Abstract
Objectives:
Work schedule demands contribute to circadian disruption and may influence health via an inflammatory response. We examined the impact of shiftwork and long work hours on inflammation in a national U.S. sample.
Methods:
Participants included 12,487 employed Black and White men and women aged ≥45 years enrolled in the REasons for Geographic and Racial Differences in Stroke (REGARDS) Study who completed an occupational questionnaire (2011-2013) and clinical exam (2013-2016). Cross-sectional associations between shiftwork and work hours with log-transformed high-sensitivity C-reactive protein (CRP) and white blood cell count (WBC) were examined by multiple linear regression analysis, overall and by race-sex subgroups.
Results:
Overall, rotating shift workers had higher log-CRP concentration compared to day workers (β = 0.09, 95% CI:0.02-0.16) and findings for WBC were null. Black women had the highest geometric mean CRP (2.82 mg/L), while White men had the highest WBC (6.35x109 cells/L). White men who worked afternoons had higher log-CRP compared to those who worked days (β=0.20, 95% CI: 0.08-0.33). Black men engaged in shiftwork <10 years working ≥55 hours/week had higher log-CRP and log-WBC compared to those working days <55 hours/week (β=0.33, 95% CI: 0.02-0.64 and β=0.10, 95% CI: 0.003-0.19). Among shift workers, non-retired White women working forward and backward shift rotations had higher log-CRP compared to those working forward only (β=0.49, 95% CI: 0.02-0.96).
Conclusions:
Shift workers had higher inflammatory markers compared to day workers and race-sex disparities should be examined further. These findings highlight a potential biological pathway linking work schedule demands and chronic disease.
Keywords: C-reactive protein, Leukocytes, White Blood Cells, Employment, Inflammation, Occupations, Shift Work Schedule
INTRODUCTION
In the United States (U.S.) more than 21 million workers are employed outside of daytime work hours (6am-6pm).(1) Shift workers are often required to work at night, to rotate their sleeping time as shifts change, and may vary sleeping patterns on work and non-workdays to balance family and social obligations, which can play a role in circadian misalignment. Those working long hours may experience poorer sleep quality and daytime dysfunction.(2) Previous studies have provided evidence of race and sex disparities in sleep, which may be influenced by differences in occupational characteristics, including shift work and work hours.(3, 4) A 2019 report by the U.S. Bureau of Labor Statistics found that shift workers are more likely to be men, Black, and Hispanic or Latino.(5) Environmental factors related to shiftwork and long work hours, such as exposure to light at night, can disrupt the body’s natural circadian rhythm and give rise to biologic changes related to hormone production and immune response.(6) Both shiftwork and working hours are associated with numerous chronic conditions.(7, 8, 9) While potential biologic mechanisms are still poorly characterized, shiftwork and working hours may contribute to immune dysfunction and increase low-grade systemic inflammation, in turn increasing the risk of diseases such as cancer, cardiovascular disease, and chronic kidney disease (CKD).(10, 11, 12)
Inflammation markers, including C-reactive protein (CRP) and white blood cell count (WBC) are partly regulated by the circadian rhythm cycle.(13, 14, 15, 16) In a small randomized crossover study of shift workers, Morris et al. found that acute circadian disruption from poor rest during typical sleep hours resulted in increased CRP levels.(14) Several cross-sectional studies have provided evidence of an association between shift work and inflammation. Puttonen et al. found that among aviation crewmembers, men who worked night shifts had higher levels of CRP.(17) In other studies, shift workers had higher WBC compared to those working days.(18, 19, 20) Despite known racial disparities in risk of inflammation-related diseases, which may exacerbated by societal factors, no studies have examined potential racial differences in shiftwork, working hours, and inflammation.(21, 22, 23)
We therefore sought to investigate the relationship between shiftwork, long working hours, and inflammation in participants of the U.S. population-based REasons for Geographic and Racial Differences in Stroke (REGARDS). We hypothesized that shiftwork and long working hours would be associated with higher CRP and WBC, and we evaluated whether these associations vary across race-sex subgroups.
MATERIALS AND METHODS
Study population
Details of the REGARDS study population and methodology have been described by Howard et al. (2005).(24) Briefly, REGARDS is a longitudinal U.S. population-based study of 30,239 non-Hispanic black and non-Hispanic white individuals aged ≥45 years. The primary aim was to evaluate geographic and racial disparities in stroke incidence and mortality. Participants were recruited using a region-, race-, and sex stratified random sampling method. The final sample comprised 21% from coastal North Carolina, South Carolina, and Georgia (the “stroke buckle”), 35% from other areas of North Carolina as well as South Carolina, Georgia, Tennessee, Mississippi, Alabama, Louisiana, and Arkansas (the “stroke belt”), and 44% from other states in the continental U.S. Around 42% of participants were Black individuals and 55% were women.
Enrollment occurred between 2003-2007 and included a computer assisted telephone interview (CATI) and an in-home clinical assessment with blood test panels, an electrocardiogram (ECG), and self-administered questionnaires. Individuals who reported chemotherapy or radiation treatment within two years of the enrollment date or who reported a serious medical condition that would prevent ongoing participation were not eligible for inclusion. The REGARDS occupational questionnaire was administered between 2011-2013 to all active participants, and 17,333 responded (87% response).(25) From 2013-2016, 14,236 active participants completed Exam 2 (a follow-up CATI and in-home examination) to collect incident risk factors and clinical outcomes. All participants provided verbal and written informed consent.
Participants who completed both the occupational ancillary questionnaire and Exam 2 were eligible for inclusion in the current study. The final study sample included 12,487 participants.
Research ethics approval
The institutional review boards at the University of Alabama at Birmingham (UAB) (ID: IRB-020925004) and the National Institute for Occupational Safety and Health (NIOSH) (ID: 10-DSHEFS-04XP) approved the study.
Patient and public involvement
No patient involved. Results of this study will be included in information shared with study participants, their proxies and the general public on the REGARDS website https://www.uab.edu/soph/regardsstudy/. In addition, newsletters that contain a brief summary of selected results are mailed to participants on a biannual basis.
Work history characteristics
Work history information was obtained by participant responses to the REGARDS occupational survey, which was administered by CATI during routine bi-annual follow-up.(25) Participants were asked if they were self-employed as the owner of a business or farm, engaged in some other form of self-employment (does not work for an employer who pays their salary or wage), or employed for wages. Participants were also asked if they were retired, out of work, unable to work, a student, or a homemaker. If they reported being retired, they were asked how many years they had been retired.
For shiftwork determinations, participants were asked the following questions: “During your entire career have you ever worked a schedule other than a regular day-time schedule (for example, have you ever worked afternoon or night shifts, split or rotating shifts, or worked on-call)?” [yes, no]; “Are you still working hours other than a “regular day-time schedule?” [yes, no]; “During your entire career, when you were not working a regular day-time schedule, which of the following best describes/described your usual work schedule? [afternoon (2pm-midnight), night (9pm-8am), rotating, split shift, irregular, and on-call]. Split shift, irregular shift, and on-call categories were combined due to small numbers. Rotating shift workers were asked “Do/Did your work hours rotate forwards, backwards, or both?”; for the duration of shiftwork, participants were asked “How many years were spent working a schedule other than a “regular day-time schedule?” [reported as months or years]. Shiftwork tenure was categorized as 0 years, 1-<10 years, ≥10 years. Work hours information was collected for the job held the longest and for the current job (if still employed) using the question “How many TOTAL hours did you work in a typical WEEK on this job?” Open-ended reports of weekly work hours were categorized as <35, 35-40, 41-48, 49-54, ≥55.(26) Tenure in the longest-held job was categorized as <10, 10-<20, 20-<30, ≥30 years. Based on previous evidence of a combined effect of shift work and work hours on chronic disease, we also considered multi-categorical groupings of shiftwork type and shiftwork tenure as well as shiftwork tenure and hours worked per week.(27, 28, 29)
Biochemical measurements
During Exam 2, serum and plasma from blood samples were shipped overnight on ice packs for processing and storage at the Laboratory for Clinical Biochemistry Research at the University of Vermont.(30) High-sensitivity CRP was measured via serum in batches by particle-enhanced immunonephelometry on a BNII nephelometer (N High Sensitivity CRP; Dade Behring, Deerfield, IL, USA), with interassay coefficients of variation (CVs) between 2.1–5.7%. Complete blood counts to obtain WBC were measured via EDTA plasma the day after collection on a Beckman Coulter LH 755 Hematology Workcell (Beckman Coulter, Fullerton, CA, USA) with CVs of 5%.
Statistical analysis
Multiple linear regression was used to estimate associations between shiftwork, long working hours, and other occupational characteristics with CRP (mg/L) and WBC (× 109 cells/L). CRP and WBC were highly positively skewed, so log-transformation was applied to both outcome variables. All models were adjusted for the following covariates collected from Exam 2: age (continuous), region (“stroke buckle”, “stroke belt”, other), education (high school or less, college or more), current smoking status (smoker, non-smoker), alcohol consumption (none: non-drinker; moderate: 1-<7 drinks/week in women,1-<14 drinks/week in men; heavy: ≥7 drinks in women, ≥14 drinks in men), physical activity (none, 1-3 times/week, ≥4 times/week), body mass index (BMI) in kg/m2 (0-<25, 25-<30, ≥30), and regular aspirin use (yes, no). Results were generated overall and stratified by race and sex since multiplicative interactions via use of cross-product terms in the age-adjusted models were observed 1) between shiftwork, race, and sex on log-CRP and 2) between shiftwork, long work hours, sex, and race on log-WBC (p≤0.10).
Sensitivity analyses were conducted by examining overall and race-sex stratified results in the following subgroups: 1) those not retired at the time the occupational survey was administered, 2) those employed for wages (i.e., they were not self-employed) in their longest-held job, and 3) those free of chronic disease associated with inflammation at Exam 2 (heart disease, diabetes, or CKD). Heart disease was defined as self-reported myocardial infarction (MI), coronary artery bypass graft (CABG), bypass, angioplasty, or stenting or evidence of MI via ECG. Diabetes was defined as a fasting glucose ≥126 milligrams per deciliter (mg/dL) or a non-fasting glucose ≥200 mg/dL or self-reported use of pills or insulin to treat diabetes. CKD was defined as an estimated GFR <60 from the CKD-Epi equation.(31) We performed all statistical procedures using SAS 9.4 (SAS Institute, Cary, NC).
RESULTS
Sociodemographic, lifestyle and inflammatory marker data are shown in Table 1 overall and by race-sex subgroups. Slightly more than half of the 12,487 study participants were women (55%) and approximately one-third were non-Hispanic Black (36%). At Exam 2 the mean age of participants was 72±8 years, 56% lived in the “stroke belt” or “stroke buckle” regions of the U.S., and 70% completed at least some college. Black women had the highest mean CRP (geometric mean ± standard error: 2.82 ± 0.07 mg/L), while White men had the highest WBC (6.35 ± 0.03 x109 cells/L).
Table 1.
Sociodemographic, lifestyle characteristics, and inflammation measures of study participants at exam 2, by race- sex subgroups
| Overall | Black | White | |||
|---|---|---|---|---|---|
| Characteristic at Exam 2, N (%) | Women | Men | Women | Men | |
| Total N | 12,487 | 2,856 (64) | 1,581 (36) | 4,046 (50) | 4,004 (50) |
| U.S. Regiona | |||||
| Belt | 4,233 (34) | 998 (35) | 529 (33) | 1,377 (34) | 1,329 (33) |
| Buckle | 2,746 (22) | 573 (20) | 307 (19) | 1,075 (27) | 791 (20) |
| Non-belt | 5,508 (44) | 1,285 (45) | 745 (47) | 1,594 (39) | 1,884 (47) |
| Educationb | |||||
| ≤High school graduate | 3,738 (30) | 1,110 (39) | 592 (37) | 1,136 (28) | 900 (22) |
| Some college | 3,300 (26) | 818 (29) | 442 (28) | 1,132 (28) | 908 (23) |
| College graduate or above | 5,446 (44) | 927 (32) | 547 (35) | 1,776 (44) | 2,196 (55) |
| Unknown | 3 (<1) | 1 (<1) | 0 (0) | 2 (<1) | 0 (0) |
| Current smokerb | |||||
| No | 11,358 (91) | 2,543 (89) | 1,353 (86) | 3,726 (92) | 3,736 (93) |
| Yes | 839 (7) | 232 (8) | 169 (11) | 242 (6) | 196 (5) |
| Unknown | 290 (2) | 81 (3) | 59 (4) | 78 (2) | 72 (2) |
| Alcohol intakec | |||||
| None | 6,672 (53) | 1,993 (70) | 887 (56) | 2,116 (52) | 1,676 (42) |
| Moderate | 4,868 (39) | 720 (25) | 578 (37) | 1,572 (39) | 1,998 (50) |
| Heavy | 622 (5) | 59 (2) | 62 (4) | 266 (7) | 235 (6) |
| Unknown | 325 (3) | 84 (3) | 54 (3) | 92 (2) | 95 (2) |
| BMI (kg/m2)b | |||||
| <18.5 | 160 (1) | 32 (1) | 16 (1) | 88 (2) | 24 (<1) |
| 18.5-<25.0 | 3,043 (24) | 451 (16) | 340 (22) | 1,287 (32) | 965 (24) |
| 25.0-<30.0 | 4,640 (37) | 886 (31) | 609 (39) | 1,368 (34) | 1,777 (44) |
| ≥30 | 4,570 (37) | 1,464 (51) | 598 (38) | 1,283 (32) | 1,225 (31) |
| Unknown | 74 (<1) | 23 (<1) | 18 (1) | 20 (<1) | 13 (<1) |
| Physical activity per weekb | |||||
| None | 4,781 (38) | 1,281 (45) | 519 (33) | 1,738 (43) | 1,243 (31) |
| 1-<4 times | 4,291 (34) | 984 (34) | 597 (38) | 1,310 (32) | 1,400 (35) |
| ≥4 times | 3,074 (25) | 503 (18) | 410 (26) | 895 (22) | 1,266 (32) |
| Unknown | 341 (3) | 88 (<1) | 55 (3) | 103 (3) | 95 (2) |
| Regular aspirin userb | |||||
| No | 5,517 (44) | 1,312 (46) | 684 (43) | 2,063 (51) | 1,458 (36) |
| Yes | 6,722 (54) | 1,475 (52) | 856 (54) | 1,909 (47) | 2,482 (62) |
| Unknown | 248 (2) | 69 (2) | 41 (3) | 74 (2) | 64 (2) |
| Characteristic at Exam 2, mean±SD | |||||
| Ageb | 72±8 | 71±8 | 72±8 | 72±9 | 74±8 |
| Log C-reactive protein (mg/L)b | 0.69±1.14 | 1.04±1.17 | 0.75±1.14 | 0.66±1.08 | 0.47±1.11 |
| Log white blood cell count (× 109 cells/L)b | 1.80±0.30 | 1.74±0.32 | 1.71±0.32 | 1.82±0.29 | 1.85±0.29 |
| C-reactive protein (geometric mean ± SE) | 2.00±0.02 | 2.82±0.07 | 2.12±0.06 | 1.94±0.03 | 1.60±0.03 |
| White blood cell count (geometric mean ± SE) | 6.04±0.02 | 5.71±0.04 | 5.51±0.05 | 6.19±0.03 | 6.35±0.03 |
Abbreviations: BMI – body mass index, kg – kilogram, m – meter, mg – milligram, L – liter, SE – standard error
Characteristic collected during study enrollment (administered 2003-2007)
Characteristic or laboratory analyte collected during the second visit to assess risk factors (Exam 2 CATI) (administered 2013-2016)
Alcohol intake defined as follows; none: non-drinker; moderate: 1-<7 drinks per week in women and 1-<14 drinks per week in men; heavy: ≥7 drinks in women and ≥14 drinks in men
Work history characteristics are shown in Table 2, overall and by race-sex subgroups. Twelve percent of participants were self-employed, and this was more common among White compared to Black participants. Half of all participants worked as a shift worker in the past (45%) or were a current shift worker (5%). White women were most likely to report never having worked outside of a daytime shift (57%), whereas Black men were least likely (31%). Twenty percent of participants worked a shift work schedule for over 10 years, while White women were the least likely (13%) and Black men the most likely (31%) to have performed shift work for a decade or more. The overall prevalence of shift work varied from a low of 10% for rotating shift work to a high of 15% for work schedules defined as split, irregular, or on-call. Black men and women were more likely than White participants to work afternoon or night shifts, while White men were more likely to work split, irregular, or on-call shifts. Over half (56%) of rotating shift workers reported working both forward and backward shift rotations. Thirteen percent of participants reported working ≥55 hours/week, with men more often reporting longer work hours than women, and White men reporting the highest prevalence of working ≥55 hours/week.
Table 2.
Work history characteristics at occupational survey (2011-2013), by race- sex subgroups
| Overall | Black | White | |||
|---|---|---|---|---|---|
| Characteristic at Occupational Survey, N (%) | Women | Men | Women | Men | |
| Total N | 12,487 | 2,856 (64) | 1,581 (36) | 4,046 (50) | 4,004 (50) |
| Employment type | |||||
| Wage-employed | 10,812 (87) | 2,647 (93) | 1,425 (90) | 3,465 (86) | 3,275 (82) |
| Self-employed | 1,479 (12) | 155 (5) | 145 (9) | 459 (11) | 720 (18) |
| Unknown | 196 (2) | 54 (2) | 11 (1) | 122 (3) | 9 (<1) |
| Ever shiftwork | |||||
| Never | 5,903 (47) | 1,339 (47) | 494 (31) | 2,310 (57) | 1,760 (44) |
| Former | 5,663 (45) | 1,300 (46) | 949 (60) | 1,444 (36) | 1,970 (49) |
| Current | 669 (5) | 152 (5) | 116 (7) | 161 (4) | 240 (6) |
| Unknown | 252 (2) | 65 (2) | 22 (1) | 131 (3) | 34 (1) |
| Shiftwork type | |||||
| Never | 5,903 (47) | 1,339 (47) | 494 (31) | 2,310 (57) | 1,760 (44) |
| Afternoon | 1,554 (12) | 432 (15) | 260 (16) | 483 (12) | 379 (9) |
| Night | 1,595 (13) | 466 (16) | 292 (18) | 385 (10) | 452 (11) |
| Rotating | 1,257 (10) | 227 (8) | 237 (15) | 255 (6) | 538 (13) |
| Split, irregular, or on-call | 1,812 (15) | 292 (10) | 260 (16) | 462 (11) | 798 (20) |
| Unknown | 366 (3) | 100 (4) | 38 (2) | 151 (4) | 77 (2) |
| Direction of shiftwork rotation | |||||
| Forwards | 461 (37) | 79 (35) | 92 (39) | 88 (34) | 202 (38) |
| Backwards | 46 (4) | 8 (4) | 6 (3) | 12 (5) | 30 (4) |
| Both | 698 (56) | 131 (58) | 132 (56) | 145 (57) | 290 (54) |
| Unknown | 52 (4) | 9 (4) | 7 (3) | 10 (4) | 26 (5) |
| Shiftwork tenure (years) | |||||
| 0 | 5,903 (47) | 1,339 (47) | 494 (31) | 2,310 (57) | 1,760 (44) |
| 1-<10 | 3,365 (27) | 798 (28) | 495 (31) | 971 (24) | 1,101 (28) |
| ≥10 | 2,519 (20) | 519 (18) | 497 (31) | 532 (13) | 971 (24) |
| Unknown | 700 (6) | 200 (7) | 95 (6) | 233 (6) | 172 (4) |
| Years worked | |||||
| ≥30 | 3,494 (28) | 736 (26) | 555 (35) | 666 (16) | 1,537 (38) |
| 20-<30 | 3,447 (28) | 785 (27) | 511 (32) | 985 (24) | 1,166 (29) |
| 10-<20 | 3,444 (28) | 799 (28) | 339 (21) | 1,396 (35) | 910 (23) |
| <10 | 1,756 (14) | 430 (15) | 149 (9) | 825 (20) | 352 (9) |
| Unknown | 346 (3) | 106 (4) | 27 (2) | 174 (4) | 39 (1) |
| Hours worked per week | |||||
| <35 | 1,328 (11) | 322 (11) | 96 (6) | 657 (16) | 253 (6) |
| 35-40 | 6,486 (52) | 1,948 (68) | 883 (56) | 2,184 (54) | 1,471 (37) |
| 41-48 | 1,200 (10) | 180 (6) | 159 (10) | 355 (9) | 506 (13) |
| 49-54 | 1,553 (12) | 158 (6) | 188 (12) | 398 (10) | 809 (20) |
| ≥55 | 1,600 (13) | 176 (6) | 229 (14) | 297 (7) | 898 (22) |
| Unknown | 320 (3) | 72 (3) | 26 (2) | 155 (4) | 67 (2) |
| Shiftwork type and shiftwork tenure | |||||
| Never | 5,903 (47) | 1,339 (47) | 494 (31) | 2,310 (57) | 1,760 (44) |
| Afternoon, night and <10 years | 1,945 (16) | 531 (19) | 306 (19) | 574 (14) | 533 (13) |
| Afternoon, night and ≥10 years | 1,033 (8) | 304 (11) | 219 (14) | 256 (6) | 254 (6) |
| Rotating, split, irregular, or on-call and <10 years | 1,380 (11) | 253 (9) | 182 (12) | 392 (10) | 553 (14) |
| Rotating, split, irregular, or on-call and ≥10 years | 1,450 (12) | 208 (7) | 273 (17) | 270 (7) | 699 (17) |
| Unknown | 776 (6) | 220 (7) | 107 (7) | 244 (6) | 205 (5) |
| Shiftwork tenure and hours worked per week | |||||
| 0 years and <55 hours/week | 5,200 (45) | 1,252 (47) | 421 (29) | 2,142 (57) | 1,385 (37) |
| 0 years and ≥55 hours/week | 642 (6) | 79 (3) | 65 (4) | 148 (4) | 350 (9) |
| <10 years and <55 hours/week | 2,930 (25) | 741 (28) | 432 (29) | 886 (23) | 871 (23) |
| <10 years and >55 hours/week | 404 (3) | 50 (2) | 60 (4) | 79 (2) | 215 (6) |
| ≥10 years and <55 hours/week | 2,002 (17) | 475 (18) | 400 (27) | 465 (12) | 662 (18) |
| ≥10 years and >55 hours/week | 493 (4) | 42 (2) | 94 (6) | 63 (2) | 294 (8) |
| Unknown | |||||
| Retired | |||||
| No | 5,004 (40) | 1,182 (41) | 595 (38) | 1,778 (44) | 1,449 (36) |
| Yes | 7,481 (60) | 1,673 (59) | 986 (62) | 2,267 (56) | 2,555 (64) |
| Unknown | 2 (<1) | 1 (<1) | 0 (0) | 1 (<1) | 0 (0) |
| Retirement duration (years) | |||||
| Not retired | 5,004 (40) | 1,182 (41) | 595 (38) | 1,778 (44) | 1,449 (36) |
| <10 | 3,073 (25) | 733 (26) | 382 (24) | 958 (24) | 1,000 (25) |
| 10-<20 | 2,964 (24) | 623 (22) | 410 (26) | 859 (21) | 1,072 (27) |
| ≥20 | 1,307 (10) | 269 (9) | 179 (11) | 397 (10) | 462 (12) |
| Unknown | 139 (1) | 49 (2) | 15 (1) | 54 (1) | 21 (1) |
Multivariable Analyses
Multivariable-adjusted associations of work characteristics with log-CRP and log-WBC are shown in Table 3. Participants with a history of shift work had a higher mean log-CRP level compared to daytime-only workers (β = 0.05, 95% CI: 0.004-0.09). When examining CRP by type of shift work, those who reported having worked rotating shifts had a higher mean log-CRP level compared to daytime-only workers (β = 0.09, 95% CI: 0.02-0.16). Findings were null for shift work and log-WBC, as well as for work hours and both inflammatory markers.
Table 3.
Multivariable -adjusted beta coefficients for mean differences in inflammatory biomarkers by shift work characteristicsa
| Characteristic | Log C-reactive protein (mg/L) |
Log white blood cell count (× 109 cells/L) |
|---|---|---|
| β (95% CI) | β (95% CI) | |
| Ever shiftwork | ||
| Never | ref | ref |
| Former | 0.05 (0.004-0.09) | 0.01 (−0.001-0.02) |
| Current | −0.02 (−0.12-0.07) | −0.002 (−0.03-0.02) |
| Shiftwork type | ||
| Never | ref | ref |
| Afternoon | 0.06 (−0.01-0.12) | −0.001 (−0.02-0.02) |
| Night | 0.01 (−0.05-0.08) | 0.01 (−0.01-0.03) |
| Rotating | 0.09 (0.02-0.16) | 0.01 (−0.01-0.03) |
| Split, irregular, or on-call | 0.02 (−0.04-0.08) | 0.01 (−0.004-0.03) |
| Direction of shiftwork rotation | ||
| Forwards | ref | ref |
| Backwards | 0.17 (−0.17-0.51) | 0.01 (−0.09-0.10) |
| Both | 0.06 (−0.08-0.19) | 0.03 (−0.01-0.07) |
| Shiftwork tenure (years) | ||
| 0 | ref | ref |
| 1-<10 | 0.05 (−0.002-0.10) | 0.01 (−0.005-0.02) |
| ≥10 | 0.03 (−0.03-0.08) | 0.01 (−0.003-0.03) |
| Hours worked per week | ||
| <35 | −0.03 (−0.10-0.04) | −0.01 (−0.03-0.01) |
| 35-40 | ref | ref |
| 41-48 | −0.02 (−0.09-0.05) | −0.01 (−0.03-0.01) |
| 49-54 | −0.04 (−0.11-0.02) | 0.001 (−0.02-0.02) |
| ≥55 | 0.05 (−0.01-0.12) | 0.001 (−0.02-0.02) |
| Shiftwork type and shiftwork tenure | ||
| Never | ref | ref |
| Afternoon, night and <10 years | 0.05 (−0.004-0.11) | 0.003 (−0.01-0.02) |
| Afternoon, night and ≥10 years | −0.01 (−0.08-0.07) | 0.01 (−0.01-0.03) |
| Rotating, split, irregular, or on-call and <10 years | 0.05 (−0.02-0.11) | 0.02 (−0.002-0.04) |
| Rotating, split, irregular, or on-call and ≥10 years | 0.05 (−0.02-0.12) | 0.01 (−0.004-0.03) |
| Shiftwork tenure and hours worked per week | ||
| 0 years and <55 hours/week | ref | ref |
| 0 years and ≥55 hours/week | 0.08 (−0.02-0.17) | −0.01 (−0.03-0.02) |
| 1-<10 years and <55 hours/week | 0.04 (−0.01-0.09) | 0.01 (−0.01-0.02) |
| 1-<10 years and ≥55 hours/week | 0.15 (0.03-0.27) | 0.02 (−0.01-0.05) |
| ≥10 years and <55 hours/week | 0.03 (−0.03-0.09) | 0.01 (−0.01-0.03) |
| ≥10 years and ≥55 hours/week | 0.06 (−0.04-0.17) | 0.01 (−0.02-0.04) |
Abbreviations: mg – milligram, g – gram, L – liter, ref – referent, CI – confidence interval
Adjusted for age, sex, race, region, education, smoking status, alcohol consumption, physical activity, BMI, and aspirin use
Multivariable results by race-sex subgroups are shown in Table 4 for CRP and Table 5 for WBC. White men with a history of working a shift work schedule had higher mean log-CRP and log-WBC compared to White men who exclusively worked a day-time shift (β = 0.11, 95% CI: 0.03-0.18 and β = 0.02, 95% CI: 0.001-0.04, respectively). Compared to White men working day shifts, associations with log-CRP were higher for White men who primarily worked afternoon shifts (β = 0.20, 95% CI: 0.08-0.33). Black men reporting both shift work and long work hours (≥55 hours/week) had higher log-CRP and log-WBC compared to Black men workings days and <55 hours/week (β = 0.33, 95% CI: 0.02-0.64 and β = 0.10, 95% CI: 0.003-0.19, respectively). White men who worked afternoon or night shifts for <10 years had higher log-CRP compared to White men who worked days (β = 0.15, 95% CI: 0.04-0.26). Additionally, White men who primarily worked rotating, split, irregular, or on-call shifts for ≥10 years also had a higher mean log-WBC than White men working days (β = 0.03, 95% CI: 0.001-0.05).
Table 4.
Multivariable -adjusted beta coefficients for mean differences in log C-reactive protein (mg/l) by shift work characteristics, stratified by race and sexa
| Characteristic | Black | White | p-interactionb | ||||
|---|---|---|---|---|---|---|---|
| Women | Men | Women | Men | Race | Sex | Race*Sex | |
| β (95% CI) | β (95% CI) | β (95% CI) | β (95% CI) | ||||
| Ever shiftwork | 0.03 | 0.96 | 0.31 | ||||
| Never | ref | ref | ref | ref | |||
| Former | 0.01 (−0.08-0.10) | −0.02 (−0.15-0.10) | 0.04 (−0.03-0.11) | 0.11 (0.03-0.18) | |||
| Current | −0.17 (−0.37-0.03) | −0.07 (−0.31-0.17) | −0.05 (−0.23-0.12) | 0.11 (−0.04-0.26) | |||
| Shiftwork type | 0.06 | 0.81 | 0.19 | ||||
| Never | ref | ref | ref | ref | |||
| Afternoon | −0.03 (−0.16-0.10) | −0.01 (−0.19-0.17) | 0.04 (−0.07-0.14) | 0.20 (0.08-0.33) | |||
| Night | −0.005 (−0.13-0.12) | −0.11 (−0.28-0.06) | 0.005 (−0.11-0.12) | 0.09 (−0.02-0.21) | |||
| Rotating | 0.10 (−0.07-0.27) | 0.09 (−0.10-0.28) | 0.09 (−0.05-0.23) | 0.10 (−0.01-0.21) | |||
| Split, irregular, or on-call | −0.01 (−0.17-0.14) | −0.04 (−0.21-0.14) | 0.01 (−0.09-0.12) | 0.08 (−0.02-0.17) | |||
| Direction of shiftwork rotation | 0.63 | 0.06 | 0.80 | ||||
| Forwards | ref | ref | ref | ref | |||
| Backwards | 0.67 (−0.39-1.74) | 0.12 (−0.81-1.04) | 0.28 (−0.40-0.96) | 0.20 (−0.29-0.69) | |||
| Both | 0.13 (−0.25-0.50) | −0.02 (−0.34-0.30) | 0.25 (−0.04-0.55) | −0.03 (−0.23-0.17) | |||
| Shiftwork tenure (years) | 0.06 | 0.63 | 0.18 | ||||
| 0 | ref | ref | ref | ref | |||
| 1-<10 | 0.01 (−0.10-0.11) | 0.01 (−0.14-0.15) | 0.04 (−0.05-0.12) | 0.10 (0.01-0.18) | |||
| ≥10 | −0.03 (−0.15-0.09) | −0.05 (−0.19-0.10) | 0.01 (−0.09-0.11) | 0.10 (0.02-0.19) | |||
| Hours worked per week | 0.54 | 0.89 | 0.66 | ||||
| <35 | −0.02 (−0.16-0.12) | −0.03 (−0.27-0.22) | −0.01 (−0.10-0.08) | −0.09 (−0.24-0.06) | |||
| 35-40 | ref | ref | ref | ref | |||
| 41-48 | 0.02 (−0.16-0.20) | −0.07 (−0.26-0.13) | −0.08 (−0.20-0.04) | −0.005 (−0.12-0.11) | |||
| 49-54 | 0.03 (−0.16-0.23) | −0.04 (−0.22-0.15) | −0.09 (−0.21-0.02) | −0.05 (−0.14-0.05) | |||
| ≥55 | −0.01 (−0.19-0.18) | 0.09 (−0.08-0.26) | 0.07 (−0.06-0.20) | 0.04 (−0.05-0.13) | |||
| Shiftwork type and shiftwork tenure | 0.41 | 0.82 | 0.57 | ||||
| Never | ref | ref | ref | ref | |||
| Afternoon, night and <10 years | −0.02 (−0.14-0.10) | 0.01 (−0.16-0.18) | 0.04 (−0.06-0.14) | 0.15 (0.04-0.26) | |||
| Afternoon, night and ≥10 years | −0.01 (−0.16-0.14) | −0.15 (−0.34-0.03) | −0.03 (−0.16-0.11) | 0.10 (−0.04-0.25) | |||
| Rotating, split, irregular, or on-call and <10 years | 0.08 (−0.08-0.25) | 0.05 (−0.15-0.24) | 0.03 (−0.09-0.14) | 0.06 (−0.05-0.17) | |||
| Rotating, split, irregular, or on-call and ≥10 years | −0.06 (−0.24-0.12) | 0.04 (−0.13-0.21) | 0.06 (−0.07-0.20) | 0.10 (−0.001-0.19) | |||
| Shiftwork tenure and hours worked per week | 0.08 | 0.62 | 0.24 | ||||
| 0 years and <55hours/week | ref | ref | ref | ref | |||
| 0 years and ≥55 hours/week | −0.06 (−0.32-0.21) | 0.07 (−0.24-0.37) | 0.01 (−0.17-0.19) | 0.14 (0.01-0.27) | |||
| 1-<10 years and <55 hours/week | 0.01 (−0.10-0.12) | −0.03 (−0.19-0.12) | 0.02 (−0.07-0.10) | 0.13 (0.03-0.22) | |||
| 1-<10 years and ≥55 hours/week | −0.03 (−0.37-0.32) | 0.33 (0.02-0.64) | 0.20 (−0.04-0.45) | 0.14 (−0.02-0.30) | |||
| ≥10 years and <55 hours/week | −0.04 (−0.16-0.09) | −0.03 (−0.19-0.13) | −0.02 (−0.12-0.09) | 0.13 (0.02-0.23) | |||
| ≥10 years and ≥55 hours/week | 0.04 (−0.32-0.41) | −0.08 (−0.34-0.18) | 0.20 (−0.07-0.46) | 0.12 (−0.02-0.26) | |||
Abbreviations: mg – milligram, g – gram, L – liter, ref – referent, CI – confidence interval
Adjusted for age, region, education, smoking status, alcohol consumption, physical activity, BMI, and aspirin use
Interaction tests performed using an age-adjusted model
Table 5.
Multivariable-adjusted beta coefficients for mean differences in log white blood cell count (× 109 cells/l) by shift work characteristics, stratified by race and sexa
| Characteristic | Black | White | p-interactionb | ||||
|---|---|---|---|---|---|---|---|
| Women | Men | Women | Men | Race | Sex | Race*Sex | |
| β (95% CI) | β (95% CI) | β (95% CI) | β (95% CI) | ||||
| Ever shiftwork | 0.24 | 0.10 | 0.53 | ||||
| Never | ref | ref | ref | ref | |||
| Former | −0.01 (−0.04-0.02) | 0.02 (−0.02-0.06) | 0.01 (−0.01-0.03) | 0.02 (0.001-0.04) | |||
| Current | −0.04 (−0.10-0.02) | 0.02 (−0.05-0.09) | 0.001 (−0.05-0.05) | −0.001 (−0.04-0.04) | |||
| Shiftwork type | 0.17 | 0.14 | 0.53 | ||||
| Never | ref | ref | ref | ref | |||
| Afternoon | −0.02 (−0.06-0.02) | 0.01 (−0.04-0.07) | 0.01 (−0.02-0.04) | −0.004 (−0.04-0.03) | |||
| Night | −0.04 (−0.07-0.001) | 0.04 (−0.01-0.09) | 0.03 (−0.005-0.06) | 0.03 (−0.003-0.06) | |||
| Rotating | 0.01 (−0.03-0.06) | 0.03 (−0.03-0.08) | −0.02 (−0.06-0.02) | 0.03 (−0.004-0.06) | |||
| Split, irregular, or on-call | 0.02 (−0.03-0.06) | 0.02 (−0.04-0.07) | −0.0001 (−0.03-0.03) | 0.02 (−0.004-0.05) | |||
| Direction of shiftwork rotation | 0.20 | 0.92 | 0.65 | ||||
| Forwards | ref | ref | ref | ref | |||
| Backwards | 0.19 (−0.07-0.45) | −0.13 (−0.42-0.17) | 0.12 (−0.09-0.32) | −0.01 (−0.14-0.12) | |||
| Both | 0.07 (−0.03-0.16) | 0.05 (−0.05-0.14) | 0.03 (−0.06-0.12) | 0.02 (−0.03-0.08) | |||
| Shiftwork tenure (years) | 0.21 | 0.05 | 0.92 | ||||
| 0 | ref | ref | ref | ref | |||
| 1-<10 | −0.01 (−0.04-0.02) | 0.03 (−0.01-0.08) | 0.004 (−0.02-0.03) | 0.02 (−0.01-0.04) | |||
| ≥10 | −0.01 (−0.04-0.03) | 0.03 (−0.02-0.07) | 0.006 (−0.02-0.03) | 0.02 (−0.002-0.05) | |||
| Hours worked per week | 0.01 | 0.78 | 0.83 | ||||
| <35 | −0.04 (−0.08-0.003) | 0.02 (−0.05-0.09) | −0.01 (−0.04-0.02) | 0.01 (−0.03-0.05) | |||
| 35-40 | ref | ref | ref | ref | |||
| 41-48 | 0.003 (−0.05-0.06) | 0.03 (−0.02-0.09) | −0.02 (−0.05-0.01) | −0.01 (−0.04-0.02) | |||
| 49-54 | −0.02 (−0.08-0.04) | 0.02 (−0.04-0.08) | −0.01 (−0.04-0.02) | 0.01 (−0.02-0.03) | |||
| ≥55 | 0.003 (−0.05-0.06) | 0.03 (−0.03-0.08) | −0.01 (−0.05-0.02) | −0.004 (−0.03-0.02) | |||
| Shiftwork type and shiftwork tenure | 0.62 | 0.26 | 0.34 | ||||
| Never | ref | ref | ref | ref | |||
| Afternoon, night and <10 years | −0.03 (−0.07-0.01) | 0.03 (−0.02-0.08) | 0.01 (−0.02-0.03) | 0.02 (−0.01-0.05) | |||
| Afternoon, night and ≥10 years | −0.03 (−0.07-0.03) | 0.04 (−0.02-0.09) | 0.03 (−0.01-0.07) | 0.01 (−0.03-0.05) | |||
| Rotating, split, irregular, or on-call and <10 years |
0.03 (−0.02-0.08) | 0.04 (−0.02-0.09) | 0.001 (−0.03-0.03) | 0.02 (−0.01-0.05) | |||
| Rotating, split, irregular, or on-call and ≥10 years |
0.03 (−0.03-0.07) | 0.02 (−0.03-0.07) | −0.02 (−0.06-0.02) | 0.03 (0.001-0.05) | |||
| Shiftwork tenure and hours worked per week | 0.35 | 0.06 | 0.87 | ||||
| 0 years and <55 hours/week | ref | ref | ref | ref | |||
| 0 years and ≥55 hours/week | 0.01 (−0.07-0.09) | 0.03 (−0.06-0.12) | −0.02 (−0.07-0.03) | −0.02 (−0.05-0.02) | |||
| 1-<10 years and <55 hours/week | −0.01 (−0.04-0.02) | 0.03 (−0.02-0.08) | 0.01 (−0.02-0.03) | 0.01 (−0.02-0.03) | |||
| 1-<10 years and ≥55 hours/week | −0.02 (−0.12-0.09) | 0.10 (0.003-0.19) | −0.02 (−0.09-0.05) | 0.02 (−0.02-0.07) | |||
| ≥10 years and <55 hours/week | −0.01 (−0.05-0.03) | 0.04 (−0.01-0.09) | −0.003 (−0.03-0.03) | 0.02 (−0.01-0.05) | |||
| ≥10 years and ≥55 hours/week | 0.003 (−0.10-0.11) | −0.01 (−0.08-0.07) | 0.05 (−0.03-0.12) | 0.01 (−0.02-0.05) | |||
Abbreviations: mg – milligram, g – gram, L – liter, ref – referent, CI – confidence interval
Adjusted for age, region, education, smoking status, alcohol consumption, physical activity, BMI, and aspirin use
Interaction tests performed using an age-adjusted model
Sensitivity analyses are shown in Appendix A. Associations between shift work, long work hours, and inflammatory markers among men appeared robust and some were stronger when restricted to those who did not report a history of inflammation-related chronic diseases. Among those not retired, working ≥55 hours/week was associated with higher log-CRP compared to working 35-40 hours/week (β = 0.10, 95% CI: 0.004-0.21). Non-retired White women working shifts in both forward and backward rotations also had higher log-CRP compared to White women only working shifts in a forward rotation (β = 0.49, 95% CI: 0.02-0.96). In several sensitivity analyses, night shift work was inversely associated with log-WBC in Black women. When results were restricted to wage-employed workers, Black men who worked night shifts had a higher mean log-WBC compared to Black men working day shifts (β = 0.07, 95% CI: 0.01-0.12). Wage-employed Black men engaged in shiftwork for more than 10 years also had higher log-WBC compared to Black men who never worked a shiftwork schedule (β = 0.05, 95% CI: 0.01-0.10).
DISCUSSION
This is the first large U.S population-based study we are aware of to examine associations between work schedule characteristics and markers of inflammation and explore potential disparities by race and sex. In the overall sample, we observed an association between shiftwork schedule and higher CRP. Among race-sex subgroups, shiftwork was associated with higher CRP among White men and shiftwork tenure and long working hours were associated with higher CRP and WBC among Black men. In sensitivity analyses, new findings emerged indicating an association between shiftwork rotation and higher CRP among non-retired White women as well as an inverse association between night shift work and WBC among Black women.
The findings are consistent with previous studies examining the relationship between work schedule demands and markers of inflammation. In a cross-sectional study of 1,877 aviation crewmembers, Puttonen and colleagues (2011) reported higher CRP levels among men working nights compared to men working days and, consistent with our findings, this association was not observed among women.(17) In a cohort of 464 police officers in the Buffalo Cardio-Metabolic Occupational Police Stress study, those working evenings or nights had significantly higher WBC (×109/L) compared to those working days.(20) In contrast, two studies did not find statistically significant associations between shift work and inflammation, including a prospective cohort of 2,323 participants from the Tromsø Study that did not find an association between a dichotomous measure of shift work and serum CRP measured seven years later.(32) Additionally, Buss and colleagues (2018) observed no cross-sectional association in the difference in mean total WBC among 8,446 2005-2010 National Health and Nutrition Examination Survey study participants working evenings or nights compared to those working days.(33) In a study of male office workers in Japan, Nakanishi et al. reported an inverse association of WBC with greater number of hours worked per day, whereas we report a positive association.(34) Differences in findings across studies could be attributed to inadequate sample size, differences in how work schedule characteristics were defined, measured, and distributed in the sample, and differences in how inflammation data were collected and treated in analyses. Interestingly, results of the current study indicate a longer-term impact of shiftwork on systemic inflammation, as many participants had been retired for several years prior to completing the occupational survey. This lends some support for the hypothesis that shiftwork may influence chronic disease risk later in life through an inflammatory mechanism.
We report novel findings regarding work organization and inflammation among race-sex subgroups and found that additional factors warrant further consideration. Shiftwork and long working hours were associated with higher CRP and WBC count among Black men. Associations for inflammatory response appeared more pronounced among wage-employed Black men and women, which may be due in part to sex- and race-based job segregation. Additionally, Black participants were more likely to work the night shift and report working more than 10 years in a shiftwork position, which may indicate racial inequality in exposure to more demanding work shifts. Cumulative stress from work-related factors, such as workplace discrimination, psychosocial job stress, and job control may disproportionately affect Black men and women and may play a role in the body’s inflammatory response. In the REGARDS cohort, Black men reported a 71% higher prevalence of discrimination compared to White men, and Black women reported a 51% higher prevalence of discrimination compared to White women.(35) Several studies have provided evidence of an association between discrimination and systemic inflammation, though studies examining racial differences are still limited.(36)
Interestingly, some associations appeared greater for afternoon shifts compared to night shifts and for those reporting ≥55 hours worked per week but <10 years of shiftwork compared to those reporting ≥55 hours worked per week and ≥10 years of shiftwork. These associations may be indicative of a healthy worker effect related to participation in shiftwork, where those who are able to maintain a shiftwork schedule for most of their career may be healthier than those who leave shiftwork positions or opt for day or afternoon/evening shifts. A 2011 systematic review identified factors that may be associated with greater shiftwork tolerance, including age, sex, and biologic chronotype.(37)
This study has several key strengths. To our knowledge, this is the first study to explore race and sex differences in work characteristics and inflammation markers in a large biracial U.S. population-based cohort. Information was collected on a wide range of occupational factors including occupation, shiftwork type and tenure, work hours, employment type, and retirement status, which allowed us to examine numerous aspects of participants’ work history as well as assess the robustness of our results in sensitivity analyses. Inflammatory markers were measured in a central laboratory using clinically used tests. The REGARDS study also collected detailed information on important potential confounders which were accounted for in our analysis, including lifestyle and clinical characteristics.
This study has several limitations. Firstly, the quasi-cross-sectional design of the study limits our ability to interpret the temporal relationship between work organization and inflammation. Additionally, since information on participants’ work hours was collected at the time of the occupational ancillary questionnaire (administered 2011-2013), responses may not reflect work scheduling at the time of the second examination (administered 2013-2016) when biochemical measurements were taken. This study may have been statistically underpowered to detect meaningful differences in some subgroup analyses. It is possible that some results may be false-positives due to the number of hypothesis tests performed. There may be residual confounding by other factors that may be associated with both work organization and inflammation, such as job strain. Clinical characteristics that may modify this association (e.g., statin use), were not evaluated in our models but may be considered in the future. Further, while all models were adjusted for age it is possible residual confounding may still be present due to the older age of REGARDS participants and the strong association of age with increases in inflammatory markers. Since the study population was restricted to non-Hispanic Black and White persons, we could not examine differences among other racial/ethnic groups. There was potential for selection bias as those who participated initially and remained active to participate in exam 2 may not be representative of the general population with the work characteristics under study.
Overall, we found that shift workers had higher CRP and WBC compared to day workers, and that these associations warrant further consideration of potential race-sex disparities, particularly for other racial/ethnic groups overrepresented in shift work positions, such as Hispanic Americans. From a clinical perspective, a history of shiftwork was associated with a 1.05 mg/L higher CRP value compared to those who did not engage in shiftwork, which guidelines from the American Heart Association and Centers for Disease Control and Prevention indicate conveys a moderate risk of cardiovascular disease (1-3 mg/L).(38) Collection of occupational characteristics can provide a more complete picture of an individual’s disease risk profile. This information can be used to better personalize recommendations for primary prevention activities and guide screening considerations.
These findings should be further evaluated in prospective studies to better elucidate the role of work organization and job segregation in understanding disparities in the occurrence of systemic low-grade inflammation and associated chronic disease incidence among worker populations. Additional factors that may influence work schedule demands should also be considered, including working multiple jobs and balancing work with caregiving responsibilities. Factors that may mediate the association between shiftwork, long work hours, and inflammation should also be considered: work schedule control, the frequency and timing of rest breaks, biologic chronotype, and sleep quality. Finally, inflammation as a contributing factor to any adverse health outcomes related to work characteristics merits investigation.
Supplementary Material
KEY MESSAGES.
What is already known about this subject?
Work schedule demands are associated with chronic disease, but little is known about the biological pathways.
Shiftwork and long work hours may promote systemic low-grade inflammation, but race and sex differences are not well understood.
What are the new findings?
In this cross-sectional study of 12,487 Black and White men and women, aged 45 years and older, shift work was associated with higher C-reactive protein concentration in White men and higher C-reactive protein and white blood cell count in Black men.
How might this impact on policy or clinical practice in the foreseeable future?
Routine health screenings for those who work outside of a daytime shift and who work long hours should include consideration of inflammatory and immune response.
FUNDING AND ACKNOWLEDGEMENTS
The REGARDS study is supported by cooperative agreement U01 NS041588 co-funded by the National Institute of Neurological Disorders and Stroke (NINDS) and the National Institute on Aging (NIA), National Institutes of Health, Department of Health and Human Services. The occupational ancillary study is supported by intramural funding by the National Institute for Occupational Safety and Health (NIOSH), Centers for Disease Control and Prevention (CDC). The content is solely the responsibility of the authors and does not necessarily reflect the official views of NINDS, NIA, CDC or NIOSH. Representatives of the NINDS were involved in the review of the manuscript but were not directly involved in the collection, management, analysis, or interpretation of the data. The authors thank the other investigators, the staff, and the participants of the REGARDS study for their valuable contributions. A full list of participating REGARDS investigators and institutions can be found at: https://www.uab.edu/soph/regardsstudy/
Footnotes
Disclosure summary: The findings and conclusions in this article are those of the authors and do not necessarily represent the views of the Centers for Disease Control, National Institute for Occupational Safety and Health, National Institute of Neurological Disorders and Stroke, or National Institute on Aging.
Competing interest statement: None declared
Data sharing:
This study uses data from the Reasons for Geographic and Racial Differences in Stroke (REGARDS) cohort. REGARDS facilitates data sharing through formal data use agreements. Requests for data access may be sent to regardsadmin@uab.edu.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
This study uses data from the Reasons for Geographic and Racial Differences in Stroke (REGARDS) cohort. REGARDS facilitates data sharing through formal data use agreements. Requests for data access may be sent to regardsadmin@uab.edu.