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

• Published in: Canadian Medical Association journal (CMAJ) Vol. 188; no. 4; pp. E67 - E72
• Main Authors: Gladstone, Emilie, Smolina, Kate, Morgan, Steven G., Fernandes, Kimberly A., Martins, Diana, Gomes, Tara
• Format: Journal Article
• Language: English
• Published: Canada Elsevier Inc 01.03.2016; Joule Inc; CMA Impact, Inc; 8872147 Canada Inc
• Subjects: Adverse and side effects; Algorithms; Analgesics, Opioid - adverse effects; Analgesics, Opioid - therapeutic use; Canada; Canada - epidemiology; Complications and side effects; Drug addiction; Drug Overdose - mortality; Drug Prescriptions - statistics & numerical data; Drug use; Drugs; Humans; Medical research; Medicine, Experimental; Mortality; Narcotics; Ontario - epidemiology; Opioids; Sensitivity and Specificity; Vital Statistics; Canada; Ontario
• ISSN: 0820-3946; 1488-2329; 1488-2329
• DOI: 10.1503/cmaj.150349

Comprehensive systems for surveilling prescription opioid–related harms provide clear evidence that deaths from prescription opioids have increased dramatically in the United States. However, these harms are not systematically monitored in Canada. In light of a growing public health crisis, accessible, nationwide data sources to examine prescription opioid–related harms in Canada are needed. We sought to examine the performance of 5 algorithms to identify prescription opioid–related deaths from vital statistics data against data abstracted from the Office of the Chief Coroner of Ontario as a gold standard. We identified all prescription opioid–related deaths from Ontario coroners’ data that occurred between Jan. 31, 2003, and Dec. 31, 2010. We then used 5 different algorithms to identify prescription opioid–related deaths from vital statistics death data in 2010. We selected the algorithm with the highest sensitivity and a positive predictive value of more than 80% as the optimal algorithm for identifying prescription opioid–related deaths. Four of the 5 algorithms had positive predictive values of more than 80%. The algorithm with the highest sensitivity (75%) in 2010 improved slightly in its predictive performance from 2003 to 2010. In the absence of specific systems for monitoring prescription opioid–related deaths in Canada, readily available national vital statistics data can be used to study prescription opioid–related mortality with considerable accuracy. Despite some limitations, these data may facilitate the implementation of national surveillance and monitoring strategies.