Table 1.
Characteristics of the samples by cohort
| FPS | HeSSup | HILDA | SLOSH | WHII | Mean or proportion pooled across cohorts | |
| Eligible for analyses on cardiometabolic disease | 28 491 | 6065 | 3681 | 3505 | 6015 | |
| Person-years (mean follow-up time in years) | 191 829.9 (6.7) | 54 315.4 (9.0) | 23 636.0 (6.4) | 17 766.5 (5.1) | 107 904.7 (17.9) | 7.3* |
| Number of cases (rate per 1000 person-years) | 616 (3.2) | 122 (2.3) | 161 (6.8) | 108 (6.1) | 1512 (14.0) | |
| Number (%) of participants experiencing increase in job strain, T1–T2 | 5015 (18) | 839 (14) | 520 (14) | 544 (16) | 1087 (18) | 16† |
| Number (%) of participants experiencing no major change in job strain, T1–T2 | 19 295 (68) | 4020 (66) | 2552 (69) | 2249 (64) | 3973 (66) | 67† |
| Number (%) of participants experiencing decrease in job strain, T1–T2 | 4181 (15) | 1206 (20) | 609 (17) | 712 (20) | 955 (16) | 17† |
| Number (%) of participants with poor mental health | 5245 (18) | 915 (15) | 445 (12) | 515 (15) | 740 (12) | 14† |
| Number of women (%) | 23 032 (81) | 3437 (57) | 1765 (48) | 1919 (55) | 1801 (30) | 55† |
| Mean age, T1 (SD) | 47.1 (8.6) | 44.3 (9.4) | 40.0 (12.8) | 49.2 (9.5) | 44.8 (6.0) | 45.4 |
| Number (%) of participants according to socioeconomic status, T1 | ||||||
| Low | 5121 (18) | 2487 (41) | 923 (25) | 1116 (32) | 1985 (33) | 29† |
| Intermediate | 15 454 (54) | 2235 (37) | 993 (27) | 1630 (47) | 2941 (49) | 42† |
| High | 7916 (28) | 1343 (22) | 1765 (48) | 759 (22) | 1089 (18) | 27† |
| Number (%) of participants with obesity, T1‡ | 2780 (10) | 474 (8) | 674 (18) | 343 (10) | 374 (6) | 10† |
| Number (%) of participants with physical inactivity, T1‡ | 4831 (17) | 1045 (17) | 702 (19) | 571 (16) | 1437 (25) | 19† |
| Number (%) of participants with high alcohol consumption, T1‡ | 3557 (13) | 782 (13) | 389 (14) | 142 (4) | 914 (15) | 11† |
| Number (%) of participants smoking, T1‡ | 4712 (17) | 1211 (22) | 711 (19) | 465 (13) | 915 (15) | 17† |
*Test for heterogeneity showed p<0.001.
†The proportions were pooled through meta-analyses using metaphor with logit transformation to increase the probability of a normal distribution suitable for estimation of summary proportions. Tests for heterogeneity showed p<0.001.
‡Missing values: obesity (FPS n=569, HeSSup n=27, HILDA n=0, SLOSH n=41 and WHII n=8), physical inactivity (FPS n=263, HeSSup n=26, HILDA n=1, SLOSH n=12 and WHII n=183), alcohol consumption (FPS n=65, HeSSup n=1, HILDA n=897, SLOSH n=235 and WHII n=50) and smoking (FPS n=604, HeSSup n=490, HILDA n=5, SLOSH n=7 and WHII n=5).
FPS, Finnish Public Sector; HILDA, Household, Income and Labour Dynamics in Australia; HeSSUP, Health and Social Support; SLOSH, Swedish Longitudinal Occupational Survey of Health; T1, time 1; T2, time 2; WHII, Whitehall II.