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Abstract
Occupational infectious disease risks between men and women have often been attributed to the gendered distribution of the labour force, with limited comparative research on occupation-specific infectious disease risks. The objective of this study was to compare infectious disease risks within the same occupations by gender. A systematic review of peer-reviewed studies published between 2016 and 2021 was undertaken. To be included, studies were required to report infectious disease risks for men, women or non-binary people within the same occupation. The included studies were appraised for methodological quality. A post hoc power calculation was also conducted. 63 studies were included in the systematic review. Among high-quality studies with statistical power (9/63), there was evidence of a higher hepatitis risk for men than for women among patient-facing healthcare workers (HCWs) and a higher parasitic infection risk for men than for women among farmers (one study each). The rest of the high-quality studies (7/63) reported no difference between men and women, including for COVID-19 risk among patient-facing HCWs and physicians, hepatitis risk among swine workers, influenza risk among poultry workers, tuberculosis risk among livestock workers and toxoplasmosis risk among abattoir workers. The findings suggest that occupational infectious disease risks are similarly experienced for men and women within the same occupation with a few exceptions showing a higher risk for men. Future studies examining gender/sex differences in occupational infectious diseases need to ensure adequate sampling by gender.
- Sexual and Gender Disorders
- Communicable diseases
- Occupational Health
- Risk assessment
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WHAT IS ALREADY KNOWN ON THIS TOPIC
There is a recognition that gender/sex differences in occupational exposures are largely attributed to the gendered nature of occupational participation in the labour market, with limited comparative research on occupation-specific risks. Previous systematic reviews on gender/sex differences in occupational exposures have not focused on infectious disease risks, with the limited research suggesting that more is known about gender/sex differences in non-communicable occupational disease exposures than in infectious disease occupational exposures.
WHAT THIS STUDY ADDS
This systematic review focuses on synthesising the evidence from studies that compared occupational infectious disease differences by gender within the same occupation to remove the effects of the gendered labour market. Furthermore, we summarise findings according to methodological study quality and post hoc statistical power to inform the strength of evidence.
HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY
The comparison of occupation-specific infectious disease risks for men, women or non-binary people can inform the need for gender-focused or gender-neutral occupational health and safety strategies in specific occupations. More high-quality studies with adequate recruitment are required to better understand the nature and extent of occupational infectious disease risks between men and women.
Introduction
Worldwide, an estimated 320 000 workers die annually from occupational infectious diseases.1–3 Moreover, lost productivity due to sickness absenteeism caused by infectious diseases is a major workplace and societal concern.4 Historically, the risk of infectious diseases was concentrated in specific occupations, that is, healthcare workers (HCWs), workers in contact with animals, outdoor workers, laboratory workers and refuse workers who have the highest risk of infection from a variety of pathogens.1 The COVID-19 pandemic also highlighted an increased risk of infection for previously low-risk occupations, primarily those that required working with the public or working in close proximity to others (eg, retail trade, food services and processing).5 6 The COVID-19 pandemic also highlighted differences in risk experienced within the population by sociodemographic characteristics.
Sex, referring to biological differences, and gender, referring to differences shaped by social and cultural circumstances, can affect the health of men and women in different ways (hereinafter the interconnected dimensions of gender and sex will also be referred to as ‘men and women’ unless otherwise specified). Studies have reported that women are more exposed to infectious diseases through caregiving occupations (eg, nurses, home support workers) while men have greater exposure to infectious agents through outdoor occupations (eg, agriculture, fishing).7–9 Segregation of work tasks within the same occupation by gender/sex can also result in differing doses or duration of infectious pathogen exposure.10 Biological factors such as differences in immunity to infections between women and men as a consequence of immunological pathways affected by sex hormones, as well as consequences of differential expression of X-chromosome-encoded genes on immune responses to pathogens can also result in differential susceptibility of infectious disease.11 12 Furthermore, the design of personal protective equipment, for example, facepiece respirators, has typically been based on average male anthropometric data, leaving many workers with inadequate protection from infectious diseases, especially among women.13
Previous systematic reviews on gender/sex differences in occupational diseases have not focused on infectious diseases14 15 or have not adequately included infectious disease-related search terms.16 17 We know of one other systematic review of infectious disease risks associated with occupational exposures, but in this review of 242 studies, there was no examination of the differences in exposure or outcome using a gender/sex lens.1 The limited research suggests that more is known about gender/sex differences in non-communicable occupational exposures to physical and psychosocial working conditions than in infectious disease occupational exposures.16 However, there has been a large swell in recent occupational health research related to the COVID-19 pandemic, which necessitates an update of the evidence to include COVID-19 studies with a focus on infectious disease. Focusing on studies that compare exposure differences for men, women or non-binary people within the same occupation is important from a primary prevention standpoint as it removes the effects of the gendered nature of occupational participation in the labour market in general as an explanation.18 However, task-based differences within occupations may still persist.10
The objective of this systematic review was to synthesise evidence from research studies to compare risks of occupation-specific infectious disease by gender.
Methods
This systematic review used a process developed by the Cochrane Collaboration that was adopted by the Institute for Work & Health (IWH) Systematic Review Programme19 and IWH’s stakeholder collaboration model.20 The review adheres to the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement.21 The protocol was not preregistered.
Identifying the research question
A meeting was held with a committee of stakeholder advisors comprised of eight individuals with diverse and knowledgeable perspectives on occupational health and safety and gender/sex-based health research (union representatives at the national level and representing municipal services, nurses and agricultural workers; the president of an industry association and a representative from the review funder: the Workers’ Compensation Board of British Columbia). Stakeholder advisors provided input on the research question to ensure it was relevant and answerable within the project time frame, helped refine the search strategy and recommended studies relevant to the review.
Identifying relevant studies
The MEDLINE (Ovid), Embase+Embase Classic (Ovid) and APA PsycINFO (Ovid) electronic databases were searched for peer-reviewed studies published from 1 January 2016 to 31 December 2021 (the date when the article retrieval was completed). The inclusion of studies was not limited by language. The search strategies were created by a research librarian (MT). After the initial search strategy was developed, the reviewers consulted with the stakeholder advisors to discuss the relevance of the terms and identify any missing terms. As controlled vocabularies differ significantly in the electronic databases, search terms were customised to each database as needed. The search terms used for the MEDLINE database are provided in online supplemental table 1. In addition to the formal search strategy, the reviewers and stakeholders were also solicited for studies that were at the preprint stage, in-press (accepted by a journal but not yet published) or articles that were not captured by the formal search strategy but could be important for the review. Reference lists of included studies and relevant review articles were also scanned for references not previously captured. EndNote was used to store references from all literature searches. Duplicates were removed and references were loaded into DistillerSR, an online systematic review management software designed specifically for the screening, quality appraisal and data extraction phases of a systematic review.
Supplemental material
Study selection
The following criteria were applied at the title/abstract and full-review stages: Using a P.I.C.O. structure (population, intervention/exposure, context and outcome), the population of interest were workers aged 18 years to retirement, and who were described as employed at a workplace at the time occupational exposure was assessed. The intervention/exposure of interest was exposure for men, women and non-binary people, the context was the same/similar occupation and outcomes were occupational infectious diseases. Original studies using case–control, cross-sectional, and retrospective and prospective cohort study designs were accepted. Studies also had to provide estimates of risk by men and women within an occupation to be included. Exclusion criteria included the general populations or where there is no clarity as to if a working population is sampled, non-primary studies, an infectious disease not attributed to occupational exposure and no sex/gender comparison provided. The search returned only studies treating gender as a simplistic binary definition, and therefore, an exploration of differences between non-binary people was not possible.
Regular meetings were held between the core review team to monitor the review process, address questions and troubleshoot difficulties in assessing the studies. Non-English language studies were examined by the reviewers and individuals who were fluent in the language. Reviewers were not blinded to the authors of the studies and did not appraise studies that they consulted on, authored or coauthored. Standardised relevance screening forms were created and piloted in DistillerSR software to ensure that the reviewers uniformly applied the inclusion/exclusion criteria.
The selection of relevant studies took place in two stages. In the first stage, the titles and abstracts of identified references were reviewed based on the inclusion/exclusion criteria. In the second stage, full-text articles were retrieved for those studies that (1) were assessed by two reviewers as meeting the inclusion criteria or (2) there was insufficient information based on the title and abstract to determine relevance. Many studies included both patient-facing HCWs (eg, physicians, clinical nurses and medical technicians) and non-patient-facing HCWs (eg, janitorial custodians and clerical staff). The review team made the decision to exclude studies that combined both patient-facing and non-patient-facing HCWs in their analysis as this was not helpful to the investigation of occupational task-specific gender/sex differences.
Due to the large number of studies retrieved by the search, the artificial intelligence (AI) feature of the DistillerSR software was used, pairing a human reviewer with the AI feature to double-review each reference at the title/abstract stage of relevance screening. This required ‘training’ the AI on a portion of studies reviewed by two humans at both stages so that the AI ‘learnt’ which types of studies were relevant to the review before ‘running’ the AI as a second reviewer to the single human reviewer. Disagreements between the human and AI features were reviewed by a third (human) reviewer until consensus was achieved.
Quality appraisal
Studies were appraised for methodological quality using the Newcastle-Ottawa Scale (NOS) for cohort studies, cross-sectional and case–control studies.22 The scale assesses studies from three broad perspectives: the selection of the study groups; the comparability of the groups and the determination of either the exposure or outcome of interest. The instrument is scored by awarding a point for each answer. Possible points are 4 points for selection (5 points for cross-sectional studies), 2 points for comparability and 3 points for outcomes. With the NOS tool, each of the study components was summed for a score ranging from 0 to 9, and up to 10 for cross-sectional studies, with those studies rating 0–2 appraised as low quality, 3–5 as fair quality and 6–9/10 as high quality. A pilot test of the NOS quality assessment forms was completed to ensure reviewers consistently interpreted them. A 10% sample of eligible studies was double reviewed by the review team to ensure good agreement23; conflicts were resolved by discussion. Once consensus was reached on the reviewing process, the remaining studies were reviewed by individual reviewers for quality appraisal. Review team members did not appraise studies that they consulted on, authored or coauthored.
Data extraction
A data extraction form was created in the DistillerSR software based on input from review team members. The following information was extracted from studies: the last name of the lead author and year the study was published, study location, study design, main occupation of participants, main occupational disease outcome/exposure, the data source, the sample size and proportion of women, the main findings and statistics relevant to outcome/exposure differences between men/males and women/females, and the study’s interpretation of the findings.
The data extraction form also incorporated the Sex and Gender Equity in Research (SAGER) guidelines to examine the extent to which sex and gender were considered by study authors.24 The form adapted SAGER criteria on whether the research question(s) or hypothesis/es make reference to gender and/or sex, or relevant groups or understand a gendered phenomenon such as masculinity; whether the literature review cites prior studies that support the existence (or lack) of significant differences between women and men or males and females; and, if the literature review points to the extent to which past research has taken gender or sex into account.
Evidence synthesis
Effect estimates were extracted from studies that stratified or matched their results for men and women by occupation, prioritising estimates from statistical models adjusted for confounders if multiple estimates were available. If studies reported separate effect estimates for men and women, the research team compared whether the effect estimates indicated a greater risk in one group compared with the other, or if there was no statistically significant risk difference, based on the difference in means estimated at the 5% level.25 26 This approach has the advantage of specifically comparing if estimates for men and women are different from each other—consistent with the objectives of the review—rather than qualitatively assessing if an estimate is statistically significant in one group but not the other, which can be due to a variety of reasons (eg, low power in one group but not the other) and does not indicate if differences are present between men and women per se.27 28 Studies that did not reach statistically significant differences but that reported meaningful practical differences (ie, Cohen’s d≥0.2 or ≥60% higher or lower odds),14 were identified as having no comparative difference between men and women. Studies that provided a qualitative reporting of differences for men and women by occupation were also included and narratively reported.
The review team conducted a post hoc power analysis to determine if each study was adequately powered to detect a difference between men and women using equations described by Rosner29 where the number of men and women, the baseline incidence of infectious disease for each group and an alpha equal to 0.05 were used in calculations.29 As described elsewhere, a statistical power of 80% or higher was considered acceptable to detect a difference even if the results show statistical significance.30
Results
Study characteristics
Figure 1 outlines the number of articles identified, included and excluded, and the reasons for exclusions. The search strategy identified 26 220 records from electronic databases, of which 1525 full texts were screened. Full-text articles that did not meet eligibility criteria were excluded in two rounds of screening (1322 articles excluded), and data were extracted from 203 studies, of which 105 studies were reviewed for methodological quality assessment. A further 42 studies were excluded after data extraction by the investigators because of ineligible estimates (eg, combined both patient-facing and non-patient-facing HCWs in their analysis and thereby it was not possible to compare occupation-specific risk differences). A total of 63 studies were included in the final review of evidence of gender/sex differences in occupation-specific infectious disease risk.
Flow chart of article selection process. **Articles screened and removed after title/abstract review.
One study fulfilled the SAGER criteria of having a research question(s) or hypothesis/es that made reference to gender and/or sex, or relevant groups or phenomena. Five studies (5/63) provided a literature review citing studies in support of the existence (or lack) of significant differences between women and men or males and females, and one study (1/63) provided a literature review pointing to the extent to which past research has taken gender or sex into account. No studies fulfilled all SAGER criteria.
Patient-facing HCWs were the most frequently studied occupations (23/63), followed by workers occupationally exposed to raw meat (eg, slaughterhouse workers, butchers and meat handlers) (12/63), farm workers (8/63), first responders (eg, paramedics, firefighters and police officers) (5/63) and veterinarians (4/63). The most frequent occupational infection studied was SaRS-CoV-2/COVID-19 infection or seroprevalence (22/63), followed by infection from the hepatitis family of viruses (14/63), tuberculosis (7/63), Staphylococcus aureus infection (3/63) and influenza infection (3/63). Women made up more than half of the study sample in the included studies (32/63 studies). The proportion of women in the study samples ranged from 4%31 to 99%.32 Most studies originated from the USA (seven studies), China, Italy and Nigeria (six studies each) and France (four studies). Most studies used cross-sectional study designs (52/63), followed by retrospective cohort (5/63), prospective cohort (3/63), mixed methods involving a combination of retrospective and prospective designs (2/63), and one case–control study.
Online supplemental table 2 describes the comparison of risk of occupational infectious disease for men and women in the same occupations, with information on the study and reviewer’s interpretation of risks.
Supplemental material
20 out of 63 studies (32%) did not explicitly interpret the differences between men and women within the article, although estimates from these studies were retrievable from tables or could be calculated by the reviewers (ie, through difference in means estimates). Most studies (47/63) were inadequately statistically powered (<80%) to detect meaningful gender/sex differences. There was disagreement between study author and reviewer’s interpretations for six studies: two examining hepatitis risk,33 34 two examining brucellosis risk35 36 and one examining Middle East respiratory syndrome coronavirus (MERS-CoV) risk.37 The following sections describe the reviewers’ interpretation of infectious disease risks for men and women in the same occupations.
COVID-19 risk
Nine high-quality studies examined the risk of occupational COVID-19 exposure.32 38–58 All nine of these studies found no differences in risk between men and women in the same occupation, although only three of these studies had statistical power to detect meaningful differences.49 53 56 Among the three studies powered to detect differences, Jacobson et al 49 and Ran et al 56 found no difference in COVID-19 risk for men and women employed as patient-facing HCWs,49 56 and Morcuendee et al found no difference in risk for men and women employed as physicians.53 The six other high-quality studies, but with <80% statistical power, all reported no difference in COVID-19 risk between men and women in healthcare, daycare, retail and gold mining occupations.32 47 48 50 51 54 Among 11 studies rated fair quality,38 40–46 52 55 58 2 studies observed a higher risk of COVID-19 among men employed as patient-facing HCWs,41 44 with 1 study adequately powered to detect differences.41 The nine remaining fair-quality-rated studies reported no difference in risk.38 40 42 43 45 46 52 55 58 Two low-quality and low statistically powered studies also reported no difference in COVID-19 risk between men and women working in fruit and vegetable packing39 and as dental workers.57
Hepatitis risk
Gender/sex differences in hepatitis risk were variable across studies regardless of quality, statistical power or occupation. Three high-quality studies examined the risk of hepatitis infection34 59 60 and two of these studies had statistical power to detect gender/sex differences.34 59 Shu et al 59 reported no difference in hepatitis risk between men and women employed as swine workers,59 while Taus et al reported a higher hepatitis risk for men employed as patient-facing HCWs.34 Four studies were fair quality and all with low statistical power.61–64 Oluremi et al reported a higher risk of hepatitis infection among men in animal handling occupations64 while the other three studies reported no difference in risk between men and women working as farm workers, veterinarians, animal handlers and patient-facing HCWs.61–63 Seven low-quality studies examined hepatitis risk.33 65–70 Of the seven, one study had statistical power and reported no difference in risk between men and women among patient-facing HCWs.70 Among the remaining low-quality studies with low statistical power, Karadeniz et al reported a higher risk of hepatitis among women employed as patient-facing HCWs,67 and Rachiotis et al reported a higher risk for hepatitis among men employed as waste collectors.33 The remaining low-quality studies reported no difference in risk among slaughterhouse workers, veterinarians and university staff.65 66 68 69
Influenza risk
Two high-quality studies examined the risk of influenza infection.31 71 One of these two studies with statistical power reported no difference in risk between men and women employed as poultry workers31 while the other study with low statistical power reported no difference in risk between men and women employed as patient-facing HCWs.71 One fair-quality study with low statistical power reported that men employed as poultry workers were at higher risk of influenza infection compared with women.72
Tuberculosis risk
Three high-quality studies examined tuberculosis risk.73–75 One of these three studies with statistical power reported no difference in risk between men and women employed as livestock workers.73 The remaining two studies with low statistical power reported higher tuberculosis risk among men75 and no difference in risk between men and women64 employed as patient-facing HCWs. Two fair-quality studies with low statistical power reported no difference in tuberculosis risk between men and women among police officers76 and patient-facing HCWs.77 Two low-quality studies with low statistical power reported no difference in tuberculosis risk between men and women employed as patient-facing HCWs78 including among medical students.79
Query (Q) fever risk
One high-quality study reported a higher risk of Q fever among men compared with women working as cattle farmers, although a post hoc statistical power calculation was not possible for this study.80 Two fair-quality studies with low statistical power reported no difference in the risk of Q fever for men and women employed as farm workers.81 82 One low-quality study with statistical power reported a higher risk of Q fever for men compared with women employed as veterinarians.83
Other infectious disease risks
Two fair-quality studies with low statistical power reported no difference in risk of staphylococcal infection between men and women employed as gold mine workers84 or as patient-facing HCWs.85
One high-quality study with statistical power reported no difference in the risk of toxoplasmosis between men and women employed as abattoir workers,86 although one fair-quality study with low statistical power reported a higher risk of toxoplasmosis among women compared with men working in gold mines.87
Two studies reported no differences between men and women in the risk of brucellosis among abattoir workers, one of high quality36 and the other of low quality,35 and both with low statistical power.
Two high-quality studies examined the risk of parasitic infection (malaria-causing parasites and intestinal parasites).88 89 One study with statistical power reported a higher risk for men than women employed as farmers89 while the other study with low statistical power reported a higher risk for men than women among food handlers.88
One fair-quality study with no statistical power available reported no gender/sex difference in the risk of MERS-CoV infection among patient-facing HCWs.37
One fair-quality study with low statistical power reported no gender/sex difference in the risk of tickborne encephalitis among nature management workers.90
One low-quality study with statistical power reported a higher risk of parasitic infection for men compared with women among farmers.91
One low-quality study with statistical power reported no difference in onychomycosis risk between men and women working at swimming pools.92
One low-quality study with low statistical power reported no difference in the risk of leptospirosis among men and women employed as butchers.93
Discussion
This systematic review examined the evidence on occupational infectious disease risks for men and women in the same occupations. Most studies (n=47/63) were inadequately powered to detect meaningful differences between men and women, and most did not observe differences in occupational infectious disease risk. Among the few high-quality studies with adequate statistical power (9/63 studies), there was evidence of a higher hepatitis risk for men than women among patients facing HCWs and parasitic infection among farmers. The rest of these studies reported no difference between men and women in COVID-19 risk among patients facing HCWs and physicians, hepatitis risk among swine workers, influenza risk among poultry workers, tuberculosis risk among livestock workers and risk of toxoplasmosis among abattoir workers. One study (1/63) was rated fair quality and with statistical power and reported a higher risk of COVID-19 for men than women among patient-facing HCWs.
Previous studies have highlighted a higher risk of infectious disease in men compared with women due to their greater representation in high-risk occupations and industries.16 94 95 This review’s focus on occupation-specific differences highlights other potential gender/sex influences on infectious disease risk. For example, while few within the context of all studies, a risk difference was reported in the direction of a higher risk for men, including in occupations with a greater representation of women (eg, patient-facing healthcare work). Conversely, a higher risk of toxoplasmosis was reported in women in the male-dominated gold mining sector. Evidence suggests that women HCWs were more likely than men to practice hand hygiene and wear personal protective equipment during the COVID-19 outbreak,96 which may explain the evidence of a higher risk of COVID-19 infection among men employed in patient-facing healthcare work. Furthermore, innate biological factors, for example, genetic, epigenetic and hormonal factors, can help females exhibit a stronger immunity response against these infections.12 97 Women may also have protection from infection by social, professional and clothing practices related to conservative, religious and traditional norms in some countries (eg, men’s beards and facial hair can compromise the seal of P2/N95-standard filtering respirators and surgical masks).98 The personal protective equipment of miners may be designed and sized for men leaving women at greater risk of virus transfer from equipment and infected sources to skin and clothing.99 100 The higher risk of infectious disease for men than women in animal handling occupations may be due to women being more cautious when in contact with animals, thus receiving a lower exposure to infectious bacteria particles.7 101
It is possible that gender/sex differences are not evident in some studies due to gendered differences in the self‐reporting of health conditions, especially if reporting is influenced by the experience with, as well as the anxiety about, a risk factor.102 Furthermore, the unpaid caregiving that disproportionally falls on women may expose more women to higher overall infection risk than men if accounting for daily contact at work and outside of work hours.103 104 This review also uncovered an inadequate focus on gender/sex differences in the recent occupational infectious disease literature, which suggests that potential differences between men and women may be underestimated. Only one study fulfilled all SAGER criteria of reporting sex and gender information while most studies were inadequately powered (<80%) to detect important differences if they were present. It is important that researchers aim for adequate recruitment through stratified sampling approaches to detect a priori defined important differences in occupational infectious disease risks between genders.
Most studies in the current review reported that the risks of occupation-specific infectious diseases were similar for men and women. Gender‐neutral occupational health and safety strategies and standards may apply in situations where there appear to be no differences in infectious disease risks between men and women. However, not acknowledging gender/sex differences in biological makeup and exposure due to disproportionate representation in the labour force, task differences in the same occupations and inadequate protective equipment, may mean that apparently neutral policies may impact people of different genders differently and reinforce existing inequalities.
The strengths of this review include a comprehensive and systematic evidence synthesis, and a focus on fair-to-high-quality studies adequately powered to assess differences between men and women in occupational infectious disease risks. There are also limitations that should be considered. First, given the breadth of the occupational health research literature, it is possible that the search strategy did not capture all relevant studies that have been published in the field over the search period of 2016–2021. Thereby, some studies that provide evidence of differences, but were not a focus of this paper, are not captured by search terms. To mitigate missing important studies, we involved a research librarian and stakeholders knowledgeable about sex and gender differences in occupational health and occupational infectious diseases in the development of the search strategy. We also included studies in several different languages to the best of our ability. Also, this systematic review was not registered at inception which would have countered the potential for publication and reporting bias by enabling comparison of the completed review with what was planned in the protocol. Lastly, while we compared risks within an occupation, there may be gender-specific task differences within an occupation that are unreported or unmeasured in the reviewed studies.
In conclusion, this systematic review found that most fair to high-quality studies reported no difference in the risk of occupational infectious diseases between men and women, suggesting that these risks are similarly experienced by sex/gender. A small number of high to fair-quality studies reported a higher risk of infectious diseases in men working in patient-facing healthcare, occupations requiring a proximity to animals and one study reporting a higher risk of toxoplasmosis for women in mining which points to the need for gender/sex-specific design of personal protective equipment. These differences might be explained by occupation-specific task differences. There was a lack of comprehensive sex and gender information in the design and interpretation of study findings, highlighting the need to better understand the role gender/sex play in occupational infectious diseases as part of well-designed studies with a priori research questions.
Supplemental material
Ethics statements
Patient consent for publication
Ethics approval
The study did not receive ethics approval as human participants were not required.
Acknowledgments
We would like to acknowledge the contributions of Tarnbir Aulakh (Queen’s University) in the review steps, the translation services of various people connected to the Institute for Work & Health and feedback from our Stakeholder Advisory Committee.
References
Supplementary materials
Supplementary Data
This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.
Footnotes
Contributors AB, EI, PMS, HJ and MK conceived the study idea and received grant funding. AB, EI and MT performed the study selection and analysis with assistance from HJ, GZ and MS. All authors were involved in the interpretation of the study findings. AB wrote the first draft of the manuscript. All authors provided substantive comments and suggestions. All authors have received the final version of the manuscript and have approved it for submission. We used the AI/machine learning feature of DistillerSR, the software we used to conduct our systematic review. The AI feature served as a second reviewer in the screening step of the systematic review process. The use of AI in the systematic review process is detailed clearly in the manuscript. AI was not used for the writing or preparation of the manuscript.
Funding This study was funded by a 2021 Innovation at Work Grant from WorkSafeBC (RS2021-IG16 Biswas). The Institute for Work & Health operates with the support of the Province of Ontario.
Disclaimer The views expressed in this review are those of the authors and do not necessarily reflect those of the Canadian Provinces of British Columbia or Ontario.
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.