European Journal of Clinical Pharmacology

, Volume 68, Issue 2, pp 123–129 | Cite as

Utilization of health care databases for pharmacoepidemiology

  • Yasuo Takahashi
  • Yayoi Nishida
  • Satoshi AsaiEmail author
Review Article


Pharmacoepidemiology is the study of the utilization and effects of drugs in clinical and population settings, and the outcomes of drug therapy. The growing trend of recording computerized data that will increasingly be automated into health care delivery is making the use of large datasets more and more common in pharmacoepidemiologic research. Most retrospective databases offer large populations and longer observation periods with real-world practice and can answer a variety of research questions quickly and cost-effectively. Observational studies, specifically using large databases, can complement findings from randomized clinical trials (RCTs) by assessing treatment effectiveness in patients encountered in daily clinical practice, although they are more exposed to bias and certainly are lower on the hierarchy of evidence than RCTs. Furthermore, careful defining of the research question with appropriate design and application of advanced statistical techniques, e.g., propensity-score analysis or marginal structural models, can yield findings with validity and improve causal inference of treatment effects. Some existing guidelines for comparative effectiveness help decision makers to evaluate the quality of observational studies comparing the effectiveness of various medical products and services. Thus, the trend for utilization of databases for pharmacoepidemiology will continue to grow in coming years.


Pharmacoepidemiology Health care database Observational database study Propensity score analysis Comparative effectiveness 


  1. 1.
    Guidelines for good pharmacoepidemiology practices (GPP) (2008) Pharmacoepidemiol Drug Saf 17 (2):200–208Google Scholar
  2. 2.
    Silverman SL (2009) From randomized controlled trials to observational studies. Am J Med 122(2):114–120PubMedCrossRefGoogle Scholar
  3. 3.
    Wong IC, Murray ML (2005) The potential of UK clinical databases in enhancing paediatric medication research. Br J Clin Pharmacol 59(6):750–755PubMedCrossRefGoogle Scholar
  4. 4.
    Dreyer NA, Schneeweiss S, McNeil BJ, Berger ML, Walker AM, Ollendorf DA, Gliklich RE (2010) GRACE principles: recognizing high-quality observational studies of comparative effectiveness. Am J Manag Care 16(6):467–471PubMedGoogle Scholar
  5. 5.
    Trojano M, Pellegrini F, Paolicelli D, Fuiani A, Di Renzo V (2009) Observational studies: propensity score analysis of non-randomized data. Int MS J 16(3):90–97PubMedGoogle Scholar
  6. 6.
    Concato J, Shah N, Horwitz RI (2000) Randomized, controlled trials, observational studies, and the hierarchy of research designs. N Engl J Med 342(25):1887–1892PubMedCrossRefGoogle Scholar
  7. 7.
    Berger ML, Mamdani M, Atkins D, Johnson ML (2009) Good research practices for comparative effectiveness research: defining, reporting and interpreting nonrandomized studies of treatment effects using secondary data sources: the ISPOR Good Research Practices for Retrospective Database Analysis Task Force Report—part I. Value Health 12(8):1044–1052PubMedCrossRefGoogle Scholar
  8. 8.
    Cox E, Martin BC, Van Staa T, Garbe E, Siebert U, Johnson ML (2009) Good research practices for comparative effectiveness research: approaches to mitigate bias and confounding in the design of nonrandomized studies of treatment effects using secondary data sources: the International Society for Pharmacoeconomics and Outcomes Research Good Research Practices for Retrospective Database Analysis Task Force Report—part I. Value Health 12(8):1053–1061PubMedCrossRefGoogle Scholar
  9. 9.
    Johnson ML, Crown W, Martin BC, Dormuth CR, Siebert U (2009) Good research practices for comparative effectiveness research: analytic methods to improve causal inference from nonrandomized studies of treatment effects using secondary data sources: the ISPOR Good Research Practices for Retrospective Database Analysis Task Force Report—part II. Value Health 12(8):1062–1073PubMedCrossRefGoogle Scholar
  10. 10.
    Schneeweiss S, Avorn J (2005) A review of uses of health care utilization databases for epidemiologic research on therapeutics. J Clin Epidemiol 58(4):323–337PubMedCrossRefGoogle Scholar
  11. 11.
    Hennessy S (2006) Use of health care databases in pharmacoepidemiology. Basic Clin Pharmacol Toxicol 98(3):311–313PubMedCrossRefGoogle Scholar
  12. 12.
    Strom BL, Carson JL, Halpern AC, Schinnar R, Snyder ES, Stolley PD, Shaw M, Tilson HH, Joseph M, Dai WS et al (1991) Using a claims database to investigate drug-induced Stevens-Johnson syndrome. Stat Med 10(4):565–576PubMedCrossRefGoogle Scholar
  13. 13.
    Freedman AN, Sansbury LB, Figg WD, Potosky AL, Weiss Smith SR, Khoury MJ, Nelson SA, Weinshilboum RM, Ratain MJ, McLeod HL, Epstein RS, Ginsburg GS, Schilsky RL, Liu G, Flockhart DA, Ulrich CM, Davis RL, Lesko LJ, Zineh I, Randhawa G, Ambrosone CB, Relling MV, Rothman N, Xie H, Spitz MR, Ballard-Barbash R, Doroshow JH, Minasian LM (2010) Cancer pharmacogenomics and pharmacoepidemiology: setting a research agenda to accelerate translation. J Natl Cancer Inst 102:1–8Google Scholar
  14. 14.
    Arnold RG, Kotsanos JG (1999) Panel 3: methodological issues in conducting pharmacoeconomic evaluations—retrospective and claims database studies. Value Health 2(2):82–87PubMedCrossRefGoogle Scholar
  15. 15.
    Sorensen HT, Sabroe S, Olsen J (1996) A framework for evaluation of secondary data sources for epidemiological research. Int J Epidemiol 25(2):435–442PubMedCrossRefGoogle Scholar
  16. 16.
    Motheral B, Brooks J, Clark MA, Crown WH, Davey P, Hutchins D, Martin BC, Stang P (2003) A checklist for retrospective database studies—report of the ISPOR Task Force on Retrospective Databases. Value Health 6(2):90–97PubMedCrossRefGoogle Scholar
  17. 17.
    Iezzoni LI (1997) Assessing quality using administrative data. Ann Intern Med 127(8 Pt 2):666–674PubMedGoogle Scholar
  18. 18.
    Glynn RJ, Schneeweiss S, Sturmer T (2006) Indications for propensity scores and review of their use in pharmacoepidemiology. Basic Clin Pharmacol Toxicol 98(3):253–259PubMedCrossRefGoogle Scholar
  19. 19.
    D'Agostino RB Jr (1998) Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Stat Med 17(19):2265–2281PubMedCrossRefGoogle Scholar
  20. 20.
    Rubin DB (1997) Estimating causal effects from large data sets using propensity scores. Ann Intern Med 127(8 Pt 2):757–763PubMedGoogle Scholar
  21. 21.
    Weitzen S, Lapane KL, Toledano AY, Hume AL, Mor V (2004) Principles for modeling propensity scores in medical research: a systematic literature review. Pharmacoepidemiol Drug Saf 13(12):841–853PubMedCrossRefGoogle Scholar
  22. 22.
    Shah BR, Laupacis A, Hux JE, Austin PC (2005) Propensity score methods gave similar results to traditional regression modeling in observational studies: a systematic review. J Clin Epidemiol 58(6):550–559PubMedCrossRefGoogle Scholar
  23. 23.
    McWilliams JM, Meara E, Zaslavsky AM, Ayanian JZ (2007) Use of health services by previously uninsured Medicare beneficiaries. N Engl J Med 357(2):143–153PubMedCrossRefGoogle Scholar
  24. 24.
    Fu AZ, Liu GG, Christensen DB, Hansen RA (2007) Effect of second-generation antidepressants on mania- and depression-related visits in adults with bipolar disorder: a retrospective study. Value Health 10(2):128–136PubMedCrossRefGoogle Scholar
  25. 25.
    Linden A, Adams JL (2010) Evaluating health management programmes over time: application of propensity score-based weighting to longitudinal data. J Eval Clin Pract 16(1):180–185PubMedCrossRefGoogle Scholar
  26. 26.
    Leslie S, Thiebaud P (2007) Using propensity score to adjust for treatment selection bias. SAS global forum 2007 paper 184. Accessed 16 March 2011
  27. 27.
    Patel BV, Leslie RS, Thiebaud P, Nichol MB, Tang SS, Solomon H, Honda D, Foody JM (2008) Adherence with single-pill amlodipine/atorvastatin vs a two-pill regimen. Vasc Health Risk Manag 4(3):673–681PubMedGoogle Scholar
  28. 28.
    Nishida Y, Takahashi Y, Nakayama T, Soma M, Kitamura N, Asai S (2010) Effect of candesartan monotherapy on lipid metabolism in patients with hypertension: a retrospective longitudinal survey using data from electronic medical records. Cardiovasc Diabetol 9:38PubMedCrossRefGoogle Scholar
  29. 29.
    Kitamura N, Takahashi Y, Yamadate S, Asai S (2007) Angiotensin II receptor blockers decreased blood glucose levels: a longitudinal survey using data from electronic medical records. Cardiovasc Diabetol 6:26PubMedCrossRefGoogle Scholar
  30. 30.
    Schneeweiss S (2007) Developments in post-marketing comparative effectiveness research. Clin Pharmacol Ther 82(2):143–156PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Yasuo Takahashi
    • 1
    • 2
  • Yayoi Nishida
    • 1
    • 2
  • Satoshi Asai
    • 1
    • 2
    Email author
  1. 1.Division of Genomic Epidemiology and Clinical Trials, Advanced Medical Research CenterNihon University School of MedicineTokyoJapan
  2. 2.Division of Clinical Trial Management, Advanced Medical Research CenterNihon University School of MedicineTokyoJapan

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