Geographical Variation in Opioid Prescribing and Opioid-Related Mortality in Ontario
Overdoses and deaths involving prescription opioids are a major public health concern. Recent data from the United States indicate an opioid-related death rate of 6.4 per 100,000 population annually, which exceeds the annual human immunodeficiency virus–related death rate at 4.0 per 100,000 population (Centers for Disease Control and Prevention 2009; Heron et al. 2009). Although the relationship between opioid prescriptions and the risk of adverse events is becoming more widely appreciated (Dhalla et al. 2009; Dunn et al. 2010), opioid prescribing practices, abuse and diversion have been shown to exhibit substantial geographical variability, and this may have implications for public health policy decisions and interventions (Curtis et al. 2006; Webster et al. 2009).
We sought to investigate the association between opioid prescribing and opioid-related mortality in Ontario and to examine geographical variation in these measures among Ontario's counties. The main outcome of interest for each county was the annualized age- and sex-standardized opioid-related mortality rate per 100,000 residents aged 15–64 years between January 1, 2004, and December 31, 2006. Opioid-related deaths were identified from the records of the Office of the Chief Coroner of Ontario using methodology described elsewhere (Dhalla et al. 2009), and the 2006 Statistics Canada census population was used as the standard population. Opioid prescribing rates were calculated for each calendar year over the study period and were defined as the total number of opioid prescriptions dispensed to Ontario Public Drug Program (OPDP) beneficiaries aged 15–64, expressed per 1,000 OPDP beneficiaries. An Ontario resident was deemed to be an OPDP beneficiary if he or she received a prescription for any drug that was paid for by the OPDP and was between the ages of 15 and 64 at the time of the prescription. Annual prescribing rates were averaged over the three-year study period in each county.
The relationship between opioid prescribing rate and opioid-related mortality was examined with linear regression, using the county as the unit of analysis and adjusting for other county-level characteristics (percentage of residents aged 15–64 who were OPDP beneficiaries, number of physicians and number of pharmacies per 1,000 OPDP beneficiaries, median family income, proportion of residents who were unemployed). The global Moran's I estimate was close to zero, indicating no significant spatial autocorrelation in the model, and the Shapiro-Wilk test statistic confirmed normality of the opioid-related mortality rate. We used backward stepwise elimination using a p value of .05 to remove non-significant covariates from the model.
We found a highly significant association between county-level opioid prescribing rates and opioid-related mortality. The adjusted regression analysis found that for every additional opioid prescription dispensed per OPDP beneficiary annually, the annual opioid-related mortality rate increased by 0.54 per 100,000 residents (p < .0001; Figure 1). A sensitivity analysis modelling the median quantity of morphine (or equivalent) dispensed per person per day revealed consistent findings.
The spatial distribution of opioid prescription rates and high opioid-related mortality rates (defined as more than one standard deviation above the provincial mean of 4.7 per 100,000 population) is illustrated in Figure 2. Thunder Bay District had the highest prescription rate (12,635 per 1,000 OPDP eligible) and the second highest opioid-related death rate (9.3 per 100,000 population). Four other counties (the Regional Municipality of Sudbury, the County of Lennox and Addington, Nipissing District, and the County of Peterborough) also ranked in the top 10 counties with respect to both prescription and opioid-related death rates. Of note, Manitoulin District had a prescription rate that approximated the provincial average (5,502 per 1,000 OPDP eligible), yet it exhibited the highest opioid-related death rate (11.9 per 100,000 population). This may be explained by the fact that Manitoulin District is the least populous county in Ontario (the population aged 15–64 in 2005 was 9,255 [Statistics Canada 2006]), and the 95% confidence interval (CI) around the observed death rate is therefore wide (95% CI 2.3–34.7 per 100,000 population).
An important limitation of our study that merits emphasis is that we were unable to identify prescriptions paid for in cash or by another payer. However, there is no obvious reason why this should vary substantially from one jurisdiction to another. Despite this, we observed a strong correlation between opioid prescribing rates and mortality rates at the level of individual counties.
In summary, we found large regional variations in opioid prescribing and outcomes and a strong association between regions with high opioid prescription utilization and those with high opioid-related death rates. Most opioid-related deaths in Ontario are unintentional and occur in individuals receiving opioids via prescription (Dhalla et al. 2009). Our findings allow clinicians and policy makers to target public health interventions and education to areas where opioid prescribing poses an especially large health risk.
About the Author
Tara Gomes, MHSc, is the project lead of the Ontario Drug Policy Research Network, an epidemiologist at the Institute for Clinical Evaluative Sciences (ICES) and an assistant professor in the Leslie Dan Faculty of Pharmacy at the University of Toronto, Toronto, Ontario.
David Juurlink, MD, PhD, is head of the Division of Clinical Pharmacology and Toxicology at Sunnybrook Health Sciences Centre in Toronto, Ontario. He is a medical toxicologist at the Ontario Poison Information Centre, a scientist at ICES and associate professor of medicine, pediatrics and health policy, management and evaluation at the University of Toronto.
Rahim Moineddin, PhD, is a senior biostatistician and an assistant professor with the Department of Family and Community Medicine at the University of Toronto. He is also an assistant professor with the Dalla Lana School of Public Health and an adjunct scientist at ICES.
Piotr Gozdyra, MA, is a medical geographer at ICES and is affiliated with the Centre for Research on Inner City Health at St. Michael's Hospital, Toronto, Ontario.
Irfan Dhalla, MD, MSc, is a lecturer in the Departments of Medicine and Health Policy, Management and Evaluation at the University of Toronto. He is also a staff physician in the Department of Medicine and a scientist in the Keenan Research Centre of the Li Ka Shing Knowledge Institute of St. Michael's Hospital.
Michael Paterson, MSc, is a scientist at ICES; an assistant professor in the Department of Health Policy, Management and Evaluation, University of Toronto; an assistant professor in the Department of Family Medicine, McMaster University, Hamilton, Ontario; and a member of the Centre for Evaluation of Medicines, St. Joseph's Healthcare Hamilton.
Muhammad Mamdani, PharmD, MA, MPH, is the director of the Applied Health Research Centre, the Keenan Research Centre, Li Ka Shing Knowledge Institute of St. Michael's Hospital. He is also associate professor in the Department of Health Policy, Management and Evaluation of the Faculty of Medicine and the Leslie Dan Faculty of Pharmacy of the University of Toronto; adjunct professor at King Saud University; and an adjunct scientist at ICES.
This study was supported by a grant from the Ontario Ministry of Health and Long-Term Care (MOHLTC) Drug Innovation Fund and the Institute for Clinical Evaluative Sciences (ICES), a non-profit research institute funded in part by MOHLTC. The opinions, results and conclusions reported in this paper are those of the authors and are independent from the funding sources. No endorsement by ICES or MOHLTC is intended or should be inferred. Irfan Dhalla receives salary support in the form of a post-doctoral fellowship from the Canadian Institutes of Health Research. The authors thank Brogan Inc. for the use of their Drug Product and Therapeutic Class Database.
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