04 Jan Algorithm Can Help Predict Deaths Related to Prescription Opioids
Medical Research: What is the background for this study? What are the main findings?
Response: Surveillance of the harms associated with chronic opioid use is imperative for clinicians and policy-makers to rapidly identify emerging issues related to this class of medications. However, data regarding opioid-related deaths is difficult to obtain in Canada as it is collected by local coroners and is not widely available to researchers. We conducted a validation study to evaluate whether regularly collected vital statistics data collected by Statistics Canada can be used to accurately identify opioid-related deaths in Canada.
We compared deaths identified from charts abstracted from the Office of the Chief Coroner of Ontario to those identified using several coding algorithms in the Statistics Canada Vital Statistics database. We found that the optimal algorithm had a sensitivity of 75% and a positive predictive value of 90%. When using this algorithm, the death data obtained from the Vital Statistics database slightly underestimated the number of opioid-related deaths in Ontario, however the trends over time were similar to the data obtained from the coroner’s office.
Medical Research: What should clinicians and patients take away from your report?
Response: Using the algorithm identified in this study, we show that regularly collected vital statistics data can be used to approximate the number of opioid-related deaths in Ontario. Application of this method to other jurisdictions would allow for clinicians and policy-makers to undertake ongoing surveillance of trends and rates of these drug poisoning deaths. Given the high public health importance of this issue, this would be an important tool for those looking to address opioid abuse and overdose at a national level.
Medical Research: What recommendations do you have for future research as a result of this study?
Response: In the future, researchers should consider regularly applying this algorithm to vital statistics data at the national level. This would allow us to estimate the prevalence of opioid-related deaths nationally for the first time, and would be valuable in undertaking ongoing surveillance and monitoring of opioid overdoses into the future.
Emilie Gladstone, Kate Smolina, Steven G. Morgan, Kimberly A. Fernandes Diana Martins, and Tara Gomes
Sensitivity and specificity of administrative mortality data for identifying prescription opioid-related deaths
CMAJ cmaj.150349; published ahead of print November 30, 2015,doi:10.1503/cmaj.150349
Tara Gomes (2016). Algorithm Can Help Predict Deaths Related to Prescription Opioids