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Review
. 2019 Mar 11;19(1):53.
doi: 10.1186/s12874-019-0695-y.

Bias in pharmacoepidemiologic studies using secondary health care databases: a scoping review

Guillermo Prada-Ramallal  1   2 , Bahi Takkouche  1   2   3 , Adolfo Figueiras  4   5   6
Affiliations
Review

Bias in pharmacoepidemiologic studies using secondary health care databases: a scoping review

Guillermo Prada-Ramallal et al. BMC Med Res Methodol. .

Abstract

Background: The availability of clinical and therapeutic data drawn from medical records and administrative databases has entailed new opportunities for clinical and epidemiologic research. However, these databases present inherent limitations which may render them prone to new biases. We aimed to conduct a structured review of biases specific to observational clinical studies based on secondary databases, and to propose strategies for the mitigation of those biases.

Methods: Scoping review of the scientific literature published during the period 2000-2018 through an automated search of MEDLINE, EMBASE and Web of Science, supplemented with manually cross-checking of reference lists. We included opinion essays, methodological reviews, analyses or simulation studies, as well as letters to the editor or retractions, the principal objective of which was to highlight the existence of some type of bias in pharmacoepidemiologic studies using secondary databases.

Results: A total of 117 articles were included. An increasing trend in the number of publications concerning the potential limitations of secondary databases was observed over time and across medical research disciplines. Confounding was the most reported category of bias (63.2% of articles), followed by selection and measurement biases (47.0% and 46.2% respectively). Confounding by indication (32.5%), unmeasured/residual confounding (28.2%), outcome misclassification (28.2%) and "immortal time" bias (25.6%) were the subcategories most frequently mentioned.

Conclusions: Suboptimal use of secondary databases in pharmacoepidemiologic studies has introduced biases in the studies, which may have led to erroneous conclusions. Methods to mitigate biases are available and must be considered in the design, analysis and interpretation phases of studies using these data sources.

Keywords: Administrative claims; Bias; Confounding factors; Electronic health records; Medical record linkage; Medical records; Observational studies; Pharmacoepidemiology.

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The authors declare that they have no competing interests.

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Figures

Fig. 1
Fig. 1
Flow chart of the article selection process. * Subgroup 1: Its principal objective was to describe, compare, evaluate, validate or develop a bias-control strategy for a known bias or limitation. † Subgroup 2: Estimated a measurement or identified risk factors for a disease, with the existence of bias being mentioned as a limitation of the study, regardless of whether or not strategies for its control were used. ‡ Subgroup 3: Had characteristics different from those indicated above or was a conference paper with no abstract/full-text available
Fig. 2
Fig. 2
Publication timeline of the 117 articles included in the review (left Y axis) and the 863 references identified through the automated search (right Y axis) unadjusted and adjusted by the number of indexed citations added to MEDLINE
Fig. 3
Fig. 3
a Distribution of included articles across medical disciplines. b Timeline of included articles by most prevalent indexed disciplines
Fig. 4
Fig. 4
Frequency of the biases mentioned in the included articles stratified by time periods
See this image and copyright information in PMC

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