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Sensitivity and specificity of administrative mortality data for identifying prescription opioid–related deaths

Gladstone E, Smolina K, Morgan SG, Fernandes KA, Martins D, Gomes T. CMAJ. 2016; 188(4):E67-72. Epub 2015 Nov 30.

Background — Comprehensive systems for surveillance of prescription opioid-related harms provide clear evidence that deaths from prescription opioids have increased dramatically in the United States. However, prescription opioid-related harms in Canada are not systematically monitored. In light of this growing public health crisis, accessible, nation-wide data sources to examine prescription opioid-related harms in Canada are desperately needed. We examine the performance of several algorithms to identify prescription opioid-related deaths in Vital Statistics data against a gold standard.

Methods — We used data abstracted from the Office of the Chief Coroner of Ontario to identify all prescription opioid-related deaths in Ontario from January 2003 to December 2010. We identified prescription opioid-related deaths in Vital Statistics death data in 2010 using five different algorithms. We selected the algorithm with the highest sensitivity and a positive predictive value above 80% as the optimal algorithm to identify prescription opioid-related deaths.

Results — Four out of five of the algorithms had positive predictive values above 80%. The algorithm with the highest sensitivity (75%) in 2010 and improved slightly in its predictive performance from 2003 to 2010.

Interpretation — In the absence of existing systems for monitoring prescription opioid-related harms in Canada, readily-available national Vital Statistics mortality data can be used to study prescription opioid-related mortality with considerable accuracy. Despite some limitations, the use of these data may facilitate the implementation of national surveillance and monitoring strategies.

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Keywords: Addiction Opioids Data validation