Population-Based Networks for ComparativeEffectiveness Research:Promises and PotholesTracy Lieu, MD, MPHJanuary 8, 2013   ...
Kaiser Permanente is a resource forcomparative effectiveness research 3.3 million patients 7,000 physicians 21 hospital...
Our Division of Research has commoninterests with UCSF   50+ research scientists in:    Cancer    Cardiovascular and met...
Our research and funding are largelypublic-domain                         % of total funding in 2011 ($107M)             P...
Population-based research networks    can facilitate CER      Patients drawn from a defined group,       representative o...
Networks have supported safety and    epidemiologic research      Vaccine Safety Datalink (CDC, 1991)      Mini-Sentinel...
In 2010, AHRQ sponsored 11 new    networks for CER     Examples:      Population-Based Effectiveness in       Asthma and ...
New approaches have increased the    power of these networks      Distributed data network approaches      Example – ast...
Distributed data networks are versatile  Standard, multi-purpose, multi-   institutional infrastructure  Can support bot...
Example: The Population-BasedEffectiveness in Asthma and Lung Diseases(PEAL) Network  6 sites with diverse populations  ...
Collaborators in the PEAL Network                          HealthPartners KP Northwest                                    ...
PEAL Virtual Data Warehouse                                                PEAL Data                            Population...
Comparative effectiveness research     PEAL Data                   and other studies     Warehouse                        ...
Data confidentiality is a key hurdle for data networks      Pooling individual level-data poses risk      De-identificat...
PEAL builds on standard datasets from theHMO Research Network’s Virtual DataWarehouse                                     ...
The PEAL Network has succeeded inits basic purpose Established understandings –governance, data  use, IRB Created data d...
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Example of potential confounding:Outcomes after leukotriene inhibitors comparedwith inhaled corticosteroids Retrospective...
Example of potential confounding:Outcomes after leukotriene inhibitors comparedwith inhaled corticosteroidsPreliminary fin...
Retrospective cohort designs for CERare prone to selection bias(confounding by indication) Patients who receive a newer t...
We’re testing analytic approaches toreducing confoundingIn the PEAL cohort analysis, we are comparing: Propensity score w...
Stronger designs may better reduceconfounding Instrumental variable – find a covariate that is  associated with the expos...
Temporal trend or intervention effect?               Intervention                   group                                 ...
Difference-in-difference design candistinguish between temporal trend . . .                          Comparison           ...
and intervention effect                               Comparison                                 group                Inte...
Interrupted Time Series Design                                 28
Interrupted time series analysisBenzodiazepine (BZ) use and hip fractures in women inMedicaid before and after NY policy r...
Number of albuterol inhalers dispensed before and                                         after an increase in co-payment ...
Comparative effectiveness research:Is there hope for this half-baked cake?                                          31
Population-based networks are usefulfor:  • Observational comparative    effectiveness research (including    quasi-experi...
You can also use population-basednetworks for:  • Epidemiology, including genetic    epidemiology  • Safety surveillance  ...
Population-based research data may beuseful for clinical system needs                                Firewall Research    ...
Electronic Data Methods (EDM) forumis a national resource  • Facilitates learning across AHRQ projects    that build infra...
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UCSF CER - Population Based Networks for Comparative Effectiveness Research (Symposium 2013)
UCSF CER - Population Based Networks for Comparative Effectiveness Research (Symposium 2013)
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UCSF CER - Population Based Networks for Comparative Effectiveness Research (Symposium 2013)

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UCSF CER - Population Based Networks for Comparative Effectiveness Research (Symposium 2013)

  1. 1. Population-Based Networks for ComparativeEffectiveness Research:Promises and PotholesTracy Lieu, MD, MPHJanuary 8, 2013 Kaiser Permanente Research
  2. 2. Kaiser Permanente is a resource forcomparative effectiveness research 3.3 million patients 7,000 physicians 21 hospitals 234 medical offices Regional quality improvement programs 2
  3. 3. Our Division of Research has commoninterests with UCSF 50+ research scientists in:  Cancer  Cardiovascular and metabolic  Health care delivery and policy  Infectious disease  Behavioral health and aging  Women’s and children’s health 3
  4. 4. Our research and funding are largelypublic-domain % of total funding in 2011 ($107M) Pharma/ biotechCentral ResearchCommittee awards Federal KP Community Benefit TPMG Foundation4
  5. 5. Population-based research networks can facilitate CER  Patients drawn from a defined group, representative of the general population  Multiple geographic sites  Sites have: – Computerized data on exposures and outcomes – Access to clinicians and patients5
  6. 6. Networks have supported safety and epidemiologic research  Vaccine Safety Datalink (CDC, 1991)  Mini-Sentinel (FDA, 2010)  Cancer Research Network (NCI)  Cardiovascular Research Network (NHLBI, 2007)  Mental Health Research Network (NIMH, 2009)6
  7. 7. In 2010, AHRQ sponsored 11 new networks for CER Examples:  Population-Based Effectiveness in Asthma and Lung Diseases (PEAL)  Surveillance, Prevention, and Management of Diabetes Mellitus (SUPREME-DM)  Scalable Partnering Network (SPAN)7
  8. 8. New approaches have increased the power of these networks  Distributed data network approaches  Example – asthma network for CER  Methodologic potholes and potential solutions  Resources for using distributed data networks for CER8
  9. 9. Distributed data networks are versatile  Standard, multi-purpose, multi- institutional infrastructure  Can support both observational and intervention studies  Local data holder control over access and uses of data  Mitigates need to share or exchange protected health information 9
  10. 10. Example: The Population-BasedEffectiveness in Asthma and Lung Diseases(PEAL) Network  6 sites with diverse populations  Sponsored by AHRQ, 2010-2013  Purpose: Establish infrastructure and conduct CER in asthma  Lay foundation for research in other lung diseases and in other fields, e.g. pharmacogenetics 10
  11. 11. Collaborators in the PEAL Network HealthPartners KP Northwest Harvard PilgrimKP Northern Health Care California Vanderbilt KP Georgia www.pealnetwork.org
  12. 12. PEAL Virtual Data Warehouse PEAL Data Population Warehouse HPHC selection and data HPHC KPNC warehouse KPNC KPSE building using KPSE HPRF distributed HPRF VAND programs and VAND KPNW site-specific KPNW translation programs Local databases, some standard PEAL databases with(VDW) and others w/varying structure common structure12
  13. 13. Comparative effectiveness research PEAL Data and other studies Warehouse Study-specific HPHC analysis programs KPNC based on common KPSE data dictionary HPRF VAND KPNW Compatible de-identified Research Team datasets from eachPEAL databases with site common structure13
  14. 14. Data confidentiality is a key hurdle for data networks  Pooling individual level-data poses risk  De-identification doesn’t always work  Distributed analysis gives stronger protection -- only aggregated, count data are shared  Example: Vaccine Safety Datalink Project and Congressman Dan Burton14
  15. 15. PEAL builds on standard datasets from theHMO Research Network’s Virtual DataWarehouse New, from Derived from the HMORN VDW source dataDemographics Specialty PrescribingEnrollment Dispensing BenefitsUtilization tables: Geocode & copaymentEncounter VitalsDiagnosis DeathProcedure 17
  16. 16. The PEAL Network has succeeded inits basic purpose Established understandings –governance, data use, IRB Created data dictionaries & datasets Identified the study cohorts; descriptive analyses Completed studies of controller medication effectiveness and statins in asthma Studies of adherence, methodology, cost- sharing, and insurance benefit design underway 18
  17. 17. 19
  18. 18. Example of potential confounding:Outcomes after leukotriene inhibitors comparedwith inhaled corticosteroids Retrospective cohort analysis of >44,000 children with probable persistent asthma 70% filled an inhaled corticosteroid (ICS); 26% filled a leukotriene inhibitor (and not an ICS) Proportional hazards models Adjusted for age, sex, insurer, asthma risk (prior ED visits, hospitalizations, oral steroid bursts), Charlson score, comorbidities, and adherence as a time-varying covariate 20
  19. 19. Example of potential confounding:Outcomes after leukotriene inhibitors comparedwith inhaled corticosteroidsPreliminary findings – confidential: In TennCare, users of leukotriene inhbitors were less likely to experience an asthma-related emergency department visit (HR 0.7, CI 0.5-0.8) in the next 12 months In HMO populations, users of leukotriene inhibitors were less likely to have subsequent oral steroid bursts (HR 0.6, CI 0.4 – 0.9)Wu AC, under review 21
  20. 20. Retrospective cohort designs for CERare prone to selection bias(confounding by indication) Patients who receive a newer treatment often differ from patients who don’t Or, better clinicians or better health care systems may adopt better interventions sooner Traditional multivariate regression often cannot resolve this confounding 22
  21. 21. We’re testing analytic approaches toreducing confoundingIn the PEAL cohort analysis, we are comparing: Propensity score weighting High-dimensionality propensity scores Proportional hazards regression with time- dependent covariates Marginal structural models Adding patient-reported information to computerized data 23
  22. 22. Stronger designs may better reduceconfounding Instrumental variable – find a covariate that is associated with the exposure and not the outcome, and use this to create “randomized” groups – if you are lucky Difference-in-difference – change in time between intervention and comparison groups Interrupted time series (regression discontinuity) 24
  23. 23. Temporal trend or intervention effect? Intervention group 25
  24. 24. Difference-in-difference design candistinguish between temporal trend . . . Comparison group Intervention group 26
  25. 25. and intervention effect Comparison group Intervention group 27
  26. 26. Interrupted Time Series Design 28
  27. 27. Interrupted time series analysisBenzodiazepine (BZ) use and hip fractures in women inMedicaid before and after NY policy restricting BZ use 50 P o licy Bz Use among Female Users before Policy,% 40 30 20 10 N ew Y ork 60% decrease N ew Jersey in bz use in NY 0 Female Users before Policy 0.025 Hip Fracture per 100000 Cumulative Incidence of P o lic y 0.02 0.015 0.01 No change in risk of hip fracture 0.005 0 1 11 M o n th 21 31Wagner AK Ann Intern Med 2007 (from Soumerai S)
  28. 28. Number of albuterol inhalers dispensed before and after an increase in co-payment due to branding changes – Preliminary data, confidential: 180 160 140number of inhalers per 1,000 children Cases (changed to brand cost-sharing) 120 100 Controls (kept generic 80 cost-sharing) 60 40 20 0 2007M03 2007M07 2007M09 2009M11 2010M03 2010M05 2007M01 2007M05 2007M11 2008M01 2008M03 2008M05 2008M07 2008M09 2008M11 2009M01 2009M03 2009M05 2009M07 2009M09 2010M01 2010M07 2010M09 2010M11 Policy change
  29. 29. Comparative effectiveness research:Is there hope for this half-baked cake? 31
  30. 30. Population-based networks are usefulfor: • Observational comparative effectiveness research (including quasi-experimental designs) • Interventional comparative effectiveness research • Delivery science / implementation research 32
  31. 31. You can also use population-basednetworks for: • Epidemiology, including genetic epidemiology • Safety surveillance • Identifying patients with specific conditions, especially uncommon ones, for all types of studies 33
  32. 32. Population-based research data may beuseful for clinical system needs Firewall Research Clinical and Data OperationalWarehouses Users& Data Marts collaborative research direct access reports direct distribution report repository research staff
  33. 33. Electronic Data Methods (EDM) forumis a national resource • Facilitates learning across AHRQ projects that build infrastructure for comparative effectiveness research • Led by AcademyHealth with AHRQ support • Holds stakeholder symposia • Organizes reports on specific topics, e.g. building cohorts for research, deidentifying data 35
  34. 34. 36
  35. 35. 37
  36. 36. 38

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