Abstract
Background
Workplace environmental exposures (WEEs) and shift work are common occupational hazards, but their joint associations with working-age health and healthy working years are not well characterised.
Methods
Data were from the UK Biobank. Lifetime WEEs (toxic substances, passive smoking, extreme temperatures, noise) and shift work (day, mixed or night shifts) were derived from complete job histories and classified as cumulative years (never, <10, >=10 years). 20 prevalent multisystem long-term work-limiting health conditions before age 70 were ascertained via linked health records. Survival analyses including Cox proportional hazards and accelerated failure time models were conducted.
Results
Among 34 413 workers aged 38–54 years, half experienced WEEs or shift work. Over 13.9±1.0 years, WEEs and shift work were associated with all 20 conditions, and some associations were more pronounced or only significant in women. Specifically, workers exposed to both exposures for >=10 years (vs both never) had 154% higher risks of new-onset carpal tunnel syndrome, followed by chronic obstructive pulmonary disease, spondylopathy, diabetes and depression (increased risks: 124%–100%). They also lost 8.88 healthy working years due to incident carpal tunnel syndrome, followed by depression, spondylopathy, intervertebral disc diseases and psoriasis (years lost: 7.33–6.44). The joint impacts of multifaceted WEEs and shift work varied, but few multiplicative/additive interactions were observed.
Conclusions
Co-occurrence of WEEs and shift work is associated with substantial excess risks of long-term work-limiting health conditions and loss of healthy working years. Strengthening control of WEEs and health surveillance for shift workers may be needed to protect working-age health and sustain productive employment.
Keywords: Epidemiology, Occupational Medicine, Environmental Medicine, Environmental Pollution, Public Health
WHAT IS ALREADY KNOWN ON THIS TOPIC
Workplace environmental exposures (WEEs) and shift work are significant risk factors for various multisystem health conditions. Shift work can increase susceptibility to WEEs, whereas existing research has predominantly examined them separately, with limited studies exploring their joint impacts on working-age health. Additionally, many studies have methodological limitations, such as small sample sizes and short follow-up periods, leading to inconsistent findings.
WHAT THIS STUDY ADDS
Using a large, national cohort of mid-life workers with detailed lifetime occupational histories, this study jointly examines cumulative WEEs and shift work in relation to 20 common long-term work-limiting health conditions. Co-occurrence of WEEs and shift work is common and is associated with elevated risks across multiple organ systems and with substantial losses in healthy working years. The study also demonstrates that sex differences are evident for several outcomes, while multiplicative and additive interactions between WEEs and shift work are generally modest, suggesting that the main concern is the overall burden of joint exposure rather than strong statistical interaction.
HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY
Enhanced monitoring and protective measures against harmful WEEs, alongside continuous health surveillance for shift workers, are required. These proactive strategies can help improve workforce productivity and foster sustainable economic growth.
Introduction
Globally, the working-age population is grappling with substantial health challenges.1 In the UK, for instance, over 8 million working-age adults report work-limiting health conditions, including 3.9 million currently employed—an increase of 1.5 million in the past decade.2 According to the Office for Budget Responsibility, the deteriorating working-age health and resultant economic inactivity have cost nearly £16 billion annually since the pandemic.3
In recent decades, occupational risk factors have garnered considerable attention, with workplace environmental exposures (WEEs)—such as toxic substances, passive smoking, extreme temperatures and excessive noise—emerging as critical concerns due to their profound impacts on a wide spectrum of health outcomes.4 Evidence has further established positive associations between lifetime cumulative WEEs and certain work-limiting health conditions, particularly cancer, respiratory, neurological and musculoskeletal diseases.5,8
Shift work is also an important occupational risk factor, demonstrated to elevate the risk of various chronic diseases.9 The underlying mechanisms involve circadian rhythm disruption, which can impair the liver function responsible for metabolising and detoxifying workplace toxins.10 In addition, shift work can induce heightened inflammation, immune dysfunction, accelerated biological ageing and frailty, thereby increasing vulnerability to long-term work-limiting health conditions associated with WEEs.911,13 This body of evidence implies potential synergistic interactions between WEEs and shift work. Compared with day workers, shift workers are more likely to be employed in jobs with physically and chemically hazardous conditions, such as higher exposure to noise, extreme temperatures, dust, chemical fumes and environmental tobacco smoke and to experience multiple WEEs simultaneously.14 15 A prior review collated existing evidence on the role of shift work in the association between chemicals and cardiovascular and respiratory health.16 Recent studies have also identified interactions between excessive noise and shift work concerning liver function17 and thyroid hormone levels.18 The controversial findings, limited variety of WEEs and health conditions, and small sample sizes largely limited the robustness of existing evidence. To date, no large-scale research has comprehensively examined the interactions between cumulative multifaceted WEEs and shift work in their associations with prevalent long-term work-limiting health conditions.
To address these research gaps, this prospective cohort study aimed to investigate the joint associations of cumulative multifaceted WEEs and shift work with the risks of common long-term work-limiting health conditions among workers.
Methods
Study population
Data were obtained from the UK Biobank (UKB, Application 105435), an ongoing population-based prospective cohort study that recruited over 500 000 participants aged 37 to 73 years between 2006 and 2010 across 22 centres in England, Scotland and Wales.19 At baseline, participants underwent a comprehensive assessment, including biological sample collection, touch-screen questionnaires, nurse-led verbal interviews and physical examinations. The UKB was approved by the National Health Service North West Centre for Research Ethics Committee. All participants provided written informed consent.
To quantify healthy working years lost due to incident long-term work-limiting health conditions and minimise recall bias, participants aged >=55 years were excluded, as retrospective life-course data in this age group are comparable to prospective data.20 In total, 34 413 currently employed participants aged 38–54 years with complete data on lifetime multifaceted WEEs and shift work were included (online supplemental figure S1).
Lifetime cumulative WEEs and shift work
Between June and September 2015, participants completed an online Healthy Work questionnaire, which gathered detailed data on their lifetime work history, including job start/end years, job type, weekly working hours and shift work history (Category 130). This study used work history information up to the baseline interview.
Participants self-rated exposure to ten aspects of WEEs across all jobs held, covering six toxic substances (dust, chemical or other fumes, asbestos, paints/thinners/glues, pesticides and diesel exhaust), passive smoking, extreme temperatures (very hot or very cold) and excessive noise. Responses included ‘Do not know’, ‘Rarely/never’, ‘Sometimes’ and ‘Often’, with ‘Often’ indicating exposure. Due to numerous ‘Do not know’ responses for asbestos, these participants were considered unexposed. Exposure to any WEE was treated as exposure to WEEs for that job, with a similar approach applied to toxic substances and extreme temperatures. Lifetime cumulative WEEs were calculated by summing exposure years across all jobs and were categorised as never, <10 years and >=10 years.
For shift work, participants reported the type of shift (day, mixed or night (working for at least 3 hours between midnight and 5:00 a.m.)), the duration of shift work in each job and, for mixed/night shifts, the number of night shifts per month. Cumulative years of shift work were calculated by summing years spent in day, mixed and night shifts across all jobs, as long-term day shifts may also present health risks, though to a lesser extent than night shifts.21,23 Additionally, cumulative years of day and night shift work were assessed separately, with categories of never, <10 years and >=10 years. The average number of night shifts per month was also calculated and categorised as never, <8 days/month and >=8 days/month.
Participants were further classified into five categories based on the joint exposure to WEEs and shift work: both >=10 years, both never, WEEs >=10 years only, shift work >=10 years only and both <10 years.
Long-term work-limiting health conditions
We selected 20 long-term work-limiting health conditions by translating the health-condition categories used by the UK Office for National Statistics to describe long-term sickness-related economic inactivity among working-age adults into incident endpoints in the UKB.24 Notably, we defined a composite ‘sensory impairment’ endpoint that combines physician-diagnosed hearing and visual impairments, reflecting difficulties in seeing or hearing as long-term work-limiting health problems and their shared functional impact on work participation. Conditions with very low incidence (eg, epilepsy, autism and Parkinson’s disease) were not modelled as separate outcomes.
All conditions were ascertained using the International Classification of Diseases 10th revision from primary care records (Category 3000), hospital inpatient records (Category 2000), death registers (Fields 40 001 and 40 002), self-reported illness data (Fields 20 001 and 20 002) and/or cancer registers (Category 100 092) (online supplemental table S1). Diabetes at baseline was further diagnosed if haemoglobin A1c >=6.5%.
Given the rising retirement age, participants were followed until age 69, with the follow-up endpoint defined as the first occurrence of conditions, age 70, loss to follow-up, death or the latest available record, whichever came first.
Covariates
Covariates were selected a priori as potential confounders of the associations between cumulative WEEs and shift work and the outcomes. They were treated as determinants of long-term exposure patterns and underlying health risk, rather than as consequences of cumulative WEEs or shift work. Regression coefficients for covariates were included to control for confounding of the exposure-outcome associations and were not interpreted as independent causal impacts, in line with guidance on the ‘table 2 fallacy’.25
Socioeconomic and demographic covariates collected at baseline included age, gender, race (White, Black, Asian, mixed, others), residence (town or rural, urban), assessment centre location (Wales, Scotland, northern England, central England, southern England, London area),education (basic (O levels, General Certificate of Secondary Education (GCSEs), Certificate of Secondary Education (CSEs)), intermediate (Advanced Subsidiary (AS) levels, National Vocational Qualification (NVQ), Higher National Diploma (HND), Higher National Certificate (HNC)), high level/professional (college, university, other professional qualifications like nursing and teaching), others), and birth country (the UK, Ireland, elsewhere). The Index of Multiple Deprivation was standardised within each country (England, Scotland and Wales) and divided into equal tertiles, representing mild, moderate and severe deprivation.
Total number of jobs in life (1, 2, 3, 4 or more), type of main job (managerial and professional, associate professional and technical, administrative and secretarial, skilled/service/sales, operative and elementary) and years worked 40 or more hours per week (never, <20, >=20) were derived from the online Healthy Work questionnaire.
Statistical analysis
Our primary estimates were the total impacts of lifetime cumulative WEEs and of shift work (and their joint categories) on incident long-term work-limiting health conditions. Cox proportional hazards regression was performed to investigate HR and 95% CI for the associations of cumulative WEEs and shift work with incident long-term work-limiting health conditions during follow-ups, after excluding those with corresponding conditions at baseline. HRs from Cox models were used as measures of relative incidence rates under the proportional hazards assumption.26 The proportional hazards assumption was assessed using Schoenfeld residuals, with no major violations that would qualitatively change the conclusions. To aid interpretation, the accelerated failure time model with the Weibull distribution was employed to estimate differences in expected healthy working years lost up to age 70 and cumulative incidence at selected ages across exposure categories. The Cox-Snell residual plot for the Weibull model (using incident arthritis as an example given its highest incidence) can be found in online supplemental figure S2. All models were adjusted for age, gender, race, residence, assessment centre location, education, multiple deprivation, total number of jobs in life, type of main job, years worked 40 or more hours per week and birth country.
Following VanderWeele,27 we distinguished joint effects, interaction and effect modification. Cumulative WEEs and shift work were treated as two exposures of interest when estimating joint effects and interaction (on multiplicative and additive scales). Multiplicative interaction was assessed by including a product term of WEE and shift work in the confounder-adjusted Cox models. Additive interaction was quantified as the relative excess risk due to interaction, interpreted as a departure from additivity of relative risks on the HR scale. Sex was treated as a stratifying variable for effect modification of the exposure-outcome associations.
Missing values for covariates were categorised as ‘Missing’ in the primary analyses, given their minimal proportions (<3%). Three sensitivity analyses were conducted to validate the results: (1) treating uncertain workplace asbestos exposures as missing; (2) applying Fine-Gray subdistribution hazard models to account for the competing risk of death; and (3) excluding incident conditions occurring within 2 years post-baseline to mitigate potential reverse causality.
Reporting of this study was done under Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.28 Statistical analyses were performed using SAS statistical software V.9.4 (SAS Institute) and R statistical software V.4.4.1 (R Project for Statistical Computing). All analyses were two-sided, and P values <0.05 were considered statistically significant.
Patient and public involvement
Patients or the public were not involved in the design, conduct, reporting or dissemination plans of our research.
Results
Of the 34 413 currently employed workers, 17 214 (50.0%) were exposed to either WEEs or shift work, with 1900 (5.5%) experiencing both for at least 10 years (table 1). Men, those of lower socioeconomic status, workers in skilled/service/sales roles and those working longer hours per week were more likely to encounter these exposures. Over an average follow-up of 13.9±1.0 years, participants with >=10 years of both exposures had the highest incidence of arthritis (17.3%) and the lowest of psoriasis (1.2%).
Table 1. Baseline characteristics of workers aged 38–54 years, 2006–2010.
| Cumulative years of exposure to WEEs and shift work | |||||
|---|---|---|---|---|---|
| Both never (n=17 199) |
Both <10y (n=7553) |
WEEs >=10 y only (n=4995) | Shift work >=10 y only (n=2766) | Both >=10y (n=1900) |
|
| Age (median (IQR)) | 48.7 (44.8–52.1) | 48.3 (44.6–51.7) | 49.2 (45.4–52.3) | 48.8 (45.1–51.9) | 48.8 (45.3–52.1) |
| Gender, N (%) | |||||
| Men | 7083 (41.2) | 2947 (39.0) | 2130 (42.6) | 1195 (43.2) | 1160 (61.1) |
| Women | 10 116 (58.8) | 4606 (61.0) | 2865 (57.4) | 1571 (56.8) | 740 (38.9) |
| Race, N (%) | |||||
| White | 16 614 (96.6) | 7241 (95.9) | 4820 (96.5) | 2641 (95.5) | 1823 (96.0) |
| Black | 110 (0.6) | 71 (1.0) | 46 (0.9) | 42 (1.5) | 18 (0.9) |
| Asian | 253 (1.5) | 112 (1.5) | 41 (0.8) | 39 (1.4) | 23 (1.2) |
| Mixed | 103 (0.6) | 70 (0.9) | 35 (0.7) | 21 (0.8) | 17 (0.9) |
| Others | 85 (0.5) | 48 (0.6) | 36 (0.8) | 15 (0.5) | 13 (0.7) |
| Missing | 34 (0.2) | 11 (0.1) | 17 (0.3) | 8 (0.3) | 6 (0.3) |
| Residence, N (%) | |||||
| Town or rural | 2450 (14.2) | 1062 (14.1) | 593 (11.9) | 377 (13.6) | 228 (12.0) |
| Urban | 14 546 (84.6) | 6390 (84.6) | 4327 (86.6) | 2349 (84.9) | 1646 (86.6) |
| Missing | 203 (1.2) | 101 (1.3) | 75 (1.5) | 40 (1.5) | 26 (1.4) |
| Assessment centre location, N (%) | |||||
| Wales | 708 (4.1) | 283 (3.7) | 252 (5.0) | 118 (4.3) | 96 (5.1) |
| Scotland | 1320 (7.7) | 576 (7.6) | 403 (8.1) | 222 (8.0) | 140 (7.4) |
| Northern England | 4346 (25.3) | 1973 (26.1) | 1525 (30.5) | 826 (29.9) | 667 (35.1) |
| Central England | 2453 (14.3) | 1084 (14.4) | 775 (15.5) | 400 (14.5) | 284 (14.9) |
| Southern England | 6575 (38.2) | 2867 (38.0) | 1674 (33.6) | 980 (35.3) | 596 (31.3) |
| London area | 1797 (10.4) | 770 (10.2) | 366 (7.3) | 220 (8.0) | 117 (6.2) |
| Education, N (%) | |||||
| Basic level | 2912 (16.9) | 1605 (21.2) | 1616 (32.4) | 725 (26.2) | 804 (42.3) |
| Intermediate level | 2907 (16.9) | 1465 (19.4) | 1027 (20.6) | 579 (20.9) | 421 (22.2) |
| High level/professional | 11 147 (64.8) | 4322 (57.2) | 2137 (42.8) | 1410 (51.0) | 592 (31.2) |
| Others | 106 (0.6) | 101 (1.4) | 166 (3.2) | 39 (1.4) | 72 (3.8) |
| Missing | 127 (0.8) | 60 (0.8) | 49 (1.0) | 13 (0.5) | 11 (0.5) |
| Multiple deprivation, N (%) | |||||
| Mild | 6198 (36.0) | 2351 (31.1) | 1360 (27.2) | 844 (30.5) | 424 (22.3) |
| Moderate | 5620 (32.7) | 2462 (32.6) | 1554 (31.1) | 901 (32.5) | 614 (32.3) |
| Severe | 4950 (28.8) | 2529 (33.5) | 1960 (39.2) | 928 (33.6) | 800 (42.1) |
| Missing | 431 (2.5) | 211 (2.8) | 121 (2.5) | 93 (3.4) | 62 (3.3) |
| Total number of jobs in life, N (%) | |||||
| 1 | 5266 (30.6) | 443 (5.9) | 1221 (24.4) | 700 (25.3) | 427 (22.5) |
| 2 | 4158 (24.2) | 1269 (16.8) | 1071 (21.5) | 607 (21.9) | 402 (21.2) |
| 3 | 3336 (19.4) | 1720 (22.8) | 1009 (20.2) | 569 (20.6) | 424 (22.3) |
| 4 or more | 4439 (25.8) | 4121 (54.5) | 1694 (33.9) | 890 (32.2) | 647 (34.0) |
| Type of main job, N (%) | |||||
| Managerial and professional | 10 014 (58.2) | 3579 (47.4) | 1868 (37.4) | 811 (29.3) | 327 (17.2) |
| Associate professional and technical | 3204 (18.6) | 1832 (24.3) | 921 (18.4) | 1421 (51.4) | 718 (37.8) |
| Administrative and secretarial | 2665 (15.5) | 1078 (14.3) | 747 (15.0) | 139 (5.0) | 81 (4.3) |
| Skilled/service/sales | 1075 (6.3) | 798 (10.5) | 1029 (20.6) | 267 (9.7) | 393 (20.7) |
| Operative and elementary | 241 (1.4) | 266 (3.5) | 430 (8.6) | 128 (4.6) | 381 (20.0) |
| Years worked 40 or more hours/week, N (%) | |||||
| Never | 9011 (52.3) | 3201 (42.4) | 2071 (41.4) | 1179 (42.6) | 568 (29.9) |
| <20 | 4120 (24.0) | 2929 (38.8) | 1437 (28.8) | 739 (26.7) | 531 (27.9) |
| >=20 | 4068 (23.7) | 1423 (18.8) | 1487 (29.8) | 848 (30.7) | 801 (42.2) |
| Birth country, N (%) | |||||
| UK | 15 827 (92.0) | 6904 (91.4) | 4695 (94.0) | 2551 (92.2) | 1766 (92.9) |
| Ireland | 102 (0.6) | 42 (0.6) | 25 (0.5) | 18 (0.7) | 12 (0.6) |
| Elsewhere | 1266 (7.4) | 606 (8.0) | 274 (5.5) | 196 (7.1) | 121 (6.4) |
| Missing | 4 (0.0) | 1 (0.0) | 1 (0.0) | 1 (0.0) | 1 (0.1) |
| Incident conditions*, N (%) | |||||
| Cancer | 1592 (9.8) | 742 (10.3) | 494 (10.5) | 280 (10.6) | 167 (9.2) |
| Diabetes mellitus | 292 (1.7) | 151 (2.0) | 116 (2.4) | 88 (3.3) | 87 (4.7) |
| Depression | 475 (3.0) | 248 (3.6) | 195 (4.3) | 100 (4.0) | 110 (6.3) |
| Anxiety | 539 (3.2) | 333 (4.6) | 228 (4.8) | 111 (4.1) | 87 (4.7) |
| Migraine | 210 (1.3) | 102 (1.4) | 89 (1.9) | 38 (1.5) | 25 (1.4) |
| Carpal tunnel syndrome | 147 (0.9) | 90 (1.2) | 90 (1.8) | 54 (2.0) | 42 (2.2) |
| Sensory impairment | 303 (1.8) | 149 (2.0) | 117 (2.4) | 47 (1.7) | 57 (3.0) |
| Heart disease | 1023 (6.2) | 533 (7.3) | 381 (8.0) | 211 (8.0) | 186 (10.4) |
| Cerebrovascular disease | 201 (1.2) | 96 (1.3) | 83 (1.7) | 51 (1.9) | 39 (2.1) |
| Peripheral arterial disease | 145 (0.9) | 87 (1.2) | 63 (1.3) | 25 (0.9) | 30 (1.6) |
| Chronic obstructive pulmonary disease | 75 (0.4) | 58 (0.8) | 59 (1.2) | 23 (0.8) | 25 (1.3) |
| Asthma | 284 (1.9) | 159 (2.5) | 129 (3.0) | 55 (2.3) | 49 (2.9) |
| Gastritis and duodenitis | 694 (4.1) | 399 (5.4) | 309 (6.4) | 149 (5.5) | 140 (7.6) |
| Irritable bowel syndrome | 269 (1.6) | 132 (1.8) | 115 (2.4) | 50 (1.9) | 39 (2.2) |
| Liver disease | 262 (1.5) | 144 (1.9) | 112 (2.3) | 68 (2.5) | 62 (3.3) |
| Psoriasis | 101 (0.6) | 65 (0.9) | 38 (0.8) | 25 (0.9) | 23 (1.2) |
| Arthritis | 1548 (9.6) | 818 (11.6) | 662 (14.5) | 303 (11.9) | 292 (17.3) |
| Spondylopathy | 332 (2.0) | 204 (2.8) | 153 (3.1) | 78 (2.9) | 83 (4.5) |
| Intervertebral disc disease | 293 (1.7) | 151 (2.1) | 126 (2.6) | 72 (2.7) | 65 (3.6) |
| Renal failure | 291 (1.7) | 150 (2.0) | 110 (2.2) | 62 (2.3) | 56 (3.0) |
Calculated after excluding patients with corresponding conditions at baseline.
WEEs, workplace environmental exposures.
Compared with workers never exposed to WEEs, those with >=10 years of WEEs were more likely to develop 17 long-term work-limiting health conditions (except for cancer, psoriasis and renal failure), notably carpal tunnel syndrome (HR 1.81, 95% CI 1.43 to 2.28), chronic obstructive pulmonary disease (HR 1.61, 1.19 to 2.19) and asthma (HR 1.50, 1.24 to 1.82) (table 2). Workplace toxic substances, passive smoking, extreme temperatures and excessive noise were associated with 16, 10, 13 and 14 health conditions, respectively (online supplemental tables S2–S5). The health impacts of multifaceted WEEs varied. The >=10 years of toxic substance exposure was most strongly linked to liver disease (HR 1.82, 1.43 to 2.33), while passive smoking was notably associated with migraine (HR 1.60, 1.13 to 2.26). Furthermore, exposure to extreme temperatures and excessive noise for >=10 years was more associated with higher risks of carpal tunnel syndrome (HR 1.62, 1.21 to 2.18 and 1.84, 1.40 to 2.41, respectively). No associations were found between multifaceted WEEs and incident cancer or psoriasis, but toxic substances and extreme temperatures were linked to increased risks of renal failure.
Table 2. Cumulative years of exposure to WEEs and incident long-term work-limiting health conditions among workers aged 38–54 years.
| Long-term work-limiting health condition | Analytic sample | Incident event | Incidence density/100 000 person-years | Never | <10 years | >=10 years | P for continuous |
|---|---|---|---|---|---|---|---|
| HR (95% CI) | |||||||
| Cancer | 32 644 | 3275 | 754.4 | 1.00 (Ref) | 1.07 (0.97–1.18) | 1.01 (0.92–1.11) | 0.999 |
| Diabetes mellitus | 33 709 | 734 | 152.4 | 1.00 (Ref) | 1.27 (1.03–1.57) | 1.32 (1.10–1.59) | 0.003 |
| Depression | 31 807 | 1128 | 253.1 | 1.00 (Ref) | 1.10 (0.93–1.31) | 1.41 (1.22–1.64) | <0.001 |
| Anxiety | 33 292 | 1298 | 278.1 | 1.00 (Ref) | 1.35 (1.16–1.57) | 1.36 (1.18–1.56) | <0.001 |
| Migraine | 32 542 | 464 | 105.0 | 1.00 (Ref) | 1.08 (0.82–1.41) | 1.32 (1.05–1.66) | 0.195 |
| Carpal tunnel syndrome | 34 176 | 423 | 90.5 | 1.00 (Ref) | 1.75 (1.33–2.30) | 1.81 (1.43–2.28) | <0.001 |
| Sensory impairment | 33 937 | 673 | 145.3 | 1.00 (Ref) | 1.17 (0.94–1.46) | 1.28 (1.06–1.55) | 0.006 |
| Heart disease | 33 079 | 2334 | 504.8 | 1.00 (Ref) | 1.23 (1.09–1.38) | 1.22 (1.10–1.36) | 0.007 |
| Cerebrovascular disease | 34 220 | 470 | 95.7 | 1.00 (Ref) | 1.23 (0.94–1.60) | 1.37 (1.09–1.73) | 0.007 |
| Peripheral arterial disease | 34 125 | 350 | 75.0 | 1.00 (Ref) | 1.28 (0.94–1.73) | 1.34 (1.03–1.75) | 0.087 |
| Chronic obstructive pulmonary disease | 34 241 | 240 | 51.1 | 1.00 (Ref) | 1.13 (0.76–1.67) | 1.61 (1.19–2.19) | 0.002 |
| Asthma | 29 841 | 676 | 166.6 | 1.00 (Ref) | 1.52 (1.23–1.88) | 1.50 (1.24–1.82) | 0.003 |
| Gastritis and duodenitis | 33 692 | 1691 | 373.5 | 1.00 (Ref) | 1.23 (1.07–1.42) | 1.38 (1.22–1.55) | <0.001 |
| Irritable bowel syndrome | 32 775 | 605 | 135.5 | 1.00 (Ref) | 0.96 (0.75–1.22) | 1.35 (1.11–1.65) | 0.005 |
| Liver disease | 34 225 | 648 | 138.6 | 1.00 (Ref) | 1.08 (0.86–1.36) | 1.32 (1.09–1.61) | 0.002 |
| Psoriasis | 33 685 | 252 | 53.5 | 1.00 (Ref) | 1.02 (0.71–1.46) | 1.15 (0.84–1.58) | 0.636 |
| Arthritis | 32 037 | 3623 | 831.8 | 1.00 (Ref) | 1.21 (1.10–1.34) | 1.38 (1.27–1.50) | <0.001 |
| Spondylopathy | 33 811 | 850 | 178.4 | 1.00 (Ref) | 1.32 (1.09–1.61) | 1.48 (1.25–1.75) | <0.001 |
| Intervertebral disc disease | 33 428 | 707 | 148.2 | 1.00 (Ref) | 1.17 (0.94–1.46) | 1.37 (1.14–1.65) | <0.001 |
| Renal failure | 34 264 | 669 | 142.9 | 1.00 (Ref) | 1.00 (0.79–1.26) | 1.14 (0.93–1.38) | 0.149 |
Models were adjusted for age, gender, race, residence, assessment centre location, education, multiple deprivation, total number of jobs in life, type of main job, years worked 40 or more hours per week and birth country.
WEEs, workplace environmental exposures.
As shown in table 3, cumulative shift work was associated with 16 long-term work-limiting health conditions. Workers with >=10 years (vs never) of shift work were most likely to develop carpal tunnel syndrome (HR 1.94, 1.50 to 2.50), diabetes (HR 1.79, 1.47 to 2.17) and liver diseases (HR 1.61, 1.29 to 1.99). Day shifts were linked only to incident depression and irritable bowel syndrome (online supplemental table S6), whereas night shifts were associated with 15 incident conditions, with >=10 years of night shifts increasing risks of carpal tunnel syndrome (HR 1.90, 1.45 to 2.50), diabetes (HR 1.86, 1.52 to 2.28) and chronic obstructive pulmonary disease (HR 1.76, 1.21 to 2.57) (online supplemental table S7). Additionally, working night shifts for >=8 days per month (vs never) was associated with a 142% higher risk of chronic obstructive pulmonary disease, followed by intervertebral disc diseases (64%) and diabetes (61%) (online supplemental table S8).
Table 3. Cumulative years of shift work and incident long-term work-limiting health conditions among workers aged 38–54 years.
| Long-term work-limiting health condition | Never | <10 years | >=10 years | P for continuous |
|---|---|---|---|---|
| HR (95% CI) | ||||
| Cancer | 1.00 (Ref) | 1.09 (0.99 to 1.20) | 1.03 (0.93 to 1.15) | 0.757 |
| Diabetes mellitus | 1.00 (Ref) | 1.20 (0.98 to 1.48) | 1.79 (1.47 to 2.17) | <0.001 |
| Depression | 1.00 (Ref) | 1.10 (0.93 to 1.30) | 1.45 (1.23 to 1.72) | <0.001 |
| Anxiety | 1.00 (Ref) | 1.22 (1.05 to 1.41) | 1.25 (1.06 to 1.47) | 0.017 |
| Migraine | 1.00 (Ref) | 1.37 (1.08 to 1.74) | 1.04 (0.78 to 1.38) | 0.661 |
| Carpal tunnel syndrome | 1.00 (Ref) | 1.40 (1.07 to 1.82) | 1.94 (1.50 to 2.50) | <0.001 |
| Sensory impairment | 1.00 (Ref) | 0.97 (0.78 to 1.20) | 1.03 (0.82 to 1.29) | 0.746 |
| Heart disease | 1.00 (Ref) | 1.20 (1.07 to 1.34) | 1.23 (1.09 to 1.39) | <0.001 |
| Cerebrovascular disease | 1.00 (Ref) | 1.20 (0.93 to 1.54) | 1.54 (1.19 to 1.99) | 0.006 |
| Peripheral arterial disease | 1.00 (Ref) | 1.24 (0.93 to 1.64) | 1.14 (0.83 to 1.57) | 0.036 |
| Chronic obstructive pulmonary disease | 1.00 (Ref) | 1.62 (1.16 to 2.26) | 1.59 (1.12 to 2.28) | 0.005 |
| Asthma | 1.00 (Ref) | 1.07 (0.86 to 1.32) | 1.11 (0.88 to 1.40) | 0.250 |
| Gastritis and duodenitis | 1.00 (Ref) | 1.19 (1.04 to 1.36) | 1.28 (1.12 to 1.48) | 0.007 |
| Irritable bowel syndrome | 1.00 (Ref) | 1.17 (0.94 to 1.46) | 1.20 (0.94 to 1.53) | 0.187 |
| Liver disease | 1.00 (Ref) | 1.37 (1.11 to 1.69) | 1.61 (1.29 to 1.99) | <0.001 |
| Psoriasis | 1.00 (Ref) | 1.29 (0.93 to 1.79) | 1.50 (1.06 to 2.13) | 0.049 |
| Arthritis | 1.00 (Ref) | 1.15 (1.05 to 1.26) | 1.23 (1.12 to 1.36) | <0.001 |
| Spondylopathy | 1.00 (Ref) | 1.38 (1.15 to 1.65) | 1.51 (1.25 to 1.83) | <0.001 |
| Intervertebral disc disease | 1.00 (Ref) | 1.45 (1.19 to 1.77) | 1.54 (1.25 to 1.90) | <0.001 |
| Renal failure | 1.00 (Ref) | 1.26 (1.02 to 1.55) | 1.36 (1.09 to 1.69) | 0.004 |
Models were adjusted for age, gender, race, residence, assessment centre location, education, multiple deprivation, total number of jobs in life, type of main job, years worked 40 or more hours per week and birth country.
The sensitivity analyses, using complete data on workplace asbestos, accounting for the competing risk of death or excluding incident conditions occurring within 2 years, yielded slightly weaker but similar results (online supplemental tables S9–S11). For gender differences, the association between cumulative WEEs and incident heart disease was more pronounced in women (HR 1.40, 95% CI 1.18 to 1.67 for <10 years; HR 1.34, 95% CI 1.14 to 1.58 for >=10 years), whereas the corresponding estimates in men were 1.10 (95% CI 0.93 to 1.29) and 1.17 (95% CI 1.02 to 1.35), with P for interaction=0.006 (online supplemental table S12). Similarly, shift work was associated with incident sensory impairment (HR 1.45, 95% CI 1.09 to 1.95 for >=10 years) and intervertebral disc disease (HR 1.88, 95% CI 1.46 to 2.41 for <10 years; HR 2.10, 95% CI 1.61 to 2.75 for >=10 years) in women, whereas no clear associations were observed in men (P for interaction=0.003 and <0.001, respectively, see online supplemental table S13).
Compared with never exposure, exposure to cumulative WEEs or shift work was associated with all 20 long-term work-limiting health conditions, though >=10 years of both was not linked to cancer or migraine (figure 1). Workers with >=10 years of both exposures had the highest risks of incident carpal tunnel syndrome (HR 2.54, 95% CI 1.76 to 3.68), followed by chronic obstructive pulmonary disease, spondylopathy, diabetes and depression (ranging from 2.24 to 2.00). Furthermore, >=10 years of both exposures were associated with the greatest losses in median healthy working years due to incident carpal tunnel syndrome (8.88, 95% CI 6.33 to 10.56), followed by depression, spondylopathy, intervertebral disc diseases and psoriasis (ranging from 7.33 to 6.44 years). No statistically significant multiplicative or additive interactions were observed between cumulative WEEs and shift work (online supplemental table S14). Regarding the joint associations of multifaceted WEEs and shift work, >=10 years of both workplace toxic substances and shift work, as well as excessive noise and shift work, were most strongly linked to incident chronic obstructive pulmonary disease (HRs: 2.79 and 2.54). In contrast, >=10 years of both passive smoking and shift work, and of extreme temperatures and shift work, were more associated with 245% and 172% higher risks of carpal tunnel syndrome, respectively (online supplemental tables S15–S18). Only a significant multiplicative interaction between cumulative workplace passive smoking and shift work was found in association with incident gastritis and duodenitis (P for interaction=0.047).
Figure 1. Joint association of lifetime cumulative WEEs and shift work with risks of and median healthy working years lost due to incident long-term work-limiting health conditions and among currently employed workers aged 38-54 years. Models were adjusted for age, gender, race/ethnicity, residence, assessment centre location, education, multiple deprivation, total number of jobs in life, type of main job, years worked 40 or more hours per week and birth country. WEEs, workplace environmental exposures.
Discussion
In this prospective cohort study, we demonstrated that workers simultaneously exposed to cumulative WEEs and shift work faced largely elevated risks of long-term work-limiting health conditions, resulting in substantial losses in healthy working years, though limited significant multiplicative or additive interactions. Women can represent a key group for interventions targeting the health risks attributed to these exposures.
Our findings align with previous literature demonstrating that prolonged exposure to WEEs and shift work is associated with increased health risks.4,929 However, we extended the current understanding by comprehensively examining multifaceted WEEs, various types and frequencies of shift work and a broad range of long-term work-limiting health conditions. Furthermore, we quantified the loss of healthy working years, providing a tangible metric to assess the impacts on workforce productivity and wider economic and social consequences. WEEs can induce oxidative stress, inflammation and DNA damage by upregulating pro-inflammatory cytokines (eg, interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α)) and generating reactive oxygen species (ROS), thereby heightening health risks.35,37 For instance, diesel exhaust can induce persistent endothelial dysfunction, contributing to vascular issues and impaired fibrinolysis.38 39 Similarly, passive smoking introduces a complex array of chemicals, including ROS and nitrogen species, which damage cellular components such as lipids, proteins and nucleic acids.40 Chronic exposure to excessive noise can activate the sympathetic nervous system and promote systemic inflammation.41 42 Extreme heat, particularly in physically demanding environments, raises health risks through dehydration and electrolyte imbalances,43 while cold exposure can induce vasoconstriction, elevating blood pressure and exerting additional strain on the cardiovascular system.44 Shift work, especially night shifts, can disrupt the body’s natural circadian rhythm, resulting in chronic sleep deprivation.9 This circadian misalignment alters melatonin secretion, dysregulates the hypothalamic-pituitary-adrenal axis and impairs detoxification processes (eg, those mediated by cytochrome P450 enzymes), leading to increased systemic inflammation, immune dysfunction, insulin resistance and endothelial dysfunction.10 45 Also, shift work can induce accelerated biological ageing and frailty, thereby increasing vulnerability to a series of health events.12 13 Notably, while day shifts induced less severe health risks than night shifts, they were still linked to depression and irritable bowel syndrome, both of which are largely attributed to stress. Chronic stress from day shift work can elevate cortisol levels, negatively impacting both mental and physical health, particularly in high-demand jobs with poor work-life balance.21 22 In the case of depression, stress can alter neurotransmitter function and brain structure, increasing vulnerability to mood disorders.46 For irritable bowel syndrome, stress exacerbates gut motility and intestinal permeability, triggering symptoms like abdominal pain, bloating and irregular bowel movements.47 These findings highlight that work-related stress, regardless of the shift timing, plays a crucial role in the development of these conditions. However, we found limited interactions between cumulative WEEs and shift work, suggesting that these exposures may independently contribute to health risks without amplifying each other’s impacts.
Strengths and limitations
To the best of our knowledge, this is the first and most comprehensive study to investigate the joint associations of cumulative multifaceted WEEs and shift work with a broad spectrum of long-term work-limiting health conditions. Leveraging data from the UKB, this study benefits from a large cohort, enabling precise exposure assessments and robust tracking of health outcomes. Our findings carry significant public health implications by demonstrating the substantial joint impacts of WEEs and shift work on health and the reduction of healthy working years. Given the rising burden of work-limiting health conditions,2 3 greater attention must be devoted to improving workplace environments and adjusting work schedules to support the physical and mental well-being of workers. Our findings can be anticipated to help improve occupational health policies to better safeguard workers in settings where both adverse WEEs and shift work are prevalent, thereby enhancing productive workforce participation and contributing to sustainable economic growth. Moreover, women appear particularly vulnerable to WEEs and shift work, suggesting the need for targeted public health interventions to protect female workers.
This study has several limitations that must be considered. First, the retrospective collection and self-assessment of WEEs and shift work could lead to misclassification bias. Second, survival bias is a concern, as healthier individuals are more likely to be included in the cohort, though we included relatively young workers. Moreover, our ‘sensory impairment’ outcome combined hearing and visual impairments. This reflects our focus on work-limiting functional consequences, as both domains can restrict communication and safe task performance and are recognised as long-term work-limiting conditions. However, hearing and visual impairments have distinct aetiologies and occupational risk profiles, which we could not disentangle in the present analysis; future studies with larger numbers of events and more detailed sensory assessments should examine them separately. In addition, we must acknowledge that potential confounders, such as early-life factors, were not fully adjusted, and over-adjustment cannot be completely excluded if some work-history variables were also located on the causal pathway. Finally, our study is observational in nature and cannot establish causality; therefore, the reported associations should be interpreted with caution and not taken as definitive causal effects.
Conclusions
In conclusion, this study underscores the joint associations of cumulative WEEs and shift work with increased risks of various long-term work-limiting health conditions. To address working-age health risks, enterprises should enhance workplace monitoring and protective measures against WEEs while implementing long-term health monitoring for shift workers, focusing on musculoskeletal, mental and other chronic conditions. Given the rising burden of long-term work-limiting health conditions, proactive interventions are critical to enhancing workforce productivity and sustainable economic growth.
Supplementary material
Acknowledgements
This research has been conducted using the UK Biobank Resource (project 105435). The authors thank everyone who made this work possible, particularly the UK Biobank team, their funders, the professionals from the member institutions who contributed to and supported this work and the UK Biobank participants.
Footnotes
Funding: This study is funded by the National Key Research and Development Program, Ministry of Science and Technology of the People's Republic of China (2025YFC2511501), the National Key Research and Development Program of China (2022YFC3600800), the Key Project of the National Natural Science Foundation of China Regional Innovation and Development Joint Fund, National Natural Science Foundation of China (U23A20420), and the Clinical Medicine Plus X - Young Scholars Project, Peking University, the Fundamental Research Funds for the Central Universities (PKU2025PKULCXQ010). The funding source had no involvement in the collection, analysis, or interpretation of data; in the writing of the report; or in the decision to submit the paper for publication.
Provenance and peer review: Not commissioned; externally peer-reviewed.
Patient consent for publication: Not applicable.
Ethics approval: This study involved human participants. The UKB was approved by the North West Multi-centre Research Ethics Committee as a Research Tissue Bank approval (reference no. 21/NW/0157). All participants provided written informed consent. Participants gave informed consent to participate in the study before taking part.
Data availability free text: The UK Biobank data are available on application to the UK Biobank (www.ukbiobank.ac.uk/) with access fees. Research codes are available on reasonable request.
Patient and public involvement: Patients and/or the public were not involved in the design, conduct, reporting or dissemination plans of this research.
Data availability statement
Data are available upon reasonable request.
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Supplementary Materials
Data Availability Statement
Data are available upon reasonable request.