Pharmacoepidemiological Approaches in Health Care

  • Christine Y. LuEmail author


Pharmacoepidemiology studies patterns of medicines use—also known as drug utilization research—which is an important component of pharmacy practice research. Pharmacoepidemiology also studies the relationship between treatment or exposure and outcomes in large populations under nonexperimental situations over time. This chapter provides an introduction to pharmacoepidemiology. It discusses the key concepts involved in studying the association between medicines and outcomes. These include forming a research question, considering sources of data, defining the study population, and defining drug exposures and outcomes. This chapter also discusses a range of study designs used in pharmacoepidemiological research including cohort studies, case-control design, within-subject methods, cross-sectional studies, ecological studies, and quasi-experimental designs. Frequently used metrics to understand drug utilization and medication adherence are also introduced. This chapter also draws on key challenges such as selection bias as well as commonly used analytical techniques to overcome these challenges.


Propensity Score Medication Adherence Drug Utilization Pharmacoepidemiological Study Electronic Medical Record Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Adams AS, Zhang F, LeCates RF et al (2009) Prior authorization for antidepressants in Medicaid: effects among disabled dual enrollees. Arch Intern Med 169(8):750–756CrossRefPubMedCentralPubMedGoogle Scholar
  2. Agency for Healthcare Research & Quality (2013) Linking data for health services research: a framework and guidance for researchers (a draft report)Google Scholar
  3. Andrade SE, Kahler KH, Frech F, Chan KA (2006) Methods for evaluation of medication adherence and persistence using automated databases. Pharmacoepidemiol Drug Saf 15(8):565–574, discussion 575–567CrossRefPubMedGoogle Scholar
  4. Austin PC, Mamdani MM, Juurlink DN, Hux JE (2006) Testing multiple statistical hypotheses resulted in spurious associations: a study of astrological signs and health. J Clin Epidemiol 59(9):964–969CrossRefPubMedGoogle Scholar
  5. Avorn J (2004) The role of pharmacoepidemiology and pharmacoeconomics in promoting access and stimulating innovation. Pharmacoeconomics 22(Suppl 2):81–86CrossRefPubMedGoogle Scholar
  6. Benson K, Hartz AJ (2000) A comparison of observational studies and randomized, controlled trials. N Engl J Med 342:1878–1886CrossRefPubMedGoogle Scholar
  7. Bertoldi AD, Barros AJ, Wagner A, Ross-Degnan D, Hallal PC (2008) A descriptive review of the methodologies used in household surveys on medicine utilization. BMC Health Serv Res 8:222CrossRefPubMedCentralPubMedGoogle Scholar
  8. Breslow N (1982) Design and analysis of case-control studies. Annu Rev Public Health 3:29–54CrossRefPubMedGoogle Scholar
  9. Brookhart MA, Schneeweiss S, Rothman KJ, Glynn RJ, Avorn J, Sturmer T (2006a) Variable selection for propensity score models. Am J Epidemiol 163(12):1149–1156CrossRefPubMedCentralPubMedGoogle Scholar
  10. Brookhart MA, Wang PS, Solomon DH, Schneeweiss S (2006b) Evaluating short-term drug effects using a physician-specific prescribing preference as an instrumental variable. Epidemiology 17:268–275CrossRefPubMedCentralPubMedGoogle Scholar
  11. Brown JS, Holmes JH, Shah K, Hall K, Lazarus R, Platt R (2010) Distributed health data networks: a practical and preferred approach to multi-institutional evaluations of comparative effectiveness, safety, and quality of care. Med Care 48(6 Suppl):S45–S51CrossRefPubMedGoogle Scholar
  12. Brown JS, Kahn M, Toh S (2013) Data quality assessment for comparative effectiveness research in distributed data networks. Med Care 51(8 Suppl 3):S22–S29CrossRefPubMedCentralPubMedGoogle Scholar
  13. Chung Y, Lu CY, Graham GG, Mant A, Day RO (2008) Utilization of allopurinol in the Australian community. Intern Med J 38(6):388–395CrossRefPubMedGoogle Scholar
  14. Concato J, Shah N, Horwitz RI (2000) Randomized, controlled trials, observational studies, and the hierarchy of research designs. N Engl J Med 342:1887–1892CrossRefPubMedCentralPubMedGoogle Scholar
  15. Cook NR, Cole SR, Hennekens CH (2002) Use of a marginal structural model to determine the effect of aspirin on cardiovascular mortality in the Physicians’ Health Study. Am J Epidemiol 155(11):1045–1053CrossRefPubMedGoogle Scholar
  16. Etminan M (2004) Pharmacoepidemiology II: the nested case-control study–a novel approach in pharmacoepidemiologic research. Pharmacotherapy 24:1105–1109CrossRefPubMedGoogle Scholar
  17. Farrington CP (2004) Re: “Risk analysis of aseptic meningitis after measles-mumps-rubella vaccination in Korean children by using a case-crossover design”. Am J Epidemiol 159:717–718, author reply 718–720CrossRefPubMedGoogle Scholar
  18. Farrington P, Pugh S, Colville A et al (1995) A new method for active surveillance of adverse events from diphtheria/tetanus/pertussis and measles/mumps/rubella vaccines. Lancet 345:567–569CrossRefPubMedGoogle Scholar
  19. Glynn RJ, Knight EL, Levin R, Avorn J (2001) Paradoxical relations of drug treatment with mortality in older persons. Epidemiology 12(6):682–689CrossRefPubMedGoogle Scholar
  20. Goldacre M (2001) The role of cohort studies in medical research. Pharmacoepidemiol Drug Saf 10:5–11CrossRefPubMedGoogle Scholar
  21. Gram LE, Hallas J, Andersen M (2000) Pharmacovigilance based on prescription databases. Pharmacol Toxicol 86(Suppl 1):13–15CrossRefPubMedGoogle Scholar
  22. Greenland S (2000) An introduction to instrumental variables for epidemiologists. Int J Epidemiol 29:722–729CrossRefPubMedGoogle Scholar
  23. Ho PM, Bryson CL, Rumsfeld JS (2009) Medication adherence: its importance in cardiovascular outcomes. Circulation 119(23):3028–3035CrossRefPubMedGoogle Scholar
  24. Hubbard R, Farrington P, Smith C, Smeeth L, Tattersfield A (2003) Exposure to tricyclic and selective serotonin reuptake inhibitor antidepressants and the risk of hip fracture. Am J Epidemiol 158:77–84CrossRefPubMedGoogle Scholar
  25. Johnson ES, Bartman BA, Briesacher BA et al (2012) The incident user design in comparative effectiveness research. Effective Health Care Program Research Report No. 32. Agency for Healthcare Research and Quality, Rockville, MDGoogle Scholar
  26. Kelly E, Lu CY, Albertini S, Vitry A (2014) Longitudinal trends in utilization of endocrine therapies for breast cancer: an international comparison. J Clin Pharm Ther. doi: 10.1111/jcpt.12227
  27. Kush RD, Helton E, Rockhold FW, Hardison CD (2008) Electronic health records, medical research, and the Tower of Babel. N Engl J Med 358(16):1738–1740CrossRefPubMedGoogle Scholar
  28. Lu CY (2009) Pharmacoepidemiologic research in Australia: challenges and opportunities for monitoring patients with rheumatic diseases. Clin Rheumatol 28(4):371–377CrossRefPubMedGoogle Scholar
  29. Lu CY, Williams KM, Day RO (2007a) Has the use of disease-modifying anti-rheumatic drugs changed as a consequence of controlled access to high-cost biological agents through the Pharmaceutical Benefits Scheme? Intern Med J 37(9):601–606CrossRefPubMedGoogle Scholar
  30. Lu CY, Williams KM, Day RO (2007b) The funding and use of high-cost medicines in Australia: the example of anti-rheumatic biological medicines. Aust New Zealand Health Policy 4:2CrossRefPubMedCentralPubMedGoogle Scholar
  31. Lu CY, Soumerai SB, Ross-Degnan D, Zhang F, Adams AS (2010) Unintended impacts of a Medicaid prior authorization policy on access to medications for bipolar illness. Med Care 48(1):4–9CrossRefPubMedGoogle Scholar
  32. Lu CY, Law MR, Soumerai SB et al (2011) Impact of prior authorization on the use and costs of lipid-lowering medications among Michigan and Indiana dual enrollees in Medicaid and Medicare: results of a longitudinal, population-based study. Clin Ther 33(1):135–144CrossRefPubMedCentralPubMedGoogle Scholar
  33. Lu CY, Srasuebkul P, Drew AK, Ward RL, Pearson SA (2012) Positive spillover effects of prescribing requirements: increased cardiac testing in patients treated with trastuzumab for HER2+ metastatic breast cancer. Intern Med J 42(11):1229–1235CrossRefPubMedGoogle Scholar
  34. Lu CY, Zhang F, Lakoma MD et al (2014) Changes in antidepressant use by young people and suicidal behavior after FDA warnings and media coverage: quasi-experimental study. BMJ 348:g3596CrossRefPubMedCentralPubMedGoogle Scholar
  35. Lu CY (2014) Uncertainties in real-world decisions on medical technologies. Int J Clin Pract 68(8):936–940. doi:10.1111/ijcp.12434.CrossRefPubMedGoogle Scholar
  36. Maclure M (1991) The case-crossover design: a method for studying transient effects on the risk of acute events. Am J Epidemiol 133:144–153PubMedGoogle Scholar
  37. Maclure M, Fireman B, Nelson JC et al (2012) When should case-only designs be used for safety monitoring of medical products? Pharmacoepidemiol Drug Saf 21(Suppl 1):50–61CrossRefPubMedGoogle Scholar
  38. Mamdani M, Sykora K, Li P et al (2005) Reader’s guide to critical appraisal of cohort studies: 2. Assessing potential for confounding. BMJ 330:960–962CrossRefPubMedCentralPubMedGoogle Scholar
  39. McKnight J, Scott A, Menzies D, Bourbeau J, Blais L, Lemiere C (2005) A cohort study showed that health insurance databases were accurate to distinguish chronic obstructive pulmonary disease from asthma and classify disease severity. J Clin Epidemiol 58(2):206–208CrossRefPubMedGoogle Scholar
  40. McWilliams JM, Meara E, Zaslavsky AM, Ayanian JZ (2007) Use of health services by previously uninsured Medicare beneficiaries. N Engl J Med 357(2):143–153CrossRefPubMedGoogle Scholar
  41. Morgenstern H (1995) Ecologic studies in epidemiology: concepts, principles, and methods. Annu Rev Public Health 16:61–81CrossRefPubMedGoogle Scholar
  42. Motheral BR, Fairman KA (1997) The use of claims databases for outcomes research: rationale, challenges, and strategies. Clin Ther 19(2):346–366CrossRefPubMedGoogle Scholar
  43. Normand SL, Sykora K, Li P, Mamdani M, Rochon PA, Anderson GM (2005) Readers guide to critical appraisal of cohort studies: 3. Analytical strategies to reduce confounding. BMJ 330:1021–1023CrossRefPubMedCentralPubMedGoogle Scholar
  44. Organisation for Economic Cooperation and Development (2007) OECD guidelines for quality assurance in genetic testing.
  45. Paniz VM, Fassa AG, Maia MF, Domingues MR, Bertoldi AD (2010) Measuring access to medicines: a review of quantitative methods used in household surveys. BMC Health Serv Res 10:146CrossRefPubMedCentralPubMedGoogle Scholar
  46. Perrio M, Waller PC, Shakir SA (2007) An analysis of the exclusion criteria used in observational pharmacoepidemiological studies. Pharmacoepidemiol Drug Saf 16(3):329–336CrossRefPubMedGoogle Scholar
  47. Psaty BM, Koepsell TD, Lin D et al (1999) Assessment and control for confounding by indication in observational studies. J Am Geriatr Soc 47:749–754PubMedGoogle Scholar
  48. Robins JM, Hernan MA, Brumback B (2000) Marginal structural models and causal inference in epidemiology. Epidemiology 11(5):550–560CrossRefPubMedGoogle Scholar
  49. Rochon PA, Gurwitz JH, Sykora K et al (2005) Reader’s guide to critical appraisal of cohort studies: 1. Role and design. BMJ 330:895–897CrossRefPubMedCentralPubMedGoogle Scholar
  50. Rosenbaum PR, Rubin DB (1984) Reducing bias in observational studies using subclassification on the propensity score. J Am Stat Assoc 79:516–524CrossRefGoogle Scholar
  51. Schneeweiss S (2007) Developments in post-marketing comparative effectiveness research. Clin Pharmacol Ther 82(2):143–156CrossRefPubMedGoogle Scholar
  52. Schneeweiss S, Sturmer T, Maclure M (1997) Case-crossover and case-time-control designs as alternatives in pharmacoepidemiologic research. Pharmacoepidemiol Drug Saf 6(Suppl 3):S51–S59CrossRefPubMedGoogle Scholar
  53. Schneeweiss S, Patrick AR, Sturmer T et al (2007) Increasing levels of restriction in pharmacoepidemiologic database studies of elderly and comparison with randomized trial results. Med Care 45(10 Suppl 2):S131–S142CrossRefPubMedCentralPubMedGoogle Scholar
  54. Strom BL, Carson JL, Halpern AC et al (1991) Using a claims database to investigate drug-induced Stevens-Johnson syndrome. Stat Med 10(4):565–576CrossRefPubMedGoogle Scholar
  55. United Nations Educational, Scientific and Cultural Organization (2003) International declaration on human genetic data.
  56. Vandenbroucke JP, von Elm E, Altman DG et al (2007) Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration. Epidemiology 18(6):805–835CrossRefPubMedGoogle Scholar
  57. Vitry AI, Thai LP, Lu CY (2011) Time and geographical variations in utilization of endocrine therapy for breast cancer in Australia. Intern Med J 41(2):162–166CrossRefPubMedGoogle Scholar
  58. Wettermark B (2013) The intriguing future of pharmacoepidemiology. Eur J Clin Pharmacol 69(Suppl 1):43–51CrossRefPubMedGoogle Scholar
  59. Whitaker HJ, Farrington CP, Spiessens B, Musonda P (2006) Tutorial in biostatistics: the self-controlled case series method. Stat Med 25:1768–1797CrossRefPubMedGoogle Scholar
  60. Wilchesky M, Tamblyn RM, Huang A (2004) Validation of diagnostic codes within medical services claims. J Clin Epidemiol 57(2):131–141CrossRefPubMedGoogle Scholar
  61. World Health Organisation (1993) How to investigate drug use in health facilities: selected drug use indicators – EDM Research Series No. 007. World Health Organisation, GenevaGoogle Scholar
  62. World Health Organisation (2003) Introduction to drug utilization research. World Health Organisation, GenevaGoogle Scholar
  63. World Health Organization (2009) DDD – Definition and General Considerations. Accessed 25 Feb 2014

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Population MedicineHarvard Medical School and Harvard Pilgrim Health Care InstituteBostonUSA

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