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

*Kontaktforfatter for dette arbejde

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Resumé

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.

OriginalsprogEngelsk
TidsskriftEuropean Journal of Epidemiology
Vol/bind33
Udgave nummer6
Sider (fra-til)545–555
ISSN0393-2990
DOI
StatusUdgivet - 2018

Fingeraftryk

Pharmaceutical Databases
Pharmacovigilance
Denmark
Drug-Related Side Effects and Adverse Reactions
Causality
Pharmaceutical Preparations
Databases
Datasets

Citer dette

Hallas, Jesper ; Wang, Shirley V. ; Gagne, Joshua J. ; Schneeweiss, Sebastian ; Pratt, Nicole ; Pottegård, Anton. / Hypothesis-free screening of large administrative databases for unsuspected drug-outcome associations. I: European Journal of Epidemiology. 2018 ; Bind 33, Nr. 6. s. 545–555.
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abstract = "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.",
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author = "Jesper Hallas and Wang, {Shirley V.} and Gagne, {Joshua J.} and Sebastian Schneeweiss and Nicole Pratt and Anton Potteg{\aa}rd",
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Hypothesis-free screening of large administrative databases for unsuspected drug-outcome associations. / Hallas, Jesper; Wang, Shirley V.; Gagne, Joshua J.; Schneeweiss, Sebastian; Pratt, Nicole; Pottegård, Anton.

I: European Journal of Epidemiology, Bind 33, Nr. 6, 2018, s. 545–555.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - Hypothesis-free screening of large administrative databases for unsuspected drug-outcome associations

AU - Hallas, Jesper

AU - Wang, Shirley V.

AU - Gagne, Joshua J.

AU - Schneeweiss, Sebastian

AU - Pratt, Nicole

AU - Pottegård, Anton

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

KW - Databases

KW - Pharmacovigillance

KW - Pharmcoepidemiology

KW - Screening

KW - Self-controlled design

U2 - 10.1007/s10654-018-0386-8

DO - 10.1007/s10654-018-0386-8

M3 - Journal article

C2 - 29605890

AN - SCOPUS:85044606754

VL - 33

SP - 545

EP - 555

JO - European Journal of Epidemiology

JF - European Journal of Epidemiology

SN - 0393-2990

IS - 6

ER -