SYSTEM DYNAMICSIN EARLY HEALTH TECHNOLOGY ASSESSMENT (eHTA):PRENATAL SCREENING TECHNOLOGY (ELI-P COMPLEX)

by

Leeza Osipenko

A DISSERTATION

Submitted to the Faculty of the Stevens Institute of Technology in partial fulfillment of the requirements for the degree of

DOCTOR OF PHILOSOPHY

___________________________________________ Leeza Osipenko, Candidate

ADVISORY COMMITTEE:

___________________________________________Dr. John Vail Farr, Chairman Date

___________________________________________Dr. Leon Bazil, Co-Chairman Date

___________________________________________ Dr. Alexander Poletaev Date

___________________________________________ Dr. Donald Merino Date

STEVENS INSTITUTE OF TECHNOLOGYCastle Point on Hudson

Hoboken, NJ 070302005

SYSTEM DYNAMICSIN EARLY HEALTH TECHNOLOGY ASSESSMENT (eHTA):

PRENATAL SCREENING TECHNOLOGY (ELI-P COMPLEX)

Abstract

Rapid growth of technological innovations in the medical field makes health technology assessment (HTA) an essential process in the operation of modern healthcare systems. HTA, inherently multidisciplinary and complex, is evolving in many directions and seeking for greater efficiency and effectiveness through the adoption of new methodologies and practices. This dissertation uses system dynamics (SD) as an assessment tool for prenatal screening technology and policy analysis.

The evaluated ELI-P Complex test is a biochemical system for pre-pregnancy/pre-natal screening used to determine the probability of pathology in pregnancy through the evaluation of the immunoregulatory state of fertile females. Simulation is designed to run at a relatively high level of aggregation for the time period between 2010 and 2035. It allows tracing of the model’s dynamics at the population (US) level of technology application in order to conduct an integrated policy analysis for prenatal care under various implementation scenarios of the ELI-P Complex.

US prenatal care at the beginning of the 21st century is costly and rather cultural than effective: the consequences of unsuccessful pregnancies, birth defects and children with congenital defects that become apparent later in life, have very high economic and social costs.

Initial investment into the introduction and dissemination of the technology and screening costs per year are estimated to be significantly lower than the savings that result from the number of birth defects and pathological pregnancy outcomes prevented. Simulation results clearly point to the benefits of the ELI-P Complex screening which, if applied at the population level, helps monitor female reproductive health and achieve noticeable improvements in

the overall health status of new generations. The estimate for the cost of intervention is less than US$25,000 per case avoided, which makes the screening program cost-effective.

This dissertation is accompanied by a CD with the ELI-P Complex Simulator developed in STELLA: user-friendly interface allows people with no prior knowledge of software to investigate various scenarios of the ELI-P Complex screening program introduction.

Key words: simulation, system dynamics, prenatal care, HTA – Health Technology Assessment, ELI-P Complex, screening technology.

Author: Leeza OsipenkoAdvisor: Dr. John FarrDate: September 8, 2005Department: Systems Engineering & Engineering ManagementDegree: Doctor of Philosophy

To my Grandparents: Osipenko Vladimir & Elena

Моим Дедушке и Бабушке Посвящается:

Спасибо за Всю Вашу Любовь и Заботу…

Acknowledgements

I thank my advisor Dr. John Farr for his guidance and support during my years at Stevens and my committee (Dr. Farr, Dr. Merino, Dr. Bazil) for their supervision of my work at the Institute, their interest in my research and constructive criticism. I am especially grateful to Dr. Poletaev for our daily correspondence and his valuable feedback to my endless dilettantish enquiries. The Immunculus Lab staff and Dr. Poletaev provided me with an invaluable opportunity to use their findings for my research. I hope this is the beginning of the real project, which can benefit humanity.

I very much appreciate the time Dr. Kim Warren took to evaluate my model and make insightful comments, which helped me improve my work. I thank for interesting discussions Dr. Mikhail Sokolov, Sergey Shikhov, Dr. Leon Bazil, Dr. Jack Homer, and Dr. Matthias Staudacher. Lieber Matthias, du warst ein wunderbares Vorbild. Ich habe von dir gelernt, nach Perfektion zu streben.

Since my early teenage years I had been at a safe distance from home, but 6,000 miles were not an obstacle for my family’s love and moral support, which were my strongest pillars during the university days. Thank you for spoiling me, loving me and of course, for sending the money, which I never earned in sufficient amounts to enjoy New York. Я очень благодарна своей маме, Осипенко Лидии, которая вложила в меня всё, что было возможно в душевном и материальном плане. Также отдельное спасибо Панюшкину Альберту за все возможности, которые стали реальностью, благодаря его помощи. From the depth of my heart I thank Timur Shadyev for his incredible emotional support during the last year. Тима, спасибо за всё прекрасное, на что человеческая душа способна: ты не отступал, во всём поддерживал, всегда верил и заставлял меня верить, что всё будет хорошо. My friends from all over the world cheered me up with their regular sympathetic inquiries wondering if I am ever going to finish “this thing” and do something with my life. Thanks for keeping me in touch with the real world, which I truly missed at times, thanks for listening to my endless complaints over the phone, for visiting me, paying for my dinners, and sending me chocolates. My special gratitude goes to Mrs. Hilary Morris for editing my work and keeping me a good company in England during the final stages of this research.

Thank you to the marvelous New York City for being so close and making the best years of my life so much fun (which certainly extended the duration of the PhD Program).

My travels around the world, fantastic books, numerous new acquaintances, and all new knowledge about the people, cultures, languages, arts and sciences that I acquired over the last four years marked my remarkable journey towards adulthood and composed a brilliant overture to my intellectual inquiry of the world. I’m eagerly looking into the future!

The financial support from the Department of Systems Engineering at Stevens Institute of Technology has been much appreciated.

Leeza OsipenkoAugust 2005

Table of Contents

Acknowledgements.............................................................................iiList of Acronyms.................................................................................viList of Tables.....................................................................................viiList of Figures...................................................................................viii

I. Introduction...................................................................11.1................................................................................Overview

.............................................................................................21.1.1 Healthcare System.................................................21.1.2 Prenatal Care..........................................................41.1.3 Health Technology Assessment (HTA)....................51.1.4 Policymaking in Healthcare....................................7

1.2..........................Modeling and Simulation in Health Sciences.............................................................................................9

1.2.1 Complex Systems Engineering.............................101.2.2 System Dynamics Approach.................................11

1.3...............................................................Problem Articulation...........................................................................................13

1.4...........................................................................Methodology...........................................................................................16

1.5...............................................................Research Objectives...........................................................................................18

1.6......................................................................................Notes...........................................................................................19

II. Literature Review........................................................212.1..................................Modeling and Simulation in Healthcare

222.1.1 Methodologies......................................................22

2.1.1.1 System Dynamics ................................................262.2..................................................................Medical Literature

...........................................................................................282.2.1 Prenatal Care.......................................................28

2.2.1.1 Birth Defects..........................................302.2.2 Immunology & Pathology of Pregnancy...............32

2.3....................................................................Socio-Economics...........................................................................................34

2.3.1 HTA...........................................................................37

2.3.1.1...............................Diffusion of Medical Technologies39

2.3.1.2....................................................Ethics & Regulations42

2.3.2 Health Policy & Systems Research.......................442.4.........................................Identification of Gaps in Literature

...........................................................................................462.5......................................................................................Notes

...........................................................................................50

III. Formulation of Dynamic Hypothesis.............................53 3.1 ELI-P Complex in Socio-economic Context......................54

3.1.1 Pregnancy Outcomes’ Indicators.............................55 3.1.1.1 Congenital Anomalies’ Indicators.......................60

3.1.2 Female Health and Other Indicators.......................633.1.3 ELI-P Complex.........................................................65

3.2 Stakeholders...................................................................70 3.3 Dynamic Hypothesis.......................................................72

3.4 Notes.................................................................................82Appendix 3.1 Female Health – Selected Trends......................84Appendix 3.2 Causal Loop Diagrams.......................................89

IV. Modeling Process.........................................................91

4.1...........................................................Structure Specification...........................................................................................92

4.1.1 Models’ Boundary and Scope...............................924.2..................................Economic Evaluation of ELI-P Complex

...........................................................................................944.3 Screening Technology Model.........................................108

4.3.1 Parameter Estimation....................................1104.3.2.................................................Equations’ Description

................................................................................1174.4....................................................................Aggregate Model

.........................................................................................1214.4.1 Parameter Estimation....................................1224.4.2 Equations’ Description...................................124

4.5......................................................................................Notes.........................................................................................126

V. Model Validation........................................................ 1275.1 Summary of Simulation Results ....................................128

5.1.1 Base Case.................................................................128

5.2 Validation & Verification ................................................1345.2.1 Structural Validity................................................ 1355.2.2 Behavioral Validity................................................138

5.2.2.1 Reference Model...............................................138 5.2.2.2 Extreme-Condition Test ...................................145 5.2.2.3 Sensitivity Analysis...........................................149

5.2.3 Expert Opinion......................................................1565.3 Notes...............................................................................157

VI. Policy Analysis and Framework Development..........1586.1............................................................Scenario Specification

.........................................................................................1596.1.1 “What If” Analysis..............................................159

6.2..........................................................................Policy Design.........................................................................................165

6.2.1 Diffusion Paradigm.............................................1656.3.........................................................American Prenatal Care

.........................................................................................1706.3.1....................................Suggestions for Improvements

1706.3.2........................................................Future Framework

175

6.4 Notes.............................................................................180

VII. Conclusion................................................................1827.1..........................................................................Contributions

........................................................................................183 7.1.1 Discussion of Research Findings........................1837.1.2 Novelty...............................................................1857.1.3 Evaluation of the Approach: SD in Early HTA.....185

7.2..............................................................................Limitations.........................................................................................187

7.3............................................................................Future Work.........................................................................................189

7.4......................................................................................Notes.........................................................................................194

Appendix 1: ELI-P Complex Technology Description....................195Appendix 2: Model Documentation..............................................206

Population Screening Program: ELI-P Complex..........206 Aggregate Model........................................................216

References.....................................................................221

List of Acronyms

Abs AntibodiesAGs AntigensCDC Center for Disease ControlDT Delta Time (time step between calculations)EIA Enzyme ImmunoassayELISA Enzyme-Linked Immunosorbent AssayELI-P (ELI from ELISA) P- Probability of Pathology in Pregnancy HTA Health Technology AssessmentIOM Institute of MedicineLBW Low Birth WeightM&S Modeling and SimulationNIH National Institute of HealthOECD Organization for Economic Co-operation and DevelopmentSD System DynamicsST System ThinkingR&D Research and DevelopmentQALY Quality Adjusted Life Year

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List of Tables

Table 3.1 Scope of ELI-P Complex Technology.........................................70Table 3.2 Stakeholders.............................................................................71Table 3.3 Adjusted Indicators: Pregnancy Outcomes................................82Table 3.4 Women with Health Problems Giving Birth...............................82Table 3.1.1 Obesity Trends in Women US 1988-2000.............................88 Table 4.0 Model Boundary Chart .............................................................93Table 4.1 US Demographics, 2003 ...........................................................95Table 4.2 Microplate Readers.................................................................100Table 4.3 Equipment Requirements.......................................................101Table 4.4 Prices of Microplate Readers’ & Triturus System....................102Table 4.5 Test & Kit Costs.......................................................................103Table 4.6 Poor ELI-P Complex Results: Causes and Treatments.............104Table 4.7 Working Capital.......................................................................107Table 4.8 Stocks & Flows Population Screening Model...........................109Table 4.9 System Parameters.................................................................110Table 4.10 Graphical Functions..............................................................113Table 4.11 Estimates of Selected Parameters........................................115Table 4.12 Stocks & Flows: Aggregate Model.........................................121Table 4.13 System Parameters: Aggregate Model..................................122Table 4.14 Estimates of Selected Parameters: Aggregate Model...........124Table 5.1a Base Case Values..................................................................128Table 5.1b Simulation Output.................................................................129Table 5.2a Base Case Values: Aggregate Model.....................................131Table 5.2b Simulation Output No Screening: Aggregate Model..............132Table 5.2c Simulation Output Under Screening: Aggregate Model ........132Table 5.3 Direct Structure Test...............................................................135Table 5.4 Setting for the Reference Simulation......................................139Table 5.5 Reference Model: Comparative Numerical Output..................143Table 5.6 Setting for the Reference Simulation: Aggregate Model.........144Table 5.7 Numerical Output of Extreme Condition 1..............................146Table 5.8 Numerical Output of Extreme Condition 2..............................148Table 5.9 Numerical Output of Extreme Condition 3..............................149Table 5.10 Numerical Output: Sensitivity Test 1....................................150Table 6.1 ELI-P Complex in the Context of Innovation............................166Table 7.1 SD and HTA ............................................................................187

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List of Figures

Figure 3.0 Project Definition.....................................................................54Figure 3.1 Pregnancy Outcomes US, 2000...............................................56Figure 3.2 Pregnancy Outcomes US, 1990-2000......................................56Figure 3.3 Infant/Neonatal Mortality Trends US, 1990-2001....................57Figure 3.4 US Newborns, 2000.................................................................58Figure 3.5 US Newborns, 1990-2000........................................................59Figure 3.6 Pathological Outcomes of Pregnancy US, 1990-2000..............59Figure 3.7 Trends for Selected Birth Defects US, 1985-2001...................60Figure 3.8 Potential Years of Life Lost US, 1985-2000.............................61 Figure 3.9 Hospital Discharge US, 1981- 2001.........................................62Figure 3.10 Prenatal Care Utilization 1st Trimester US, 1980-2002..........63Figure 3.11 Pregnancy Outcomes: ELI-P Test Evaluated Females............67Figure 3.12 Influence Diagram: Example.................................................72Figure 3.13 Causal Loop Diagram 1.........................................................73Figure 3.14 Causal Loop Diagram 2.........................................................75Figure 3.15 Causal Loop Diagram 3.........................................................76Figure 3.16 Causal Loop Diagram 4 ........................................................76Figure 3.17 Causal Loop Diagram 5 ........................................................77Figure 3.18 Causal Loop Diagram 6 ........................................................78Figure 3.19 Causal Loop Diagram 7 ........................................................79Figure 3.20 Causal Loop Diagram 8 ........................................................80Figure 3.21 Causal Loop Diagram 9 ........................................................81Figure 3.1.1 Perceived Health Status US Women (aged15-44)................84Figure 3.1.2 Chlamydia – Rates by Sex US, 1984-2003...........................84Figure 3.1.3 Gonorrhea - Rates by Sex US, 1981-2003............................85Figure 3.1.4 Syphilis - Rates by Sex US, 1981-2003................................85Figure 3.1.5 Genital Herpes US, 1966-2003 ............................................86Figure 3.1.6 Genital Warts US, 1966-2003...............................................86Figure 3.1.7 Trichomoniasis and Other Vaginal Infections US, 1966-2003.87Figure 3.1.8 Diabetes Incidence in US Women (18-44), 1997-2003.........87Figure 3.2.1 Causal Loop Diagram 10......................................................89Figure 3.2.2 Causal Loop Diagram 11......................................................89Figure 3.2.3 Causal Loop Diagram 12......................................................90Figure 3.2.4 Causal Loop Diagram 13......................................................90Figure 4.1a Fragment of Stock & Flow Diagram Pregnancy Onset and Screening.................................................................................................111Figure 4.1b Fragment of Stock & Flow Diagram Screening Cost............112Figure 4.1c Fragment of Stock & Flow Diagram Pregnancy Outcomes. .112Figure 4.2 Stock & Flow Diagram: Aggregate Model.............................123Figure 5.1a Base Case: Pregnancy Outcomes........................................129

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Figure 5.1b Base Case: Costs................................................................129 Figure 5.2a Base Case: Aggregate Model No Screening........................132Figure 5.2b Base Case: Aggregate Model Under Screening...................133Figure 5.2c Base Case: Aggregate Model Becoming Healthy Women....133Figure 5.2d Base Case: Aggregate Model Unhealthy Newborns.............134Figure 5.3a Historical Fit (1985-2000): Newborns..................................141Figure 5.3b Historical Fit (1985-2000): Pregnancies..............................141Figure 5.3c Historical Fit (1985-2000): Population.................................142Figure 5.3d Historical Fit (1985-2000): Fertile Women..........................142Figure 5.4 Historical Fit (1985-2000): Other Indicators..........................143Figure 5.5a Historical Fit: Aggregate Model (1985-2000): Pregnancies. 144Figure 5.5a Historical Fit: Aggregate Model (1985-2000): Fertile Women145Figure 5.6 Extreme Condition 1: Females Deaths..................................146Figure 5.7a Extreme Condition 2: Very Low Pregnancy Planning and Prenatal

Care Utilization Rates.............................................................147Figure 5.7b Extreme Condition 2: Very Low Pregnancy Planning and Prenatal

Care Utilization Rates.............................................................148Figure 5.7c Extreme Condition 3: No Healthy Women...........................149Figure 5.8 Sensitivity Test 1...................................................................150Figure 5.9 Sensitivity Test 2...................................................................151Figure 5.10a Sensitivity Test 3: Screening Cost.....................................152Figure 5.10b Sensitivity Test 3: Other Indicators..................................152 Figure 5.11 Sensitivity Test 4.................................................................153Figure 5.12a Sensitivity Test Aggregate Model: Unhealthy Newborns...154Figure 5.12b Sensitivity Test Aggregate Model: Unhealthy Women......155Figure 5.12c Sensitivity Test Aggregate Model: Healthy Newborns.......155Figure 6.1a Technology Adoption Rate at 20%......................................160Figure 6.1b Healthy Newborns Scenario 1.............................................160Figure 6.1c Unhealthy Newborns Scenario 1..........................................161Figure 6.2a Healthy Newborns Scenario 2.............................................161Figure 6.2b Newborns Scenario 2..........................................................162Figure 6.3a Treatment Effectiveness in Pregnancy................................162Figure 6.3b Treatment Effectiveness in Pregnancy: Healthy Newborns.163Figure 6.3b Treatment Effectiveness in Pregnancy: Newborns with Birth Defects.....................................................................................................163Figure 6.4a Costs and Procedures..........................................................164Figure 6.4b Poor Pregnancy Outcomes..................................................165Figure 6.5 Prenatal Care Visits...............................................................177Figure 6.6 Prenatal Care Framework......................................................179Figure 6.7 Prenatal Care Interventions and Spending............................180Figure A.1 ELI-P Complex.......................................................................197

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Chapter 1Summary: The chapter begins with a brief description of the US healthcare system and methodologies used for healthcare policymaking in the beginning of the 21st century. An overview of prenatal care emphasizes its ineffectiveness and draws attention to birth defects and pathologies in newborns. In consequence, the research question is being formulated: health technology assessment of the intervention (ELI-P Complex1 test) is needed to design a set of policies to improve poor pregnancy outcomes. The problem is articulated and system dynamics is chosen as an appropriate tool for simulation modeling. The chapter concludes summarizing research objectives.

Introduction1.1 Overview

1.1.1 Healthcare System1.1.2 Prenatal Care1.1.3 Health Technology Assessment (HTA)1.1.4 Policymaking in Healthcare

1.2 Modeling and Simulation in Health Sciences1.2.1 Complex Systems Engineering1.2.2 System Dynamics

1.3 Problem Articulation1.4 Methodology1.5 Research Objectives1.6 Notes

OVERVIEW

Healthcare System

1 ELI-P Complex test: (from ELISA - enzyme-linked immunosorbent assay) – probability of pathology in pregnancy test.

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The United States’ healthcare system is monstrous in size and complexity: at the beginning of the 21st century it employs over 11 million people and consumes 1/7 (GDP) of the largest economy in the world [1]. Americans are living with the first-rate medicine and a third-rate healthcare system [2], which unfortunately ranks as the third leading cause of death in the US [3]. And the situation is getting worse. Many causes of this problem, mostly of fiscal nature, are out of the scope of this research however, the given thesis explores the issue, which directly affects the healthcare system and especially its future performance. The US is not different from other advanced or backwards systems with regards to the general attitude towards healthcare. The mentality of patients and medical service providers is similar across the cultures and systems: in general, people turn to doctors when they are not feeling well and medical professionals are routinely being trained in curing or treating various conditions, rather than maintaining health.

The fast-paced technological progress transforms all medical fields and now many diseases can be diagnosed at much earlier stages and cured in time to save more lives. The future holds for us the wonders of nano-medicine, the fruits of the Genome Project and stem cell research, etc. but these endeavors are decades away from offering practicing doctors useful tools, which can be applied to help their patients.2 Hence, the recently initiated healthcare reform ought to focus on the currently available technologies and resources, and ensure their most efficient utilization.

Back in 1992 N. Fost concluded that presymptomatic screening is central to contemporary medical practice [29]. Is this wishful thinking or a true state of affairs? Basic lab tests, yearly physical examinations, 2 Numerous experimental projects are underway but the transfer of new findings into clinical practice is slow due to ethical reasons and many unanswered scientific questions.

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increasing number of MRIs, mammograms, etc. point into the right direction of the system’s development. Vaccinations, diagnostic technologies, control of environmental hazards, epidemics prevention programs, and healthy life-style propaganda produce a marginal positive impact but are far from fulfilling their theoretical potential3. Meanwhile, presymptomatic screening continues to represent only a small share of operations in today’s healthcare system. Although the benefits of preventive medicine4 are difficult to measure, a focus on prevention would certainly lead to long-term cost savings [2] by increasing life expectancy and decreasing the occurrence of many diseases. Targeting symptoms instead of causes is unlikely to yield efficiency in the long-run thus, preventive medicine will continue to grow, slowly cutting the share of today’s treating and curing medical practice.

The problems of the US healthcare system are being addressed through programs launched by The National Institute of Health, The Institute of Medicine and other organizations managing the long-needed healthcare reform, which is a very complicated process. This work attempts to investigate a small, but important aspect of the healthcare system, and propose policies, which can serve as a positive addition to this paramount initiative.

Prenatal CarePrenatal care5 in the US faces the same problems (not

necessarily from the same causes) as the healthcare system in 3 The number of obese and diabetic people is growing, the number of heart failures and birth defects is not decreasing, etc.4 Preventive Medicine - specialty of medical practice, which focuses on the health of individuals and defined populations in order to protect, promote, and maintain health and well-being and prevent disease, disability, and premature death [http://www.ph.ucla.edu/pmr/training.htm].5 Prenatal care is the complex of interventions that a pregnant woman receives from organized healthcare services [WHO HEN report].

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general. Increased spending on prenatal care and introduction of new technologies do not decrease the number of low birth weight newborns (LBW), pre-term births6 or birth defects in the US [4]. Every year about 120,000-150,000 U.S. babies are born with a birth defect (3-4% of newborns) [5,12] - the leading cause of infant mortality. 7.5% of children manifest a congenital defect by age 5 [19]. About 65-70% of birth defects have unexplained causes [6,12]. The social and economic impact of birth defects remains very troublesome and direct and indirect costs amount to billions of dollars annually [7].

Not considering genetic causes, which account only for 13% of all birth defects [8], it has been determined that a large number of birth defects occurs due to the mother’s poor health [9,10,14,15,16,17,20]. Most of the time a woman and her physician may not be aware of some health problems that might exist unless a patient has a documented history of a chronic illness, detected infection, or another diagnosed condition before or at the beginning of pregnancy. However, a woman’s body is not always at its best state to create a safe environment for the fetus and produce a healthy baby as a result.

Today, the science of birth defects prevention does not extend beyond anti-alcohol/anti-drug campaign, folic acid and healthy diet/exercise recommendation. Usually, only the risk group7

undergoes genetic/hormonal counseling and no population screening programs exist to lower the occurrence of birth defects. The rapid development of new technologies allows detecting (not preventing!)8

of some birth defects (mostly of genetic nature), but the result is either an abortion (which is emotionally difficult for future parents) or a sick child. Very few congenital anomalies in fetuses can be treated [19],

6 Infant mortality has been decreased through the use of advanced and expensive neonatal care units.7 Women over 35 or those with history of genetic or hormonal problems.8 Non-invasive prenatal diagnostics technologies are under development: [www.safenoe.org]

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overall the detection technique does not decrease the number of birth defects (besides through the abortion procedure).

This research evaluates a screening technology developed by Russian bio-scientists at the Immunculus Medical Research Laboratories [13]. ELI-P Complex is a biochemical test system for pre-pregnancy/pre-natal diagnostics used to determine the probability of pathology in pregnancy [refer to Appendix 1 for technical description]. ELI-P Complex screening reveals immunoregulatory reproductive state of a woman. Abnormal test results indicate the presence of a chronic condition or an acute process, which may be harmful for the future fetus. Hence, screening fertile women planning pregnancy or in the first trimester with ELI-P Complex and treating those who have poor test results, can reduce the number of birth defects, increase the number of healthy newborns and decrease the number of newborns with pathologies [9,14,15,16]. Consequently, the application of this technology at the population level has a potential to revolutionize prenatal care, produce a noticeable economic impact, and improve the socio-demographic situation.

Health Technology Assessment Most countries regulate drugs and devices by law, by payment,

or by placement of services. A multidisciplinary research called health technology assessment (HTA) assists policymakers on matters of medical, economic, social, and ethical implications of the dissemination and use of health technology [37]. HTA is a systematic process by which the direct and indirect consequences of a particular technology are assessed [11]. The field of HTA is substantial, diverse and complex: besides providing valuable information on technical

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performance of healthcare technologies, HTA also contributes by improving effectiveness, cost-effectiveness and societal accountability of healthcare overall. Thus, with increasing political and social demand for efficiency in the allocation and use of healthcare resources, the economic evaluation of health technologies will continue to dominate the field [26].

Most health technologies receive some type of assessment, which extent and quality depend on the demands of the industry and/or society and the resources available. Similar circumstances influence the timing of HTA: some analyses are directed towards determining the most potent technologies in development, while others evaluate technologies which are already in use. One of the branches of HTA is Horizon Scanning Systems (HSSs); its purpose is to help control and rationalize the adoption and diffusion of new technologies in healthcare practice, by providing policymakers with information on the consequences of introduction of the health technology into the healthcare system [25]. But HSSs usually reach out only to the technologies coming from under the umbrella of well-established companies in the healthcare industry and not the technologies from small labs or independent inventors.

This research is designed to conduct a partial HTA of an emerging screening technology. ELI-P Complex has had a limited assessment through clinical trials in Russia, however the potential of the technology has only been hypothesized but not assessed from socio-demographic and economic points of view. The HTA of ELI-P Complex will be an essential bridge between basic research and development (R&D) and suitable application of this technology in the US market9.

9 The technology has been patented in Russia but not in the US

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Policymaking in Healthcare

The policymaking process (healthcare included) takes place on many fronts—political, economic, social, technological, and local, national, international. Depending whether the policy is designed on the federal, state, or local level, different players and sources of funding are involved into the process. Many factors from evidence-based research and socio-economic and cultural conditions to dissemination through lobbying and marketing contribute to health policy formulation and its successes. The use of health services research is one of the most influential factors in the policymaking process. The translation of research findings complemented with explicitly defined problems, which need to be addressed in all areas of healthcare into the policymaking process, remains complicated, slow and often is inefficient. Since not all academic theories and scientific paradigms are policy relevant [21], the recognition of policy-able ideas is as difficult as the recognition of innovations with a multi-million dollar commercialization potential. Consequently, only a few ideas are becoming policies especially at the federal level.

Policymaking for preventive care is more difficult than in other branches of healthcare since the evidence is limited and future outcomes are usually hypothesized rather than demonstrated. However, the investment in preventive care can be justified and desirable since it is directed towards the elimination of causes of future problems. Preventive healthcare generally covers large population segments, and the bulk of costs is incurred in the beginning, whereas health benefits may not start to accrue until decades later [22]. So, while the majority of policies can be designed using the information from cost-benefit analysis or clinical trials, the preventive care policy

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formulation can arise from the results of models and simulations, demonstrating the robustness and potential of various programs.

In the evaluation of healthcare technologies, policy analysts rely on both qualitative and quantitative methods. The former approach includes expert evaluation, focus groups, in-depth interviews, collection of information, and literature reviews. Some groups lobby specific policies, others do not advocate or ban policies, but analyze the options providing well-considered policy options so that the public and policymakers can make informed choices on the use, application, and future direction of these technologies [20]. The quantitative methods include various tools applied in HTA, as well as specific studies (i.e. econometric or mathematical models) for the adoption of selected programs or interventions. Policymakers prefer to look at cost-benefit analysis since it attributes numerical values to the assessed interventions, however, rarely such approach can be viewed as sufficient or the most credible.

Various organizations and interest groups shape the health policy in the US. The loudest voice in lobbying for mother-newborn policies perhaps belongs to the March of Dimes, which aims to improve the health of babies by preventing birth defects and infant mortality. With almost seventy years in action and 3 million volunteers across the country, the March of Dimes carries its mission through research, community services, education and advocacy [12]. CDC, NIH, IOM10, US Dept of Health, universities and research centers, as well as other organizations also spend substantial resources to improve reproductive health, birth outcomes and the overall health status of the population.

Despite the fact that up to 70% of birth defects remain unexplained, a tremendous share of resources and interest is currently

10 Center for Disease Control, National Institute of Health, Institute of Medicine

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invested into studying and preventing genetic birth defects [5,6,7,8,12,20,etc] (accounting for 13-15% of total + ~15% of multi-factorial) while the remaining 60-70% are being given considerably less attention. Whether this is an attempt to justify investments into the Genome project, or a natural course of scientific development, current research is bringing interesting results which hold explosive potential for the future clinical applications. However, the issues involved in genetic screening programs are fairly complicated from the ethical, financial, regulatory, and evidence points of view thus, for the next few decades most of these programs should be thought of as experiments, with unknown benefits and risks [29]. Those enthusiasts willing to explore the reasons for the seventy percent of the unknown causes of birth defects place high hopes on the evolving fields of proteomics and post-genomics but, there are also more pragmatic movements in the field where scientists are focusing on the solutions which can make a difference already today.

The aim of this research is to conduct a policy analysis (the process, through which alternative policies or programs that are intended to lessen or resolve social, economic, or physical problems are identified and evaluated [28]) for the introduction of the new (non-genetic) technology into pre-conception/prenatal care in the US using modeling and simulation11. However, the actual implementation of the suggested policies in this study will require a considerable amount of work, ranging from obtaining additional clinical trials data of the ELI-P Complex screening, to lobbying for the funding and promotion of the technology.

11 Policy analysis associated with genetic tests for pre-conception/prenatal care is out of scope of this research

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MODELING AND SIMULATION IN HEALTH SCIENCESModeling and simulation (M&S) in health sciences has been

growing at a rapid pace over the last few decades. From sophisticated mathematical models of physiological processes and epidemics control, to decision-making tools for doctors and nurses, the healthcare sector has been applying new technologies to improve its performance and efficiency; and most importantly, to create alternatives to human/animal testing.

While M&S for physical products has become a robust and necessary tool, the modeling of social aspects of the healthcare system can still be regarded as developing. Systems engineering, mathematical modeling, decision analysis, economic evaluation, agent-based and system dynamics, discrete and stochastic simulations have been applied to study various problems and issues within the healthcare system. Besides economic modeling typical for HTA, a few other M&S methodologies have been used in health technology assessment and policymaking [11,26,31,32], but have not become a common trend. HTA specialists apply a combination of various approaches to conduct their analysis and come to the consensus that better multifunctional tools are necessary [11, 26].

Complex Systems Engineering

Even though, mechanical systems thinking has significantly helped learning about social systems over the past century [27], the limitations of this approach become obvious producing the need for more sophisticated tools. Complex systems engineering has been adopted to initiate the reform of the healthcare system. The main principles of this approach, focusing on the rules, rather than specific system requirements, treat the system as an evolving entity with an

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evolutionary development [30], and realize that only a modest level of certainty exists regarding what actions lead to what outcomes [27].

The US healthcare system is under a lot of criticism from all of its stakeholders. Modifications are compelling and already take place to make the system, as a whole, more efficient and effective. The ongoing changes in the US healthcare system must co-exist with the development and introduction of new healthcare technologies, which, in turn, should be designed to adhere to the rules12 of the healthcare system at the point of their introduction and be flexible to change along the way with the system they intend to enter. Most parts of the healthcare system should be viewed and designed as natural adaptive systems13, which have the freedom and ability to respond to stimuli in many different and fundamentally unpredictable ways. In complex adaptive systems, systems exist within systems and co-evolve producing nonlinear (small changes can lead to large effects), novel, and emergent behaviors [27]. Forecasting in such systems is inherently inexact but general patterns can be determined.

This research is not using the complex systems engineering approach to design a model, but the system dynamics approach described below which meets the above criteria. Consequently, the integration of the ELI-P Complex screening technology into the complex system should be possible, since the policies regarding its introduction will be designed to comply with the features of the evolving healthcare system. Chapters 3 & 6 describe this process in greater detail.

12 Refer to the IOM Crossing Quality Chasm for the set of 10 rules developed for the US healthcare system.13 Natural adaptive system: any system capable of learning; change in behavior can occur from within

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System Dynamics ApproachSystem Dynamics (SD) is a methodology for studying and

managing complex feedback systems in managerial, organizational and socioeconomic context [23]; and a method for enhancing learning within these systems. SD adopts a holistic approach and helps understand the basic structure of the system and the behavior it produces. SD has been applied in health sciences to model various problems [23,31,32], however its share is minuscule, compared to the use of econometric or stochastic models. This is a paradox, since the latter are not proved to provide better answers but rather a reductionist picture of the system. The mathematical and economic models are relatively well suited for the short-term forecasting and poorly reflect the long-term perspectives [33]. This has happened for two reasons: first, there are many more trained mathematicians and economists than SD professionals, and second, the clients are more willing to believe extrapolated results, which look familiar, than to learn to think systematically to understand the causes and nonlinear effects which challenge their mental models.

System dynamics has a great potential when applied to social sciences. It uses the principles of systems thinking (ST) to model a problem (not a system) providing an opportunity for stakeholders to get involved into the process. The dynamic characteristics are defined though the qualitative modeling process using causal loop diagrams, which are logic-based descriptions of causes and effects. In the quantitative part of the modeling process, parameters are estimated (given numerical values) and the relationships between the variables are defined in the form of equations. The software tools (Stella®,

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Vensim®, Powersim®, My Strategy®) simplify the process and provide user-friendly interfaces for the simulation design. Then, the system dynamics approach is used to prescribe for the decision-making to respond in time to any changes and depict how to change the physical structure [23]. This is being done with the incorporation of the “what if” scenarios (dynamic sensitivity analysis).

While economic analysis maybe more useful for healthcare technologies associated with treatments to demonstrate their financial performance, preventive technologies (diagnostics/screening tools) often cannot benefit from this analysis due to the lack of evidence; and this is when system dynamics modeling offers its superiority. SD is one of many tools for M&S and if it is properly applied in the context of healthcare, it maybe more helpful to policymakers than some other methodologies.

PROBLEM ARTICULATIONProblem articulation is the first and crucial step of the SD model

design process [23]. Chapter 3 in detail evaluates the problem from the systems thinking perspective and expatiates on the following statements.

What is the Problem? ~ 90,000 kids are born with birth defects of UNKNOWN causes

every year in the US [12].

The care for children (babies) with birth defects is ~US$8 billion/year (1992 CDC estimate for 18 major birth defects out of 4,000 known)14 [5].

14 Current cost of care must be a lot higher due to the inflation and inclusion of indirect costs.

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A technology, which can address this problem, is available, but its potential at the population level has not been assessed. Thus, the innovation remains in the lab, instead of helping improve prenatal care by evaluating immunoregulatory health of future mothers across the country.

What are the symptoms/difficulties? Poor health of fertile women.

Majority of the biological symptoms of the problem remain unknown.

The US healthcare system is an impediment (uninsured females, reduced funding for clinical research, increasing cost of healthcare, etc.)

Why is it a problem? While it is an obvious statement that female health affects the

health of the future offspring, women and physicians are unaware that in many cases the poor state of the woman’s body [unsuitable for pregnancy] can be identified through the measurements of antibodies in the blood of an otherwise healthy female.

The current practices are unable to explain the causes of 70% of birth defects and decrease the number of pre-term births, miscarriages, and LBW newborns.

While genetic testing demonstrates phenomenal progress, overall prenatal care remains at a very basic level, which serves rather a cultural function than provides an actual effective service to future mothers.

No population screening programs exist to lower the occurrence of birth defects.

Who is the client? The list of stakeholders is expansive (see Chapter 3 for

description), but the principle ones include the society, fertile or pregnant females, newborns, investors, hospitals/clinics. There are

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also secondary stakeholders such as other family members, insurance companies, federal/local governments, physicians, researchers, teachers, social workers, etc. .What is the purpose of the model?

The primary goal is to model one aspect of prenatal care (not the entire prenatal care system) where a new intervention (screening technology) can demonstrate its impact on pregnancy outcomes.

The secondary goal is to create a simplified representation of reality, which can be understood not only by medical or system dynamics professionals, but by all stakeholders with the basic knowledge of concepts.

The tertiary purpose is to design a set of policies for the future prenatal care in the US, which can help address some of the identified problems.

Finally, the purpose of the model is to demonstrate an efficient method for early health technology assessment using system dynamics simulation.

The time horizon chosen for the simulation is 25 years (2010-2035), which is sufficient to address the dissemination of technology, its adoption, and some of the effects. The aggregate model, tracking the improvement of female health, is designed for 40 years (2010-2050) to demonstrate population health re-generation results.

Dynamic problem definition: What is the historical behavior of the concepts?

Infant mortality has been declining in the US almost every year (mostly due to the development of new technologies that increase survivability of premature babies).

The number of pre-term births and LBW newborns has been steadily troublesome producing a less healthy population and elevating morbidity and mortality rates.

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The expenditures on healthcare have been increasing and the mother-child health has not been an exception.

Prenatal care remains to be ineffective [4].

The US population has been growing but lately primarily from immigration.

The number of uninsured females in the US has been growing and currently is at 14% [34].

What their (concepts) behavior might be in the future? The model presented in this work has a narrow scope and does not incorporate all of the above concepts, thus necessary assumptions are made (see below and Chapter 4) to set model boundaries and focus on the importance of the female health improvement.

Infant mortality is expected to decrease due to the greater sophistication of neonatal technologies.

Marginal decreases in LBW and pre-term births are expected from the enforcement of programs which are already in place.

The population in the US will continue to increase through immigration rather than through natural growth.

Hopefully, the US healthcare system will be redesigned towards universal healthcare and provide all females with basic insurance coverage for maternity.

If policies suggested in this research are introduced, along with further developments in genetic counseling and new tests, prenatal care should become more efficient and bring positive changes to pregnancy outcomes.

If the population becomes healthier through lowering the occurrence of newborn pathologies and birth defects, the expenditures on people with congenital abnormalities should decline.

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METHODOLOGY Since the modeling process is inherently creative [23], there is no single proper recipe to follow. The methodology for this research evolves from the nature of the problem, which is being investigated, available information, and the outlined objectives; however the modeling process follows the core activities typical for most SD projects: 1) identification of a problem, 2) development of a dynamic hypothesis, 3) building a computer simulation model of the system, 4) testing the model to be certain that it reproduces the behavior seen in the real world, 5) devising and testing in the model alternative policies, and 6) implementation of the solution [23].

The comprehensive literature review (Chapter 2) of medical, HTA, and M&S fields allows for the conceptualization of the research question, as well as helps define gaps in current research, which are being discussed in the policy analysis section of this dissertation (Chapter 6). Computer modeling is done using Consideo® and Stella® software. The ELI-P Complex Population Screening simulation consists of two submodels: 1) pregnancy onset and screening and 2) pregnancy outcomes. The Aggregate model traces pregnancy outcomes as a feedback to the overall female health. The concepts for both models are first studied at the systems thinking level (Chapter 3) then, the quantitative part begins with the parameter estimation, which is done using available data (derived from literature review, clinical trials, economic and population indicators), experts’ opinion, and informed guesses based on the accumulated knowledge. Then quantitative relationships among the variables are expressed in the form of equations and the information is entered into the computer simulation (Chapter 4). In order to design a manageable model, the focus is only on the essential features and factors relevant to the questions at hand,

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thus the selection of variables is limited and where possible, variables are aggregated to give clarity to the model.

The validation of the model (Chapter 5) is performed through a number of tests: direct structure test, extreme-condition test, parameter verification test, and experts’ opinion. The behavioral validation is performed by assessing the quality of historical fit: after changing the initial settings in the simulation to 1985 data, the model is run to produce the outputs up to the year 2000. The results of this simulation are compared to the actual historical data. Finally, sensitivity analysis is performed to assess uncertainty of the estimated parameters and relationships between the structure and the behavior of the model, as well as to identify points of high leverage for policy intervention [23].

In Chapter 6, using “what if” analysis, the models’ behavior is evaluated for new insights, which yield suggestions for policy generation. The integrated approach to policy analysis, which allows continuous monitoring and evaluation of policies over time, is suggested to further carry out the introduction of the ELI-P Complex into the US prenatal care system.

The proposed methodology is well suited to examine the dynamic effects of policy initiatives in prenatal care. It helps answer the questions of how the ELI-P Complex technology can be implemented in the US for the population-wide screening and how such intervention can affect the socio-economic situation.

RESEARCH OBJECTIVESThe diffusion of technologies in healthcare continues with little

reference to research [35,36] since it is either insufficient or poorly presented for the efficient utilization by policymakers. The impact of

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HTA depends partly on how relevant and useful the information is at the time decisions are being made [35] but rarely policymakers can rely on the informative HTA models to gain proper understanding. Most of the time they deal with one or the other aspect of presented evidence, without an opportunity to evaluate the “whole picture.” The earlier a technology is assessed, the more likely its diffusion to be rationalized [11]. Now is a good time for the ELI-P Complex assessment, since the technology is still relatively new, has potential for future evolvement, and has already demonstrated convincing benefits. The goal of this research is to provide an enriched model of the ELI-P Complex technology assessment, in order to equip policymakers with a proper tool to influence the future of this technology and propose various paths for adoption.

The means for achieving this objective is building a system dynamics simulation which is designed to demonstrate how pregnancy outcomes can be influenced by the introduction of the new intervention and helps identify conditions under which the number of healthy newborns is maximized (Chapter 6). Novel insights arising from this process can be used to propose changes in American prenatal care.

Simulations developed in this research can be used as a framework for producing sophisticated policy analysis tools for prenatal care decision-making.

This interdisciplinary work presents system dynamics as a useful methodology for early HTA of the emerging technologies which, usually possess little evidence-based results, and which potential has to be estimated with little if any historical data. This research can serve as groundwork for further studies to evaluate how early HTA can use SD simulations to provide more informative

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reports to policymakers. The dissertation is accompanied by a CD with the Population

Screening and the Aggregate Model simulations, both of which, have a graphic user interface (GUI). This functionality allows untrained users to easily operate the simulations and test models’ outputs under various settings and improve the accuracy of the models’ parameters.

NOTES: Numbers in [ ] correspond to the title in References p. 221

1. [210] CMS DATA. Accessed on October 4, 2004. [http://www.cms.hhs.gov/charts/healthcaresystem/chapter1.asp]2. [40] Barrett K, Greene R, Mariani M. 3. [239] World Prosperity Ltd. The Healthcare Reform. [http://www.world-

prosperity.org/healthcare.htm]. Accessed on December 9, 2004.4. [31] Strong, T. 5. [208] CDC. Birth Defects data. Accessed on October 4, 2004. [http://www.cdc.gov/node.do/id/0900f3ec8000dffe]6. [104] Nelson K, Holmes LB. 7. [143] Waitzman, N., Romano, P., Scheffler R. 8. [37] Baird P. 9. [163] Poletaev A. Information Letter. 200410. [118] Poletaev A, Morozov S. 200011. [32] Szczepura A., Kankaanpää J. (eds)12. [222] The March of Dimes Data. Accessed on November 2, 2004.

[http://www.modimes.org/professionals/681_1206.asp] 13. [218] Immunculus Medical Research Center [www.immunculus.com]14. [149] Litvak, O. 15. [154] Serova, O. 16. [156] Zamaleeva, R. 17. [106] Patterson, P. 18. [161] The Merck Manual on Diagnosis and Therapy19. [91] Koskimies, O.20. [217] Genetics & Public Policy Center21. [196] Start D., Hovland, I.22. [77] Heidenberger K, 199223. [30] Sterman, J. Business Dynamics24. [178] Bar-Yam, Y. 200425. [54] Douw K., et. al. 26. [34] Topfer L., Auston I. (eds)27. [18] Plsek, P. Appendix to IOM, Crossing the Quality Chasm28. [24] Patton C. & Sawicki D. 29. [64] Fost, N. 30. [41] Bar-Yam, Y. 200331. [17] Hargrove, J.

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32. [148] Homer, J 198333. [159] Meadows, D. 198034. [198] Women’s Health USA 200435. [126] Rosen, Gabbay36. [75] Hanney, S. et. al.37. [88] Jonsson, E., Banta, D.

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Literature Review

Chapter 2Summary: The literature review places an emphasis on existing applications of modeling and simulation in health sciences, focusing on decision-making models and diffusion of medical innovations. The chapter describes the current state of prenatal care in the US and the field of health technology assessment. From the thorough review of the various bodies of literature, it becomes apparent that modeling and simulation in HTA remains in its nascent state and the current models and methodologies need further elaborations on the part of theoretical and practical work. The chapter concludes highlighting gaps in literature in the fields under investigation thus, presenting incentives for the given research.

Literature Review2.1 Modeling and Simulation in Healthcare

2.1.1 Methodologies2.1.1.1 System Dynamics

2.2 Medical Literature2.2.1 Prenatal Care

2.2.1.1 Birth Defects2.2.2 Immunology & Pathology of Pregnancy

2.3 Socio-Economics2.3.1 HTA

2.3.1.1 Diffusion of Medical Technologies2.3.1.2 Ethics & Regulations

2.3.2 Health Policy & Systems Research2.4 Identification of Gaps in Literature2.5 Notes

Literature Review

MODELING AND SIMULATION IN HEALTHCARE

Computer-based modeling and simulation (M&S) in medicine is applied in diagnostics, therapeutics, research, training, etc and over the last few decades the field has witnessed significant progress. The latest achievements range from predictive models of generic cell types to the working models of actual human organs [1], from dynamic systems [2,3] and mathematical models of drug kinetics [4] to decision-support simulations in medicine [5,6,7,8,9,10] and models of healthcare products’ potential [11,12]. It is needless to prove the benefits already brought and that are yet to be achieved through the application of new technologies in healthcare. Medical sector, compared to other fields (defense, finance, construction, engineering, etc) is rather a slow adopter of M&S, due to its sensitivity to a number of issues. Since healthcare is concerned with promoting, protecting, and improving the well-being of individuals, a full reliance on computer technologies is not always possible, ethical, safe or affordable.

When or if the gap between the in vivo/in vitro testing and M&S can be closed or maximally minimized is unknown, but a wider application of various M&S techniques should certainly bring beneficial results into healthcare, increase its safety, improve performance, decrease costs, and speed up many processes. Thus, the focus today should be on finding the most efficient ratio between the M&S applications and live testing. Modern developments and resources allow for a better and faster integration of M&S techniques into various sectors of American healthcare.

Methodologies

Literature Review

The immensity of the healthcare industry, products, services and technologies does not allow for a full overview of available methodologies. Thus, this chapter focuses primarily on the M&S techniques used in prenatal care, technologies associated with it and the approaches to policymaking.

Let us classify the existing M&S methodologies used to study the above issues into soft and hard. The hard methodologies, focusing on validity and computational issues, have been widely used for many years and include mathematical models, econometric and statistical analysis. Discrete stochastic simulations continue to be a very popular tool and are used to build models of disease prevention [13], health programs [14], various optimization processes [8] in medicine and healthcare, etc. Most current methods of data analysis in medicine continue to use linear models, which are based on proportionality between two variables and/or relationships described by linear differential equations. However, nonlinear behavior commonly occurs within human systems due to their complex dynamic nature and linear models cannot adequately describe this [3]. If models are to be good representations of social systems, there must be an unrestricted willingness to incorporate nonlinearity [120]. Thus lately, there has been a growing trend of adopting such models to study various issues in healthcare especially those, influenced by the human behavior.

Soft methodologies include participation, development of shared understanding, and usability of tools and approaches. Usually soft methodologies are classified as qualitative modeling, which is the opposite of quantitative (hard) modeling. The obvious benefit of soft methodologies is that they allow for addressing nonlinearities that reflect the complexity of the studied systems, but at the same time preserve the level of modeling simplicity compared to the

Literature Review

mathematical models, which portray the system behavior only under strong assumptions. Already today a myriad of examples show how nonlinear modeling15 is helping to explain some system behaviors that linear systems cannot and thus augment our understanding of the nature of complex dynamic systems within the human body in health and in disease states [2,3,15,16,17,18,19,20]. Another set of studies uses soft methodologies to address the complexity in healthcare system, management, resources, etc. [21,22,23,24,25,26,27,28,29,30

]. Various combinations of M&S methodologies have been proposed

to study the complexity in healthcare and medicine. For example Baldwin L., et al. [31] use MAIPU - modeling approach that is participatory iterative for understanding and not restricted by formal and logical rules; instead it is aimed at being adaptive to changing requirements. This methodology helps to successfully facilitate the modeling process and finds that involving stakeholders throughout enables them to fully appreciate the findings. Stausberg J. and Person M. [10] introduce a process model of diagnostic reasoning in medicine, which combines static and dynamic elements and reasoning control to implement a decision-support system. Hall J. et al. [14] use the stated preference discrete choice modeling to evaluate health programs. Their approach is based on the random utility theory and the survey data to investigate the individual decision-making. Heymann A. et al. [32] propose systematic inventive thinking, a technique for problem-solving in the field of engineering to solve complex problems facing primary care (antibiotics over-prescription) and find it to be a useful technique for problem-solving and idea generation within the medical

15 Referring to soft modeling here. There are many examples of mathematical non-linear models, which are infinitely simplified to produce solvable equations, creating a mathematical approximation of a complex system rather than a reflection of the complexity of its behavior.

Literature Review

framework. Hirsch G. & Immediato S. [22] describe system dynamics-based microworld learning environments and how healthcare providers use them as they navigate through complexity and uncertainty. In particular, they focus on how system archetypes (or generic structures) are used as a device to capture what is learned about the dynamics of changes and to help microworld users apply this learning to similar situations in other settings.

The above examples combine the already existing methodologies into at times cumbersome frameworks, which can be interesting for studying some specific problems, but rarely such ‘framework creation’ projects take off to establish a recognizable and utile tool. There are plenty of various methodologies for M&S available and many more are in development. Some methodologies gain popularity, others find specific applications, and the rest never get more than one try and become forgotten, while the search for the most suitable ones continues. Many research organizations around the world use M&S to study various problems within the healthcare system. Such centers as the Santa Fe Institute and NECSI16 work on developing complex systems (molecular biology, epidemiology), while hundreds of university centers are developing dynamic modeling and control system simulations supporting decision-making in relation to diagnosis, patient management, etc.

In addition to the research activities, M&S has become a very popular practical tool for the majority of consulting companies providing services for the healthcare industry and offering optimization solutions using the whole array of methodologies including those described above. Fone, D. et al. conducted a review of the world literature to evaluate the extent, quality and value of computer

16 NECSI - New England Complex Systems Institute

Literature Review

simulation modeling in population health and healthcare delivery (applied to a wide variety of healthcare problems) and concluded that M&S is a powerful method for modeling both small and large populations to inform policymakers in the provision of healthcare [33].

System Dynamics The use of systems thinking and system dynamics became widespread in various fields and both are getting more exposure in healthcare and medicine. Systems thinking is regarded as a powerful qualitative analysis tool [34,35,36], while system dynamics also includes a quantitative simulation of the problem to provide a more in-depth analysis.

System dynamics theory with the prominent application examples has been introduced by Jay Forrester in his seminal works: Industrial Dynamics, Urban Dynamics and World Dynamics. Further theory development, especially in managerial applications, is reflected in the works of Peter Senge [36], Donella Meadows [37,38], and John Sterman [39,40,41]. The field’s popularity has been growing since the mid 1960s. Today there are thousands of model examples, articles and dozens of books describing the SD techniques, approaches and case studies [39,42,43,44,45,46]. The next generation of theoretical work on system dynamics addresses many important points in model building such as the diagnosis of surprising model behavior [47], formal aspects of model validity and validation [48], effects of feedback complexity on dynamic decision-making [49], etc.

As healthcare is concerned, the works of J. Hargrove, B. Hannon, and M. Ruth through textbook examples show how helpful and interesting system dynamics can be when studying problems in biological systems and health sciences [19,20]. Jack Homer, beginning with his dissertation (1983) [50] where he evaluated the potential of

Literature Review

two healthcare technologies (clindamycin and pacemaker) chose to apply system dynamics as a tool for policy-formulation/analysis in healthcare. His diabetes and antibiotics resistance models, as well as later works of others [16,18,19,20,23] provide good examples of how system dynamics can be a useful analysis tool to evaluate the diffusion, performance, and potential of medical technologies. Selective use of SD models in medical field is demonstrated by the study of energy regulation and obesity treatment [15], HIV/AIDS HAART therapy model [16], epidemics control program simulation [17], chronic disease prevention model [23], etc.

In addition to modeling specific problems in medicine and healthcare, there are examples of a more holistic approach to address the issues of complexity using systems thinking. Kurt Heidenberger in his works [6,7] gives a thorough review of the preventive healthcare literature and suggests the use of systems thinking and system dynamics as essential tools for socio-economic decision support for policymakers in this field. He defines preventive healthcare to be a strategic investment and presents an example of quantitative modeling for the program selection and resource allocation. A recent initiative by the Institute of Medicine builds upon this approach and proposes the reform of the American healthcare system using systems thinking [29,34] to understand and solve complex problems. The important accomplishment of this work is the recognition that the US healthcare is a complex adaptive system demonstrating emergent behaviors at both macro- and micro- levels. Thus, the use of classic tools (discrete-choice modeling, pan-sector centralized regulation, etc.) to address the current problems of the system is inappropriate and the aim is to create the conditions for self-organization through simple

Literature Review

rules under which massive and diverse experimentation can happen [29].

System dynamics is a prominent example of a nonlinear approach to modeling and while in nonlinear systems results are less generalizable, they are considered to be more relevant [120] which makes SD a profoundly useful tool to study problems in medicine and healthcare where generalizability is not always an objective.

MEDICAL LITERATURE

Every year thousands of articles dedicated to studies of prenatal care, birth defects, health technology assessment, preventive medicine, etc. are being published. The growing number of researchers and the availability of resources including easy access to data, publications and other information enabled by digital technologies speed up the scientific progress, however, the ability of the real world to adopt new applications, is not always matching the pace of the research activities. Although, medical research itself is not without pitfalls. Due to inadequate designs and investigator biases, the overall quality is not always exemplary: informal methods are simple but unreliable, while large-scale randomized clinical trials can provide unbiased, statistically significant, and often conclusive evidence but at the cost of years of work and millions of dollars [50]. Significant efforts are needed on both sides to fine-tune the research activities and increase the applicability potential of the available research.

Prenatal Care

Literature Review

Preventive medicine continues to constitute only a small part of today’s healthcare in the US and around the world. Clinical preventive medicine is bringing some improvements [51] into the system, but its positions are far from dominating. If we look at the field of healthcare as a whole, maybe only 5% of its operations are of a preventive nature while the overwhelming share (~95%) of resources, attention, and research in healthcare are directed to treating various conditions17.

Naturally, most doctors are trained to treat disease, not to maintain health; but pregnancy is not a disease [52]. Prenatal care18 is being attributed to preventive medicine unless a treatment is required for a problematic condition. Thomas Strong gives a very critical overview of the American prenatal care in his book Expecting Trouble: The Myth of Prenatal Care in America. He highlights the stagnation in the current prenatal care where improvement is impeded by the organization of the healthcare system in the US and points to the predominantly cultural value of prenatal care visits. Pregnancy outcomes in America do not seem to be getting better, since the number of low birth weight newborns (LBW), pre-term labor cases and birth defects is not decreasing. Thomas Strong firmly believes that the current system does not serve the children and the mothers as well as it could [52] and even in the 21st century it is still a mystery as to what actually constitutes prenatal care. Other researchers in the field support T. Strong’s views. For example, Misra et al. find that despite great strides in improving prenatal care utilization among American women, key perinatal indicators have remained stagnant or worsened in the past decade, and the United States continues to rank near the bottom compared to other developed countries [53]. The Institute of

17 Poletaev A.B.18 Prenatal care is a complex of interventions that a pregnant woman receives from organized healthcare services.

Literature Review

Medicine acknowledges that the term “prenatal care” represents an inexact collection of interactions and procedures which focus more on the total number of prenatal care visits than on the actual content or quality of those visits [52]. Also, research shows that many prenatal interventions are unnecessary or of unproven benefit [118].

Infant mortality has been declining and continues to do so, but it is happening mostly due to advancements in neonatal technologies [52] not the improvements in prenatal care or decrease in pre-term birth cases, or the overall improved health of newborns. Lu M. C. et al. state that neither pre-term birth nor IUDR19 can be effectively prevented by prenatal care in its present form. Preventing LBW requires reconceptualization of prenatal care as part of a longitudinally and contextually integrated strategy to promote optimal development of women's reproductive health not only during pregnancy, but over the life course [54]. Very slowly these concepts are being introduced into the preventive care, but more effort is needed to make this system efficient so that it shows positive results [55].

Besides improving prenatal care itself, the methodologies used to study its efficiency should be refined as well in order to accurately reflect important parameters, whether they are quantifiable or not. For example, measuring the benefits of prenatal care conclude that the researchers should be explicit and rigorous in their application of preference-based approaches to benefit measurement, since in many studies the units cannot incorporate several health changes that might occur within a single measure, and they overlook individuals’ preferences for those health changes [56].

19 Intra-uterine death rate

Literature Review

Birth Defects Today it is believed (and the actual results show) that the majority of birth defects cannot be prevented by prenatal care [52,57]. Whether this situation changes, we are yet to see, but currently a lot of resources around the world are being dedicated to study the causes of birth defects, prevention methods, treatment during prenatal, neonatal, and early childhood periods.

In the US, in collaboration with the research hospitals and institutions, many states have expanded centralized programs studying, tracking and preventing birth defects (CA, AZ, AL, GA, NC, etc.) but the leading role in the promotion of awareness, research, support and lobbying efforts belongs to the March of Dimes. For almost 80 years the incredible work of this organization has benefited society. Today, the March of Dimes20 estimates that every 28th baby in the US is born with a birth defect and 60-70% of birth defects have unexplained causes [57]. The socio-economic costs to society are tremendous. Stigma, discrimination, social isolation, lost hopes and opportunities, and the daily stresses associated with lifelong impairment, add to the physical and economic burdens [58]. In addition to hardships produced by birth defects, studies show that people born with low birth weight are more likely to lead a lower quality of life and have more learning disabilities [59,60], succeed less than their counterparts [61], and suffer from various health problems later in life [54,62].

Various sources confirm that the current state of knowledge regarding the etiology of birth defects is quite limited [57,62,63] and research is the first stage in the development of new prevention strategies [62]. Some authors claim that prevention of birth defects can bring ethical concerns from parties opposing eugenics and

20 [http://www.modimes.org/]

Literature Review

lobbying groups of handicapped people [64], but the research and all the efforts to prevent birth defects will continue, even if only marginal success rates are feasible at the moment. The Institute of Medicine reports on Reducing Birth Defects (2003) and Improving Birth Outcomes (2004) take a population approach to studying these problems in developing countries, for these works yield a lot of important information for the developed healthcare systems as well. In particular, pointing that the policymakers should recognize the enormous personal and societal consequences imposed by birth defects [65,66]. It should be a responsibility of any healthcare system to provide women with the care they need and give children the best possible start in life [58].

Patricia Baird in her analysis of the consequences of the population-wide implementation of a genetic screening program in Canada concludes that, while the population approach can bring a lot of benefits, it promises to be difficult to implement from the economic and individual decision-making perspective [58]. Norman Fost [67], studying mass population screening for genetic disorders in asymptomatic individuals, draws special attention to a careful analysis of risks and benefits highlighting that the right to decide whether to test or not should be left to the individual. With time, for the healthcare systems to be efficient, they will be forced to establish population-level programs for conditions homogenously affecting the society, and customized programs for focusing on specific problems and targeting selected groups. Before such levels of operational efficiency are achieved, the rapid development of technologies and programs will continue emphatically.

Currently, a large share of research literature on birth defects focuses on genetic causes. Even though they constitute only a small

Literature Review

percentage of all birth defects and are not a subject of this research work, the findings of genetic studies are very useful to learn from since they cover economic, ethical and social aspects, which are common to prenatal care overall. The reason the studies of genetic causes of birth defects prevail is perhaps due to the negative popularity, which is produced by the severity of most conditions, the availability of funding and the latest achievements of the Genome project stipulating further scientific interest. This research evaluates a technology (ELI-P Complex) which, through determining the probability of pathology in pregnancy, is capable to some degree of predicting and preventing birth-defects of a non-genetic nature: the development of an embryo/fetus is influenced by various teratogenic factors: pollution, mother’s infections, medications, etc., which do not significantly influence the genome but have one important common characteristic – they all lead to changes in the activity of the immune system of a pregnant woman [68].

Immunology & Pathology of Pregnancy

Back in 1978 an interesting study of the paired serum samples was published and concluded that infections and other maternal factors are the risk indicators for congenital malformations [69]. Many other examples associating female health status with the pregnancy outcomes followed [70,71,72]. Recently, the growing body of literature emerged analyzing the immunology of pregnancy. For example, Dr. Peltier’s work explained certain immunological aspects of pre-term labor [73], Dr. Coulam studied pregnancy failures due to the lack of tolerance of a genetically incompatible fetus by the maternal immune system [74], Dr. Serova studied immunological, infectious and endocrine aspects of unsuccessful pregnancies [78], etc.

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By the end of the 20th century, enough evidence was obtained to state that antibodies, present in the serum of healthy individuals in the absence of deliberate immunization with any antigen, are referred to as natural antibodies [75]. This led to the entirely new wave of studies and experiments in many fields21, but also – the reproductive health. Poletaev A.B. & Morozov S. studied the changes of maternal serum natural antibodies of the IgG class to the four antigens affecting the embryo/fetus development. Later works of Dr. Poletaev A.B. et al. [68,72,76] found that the determination of embryotropic auto-Abs contents, synthesized by an organism of a mother and delivered to an embryo/fetus, in the blood serum of a pregnant female can provide a lot of important information necessary to make a prognosis of the pregnancy development. Failures in the production of such auto-Abs are directly related to a wide spectrum of problems: from pathology in pregnancy development and infertility of unknown genesis to habitual miscarriages and congenital defects [71]. These findings led to the development of the screening technology – the ELI-P test (currently ELI-P Complex – see Appendix 1) for the determination of probability of pathology in pregnancy. A few dozen of experimental projects followed in order to assess the clinical potential of this tool [77,78,79,80,81,82,etc]. The approbation of the ELI-P test and later the ELI-P Complex was done at hospitals and research centers around Russia [77,78,80,81,82]. All these studies confirmed that the ELI-P test is an efficient method for determining the pathology in pregnancy and recommended it for clinical practice. The findings of Dr. Budykina and Dr. Serova also suggest that ELI-P test can be used to examine women planning pregnancy and to determine the state of the immune regulation of an embryo development; to examine women at early

21 Works by Irun Cohen, Yehuda Shoenfeld, etc.

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stages of pregnancy (1st trimester) with the goal to detect as early as possible patients who belong to the groups of an elevated risk for the pathological pregnancy development and conduct the necessary treatment and prophylactics; and to screen women planning pregnancy in order to detect females who may possibly have infectious diseases and/or endocrine problems and if necessary, provide further analysis and treatment [81].

Demin V. and Kluchnikov C. conducted a very interesting study of the health state of children from mothers evaluated with the ELI-P test during pregnancy and found the screening technology to be also a good prognostic tool for determining possible problems in the health status of a future child [80].

SOCIO-ECONOMICSOver ten years ago, Waitzman N. and Romano P. produced an

important comprehensive work estimating economic costs of major birth defects. Using the standard cost-of-illness methodology, they found that extensive indirect costs arise due to morbidity from congenital defects [62]. Their estimate from 25 years ago excluding direct medical costs was US$6.3 billion in 1980 (18 major birth defects). At today’s prices the current costs must be double that number, but perhaps much more if at least 50 common birth defects were studied and evaluated including direct and indirect costs. Metkus A. et al. also recognized tremendous costs of birth defects in their study of diaphragmatic hernia and associated treatments [83], which exceed US$230 million per year in the US. Since most children with birth defects do not die in infancy, they require special treatment into adulthood, special education, rehabilitation, and non-medical services, which increase costs [61,62]. The productivity loss due to the limited

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participation by the members of society with birth defects also affects the economy [62,83]. Because many birth defects (congenital abnormalities) are common for the LBW and pre-term infants, the more recent studies by Petrou S. et al. find that pre-term birth and LBW22

also impose a substantial burden on special education and social services, on families and care givers of the infants and on the society in general [59].

Economics and societal costs of various medical conditions, healthcare technologies, and procedures are being extensively studied and as the availability of new and improved techniques of prenatal diagnosis increases, so does the relevance of economic evaluation [84]. There are numerous examples of cost-utility [85] (including the estimation of QALYs, HYEs, SAVEs23) models of prenatal screening programs, technologies, procedures [55,58,86,87,88,89,90,91] and diagnostics [55,84,89,91,92]. A few studies evaluate costs-per-procedure to fix various birth defects [62,93,83] and estimate costs (economic consequences) associated with stillbirth [94], LBW [59,60,61] and pre-term labor [59,60] applying cost-effectiveness [95], cost-utility [96], cost-of-illness [62,97], cost-benefit analyses, etc.

Special conditions aside, prenatal care itself is relatively expensive24 [118] therefore it needs to be scrutinized and planned carefully. The average pregnant woman in many countries receives 150 or more specific tests, examinations, and interventions during pregnancy [118]. This broad range of options makes evaluation of prenatal care a challenge. Some authors suggest that economic analysis of prenatal diagnostic techniques should incorporate as wide

22 For babies born at less than 1000 g, the an additional cost per additional survivor at discharge was between ₤84,490 and ₤174,040 (1998 ₤ sterling) [59].23 QALY – quality-adjusted life year, HYE-healthy years equivalent, SAVE-saved young life equivalent24 ~US$3000 per pregnant woman (1996 estimate)

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range of benefits as possible [84]. For example, some monetary-based techniques include the revealed preference approach, which involves observing decisions that individuals actually make concerning health risks, and the willingness-to-pay approach, which provides a framework for investigating individuals' willingness to pay for benefits of healthcare interventions. There are also many examples of discrete choice experiments that describe healthcare interventions in terms of their attributes, and elicit preferences for scenarios that combine different levels of those attributes [56]. However, Stavros Petrou very rightly points methodological limitations of economic evaluations of prenatal screening [60,97] since most of the approaches still fail to take into consideration a variety of non-quantifiable parameters.

Today the outcomes of the socio-economic analysis in the field lead us to believe that costs of birth defects can be decreased through the use of new technologies and the prudent use of laboratory and radiological examinations [83], and genetic prenatal diagnostic testing can be cost-effective at any age or risk level [92]. Opinions with regards to the costs of population-level prenatal screening differ: some studies find socio-economic benefits [57,63,64], while others find such approach to be unfeasible [58]. As per the ELI-P test technology evaluation, Demin V. and Kluchnikov C. conclude that the method can have a socio-economic importance for pediatrics because its results allow an early, active, and justified prophylactics of children’s illnesses which, through the prevention of chronic and life-threatening conditions in newborns and children, should yield noticeable economic savings [80]. But this is only one aspect of the socio-economic potential of the ELI-P technology and many others are yet to be assessed.Health Technology Assessment (HTA)

Literature Review

The field of HTA is dominated by health economists who have produced a significant body of literature on the subject over the past 20 years. There has been a noticeable increase in the number of published studies on healthcare programs and interventions [98]. Nowadays the field is very vibrant and continues to foster research through INAHTA25, government led programs such as NHSHTA26 in Britain, CHSPR27 in Canada, ECAHI/ECHTA28 in EU, independent HTA agencies and research units within universities, institutes, pharmaceutical/medical technology corporations and hospitals. The wide scope of the HTA literature covers the findings from clinical trials to economic evaluation to technology reports by the experts: most of the time these studies do not carry the HTA title. Thus, the literature from various fields actually finds itself under the umbrella of the HTA publications, which makes it difficult to classify and identify all the available sources.

The HTA theory and tools are well summarized in the collection of case studies edited by Szczepura A. and Kankaanpää J. [99] along with an ample online collection of materials assembled by Topfer L., and Austron, I. in their HTA E-text [98]. The methodologies used in HTA studies depend on the type of the technology itself, the stage of HTA and the purpose of application: some health technologies must undergo ethical evaluation for their deployment while others could be safe from this perspective but very expensive to implement and need a thorough cost-utility or cost-effectiveness analysis. HTA may include “scientific” methods such as randomized controlled trials, expert consensus techniques [99], economic analyses, decision analyses, 25 The International Network of Agencies for Health Technology Assessment26 http://www.ncchta.org/27 http://www.chspr.ubc.ca/28 http://www.ecahi.org/

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relevant literature reviews of complex evidence, etc. These techniques have been independently developed and vastly practiced in different fields; the application in healthcare has been characterized by a variable degree of success.

Being a recommendatory field rather than an exact science, medicine often provides conflicting reports of questionable quality, which makes it difficult to distinguish reliable sources in the pool of existing findings. The cost-effectiveness guidelines and studies in health and medicine [101] have been in place for a few decades now and are used by an overwhelming majority of health economists and HTA specialists but the current focus on clinical cost-effectiveness produces work of limited relevance to managerial decision-makers [102]. Therefore, many new approaches are being developed either for specific studies or as an attempt to propose a generalizable methodology to address the peculiarities of the healthcare field. For example, Greenhalgh T., et al. propose a meta-narrative review to evaluate multiple sources and make sense of seemingly contradictory data in diffusion of medical innovations research [100], Sloane E. et al. use the analytic hierarchy process (AHP) as an HTA tool [9] and demonstrate how AHP can be applied to rapidly and efficiently build a neonatal ventilator evaluation model. Their methodology allows a diverse set of decision factors to be assessed before making a product evaluation. But much HTA still relies on a linear model which aim is to identify technology, evaluate it in a timely manner to produce definitive results to be disseminated to the target audience who, if properly primed, can implement them. Many authors come to the realization that linear models do not accurately describe what happens in practice [3,99,102,103]. Perhaps the biggest challenge for HTA is to refine our understanding of the limitations of the model so that it can

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be developed into an exercise that is more in tune with the social, political, and organizational world it serves.

Many of the methodologies described previously in this chapter (socio-economic analyses, systems thinking, system dynamics, discrete choice modeling, etc.) are becoming increasingly popular in HTA. But the choice of the methodology (or combinations) ought to be determined by the type of the technology and the reason for which it is being assessed (specific performance properties, for a chosen market, for a diffusion potential, etc.). HTA should combine technical assessments of effects with a review of wider social issues such as ethical and legal implications, and the effect of technology on the whole organization (system). Knowledge of the “whole system” effects might alert managers and clinicians to potential pressures and opportunities created by the technology and better prepare them to manage their introduction. Any shift to such a broad approach in HTA would entail complex methodological problems, that can be addressed through the standardization of HTA methodologies, which is yet to come along with the international guidelines needed to aid the process [99].

Diffusion of Medical Technologies Various diffusion paradigms have been developed across the industries. The seminal work of E. Rogers Diffusion of Innovations aggregates decades of studies in diffusion research providing a valuable resource for all fields characterized by technical sophistication and a high innovation potential. According to him, the diffusion research focuses on five elements: 1) the characteristics of an innovation which may influence its adoption; 2) the decision-making process that occurs when individuals consider adopting a new idea, product, or practice; 3) the

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characteristics of individuals likely to adopt the innovation; 4) the consequences for the individuals and the society for adopting the innovation; and 5) communication channels used in the adoption process [104]. A popular example of a diffusion model is the Bass model29 [39] of new products and technologies’ adoption. It has been widely employed, including the healthcare sector, and has become a classic tool for the development of other more descriptive and complex diffusion models.

As per medical technologies, early work on diffusion dates back to the 1920s30 but mainly, the research on the diffusion of medical innovations has proliferated since the 1950s [50]. Ann Bonair and Jan Persson in their case study “Innovation and Diffusion of Healthcare Technologies” provide a comprehensive overview of these processes, aggregating information from historical examples to contemporary methodologies [99].

The applications departing from the classical diffusion paradigm have mostly concerned drugs [99]. Poh-Lin T. identified that diffusion in the pharmaceutical industry is influenced by such factors as technological familiarity, product differentiation, competitive intensity, source of technology and national origin [12]. However, regulations play a very important role when medical technologies are concerned. In countries, where medical equipment is not regulated, technologies can diffuse without controls, even if they are not reimbursed by health insurance agencies. This leads to conflict among interested parties or increases risks (side effects) from the use of these technologies [106]. Besides the technologies, there are also interesting studies of healthcare programs’ diffusion/dissemination. King L., et al. argue that 29 Linear model depicting the relationship between the market potential, external influence, and internal influence producing an S-shaped curve for the diffusion of innovations over time 30Works by Ogburn, WF (1922) Social Change and Stern, BJ (1927) Social Factors in Medical Progress

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despite repeated expressions of concern, both academic researchers and program delivery practitioners tend to neglect the dissemination of new knowledge about health promotion programs, so the effective programs are often not implemented as widely as they could be and that health promotion programs are not achieving their full potential. A two-way construction of dissemination is suggested to show how research can benefit from collaboration with program delivery practitioners [107].

The majority of statements from policymakers and in policy documents on evidence-based medicine implementation continue to draw on classic diffusion of innovation models [103]. But, the identification of new emerging healthcare technologies [105] often follows a different path than consumer technologies and the diffusion of innovations in the medical field does not always demonstrate the S-shaped growth [99,103]. For example, IT/consumer products usually exist in the environment of minimized regulations and maximized competition, while healthcare products can be regulated by policies, many levels of quality control and very sensitive demand (based on need, not popularity). As benefits and risks for individuals as well as public health vary with each new [medical] test [67], estimation of demand for a given technology in a given setting is more complex than for other goods. In his dissertation (1974) on the diffusion of leukemia chemotherapy, Warner K. demonstrated that not all innovations diffuse according to an S-shaped curve; that diffusion is not an exclusively economic phenomenon; and that the complexity of diffusion should be reflected in the mix of variables studied [99].

Further explorations of diffusion of medical technologies are presented in dissertations of Homer J. (1983), Bonair A. (1990) and studies from other areas of healthcare practice [50,99], which identify

Literature Review

a variety of factors responsible for the diffusion in healthcare. Dopson S. et al. find that clinician’s own experience often has a greater influence on their practice than research published in journals [103]. Also, prevailing personal, social, and professional values, along with economic and organizational constraints, affect the adoption of new technologies. Williams and Gibson [103] state that diffusion in medicine is influenced by science push, strong networks, communications, boundary spanners, knowledge utilization, demand, problem-solving needs among users which makes the dissemination process far more chaotic than the S-curve used in Rogers’ work [104]. It is important to keep in mind that research knowledge about new medical technologies should not be considered as a fixed entity as technologies change and develop after they are launched [102] and dynamic observations and adjustments are necessary in technology evaluation. While many authors acknowledge the importance of feedback loops, they do not provide an analysis of the complex social context that is often referred to in the literature as the “receiving system”. More and more works address the need for more complex models to explain such complex social processes since current theories and frameworks of innovation and diffusion from sociology and economics do not provide a comprehensive model for understanding technological change in general or in the healthcare system [99]. All of the above examples draw attention to the fact that the linear models of diffusion are not applicable and the need for the synthesis of methodologies and diffusion studies is profound.

Ethics & Regulations in healthcare play a dominant role. A vast body of literature is dedicated to studying the influence of ethical and regulatory aspects on the success of healthcare programs,

Literature Review

technologies, and procedures. They comprise a very important part of many HTA initiatives, providing valuable information to the society and researchers, helping make proper decisions and find optimum solutions.

As the reproductive technologies are concerned, the following ethical and regulatory aspects come into play: educational and socio-economic status, religion, availability of care and promotion, estimation of risks and benefits, etc. In the current literature, the most attention to ethical and regulatory issues is given in the evaluation of genetic technologies due to the high risk involved. Ethical implication of [genetic] screening in asymptomatic individuals concludes that as with all new technologies, benefits and risks have to be assessed in well-designed and well-reviewed studies if individuals are to be allowed to make informed decisions regarding whether or not to be tested [67]. Various studies evaluate women’s preferences towards prenatal screening [57,91,109,110] and while a lot of reasonable concerns are expressed towards invasive screening procedures for genetic defects, practically all females are ready to do whatever it takes to ensure the healthy outcomes of pregnancy and healthy newborns.

With regards to the population-based approach and a common overseeing body, Europe is more proactive seeing advantages in balancing potential benefits and disadvantages of commercial prenatal testing for all parties involved [108]. In the US a lot of progress is being made at the individual research centers but thus far there have been very few attempts to organize state wide regulatory programs. Thus, the major ethical issue in prenatal care, which is access, remains the major issue of the American prenatal care: when an intervention clearly benefits maternal and child health, is it ethical not to provide it routinely? On the other hand, is it ethical to provide low-

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risk pregnant women with intensive prenatal care procedures, which may be causing harm or wasting scarce resources? [118]

Hopefully with time, the regulatory system in the US will direct proper resources to offer various population-wide programs for prenatal screening and testing (genetic and non-genetic) guided by a well-developed regulatory scheme and benefiting the society. This research acknowledges the importance of ethical and regulatory evaluation, but emphasizes it only as the access to the technology use is concerned, since the ELI-P Complex is associated with minimal risks31

to the patient and does not require special approvals from such regulatory bodies as FDA.

Health Policy & Systems Research

Health policy’s role in the pursuit of health is played out across many fronts because health is determined by many variables: the physical environment in which people live and work, their biology and behavior, social factors, and access to health services [111]. Thus, health policy receives a great deal of attention at all levels of government and promotes a demand for the health policy research, focusing on the development of better methodologies for policymaking and analysis. There are many books and articles discussing the techniques of health policymaking: analysis and formulation [111,112,113,114,115]. Beaufort Longest provides a comprehensive model of this process [111] and Bodenheimer S. and Grumbach K. cover such fundamental topics as cost containment, health insurance, managed care, and physician and hospital payment, bringing up

31 The test imposes no direct risk on the patient, but only provides additional information about the health status which theoretically can be emotionally disturbing (since NO exact diagnosis is being given to a patient, the emotional damage is minimized)

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important policy issues and pinpointing individual encounters within the American healthcare system [112].

Every year more than US$70 billion is spent on health research and development by the public and private sectors. An estimated 10% of this is used for research into 90% of the world's health problems [116]. But even these ten percent allow for immense amounts of new discoveries, studies, evaluations, etc. The utilization of health research in policymaking should contribute to policies that may eventually lead to desired outcomes, including health gain [115], however, very often many policies are being formulated without sufficient reference to research. This is happening not because of lack of research, but poor links between the available research and its users (all levels).

For decades, evidence-based medicine has been viewed by many policymakers, managers and clinicians as an important lever to ensure clinical practice is more effective and represents value for money [103] but the robust clinical evidence is no longer considered to be sufficient for many policy decisions. Despite growing acceptance of the principle of evidence-based practice amongst clinicians, there is still a weak relationship between the available evidence and clinical behavior change [103]. HTA provides valuable information but is not yet dominating the field or constituting the focal point of decision-making in healthcare either. The reason for the limited use of HTA is the failure to assess the effect of new technologies on the organizations which adopt them, the complex nature of knowledge about new technologies, and the personal and social values through which results are interpreted [102]. This situation is changing and stronger links between research and practice are being formed as many new developments take place within the heath systems research.

Literature Review

The field of health outcomes research (HOR) is conformable to HTA and also treats clinical findings alone as an insufficient measure and looks beyond physiological measures of success to examine effects of the healthcare process on patients and populations. HOR seeks to understand the end results of particular healthcare practices and interventions, and it places a greater emphasis on the patient [117]. Outcomes research can affect health policy decision-making at local, state, and national levels and in both private and public sectors [117]. This research, while predominantly focusing on HTA, finds useful links in the health outcomes research and incorporates them into the model.

Prenatal/preconception care is influenced by a myriad of different policies at all levels, some of which are more effective than others. Policymaking in mother-child health has traditionally been characterized by a greater reliance on regulatory and judicial bodies and the frequent use of mothers and children as political symbols, despite their lack of direct influence on policymaking [52]. This situation should and eventually will be changed.

IDENTIFICATION OF GAPS IN LITERATUREThe literature from various fields has been analyzed in this

chapter and, while we must acknowledge a phenomenal progress over the last few decades in improving all aspects of healthcare, (especially making it more technologically-savvy), and research in related fields, each area is characterized by many gaps which have yet to be addressed in theoretical and applied studies.

Healthcare While there is a large body of literature covering socio-economic costs of birth defects, prenatal care procedures, or specific

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screening tests, there are very few population-wide studies and forecasts, which are essential for the development and introduction of proper policies. Pregnancy is of a ubiquitous interest to the world’s population, not an issue concerning a specific group; thus, population-wide policies should be developed and implemented. However, at this point we are still very far from an efficient prenatal care system in the US due to the following reasons: 1) not all population groups understand the importance of the procedures; 2) there is a considerable disagreement regarding the elements of even the most basic type of prenatal care; 3) insurance coverage and availability is an impediment in certain population groups; 4) etc.

A fair amount of inconsistency exists regarding various official bodies’32 recommendations about the nature and delivery of prenatal healthcare services (organization of care, clinical testing and screening and pregnancy education) in the US [52]. The WHO proposed a new model involving fewer visits and fewer interventions aside from those identified for particular cases [118], but it remained in an experimental state. Research needs to focus on factors that identify high-risk pregnancies and on the health benefits of specific interventions [118]. This, with the help of proper resources, should help design and implement an effective prenatal care system in the US.

Few studies of the cost or cost-effectiveness of prenatal care have been carried out [118] and many prenatal interventions have never been evaluated at all, thus there is a great need for more research. The current focus in prenatal care on genetic technologies is understandable, since a lot of interesting findings come out of the Genome project but let us not forget that genetic problems constitute only ~13% of all birth defects and significantly less efforts and

32 US Preventive Services Task Force and Public Service Expert Panel.

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resources are being dedicated to studying other reasons of pathological pregnancy outcomes. Unfortunately, most results from the ELI-P and ELI-P Complex exist only in gray literature and memos of hospital reports and not yet exposed to the world community of researchers. We can say that HTA of the ELI-P Complex has began, but while the old results of the ELI-P test33 speak of a technology with a great potential, the evidence overall is incomplete. Obviously more ELI-P Complex controlled studies are needed to provide sufficient clinical information for general and specific cases (examples given in Chapter 7). There is also a need for socio-economic evaluation of the ELI-P Complex (which this research attempts to address) and the real-life (not simulation based) population-wide studies.

Misra D. et al. suggest the life span perspective, which focuses attention towards the preconceptional and interconceptional periods as targets for intervention in improving prenatal health. The multiple determinants model distinguishes among concepts of disease, health and functioning, and well-being for both women and their offspring [53]. It should be interesting to apply this approach to the evaluation of the ELI-P Complex and to some degree, the system dynamics model described in this thesis attempts to do so.

The research in prenatal care will continue at all levels offering new findings, methodologies and policies striving for a better system and improving the health of population.

HTA and M&S Medicine and technology have been integrated decades ago, and medical engineering has been very successful. Today there is plenty of decision support software for physicians and biomedical engineering products from artificial limbs and eyes to implantable 33 Most clinical trials data which exists and has been used in this research comes from the ELI-P test (4antigens), not the advanced ELI-P Complex test (8antigens).

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chips. However, M&S is far from extinguishing its full potential and animals and people continue to be used for medical research, enormous resources are being spent and mistakes made. The reason the simulation development in medicine is slow is due to its extreme complexity and a high level of risk involved (if the live system is being modeled and used in lieu of the in vivo trials) but the necessity of simulations is obvious and with time and growth of technological sophistication, this barrier will be crossed. D. Fone and his colleagues reviewed 182 publications and concluded that further research is required to assess the value of modeling in healthcare and medicine but found M&S to be a powerful and useful method for policymakers [33]. The leading researchers applying M&S in their respective medical fields find that the tremendous potential of computer-based modeling and simulation in medicine could be realized within ten years given a significant commitment of resources [1]. There have been some attempts to use system dynamics to help develop and implement policies and programs in healthcare [18,23,27], but only in an exemplary fashion and the use of SD for health policy formulation and analysis is still far from being a common practice.

Szczepura A. and Kankaanpää J. in their book summarized HTA achievements and challenges in case studies of HTA application around the world. From this work, it is clear that the field is still developing and many gaps in information and understanding currently exist [99] and HTA still has relatively little impact in the political world of healthcare organization [102].

As we see, there are a number of gaps in theory and practice that need to be addressed by the joint efforts of specialists from various fields. This thesis is aiming to fill one of these gaps through a comprehensive example of the HTA methodology, which incorporates

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into one model various techniques that have been used separately and provided insufficient information for policymakers. The application of system dynamics to the assessment of a medical technology allows evaluating the potential of the “whole system” involving the social context in which the technology is designed to operate, while incorporating examples of traditional cost-utility analysis and results of clinical trials.

NB Current popularity of multidimensional research yields a lot of rich and interesting information, but at the same time increases complexity and redundancy. For example, there are a few new fields which are developing their own approaches and theories to explain and explore the same questions and with time some of them should either disappear or merge to create more substantial research fields (for example, such pairs/groups as 1) sociocybernetics34, complexity science and system dynamics, 2) health outcomes research and HTA seem to be focusing on similar goals and using comparable technologies but continue to be pursued as separate research fields).

NOTES: Numbers in [ ] correspond to the title in References p. 221

1. [81] Higgins, G.2. [71] Granic, I, Hollenstein, T. 3. [80] Higgins, J. 4. [50] Danhof, M. 5. [57] Fahley, D. 6. [77] Heidenberger, K 19927. [78] Heidenberger, K 19968. [117] Poh K-L9. [135] Sloane, E. et al.

34 Sociocybernetics provides a theoretical framework as well as information technology tools for responding to the basic challenges social groups (individuals, families, companies, countries, etc) are facing today: giving software and database solutions of problems mostly coming from the competitive nature of human relationships [119].

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10. [136] Stausberg, J. Person, M. 11. [85] Homer, J. 198712. [116] Poh-Lin, T. 13. [51] Davies, R. et al.14. [74] Hall, J. et al.15. [35] Abdel-Hamid, T.16. [49] Dangerfield, B. et al.17. [59] Flessa, S. 18. [86] Homer, J. 200019. [15] Hannon, B. 199720. [17] Hargrove, J. 21. [65] Fraser, S. Greenhalgh, T. 22. [82] Hirsch, G. Immediato, S. 23. [87] Homer, J. 200424. [94] Liddell, W., Powell, J.25. [114] Plsek, P., Greenhalgh, T.26. [115] Plsek, P., Wilson, T. 27. [128] Royston, G. et al.28. [144] Wilson, T. Holt, T.29. [18] IOM Crossing the Quality Chasm30. [151] Peters, J. 1971.31. [38] Baldwin, L, Eldbi, T., Paul, R. 32. [79] Heymann, A. et al.33. [60] Fone D. et al.34. [55] Doyle, J. 35. [56] Doyle, J., Ford, D.36. [29] Senge, P. 37. [22] Meadows, D. Limits to Growth, 200438. [98] Meadows, D. 198939. [30] Sterman, J. Business Dynamics, 200040. [137] Sterman, J. 198841. [138] Sterman, J. 199242. [7] Coyle, R. 199643. [26] Richmond, B. 200444. [33] The MIT System Dynamics Group Lit Collection45. [21] Maani,K., Cavana, R.,46. [124] Richardson, G. 198647. [97] Mass, N.48. [42] Barlas, Y.49. [52] Diehl, E. Sterman, J. 50. [148] Homer, J. 198351. [45] Corcoran, J., White, S. 52. [31] Strong, T.53. [102] Misra et al.54. [95] Lu M. C. et al.55. [47] Cunniff C. 56. [112] Petrou, S. Birth 200357. [222] March of Dimes58. [37] Baird, P.59. [109] Petrou, S. 200060. [111] Petrou, S. IJOG 200361. [39] Barker, D. et al.

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62. [143] Waitzman, N., Romano, P. 63. [208] CDC data64. [43] Bassett, K. et al.65. [2] Bale J. et al. 200366. [3] Bale J. et al. 200467. [64] Fost N.68. [164] Poletaev, A.B., Kuzmenko L, 200569. [91] Koskimies, O. et al.70. [106] Patterson, P.71. [118] Poletaev A.B., Morozov S.72. [121] Poletaev A.B. 200373. [108] Peltier, M.74. [46] Coulam CB.75. [93] Lacroix-Desmazes, S. et al.76. [119] Poletaev A.B. 200277. [156] Zamaleeva R. 199978. [154] Serova, O. 200079. [149] Litvak, O. 200180. [182] Demin, V., Kluchnikov C.81. [180] Budykina, T. , Serova, O.82. [200] Zhigulina, S.83. [99] Metkus et al.84. [133] Shackley, P.85. [69] Gold, M., Stevenson, D., Fryback, D.86. [110] Petrou, S. 200187. [113] Petrou, S., Mugford, M.88. [127] Rouse D., Stringer J.89. [129] Sansom, S. et al.90. [131] Schrag S. et al.91. [136] Sherlaw-Johnson C., Gallivan S., Jenkins D.92. [76] Harris, R.93. [68] Gilbert, R.94. [100] Michalski, S., Porter, J., Pauli, R.95. [36] Anderson, J. et al.96. [66] Fryback, D., Lawrence, W.97. [125] Rice, D.98. [34] Topfer L., Auston I. (eds)99. [32] Szczepura A., Kankaanpää J. (eds)100. [72] Greenhalgh, et al. 101. [69] Gold, M., Russell, L., Siegel J. 1996102. [126] Rosen, R., Gabbay, J.103. [53] Dopson, S. et al. 104. [27] Rogers, E. 2003105. [54] Douw, K. e al106. [142] Vondeling, H. et al.107. [90] King, et al.108. [83] Hoedemaekers, R.109. [123] Press, N., Browner, C.110. [130] Santalahti, P. et al.111. [20] Longest, B. 2002112. [4] Bodenheimer, S., Grumbach, K. 113. [186] Gallagher, J. et al.

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114. [187] Hall, E., Berlin, M.115. [75] Hanney S., et al. 116. [1] Alliance for Health Policy and Systems Research117. [175] Arnold, S., et al.118. [177] Banta, D. HEN Report 2003119. [http://www.absoluteastronomy.com/encyclopedia/S/So/

Sociocybernetics.htm]120. [167] Forrester, J. 1985.

Chapter 3Summary: A dynamic hypothesis is formulated using the standard approach from system dynamics but adjusted to suit this research to properly identify and address the problem, which is being studied. Historical data is aggregated to give credibility to the process of placing ELI-P Complex technology into the socio-economic context, which in turn, helps identify stakeholders and main variables. Causal loop diagrams (CLD) are developed using Consideo® software. Influence diagrams [causal loops] help identify gaps in knowledge and data, determine some parameters and yield interesting informative insights about the problem, thus providing the qualitative analysis for the development of the simulation.

Literature Review

Formulation of Dynamic Hypothesis 3.1 ELI-P Complex in Socio-economic Context

3.1.1 Pregnancy Outcomes’ Indicators3.1.1.1 Congenital Anomalies’ Indicators

3.1.2 Female Health and Other Indicators3.1.3 ELI-P Complex

3.2 Stakeholders 3.3 Dynamic Hypothesis

3.4 NotesAppendix 3.1 Female Health – Selected TrendsAppendix 3.2 Causal Loop Diagrams

ELI-P COMPLEX IN SOCIO-ECONOMIC CONTEXT

In Chapter 1 we have identified the problem, which this research is aiming to address, using the M&S approach, and suggested the time horizon of 25 years. This chapter is dedicated to the development of the dynamic hypothesis to detect the problematic behavior, if such exists, through the identification and evaluation of the cause-effect relationships. Figure 3.0 summarizes the problem description, objectives and system boundaries (see Chapter 4 for complete overview of the model boundary and structure). Figure 3.0 – Project Definition

Literature Review

At a high level of aggregation, the “client” of this model is the society; if we dissect the model, the client for some submodels may be either a female, or a given population group that undergoes the screening process. The source of the problem (birth defects and inborn pathologies35) is biological and evolutionary, and its outcomes produce a negative socio-economic impact. The list of causes contributing to the problem is extensive: genetics, teratogens, behavior, chronic or acute conditions, malnutrition before or during pregnancy, hygiene, ineffectiveness of prenatal care and low utilization rate, lack of awareness among women about healthy pregnancy and available procedures, screenings, tests, etc. The identification of how the problem (poor pregnancy outcomes) arises is out of the scope of this research.36 The focus of this work is to take the bio-socio-economic situation as a starting point, recognize the existing problem 35 In this research birth defects are defined as severe structural defects; Inborn pathologies are the deviations of perfect health status where a child does not have a clearly expressed birth defect but may suffer from developmental problems, mental retardation, chronic inherited diseases, etc. (many of these pathologies are detected between 1st and 3rd years of life and not at birth.36 Some issues underlying the causes of the problem will be addressed when discussing the assumptions for the model development (Chapter 4) and designing the prenatal care framework (Chapter 6).

Literature Review

and suggest how an intervention in the form of a new screening technology can address this problem.

Pregnancy Outcomes’ IndicatorsChapter 2, in addition to identifying current M&S methodologies,

covered many aspects of the US healthcare (prenatal care specifically), and socio-economic conditions concerning female reproductive health. Now, let us look at some specific trends in the US. Figure 3.1 shows that out of 6,401,000 registered pregnancies in 2000 in the US only 63% resulted in live births. The number of induced abortions slightly declined from 1990 and composed 21%37. The ten-year chart (Figure 3.2) shows no noticeable changes in fetal losses or increase in live births over the last decade of the 20th century. However, mortality rates decreased significantly over the same period of time. Figure 3.3 shows a noticeable decrease in tobacco consumption, slight decrease in alcohol consumption, and a considerable decrease in infant (23%) and neonatal mortality.

37 The number combines 850,293 legal cases plus illegal and those of unknown type.

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EMBED Excel.Chart.8 \s

Figure 3.2Pregnancy Outcomes US Data 1990-2000

0

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Year 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Rate

s (p

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Data Source: NVSR Vol 52, No. 23, June 15, 2004Available on CDC website

It has been mentioned that the decrease in infant and neonatal mortality is mostly associated with the improvement of post-natal technologies and especially the neonatal units providing a higher survivability to the low birth weight and very low birth weight newborns. Meanwhile, the pre-term birth rate increased from 10.6% to 11.6% over the same decade38.Figure 3.3

38 http://www.marchofdimes.com/aboutus/1531.asp Pre-term birth: birth before 37 weeks of gestation

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Figure 3.4 shows the outcomes of live births in the US for the year 2000. Only 81% of newborns are characterized as healthy. The average statistics for structural birth defects is 3-4%, and according to the March of Dimes studies, about 7% of children are born with small pathologies (developmental retardation, learning disabilities, inherited chronic diseases, etc). The low birth weight children account, on average, for 8% of newborns. In order to more accurately represent the fractions, we had to account for the following: since deaths from birth defects account for 31% of all infant deaths, we cannot actually use the 3% number and since 12.1% of infant deaths are attributed to low or very-low birth weight, the 8% figure cannot be used to represent the actual number for the year 2000. Hence, not to double-count newborns which are born with birth defects or low birth weight and who do not survive, some adjustments have been made (calculations in Notes: [1]).

Literature Review

Figure 3.4

US Newborns 2000 data

Infant Mortality1%

LBW Adjusted8%

Healthy Newborns81%

Birth Defects Adjusted3%

Inborn Pathologies7%

Data Source: OECD, March of Dimes

Figure 3.5 presents the summary of the live births’ outcomes in the US during the last decade of the 20th century. The homogeneity of the results throughout the years indicates no noticeable change in the newborns’ health and pregnancy outcomes! This is an alarming finding, which is supported by the reviewed literature. While the pregnancy outcomes in the US are still better than in many countries (but worse than in Japan and Western Europe), there are no positive

Dynamic Hypothesis

trends for healthier newborns or decreasing trends for birth defects and small pathologies. Figure 3.6 depicts only pathological pregnancy outcomes, eliminating healthy birth outcomes to better adjust the scale, and track the small decrease in infant mortality.

Congenital Anomalies’ Indicators Over fifteen years of data on the rates for the three major birth defects also look troubling. While the total expenditure (largest in the world) on health in the US has been growing rapidly, few positive trends as per reduction of birth defects have been registered.39 Figure 3.7

Unfortunately, birth defects have a very strong socio-economic impact not only because of high costs of care and emotional burden, but also, because of life years lost due to morbidity and mortality. In 39 There are positive trends in the reduction of brain and spine birth defects due to the promotion of folic acid consumption + other vitamins and healthier life styles. Also some birth defects have been decreased thanks to proper vaccinations, and treatment of women with chronic diseases.

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Figure 3.8 the leading cause of death in the US – the heart disease - is depicted along with HIV, congenital anomalies, perinatal conditions40

and the lost years of life associated with them. While 4.6 million Americans suffer from some form of heart disease [2] (certainly a much higher number than that of birth defects), we see that the years of life lost, due to perinatal conditions and congenital anomalies combined, is around 2/3 of the years of life lost from heart disease. There is a correlation between these variables since about 500,000 adults in the US have grown into adulthood with congenital heart disease and this number is increasing by about 20,000 each year [2]. Also, we can see a sharp decrease in the years of life lost since 1995 due to HIV, and a steady decrease for heart disease but rather stable levels for congenital anomalies and perinatal conditions.

Over the past 25 years there has been a sharp decline in the 40 Perinatal refers to conditions that occur in the time surrounding childbirth and that affect the newborn baby.

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hospitalization rates for normal delivery, pregnancy and childbirth related conditions however, hospital discharge rates for perinatal conditions and congenital anomalies remain virtually unchanged (Figure 3.9). This trend, once again, indicates no or very marginal improvements in poor pregnancy outcomes over the last decades and, if we look at Figure 3.10, we see a clear proof of the ineffectiveness of

American prenatal care. The utilization rate of the latter increased by almost 10% since 1990s but no decrease of most birth defects has been registered, no decrease in inborn pathologies, no increase in the number of healthy newborns though, there has been a troubling increase in low birth weight infants and the number of pre-term births.

This numeric evidence supports the literature review findings and proves the statements made in previous chapters about the poor

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performance of US prenatal care, ineffectiveness of the healthcare system, an alarming situation

with poor pregnancy outcomes (common all over the world), and the absence of necessary tools and technologies to prevent and treat birth defects or less significant congenital abnormalities. Unfortunately the federal budget for birth defects is less than 1% of the amount devoted to less common problems such as childhood cancer or AIDS [3].

Female Health and Other IndicatorsAccording to the March of Dimes statistics (2000 Data US),

21.2% of females smoke, 7% of females binge alcohol, 128.3/100,000 have gonorrhea, 404/100,000 have Chlamydia (the registered cases, but many women have these infections without knowing and of course, many conditions go under-reported) [4]. Almost 20% of births in 2000 were from women with diagnosed medical conditions (listed in Notes [5]) and these do not include such as syphilis, HIV, herpes, hepatitis or

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other serious infections in mothers that put the fetus’ or newborn’s health at risk. Regardless of these indicators, in the same year, 94.9% of females of fertile age41 reported that they were in good health. This can serve as another indicator of the problem: women’s perception causes them to be less serious about their health status so that while over 20% (moderate estimation) might need to be examined and treated before pregnancy, there maybe 5% who do not think they are in good health, who might voluntarily seek physician’s consultation and maybe another 2-3% of females with health problems may be detected by physicians. Consequently, more than 10% of fertile females with various healthcare problems, of which they are not aware, become pregnant and thus, place their pregnancy and future newborns at greater risk. It has been found that at least 10% of mother’s immune system is being “imprinted” in the future child [16]. Thus, a woman with immune deviations is already passing at least 10% of her health problems to her future offspring!

Appendix 3.1 of this chapter summarizes 10-year trends for some infectious and chronic diseases, which can have negative impacts on pregnancy development and outcomes. Sadly, the statistics show significant decrease only in gonorrhea and syphilis cases, but sharp increases in Chlamydia infections, genital herpes and warts, vaginitis; stable levels of diabetes, and alarming rates of obesity growth. With all the efforts and programs, American females do not seem to be getting healthier according to the data and the number of poor pregnancy outcomes is not decreasing either. Hence, the empirical examples portrayed in this chapter are consistent.

In addition, 13.4% of US females of fertile age live below poverty level, 11.9% do not receive adequate prenatal care (which

41 OECD 2004 Health DATA

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increases risks since the basic care offered in the US at least manages to sustain the current figures of pregnancy outcomes), 22.7% of births are from women with less than 12 years of education, and around 14% of females of fertile age remain uninsured [4].

CDC estimates that in the US there are about 17% of children under 18 years of age who have a developmental disability42 [6]. If we once again look at the Figure 3.4, we may see that some portion of this number comes from children who were born with birth defects, inborn pathologies and low birth weight, but a simple calculation43 shows that there is also a certain share of children (5-7% -experts’ estimate) who are considered healthy at birth (in the 81%), may have developmental disabilities. This fact adds more negatives to the issue of birth defects and poor pregnancy outcomes. US$36 billion each year is spent on special education programs for individuals with developmental disabilities [6]. In addition, the average cost of care for one person with mental retardation is US$1 million (in Y2003 dollars) and it is estimated that the lifetime costs for all people with mental retardation who were born in 2000 will total US$51.2 billion (in Y2003 dollars) [7]. The socio-economic burden produced by poor female health and poor pregnancy outcomes is obvious.

ELI-P Complex

We have introduced the ELI-P Complex technology and described its purpose, some technical parameters, and medical fieldwork results available to date. In order to conduct the health technology 42 Developmental disability – diverse group of physical, cognitive, psychological, sensory, and speech impairments that begin anytime during development up to 18 years.43 1% dies in the first year, a few more % die during childhood, some birth defects can be fixed without any developmental consequences, up to 30% of low birth weight infants experience absolutely normal development, which allows us to assume that about 10% of developmental disabilities can be attributed to the diagnosed inborn problems, the rest must be coming from the pool of healthy children. Apparently, not ALL children as classified normal/healthy at birth actually correspond to this status.

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assessment of ELI-P Complex, it is important to review its features, capabilities and impacts within the US socio-economic context, because the aim of this work is to determine proper application and dissemination of the technology.

First and foremost, ELI-P Complex is an information technology. It is not a treatment, it is not a device detecting a specific condition, it is a screening technology, providing physicians with the information about the patients’ body state, specifically, females’ immunoregulatory status responsible for reproduction. Thus, ELI-P Complex should be integrated into an already developed screening/diagnosis/treatment framework since its aim is to augment the value of the already existing procedures. Currently women are undergoing a lot of unnecessary tests [8] or being treated with wrong medications during pregnancy [8,9], which can be harmful to the fetus or the mother, or the opposite: those women who should be given special attention are not examined and treated properly.

Thus far, ELI-P test and ELI-P Complex have been evaluated on fertile females in Russia and a valid argument is how can the Russian data be applied to the American population? It is true that the health status of American females is better than that of Russian females due to the higher socio-economic status, better healthcare and a better access to it. This work makes necessary adjustments to account for these differences, however at all other levels, the comparisons are fairly legitimate. Biologically, females are the same around the world, they suffer from the same problems, and the Chlamydia infection in an American female might lead to the adverse pregnancy outcomes as well as in the Russian. Both populations suffer a comparable miscarriage/infertility rate. Higher infant mortality in Russia is attributed to the lack of the latest technologies (neonatal units),

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unavailability of professional services in many areas, economic hardship, and the growth of infectious diseases in the female population. But the trends with birth defects unfortunately remain similar across the borders: around 70% of them remain unexplained and the world community is working to resolve similar issues to improve the health of people.

The seven-year study of pregnant women (N=2000) in the Moscow region has shown incredible benefits of the ELI-P test. Women who were screened with this technology and placed in Group 144 (Figure 3.11) had over 90% of healthy newborns and women in Group 6 had no

44 See Appendix 1 for detailed description of the ELI-P Complex technology and classification groups.

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healthy newborns and over 80% (combined) outcomes of fetal deaths, stillbirths, miscarriages or newborns with birth defects. Other studies [10,11,12] on population groups in different regions confirmed these trends.

The downside of most of these clinical trials is that they provide limited information. Women were predominantly screened in the first trimester of pregnancy and many of them were not treated if the treatment was necessary. Studies conducted by Drs. R. Zamaleeva, O. Litvak and O. Serova produced very interesting results showing the benefits of the ELI-P test technology as a screening tool to identify and monitor the health status of females with various congenital conditions during pregnancy. But these studies did not use a random sample size and were not performed on the large scale to assess the benefits of the technology as a population screening tool.

There is a lack of evidence-based clinical trials of the ELI-P Complex technology. For example, there is no data on how the ELI-P Complex screening can decrease the number of pre-term births but there is a theoretical assumption, that women with poor ELI-P Complex test results are more likely to have LBW children since the low birth weight is attributed to the abnormal intrauterine conditions, which can be determined by ELI-P Complex in the majority of cases.

The error term of the ELI-P Complex test is 6-8% during the first screening and under 1% during the repeated screening. The error in the first screening is not from the quality of the test system or the interpretation of the results, but from a possible temporary poor state of a female organism (for instance, after an infectious disease a tooth inflammation) when the body is fighting a temporary infection and thus, embryotropic antibodies can be over or under-produced and indicate a pathology which does not actually exist. Consequently, the

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ELI-P Complex screening is recommended at least twice in the case of poor original results to control for such error.

This study is based on the assumption that ELI-P Complex will be a ubiquitous screening system for fertile females planning pregnancy or for females in the 1st trimester of pregnancy. Women with already diagnosed conditions such as HIV, diabetes, Hepatitis, etc. and whose pregnancy is already supervised by physicians and their conditions are properly treated during pregnancy can be exempt from the ELI-P Complex screening to save resources since the test results are unlikely to provide valuable information in the cases where diagnoses are known and attended by the specialists45.

The real value of the screening test comes from identifying, in seemingly healthy females, the risk groups for poor pregnancy outcomes and treating these females beforehand, or treating them properly during pregnancy to ensure better outcomes. The expert opinion today is that if ELI-P Complex is properly applied in the US, birth defects can be brought down to 1.5% from the current 3% at the population level. Correspondingly, the inborn anomalies can be decreased from 7% down to 3-4% and thus the healthy newborns’ numbers may increase up to 90% [9]. Pre-term births resulting in low birth weight infants are estimated to decrease with the efforts of other programs and account for about 7% [4].

In HTA decision analysis can be used to predict the likelihood of potential outcomes of alternative clinical strategies (and associated costs of care); to identify the clinical strategy that has the greatest utility for specific types of patients; to support the development of practice guidelines; and to identify areas for future research [13]. Hence, health technology assessment is an iterative process involving

45 However, it can be used as a monitoring tool to track the treatment effectiveness.

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many levels of analysis, usually non-linear and system dynamics is an iterative modeling tool, which seems to be perfectly suited to aggregate various HTA steps under one umbrella in order to study different aspects of the technology’s impact and dissemination.

Within the HTA framework, the scope of the ELI-P Complex technology is summarized in Table 3.1:

Table 3.1 Scope of ELI-P Complex Technology Physical Nature ELI-P Complex Test-System

Purpose of Application: Evaluation of the immunoregulatory state of fertile femalesa) Prevention Some cases of: miscarriages, infertility, birth defects,

pathological pregnancy development, pathologies in newborns

b) Screening Primary purpose of the technology – screening of the serum samples on the detection of specific autoantibodies.

c) Diagnosis Preliminary diagnosis of pregnancy development. If problems are detected, further evaluation is needed to give a concrete diagnosis.

d) Treatment Test results can indicate what treatment is necessary for a given female.

e) Rehabilitation Rehabilitation results: improved ELI-P Complex indicators after a successful treatment. Secondary, tertiary screening with the ELI-P Complex can be viewed as a rehabilitation function of the technology.

NB: The left side of the table adopted from [13].

The interviews and surveys were found to be unnecessary and resource-consuming in this study since the ELI-P Complex technology does not involve many complicated issues (ethical, legal) that are usually associated with genetic technologies. This is rather unusual, since the survey/trial stage is almost always necessary or required when evaluating technologies in healthcare to account for the possible ethical issues. The existing research, literature, case studies, empirical data etc. provide sufficient information for inferences to make solid assumptions (see table 4.2) about women’s preferences, possible

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scenarios for the dissemination of this technology and the technological aspects (automation).

STAKEHOLDERS

The model developed in this research considers the forces shaping the use of a new medical technology and the consequences of that use for its stakeholders. Table 3.2 lists the stakeholders of the ELI-P Complex technology and their involvement where the use or implementation (investment) or the evaluation of the technology benefits are concerned.Table 3.2 Stakeholders

Stakeholder InvolvementWomen:

a) fertile (pregnant and planning)

b) girls (will be planning pregnancy)

Fertile women are the primary consumers of the technology, who are influenced by most parameters of the system/model. Girls are stakeholders as recipients of proper healthcare and education to become the future users of technology.

Newborns-only future generations

Newborns from women screened by ELI-P can be compared to newborns from non-screened women.

Medical Personnel:a) obstetricians/gynecologistsb) family doctorsc) nursesd) lab workers

Education and training must be provided to all medical personnel. Obstetricians/Gynecologists and nurses are viewed as primary providers of the service, other doctors must be aware of the procedure to advise women. Lab workers should be trained to conduct the analysis or operate an automated system.

Research Centersa) Immunculusb) other labs

Patents and know-how belongs to the Immunculus Medical Research Lab and the dissemination of the technology will depend on their willingness to distribute the rights, control the quality and improve the technology.

Policymakersa) publicb) government

All levels are involved: local, state and federal.

Health Ins. Providersa) private companiesb) Medicare

Government and private insurance companies will be influenced regardless of the degree that they participate in the coverage of the procedure.

Healthcare organizations:a) hospitalsb) labs

Healthcare organizations should provide proper facilities and equipment.

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c) clinicsSociety: Society is involved at many levels as a

beneficiary and a resource provider. Possibly a share of tax $ will be used to launch or support ELI-P Complex. Society will benefit with healthier children, families will benefit from better pregnancy outcomes (treated infertility, less miscarriages, healthier newborns), a more productive population makes the society more successful, places less burden on future healthcare spending to treat sick newborns (children/adults)

Industry:a) equipment producersb) antigens/reagent suppliersc) softwared) pharmaceuticals

A number of companies is involved as suppliers of equipment, software, hardware, biochemical material, packaging, treatments and test systems.

Investors:a) governmentb) private funds (VCs)c) other

Investors of all levels willing to finance the project

Lobbying/Promotional Groups Dissemination of the information online, in print, on TV, in person, through direct mailing, at conferences, training courses, etc.

Other FDA, Institute of Health, WHO, CDC, etc.

DYNAMIC HYPOTHESISThe dynamic hypothesis helps develop appreciation for the

dynamic complexity [1] of the integration process of new technology into the healthcare system.

We have developed a set of causal loop diagrams to portray the essential components and their interactions in the system. This is an important part of qualitative modeling, however, it should be viewed as an initial stage, where later, the feedback structure is described by the stock-and-flow diagrams and finally by the equations. For early HTA of ELI-P Complex, the influence diagrams are very helpful to begin the process of parameter identification, but are not sufficient since we run into the problems typical for causal loop diagrams. George Richardson

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very rightly noticed that the traditional definitions of positive and negative links fail in a wide variety of cases [15].

Figure 3.12

For example on a simple population diagram (Figure 3.12), in a positive loop involving population and births per year, the link from births to population fails the traditional definition: a decrease in births per year will not result in a decrease in population, since births can only increase population.

Thus, in this work, the causal loop diagrams46 are accompanied by a detailed description which must be carefully followed to understand the diagram properly, because otherwise, a viewer may misinterpret the meaning of pluses and minuses. In most cases they traditionally signify an increase and a decrease, but in some cases, they can be interpreted as a positive or negative impact and the reinforcing loops leading to the growth behavior might not necessarily build on growth in all of its variables.

The reason we have chosen not to abandon causal loop diagrams (even

acknowledging their imperfections) is because the tool provides with a lot of helpful

information about the feedbacks, which is essential for quick and easy development of

stock-and-flow structure and later the submodels, which take away possible

misinterpretations from the influence diagrams.

46 Causal loop diagram [CLD] = influence diagram

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Population

Birth Rate

DeathsBirths

Death Rate

+

++

++

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Dynamic Hypothesis

Figure 3.13

The highest level of aggregation produces a simple balancing loop, where the necessary investment promotes the adoption of the technology, which reduces poor pregnancy outcomes at the population level and this, in turn, increases the demand for the technology, which stimulates further investment.

The placement of ELI-P Complex into the socio-economic context discussed earlier in this chapter has been necessary for identifying factors which influence the diffusion. E. Rogers argues that the adoption of innovations is influenced by the interaction among the innovation, the adopter and the environment [14]. A. Bonair and J. Persson, along the same lines, suggest the three determinants of diffusion of medical technology: the actors and the processes, the structure or the environment and the characteristics of the innovations [13]. For the ELI-P Complex technology the unit of adoption has been identified as the number of procedures. Patients are likely to play a passive role since the serum samples can be studied with or without their consent. There is a number of barriers that the technology adoption faces: lack of pregnancy planning, cultural and educational attitudes47, complexity of the healthcare system, which is an impediment at many levels and a slow adopter of new policies, increased demands on the healthcare infrastructure and available resources, lack of sufficient evidence-based results, etc.

47 Religious beliefs, poor education or knowledge about pregnancy process and its complications, etc.

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The causality analysis shows that the technology leads to better pregnancy outcomes in the US population and promotes its’ adoption (Figure 3.13). An expanded overview of technology adoption is depicted in Figure 3.14 where we can see that better pregnancy outcomes also increase the demand for the procedure. The latter increases the number of procedures performed and drives down the cost-per-test, making it more affordable, and increasing accessibility and technology adoption. From Figure 3.15 we see that technology adoption is also influenced by the accuracy rate of the procedure, which is achieved through the automation of the ELI-P Complex technology. The description of the automation process48 is presented in Chapter 4. The automation is essential for the mass implementation of the technology and, while it requires larger initial investment in equipment, development, and personnel training, the potential number of procedures can be increased hundreds of times and the lab error will be eliminated. In this work, due to the lack of data for depicting mathematical relationships among the above parameters, the technology adoption is assumed to follow the S-shaped growth curve over the 25-year period, reaching market saturation in about 17 years.

Figure 3.14

48 Now ELI-P Complex results are obtained manually in the laboratory settings using similar approach to the standard ELISA procedures.

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Now, let us take a look at other socio-economic aspects of the model. Figure 3.16 shows that better pregnancy outcomes decrease the cost of care for newborns with poor health, which decreases socio-economic burden on the society and increases quality of life. The latter produces a positive impact on population health. From Figure 3.17 we see that better population health results in better female health which together create a strong reinforcing loop: healthier females produce healthier generations. Also from Figure 3.17 it can be seen that there are many factors affecting female health such as age, socio-economic status, diseases, environmental hazards, lifestyle and the access to and quality of healthcare.

Figure 3.15

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Figure 3.16

Apparently, today, the negative effect of these factors combined is stronger than the factors directed on the improvement of female

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health (availability of services, programs, education, insurance, active/healthy lifestyle and healthy environment). As it has been mentioned, the dynamics of the problematic behavior is hard to trace using causal loop diagrams, since we do not have sufficient bio-social information and cannot make such statements that the contemporary ineffective US prenatal care increases pre-term births or has no effect on poor pregnancy outcomes. The data supports such statements, but the causality of such nature can be rightly debated since many more issues come into the picture to explain various biological trends.Figure 3.17

Reverting to the modeling process: Figures 3.18 and 3.19 depict how better population health increases life expectancy, decreases needs and costs for special education, which increases the overall education attainment and this, in turn, increases productivity. An increase in

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productivity leads to an increase in GDP, which, along with the education attainment and life expectancy, are the composites of the human development index (HDI). An interesting dynamics emerges: there is a possibility that major economic indicators can be affected to some degree by the population-wide application of a technology which can in the long-run improve the overall population health.

Figure 3.18

If women planning pregnancy or already pregnant have the ELI-P Complex results (along with the rest of the blood work for major viruses), and if their ELI-P Complex results are in the range of norm, they may be exempt from unnecessary prenatal care visits (which sum up to 12 or 14!) during 9 months. A few more ELI-P Complex screenings during first and second trimesters for monitoring purposes can be sufficient to ensure a good pregnancy development and positive outcomes (refer to Chapter 6 for further description). Figure 3.19

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This way the resources (facilities, professionals’ time, medications, equipment) become focused only on those females who require special attention to correct their levels of embryotropic antibodies (usually through treatment of infectious diseases or chemical monitoring of some chronic conditions) and others with at risk pregnancies. Such approach should improve the effectiveness of prenatal care since sufficient resources may become available for the introduction of sophisticated genetic prenatal screening technologies, and other programs directed towards improving reproductive health. In the long-run, better pregnancy outcomes may yield savings, which can be used for the improvement of the American prenatal care to ensure better services for mothers and future children and promote the development of healthier generations (Figure 3.20).

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Figure 3.20

The variables in the model are tightly interconnected and in Figure 3.21 we can track how an increasing demand for the procedure promotes technology automation, which leads to more savings (short-term), which can be reinvested into the processes within the model. The interactions among the variables occur with time delays and in many instances these delays are substantial. Some of them are incorporated in the Aggregate model which is designed to run for 40 years (Chapter 4) to demonstrate the dynamics of technology implementation and its impact for a few generations into the future (Chapter 6).

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Figure 3.21

Appendix 3.2 presents additional examples of causal loop diagrams depicting the relationships among other variables.

NB: Data Limitations: There is a lack of socio-economic studies on many conditions and there are no recent or expanded studies on the costs of birth defects after 1992. Overall, most data in this chapter is underestimated. Each indicated source of data provides an outline of its limitations in their respective publications. In addition, many cases of pregnancies, infections, and abortions go underreported. All estimations of costs are very approximate since they fail to include complete sets of direct medical, other direct and indirect costs. For example, some estimates do not include expenses, such as hospital outpatient visits, emergency department visits, residential care, and family out-of-pocket expenses, productivity loss due to parents/guardians taking time off from work to care for the disable. However, most of the data in this chapter is an up to date summary of the best available, which could be found on the subject. The author has made an effort to better explain the choices of data and adjusted the values of some indicators to better reflect the real world situation.

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NOTES:Numbers in [ ] correspond to the title in References p. 221

1. Table 3.3 Adjusted Indicators: Pregnancy Outcomes

OutcomeNumber in

2000 CalculationLive Births (LB) 4,059,000Infant Mortality (IM) 28,007 LB*6.9/1000Inborn Pathologies (IP) 284,130 LB*0.07Healthy Newborns 3,292,121Low birth weight (LBW) 324,720 LB*0.08adjLBW 321,359 LBW-(IM*0.12)Birth Defects (BD) 142,065 LB*0.35adjBD 133,383 BD-(IM*0.31)

2. [236] WebMD Health Heart Disease: Diseases That Affect the Heart and Cardiovascular System [http://my.webmd.com/content/pages/9/1675_57860.htm?z=1675_00000_

1034_tn_01]3. [207] California BD Monitoring Program4. [198] Women’s Health USA 20045. [208] CDC 2000 data Table 3.4 Women with Health Problems Giving Birth

Totalper 1000

liveMedical Condition Cases births

Anemia 102,788 25.7Cardiac disease 20,308 5.1Acute or chronic lung disease 49,263 12.3Diabetes 131,027 32.8Genital herpes 33,644 9.3Hydramnios/Oligohydramnios 55,590 13.9Hemoglobinopathy 3,152 0.8Hypertension, chronic 33,442 8.4Hypertension, pregnancy-associated 150,854 37.8Eclampsia 12,920 3.2Incompetent cervix 11,703 2.9Previous infant 4,000+ grams 42,102 10.5Previous pre-term or small-for-gestational-age infant 49,934 12.5Renal disease 12,185 3.1Rh sensitization 26,648 6.7Uterine bleeding 20,666 5.7

190.7

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19.70% babies born from mothers with these medical problems

6. Prevention News [http://www.prevention-news.com/general/cdc-prevention.htm]7. [208] CDC http://www.cdc.gov/ncbddd/dd/ddmr.htm8. [31] Strong T.9. [139] Poletaev A.B – Interview June 6, 200410. [182] Demin, V., Kluchnikov C.11. [180] Budykina, T. , Serova, O.12. [200] Zhigulina, S.13. [32] Szczepura A., Kankaanpää J. (eds)14. [27] Rogers, E. 200315. [124] Richardson, G. 198616. [122] Poletaev, A.B., Osipenko, L. 2003

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Appendix 3.1Female Health – Selected Trends

Figure 3.1.1

Sexually Transmitted Disease Surveillance 2003 U.S. Department of Health and Human Services, Centers for Disease Control and Prevention. Available online at:

[http://www.cdc.gov/std/]

Figure 3.1.2

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SOURCE: National Disease and Therapeutic Index (IMS Health)

Figure 3.1.3

SOURCE: National Disease and Therapeutic Index (IMS Health)Figure 3.1.4

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SOURCE: National Disease and Therapeutic Index (IMS Health)

Figure 3.1.5

SOURCE: National Disease and Therapeutic Index (IMS Health)

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Figure 3.1.6

SOURCE: National Disease and Therapeutic Index (IMS Health)

Figure 3.1.7

SOURCE: National Disease and Therapeutic Index (IMS Health)

Figure 3.1.8

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Data Source: Centers for Disease Control and Prevention (CDC), National Center for Health Statistics, Division of Health Interview Statistics, data from the National Health

Interview Survey. Available online at: [http://www.cdc.gov/diabetes/statistics/incidence/table5.htm]

Table 3.1.1 Obesity Trends

Increase in Overweight, Obesity and Severe ObesityPrevalence Among U.S. Women*

Weight Category 1988 to 1994Prevalence (%)

1999 to 2000Prevalence (%)

Overweight(BMI > 25) 51.2 62

Obesity(BMI > 30) 26 34

Severe Obesity(BMI >40) 4 6.3

Source: CDC, National Center for Health Statistics, National Health and Nutrition Examination Survey. Health, United States (Table 70) 2002. Flegal et.

al. 2002;288:1723-7. *Ages 20 to 74 for overweight and obesity prevalence and ages 20 and older for severe obesity.

Increase in Overweight Prevalence with Age Among U.S. Women (1999 to 2000)

Age (Years) Prevalence (%)

20 to 39 54.3Source: CDC, National Center for Health Statistics, National Health and

Nutrition Examination Survey. Flegal et. al. JAMA. 2002;288:1723-7.

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Increase in Obesity Prevalence (%) Among Women by Age Group

Age (Years) 1988 to 1994 1999 to 2000

20 to 29 14.6 23.3

30 to 39 25.8 32.5

40 to 49 26.9 35.4Source: CDC, National Center for Health Statistics, National Health and

Nutrition Examination Survey. Flegal et. al. JAMA. 2002;288:1723-7.

Available online at: [http://www.obesity.org/subs/fastfacts/obesity_women.shtml]

Appendix 3.2Selected Causal Loops of the ELI-P Complex HTA Model to study the behavior of the variables.

Figure 3.2.1

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Figure 3.2.2

Figure 3.2.3

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Figure 3.2.4

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Chapter 4Summary: In this chapter the boundary and scope of the models are described. The models’ structures are identified through selected feedback processes from influence diagrams developed in Chapter 3. Parameter estimation procedure is described in detail for all significant variables and supported by the economic evaluation of the ELI-P Complex technology. The main equations for the models are derived and described in this chapter as well. Computer simulation is developed for Population Screening and Aggregate models using Stella® software V8.1 from Isee Systems, Inc. Complete documentation for both models is presented in Appendix 2.

Modeling Process4.1 Structure Specification

4.1.1 Models’ Boundary and Scope 4.2 Economic Evaluation of ELI-P Complex 4.3 Screening Technology Model

4.3.1 Parameter Estimation4.3.2 Equations’ Description

4.4 Aggregate Model4.4.1 Parameter Estimation4.4.2 Equations’ Description

4.5 Notes

STRUCTURE SPECIFICATION

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The dynamic hypothesis developed in the previous chapter does not serve as a working theory of how the problem arises, which is atypical for SD modeling, but it has been stated that the aim of this work is to capture not the dynamics of the biological problems creating the problem but the dynamics of those behaviors/initiatives which can solve it. This chapter, using selected causal relationships, develops the underlying stock and flow structure for the system, to trace the dynamic characteristics of the two models: the main Population Screening Program Model evaluating the technology’s potential and the Aggregate Model evaluating a wider technology impact on population.

Models’ Boundary and ScopeSystem dynamics seeks endogenous explanation of the problem

[1], thus all variables influencing the dynamics of the system behavior should be included into the model so, the use of exogenous variables is limited. In order to make the decision on what variables to treat endogenously and what variables to treat exogenously we clearly identified the purpose of the model in the previous chapter and described the relationships among the main variables with the help of causal loop diagrams. Not all variables identified by the influence diagrams are used in this simulation to keep the models’ complexity level under control. The purpose of these models is to provide a theory explaining how poor pregnancy outcomes can be decreased, along with the associated costs, in the US healthcare system. In other words, we are seeking to provide a possible solution (not explanation!) to the given problem, to help policymakers make proper decisions and introduce programs that can change the current situation in prenatal care. Table 4.0 describes the

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model boundary by classifying the variables as endogenous or exogenous and those that are excluded from the given modeling activity. Variables in italic are from the Aggregate Model.

Table 4.0 Model Boundary Chart Endogenous Exogenous Excluded

Fertile women:Healthy womenUnhealthy women

Population:Immigration rate

Customer opinion Decision to test Demand for treatment

Pregnant women:Planning pregnancyNot planning pregnancyIn prenatal care

Number of women in treatment

Female demographics, socio-economic status, age, lifestyle

Pregnant screened/treated Retests Ethical evaluationPregnant unscreened/untreated

Planning pregnancy rate Suppliers, manufacturers

Treatment outcomes (good, bad)

In prenatal care rate Teratogen and natural factors

Newborns:Healthy newbornsUnhealthy newborns

Cost per testCost of treatmentCost per congenital defect

Other prenatal care initiatives: Genetic screening

Newborns with birth defectsNewborns with pathologies

Healthy newborns fraction Diseases/epidemic

Technology adoption: Planning 1st Trimester prenatal care

Death ratesPregnancy loss rateInfant mortality

Financing scheme Insurance

Number of tests Treatment effectiveness Other interventions (for ex. public education programs)

DeathsPregnancy loss

ELI-P Screening (binary) Legal considerations

Being treated Pregnancy Rate Physicians’ trainingCost of care (direct+indirect)Screening cost

Investment

Healthy Girls Healthcare policiesUnhealthy Girls Screening accuracyBecoming Healthy WomenBecoming Unhealthy Women

Prenatal care quality & effectiveness

Many of the excluded variables are very interesting and the reason for their omission is either the lack of data or the increasing complexity of the models. The incorporation of these variables will be addressed in

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Chapter 7, where the limitations of the models and future work are described.ECONOMIC EVALUATION OF ELI-P-COMPLEX

In order to move to parameter estimation, the economics of the ELI-P Complex technology needs to be discussed. Economic evaluation is one of the main pillars of HTA. Health economists use various methodologies to measure in monetary terms the potential of the products’ introduction to the market or their use in clinical practice and benefits generated as a consequence. The choice of methodology depends on the availability of data and the actual purpose of the study. Cost-effectiveness analysis (CEA) was designed for selecting among competing wants wherever resources are limited [2] and gained its popularity in healthcare sciences. Other methods such as cost-benefit analysis and cost-utility analysis are also being widely used in healthcare; especially cost-utility analysis since it allows measuring benefits in non-monetary terms.

Here the economic evaluation of the ELI-P Complex and its introduction into clinical practice as a screening tool applied at the population level is limited to a basic cost-utility analysis due to the lack of data. If the test is selected for the actual introduction across the US or specific areas, a detailed cost-utility analysis must be conducted, taking into consideration the current and forecasted numbers, and the circumstances in which the technology will be used.

Estimating the number of proceduresIn practice, the uptake of the ELI-P Complex screening test

should begin in selected areas and eventually spread across the country thus increasing the number of procedures performed per year. While the US population is growing and will continue to do so, it is

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unlikely that birth rates increase steadily, however, society’s awareness, educational programs and greater uptake of prenatal care early in pregnancy, should encourage more women to undergo screening before pregnancy and after its onset. Most data in Table 4.1 is taken from the CDC National Vital Statistics Reports. 2000 is used as a base year for this economic analysis, since it is the year of comprehensive data to date. The following assumptions are made to estimate the maximum number of the ELI-P Complex screening tests, which should be done per year in the US if the national population screening program is implemented: 1) clinical trials of ELI-P Complex have demonstrated convincing results on thousands of samples under various conditions; 2) the test has been recommended by the US regulatory bodies and policymakers into clinical practice. Whilst in the real world, local or regional adoption of the test is more likely to lead to a greater dissemination across geographic regions and socio-economics classes, we consider for 2000 3) a national level of program uptake in order to calculate the infrastructure requirements and trace potential effects on population health. This approach proposes a scenario, which can be easily modified to produce calculations for selected states or counties.

Table 4.1 US Demographics 2000Events Cases

Number of Pregnancies 6,401,000Abortions 1,344,210Fetal Losses 1,024,160Live Births 4,032,630Prenatal care 1st Trimester (total) 5,312,830Planned Pregnancies 3,264,510Unplanned Pregnancies Prenatal Care 1st Trim 2,048,320Complicated Pregnancies 531,283

Maximum Number of ELI-P tests per year ~14,657,000

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Estimates are based on US 2000 data [3]

The diffusion of this technology can take many different routes, which at the moment can only be theorized: the technology may start off by being offered at selected clinics, then regions may implement special programs, alternatively states can issue legislation introducing ELI-P Complex into prenatal care. The screening can be offered for out-of-pocket payment only, or there could be a government-subsidized program, which would support the dissemination of the technology. The uptake may also depend on the women’s willingness to undergo screening, educational campaigns, and physicians’ encouragement. Selecting one of these routes and hypothesizing the adoption of ELI-P Complex in the US and calculating the benefits at this stage would be comparable to shooting in the dark. Producing a credible diffusion scenario is not possible49 before first results of large-scale clinical trials of the test in the US become available and the feedback from females and physicians is evaluated. Thus the purpose of the given calculations is to create a framework for the economic assessment of the ELI-P Complex introduction into clinical practice and to see what effect this screening might produce in the US if it were to be implemented at the population level with an intention to boost health of future generations by improving birth outcomes.

Hence, if we were to introduce ELI-P Complex across the USA in 2000, (assuming there is sufficient public awareness about the implemented program) there would be two different pools of females to evaluate: those planning pregnancy [] (51%), and those utilizing prenatal care [] during the first trimester (83%)[3]. Obviously, almost all of the planning pregnancy females are likely to be in the pool of 49 To produce an actual proposal for venture capitalists or policymakers a detailed cost-benefit analysis must be conducted requiring additional funding and collaboration of various stakeholders in order to collect all the necessary data and produce the estimates.

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those utilizing the 1st trimester prenatal care. About 20% of pregnancies are complicated due to various reasons [4]. Many of these females will not have the ELI-P Complex screening if their condition is known: diabetes, heart problems, obesity, etc. These women should be closely supervised by their physicians and treated accordingly. However, about half of complicated cases will be of unknown cause [] and these women instead of undergoing 1 or 3 ELI-P Complex screenings will have to be monitored with 3 or 4 and treated in the process to restore their immune systems. The adoption rate [R] for those enrolled in prenatal care is assumed to be 85% and the adoption rate [r] for those planning pregnancy is set to 65%. Women in prenatal care will be offered the test during their first visit and the majority is likely to accept, however, we cannot hope for the 100% uptake. Women with diagnosed health conditions (chronic diseases for which women have been supervised for years, inherited problems, women with a high risk of passing mutated genes, etc.) might choose not to be screened with ELI-P Complex or be offered such procedure. Some women are likely to decline the screening due to their religious or cultural preferences, ignorance, or absence of health insurance coverage, etc. The ratio of women, who plan pregnancy and choose to get screened, is set at an even lower percentile, since making an extra step and visiting a physician for a prophylactic measure before deciding to conceive cannot be expected from all planning women. In the forthcoming decades people may remain reluctant to exercise prevention practices thus, it is unreasonable to expect that women will be screened at higher rates, unless of course, such screening is promoted and enforced by insurance companies as pre-requisite for further coverage. The uptake of screening is not hampered by risk since, the test is harmless (as a procedure), and

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according to numerous studies on screening in pregnancy [5,6,7] women are very keen to find out all possible risks, to ensure that their baby will be healthy50. If [n] is the number of tests, then the approximate number of the ELI-P Complex screenings per year can be calculated using the following formula:

(1)The number of tests for different pools of women [n, n2, n3] can be calculated the following way: according to this set up, women in the first trimester prenatal care, with an unplanned pregnancy (-) should undergo two tests (the first - at the booking appointment and the second - during the 2nd trimester). Women with a planned pregnancy () have the opportunity to undergo the screening before conception and our assumption is that more than half of them would be willing to check their health status before becoming pregnant. Then they join the pool of those who are screened in the 1st and 2nd trimesters. The group of females with poor original ELI-P Complex indicators will be identified in both pools (planning and pregnant) who either need to be re-tested or treated and then re-tested and further monitored with the ELI-P Complex screenings. The number of women who ought to be treated is estimated to be about half of those females with poor ELI-P Complex results51 and they should undergo about three tests on average52 during pregnancy. We expect that some women refuse treatment especially if they are already pregnant.

50 Hopefully, the uptake will be higher than 90%. Extensive socio-ethical studies are needed to evaluate women’s perception and willingness to screen. Also, the policy on test coverage by insurance companies or government programs will have a significant impact.51 Women with clearly known conditions might not need to undergo ELI-P Complex screening since the goal of the test is to screen those seemingly healthy females to detect deviations in their health status. However, ELI-P Complex or similar tests can be used to monitor the effectiveness of treatment.52 Some women may need only two tests others may need up to five depending on the level of complications.

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The formula (1), estimating the number of tests, can be used for any population pool in any region, after collecting sufficient data on the service provision and on female attitudes to estimate the rates of uptake.

Equipment (Estimates and Automation)Currently biochemical analysis of the test results is being

performed manually, using absorbance method to read the optical density. The manual procedure has numerous disadvantages: lab error, long processing time, low throughput and reliance on the laboratory technicians’ skills and time. If the test is to be introduced into clinical practice at the population level (state, county) it must be automated. Automation technology exists and is produced and distributed by various international corporations: Abbott Laboratories, Molecular Devices, Tecan, and dozens of smaller companies. Automated ELISA tests are done routinely in most clinics and hospitals around the world and manual ELISA tests are performed mostly in research labs. No additional R&D is required to automate the test since only the optimization of a suitable microplate reader system is needed. The readers have different capacities and configurations. Primarily they are categorized by capacity (using 96-well, 384-well, 1536-well plates) and provide luminescence, fluorescence and absorbance reading functions. Some readers such as SpectraMax M5

and SpectraMax M2 from Molecular Devices and Safire 2, Ultra Evolution from Tecan offer multiple (up to nine) different detection modes. The price of the equipment depends on its functionality and ranges from US$9,000 to US$100,000 [8,9,10].

Evaluation of the technologies available in the market helped identify the most suitable for the ELI-P Complex automation. The

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Grifols Triturus immunoassay analyzer allows programming and performing of all steps of any micro-well EIA test, regardless of the manufacturer of the assay. The system is fully automated and includes sample dilution, dispensing, incubation, washing, reagent addition, absorbance reading, calculation and interpretation of the results [11]. The advantage of such system is that it saves time and it eliminates the need for a lab worker to supervise the process. The Triturus system can be loaded for the night and left to work for the results to be ready in the morning. Each run takes about eight hours, so up to three runs can be performed on one machine per day. All microplate readers do the reading within a matter of seconds, but Triturus performs all the other functions as well, including timed incubation. This is the best technology currently available on the market. Table 4.2 shows prices of different microplate readers from three different suppliers. While Molecular Devices and Tecan53 readers are cheaper, they are not fully automated systems and would drive the cost per test up, since more manual labor is needed to operate the machines and to prepare samples for reading.

Table 4.2 Microplate ReadersMolecular Devices

Tecan Triturus

96 well $14,000 $9,000 $65,000384 well $34,000 $24,000 $70,000

1536 well $55,000 $34,000Data Source: Phone interviews with equipment vendors and e-mail

correspondence [10]

ELI-P Complex evaluates antibodies in blood samples not towards one but eight different antigens, therefore for one test sixteen wells must be used (all tests are run in duplicate) on a plate and Triturus is 53 Prices for the readers vary depending not only on the capacity but also on the reading method or multifunctionality. Average prices were selected for the Table 4.3

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the only multi-test, multi-batch system, which can perform simultaneously up to 8 different tests per batch on a given sample or a group of samples, and, independently, schedule several consecutive groups of tests, and new work batches can be loaded, while other batches are processed. On each plate, regardless of capacity, 16 wells must be used for the controls [12] (2 wells per antigen).

Table 4.3 shows the number of tests that can be done per year on the system of a given capacity, and the number of machines that are needed to conduct the maximum number of ELI-P Complex tests that have been estimated for the year 2000. The number of tests per year is calculated assuming that the laboratory facilities operate 310 days per year with at least two loads per day. The number of machines required is calculated by dividing the total number of tests, which need to be performed per year (14,657,000), by the number of tests, which can be done on a given machine.

Table 4.3 Equipment RequirementsNumber of

wellsControls added

Possible number of

tests

No of tests per year

No of machines needed

96 80 5 3100 4728384 368 23 14260 1028

1536 1520 95 58900 249

Table 4.4 shows the average total costs for microplate readers54. 96-well readers are not efficient or cost-effective due to their low throughput. 1536-well readers are highly efficient and cost-effective, but are too big to be placed at every other hospital, however, large medical centers may benefit from high-throughput machines. The most suitable configuration is 384-well systems, since they can be located in all large and many middle-sized hospitals. This would 54 Tecan and MD offer microplate readers, but Grifols Triturus offers an integrated multifunctional system.

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evenly distribute machines around the states, so that delivery of the samples can be done within a day. At maximum adoption rate, ~20-25 tests will be available to be analyzed on each machine per day55. These machines can run any assay, and if the ELI-P Complex due to the demand, is loaded once and not twice a day, the rest of the time, machinery can be used to run any other type of ELISA-based tests (HIV, prostate, hepatitis, DNA quantifications, etc.) Hence, the 384-well machines would be the most efficient and effective for most clinics of the three presented configurations; but, large laboratories, in densely populated areas, should consider the 1536 well systems. The current configuration of the Triturus system does not come with 1536 plates but hopefully can be developed in the near future.

Table 4.4 Prices of Microplate Readers’ & Triturus System

No. of wells No. of Machines

Molecular Devices

Tecan Triturus

96 4728 $66,192,947 $42,552,609384 1028 $34,946,587 $24,668,179 $64,753,970

1536 249 $13,686,512 $8,460,753

Although Triturus has the highest cost for 1028 machines, it might be the best since it is all in one system, which meets the requirements of the ELI-P Complex test perfectly. If Molecular Devices or Tecan technology is chosen, the price of extra technology needed (washers, incubators, ect) plus 1028 (even if part time) of additional lab personnel would significantly increase costs, while in the near future Triturus systems can be expected to become much cheaper, if they are produced in larger quantities. Compared to Molecular Devices 55 About 17,000 women per day enter the 1st trimester prenatal care: if 85% of them undergo the ELI-P screening, 14 tests per day on average must be run on each machine in the country, plus 5-10 tests from planning women or retests from monitored women. The population is unevenly distributed, so in some areas the systems will have to be loaded 3 times a day, and in other areas once in 2-3 days.

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and Tecan systems, Grifols’ Triturus has been introduced to the market very recently, and that explains its current high price.

To customize the system for the ELI-P Complex test, Triturus software needs to be re-programmed to properly interpret and report test results. The system should be able to 1) identify results in the state of norm and email them to a woman and her physician and to 2) identify the pathology state and email the results to a physician who should interpret the indicators further and schedule a counseling appointment with a patient. The Triturus system allows for a bi-directional host communication and on-line connection for data management and reporting [11], which means that the results can be reported immediately.

Cost per test

In order to use Triturus or any other automated system, ELI-P Complex kits must be developed to include proper antigens and assays. Currently existing manual test ELI-P Complex kits are not suitable for an automated system. Table 4.5 depicts how the cost-per-test and cost-per-kit (different configurations) have been derived. Prices of antigens and reagents are quoted from international manufacturers in Y2005 dollars. The average cost per test is estimated to be ~ US$22 and depends on prices set by suppliers of components, variable costs of labor and processing fees. It is unlikely for the test to cost more than US$30 (to a patient or insurance company) and less than US$15. Table 4.5 Test & Kit (automated) Costs

ComponentCost per

TestCost per Kit Notes

384 wells 1584 wellsPlates 0.1 2.50 32.00 100 plates (384)-200$[13]Antigens (8) 6.22 143.06 590.90 ~350$/antigen[14] for 350

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testsReagent tips 0.84 19.32 77.28 Triturus[11]Sample tips 0.05 1.15 4.60 Triturus[11]Substrate 0.05 1.14 4.58 120ml=28$[15]Buffer 0.22 5.06 20.24 1000ml-3$[15] 1.5ml/well

Conjugate0.60

13.8 55.21l solution=300$[15]

(10ml/96well plate)Controls 0.27 6.22 6.22 16 wellsStop solution 0.26 2.9 11.6 500ml=11.5$[16]Reagent Boat 0.1 4 4 Triturus[11]Waste Tray 0.2 5 5 Triturus[11]

Operation0.1

2 3Water, light, lab facilities,

etc.Packaging 0.1 5.00 20.00Labor 2 5.00 20.00

Processing10

NA NAStorage, Administration,

S&HTotal 21.11 216. 15 854.62

10platesNA

2,132.60 8,488.0010 plates – reusable

componentsData Source: see appropriate references [ ] in Notes

Eventually, the mass production of antigens and more efficient automation procedures should bring the cost of the test down. The cost of kits is estimated according to those consisting of 384 well 1584 well plates. The former is more likely to be used in practice.

If the population level of the ELI-P Complex screening implementation would have been possible in 2000, then the total cost of the program to the healthcare industry would have been ~ US$ 322,454,000, which is the number of tests per year (14,657,000) times the cost of a single test ~(US$22). It is difficult to estimate the cost of treatment for those females with poor ELI-P Complex results who need to undergo a 1-2 months therapy to contain their chronic conditions or stabilize their immune state and ensure healthy pregnancy. Depending on the main cause of the results’ deviation, specific forms of therapies should be recommended (Table 4.6). Table 4.6 Poor ELI-P Complex Results: Causes and Treatments

Above Norm Below Norm VariableProbable Cause Treatment Probable Treatment Probable Treatme

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Cause Cause nt1.Acute infection2. Exacerbation of chronic infection or inflammatory disease3.System auto-immune disease4.Organ specific autoimmune disease5.Early stages of HIV6.Early stages of malignant process

Immuno-suppressants For example: (cause 1):Antibiotics + non-steroid anti-inflammatory drugs (ThromboASS, etc).

1. Chronic massive viral infection (no exacerbation)2. Chronic intoxication (heavy smocking including)3. Prolonged treatment with some medications (steroids, cytostatics, some antibiotics, anti seizure drugs, etc.)4. Radioactive influences

Immune-modulating therapy.For example: (cause 1): Viferon 3 + Amixin(immune stimulating medicines) + Valtrex (anti-viral drug)

1. Endocrine and/or metabolic dysfunctions2. Autoimmune disease (organ-specific)

Donor IgGFor example: (cause 1): Specific hormone-replacing therapy + herbal therapy

Source: [149,154,156,240]

If the average cost of treatment per woman is ~ US$ 200, then the total treatment cost would account for US$106,256,600 in Y2000 dollars. Hence, the total cost of ELI-P Complex to the healthcare system for the year 2000 would have been US$428,710,600. Almost half a billion dollars per year might seem to be a very high price to pay for a screening program, but according to the CDC data, in 2002 the screening program for breast and cervical cancer cost US$192,000,000 and screened about 2,500,000 females providing ~4,300,000 tests [17]. Thus, as we see, the estimated cost of the proposed program is within realistic spectra as far as the spending of the US healthcare system is concerned.

Savings

The aims of ELI-P Complex is to 1) explain a share of ~60% of unexplained birth defects 2) decrease the number of birth defects and

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inborn pathologies 3) improve the health of future generations and 4) help re-design prenatal care. To measure the benefits of the ELI-P Complex screening is very difficult since the results (money-saving) will be seen years after the original screening procedures are performed. First results will be obtained within a year, by tracking improvements in birth outcomes, but since many pathologies are not revealed at birth, only by supervising children further, we can collect more comprehensive data. However, if with this population-wide theorized program, which costs almost half a billion dollars per year to the healthcare system, the number of birth defects can be decreased by 0.5% (a modest estimate, since if nearly 80% of pregnant females are screened, the number of birth defects should decrease further) [18,19], we can attempt to measure some benefits in monetary terms. A very conservative estimate of 8 billion dollars per year is being quoted in literature as cost of birth defects [20,21]. Relying on this number, we can estimate that savings may total up to US$US1.3 billion in treatment for the birth defects that are prevented with the help of the ELI-P Complex screening. Further savings may result from fewer newborns with inborn pathologies (less children would enroll into special education programs, and parents of those children who are born healthy or with small pathologies would be able to work instead of caring for them, etc). Certainly, the value of social benefits, which cannot be measured in monetary terms, should have a significant and positive impact on society. Finally, a new healthier generation will provide a more potent labor-force capable of actively participating in life and serving the economy as a positive indicator.

The Quality Adjusted Life Year (QALY) is measured on the scale from 0 to 1, where 0 stands for death and 1 for perfect health. This is a common parameter widely used in HTA even though it has numerous

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limitations. QALY places a rough abstraction of a numerical value on human life and its quality. However, not having a better way to assess cost-effectiveness of the screening intervention we decided to use this measure. Preventing ~22,000 (1/6 of total) birth defects by spending US$ 498,709,720 (where US$69,999,120 is 20% of working capital required for the program implementation)56 costs US$22,434 per case avoided, which is significantly less than US$60,000 per life year saved [22]. According to the cost-utility theory, it is good value for money and such intervention can be considered cost-effective.

Working Capital and Additional CostsThe most costly component of working capital would be the

phase of clinical trials, since before the screening test is introduced at the population level, convincing results on thousands of samples are needed (see Chapter 7 for the proposed plan of clinical trials). It has been assumed that the program conducted in multiple centers across the world would cost ~ US$200,000,000.57 Table 4.7 presents additional expenses, which are likely to be associated with the population level of program implementation. The estimates include expenses for education of women, physicians and nurses, publications, and promotional activities.

Table 4.7 Working Capital$US

Clinical Trials 200,000,000Education/Publications/Promotion 50,000,000Training: GPs, Nurses, Lab Personnel 20,000,00056 See estimates for working capital in the next section: 1/5 of this estimation is used for the year 2000 since costs can be distributed over a five-year period (results of clinical trials, training, education, equipment, etc).57 Detailed outline of activities and budgeting for clinical trials needs to be developed in order to establish a 3-4 year research program. The $US 200 million estimate is comparable to the cost of major research projects in biological sciences funded by the EU 6th and 7th Frameworks.

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Kit Production Lines 3,000,000Equipment 64,700,000Overhead 50,000,000Total 387,700,000

Training of physicians and nurses has been calculated by taking the number of obstetricians and gynecologists in the country (36,380) [23] and multiplying it by US$400, which could be the average cost per education course. Half of this cost was considered for nurses’ education. Equipment costs have been calculated in previous sections of the chapter; the kit production line should not be expensive to set up and run, and all unaccounted costs are included in the overhead of US$50,000,000. The working capital is likely to be overestimated, since the economies of scale ought to bring down some costs significantly, however, the goal of these calculations is not to provide a roadmap for investors, but to outline all the necessary components, which would be considered when the screening technology can actually be evaluated for the implementation under a specifically designed scenario. At this point, due to the lack of more precise data, further detailed estimations are not appropriate. The above analysis provided a theoretical economic evaluation of the key financial parameters. Later they will be used as a reference to evaluate simulation results (Chapter 5).

SCREENING TECHNOLOGY MODEL

The model is designed for 25 years and focuses on the dynamics of technology penetration into the healthcare system and the results it brings along. This is not a conserved system because health improvement effects cannot be traced within such time period. Variables included into the model are depicted in Table 4.0. The model

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has 96 system parameters, of which 19 are state variables and 7 are graphical functions. Table 4.8 summarizes the model structure in terms of stocks and flows and the graphic representation of this summary is presented in Figures 4.1a, 4.1b and 4.1c. From the diagrams it can be seen that the model consists of two subsystems: 1) pregnancy onset and screening and 2) pregnancy outcomes. In Table 4.8 system parameters are assigned symbols to help interpret the described below equations.

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Table 4.8 (Continued)

Table 4.9 lists the remaining system parameters and their symbols and Table 4.10 summarizes seven graphical functions used in the model.Table 4.9 System ParametersBirth Defects Fraction Df In Prenatal Care pcCost of Care cc In Treatment Frac PL tPLCost of Treatment ct In Treatment Frac NP tNPCost per Test ce Multiple Births mbCost per BD Case cD Not Enrolling in PC Fraction nPCCost per Pathology Case cP Planning Rate rPLDeath Rate dr Poor Test Results PL PtrPLDeath Rate Healthy Women drH Poor Test Results NP PtrNPDeath Rate Unhealthy Women drU Pregnancy Rate NP rNPDeciding to Wait dW Retests rtELIPS e Screened Pregnant SPRElips for Planning ePP Screened Not Planning SNPElips for Pregnant ePr Screened Planning sPPGrowth Rate Fertile Females gr Screening Cost scImmigration Rate imr Total Pregnant tpInfant Mortality Rate ir Unhealthy Conceiving Frac uC

Parameter EstimationEach equation and each parameter in the model is described

with a convincing amount of evidence supporting the nature of relationships among parameters and the actual choice of variables. The evidence is drawn from expansive literature reviews, experts’ opinions, historical data, and currently available statistical data.

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Table 4.10 Graphical Functions

Graph 1. Technology Adoption in Prenatal Care over time (AD)

Graph2. Technology Adoption Among Planning Females (ADp)

Graph 3. Treatment Effectiveness Before Pregnancy (TEb)

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Graph 4. Treatment Effectiveness in Pregnancy (TEp)

Graph 5.Pregnancy Loss Rate Screened Treated (PLrS)

Graph 6.Pregnancy Loss Rate Unscreened/Untreated (PlrU)

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Graph 7.Healthy Newborns Fraction (HNf):controlled by AD and TEp

Table 4.11 summarizes the parameter estimation process; the description of parameters not shown in this table can be found in Appendix 2.Table 4.11 Estimates of Selected Parameters

Healthy Women[61,660] in thousands

H The initial value for the stock was forecasted in Excel for the year 2010 from OECD Health DATA 2004 (77% of all women in the age cohort 15-49)

Unhealthy Women[18,418] in thousands

U The initial value for the stock was forecasted in Excel for the year 2010 from OECD Health DATA 2004 (23% of all women in the age cohort 15-49)

Fertile Women[80,078] in thousands

N The initial value for the stock is forecasted in Excel for the year 2010 from OECD Health DATA 2004

Planning Pregnancy[3,467] in thousands

PP The initial value for the stock is estimated number of planned pregnancies for 2010 considering that 53% of all pregnancies that year will be planned.

Not Planning[77,571] in thousands

MPL Fertile women not planning pregnancy.

Pregnancy Rate Not Planning Entering PC[0.027]

rNP Overall pregnancy rate was calculated from taking the population data (females age 15-49) OECD Health DATA 2004 and pregnancy statistics from National Vital Statistics Report, Vol. 52, No. 23, June 15, 2004. Average pregnancy rate was derived (~0.084); 85%-53%=32% (prenatal care utilization-planning rate) 32% of 0.084 is the rate at which not planning women get pregnant in the age cohort 15-49.

Planning Rate[0.044]

rPL According to http://www.plannedparenthood.org/ only 51% of pregnancies were planned in 2000. The rate in simulation is adjusted to 2010. 53% of all planned pregnancies for year 2010 = 0.044 of fertile females in the age cohort 15-49

Not Enrolling in PC nPC 0.04 fraction = 100% of pregnant non-planning

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Fraction [0.04- rNP] pregnancy womenPoor Test Results [0.2]

PtrPLPtrNP

According to the clinical trials of the ELI-P Complex, ~20% of females have ELI-P indicators out of the normal range.

Growth Rate (fertile females)[0.0085]

gr Population growth rate is calculated from the Census forecast available at [http://www.census.gov/ipc/www/usinterimproj/natprojtab01a.pdf] and adjusted to the growth rate for the pool of females in the age cohort 15-49 for the years 2010-2035

Pregnancy Loss Rates

PLrSPLrU

National Vital Statistics Report, Vol. 52, No. 23, June 15, 2004. 2000 data indicates 21% of aborted pregnancies. The adjusted number for 2010 assumes a decrease in induced abortions.2000 data indicates 16% in fetal losses. Pregnancy Loss rates are combined rates of abortions and fetal losses adjusted to 2010 [0.31]. Graphs 5&6 in Table 4.10 represent the changes in pregnancy losses over time as screening is being implemented.

Multiple Births Fraction [0.035]

mb [http://www.cdc.gov/nchs/fastats/multiple.htm] 2002 Data

Immigration Rate[0.002]

imr According to Migration News[http://migration.ucdavis.edu/mn/comments.php?id=1246_0_2_0] net immigration in the US between 1990-1996 was ~690,000 per year, which is about 246 per 100,000 of US population.

US Population [308,936] in thousands

P Population forecast for 2010 was taken from US Census data: [http://www.census.gov/ipc/www/usinterimproj/natprojtab01a.pdf]

Cost per BD Case[66,000] $US

cD According to California birth defects registry, cost of Birth Defects was estimated ~ US$8billion per year [1992]. The average cost per BD was calculated from 3% (of total births) birth defects occurring every year.

Cost per Pathology Case[15,000] $US

cP The average cost per Pathological birth outcome was assumed to be US$15,000. 7.5% (of total births) pathological pregnancy outcomes occur every year (March of Dimes).

Cost of Treatment[200] $US

ct Average price of treatments which are listed in Table 4.6

Cost per Test[22] $US

ce Estimated in previous section of this chapter

Death Rate Healthy Women[0.0005]Death Rate Unhealthy Women [0.0022]

drH

drU

Deaths data is taken from National Vital Statistics Reports, Vol. 53, No. 15, February 28, 2005. In the age cohort 15 - 49 death rate is ~272 per 100,000 females (0.00272).

Infant Mortality Rate[0.0058]

ir OECD Health Data 2004; 2001 - 6.8 deaths per 1000 life births. Rate adjusted for 2010-2035

Death Rate dr Deaths data is taken from National Vital Statistics

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[0.0084] Reports, Vol. 53, No. 15, February 28, 2005DR for 2003~840 per 100,000 of US population

The following units are used in the model: {year} {/yr} {thousands of women} {thousands of women/yr} {$US} {thousands $US} {thousands $US/yr} {number of tests} {thousands of people} {thousands of people/yr}

Equations’ DescriptionThe Population Screening model has 62 equations. Below is the description of the most important ones, the rest of the equations (mostly inter-step calculations) are summarized in Appendix 2. The main part of the model (Figure 4.1a) is designed to calculate how many women in the age cohort 15-49 get pregnant every year, how many of them plan pregnancy and enroll into prenatal care, and how different categories of women undergo screening and treatment. The number of Women who are Potentially Childbearing58 (wC) per year is the sum of Healthy (H) and Unhealthy (U) fertile females:

wC=H+U (2)

This gives us a pool of Fertile Women (F), which is the main feedback hub of this submodel:

F(t)=F(t-dt)+(wC+stG+uuG+nc-wP-wS-wNP)*dt (3)

Then women are divided into those Planning Pregnancy [PP] and Not Planning [NPL] which are calculated as:

PP(t) = PP(t - dt) + (wP - pPR) * dt (4)

58 First letters of variables’ names are capitalized to match their respective names in the Stella model

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NPL(t) = NPL(t - dt) + (wNP - pcPR - npPR - nc) * dt (5)

In turn, the pool of Not Planning is divided further into those in prenatal care [NPPC] and those not enrolling in prenatal care [PR]. The model assumes that all women planning pregnancy are enrolling into the 1st

trimester prenatal care. In this model we refer to planning women as those who actually prepare to have a child by changing their habits, diet, watching their health, consulting a physician, etc. Thus for example, an Amish woman planning to have a child will not be attributed to the pool of planning pregnancy females in America since despite her deliberate decision to have a baby, she is not going to utilize modern means of health monitoring, screening, physicians’ advise, 1st trimester prenatal care, etc. This woman will be attributed to the Not Planning Not in Prenatal [PR] Care pool.

PR(t) = PR(t - dt) + (npPR - eUU) * dt (6)

NPPC (t) = NPPC(t - dt) + (pcPR - mPR) * dt (7)where pcPR variable incorporates not just a share of Not Planning Pregnancy women, but also those planning who were not screened in the planning phase but entered prenatal care:

pcPR = ac*NPL+(pPR-spPR) (8)

The rest of the planning women are not added to this pool to avoid double counting. The number of screening procedures (e) corrects for this and Planning and Not Planning women are later accumulated into the common pool of Screened Treated Pregnant [ST], which is comprised of the pools of women screened during the 1st trimester [S]

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and those screened first time before becoming pregnant [SPL]. Technology adoption [AD] by women in prenatal care is defined by the adoption rate over time shown in Table 4.10 (Graph 1), but the adoption among planning women [ADp] differs (Graph 2). These graphical functions define the flow of women who are being screened in both pools.

S (t) = S (t - dt) + (gNP + scPC - trNP - eST) * dt (9)

SPL(t) = SPL(t - dt) + (gPL+scPL- trPL-spPR) * dt (10)

where the number of treated women in both pools is calculated as:

T(t) = T(t - dt) + (trNP - bNP - gNP) * dt (11)

and TPL(t) = TPL(t - dt) + (trPL – bPL-gPL) * dt

(12)

Treatment outcomes are divided into good and bad, which are defined by graphical functions TEb and TEp presented in Table 4.10 (Graphs 3&4). About 20% of screened women have poor test results and need to be re-screened and treated, but the uptake of treatment before pregnancy and during pregnancy is different due to the risk involved. TEb > TEp, because during pregnancy the therapy must be restricted to medications, which do not harm the fetus. Women who have poor treatment outcomes before pregnancy are divided with the help of a decision box into two groups: those Deciding to Wait [dW] and undergo further investigations, and those willing to conceive [bPL-dW]. Hence, a share of bPL – deciding to conceive is added to the Entering Pool of Unscreened Untreated [eUU] and Deciding to Wait [dW] is added to the pool of Not Conceiving [nc].

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Finally, pregnant women are being accumulated into two pools of Pregnant Unscreened Untreated [UU] and Pregnant Screened Treated [ST]:

UU(t) = UU(t - dt) + (eUU + pcUU - uB -plUU) * dt (13)

ST(t) = ST(t - dt) + (eST + spPR - sB - plST) * dt (14)

which outflows are Unscreened Untreated Giving Birth [uB] and Screened Treated Giving Birth [sB]. Both are derived from the number of pregnancies in the respective pools minus Pregnancy Losses [plUU and plST]. The latter are calculated with the help of graphical functions depicted in Table 4.10 (Graphs 5&6) indicating a decrease in pregnancy losses among screened/treated women and an increase among unscreened/untreated women. The number of screening procedures per year (e) and screening/treatment cost per year (sc) (Figure 4.1b) are calculated as:

e = S*ePr+sPL*ePP+(PtrPL+PtrNP)*rt (15)

sc = (T+TPL)*ct+ce*e (16)

The pregnancy outcomes submodel focuses on tracking various birth outcomes. The total number of Newborns (NB) per year is:

NB(t) = NB(t - dt) + (bb - dN - bH - bU) * dt (17)

where Being Born (bb) is the sum of births from screened treated [sB] and births from unscreened untreated females [uB] times Multiple Births fraction (mb). Figure 4.1c shows the Newborns stock divided into two other stocks: Healthy (HN) and Unhealthy Newborns (UN)

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where the latter is divided into Newborns with Birth Defects (ND) and Newborns with Pathologies (NP).

HN(t) = HN(t - dt) + (bH - lH) * dt (18)

UN(t) = UN(t - dt) + (bU- bP- bD) * dt (19)

In the above equations bH and bU are calculated with the help of Healthy Newborns Fraction (HNf) depicted in Table 4.10 (Graph 9) and controlled by the pregnancy planning rate to indicate that if more pregnancies are planned, more women are likely to enroll into prenatal care. This would lead to a lower number of pregnancy losses, an increase in women screened and treated, an increase in newborns and a decrease in poor pregnancy outcomes. Thus, Beginning Life Healthy (bH) and Beginning Life Unhealthy (uB) are defined as:

bH=IF(rPL>0.044)THEN(NB*(HNf+0.005))ELSE(HNf*NB) (20)

and bU=IF(rPL >0.044)THEN(NB*(1-(HNf+0.005)))ELSE(NB*(1-HNf))

(21)Then newborns with pathologies and birth defects are calculated with the use of the birth defects fraction (Df):

NP(t) = NP(t - dt) + (bP - lP) * dt (22)

ND(t) = ND(t - dt) + (bD - lD) * dt (23)

Which allows us to calculate the Cost of Care (cc): CC = cD*lD+cP*lP

(24)

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Finally the total US Population (P) is being calculating using the birth results and immigration per year.

P(t) = P(t - dt) + (lH + lP + lD+ im - d) * dt (25)

AGGREGATE MODEL

This model is an aggregate version of the Population Screening model presented above and it has developed as an auxiliary tool to demonstrate the potential of the ELI-P Complex technology. The change of scale from 25 years to 40 (2010-2050) allows tracing the feedback effects on female health improvement. In the simplified Aggregate model the ELI-P Complex screening is presented as a binary variable to evaluate pregnancy outcomes without any changes (current system) and with the technology intervention.

Table 4.12 Stocks and Flows for Aggregate ModelInflow Stock Outflow

Entering Pool of Healthy Women

eH Healthy Women H DeathsHW DHW

Entering Pool of Unhealthy Women

eU Unhealthy Women

U Deaths UnH DUW

Women Potentially ChildbearingWomen Giving Birth

wC

wG

Fertile Women F Women Getting PregnantWomen Ceasing to Potential Child Bearing

wP

wS

Women Getting Pregnant

wP Pregnant PR Pregnancy TerminationWomen Giving Births

XwG

Healthy Births pY Hb Healthy Newborns

HN Early DeathsHHealthy Leave Infancy

DHHi

Unhealthy Births pY Ub Unhealthy Newborns

UN Early Deaths NHUnhealthy Leave Infancy

DNHUHi

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Most of the rates and variables are adapted from the Population Screening model. The new variables, which are included in the model, are depicted in Table 4.0 in italics. The Aggregate model has 37 system parameters, of which 6 are state variables. Table 4.12 presents the stock and flow set up for the Aggregate model and its complete structure is presented in Figure 4.2. Table 4.13 summarizes the rest of the model’s parameters and their symbols.

Table 4.13 System Parameters for Aggregate ModelPregnancy Termination Rate AFr ELI-P Screening eAge of Entering the Pool of Fertile Women

aa Girls Fraction gf

Becoming Healthy Women bHW Growth Rate (fertile females) grBecoming Unhealthy Women bUW Healthy Births Fraction hbfDeath Rate Healthy Women drH Healthy Girls hGDeath Rate Unhealthy Women drU Multiple Births Fraction mbDeath Rate Healthy Babies drHN Pregnancy Rate PRrDeath Rate Unhealthy Babies drUN Unhealthy Girls uG

Parameter EstimationSince some of the parameters are borrowed from the first model, Table 4.14 describes only the newly introduced ones. The following units are used in the Aggregate model:

{year} {/yr} {thousands of women} {thousands of women/yr} {thousands of newborns} {thousands of newborns/yr} {thousands of girls}

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Table 4.14 Estimates of Selected Parameters for Aggregate ModelPregnancy Termination Rate[0.31]

AFr National Vital Statistics Report, Vol. 52, No. 23, June 15, 2004. 2000 data indicates 16% of fetal loses and 21% of aborted pregnancies. The aggregated estimate of 31% is adjusted number for 2010 considering the number of induced abortions and fetal losses decreases due to the use of contraception and better medical treatments during pregnancy.

Death Rate Healthy Newborns [0.001]Death Rate Unhealthy Newborns [0.004]

DrH

drU

Neonatal/Infant Mortality data was taken from OECD Health DATA 2004. Death rates for healthy/unhealthy newborns were adjusted for 2010-2050. Infant mortality in 2001 was ~6.8 per 1000 live births.

Healthy Births Fraction[0.85]

HBf It has been assumed that ~85% of babies are born healthy. The users can change this value if they have more precise data.

Girls Fraction[0.45]

gf There are more boys than girls born in the US for many decades now, plus the fraction does not include those girls who as they become women choose not to have children or won’t be able to due to genetic problems. The user can change this age for the value he/she finds the most suitable.

Age of Entering the Pool of Fertile Women [15]

aa Even though CDC estimate for the first pregnancy in the US is 25.1 years, the fertile age is considered 15-44, in this model it is 15-49 due to the growing number of late pregnancies and innovative reproductive technologies which promise to postpone pregnancies to more advanced female age in the future.

Equations’ Description

This model has 17 equations and it is a simplified version of the above model where pregnant women are not being tracked in separate pools and the screening intervention is defined as a binary variable. The number of women who are potentially childbearing in the age cohort 15-49 is calculated as (2), however Healthy Women (H) and Unhealthy Women (U) in this case are derived as:

H(t) = H(t - dt) + (eH- drH) * dt (26)

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U(t) = U(t - dt) + (eU - drU) * dt (27)

where Entering the Pool of Healthy Women (eH) and Entering the Pool of Unhealthy women (eU) are not simply the products of females and growth rates but:

EH =IF(e=0) THEN(H*gr) ELSE (IF(TIME<=2025) THEN(H*gr) ELSE((H+bHW)*gr))

(28) EU= IF(e=0) THEN(U*gr) ELSE

(IF(TIME<=2025) THEN(U*gr) ELSE((U+bUW)*gr)) (29)

In this model the time delay of 15 years is introduced for if the ELI-P Complex screening is implemented, then we can trace the improvement in female health as the girls born from screened mothers reach the reproductive age. Becoming Healthy Women (bHW) is expressed:

bHW=DELAY(hG,aa,1601) (30)

where 1601 is the initial value of healthy girls which is being retained constant until they become of fertile age. Pregnancy Termination [X] in this model is calculated as:

X=PR*(AFr-e/20) (31)

which emphasizes that if the screening is introduced, pregnancy loss would decrease by 5%. Healthy (HB) and Unhealthy (UB) Births in this model are expressed from one pool of screened females but with the use of Healthy Birth fraction (hbf), which can be changed by the users of the simulation if they have better evidence.

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HN(t) = HN(t - dt) + (Hb - Hi - DH) * dt (32)

UN(t) = UN(t - dt) + (Ub - Uhi - DNH) * dt (33)

Healthy Births per year (Hb) and Unhealthy Births per year (Ub) are expressed as:

Hb = wG*(hbf+e/10) (34)

Ub = wG*(1-hbf-e/10) (35)

If no screening is implemented then e=0, but if the screening is implemented (e=1), the above formulas allow us to influence the healthy birth rate fraction accordingly, increasing it by 10% for healthy births and decreasing it by 10% for unhealthy births.

NOTES:Numbers in [ ] correspond to the title in References p. 221

1. [30] Sterman, J.2. [14] Gold M, et. al.3. [208] CDC data4. [226] OECD Health Data: over 500,000 pregnancy associated

hospitalizations per year + ~the same number of complicated pregnancies not requiring hospitalization.

5. [112] Petrou, S.6. [123] Press, N., Browner C. 7. [130] Santalahti, P. et al.8. [223] Molecular Devices Corp.9. [234] [241] Tecan Group10. [241] [242] Phone interviews with sales representatives from MD and Tecan11. [233] [143] Grifols, Triturus Manual12. Appendix 1. ELI-P Complex Technical Description13. E&K Scientific [http://www.eandkscientific.com/1536%20well.htm]14. Sigma Aldrich USA [http://www.sigmaaldrich.com]15. The Buyer’s Guide for Life Scientists [http://www.biocompare.com]16. Chemicon Intl. [http://www.chemicon.com]17. [208] CDC Data [http://www.cdc.gov/cancer/nbccedp/Reports/NationalReport

summary.htm]

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18. [240] Poletaev A.B.

19. [156] Zamaleeva, R.

20. [222] March of Dimes

21. [143] Waitzman, N., Romano, P.

22. Cost-effectiveness of HAART study [http://www.aidsmap.com/en/docs/ 673FBA30-FF55-455E-9B53-54BCC9B6421E.asp]

23. ACOG [http://www.acog.org/]

Chapter 5Summary: This chapter begins with a summary of the simulation results, which are examined under various conditions in order to study the model’s behavior. After the analysis of the model’s structure is performed using a structure-confirmation test and a parameter-confirmation test, the model’s behavioral validity is then assessed by running the simulation on reference data (1985-2000), testing it for various extreme conditions and conducing sensitivity analysis to identify parameters which have significant impacts on the model’s behavior. As a part of the validation process, the model is checked for consistency and examined by experts from fields of system dynamics

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and preventive medicine. The Aggregate model is evaluated in a similar manner.

Model Validation 5.1 Summary of Simulation Results

5.1.1 Base Case 5.2 Validation & Verification

5.2.1 Structural Validity 5.2.2 Behavioral Validity

5.2.2.1 Reference Model 5.2.2.2 Extreme-Condition Test 5.2.2.3 Sensitivity Analysis

5.2.3 Expert Opinion5.3 Notes

SUMMARY OF SIMULATION RESULTS

Base CaseAs previously mentioned, the Population Screening model is

designed for the early (has not yet entered the market) health technology assessment, and the goal of the model is to produce various scenarios of how this technology might influence prenatal care. Before examining outputs of the simulation under different settings, the base case (run) results are presented, to be able to compare further experimental outputs with the original. The base case is the simulation output (continuous data for 25 years) under the assumptions outlines in preceding chapters of this work. Delta time (DT) chosen for both simulations is 0.25, (quarter of a year), which is a time interval between the calculations. This interval is optimal because it is small enough not to miss any interesting changes in between the steps and large enough to allow the computer to run the simulation fast. Table 5.1a lists the initial inputs of the parameters and Table 5.1b summarizes the output for selected years. The initial

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values for the stocks (set by the modeler) are listed in the 2010 column.Table 5.1a Base Case Values

Parameters Base Case Values

Planning Rate 0.044Pregnancy Rate Not Planning 0.027Unhealthy Conceiving Fraction 0.5Cost per Test 22Cost of Treatment 200Cost per BD Case 66000Cost per Pathology Case 15000Number of Tests (Pregnant) 2Number of Tests (Planning) 3Number of Test (In Treatment) 2.5In Treatment Fraction (Planning) 0.9In Treatment Fraction (Pregnant) 0.5Technology Adoption Rates, Treatment Effectiveness, Pregnancy Loss Rates

and Healthy Newborns Fraction are defined by graphical functions as depicted in Table 4.10

Table 5.1b Simulation Output (in thousands)

Figure 5.1a Graphical Output Base Case: Pregnancy Outcomes

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Figures 5.1a and 5.1b present selected graphical outputs of the simulation. It can be seen that under the proposed scenario the number of healthy newborns increases from 3,898,000 to 5,511,000 in the 25-year period while the number of newborns with pathologies and birth defects decreases from 349,000 to 282,000 and from 141,000 to 139,000 respectively. While the decrease in the actual number of newborns with pathologies and birth defects does not seem to be as great as expected, we have to keep in mind that in 25 years the total number of newborns increases by ~1,617,000, relative to which, the decrease in birth defects and births with pathologies by the year 2035, demonstrates significant improvement in pregnancy outcomes, compared to the year 2010. During this period the simulation shows that the US population is expected to increase from 308,936,000 to 381,677,000, which is consistent with the US Census forecast59. By the year 2035 there will be ~18.2 million ELI-P Complex procedures performed per year and the cost of screening will be ~ US$564 million (in Y2005 dollars). In this study inflation is not taken into consideration and costs are not discounted. This is a game simulator, and the user is free to choose the cost per test and the cost per treatment and set adjusted values, if she has more accurate estimates at her disposal.

59 US Census Population Forecast [http://www.census.gov/ipc/www/usinterimproj/natprojtab01a.pdf]

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According to the base run, the cost of care under the given settings can be brought down from US$14.5 to US$13.4 billion (in 2005 dollars) between the years 2010 and 2035.

For the base case scenario the S-shaped adoption curve is chosen, where the full integration of the ELI-P Complex screening into clinical practice occurs around the year 2030. It can be argued that the curve ought to have a much sharper trajectory reaching the full market penetration within 7-10 years after the technology’s implementation. In this case, the user is free to change the adoption trajectory as she finds most appropriate, however, the author’s judgment for choosing a much slower adoption pattern is based on the evidence that even having great technologies at their disposal, people are reluctant to implement them, especially when the shift of paradigm is required. It will take a generation if not longer to change the mindset of women so they are willing to get screened before pregnancy. Also, it is possible that the technical modification of the test will be an ongoing process, extended for decades. The search for the most suitable sets of antigens to represent the system responsible for the development of an embryo and fetus is a very complicated task.Figure 5.1b Graphical Output Base Case: Costs

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Table 5.2a presents the Base Case values (those which differ from the Population Screening model) for the Aggregate model. This simulation is designed to operate in the binary mode: under the ELI-P Complex screening and without this intervention. The results of the simulation output are shown in Tables 5.2b and 5.2c below and accompanied by the graphical representation of the simulation runs (Figures 5.2a and 5.2b).

Table 5.2a Base Case Values (Aggregate Model)

Parameters Base Case Values

Pregnancy Rate 0.084Abortions/Fetal Loss Rate 0.31Healthy Birth Fraction 0.85Girls Fraction 0.45

From the following tables we can see that the introduction of screening (assuming that necessary treatment and counseling follows) increases the number of healthy newborns almost by 1 million and decreases the number of unhealthy newborns (Figure 5.2d) ~ by 600,000 in the year 2035. According to the simulation output, it is obvious that the screening intervention has a significant positive impact on pregnancy outcomes. Table 5.2b Simulation Output (in thousands) (Aggregate Model)No ELI-P Complex Screening

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Table 5.2c Simulation Output (in thousands) (Aggregate Model)Under ELI-P Complex Screening

Figure 5.2a Graphical Output Base Case: No ELI-P Screening

Figure 5.2b Graphical Output Base Case: Under ELI-P Screening

The following two Figures (5.2c and 5.2d) present comparative simulation outputs for the two selected parameters: Becoming Healthy

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Women and Unhealthy Newborns. Run1 is under the ELI-P Complex screening and Run2 is under no intervention. A significant increase in the number of healthy women can be observed as the ELI-P complex screening is introduced. This stock incorporates the number of girls born from ELI-P screened mothers who become fertile (the values are constant for 15 years as girls are growing up and not entering the pool of fertile women).

Figure 5.2c Graphical Output Base Case: Becoming Healthy Women

Figure 5.2d Graphical Output Base Case: Unhealthy Newborns

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In order to assess the quality of outputs of both simulations, the models must be evaluated and validated using various tests.

VALIDATION AND VERIFICATION

In system dynamics compared to other M&S methodologies, the term verification is not used. It mostly applies in the context of hardware and software systems to prove or disprove the correctness of a system with respect to a certain formal specification or property, using formal methods (mathematically based techniques) [1]. The process of validation in system dynamics relies on many various tests, but the field continues to lack a formalized validation methodology and tools. There is no SD software package, which has a full validation environment, but this does not mean that there are no robust techniques, which help find flaws in the model’s structure and behavior. In system dynamics, validation is a continuous process of testing and building confidence in the model [2] and no model can simply be validated by a single test or the ability to fit the historical data, thus the model cannot be classified as correct or incorrect [4,6], but it can be of good quality or of poor quality [4], suitable or not suitable. J. Forrester back in the 1960s argued that validity of a simulation model cannot be discussed without reference to a specific purpose [3]. Thus when applying standardized tests, it is very important to always keep in mind the environment in which the simulation is designed to operate, and the type of questions which it aims to answer.

To validate the two models discussed in this work, the following methodology was chosen: first, both models are examined in the context of the US prenatal care for their structural validity, second, the

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behavioral validity is assessed, and finally the qualitative evaluation of the model performance is carried out.

Structural Validity Generating an “accurate” output behavior is not sufficient for establishing the model’s validity. What is crucial is the validity of the internal structure [4]. The validation process of both models began when the first few parameters were estimated and their relationships defined and has been carried out though the entire model building process which, to some degree, facilitates model validation at this stage. Direct Structure Test, which assesses the validity of the model’s structure by direct comparison with knowledge about the real system’s structure, is performed first. Each mathematical relationship is evaluated and compared with the real prenatal care system. Table 5.3 lists selected equations from the models and their explanations in relation to the real system in the US in 2005. Table 5.3 Direct Structure TestwC = U+H Women who are potentially childbearing in the society

consist of those who are healthy and who are not healthy, while the latter have a lower potential for successful childbearing.

plUu=UU-PLrUplST=ST-PLrS

While many abortions are underreported in any society, the number, which is usually considered in the studies is the share of those pregnancies which were terminated as a part of medical procedure or woman’s choice. Many fetal losses go underreported every year, some women do not even know that they miscarry. This study assumes the known rate of miscarriages and fetal losses occurring every ever and reported to/by medical practice.

wP=rPL*FwNP= F-PLpcPR= rNP*NPL+

Women who are getting pregnant per year come from the pool of fertile women who conceive at the rate of pregnancy for that given year, while a small number of pregnancies occur in girls under the age of 15 and in women over the age of 49, they are excluded from this

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(pPR-spPR) study. sB = ST-plSTuB=UU-plUu

Women who give birth per year are the total number of pregnant women minus the total number of abortions and fetal losses that year.

wS = F-sB-uB Women ceasing to potential childbearing are those fertile women who did not get pregnant that year.

scPC = mPR*AD Screened Pregnant women are those who enter prenatal care and choose to go through screening. Adoption rate in this case is an aggregates variable representing the combination of factors leading to technology adoption such as woman’s choice, technology availability, physician’s proper counseling to convince a woman, etc.

PP = F*rPL Women who plan pregnancy are fertile women who prepare for pregnancy and become pregnant within the considered time period.

sPP = ADp*PP Screened planning women are those who choose to go through screening before they conceive. Adoption rate in this case is also an aggregates variable representing the combination of factors leading to technology adoption such as woman’s choice, technology availability, public awareness, word of mouth, woman’s level of education, previous history of problematic pregnancies, etc.

SNP = SPR-sPP Screened women who did not plan pregnancy are those who entered prenatal care after they found out they were pregnant and decided to undergo screening procedures.

PtrPL = SPL*0.2PtrNP = S*0.2

Poor test results were considered to be 20% from previous clinical trials, approximately similar number for the pool of planning pregnancy women and not planning.

bb = (sB+uB)*mb+(sB+uB)

Number of babies being born per year is the sum of screened and unscreened women giving birth plus all the multiple births to account for twins, triplets, etc.

dN= ir*NB Infant deaths are the number of newborns who die before age one and it is defined by the fraction of newborns who are going to die per 1000 live births that year

bH = HNf*NB Newborns who are healthy at birth represent a relatively large fraction of all live births (graphically defined in table 4.10)The rest of the newborns are considered to be unhealthy (which varies from severe defects to mild pathologies).

bP = UN*(1-Df) Newborns beginning life with pathologies are usually

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attributed to unhealthy newborns but distinguished from the newborns with birth defects.

e = S*ePr+sPL*ePP+(PtrPL+PtrNP)*rt

See (1) and explanation in Chapter 4.

sc = (T+TPL)*ct+ce*e

Screening Cost in the real world might incorporate other unaccounted costs, but its two major components will be the cost of treatment and the cost of tests (which already include associated administrative costs)

All equations of the Aggregate model have been evaluated in a similar fashion. The relationships between main variables are maintained to support a consistent flow of events and the rate of their occurrence in the real system. A well-checked base case allows for further model development and greater complexity, while preserving the accuracy and proper depiction of the actual predictions that the models are designed to simulate. Conclusions have been drawn that both models are robust simplifications of the processes occurring in the real world.

Parameter confirmation testing was performed during the model-building phase. As summarized in Table 4.11, each constant is defined using the knowledge about the real system of the US prenatal care and indicators associated with it. Those parameters in question are presented in the model as control buttons where the user can set the value she thinks is the most accurate within the range specified by the model-builder. The range selection, once again, is supported by historical data and forecasted trends. All parameters in the model correspond to the elements of the real system. The only parameters which do not have equivalence in the current US prenatal care are the following: ELI-P complex technology adoption rate, treatment effectiveness rate, healthy newborns fraction, and in treatment fraction. However, numerous comparisons have been made to estimate these parameters as accurately as possible. Some relied on

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the results of clinical trials (treatment effectiveness and healthy newborns fraction), others were determined from extensive literature reviews (adoption rates) [6,8,9].

Unfortunately the Stella software has a very limited selection of validation tools. A useful functionality of the built-in unit check helped assess both models for unit consistency. The program also has a tracing capability, which was used to check the models’ consistency. GUI, set up with various buttons representing model parameters, allowed for a quick and efficient scenario and extreme condition testing, which are described further in this chapter and in Chapter 6. Standard statistical measures cannot be applied to the evaluation of this model because it involves a transient, non-stationary behavior (S-shaped growth) hence, since the modeled problem is of no statistical nature, validating it statistically is not suitable because of problems of autocorrelations and multicollinearity [4]. Barlas Y. suggests that the best approach for such models is to compare graphical/visual measures of the most typical behavior-pattern characteristics [4].

Behavioral Validity

A few approaches of placing the model into different contexts and evaluating its outputs were used to assess the behavioral validity.

Reference Model Running the simulation for a few decades in the past presents an interesting test, which shows how the model reproduces major indicators such as the number of pregnancies, number of newborns, population, number of healthy and unhealthy women, and poor pregnancy outcomes. For the reference model test years 1985 to 2000 were chosen. CDC, Census and OECD provide

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comprehensive sets of data on the following parameters: Population, Immigration, Births, Deaths, Pregnancies, Poor Pregnancy Outcomes (Fetal Losses, Abortions), and Infant Mortality. This information was graphed in Microsoft Excel, then, the simulation settings were adjusted to run for the Years 1985-2000. The initial inputs for all of the above stocks were set to the 1985 values. Pregnancy rates were derived for the pool of females in the age cohort 15-49. Because sufficient data were available, instead of deriving the average pregnancy rate for 15 years, it has been decided to depict pregnancy rate in Not Planning Women as a graphical function to increase accuracy. Table 5.4 presents new settings for the historical model to run the simulation for 1985-2000.

Table 5.4 Settings for the Reference SimulationParameters Base Case

ValuesEntering Prenatal Care Not Planning Rate 0.029Pregnancy Loss rate [0.40-

0.34]Planning Women (Initial Stock Value) 3,072Healthy Women (Initial Stock Value) 49,081Unhealthy Women (Initial Stock Value) 14,661Fertile Women (Initial Stock Value) 63,742US Population (Initial Stock Value) 237,924Immigration Rate 0.0043Death Rate HW 0.0003Death Rate UW 0.0007Death Rate (Population) 0.0086Newborns (Initial Stock Value) 3,761Healthy Newborns (Initial Stock Value) 3,328Unhealthy Newborns (Initial Stock Value) 433Infant Mortality 0.0086Pregnancy Planning Rate

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Data Source: CDC, OECD, Census [7]

In the reference model pregnancy loss rates for screened/treated and unscreened /untreated females were set to their original values (as in the Table 5.4 above) instead of being adjusted with the help of graphical functions as in the forecasting model. Since there was no ELI-P Complex screening between 1985 and 2000, abortion rate and fetal loss rate could not decrease because of this intervention, though they decreased due to other interventions. Hence, while for initial stocks we place 1985 numbers, all other rates and fractions are the averages for the time period between 1985 and 2000.

The historical fit produced results depicted in Figures 5.3a – 5.3d. Each shows the simulation outcome (Stella: line 1) and the graphical representation of the actual data for those years (Excel: line with dimes). As we see, most variables were reproduced almost exactly and their trajectories are fairly similar. Since the Excel and Stella

software use different graphing scales, the exact overlay was difficult to achieve. Therefore Table 5.5 was created to provide the summary of the obtained numerical results. Most of the simulated results are very close to the actual historical data.

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Figure 5.3a – Comparison of statistical historical data and simulated output: Newborns

Figure 5.3b – Comparison of Historical Data and Simulated Output: Pregnancies

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Figure 5.3c – Comparison of Historical Data and Simulated Output: Population

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Figure 5.3d – Comparison of Historical Data and Simulated Output: Fertile Women

Table 5.5 Numerical Comparison of Simulation Output and Historical Data

YEAR Fertile Women Pregnant Newborns PopulationSim Actual Sim Actual Sim Actual Sim Actual

1985 63,742 63,742 6,144 6,144 3,761 3,761237,924 237,9241989 64,872 66,536 6,395 6,527 3,838 4,041248,228 246,8191993 66,843 69,381 6,518 6,494 3,947 4,000258,699 259,9192000 70,778 73,744 6,397 6,401 3,937 3,935278,372 282,224

The original model structure assumes that there is some ELI-P Complex screening in place before the population uptake of the program begins. Thus, for the years 1985-2000 the Reference model produced realistic results showing a constant number of the ELI-P Complex procedures (which were supplied by the modeler) and a constant limited number of screened pregnancies, while keeping the level of unscreened

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pregnancies similar to the overall pregnancy level. The Reference model confirmed the robustness of the original model.

Figure 5.4: Reference Model Output: Other Indicators

The Aggregate model was checked for the historical fit as well. Its settings for the years 1985-2000 have been changed accordingly (Table 5.6).

Table 5.6 Settings for the Reference Simulation (Aggregate Model)Parameters Base Case

ValuesPregnancy Termination 0.4Pregnant Women (Initial Stock Value) 6,144Healthy Women (Initial Stock Value) 49,081Unhealthy Women (Initial Stock Value) 14,661Fertile Women (Initial Stock Value) 63,742Death Rate HW 0.0003Death Rate UW 0.0007Newborns (Initial Stock Value) 3,761Healthy Newborns (Initial Stock Value) 3,328Unhealthy Newborns (Initial Stock Value) 433Death Rate HN 0.002

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Death Rate UN 0.0066

Figure 5.5a – Comparison of Historical Data and Simulated Output (Aggregate Model): Pregnancies

Figure 5.5b – Comparison of Historical Data and Simulated Output (Aggregate Model): Fertile Women

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The output produced by this simulation closely matches historical data for the years 1985-2000 (comparative graphs are presented in Figures 5.4a and 5.4b). Once again, the overlay of scales was not possible, but the numerical outputs of the Aggregate Reference model are as closely matched to the historical data (Table 5.5) as in the Population Screening model (1985-2000).

Extreme-Condition Test There are two types of extreme-condition tests used in system dynamics: direct and structure-oriented behavior test. The latter was used to evaluate this model and assess plausibility of the resulting values against the knowledge/anticipation of what would happen under a similar condition in real life [10]. The test was carried out by assigning extreme values to selected parameters and comparing the model-generated behavior to the observed (or anticipated) behavior of the real system under the same extreme condition. A few interesting outputs of this test are described below:

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Test 1: Extreme death rates in females. DrHW was set to 0.055 and drUnH was set to 0.25, which is 10% of all fertile females. Under these settings, the population death rate was increased to 0.034. The simulation produced results depicted in Figure 5.6 and Table 5.7. If in reality such situation happens and beginning with 2010 10% of all fertile females would be dying every year, then pregnancy and birth rates would begin to drop sharply in a matter of years. It would certainly affect the overall population to a great extent. The simulation shows that in 25 years under such extreme condition, the population might decrease by 120 million people. It is apparent that the simulation properly responds to this extreme condition.Table 5.7 Numerical Output of Extreme Condition 1

Figure 5.6 Extreme-Condition 1: Female Deaths

Test 2: Planning rate and prenatal care utilization rate set to 10%. Both prenatal care utilization rate and pregnancy planning rate were used to calculate proper rates for the models, which are rPL and rNP.

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The assumptions were made on the available 2000 data [prenatal care utilization rate of 83% and pregnancy planning rate of 51%], and forecasted for 2010 as 85% and 53% respectively. When both of these rates were brought down to 10%, rPL dropped to 0.008 and rNP dropped to 0.003. Simulation responses to this condition are presented in Figures 5.7a and 5.7b as well as in Table 5.8. Such conditioning in the real world would increase the number of unsuccessful pregnancy outcomes. Once again, the simulation responded properly and this extreme condition decreased the total number of pregnancies and the number of newborns, and decreased the cost of care (both healthy and unhealthy outcomes decreased in relation to original values) and the screening cost.

Figure 5.7a Extreme-Condition 2: Very Low Pregnancy Planning and Prenatal Care Utilization

Figure 5.7b Extreme-Condition 2: Very Low Pregnancy Planning and Prenatal Care Utilization

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Table 5.8 Numerical Output of Extreme Condition 2

Test 3: Healthy Women Stock = 0 If the initial number of healthy women is set to zero, it means there were no healthy fertile females in the year 2010 and on for the simulated 25 years. Obviously, in real life the absence of 77% females would dramatically bring down the number of all fertile females, thus bringing down the number of pregnancies and newborns, and tremendously shrinking the total population. As it can be seen from Figure 5.8 and Table 5.9, the simulation very accurately produced the response, which would happen in the real system.

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Figure 5.7c Extreme-Condition 3:

Table 5.9 Numerical Output of Extreme Condition 3

From the extreme-condition tests we can conclude that the model generates the right behavior for the right reasons.

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Sensitivity Analysis Stella software package provides a robust sensitivity testing environment. The GUI of both models is equipped with sensitivity testing controls allowing users to experiment with their own sensitivity tests if they wish to check the models’ responsiveness to changes in various inputs. As a part of the validation process, the first phase of the sensitivity analysis focused on changing values of selected constants (conveyors) in order to see how they impact the behavior of the model. The second phase was designed around changing initial values of the stocks and observing the models’ behavior.

Sensitivity Test 1: Planning rate [0.044] is set to values: 0, 0.03, and 0.06Figure 5.8 Sensitivity Test 1:

In Figure 5.8 and in Table 5.10 Run 1 corresponds to a zero planning rate, Run 2 to 0.03 planning rate, and Run 3 to 0.06 planning rate. As the simulation results show, the pregnancy rate parameter is very sensitive and produces significant changes in the model behavior when it takes upon different values (within the realistic paradigm). Since pregnancy rate directly affects the total number of pregnancies per

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year, it also affects the total number of newborns, the cost of screening, the cost of care and total population.

Table 5.10 Numerical Output of Sensitivity Test 1

Sensitivity Test 2: Pregnancy rate in Not Planning [0.027] is set to values: 0, 0.02, and 0.04Figure 5.9 Sensitivity Test 2:

In Figure 5.9 Run 1 corresponds to a zero planning rate, Run 2 to 0.02 planning rate, and Run 3 to 0.04 planning rate. As the simulation results show, pregnancy rate in Not Planning Women is a less sensitive parameter than planning rate. These rates are logically connected as an increase in pregnancy planning should decrease pregnancy rate in Not Planning Women however, the current version of Stella does not

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allow to set the control knobs so, the change in one, automatically adjusts the rate in the other to a corresponding level. But together, both parameters are sensitive in this model, as they significantly change its behavior.Sensitivity Test 3. Cost per test is set between 0 and US$100. This test was designed for 5 runs (5 different cost-per-test values in the range between 0 and US$100 were analyzed). When the cost per test is set to US$100, the screening cost per year goes up to US$2.3 billion (Run 5); when the cost per test is set to 0, the model properly responds so that the total screening cost equals the total cost of treatment only. Run 3 corresponds to the realistic value where the cost of the test is ~US$25.Figure 5.10a Sensitivity Test 3:

From Figure 5.9b we see that the change in this parameter noticeably affects only the total screening cost and not other parameters in the model, since the cost of tests is not connected to the adoption rate of the technology. In the realistic world, the cost-per-test could be directly affected by the willingness to test or the test’s availability, however, in this research the assumption has been made that the test is covered by insurance and its cost is not higher than US $30, which

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presents no concern to insurance companies and they could be willing to cover the screening.

Figure 5.10b Sensitivity Test 3:

Sensitivity Test 4: Change in the initial value of the Newborns stock. Five Runs with values ranging from 0 to 7000 were set up. From the simulation outputs depicted in Figure 5.11 it can be seen that within 8 years, the system comes into equilibrium. The stocks of newborns and healthy newborns under five different conditions reach equilibrium within 4.5 years and obviously, for more extreme values (Runs 1 and 5) it takes longer to reach the equilibrium than for the moderate values (Runs 3 and 4). The population parameter responded correctly to this change as well producing five different slopes (which are very close in values) corresponding to each of the runs. Thus, the model behavior has not been taken out of the equilibrium, but proper changes occurred reflecting modifications in the system. When the number of newborns is set to 0 (for the year 2010), the population for that Run 1 is at its lowest point compared to Figure 5.11 Sensitivity Test 4:

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Run 5 where the number of newborns is set to its maximum value. In the extreme condition test the number of healthy women was set to 0 and the system was taken out of the equilibrium. In this case, the equilibrium condition is quickly regenerated, since each year the number of newborns increases and adds to the initial value of newborns, while in the extreme condition test, the number of healthy women was held constant at zero for the duration of the simulation.

Sensitivity Test 5. Aggregate Model: Change in Healthy Newborns Fraction. The fraction is set to three different values: 0; 0.495 and 0.99. In the aggregate model it turned out to be a sensitive parameter affecting the number of both cohorts of newborns and women becoming healthy and unhealthy. In Figure 5.12a, Run 1 corresponds to 0 fraction value, Runs 2 and 3 to fractions 0.495 and 0.99

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respectively. Figure 5.12b shows how setting the fraction of healthy newborns to zero (Run2) increases the number of unhealthy females entering the pool of unhealthy women.

Figure 5.12a Sensitivity Test Aggregate Model

Figure 5.12b Sensitivity Test Aggregate Model

Figure 5.12c Sensitivity Test Aggregate Model

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In Figure 5.12c, Runs 1 and 2 are the actual simulation runs under original settings where Run 1 is without the ELI-P screening and Run 2 is with the screening implemented. Run 3 is when the healthy newborns fraction is set to zero; Run 4 produces very interesting results: while the healthy newborns fraction is kept at level zero, the introduction of screening starts to produce healthy newborns. The above test was done by only changing the fraction and not the initial values of the stocks (Healthy and Unhealthy Newborns), thus no extreme results were produced and the model continued to exhibit logical behavior which could be expected in the real world.

From the sensitivity analysis it can be concluded that both simulations produced similar high sensitivity to parameter changes, as the real system would, under the given conditions.

Expert Opinion

As a part of qualitative validation, expert opinion was used to assess the model’s quality, usefulness, and functionality. First, the model was evaluated by a system dynamics professional who suggested possible model improvement strategies from the technical point of view. Most of these suggestions were taken into consideration

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and implemented and others remained for future work, which is described in Chapter 7. Overall, the expert’s opinion on the model’s structure and performance was satisfactory. Then the model was evaluated but the main stakeholder – the Immunculus Labs where ELI-P Complex was developed. This evaluation focused mostly on the quality of the parameters, simulation’s ability to produce the behaviors observed in the real world, and the usefulness of the model in the evaluation of the technology’s effectiveness. Very positive feedback was received from this exercise. The laboratory offered to provide the modeler with more results of clinical trials in the near future to improve the model’s parameters and help develop the model’s structure in greater detail.

Further research and collection of more precise information, preferably in the specific area where the technology is likely to begin its implementation, are needed to present the model for the evaluation to other stakeholders such as policymakers, women planning pregnancy, various social groups and venture capitalists.

Simulations provide consistent stories about the future but not predictions. Both, the Population Screening Model and the Aggregate Model presented realistic scenarios about the future under the assumption of the ELI-P Complex technology implementation. Within the context of this research and the developed dynamic hypothesis the model did not fail any of the validation tests hence, it can be concluded that the model is valid.

NOTES:Numbers in [ ] correspond to the title in References p. 221

1. [237] Wikipedia2. [165] Sterman, J. 1988

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3. [10] Forrester, J. 19614. [42] Barlas, Y. 19965. [150] Oliva, R. 19966. [30] Sterman, J. 20007. [208] [209] [226] Data Tables 8. [27] Rogers, E. 20049. [148] Homer, J. 198010. [184] Forrester, J. 1971

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Chapter 6Summary: Policy design and analysis are discussed in this chapter, building upon the findings from systems thinking modeling, simulation results and their evaluation under various “what if” scenarios. The integrated policy analysis is suggested and the summary of future hypothetical policies for prenatal care is produced. This chapter aggregates results of the modeling activity, literature review, current information about HTA and prenatal care, and suggests a framework for the US prenatal care modification to make it more efficient, safe, enjoyable, and above all, to provide long- and short-term benefits to mothers, their children and the society.

Policy Analysis and Prenatal Care6.1 Scenario Specification

6.1.1 “What If” Analysis6.2 Policy Design

6.2.1 Diffusion Paradigm6.3 American Prenatal Care

6.3.1 Suggestions for Improvements6.3.2 Future Framework

6.3 NotesSCENARIO SPECIFICATION

Simulation modeling is a process of exploration; it is not a solution to an existing problem or an answer to a stated question. Most system dynamics models are built with the goal of reaching a new understanding of how a problem arises and then using that understanding to design high leverage for improvement [1]. In this work, the cause of the problem was clearly identified. However, the modeling process was not focused around the assessment of all causes (which are numerous) contributing to the problem, but around addressing the influence of one specific cause and its effect on the

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problem (the health of pregnant women, or those planning to become pregnant, and pregnancy outcomes) in the futuristic dimension. The following policy design process focuses on producing a set of decisions, which can improve some of the current problems in the US prenatal care system in the coming decades. Policy design includes the creation of entirely new strategies, structures, and decision rules [1] and in this chapter it is described as direct outcome of the simulation process but within the prenatal care framework which incorporates multiple other criteria.

“What If” Analysis The graphic interface developed for both models allows for quick and easy creation of various “what if” scenarios. Only a few of them, which are relevant to policymaking, are described in this chapter, but many others can be derived when the model is expanded to a higher level of complexity.

1. What if adoption rate of this technology does not follow the S-shaped growth but remains around 20%?

Under these settings the simulation produces expected results: the number of healthy newborns decreases, while the number of unhealthy newborns increases. The total number of newborns decreases slightly as well since the benefits of treatment recede and the number of fetal losses increases. Figures 6.1b and 6.1c summarize the outputs of the simulation for scenario 1. In both, Run 1 is the original run under the S-shaped growth (adoption rate up to 85%) and Run2 is an output under the adoption level depicted in Figure 6.1a.

Figure 6.1a Technology Adoption rate at 20%

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In the model this change leads to an automatic adjustment in pregnancy losses for the unscreened/ untreated group to maintain it at the average value of 0.32, instead of being controlled by the graphical function as shown in Table 4.10.

Figure 6.1b Healthy Newborns Scenario 1

Figure 6.1c Unhealthy Newborns Scenario 1

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2. What if pregnancy rate drops after 2025 when 30% of women decide to have cloned children?

To run this scenario, Planning rate (rPL) is set to 0.044+STEP(-0.013,2025) and Pregnancy rate in Not Planning (rNP) is set to 0.027+STEP(-0.0081,2025), where the first number in brackets indicates a 30% decrease and the second number indicates the year when the decrease begins. Such adjustment leads to major changes in the model behavior. Figures 6.2a and 6.2b show a dramatic drop in newborns after the year 2025.

Figure 6.2a Healthy Newborns Scenario 2

Figure 6.2b Newborns Scenario 2

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3. What if clinical trials show that treatment effectiveness is higher than expected, or what if clinical trials show that treatment effectiveness is lower than expected during pregnancy?

Figure 6.3a exhibits two different settings under which the simulation is run and Figures 6.3b and 6.3c present the outputs of these simulations. In both cases Run 1 corresponds to the original setting of treatment effectiveness as shown in Table 4.10, Run 2 is under the condition of low treatment effectiveness in pregnancy and Run 3 is under the condition of high treatment effectiveness in pregnancy. Once again, the simulation produces a logical output: under the low treatment effectiveness the number of healthy newborns decreases and the Figure 6.3a Treatment Effectiveness in Pregnancy 1 2

number of birth defects and pathology cases increases, while the opposite happens when the treatment effectiveness in pregnancy is high (incorporates positive outcome – female’s condition improves while treatment has a positive effect on the fetus).

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Figure 6.3b Treatment Effectiveness in Pregnancy

Figure 6.3c Treatment Effectiveness in Pregnancy

4. Assessing the InterventionAccording to the simulation results, screening cost for the year 2035 is US$564,552,000 in Y2005 dollars. By this time the adoption level of the technology reaches its potential and allows for 18,166,000 screening procedures to be performed annually (Figure 6.4a) (assuming that planning women continue to undergo on average 3 screenings per pregnancy, those not planning but enrolling into prenatal care undergo 2, and those requiring treatment undergo 2.5 screenings on average). Under such settings pathologies decrease from 349,000 (8% of 2010 births) to 282,000 (4.7% of 2035 births) and birth defects decrease from 141,000 (3.2% of 2010 births) to 139,000

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(2.3% of 2035 births) between years 2010 and 2035 (Figure 6.4b). The cost of care decreases from US$14.5 billion to US$13.4 billion (Y2005 dollars). Hence, the ELI-P Complex screening intervention allows the prevention of 53,000 birth defects cases (if in 2035 birth defects rate remains at 3.2% it would result in 192,000 cases). If the total spending is screening cost + 1/5 of working capital60 (original working capital distributed over the years), then QALY can be calculated as:

With time, the screening intervention becomes more cost-effective (if the cost of the test is maintained around US$20-30 and the number of birth defects decreases from 3.2% to 2.3%). Considering ELI-P Complex also helps prevent a large share of pathological pregnancy outcomes, the actual screening program should be even more cost-effective than the above calculations show.Figure 6.4a Costs and Procedures

Figure 6.4b Poor Pregnancy Outcomes

60 See estimates in Ch 4.

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The “what if” analysis has assisted with the development of policy suggestions for the implementation of the ELI-P Complex technology and the improvement of the US prenatal care system.

POLICY DESIGN

Diffusion Paradigm

To get a new idea adopted, even when it has obvious advantages, is difficult since there is often a lengthy period between invention and adoption. This study takes a simplified approach towards the very complex issue of technology diffusion. However, possible scenarios of the latter for ELI-P Complex should be given more consideration with the assistance of policymakers who have the power to influence the course of technology’s penetration into prenatal care. Overall, the diffusion is likely to follow the S-shaped curve if the technology continues to demonstrate accurate clinical performance but, as it has been mentioned, the slope of this curve is hard to predict since it depends on many factors described below. Table 6.1 summarizes characteristics of the ELI-P Complex technology in the realm of the ten categories, which describe innovations.

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Table 6.1 ELI-P Complex in the context of 10 critical dynamics of innovation Source: [2]

Categories ELI-P ComplexRelative Advantage Provides new valuable information about

the health state of a female and provides a prognosis of her pregnancy development. The method is unique (does not replace an old technology) but has a potential of avoiding unnecessary tests and offering necessary ones to those pregnant women who might have complications. Low health risks are associated with the technology, which result not from the technology implementation but poor interpretation of the results and inappropriate prescriptions.

Trialability The test can be tried at low cost, no commitment for implementation and no harm to the patient and little training required from physicians/lab specialists.

Observability The field trials and the word of mouth in professional circles should encourage more obstetricians/gynecologists to try the technology. The benefits are long-term. There is a gap between the application of technology and observation of benefits.

Complexity The technology is easy to understand and to use since it’s built on the long-adopted popular ELISA technology, however complexity increases if the test is modified and new antigens added.

Homophilous groups Technology is for the use in obstetrics/gynecology practice: very purposeful application.

Pace of innovation/reinvention Technology has a great potential to evolve. It can be modified from the component perspective (adding more antigens) and automation of technological process and interpretation of the results.

Norms, roles, and social networks

Policy regulation would greatly help the diffusion of this technology. Since the screening processes presents no harm, patient’s consent should not be an obstacle and later might be eliminated. Informal mechanism (physician’s unwillingness to learn new tool, reluctance of women to test) can be an impediment, the cost of

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implementation can also be a an obstacleOpinion leaders Technology needs to be evaluated by

experts and leading clinics; reports at the conferences, approvals by the health authorities and the proper policy recommendations are necessary to help technology diffusion.

Compatibility The technology fits very well into the current prenatal care framework and with all the tests/procedures performed in this field. It can serve as an aiding tool to better co-ordinate other activities in the field to achieve better results.

Infrastructure Insignificant special infrastructure adjustments are needed. The technology fits well within the currently available laboratory equipment and the physician’s practice. However, more machinery will be required for the population level application.

Communications channels A number of influential channels exists: physician to physician, female to female, female to physician, promotion to female, promotion to physician, authority (expert, scientist, policy maker, publication) to physician, etc.

It is obvious from the above summary that the technology possesses a lot of positive features, which make it an innovation with a high potential. ELI-P Complex does not require patient selection. The test administration is simple, as well as should be the physician’s decision-making, especially if accurate results are produced by an automated system. The risk to patients is minimized and, the feedback of test results provides more information about possible pregnancy outcomes. These and many other positive characteristics of ELI-P Complex suggest that the technology should follow a quick and easy path to adoption. The willingness to use the test should increase, as more positive results of its performance become available [3]. Females may not be able to report the benefits of specific interventions, since it would be more likely for women to perceive a combined effect of all

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procedures they undergo while in prenatal care. However, physicians, medical statisticians, hospitals, etc. should see the impact of the screening and early intervention from a different perspective and identify the benefits of the technology at the population level.

However, there are many concerns, which cannot be ignored since they may hamper the test diffusion and may postpone the test implementation for many years. ELI-P Complex above all is an information technology, thus the accuracy of the test’s indicators and their proper interpretation are the keys to the technology’s success. Policymakers’ decisions will directly depend on the technology’s performance in these categories. If over the years of clinical trials, ELI-P Complex demonstrates its prognostic value, there would still remain the challenge of moving from “there is a technology that tells you” to the state when it is obvious that such technology exists and that every woman can and is offered to have her ELI-P Complex results just as she has the right to have an ultrasound during pregnancy.

Careful consideration should be given to the influence of the environment in which the technology implementation is about to take place. Policymakers must be prepared for the fact that professional groups are well able to resist change [3]. At the early stages of adoption it might be difficult to convince physicians to use ELI-P Complex, as it would require extra training and effort. Also, it should not be forgotten that the offered screening is an extra luxury service, without which most women would still have healthy children. Social changes and social problems facing the world, of course, affect the diffusion of innovations [27], thus there may be many more obstacles for adoption, which are not considered at the moment and will appear at a later stage. So, the move from the available technology to a customary practice is likely to be a long process, which would take

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more than promotional efforts and would require a change of mindset to manage to depart from the old-fashioned practices.

This work using the results of the system dynamics simulation attempts to help policymakers make proper decisions regarding the ELI-P Complex technology. Below are five policy suggestions, which have been derived as a result of this study:Policy Suggestion 1

Large clinical trials should be carried out to determine the technology’s performance, accuracy and benefits in order to identify the proper course for its adoption. The simulation suggests that a population level of adoption would be the most efficient and effective.

Policy Suggestion 2If the technology is in the implementation phase at the population level, there should be all incentives created to encourage women to screen before getting pregnant and get treated if necessary.

Policy Suggestion 3

Regardless of the ELI-P Complex implementation, pregnancy planning rate should be increased through various education/public awareness programs.

Policy Suggestion 4Regardless of the ELI-P Complex implementation, prenatal care utilization rate and prenatal care effectiveness and efficiency should be increased.

Policy Suggestion 5 More research should be conducted to identify proper treatments restoring female immunoregulatory systems and producing beneficial results on pregnancy development.

But since the results of specific interventions vary according to context [3] and so many factors lead to technology diffusion, policymakers ought to use an integrated approach to policy analysis. Their decisions should be made overtime, as an appropriate reaction to the system’s61

61 American healthcare

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response and new evidence as technology evolves or enters different stages of implementation.

The purpose of prenatal care is to prevent or identify and treat conditions that may threaten the health of the fetus/newborn and/or the mother, and to help a woman

approach pregnancy and birth as positive experience.D. Banta 2003

AMERICAN PRENATAL CARE

This research has aggregated the evidence from various sources to be able to conclude that the current state of the US prenatal care system is far from satisfactory. Many experts agree that the system should undergo major changes to increase its efficiency and effectiveness. While some initiatives are already being undertaken or considered by US policymakers [4,5], collaborative efforts, re-organization, and involvement of many stakeholders and resources should help make a difference. This work suggests one of the scenarios, which in whole or in part can be used to help improve US prenatal care and decrease poor pregnancy outcomes. Obviously, it is not the only way to advance the system and provide better care to women and children, neither is it a panacea to all the problems that prenatal care in the US is facing today.

Suggestions for Improvements

Because pregnancy problems occur in 10-15% of cases, it does not place the issue of poor pregnancy outcomes into the ranks of AIDS, heart disease or diabetes, but it does not mean that the matter can be given as little attention as today. Does not it seem logical to invest into the health of newborns, so they do not have to be treated for diabetes or cancer a few decades later? Should not we try to fix the problems before they occur through the use of various screening

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programs? Most people would agree that we should, but our societies continue to expand billions and billions of dollars into the care for the sick rather than into the care for the healthy or unborn. Women’s Health & ELI-P Complex

Many researchers and doctors are pointing to the importance of maintaining female health from early age, but it continues to be poorly funded and ignored for the most part. Misra D., et al. proposes a "life span" approach with a multiple determinants model to ensure good pregnancy outcomes. He states that:

1) powerful influences on outcome occur long before pregnancy begins;

2) pregnancy outcome is shaped by social, psychological, behavioral, environmental, and biological forces;

3) the demography of pregnancy is changing dramatically with more women delaying their first birth [8].

Unfortunately, non-pregnant women in the US remain nearly invisible to prenatal researchers and policymakers. The health of reproductive-age women is considered only to the degree that it affects their babies – some healthcare plans historically only provided coverage to women while they were pregnant [6]. As we have seen from the women’s health indicators presented in Chapter 3, it is a big mistake of the current healthcare system and not only in the US. Unhealthy women are very unlikely to produce healthy children. This obvious fact is often ignored and unless a woman has a clearly serious health condition, she is considered as a low risk case and not given the attention, screenings and treatments she might need. Without the assumption that women in their 20s and 30s are generally healthy and require less preventive interventions, female health should be prioritized across the age groups. Generally healthy is often not a good enough definition and as healthcare gets more customized we must begin to step away from

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such “generalities”. Eventually, complete genetic profiling should provide sufficient information about any person’s health status, but currently, at the very least, immunological profiling should be done to monitor the reproductive health of women.

Tremendous progress in the field of medicine over the past century led to phenomenal results increasing people’s life expectancy and holding a promise that it will increase even further. But the other side of the coin is that these new medical advances are confronting the “survival of the fittest” theory under which only the healthiest and the strongest would survive and reproduce thus making new generations only healthier and stronger. Under the current system, women who are not really healthy, with the help of new technologies have a chance to have children who turn out to be not very healthy themselves, and continue the trend of increasing this mildly unhealthy population, which results in an incredible expansion of the healthcare market to support a good level of life for all of those people who are either born unhealthy or become unhealthy later in life due to congenital problems or other reasons.62

ELI-P Complex gives an opportunity to help correct to some degree this situation by screening women who plan to become pregnant and improving their health through proper treatment before pregnancy or at its early stages so future mothers pass the minimized number of their health problems to future children. It is important to remember that if a birth defect or pathology is not detected at birth, it does not yet mean that the child is healthy. Many inborn health problems become apparent later in life. On numerous occasions throughout this work it has been emphasized that treatment in pregnancy might not be proper or effective, and since many birth 62 The author has no intentions to make any reference to eugenics in this case but summarizes the sad reality of our current healthcare and suggests a possible mean for improvement.

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defects occur very early in pregnancy, the emphasis should be on the phase of pregnancy preparation. Whether it includes the ELI-P Complex screening or not, pregnancy outcomes will be improved if women take appropriate vitamins, undergo genetic counseling, necessary treatments (if needed) and lead a healthy lifestyle before conceiving.

ELI-P Complex screening is aiming for the population level of implementation, which on one hand makes the task easy (no patient selection, less complexities), on the other, the scope of the program and synthesis of efforts and resources required might be an obstacle. There are a few examples where population programs in prenatal care have been considered. For example, Patricia Baird in her study of genetic screening programs discusses the population level approach in Canada and concludes that if all currently existing programs were implemented population-wide, about 60-75% of major malformations would be detected [9]. She also finds, whether birth defects occur due to chromosomal errors, single genes, unknown or multifactorial causes, it is not feasible to test all pregnancies for all of these causes, and it cannot be predicted which pregnant women are more likely to be carrying a fetus with a birth defect except in a few instances [9]. This statement will hold true at least until full genetic profiling is available which is decades away, but ELI-P Complex can help detect pregnancies, which need to be closely monitored (excluding those where a fetus might have genetic problems). Since the test can identify those women whose immuno-regulatory status is out of norm and the likely cause of such state, a woman can be sent for a more detailed examination of the probable set of causes (Table 4.6).

Any population-wide program, if implemented, should include maternal counseling and informed consent, and must have

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demonstrated high accuracy rates [9]. ELI-P Complex is not an exception to this rule however, the issue of consent might be eventually avoided if the program becomes a routine blood test. The result is not the diagnosis of a particular birth defect but simply an indicator of poor health status of a woman and a recommendation for further investigations, which might require more rigorous ethical considerations. Finally, since the treatment and care for individuals with major birth defects is so costly, and in practice most couples opt to terminate, it is likely that carefully planned screening programs will be cost-effective. But it should be kept in mind that mass screening programs are often driven by political considerations or a desire for profit. ELI-P Complex might fall into this trap, thus the policy, regarding this technology, should include review by those without vested interests [10]. While there are many successful examples of population level programs such as vaccinations, cancer screening, etc., prenatal care offers few examples to learn from, but it should not prevent ELI-P Complex from pioneering in this area.

Education

From the author’s personal observations and further research in the field, one thing becomes apparent: across the systems (US, Canada, Europe, developing countries) there is a major impediment – the lack of proper education about reproductive health and pregnancy. This is a paradox of the modern society: we are overwhelmed by information and know more about computers, cell phones and politics than about the most important process in human life. In their majority, even very well educated women do not possess basic knowledge of how to prepare for pregnancy (most think it does not require any preparation and just happens), which procedures to be aware of when

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pregnant, what to do to prevent poor pregnancy outcomes, etc. This is more a fault of the system rather than of the women themselves. At school, girls are being taught the minimum, which does not supply them with much knowledge regarding female health. Many women considering pregnancy find out about folic acid recommendation when they are already pregnant (which is too late!), others learn about genetic disorders in their first trimester of pregnancy unless they come from a family with a history of genetic problems. This list can be continued for dozens of pages.

Unfortunately today, mothers and sisters also do not know enough to pass proper information to their children or siblings who plan to have children, which is a major pitfall in the system. Pregnancy is an intimate family decision, thus family is usually the primary source of information and today’s future mothers are not being adequately equipped with knowledge regarding their health and pregnancy. Policymakers should consider spending significant resources to improve this situation and create educational programs which would focus on the provision of informational brochures in simple understandable language, promote group counseling, television or video information sessions, introduce revised programs at schools and courses at colleges, etc. It is important to increase people’s awareness and expand preventive programs through the distribution of information and proper education on the subject.

Future Framework

Compared to European countries, where over 99% of pregnant women enroll into 1st trimester prenatal care, the US, with its 83% enrollment rate as of 2000, is lagging far behind. However, simply increasing this number can merely increase costs without necessarily

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improving birth outcomes, since the current prenatal care system is ineffective [6,7] and neither women nor future babies actually benefit from it. But we should not forget that prenatal care does serve an important function giving future mothers psychological assurance that they are doing what they should in order to have a healthy child. Now the focus should be on ensuring that prenatal care also serves all other functions that the society expects. Hence, it should be advanced by gradually introducing new procedures, customizing prenatal services and making them available to all planning pregnancy and pregnant women.

Taking into consideration all the criticism produced by Dr. Strong [6] and empirical evidence of poor female health and pregnancy outcomes summarized in Chapter 3, the current situation obviously cannot be maintained in the country, which is aiming to hold a superior position in the world and spend more than any other country on its healthcare. But even if proper resources are allocated for proper programs, even under the ideal case scenario, we are decades away from a properly working prenatal care system in the US, which makes a difference and helps ensure better health for future generations. Thus, we have to prepare for decades of changes without expecting an overnight miracle, but we have to make sure these changes are taking a proper course to arrive at the desired results.

David Banta in his report to the WHO in 2003 proposed a framework for prenatal care re-organization and stated that:

1) prenatal care should be appropriate, cost-effective and based on the needs of the specific pregnant woman;

2) the care provided should not be excessive;3) new technologies need to be implemented continually, while

older services need to be reconsidered; 4) the care for each pregnant woman needs to be individualized

based on her own needs and wishes [7].

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These recommendations are in line with the framework designed by the WHO suggesting a fewer number of prenatal care visits [11]. Their randomized trial and similar ones, done in Britain and the USA, reveal that such approach does not make pregnancy outcomes worse. Decreasing the number of prenatal care visits may be increasing the efficiency of prenatal care, but it is unlikely to increase its effectiveness before the quality of the offered procedures changes. However, the push for such change may face serious resistance. In the US, the courts have found physicians guilty for not providing low-risk women with services appropriate for only high-risk pregnancies. Therefore, obstetricians may provide services that they feel to be unnecessary [7], which makes the prenatal care system expensive, inefficient and perhaps reduces the chances of introducing a new system with fewer visits. However, ELI-P Complex can be one of the tools helping to effectively decrease the number of prenatal visits. If the screening with a high degree of accuracy separates high risk women from low risk women, it may become a useful tool protecting physicians from being accused of not providing at risk women with proper care. Figure 6.6 depicts a possible layout of how the visits can be scheduled for different pools of women. Figure 6.5 Prenatal Care Visits

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Those with good ELI-P results and no identified predisposition to having a child with genetic problems can be attributed to the group of “easy pregnancies.” Those women who need to be re-screened and treated, women of advanced age, women from certain ethnic groups, women from the disadvantaged socio-economic class, etc. can be attributed to the “monitored” group and require more visits. Only about 10% of pregnancies need to be closely monitored with frequent visits or even hospitalizations to ensure good pregnancy outcomes. Indeed if the implementation of the ELI-P Complex screening leads to the decrease in visits, then the money saved can go to cover the costs of the screening for genetic disorders or the dissemination of the ELI-P Complex itself.

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Optimization of prenatal care becomes possible thanks to great technological advancements. From over-the-counter pregnancy tests to online resources where women can read everything they need to know about their pregnancy development on the daily basis63, to telemedicine and online medicine where physician’s counseling can be delivered in virtual environment, to using fetal Doppler heartbeat monitors [http://www.babycenter.com/], and baby gender tests.

Currently many advances are made in the fields of genomics and proteomics and many tests are being developed for use in prenatal care. Figure 6.6 summarizes procedures which should be done at different stages of pregnancy. This is a comprehensive list and each woman would have to undergo only a selected set of procedures suitable to her and identified by her physician. As seen from the list, the ELI-P Complex screening may eliminate the need for many interventions for most women and make prenatal care more efficient.

63 http://www.pregnancyweekly.com/

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Figure 6.7 depicts a comparative summary of genetic and non-genetic technologies and healthcare spending. Genetic technologies will remain cost-intensive for many decades to come while other procedures can certainly be done at much lower costs and provide a ubiquitous service. However, the comparison of the benefits from different interventions should help decide which option yields most savings and which one most expenditures. Both genetic and non-genetic prenatal care technologies will be pursued but the focus should shift from providing ineffective and inefficient prenatal care in America towards customized and effective universally available service making pregnancy and its outcomes an enjoyable experience for the mothers, their children and families.

Figure 6.7 Prenatal Care Interventions and Spending

NOTES:

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Numbers in [ ] correspond to the title in References p. 221

1. [30] Sterman, J. 20002. [181] Cain, M, Mittman, R.3. [53] Dopson, S. et al.4. [18] IOM Crossing the Quality Chasm5. [178] Bar-Yam, Y.6. [31] Strong, T.7. [177] Banta, D. 20038. [63] Misra D., et. al.9. [37] Baird, P.10. [64] Fost, N.11. [238] WHO Antenatal Care Randomized Trial

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Chapter 7Summary: The final chapter summarizes the contributions and findings of this research. It highlights novel aspects, discusses the limitations of this work and suggests how these limitations can be addressed. Further improvements to the models presented in this dissertation are also discussed. Special attention is given to various scenarios for future projects, which should be continued in the fields of early HTA and prenatal care. While the focus of this work is on demonstrating system dynamics’ usefulness for early HTA, this interdisciplinary research can be considered a starting point for delivering a worthy contribution to the grand initiative of redesigning American prenatal care through the introduction of preventive measures (screening).

Conclusion 7.1 Contributions

7.1.4 Discussion of Research Findings7.1.5 Novelty7.1.6 Evaluation of the Approach: SD in Early HTA

7.2 Limitations7.3 Future Work 7.4 Notes

Never doubt that a small group of thoughtful, committed citizens can change the world. Indeed, it's the only thing that ever has.

Margaret Mead

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CONTRIBUTIONS

This work simulated the effect of the ELI-P Complex screening technology on pregnancy outcomes as a part of early health technology assessment. Extensive research covering various bodies of literature has been done in order to learn about the state of the US prenatal care system and identify multiple problems, which helped develop the dynamic hypothesis for the study. System dynamics was chosen as a methodology to conduct the study and proved to be a useful tool capable of capturing many phases of early HTA in the process of modeling and simulation.

Discussion of Research Findings

In the 20th century the US has passed the stage of integrating prenatal care into its healthcare system, achieved great results in decreasing maternal and infant mortality, succeeded in preventing some birth defects, and assured safer pregnancies through the adequate provision of preventive measures. Nevertheless, the current prenatal care in the US is not exemplary and lacks improvements in many spheres. This research suggested a set of policies, which should be considered by policymakers to reform prenatal care service. The socio-economic assessment of the ELI-P Complex technology conducted with the help of M&S has provided new insights into how prenatal care and society can benefit from the use of this technology. The process of model building was a very important exercise assisting in discovering of possible problems, which may arise in the course of technology implementation. It also helped identify gaps in knowledge and data, and indicated a clear venue for the future work in the field.

This research has emphasized that the healthcare system’s focus is on disease treatment, not disease prevention and even the most

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advanced healthcare systems in the world are just at an embryonic state of initiating the transformation of medicine from disease treatment to its avoidance. Unfortunately, prenatal care has very little potential at the moment to prevent birth defects and incredible efforts and resources go to the management and future consequences of poor pregnancy outcomes. Change in the healthcare system will occur eventually though the push from outside (institutional reforms) and the one from inside (technologies and practices). Both directions are being pursued but this process may take many decades even in the fast-changing environment as the one we live in today. Many initiatives “from inside” have been discussed or mentioned in this research as prenatal care is concerned, but from the organizational/institutional points of view, some thinking has been done on the subject as well. One of the approaches suggests separating the healthcare system into two types of tasks: simple and repetitive from complex and unique - and to have different organizational forms address each type of task to enable both efficiency and effectiveness [1]. The population level of the ELI-P Complex introduction falls under the first category of simple and repetitive procedures and should easily demonstrate its efficiency since it does not require individual decision-making. Then, if only women who require treatment, are directed towards customized diagnostics, the main pool of patients “travels through the system” quickly. Hence, the screening program should be able to demonstrate its benefits and lead to the implementation of additional programs, which would slowly shift the healthcare towards the prioritization of preventive measures.

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Novelty

While the main novel aspect of this work was to introduce system dynamics as a comprehensive tool for early health technology assessment, this research also presented novel insights by:

giving exposure to the screening technology designed to monitor female immunoregulatory state and possibly decrease the number of birth defects and inborn abnormalities if the identified females are properly treated;

providing scenarios for mass implementation of the technology in the US market;

suggesting further enhancements of the given technology to improve its efficiency;

proposing a set of suggestions for the modification of the US prenatal care system;

producing guidelines for future work in the fields covered in this research;

partially fulfilling some of the long-term goals that eventually should be achieved by the US and other healthcare systems to improve population health.

Evaluation of the Approach: SD in Early HTA

Health technology assessment synthesizes findings from clinical research and includes analysis of costs, cost-effectiveness, and the broader social aspects of health technology [2]. The goal of this work was to conduct an early HTA of the ELI-P Complex, which has been done with the help of system dynamics simulation tool. This initiative allowed to study the potential of a new screening technology in prenatal care and provided interesting insights.

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System dynamics is a suitable methodology for working on problems with many variables connected though non-liner relationships. It allows for the use of imprecise information thanks to the methodology’s flexibility to incorporate data at different stages (as it becomes available). This is an especially useful feature for early HTA studies which are usually complicated by the large number of unknowns. SD also enables stakeholders with different objectives to assess the model under various settings in a matter of minutes, which differentiates it from other HTA methodologies. Since the effects or impacts of a technology are not typically realized at once or at a constant rate, HTA presents a clear problem of dynamic nature, which makes the use of ST and SD very suitable because both methodologies provide good tools for the analysis of evolving technologies producing a “moving target” effect [3]. As the technology or its’ application are changing, the SD model can be easily and efficiently modified (parameters) and the simulation can show new outcomes under various scenarios. Finally, neither HTA nor SD can supply policymakers with the answers; both methodologies are there to only inform the decision-making process [4]. However, SD can provide a more effective way of dealing with available information and modeling the existing evidence. A working simulation allows policymakers to actually play with different scenarios and assess many possible combinations very quickly. Thus, the decision can be made by choosing the best alternative out of many. Table 7.1 summarizes main stages of HTA and compares them to the steps undertaken by the system dynamics M&S process. Each of the HTA phases is accomplished with the help of various methodologies described in Chapter 2. In turn, SD is not isolated to systems thinking and computer modeling and also involves information gathering for the

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dynamic hypothesis identification, and overall provides a more integrated framework for the entire process of HTA. Since SD is designed to model social systems and incorporates very similar phases to HTA, it might become a comprehensive solution for the health technology assessment specialists, adding another dimension to their work and helping produce more convincing scenarios and easily interpretable models with graphic interfaces.

Table 7.1 SD and HTAHTA SD

Identify assessment topics Problem identificationSpecify the assessment problem Dynamic hypothesisRetrieve available evidence Data collectionCollect new (primary data) If applicableInterpret evidence Systems thinking (qualitative model)Synthesize (consolidate evidence) SD simulation and scenario analysisFormulate findings “What if”2 analysis and policy

formulationDisseminate findings and recommendations

Policy application

Monitor impact Monitor impactSource [4]

This research confirms that system dynamics and health technology assessment have a lot in common which makes their integration eventual. The application of SD to HTA might be able to fulfill most of its potential by evaluating those technologies that have a long-term benefit potential and technologies coming out from small labs or independent inventors rather than from under the umbrella of a large corporation in the healthcare industry.

LIMITATIONS

We took upon a difficult task of trying to address a problem within an extremely complex healthcare system. Whilst only a small

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but vitally important sector of the system was considered (prenatal care), its real-life complexity is beyond the capabilities of any modeling effort. Hence, a number of assumptions has been made to simplify the system. Many variables were aggregated, and some processes were ignored in order to be able to create a manageable robust model to simulate the impact of an intervention entering the system.

Health is affected by many factors other than healthcare, often making it difficult to isolate the effect of particular healthcare technologies [4]. Prenatal care is not an exception to this rule: a) many research initiatives are being undertaken in various fields to decrease the number of birth defects and pathologies, b) the risk of epidemics and new diseases always exists, c) cloning and IVF (in-vitro fertilization) have not been addressed in this research either, but IVF might have a significant impact on reducing the number of birth defects (especially genetic ones). Hence, in the long-term the combined effect of all interventions should be considered. At this stage the incorporation of these factors was not possible due to the lack of data and growing complexity. Thus this work considered an isolated scenario of one technology effect on pregnancy outcomes, which removes the simulation output from reality to a certain degree. The lack of evidence from clinical trials also prevented the incorporation of more variables into the model and proper identification of some of the relationships among the parameters, which is why this model has too many exogenous variables. Eventually, as the relationships among variables can be identified with greater accuracy, most of exogenous variables should become endogenous and only those characterized by highly imperfect information should be kept as exogenous.

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Negative outcomes of the ELI-P Complex screening were not incorporated into the model, but it does not mean that they do not exist. The test is not physically harmful and does not require additional accommodations (an extra visit or an extra blood drawing), but there is a chance that poor ELI-P Complex results might bring women anxiety, create more stress and postpone or terminate a pregnancy (which can be successful). Also, risk might be involved in misinterpreting results of the screening and prescribing wrong treatments to women. These issues should be studied in the course of clinical trials and incorporated into the model at a later stage.

The model has not been evaluated by all stakeholders, which means, that their opinion has not been used for the model’s improvement. This is a very important part of the modeling process and the feedback from some stakeholders was very valuable, thus this initiative should be continued to address weaknesses of the model and help identify improper system behavior. Finally, the detailed economic evaluation of the intervention was out of scope of this study but certainly must be incorporated when more precise financial and demographic data becomes available and the project’s implementation scheme is limited to a specific initiative where parameters are known or well defined.

The aim of the model designed in this research is not to predict the future with certainty but to help decision-makers reduce risk and uncertainty when confronting complex interdependent environments like healthcare. Nevertheless, it should always be remembered that any model is just an approximation of reality, no model is ever correct and no model is ever perfect, which is the main limitation of this work. However, considering the fact that we live in the imperfect world, a

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useful, suitable model, which helps make decisions beneficial to the society compensates for the absence of perfect information.

FUTURE WORK

General Adequate improvements should be made at the institutional level

as well as at the social and technological ones thus, well-coordinated work should be undertaken in many directions to arrive to successful results and enable the actual implementation of the ELI-P Complex technology. Prenatal care is one of the most important services in healthcare and every pregnant woman should have full access to it [5]. Hopefully, the US realizes the importance of universal healthcare provision and all pregnant women in the country will have access to proper services. The composition of prenatal care programs should be revised continually to properly deliver and fund prenatal programs to help families avoid the suffering and society avoid the costs entailed in having severely handicapped children [6]. Considering the fact that many prenatal interventions have never been evaluated [5], there is an obvious need for more research in the field. Current programs and upcoming technologies should be assessed to offer American women the best and most efficient services possible.

ELI-P Complex A lot more work needs to be done before the national screening

program becomes a reality. First, more biochemical research is needed to set uniform standards, collect, interpret, and act on the obtained data from the studies. The composition of the ELI-P Complex based on eight antigens is the best available at the moment, but it

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cannot be argued that it is the best possible. Second, significant financial resources are required to set up collaborative work among biochemical laboratories, which would focus on identifying the most suitable antigens (AGs) responsible for the female reproductive system. The number of AGs in the ELI-P Complex might increase by 2 or 4 and some of the currently existing ones may be replaced (possibly, AGs of ovarium, luteinizing hormone or follicle stimulating hormone [7] can be added). Third, after the ELI-P Complex formula is finalized, the Triturus system should be adjusted for the new kits and help facilitate the second pre-implementation phase of work which is clinical trials.

A major international research network should be created for conducting comprehensive clinical trials. Randomized clinical trials should assess thousands of women (pregnant and planning) as well as their children (born from screened mothers) and can be designed according to the following scenarios:

Pre-pregnancy screening, no treatment, follow up to pregnancy outcomes, follow a child’s health up to age 2;

Pre-pregnancy screening, treatment if necessary, follow up to pregnancy outcomes, follow a child’s health up to age 2;

First trimester screening, no treatment, follow up to pregnancy outcomes, follow a child’s health up to age 2;

First trimester screening, treatment if necessary, follow up to pregnancy outcomes, follow a child’s health up to age 2;

Infertile women screening, appropriate treatment, follow up to conception success, follow to pregnancy outcomes;

Women with identified health problems (heart disease, renal disease, diabetes, etc) pre-pregnancy treatment, ELI-P Complex

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screening to monitor results, other treatments if required, follow up to pregnancy outcomes, follow a child’s health up to age 2;

Women with identified health problems (heart disease, renal disease, diabetes, etc) first trimester treatment, ELI-P Complex screening to monitor results, other treatments if required, follow up to pregnancy outcomes, follow a child’s health up to age 2.

From these studies, data should be collected on frequency of miscarriages/fetal losses and abortions, types of treatments, diagnostic accuracy, cost of treatments, positive and negative outcomes of treatments, pregnancy outcomes, child’s health status, women’s attitude towards screening, women’s perception towards risks associated with treatment, physicians ability to interpret ELI-P Complex results and suggest proper therapy, women’s willingness to undergo therapy, other interventions (screening, treatments) done along with ELI-P Complex, and any other interesting findings that might become available during the study. After such comprehensive data is obtained and if the performance of the ELI-P Complex screening technology is as satisfactory as currently suggested, then the success of the technology uptake across the nation should be guaranteed.

Simulation M&S work should be continued in parallel with the biochemical

and clinical efforts to improve the screening system simulation. As data or new findings (correlations between the variables) become available, they should be gradually incorporated into the model. During this process it is very important to maintain the model’s validity, and necessary checks should be done after each addition to the model’s structure, which should reflect its purpose and not explain every exception or idiosyncrasy that may exist in the real system.

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As more evidence becomes available, the accuracy of the model should increase. The model can benefit from providing outputs on more variables and better defining the impact of change in these variables on other parameters of the system. For example, in the current model, pregnancy loss is defined by a ~5% decrease from the current value as more women are getting screened, but we need to have more evidence from clinical trials to make sure that the change in this parameter is depicted accurately.

The model should, at least at the aggregate level, incorporate a number of other important interventions, which cannot be isolated from the prenatal care system (such as genetic screening, actual female health status and behaviors, teratogen factors, etc.). Then, if the model is to be presented to venture capitalists interested in funding the first phase of the project, the socio-economic part of the model should be expanded to incorporate the supplier, more specific costs, overhead costs, precise market demand, insurance companies involvement, costs associated with training physicians and nurses and infrastructure-related costs. Finally, it should be remembered that beyond the explanatory scope of the model lies a large number of social, cultural, demographic, economic, organizational, legal, and technological conditions, which comprise an overall context for emergence of the technology. Many of these parameters will not be entered into the model to keep complexity within manageable boundaries, but these conditions cannot be ignored and should have some presence in either the evaluation of the model’s behavior in related context or incorporation of some of these parameters at a high level of aggregation.

In the end of this process, the model should reflect the decisions made by the medical community, physicians, manufacturers, females,

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and policymakers to have enough confidence that this is the most robust model, which can be created for the given purpose. Since this simulation provides an experimental arena for discovering the sources of real-life problems and evaluating alternative policy options in relatively little time and at low cost, it should be a useful tool for all the stakeholders involved and help speed up the process of the technology introduction and relevant decision-making.

Hopefully this research managed to draw attention to the necessity of collaborative efforts required across all of the discussed dimensions in order to improve pregnancy outcomes. Let us hope the future brings satisfactory changes sooner than expected and more pregnancies will have a non-incidental but a carefully planned character, pregnancy outcomes will be improved to minimize the number of birth defects, prenatal care will become enjoyable and effective, and overall population health will begin to show trends for improvement.

NOTES:Numbers in [ ] correspond to the title in References p. 221

1. [178] Bar-Yam, Y. 20042. [88] Jonsson E., Banta, D.3. [53] Dopson, S. et al.4. [32] Szczepura A., Kankaanpää J. (eds) 5. [177] Banta, D. 20036. [37] Baird, P.7. [240] Poletaev A. B.

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Appendix 1ELI-P Complex Test Description

ELI-P Complex is a biochemical test system for pre-pregnancy/pre-natal diagnostics used to determine the probability of pathology in pregnancy through the evaluation of the immunoregulatory state64 of a woman. ELI-P Complex is one of the tests of the ELI-Test Group and works according to the general principles of other tests within this group. Thus, to understand how ELI-P Complex works, it is important to first, describe the Immnological Homunculus and the ELI-Test Group paradigm.

Normal auto-antibodies (auto-Abs)65 are produced in the organism of any person through the entire course of life [1].

64 Quantitative determination of the auto-Abs characterized by the embryotropic activity.65 Auto-Abs -an antibody that reacts with the cells, tissues, or native proteins of the individual in which it

is produced.

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Production of normal auto-Abs of various specificity is regulated by the type of corresponding antigens66-targets [2].

Combination of normal auto-Abs of various specificity, representing an organism, forms a dynamic illustration of the molecular content of human bodies. This system of normal auto-Abs is called Immnological Homunculus - a ‘mirror’ of the metabolic state of cells in our bodies, using which the immune system operates [2].

Any small serum sample is represented by the complete combination of the auto-Abs, reflecting the molecular structure of the organism (i.e. blood can be taken for analysis from any part of the body) [3].

Development of diseases is accompanied by steady changes in the synthesis or breakdown of the molecular components of the body cells, which is reflected in the content amounts of various auto-Abs in the serum [2].

This makes the detection of quantitative amounts of auto-Abs the ‘marker’ characteristics for various forms of pathology [3].

A healthy person has normal levels (determined to each antigen) of specific auto-Abs. The quantitative changes (increase or decrease) of auto-Abs levels indicate pathology67.

The ELI-group tests are designed to work with the panels of antigens68

to determine the quantitative changes in auto-Abs. It provides the picture of molecular changes happening in the body and helps diagnose developing diseases prior to their clinical manifestation [3].

The type of pathology is being determined by the collection of selected antigens responsible for different organs or processes in the human body, thus there exist specific ELI-Tests (ELI-P Complex, ELI-D, ELI-N, etc) and many more are being currently developed [3].

The benefits of targeting a disease before its clinical manifestation are obvious since it helps prevent many negative consequences, from the deterioration of a human body to the increased cost of treatment. When a prominent molecular misbalance is registered, the treatment should be directed towards the elimination of the cause of molecular changes in an organism and only then towards the normalization of the auto-Abs’ production (such treatment usually is prophylactic in nature and is associated with much lower costs).

The ELI-P Complex test is based on the detection of the amounts of embryotropic antibodies in the blood of a female before and during pregnancy [4]. ELI-P Complex was developed from the ELI-P test, which was based on four antigens. This is a patented technology with over ten years of clinical trials results, but it has not yet been widely implemented due 66 Antigen – any substance (as a protein or enzyme) that stimulates the production of antibodies67 Mayansky A.N. Microbiology for Physicians. N Novgorod: NMGA 1999 [in Russian]68 For decades ELISA has been used all over the world as a successful tool to determine quantities of auto-

Abs to a specific antigen (chosen for a given test). Such tests provide a binary answer (yes/no) regarding the presence of a specific pathology. Not always such answers are correct.

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to various reasons (economic, technical, etc.). However, a decade of clinical results shows undisputable benefits of this technology [3,4,8]: it has been determined that consistent under- or over-production of various embryotropic autoantibodies can impact the physiological process of pregnancy, negatively influence a developing embryo, and cause many forms of female infertility, and/or repeated miscarriages [5,6,7,8].

Ideally, the ELI-P Complex screening should be performed before pregnancy and then during the 1st and 2nd trimesters to evaluate the immunoregulatory state of females. Women, who have been diagnosed with chronic diseases (diabetes) or have a virus (HIV, herpes, etc), other conditions or predisposition to having a baby with genetic problems do not necessarily need to be screened by ELI-P Complex since the cause of their problem is already determined and should be addressed with the means of the current practice. The goal of ELI-P Complex is to screen seemingly healthy females to make sure they are in the best possible state to carry a healthy child, thus preventing many of those birth defects with ‘unknown’ causes.

The ELI-P Complex test detects the quantities of embryotropic auto-Abs to the eight reproductive antigens in the blood of females (Figure 1). It has been determined that women with good (normal levels of embryotropic auto-Abs) ELI-P Complex results (groups k1 and k2) have a much higher number of healthy children and very few newborns with small pathologies and even fewer if any with birth defects, while women in groups k5 and k6 have less healthy babies (most of whom develop various health problems in the future), high number of birth defects and many newborns with small pathologies [5,6,7] (Figure 3.11). Women who are tested before pregnancy or during the first trimester and are placed in groups k3-k6, most of the time can be treated with widely-available medications (Table 4.6) to restore the activity of their embryotropic autoantibodies to a proper level, which significantly decreases the number of newborns with birth defects and increases the number of healthy newborns. Obviously, there should be a lot of effort placed into encouraging females to screen in the planning stage since the therapy can be more effective than during the pregnancy. However, many women can be treated to some degree during pregnancy to decrease the probability of pathology developments in their fetus thus, ELI-P Complex is also recommended during the 1st and 2nd trimesters of pregnancy.

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Figure A.1 ELI-P Complex

There are also supplementary tests ELI-SPR for women suffering infertility of unknown causes and ELI-D developed specifically for diabetics. The basic research continues on developing various ELI-tests to screen for multitude of other pathologies.

The ELI-P Complex method is not designed to replace any of the existing screening tools and programs, but to be used in combination. This test helps identify reproductive immonoregulatory problems in females of fertile age and indicate the necessary treatments after which successful pregnancy outcomes increase. Healthy women have fewer children with birth defects and pathologies and fewer miscarriages, but of course, this screening test is not a panacea to the problem. Many birth defects, which are caused by teratogenic factors: stress, environment, malnutrition, drugs, infections, etc., can be detected with ELI-P Complex but not the problems of genetic nature.

Appendix 1A presents instructions for practical use of the ELI-P-Complex Kit (manual).

NOTES:

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Numbers in [ ] correspond to the title in References p. 221

1. [28] Rose N., Mackay, I.2. [19] Kovalev I., Polevaia O.3. [163] Poletaev A. Information Letter. 2004.4. [162] Poletaev A, Morozov S. Information Letter. 2001.5. [149] Litvak, O. 6. [154] Serova, O. 7. [156] Zamaleeva, R.8. [122] Poletaev A., Osipenko L.

Appendix 1A

INSTRUCTIONS FOR PRACTICAL USE OF THE ELI-P-COMPLEX KIT

(semi-quantitative determination of embryotropic autoantibodies in the blood serum)

Prepared by Alexander B. Poletaev, MD, PhD

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1. PURPOSE

1.1. The ELI-P-Complex Kit is designed for a semi-quantitative determination of regulatory embryotropic antoantibodies (Abs) of the IgG class, interacting with the following antigens: human chorionic gonadotropin (hCGT), ds-DNA, -glycoprotein I, IgG antibodies against -glycoprotein I, Fc-fragments of the rabbit’s IgG, collagen-IV, S100 protein, and МР-65 protein in the blood serum of females planning pregnancy, or at the beginning of pregnancy. The method is based on the ELISA technology.

hCGT – peptide hormone necessary for the formation of placenta. An abnormal rise of anti - hCGT Abs may lead to the fetus growth retardation, death of the fetus, and/or miscarriages.

ds-DNA – main component of nuclear chromatin; its excess is the reason for induction of anti-dsDNA Abs, and may indicate a general activation of the apoptotic processes. An abnormal rise of anti-dsDNA Abs may lead to the fetus growth retardation, death of the fetus, and/or miscarriages.

beta-2-glycoprotein I – main phospholipid-binding serum protein. An increase of Abs against beta2-glycoprotein indicates a possible beginning of the antiphospholipid syndrome (APS) and may lead to the fetus growth retardation, death of the fetus, and/or miscarriages.

beta-2-glycoprotein I-binding Abs - a component used for the evaluation of anti-idiotypic Abs (that is specific anti-antibodies against -glycoprotein). A long-term (more than 1-2 months) elevation of Abs against beta2-glycoprotein causes the induction of specific anti-antibodies synthesis. The ratio of Abs against beta2-glycoprotein and anti-Abs allows analyzing the dynamics of APS syndrome development.

Fc-fragment of IgG is used for the evaluation of the “rheumatoid factor” of Abs against Fc-fragments of the Ig molecules. An increase in the “rheumatoid factor” indicates the elevated production of Igs. An abnormal rise of anti-Fc Abs may lead to the fetus growth retardation, death of the fetus and/or miscarriages.

Collagen – the main protein of intercellular matrix. An abnormal rise of anti-collagen Abs may indicate the inflammatory processes which involve the interstitial tissues, and may lead to the fetus growth retardation, death of the fetus and/or miscarriages.

S100 protein is directly related to the differentiation and maturation of the fetal nervous system. An abnormal increase or decrease of anti-S100 Abs may lead to the fetus growth retardation or death, as well as different pathologies of the nervous system.

МР-65 – a protein directly related to mechanisms of the fetal morphogenesis (formation of anatomically normal organs). An abnormal increase or decrease of anti-MP-65 Abs may lead to the fetus growth retardation, death of the fetus and/or miscarriages, as well as various anatomical malformations.

1.2. The ELI-P-Complex Kit is developed for use in obstetrics, gynecology and immunology for:a) Screening females 1-2 months before pregnancy (especially for investigation of women who could be attributed to a risk group);b) Screening pregnant females in the 1st and 2nd trimesters to identify women with the prominent risk of anomalous pregnancy development.

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c) Investigation of women suffering from infertility from unknown causes to reveal possible deviations in mechanisms of the immune regulation of the embryo development.

1.3. Certain serum contents of Abs against S100 and MP-65 are important for normal embryogenesis. An abnormal increase or decrease of these Abs may lead to various problems in the embryo development.

Abnormally high serum levels of Abs against hCGT, dsDNA, -glycoprotein I, IgG antibodies against -glycoprotein I, Fc-fragments of the rabbit’s IgG, collagen-IV may reveal embryotoxic effects and be the most frequent causes of miscarriages and intrauterine embryo deaths. Many environmental factors (chemical pollutants, ionizing radiation, infection agents, etc.) may influence and change the production of the investigated Abs. The evaluation of the serum content of the above Abs may produce additional clinically important information about conditions and influences of the woman’s organism upon the developing embryo and fetus.

2. GENERAL CHARACTERISTICS OF ELI-P-COMPLEX KIT

2.1. Content:- 96-wells polystyrene immunoplates with adsorbed antigens – 10 pcs.;- Reference serum sample with the normal content (average to population) of investigated Abs –

1 Expender tube (0.25 ml); NB: Checked for absence of Hepatitis B and C, and HIV1 and 2.- Polyclonal rabbit’s antibodies against human IgG conjugated with HRP (in 50% glycerol – 2 tubes x 0.06 ml);- Buffer for dilution of the serum samples - 1 pack;- Buffer for plates washing - 1 pack;- Buffer for development of enzyme reaction - 1 pack;- Stop-solution (1М H2SO4) - 3 flacks x 10 ml;- Tween-20 - 1 flack x 12 ml;- TMB-solution - 1 tube x 1.2 ml.

2.2. ELI-P-Complex kit contains all components necessary for double investigation of 50 serum samples (5 serum samples + 1 reference serum are investigated in each immunoplate).

2.3. Principle of investigation:

The eight investigated antigens are preliminary adsorbed in corresponding wells of immunoplates (Table 1);

The diluted reference serum and diluted serum samples of investigated individuals are placed into the wells;

An equilibrium between free and binding Abs of the serum samples is formed during the period of incubation;

The wells are washed extensively from the unbound Abs and polyclonal rabbit’s antibodies against human IgG conjugated with HRP placed into each well.

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After an additional incubation and washing the TMB solution is added into the wells for the reaction development.

The intensity of the color reaction is proportional to human Abs of the IgG class bound in each well.

The photometric evaluation of the reaction intensity is executed at 450 nm The relative immune reactivity of Abs against the eight investigated antigens is

calculated as a percentage to the reactivity of the corresponding Abs in the reference serum sample.

3. ANALYTICAL CHARACTERISTICS OF THE KIT

3.1. Sensitivity: The kit allows the evaluation of the serum content of Abs against CGT, ds-DNA, -glycoprotein I, IgG antibodies against -glycoprotein I, Fc-fragments of the rabbit’s IgG, collagen-IV, S100 protein, and МР-65 protein in a range from -90% to +400% in accordance to the reference serum.

3.2. Physiological (normal) levels of the investigated Abs immunoreactivities range from -15% to +40% (this arbitrary range was determined during the analysis of 500 serum samples obtained from clinically healthy women of the fertile age; all of them had normal pregnancy development and healthy newborns).

3.3. The optical densities in the wells with the reference serum should be in the range from 0.15 to 0.45 units.

4. NECESSARY EQUIPMENT

- ELISA-plate photometer (ELISA-Reader) with the wave length of 450 nm;- Thermostat for +37 + 1о С; - Thermostat for +56 + 1о С;- ELISA plate washer;- Autopipettes 1-channel (changeable or constant volumes) for 10ul, 20ul, 200ul, 1000ul,

5000ul; and tips.- Autopipettes 8-channels (changeable volume) for 25ul, 90ul, 100ul, 200ul) and tips;- Cylinders for 10, 100 and 1000ml;- Stirring;- Flasks (glass and plastic) for 5-10ml;

5. REAGENTS PREPARATION

(All calculations are for operation on a single 96-wells immunoplate).

5.1. Buffer for the dilution of serum samples. The contents of the package marked as “Buffer for the dilution of serum samples” should be dissolved in 30 ml of distilled water; add 1.5 ml Tween-20. Final 10-fold concentrated solution can be filtrated through 0.45 micron filter and kept up to 2 months at +2...8о С. Just before use, 3 ml of concentrated buffer should be diluted with 27 ml of distilled water and stirred. Working solution of the sample buffer can be stored for 1 week at +2...8о С.

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5.2. Preparation of the analyzed serum samples. The obtained serum samples can be stored at +2...4оС up to 1 week or at -20о С up to 3 months. Right before the investigation, the serum samples should be heated at +56о С for 30 minutes. After heating 10ul of each serum sample, dilute by 2000ul of the buffer working solution (5.1), stir thoroughly and place into the wells of immunoplate. The diluted serum samples should be used immediately and not kept.

5.3. Preparation of the reference serum for analysis. The reference serum should not be heated! 10 ul of the reference serum should be diluted by 2000ul sample buffer (6.1) and stirred thoroughly. The diluted reference serum should be used immediately and not kept.

5.4. Washing buffer. Dissolve the content of the package marked as “Washing buffer” in 200 ml of distilled water, add 10 ml of Tween-20. The final 10-fold concentrated solution should be filtrated through the 0.45 micron filter and kept up to 2 months at +2...8о С. Just before use, 25 ml of the concentrated buffer should be diluted by adding 225 ml of distilled water and stirred. The working solution of the sample buffer can be stored for 1 week at +2...8о С.

5.5. Dilution of polyclonal rabbit’s conjugate with HRP. 10ul of polyclonal rabbit’s antibodies against human IgG conjugated with HRP should be diluted by 10ml sample buffer (5.1) and stirred thoroughly. Diluted reference serum should be used immediately and not kept.

5.6. Buffer for the development of immuno-enzyme reaction. The content of the package marked as “Buffer for the reaction development” should be dissolved in 10 ml of distilled water. The final 10-fold concentrated solution should be filtrated through the 0.45 micron filter and can be kept in a dark flask up to 2 months at +2...8о С.

5.7. Chromogenic solution. Just before use, 1 ml of the 10-fold concentrated buffer for the reaction development should be added to 9.0 ml of distilled water and 0.1 ml of TMB solution, and stirred thoroughly. The solution should be used immediately and not kept.

6. ANALYSIS

6.1. The reference serum (100 ul/well) in the working dilution and the investigated serum samples (100 ul/well) in the working dilution should placed into the plate wells (see the scheme below) and incubated for 15-20 hours (over night) at +2...8о С.

1 2 3 4 5 6 7 8 9А CGT RS RS 1 1 2 2 3 3 4 4 5 5B DNA RS RS 1 1 2 2 3 3 4 4 5 5C GP RS RS 1 1 2 2 3 3 4 4 5 5D Ab- RS RS 1 1 2 2 3 3 4 4 5 5E Fc RS RS 1 1 2 2 3 3 4 4 5 5F CLG RS RS 1 1 2 2 3 3 4 4 5 5G S100 RS RS 1 1 2 2 3 3 4 4 5 5

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Table 1. The scheme of the serum samples (reference and analyzed) placed into the wells of 96-well immunoplate

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H МР-65 RS RS 1 1 2 2 3 3 4 4 5 5Notes: Absorbed antigens (lines A though H) are placed in the horizontal lines of the plate:

hCGT – human chorionic gonadotropin DNA – double spiral DNA

-GP - GlycoproteinAb- - antibodies against -GlycoproteinFc – Fc-fragments of IgGCLG - collagen S100 – S100 proteinMP-65 – МР-65 protein

RS - Reference serum (rows 1 and 2) Numbers 1 though 5 – five analyzed serum samples (rows 3 though 12)

6.2. After incubation the wells should be washed 4 times with the washing buffer (300ul per well x 4).

6.3. Right after the washing, the anti-human IgG-HRP conjugate solution (100ul per well) should be placed into the wells using 8-channel pipettes. The plates should be incubated for 90 min. at +37о С.

6.4. After incubation the wells should be washed 4-times with the washing buffer (300ul per well x 4).

6.5 Right after the washing, the chromogenic solution (100ul per well) should be placed into the wells using 8-channel pipettes.

6.6. The plates should be incubated in a dark place for 5-20 min. at +18- +24 о С (up to a light blue color) and then the development should be stopped by a stop-solution adding (25ul per well) into the wells using 8-channel pipettes.

6.7. The reaction intensity should be evaluated by the ELISA-reader in units of optical density at 450 nm.

7. CALCULATIONS

7.1. The average meaning of the reaction’s optical density with each antigen is calculated for the reference serum as well as for the analyzed serum samples.

7.2. The relative immune reactivity of analyzed serum samples against each of the antigens are calculated using the following equations:

R(AG1) х 100 R(AG2) х 100 R(AGn) х 100 ___________________ - 100; ___________________ - 100 _________________ - 100 R(Rs1) R(Rs2) R(Rsn)

Where: R(AG1, 2,…n) – average meaning of optical density of analyzed serum sample in the wells which contain antigens 1, 2,... n;

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R(Rs1, Rs2, …Rsn) - average meaning of optical density of the reference serum sample in the wells which contain antigens 1, 2,... n;

PC software can be used for calculations.

8. INTERPRETATION OF OBTAINED DATA

8.1. If the relative immune reactivity of the analyzed serum sample with any of the antigens is in the range from -20 ... to +20, it should be assumed that this serum sample was obtained from women which can be attributed to the group with the normal content of embryotropic Abs.

8.2. If the relative immune reactivity of the analyzed serum sample with any of the antigens is out of the normal reaction, but is in the range no less than -40 ... and not more +40, it should be assumed that this serum sample was obtained from women which may be attributed to the group with the slight deviations in the content of embryotropic Abs.

8.3. If the relative immune reactivity of the analyzed serum sample with any of the antigens has been placed in the range from -40 ... to +40, it should be assumed that this serum sample was obtained from women which may be attributed to the group with the prominent deviations in the content of embryotropic Abs.

8.4. The risk of intrauterine fetal death, miscarriage, or developmental anomalies is minimal in women who are attributed to the normal group. The risk of intrauterine fetal death, miscarriage, or developmental anomalies may be 10-15% in women who are attributed to the group with the slight deviations in the content of embryotropic Abs. The risk of intrauterine fetal death, miscarriage, or developmental anomalies may be high in women who are attributed to the group with the prominent deviations in the content of embryotropic Abs.

The most common reason for deviations in the production of embryotropic Abs are viral or bacterial microorganisms (HSV, CMV, Ch. trachomatis, ureaplasma, etc.), which influence the general state of the woman’s immune system.

9. STORAGE AND USAGE OF THE KIT

9.1. Components of the ELI-P-Complex kit can be kept safely for 12 months using the following methods:

- Pack № 1 (Polyclonal rabbit’s antibodies against human IgG conjugated with HRP and the reference serum sample) should be kept at -20о С;- Pack № 2 (Buffer for dilution of the serum samples; Buffer for the plates’ washing;

Buffer for the development of enzyme reaction; TMB stock solution) should be kept at +2...8о С;- Polystyrene plates should be kept at +2...8о С b.

9.2. The prepared 10-fold concentrate buffer for the dilution of serum samples and the 10-fold concentrated washing buffer can be kept for up to 2 months at +2...8о С.

9.3. The working (diluted) buffer for the dilution of the serum samples and the washing buffer

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can be kept for up to 2 months at +2...8о С. 9.4. The stop-solution can be kept at the room temperature (+18...25о С) for 12 months.

9.5. Tween-20 and TMB can be kept at +2...8о С for 12 months.

9.6. Intensively hemolized serum samples and samples with putrescent smell should not be used for investigation.

10. ADDITIONAL NOTES

10.1. An abnormal increase of immunoreactivity against chorionic gonadotropin is a sign of “anti-hCGT syndrome” and may negatively affect the placenta formation (often leads to miscarriages in the 1st trimester of pregnancy).

10.2. An abnormal increase of immunoreactivity against -glycoprotein I and against IgG antibodies against -glycoprotein I is a sign of “anti-phospholipid syndrome” and may cause miscarriages (especially during 2-nd and 3-rd trimesters of pregnancy) or low birth weight.

10.3. An abnormal increase of immunoreactivity against dsDNA Fc-fragments of IgG, collagen may negatively influence the fetus development and cause miscarriages (especially during the 2nd and 3rd trimesters of pregnancy) or low birth weight.

10.4. An abnormal increase of immunoreactivity against the S100 protein may indicate the presence of the human papilloma virus infection (molecular mimicry) and may lead to different malformations in the neural tube/nervous system of a fetus and a newborn.

Most cases of pathology in pregnancy can be avoided if a woman is investigated BEFORE her pregnancy with the ELI-P-Complex and treated if necessary. ELI-P can be used as a screening and monitoring tool for the determination of the reproductive immunoregulatory state of females.

Appendix 2Model Documentation

Population Screening Program: ELI-P Complex

Stock Flow Converter Graph

{ INITIALIZATION EQUATIONS }

INIT Fertile__Women = 80078

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DOCUMENT: The initial value for the stock is forecasted in Excel for the year 2010 from OECD Health DATA 2004

INIT Pregnant_Unscreened__Untreated = 6300

INIT Pregnant_Screened__Treated = 319

Total__Pregnant = Pregnant_Unscreened__Untreated+Pregnant_Screened__Treated

INIT UnHealthy_Women = 18418

DOCUMENT: The initial value for the stock was forecasted in Excel for the year 2010 from OECD Health DATA 2004 (23% of all women in the age cohort 15-49).

INIT Healthy__Women = 61660

DOCUMENT: The initial value for the stock was forecasted in Excel for the year 2010 from OECD Health DATA 2004 (77% of all women in the age cohort 15-49).

Women___Potentially__Childbearing = UnHealthy_Women+Healthy__Women

Pregnancy_Loss__Rate_ScTr = GRAPH(TIME) (2010, 0.288), (2013, 0.292), (2015, 0.288), (2018, 0.282), (2020, 0.274), (2023, 0.264), (2025,

0.256), (2028, 0.246), (2030, 0.23), (2033, 0.224), (2035, 0.214)

INIT Screened = 146

INIT Treated = 14.6

Treatment_Effect_in__Pregnancy = GRAPH(TIME) (2010, 0.465), (2013, 0.48), (2015, 0.495), (2018, 0.505), (2020, 0.515), (2023, 0.54), (2025, 0.555),

(2028, 0.58), (2030, 0.6), (2033, 0.615), (2035, 0.64) DOCUMENT: Treatment effectiveness during pregnancy is much lower.

In_Treatment__Fraction_not_Planning = 0.5

Poor_Test__Results_NP = Screened*0.2 DOCUMENT: According to the clinical trials of the ELI-P Complex, ~20% of females have ELI-P

indicators out of the normal range.

INIT N_Plan_in__PC = 5465 DOCUMENT: Intial value... + planning who are not screened

Preg_Rate__Not_Plan = 0.027 DOCUMENT: Pregnancy rate was calculated from taking the population data (females age 15-49)

OECD Health DATA 2004 and pregnancy statistics from National Vital Statistics Report, Vol. 52, No.

215

Dynamic Hypothesis

23, June 15, 2004. Average pregnancy rate was derived (~0.084) 32% of this estimate is the rate at which not planning women get pregnant in the age cohort 15-49.

INIT Not__Planning = 77571

INIT Planning = 3467DOCUMENT: The initial value for the stock is estimated number of planned pregnancies for 2010 considering that 53% of all pregnancies that year will be planned.

Planning__Rate = 0.044 DOCUMENT: According to http://www.plannedparenthood.org/ only 51% of pregnancies were

planned in 2000. The rate in simulation is adjusted to 2010. 53% of all planned pregnancies for year 2010 = 0.044 of fertile females in the age cohort 15-49

Not_Enrolling_in_PC_Fraction = 0.04-Preg_Rate__Not_Plan

Not__Planning__per_Y = Fertile__Women-Planning

Becoming_Preg__Not_Plan = Not__Planning*Not_Enrolling_in_PC_Fraction

Planning__Preg = Planning__Rate*Fertile__Women

Preparing_for_Preg = Planning

INIT Screened__Planning = 173

Adoption__Rate_Plan = GRAPH(TIME) (2010, 0.05), (2013, 0.065), (2015, 0.115), (2018, 0.195), (2020, 0.3), (2023, 0.445), (2025, 0.535),

(2028, 0.595), (2030, 0.62), (2033, 0.645), (2035, 0.655) DOCUMENT: S-curve is used to forecast the adoption of screening among females planning

pregnancy. A lower adoption rate is indicated for planning women than for those in the first trimester, due to the reluctance of the population to monitor their health

Being_Screened_pY = Preparing_for_Preg*Adoption__Rate_Plan

INIT Treated__Planning = 31

Treatment_Effect_Before__Pregnancy = GRAPH(TIME) (2010, 0.64), (2013, 0.64), (2015, 0.645), (2018, 0.665), (2020, 0.68), (2023, 0.695), (2025, 0.745),

(2028, 0.765), (2030, 0.79), (2033, 0.84), (2035, 0.88) DOCUMENT: Treatment effectiveness before pregnancy is very high. Complete re-reneration of

the female immune status can be expected in over 90% of cases (Poletaev AB, et. al).

Poor_Test_Results_Plan = Screened__Planning*0.2 DOCUMENT: According to the clinical trials of the ELI-P Complex, ~20% of females have ELI-P

indicators out of the normal range.

In_Treatment__Fraction_Planning = 0.9

Plan_In__Treatment = Poor_Test_Results_Plan*In_Treatment__Fraction_Planning

216

Dynamic Hypothesis

GOTrPlan = Treated__Planning*Treatment_Effect_Before__Pregnancy

ScPlan_Becoming__Pregnant = Screened__Planning

Becoming_Preg__in_PC = Preg_Rate__Not_Plan*Not__Planning+(Preparing_for_Preg-ScPlan_Becoming__Pregnant)

Monitored__Pregnancies = N_Plan_in__PC

Adoption_Rate__in_PC = GRAPH(TIME) (2010, 0.07), (2013, 0.08), (2015, 0.095), (2018, 0.13), (2020, 0.215), (2023, 0.425), (2025, 0.645),

(2028, 0.795), (2030, 0.845), (2033, 0.86), (2035, 0.865) DOCUMENT: The S-curve is chosen to forecast the adoption rate. 100% adoption does not equal

100% of planning/pregnant females being screened, since some will refuse, others don't need to undergo screening.

Screened_in__PC = Monitored__Pregnancies*Adoption_Rate__in_PC

In_Treatment__NP = In_Treatment__Fraction_not_Planning*Poor_Test__Results_NP

GOTrNP = Treated*Treatment_Effect_in__Pregnancy

Ent_Pool_of__ScTr = Screened

Pregnancy__Loss_ScTr = Pregnant_Screened__Treated*Pregnancy_Loss__Rate_ScTr

ScTr_Giving__Birth_pY = Pregnant_Screened__Treated-Pregnancy__Loss_ScTr

BOTrPlan = Treated__Planning-GOTrPlan

Unhealthy_Conceiving_Fraction = 0.8

Deciding_to_Wait = BOTrPlan*Unhealthy_Conceiving_Fraction

NC__pY = Not__Planning-Becoming_Preg__Not_Plan-Becoming_Preg__in_PC+Deciding_to_Wait

Pregnancy_Loss__Rate_UU = GRAPH(TIME) (2010, 0.31), (2013, 0.313), (2015, 0.315), (2018, 0.323), (2020, 0.333), (2023, 0.343), (2025, 0.355),

(2028, 0.375), (2030, 0.39), (2033, 0.405), (2035, 0.417) DOCUMENT: National Vital Statistics Report, Vol. 52, No. 23, June 15, 2004. 2000 data indicates

21% of aborted pregnancies. The adjusted number for 2010 assumes a decrease in induced abortions.2000 data indicates 16% in fetal losses. Pregnancy Loss rates are combined rates of abortions and fetal losses adjusted to 2010 [0.31]. Graphs 5&6 in Table 4.10 represent the changes in pregnancy losses over time as screening is being implemented.

INIT NotPlan__Not_in_PC = 981

Ent_Pool_UU = NotPlan__Not_in_PC

BOTrNP = Treated-GOTrNP

Ent_Pool__UU_from_PC = Monitored__Pregnancies-Screened_in__PC+BOTrNP+(BOTrPlan- Deciding_to_Wait)

217

Dynamic Hypothesis

Pregnancy__Loss_UU = IF(Screened_in__PC<700) OR (Being_Screened_pY<500) THEN(Pregnant_Unscreened__Untreated*0.3)ELSE(Pregnant_Unscreened__Untreated*Pregnancy_Loss__Rate_UU)

UU_Giving__Birth = Pregnant_Unscreened__Untreated-Pregnancy__Loss_UU

Women_Ceasing_to_Potential_Child_Bearing = Fertile__Women-Total__Pregnant

DrHW = 0.0005 DOCUMENT: Deaths data is taken from National Vital Statistics Reports, Vol. 53, No. 15, February 28, 2005 For the age cohort between 15 and 49 death rate is ~272 per 100,000 females (0.00272).

It's been assumed that for unhealthy women DR is 0.0022 and for healthy 0.0005

Growth_Rate_fertile_females = 0.0085 DOCUMENT: Population growth rate is calculated from the Census forecast available at

http://www.census.gov/ipc/www/usinterimproj/natprojtab01a.pdf and adjusted to the growth rate for the pool of females in the age cohort 15-49 for the years 2010-2035

Entering_Pool_of__Healthy_Women = Growth_Rate_fertile_females*Healthy__Women

DeathsHW = Healthy__Women*DrHW

DrUnH = 0.0022 DOCUMENT: Deaths data is taken from National Vital Statistics Reports, Vol. 53, No. 15, February

28, 2005 . For the age cohort 15 -49 death rate is ~272 per 100,000 females (0.00272). It's been assumed that for unhealthy women DR is 0.0022 and for healthy 0.0005

Entering_Pool_of___Unhealthy_Women = UnHealthy_Women*Growth_Rate_fertile_females

DeathsUnH = DrUnH*UnHealthy_Women

Infant_Mortality_Rate = 0.0058 DOCUMENT: OECD Health Data 2004 2001 - 6.8 deaths per 1000 life births. Rate adjusted for 2010-2035

INIT Newborns = 4400

Multiple_Birth__Fraction = 0.035 DOCUMENT: [http://www.cdc.gov/nchs/fastats/multiple.htm] 2002 Data

Being_Born_pY = (ScTr_Giving__Birth_pY+UU_Giving__Birth)*Multiple_Birth__Fraction+ +(UU_Giving__Birth+ScTr_Giving__Birth_pY)

Infant_Deaths = Infant_Mortality_Rate*Newborns

Healthy_Fraction = GRAPH(Adoption_Rate__in_PC) (0.007, 0.889), (0.0963, 0.89), (0.186, 0.889), (0.275, 0.893), (0.364, 0.896), (0.454, 0.905), (0.543,

0.913), (0.632, 0.922), (0.721, 0.928), (0.811, 0.928), (0.9, 0.928) DOCUMENT: Estimates from ELI-P & ELI-P Comlex Clinical trials: Poletaev, Zamaleeva,

Budykina, Zhigulina, Demin, Kluchnikov, Litvak, Serova, Morozov, Immunculus Labs 1998-2005. Screening and treatment can increase positive health outcomes by 4-5%

218

Dynamic Hypothesis

Beginning_Life__Healthy_pY=IF(Planning__Rate>0.044)THEN(Newborns*(Healthy_Fraction+ +0.005)) ELSE(Healthy_Fraction*Newborns)

Beginning_Life_Unhealhy_pY = IF(Planning__Rate>0.044)THEN(Newborns* * (1-(Healthy_Fraction+0.005)))ELSE(Newborns*(1-Healthy_Fraction))

Death__Rate = 0.0084 DOCUMENT: Deaths data is taken from National Vital Statistics Reports, Vol. 53, No. 15, February

28, 2005 DR for 2003~840 per 100,000 of US population

INIT US_Population = 308936

INIT Healthy_Newborns = 3898

Leaving_Infancy__Healthy_pY = Healthy_Newborns

INIT Newborns_with_Pathologies = 349

INIT Unhealthy_Newborns = 490

Birth_Defects__Fraction = 0.33DOCUMENT: March of Dimes: 3-3.5% of newborns are born with birth defects in the US every year.

Beginning_Life_with_Pathologies_pY = Unhealthy_Newborns*(1-Birth_Defects__Fraction)

Leaving_Infancy__w_Pathologies_pY = Newborns_with_Pathologies

INIT Newborns_with__Birth_Defects = 141

Beginning_Life_with_Birth_Defects_pY = Unhealthy_Newborns*Birth_Defects__Fraction

Leaving_Infancy_w__Birth_Defects_pY = Newborns_with__Birth_Defects

Immigration__Rate = 0.0022DOCUMENT: According to Migration News [http://migration.ucdavis.edu/mn/comments.php?id=1246_0_2_0]net immigration in the US between 1990-1996 was ~690,000 per year, which is about 246 per 100,000 of US population.

Immigrants_Coming__to_the_US_pY = Immigration__Rate*US_Population

Deaths = Death__Rate*US_Population

In_Prenatal_Care = ScPlan_Becoming__Pregnant+N_Plan_in__PC

Cost_per__BD_case = 66000DOCUMENT: March of Dimes: the cost of Birth Defects was estimated ~ $US 8billion per year. The average cost per BD was calculated from 3% (of total births) birth defects occurring every year.

Cost_per__Path_case = 15000DOCUMENT: The average cost per Pathological birth outcome was assumed to be $US15,0007.5% (of total births) pathological pregnancy outcomes occur every year (March of Dimes).

219

Dynamic Hypothesis

Cost_of_Care = Cost_per__BD_case*Leaving_Infancy_w__Birth_Defects_pY+ Cost_per__Path_case*Leaving_Infancy__w_Pathologies_pY

Elips_for__Pregnant = 2

Elips_for__Planning = 3

Retests = 2.5

ELIPS = Screened*Elips_for__Pregnant+Elips_for__Planning*Screened__Planning+ (Poor_Test_Results_Plan+Poor_Test__Results_NP)*Retests

Cost_of_Treatment = 200

Cost_per_Test = 22 DOCUMENT: Cost per test is derived in Chapter 4. Estimates from Immunculus Laboratories.

Screening__Cost = (Treated__Planning+Treated)*Cost_of_Treatment+Cost_per_Test*ELIPS

{ RUNTIME EQUATIONS }

Fertile__Women(t) = Fertile__Women(t - dt) + (Women___Potentially__Childbearing + ScTr_Giving__Birth_pY + NC__pY + UU_Giving__Birth - Women_Ceasing_to_Potential_Child_Bearing - Not__Planning__per_Y - Planning__Preg) * dt

DOCUMENT: The initial value for the stock is forecasted in Excel for the year 2010 from OECD Health DATA 2004

Pregnant_Unscreened__Untreated(t) = Pregnant_Unscreened__Untreated(t - dt) + (Ent_Pool_UU + Ent_Pool__UU_from_PC - Pregnancy__Loss_UU - UU_Giving__Birth) * dt

Pregnant_Screened__Treated(t) = Pregnant_Screened__Treated(t - dt) + (Ent_Pool_of__ScTr + ScPlan_Becoming__Pregnant - ScTr_Giving__Birth_pY - Pregnancy__Loss_ScTr) * dt

UnHealthy_Women(t) = UnHealthy_Women(t - dt) + (Entering_Pool_of___Unhealthy_Women - DeathsUnH) * dt

DOCUMENT: The initial value for the stock was forecasted in Excel for the year 2010 from OECD Health DATA 2004 (23% of all women in the age cohort 15-49).

Healthy__Women(t) = Healthy__Women(t - dt) + (Entering_Pool_of__Healthy_Women - DeathsHW) * dt

DOCUMENT: The initial value for the stock was forecasted in Excel for the year 2010 from OECD Health DATA 2004 (77% of all women in the age cohort 15-49).

Screened(t) = Screened(t - dt) + (GOTrNP + Screened_in__PC - In_Treatment__NP - Ent_Pool_of__ScTr) * dt

220

Dynamic Hypothesis

Treated(t) = Treated(t - dt) + (In_Treatment__NP - BOTrNP - GOTrNP) * dt

N_Plan_in__PC(t) = N_Plan_in__PC(t - dt) + (Becoming_Preg__in_PC - Monitored__Pregnancies) * dt

DOCUMENT: Intial value... + planning who are not screened

Not__Planning(t) = Not__Planning(t - dt) + (Not__Planning__per_Y - Becoming_Preg__in_PC - Becoming_Preg__Not_Plan - NC__pY) * dt

Planning(t) = Planning(t - dt) + (Planning__Preg - Preparing_for_Preg) * dt

DOCUMENT: The initial value for the stock is estimated number of planned pregnancies for 2010 considering that 53% of all pregnancies that year will be planned.

Screened__Planning(t) = Screened__Planning(t - dt) + (Being_Screened_pY + GOTrPlan - Plan_In__Treatment - ScPlan_Becoming__Pregnant) * dt

Treated__Planning(t) = Treated__Planning(t - dt) + (Plan_In__Treatment - BOTrPlan - GOTrPlan) * dt

NotPlan__Not_in_PC(t) = NotPlan__Not_in_PC(t - dt) + (Becoming_Preg__Not_Plan - Ent_Pool_UU) * dt

Newborns(t) = Newborns(t - dt) + (Being_Born_pY - Infant_Deaths - Beginning_Life__Healthy_pY - Beginning_Life_Unhealhy_pY) * dt

US_Population(t) = US_Population(t - dt) + (Leaving_Infancy__Healthy_pY + Leaving_Infancy__w_Pathologies_pY + Leaving_Infancy_w__Birth_Defects_pY + Immigrants_Coming__to_the_US_pY - Deaths) * dt

Healthy_Newborns(t) = Healthy_Newborns(t - dt) + (Beginning_Life__Healthy_pY - Leaving_Infancy__Healthy_pY) * dt

Newborns_with_Pathologies(t) = Newborns_with_Pathologies(t - dt) + (Beginning_Life_with_Pathologies_pY - Leaving_Infancy__w_Pathologies_pY) * dt

Unhealthy_Newborns(t) = Unhealthy_Newborns(t - dt) + (Beginning_Life_Unhealhy_pY - Beginning_Life_with_Pathologies_pY - Beginning_Life_with_Birth_Defects_pY) * dt

Newborns_with__Birth_Defects(t) = Newborns_with__Birth_Defects(t - dt) + (Beginning_Life_with_Birth_Defects_pY - Leaving_Infancy_w__Birth_Defects_pY) * dt

Total__Pregnant = Pregnant_Unscreened__Untreated+Pregnant_Screened__Treated

221

Dynamic Hypothesis

Women___Potentially__Childbearing = UnHealthy_Women+Healthy__Women

Pregnancy_Loss__Rate_ScTr = GRAPH(TIME)(2010, 0.288), (2013, 0.292), (2015, 0.288), (2018, 0.282), (2020, 0.274), (2023, 0.264), (2025, 0.256), (2028, 0.246), (2030, 0.23), (2033, 0.224), (2035, 0.214)

Treatment_Effect_in__Pregnancy = GRAPH(TIME) (2010, 0.465), (2013, 0.48), (2015, 0.495), (2018, 0.505), (2020, 0.515), (2023, 0.54), (2025, 0.555),

(2028, 0.58), (2030, 0.6), (2033, 0.615), (2035, 0.64) DOCUMENT: Treatment effectiveness during pregnancy is much lower.

Poor_Test__Results_NP = Screened*0.2 DOCUMENT: According to the clinical trials of the ELI-P Complex, ~20% of females have ELI-P

indicators out of the normal range.

Not_Enrolling_in_PC_Fraction = 0.04-Preg_Rate__Not_Plan

Not__Planning__per_Y = Fertile__Women-Planning

Becoming_Preg__Not_Plan = Not__Planning*Not_Enrolling_in_PC_Fraction

Planning__Preg = Planning__Rate*Fertile__Women

Preparing_for_Preg = Planning

Adoption__Rate_Plan = GRAPH(TIME)(2010, 0.05), (2013, 0.065), (2015, 0.115), (2018, 0.195), (2020, 0.3), (2023, 0.445), (2025, 0.535), (2028, 0.595), (2030, 0.62), (2033, 0.645), (2035, 0.655)DOCUMENT: S-curve is used to forecast the adoption of screening among females planning pregnancy. A lower adoption rate is indicated for planning women than for those in the first trimester, due to the reluctance of the population to monitor their health

Being_Screened_pY = Preparing_for_Preg*Adoption__Rate_Plan

Treatment_Effect_Before__Pregnancy = GRAPH(TIME) (2010, 0.64), (2013, 0.64), (2015, 0.645), (2018, 0.665), (2020, 0.68), (2023, 0.695), (2025, 0.745),

(2028, 0.765), (2030, 0.79), (2033, 0.84), (2035, 0.88) DOCUMENT: Treatment effectiveness before pregnancy is very high. Complete re-generation of the

female immune status can be expected in over 90% of cases (Poletaev AB, et. al).

Poor_Test_Results_Plan = Screened__Planning*0.2 DOCUMENT: According to the clinical trials of the ELI-P Complex, ~20% of females have ELI-P

indicators out of the normal range.

Plan_In__Treatment = Poor_Test_Results_Plan*In_Treatment__Fraction_Planning

GOTrPlan = Treated__Planning*Treatment_Effect_Before__PregnancyScPlan_Becoming__Pregnant = Screened__Planning

222

Dynamic Hypothesis

Becoming_Preg__in_PC = Preg_Rate__Not_Plan*Not__Planning+(Preparing_for_Preg-ScPlan_Becoming__Pregnant)

Monitored__Pregnancies = N_Plan_in__PC

Adoption_Rate__in_PC = GRAPH(TIME) (2010, 0.07), (2013, 0.08), (2015, 0.095), (2018, 0.13), (2020, 0.215), (2023, 0.425), (2025, 0.645),

(2028, 0.795), (2030, 0.845), (2033, 0.86), (2035, 0.865) DOCUMENT: The S-curve is chosen to forecast the adoption rate. 100% adoption does not equal

100% of planning/pregnant females being screened, since some will refuse, others don't need to undergo screening.

Screened_in__PC = Monitored__Pregnancies*Adoption_Rate__in_PC

In_Treatment__NP = In_Treatment__Fraction_not_Planning*Poor_Test__Results_NP

GOTrNP = Treated*Treatment_Effect_in__Pregnancy

Ent_Pool_of__ScTr = Screened

Pregnancy__Loss_ScTr = Pregnant_Screened__Treated*Pregnancy_Loss__Rate_ScTr

ScTr_Giving__Birth_pY = Pregnant_Screened__Treated-Pregnancy__Loss_ScTr

BOTrPlan = Treated__Planning-GOTrPlan

Deciding_to_Wait = BOTrPlan*Unhealthy_Conceiving_Fraction

NC__pY = Not__Planning-Becoming_Preg__Not_Plan-Becoming_Preg__in_PC+Deciding_to_Wait

Pregnancy_Loss__Rate_UU = GRAPH(TIME) (2010, 0.31), (2013, 0.313), (2015, 0.315), (2018, 0.323), (2020, 0.333), (2023, 0.343), (2025, 0.355),

(2028, 0.375), (2030, 0.39), (2033, 0.405), (2035, 0.417) DOCUMENT: National Vital Statistics Report, Vol. 52, No. 23, June 15, 2004. 2000 data indicates

21% of aborted pregnancies. The adjusted number for 2010 assumes a decrease in induced abortions.2000 data indicates 16% in fetal losses. Pregnancy Loss rates are combined rates of abortions and fetal losses adjusted to 2010 [0.31]. Graphs 5&6 in Table 4.10 represent the changes in pregnancy losses over time as screening is being implemented.

Ent_Pool_UU = NotPlan__Not_in_PC

BOTrNP = Treated-GOTrNP

Ent_Pool__UU_from_PC = Monitored__Pregnancies-Screened_in__PC+BOTrNP+(BOTrPlan- Deciding_to_Wait)

Pregnancy__Loss_UU = IF(Screened_in__PC<700) OR (Being_Screened_pY<500) THEN(Pregnant_Unscreened__Untreated*0.3)ELSE(Pregnant_Unscreened__Untreated*Pregnancy_Loss__Rate_UU)

UU_Giving__Birth = Pregnant_Unscreened__Untreated-Pregnancy__Loss_UU

Women_Ceasing_to_Potential_Child_Bearing = Fertile__Women-Total__Pregnant

Entering_Pool_of__Healthy_Women = Growth_Rate_fertile_females*Healthy__Women

DeathsHW = Healthy__Women*DrHW

223

Dynamic Hypothesis

Entering_Pool_of___Unhealthy_Women = UnHealthy_Women*Growth_Rate_fertile_females

DeathsUnH = DrUnH*UnHealthy_Women

Being_Born_pY = (ScTr_Giving__Birth_pY+UU_Giving__Birth)*Multiple_Birth__Fraction++ (UU_Giving__Birth+ScTr_Giving__Birth_pY)

Infant_Deaths = Infant_Mortality_Rate*Newborns

Healthy_Fraction = GRAPH(Adoption_Rate__in_PC) (0.007, 0.889), (0.0963, 0.89), (0.186, 0.889), (0.275, 0.893), (0.364, 0.896), (0.454, 0.905), (0.543,

0.913), (0.632, 0.922), (0.721, 0.928), (0.811, 0.928), (0.9, 0.928) DOCUMENT: Estimates from ELI-P & ELI-P Comlex Clinical trials: Poletaev, Zamaleeva,

Budykina, Zhigulina, Demin, Kluchnikov, Litvak, Serova, Morozov, Immunculus Labs 1998-2005. Screening and treatment can increase positive health outcomes by 4-5%

Beginning_Life__Healthy_pY = IF(Planning__Rate>0.044)THEN(Newborns*(Healthy_Fraction+0.005))

ELSE(Healthy_Fraction*Newborns)

Beginning_Life_Unhealhy_pY = IF(Planning__Rate>0.044)THEN(Newborns*(1-(Healthy_Fraction+0.005)))ELSE(Newborns*(1-Healthy_Fraction))

Leaving_Infancy__Healthy_pY = Healthy_Newborns

Beginning_Life_with_Pathologies_pY = Unhealthy_Newborns*(1-Birth_Defects__Fraction)

Leaving_Infancy__w_Pathologies_pY = Newborns_with_Pathologies

Beginning_Life_with_Birth_Defects_pY = Unhealthy_Newborns*Birth_Defects__Fraction

Leaving_Infancy_w__Birth_Defects_pY = Newborns_with__Birth_Defects

Immigrants_Coming__to_the_US_pY = Immigration__Rate*US_Population

Deaths = Death__Rate*US_Population

In_Prenatal_Care = ScPlan_Becoming__Pregnant+N_Plan_in__PC

Cost_of_Care = Cost_per__BD_case*Leaving_Infancy_w__Birth_Defects_pY++Cost_per__Path_case*Leaving_Infancy__w_Pathologies_pY

ELIPS = Screened*Elips_for__Pregnant+Elips_for__Planning*Screened__Planning+ +(Poor_Test_Results_Plan+Poor_Test__Results_NP)*Retests

Screening__Cost = (Treated__Planning+Treated)*Cost_of_Treatment+Cost_per_Test*ELIPS

Aggregate Model

{ INITIALIZATION EQUATIONS }

INIT Fertile__Women = 80078

224

Dynamic Hypothesis

DOCUMENT: The initial value for the stock is forecasted in Excel for the year 2010 from OECD Health DATA 2004

Pregnancy__Rate = 0.084 DOCUMENT: Pregnancy rate was calculated from taking the population data (females age 15-49)

OECD Health DATA 2004 and pregnancy statistics from National Vital Statistics Report, Vol. 52, No. 23, June 15, 2004. Then the average pregnancy rate was derived.

INIT Pregnant__Women = 6542

DOCUMENT: The initial value for the stock is estimated number of pregnancies for 2010 (7% of 2010 US female population in the age cohort 15-49)

Multiple_Birth__Fraction = 0.035DOCUMENT: CDC data 2002 http://www.cdc.gov/nchs/fastats/multiple.htm

AbortionsFetal__LossRate = 0.31DOCUMENT: National Vital Statistics Report, Vol. 52, No. 23, June 15, 2004. 2000 data indicates 16% of fetal loses and 21% of aborted pregnancies. The aggregated estimate of 31% is adjusted number for 2010 considering the number of induced abortions and fetal losses decreases due to the use of contraception and better medical treatments during pregnancy.

ELIP__Screening = 1 DOCUMENT: In this model ELI-P is set as binary variable to be able to see the results with and

without the screening program.

Pregnancy__Termination = Pregnant__Women*(AbortionsFetal__LossRate- ELIP__Screening/20)

Women__Giving_Birth_pY= =(Pregnant__Women+Pregnant__Women*Multiple_Birth__Fraction)-Pregnancy__Termination

INIT Healthy__Women = 61660

DOCUMENT: The initial value for the stock was forecasted in Excel for the year 2010 from OECD Health DATA 2004 (77% of all women in the age cohort 15-49).

INIT UnHealthy_Women = 18418

DOCUMENT: The initial value for the stock was forecasted in Excel for the year 2010 from OECD Health DATA 2004 (23% of all women in the age cohort 15-49).

Women_Becoming__Potentially_Childbearing = Healthy__Women+UnHealthy_Women

Women_Becoming__Pregnant_pY = Fertile__Women*Pregnancy__RateWomen_Ceasing_to_be_Potentially_Child_Bearing = Fertile__Women- Women_Becoming__Pregnant_pYINIT Healthy__Newborns = 3947

DOCUMENT: 75% of Total Births in 2010

Healthy_Births_Frac = 0.85

225

Dynamic Hypothesis

Health__Births_pY = Women__Giving_Birth_pY*(Healthy_Births_Frac+ELIP__Screening/10)

Healthy_Leave__Infancy_pY = Healthy__Newborns

drHN = 0.001 DOCUMENT: Neonatal/Infant Mortality data was taken from OECD Health DATA 2004. DR for healthy newborns was adjusted to years 2010-2050

Early_Deaths__H_pY = Healthy__Newborns*drHN

INIT Unhealthy__Newborns = 796

DOCUMENT: 25% of total births in 2010

Unhealthy__Births_pY = Women__Giving_Birth_pY*(1-Healthy_Births_Frac- ELIP__Screening/10)

Unhealthy_Leave__Infancy_pY = Unhealthy__Newborns

drUN = 0.004 DOCUMENT: Neonatal/Infant Mortality data was taken from OECD Health DATA 2004. DR for healthy newborns was adjusted to years 2010-2050. Neonatal mortality was 4.6 % in 2000

Early_Deaths_pY = drUN*Unhealthy__Newborns

DrHW = 0.0005 DOCUMENT: Deaths data is taken from National Vital Statistics Reports, Vol. 53, No. 15, February

28, 2005 For the age cohort between 15 and 49 death rate is ~272 per 100,000 females (0.00272). It's been assumed that for unhealthy women DR is 0.0022 and for healthy 0.0005

Growth_Rate_fertile_females = 0.0085 DOCUMENT: Population growth rate is calculated from the Census forecast available at

http://www.census.gov/ipc/www/usinterimproj/natprojtab01a.pdf and adjusted to the growth rate for the pool of females in the age cohort 15-49 for the years 2010-2050

Girls__Fraction = 0.45

Healthy__Girls = Healthy_Leave__Infancy_pY*Girls__Fraction

Age_of_Entering__the_Pool_of__Fertile_Women = 15 DOCUMENT: Even though CDC estimate for the first pregnancy in the US is 25.1 years, the fertile

age is considered 15-44, in this model it is 15-49 due to the growing number of late pregnancies and innovative reproductive technologies which promise to postpone pregnancies to more advanced female age in the future.

Becoming_Healthy__Women = DELAY(Healthy__Girls,Age_of_Entering__the_Pool_of__Fertile_Women,1601)

Entering_Pool_of__Healthy_Women = IF(ELIP__Screening=0) THEN(Healthy__Women*Growth_Rate_fertile_females) ELSE(IF(TIME<=2025) THEN(Healthy__Women*Growth_Rate_fertile_females) ELSE((Healthy__Women+Becoming_Healthy__Women)*Growth_Rate_fertile_females))

Deaths_of_Healthy__Women__pY = Healthy__Women*DrHW

DrUnH = 0.0022

226

Dynamic Hypothesis

DOCUMENT: Deaths data is taken from National Vital Statistics Reports, Vol. 53, No. 15, February 28, 2005For the age cohort between 15 and 49 death rate is ~272 per 100,000 females (0.00272).It's been assumed that for unhealthy women DR is 0.0022 and for healthy 0.0005

Unhealthy_Girls = Unhealthy_Leave__Infancy_pY*Girls__Fraction

Becoming__Unhealthy_Women = DELAY(Unhealthy_Girls,Age_of_Entering__the_Pool_of__Fertile_Women,585)

Entering_Pool_of___Unhealthy_Women = IF(ELIP__Screening=0) THEN(UnHealthy_Women*Growth_Rate_fertile_females) ELSE(IF(TIME<=2025) THEN(UnHealthy_Women*Growth_Rate_fertile_females) ELSE((UnHealthy_Women+Becoming__Unhealthy_Women)*Growth_Rate_fertile_females))

Deaths_of_Unhealthy__Women_pY = DrUnH*UnHealthy_Women

{ RUNTIME EQUATIONS }

Fertile__Women(t) = Fertile__Women(t - dt) + (Women__Giving_Birth_pY + Women_Becoming__Potentially_Childbearing - Women_Becoming__Pregnant_pY -Women_Ceasing_to_be_Potentially_Child_Bearing) * dt

DOCUMENT: The initial value for the stock is forecasted in Excel for the year 2010 from OECD Health DATA 2004

Pregnant__Women(t) = Pregnant__Women(t - dt) + (Women_Becoming__Pregnant_pY - Women__Giving_Birth_pY - Pregnancy__Termination) * dt

DOCUMENT: The initial value for the stock is estimated number of pregnancies for 2010 (7% of 2010 US female population in the age cohort 15-49)

Healthy__Women(t) = Healthy__Women(t - dt) + (Entering_Pool_of__Healthy_Women - Deaths_of_Healthy__Women__pY) * dt

DOCUMENT: The initial value for the stock was forecasted in Excel for the year 2010 from OECD Health DATA 2004 (77% of all women in the age cohort 15-49).

UnHealthy_Women(t) = UnHealthy_Women(t - dt) + (Entering_Pool_of___Unhealthy_Women - Deaths_of_Unhealthy__Women_pY) * dt

DOCUMENT: The initial value for the stock was forecasted in Excel for the year 2010 from OECD Health DATA 2004 (23% of all women in the age cohort 15-49).

Healthy__Newborns(t) = Healthy__Newborns(t - dt) + (Health__Births_pY - Healthy_Leave__Infancy_pY - Early_Deaths__H_pY) * dt

DOCUMENT: 75% of Total Births in 2010

227

Dynamic Hypothesis

Unhealthy__Newborns(t) = Unhealthy__Newborns(t - dt) + (Unhealthy__Births_pY - Unhealthy_Leave__Infancy_pY - Early_Deaths_pY) * dt

DOCUMENT: 25% of total births in 2010

Pregnancy__Termination = Pregnant__Women*(AbortionsFetal__LossRate-ELIP__Screening/20)

Women__Giving_Birth_pY = (Pregnant__Women+Pregnant__Women*Multiple_Birth__Fraction)-Pregnancy__Termination

Women_Becoming__Potentially_Childbearing = Healthy__Women+UnHealthy_Women

Women_Becoming__Pregnant_pY = Fertile__Women*Pregnancy__Rate

Women_Ceasing_to_be_Potentially_Child_Bearing = Fertile__Women-Women_Becoming__Pregnant_pY

Health__Births_pY = Women__Giving_Birth_pY*(Healthy_Births_Frac+ELIP__Screening/10)

Healthy_Leave__Infancy_pY = Healthy__Newborns

Early_Deaths__H_pY = Healthy__Newborns*drHN

Unhealthy__Births_pY = Women__Giving_Birth_pY*(1-Healthy_Births_Frac- ELIP__Screening/10)

Unhealthy_Leave__Infancy_pY = Unhealthy__Newborns

Early_Deaths_pY = drUN*Unhealthy__Newborns

Healthy__Girls = Healthy_Leave__Infancy_pY*Girls__Fraction

Becoming_Healthy__Women = DELAY(Healthy__Girls,Age_of_Entering__the_Pool_of__Fertile_Women,1601)

Entering_Pool_of__Healthy_Women = IF(ELIP__Screening=0) THEN(Healthy__Women*Growth_Rate_fertile_females) ELSE(IF(TIME<=2025) THEN(Healthy__Women*Growth_Rate_fertile_females) ELSE((Healthy__Women+Becoming_Healthy__Women)*Growth_Rate_fertile_females))

Deaths_of_Healthy__Women__pY = Healthy__Women*DrHW

Unhealthy_Girls = Unhealthy_Leave__Infancy_pY*Girls__Fraction

Becoming__Unhealthy_Women = DELAY(Unhealthy_Girls,Age_of_Entering__the_Pool_of__Fertile_Women,585)

Entering_Pool_of___Unhealthy_Women = IF(ELIP__Screening=0) THEN(UnHealthy_Women*Growth_Rate_fertile_females) ELSE(IF(TIME<=2025) THEN(UnHealthy_Women*Growth_Rate_fertile_females) ELSE((UnHealthy_Women+Becoming__Unhealthy_Women)*Growth_Rate_fertile_females))

Deaths_of_Unhealthy__Women_pY = DrUnH*UnHealthy_Women

228

Dynamic Hypothesis

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151. Peters, J. A Health Dynamics Model. MS Thesis, MIT, 1971.152. Rigler I. A Competitive Study of the HMO Industry in Massachusetts. MS

Thesis. MIT, 1986.153. Schuster, R. Systems Thinking View on the Situation of Unemployment in the

USA. Diploma Thesis. UCSD & Vienna University of Technology, 2003.154. Serova, O. Unsuccessful Pregnancy. Complex Analysis of the Problem:

Immunological, Infectious and Endocrine Aspects. DSc. Dissertation. Moscow Regional Science Research Institute of Obstetrics and Gynecology. Moscow 2000 [in Russian].

155. Sterman, J. The Energy Transition and the Economy: A System Dynamics Approach. PhD Dissertation. MIT, 1981.

156. Zamaleeva, R. Pathology in Development of a Fetus in Pregnant Women with Congenital Diseases: Prophylactics and Treatment. DSc. Dissertation. Kazan State Medical University of S.V. Kurashov. Kazan, 1999 [in Russian].

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Book Chapters & Information Letters

157. Engineering Fundamentals: Modeling and Simulation. Chapter 13. Acquisition Community Connection. Available Online at: [http://acc.dau.mil/simplify/ev.php

?ID=7109_201&ID2=DO_TOPIC]. Accessed on October 21, 2004.158. Leshkevich, I, et al. Medico-economic evaluation: Accessing the effectiveness

of decreasing infant mortality in Moscow. Methodical Recommendations Booklet. Committee on Health Protection. Moscow Government 1999. [in Russian]

159. Meadows, D. The Unavoidable A Priori. in Randers, J. Elements of the System Dynamics Method. Waltham, MA: Pegasus Communications; 1980: 23-57.

160. Norman, D., Kuras, M. Engineering Complex Systems. Chapter α The MITRE Corporation. Available online at: [www.mitre.org/work/tech_papers/tech_papers_04/ norman_engineering/norman_engineering.pdf]. Accessed on October 21, 2004.

161. The Merck Manual on Diagnosis and Therapy. Ch 19. Pediatrics. Chapter 261. Congenital Anomalies Available online at [http://www.merck.com/mrkshared/mmanual/section19/chapter261/261a.jsp]. Accessed on December 1, 2004.

162. Poletaev A., Morozov S. ELI-P-Test methodology for the evaluation of the reproduction health in women of fertile age. Information Letter. The Ministry of Health of the Russian Federation: Moscow 2001. [in Russian]

163. Poletaev A. New tendencies and perspectives in the medical laboratory diagnostics: General characteristics of the ELI-test group methods. Information Letter. Immunculus Medical Research Center Moscow: 2004. [in Russian]

164. Poletaev A., Kuzmenko L. Immunological Homunculus (Immunculus) and new approaches in clinical laboratory practice. Information Letter for the 1st Moscow Intl. Conf.: Natural Autoimmunity in Physiology and Pathology. Moscow: 2005

165. Sterman, J. A Skeptic’s Guide to Computer Models in Grant, L. Foresight and National Decision. Lanham, MD: University Press of America, 1988: 133-169.

166. Wajcman, J. Delivered into men’s hands? The social construction of reproductive technology. Power and Decision: the Social Control of Reproduction. Cambridge: Harvard School of Public Health, 1994: 153-175.

Conference Proceedings/Talks

167. Forrester, J. Nonlinearity in high-order models of social systems. The Workshop on Modeling Complex Systems. University of Texas, Austin, March 1985.

168. Homer J., et al. The CDC’s Diabetes Systems Modeling Project: Developing a New Tool for Chronic Disease Prevention and Control. Proceedings of the 22nd System Dynamics Conference. Oxford, UK 2004.

169. Hopkins, M. What happens when the emergence of new medical technologies does not depend on industry? DRUID Academy Winter 2003 PhD Conference [www.druid.dk/conferences/winter2003/Paper/Hopkins.pdf]

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170. Kingsbury, L, Korsan, R. The Collaborative and Strategic Benefits of Exploring Drug Models in Clinical Development. Presentation by Cephalon, Inc. and Pharsight Corp. DIA 39th Annual Meeting June 2003. Available online at [http://www.pharsight.com/news/sci_presentations.php]

171. Ring, J. Beyond the System Operator Paradigm; Systems Engineering as a Socio-technical System. Invited Paper. Conference on Systems Engineering Research, USC and Stevens Institute of Technology 2004

172. Standridge, C. A tutorial on simulation in health care: applications and issues. Proceedings. Winter Simulation Conference 1999.

173. Tanenbaum, S. Think before you prep: Defining the terms of change in American healthcare. The Society of Thoracic Surgeons Address. Atlanta, GA 1995.

174. Zaharov V. P., et al. Analysis of specific serum immunoreactivity of fetile age women of city Voronezh by using ELI-P-TEST method. Proceedings of the 1st Moscow International Symposium Mother-Fetus-Newborn: Immunology and Immunopathology (and related problems) Moscow, September 24-26, 2001.

White Papers, Monographs, Reports

175. Arnold, S., et al. Health outcomes core library project. Academy Health. July, 2004. Available online at [http://www.academyhealth.org/publications/healthoutcomesreport.htm] Accessed on February 28, 2005.

176. Alexander, G. Creating a population-based maternal and child health surveillance system in Arizona. Prepared for AZ Dept of Health Services 1996. Available online at [http://www.modimes.org/professionals/855_2104.asp] Accessed on December 14, 2004

177. Banta, D. What is the efficacy/effectiveness of antenatal care? WHO Regional Office for Europe’s Health Evidence Network (HEN). December, 2003. Available online at [http://www.euro.who.int/Document/e82996.pdf]. Accessed on February 24, 2005.

178. Bar-Yam, Y. Multiscale analysis of the healthcare and public health system: Organizing for achieving both effectiveness and efficiency, NECSI Technical Report, 2004. Available online at [http://necsi.org/cxworld/healthcare.html]. Accessed on December 10, 2004.

179. Breierova, L, Choudhari, M. An Introduction to Sensitivity Analysis. 1996 D-4526-2 MIT System Dynamics Group Literature Collection. System Dynamics Society. 2003.

180. Budykina T., Serova, O. Report on the results of using ELI-P Test at the Moscow region research institute of obstetrics and gynecology of the Russian Ministry of Health during 1995-1998. Available online at [http://www.eliptest.net].

181. Cain, M, Mittman, R. Diffusion of Innovation in Health Care. Institute for the Future. Prepared for CA HealthCare Foundation. 2002. Available online at [http://www.iftf.org/docs/SR-778_Diffusion_of_Innovation_in_HC.pdf].

182. Demin, V.F., Kluchnikov, C.C., Report on the results of catamnestic observations of the health status of children from women, evaluated during pregnancy with the ELI-P test. Russian State Medical University, Faculty of Child Diseases № 3. Available online at [http://www.eliptest.net].

183. Eberlein, R., Wang, Q. Statistical estimation and system dynamics models. 1984 D-3506 MIT System Dynamics Group Literature Collection. System Dynamics Society. 2003.

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184. Forrester, J. Counterintuitive behavior of social systems, 1971 D-4468 MIT System Dynamics Group Literature Collection. System Dynamics Society. 2003.

185. Forrester, J. Understanding the nature of systems, 1996 D-4578 MIT System Dynamics Group Literature Collection. System Dynamics Society. 2003.

186. Gallagher, J. et al. Influencing interventions to promote positive pregnancy outcomes and reduce the incidence of low birth weight and pre-term infants. Prepared for The March of Dimes Public Policy Studies Reports. 2004. Available online at [http://www.modimes.org/professionals/855_2104.asp] Accessed on December 4, 2004

187. Hall, E., Berlin, M. Using Medicaid to support pre-term birth prevention: Five case studies. Prepared for The March of Dimes Public Policy Studies Reports. 2004. Available online at [http://www.modimes.org/professionals/855_2104.asp] Accessed on December 4, 2004.

188. Howick, S. Using System Dynamics to Analyze Disruption and Delay for Litigation: Can the Modeling Purposes be Met? Research Paper No. 2001/21. Strathclyde Business School, Glasgow, Scotland.

189. Lamph, S, Wheeler, M. Halloran, S. Grifols Triturus immunoassay analyzer. Evaluation report MHRA 04014. Surrey, UK. 2004.

190. Maltseva, L. I., Zamaleeva, R.S. Report on the evaluation of the ELI-DIA test kit. The Faculty of Obstetrics and Gynecology #1 Kazan State Medical Academy. 2002. Available online at [http://www.eliptest.net]

191. Neuschler, E. Policy brief on tax credits for the uninsured and maternity care. IHPS. Prepared for The March of Dimes Public Policy Studies Reports. 2004. Available online at [http://www.modimes.org/professionals/855_2104.asp] Accessed on December 4, 2004.

192. Poletaev, A. B. Instructions for practical use of the ELI-P Complex kit (semi-quantitative determination of embryotropic autoantibodies in the blood serum). Medical Research Center “Immunculus.” 2005. Available online at [http://www.eliptest.net].

193. Schwalber, R. et al. System development and analytic/quantitative skills: A review of current literature. Prepared for US Dept of Health & Human Services. Health Systems Research, Inc. 1997. Available online at [http://www.modimes.org/

professionals/855_2104.asp] Accessed on December 4, 2004194. Senge, P. Some thoughts at the boundaries of classical system dynamics:

structuration and wholism. 1998 D-4814 MIT System Dynamics Group Literature Collection. System Dynamics Society. 2003.

195. Shreckngost, R. Dynamic simulation models: How valid are they? 1984 D-4463 MIT System Dynamics Group Literature Collection. System Dynamics Society. 2003.

196. Start, D., Hovland, I. Research and Policy in Development (RAPID) Programme Overseas Development Institute 2004. Available online at: [http://www.odi.org.uk/RAPID/Publications/Documents/Tools_handbook_final_web.pdf]. Accessed on December 10, 2004.

197. Sterman, J. Documenting system dynamics models. 1990 D-4330 MIT System Dynamics Group Literature Collection. System Dynamics Society. 2003

198. U.S. Dept. of Health and Human Serv. HRSA. Women’s Health USA 2004. Rockville, MD: 2004.

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199. Wang, Q. Using the Liapunov principles in the analysis of system dynamics models. 1983 D-3498 MIT System Dynamics Group Literature Collection. System Dynamics Society. 2003.

200. Zhigulina, S. G. Report on the evaluation of the ELI-DIA test kit. Center for Family Planning and Reproductive Health. Kaliningrad. 2002 Available online at [http://www.eliptest.net].

Presentation (.ppt)

201. A Brief Introduction to KMCI’s Conceptual Frameworks as Taught in the CKIM and K-STREAM Programs. KMCI. 2004 Available Online at: [www.kmci.org/media/Intro_to_KMCIs_Frameworks.pdf] Accessed on October 15. 2004.

202. Gillespie W. Case Study in the Use of Bayesian Hierarchical Modeling and Simulation for Design and Analysis of a Clinical Trial. Pharsight Corp. 2003 FDA/Industry Statistics Workshop September 18-19. Available online at [http://www.pharsight.com/news/sci_presentations.php]

203. Stevens Institute of Technology. SYS 625 Fundamentals of Systems Engineering. Lecture Slides. Property of SEEM Department.

WWW and Data Sources:

204. AAA Healthcare [http://www.aaa-healthcare.com/]205. American College of Preventive Medicine [http://www.acpm.org/]206. Alliance for Health Policy and Systems Research

[http://white.collexis.net/collexis_evidencebase/www/]207. California Birth Defect Monitoring Program [http://www.cbdmp.org]208. CDC – Centers for Disease Control and Prevention [http://www.cdc.gov/]

a. Birth Defects [http://www.cdc.gov/ncbddd/default.htm]b. NCHS [http://www.cdc.gov/nchs/]c. PRAMS [http://www.cdc.gov/reproductivehealth/srv_prams.htm]

209. Census Bureau [http://www.census.gov/]210. CMS – Center for Medicare and Medicaid Services [http://www.cms.hhs.gov/]211. Cochraine [http://www.cochrane.org/index0.htm]212. Consideo [www.consideo.de]213. Defined Care [http://www.definedcare.com]214. Dictionary.com [http://dictionary.reference.com/]215. Entrez PubMed [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi]216. FDA Center for Devices [http://www.fda.gov/cdrh/index.html]217. Genetics & Public Policy Center [http://www.dnapolicy.org/index.jhtml]218. Human Development Reports [http://hdr.undp.org/]219. Immunculus Medical Research Center [www.immunculus.com]220. INAHTA- The International Network of Agencies for Health Technology

Assessment [http://www.inahta.org/inahta_web/index.asp]221. Institute of Medicine (IOM) [http://www.iom.edu/]222. March of Dimes [http://www.marchofdimes.com/]

a. PeriStats [http://www.marchofdimes.com/peristats/]b. Public Policy

[http://www.marchofdimes.com/aboutus/855_4400.asp]223. Molecular Devices [http://www.moleculardevices.com/pages/instruments/ readers_main.html]224. National Institute of Health (NIH) [http://www.nih.gov/]

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225. NHS Health Technology Assessment Programme [http://www.ncchta.org/index.htm]

226. OECD Health Project [http://www.oecd.org/document/28/0,2340,en_ 2649_33929_2536540_1_1_1_1,00.html]

a. OECD Health Data CD 2004227. Pegasus Communications [http://www.pegasuscom.com/]228. Principia Cybernetica [http://pespmc1.vub.ac.be/DEFAULT.html]229. Reproductive Health Technologies Project [http://www.rhtp.org/]230. Science Direct [http://www.sciencedirect.com/]231. Stella 8.1 [www.iseesystems.com]232. System Dynamics/Systems Thinking Mega Link List. The Gunter Ossimitz

Collection [http://go.just.to/sd]233. Triturus Analyzer [http://www.triturus.com/analyzer.html]234. Tecan Group [http://www.tecan.com/platform/apps/product/index.asp?

MenuID=1223&ID=627&Menu=1&Item=21.2.5]235. US Dept of Health and Human Services: Maternal and Child Health

[http://www.ask.hrsa.gov/MCH.cfm?content=MCH]236. WebMD [www.webmd.com]237. Wikipedia [www.wikipedia.org]238. World Health Organization (WHO) Antenatal Care Randomized Trial:

Manual for the Implementation of the New Model. [http://www.who.int/reproductive-health/publications/RHR_01_30/]

239. World Prosperity Ltd. The Healthcare Reform. Available online at [http://www.world-prosperity.org/healthcare.htm]. Accessed on December 9, 2004.

Interviews

240. Poletaev A.B. – Numerous interview and discussions in person and over the phone 2003-2005

241. Tecan Corporation. Phone Interview: Sales department May 19, 2005.242. Molecular Devices. Phone Interview: Sales department June 15, 2005.243. Grifols Triturus USA. Phone Interview: Sales department June 23, 2005.

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Leeza Osipenko was born in Russia in 1979. In 1995 she moved to the USA and in 2000 received BA in Economics/International Relations from Denison University. In 1999 Leeza spent one semester abroad at La Universidad de Complutense, Madrid, Spain. She received MS in Information Systems from Syracuse University in 2001. While at Stevens, she lectured and served as a teaching assistant for various undergraduate and graduate courses at the Department of Systems Engineering. Leeza is the founder and president of the Russian Chapter of System Dynamics Society [www.sdrus.org.ru]. Currently, she resides in England and holds an appointment as Senior Research Fellow at Warwick University Medical School, which is effective until 2009. She is working on the SAFE Network of Excellence [www.safenoe.org] funded by the 6th European Research Framework. Her work focuses on conducting health technology assessment of non-invasive prenatal screening and diagnostic technologies based on the extraction and evaluation of fetal DNA from maternal blood.

Publications:

Osipenko, L., Freeman, K. Szczepura, A. Health Technology Assessment of Non-Invasive (NI) RhD Testing. Poster Presentation. 4th Intl. Conference on Circulating Nucleic Acids in Plasma/ Serum (CINAPS-IV) London, UK 2005.

Osipenko, L., Szczepura, A., Freeman, K. Cost-effectiveness of management strategies for pregnancies at risk of RhD alloimmunisation BMJ online

[http://bmj.bmjjournals.com/cgi/eletters/330/7502/1255]: 8 June 2005.Rapid response to S. Kumar and F. Regan Management of pregnancies with RhD alloimmunisation. BMJ 2005; 330: 1255-1258.

Osipenko, L., Farr, J. System Dynamics and Dynamic Systems in Regulatory Environments: Pharmaceuticals Application” System Dynamics Conference, Oxford, UK 2004 Proceedings (peer-reviewed).

Poletaev, A. B., Osipenko, L. General Network of Natural Autoantibodies as Immunological Homunculus (Immunculus) Autoimmunity Review. (2003) 2: 264-271.

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Bazil, L., Osipenko, L. Integrated System Dynamics Models for Global Telecommunications Industry: Corporate/National/Global and Corporate/Industry/Global dimensions ASEM Proceedings Tampa, FL. 2002: 124-8 (peer-reviewed).

Eseryel, Y., Osipenko, L., Wigand R., et al. An Analysis of the Emerging Electronic Trading Network Market”: A Study for Giga Information Group, GIGA Press 2001.

Working papers:

Osipenko, L. Prenatal Screening Technology: Socio-economic Effects of Wide Implementation in the USA.Osipenko, L. et al. HTA of RhD Non-invasive Diagnostic Technology in the UK, Germany, & the Netherlands.

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