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Economic evaluation of complex population health interventions: The role of simulation models. Beate Sander, PhD

Scientist, Public Health Ontario Assistant Professor, Institute of Health Policy, Management and Evaluation, University of Toronto Adjunct Faculty, Department of Mathematics and Statistics, York University Adjunct Scientist, Institute for Clinical Evaluative Sciences


Agenda

• Background • Economic evaluation / Economic evaluation of population health

interventions • Simulation / data-driven simulation

• Example: West Nile virus

• Key points

2



Health policy decision-making: Choices

“The first lesson of economics is scarcity: there is never enough of anything to fully satisfy all those who want it. The first lesson of politics is to disregard the first lesson of economics.” (Thomas Sowell 1993)



Evidence relevant for decision-making

Assessment of

• Technical properties

• Safety

• Efficacy and/or effectiveness

• Economic attributes or impacts

• Social, legal, ethical and/or political impacts

• Context



Economic evaluation

• Economic evaluation: • A solution to a constrained maximisation problem.

• Maximise health, cases prevented, patients treated, quality of care. . . • Constraints: limited money, people, time, space.

• Considers benefits and cost simultaneously.

• Economic Evaluation is a “comparative analysis of alternative courses of action in terms of both their costs and consequences” (Drummond 1997)

• Translation: “bang for the buck”, efficiency, value

• Generally conducted to support decision-making, often for drugs and (increasingly) technologies

5

ΔC/ΔB = (C1-C2)/(B1-B2)



Population Health Interventions

• Seldom single intervention, encompass prevention & treatment

• Primarily non-medical interventions , often large scale environmental or policy changes

• Large and diverse population groups / selected priority groups

• Aimed at risk factors, not disease-specific

• Often significant time lag between intervention and benefit

• Differences between who bears risk and benefit (e.g., vaccines)

• Difficult to identify who benefits / in whom disease is averted



Economic Evaluation of Population Health Interventions

• Are scarce and lack consistent quality, evaluations of interventions at the environmental level are nearly non-existent.1

• Why? Methodologically complex and requires different methods than individual-level research

• Several barriers exist:1,2

• Complexity of interventions • Measurement of effectiveness,

benefits and costs • Equity • Discounting

8

The health impact pyramid3

1 Curtis L. Health Economics and Public Health. 2012 2 Weatherly H et al. Methods for assessing the CE of PH interventions: Key challenges and recommendations. 2009 3 Frieden TR. A Framework for Public Health Action: The Health Impact Pyramid. 2010

Counselling and

Education

Clinical Interventions

Long-Lasting Protective Interventions

Changing the Context to Make Individuals’ Default Decisions Healthy

Socioeconomic Factors

Increasing Individual Effort

Needed

Increasing Population Impact



Simulation

• Computer simulation pioneered as a scientific tool in meteorology and nuclear physics post-World War II

• Indispensable in a growing number of disciplines, including astrophysics, particle physics, materials science, engineering, fluid mechanics, climate science, evolutionary biology, ecology, economics, decision theory, medicine, sociology, epidemiology...

9 Stanford Encyclopedia of Philosophy. Computer Simulations in Science. 2015



Data-driven Simulation

• Simulation • Definition: “a comprehensive method for studying systems. ... includes

choosing a model; finding a way of implementing that model in a form that can be run on a computer; calculating the output of the algorithm; and visualizing and studying the resultant data.”

• Types: equation-based (compartmental), agent-based (individual-based), multiscale simulations

• Data-driven: determined by or dependent on the collection or analysis of data

10 Stanford Encyclopedia of Philosophy. Computer Simulations in Science. 2015



So. . .

• Population health interventions: complex, demand novel systems approaches, which integrate interventions within and outside the healthcare system

• Simulation: a comprehensive method for studying complex systems that allows researchers to test hypotheses that are difficult, if not impossible, to test in the field

11

Real-world problem

Model

Solution at model level

Solution in real world



The Cost-effectiveness of West Nile Virus Intervention Strategies. A Computer Simulation Model.

12



Acknowledgments

• Team • Huaiping Zhu, PhD - York University • Xin Gao, PhD - York University • Mark Loeb, MD, MSc, FRCPC - McMaster University • Howard Shapiro, MD, MSc – Toronto Public Health • Robbin Lindsay, PhD – NML, PHAC • Doug Sider, MD, MSc - PHO • Mark Nelder, PhD - PHO • Dimitra Kasimos, BSc, BASc - Halton Region Health Department • Elaine Pacheco, BAA - Toronto Public Health • Paul Proctor, BA - Peel Region Health Department • Wendy Pons, PhD - Peel Region Health Department • Lyle Petersen, MD, MPH - National Center for Emerging and Zoonotic Infectious Diseases (NCEZID), CDC • Emily Shing, MPH – PHO, • Man Wah Yeung, MSc – PHO • Longbin Chen (PhD candidate), York University • Wenzhe Li (PhD candidate), York University • Don Yu (PhD candidate), York University

• CIHR Operating Grant (MOP 133571)

TOPHC 2017 13



West Nile Virus (WNV): Epidemiology

14

1999

2002 2002

2003

2009

2003 2003

West Nile virus (WNV) is a mosquito-borne Flavivirus (Flaviviridae), first isolated from a febrile patient in Uganda in 1937.

The rapid spread of WNV throughout North America makes WNV the most widely distributed arbovirus in the world.



West Nile Virus (WNV): Epidemiology

2002-2014:

• 1,103 confirmed and probable WNV cases

• 2012 was the second worst year for WNV cases: ~250 cases (2002 was the worst with ~ 400 cases)

• Highest incidence rate 3.27 cases per 100,000 population Substantially higher than rates reported for other diseases, e.g. invasive meningococcal disease (0.26 in 2010 to 0.94 in 2001 per 100,000 population)

15

Number of confirmed and probable human WNV cases by year

Ontario Agency for Health Protection and Promotion (Public Health Ontario). Vector-borne diseases 2014 summary report. Toronto, ON: Queen's Printer for Ontario; 2015.



West Nile Virus (WNV): Human Disease

WNV illness can present as

• Asymptomatic infection (70-80%)

• Nonneuroinvasive disease • Flu-like illness, accompanied by fever, headache, anorexia, body aches,

nausea, photophobia, vomiting, periocular pain, and other non-specific symptoms

• Neuroinvasive disease (1 in 150 to 250 of infections) • Patients present with: meningitis, encephalitis, acute flaccid paralysis

and/or myelitis accompanied by gastroenteritis, general malaise and fatigue • More likely to experience long term and permanent sequelae (mental and

physical deficits), • Case fatality ratio: 9.8% (range: 6.4-15.6%)

• Associated with increased age, male sex, immunosuppression and co-morbidities

16



West Nile Virus (WNV): Ecology

• WNV is maintained in a mosquito-bird-mosquito enzootic cycle

• Mosquitoes are the primary enzootic vectors, but also bridge vectors, spreading WNV from the enzootic cycle to the epizootic/epidemic cycle.

• Important ecological factors: • Number and distribution of susceptible and infected birds • Numbers and species of vector mosquitoes • Virus levels in mosquito populations • Weather (temperature, precipitation, humidity), Climate • Location of susceptible humans

17



West Nile Virus (WNV): Interventions

• Low seroprevalence → protective immunity in the population is too low to prevent outbreaks

• WNV prevention: reduce the number of WNV-infected mosquitoes and minimize contact between humans and biting mosquitoes • Personal protection: avoidance of mosquitoes during peak biting

periods (evening), mosquito repellents, removal of standing water, window screens

• Larviciding: insecticides to catch basins to kill Culex larvae • Adulticiding: aerial pesticides to kill adult mosquitoes • Timing important, need to incorporate surveillance (mosquitoes, birds, humans)

• Human vaccines: several vaccine candidates are under development

18



Motivation

• WNV human cases in Canada since 2002

• Outbreaks continue to occur despite WNV prevention programs

• Climate change may increase WNV incidence

• Risk of WNV illness needs to be balanced against the costs of interventions but economic impact of WNV illness, surveillance and mitigation strategies is largely unknown

Comparative effectiveness and cost-effectiveness of WNV mitigation strategies is poorly understood

19



Objective

The goal of this study is to evaluate the cost-effectiveness of WNV mitigation strategies using three public health units (Halton, Peel, Toronto) of Southern Ontario as the base population.

Specific objectives:

(1) To predict mosquito abundance, WNV risk and WNV transmission.

(2) To estimate health and economic consequences.

(3) To determine the comparative effectiveness, economic impact and cost-effectiveness of WNV mitigation strategies.

20



Simulation

21



Multiscale Simulation Model

22

Asymptomatic

Symptomatic

WNV Non-NS

WNNS

[+]

[+]

Enzootic Transmission Cycle

Dead-End Host

Alternate Modes of Transmission

Mosquito Life Cycle

Pupa Egg

Larva

MOSQUITO ABUNDANCE

WNV TRANSMISSION

Infected

DISEASE HISTORY

Dead-End Host



Geography

• 3 PHUs: Halton, Peel and Toronto • Mix of urban centers (Toronto) and urban/rural landscapes (Halton,

Peel), among the most affected areas in Ontario

• Mosquito abundance and WNV risk assessment models have been developed for Peel

• Each PHU’s geographic area are divided into subregions • Based on aggregated census tracts such that geographic areas covered

by subregions are homogenous in terms of key WNV transmission characteristics, including temperature, precipitation, land use, population density, and social-economic variables

• Each subregion has at least one mosquito trap, which provides local information on mosquito abundance and mosquito WNV infection rates

23



Economic Evaluation

Cost-utility analysis • Perspectives:

• Healthcare payer (Ministry of Health and Long-Term Care [MOHLTC]) • Society

• Time horizon: • Lifetime time

• Intervention strategies: separately and in combination • Health outcomes:

• All clinical outcomes (number of symptomatic cases, nonneuroinvasive disease, neuroinvasive disease, deaths)

• Health-related quality of life, expressed as quality adjusted life-years (QALYs )

24



Mosquito abundance (life cycle)

• Culex pipiens/restuans, the primary WNV bridge vector to humans in Ontario is considered

• The aquatic life cycle stages of egg, larva and pupa are combined into one stage, i.e. only the combined aquatic stage and adult mosquito stage is modeled

• The abundance of Culex is driven by weather conditions (temperature and precipitation) and geography

• Intervention: Larviciding

25



Methods

• Define clusters of traps with similarities in weather conditions and geography, affecting mosquito reproduction and activity • Based on mosquito surveillance data, weather and landscape data • K-means clustering, popular for cluster analysis in data mining

• Define modeling unit following census tract boundaries and: • ≥ 3 traps of same cluster • ≤ 1 trap of different unit • Taking roads and rivers into consideration

• Predictive model: • Generalized linear model • Considering temperature,

precipitation, landscape



Validation



Results

Comparison of mosquito abundance in different modeling units of Peel region



WNV transmission

• Mosquito, bird and human populations • Mosquitoes: Culex pipiens/restuans • Birds: corvid and non-corvid • Humans: age-structured (young children, school-aged children,

adolescents, adults, older adults)

• WNV transmission is determined by • Contact rates (mosquitoes and birds, mosquitoes and humans), • Infectiousness and duration of infectiousness (mosquitoes and birds) • Susceptibility (mosquitoes, birds and humans) • Weather

• Mosquito abundance, behavior, mortality • WNV’s extrinsic incubation period.

• Interventions: adulticiding, personal protection, vaccines (humans, birds)

29


PublicHealthOntario.ca 30

Birds (stratified by species and age)

Vector Mosquitoes Total mosquito count NM from forecasting model Humans

(stratified by age)

NM−Mi Mi

Bi

Hi

Br Bs Hs

Hr

Stochastic, compartmental SIR-type (Susceptible, Infected, Recovered) dynamic model, weather-driven, predicts human infection rates on a weekly basis throughout the WNV season (May to October) for each subregion.



Human Disease History

• Model population: ~3.1 million individuals, representing the 4.4 million individuals living in the 3 PHUs.

• Each individual will be assigned characteristics relevant for WNV infection and disease severity, including age, sex, and co-morbidities (history of cancer, diabetes, hypertension, alcohol abuse, renal disease, immunocompromised)

• Model includes acute disease and long-term sequelae. Probability of an individual being in a health state (e.g., symptomatic, neuroinvasive disease, death) will be determined by that individual’s characteristics (age, sex, and co-morbidities).

31



Model Schematic (1)

32

Symptomatic WNV

Neuroinvasive Disease (ND)

Non-Neuroinvasive Disease (NND)

― Meningitis (WNM) ― Encephalitis (WNE) ― Meningoencephalitis (WNME) ― Acute flaccid paralysis (AFP) ― AFP + Meningitis ― AFP + Encephalitis ― AFP + Meningoencephalitis

Health state transitions: • Acute phase (1st year post infection): Weekly • Post-acute phase (> 1 year post infection): Yearly



Model Schematic (2)

TOPHC 2017 33

Meningitis

Hospitalized

Sequelae

― Physical/functional ― Neuropsychological/ cognitive ― Physical/functional &

Neuropsychological/ cognitive

Healthy

Die, unrelated

Die, WNV



Model Schematic (3)

Non-Neuroinvasive Disease (NND)

Other healthcare

contact

No healthcare contact Hospitalized

Sequelae

― Physical/functional ― Neuropsychological/ cognitive ― Physical/functional &

Neuropsychological/ cognitive

Healthy

Die, unrelated

Die, WNV

TOPHC 2017 34



Data Sources

Surveillance data – Humans (Integrated Public Health Information System, iPHIS)

Surveillance data - Mosquitoes

Administrative data (ICES) - population profiles: e.g., comorbidity by age, sex, region

Administrative data (ICES) - healthcare costs attributable to WNV

Rapid Risk Factor Surveillance System (RRFSS) – data on household and personal protection

Data from health units (Peel, Halton, Toronto – data on WNV program (program description, roll-out, cost)

McMaster clinical data (Dr. Mark Loeb) - utilities

Weather data (Canada’s National Climate Archive)

Land use data (Landset ETM image, mapped using GIS software)

Statistics Canada (census tract-level population data, earnings, employment data)

Literature 35



Key Data

36

Parameter Estimate Source

Symptomatic | infection 20% Huhn 2003

Non-Neuroinvasive Disease (Non-ND) | symptomatic 95% Huhn 2003

Neuroinvasive Disease (ND) | symptomatic 5% Huhn 2003

Meningitis │ ND 37% CDC MMWR 2001

Encephalitis │ ND 30% CDC MMWR 2001

Meningoencephalitis │ ND 25% CDC MMWR 2001

Acute flaccid paralysis │ ND 8% Lindsay 2014

Utility│Non-ND 0.63 Clinical data, Loeb

Utility│Meningitis 0.52 Clinical data, Loeb

Utility │Encephalitis 0.57 Clinical data, Loeb

Utility │Meningoencephalitis 0.53 Clinical data, Loeb

Utility │Acute flaccid paralysis 0.52 Clinical data, Loeb



Validation

37

Parameter Predicted Observed (95% CI) Source

Non-ND 60% Loeb 2008

ND 40% Loeb 2008

Meningitis │ ND 22% 4% (1%; 11%) Clinical data, Loeb

Encephalitis │ ND 33% 34% (23%; 46%) Clinical data, Loeb

Meningoencephalitis │ ND 38% 52% (39%; 64%) Clinical data, Loeb

Acute flaccid paralysis │ ND 7% 10% (4%; 18%) Clinical data, Loeb

Hospitalization 5% 15% (10%; 22%) Loeb 2008

Case fatality │non-ND <1% - Loeb 2008

Case fatality │ND 8% 8% (3%; 16%) Loeb 2008

Case fatality │Meningitis 2% - Loeb 2008

Case fatality │Encephalitis 7% 5% (<1%; 16%) Loeb 2008

Case fatality │Meningoencephalitis 11% 9% (2%; 21%) Loeb 2008

Case fatality │Acute flaccid paralysis 24% 14% (<1%; 46%) Loeb 2008



Results

• Mean age of infection: 55 years

• Expected Quality-adjusted life-years, QALYs (undiscounted) • No WNV infection: 34.4 QALYs • WNV infection (mean): 33.4 QALYs

• Non-neuroinvasive disease: 34.1 QALYs • Neuroinvasive disease: 23 QALYs

38



Current Work

• Fine-tune model

• Add cost data

• Link to WNV prediction and transmission model components

• Conduct cost-effectiveness analyses

39



Asymptomatic

Symptomatic

WNV Non-NS

WNNS

[+]

[+]

Enzootic Transmission Cycle

Dead-End Host

Alternate Modes of Transmission

Mosquito Life Cycle

Pupa Egg

Larva

MOSQUITO ABUNDANCE

WNV TRANSMISSION

Infected

DISEASE HISTORY

Dead-End Host



Limitations

• General limitations related to modeling • Simplifying assumptions • Limited data, especially on WNV transmission

• Limited transferability of findings (context specific) => findings will be reported for key scenarios defined by important ecological factors in addition to health unit-specific results

• Scope: • Geographic spread (spatial movement, overwintering of birds) • Climate change and extreme weather. => Simulation model will be developed to facilitate easy integration with spatial transmission and climate change models in the future

41



Significance

• Understanding the comparative effectiveness and cost-effectiveness of WNV mitigation strategies

• Support public health decision-making in Ontario and other jurisdictions (strategic planning as well as near real-time predictions to optimize situational response)

• Lay the foundation for future work in the One Health arena, particularly as it relates to simulating emerging vector-borne diseases (e.g. Zika, Lyme disease) and the impact of climate change

42



SUMMARY

43



System perspective Complex questions

Data-driven simulation

Engage broadly, transdisciplinary



THANK YOU!

45


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