Drug Safety

, Volume 25, Issue 6, pp 453–458 | Cite as

Use of Measures of Disproportionality in Pharmacovigilance

Three Dutch Examples
  • Antoine C.G. Egberts
  • Ronald H.B. Meyboom
  • Eugène P. van Puijenbroek
Short Communication

Abstract

Spontaneous reporting systems for suspected adverse drug reactions (ADRs) remain a cornerstone of pharmacovigilance. In The Netherlands ‘the Netherlands Pharmacovigilance Foundation Lareb’ maintains such a system. A primary aim in pharmacovigilance is the timely detection of either new ADRs or a change of the frequency of ADRs that are already known to be associated with the drugs involved, i.e. signal detection. Adequate signal detection solely based on the human intellect (case by case analysis or qualitative signal detection) is becoming time consuming given the increasingly large number of data, as well as less effective, especially in more complex associations such as drug-drug interactions, syndromes and when various covariates are involved. In quantitative signal detection measures that express the extent in which combinations of drug(s) and clinical event(s) are disproportionately present in the database of reported suspected ADRs are used to reveal associations of interest. Although the rationale and the methodology of the various quantitative approaches differ, they all share the characteristic that they express to what extent the number of observed cases differs from the number of expected cases.

In this paper three Dutch examples are described in which a measure of disproportionality is used in quantitative signal detection in pharmacovigilance: (i) the association between antidepressant drugs and the occurrence of non-puerpural lactation as an example of an association between a single drug and a single event; (ii) the onset or worsening of congestive heart failure associated with the combined use of nonsteroidal anti-inflammatory drugs and diuretics as an example of an association between two drugs and a single event (drug-drug interaction); and (iii) the (co)-occurrence of fever, urticaria and arthralgia and the use of terbinafine as an example of an association between a single drug and multiple events (syndrome).

We conclude that the use of quantitative measures in addition to qualitative analysis is a step forward in signal detection in pharmacovigilance. More research is necessary into the performance of these approaches, especially its predictive value, its robustness as well as into further extensions of the methodology.

Notes

Acknowledgements

The authors had no external funding for the preparation of this article, nor was there any conflict of interest relevant to the content.

References

  1. 1.
    Meyboom RHB, Egberts ACG, Edwards IR, et al. Principles of signal detection in pharmacovigilance. Drug Saf 1997; 16: 355–65PubMedCrossRefGoogle Scholar
  2. 2.
    Edwards IR, Aronson JK. Adverse drug reactions: definitions, diagnosis, and management. Lancet 2000; 356: 1255–9PubMedCrossRefGoogle Scholar
  3. 3.
    Finney DJ. The design and logic of a monitor of drug use. J Chroniv Dis 1964; 18: 77–98CrossRefGoogle Scholar
  4. 4.
    Finney DJ. Statisical logic in the monitoring of reactions to therapeutic drugs. Methods Inf Med 1971; 10: 237–45PubMedGoogle Scholar
  5. 5.
    Stricker BHCh, Tijssen JGP. Serum sickness-like reactions to cefaclor. J Clin Epidemiol 1992; 45: 1177–84PubMedCrossRefGoogle Scholar
  6. 6.
    Evans SJW, Waller P, Davis S. Proportional reporting ratios: the uses of epidemiological methods for signal generation [abstract]. Pharmacoepidemiol Drug Saf 1998; 7Suppl. 2: S102Google Scholar
  7. 7.
    Egberts ACG, Van der Hofstede JW, Meyboom RHB, et al. Transformation of a database of spontaneously reported suspected adverse drug reactions and its use as a tool in signal detection. In: Egberts ACG, editor. Pharmacoepidemiologic approaches to the evaluation of antidepressant drugs [thesis]. Utrecht University: 1997: 4Google Scholar
  8. 8.
    Evans SJW. Pharmacovigilance: a science or fielding emergencies? Stat Med 2000; 19: 3199–209PubMedCrossRefGoogle Scholar
  9. 9.
    Moore N, Kreft-Jais C, Haramburu F, et al. Reports of hypoglycaemia associated with the use of ACE inhibitors and other drugs: a case/non-case study in the French pharmacovigilance system database. Br J Clin Pharmacol 1997; 44: 513–8PubMedCrossRefGoogle Scholar
  10. 10.
    Tubert P, Begaud B, Pere JC, et al. Power and weakness of spontaneous reporting: a probabilistic approach. J Clin Epidemiol 1992; 45: 283–6PubMedCrossRefGoogle Scholar
  11. 11.
    Hayes WL. Statistics 4th ed. Fort Worth: Holt, Rinehart & Winston, 1988: 3.18, The Poisson distribution, 144–6Google Scholar
  12. 12.
    Bate A, Lindquist M, Edwards IR, et al. A Bayesian neural network method for adverse drug reaction signal generation. Eur J Clin Pharmacol 1998; 54: 315–21PubMedCrossRefGoogle Scholar
  13. 13.
    Lindquist M, Ståhl M, Bate A, et al. From association to alert: a revised approach to international signal analysis. Pharmacoepidemiol Drug Saf 1999; 8: S15–25PubMedCrossRefGoogle Scholar
  14. 14.
    Lindquist M, Ståhl M, Bate A, et al. A retrospective evaluation of a data mining approach to aid finding new adverse drug reaction signals in the WHO international database. Drug Saf 2000; 23: 533–42PubMedCrossRefGoogle Scholar
  15. 15.
    DuMouchel W. Bayesian data mining in large frequency tables, with an application to the FDA spontaneous reporting system. Am Statistician 1999; 53: 177–89Google Scholar
  16. 16.
    Egberts ACG, Meyboom RHB, de Koning GHP, et al. Non-puerperal lactation associated with antidepressant drug use. Br J Clin Pharmacol 1997; 44: 277–81PubMedCrossRefGoogle Scholar
  17. 17.
    van Puijenbroek EP, Egberts ACG, Meyboom RHB, et al. Signalling possible drug-drug interactions in a spontaneous reporting system: delay of withdrawal bleeding during concomitant use of oral contraceptives and itraconazole. Br J Clin Pharmacol 1999; 47: 689–93PubMedCrossRefGoogle Scholar
  18. 18.
    van Puijenbroek EP, Egberts ACG, Meyboom RHB, et al. Association between terbinafine and arthralgia, fever and urticaria: symptoms or syndrome? Pharmacoepidemiol Drug Saf 2001; 10: 135–42PubMedCrossRefGoogle Scholar
  19. 19.
    Meyboom RHB, Hekster YA, Egberts ACG, et al. Causal or casual: the role of causality assessment in pharmacovigilance. Drug Saf 1997; 17: 374–89PubMedCrossRefGoogle Scholar
  20. 20.
    de Bruin ML, van Puijenbroek EP, Egberts ACG, et al. Non-sedating antihistamine drugs and cardiac arrhytmias: biased risk estimates from spontaneous reporting systems? Br J Clin Pharmacol 2002; 53: 370–4PubMedCrossRefGoogle Scholar
  21. 21.
    van Puijenbroek EP, Bate A, Leufkens HGM, et al. A comparison of measures of disproportionality for signal detection in spontaneous reporting systems for adverse drug reactions. Pharmacoepidemiol Drug Saf 2002; 11: 3–10PubMedCrossRefGoogle Scholar

Copyright information

© Adis International Limited 2002

Authors and Affiliations

  • Antoine C.G. Egberts
    • 1
    • 2
  • Ronald H.B. Meyboom
    • 1
    • 3
    • 4
  • Eugène P. van Puijenbroek
    • 3
  1. 1.Department of Pharmacoepidemiology and PharmacotherapyUtrecht Institute for Pharmaceutical SciencesUtrechtThe Netherlands
  2. 2.Hospital Pharmacy Midden-Brabant, TweeSteden Hospital and St Elisabeth HospitalTilburgThe Netherlands
  3. 3.Netherlands Pharmacovigilance Foundation Lareb’s-HertogenboschThe Netherlands
  4. 4.The Uppsala Monitoring CentreUppsalaSweden

Personalised recommendations