Abstract
Background
Shift work is known to be a risk factor for metabolic diseases. Although not established within literature, socioeconomic disadvantage may be associated with increased risk of being engaged in these shift work patterns.
Aim
To examine whether socioeconomic factors are associated with the likelihood of engaging in shift work.
Methods
Using data from the Canadian Longitudinal Study on Aging (CLSA), we conducted a cross-sectional analysis of adults aged 45–85 years. We created a Social Disadvantage Score composed of points derived from immigration status, language, education and race, ranging from 0 to 6. Logistic regression models were used to examine the association between social disadvantage and shift work, while adjusting for relevant covariates. We performed similar analyses using data from the Hoorn Study in the Netherlands—focused on the association between educational attainment and shift work.
Results
Of 18,393 adults employed in the CLSA at baseline, 16.6% reported currently working in a shift work scheduled job. Odds of shift work employment increased progressively with Social Disadvantage Score. Individuals with the highest scores (4+) had 3.25 times the odds (95% CI: 2.24–4.72) of being employed in shift work compared to those with a score of 0. Of 709 adults in the Hoorn Study, 11.4% worked in shift work patterns. Lower education was associated with shift work employment (OR 1.74, 95% CI: 1.1–2.9).
Conclusions
Social disadvantage is strongly associated with shift work in Canada, and in the Netherlands, lower education is associated with shift work.
Keywords: circadian alignment, CLSA, Hoorn Study, shift work, socioeconomic disadvantage
Introduction
In recent years, evidence of the negative impacts of circadian misalignment between the endogenous circadian system and 24-hour behavioral/environmental cycles on human health has continued to build (1, 2). Circadian rhythms are established by a central biological clock located in the suprachiasmatic nucleus of the hypothalamus with numerous peripheral clocks in tissues throughout the body (3). Ideally, the endogenous internal clocks are synchronized with the environmental rhythm (3), which is comprised of various behavioral “Zeitgebers”—or signals—such as food intake, sleep, light, and physical activity (4).
Unfortunately, all too common in our modern lifestyle, our behaviors are dyssynchronous with our endogenous clock, including prolonged screen time, light at night, late night eating, reductions in sleep, and working long hours (5, 6). These disruptions in circadian rhythms have been implicated in various conditions, particularly in cardiometabolic diseases including hypertension (7), obesity (8), cardiovascular disease (CVD) (9, 10), and type 2 diabetes (7, 11, 12). Engaging in shift work is particularly problematic, as it affects a number of Zeitgebers, including light exposure, sleep timing, and meal timing (6). Poor sleep quantity and quality, known to result from shift work (7), have also been associated with cardiometabolic diseases (13, 14). Circadian misalignment has also been associated with a range of other adverse health outcomes, including cognitive impairment; however, cardiometabolic disease provides the primary biological context motivating the present study (15).
It is also well understood that those facing socioeconomic disadvantage are at highest risk of developing cardiometabolic diseases (16) and also have worse outcomes, compared to counterparts who face fewer socioeconomic disadvantages (17). However, it is unclear if there is an association between socioeconomic disadvantage and shift work, though it is postulated that those who face a greater degree of social disadvantage may be more likely to work in jobs where shift work and long hours are common. This, however, has not been clearly established in the literature to date.
The objective of this study was to determine whether socioeconomic factors such as education, race/ethnicity, and immigration status are associated with an increased odds of being engaged in shift work, both in Canada and in the Netherlands. Understanding this association will help fill the gap in understanding some of the potential mechanisms underlying the increased risk of cardiometabolic diseases faced by those with socioeconomic disadvantages.
Materials and methods
Design and setting
We performed paired cross-sectional analyses in Canada and the Netherlands. In Canada, we used self-reported baseline data from the Canadian Longitudinal Study on Aging (CLSA) from 2011–2015. The study was approved by the Hamilton Integrated Research Ethics Board at McMaster University (Project ID: 14106). In the Netherlands, we used data from Wave two of the Hoorn study, collected in 2013–2015. The Ethics Committee of the VU University Medical Center approved the Hoorn Study (2012/222), and written informed consent was obtained from all participants.
Study population
The Canadian Longitudinal Study on Aging (CLSA) is a national, longitudinal research platform including 51,338 participants aged 45–85 years at baseline from all Canadian provinces. This age range was intended by the investigators to be able to capture middle aged experiences that influence aging as well as older adults who have reached advanced age while living in the community. Details on the study design have been described elsewhere (18).
The sampling frames for the study included: [1] Participants in the Canadian Community Health Survey who provided their contact information and consent to be contacted for the CLSA; [2] Information packages were mailed to randomly chosen individuals from provincial healthcare databases on behalf of the CLSA; and [3] Random digit dialing of landlines based on area codes, if an eligible individual within contacted households were willing, they provided their individual contact information for further interviews and assessments (19). All participants in the study were selected using stratified random sampling from the community-dwelling population. To be eligible for the study, participants had to be physically and cognitively able to participate on their own and communicate in English or French. Members of the armed forces, individuals living in Canada's three territories or institutions such as long-term care facilities, and persons living on federal First Nation reserves and other First Nations settlements in the provinces were not eligible to participate in the study. Participants were recruited in the tracking cohort (n = 21,241) and the comprehensive cohort (n = 30,097). Tracking cohort participants were randomly selected from Canadian provinces and completed interviews by phone. Participants in the comprehensive cohort were randomly selected from within 25–50 km distance of 11 data collection sites, which are located in seven Canadian provinces. In addition to being interviewed in-person, comprehensive cohort participants completed in-depth physical assessments and provided blood and urine samples (18).
For the present study, we excluded those who were not working at the time of baseline survey administration or those partially retired (n = 31,343), as the nature of the survey questions surrounding employment schedules most directly applied to current workers rather than those who had retired or taken leave. We also excluded those who report a current casual or seasonal position (n = 755), as we did not have sufficient detail on their actual day-to-day work schedule. Lastly, we excluded those with missing data (n = 847) in variables related to our Social Disadvantage Score (described below).
The Hoorn Study is a population-based cohort started in 1989, representative of the general Dutch population. To date, two waves of data have been collected, one in 1989 and one in 2006. In wave two, a total of 6,180 inhabitants of the city of Hoorn, aged 40–65 years and able to provide consent were randomly selected from the municipal registry. Of these, 45.4% agreed to participate in the cohort. Between January 2013 and December 2015, 1,734 participants agreed to participate in the follow-up. A complete overview of the study design is provided elsewhere (20). The primary objective of the Hoorn Study is to identify biological, behavioral, and environmental factors that influence the development and progression of type 2 diabetes and its complications. Consequently, the dataset contains limited socioeconomic variables, and proxy indicators of socioeconomic position were used to approximate these factors. For this current analysis, we only used data from the 2013–2015 measurements, as questions on shift work were only assessed at that time point. Of the 1,734 individuals who participated in the follow-up, we excluded participants who were not working at the time of administration (n = 982), had missing data on education level (n = 4) or had missing data on included covariates (n = 39).
Variables
Outcome: in the CLSA, shift work was defined based on response to the following question: “Which of the following best describes your working schedule currently?”. Response options included “Daytime schedule or shift”, “evening shift”, “night shift”, “rotating shift changing periodically from days to evenings or nights”, and “other”. In our analysis, we dichotomized these options into “daytime schedule/shift” and “all others” (which were classified as shift workers).
In the Hoorn Study, shift work was defined based on the response to the survey questions: “Do you currently have a paid job” and “Do you work in shift work or at a nighttime schedule?”. In our analysis, we only included participants that were working at the time of administration and dichotomized this group into “daytime workers” and “shift/nighttime workers”.
Exposure: in the CLSA, self-reported indicators of socioeconomic disadvantage included race (categorized as: white or racialized population), immigration status (Canadian-born, immigrated >10 years ago, and immigrated < 10 years ago), first language (categorized as non-minority or minority first language, based on province of residence [non-French in Quebec, and non-English outside of Quebec]), and educational attainment (high education: university degree or higher; medium education: high-school degree or some post-secondary; low education: no high school degree). We chose these indicators as they are included in the Statistics Canada's dimensions of multiple deprivation (21). To understand the compounding and incremental effect of these various socioeconomic contributors, we created a bespoke Social Disadvantage Score ranging from 0–6 with points given for increasing levels of social disadvantage. That said, we merged scores of 4+ into one category as few individuals had scores of 5 and 6 (see Box 1). We did not include income in our Social Disadvantage Score as it potentially represents a downstream consequence of several other included factors and could therefore disproportionately drive the observed association between the overall score and shift work.
Box 1.
Parameters of the social disadvantage score.
| Category | Points given |
|---|---|
| Immigration status | 0—Canadian-born |
| 1—Immigrated >10 years ago | |
| 2—Immigrated < 10 years ago | |
| Language | 0—Non-minority language |
| 1—Minority language | |
| Education | 0—High education (university degree) |
| 1—Medium education (high-school degree or some post-secondary) | |
| 2—Low education (no high-school degree) | |
| Race | 0—White |
| 1—Visible minority or Indigenous ancestry |
In the Hoorn Study, educational attainment was used as a proxy for socioeconomic disadvantage because other related socioeconomic variables were not available. Education levels were dichotomized into two groups: low and high. Low education included no formal education, primary or elementary school, lower vocational education, or intermediate general secondary education. High education included intermediate vocational education, higher general or pre-university education, higher professional education, or university-level education. This dichotomization was applied to maintain adequate cell sizes across categories and is consistent with previous analyses of the Hoorn Study (22).
Covariates: there are several other factors which are associated with both socioeconomic disadvantage and shift work and may therefore act as confounding factors. These were controlled for in our regression analysis. These include age (grouped as 45–54, 55–64, 65+ years), sex (self-reported male vs. female), and location of residence (urban vs. rural). In the CLSA, urban/rural status was determined by Statistics Canada's Postal Code Conversion File (23). In the Hoorn Study, this was based on the total number of addresses per km2 and dichotomized into urban in case of one thousand or more addresses per km2 and into rural in case of fewer than one thousand addresses per km2 (24).
Statistical analyses
For both CLSA and Hoorn analyses, we calculated descriptive statistics stratified by current employment schedule (shift/night work vs. regular daytime schedule). The CLSA data were weighted in order to account for sample misrepresentation due to unequal sampling probabilities, frame coverage error and non-response, and to improve the precision of estimates. Inflation weights, derived from weights given by Statistics Canada, were used to balance the sample to be more representative of the Canadian population (19).
We performed a multivariable binary logistic regression to calculate odds ratios of shift work associated with socioeconomic disadvantage. Fully adjusted models included the potential confounders of interest: age, sex, and urban/rural as covariates. With the CLSA data, we used frequency weights which were derived from normalized analytic weights. These analytic weights were rescaled inflation weights summed to the sample size within each province, so that their mean value was 1 within each province. In the CLSA data, we first modeled each indicator individually, followed by modeling our Social Disadvantage Score with the same adjustment variables. Analyses were done using Stata, version 17 (StataCorp) for CLSA data and SPSS, version 28 for Hoorn Study data.
Results
Study cohort and respondent characteristics
A total of 18,393 individuals who reported being currently employed were included in the analysis from the CLSA cohort. Average age of CLSA participants was 54.7 years (SD: 6.4) with 47.9% being female. The majority (15,347 participants, 83.4%) reported currently working in a regular daytime schedule leaving 3,046 participants (16.6%) working in some form of a shift work schedule. Each of the covariates showed similar distribution when stratified by work schedule (Table 1).
Table 1.
Descriptive characteristics of participants in the CLSA at baseline, stratified by work schedule (n = 18,393).
| Variable | Total (n = 18,393) | Daytime workers (n = 15,347) | Shift workers (n = 3,046) | |
|---|---|---|---|---|
| Age group | 45–54 years | 10,448 (56.8%) | 8,670 (56.9%) | 1,778 (56.4%) |
| 55–64 years | 6,669 (36.3%) | 5,507 (36.1%) | 1,162 (36.9%) | |
| 65–85 years | 1,276 (6.9%) | 1,063 (7.0%) | 213 (6.8%) | |
| Sex | Male | 9,576 (52.1%) | 7,980 (52.4%) | 1,596 (50.6%) |
| Female | 8,817 (47.9%) | 7,260 (47.6%) | 1,557 (49.4%) | |
| Residence | Urban | 15,085 (82.0%) | 12,652 (83.0%) | 2,433 (77.1%) |
| Rural | 2,450 (13.3%) | 1,944 (12.8%) | 506 (16.1%) | |
| Missing | 858 (4.7%) | 644 (4.2%) | 214 (6.8%) | |
| Education | High education (university degree) | 15,001 (81.6%) | 12,636 (82.9%) | 2,365 (75.0%) |
| Medium education (high-school degree or some post-secondary) | 2,844 (15.5%) | 2,229 (14.6%) | 624 (19.8%) | |
| Low education (no high-school degree) | 548 (3.0%) | 384 (2.5%) | 164 (5.2%) | |
| Immigration status | Canadian-born | 15,624 (85.0%) | 12,933 (84.9%) | 2,691 (85.4%) |
| Immigrated >10 years ago | 2,564 (13.9%) | 2,166 (14.2%) | 398 (12.6%) | |
| Immigrated < 10 years ago | 205 (1.1%) | 141 (0.9%) | 64 (2.0%) | |
| Language | Non-minority language | 15,639 (85.0%) | 12,937 (84.9%) | 2,702 (85.7%) |
| Minority language | 2,754 (15.0%) | 2,303 (15.1%) | 451 (14.3%) | |
| Race | White | 17,217 (93.6%) | 14,306 (93.9%) | 2,911 (92.3%) |
| Visible minority or indigenous ancestry | 1,176 (6.4%) | 934 (6.1%) | 242 (7.7%) | |
n represents the number of individuals in the sample. Percentages reflect weighted prevalence estimates.
A total of 709 individuals were included in the analysis involving the Hoorn cohort. The average age in the included cohort was 56.8 years (SD: 5.12 years), and 50.5% were males. There were 628 (88.6%) participants currently working in a regular daytime schedule, leaving 81 (11.4%) working in shift work schedules (Table 2).
Table 2.
The Hoorn Study cohort characteristics, stratified by work schedule (n = 709).
| Variable | Total (n = 709) | Daytime workers (n = 628) | Shift workers (n = 81) | |
|---|---|---|---|---|
| Age group | 45–54 years | 251 (35.4%) | 214 (34.1%) | 37 (45.7%) |
| 55–64 years | 408 (57.5%) | 365 (58.1%) | 43 (53.1%) | |
| 65–74 years | 50 (7.1%) | 49 (7.8%) | 1 (1.2%) | |
| Sex | Male | 358 (50.5%) | 320 (51.0%) | 38 (46.9%) |
| Female | 351 (49.5%) | 308 (49.0%) | 43 (53.1%) | |
| Residence | Urban | 545 (76.9%) | 481 (76.6%) | 64 (79.0%) |
| Rural | 164 (23.1%) | 147 (23.4%) | 17 (21.0%) | |
| Education level | Low | 188 (26.5%) | 160 (25.5%) | 28 (34.6%) |
| High | 521 (73.5%) | 468 (74.5%) | 53 (65.4%) | |
n represents the number of individuals in the sample. Percentages reflect weighted prevalence estimates.
Socioeconomic factors and shift work
When included individually in separate adjusted models in the CLSA, education, race, language, and immigration within the previous 10 years were all associated with shift work while immigration beyond 10 years and language were not individually associated with shift work (Figure 1). Of these indicators, immigration within 10 years showed the strongest association with those individuals having 2.27 times the odds (95% CI: 1.60–3.23) of working in a shift work scheduled job, compared to Canadian-born workers. This association was followed by education, where those with the lowest level of education (no high-school diploma) had 2.15 times the odds (95% CI: 1.74–2.66) of those who had the highest educational attainment (university degree); and race, where individuals from visible minority populations had 1.46 times the odds of shift work (95% CI: 1.22–1.74), compared to white individuals. Interestingly, individuals whose first language was a minority within their province of residence had lower odds of engaging in shift work (OR: 0.86; 95% CI: 0.75–0.98) compared to those whose first language was not a minority.
Figure 1.
Forest plot showing adjusted odds ratios for shirt work employment associated with each component of the Social Disadvantage Score and selected demographic characteristics from a multivariable model (n = 18,393).
With respect to the Social Disadvantage Score, the total proportion of respondents with a score of 0 was 11,052 (60.1%), 1 was 4,814 (26.2%), 2 was 1,731 (9.4%), 3 was 627 (3.4%), and 4+ was 169 (0.9%). Modeling our Social Disadvantage Score showed increasing odds of engaging in shift work with higher Social Disadvantage Score. Individuals with a Social Disadvantage Score of 4+ had 3.25 (95% CI: 2.24–4.72) times higher odds of engaging in shift work compared to the reference group (score of 0; Figure 1).
Using the Hoorn data, the crude model assessing the association between education level and shift work found that those with low education had an odds ratio of 1.55 (95% CI: 0.95–2.53), compared to those with high education. After adjustment for age, sex and urban vs. rural residency, the association was significant, with odds of shift work for individuals with a low education being 1.74 (95% CI: 1.1–2.9) times higher, compared to participants with a higher education.
Discussion
Of the almost 20,000 working adults in the CLSA cohort, about 17% reported currently working in a shift work scheduled job. In the CLSA, odds of shift work employment increased progressively with Social Disadvantage Score, with individuals in the group with the most social disadvantage (score of 4+) having 3.25-times higher odds of performing shift work compared to those who did not experience social disadvantage (score of 0). Those with lower levels of education, and those from visible minority populations were more likely to engage in shift work, while the strongest predictor was immigration within 10 years. In the Dutch population, 11.4% of individuals were working a shift work scheduled job. When adjusting for covariates, those with low levels of education had 1.75-fold higher odds of engaging in shift work, when compared to those with the highest levels of education—a very similar finding to the results in the Canadian cohort.
Our study findings are consistent with those from other studies that have looked at risk factors for shift work. One study from the US found that immigrants from African/Caribbean countries (32.5%) and those with Hispanic ethnicity (31.9%) had twice the likelihood of working night shifts compared to non-Hispanic white individuals (15.6%) (25). Interestingly, the rate of shift work in these minority groups was considerably higher than the rates observed in our cohorts where 20.6% of racialized minorities worked in shifts, whereas our studies had a relatively comparable rate of white individuals working in shifts (16.9%). In Singapore, ethnicity was also noted to be a key determinant of being occupied in a shift work schedule (26). In Canada, specifically, one systematic review demonstrated that education, and subjective socioeconomic status have been linked with disturbed sleep patterns, some of which is due to shift work (27). In the Netherlands, one other study has shown a similar association between education and shift work, with shift work rates of 26.5% and 16.6% for low education and high education, respectively (28).
One's work environment and colleagues can have a significant impact on health behaviors and practices. For example, it has been shown that people are more likely to engage in healthy behaviors when encouraged to do so by their fellow employees (29). To mitigate the increased risks of shift work patterns and the resulting social disparity, employers could create policies to mitigate the risks of shift work on employees' long-term health. There is already work being done on this front as a number of studies have evaluated the impact of workplace initiatives that encourage healthy behaviors, physical activity programs (30), occupational health guidelines targeted at weight gain prevention among employees (31), and employer-sponsored financial incentives for healthy behaviors (32). A European systematic review found many of these programs and initiatives to be effective at improving participant dietary outcomes (33) while another systematic review targeted specifically at shift workers found evidence for physical activity interventions in weight loss and improved physical fitness (34). In addition to promoting healthy behaviors in the workplace, there are specific strategies to address the unique challenges of shift work and mitigate its negative effects. Evidence suggests that limiting consecutive night shifts, ensuring adequate recovery time between shifts, and reducing overall shift durations can help reduce long-term health issues (35).
Given the increasing knowledge we have of the impact of circadian misalignment on metabolic dysfunction, the increased exposure to shift work seen in these populations may account for some degree of this increased risk of conditions such as type 2 diabetes, and CVD.
One strength of our study was the large population-based cohorts in two different countries. The CLSA included a nationally generalizable sample of participants with a wealth of information on socioeconomic disadvantage indicators and demographic characteristics. Though the findings from the CLSA may be hard to generalize outside of a Canadian context, the use of Hoorn study data, though limited by the number of socioeconomic variables available, helps illustrate similar results in a different setting. A principal weakness of this study was the older age of the cohort. With a minimum age of 45 years in the CLSA, we did not have access to much of the younger working population where people may be even more likely to work in shift-type schedules. Therefore, our findings may not be generalizable to a younger cohort, who may have different patterns of employment than those of older generations. However, cardiometabolic diseases have higher incidence and prevalence in middle-aged and older individuals. Finally, our study only looked at the association between social disadvantage and shift work but was not powered to examine for associations with health outcomes such as dysglycemia, obesity, or cardiovascular events. In future studies, with appropriate data and sample size, one could examine if shift work plays a mediating role between socioeconomic disadvantage and specific clinical outcomes like those mentioned above.
In conclusion, we have shown that many dimensions of social disadvantage are associated with shift work exposure in Canada, while in the Netherlands specifically, lower education was associated with shift work. These work-related disparities which pre-dispose individuals to circadian misalignment may partially explain the increased rate of cardiometabolic diseases seen in groups that face socioeconomic disadvantage. The potential mediating effect of shift work in these associations remains to be tested in future studies.
Acknowledgments
This research was made possible using the data/biospecimens collected by the Canadian Longitudinal Study on Aging (CLSA). Funding for CLSA is provided by the Government of Canada through the Canadian Institutes of Health Research (CIHR) under grant reference: LSA 94473 and the Canada Foundation for Innovation, as well as the following provinces, Newfoundland, Nova Scotia, Quebec, Ontario, Manitoba, Alberta, and British Columbia. This research has been conducted using the CLSA dataset Tracking Baseline v3.7 and Baseline Comprehensive Dataset v6.1, under Application Number 2104051. The CLSA is led by Parminder Raina, Christina Wolfson, and Susan Kirkland. We are grateful for the dedication of the participants and all individuals that contribute to the Hoorn Studies.
Funding Statement
The author(s) declared that financial support was received for this work and/or its publication. This study was funded by The Netherlands Organization for Health Research and Development (ZonMw) [459001021], Dutch Diabetes Research Foundation (Diabetes Fonds) [2019.11.101], the Canadian Institutes of Health Research (CIHR) [TNC-174963], and HealthHolland [LSHM20107].
Footnotes
Edited by: Susana Viegas, New University of Lisbon, Portugal
Reviewed by: Melody L. Greer, University of Arkansas for Medical Sciences, United States
Pasquale Bufano, National Research Council (CNR), Italy
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Ethics statement
The studies involving humans were approved by the Hamilton Integrated Research Ethics Board at McMaster University (Project ID: 14106) and the Ethics Committee of the VU University Medical Center approved the Hoorn Study (2012/222), and written informed consent was obtained from all participants. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.
Author contributions
DeC: Writing – original draft, Formal analysis, Writing – review & editing. TR: Writing – review & editing, Writing – original draft. RS: Data curation, Formal analysis, Writing – review & editing, Writing – original draft. PR: Methodology, Supervision, Data curation, Funding acquisition, Writing – review & editing, Conceptualization, Project administration. FR: Methodology, Supervision, Data curation, Writing – review & editing, Conceptualization, Project administration, Funding acquisition. DJ: Writing – review & editing, Formal analysis, Data curation. RM: Conceptualization, Funding acquisition, Writing – review & editing. J-PD: Writing – review & editing, Funding acquisition, Conceptualization. JH: Funding acquisition, Writing – review & editing, Conceptualization. DB: Writing – review & editing, Funding acquisition, Conceptualization. AC: Writing – review & editing, Funding acquisition, Conceptualization. PS: Funding acquisition, Conceptualization, Writing – review & editing. DaC: Methodology, Data curation, Supervision, Writing – review & editing, Project administration, Conceptualization, Funding acquisition.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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The author(s) declared that generative AI was not used in the creation of this manuscript.
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References
- 1.Morris CJ, Purvis TE, Hu K, Scheer FA. Circadian misalignment increases cardiovascular disease risk factors in humans. Proc Natl Acad Sci U S A. (2016) 113:E1402–11. doi: 10.1073/pnas.1516953113 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Scheer FA, Hilton MF, Mantzoros CS, Shea SA. Adverse metabolic and cardiovascular consequences of circadian misalignment. Proc Natl Acad Sci U S A. (2009) 106:4453–8. doi: 10.1073/pnas.0808180106 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Ikegami K, Refetoff S, Van Cauter E, Yoshimura T. Interconnection between circadian clocks and thyroid function. Nat Rev Endocrinol. (2019) 15:590–600. doi: 10.1038/s41574-019-0237-z [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Neves AR, Albuquerque T, Quintela T, Costa D. Circadian rhythm and disease: Relationship, new insights, and future perspectives. J Cell Physiol. (2022) 237:3239–56. doi: 10.1002/jcp.30815 [DOI] [PubMed] [Google Scholar]
- 5.Poggiogalle E, Jamshed H, Peterson CM. Circadian regulation of glucose, lipid, and energy metabolism in humans. Metabolism. (2018) 84:11–27. doi: 10.1016/j.metabol.2017.11.017 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Boivin DB, Boudreau P, Kosmadopoulos A. Disturbance of the circadian system in shift work and its health impact. J Biol Rhythms. (2022) 37:3–28. doi: 10.1177/07487304211064218 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Guo Y, Liu Y, Huang X, Rong Y, He M, Wang Y, et al. The effects of shift work on sleeping quality, hypertension and diabetes in retired workers. PLoS ONE. (2013) 8:e71107. doi: 10.1371/journal.pone.0071107 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Sun M, Feng W, Wang F, Li P, Li Z, Li M, et al. Meta-analysis on shift work and risks of specific obesity types. Obes Rev. (2018) 19:28–40. doi: 10.1111/obr.12621 [DOI] [PubMed] [Google Scholar]
- 9.Lee DW, Kim HR, Myong JP, Choi J, Hong YC, Kang MY. Does working long hours increase the risk of cardiovascular disease for everyone? J Occup Health. (2019) 61:431–41. doi: 10.1002/1348-9585.12069 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Wang N, Sun Y, Zhang H, Wang B, Chen C, Wang Y, et al. Long-term night shift work is associated with the risk of atrial fibrillation and coronary heart disease. Eur Heart J. (2021) 42:4180–8. doi: 10.1093/eurheartj/ehab505 [DOI] [PubMed] [Google Scholar]
- 11.Gan Y, Yang C, Tong X, Sun H, Cong Y, Yin X, et al. Shift work and diabetes mellitus: a meta-analysis of observational studies. Occup Environ Med. (2015) 72:72–8. doi: 10.1136/oemed-2014-102150 [DOI] [PubMed] [Google Scholar]
- 12.Bescos R, Boden MJ, Jackson ML, Trewin AJ, Marin EC, Levinger I, et al. Four days of simulated shift work reduces insulin sensitivity in humans. Acta Physiol (Oxf). (2018) 223:e13039. doi: 10.1111/apha.13039 [DOI] [PubMed] [Google Scholar]
- 13.Cappuccio FP, D'Elia L, Strazzullo P, Miller MA. Quantity and quality of sleep and incidence of type 2 diabetes: a systematic review and meta-analysis. Diabetes Care. (2010) 33:414–20. doi: 10.2337/dc09-1124 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Jehan S, Myers AK, Zizi F, Pandi-Perumal SR, Jean-Louis G, Singh N, et al. Sleep health disparity: the putative role of race, ethnicity and socioeconomic status. Sleep Med Disord. (2018) 2:127–33. doi: 10.15406/smdij.2018.02.00057 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Leahy S, Xiao Q, Yeung CHC, Figueiro MG. Associations between circadian alignment and cognitive functioning in a nationally representative sample of older adults. Sci Rep. (2024) 14:13509. doi: 10.1038/s41598-024-64309-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Winkleby MA, Jatulis DE, Frank E, Fortmann SP. Socioeconomic status and health: how education, income, and occupation contribute to risk factors for cardiovascular disease. Am J Public Health. (1992) 82:816–20. doi: 10.2105/AJPH.82.6.816 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Stringhini S, Carmeli C, Jokela M, Avendaño M, Muennig P, Guida F, et al. Socioeconomic status and the 25 × 25 risk factors as determinants of premature mortality: a multicohort study and meta-analysis of 1·7 million men and women. Lancet. (2017) 389:1229–37. doi: 10.1016/S0140-6736(16)32380-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Raina P, Wolfson C, Kirkland S, Griffith LE, Balion C, Cossette B, et al. Cohort profile: the Canadian longitudinal study on Aging (CLSA). Int J Epidemiol. (2019) 48:1752–3j. doi: 10.1093/ije/dyz173 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Canadian Longitudinal Study on Aging. CLSA technical document 2020: sampling and computation of response rates and sample weights for the tracking (telephone interview) participants and comprehensive participants (2020). Available online at: https://www.clsa-elcv.ca/ (Accessed January 3, 2025).
- 20.Rutters F, Nijpels G, Elders P, Stehouwer CDA, van der Heijden AA, Groeneveld L, et al. Cohort profile: the hoorn studies. Int J Epidemiol. (2018) 47:396-j. doi: 10.1093/ije/dyx227 [DOI] [PubMed] [Google Scholar]
- 21.Statistics Canada. The Canadian index of multiple deprivation: user guide. Ottawa: Statistics Canada (2019). [Google Scholar]
- 22.Rutters F, Pilz S, Koopman ADM, Rauh SP, Pouwer F, Stehouwer CDA, et al. Stressful life events and incident metabolic syndrome: the Hoorn study. Stress. (2015) 18:507–13. doi: 10.3109/10253890.2015.1064891 [DOI] [PubMed] [Google Scholar]
- 23.Canadian Longitudinal Study on Aging. Data support document: urban/rural classification. Hong Kong: CLSA (2018). [Google Scholar]
- 24.Statistics Netherlands—CBS. Narrowing gap between number of facilities available in urban and rural areas (2010). Available online at: https://www.cbs.nl/en-gb/figures/detail/80305ENG (Accessed January 3, 2025).
- 25.Ertel KA, Berkman LF, Buxton OM. Socioeconomic status, occupational characteristics, and sleep duration in African/Caribbean immigrants and US White health care workers. Sleep. (2011) 34:509–18. doi: 10.1093/sleep/34.4.509 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Dicom AR, Huang X, Hilal S. Association between shift work schedules and cardiovascular events in a multi-ethnic Cohort. Int J Environ Res Public Health. (2023) 20. doi: 10.3390/ijerph20032047 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Etindele Sosso FA, Kreidlmayer M, Pearson D, Bendaoud I. Towards a socioeconomic model of sleep health among the Canadian population: a systematic review of the relationship between age, income, employment, education, social class, socioeconomic status and sleep disparities. Eur J Investig Health Psychol Educ. (2022) 12:1143–67. doi: 10.3390/ejihpe12080080 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Hulsegge G, Picavet HSJ, van der Beek AJ, Verschuren WMM, Twisk JW, Proper KI. Shift work, chronotype and the risk of cardiometabolic risk factors. Eur J Public Health. (2019) 29:128–34. doi: 10.1093/eurpub/cky092 [DOI] [PubMed] [Google Scholar]
- 29.van der Put A, Ellwardt L. Employees' healthy eating and physical activity: the role of colleague encouragement and behaviour. BMC Public Health. (2022) 22:2004. doi: 10.1186/s12889-022-14394-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Cairns JM, Bambra C, Hillier-Brown FC, Moore HJ, Summerbell CD. Weighing up the evidence: a systematic review of the effectiveness of workplace interventions to tackle socio-economic inequalities in obesity. J Public Health (Oxf). (2015) 37:659–70. doi: 10.1093/pubmed/fdu077 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Verweij LM, Proper KI, Weel ANH, Hulshof CTJ, van Mechelen W. Design of the Balance@Work project: systematic development, evaluation and implementation of an occupational health guideline aimed at the prevention of weight gain among employees. BMC Public Health. (2009) 9:461. doi: 10.1186/1471-2458-9-461 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Kullgren JT, Williams GC, An LC. Patient-centered financial incentives for health: Can employers get change for their dollars? Healthc (Amst). (2013) 1:82–5. doi: 10.1016/j.hjdsi.2013.08.001 [DOI] [PubMed] [Google Scholar]
- 33.Maes L, Van Cauwenberghe E, Van Lippevelde W, Spittaels H, De Pauw E, Oppert JM, et al. Effectiveness of workplace interventions in Europe promoting healthy eating: a systematic review. Eur J Public Health. (2012) 22:677–83. doi: 10.1093/eurpub/ckr098 [DOI] [PubMed] [Google Scholar]
- 34.Demou E, MacLean A, Cheripelli LJ, Hunt K, Gray CM. Group-based healthy lifestyle workplace interventions for shift workers: a systematic review. Scand J Work Environ Health. (2018) 44:568–84. doi: 10.5271/sjweh.3763 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Garde AH, Begtrup L, Bjorvatn B, Bonde JP, Hansen J, Hansen Å M, et al. How to schedule night shift work in order to reduce health and safety risks. Scand J Work Environ Health. (2020) 46:557–69. doi: 10.5271/sjweh.3920 [DOI] [PMC free article] [PubMed] [Google Scholar]
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Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.