research team meeting mrs

CER Team Meeting: Data Dictionary Planning

November 12, 2010

Agenda

• Review CER research questions and hypotheses– Specific aims of the grant– Conceptual model – Refining hypotheses– Sources of data

• Outline for systematic identification of data domains and elements

Agenda

• Review CER research questions and hypotheses– Specific aims of the grant– Conceptual model – Refining hypotheses– Sources of data

• Outline for systematic identification of data domains and elements

Specific Aims of the Grant

• Specific Aim Related to CER (Aim 3): Develop and enhance four sentinel cohort pairs of patients with asthma (pediatric and adult), hypertension, and hypercholesterolemia distinguished by their care delivery characteristics which can support comparative effectiveness research.

Specific Aims of the Grant

• Overall Goals: • Demonstrate the capability of the SAFTINet data

system to collect and accurately link relevant and valid patient-level information necessary for comparing the effectiveness of different delivery system strategies

• Lay the groundwork (cohort identification, outcomes measurement, sample size estimates, etc.) to conduct prospective observational studies and clinical trials

Specific Aims of the Grant

• The SPECIFIC SUB-AIMS for Aim 3 are:– Specific Aim 3.1 Specify the data elements required

for optimal cohort creation. – Specific Aim 3.2 Develop and use multivariable

models of asthma, blood pressure and cholesterol control to identify system-level, individual care provider-level, and patient-level factors associated with the control of these conditions.

– Specific Aim 3.3 Enhance the data set by implementing point-of-care data collection tools for health-related quality of life.

Agenda

• Review CER research questions and hypotheses– Specific aims of the grant– Conceptual model – Refining hypotheses– Sources of data

• Outline for systematic identification of data domains and elements

Conceptual Model

Relatively Mutable

CLINICAL INERTIACounselingDrug selectionDosage selectionConcomitant medsFollow-upDecision support

PATIENT-CENTERED MEDICAL HOME

Integrated Mental Health Care

Disease-specific case mngmnt

Access to careOutcomes feedback

THERAPY ADHERENCETherapy persistenceMental health statusHealth knowledgePerceived need for

careSymptomsDrug side effects

PROCESSES OF CARE

(clinician factors)

+STRUCTURES OF

CARE(system factors)

+ PATIENT FACTORS →

OUTCOMES(chronic disease control)

Relatively immutable

Appointment timePatient loadPhysical facilitiesPractice typeSupport personnelGeneralist vs.

specialist

AgeGenderRace/ethnicitySESMarital statusReligious/cultural

beliefsComorbidity

Agenda

• Review CER research questions and hypotheses– Specific aims of the grant– Conceptual model – Refining hypotheses– Sources of data

• Outline for systematic identification of data domains and elements

Refining Hypotheses

• Hypothesis from Research Design: “We hypothesize that health care delivery system factors, such as the patient-centered medical home, outweigh individual care provider factors, patient factors, and medication effectiveness in the control of asthma, high blood pressure and hypercholesterolemia. “

Example Hypotheses• Pediatric asthma outcomes (define) are better by

some amount X (define) at health centers that implement PCMH functions (define specific function(s)).

• A greater proportion of adult hypertension patients with a dx of depression are appropriately controlled at practices that have integrated mental health services (IMH).

• The level (intensity) of IMH services is correlated with improved BP control in adult HTN patients whom also have a dx of depression.

Refining Hypotheses

CLINICAL INERTIADrug selectionDosage selectionConcomitant meds

PATIENT-CENTERED MEDICAL HOME

Intgrtd Mental HealthDisease-specific case

managementAccess to care

THERAPY ADHERENCETherapy persistenceMental health status

PROCESSES OF CARE

(clinician factors)+

STRUCTURES OFCARE

(system factors)+ PATIENT FACTORS →

OUTCOMES(chronic disease

control)

Generalist vs. specialist

Age, GenderRace/ethnicitySESMarital statusComorbidity

Hypothesis: health care delivery system factors, such as the patient-centered medical home, outweigh individual care provider factors, patient factors, and medication effectiveness in the control of asthma, high blood pressure and hypercholesterolemia.

Refining Hypotheses

PROCESSES OF CARE

(clinician factors)+

STRUCTURES OFCARE

(system factors)+ PATIENT FACTORS →

OUTCOMES(chronic disease

control)

•Primary explanatory variable: patient-centered medical home•Dependent variables: meeting national, evidence-based guidelines for control

•Independent variables: Factors associated with disease control, identified from literature, research experience, and clinical judgment

•Statistical analysis: Mixed effects models will be used to determine factors associated with chronic disease control; the primary explanatory variable of the PCMH clinic, and the other factors impacting chronic disease control

Agenda

• Review CER research questions and hypotheses– Specific aims of the grant– Conceptual model – Refining hypotheses– Sources of data

• Sample data dictionary• Outline for systematic identification of data

domains and elements

Sources of Data

• EHR• Medicaid claims• Enhanced point-of-care data collection• Organizational or practice-level survey

Agenda

• Review CER research questions and hypotheses– Specific aims of the grant– Conceptual model – Refining hypotheses– Sources of data

• Sample data dictionary• Outline for systematic identification of data

domains and elements

Sample Data Dictionary

SAFTINet data dictionary structure

Construct Measure Elements Values Reference Data Source

Agenda

• Review CER research questions and hypotheses– Specific aims of the grant– Conceptual model – Refining hypotheses– Sources of data

• Sample data dictionary• Outline for systematic identification of data

domains and elements

Outline for Systematic Identification of Data Domains and Elements

• Establish:– Hypotheses and research questions– Cohort definition– Outcome measures– Primary explanatory variable– Covariates

• Establish these in order to:– Make a list of needed data elements for current work– Lay the groundwork for future directions

• Document a rationale for hypotheses and selection of measures (constructs and data elements)

Outline for Systematic Identification of Data Domains and Elements

• An example from an asthma cohort• Purpose of example to illustrate

– Selecting hypotheses and measures– Listing data elements– Documentation of a rationale for hypotheses and

selection of measures (constructs and data elements)

PROCESSES OF CARE

(clinician factors)+

STRUCTURES OFCARE

(system factors)+ PATIENT FACTORS →

OUTCOMES(chronic disease

control)

Outline for Systematic Identification of Data Domains and Elements

• Establish:– Hypotheses and research questions– Cohort definition– Outcome measures– Primary explanatory variable– Covariates

• Establish these in order to:– Make a list of needed data elements for current work– Lay the groundwork for future directions

• Document a rationale for hypotheses and selection of measures (constructs and data elements)

Outline for Systematic Identification of Data Domains and Elements

•Establish hypothesis: clinical inertia is associated with worse asthma control

CLINICAL INERTIA

PROCESSES OF CARE

(clinician factors)+

STRUCTURES OFCARE

(system factors)+ PATIENT FACTORS →

OUTCOMES(chronic disease

control)

Outline for Systematic Identification of Data Domains and Elements

•Primary explanatory variable: clinical inertia•Dependent variables: meeting evidence-based guidelines for control

•Independent variables: Factors associated with disease control, identified from literature, research experience, and clinical judgment

CLINICAL INERTIA

PROCESSES OF CARE

(clinician factors)+

STRUCTURES OFCARE

(system factors)+ PATIENT FACTORS →

OUTCOMES(chronic disease

control)

Selection of hypothesis: rationale

Outline for Systematic Identification of Data Domains and Elements

• Establish:– Hypotheses and research questions– Cohort definition– Outcome measures– Primary explanatory variable– Covariates

• Establish these in order to:– Make a list of needed data elements for current work– Lay the groundwork for future directions

• Document a rationale for hypotheses and selection of measures (constructs and data elements)

Cohort Definition• Concept: patients with persistent asthma• Options

– Research Design’s definition– HEDIS criteria– Adjusted HEDIS criteria– Enhanced data collection– Others

Cohort Definition• Research Design: “As per the 2007 …EPR-3, we will

define persistent asthma as” > 1 of the following criteria in 12 months – > 1 prescriptions for an asthma maintenance therapy– > 2 asthma-related ED visits– > 1 asthma-related hospitalization

Cohort Definition• HEDIS: > 1 of the following criteria in 12 months

– > 4 asthma medication dispensing events– > 2 asthma medication dispensing events + 4 asthma-related

outpatient visits – > 1 asthma-related hospitalization– > 1 asthma-related ED visit

• Adjusted HEDIS criteria: improved validity if patients meeting criteria for >2 consecutive years (Mosen et al., 2005)

• Enhanced data:– Patient-entered chronic severity (kiosk) to assess current impairment and

future risk (Porter et al., 2004)– Provider-entered assessment of severity

Data Elements for Cohort DefinitionConstruct Measure Elements Values Reference Data

Source

Cohort definition

HEDIS definition of persistent asthma 1: > 4 asthma medication dispensing events in 12 months

Asthma medication dispensed (date, medication)

y/n HEDIS manual

Claims data

Cohort definition

HEDIS definition of persistent asthma 2: > 2 asthma medication dispensing events + 4 asthma-related outpatient visits in 12 months

Asthma medication dispensed (date, medication)

Asthma-related outpatient visits (date, ICD-9 code)

y/n HEDIS manual

Claims data

Cohort definition

HEDIS definition of persistent asthma 3: > 1 asthma-related hospitalization in 12 months

Asthma-related inpatient visits (date, ICD-9 code)

y/n HEDIS manual

Claims data

Cohort definition

HEDIS definition of persistent asthma 4: > 1 asthma-related ED visits in 12 months

Asthma-related ED visits (date, ICD-9 code)

y/n HEDIS manual

Claims data

Rationale for Selection of MeasuresThe current HEDIS measure for asthma uses administrative data collected during 1 year to identify patients with presumed persistent asthma and evaluates controller therapy during the next year. The current HEDIS asthma inclusion include a significant portion of patients with intermittent asthma;1, 2 thus, we chose to use the methods validated by Moser et al., who adapted the HEDIS measure to require at least 2 consecutive years meeting qualification criteria to identify persistent asthma.3

1 Kozyrskyj AL, Mustard CA, Becker AB. Identifying children with persistent asthma from health care administrative records. Can Respir J. 2004;11:141-145.2 Cabana MD, Slish KK, Nan B, Clark NM. Limits of the HEDIS criteria in determiningasthma severity for children. Pediatrics. 2004;114:1049-1055.3 Mosen DM, Macy E, Schatz M, et al. How well do the HEDIS asthma inclusion criteria identify persistent asthma? Am J Manag Care. 2005 Oct;11(10):650-4.

Outline for Systematic Identification of Data Domains and Elements

• Review– Hypotheses and research questions– Cohort definition– Outcome measures– Primary explanatory variable– Covariates– Other future directions

• Review these in order to– Make a list of needed data elements for current work– Lay the groundwork for future directions

Outcome Measures

• Patient-Reported Control Measures• Utilization Measures• Health-Related Quality-of-Life (HRQoL)

PROCESSES OF CARE

(clinician factors)+

STRUCTURES OFCARE

(system factors)+ PATIENT FACTORS →

OUTCOMES(chronic disease

control)

• Asthma Control Test (ACT) (Nathan et al. 2004)• Childhood Asthma Control Test (Liu et al.

2007)• Asthma Control Questionnaire (Juniper et al.

1999)• Asthma Therapy Assessment Questionnaire

(ATAQ) control index (Vollmer et al. 1999) – mentioned in Research Design

Patient-Reported Control Measures

Outcome Measures: Utilization Measures

• Ratio of controller to total asthma medications—mentioned in Research Design– > 0.5 is suggested cut-point– better associated with utilization (ED visits) than is

HEDIS outcome measure– Weighted vs. not

• HEDIS outcome measure– prescription of at least one controller medication– found to be more of a severity indicator than

quality/control measure• Acute hospital visits (ED, inpatient)

Outcome Measures: HRQoL• Asthma-Specific Quality of Life

– Mini Asthma Quality of Life Questionnaire (Juniper et al. 1999a)

– Asthma Quality of Life Questionnaire (Katz et al. 1999; Marks et al. 1993)

– ITG Asthma Short Form (Bayliss et al. 2000)– Asthma Quality of Life for Children (Juniper et al. 1996)– Others?

• Generic Quality of Life– SF-36 (Bousquet et al. 1994)– SF-12 (Ware et al. 1996)

Data Elements for Outcome Measures Definition

Construct Measure Elements Values Reference Data Source

Asthma control

Childhood Asthma Control Test 7 components 0-27 (poor control <19)

Liu et al. 2007

POC measure

Asthma control

Ratio of controller to total asthma medications

Asthma medication dispensed (date, medication)

0-1 (dichotomize at 0.5)

HEDIS manual

Claims data

Asthma control

Acute hospital resource utilization

Asthma-related inpatient visits (date, ICD-9 code)

# visits Claims data

Asthma control

Acute hospital resource utilization

Asthma-related ED visits (date, ICD-9 code)

# visits Claims data

Rationale for Selection of Measures

Outline for Systematic Identification of Data Domains and Elements

• Establish:– Hypotheses and research questions– Cohort definition– Outcome measures– Primary explanatory variable– Covariates

• Establish these in order to:– Make a list of needed data elements for current work– Lay the groundwork for future directions

• Document a rationale for hypotheses and selection of measures (constructs and data elements)

Primary Explanatory Variable• Clinical inertia: “the failure of clinicians to

initiate or intensify drug therapy appropriately in a patient with uncontrolled asthma, blood pressure or cholesterol”

CLINICAL INERTIA

PROCESSES OF CARE

(clinician factors)+

STRUCTURES OFCARE

(system factors)+ PATIENT FACTORS →

OUTCOMES(chronic disease

control)

Guideline-concordant intensification steps

• Intensify = follow EPR3 steps

• Uncontrolled = based on POC control test (ACT, ATAQ, etc)

Data Elements for Primary Explanatory Variable Definition

Construct Measure Elements Values Reference Data Source

Clinical inertia

Childhood Asthma Control Test 7 components, date 0-27 (poor control <19)

Liu et al. 2007

POC measure

Clinical inertia

Medications Asthma medication dispensed (date, medication)

Claims data

Rationale for Selection of Measures???

Outline for Systematic Identification of Data Domains and Elements

• Establish:– Hypotheses and research questions– Cohort definition– Outcome measures– Primary explanatory variable– Covariates

• Establish these in order to:– Make a list of needed data elements for current work– Lay the groundwork for future directions

• Document a rationale for hypotheses and selection of measures (constructs and data elements)

Covariates• Processes of Care

– Clinical inertia (primary explanatory variable)

– Medication prescription• Structures of Care

– Practice demographics– PCMH, IMH

PROCESSES OF CARE

(clinician factors)+

STRUCTURES OFCARE

(system factors)+ PATIENT FACTORS →

OUTCOMES(chronic disease

control)

• Patient Factors– Demographics, access– Co-morbidity (medical,

mental health)– Severity of illness– Therapy adherence

Covariates• Processes of Care

– Clinical inertia (primary explanatory variable)

– Medication prescription• Structures of Care

– Practice demographics– PCMH, IMH

PROCESSES OF CARE

(clinician factors)+

STRUCTURES OFCARE

(system factors)+ PATIENT FACTORS →

OUTCOMES(chronic disease

control)

• Patient Factors– Demographics, access– Co-morbidity (medical,

mental health)– Severity of illness– Therapy adherence

Covariates

• Medical Comorbidity: – “Chronic medical co-morbidity will be...grouped

into 30 comorbidities as described by Elixhauser and Quan.” (AHRQ co-morbidity measures)

– Body Mass Index (do we need other measures for children?)

– Smoking status (also 2nd hand smoke exposure?)

PROCESSES OF CARE

(clinician factors)+

STRUCTURES OFCARE

(system factors)+ PATIENT FACTORS →

OUTCOMES(chronic disease

control)

Comorbidity

AHRQ comorbidity measures

Data Elements for Comorbidity Variable Definition

Construct Measure Elements Values Reference Data Source

Medical co-morbidity

HCUP comorbidity measure ICD-9 codes from encounters and problem list

0-27 (poor control <19)

Liu et al. 2007

EHR

Rationale

Disease-specific discussions

• Hypertension• Hyperlipidemia• Asthma (Peds)• Asthma (Adults)

Outline for Systematic Identification of Data Domains and Elements

• Establish:– Hypotheses and research questions– Cohort definition– Outcome measures– Primary explanatory variable– Covariates

• Establish these in order to:– Make a list of needed data elements for current work– Lay the groundwork for future directions

• Documenting a rationale for hypotheses and selection of measures (constructs and data elements)

Hypertension

• Hypotheses:

Hypertension

• Cohort definition:

Hypertension

• Outcome measures:

Hypertension

• Covariates:

Structures of Care

• PCMH• IMH

Patient Centered Med Home Standards- NCQA

1. Access and Communication 2. Patient Tracking and Registry Functions3. Care Management 4. Patient Self‐Management Support5. Electronic Prescribing 6. Test Tracking 7. Referral Tracking8. Performance Reporting and Improvement 9. Advanced Electronic Communications

Integrated Mental Health

• ????

Disease-specific PCMH/IMH Factors

• E.g., Asthma educators?

System Level Factors

• Applied differently based on patient/family/doctor-- can we account for this or not??

Considerations for Future Research

Asthma: • Asthma epidemiology has focused on individual-

level and family risk factors. • Less focus on social and environmental context. • Low-income individuals more likely to be exposed

to irritants, pollutants, indoor allergens, and psychosocial stress, which may influence asthma morbidity.

• Future vision: enhance our cohort with data on suspected biological and environmental determinants of asthma disparities.

Considerations for Future Research

Hypertension: • Prevalence and rate of diagnosis of hypertension

in children and adolescents are increasing, due in part to the increasing obesity prevalence and growing awareness of hypertension.

• Future vision: expand our cohort to include adolescents with hypertension in an effort to identify health care delivery strategies appropriate for the lifespan of patients with hypertension.

Considerations for Future Research

Hypercholesterolemia: • American Academy of Pediatrics recommends

screening overweight children with a fasting lipid profile

• Rising obesity epidemic in U.S. children• Future vision: expand our

hypercholesterolemia cohort to include overweight children.