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. 2016 Jul;82(1):17-29.
doi: 10.1111/bcp.12944. Epub 2016 May 9.

Adverse drug event reporting systems: a systematic review

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Adverse drug event reporting systems: a systematic review

Chantelle Bailey et al. Br J Clin Pharmacol. 2016 Jul.

Abstract

Aim: Adverse drug events (ADEs) are harmful and unintended consequences of medications. Their reporting is essential for drug safety monitoring and research, but it has not been standardized internationally. Our aim was to synthesize information about the type and variety of data collected within ADE reporting systems.

Methods: We developed a systematic search strategy, applied it to four electronic databases, and completed an electronic grey literature search. Two authors reviewed titles and abstracts, and all eligible full-texts. We extracted data using a standardized form, and discussed disagreements until reaching consensus. We synthesized data by collapsing data elements, eliminating duplicate fields and identifying relationships between reporting concepts and data fields using visual analysis software.

Results: We identified 108 ADE reporting systems containing 1782 unique data fields. We mapped them to 33 reporting concepts describing patient information, the ADE, concomitant and suspect drugs, and the reporter. While reporting concepts were fairly consistent, we found variability in data fields and corresponding response options. Few systems clarified the terminology used, and many used multiple drug and disease dictionaries such as the Medical Dictionary for Regulatory Activities (MedDRA).

Conclusion: We found substantial variability in the data fields used to report ADEs, limiting the comparability of ADE data collected using different reporting systems, and undermining efforts to aggregate data across cohorts. The development of a common standardized data set that can be evaluated with regard to data quality, comparability and reporting rates is likely to optimize ADE data and drug safety surveillance.

Keywords: adverse drug events; data elements; medicines; pharmacovigilance; reporting systems; systematic review.

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Figures

Figure 1
Figure 1
Flow diagram of included studies and systems
Figure 2
Figure 2
Iterations of data fields and reporting concepts sorted using visual thinking software: (a) Example of raw data prior to being sorted. (b) STEP 1: Example of sorting into reporting concepts. (c) STEP 2: Elimination of duplicate fields. (d) STEP 3: Identification of relationships between concepts
Figure 3
Figure 3
Quality assessment scores of reporting systems using QA scales proposed by WHO – UMC and Bandekar
Figure 4
Figure 4
Example of relationships and hierarchies within one reporting concept

References

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