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Methodological challenges when evaluating potential off-label prescribing of drugs using electronic health care
Miguel Cainzos-Achirica1, Cristina Varas-Lorenzo1, Anton Pottegård2
1Epidemiology, RTI Health Solutions, Barcelona, Spain.
Purpose:
To report and discuss estimated prevalence of potential off-label use and associated methodological challenges using a case study of dabigatran.
Methods:
Observational, cross-sectional study using 3 databases with different types of clinical information available: Cegedim Strategic Data Longitudinal Patient Database (CSD-LPD), France (cardiologist panel, n = 1706; general practitioner panel, n = 2813; primary care data); National Health Databases, Denmark (n = 28 619; hospital episodes and dispensed ambulatory medications); and Clinical Practice Research Datalink (CPRD), UK (linkable to Hospital Episode Statistics [HES], n = 2150; not linkable, n = 1285; primary care data plus hospital data for HES-linkable patients).
Study Period:
August 2011 to August 2015. Two definitions were used to estimate potential off-label use: a broad definition of on-label prescribing using codes for disease indication (eg, atrial fibrillation [AF]), and a restrictive definition excluding patients with conditions for which dabigatran is not indicated (eg, valvular AF).
Results:
Prevalence estimates under the broad definition ranged from 5.7% (CPRD-HES) to 34.0% (CSD-LPD) and, under the restrictive definition, from 17.4% (CPRD-HES) to 44.1% (CSD-LPD). For the majority of potential off-label users, no diagnosis potentially related to anticoagulant use was identified. Key methodological challenges were the limited availability of detailed clinical information, likely leading to overestimation of off-label use, and differences in the information available, which may explain the disparate prevalence estimates across data sources.
Conclusions:
Estimates of potential off-label use should be interpreted cautiously due to limitations in available information. In this context, CPRD HES-linkable estimates are likely to be the most accurate.
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