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Original research
Precarious employment and the workplace transmission of COVID-19: evidence from workers’ compensation claims in Ontario, Canada
  1. Faraz V Shahidi1,2,
  2. Qing Liao1,
  3. Victoria Landsman1,2,
  4. Cameron A Mustard1,2,
  5. Lynda Robson1,3,
  6. Aviroop Biswas1,2,
  7. Peter M Smith1,2
  1. 1Institute for Work and Health, Toronto, Ontario, Canada
  2. 2Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
  3. 3Toronto Metropolitan University, Toronto, Ontario, Canada
  1. Correspondence to Dr Faraz V Shahidi, Institute for Work and Health, Toronto, Canada; fshahidi{at}iwh.on.ca

Abstract

Objective To examine the association between precarious employment and risk of work-related COVID-19 infection in Ontario, Canada.

Methods We combined data from an administrative census of workers’ compensation claims with corresponding labour force statistics to estimate rates of work-related COVID-19 infection between April 2020 and April 2022. Precarious employment was imputed using a job exposure matrix capturing temporary employment, low wages, irregular hours, involuntary part-time employment and a multidimensional indicator of ‘low’, ‘medium’, ‘high’ and ‘very high’ overall exposure to precarious employment. We used negative binomial regression models to quantify associations between precarious employment and accepted compensation claims for COVID-19.

Results We observed a monotonic association between precarious employment and work-related COVID-19 claims. Workers with ‘very high’ exposure to precarious employment presented a nearly fivefold claim risk in models controlling for age, sex and pandemic wave (rate ratio (RR): 4.90, 95% CI 4.07 to 5.89). Further controlling for occupational exposures (public facing work, working in close proximity to others, indoor work) somewhat attenuated observed associations. After accounting for these factors, workers with ‘very high’ exposure to precarious employment were still nearly four times as likely to file a successful claim for COVID-19 (RR: 3.78, 95% CI 3.28 to 4.36).

Conclusions During the first 2 years of the pandemic, precariously employed workers were more likely to acquire a work-related COVID-19 infection resulting in a successful lost-time compensation claim. Strategies aiming to promote an equitable and sustained recovery from the pandemic should consider and address the notable risks associated with precarious employment.

  • COVID-19
  • OCCUPATIONAL HEALTH
  • Health inequalities
  • EMPLOYMENT
  • WORKPLACE

Data availability statement

Data may be obtained from a third party and are not publicly available. Data may be obtained via request from the Statistics Canada Research Data Centre Network (https://crdcn.ca/) and the Workplace Safety and Insurance Board (https://www.wsib.ca/en).

https://doi.org/10.1136/jech-2024-222373

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    WHAT IS ALREADY KNOWN ON THIS TOPIC

    • The theme of precarious employment has featured prominently in discussions about the social and economic drivers of the COVID-19 pandemic.

    • From the outset of the pandemic, precariously employed workers were hypothesised to be at greater risk of acquiring a workplace infection.

    • While precarious employment is widely hypothesised to have contributed to the workplace transmission of COVID-19, there is very limited evidence to support that claim.

    WHAT THIS STUDY ADDS

    • This study examines the relationship between precarious employment and work-related COVID-19 (WR-C19) infections in Ontario, Canada.

    • We find that workers exposed to precarious employment were more likely to file a successful workers’ compensation claim for COVID-19.

    • Even after accounting for underlying differences in the nature of their work, the risk of a WR-C19 claim was nearly four times greater among precariously employed workers.

    HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

    • Strategies aiming to promote an equitable and sustained recovery from the COVID-19 pandemic should consider and address the notable individual and collective health risks associated with precarious employment.

    • Future research should investigate the mechanisms linking precarious employment to the workplace transmission of respiratory infections with the aim of informing effective policy and programmatic interventions.

    Introduction

    The COVID-19 pandemic changed the landscape of occupational health and safety, with significant measures having been introduced to prevent and mitigate the harms arising from this novel hazard in the workplace.1 2 Like other hazards before it, the occupational risks associated with COVID-19 were not shared equally by all.3 While the workplace was promptly identified as an important arena of transmission,4 early evidence pointed to substantial inequities both within and across occupational settings, leading certain segments of the workforce to bear a disproportionate burden of infection and illness.1 2 5 Against this backdrop, the concept of precarious employment emerged as a pivotal lens through which to understand occupational disparities in the incidence and severity of COVID-19.6 7

    Precarious employment describes various forms of works that are characterised by insecurity, uncertainty or vulnerability in the terms and conditions of employment.8 9 While no standardised definition of precarious employment exists, it is generally conceptualised as a multidimensional construct whose distinguishing features include contractual instability (eg, temporary employment), earnings inadequacy (eg, low wages), schedule unpredictability (eg, irregular hours), working time mismatch (eg, involuntary part-time employment) and a lack of rights and protections (eg, the absence of a union).10

    From the outset of the pandemic, precariously employed workers were hypothesised to be at greater risk of contracting COVID-19 in the workplace.6 7 Precariously employed workers were concentrated in ‘essential’ industries, such as retail and food services, where baseline risk of transmission was high.11 12 Within the workplace, precariously employed workers reported lower rates of personal protective equipment and other workplace infection control measures.13 14 They often lacked access to paid sick leave and other forms of income support, contributing to a climate of presenteeism—working while sick—and increasing the risk of workplace outbreaks.15 16 Financial hardship, fear of job loss and an overall sense of powerlessness may have hindered the ability of workers in precarious employment situations to negotiate for safer working conditions and refuse unsafe work, further increasing their risk of exposure to COVID-19.17 18

    While precarious employment is widely hypothesised to have contributed to the workplace transmission of COVID-19, there is limited empirical evidence to support that claim. Previous studies have documented disparities in the incidence and severity of infections across industries and occupations.19–21 Other studies have described how specific occupational exposures—such public facing work, working in close proximity to others and an indoor work environment—are associated with infection risk.22–24 However, there is limited research specifically investigating the association between precarious employment and risk of COVID-19 infection.24 25 To fill this gap, the present study examines precarious employment in relation to the workplace transmission of COVID-19. Drawing on an administrative census of workers’ compensation claims from Ontario, Canada, we ask whether workers exposed to precarious employment were more likely to acquire a work-related COVID-19 (WR-C19) infection during the first 2 years of the pandemic.

    Methods

    Data

    Data on WR-C19 infections were drawn from an administrative census of workers’ compensation claims registered with the Workplace Safety and Insurance Board (WSIB). The WSIB is the sole provider of workers’ compensation insurance in Ontario, Canada. WSIB coverage is mandatory in most industries and extends to approximately 76% of the workforce.23 From the census of registered claims, we identified all accepted lost-time compensation claims for WR-C19 infections between April 2020 and April 2022. The WSIB assessed every WR-C19 claim to determine the work-relatedness of the associated infection. For a WR-C19 claim to be accepted, it was necessary to establish (1) that the risk of transmission at work was greater than the risk of transmission in the community and (2) that work played a significant role in the aetiology of the infection.26 Not all WR-C19 infections satisfied these criteria and, as a result, not all WR-C19 infections resulted in a successful claim. However, we can assume that all accepted claims for WR-C19 are the result of infections that were acquired in the workplace. WSIB claim abstracts included information on the age, sex and occupation of the claimant as well as the date of the event (ie, infection).

    To calculate claim rates, we combined accepted compensation claims for WR-C19 with corresponding information on hours of work exposure obtained from the Labour Force Survey (LFS), which provides routine and reliable estimates of the labour force in Canada. We used the LFS to derive monthly estimates of hours worked (ie, time at risk) among employed persons in Ontario between April 2020 and April 2022. See figure 1 for an overview of our approach. Importantly, for our investigation, starting in April 2020, supplemental questions were introduced to the LFS with the aim of understanding the labour market impacts of the COVID-19 pandemic. One of these questions asked respondents to describe their usual place of work. To accurately estimate the population at risk of WR-C19 infection, we restricted the LFS sample to respondents who indicated that they usually worked outside their home. Because the supplemental questions were only asked of LFS respondents aged 15–69 years, we limited our sample of WR-C19 claims to this age range. We also excluded self-employed individuals and individuals employed in industry groups with no or incomplete WSIB coverage, given that not all workers in these industries are eligible for compensation insurance.23

    Figure 1
    Figure 1

    Analytic approach to estimating work-related COVID-19 (WR-C19) claim rates by combining accepted lost-time compensation claims from the Workplace Safety and Insurance Board (WSIB) with corresponding labour force estimates from the Labour Force Survey (LFS). Adapted from Smith et al.22

    Exposure: precarious employment

    We examined four dimensions of precarious employment: contractual instability (ie, temporary employment), earnings inadequacy (ie, low wages), schedule unpredictability (ie, irregular hours) and working-time mismatch (ie, involuntary part-time employment). WSIB claim abstracts do not contain information on hours, wages and other employment conditions. To address this gap, we constructed a job exposure matrix describing a person’s probability of exposure to precarious employment based on their reported occupation, adopting a similar approach to previous research.27 28 Briefly, we used LFS data to calculate the proportion of Ontario workers exposed to a given employment condition (ie, the proportion exposed to temporary employment, the proportion exposed to low wages, etc) within each of the 500 occupations listed in the 2016 National Occupational Classification (NOC). Temporary employment was assessed using the question ‘Is your job permanent?’ Low wages was assessed using the question ‘What is your hourly rate of pay?’ and defined as earning less than two-thirds of the median hourly wage. Irregular hours was assessed using the question ‘Does the number of paid hours you work vary from week to week?’ Finally, involuntary part-time employment was assessed using the pair of questions ‘How many paid hours do you usually work per week?’ and ‘Did you want to work 30 or more hours per week?’ For each employment condition, we grouped occupations into four quartiles of exposure, with the first quartile (Q1) representing occupations with the lowest prevalence of the employment condition and the fourth quartile (Q4) representing occupations with the highest prevalence of the employment condition. Absolute quartile ranges (ie, minimum and maximum probabilities of exposure) are presented in Table A1 of the online supplemental material. We also created a multidimensional index describing overall exposure to precarious employment. This index was evaluated based on the frequency that a given occupation was assigned the highest exposure level (ie, Q4). We then categorised occupations into four groups: those with ‘low’ overall exposure (ie, never assigned to Q4), ‘moderate’ overall exposure (ie, assigned to Q4 one time), ‘high’ overall exposure (ie, assigned to Q4 two times) and ‘very high’ overall exposure (assigned to Q4 three or more times). In a final step, WSIB claimants were assigned exposure values (eg, Q1, Q2, etc) based on their reported NOC occupation through a one-to-one matching process.

    Supplemental material

    Other measures

    Other measures included sex (male or female), age (15–24, 25–34, 35–44, 45–54, or 55 and above), wave of the pandemic (wave 1: April 2020 to August 2020, wave 2: September 2020 to February 2021, wave 3: March 2021 to June 2021, wave 4: July 2021 to November 2021; or wave 5: December 2021 to April 2022) and occupational risk factors associated with COVID-19. Sex, age and wave of the pandemic were measured directly in both the WSIB claims dataset and the LFS. Occupational risk factors associated with COVID-19 were imputed from occupational codes using the SARS-CoV-2 Occupational Exposure Matrix (SOEM), a job exposure matrix developed by the US Council of State and Territorial Epidemiologists Occupational Health Subcommittee based on data collected from the Occupational Information Network (O*Net).29 Occupational exposures assessed by the SOEM include public facing work, working in close proximity to others, and an indoor work environment. Using a previously developed and tested rubric,23 29 occupations were sorted into one of three categories describing low-risk, medium-risk and high-risk occupations respectively.

    Analyses

    We calculated crude claim rates and rate ratios (RRs) by matching WR-C19 claims (the numerator) with corresponding information on hours of exposure (the denominator). These data were combined into a single dataset, in which claims and hours of exposure were aggregated at a monthly level and then further stratified across all levels of the study variables (ie, across strata of sex, age, wave and occupational risk). Thus, each observation (or row) in this aggregated dataset described the number of claims and corresponding hours of exposure within a given month and for a given combination of sex, age, wave and occupational risk group. Rates were then calculated in terms of the number of claims per full-time equivalent (FTE) hours of exposure, with one FTE equating to 4071 hours (ie, 37.5 hours per week across each of the 108.6 weeks between 1 April 2020 and 30 April 2022). We next estimated a series of negative binomial regression models to examine the relationship between exposure to precarious employment and accepted claims for WR-C19. These models used the same aggregated dataset describing the number of claims and hours of exposure within a given month and for a given combination of sex, age, wave and occupational risk group. Models were estimated with count of claims as the outcome and log-transformed hours of exposure as the model offset. A more detailed description of our modelling approach is provided elsewhere.23 We modelled each precarious employment indicator separately. We initially adjusted or sex, age and wave as potential confounders (model 1). We then further adjusted for occupational risk factors for COVID-19 (model 2). We took this approach to examine the role of occupational risk factors in explaining the relationship between precarious employment and the risk of a WR-C19 claim—that is, Were precariously employed workers more likely to acquire a work-related infection because they were employed in ‘risker’ occupations? Model estimates are presented as RR with corresponding 95% CIs.

    Results

    Table 1 presents the number of accepted loss-time WR-C19 claims, FTEs of exposure, crude claim rates per 1000 FTEs and unadjusted RRs across study variables. During the 2-year period between April 2020 and April 2022, there were 43 227 accepted lost-time claims for WR-C19. Claim rates were higher among women than men (23.3 per 1000 FTEs vs 8.7 per 1000 FTEs) and varied substantially across waves of the pandemic (eg, 10.1 per 1000 FTEs during wave 1 vs 26.4 per 1000 FTEs during wave 5). Claim rates also varied across levels of exposure to occupational risk factors associated with COVID-19, ranging from 5.5 per 1000 FTEs among those in low-risk occupations to 27.3 per 1000 FTEs among those in high-risk occupations. With respect to the four dimensions of precarious employment, claim rates were generally highest among workers in Q4 and Q3 occupations (ie, those with the highest probabilities of exposure). An exception to this rule was the indicator for low wages, for which the highest claim rates were observed among workers in Q3 and Q1 occupations. A more or less monotonic gradient in claim rates was observed across increasing levels of multidimensional (ie, overall) exposure to precarious employment. Claim rates varied from a low of 5.9 per 1000 FTEs in occupations with low overall exposure to a high of 28.9 per 1000 FTEs in occupations with very high overall exposure to precarious employment. The claim rate among workers with very high overall exposure to precarious employment was comparable to that observed among high-risk occupations involving public facing work, close proximity to others and an indoor work environment (28.9 per 1000 FTEs vs 27.3 per 1000 FTEs).

    View this table:
    Table 1

    Number of accepted loss-time claims for work-related COVID-19 infections, full-time-equivalents, claim rates and unadjusted rate ratios across study variables: Ontario, Canada

    Table 2 presents RRs describing associations between the precarious employment indicators and WR-C19 claims, first adjusted by sex, age and wave (model 1), and then additionally adjusted for occupational risk factors (model 2). After adjusting for sex, age and wave, the risk of a WR-C19 claim was highest among workers in occupations with the highest probability of exposure to temporary employment, irregular hours and involuntary part-time employment. Compared with workers in Q1 occupations, the age-adjusted, sex-adjusted and wave-adjusted RRs for workers in Q4 occupations were 6.18 (95% CI 5.09 to 7.50) for temporary employment, 5.30 (95% CI 4.40 to 6.40) for irregular hours and 3.07 (95% CI 2.53 to 3.73) for involuntary part-time employment. For these three indicators, relationships were generally graded, such that workers in Q3 occupations presented the next highest relative risk. We did not observe significant differences in WR-C19 claim risk across levels of exposure to low wages, with the exception of a lower relative risk among workers in Q2 vs Q1 occupations (RR: 0.67, 95% CI 0.44 to 0.81). The risk of WR-C19 claim became progressively higher across increasing levels of multidimensional exposure to precarious employment. Compared with low overall exposure to precarious employment, medium overall exposure was associated with a 165% greater risk (RR: 2.65, 95% CI 2.20 to 3.18), high overall exposure was associated with a 214% greater risk (RR: 3.14, 95% CI 2.61 to 3.78), and very high overall exposure was associated with a 390% greater risk (RR: 4.90, 95% CI 4.07 to 5.89).

    View this table:
    Table 2

    Rate ratios describing the association between exposure to precarious employment and work-related COVID-19 infection claims in Ontario, Canada

    Further adjustment for occupational risk factors was associated with COVID-19-attenuated associations (model 2). For the most part, however, they remained statistically and substantively significant. In this fully adjusted model, all four dimensions of precarious employment, including low wages, were associated with increased risk of a WR-C19 claim. A monotonic relationship also persisted between multidimensional exposure to precarious employment and claims for WR-C19. Compared with low overall exposure to precarious employment, medium overall exposure was associated with a 78% elevated risk (RR: 1.78, 95% CI 1.55 to 2.06), high overall exposure was associated with 239% elevated risk (RR: 3.39, 95% CI 2.91 to 3.94), and very high overall exposure was associated with 278% elevated risk (RR: 3.78, 95% CI 3.28 to 4.36).

    Discussion

    This study examined precarious employment in relation to the workplace transmission of COVID-19 in Ontario, Canada. Combining workers’ compensation claims with corresponding labour force statistics during the first 2 years of the pandemic, we estimated lost-time claim rates for WR-C19 infections across levels of exposure to precarious employment. We found that workers in occupations with the greatest exposure to precarious employment were more likely to file a successful compensation claim for COVID-19, with a nearly fivefold risk compared with those in occupations with the lowest probability of exposure. Controlling for occupational risk factors associated with COVID-19 somewhat attenuated observed associations. However, even in fully adjusted models, the risk of a WR-C19 claim was nearly four times greater among precariously employed workers compared with their non-precarious counterparts.

    Our findings support the hypothesis that precariously employed workers faced a greater risk of acquiring a workplace COVID-19 infection.6 7 These findings contrast previous research reporting weak or non-existent associations between job insecurity, income insecurity and risk of infection in Denmark and the United Kingdom.24 25 Our results suggest that precariously employed workers were at increased risk of infection in some part because of the nature of the work they performed during the pandemic (ie, in public facing jobs that involve working indoors and in close proximity to others).30 31 Sizeable associations between precarious employment and claims for WR-C19 nevertheless persisted even after we accounted for underlying differences in the nature of precarious and non-precarious jobs. The persistence of that relationship implies that, at a given level of exposure to occupational risk factors for infection, precariously employed workers were still more vulnerable to the workplace transmission of COVID-19. Such vulnerability may be attributable to the limited rights and protections that precariously employed workers are afforded in the workplace. Precarious employment often coincided with a lack of paid sick leave, an inability to exercise power in the workplace, and the inadequate provision of personal protective equipment and other workplace infection control measures.13–18 Such factors are likely to have contributed to the excess risk of infection among precariously employed workers. Ultimately, our findings reveal an additional layer to the challenges that precariously employed workers faced during the pandemic, including heightened economic insecurity and declining mental health and well-being.32 33 They also reinforce the importance of recognising precarious employment as an occupational hazard and a target of primary prevention activities.10 34

    The study findings highlight the importance of addressing the challenge of precarious employment in controlling the spread of infectious diseases such as COVID-19, with wider implications for public health and health equity. In Canada and many other jurisdictions, socioeconomic and racial inequities in the incidence of COVID-19 infections in general have been widely documented.35 36 It is also well established that socioeconomically and racially disadvantaged groups are more likely to occupy precarious positions in the labour market.37 38 Our results in turn lend support to the notion that socioeconomic and racial inequities in the burden of COVID-19 in part reflect a segmented labour market, which led socioeconomically and racially disadvantaged groups to face heightened risks in the workplace.3 5 It stands to reason that addressing the problem of precarious employment may have reduced not only the overall burden of infections but also the disproportionate impacts of the pandemic and resulting health inequalities arising from racism and other forms of structural inequality.39

    Our findings should be interpreted in light of the following strengths and limitations. Strengths of our analysis include the use of workers’ compensation claims to identify WR-C19 infections as well as our use of labour force estimates to accurately estimate time spent in the workplace and therefore ‘at risk’ of acquiring a WR-C19 infection. The most important limitation of our study concerns the use of a job exposure matrix to assess precarious employment in terms of the occupation-level probability of exposure to adverse employment conditions. Heterogeneity in exposure levels within occupations is likely to have resulted in biased estimates due to misclassification error. A second limitation concerns the differential selection of precarious and non-precarious workers into the workers’ compensation system. Previous studies have shown that precariously employed workers are less likely than their non-precarious counterparts to report work-related injuries and illnesses.40 However, in the case of WR-C19 infections, precariously employed workers may have depended more heavily on compensation insurance, given that they would have had disproportionately less access to alternative recourse in the event of an infection (eg, paid sick leave, the option to work from home). If this assumption is correct and precariously employed workers were more likely to rely on the compensation system for wage recovery, then it is likely that we have overestimated the magnitude of disparities in WR-C19 between precarious and non-precarious workers. This limitation notwithstanding, compensation claims provide what is arguably the most valid source of information on infections acquired specifically in the workplace. For that reason, they offer a particularly effective means of examining the association between precarious employment and work-related infections. Finally, due to the limited information contained in WSIB claim abstracts on the characteristics of individual claimants, we were not able to account for factors that potentially confound the association between precarious employment and claims for WR-C19 infections, such as race, immigration status, household size and pre-existing health conditions.

    Conclusions

    During the first 2 years of the pandemic, Ontario workers exposed to precarious employment were more likely to file a successful workers’ compensation claim for COVID-19. Claim rates among the most precariously employed workers were comparable to those observed among workers employed in high-risk occupations (ie, public facing jobs that involve working indoors and in close proximity to others). These findings suggest that precarious employment contributed substantially to the burden of workplace compensation claims for work-related infections. Efforts to promote an equitable and sustained recovery from the pandemic should consider and address the notable individual and collective health risks associated with precarious employment.

    Data availability statement

    Data may be obtained from a third party and are not publicly available. Data may be obtained via request from the Statistics Canada Research Data Centre Network (https://crdcn.ca/) and the Workplace Safety and Insurance Board (https://www.wsib.ca/en).

    Ethics statements

    Patient consent for publication

    Ethics approval

    This study was approved by the University of Toronto, Health Sciences Research Ethics Board (Protocol# 39267).

    References

    1. ↵
      1. Michaels D,
      2. Wagner GR,
      3. Ryan L
      . Lessons from COVID-19 for protecting workers in the next pandemic. JAMA 2023;330:23–4. doi:10.1001/jama.2023.8229
      OpenUrlCrossRefPubMed
    2. ↵
      1. Peters SE,
      2. Wagner GR
      . Work during and after the pandemic. In: Duncan DT, Kawachi I, Morse S, eds. The social epidemiology of the COVID-19 pandemic. Oxford: Oxford University Press, 2024.
    3. ↵
      1. Bambra C,
      2. Riordan R,
      3. Ford J, et al
      . The COVID-19 pandemic and health inequalities. J Epidemiol Community Health 2020;74:964–8. doi:10.1136/jech-2020-214401
      OpenUrlAbstract/FREE Full Text
    4. ↵
      1. Carlsten C,
      2. Gulati M,
      3. Hines S, et al
      . COVID-19 as an occupational disease. Am J Ind Med 2021;64:227–37. doi:10.1002/ajim.23222
      OpenUrlCrossRefPubMed
    5. ↵
      1. McClure ES,
      2. Vasudevan P,
      3. Bailey Z, et al
      . Racial capitalism within public health-how occupational settings drive COVID-19 disparities. Am J Epidemiol 2020;189:1244–53. doi:10.1093/aje/kwaa126
      OpenUrlCrossRefPubMed
    6. ↵
      1. Matilla-Santander N,
      2. Ahonen E,
      3. Albin M, et al
      . COVID-19 and precarious employment: consequences of the evolving crisis. Int J Health Serv 2021;51:226–8. doi:10.1177/0020731420986694
      OpenUrl
    7. ↵
      1. McNamara CL,
      2. McKee M,
      3. Stuckler D
      . Precarious employment and health in the context of COVID-19: a rapid scoping umbrella review. Eur J Public Health 2021;31:iv40–9. doi:10.1093/eurpub/ckab159
      OpenUrlCrossRefPubMed
    8. ↵
      1. Kalleberg AL
      . Precarious Lives: Job Insecurity and Well-Being in Rich Democracies. Cambridge: Polity Press, 2018.
    9. ↵
      1. Kreshpaj B,
      2. Orellana C,
      3. Burström B, et al
      . What is precarious employment? A systematic review of definitions and operationalizations from quantitative and qualitative studies. Scand J Work Environ Health 2020;46:235–47:3875. doi:10.5271/sjweh.3875
      OpenUrlCrossRefPubMed
    10. ↵
      1. Bodin T,
      2. Çağlayan Ç,
      3. Garde AH, et al
      . Precarious employment in occupational health - an OMEGA-NET working group position paper. Scand J Work Environ Health 2020;46:321–9:3860. doi:10.5271/sjweh.3860
      OpenUrlPubMed
    11. ↵
      1. Matz M,
      2. Rhodes S,
      3. Tongeren MV, et al
      . Excess mortality among essential workers in England and Wales during the COVID-19 pandemic: an updated analysis. J Epidemiol Community Health 2023;77:481–4. doi:10.1136/jech-2023-220391
      OpenUrlAbstract/FREE Full Text
    12. ↵
      1. Rao A,
      2. Ma H,
      3. Moloney G, et al
      . A disproportionate epidemic: COVID-19 cases and deaths among essential workers in Toronto, Canada. Ann Epidemiol 2021;63:63–7. doi:10.1016/j.annepidem.2021.07.010
      OpenUrlCrossRefPubMed
    13. ↵
      1. Purkayastha D,
      2. Vanroelen C,
      3. Bircan T, et al
      . Work, health and Covid‑19: a literature review. SSRN Journal 2021. doi:10.2139/ssrn.3856915
    14. ↵
      1. Smith PM,
      2. Smith BT,
      3. Warren C, et al
      . The prevalence and correlates of workplace infection control practices in Canada between July and September 2020. Health Rep 2021;32:16–27. doi:10.25318/82-003-x202101000002-eng
      OpenUrlPubMed
    15. ↵
      1. Capasso A,
      2. Kim S,
      3. Ali SH, et al
      . Employment conditions as barriers to the adoption of COVID-19 mitigation measures: how the COVID-19 pandemic may be deepening health disparities among low-income earners and essential workers in the United States. BMC Public Health 2022;22:870. doi:10.1186/s12889-022-13259-w
    16. ↵
      1. Quinlan M
      . COVID-19, health and vulnerable societies. Ann Work Expo Health 2021;65:239–43:wxaa127. doi:10.1093/annweh/wxaa127
      OpenUrl
    17. ↵
      1. Rho HJ,
      2. Riordan C,
      3. Ibsen CL, et al
      . Do workers speak up when feeling job insecure? Examining workers’ response to precarity during the COVID-19 pandemic. Work Occup 2023;50:97–129. doi:10.1177/07308884221128481
      OpenUrl
    18. ↵
      1. Shoss MK,
      2. Min H,
      3. Horan K, et al
      . Risking one’s life to save one’s livelihood: precarious work, presenteeism, and worry about disease exposure during the COVID-19 pandemic. J Occup Health Psychol 2023;28:363–79. doi:10.1037/ocp0000366
      OpenUrl
    19. ↵
      1. Bonde JPE,
      2. Begtrup LM,
      3. Jensen JH, et al
      . Occupational risk of SARS-CoV-2 infection: a nationwide register-based study of the Danish workforce during the COVID-19 pandemic, 2020-2021. Occup Environ Med 2023;80:202–8. doi:10.1136/oemed-2022-108713
      OpenUrlAbstract/FREE Full Text
    20. ↵
      1. Kelsall HL,
      2. Di Donato MF,
      3. McGuinness SL, et al
      . Workers’ compensation claims for COVID-19 among workers in healthcare and other industries during 2020-2022, Victoria, Australia. Occup Environ Med 2023;80:667–73. doi:10.1136/oemed-2023-108982
      OpenUrlAbstract/FREE Full Text
    21. ↵
      1. Rhodes S,
      2. Wilkinson J,
      3. Pearce N, et al
      . Occupational differences in SARS-CoV-2 infection: analysis of the UK ONS COVID-19 infection survey. J Epidemiol Community Health 2022;76:841–6:jech-2022-219101. doi:10.1136/jech-2022-219101
      OpenUrlAbstract/FREE Full Text
    22. ↵
      1. Smith PM,
      2. Liao Q,
      3. Shahidi F, et al
      . Variation in occupational exposure risk for COVID-19 workers’ compensation claims across pandemic waves in Ontario. Occup Environ Med 2024;81:171–7. doi:10.1136/oemed-2023-109243
      OpenUrlAbstract/FREE Full Text
    23. ↵
      1. Torén K,
      2. Albin M,
      3. Bergström T, et al
      . Occupational risks associated with severe COVID-19 disease and SARS-CoV-2 infection - a Swedish national case-control study conducted from October 2020 to December 2021. Scand J Work Environ Health 2023;49:386–94:4103. doi:10.5271/sjweh.4103
      OpenUrl
    24. ↵
      1. van der Feltz S,
      2. Schlünssen V,
      3. Basinas I, et al
      . Associations between an international COVID-19 job exposure matrix and SARS-CoV-2 infection among 2 million workers in Denmark. Scand J Work Environ Health 2023;49:375–85:4099. doi:10.5271/sjweh.4099
      OpenUrl
    25. ↵
      1. Rhodes S,
      2. Beale S,
      3. Wilkinson J, et al
      . Exploring the relationship between job characteristics and infection: application of a COVID-19 job exposure matrix to SARS-CoV-2 infection data in the United Kingdom. Scand J Work Environ Health 2023;49:171–81:4076. doi:10.5271/sjweh.4076
      OpenUrl
    26. ↵
      1. Workplace Safety & Insurance Board of Ontario
      . Adjudicative Approach Document: Novel Coronavirus (COVID-19) Claims. Toronto: ON Workplace Safety & Insurance Board of Ontario, 2020.
    27. ↵
      1. Harris MA,
      2. MacLeod J,
      3. Kim J, et al
      . Use of a Canadian population-based surveillance cohort to test relationships between shift work and breast, ovarian, and prostate cancer. Ann Work Expo Health 2020;64:387–401. doi:10.1093/annweh/wxaa017
      OpenUrl
    28. ↵
      1. Hawkins D,
      2. Punnett L,
      3. Davis L, et al
      . The Contribution of occupation-specific factors to the deaths of despair, Massachusetts, 2005-2015. Ann Work Expo Health 2021;65:819–32. doi:10.1093/annweh/wxab017
      OpenUrl
    29. ↵
      1. CSTE Occupational Health Subcommittee
      . Characterizing the Risk of Exposure to SARS-CoV-2 among Non-Health Care Occupations Based on Three Workplace Risk Factors: Public Facing Work, Working Indoors, and Working in Close Physical Proximity to Others. Council of State and Territorial Epidemiologists, 2021.
    30. ↵
      1. Rothwell J,
      2. Smith E
      . Socioeconomic status as a risk factor in economic and physical harm from COVID-19: evidence from the United States. Ann Am Acad Pol Soc Sci 2021;698:12–38. doi:10.1177/00027162211062137
      OpenUrl
    31. ↵
      1. St-Denis X
      . Sociodemographic determinants of occupational risks of exposure to COVID-19 in Canada. Can Rev Sociol 2020;57:399–452. doi:10.1111/cars.12288
      OpenUrlCrossRef
    32. ↵
      1. Mai QD,
      2. Song L,
      3. Donnelly R
      . Precarious employment and well-being: insights from the COVID-19 pandemic. Work Occup 2023;50:3–21. doi:10.1177/07308884221143063
      OpenUrl
    33. ↵
      1. Oddo VM,
      2. Jones-Smith JC,
      3. Knox MA
      . Changes in precarious employment and health in the United States amidst the COVID-19 pandemic. Prev Med Rep 2023;31:102113. doi:10.1016/j.pmedr.2023.102113
    34. ↵
      1. Koranyi I,
      2. Jonsson J,
      3. Rönnblad T, et al
      . Precarious employment and occupational accidents and injuries - a systematic review. Scand J Work Environ Health 2018;44:341–50:3720. doi:10.5271/sjweh.3720
      OpenUrlPubMed
    35. ↵
      1. Magesh S,
      2. John D,
      3. Li WT, et al
      . Disparities in COVID-19 outcomes by race, ethnicity, and socioeconomic status: a systematic-review and meta-analysis. JAMA Netw Open 2021;4:e2134147. doi:10.1001/jamanetworkopen.2021.34147
    36. ↵
      1. Mishra S,
      2. Ma H,
      3. Moloney G, et al
      . Increasing concentration of COVID-19 by socioeconomic determinants and geography in Toronto, Canada: an observational study. Ann Epidemiol 2022;65:84–92. doi:10.1016/j.annepidem.2021.07.007
      OpenUrlCrossRef
    37. ↵
      1. Côté D,
      2. Durant S,
      3. MacEachen E, et al
      . A rapid scoping review of COVID-19 and vulnerable workers: Intersecting occupational and public health issues. Am J Ind Med 2021;64:551–66. doi:10.1002/ajim.23256
      OpenUrlCrossRefPubMed
    38. ↵
      1. Oddo VM,
      2. Zhuang CC,
      3. Andrea SB, et al
      . Changes in precarious employment in the United States: a longitudinal analysis. Scand J Work Environ Health 2021;47:171–80:3939. doi:10.5271/sjweh.3939
      OpenUrlCrossRefPubMed
    39. ↵
      1. Bailey ZD,
      2. Feldman JM,
      3. Bassett MT
      . How structural racism works - racist policies as a root cause of U.S. racial health inequities. N Engl J Med 2021;384:768–73. doi:10.1056/NEJMms2025396
      OpenUrlCrossRefPubMed
    40. ↵
      1. Kreshpaj B,
      2. Bodin T,
      3. Wegman DH, et al
      . Under-reporting of non-fatal occupational injuries among precarious and non-precarious workers in Sweden. Occup Environ Med 2022;79:3–9. doi:10.1136/oemed-2021-107856
      OpenUrlAbstract/FREE Full Text

    Footnotes

    • Contributors FVS and PS developed the study idea. QL performed the analysis with assistance from FVS, PS and VL. All authors discussed and interpreted the study findings. FVS wrote the first draft of the manuscript. All authors provided substantive comments and suggestions. All authors have reviewed the final version of the manuscript and have approved it for submission. FVS is the guarantor for this study.

    • Funding This research did not receive any specific funding from agencies in the public, commercial or not-for-profit sectors. All authors worked for the Institute for Work & Health while this project was completed. The Institute for Work & Health is supported through funding from the Ontario Ministry of Labour, Immigration, Training and Skills Development (MLITSD).

    • Competing interests None declared.

    • Provenance and peer review Not commissioned; externally peer-reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.