ABSTRACT
Background
Work‐related injuries may increase risk for opioid‐related harms. Yet there remains a gap in our understanding of trends in opioid‐related harms over time among injured workers. We estimated trends in hospital encounters for opioid‐related harms among injured workers in Ontario, Canada from 2006 to 2022.
Methods
This study used linked workers' compensation (1983–2019) and hospitalization and emergency department (ED) (2006–2022) data from 1.7 million injured workers. Rates of opioid‐related poisonings and mental and behavioral disorders were estimated for all workers, as well as by age, sex, and occupational group. Joinpoint regression was used to analyze trends and estimate average annual percent change (AAPC) in rates.
Results
Rates of harms significantly increased across all age groups and sexes, with the highest increases among male workers and those aged 35–44. There was also variation by occupation. For poisonings, some of the largest increases in ED visits and hospitalizations were among workers in machining (ED visits = AAPC 18.5%, 95% CI 0.0%–40.5%; hospitalizations = AAPC 8.1%, 95% CI 3.4%–13.0%), and in construction trades (ED visits = AAPC 15.0%, 95% CI 10.4%–19.7%; hospitalizations = AAPC 6.5%, 95% CI 4.2%–8.8%). For mental and behavioral disorders, ED visits and hospitalizations were highest among workers in clerical work (ED visits = AAPC 12.4%, 95% CI 0.1%–26.2%; hospitalizations = AAPC 12.1%, 95% CI 7.9%–16.5%) and in materials handling (ED visits = AAPC 11.6%, 95% CI 1.8%–22.3%, hospitalizations = AAPC 14.2%, 95% CI 8.4%–20.3%).
Conclusion
Rates of opioid‐related harms significantly increased over time among injured workers in Ontario, with substantial increases within certain occupational groups. Findings suggest injured workers should be a priority in ongoing surveillance and policy responses.
Keywords: drug poisonings, opioid‐related disorders, surveillance, trends, workers' compensation, workplace
1. Introduction
In both Canada and the United States (US), drug‐related fatalities have emerged as a leading cause of accidental death [1, 2]. Between January 2016 and September 2025, more than 55,000 Canadians died from an opioid‐related poisoning [3], while approximately 800,000 drug‐related deaths occurred in the US over a comparable period [4]. The marked rise in drug‐related morbidity and mortality over the past two decades has been attributed to a succession of evolving drivers, beginning with liberal opioid prescribing practices in the early 2000s, and followed more recently by contamination of the illicit drug supply [5, 6]. In the United States, this pattern has been further characterized as including a distinct intermediate wave of heroin use before a shift to fentanyl [7]. The COVID‐19 pandemic coincided with a further acceleration in opioid‐related harms [8, 9, 10], although recent surveillance data suggest rates may be stabilizing or declining [4, 11].
Research on temporal trends in opioid‐related harms has primarily focused on the general population, including investigation of variation by age, sex, and other sociodemographic characteristics [4, 5, 12, 13]. A smaller body of research has explored trends within specific groups, such as people experiencing homelessness, individuals recently released from incarceration, and pregnant persons [14, 15, 16]. By contrast, relatively little is known about temporal trends in harms among the labor force, despite evidence that working‐age adults (namely males) consistently account for a disproportionate share of opioid‐related morbidity and mortality [4, 5, 11, 13].
A growing body of literature links work‐related injuries to opioid use and its harms [17]. Evidence from general working populations, largely from the US, indicates that workers in physically‐demanding occupations, including construction and trades, transportation, manufacturing, maintenance and repair, healthcare, and natural resources, are more likely to use prescribed opioids [18, 19, 20] and experience some of the highest rates of opioid‐related morbidity and mortality [21, 22, 23, 24, 25, 26, 27, 28, 29]. These occupational groups also bear a substantial burden of work‐related injury [30]. Among injured workers, opioid prescription is common after work injury [31, 32, 33, 34, 35, 36, 37], and both work injury itself and prolonged work disability after injury have been associated with increased risk of opioid‐related deaths [38, 39, 40] and non‐fatal opioid‐related harms, including dependence and abuse.
Building on this evidence, we previously used data from the Occupational Disease Surveillance System (ODSS) to examine occupational patterns in opioid‐related harms among a group of 1.7 million injured workers in Ontario, Canada. Elevated risks of hospital encounters for opioid‐related poisonings and mental and behavioral disorders were observed among workers in several occupational groups, including construction, transportation, forestry and logging, materials handling, processing (mineral, metal, chemical), processing (food, wood, textile), machining, and mining. These risks were elevated both relative to other injured workers in the ODSS and relative to the general working‐age population of Ontario [41, 42].
Collectively, these findings suggest that injured workers are an important subgroup of the labor force warranting targeted surveillance. Yet, despite accumulating evidence of elevated risk, most occupational research has focused on fatal opioid harms, and little is known about how the broader spectrum of opioid‐related harms among injured workers has evolved over time. Understanding temporal patterns is critical for identifying emerging high‐risk groups and informing prevention efforts. To address this gap, the current study aimed to estimate temporal trends in hospitalizations and emergency department visits for opioid‐related poisonings and mental and behavioral disorders among injured Ontario workers from 2006 to 2022, overall and by age, sex, and occupation.
2. Methods
2.1. Study Cohort and Data Sources
This study uses data from the Occupational Disease Surveillance System (ODSS) which was developed to monitor work‐related diseases among workers in Ontario, Canada through a series of linked administrative health databases. Briefly, the ODSS was established using the Workplace Safety and Insurance Board's (WSIB) accepted lost‐time workers' compensation claims records between 1983 and 2019 for approximately 2.37 million workers. The WSIB is Ontario's workers' compensation insurer, providing coverage to 70%–75% of Ontario workers. Workers from the claims records were linked to the Ontario Health Insurance Plan's Registered Persons Database (RPDB; 1990–2022) using deterministic and probabilistic linkage methods. The RPDB includes demographic information, death date (where applicable), residence, and a unique Health Insurance Number (HIN) for all Ontarians with provincial health insurance. A total of 1,973,312 workers were successfully linked to the RPDB and had a corresponding HIN. Using the HIN, workers were then deterministically linked to hospitalization (Discharge Abstract Database, DAD) and emergency department visit records (National Ambulatory Care Reporting System, NACRS) from 2006 to 2022. For this analysis, workers outside of prime working age (15–65 years) during follow‐up and those who died or emigrated out of Ontario before follow‐up were excluded (n = 283,677). As a result, 1,689,635 workers were included in this study. Detailed methods have been described previously [41, 42].
This study has ethics approval from the University of Toronto Health Sciences Research Ethics Board (#39013).
2.2. Outcomes
The outcomes of this study were hospitalizations and emergency department visits for opioid‐related poisonings and opioid‐related mental and behavioral disorders, identified in the DAD and NACRS, respectively, from 2006 to 2022 using diagnoses coded according to the International Classification of Diseases and Related Health Problems, 10th Revision, Canada (ICD‐10‐CA) (poisonings: T40.0–T40.4, T40.6; mental and behavioral disorders: F11.0–F11.9; see Supporting Information S1: Table 1 for a detailed list). Intent of poisoning was also identified using ICD‐10‐CA codes: X42, accidental poisonings; X62, intentional poisonings; and Y12, unknown poisonings.
2.3. Covariates
The sex and age of workers were obtained from the WSIB and supplemented using the RPDB, where this information is more up to date. Information on occupation was recorded at the time of each worker's claim in the WSIB records. Occupation was coded using the 1971 Canadian Classification Dictionary of Occupation (CCDO) at the four‐digit level. Using these codes, 12 occupational groups were created from CCDO's division groups (the broadest level of classification): medicine and health; clerical; sales; service; occupations in primary industries (combining farming, horticultural, and animal husbandry; fishing, hunting, trapping, and related; forestry and logging; mining and quarrying); processing; machining; product fabricating, assembling, and repairing; construction trades; transport and other equipment operating (combining transport equipment operating; other crafts and equipment operating); materials handling; and other occupations (combining managerial, administrative and related; natural sciences, engineering, and mathematics; social sciences and related; teaching and related; artistic, literary, recreational, and related).
2.4. Statistical Analyses
Annual age‐standardized incidence rates for each opioid‐related outcome from 2006 to 2022 were estimated for all workers in the ODSS, as well as by sex (males, females), and occupational group. Rates were standardized in 4‐year age groups [15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65] to the annual age‐distribution of the Ontario population for the same period using data provided by ICES (formerly known as the Institute for Clinical and Evaluative Sciences). Crude incidence rates for each opioid‐related outcome from 2006 to 2022 were also estimated by the same age groups. For poisonings, specifically, age‐standardized rates by intent of poisoning were estimated. All incidence rates were expressed as the number of cases per 100,000 person‐years.
Temporal trends in the age‐standardized incidence rates of each outcome were modeled using the Joinpoint Regression Program version 4.9.1.0 (National Cancer Institute, Bethesda, MD, USA—April 2022, available through the Surveillance Research Program of the US National Cancer Institute) [43]. Joinpoint regression is an analytic approach designed to capture the points (joinpoints) where a statistically significant change in the slope of a linear trend is identified, and to connect the points with different line segments on a log‐scale to the observed rates. We specified a minimum of zero joinpoints (i.e., representing a straight line) and a maximum of three joinpoints, as recommended for 17 data points [44] The Monte Carlo permutation method was used to identify statistically significant changes in trend at p < 0.05 and to determine the optimal number of joinpoints. For each regression line segment identified, an annual percent change (APC) in the incidence rate and associated 95% confidence interval (CI) was estimated by transforming the slope coefficient and tested to determine whether it was different from the null hypothesis (that APC = 0). For the full study period, the average annual percent change (AAPC) and its 95% CI was calculated as a summary measure of the trend from 2006 to 2022. The AAPC is estimated as a weighted average of the slope coefficients from all line segments, with weights equal to the number of years in each line segment. Both APC and AAPC were calculated overall and by sex, age, and occupational group. In all analyses, a p‐value less than 0.05 was considered statistically significant.
3. Results
A total of 1.69 million workers from the ODSS were included in this analysis. The characteristics of these workers, overall in the cohort and among cases of opioid‐related harms, are presented in Table 1. Workers in the ODSS were primarily over the age of 35 (80.9%), male (66.3%), and in service (15.3%) and product fabricating, assembling, and repairing occupations (12.0%).
Table 1.
Characteristics of injured workers in the ODSS (N = 1,689,635) and cases of opioid‐related poisonings and mental and behavioral disorders from 2006 to 2022.
| Characteristic | Injured workers in the ODSS | Cases of opioid‐related poisonings | Cases of opioid‐related mental & behavioral disorders | ||
|---|---|---|---|---|---|
| ED visits | Hospitalizations | ED visits | Hospitalizations | ||
| N (%) | N (%) | N (%) | N (%) | N (%) | |
| Overall | 1,689,635 (100.0) | 15,277 (100.0) | 5125 (100.0) | 19,724 (100.0) | 4778 (100.0) |
| Sex | |||||
| Males | 112,0120 (66.3) | 11,926 (78.1) | 3533 (68.9) | 15,960 (80.9) | 3609 (75.5) |
| Females | 569,515 (33.7) | 3351 (21.9) | 1592 (31.1) | 3764 (19.1) | 1169 (24.5) |
| Age a | |||||
| 15–34 | 410,687 (24.3) | 2542 (16.6) | 496 (9.7) | 4613 (23.4) | 491 (10.3) |
| 35–44 | 491,072 (29.1) | 4143 (27.1) | 1068 (20.8) | 6021 (30.5) | 1104 (23.1) |
| 45–54 | 480,860 (28.5) | 5063 (33.2) | 1790 (34.9) | 6096 (30.9) | 1656 (34.7) |
| 55–65 | 307,016 (18.2) | 3529 (23.1) | 1771 (34.6) | 2994 (15.2) | 1527 (32.0) |
| Intent of poisoning | |||||
| Accidental | 9170 (60.0) | 2543 (49.6) | |||
| Intentional | 3240 (21.2) | 1607 (31.4) | |||
| Unknown | 2350 (15.4) | 734 (14.3) | |||
| Occupation | |||||
| Medicine and health | 111,761 (6.4) | 577 (3.8) | 299 (5.8) | 596 (3.0) | 220 (4.6) |
| Clerical | 136,431 (7.8) | 838 (5.5) | 297 (5.8) | 963 (4.9) | 293 (6.1) |
| Sales | 114,992 (6.6) | 808 (5.3) | 296 (5.8) | 899 (4.6) | 207 (4.3) |
| Service | 267,701 (15.3) | 2,015 (13.2) | 723 (14.1) | 2536 (12.9) | 643 (13.5) |
| Occupations in primary industries b | 48,714 (2.8) | 432 (2.8) | 123 (2.4) | 801 (4.1) | 138 (2.9) |
| Processing | 108,420 (6.2) | 949 (6.2) | 355 (6.9) | 1242 (6.3) | 288 (6.0) |
| Machining | 116,183 (6.6) | 1158 (7.6) | 363 (7.1) | 1398 (7.1) | 344 (7.2) |
| Product fabricating, assembling, and repairing | 210,556 (12.0) | 1320 (8.6) | 504 (9.8) | 1932 (9.8) | 439 (9.2) |
| Construction trades | 151,837 (8.7) | 2141 (14.0) | 564 (11.0) | 2758 (14.0) | 671 (14.0) |
| Transport and other equipment operatingc | 138,190 (7.9) | 1009 (6.6) | 416 (8.1) | 1269 (6.4) | 345 (7.2) |
| Materials handling | 90,004 (5.1) | 953 (6.2) | 270 (5.3) | 1395 (7.1) | 262 (5.5) |
| Other occupations d | 134,320 (7.7) | 462 (3.0) | 182 (3.6) | 563 (2.9) | 130 (2.7) |
Abbreviations: ED, emergency department; ODSS, Occupational Disease Surveillance System.
Age at start of follow‐up for workers in the ODSS and age at diagnosis for cases of opioid‐related poisonings and mental and behavioral disorders.
Occupations in primary industries include the following occupational groups: farming, horticultural, and animal husbandry; fishing, hunting, trapping, and related; forestry and logging; mining and quarrying.
Transport and other equipment operating include the following occupational groups: transport equipment operating; other crafts and equipment operating.
Other occupations include the following occupational groups: managerial, administrative and related; natural sciences, engineering, and mathematics; social sciences and related; teaching and related; artistic, literary, recreational, and related.
Between 2006 and 2022, a total of 15,277 poisoning ED visits and 5,125 hospitalizations were identified among workers in the ODSS (Table 1). Similarly, a total of 19,724 ED visits and 4778 hospitalizations for opioid‐related mental and behavioral disorders were observed. For both types of harms, the largest number of cases occurred among males, workers between the ages of 35 and 65 years, and workers in service and construction trades occupations. Approximately half of all poisonings were accidental.
3.1. Trends in Opioid‐Related Poisonings
Among all workers in the ODSS, the overall age‐standardized rate of ED visits for opioid‐related poisonings increased from 25.0 cases per 100,000 person‐years in 2006 to 174.5 cases per 100,000 person‐years in 2022, for an AAPC of 12.1% (95% CI 4.9%, 19.8%). The largest increases were observed between 2015 and 2019, when the rates increased 35.0% per year (95% CI 6.6%, 70.9%) (Table 2; Figure 1). A more gradual increase occurred in the rates of poisoning hospitalizations over the course of follow‐up (AAPC 3.8%, 95% CI 2.3%, 5.4%).
Table 2.
Age‐standardized rates (per 100,000 person‐years), annual percent changes, and average annual percent changes for opioid‐related poisonings among injured workers in the ODSS (N = 1,689,635) from 2006 to 2022.
| Emergency Department Visits for Poisonings | Hospitalizations for Poisonings | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Observed Age‐Standardized Ratesa | AAPC (95% CI)b | Year Segments | APC (95% CI)b | Observed Age‐Standardized Ratesa | AAPC (95% CI)b | Year Segments | APC (95% CI)b | |||
| 2006 | 2022 | 2006 | 2022 | |||||||
| Overall cohort | 25.0 | 174.5 | 12.1 (4.9, 19.8) | 2006–2015 | 5.9 (−0.1, 12.2) | 11.4 | 23.3 | 3.8 (2.3, 5.4) | 2006–2022 | 3.8 (2.3‐5.4) |
| 2015–2019 | 35.0 (6.6, 70.9) | |||||||||
| 2019–2022 | 3.9 (−14.0, 25.5) | |||||||||
| Sex | ||||||||||
| Males | 24.4 | 219.0 | 13.5 (8.0, 19.2) | 2006–2015 | 7.1 (2.1, 12.4) | 9.8 | 27.3 | 5.0 (3.3, 6.7) | 2006–2022 | 5.0 (3.3, 6.7) |
| 2015–2020 | 35.0 (20.6, 51.1) | |||||||||
| 2020–2022 | −4.8 (−28.6, 26.9) | |||||||||
| Females | 26.7 | 88.9 | 8.8 (6.7, 11.0) | 2006–2022 | 8.8 (6.7, 11.0) | 15.1 | 14.9 | 1.4 (−0.6, 3.5) | 2006–2022 | 1.4 (−0.6, 3.5) |
| Age c | ||||||||||
| 15–34 | 24.1 | 213.6 | 12.8 (2.9, 23.7) | 2006–2015 | 6.4 (−0.1, 13.5) | 9.4 | 18.0 | 5.0 (2.8, 7.2) | 2006–2022 | 5.0 (2.8, 7.2) |
| 2015–2018 | 46.4 (−11.8, 142.8) | |||||||||
| 2018–2022 | 5.8 (−6.5, 19.9) | |||||||||
| 35–44 | 30.4 | 230.8 | 13.2 (8.9, 17.6) | 2006–2015 | 4.7 (0.7, 8.8) | 14.0 | 27.0 | 4.2 (2.7, 5.7) | 2006–2022 | 4.2 (2.7, 5.7) |
| 2015–2020 | 40.1 (27.9, 53.5) | |||||||||
| 2020–2022 | −5.6 (−23.5, 16.4) | |||||||||
| 45–54 | 26.3 | 141.9 | 9.7 (3.5, 16.3) | 2006–2015 | 4.0 (−1.6, 10.0) | 11.9 | 24.0 | 4.4 (1.5, 7.4) | 2006–2009 | 27.1 (8.6, 48.8) |
| 2015–2020 | 26.1 (10.6, 43.8) | 2009–2022 | −0.3 (−1.6, 1.1) | |||||||
| 2020–2022 | −1.4 (−30.0, 38.9) | |||||||||
| 55–65 | 17.7 | 85.6 | 10.1 (7.1, 13.2) | 2006–2016 | 8.7 (6.0, 11.4) | 11.2 | 28.3 | 5.7 (2.7, 8.8) | 2006–2012 | 16.5 (7.8, 25.8) |
| 2016–2020 | 22.5 (12.4, 33.5) | 2012–2022 | −0.2 (−2.4, 2.0) | |||||||
| 2020–2022 | −4.8 (−17.3, 9.6) | |||||||||
| Intent of poisoning | ||||||||||
| Unintentional | 8.6 | 137.0 | 18.7 (13.5, 24.1) | 2006–2015 | 11.2 (5.5, 17.2) | 3.3 | 13.7 | 8.4 (6.3, 10.5) | 2006–2022 | 8.4 (6.3, 10.5) |
| 2015–2020 | 46.1 (32.1, 61.5) | |||||||||
| 2020–2022 | −5.3 (−24.3, 18.4) | |||||||||
| Intentional | 8.1 | 19.0 | 4.5 (−0.4, 9.7) | 2006–2008 | 31.6 (0.9, 71.6) | 5.2 | 5.8 | −0.4 (−2.5, 1.6) | 2006–2022 | −0.4 (−2.5, 1.6) |
| 2008–2015 | −0.4 (−4.4, 3.7) | |||||||||
| 2015–2018 | 19.1 (−5.3, 49.7) | |||||||||
| 2018–2022 | −9.1 (−15.3, −0.2) | |||||||||
| Unknown | 5.7 | 18.8 | 8.7 (6.2, 11.2) | 2006–2022 | 8.7 (6.2, 11.2) | 1.8 | 3.7 | 3.5 (1.0, 6.1) | 2006–2012 | 14.4 (8.1, 21.0) |
| 2012–2022 | −2.5 (−5.1, 0.2) | |||||||||
| Occupational group | ||||||||||
| Medicine and health | 41.1 | 103.1 | 8.5 (5.0, 12.2) | 2006–2022 | 8.5 (5.0, 12.2) | 21.7 | 25.1 | 0.4 (−4.9, 6.0) | 2006–2022 | 0.4 (−4.9, 6.0) |
| Clerical | 19.8 | 135.7 | 11.4 (−0.7, 24.9) | 2006–2015 | 6.8 (−0.2, 14.4) | 8.1 | 21.6 | 4.4 (1.4, 7.4) | 2006–2022 | 4.4 (1.4, 7.4) |
| 2015–2018 | 42.6 (−25.0, 170.9) | |||||||||
| 2018–2022 | 1.6 (−13.3, 19.0) | |||||||||
| Sales | 35.6 | 118.2 | 11.0 (8.1, 14.0) | 2006–2022 | 11.0 (8.1, 14.0) | 9.8 | 16.2 | 3.5 (−1.0, 8.2) | 2006–2012 | 18.8 (6.3, 32.8) |
| 2012–2022 | −4.7 (−8.7, −0.5) | |||||||||
| Service | 20.4 | 111.7 | 9.5 (0.2, 19.7) | 2006–2015 | 5.0 (−0.8, 11.3) | 8.5 | 14.2 | 1.8 (−1.4, 5.1) | 2006–2022 | 1.8 (−1.4, 5.1) |
| 2015–2018 | 38.4 (−15.4, 126.5) | |||||||||
| 2018–2022 | 0.8 (−11.0, 14.2) | |||||||||
| Occupations in primary industries d | 15.3 | 182.6 | 11.2 (2.4, 20.8) | 2006–2012 | −2.0 (−21.2, 22.0) | 5.2 | 26.0 | 2.0 (−4.7, 9.1) | 2006–2022 | 2.0 (−4.7, 9.1) |
| 2012–2022 | 20.0 (12.3, 28.3) | |||||||||
| Processing | 28.7 | 237.7 | 16.1 (12.5, 19.8) | 2006–2022 | 16.1 (12.5, 19.8) | 17.8 | 23.6 | 1.6 (−3.1, 6.6) | 2006–2022 | 1.6 (−3.1, 6.6) |
| Machining | 14.0 | 301.3 | 18.5 (0.0, 40.5) | 2006–2011 | 25.3 (2.7, 52.9) | 7.9 | 37.4 | 8.1 (3.4, 13.0) | 2006–2022 | 8.1 (3.4, 13.0) |
| 2011–2014 | −8.7 (−65.4, 140.6) | |||||||||
| 2014–2022 | −6.7 (−46.1, 61.3) | |||||||||
| Product fabricating, assembling, and repairing | 26.0 | 182.6 | 14.1 (11.5, 16.7) | 2006–2022 | 14.1 (11.5, 16.7) | 9.0 | 12.2 | 3.5 (−0.8, 7.8) | 2006–2022 | 3.5 (−0.8, 7.8) |
| Construction trades | 32.4 | 293.1 | 15.0 (10.4, 19.7) | 2006–2015 | 9.7 (4.9, 14.8) | 16.1 | 40.2 | 6.5 (4.2, 8.8) | 2006–2022 | 6.5 (4.2, 8.8) |
| 2015–2020 | 38.4 (26.8, 51.1) | |||||||||
| 2020–2022 | −10.9 (−28.6, 11.2) | |||||||||
| Transport and other equipment operatinge | 18.5 | 123.6 | 9.3 (3.7, 15.2) | 2006–2022 | 9.3 (3.7, 15.2) | 8.0 | 16.3 | 0.8 (−3.3, 5.1) | 2006–2022 | 0.8 (−3.3, 5.1) |
| Materials handling | 20.1 | 201.9 | 16.3 (12.4, 20.3) | 2006–2022 | 16.3 (12.4, 20.3) | 14.0 | 22.4 | 3.9 (−0.4, 8.4) | 2006–2022 | 3.9 (−0.4, 8.4) |
| Other occupations f | 14.4 | 51.8 | 7.6 (4.3, 10.9) | 2006–2022 | 7.6 (4.3, 10.9) | 10.8 | 24.8 | 1.2 (−3.1, 5.7) | 2006–2022 | 1.2 (−3.1, 5.7) |
Abbreviations: AAPC, average annual percent change; APC, annual percent change; CI, confidence interval.
Age‐standardized rates per 100,000 person‐years, overall and by sex, intent, and occupation. Age‐specific crude rates by age.
Bold denotes statistically significant (p < 0.05) increase in percent change. Bold italicized denotes statistically significant (p < 0.05) decrease in percent change.
Age at diagnosis of opioid‐related poisonings.
Occupations in primary industries include the following occupational groups: farming, horticultural, and animal husbandry; fishing, hunting, trapping, and related; forestry and logging; mining and quarrying.
Transport and other equipment operating include the following occupational groups: transport equipment operating; other crafts and equipment operating.
Other occupations include the following occupational groups: managerial, administrative and related; natural sciences, engineering, and mathematics; social sciences and related; teaching and related; artistic, literary, recreational, and related.
Figure 1.
Age‐standardized rates (per 100,000 person‐years) for opioid‐related poisonings among injured workers in the ODSS (N = 1,689,635) from 2006 to 2022.
Increases in age‐standardized rates of opioid‐related poisonings were seen among both sexes (Table 2). This was particularly evident for males, for whom rates of ED visits increased an average of 13.5% per year (95% CI 8.0%, 19.2%) and rates of hospitalizations increased an average of 5.0% per year (95% CI 3.3%, 6.7%). The pattern of findings also mirrored that seen in the overall sample (Table 2; Supporting Information S1: Figure 1).
Age‐specific rates of opioid‐related poisonings significantly increased for all age groups (Table 2). For ED visits, the largest rate increases were seen among workers 15–34 years (AAPC 12.8%, 95% CI 2.9%, 23.7%) and 35–44 years (AAPC 13.2%, 95% CI 8.9%, 17.6%). The pattern of findings in ED visits was similar across age groups, where the largest rate increases occurred between 2015 and 2020 (Table 2; Supporting Information S1: Figure 2). For hospitalizations, gradual increases were seen among workers aged 15–44, while workers aged 45–65 experienced steeper increases earlier in the follow‐up period, leveling off thereafter (Table 2; Supporting Information S1: Figure 2).
Increases in the age‐standardized rates of accidental poisonings were observed (Table 2; Supporting Information S1: Figure 3). Rates of ED visits for accidental poisonings increased on average 18.7% per year (95% CI 13.5%, 24.1%), with the sharpest increases occurring from 2015 to 2020 (Table 2). The rate of hospitalizations for accidental poisonings gradually rose over the course of follow‐up (AAPC 8.4%, 95% CI 6.3%, 10.5%). Smaller increases in the age‐standardized rates of poisonings of unknown intent were also observed (ED visits AAPC 8.7%, 95% CI 6.2%, 11.2%; hospitalizations AAPC 3.5%, 95% CI 1.0%, 6.1%) (Table 2; Supporting Information S1: Figure 3). On the other hand, fluctuations in the rates of ED visits for intentional poisonings were observed over the course of follow‐up (Table 2; Supporting Information S1: Figure 3). Intentional poisonings increased significantly from 2006 to 2008 (AAPC 31.6%, 95% CI 0.9%, 71.6%) and then plateaued until 2015, when another non‐statistically significant rise in rates was seen (2015–2018 AAPC 19.1%, 95% CI −5.3%, 49.7%). From 2018 to 2022, rates significantly decreased 9.1% per year (95% CI −15.3%, −0.2%). No significant change in the rates of hospitalizations for intentional poisonings was observed.
Age‐standardized rates of poisoning ED visits increased for workers in all occupational groups, though increases were not always statistically significant (Table 2). Some of the largest increases over the 2006–2022 time period were seen among workers in machining (AAPC 18.5%, 95% CI 0.0%, 40.5%), materials handling (AAPC 16.3%, 95% CI 12.4%, 20.35), processing (AAPC 16.1%, 95% CI 12.5%, 19.8%), construction trades (AAPC 15.0%, 95% CI 10.4%, 19.7%), and product fabricating, assembling, and repairing (AAPC 14.1%, 95% CI 11.5%, 16.7%). In this group, observed rates in 2022 ranged from 182.6 cases per 100,000 person‐years among workers in product fabricating, assembling, and repairing to 301.3 cases per 100,000 person‐years among workers in machining. Rates of opioid‐related poisoning hospitalizations increased significantly from 2006 to 2022 for workers in machining (AAPC 8.1%, 95% CI 3.4%, 13.0%), construction trades (AAPC 6.5%, 95% CI 4.2%, 8.8%), and clerical occupations (AAPC 4.4%, 95% CI 1.4%, 7.4%). For workers in sales, rates of hospitalizations increased significantly from 2006 to 2012 (APC 18.8%, 95% CI 6.3%, 32.8%), but decreased thereafter (APC −4.7%, 95% CI −8.7%, −0.5%). Trends for occupations are presented in Figure 2.
Figure 2.
Age‐standardized rates (per 100,000 person‐years) for opioid‐related poisonings by occupation among injured workers in the ODSS (N = 1,689,635) from 2006 to 20.
3.2. Trends in Opioid‐Related Mental and Behavioral Disorders
Among all workers in the ODSS, the overall age‐standardized rate of ED visits for opioid‐related mental and behavioral disorders increased from 57.5 cases per 100,000 person‐years in 2006 to 179.0 cases per 100,000 person‐years in 2022 (AAPC 7.9%, 95% CI 3.5%, 12.4%), with increases primarily seen between 2006 and 2010 (APC 24.2%, 95% CI 12.2%, 37.5%) and from 2014 to 2022 (AAPC 8.4%, 95% CI 5.0%, 12.0%) (Table 3; Figure 3). A steady increase in the rate of hospitalizations was observed, increasing from an observed age‐standardized rate of 7.9 cases per 100,000 person‐years in 2006 to 46.5 cases per 100,000 person‐years in 2022 (AAPC 11.6%, 95% CI 9.9%, 13.5%).
Table 3.
Age‐standardized rates (per 100,000 person‐years), annual percent changes, and average annual percent changes for opioid‐related mental and behavioral disorders among injured workers in the ODSS (N = 1,689,635) from 2006 to 2022.
| Emergency Department Visits for Mental & Behavioral Disorders | Hospitalizations for Mental & Behavioral Disorders | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Observed Age‐Standardized Ratesa | AAPC (95% CI)b | Year Segments | APC (95% CI)b | Observed Age‐ Standardized Ratesa | AAPC (95% CI)b | Year Segments | APC (95% CI)b | |||
| 2006 | 2022 | 2006 | 2022 | |||||||
| Overall cohort | 57.5 | 179.0 | 7.9 (3.5, 12.4) | 2006–2010 | 24.2 (12.2, 37.5) | 7.9 | 46.5 | 11.6 (9.9, 13.5) | 2006–2022 | 11.6 (9.9, 13.5) |
| 2010–2014 | −7.2 (−20.0, 7.5) | |||||||||
| 2014–2022 | 8.4 (5.0, 12.0) | |||||||||
| Sex | ||||||||||
| Males | 59.9 | 216.5 | 8.5 (3.4, 13.8) | 2006–2010 | 25.1 (11.0, 41.0) | 7.6 | 54.0 | 13.2 (11.2, 15.3) | 2006–2022 | 13.2 (11.2, 15.3) |
| 2010–2014 | −7.3 (−21.9, 10.0) | |||||||||
| 2014–2022 | 9.3 (5.3, 13.6) | |||||||||
| Females | 47.1 | 107.6 | 6.6 (−1.6, 15.5) | 2006–2011 | 17.8 (6.1, 30.8) | 9.0 | 31.1 | 7.3 (4.8, 9.9) | 2006–2022 | 7.3 (4.8, 9.9) |
| 2011–2014 | −10.7 (−42.6, 39.0) | |||||||||
| 2014–2022 | 7.1 (2.0, 12.3) | |||||||||
| Age c | ||||||||||
| 15–34 | 73.4 | 219.5 | 7.9 (3.1, 12.9) | 2006–2010 | 31.1 (16.5, 47.6) | 5.8 | 36.0 | 11.5 (7.8, 15.3) | 2006–2022 | 11.5 (7.8, 15.3) |
| 2010–2014 | −8.2 (−21.5, 7.2) | |||||||||
| 2014–2022 | 6.1 (2.0, 10.3) | |||||||||
| 35–44 | 59.5 | 288.3 | 10.5 (5.9, 15.3) | 2006–2009 | 23.0 (3.2, 46.4) | 9.3 | 72.7 | 13.1 (9.5, 16.9) | 2006–2016 | 7.5 (3.0, 12.3) |
| 2009–2014 | −2.0 (−11.8, 8.9) | 2016–2022 | 23.1 (15.4, 31.3) | |||||||
| 2014–2022 | 14.4 (10.8, 18.1) | |||||||||
| 45–54 | 40.6 | 116.0 | 7.3 (1.7, 13.2) | 2006–2009 | 29.9 (0.4, 68.1) | 7.9 | 52.2 | 10.6 (8.7, 12.5) | 2006–2022 | 10.6 (8.7, 12.5) |
| 2009–2016 | −3.6 (−9.9, 3.1) | |||||||||
| 2016–2022 | 10.5 (3.2, 18.3) | |||||||||
| 55–65 | 40.7 | 66.5 | 6.1 (4.2, 8.0) | 2006–2022 | 6.1 (4.2, 8.0) | 10.8 | 35.8 | 10.3 (8.7, 12.0) | 2006–2022 | 10.3 (8.7, 12.0) |
| Occupation | ||||||||||
| Medicine and health | 45.8 | 68.0 | 3.6 (0.2, 7.1) | 2006–2022 | 3.6 (0.2, 7.1) | 18.0 | 14.5 | 2.2 (−3.1, 7.9) | 2006–2022 | 2.2 (−3.1, 7.9) |
| Clerical | 23.4 | 88.5 | 12.4 (0.1, 26.2) | 2006–2011 | 38.7 (18.3, 62.7) | 6.0 | 38.4 | 12.1 (7.9, 16.5) | 2006–2022 | 12.1 (7.9, 16.5) |
| 2011–2022 | 10.6 (2.4, 19.4) | |||||||||
| Sales | 30.8 | 126.1 | 10.2 (3.8, 17.1) | 2006–2010 | 33.1 (10.2, 60.8) | 2.7 | 26.8 | 12.2 (7.6, 17.1) | 2006–2022 | 12.2 (7.6, 17.1) |
| 2010–2015 | −4.4 (−17.1, 10.2) | |||||||||
| 2015–2022 | 9.6 (3.4, 16.1) | |||||||||
| Service | 37.3 | 153.0 | 8.5 (0.8, 16.8) | 2006–2008 | 54.9 (−18.4, 194.1) | 4.1 | 43.1 | 11.3 (8.1, 14.6) | 2006–2022 | 11.3 (8.1, 14.6) |
| 2008–2022 | 3.1 (1.1, 5.2) | |||||||||
| Occupations in primary industries d | 68.5 | 137.2 | 4.7 (1.4, 8.1) | 2006–2022 | 4.7 (1.4, 8.1) | 20.8 | 42.1 | 5.3 (−3.6, 15.1) | 2006–2008 | −25.2 (−65.0, 59.9) |
| 2008–2022 | 10.6 (7.4, 13.9) | |||||||||
| Processing | 70.5 | 229.4 | 7.9 (2.8, 13.2) | 2006–2010 | 24.9 (3.2, 51.1) | 4.7 | 60.8 | 15.7 (−1.2, 35.5) | 2006–2010 | 51.7 (−11.3, 159.4) |
| 2010–2022 | 2.8 (−0.6, 6.2) | 2010‐2017 | −8.5 (−27.4, 15.2) | |||||||
| 2017‐2022 | 29.5 (3.4, 62.3) | |||||||||
| Machining | 75.9 | 267.4 | 8.5 (6.3, 10.7) | 2006–2022 | 8.5 (6.3, 10.7) | 6.2 | 40.8 | 15.9 (10.5, 21.6) | 2006–2022 | 15.9 (10.5, 21.6) |
| Product fabricating, assembling, and repairing | 58.0 | 117.9 | 4.7 (1.8, 7.6) | 2006–2022 | 4.7 (1.8, 7.6) | 7.0 | 43.1 | 10.5 (1.9, 19.8) | 2006–2018 | 2.3 (−4.4, 9.5) |
| 2018–2022 | 39.1 (3.7, 86.7) | |||||||||
| Construction trades | 88.9 | 343.2 | 9.8 (5.0, 14.7) | 2006–2010 | 25.4 (9.0, 44.3) | 13.8 | 90.9 | 13.6 (6.9, 20.8) | 2006–2012 | −0.6 (−15.6, 16.9) |
| 2010–2016 | −4.2 (−11.7, 3.9) | 2012–2022 | 23.1 (17.4, 29.1) | |||||||
| 2016–2022 | 15.2 (8.8, 21.9) | |||||||||
| Transport and other equipment operatinge | 50.2 | 117.5 | 4.2 (0.8, 7.8) | 2006–2022 | 4.2 (0.8, 7.8) | 3.4 | 26.6 | 9.4 (4.7, 14.3) | 2006–2022 | 9.4 (4.7, 14.3) |
| Materials handling | 33.5 | 240.4 | 11.6 (1.8, 22.3) | 2006–2008 | 103.7 (−1.2, 320.3) | 4.6 | 69.7 | 14.2 (8.4, 20.3) | 2006–2022 | 14.2 (8.4, 20.3) |
| 2008–2015 | −9.0 (−16.9, −0.3) | |||||||||
| 2015–2022 | 15.2 (5.7, 25.6) | |||||||||
| Other occupations f | 106.9 | 34.1 | −4.2 (−8.1, −0.2) | 2006–2022 | −4.2 (−8.1, −0.2) | 5.6 | 5.7 | 3.5 (−2.3, 9.7) | 2006–2022 | 3.5 (−2.3, 9.7) |
Abbreviations: AAPC, average annual percent change; APC, annual percent change; CI, confidence interval.
Age‐standardized rates per 100,000 person‐years, overall and by sex and occupation. Age‐specific crude rates by age.
Bold denotes statistically significant (p < 0.05) increase in percent change. Bold italicized denotes statistically significant (p < 0.05) decrease in percent change.
Age at diagnosis of opioid‐related mental and behavior disorders.
Occupations in primary industries include the following occupational groups: farming, horticultural, and animal husbandry; fishing, hunting, trapping, and related; forestry and logging; mining and quarrying.
Transport and other equipment operating include the following occupational groups: transport equipment operating; other crafts and equipment operating.
Other occupations include the following occupational groups: managerial, administrative and related; natural sciences, engineering, and mathematics; social sciences and related; teaching and related; artistic, literary, recreational, and related.
Figure 3.
Age‐standardized rates for opioid‐related mental and behavioral disorders among injured workers in the ODSS (N = 1,689,635) from 2006 to 2022.
Age‐standardized rates of opioid‐related mental and behavioral disorders increased among both sexes in a pattern similar to that seen in the overall sample (Table 3; Supporting Information S1: Figure 4). For males, the rate of ED visits increased on average 8.5% per year (95% CI 3.4%, 13.8%), while hospitalizations increased an average of 13.2% annually (95% CI 11.2%, 15.3%). For females, the average annual percent change in the rates of ED visits was not statistically significant (AAPC 6.6%, 95% CI −1.6%, 15.5%), although statistically significant increases were seen from 2006 to 2011 and from 2014 to 2022. The rate of hospitalizations among females increased significantly by 7.3% on average per year (95% CI 4.8%, 9.9%).
Significant increases in the rates of both ED visits and hospitalizations for mental and behavioral disorders were observed across all age groups (Table 3; Supporting Information S1: Figure 5). Overall, the largest observed rates and increases in rates were seen among workers aged 35–44 for both ED visits (AAPC 10.5%, 95% CI 5.9%, 15.3%) and hospitalizations (AAPC 13.1%, 95% CI 9.5%, 16.9%). The pattern of findings across age groups mirrored that seen overall in the ODSS cohort (increases in the first and last third of follow‐up), with some exceptions. Notably, workers aged 55–65 had a more gradual increase in the rate of ED visits, while workers aged 35–44 had a sharp increase in hospitalizations from 2016 to 2022 (Table 3; Supporting Information S1: Figure 5).
Age‐standardized rates of ED visits for mental and behavioral disorders increased for workers in all occupations from 2006 to 2022, except for those in other occupations (i.e., managerial, administrative and related; natural sciences, engineering, and mathematics; social sciences and related; teaching and related; artistic, literary, recreational, and related) where a significant decrease over the course of follow‐up was observed (AAPC −4.2%, 95% CI −8.1%, −0.2%) (Table 3). Some of the largest increases were seen among workers in clerical occupations (AAPC 12.4%, 95% CI 0.1%, 26.2%), materials handling (AAPC 11.6%, 95% CI 1.8%, 22.3%), sales (AAPC 10.2%, 95% CI 3.8%, 17.1%), and construction trades (AAPC 9.8%, 95% CI 5.0%, 14.7%) from 2006 to 2022. Increases in rates of hospitalizations for opioid‐related mental and behavioral disorders over the same time period were of a similar magnitude and statistically significant for workers in most occupational groups, except medicine and health, occupations in primary industries, processing, and other occupations. Across both data sources, workers in construction and trades, materials handling, processing, and machining had some of the highest observed rates of mental and behavioral disorders in 2022, ranging from 229.4 cases to 343.2 cases per 100,000 person‐years for ED visits, and from 40.8 cases to 90.0 cases per 100,000 person years for hospitalizations. Trends for occupations are presented in Figure 4.
Figure 4.
Age‐standardized rates for opioid‐related mental and behavioral disorders by occupation among injured workers in the ODSS (N = 1,689,635) from 2006 to 2022.
4. Discussion
This study examined trends in opioid‐related harms from 2006 to 2022 among a large cohort of injured workers in Ontario, Canada. Overall, age‐standardized rates of hospital encounters for both opioid‐related poisonings (namely those of unintentional and undetermined intent) and mental and behavioral disorders rose significantly over the follow‐up period, with the steepest increases after 2014. Although rate increases were seen across all demographic and occupational groups, the magnitude of increase varied, with some workers experiencing disproportionately larger increases.
The overall temporal pattern observed in this cohort of injured workers closely mirrors population‐level trends reported in Canada [5, 11, 45, 46, 47]. Broad drivers of the opioid crisis, including the earlier era of liberal opioid prescribing and more recent contamination of the unregulated drug supply with illicit synthetic opioids [6], have likely contributed to the timing of the increases in this cohort. However, our data do not permit direct measurement of the relative contributions of opioid prescribing history, changes in access to prescribed opioids due to policy and practice shifts, or use of illicit opioids to the trends in harms observed here, representing an important avenue for future research.
Our study found that opioid‐related harms increased across all age groups, but workers aged 35–44 years generally demonstrated the highest rates of harm and the steepest rate increases over time. Similarly, while rates of harm were comparable between sexes at baseline, subsequent increases were markedly more pronounced among males. This widening divergence in rates aligns with population‐level evidence documenting a disproportionate burden of opioid‐related harm among males [5, 11, 45, 46, 48]. These distinct patterns in harms by sex likely reflect a complex interplay of biological, behavioral, and social factors, including sex‐based differences in physiological responses to pain and opioids, and gendered differences in prescribing patterns, motivations for use, risk‐taking propensities, and help‐seeking behavior [49, 50, 51, 52]. The findings may also be driven by occupational segregation by sex, whereby men and women are unequally distributed across occupations, for example by a higher proportion of men employed in high hazard occupations. This pattern is explored further below in relation to occupational shifts.
Indeed, occupational patterns in opioid‐related harms also evolved over time. In 2006, the highest rates were most consistently observed among workers in health and construction occupations, with higher rates for some harms also seen in processing, sales, primary industry, and other occupations. By 2022, however, the highest rates of harms had become increasingly concentrated among workers in machining, construction, processing, and materials handling. Although the reasons for this shift cannot be directly assessed in the present study, the pattern may reflect, in part, how broader changes in the drivers of the opioid crisis intersected with the sex composition and working conditions of specific occupations. For instance, the early prominence of health occupations during the prescription‐driven wave of the crisis may have been shaped by healthcare workers' greater access to medications in clinical settings [53, 54, 55] and higher prescribing rates and help‐seeking among women [49, 50], who make up a substantial share of the health workforce [56]. As prescribing practices tightened and harms became increasingly linked to the illicit drug supply, the highest rates shifted toward predominantly male‐dominated, physically demanding occupations. In these occupations, elevated injury burden and chronic pain may increase opportunities for opioid use [30], masculine norms may promote working through pain and discourage help‐seeking [57], and a higher propensity for illicit drug use among men may increase likelihood of exposure to a contaminated drug supply [49, 58, 59]. Together, these intersecting factors may help explain the growing concentration of harms in manual occupations over time.
Notable increases in some opioid‐related harms were also observed among workers in occupations not typically characterized by heavy physical demands, including clerical and sales occupations. These findings point to the potential importance of broader psychosocial and organizational stressors beyond physical job demands that may contribute to opioid‐related harm through stress‐related and self‐medication pathways, such as high psychological and time demands, low autonomy, effort‐reward imbalance, long or irregular work hours, repetitive work, and job insecurity [17, 60, 61, 62, 63]. Further research is needed to identify which factors are most relevant within different occupational groups. Nonetheless, these patterns suggest that workers in occupations not traditionally considered high‐risk may warrant greater attention in surveillance and prevention planning.
Results from this study collectively underscore the importance of incorporating occupational information into surveillance and research on opioid‐related harms. Aggregate population‐level analyses of trends may obscure meaningful variation across worker subgroups, while occupation‐specific analyses can help identify groups that may benefit from more targeted prevention efforts. The magnitude of the rates observed in this cohort further highlights injured workers as a priority population. In a previous analysis of these data, we found that rates of opioid‐related hospital encounters among injured workers were significantly higher than those in the Ontario general working‐age population [41]. Consistent with this prior work, the present study found that by 2022, the age‐standardized rate of ED visits for opioid‐related poisonings had risen 598% since 2006 (AAPC 12.1, 95% CI 4.9, 19.8), reaching 174.5 per 100,000 workers, compared with a corresponding rate of 111.9 per 100,000 among Ontarians aged 15–64 in the general population [45].
Although this comparison should be interpreted cautiously given potential differences between injured workers and the broader population, it nonetheless emphasizes the substantial burden of opioid‐related harms in the former. This burden is likely multifactorial, reflecting complex interactions between workers' experiences of pain and poor mental health following injury [64, 65, 66, 67, 68, 69], historically high rates of opioid prescribing among injured workers [31, 32, 33, 34, 35, 36], and systemic barriers to recovery, including return‐to‐work difficulties and limited access to alternative pain management [17, 70].
This research builds upon the growing body of evidence in this area by shedding light on trends in opioid‐related harms within a cohort of injured workers. The use of linked administrative health data from 1.7 million injured workers spanning 17 years stands as a significant strength of this study, providing substantial statistical power to examine temporal trends across sex, age, and broad occupational groups.
Several limitations of this study also warrant consideration. The ODSS cohort includes only workers with accepted lost‐time workers' compensation claims, thereby overrepresenting workers in occupations with heightened injury risks. Consequently, absolute rates within occupational groups may not be representative of all injured workers. For instance, workers captured in lower‐injury occupations (e.g., clerical and sales) may represent a more selected subset of injured workers whose injuries are severe enough to warrant an accepted claim, potentially resulting in higher observed absolute rates. However, we could not evaluate this possibility as information on injuries that did not result in accepted lost‐time claims was unavailable. Importantly, given that our analyses examined temporal changes within occupations, such selection would be unlikely to have substantially affected the observed trends, provided the composition of workers captured by the compensation system remained relatively stable over time.
The cohort does not capture injured workers whose claims were not filed or accepted. Occupational data are only captured at the time of the worker's compensation claim, with no information on subsequent changes in occupation. We also could not confirm that individuals remained employed throughout follow‐up or at the time of the opioid‐related harm, although analyses were restricted to those aged 15–65 years during follow‐up to increase the likelihood that observed harms occurred while individuals were in the labor force. Further, although the extended follow‐up period was a strength of this study, allowing the capture of opioid‐related harms that may develop over time following a work injury, harms occurring many years after injury may also have been influenced by subsequent clinical, occupational, and social factors. Consequently, the extent to which these harms can be attributed to the original work injury could not be determined from the available data. Finally, follow‐up ended in 2022, limiting our ability to characterize the most recent trends. While rates of opioid‐related harms in the Ontario general population have declined since 2021 [45], our analyses did not identify a statistically significant joinpoint in 2022, which may have been detected with further years of observation.
This study has demonstrated that opioid‐related poisonings and mental and behavioral disorders increased significantly among injured workers in Ontario from 2006 to 2022, with particularly large increases among men, workers aged 35–44 years, and several physically‐demanding occupations. These findings support the need to consider occupation and work‐related injury in opioid harm prevention and harm reduction activities, and suggest injured workers should be a priority population in ongoing surveillance and policy responses.
Author Contributions
Jeavana Sritharan: conceptualization, analysis, methodology, interpretation of data, drafting the original work, reviewing and editing work, final approval of the version being published, and agreement for accountability in all aspects of the work. Chaojie Song: analysis, methodology, reviewing and editing of work, final approval of the version being published, and agreement for accountability in all aspects of the work; Paul A. Demers: funding acquisition, conceptualization, reviewing & editing of work, final approval of the version being published, and agreement for accountability in all aspects of the work. Fateme Kooshki: reviewing and editing of work, final approval of the version being published, and agreement for accountability in all aspects of the work. Nancy Carnide: funding acquisition, conceptualization, analysis, methodology, interpretation of data, reviewing and editing work, final approval of the version being published, and agreement for accountability in all aspects of the work.
Disclosure
The views expressed in this report do not necessarily represent the views of the Public Health Agency of Canada. The funders had no role in the conduct of this study, in the writing of the report, or in the decision to submit the article for publication. All inferences, opinions and conclusions drawn in this report are those of the authors and do not reflect those of the Province of Ontario.
Ethics Statement
This study was approved by the University of Toronto Health Sciences Research Ethics Board (reference 39013).
Conflicts of Interest
The authors declare no conflicts of interest.
Supporting information
Supporting File 1
Acknowledgments
We would like to acknowledge Nelson Chong for his support with the ODSS data linkage. This project has been made possible through funding from the Public Health Agency of Canada (2021‐ HQ‐ 000092). The ODSS was initially funded by the Ontario Ministry of Labour, Immigration, Training and Skills Development (14‐ R‐ 029) and the Public Health Agency of Canada (1516‐ HQ‐ 000066). Both the Institute for Work & Health and the Occupational Cancer Research Centre receive support from Ontario's Ministry of Labour, Immigration, Training and Skills Development. The Occupational Cancer Research Centre also receives support from Ontario Health.
Sritharan J., Song C., Demers P. A., Kooshki F., and Carnide N., “Trends in Opioid‐Related Poisonings and Mental and Behavioural Disorders From 2006 to 2022 Among a Large Cohort of Injured Workers in Ontario, Canada,” American Journal of Industrial Medicine 69 (2026): 1060‐1079. 10.1002/ajim.70136.
Institution at which the work was performed: Occupational Cancer Research Centre, Ontario Health.
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supporting File 1
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.