1. Realizing the potential of health administrative data: study design, inference and future directions Bohdan Nosyk, PhD Associate Professor, St. Paul’s Hospital Canfar Chair in HIV/AIDS Research Faculty of Health Sciences, Simon Fraser University Research Scientist, Health Economics Michael Smith Foundation for Health Research Scholar BC Centre for Excellence in HIV/AIDS 1

2. Disclosure Funding from: NIH/NIDA, CIHR, VCH, BC MoH, MSFHR, St. Paul’s Hospital Foundation, Canfar No conflicts of interest to declare 2

3. My ‘Big Data’ Experience 1. BC MMTOS – Cohort Definition: All individuals receiving methadone for opioid dependence in BC: 1996-2007; N~18,000 – Composition: PharmaNet, DAD, MSP, Vital stats, HCC, MH file 2. CA OTP – Cohort Definition: All individuals accessing publicly-funded treatment for opioid dependence; N~=32,000 – Composition: CADDS/CalOMS, DHCS, DOJ, NDI 3. STOP HIV/AIDS Cohort – Cohort Definition: All individuals diagnosed* with HIV/AIDS in British Columbia – 1996-2014 (annually updated); N~13,000 – Composition: BC-CfE DTP database, BC CDC HIV testing database, PharmaNet, DAD, MSP, Vital stats, HCC, AIMS 3

4. Outline Background Health administrative data applications in: – Health system performance measurement – Inference in secondary analysis – Empirical basis to evaluate change in policy, practice – Supporting health economic evaluation Future directions 4

5. Context High level of expenditure on health care Demand for quality, public accountability Sustainability of healthcare delivery All reliant on timely research to inform evidence-based practice 5

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7. Increasingly scarce resources to fund health research CIHR – nominal annual operating budget flat since 2007-08; to decline in 2018-19 (declining in real terms throughout) 7 NIH – declining in real terms since 2003

8. Research with health administrative data Becoming an imperative: – Infeasibility, inadequacy of RCTs – Decreasing grant funding available for RCTs, prospective cohort studies -> increasing importance to work with HAs, MoH Emphasis on timeliness Advantages: – Population-level data capture External validity Detailed, longitudinal analysis of rare outcomes Uniquely suitable for TTE analysis – Captures real-world data on clinical process, health care delivery – Flexible counting process 8

9. Research with health administrative data Not all health administrative databases are alike! – Typical health administrative databases (Pharmanet, MSP, DAD, Vital stats) Including standardized patient assessment (CA OTP database) Linked with disease registries (STOP database) Linked to a prospective cohort study Linked to an RCT 9

10. 10 A well-designed observational study can approximate the results of an RCT

11. Some valuable applications: – Health system performance metrics – Inference in secondary analysis – Policy evaluation – Health economic model development Opportunity to inform clinical practice, resource allocation decisions, policy change prospectively 11 Research with health administrative data

12. Health System Performance Measurement 12

13. Health system performance Measures of disease prevalence, incidence, burden – Standard surveillance metrics Measures of quality of care – Wait times, re-admission, etc. Adverse health outcomes – May be rare, due to many causes – May provide non-specific information to improve healthcare delivery Clinical process measures – More frequently observed – Directly tied to best practices – Actionable 13

14. Nosyk et al, Lancet Infect Dis, 2014; 14(1): 40-9.

15. Quarterly HIV Surveillance Report Developed collaboratively with input from all partners Content, methodology described in Lourenco et al, JAIDS, 2015

16. Nosyk et al, JSAT 2010; 39: 22-31.

17. BC MMT Dosing Guidelines (2007) StageRecommendations Starting Dose- Non-tolerant/opiate naïve: 5-10mg pd - Unknown Tolerance: 15-25 mg pd. - Known Tolerance: 20-40 mg pd. Stabilization (Titration) Dosing -Stabilization dose: reduces/eliminates withdrawal symptoms and drug craving, will not induce sedation or respiratory depression, blocks euphoric effects of illicit opioids. -Dose adjustments: 5-10mg range, not more frequently than every 3-5 days. Stabilized (Maintenance) Dose-Most patients will achieve stability on doses between 60-100mg daily. Carry Policy-Recommended that carries not exceed 4 days or 400mg. -Criteria: clinical stability (stable dose, social stability (incl. urine drug screens free of all mood-altering drugs for a min. of 12 week), and ability to store methadone safely. -Reasons for initiation of carry privileges must be documented by physician. Tapering-Maximum weekly reduction should be no more than 5% of total dose 17

18. Definitions: titration, tapering dose t > dose t-1 ; dose t ≥ dose t+1 ; dose t ≥ dose t+2 ; dose t ≥ dose t+3 ; dose t ≥ dose t+4. dose t < dose t-1 ; dose t < dose t-2 ; dose t < dose t-3 ; dose t < dose t-4 ; dose t ≥ dose t+1 ; dose t ≥ dose t+2 ; dose t ≥ dose t+3 ; dose t ≥ dose t+4.; dose t > dose t-1 ; dose t-1 ≤ dose t-2 ; dose t-1 ≤ dose t-3 ; dose t-1 ≤ dose t-4 ; dose t-1 < dose t+1 ; dose t-1 < dose t+2 ; dose t-1 < dose t+3 ; dose t-1 < dose t+4 Titration end Taper start Taper Interruption 18

19. Trends in Dosing Guideline Compliance 19

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21. DTES-2GS: Clinical process Measures: Integration 21

22. Inference in Secondary Analysis 22

23. “In an ideal world, all decisions would be based on randomized experiments.... Unfortunately, randomized experiments are often unethical, impractical, or simply too lengthy for timely decisions. The next best thing to a randomized experiment is an observational study that closely mimics a randomized experiment. Though causal inferences from observational data are risky, the best available evidence for decision- making will often come from well designed and properly analyzed observational studies. Because there is no alternative to observational studies, we need to keep improving them.” - Miguel Hernan, Harvard School of Public Health 23

24. Causality No unmeasured confounding – not a testable hypothesis Counterfactual framework – Conditional exchangeability – Consistency – Positivity Bradford Hill ‘viewpoints’ – Temporality, strength of association, consistency in findings, dose-response, biological plausibility 24

25. Treatment Evaluation Selection on observables or unobservables? Selection into treatment on observable factors – Naturalistic policy change – Multiple regression techniques, propensity scores, DID Selection into treatment on unobservable factors – Choosing drug A over drug B – Instrumental variable methods? 25

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27. Study Design Population-based retrospective cohort study Study selection criteria: – completed (non-censored) episodes in which the mean dose was either decreasing, or had decreased to ≤5mg per day over the final four weeks of the episode – (included if (i) ((mean dose t-3 – mean dose T ) / mean dose T )<0; or (ii) mean dose t ≤5 mg, where t = (t-3, t-2, t-1, T) and T is the terminal week of treatment. – The durations of tapers were therefore a minimum of 4 weeks. 27

28. Outcome Sustained successful taper definition – (i) methadone dose tapered to ≤ 5mg/day in the final week of treatment – within 18 months of treatment discontinuation: (ii) no treatment re-entry (iii) no drug-related hospitalization (iv) no mortality 18 month reference period was chosen based on distributions of time to treatment re-entry, to capture the majority of re- entries following successful tapering while maintaining sufficient patient follow-up 28

29. Conclusions Of all completed MMT episodes, 2.5% were classified as ‘sustained successful taper’ – The majority of patients treated with methadone maintenance will not succeed at tapering Longer tapers had substantially higher odds of success, regardless of how early in the treatment episode they were initiated A more gradual tapering schedule, with dose decreases scheduled in only 25-50% of the weeks in treatment, provided the highest odds of sustained success in tapering 29

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31. Nosyk et al., AIDS 2015; 29:965-73.

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33. Empirical basis to evaluate change in policy, practice 33

34. 34 Anglin et al, AJPH 2013; 103: 1096-1102

35. Cost-effective Integration of HIV Care in OST Nosyk et al, Contemp Clin Trials. 2015; 45: 201-9.

36. Cost-effective Integration of HIV Care in OST

37. 6m12m18m 24m OST site cluster 1 OST site cluster 3 OST site cluster 2 expanded HIV care intervention Cluster-randomized, stepped-wedge trial design in N=51 unique OST sites An estimated 40-45% of HIV-positive individuals in BC are IDU PHAC estimates 3,400 undiagnosed HIV-positive individuals in BC (2011) Among diagnosed IDU: 530 not on HAART; another 752 not suppressed (2011)

38. Cost-effective Integration of HIV Care in OST

39. Supporting health economic evaluation 39

40. Economic evaluation defined Born out of welfare economics – objective is to maximize social welfare (health) subject to constraints Incremental Cost-Effectiveness Ratio: –For an assessment of efficiency, the additional costs of one intervention over another should be compared to the additional benefits, or effectiveness:

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42. BC HIV Transmission Model Diagram 42 I 1 Infected CD4 ≥500 S 1 Susceptible Not Screened S 2 Susceptible Screened ψ ω ρ I 4 Infected CD4<200 I 2 : Infected CD4: 350-499 I 3 : Infected CD4: 200-349 T 2 On HAART CD4: 350-499 T 3 On HAART CD4: 200-349 T 4 On HAART CD4<200 T 1 ON HAART CD4 ≥500 D 4 : Diagnosed CD4<200 D 1 Diagnosed CD4≥500 D 2 Diagnosed CD4:350-499 D 3 Diagnosed CD4: 200-349 Ψ+ν Key model features: Key risk groups represented: {MSM, IDU, MSM/IDU, HETERO}; proportional mixing Built with extensive BC population-level data; captured changes in practice over time HAART dropout modeled explicitly

43. Selected Model Validation Results 43

44. Focus on local HIV microepidemics: A case study for Kenya 44 Andersen et al., Lancet 2014; 384: 249-58. Same investment, greater health benefits for a localized vs. generalized HIV public health strategy

45. Health Production Function for hypothetical HIV care intervention scenarios: British Columbia, Canada: 2015-2035 45 Nosyk et al, in preparation

46. A focused on localized microepidemics 46 Figure 4. Evidence Synthesis Nosyk et al, in preparation

47. Looking ahead Annual, ‘near real-time’ guidance on resource utilization decisions at the local level – Recognizing differences in localized HIV microepidemics Administrative data linkage for prospective cohort studies, RCTs – Address limitations in each study design; maximize value of research investments Increasing emphasis on evaluation of health system performance, policy change Emphasis on timeliness, automation: – Data visualization of individual-level records to aid physician decision- making 47

48. Summary HA data – a valuable resource that will only increase in prominence to public health research community Requires a balance between methodological development, timeliness of analysis Our responsibility – realize public health value of these data 48

49. Acknowledgements The STOP HIV/AIDS Study team: BC Centre for Excellence in HIV/AIDS BC Centre for Disease Control BC Ministry of Health BC Regional Health Authorities First Nations Health Authority Funding: BC Ministry of Health Vancouver Coastal Health Authority Canadian Institutes of Health Research National Institutes of Health/National Institute on Drug Abuse Michael Smith Foundation for Health Research St. Paul’s Hospital Foundation CANFAR The BC-CfE Health Economic Research Unit Dimitra Panagiotoglou, PhD(c) Michelle Olding, MPH Jeong Min, MSc Linwei Wang, MSc Emanuel Krebs, MA Ben Enns, MA Xiao Zang, PhD student Batool Yazdani, MSc student Ahmed Adam, MPH student