Evaluation of the quality and value of data sources for postmarket surveillance of the safety of cough and cold medications in children

Jody L. Green, Kate M. Reynolds, William Banner, G. Randall Bond, Ralph E. Kauffman, Robert B. Palmer, Ian M. Paul, Richard C. Dart

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Background: The purpose of this report is to evaluate the quality of data sources used to study cough and cold medication (CCM) safety in children via the Pediatric Cough and Cold Safety Surveillance System. Methods: The System utilized the National Poison Data System (NPDS), FDA Adverse Event Reporting System (FAERS), English-language medical literature, manufacturer postmarket safety databases, and news/media reports to identify cases from January 2008 through September 2016. Each data source was evaluated by the proportion of detected cases determined to be eligible (met case criteria) and the proportion determined to be evaluable (able to determine causal relationship between adverse event and exposure). Results: A total of 7184 unique cases were identified from 27,597 detected reports. Of these, 6447 (89.7%) were evaluable. The data source with the highest volume of detected cases was news/media; however, only 0.3% of those cases were eligible for panel review and only 0.2% (24 out of 13,450 cases) were evaluable. The data source with the highest proportion of eligible and evaluable cases was NPDS with 7691 detected cases, 6113 (79.5%) eligible cases, and 5587 (72.6%) evaluable cases. Conclusions: The data sources utilized to evaluate the safety profile of pediatric CCMs yielded variable detection and evaluation rates, but overall provided a comprehensive look at exposures that otherwise cannot be studied in clinical trials. While this study suggests that each source made a valuable contribution and that evaluable cases are generalizable, improvements are needed in case completeness and accuracy to enhance the quality of postmarket safety evaluations.

Original languageEnglish (US)
Article number175
JournalBMC Medical Research Methodology
Volume18
Issue number1
DOIs
StatePublished - Dec 22 2018

All Science Journal Classification (ASJC) codes

  • Epidemiology
  • Health Informatics

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