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Reality check: Effects of National Health Information Technology Prescription Drug Abuse

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CHESS-seminaari 15.11.2017
Petri Böckerman

Published in: Health & Medicine
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Reality check: Effects of National Health Information Technology Prescription Drug Abuse

  1. 1. Introduction Reform Data Empirical approach Results Conclusions Reality Check: Effects of a National Health Information Technology on Prescription Drug Abuse Petri B¨ockerman1 Mika Kortelainen2 Liisa T. Laine3 Mikko Nurminen1 Tanja Saxell2 1Turku School of Economics 2VATT Institute for Economic Research 3University of Washington, and University of Jyv¨askyl¨a ∗This research is funded by Yrj¨o Jahnsson Foundation October 18, 2017
  2. 2. Introduction Reform Data Empirical approach Results Conclusions Introduction • Health care systems: often fragmented and uncoordinated (e.g. Cebul et al. 2008, Elhauge 2010) • Consequences: poor treatment decisions & adherence and wasteful spending • Main goals of Health IT: decrease fragmentation, improve quality with lower costs (e.g. Dranove et al. 2014; Freedman et al. 2015) • HITECH Act in 2009: “to stimulate the adoption of electronic health records (EHR) and supporting technology in the United States” • EU eHealth policy: cross-border electronic health care system in Europe (incl. e-prescriptions) • Health ITs: costly investments but the true effects are uncertain
  3. 3. Introduction Reform Data Empirical approach Results Conclusions Introduction • We study channels through which e-prescriptions can affect health outcomes • E-prescriptions can • Increase prescription drug use by making renewal easier • Reduce prescription drug abuse by making monitoring easier • Implications on patient health and welfare (net effects) are ambiguous • Increase in prescription drug use is beneficial for under-used drugs • Increase in prescription drug use is harmful for over-used drugs
  4. 4. Introduction Reform Data Empirical approach Results Conclusions Introduction • We focus on globally one of the most used group of pharmaceuticals: benzodiazepines • BZDs are highly addictive, a commonly abused class of drugs • In the US the deaths involving benzodiazepines has increased 4.3 fold • In Finland, almost every illegal drug, prescription drug or alcohol related death is related to BZDs (THL 2013) • Opioids do not constitute an epidemic in Finland
  5. 5. Introduction Reform Data Empirical approach Results Conclusions Introduction • Only a little is known about the underlying mechanisms behind the increase in prescription drug abuse • Especially little is known about the causal evidence • Finnish health care market provides a promising setting: • A nationwide rich patient-level register-based data • Identification: a gradual and plausibly exogenous rollout of an electronic prescription over the course of five years • Our quasi-experimental research design and rich register-based data → causal estimates • A national e-prescription system • Single-payer system
  6. 6. Introduction Reform Data Empirical approach Results Conclusions This paper • Health outcomes related to PDA • How technology reflects on prescription drug use and how it is associated with health outcomes • Substitution patterns by the most serious addicts: important for understanding potential unintended consequences • Treatment effects heterogeneity: averages, risk group
  7. 7. Introduction Reform Data Empirical approach Results Conclusions Preview of results • PDA related diagnosis increase in these patient populations • Among patients under 40 • Some indication of increase for patients with a PDA history • Identify potential mechanisms behind worse health outcomes • By making renewal easier, the E-prescription system increases BNZ use • No clear indication on substitution effects into illegal drugs • Weak evidence of increased pharmacy robberies in urban areas
  8. 8. Introduction Reform Data Empirical approach Results Conclusions Previous literature • Effects of Health IT on the costs and quality of medical care: e.g. Miller & Tucker (2011), Dranove et al. (2014), Agha (2014 JHE). • Prescription drug monitoring programs in the US: e.g. Buchmueller & Carey (2017), Dave et al. (2017) • Previous literature says fairly little about mechanisms that lead to overutilization or prescription drug abuse • There is only a little systematic evidence on substitution patterns
  9. 9. Introduction Reform Data Empirical approach Results Conclusions Outline of the talk Reform Data Empirical approach Results Health outcomes Prescribing Substitution patterns Other outcomes Conclusions
  10. 10. Introduction Reform Data Empirical approach Results Conclusions Reform • Public health care units adopted e-prescription during 2010–2014 • Exogenous variation in adoption time: • Technical difficulties. • Massive system → simultaneous adoption impossible → step-wise adoption across health care units and time • Variation in technology adoption was not health related • Private health care units • Had to adopt the technology by 2015 • Except small units (less than 5000 prescriptions per year) had to adopt by 2017 • Central nervous system (CNS) drugs became mandatory to prescribe electronically in October 2015
  11. 11. Introduction Reform Data Empirical approach Results Conclusions Rollout of E-prescriptions Figure: Adoption year of municipalities’ primary health care units.
  12. 12. Introduction Reform Data Empirical approach Results Conclusions Reform exogeneity Dependent variable: lag in adoption time (days) Covariate year 2008 2009 2010 log(Population) −14.479 −12.570 −11.730 (9.777) (8.412) (9.154) > 64 old (%) 96.061∗ 84.620 80.265 (54.198) (58.698) (56.200) log(taxable income / capita) 20.348 10.848 12.929 (20.630) (20.019) (18.576) log(health care costs / capita) 2.625∗ 2.821 2.389 (1.537) (1.859) (1.686) Drug reimbursement index 0.345 0.141 0.135 (1.023) (0.992) (1.026) Morbidity index −0.537 −0.380 −0.185 (1.053) (1.030) (1.113) Mortality index 0.104 0.134 −0.030 (0.401) (0.390) (0.448) Outpatient visits in specialities of −0.007 −0.019 −0.060 psychiatry / 1000 inhabitants (0.049) (0.048) (0.042) Psychiatric inpatient care / 1000 inhabitants 1.797 0.833 1.180 (1.319) (1.669) (1.655) Number of PDA’s −0.042 −0.028 −0.004 (0.174) (0.153) (0.127) Hospital district control X X X Observations 299 298 298 Adjusted R2 0.793 0.791 0.792
  13. 13. Introduction Reform Data Empirical approach Results Conclusions From paper-based prescribing to e-prescriptions • Increase patient and medication safety • Decrease medication errors • Make prescribing and dispensing easier and more efficient • Renewals: • Patient can request renewals also through pharmacies • Renewals also by discussion over the phone • E-prescriptions can be viewed by physicians and pharmacists involved in patient’s care • Until 2015, physicians and pharmacists needed patients’ permission to access CNS prescription drug history • → Potential selection → lower bound estimates (intent-to-treat)
  14. 14. Introduction Reform Data Empirical approach Results Conclusions Physician’s view
  15. 15. Introduction Reform Data Empirical approach Results Conclusions Reform Data Empirical approach Results Health outcomes Prescribing Substitution patterns Other outcomes Conclusions
  16. 16. Introduction Reform Data Empirical approach Results Conclusions Data • A nationwide rich patient-level register data for years 2007–2014 • Health outcomes related to a medical condition (HILMO) • Prescription drugs in the class of benzodiazepines (KELA) • Identify patient, physician, pharmacy ID’s and patients’ home municipalities • Population: Patients with BNZ and BNZ related prescriptions during 2007-2014 • Data on municipality level pharmacy robberies for years 2010–2014
  17. 17. Introduction Reform Data Empirical approach Results Conclusions Data & Outcomes • Diagnosis data and prescription: aggregated to patient half-year level observations • Health outcomes: Sum of patient’s diagnosis related to prescription drug abuse • Outcomes from prescription data: Number of Rx, Sum of DDD (Defined Daily Dose) • Substitution patterns: Illegal drug use diagnosis, pharmacy robberies • Patient-prescription daily level data: Days between new BNZ prescription • Treatment effects heterogeneity: averages, risk group
  18. 18. Introduction Reform Data Empirical approach Results Conclusions Data: limitations • We do not observe: • Prescription fraud • Purchases not covered by the social insurance • Substitution patterns: illegal drug purchases (e.g. online or dealers)
  19. 19. Introduction Reform Data Empirical approach Results Conclusions Reform Data Empirical approach Results Health outcomes Prescribing Substitution patterns Other outcomes Conclusions
  20. 20. Introduction Reform Data Empirical approach Results Conclusions Empirical approach • Our identification strategy exploits variation across regions in outcomes related to prescription drug abuse as a result of the adoption of the e-prescription system. • We estimate the following reduced form model for individual i in municipality m in time t: Yimt = αm + γt + δAftermt + XT imt β + imt • Diff-in-Diff (DD) • Main outcomes estimated at patient half-year level
  21. 21. Introduction Reform Data Empirical approach Results Conclusions Descriptive stats Figure: Age distribution by PDA diagnosis
  22. 22. Introduction Reform Data Empirical approach Results Conclusions E-prescription uptake Figure: E-prescription uptake - Age comparison
  23. 23. Introduction Reform Data Empirical approach Results Conclusions E-prescription uptake Figure: E-prescription uptake - Private vs. public
  24. 24. Introduction Reform Data Empirical approach Results Conclusions Reform Data Empirical approach Results Health outcomes Prescribing Substitution patterns Other outcomes Conclusions
  25. 25. Introduction Reform Data Empirical approach Results Conclusions Diagnosis related to PDA Figure: Age < 40 Figure: Age > 40
  26. 26. Introduction Reform Data Empirical approach Results Conclusions Diagnosis related to PDA Figure: PDA diagnose & age < 40
  27. 27. Introduction Reform Data Empirical approach Results Conclusions Reform Data Empirical approach Results Health outcomes Prescribing Substitution patterns Other outcomes Conclusions
  28. 28. Introduction Reform Data Empirical approach Results Conclusions Number of Rx Figure: Age < 40 Figure: Age > 40
  29. 29. Introduction Reform Data Empirical approach Results Conclusions Number of Rx: PDAs Figure: PDA diagnose & age < 40
  30. 30. Introduction Reform Data Empirical approach Results Conclusions Rx use vs. PDA diagnosis
  31. 31. Introduction Reform Data Empirical approach Results Conclusions Easier diagnosing?
  32. 32. Introduction Reform Data Empirical approach Results Conclusions Reform Data Empirical approach Results Health outcomes Prescribing Substitution patterns Other outcomes Conclusions
  33. 33. Introduction Reform Data Empirical approach Results Conclusions Hospital visit related to illegal drug use Figure: Age < 40 Figure: Age > 40
  34. 34. Introduction Reform Data Empirical approach Results Conclusions Pharmacy robberies: urban areas Figure: Pharmacy robbery rate Figure: Prob. of a pharmacy robbery
  35. 35. Introduction Reform Data Empirical approach Results Conclusions Reform Data Empirical approach Results Health outcomes Prescribing Substitution patterns Other outcomes Conclusions
  36. 36. Introduction Reform Data Empirical approach Results Conclusions Supplementary outcomes • Time between new prescriptions Results
  37. 37. Introduction Reform Data Empirical approach Results Conclusions Conclusions • We study the causal effects of an implementation of a health IT (e-prescriptions) on prescription drug abuse • Our focus: to analyze channels through which e-prescriptions can affect health outcomes • Contribution: to provide causal evidence about mechanisms that increase/decrease drug use • We use quasi-experimental research design and rich register-based data • Outcomes • Health outcomes: PDA related diagnosis • Physician prescribing patterns: # prescriptions, DDD • Substitution patterns: illegal drugs, pharmacy robberies • Analyze treatment effects heterogeneity: averages, risk group
  38. 38. Introduction Reform Data Empirical approach Results Conclusions Conclusions • PDA related hospitalizations • Increases in especially patients under 40 • The number prescriptions increase, especially among patients under 40 • Number of prescriptions increase also for patients with PDA diagnosis • No clear indication on substitution effects into illegal drugs but weak evidence of increase in pharmacy robberies in urban areas • Health IT • Health benefits if increases use of Rx when underutilized • Drawbacks if increases use of Rx that are overutilized and/or highly addictive • Potentially creates new addicts; potential need for tightening control of addictive Rx prescribing
  39. 39. Introduction Reform Data Empirical approach Results Conclusions Next steps • Substitution effects: effects on veterinary physicians. • Continuation projects: effects on overlapping medications, for example
  40. 40. Days between physician visits, full sample Back
  41. 41. Days between physician visits, patients younger than 40 yrs Back
  42. 42. Days between physician visits, patients with PDA diagnosis Back

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