Abstract
Introduction
As the drug toxicity crisis continues to evolve globally, harms related to non-opioid substances, including stimulants, have risen in parallel. Our study aims were to describe trends in accidental stimulant toxicity deaths and to characterize demographic characteristics of decedents and the circumstances surrounding death.
Methods
We conducted a population-based repeated cross-sectional study, of all accidental stimulant toxicity deaths between January 1, 2018, and December 31, 2021, in Ontario, Canada. We reported monthly rates of stimulant toxicity deaths per 100,000 people residing in Ontario and the circumstances surrounding death. All analyses were stratified by the type of stimulant(s) involved in death.
Results
Between 2018 and 2021, we identified 5210 stimulant toxicity deaths with the monthly rate rising from 0.4 to 1.0 per 100,000. Both cocaine and methamphetamine were involved in 16.2 % of deaths, and 56.2 % and 27.7 % involved cocaine or methamphetamine (without other stimulants), respectively. Over 80 % of deaths also involved an opioid. Among all deaths, 75.2 % of decedents were male, 53.1 % were aged 25–44, and over half of all deaths occurred in private residences (64.7 %).
Conclusions
The rate of stimulant toxicity deaths has continued to grow, more than doubling over a three-year period. As stimulant-related deaths continue to rise, comprehensive social supports and mental health services, including harm reduction and treatment programs adapted to the unique needs of people who use stimulants alone or in combination with other substances, are urgently required to meet the changing needs of people who use drugs.
Keywords: Stimulants, Cocaine, Methamphetamine, Drug overdose, Harm reduction
1. INTRODUCTION
As the drug toxicity crisis continues to evolve globally, harms related to non-opioid substances, including stimulants, have risen in parallel. Stimulant toxicities are typically characterized by both vascular and neuropsychiatric pathologies with fatal cardiac arrhythmia often listed as the most likely etiology among methamphetamine-related deaths (Coffin & Suen, 2023), while deaths resulting from the use of stimulants in combination with opioids are hypothesized to be caused by respiratory arrest (Ahmed, Sarfraz, & Sarfraz, 2022). In the United States (US), the rate of stimulant-related deaths without the involvement of opioids increased 23.0 % (1.3 to 1.6 per 100,000) between 2016 and 2017 (Hoots, Vivolo-Kantor, & Seth, 2020) as compared to Australia where accidental methamphetamine-related deaths increased 23.97 % each year between 2011 and 2016 (Stronach, Dietze, Livingston, & Roxburgh, 2024). Further, in 2017, an estimated 0.58 % of all-cause deaths globally were associated with amphetamine dependence and 0.32 % with cocaine (Farrell et al., 2019). Several factors have likely contributed to the significant rise of stimulant-related harms, including the increased use of stimulants in combination with opioids from the unregulated drug supply and the potential contamination of stimulants with fentanyl (Ciccarone, 2021; Jones, Bekheet, Park, & Alexander, 2020).
Harm reduction programs have struggled to adapt to increasing stimulant use given the difficulties implementing supervised smoking and inhalation sites, and the limited health services and social supports available for people who use stimulants (The American Society of Addiction Medicine, 2024). There are also few existing treatment options for people with stimulant use disorder, with challenges reported in the management of stimulant withdrawal given the psychological nature of symptoms experienced (Li & Shoptaw, 2023). Furthermore, in order to ensure harm reduction and treatment options are tailored to populations most at risk of experiencing stimulant-related harms it is important to understand the demographic characteristics of people who die as the result of a stimulant toxicity. However, across Canada these characteristics remain poorly understood given a historical lack of available data apart from opioid-related deaths where stimulants were involved (Cheng et al., 2022). In this study, we present findings from the analysis of data from the Office of the Chief Coroner in Ontario, which captures all deaths where stimulants directly contributed to death. Our study aims were to describe trends in accidental stimulant toxicity deaths and to characterize demographic characteristics of decedents and the circumstances surrounding death.
2. METHODS
2.1. Study design
We conducted a population-based repeated cross-sectional study of all stimulant toxicity deaths that occurred between January 1, 2018, and December 31, 2021, in Ontario Canada. The study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines (Von Elm et al., 2007) with a preliminary analysis of this data previously made available online (Gomes et al., 2023).
2.2. Data sources
We obtained administrative health data housed at ICES, an independent, non-profit research institute whose legal status under Ontario’s health information privacy law allows it to collect and analyze health care and demographic data, without consent, for health system evaluation and improvement. We used a novel database containing information from the Office of the Chief Coroner in Ontario on all stimulant, alcohol and benzodiazepine-related toxicity deaths that have occurred in Ontario since January 1, 2018, that did not involve opioids. We used these data to supplement the Drug and Drug/Alcohol Related Death Database (DDARD), which contains information related to all substances involved in opioid toxicity deaths across the province (Singh et al., 2018). Taken together, these two databases allow for a complete capture of all stimulant toxicity deaths (with or without opioid involvement) across Ontario between 2018 and 2021. We used the Registered Persons Database, which includes all individuals eligible for the Ontario Health Insurance Plan (OHIP), to determine demographic characteristics of decedents and monthly estimates of the overall population of Ontario. All datasets were linked using unique encoded identifiers and analyzed at ICES. The study excluded stimulant toxicity deaths which could not be linked (<5 % of records). The use of the data in this study is authorized under Section 45 of Ontario’s Personal Health Information Protection Act (PHIPA) and does not require review by a Research Ethics Board.
2.3. Study population
Our study population included all accidental stimulant toxicity deaths experienced by Ontario residents over the study period. We used the aforementioned combined dataset containing all death records from the Office of the Chief Coroner of Ontario where a substance-related toxicity directly contributed to death, and excluded records where the manner of death was not accidental. Deaths are determined to be accidental in nature following the coroner’s investigation and are defined as deaths resulting from an incident that happens without foresight or expectation. We only retained records where cocaine and/or methamphetamine were identified as direct contributors to death. Determining the substance attribution for the cause of death is highly complex and unique to each death investigation where coroners may consider many factors including the circumstances of death, the medical and substance consumption history of the person who died, the source and concentration of substances found in post-mortem toxicology, personal tolerance levels, metabolic pathways, and autopsy findings. Generally, when completing the cause of death statement, coroners include all substances that contributed (alone or in combination) to death, including any potential interactions such as synergistic or additive effects.
2.4. Demographic characteristics of decedents and the circumstances surrounding death
We determined demographic characteristics of decedents on the date of death, including age (categorized as 0–24, 25–44, 45–64 and 65+), sex, income quintile and location of residence (urban vs. rural). We described circumstances surrounding death as determined by the investigating coroners, including location of incident (Appendix 1), number of substances contributing to death (1, 2, 3+), and other substances (opioids, benzodiazepines, and alcohol) contributing to death. We summarized missing data as a separate category for income quintile, location of residence, and location of incident.
2.5. Statistical analysis
We reported the rate of stimulant toxicity deaths over each month of the study period, measured as a count of deaths, with crude rates reported per 100,000 people residing in Ontario. We reported trends overall, among deaths involving only stimulants (without opioids, benzodiazepines, or alcohol) and stratified by the type of stimulant(s) involved in death categorized as mutually exclusive groups of both cocaine and methamphetamine, cocaine in the absence of other stimulants, and methamphetamine in the absence of other stimulants. We used joinpoint regression models on the logarithmic scale (Joinpoint Regression Program, Version 5.0.2, 2023) to characterize changes in the trend of stimulant-related deaths among each of these five groups. Joinpoints are identified without preconceived notions regarding the location of breakpoints in the trend, which was necessary due to the shifts in death rates over the study period. The optimal model was selected by comparing the weighted Bayesian Information Criterion values for different models with ‘k’ joinpoints. We reported the monthly percent change (MPC) and corresponding 95 % confidence intervals (CIs) for each linear trend segment, along with the average monthly percent change (AMPC) and accompanying 95 % CI for the overall trend across the study period.
We used descriptive statistics to summarize demographic characteristics of decedents and circumstances surrounding death. We conducted pairwise comparisons to assess how characteristics differed among deaths involving cocaine in the absence of other stimulants compared to the two other stimulant groups (i.e., methamphetamine and both cocaine and methamphetamine). We applied the Bonferroni correction for multiple comparisons and therefore used a type I error rate of p = .025 (p = .05/2) to determine statistical significance. Finally, to assess if these characteristics changed over time, we compared deaths which occurred in the first and last year of the study period (2018 vs. 2021), using a two proportions Z-test. Small cells (≤5) were suppressed due to institutional privacy requirements, with ranges provided to prevent back-calculation.
2.6. Involvement of people with lived experience
The Ontario Drug Policy Research Network’s opioid-related research is informed by a Lived Experience Advisory Group, comprised of individuals with lived and living experience using opioids who were consulted before, during, and after the study design process. Subsequently, we identified several additional people with lived and living experience with a range of substances including opioids, stimulants, benzodiazepines, and alcohol. Throughout the conduct of this study, these individuals participated as study team members, providing feedback on the study scope, methodology, contextualization, and reporting of results. All people with lived and living experience were compensated for their time spent on this study and were offered co-authorship (or when preferred by the individual, acknowledgment) in this manuscript.
3. RESULTS
Between January 1, 2018, and December 31, 2021, there were 5210 stimulant toxicity deaths in Ontario, with the monthly rate of stimulant toxicity deaths rising from 0.4 to 1.0 per 100,000 (Average Monthly Percent Change (AMPC) = 2.2 % per month, 95 % CI: 0.2 %, 4.2 %, p = .03; Fig. 1; Appendix 2). Among these deaths, 987 involved a stimulant alone, increasing from 0.1 to 0.2 per 100,000 (AMPC = 0.8 % per month, 95 % CI: 0.3 %, 1.4 %, p = .003). When stratified by the type of stimulant directly contributing to death, 16.2 % of deaths involved both cocaine and methamphetamine, 56.2 % of deaths involved cocaine in the absence of other stimulants, and 27.7 % of deaths involved methamphetamine in the absence of other stimulants. However, these trends differed across strata with deaths involving cocaine increasing from 0.2 to 0.6 per 100,000 (AMPC = 1.5 % per month, 95 % CI: −0.6 %, 3.7 %, p = .16), deaths involving methamphetamine increasing from 0.04 to 0.3 per 100,000 (AMPC = 3.4 %, 95 % CI: −1.3 %, 8.2 %, p = .16), and deaths involving both cocaine and methamphetamine rising from 0.1 to 0.2 per 100,000 (AMPC = 2.0 %, 95 % CI: 0.2 %, 3.8 %, p = .03). Although the overall average monthly percent change for deaths involving cocaine and methamphetamine without other stimulants were not statistically significant due to large fluctuations in trends over time, we observed significant increases in the monthly percent change across several of the identified joinpoint segments. For instance, the monthly percent change for deaths involving cocaine was 10.7 % each month (95 % CI: 5.4 %, 16.3 %, p < .001) from September 2019 to June 2020 and the monthly percent change for deaths involving methamphetamine was 12.8 % each month (95 % CI: 5.6 %, 20.6 %, p < .001) from July 2019 to May 2020.
Fig. 1.
Trends in stimulant toxicity deaths between 2018 and 2021, overall and by type of stimulant(s) involved.
Among all stimulant toxicity deaths, 75.2 % occurred among males and 53.1 % among people aged 25 to 44 (Table 1). The majority of decedents resided in urban regions (87.7 %) and 41.7 % resided in neighbourhoods in the lowest income quintile. More than half of all deaths occurred in private residences (64.7 %), followed by 6.9 % occurring within rooming houses or collective dwellings and 6.7 % in other non-hospital indoor spaces. Only 18.9 % of stimulant toxicity deaths did not involve opioids, alcohol, or benzodiazepines, with 79.6 % of deaths involving both a stimulant and opioid (primarily unregulated fentanyl). The demographic characteristics and circumstances surrounding death differed when stratified by the type of stimulant involved in death. Deaths involving methamphetamine (without other stimulants) were more concentrated among people aged 25–44 (59.9 % vs. 49.0 %, p < .001), and those living in neighbourhoods of the lowest income quintile (44.5 % vs. 39.1 %, p ≤0.001), compared to deaths involving cocaine (without other stimulants). Further, deaths involving methamphetamine occurred less often in a private residence (55.1 % vs. 71.5 %, p < .001), and more commonly involved two substances (71.2 % vs. 61.4 %; p < .001). These patterns were generally consistent when comparing deaths involving cocaine to those involving both cocaine and methamphetamine. When comparing deaths over time, we observed a lower proportion of deaths involving only stimulants (15.5 % vs. 25.3 %, p < .001), fewer deaths occurring in private residences (53.0 % vs. 70.6 %, p < .001) and a higher proportion of deaths involving opioids (83.3 % vs. 72.6 %, p < .001 in 2021 as compared to 2018 (Appendix 3).
Table 1.
Descriptive characteristics and circumstances surrounding death among stimulant toxicity deaths 2018 to 2021, overall and by type of stimulant involved.
| Characteristics | Overall N = 5210 | Cocaine (without other stimulants) N = 2926 | Methamphetamine (without other stimulants) N = 1442 | Cocaine and methamphetamine N = 842 |
|---|---|---|---|---|
| Age | ||||
| 0–24 | 342 (6.6 %) | 177 (6.0%) | 105 (7.3 %) | 60 (7.1 %) |
| 25–44 | 2769 (53.1 %) | 1435 (49.0 %) | 864 (59.9 %)* | 470 (55.8 %)* |
| 45–64 | 1960 (37.6 %) | 1211 (41.4 %) | 454 (31.5 %)* | 295 (35.0 %)* |
| 65+ | 139 (2.7 %) | 103 (3.5 %) | 19 (1.3 %)* | 17 (2.0 %) |
| Sex | ||||
| Male | 3919 (75.2 %) | 2218 (75.8 %) | 1100 (76.3 %) | 601 (71.4 %)* |
| Female | 1291 (24.8 %) | 708 (24.2 %) | 342 (23.7 %) | 241 (28.6 %)* |
| Income quintile | ||||
| Q1 (lowest) | 2172 (41.7 %) | 1145 (39.1 %) | 642 (44.5 %)* | 385 (45.7 %)* |
| Q2 | 1118 (21.5 %) | 627 (21.4 %) | 315 (21.8 %) | 176 (20.9 %) |
| Q3 | 765 (14.7 %) | 447 (15.3 %) | 203 (14.1 %) | 115 (13.7 %) |
| Q4 | 493 (9.5 %) | 305 (10.4 %) | 120 (8.3 %) | 68 (8.1 %) |
| Q5 | 454 (8.7 %) | 294 (10.0 %) | 95 (6.6 %)* | 65 (7.7 %) |
| Unknown | 208 (4.0 %) | 108 (3.7 %) | 67 (4.6 %) | 33 (3.9 %) |
| Location of residence | ||||
| Urban | 4569 (87.7 %) | 2607 (89.1 %) | 1229 (85.2 %)* | 733 (87.1 %) |
| Rural | 435 (8.3 %) | 213 (7.3 %) | 146 (10.1 %)* | 76 (9.0 %) |
| Unknown | 206 (4.0 %) | 106 (3.6 %) | 67 (4.6 %) | 33 (3.9 %) |
| Location of incident | ||||
| Private residence | 3371 (64.7 %) | 2092 (71.5 %) | 794 (55.1 %)* | 485 (57.6 %)* |
| Rooming house/Collective dwelling | 362 (6.9 %) | 149 (5.1 %) | 124 (8.6 %)* | 89 (10.6 %)* |
| Other residential settings | 130 (2.5 %) | 53 (1.8 %) | 53 (3.7 %)* | 24 (2.9 %) |
| Other indoor space(non-hospital) | 348 (6.7 %) | 166 (5.7 %) | 118 (8.2 %)* | 64 (7.6 %) |
| Other | 131 (2.5 %) | 63 (2.2 %) | 49 (3.4 %)* | 19 (2.3 %) |
| Outdoors | 311 (6.0 %) | 149 (5.1 %) | 114 (7.9 %)* | 48 (5.7 %) |
| Unknown | 557 (10.7 %) | 254 (8.7 %) | 190 (13.2 %)* | 113 (13.4 %)* |
| Number of substances contributing to death | ||||
| 1 | 987 (18.9 %) | 626 (21.4 %) | 256 (17.8 %)* | 105 (12.5 %)* |
| 2 | 3454 (66.3 %) | 1797 (61.4 %) | 1026 (71.2 %)* | 631 (74.9 %)* |
| 3+ | 769 (14.8 %) | 503 (17.2 %) | 160 (11.1 %)* | 106 (12.6 %)* |
| Other substances contributing to death | ||||
| Opioids | 4148 (79.6 %) | 2252 (77.0 %) | 1172 (81.3 %)* | 724 (86.0 %)* |
| Alcohol | 533 (10.2 %) | 380 (13.0 %) | 78 (5.4 %)* | 75 (8.9 %)* |
| Benzodiazepines | 349 (6.7 %) | 198 (6.8 %) | 100 (6.9 %) | 51 (6.1 %) |
Represents p < .025 when compared to cocaine (without other stimulant) deaths.
4. DISCUSSION
In this study of stimulant toxicity deaths across Ontario, we found that the rate of stimulant-related mortality more than doubled over a three-year period. Although deaths involving cocaine in the absence of other stimulants remained the most prevalent over the study period, we observed the largest relative increase in deaths involving methamphetamine (without cocaine), with this rate increasing more than seven-fold. The finding that over 80 % of stimulant toxicity deaths also involved an opioid, and that just one in six deaths involved a stimulant without the involvement of a secondary substance aligns with findings from other jurisdictions (Ellis, Kasper, & Cicero, 2018; Hoots et al., 2020). Specifically, they likely reflect increasing polysubstance use – particularly the use of fentanyl among people who primarily use stimulants (who may have low tolerance), and changing substance use preferences among people who use drugs. Additionally, in some cases stimulants may be used to compensate for the sedating effects associated with an unregulated fentanyl supply which is increasingly adulterated with benzodiazepines (Compton, Valentino, & DuPont, 2021). Furthermore, among all stimulant toxicity deaths, three-quarters occurred among men, and over half were among people aged 25–44 and within private residences, respectively. This provides preliminary insights into the demographic groups most significantly impacted by harms related to stimulant use and the circumstances surrounding death, which can help direct the development of comprehensive health and social supports, harm reduction programs and treatment options that meet the changing needs of people who use drugs.
Overall, we found the largest relative increase in the rate of stimulant toxicity deaths involving methamphetamine, aligning with reports that the concomitant use of methamphetamine and opioids continues to rise (Steinberg et al., 2022). This also parallels trends observed in the US, where the number of deaths involving methamphetamine increased 357.0 % (6830 to 31,170) between 2016 and 2021, compared to a 129.7 % (11,404 to 26,198) increase in cocaine-related deaths over the same period (Spencer et al., 2023). This is in contrast to a study conducted in France using toxicology data which found the largest increase in polysubstance deaths involving cocaine, which increased from 30.8 % to 57.8 % of all drug-related deaths from 2011 to 2021, with no increases observed in deaths involving methamphetamine. (Revol et al., 2023). The trend toward methamphetamine and opioid polysubstance use in Canada and the US may be driven by the continuation of historical geographic trends in methamphetamine use, changes in the unregulated drug supply, where methamphetamine is used to mitigate the extreme sedation from fentanyl adulterated with synthetic benzodiazepines, and increased methamphetamine availability, affordability, and purity (Ellis et al., 2018; Jones et al., 2020). Varied motivations surrounding methamphetamine use have been reported including serving as a substitute for opioids or cocaine due to cost and availability, preventing withdrawal, and moderating opioid use given the uncertainty of the unregulated supply (Silverstein, Daniulaityte, Getz, & Zule, 2021). Furthermore, our finding that methamphetamine-related deaths in the absence of other stimulants were highest among people residing in low income neighbourhoods and that fewer deaths occurred in private residences, provides preliminary evidence that lower income and vulnerably housed people may have different patterns of use of methamphetamine, including patterns of use where the alertness provided by stimulants may be a pragmatic strategy to cope with a lack of housing or shelter space, and concomitant safety concerns when forced to spend nights outside (McKenna, 2013). These important demographic differences highlight that comprehensive solutions are necessary, including adequate income supports, affordable housing, and harm reduction strategies adapted to stimulant use (including supervised smoking and inhalation sites, counselling, and access to naloxone among people who primarily use stimulants) (Kleinman, 2023) to support people who use different types of stimulants from the unregulated drug supply.
Another notable finding of our study was the small proportion of stimulant-only deaths as most of the growth we observed in stimulant toxicity deaths were those where other substances contributed to death. This highlights the critical need for expanded knowledge and training surrounding appropriate responses to toxicities where stimulants are involved. For instance, interviews conducted with people who use stimulants in Vancouver, British Columbia, found that there was no unified understanding of the signs and proper responses to a stimulant toxicity (sometimes referred to as “overamping”), hindering the ability to adequately respond (Mansoor et al., 2022). While naloxone is an effective antagonist to opioid-involved overdoses, it is not effective in stimulant toxicity and there are currently no pharmaceutical options to reverse the effects of stimulant toxicity (Mansoor et al., 2022). Expanding harm reduction supports to include supervised smoking and inhalation within supervised consumption sites, as well as increased training on how to support and intervene in cases of stimulant toxicity may be helpful.
A core strength of this study includes the use of a novel database to provide initial insights into the trends of stimulant toxicity deaths across Ontario. However, there are limitations warranting discussion. First, only acute stimulant toxicity deaths were included in our analysis and as such this study does not capture non-fatal toxicities or other health outcomes associated with stimulant use which may impact the long-term health of people who use drugs. For example, the prolonged use of stimulants from the unregulated drug supply has been associated with increased rates of cardiovascular complications including myocardial infarctions, stroke, cardiac arrhythmias and psychiatric complications which are not reflected in this analysis (Gagnon, Sadasivan, Perera, & Oudit, 2022). Our study was not designed to capture the complete burden of stimulant-related harms in Ontario and future work should explore this issue. Second, some variables describing circumstances surrounding death including location of incident have a high degree of missingness (>25 %) given that these data are based on coroner’s investigations where there may not always be sufficient detail to classify (e.g., deaths among people experiencing homelessness). Therefore, we anticipate that the reported proportions for the location of incident, particularly in settings outside of private residences and in later years of the study period, are underestimated.
5. CONCLUSION
In this population-based study using coronial records from across Ontario, the rate of stimulant toxicity deaths doubled over a three-year period. In 2021, an average of five people lost their lives every day to an accidental stimulant toxicity with over 80 % of these deaths also having opioids contributing to death, highlighting the polysubstance nature of the current drug toxicity crisis. As stimulant-related harms rise, it is imperative that comprehensive health, mental health services and substance use treatment services are available and adequately resourced to respond to the changing needs of people who use drugs, including targeted supports for people who use stimulants alone or in combination with other substances. Given that many deaths also involved opioids, continued counselling around harm reduction interventions and access to naloxone, even among people who primarily use stimulants is also warranted.
HIGHLIGHTS.
The stimulant toxicity death rate has continued to grow, doubling over three years.
Over 80 % of deaths also involved an opioid.
More than half of deaths occurred among younger adults and in private residences.
The adaption of harm reduction programs is needed to meet the changing needs of people who use drugs.
Acknowledgements
This study was supported by ICES, an independent, non-profit research institute funded by an annual grant from the Ontario Ministry of Health (MOH) and the Ministry of Long-Term Care (MLTC). Parts of this material are based on data and information compiled and provided by the MOH and the Canadian Institute for Health Information. This document used data adapted from the Statistics Canada Postal CodeOM Conversion File, which is based on data licensed from Canada Post Corporation, and/or data adapted from the Ontario Ministry of Health Postal Code Conversion File, which contains data copied under license from ©Canada Post Corporation and Statistics Canada. The analyses, conclusions, opinions, and statements expressed herein are solely those of the authors and do not reflect those of the data sources; no endorsement is intended or should be inferred. We thank IQVIA Solutions Canada Inc. for use of their Drug Information File. We thank Clare Cheng, Emily Schneider, Paul Newcombe, Samantha Singh, Shauna Pinkerton, Tasha-Dawn Doucette, Tonya Campbell, and Zackary Bouck for their contributions to this study.
Funding
This work was supported in part by the Public Health Agency of Canada, Substance-Related Harms Division, and grants from the Canadian Institutes of Health Research (Grants #153070 and #178163). S.Ledlie is supported by an Ontario Graduate Scholarship and the Network for Improving Health Systems Trainee Award. T. Gomes is supported by a Tier 2 Canada Research Chair.
Declaration of competing interest
T. Gomes reports funding from the Ontario Ministry of Health and Indigenous Services Canada unrelated to this work. N. Bozinoff has received salary support from US National Institute on Drug Abuse (NIDA) grant R25-DA037756 unrelated to this work. She has received honoraria for continuing professional development talks from the Ontario College of Family Physicians. No other authors have any other competing interests to declare.
Appendix 1. Location of incident categories.
| Category | Description |
|---|---|
| Private residence | Includes private dwellings. |
| Rooming house/other collective dwellings | Includes sober living facilities, boarding houses, halfway houses, and rooming houses. |
| Other residential settings/shelters | Includes shelters, community housing, and residential care facilities. |
| Other indoor space(non-hospital) | Includes correctional facilities, custody, and hotel/motel/inn. |
| Other | Includes hospital, transit and other. |
| Outdoors | Includes all outdoor areas. |
| Unknown | Includes missing, unknown, and other categories where there is not sufaicient detail to classify (e.g., homeless). |
Appendix 2. Joinpoint models overall, and by type of stimulant(s) involved.
| Model | Monthly rate per 100,000 – start of period | Monthly rate per 100,000 – end of period | Monthly percent change (MPC) | p-value | 95 % CIs |
|---|---|---|---|---|---|
| Overall | |||||
| Full study period (January 2018 to December 2021) | 0.4 | 1.0 | 2.2 %a | 0.03 | 0.2 %, 4.2 % |
| Segment 1: January 2018 to April 2019 | 0.4 | 0.7 | 4.7 % | <0.001 | 2.9 %, 6.6 % |
| Segment 2: April 2019 to August 2019 | 0.7 | 0.3 | −16.9 % | 0.07 | −31.8 %, 1.3 % |
| Segment 3: August 2019 to May 2020 | 0.3 | 1.1 | 12.6 % | <0.001 | 7.9 %, 17.5 % |
| Segment 4: May 2020 to December 2021 | 1.1 | 1.0 | −0.04 % | 0.92 | −0.9 %, 0.8 % |
| Stimulants (only) | |||||
| Full study period (January 2018 to December 2021)b | 0.1 | 0.2 | 0.8 %a | 0.003 | 0.3 %, 1.4 % |
| Cocaine (without other stimulants) | |||||
| Full study period (January 2018 to December 2021) | 0.2 | 0.6 | 1.5 %a | 0.16 | −0.6 %, 3.7 % |
| Segment 1: January 2018 to April 2019 | 0.2 | 0.4 | 3.7 % | 0.002 | 1.5 %, 6.0 % |
| Segment 2: April 2019 to September 2019 | 0.4 | 0.2 | −11.0 % | 0.17 | −24.8 %, 5.4 % |
| Segment 3: September 2019 to June 2020 | 0.2 | 0.6 | 10.7 % | <0.001 | 5.4 %, 16.3 % |
| Segment 4: June 2020 to December 2021 | 0.6 | 0.6 | −0.9 % | 0.15 | −2.1 %, 0.3 % |
| Methamphetamine (without other stimulants) | |||||
| Full study period (January 2018 to December 2021) | 0.04 | 0.3 | 3.4 %a | 0.16 | −1.3 %, 8.2 % |
| Segment 1: January 2018 to April 2019 | 0.04 | 0.3 | 8.7 % | <0.001 | 5.3 %, 12.2 % |
| Segment 2: April 2019 to July 2019 | 0.3 | 0.07 | −31.3 % | 0.28 | −65.5 %, 36.7 % |
| Segment 3: July 2019 to May 2020 | 0.07 | 0.3 | 12.8 % | <0.001 | 5.6 %, 20.6 % |
| Segment 4: May 2020 to December 2021 | 0.3 | 0.3 | 1.2 % | 0.11 | −0.3 %, 2.7 % |
| Cocaine and methamphetamine | |||||
| Full study period (January 2018 to December 2021) | 0.1 | 0.2 | 2.0 %a | 0.03 | 0.2 %, 3.8 % |
| Segment 1: January 2018 to September 2019 | 0.1 | 0.04 | −0.9 % | 0.74 | −2.6 %, 1.9 % |
| Segment 2: September 2019 to May 2020 | 0.04 | 0.2 | 14.6 % | 0.003 | 5.1 %, 24.9 % |
| Segment 3: May 2020 to December 2021 | 0.2 | 0.2 | −0.5 % | 0.52 | −1.9 %, 0.9 % |
Represents the average monthly percent change over the full study period.
For the stimulant only model, the best fitting model had no joinpoints.
Appendix 3. Descriptive characteristics and circumstances surrounding death among stimulant toxicity deaths, 2018 vs 2021.
| Characteristics | 2018 N = 831 | 2021 N = 1822 |
|---|---|---|
| Age | ||
| 0–24 | 67 (8.1 %) | 111 (6.1 %) |
| 25–44 | 442 (53.2 %) | 973 (53.4 %) |
| 45–64 | 304 (36.6 %) | 686 (37.7 %) |
| 65+ | 18 (2.2 %) | 52 (2.9 %) |
| Sex | ||
| Male | 611 (73.5 %) | 1369 (75.1 %)* |
| Female | 220 (26.5 %) | 453 (24.9 %) |
| Income quintile | ||
| Q1 (lowest) | 322 (38.7 %) | 765 (42.0 %) |
| Q2 | 196 (23.6 %) | 389 (21.4 %) |
| Q3 | 131 (15.8 %) | 278 (15.3 %) |
| Q4 | 73 (8.8 %) | 166 (9.1 %) |
| Q5 | 79 (9.5 %) | 142 (7.8 %) |
| Unknown | 30 (3.6 %) | 82 (4.5 %) |
| Location of residence | ||
| Urban | 744 (89.5 %) | 1565 (85.9 %)* |
| Rural | 59 (7.1 %) | 175 (9.6 %)* |
| Unknown | 28 (3.4 %) | 82 (4.5 %) |
| Location of incident | ||
| Private residence | 587 (70.6 %) | 966 (53.0 %)* |
| Rooming house/Collective dwelling | 58 (7.0 %) | 155 (8.5 %) |
| Other residential settings | 17 (2.0 %) | 28–32 (1.5 %−1.8 %) |
| Other indoor space (non-hospital) | 60 (7.2 %) | 70 (3.8 %)* |
| Other | 32 (3.9 %) | 17–21 (0.9 %−1.2 %) |
| Outdoors | 59 (7.1 %) | 78 (4.3 %)* |
| Unknown | 18 (2.2 %) | 502 (27.6 %)* |
| Number of substances contributing to death | ||
| 1 | 210 (25.3 %) | 282 (15.5 %)* |
| 2 | 501 (60.3 %) | 1287 (70.6 %)* |
| 3+ | 120 (14.4 %) | 253 (13.9 %) |
| Other substances contributing to death | ||
| Opioids | 603 (72.6 %) | 1518 (83.3 %)* |
| Alcohol | 95 (11.4 %) | 151 (8.3 %)* |
| Benzodiazepines | 46 (5.5 %) | 136 (7.5 %) |
Represents p < .05.
Footnotes
CRediT authorship contribution statement
Shaleesa Ledlie: Writing – review & editing, Writing – original draft, Methodology, Formal analysis, Conceptualization. Pamela Leece: Writing – review & editing, Project administration, Methodology, Funding acquisition, Conceptualization. Joanna Yang: Writing – review & editing, Methodology, Formal analysis, Conceptualization. Anita Iacono: Writing – review & editing, Methodology, Investigation, Conceptualization. Gillian Kolla: Writing – review & editing, Methodology, Investigation, Conceptualization. Rob Boyd: Writing – review & editing, Methodology, Investigation, Conceptualization. Nikki Bozinoff: Writing – review & editing, Methodology, Investigation, Conceptualization. Mike Franklyn: Writing – review & editing, Methodology, Investigation, Conceptualization. Dana Shearer: Writing – review & editing, Project administration, Methodology, Investigation, Conceptualization. Ashley Smoke: Writing – review & editing, Methodology, Investigation, Conceptualization. Fangyun Wu: Writing – review & editing, Methodology, Investigation, Formal analysis, Conceptualization. Tara Gomes: Writing – review & editing, Supervision, Project administration, Methodology, Investigation, Conceptualization.
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