European Journal of Epidemiology

, Volume 33, Issue 6, pp 545–555 | Cite as

Hypothesis-free screening of large administrative databases for unsuspected drug-outcome associations

  • Jesper Hallas
  • Shirley V. Wang
  • Joshua J. Gagne
  • Sebastian Schneeweiss
  • Nicole Pratt
  • Anton Pottegård


Active surveillance for unknown or unsuspected adverse drug effects may be carried out by applying epidemiological techniques to large administrative databases. Self-controlled designs, like the symmetry design, have the advantage over conventional design of adjusting for confounders that are stable over time. The aim of this paper was to describe the output of a comprehensive open-ended symmetry analysis of a large dataset. All drug dispensings and all secondary care contacts in Denmark during the period 1995–2012 for persons born before 1950 were analyzed by a symmetry design. We analyzed all drug–drug sequences and all drug–disease sequences occurring during the study period. The identified associations were ranked according to the number of outcomes that potentially could be attributed to the exposure. In the main analysis, 29,891,212 incident drug therapies, and 21,300,000 incident diagnoses were included. Out of 186,758 associations tested in the main analysis, 43,575 (23.3%) showed meaningful effect size. For the top 200 drug–drug associations, 47% represented unknown associations, 24% represented known adverse drug reactions, 30% were explained by mutual indication or reverse causation. For the top 200 drug–disease associations the proportions were 31, 15, and 55%, respectively. Screening by symmetry analysis can be a useful starting point for systematic pharmacovigilance activities if coupled with a systematic post-hoc review of signals.


Pharmacovigillance Pharmcoepidemiology Self-controlled design Databases Screening 



Funded by Clinical Pharmacology and Pharmacy, University of Southern Denmark, Denmark and Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women’s Hospital, Harvard Medical School, MA, USA.

Author contribution

JH: Conceived the study, analyzed the data and wrote first draft. SVW, JJG, SS: Conceived the study, provided input to analysis and report. Nicole Pratt: Provided input to analysis and report. Anton Pottegård: Conceived the study, provided input to the report.

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.


  1. 1.
    Wysowski DK, Swartz L. Adverse drug event surveillance and drug withdrawals in the United States, 1969–2002: the importance of reporting suspected reactions. Arch Intern Med. 2005;165:1363–9.CrossRefPubMedGoogle Scholar
  2. 2.
    Bakke OM, Manocchia M, de Abajo F, Kaitin KI, Lasagna L. Drug safety discontinuations in the United Kingdom, the United States, and Spain from 1974 through 1993: a regulatory perspective. Clin Pharmacol Ther. 1995;58:108–17.CrossRefPubMedGoogle Scholar
  3. 3.
    Waller PC. Making the most of spontaneous adverse drug reaction reporting. Basic Clin Pharmacol Toxicol. 2006;98:320–3.CrossRefPubMedGoogle Scholar
  4. 4.
    Moride Y, Haramburu F, Requejo AA, Bégaud B. Under-reporting of adverse drug reactions in general practice. Br J Clin Pharmacol. 1997;43:177–81.CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Gaist D, Andersen M, Schou JS. Spontaneous reports of drug-induced erythema multiforme, Stevens–Johnson syndrome and toxic epidermal necrolysis in Denmark 1968–1991. Pharmacoepidemiol Drug Saf. 1996;5:79–86.CrossRefPubMedGoogle Scholar
  6. 6.
    Alvarez-Requejo A, Carvajal A, Bégaud B, Moride Y, Vega T, Arias LH. Under-reporting of adverse drug reactions. Estimate based on a spontaneous reporting scheme and a sentinel system. Eur J Clin Pharmacol. 1998;54:483–8.CrossRefPubMedGoogle Scholar
  7. 7.
    McCormick TH, Ferrell R, Karr AF, Ryan PB. Big data, big results: knowledge discovery in output from large-scale analytics. Stat Anal Data Min. 2014;7:404–12.CrossRefGoogle Scholar
  8. 8.
    Hallas J, Pottegård A. Use of self-controlled designs in pharmacoepidemiology. J Intern Med. 2014;275:581–9.CrossRefPubMedGoogle Scholar
  9. 9.
    Hallas J. Evidence of depression provoked by cardiovascular medication: a prescription sequence symmetry analysis. Epidemiology. 1996;7:478–84.CrossRefPubMedGoogle Scholar
  10. 10.
    Wahab IA, Pratt NL, Wiese MD, Kalisch LM, Roughead EE. The validity of sequence symmetry analysis (SSA) for adverse drug reaction signal detection. Pharmacoepidemiol Drug Saf. 2013;22:496–502.CrossRefPubMedGoogle Scholar
  11. 11.
    Wahab IA, Pratt NL, Ellett LK, Roughead EE. Sequence symmetry analysis as a signal detection tool for potential heart failure adverse events in an administrative claims database. Drug Saf. 2016;39:347–54.CrossRefPubMedGoogle Scholar
  12. 12.
    Pottegård A, Schmidt SAJ, Wallach-Kildemoes H, Sørensen HT, Hallas J, Schmidt M. Data resource profile: The Danish National Prescription Registry. Int J Epidemiol. 2016;46:798.PubMedCentralGoogle Scholar
  13. 13.
    Schmidt M, Schmidt SAJ, Sandegaard JL, Ehrenstein V, Pedersen L, Sørensen HT. The Danish National Patient Registry: a review of content, data quality, and research potential. Clin Epidemiol. 2015;7:449–90.CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Schmidt M, Pedersen L, Sørensen HT. The Danish Civil Registration System as a tool in epidemiology. Eur J Epidemiol. 2014;29:541–9.CrossRefPubMedGoogle Scholar
  15. 15.
    Thygesen LC, Daasnes C, Thaulow I, Brønnum-Hansen H. Introduction to Danish (nationwide) registers on health and social issues: structure, access, legislation, and archiving. Scand J Public Health. 2011;39:12–6.CrossRefPubMedGoogle Scholar
  16. 16.
    Pratt NL, Ilomäki J, Raymond C, Roughead EE. The performance of sequence symmetry analysis as a tool for post-market surveillance of newly marketed medicines: a simulation study. BMC Med Res Methodol. 2014;14:66.CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Tsiropoulos I, Andersen M, Hallas J. Adverse events with use of antiepileptic drugs: a prescription and event symmetry analysis. Pharmacoepidemiol Drug Saf. 2009;18:483–91.CrossRefPubMedGoogle Scholar
  18. 18.
    Rothman KJ. Six persistent research misconceptions. J Gen Intern Med. 2014;29:1060–4.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Morris JA, Gardner MJ. Calculating confidence intervals for relative risks, odds ratios, and standardised ratios and rates. In: Gardner MJ, Altman DG, editors. Statistics with confidence. London: British Medical Journal Publishing; 1989. p. 60–1.Google Scholar
  20. 20.
    Cole DV, Kulldorff M, Baker M, et al. Infrastructure for evaluation of statistical alerts arising from vaccine safety data mining activities in mini-sentinel.
  21. 21.
    Gruber S, Chakravarty A, Heckbert SR, Levenson M, Martin D, Nelson JC, et al. Design and analysis choices for safety surveillance evaluations need to be tuned to the specifics of the hypothesized drug-outcome association. Pharmacoepidemiol Drug Saf. 2016;25:973–81.CrossRefPubMedGoogle Scholar
  22. 22.
    Bytzer P, Hallas J. Drug-induced symptoms of functional dyspepsia and nausea. A symmetry analysis of one million prescriptions. Aliment Pharmacol Ther. 2000;14:1479–84.CrossRefPubMedGoogle Scholar
  23. 23.
    Lorberbaum T, Nasir M, Keiser MJ, Vilar S, Hripcsak G, Tatonetti NP. Systems pharmacology augments drug safety surveillance. Clin Pharmacol Ther. 2015;97:151–8.CrossRefPubMedGoogle Scholar
  24. 24.
    Lai ECC, Pottegård A, Lin SJ, Hsieh C-Y, Hallas J, Yang Y-H. Antiepileptic drugs and risk of bacterial infections: a cross-national symmetry analysis from Denmark and Taiwan (submitted).Google Scholar
  25. 25.
    Harris DG. Management of pain in advanced disease. Br Med Bull. 2014;110:117–28.CrossRefPubMedGoogle Scholar
  26. 26.
    Rasmussen L, Hallas J, Madsen KG, Pottegård A. Cardiovascular drugs and erectile dysfunction—a symmetry analysis. Br J Clin Pharmacol. 2015;80:1219–23.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Division of Pharmacoepidemiology and Pharmacoeconomics, Department of MedicineBrigham and Women’s Hospital and Harvard Medical SchoolBostonUSA
  2. 2.Clinical Pharmacology and Pharmacy, Department of Public HealthUniversity of Southern DenmarkOdense CDenmark
  3. 3.Quality Use of Medicines and Pharmacy Research Centre, Sansom InstituteUniversity of South AustraliaAdelaideAustralia

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