Conference paper International Population Conference 2017 Occupational variation in healthy worker effects: self-reported health in Belgium By Laura Van den Borre & Patrick Deboosere Affiliation: Interface Demography, Sociology Department, Vrije Universiteit Brussel, Belgium Introduction Occupational health research is complicated by the Healthy Worker Effect (HWE), referring to the continuous selection process of healthy individuals in the workforce. As a result, the comparison of workers’ health with the general population is biased.[1] Due to different hiring policies and specific job requirements, the HWE is likely to be different across industries. Physically demanding occupations should exhibit the HWE more clearly than non-manual labour jobs.[2] If so, will this persist when making the transition to the inactive population? So far occupational differences within the HWE have scarcely been investigated. To our knowledge, this is the first study to examine specific occupational groups. This study follows the total Belgian work force of 1991 and investigates variations in self-reported health for specific occupational groups 10 years later. Methodology Data were derived from an anonymous record linkage between the Belgian censuses of 1991 and 2001. Linkage at the individual level is possible because each citizen has a unique identification number. An additional linkage with the population register was performed to account for migrations or deaths between the census dates. The total Belgian working population aged 25 to 55 years was selected from the 1991 census and followed up until the 2001 census. A total of 1,773,345 men and 1,176,913 women were employed on 1 March 1991. In the period between the two censuses, 3.2% of male workers and 1.5% of female workers died. Loss to follow-up due to emigration was 2.1% and 1.5% in the male and female working population, respectively. As shown in table 1, the study population consists of approximately 1.6 million men and 1.1 million women who were at work on 1 March 1991 and resided in Belgium on 1 October 2001. Health information was derived from the 2001 census using the question ‘How is your health in general?’ Self-reported health was dichotomized into good (very good/good coded 0) and poor (fair/bad/ very bad coded 1) health. Health questions were not included in the 1991 census. Occupational groups were composed using the 2-digit codes from the International Standard Classification of Occupations (ISCO-88) as recorded in the 1991 census. Although detailed occupational information is not available for 2001, the dataset does include information on the activity status in 2001. This allows us to determine who is still active, unemployed, (pre)retired or inactive due to personal, health or familial reasons. 1
Conference paper International Population Conference 2017 In order to determine which occupational groups experienced stronger HWEs, the health situation in 2001 was compared with three possible scenarios for the 1991 working population. Healthy worker effects entail a comparison problem between the active and total population. As a result, we considered whether members of the 1991 workforce were still active 10 years later. In addition to the activity status in 2001, we also took mortality between the two census dates into account assuming deaths to be the result of poor health. Analyses were performed for men only, as inactivity among women in prime working ages may be motived by caregiving and familial reasons rather than their own health situation.[3] Table 1 Number and percentage of study subjects with the mean age at the time of the 2001 census and the percentage of subjects experiencing fair or (very) poor health in 2001 Men Women Occupation (Census 1991) N (% of total) Mean age % of N N (% of total) Mean age % of N (years) in poor health (years) in poor health Legislators and senior officials 4427 (0.28%) 53.18 16.38% 1269 (0.12%) 50.97 16.57% Corporate managers 134047 (8.37%) 50.94 15.87% 41217 (3.86%) 49.06 17.57% Managers of small enterprises 62531 (3.91%) 50.37 24.44% 39141 (3.67%) 50.16 26.28% Physical, math. and engineering science professionals 44553 (2.78%) 46.63 10.91% 6876 (0.64%) 42.34 8.70% Life science and health professionals 29762 (1.86%) 48.20 11.34% 73403 (6.87%) 45.67 13.78% Teaching professionals 66821 (4.17%) 51.45 19.07% 113020 (10.58%) 49.02 17.34% Other professionals 56647 (3.54%) 49.18 16.05% 41848 (3.92%) 46.66 15.24% Physical and engineering science assoc. professionals 118132 (7.38%) 49.64 20.69% 19564 (1.83%) 46.97 17.11% Life science and health assoc. professionals 10549 (0.66%) 47.80 13.26% 21365 (2%) 46.18 13.66% Teaching associate professionals 8161 (0.51%) 47.90 18.41% 16065 (1.5%) 46.86 18.73% Other associate professionals 60868 (3.8%) 49.17 17.75% 38950 (3.65%) 47.55 15.90% Office clerks 188232 (11.76%) 49.10 20.64% 240961 (22.57%) 46.88 17.86% Customer services clerks 6566 (0.41%) 48.11 19.52% 24995 (2.34%) 46.70 21.62% Personal and protective services workers 60747 (3.79%) 48.17 23.53% 86914 (8.14%) 47.24 26.03% Salespersons and demonstrators 23881 (1.49%) 47.31 20.47% 68484 (6.41%) 46.88 21.68% Skilled agricultural and related workers 39462 (2.46%) 49.97 25.19% 13726 (1.29%) 51.57 25.70% Extraction and building trades workers 141225 (8.82%) 48.40 30.65% 1700 (0.16%) 47.85 28.98% Metal, machinery and related trades workers 136747 (8.54%) 48.10 25.97% 11611 (1.09%) 47.44 26.45% Precision, (handi-)craft and related trades workers 20746 (1.3%) 49.19 25.27% 4665 (0.44%) 46.38 22.47% Other craft and related trades workers 45314 (2.83%) 48.32 25.74% 29739 (2.79%) 47.27 24.85% Stationary plant and related operators 23831 (1.49%) 48.41 26.13% 2086 (0.2%) 47.55 28.51% Machine operators and assemblers 41942 (2.62%) 47.58 26.01% 29982 (2.81%) 46.33 25.76% Drivers and mobile plant operators 103341 (6.45%) 48.75 28.36% 2695 (0.25%) 47.36 27.30% Services elementary occupations 63630 (3.97%) 48.74 31.04% 119135 (11.16%) 48.89 32.41% Agricultural and related labourers 444 (0.03%) 43.31 22.27% 2242 (0.21%) 52.90 29.82% Labourers in mining, constr., manuf. and transport 83206 (5.2%) 47.97 28.37% 14189 (1.33%) 47.08 28.50% Armed forces 25228 (1.58%) 46.35 17.89% 1950 (0.18%) 43.68 18.20% Total 1601040 (100%) 48.95 19.84% 1067792 (100%) 47.53 17.99% 2
Conference paper International Population Conference 2017 Standardized Morbidity Ratios (SMR) were calculated for (A) the total 1991 workforce, entailing active and non-active members in 2001 as well as workers who died between 1 March 1991 and 1 October 2001; (B) active and non-active members of the 1991 working population; (C) active workers in 1991 and 2001. We used the health distribution of the 2001 male population aged 35 to 65 years as reference for each scenario. The share of men reporting poor health in 2001 were obtained by 5-year age group. These proportions were applied to the 1991 male workforce by 5-year age group and occupational group for scenario A, B and C. Using the occupational information from the 1991 census, the observed number of workers in poor health were divided by the expected number based on the health distribution of the total male population in 2001. The result was multiplied by 100. An SMR of 100 indicates that the observed distribution of poor health is similar to the distribution in the reference category. An SMR (below) above 100 indicates that the study population has (lower) higher proportions of poor health than can be expected based on the 2001 distribution. Large differences between SMRs for the three scenarios indicate strong HWEs. Figure 1 shows the percentage of men experiencing poor health in 2001 according to their age at the time of the 2001 census. Lines represent the total male population in 2001 and the 1991 male workforce in three scenarios. The health situation in the 1991 workforce (A) and among active and non-active members in 2001 (B) are masked when comparing the self-reported health among the active population (C) with the health situation in the 2001 population. Scenario C represents the ‘ideal’ working population, assuming no- one had left employment or no-one had died between the two census. Scenario A simulates a situation where the unhealthiest workers were still at work. The comparison of scenario A and scenario C thus represents the impact of poor health on the formation of the active population in 2001 net of ageing. As can be seen in figure 1, the difference between the share of men in scenario C in poor health is still considerably lower than the share of men in the 2001 total population. This can be explained by health selection processes prior to the 1991 census, which have not been taken into account in this study design. 50% 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 Total male population 2001 A Male working population 1991 - Active & non-active in 2001 plus deceased in 1991-2001 B Male working population 1991 - Active & non-active in 2001 C Male working population 1991 - Active in 2001 Figure 1 Percentage of men experiencing poor health in 2001 total population and 1991 workforce by age in 2001. 3
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