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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.