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Shift work and long work hours and their association with chronic health conditions: A systematic review of systematic reviews with meta-analyses

  • Adovich S. Rivera ,

    Roles Data curation, Formal analysis, Methodology, Project administration, Visualization, Writing – original draft, Writing – review & editing

    adovichrivera2021@u.northwestern.edu

    Affiliation Institute for Public Health and Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States of America

  • Maxwell Akanbi,

    Roles Conceptualization, Data curation, Formal analysis, Writing – review & editing

    Affiliation Institute for Public Health and Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States of America

  • Linda C. O’Dwyer,

    Roles Conceptualization, Data curation, Methodology, Writing – review & editing

    Affiliations Institute for Public Health and Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States of America, Galter Health Sciences Library and Learning Center, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States of America

  • Megan McHugh

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Supervision, Writing – review & editing

    Affiliations Institute for Public Health and Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States of America, Department of Emergency Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States of America

Shift work and long work hours and their association with chronic health conditions: A systematic review of systematic reviews with meta-analyses

  • Adovich S. Rivera, 
  • Maxwell Akanbi, 
  • Linda C. O’Dwyer, 
  • Megan McHugh
PLOS
x

Abstract

Background

Previous reviews have demonstrated that shift work and long work hours are associated with increased risk for chronic conditions. However, these reviews did not comprehensively assessed the body of evidence, and some were not conducted in a systematic manner. A better understanding of the health consequences of shift work and long work hours will aid in creating policy and practice recommendations. This review revisits the epidemiologic evidence on the association of shift work and long work hours with chronic conditions with particular emphasis on assessing the quality of the evidence.

Methods and findings

We conducted a systematic review of systematic reviews with meta-analyses (SR-MA) that assessed the link between shift work or long work hours and chronic conditions (PROSPERO CRD42019122084). We evaluated the risk of bias of each SR-MA using AMSTAR v2 and assessed the overall evidence for each condition using the GRADE approach. We included 48 reviews covering cancers, cardiovascular diseases, metabolic syndrome and related conditions, pregnancy complications, depression, hypertension, and injuries. On average, only 7 of 16 AMSTAR items were fulfilled. Few SR-MAs had a registered protocol and nearly all failed to conduct a comprehensive search. We found moderate grade evidence linking shift work to breast cancer and long work hours to stroke. We found low grade evidence linking both shift work and long work hours with low to moderate increase in risk for some pregnancy complications and cardiovascular diseases. Low grade evidence also link long work hours and depression.

Conclusions

Moderate grade evidence suggest that shift work and long work hours increase the risk of breast cancer and stroke, but the evidence is unclear on other chronic conditions. There is a need for high-quality studies to address this gap. Stakeholders should be made aware of these increased risks, and additional screening and prevention should be considered, particularly for workers susceptible to breast cancer and stroke.

Introduction

Jobs that require work outside the traditional daytime hours of approximately 8 AM to 6 PM have become ubiquitous across economically developed nations. Some jobs such as those in healthcare, manufacturing, and law enforcement routinely require night time or prolonged shifts. In the United States (US) and European Union (EU), a fifth of employees are shift workers [1]. Additionally, a substantial share of the work force works more than the usual 40 hours per week, with 36.1% of the global workforce clocking in excessive hours (more than 48 hours) per week [1].

Shift work schedules and long work hours give rise to acute and chronic health effects ranging from metabolic syndrome to cancers that arise from shared and interacting biological pathways [25]. Shift work disrupts a person’s circadian rhythm and the internal processes controlled by this rhythm, such as clock genes for cell proliferation and melatonin secretion. These disruptions promote inflammation and oncogenesis and are immunosuppressive[35]. For example, breast cancer among female shift works has been attributed to increasing DNA methylation with increasing exposure to shift work[4]. Long work hours, meanwhile, not only cuts into non-work hours that the body needs for rest and recovery but can also be a form of psychologic stress [2,6], and if chronically exposed, this stress can lead to cardiovascular disease [7]. Non-standard work hours can also induce unhealthy coping behaviors, such as low physical activity and poor diets [2].

Recognizing the potentially harmful effects of shift work and long work hours, many governments have enacted laws and regulations restricting their use [8]. While in almost all countries there are restrictions on the maximum allowed work hours and stipulated compensation for work done in excess of these hours, regulation of shift work is more varied. The EU restricts the numbers of night work hours a person can perform per day [8]. The EU, Japan, and South Korea, also require special health examinations for night shift workers and have imposed shift work prohibitions on pregnant women [8]. The US has imposed few restrictions on non-standard work hours, and the policies that exist typically focus on the effects of non-standard work hours on productivity and safety, such as fatigue and occupational injuries [8].

Prior reviews of epidemiologic evidence have concluded that non-standard work hours are associated with increased risk for breast cancer, metabolic disease, and cardiovascular disease [912]. However, these reviews lacked a comprehensive approach for judging the body of evidence, considered only select sources of bias, and some were not conducted in a systematic manner. These weaknesses restrict the ability to confidently quantify the increased risk caused by shift work and long work hours, prevent the calculation of the associated healthcare costs, and therefore, fall short of motivating changes in policy and practice.

In this paper, we report on our systematic review of systematic reviews with meta-analysis (i.e., “umbrella review”) on the association of exposure by workers to shift work and long work hours (hereinafter referred to as “non-standard work hours”) with chronic or high cost conditions. Given the plethora of reviews on various conditions, we felt that compiling and systematically assessing the evidence would be helpful for clinicians and policy makers as they consider appropriate policies and guidelines on these two common non-standard work hour set-ups. Results may also be of interest to employees, who potentially bear an increased risk of chronic illness, and employers and insurers, which often bear the healthcare costs associated with chronic illnesses. We also identify any gaps in the available body evidence to help direct future research.

Materials and methods

Screening, inclusion and exclusion criteria

We included any study that conducted a systematic review of the literature (as opposed to purposive or undocumented selection of articles) using at least one database to examine how exposure by adult workers to shift work or long work hours affect the risk of having or acquiring a chronic or high-cost condition. We included only those that pooled the results in a meta-analysis since only these studies report quantified risk measures that are necessary for making policy decisions regarding non-standard work hours. The screening was done by two authors (AR, MM) independently and differences were resolved by consensus.

We defined shift work as any work outside the standard daytime work hours of approximately 8 AM to 6 PM, and this includes rotating shift work, fixed nights, and evening work. Long work hours was defined as work with a duration that exceeds 40 hours per week. The chronic conditions included in this review were a combination of the highest cost conditions with at least ten percent prevalence among adults in the US, and conditions with the highest personal health spending in the US, for example, diabetes, ischemic heart disease, and low back and neck pain [13,14].

We excluded reviews that investigated (1) biologic mechanisms behind the effects of non-standard work hours on health, (2) association of non-standard work hours and risk factors of chronic conditions (e.g. smoking, low physical activity), and (3) interventions to mitigate effects of non-standard work hours. We excluded reviews that measured impact of non-standard work hours on biomarkers instead of diagnosis (e.g. blood pressure as a continuous outcome vs hypertension), quality of life, sleep-related measures (e.g. disturbances, length, quality), and other employee outcomes (e.g. absenteeism, productivity, work-related stress). Reviews that only had abstracts available were excluded.

We searched the following electronic bibliographic databases from inception to April 2019: MEDLINE Pubmed, Embase (embase.com), Scopus, CINAHL (Ebsco), Web of Science, PsycINFO (Ebsco), ABI Inform Global (Ebsco), Business Source Premier (Ebsco), and Risk Abstracts (ProQuest). We also looked for grey literature in the following sites: Grey Literature Report, OpenGrey, CADTH, Systematic review repository (AHRQ), Epistomonikos, PROSPERO, Working time conference meetings/abstracts, CDC National Institute for Occupational Safety and Health, and Proquest Dissertations. The search strategy was adapted and developed using similar reviews and iterative searches by the research librarian in our team (LO), and a copy can be found in the S1 File. To capture additional relevant literature, we contacted authors of PROSPERO protocols that were marked as completed or were past the registered completion date to ask about availability of their review. We also hand searched the references of included studies to identify potentially relevant articles.

There were no language, country or date restrictions in the search, but we only included articles written in English, Spanish or French due to logistical factors. For Spanish and French articles, an English summary was produced and used for the screening.

Data extraction and quality assessment

Three authors (AR, MA, and MM) performed data extraction and quality assessment independently using standardized forms with each paper being assigned to two reviewers. Conflicts were resolved through discussion. We extracted pooled risk estimates, which were commonly reported as odds ratios (OR) or risk ratios (RR). As most meta-analyses had multiple pooled estimates, we extracted the main pooled results for each outcome of interest and any available dose-response results. We also extracted subgroup analyses based on shift type, design (e.g. cohort only), gender, age group, quality assessment (e.g. high-quality studies only), and race.

To provide a comprehensive and transparent assessment of the evidence, we utilized A MeaSurement Tool to Assess systematic Reviews (AMSTAR) v2 to assess the quality of an individual systematic review with meta-analysis (SR-MA) [15], and the Grades of Recommendation, Assessment, Development and Evaluation (GRADE) approach to judge the overall body evidence for each outcome [16]. For each paper, we counted the number of AMSTAR items that were fulfilled and divided by the total items to get a quality score. GRADE classifies evidence into high, moderate, low, or very low, which reflects the confidence that the studies capture the true effect or association of interest. Since all included SR-MAs pooled results from observational studies, the baseline quality of evidence was “low” and we down- or upgraded the assessment of each outcome following the GRADE criteria which considers risk of bias, imprecision, inconsistency (including heterogeneity), indirectness, and publication bias. For both AMSTAR and GRADE assessments, two authors (AR and MM) conducted independent assessments and resolved differences through consensus. In the Results below, we report quantitative findings of conditions with moderate or low grade evidence and the extracted results of individual studies can be found in the S4 File.

Deviations from the protocol

The review protocol (S5 File) was registered in PROSPERO (CRD42019122084), and we made three modifications. First, we revised the inclusion criteria to limit our scope to SR-MAs. This allowed us to focus on assessing the evidence on the strength of association between non-standard work hours and each outcome. Second, we modified the search strategy to better capture articles that examined the effects of long work hours. The final modification was the adoption of the GRADE approach to assess the overall quality of the evidence. The PRISMA checklist can be found in the S2 File.

Results

Overview of search results

Of the 2,936 articles identified in the initial search, we included 289 in the full text screen. Forty-eight (48) SR-MAs were ultimately included in the analysis (Fig 1) (See S3 File for list of articles excluded in full text screen). Among those articles, 41 (85%) used shift work as the exposure, and 12 (25%) used long work hours. The articles covered the following outcomes: cancers [1732] (16, 33%), cardiovascular disease [3341] (9, 19%), metabolic syndrome, diabetes mellitus, and obesity [4250] (9, 19%), complications of pregnancy [5158] (8, 17%), depression [43,45,59] (4, 8%), hypertension [60] (1, 2%), and injuries [61] (1, 2%). Some conditions we identified to be chronic or high cost (e.g. low back and neck pain, and lower respiratory tract infections) had not been a subject of eligible systematic review with meta-analysis.

Nearly all SR-MAs had a pooled estimate for the association between exposure to any shift work and an outcome, using “never exposed to shift work” or “regular day shift” as control. The definition of long work hours varied, with the lower limit usually set at or above 40 hours per week. Most SR-MAs used standard hours (35–40 hours per week) as the control. (Table 1)

On average 7 (SD: 2.37) of the 16 AMSTAR items were fulfilled yielding an average score of 44.7% (SD: 14.8). Only 19 (39.5%) SR-MAs had a score of 50% or higher. Few SR-MAs had a registered protocol and nearly all failed to conduct a comprehensive search. We flagged several SR-MAs for inappropriate analysis due to pooling of hazards ratio with odds and risk ratios. (Fig 2) GRADE assessment of evidence with the pooled risk estimate from the SR-MA with the highest AMSTAR score for each outcome is summarized in Table 2.

thumbnail
Fig 2. Quality assessment of included reviews using AMSTAR v2 (n = 41).

Red is not fulfilled, blue is fulfilled, and purple is partially fulfilled. RoB means risk of bias.

https://doi.org/10.1371/journal.pone.0231037.g002

Shift work

We found moderate grade evidence for the association between breast cancer and shift work. Most [18,20,23,26,28,31,32] (7 out of 9) included SR-MAs on breast cancer found a significantly increased risk among shift workers compared to non-shift workers. Focusing on the most recent SR-MAs, which included only high-quality articles, Li (2015) [31] found that shift workers have an 11% increased risk for breast cancer compared to non-shift workers (RR 1.11, 95% CI 1.02 to 1.20, I2 = 48%). Ijaz et al. (2013) [27] also detected a significant dose-response relationship with 5% increase in risk for every five years of exposure (RR 1.05, 95%CI 1.10 to 1.10, I2 = 55%). Lin et al (2015) [37] found a significant association between rotating shift work and breast cancer, although this should be interpreted with caution due to the review’s low quality.

Six SR-Mas [33,3638,40,41] investigated the association between cardiovascular disease and shift work. We found low grade evidence for ischemic heart disease, myocardial infarction, and ischemic stroke. Cheng et al. (2019) [33] found that there was a 13% increase in risk for ischemic heart disease among shift workers versus controls (1.13, 95%CI: 1.08 to 1.20, I2 = 52.7). While they [33] detected a significant dose-response relationship for ischemic heart disease such that each year of shift work led to a 0.9% increase in risk for ischemic heart disease (RR 1.009, 95% CI: 1.006 to 1.012, phetertogeneity > 0.05), we did not upgrade the evidence rating due to significant heterogeneity of the main and dose-response analyses. This increased risk for ischemic heart disease was present both rotating and fixed night shift work, and they also reported an increased risk for myocardial infarction (1.27, 95%CI: 1.17 to 1.39, I2 = 0) [33]. Meanwhile, Vyas et al. (2012) [40] reported a 5% increase in risk for ischemic stroke (RR 1.05, 95%CI 1.01 to 1.09, I2 = 0) among shift workers. We rated evidence that linked shift work to broadly defined cardiovascular disease (vs specific forms such as stroke) was very low (Table 2).

Among complications of pregnancy, the association between shift work and two complications, preterm delivery and small-for-gestational-age infant was supported by low-grade evidence. Bonzini et al. (2007) [53] estimated a significant increase in risk for preterm birth among shift workers (RR 1.20, 95%CI: 1.01–1.42, pheterogeneity = 0.002). They also found that results that trended towards significance on shift work and small-for-gestational-age (RR 1.07, 95%CI: 0.96–1.19, I2 = 3.3%). The lower quality update of Bonzini et al. ‘s review [52] found significant increased risk for both outcomes among shift workers.

Cai et al. (2019) [54] estimated the effects of rotating shifts and fixed night shifts on complications of pregnancy and rated the link between the two types of shift work and gestational hypertension to be low grade. Fixed shifts were associated with gestational hypertension (OR = 1.19, 95%CI: 1.1 to 1.29, I2 = 0%) and there was a trend towards significance between rotating shift and gestational hypertension (OR = 1.19, 95%CI: 0.97 to 1.45, I2 = 0%).[54] Fixed shifts were also associated with increased risk for preterm delivery (OR = 1.21, 95%CI: 1.03 to 1.42, I2 = 36%). Rotating shifts, meanwhile, was associated with increased the risk of preterm delivery (OR = 1.13, 95%CI: 1.00 to 1.28, I2 = 31%), and offspring that are small for gestational age (OR = 1.18, 95%CI: 1.01 to 1.38, I2 = 0%). We assessed the evidence for miscarriages and preeclampsia to be very low due to imprecision or inconsistency.

We found very low-grade evidence regarding shift work’s effect on risk for depression, diabetes mellitus, hypertension, miscarriages, occupational injuries, obesity, metabolic syndrome, and other cancers (colorectal, hematologic, lung, prostate, reproductive system, and skin) (Table 2). Most of the SR-MAs of these outcomes had high risk of bias. The common unmet AMSTAR items are related to protocol registration, search comprehensiveness, listing excluded studies, assessing impact of funding sources, use of appropriate pooling technique, and assessing and discussing risk of bias of included studies in the meta-analysis. (Fig 2) There were also limitations related to imprecision or unexplained heterogeneity.

Long work hours

Stroke is the only outcome that had moderate grade evidence associated with long work hours. Kivimaki et al. (2015) [35] found that there was a 33% increased risk of stroke among those who worked more than 40 hours per week, compared to those who work standard hours (RR: 1.33, 95%CI: 1.11 to 1.61, I2 = 0). Their metaregression results suggested that risk might be higher among those with high socio-economic status (compared to low socio-economic status) but there were no differences by age group or sex. They also observed a dose-response relationship with longer hours per week translating to higher risk (RR 1.11, 95%CI: 1.05 to 1.17; 11% increase per increase in work hour category).

There was low-grade evidence supporting the association between depression, coronary disease, and selected complications of pregnancy (preterm delivery and low birthweight) with long work hours. Virtanen et al. (2018) [63] found 14% higher odds (OR 1.14, 95%CI 1.03 to 1.25, I2 = 45.1%) of depression among those who worked >40 hours per week. Kivimaki et al. (2015) [54] found a 13% increased risk for coronary disease for those doing long work hours (RR 1.13, 95%CI: 1.02 to 1.26 I2 = 0).[35] Cai et al. (2019) found a 21% increase in odds for preterm delivery (OR 1.21, 95%CI: 1.11 to 1.13, I2 = 30%) and a 43% increase in odds of having low birth weight offspring (OR 1.43, 95%CI: 1.11 to 1.84, I2 = 0%) among women working >40 hours per week during pregnancy. They also reported a significant linear relationship between hours worked and the risk of preterm delivery.

We found very low-grade evidence for long work hours and the following outcomes: miscarriage, preeclampsia, gestational hypertension, small for gestational age, diabetes mellitus, and any cardiovascular disease (Table 2). SR-MAs had high risk of bias and issues with the heterogeneity of results casting doubt on the significant relationships detected by these reviews. Unmet AMSTAR items leading to the high risk of bias were similar to that of reviews on shift work.

Discussion

We found moderate grade evidence linking shift work to breast cancer and long work hours to stroke. We also found low-grade evidence linking shift work to ischemic heart disease, myocardial infarction, ischemic stroke, gestational hypertension, preterm delivery, and small-for-age babies, and low-grade evidence on the association between long work hours and coronary disease, depression, low birthweight babies, and preterm delivery (Table 2). Our conclusions align with previous reviews[10,11], though we identified specific diseases (e.g. stroke and myocardial infarction) rather than adopting broad categories (e.g., cardiovascular disease). Our study is notable because we found that not all associations of non-standard work hours to cardiovascular diseases and cancers were supported by sufficient quality evidence.

Our findings should be of interest to workers, unions, and other organizations that advocate for workplace well-being. Workers should be informed of the risks associated with these jobs and the evidence-based screenings and interventions that might mitigate the risk. The increased occurrence of these outcomes ultimately translates to increased healthcare costs, which burdens the workers and businesses. We recognize that in some industries (e.g., manufacturing, transportation, hospitals, and police/fire departments), shift work and long work hours may be inevitable; nevertheless, employers should be aware of the healthcare costs associated with their non-standard work hours, potentially consider alternative schedules, and encourage screenings and interventions to reduce risk. In countries like the US, where self-insured companies bear the additional costs of these conditions, there might be a case for restricting non-standard work hours that balances lost productivity with potential savings due to the prevention of these conditions.

Findings may also be of interest to policymakers, as several countries have enacted policies to protect shift workers such as restricting total shift work hours per week and requiring companies to offer free health exams to shift workers [8]. It is still rare for governments to require companies to provide compensation to employees harmed by shift work. The exception is Denmark, where shift workers who develop breast cancer receive compensation. The first claimants received amounts ranging from US$ 3,000 to US$ 100,000, funded through their employers’ insurance companies [65]. However, current evidence does not clearly identify advantages of fixed shifts over rotating shifts and does not suggest a specific threshold for maximum number of hours per week. Both topics warrant more research. Our findings, however, suggest that a maximum lifetime exposure cap might be warranted, particularly when the risk from exposure has meaningful dose-response association. This policy should be considered for occupations where shift work is unavoidable, such as healthcare and law enforcement. In countries with universal healthcare insurance, regulating non-standard work hours may translate into non-trivial societal savings.

Screening and behavioral changes are commonly used preventive interventions for various health conditions. While we found moderate evidence suggesting non-standard work hours may increase the risk for certain conditions captured in current screening or preventive guidelines (e.g. breast cancer and stroke); there are no specific recommendations for screening for these conditions based on work hours. It is unclear whether there should be unique guidelines for those exposed to non-standard work hours (e.g. should shift workers be screened at earlier ages?). Some research exists on behavioral interventions for shift workers [66]. Guidance for employers on managing the impact of shift work remains largely focused on designing efficient shift schedules and promoting healthy lifestyle among workers [66,67]. The effect of these interventions in the long term and on the risk for acquiring chronic conditions should continue to be investigated.

We performed a comprehensive search and assessment of the literature to arrive at our findings. We used a reproducible method for assessing the evidence, and as new reviews are produced, our assessments can be updated following the same methodology. Despite the number of SR-MAs included, several research questions related to the epidemiologic link between non-standard work hours and chronic conditions remain unanswered. Studies on differential risks are needed. Examples would be studies that compare rotating versus fixed shift and studies that look at sex or geographic differences. Dose-response meta-analyses are also needed, especially for establishing causality.

We focused on epidemiologic evidence in this review, but it should be acknowledged that mechanistic studies that investigate how shiftwork alters biological processes to increase the risk for various conditions are necessary to prove with certainty that non-standard work hours are causing these outcomes. These studies, together with epidemiologic studies, are also needed to guide intervention and policy development. We found studies that proposed disease mechanisms for two conditions with moderate grade evidence. For breast cancer, shift work leads to disruption of circadian rhythms which in turn lead to genetic and epigenetic changes that promote cancer growth [4]. For stroke, long work hours is a source of stress and this stress leads to damage to the cardiovascular system. Long work hours can also promote unhealthy behaviors that further increase risk for stroke [6,7].

Several SR-MAs included in this study failed to meet the current standards for systematic reviews and meta-analyses as outlined in AMSTAR. Requirements such as protocol registration, comprehensive search strategies, and appropriate pooling of studies were most commonly unmet. Failing to meet AMSTAR conditions was a common reason for downgrading the evidence. We recommend that future SR-MAs are conducted in accordance with these guidelines to ensure minimization of risk of bias.

We downgraded much of the evidence due to issues of heterogeneity. The individual studies pooled by meta-analyses that we reviewed often had differences in definitions, and measurement of exposures, and included populations. There were also differences in the variables used to calculate adjusted risk estimates. Despite these potential sources of heterogeneity, subgroup analyses often failed to identify any socio-demographic or study design-related factors as a significant source of heterogeneity. Fortunately, individual level cohort data is increasingly becoming available and allows for individual-level meta-analyses. These individual-level meta-analyses allow researchers to apply more consistent definitions and utilize the same regression models for getting adjusted measures of association.

Our review has several limitations. One is that we focused on SR-MAs and did not look for additional observational studies. Some of the SR-MAs we included (e.g., those regarding pregnancy complications) were published more than five years ago, and new observational studies may provide better quality evidence regarding these outcomes. We were also limited to outcomes where meta-analyses were performed. On account of these, we may have missed high quality observational studies. Another weakness is that we assessed papers based on the data published. Some AMSTAR criteria may have been fulfilled during the conduct of the study, but were excluded from the published manuscript resulting in lower quality scores. Finally, there are new work hour arrangements, such as flexible work hours or compressed workweeks, that we did not include in this review. Health effects of these arrangements might be similar to those included here if these work arrangements induce circadian rhythm disruptions or exceed the standard work hour length.

Conclusion

Non-standard work hours are likely associated with several chronic outcomes. There is moderate grade evidence linking shift work to breast cancer and long work hours to stroke. There is low-grade evidence that suggests an increased risk of depression, some forms of cardiovascular diseases, and complications of pregnancy with exposure to non-standard work hours. Differential risk across different types of shift work and diverse populations should also be studied. Our results suggest that non-standard work hours may be detrimental to employee health. Workers should be informed of the potential risks associated with these jobs, and additional screening and preventive measures for breast cancer and stroke may be warranted. Higher quality research needs to be conducted to ascertain effects on other chronic and high-cost outcomes and to guide stronger policy recommendations.

Supporting information

S3 File. List of excluded articles in full text screen.

https://doi.org/10.1371/journal.pone.0231037.s003

(XLSX)

S4 File. Pooled results of included reviews per condition.

https://doi.org/10.1371/journal.pone.0231037.s004

(PDF)

References

  1. 1. Messenger Jon. Working time and the future of work. Future of Work Research Paper Series. Geneva, Switzerland: International Labor Office; 2018. p. 44. Available: https://www.ilo.org/global/topics/future-of-work/publications/research-papers/WCMS_649907/lang—en/index.htm
  2. 2. Merkus SL, Holte KA, Huysmans MA, Van Mechelen W, Van Der Beek AJ. Nonstandard working schedules and health: The systematic search for a comprehensive model. BMC Public Health. 2015;15: 1–15. pmid:25563658
  3. 3. Puttonen S, Harma M, Hublin C. Shift work and cardiovascular disease—pathways from circadian stress to morbidity. Scand J Work Environ Health. 2010;36: 96–108. pmid:20087536
  4. 4. Haus EL, Smolensky MH. Shift work and cancer risk: Potential mechanistic roles of circadian disruption, light at night, and sleep deprivation. Sleep Med Rev. 2013;17: 273–284. pmid:23137527
  5. 5. Reiter RJ, Tan DX, Korkmaz A, Ma S. Obesity and metabolic syndrome: Association with chronodisruption, sleep deprivation, and melatonin suppression. Ann Med. 2012;44: 564–577. pmid:21668294
  6. 6. Haines VY, Marchand A, Genin E, Rousseau V. A balanced view of long work hours. Int J Work Heal Manag. 2012;5: 104–119.
  7. 7. Kivimäki M, Steptoe A. Effects of stress on the development and progression of cardiovascular disease. Nat Rev Cardiol. 2018;15: 215–229. pmid:29213140
  8. 8. Gartner J, Rosa RR, Roach G, Kubo T, Takahashi M. Working Time Society consensus statements: Regulatory approaches to reduce risks associated with shift work—a global comparison. Ind Health. 2019;57: 245–263. pmid:30700673
  9. 9. Wang XS, Armstrong MEG, Cairns BJ, Key TJ, Travis RC. Shift work and chronic disease: The epidemiological evidence. Occup Med (Chic Ill). 2011;61: 78–89. pmid:21355031
  10. 10. Kecklund G, Axelsson J. Health consequences of shift work and insufficient sleep. BMJ. 2017;356: i6599. pmid:31055304
  11. 11. Ganster DC, Rosen CC, Fisher GG. Long Working Hours and Well-being: What We Know, What We Do Not Know, and What We Need to Know. J Bus Psychol. 2018;33: 25–39.
  12. 12. Moreno CRC, Marqueze EC, Sargent C, Wright KP Jr, Ferguson SA, Tucker P. Working Time Society consensus statements: Evidence-based effects of shift work on physical and mental health. Ind Health. 2019;57: 139–157. pmid:30700667
  13. 13. Dieleman JL, Baral R, Birger M, Bui AL, Bulchis A, Chapin A, et al. US spending on personal health care and public health, 1996–2013. JAMA—J Am Med Assoc. 2016;316: 2627–2646. pmid:28027366
  14. 14. Soni A, Mitchell E. Expenditures for Commonly Treated Conditions among Adults Age 18 and Older in the US Civilian Noninstitutionalized Population, 2013. Stat Br.
  15. 15. Shea BJ, Reeves BC, Wells G, Thuku M, Hamel C, Moran J, et al. AMSTAR 2: A critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ. 2017;358: 1–9. pmid:28935701
  16. 16. Guyatt GH, Oxman AD, Vist GE, Kunz R, Falck-Ytter Y, Alonso-Coello P, et al. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ. 2008;336: 924–926. pmid:18436948
  17. 17. Du HB, Bin KY, Liu WH, Yang FS. Shift work, night work, and the risk of prostate cancer: A meta-analysis Based on 9 cohort studies. Med (United States). 2017;96. pmid:29145258
  18. 18. Erren TC, Pape HG, Reiter RJ, Piekarski C. Chronodisruption and cancer. Naturwissenschaften. 2008;95: 367–382. pmid:18196215
  19. 19. Mancio J, Leal C, Ferreira M, Norton P, Lunet N. Does the association of prostate cancer with night-shift work differ according to rotating vs. fixed schedule? A systematic review and meta-analysis. Prostate Cancer Prostatic Dis. 2018;21: 337–344. pmid:29700389
  20. 20. Megdal SP, Kroenke CH, Laden F, Pukkala E, Schernhammer ES. Night work and breast cancer risk: A systematic review and meta-analysis. Eur J Cancer. 2005;41: 2023–2032. pmid:16084719
  21. 21. Rao D, Yu H, Bai Y, Zheng X, Xie L. Does night-shift work increase the risk of prostate cancer? A systematic review and meta-analysis. Onco Targets Ther. 2015;8: 2817–2826. pmid:26491356
  22. 22. Travis RC, Balkwill A, Fensom GK, Appleby PN, Reeves GK, Wang XS, et al. Night shift work and breast cancer incidence: Three prospective studies and meta-analysis of published studies. J Natl Cancer Inst. 2016;108: 1–9. pmid:27758828
  23. 23. Wang F, Yeung KL, Chan WC, Kwok CCH, Leung SL, Wu C, et al. A meta-analysis on dose-response relationship between night shift work and the risk of breast cancer. Ann Oncol. 2013;24: 2724–2732. pmid:23975662
  24. 24. Wang X, Ji A, Zhu Y, Liang Z, Wu J, Li S, et al. A meta-analysis including dose-response relationship between night shift work and the risk of colorectal cancer. Oncotarget. 2015;6: 25046–25060. pmid:26208480
  25. 25. Gan Y, Li L, Zhang L, Yan S, Gao C, Hu S, et al. Association between shift work and risk of prostate cancer: A systematic review and meta-analysis of observational studies. Carcinogenesis. 2018;39: 87–97. pmid:29126152
  26. 26. He C, Anand ST, Ebell MH, Vena JE, Robb SW. Circadian disrupting exposures and breast cancer risk: a meta-analysis. Int Arch Occup Environ Health. 2015;88: 533–547. pmid:25261318
  27. 27. Ijaz S, Verbeek J, Seidler A, Lindbohm ML, Ojajärvi A, Orsini N, et al. Night-shift work and breast cancer—A systematic review and meta-analysis. Scand J Work Environ Heal. 2013;39: 431–447.
  28. 28. Jia Y, Lu Y, Wu K, Lin Q, Shen W, Zhu M, et al. Does night work increase the risk of breast cancer? A systematic review and meta-analysis of epidemiological studies. Cancer Epidemiol. 2013;37: 197–206. pmid:23403128
  29. 29. Kamdar BB, Tergas AI, Mateen FJ, Bhayani NH, Oh J. Night-shift work and risk of breast cancer: A systematic review and meta-analysis. Breast Cancer Res Treat. 2013;138: 291–301. pmid:23400581
  30. 30. Kolstad HA, Erlandsen M, Frost P, Bonde JP. Should we warn against night shifts to prevent breast cancer? Occup Environ Med. 2010;67: 797. pmid:20798013
  31. 31. Li M. Night Shift Work and Risk of Breast Cancer: A Case-control Study among Hong Kong Chinese Women. Chinese University of Hong Kong. 2015.
  32. 32. Liu W, Zhou Z, Dong D, Sun L, Zhang G. Sex differences in the association between night shift work and the risk of cancers: A meta-analysis of 57 articles. Dis Markers. 2018;2018. pmid:30598709
  33. 33. Cheng M, He H, Wang D, Xu L, Wang B, Ho KM, et al. Shift work and ischaemic heart disease: Meta-analysis and dose-response relationship. Occup Med (Chic Ill). 2019;69: 182–188. pmid:30923828
  34. 34. Kang MY, Park H, Seo JC, Kim D, Lim YH, Lim S, et al. Long working hours and cardiovascular disease: A meta-analysis of epidemiologic studies. J Occup Environ Med. 2012;54: 532–537. pmid:22576460
  35. 35. Kivimäki M, Jokela M, Nyberg ST, Singh-Manoux A, Fransson EI, Alfredsson L, et al. Long working hours and risk of coronary heart disease and stroke: A systematic review and meta-analysis of published and unpublished data for 603 838 individuals. Lancet. 2015;386: 1739–1746. pmid:26298822
  36. 36. Li M, Huang JT, Tan Y, Yang BP, Tang ZY. Shift work and risk of stroke: A meta-analysis. Int J Cardiol. 2016;214: 370–373. pmid:27085131
  37. 37. Lin X, Chen W, Wei F, Ying M, Wei W, Xie X. Night-shift work increases morbidity of breast cancer and all-cause mortality: A meta-analysis of 16 prospective cohort studies. Sleep Med. 2015;16: 1381–1387. pmid:26498240
  38. 38. Torquati L, Mielke GI, Brown WJ, Kolbe-Alexander T. Shift work and the risk of cardiovascular disease. A systematic review and meta-analysis including dose-response relationship. Scand J Work Environ Heal. 2018;44: 229–238. pmid:29247501
  39. 39. Virtanen M, Heikkilä K, Jokela M, Ferrie JE, Batty GD, Vahtera J, et al. Long working hours and coronary heart disease: A systematic review and meta-analysis. Am J Epidemiol. 2012;176: 586–596. pmid:22952309
  40. 40. Vyas M V., Garg AX, Iansavichus A V., Costella J, Donner A, Laugsand LE, et al. Shift work and vascular events: Systematic review and meta-analysis. BMJ. 2012;345: 1–11. pmid:22835925
  41. 41. Wang D, Ruan W, Chen Z, Peng Y, Li W. Shift work and risk of cardiovascular disease morbidity and mortality: A dose–response meta-analysis of cohort studies. Eur J Prev Cardiol. 2018;25: 1293–1302. pmid:29929393
  42. 42. Anothaisintawee T, Reutrakul S, Van Cauter E, Thakkinstian A. Sleep disturbances compared to traditional risk factors for diabetes development: Systematic review and meta-analysis. Sleep Med Rev. 2016;30: 11–24. pmid:26687279
  43. 43. Cosgrove MP, Sargeant LA, Caleyachetty R, Griffin SJ. Work-related stress and Type 2 diabetes: Systematic review and meta-analysis. Occup Med (Chic Ill). 2012;62: 167–173. pmid:22333189
  44. 44. 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: 72–78. pmid:25030030
  45. 45. Kivimäki M, Virtanen M, Kawachi I, Nyberg ST, Alfredsson L, Batty GD, et al. Long working hours, socioeconomic status, and the risk of incident type 2 diabetes: A meta-analysis of published and unpublished data from 222120 individuals. Lancet Diabetes Endocrinol. 2015;3: 27–34. pmid:25262544
  46. 46. Liu Q, Shi J, Duan P, Liu B, Li T, Wang C, et al. Is shift work associated with a higher risk of overweight or obesity? A systematic review of observational studies with meta-analysis. Int J Epidemiol. 2018;47: 1956–1971. pmid:29850840
  47. 47. Saulle R, Bernardi M, Chiarini M, Backhaus I, Torre G La Shift work, overweight and obesity in health professionals: a systematic review and meta-analysis. Clin Ter. 2018;169: e189–e197. pmid:30151553
  48. 48. 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. pmid:28975706
  49. 49. Wang F, Zhang L, Zhang Y, Zhang B, He Y, Xie S, et al. Meta-analysis on night shift work and risk of metabolic syndrome. Obes Rev. 2014;15: 709–720. pmid:24888416
  50. 50. Watanabe K, Sakuraya A, Kawakami N, Imamura K, Ando E, Asai Y, et al. Work-related psychosocial factors and metabolic syndrome onset among workers: a systematic review and meta-analysis. Obes Rev. 2018;19: 1557–1568. pmid:30047228
  51. 51. Bonde JP, Jørgensen KT, Bonzini M, Palmer KT. Miscarriage and occupational activity: A systematic review and meta-analysis regarding shift work, working hours, lifting, standing, and physical workload. Scand J Work Environ Heal. 2013;39: 325–334. pmid:23235838
  52. 52. Bonzini M, Palmer KT, Coggon D, Carugno M, Cromi A, Ferrario MM. Shift work and pregnancy outcomes: A systematic review with meta-analysis of currently available epidemiological studies. BJOG An Int J Obstet Gynaecol. 2011;118: 1429–1437. pmid:21790955
  53. 53. Bonzini M, Coggon D, Palmer KT. Risk of prematurity, low birthweight and pre-eclampsia in relation to working hours and physical activities: A systematic review. Occup Environ Med. 2007;64: 228–243. pmid:17095552
  54. 54. Cai C, Vandermeer B, Khurana R, Nerenberg K, Featherstone R, Sebastianski M, et al. The impact of occupational shift work and working hours during pregnancy on health outcomes: a systematic review and meta-analysis. Am J Obstet Gynecol. 2019. pmid:31276631
  55. 55. Mozurkewich EL, Luke B, Avni M, Wolf FM. Working conditions and adverse pregnancy outcome: A meta-analysis. Obstet Gynecol. 2000;95: 623–635. pmid:10725502
  56. 56. Palmer KT, Bonzini M, Harris EC, Linaker C, Bonde JP. Work activities and risk of prematurity, low birth weight and pre-eclampsia: An updated review with meta-analysis. Occup Environ Med. 2013;70: 213–222. pmid:23343859
  57. 57. Quansah R, Jaakkola JJ. Occupational exposures and adverse pregnancy outcomes among nurses: A systematic review and meta-analysis. J Women’s Heal. 2010;19: 1851–1862. pmid:20809857
  58. 58. van Melick MJGJ, van Beukering MDM, Mol BW, Frings-Dresen MHW, Hulshof CTJ. Shift work, long working hours and preterm birth: a systematic review and meta-analysis. Int Arch Occup Environ Health. 2014;87: 835–849. pmid:24584887
  59. 59. Angerer P, Schmook R, Elfantel I, Li J. Night Work and the Risk of Depression. Dtsch Arztebl Int. 2017;114: 404–411. pmid:28669378
  60. 60. Manohar S, Thongprayoon C, Cheungpasitporn W, Mao MA, Herrmann SM. Associations of rotational shift work and night shift status with hypertension: A systematic review and meta-analysis. J Hypertens. 2017;35: 1929–1937. pmid:28650914
  61. 61. Fischer D, Lombardi DA, Folkard S, Willetts J, Christiani DC. Updating the “Risk Index”: A systematic review and meta-analysis of occupational injuries and work schedule characteristics. Chronobiol Int. 2017;34: 1423–1438. pmid:29064297
  62. 62. Lee A, Myung SK, Cho JJ, Jung YJ, Yoon JL, Kim MY. Night shift work and risk of depression: Meta-analysis of observational studies. J Korean Med Sci. 2017;32: 1091–1096. pmid:28581264
  63. 63. Virtanen M, Jokela M, Madsen IEH, Magnusson Hanson LL, Lallukka T, Nyberg ST, et al. Long working hours and depressive symptoms: Systematic review and meta-analysis of published studies and unpublished individual participant data. Scand J Work Environ Heal. 2018;44: 239–250. pmid:29423526
  64. 64. Watanabe K, Imamura K, Kawakami N. Working hours and the onset of depressive disorder: A systematic review and meta-analysis. Occup Environ Med. 2016;73: 877–884. pmid:27852890
  65. 65. Wise J. Danish night shift workers with breast cancer awarded compensation. BMJ. 2009;338. pmid:19297443
  66. 66. Neil-Sztramko SE, Pahwa M, Demers PA, Gotay CC. Health-related interventions among night shift workers: A critical review of the literature. Scand J Work Environ Heal. 2014;40: 543–556. pmid:24980289
  67. 67. Health and Safety Executive. Managing Shiftwork. Heal Saf Guid. 2006.