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  • Research
  • Open Access
  • Open Peer Review

Investigating self-reported health by occupational group after a 10-year lag: results from the total Belgian workforce

Archives of Public Health201876:68

https://doi.org/10.1186/s13690-018-0313-1

  • Received: 2 July 2018
  • Accepted: 20 September 2018
  • Published:
Open Peer Review reports

Abstract

Background

Belgium lacks a systematic overview of health differences by occupation. This is the first study to examine self-reported health among 27 occupational groups in Belgium with a lag time of 10 years.

Methods

Individual data are derived from an anonymous linkage between the 1991 and 2001 Belgian census. The total working population (25–55 years) is selected from the 1991 Belgian census. Self-reported health (1 = fair or (very) bad health; 0 = (very) good health) was obtained from the 2001 census. Logistic regression analysis was used to analyse the health of 1.5 million men and 1.0 million women by occupational group in 1991. The active sex-specific population in 1991 and 2001 was the reference group. Controls include age, activity status and housing status at the time of 2001 census.

Results

Both male and female workers in physically demanding occupations were more likely to report poor health. The three occupations with the highest age-adjusted Odds Ratios (OR) were extraction and building trade workers (ORmale 2.08 95% Confidence Interval (CI) 2.05–2.10; ORfemale 2.15 CI 1.93–2.40); services elementary workers (ORmale 2.06 CI 2.03–2.10; ORfemale 2.37 CI 2.34–2.41); and labourers in construction, manufacturing and transport (ORmale 1.90 CI 1.86–1.93; ORfemale 2.21 CI 2.12–2.29). Men and women in teaching, scientific, health-related and managerial positions had the lowest age-adjusted ORs for poor self-reported health. The pattern in occupational health differences remained the same after controlling for activity status and socio-economic position.

Conclusions

Occupational health inequalities are apparent after a lag time of 10 years. The identification of types of workers in poor health provide valuable insights to future health promotion strategies in the Belgian workforce.

Keywords

  • Cohort study
  • Occupational health
  • Self-rated health
  • Men
  • Women
  • Occupation
  • Health inequalities

Background

Belgium has no systematic overview of occupational health differences. Yet, this issue is becoming increasingly important as policy measures are being developed to encourage workers to stay employed longer. Considering the importance of deteriorating health as a motive to leave employment [1], there is a high need to understand health inequalities in the Belgian workforce.

The available insights have been gained largely from international research. Manual work has been associated with poor health [24]. Although manual workers generally have a better health at the start of employment, their health declines more rapidly during working years than non-manual workers’ health [2, 4]. Longitudinal studies show that work-related health differences persist even after job changes or retirement [5]. Not only do manual workers have more years in poor health, they also have shorter life expectancies [6, 7]. Differences are explained partly by the physical demands of manual labour.

Research has been conducted on occupational health in Belgium, but the focus laid mainly on specific work-related diseases [8, 9], specific work contexts [10, 11] or mechanisms of health differences [12, 13]. Very little is known about health in specific occupations or how occupational health differences relate to each other. Which occupations have the best health situation? Which workers experience the most health problems? How large is occupational variation in health among Belgian workers?

This study follows the total Belgian workforce of 1991 using newly available census-linked data to investigate variations in self-reported health for specific occupational groups after a lag time of 10 years. Self-reported health is a well-established predictor of morbidity and mortality, covering physical, mental and social aspects of health [14, 15]. This research examines potential health differences by occupation in the total male and female working population. We further explore if and to what extent these results differ by age, activity status and socio-economic position.

Method

Data were derived from an anonymous record linkage between the Belgian censuses of 1991 and 2001. Statistics Belgium performed the linkage at the individual level using unique identification numbers for each citizen. An additional linkage with the population register was performed to account for migrations or deaths between the census dates [16]. The result is a rich, exhaustive dataset combining cross-sectional data at the time of the 1991 and 2001 census. 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 1.7 million men and 1.1 million women were employed on 1 March 1991. In the period between the two censuses, 3.1% of male workers and 1.5% of female workers died. An overview of the number of deaths per occupation can be found in Table 3 in Appendix. Loss to follow-up due to emigration was 2.1% and 1.4% in the male and female working population, respectively. As a result, analyses are based on data from 1.5 million men and 1.0 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. The ISCO codes discern skill levels and skill specialisation, referring to the level of complexity and the type of knowledge, tools and equipment used, respectively [17]. Persons working in sheltered workshops were not included because of the targeted health selection in specific industries, corresponding to 7368 disabled men and 4945 disabled women. Both among men and women, the largest occupational group is ‘office clerks’ with respectively 12% of working men and 23% of working women. Other important occupational groups for men include ‘extraction and building trades’ and ‘metal, machinery and related trades’, with both employing approximately 9% of working men. Among women, we find a substantial number of women working in elementary services (11%) and in professional teaching jobs (11%).

Tables 4 and 5 in Appendix provide a comprehensive overview of the classification for men and women. Detailed occupational information is not available for 2001. The dataset does include information on the activity status in 2001. Respondents were asked to which category of persons they belong. Possible answers included students, actively employed persons, first-time jobseekers, other unemployed and (early) retirees. We used this information to determine who is still active, unemployed, (pre)retired or inactive due to personal, health or familial reasons.

Logistic regression analyses were performed for poor self-reported health by occupational group. Analyses were performed for men and women separately due to well-established sex differences in the distribution of risk factors for poor self-reported health [18]. Odds Ratios (OR) and 95% Confidence Intervals (CI) were computed with the sex-specific population that is still actively employed in 2001 as reference population. The majority of the active population in 1991 was still employed ten years later with 75% of men and 70% of women. These groups represent the healthiest individuals and provide an insight in the “acceptable” health situation to remain actively employed.

Analyses were performed using STATA/MP version 13.1. Three control variables were added step-wise. First, all models controlled for age measured continuous in years at the time of the 2001 census. Age is an important factor as health deteriorates as people grow older [19].

Second, activity status in 2001 was also included as a control to investigate the associations between poor self-reported health and the transition into the non-active population. Several mechanisms can play a role in the association between activity status and health [20]. Workers may leave employment because of a work-related disease. In the case of a non-occupational disease, workers may also have to leave employment because working conditions have become too strenuous or because of the gravity of the condition.

Third, socio-economic background was examined. Multiple studies have reported an association between poor health and low socio-economic position (SEP) [2123]. Occupation is an important component of SEP which is a composite measure for an individual’s place in the social structure [24]. Because the workplace is an important source of social determinants of health, work is potentially closely related to various other material and social indicators [25]. Careful consideration of the socio-economic background is warranted when investigating occupational health differences [26]. Information on housing and ownership was used for this purpose. Housing conditions may have direct health effects [27]. In addition, housing status has also been reported to be a good indicator for material circumstances as it entails past (e.g. inheritance), present (e.g. wage) and future (e.g. mortgage) income perspectives [28]. The variable combines information on housing comfort and home ownership at the time of the 2001 census. Tenants and home owners with low, medium and high housing comfort were distinguished. Homes with low comfort require large repairs. Mid- and high-quality homes have central heating and are > 35 m2 and > 85 m2, respectively.

Results

Table 1 presents the number and share of persons in poor health among the 1991 Belgian workforce with a 10-year lag. Generally, two out of ten workers reported their health to be poor ten years later (men 23%; women 21%). For men, the highest percentage was found among services elementary occupations (31%) and extraction and building trades workers (31%). The mean age for both occupational groups was 48 years in 2001, which is slightly lower than the total male average. For women, services elementary occupations had the highest share for reporting poor health (32%). This occupational group is a little older than average with a mean age of 49 in 2001.
Table 1

Poor self-reported health as reported in the 2001 Belgian census by occupational group at the time of the 1991 Belgian census

Occupational group in 1991 (ISCO code)

Men

Women

N 2001

Age

SRH

SRH%

N 2001

Age

SRH

SRH%

Legislators and senior officials (11)

4291

53

703

16%

1213

51

201

17%

Corporate managers (12)

129,042

51

20,476

16%

39,553

49

6949

18%

Managers of small enterprises (13)

58,998

50

14,422

24%

37,197

50

9776

26%

Physical, math. and engin. science professionals (21)

43,288

47

4721

11%

6691

42

582

9%

Life science and health professionals (22)

28,794

48

3264

11%

71,199

46

9811

14%

Teaching professionals (23)

65,059

51

12,410

19%

109,756

49

19,031

17%

Other professionals (24)

54,424

49

8736

16%

40,270

47

6137

15%

Physical and engin. science assoc. professionals (31)

114,747

50

23,736

21%

18,953

47

3243

17%

Life science and health assoc. professionals (32)

10,238

48

1358

13%

20,738

46

2832

14%

Teaching associate professionals (33)

7924

48

1459

18%

15,560

47

2915

19%

Other associate professionals (34)

58,711

49

10,419

18%

37,696

48

5995

16%

Office clerks (41)

182,752

49

37,729

21%

233,708

47

41,732

18%

Customer services clerks (42)

6389

48

1247

20%

24,206

47

5233

22%

Personal and protective services workers (51)

58,194

48

13,694

24%

83,322

47

21,686

26%

Salespersons and demonstrators (52)

22,889

47

4686

20%

65,966

47

14,299

22%

Skilled agricultural and related workers (61)

37,704

50

9499

25%

13,147

52

3379

26%

Extraction and building trades workers (71)

134,765

48

41,308

31%

1622

48

470

29%

Metal, machinery and related trades workers (72)

131,878

48

34,250

26%

11,241

47

2973

26%

Precision, (handi-)craft and related trades workers (73)

20,021

49

5059

25%

4509

46

1013

22%

Other craft and related trades workers (74)

43,324

48

11,153

26%

28,683

47

7128

25%

Stationary plant and related operators (81)

23,014

48

6014

26%

2010

48

573

29%

Machine operators and assemblers (82)

40,326

48

10,489

26%

28,904

46

7445

26%

Drivers and mobile plant operators (83)

98,848

49

28,029

28%

2586

47

706

27%

Services elementary occupations (91)

60,491

49

18,779

31%

114,190

49

37,012

32%

Agricultural and related labourers (92)

431

43

96

22%

2136

53

637

30%

Labourers in mining, constr., manuf. & transport (93)

79,761

48

22,627

28%

13,648

47

3889

28%

Armed forces (100)

24,288

46

4344

18%

1890

44

344

18%

Total

1,540,591

49

350,707

23%

1,030,594

48

215,991

21%

Abbreviations: N2001 Study population census 2001, Age Mean age in 2001 in years, SRH absolute number of persons reporting poor health in 2001, SRH% percentage of persons reporting poor health from the 2001 population

The occupational groups with the fewest workers reporting poor health in 2001 were scientific professions. Approximately 10% of workers in physical, mathematical and engineering science reported poor health (men 11%; women 9%). We found a similar result among life science and health professionals (men 11%; women 14%).

Figures 1 and 2 present the share of persons in poor health per occupational group in 1991 and by activity status in 2001. The results for men show a clear gradient by activity status. Percentages were highest among those who left employment because of personal reasons with results ranging from 63% for armed forces and 94% for agricultural labourers. Unemployed men had a higher relative share for poor health than retired men. One exception to the pattern was found among agricultural labourers, where retired workers (68%) had a higher share to report poor health than unemployed workers (45%). Workers that were still active had the lowest percentages from 9% among physical, mathematical and engineering professionals to 22% among service elementary workers.
Fig. 1
Fig. 1

Percentage of men reporting poor health by occupational group in the 1991 Belgian census and activity status in the 2001 Belgian census. Legend:

Fig. 2
Fig. 2

Percentage of women reporting poor health by occupational group in the 1991 Belgian census and activity status in the 2001 Belgian census. Legend:

The pattern for women was more condensed than for men, meaning the shares by activity status do not differ as much among women as among men. This is mostly because of the relatively low share of women to report poor health after they left employment due to personal reasons. Percentages for this group ranged between 21% for physical, mathematical and engineering professionals and 62% for labourers in construction, manufacturing and transport. Results for unemployed women were highly similar to the findings for retirees. Again, active workers had the lowest share to report poor health with 7% of physical, mathematical and engineering professionals and 21% of agricultural labourers.

Table 2 presents ORs for poor self-reported health by sex. Predictor variables in model1 are respondents’ 1991 occupational group and their age at the time of the 2001 census. Model 2 adds the activity status in 2001 and model 3 finally adds the housing status in 2001.
Table 2

Results of multivariate logistic regression models predicting 2001 self-reported poor health by sex in Belgium, Odds Ratios and 95% Confidence Intervals, sorted on ORs for men in model 1

 

Men

Women

Model 1

Model 2

Model 3

Model 1

Model 2

Model 3

OR

CI

OR

CI

OR

CI

OR

CI

OR

CI

OR

CI

Occupational group 1991 (Active pop. 1991 & 2001 = Ref)

1.00

1.00

1.00

1.00

1.00

 

1.00

 

 Extraction and building trades workers

2.08

(2.05–2.10)

1.42

(1.40–1.44)

1.36

(1.34–1.38)

2.15

(1.93–2.40)

1.34

(1.19–1.50)

1.27

(1.13–1.43)

 Services elementary occupations

2.06

(2.03–2.10)

1.47

(1.44–1.50)

1.35

(1.33–1.38)

2.37

(2.34–2.41)

1.55

(1.52–1.57)

1.41

(1.39–1.43)

 Labourers in mining, constr., manuf. and transport

1.90

(1.86–1.93)

1.34

(1.32–1.36)

1.23

(1.21–1.25)

2.21

(2.12–2.29)

1.40

(1.34–1.46)

1.29

(1.24–1.34)

 Drivers and mobile plant operators

1.81

(1.79–1.84)

1.32

(1.30–1.35)

1.23

(1.21–1.25)

2.04

(1.87–2.23)

1.34

(1.22–1.47)

1.25

(1.14–1.37)

 Agricultural and related labourers

1.79

(1.42–2.26)

1.36

(1.06–1.74)

1.25

(0.97–1.60)

1.60

(1.45–1.76)

1.13

(1.03–1.25)

1.09

(0.99–1.21)

 Machine operators and assemblers

1.71

(1.67–1.75)

1.24

(1.21–1.27)

1.17

(1.14–1.20)

2.02

(1.96–2.07)

1.26

(1.23–1.30)

1.19

(1.15–1.22)

 Metal, machinery and related trades workers

1.66

(1.64–1.69)

1.24

(1.22–1.25)

1.19

(1.18–1.21)

1.94

(1.86–2.03)

1.23

(1.18–1.29)

1.18

(1.13–1.24)

 Stationary plant and related operators

1.64

(1.59–1.69)

1.23

(1.19–1.27)

1.18

(1.14–1.21)

2.15

(1.94–2.37)

1.38

(1.24–1.53)

1.30

(1.17–1.45)

 Other craft and related trades workers

1.61

(1.57–1.64)

1.07

(1.05–1.10)

1.02

(0.99–1.04)

1.79

(1.75–1.85)

1.08

(1.05–1.11)

1.04

(1.01–1.07)

 Precision, (handi-)craft and related trades workers

1.49

(1.44–1.54)

1.08

(1.04–1.12)

1.04

(1.01–1.08)

1.66

(1.54–1.78)

1.05

(0.98–1.14)

1.01

(0.94–1.09)

 Personal and protective services workers

1.44

(1.42–1.47)

1.15

(1.13–1.18)

1.11

(1.09–1.13)

1.91

(1.88–1.95)

1.34

(1.32–1.37)

1.27

(1.25–1.30)

 Skilled agricultural and related workers

1.41

(1.37–1.44)

1.08

(1.05–1.11)

1.02

(0.99–1.04)

1.39

(1.34–1.45)

0.94

(0.90–0.98)

0.91

(0.87–0.94)

 Managers of small enterprises

1.33

(1.30–1.35)

0.96

(0.94–0.98)

0.95

(0.93–0.98)

1.60

(1.56–1.64)

1.00

(0.98–1.03)

0.95

(0.93–0.98)

 Salespersons and demonstrators

1.25

(1.21–1.29)

0.91

(0.88–0.94)

0.90

(0.86–0.93)

1.51

(1.48–1.54)

0.98

(0.96–1.00)

0.95

(0.93–0.97)

 Office clerks

1.15

(1.13–1.16)

0.95

(0.94–0.96)

0.96

(0.95–0.98)

1.19

(1.18–1.21)

0.92

(0.91–0.93)

0.93

(0.92–0.94)

 Customer services clerks

1.13

(1.06–1.20)

0.91

(0.85–0.97)

0.92

(0.86–0.99)

1.54

(1.49–1.59)

1.13

(1.09–1.16)

1.11

(1.07–1.15)

 Armed forces

1.12

(1.08–1.16)

0.95

(0.91–0.98)

0.93

(0.90–0.97)

1.57

(1.40–1.77)

1.29

(1.15–1.46)

1.23

(1.09–1.39)

 Physical and engineering science assoc. professionals

1.11

(1.09–1.13)

0.91

(0.90–0.92)

0.93

(0.91–0.94)

1.12

(1.08–1.17)

0.86

(0.83–0.90)

0.88

(0.84–0.91)

 Teaching associate professionals

1.07

(1.01–1.13)

0.93

(0.87–0.98)

0.96

(0.90–1.02)

1.26

(1.21–1.32)

1.00

(0.96–1.05)

1.02

(0.97–1.06)

 Other associate professionals

0.93

(0.91–0.95)

0.74

(0.72–0.76)

0.76

(0.75–0.78)

0.98

(0.95–1.01)

0.72

(0.70–0.74)

0.74

(0.72–0.76)

 Teaching professionals

0.90

(0.88–0.92)

0.81

(0.80–0.83)

0.89

(0.87–0.91)

0.99

(0.97–1.01)

0.85

(0.83–0.86)

0.92

(0.91–0.94)

 Other professionals

0.83

(0.81–0.85)

0.72

(0.70–0.74)

0.77

(0.75–0.78)

0.99

(0.96–1.02)

0.82

(0.79–0.84)

0.86

(0.83–0.88)

 Corporate managers

0.74

(0.73–0.75)

0.60

(0.59–0.61)

0.65

(0.64–0.66)

1.00

(0.97–1.03)

0.73

(0.70–0.75)

0.74

(0.72–0.76)

 Life science and health assoc. professionals

0.72

(0.68–0.77)

0.63

(0.59–0.67)

0.68

(0.64–0.72)

0.90

(0.87–0.94)

0.73

(0.70–0.76)

0.76

(0.73–0.80)

 Legislators and senior officials

0.68

(0.62–0.73)

0.62

(0.57–0.67)

0.68

(0.63–0.74)

0.83

(0.71–0.96)

0.70

(0.60–0.82)

0.75

(0.64–0.87)

 Physical, math. and engineering science professionals

0.61

(0.59–0.63)

0.54

(0.53–0.56)

0.60

(0.58–0.62)

0.71

(0.65–0.77)

0.58

(0.53–0.64)

0.63

(0.58–0.69)

 Life science and health professionals

0.59

(0.57–0.61)

0.54

(0.52–0.56)

0.61

(0.59–0.63)

0.95

(0.92–0.97)

0.79

(0.77–0.81)

0.83

(0.81–0.85)

Intercept

0.01

(0.01–0.01)

0.02

(0.02–0.02)

0.01

(0.01–0.01)

0.01

(0.01–0.01)

0.01

(0.01–0.01)

0.01

(0.01–0.01)

Age

1.06

(1.06–1.06)

1.05

(1.05–1.05)

1.05

(1.05–1.05)

1.07

(1.07–1.07)

1.07

(1.06–1.06)

1.06

(1.06–1.06)

Activity status (Employed = Ref)

  

1.00

1.00

  

1.00

 

1.00

 

 Unemployed

  

3.61

(3.61–3.75)

3.29

(3.22–3.35)

  

2.69

(2.64–2.74)

2.58

(2.54–2.63)

 Retired

  

1.35

(1.35–1.39)

1.33

(1.32–1.35)

  

1.43

(1.41–1.46)

1.42

(1.40–1.45)

 Personal reasons

  

17.38

(17.38–18.17)

16.76

(16.40–17.14)

  

4.84

(4.77–4.92)

4.90

(4.82–4.98)

 Other

  

3.53

(3.53–3.76)

3.30

(3.20–3.41)

  

2.57

(2.49–2.65)

2.50

(2.42–2.58)

 Missing

  

2.64

(2.64–2.95)

2.38

(2.25–2.52)

  

1.91

(1.80–2.03)

1.79

(1.68–1.9)

Housing status (Own –high quality = Ref)

    

1.00

    

1.00

 

 Own -medium quality

    

1.32

(1.31–1.33)

    

1.29

(1.27–1.3)

 Own- low quality

    

1.55

(1.54–1.56)

    

1.47

(1.46–1.49)

 Rent -high quality

    

1.25

(1.23–1.27)

    

1.31

(1.28–1.33)

 Rent -medium quality

    

1.64

(1.62–1.66)

    

1.75

(1.72–1.78)

 Rent - low quality

    

2.03

(2.01–2.06)

    

2.13

(2.10–2.16)

 Missing

    

1.90

(1.88–1.93)

    

1.79

(1.76–1.82)

Model evaluation

 Degrees of freedom

28

33

39

28

33

39

 Likelihood ratio test

136,604.33***

243,474.39***

267,517.61***

94,204.91***

138,590.9***

152,124.72***

 Pseudo R2

0.05

0.09

0.10

0.06

0.08

0.09

*** p ≤ 0.001

Occupational variation in ORs for poor self-reported health was similar for men and women. Compared to active workers in both 1991 and 2001, working in services elementary occupations, craft and construction was associated with an increased likelihood to report poor health. The three occupations with the highest age-adjusted ORs were extraction and building trade workers (ORmale 2.08 CI 2.05–2.10; ORfemale 2.15 CI 1.93–2.40); services elementary workers (ORmale 2.06 CI 2.03–2.10; ORfemale 2.37 CI 2.34–2.41); and labourers in mining, construction, manufacturing and transport (ORmale 1.90 CI 1.86–1.93; ORfemale 2.21 CI 2.12–2.29). Men and women in teaching, scientific, health-related and managerial positions had lower age-adjusted ORs for poor self-reported health. Among women, the lowest ORs were found among physical, mathematical and engineering science professionals with 0.71 (CI 0.65–0.77). Their male colleagues had a similar OR (0.61 CI 0.59–0.63), but the lowest OR among men was found among life science and health professionals with 0.59 (CI 0.57–0.61).

The pattern in health differences by occupational group remained clear after controlling for activity status and housing status in 2001.The inclusion of activity status caused ORs in model 2 to decrease considerably for all occupational groups. Compared to workers that were still active in 2001, non-active statuses consistently had higher ORs for poor self-reported health. Leaving employment because of health, familial or other personal reasons was associated with the highest ORs for poor health. Generally, unemployed men and women had higher ORs than retirees with reference to the active population. ORs for occupations converged slightly in model 3 after adding housing status to the model. People living in low-quality housing were more likely to report poor health than those who live in high-quality housing. Home owners had lower ORs than tenants.

Discussion

This study investigated census-linked data to examine differences in health by occupation among the total Belgian workforce in 1991. The main aim of this research was to provide an overview of self-reported health status in 2001 among 27 occupational groups with a 10-year lag. We found large health inequalities in the Belgian workforce for both sexes, especially in lower qualified occupations. Our results indicate that workers in physically demanding jobs had an increased likelihood to report poor health with reference to the active population in 1991 and 2001. Considerable variation existed within manual jobs as age-adjusted ORs ranged between approximately 1.40 and 2.00. Even higher results were observed among women, where age-adjusted ORs were up to 2.37 (CI 2.34–2.41) for services elementary occupations. Teaching, health-related and managerial jobs were associated with lower ORs for poor self-reported health. The pattern in occupational health differences remained the same after controlling for activity status and socio-economic status.

Occupation, age, activity status and housing status explained up to 10% and 9% of health differences among men and women, respectively. The largest contribution in explained variance was found after controlling for activity status. Especially ORs among manual workers experienced a stark decline after taking activity status into account. A potential explanation lies in differential healthy worker effects by occupational group [29]. Health problems in late middle age may create a discrepancy between the individual capabilities and the job requirements. Because of the physically demanding nature of manual labour, these workers may be more likely to encounter a mismatch than non-manual workers. In addition, the accumulation of unfavourable working conditions in these occupations has been reported to affect workers’ health [30]. As a result, more manual workers may quit their jobs due to health reasons compared to other workers, creating larger health differences by activity status in manual occupations.

Housing status did not seem to explain much of the differences in self-reported health by occupation. The inclusion of housing status modified ORs only slightly. The largest changes in ORs were among occupational groups with a specific socio-economic profile, such as services elementary occupations with a high proportion of cleaners.

The observed occupational health differences can perhaps be further explained by specific psychosocial or physical working conditions. Schütte and colleagues found that poor self-reported health is associated with hazardous working conditions, as well as with high psychological demands, low rewards and work-life imbalance for both men and women [18]. Several other studies have reported an influence of high job demands, job insecurity and repetitive work on occupational health differences [3133]. Future research is needed to understand which work-related health risks are of importance for the investigated occupational groups.

The key strength of this study is the availability of a large, exhaustive dataset, which allowed us to study the total 1991 Belgian workforce. High-quality information was drawn from comprehensive and reliable national data sources [34]. The findings for 27 specific occupational groups complement existing international research based on fewer and broader categories. As a result, the findings provide a nuanced overview of occupational health in Belgium for the first time. In addition, this research adds to the growing body of literature on work-related health differences among women. Earlier studies report mixed and contradictory results [4, 6, 19, 3537]. In accordance with recent French findings [6], we found relatively large occupational differences in women’s health. The pattern in self-reported health by occupation is clearly similar for men and women although results cannot be directly compared because of the use of sex-specific reference populations. We observed a difference in self-reported health by activity status between men and women. Men who left employment because of personal, health or familial reasons have markedly higher ORs than women with reference to their respective reference populations. It is highly likely that poor health was the decisive factor for these men in leaving employment. Women in this age group may be more prone to stop working because of familial obligations such as caregiving or childrearing.

The study also has some limitations. Firstly, health was measured using a general self-reported health question. Answers reflect multiple health dimensions and are highly subject to individual perceptions, as well as to the wider socio-temporal context [38]. This entails results should be interpreted with consideration for cross-cultural differences. A common methodological issue with self-reported health is that health experiences affect the response rate. People in poor health may not be fit enough to answer the questions. Self-reported health from the 2001 Belgian census has been compared to the national health interview survey. Lorant and colleagues found fewer non-response and better representation of low socio-economic groups in the -mandatory- census [34].

Secondly, the repeated cross-sectional design does not capture the dynamic mechanisms underlying this complex relation between health and occupation. Previous research has found important effects of time-varying indicators [39, 40]. In this study, specific occupational information is only available for one point in time. Job changes may have occurred over the period of 10 year, which may alter our results slightly. In our opinion, transfers from one occupational group to another in 1991 will be scarce given the use of relatively broad categories of occupations. Health selection in and out of employment has been reported to be more important than changes between jobs [41]. By controlling for activity status in 2001, we have accounted for possible healthy worker effects due to inactivity. However, it is possible that those experiencing very poor health, may have died or emigrated in the 10-year lag period. As a result of this potential underestimation in the worst-off professions, health inequalities between occupational groups may be even larger than presented in this study.

Thirdly, potential effects from part-time and full-time work are not investigated in the present study. An association between poor health and part-time employment has been reported in previous research [42]. It is possible that our results for women may alter somewhat when considering differences in work time. According to the 1991 census, 64% of working women were employed full-time with proportions ranging from 45% among customers service clerks to 98% in the armed forces. In contrast, the overall majority (95%) of active Belgian men worked full-time in 1991 with little variation across occupations. This topic should be investigated in future research with consideration of the complexities of contextual gender differences (e.g. child rearing tasks, relationship status, different working conditions and welfare state provisions) [43, 44].

Fourthly, the lack of suitable occupational data is a major obstacle for Belgian longitudinal analyses on this topic. The results for the 1991 working population may not reflect current-day differences in health situation by occupation. Although the occupational groups under investigation are still highly relevant, the Belgian workforce has undergone some important socio-demographic and economic changes over the last decades, as most West-European countries [45]. The presented findings are based on the most recent data available for a nationwide analysis of health differences by occupation in Belgium.

This research quantifies an important policy challenge in Belgium. This study shows a continuum of health risks with a clear hierarchy by occupation even after a 10-year lag time. Policy makers should invest in reducing health disparities by occupation. We stress the importance of additional research and policy efforts targeting manual labour jobs.

We also call policymakers’ attention to the large health differences by occupation in the female working population, especially considering the increased labour market participation of women during the last decades. According to data from the International Labour Organization, the overall female labour force participation increased from 38% in 1992 to 48% in 2017 [46].

Conclusion

This study provided an overview of health differences among 27 types of Belgian workers. Both male and female workers in physically demanding occupations were more likely to report poor health. Significantly fewer workers in teaching, health-related and managerial jobs reported poor health. Large differences were observed between activity statuses -particularly in men- as found in previous research [20]. The current study confirms earlier findings of a negative association between socio-economic position and poor self-reported health [2123]. To our knowledge, this is the first Belgian study to provide insights in the health situation by occupation with a ten year-lag. For now, we can only speculate on which health problems are at the root based on these results. Future research is required to determine the underlying mechanisms of the presented occupational health differences.

Abbreviations

CI: 

95% Confidence Intervals

OR: 

Odds Ratio

SEP: 

Socio-Economic Position

Declarations

Acknowledgements

We would like to thank Statistics Belgium for performing the linkage procedure. The authors would like to thank Dr. Katrien Vanthomme for her thoughtful advice during the writing process.

Funding

LVdB is a PhD fellow at the Research Foundation-Flanders (FWO).

Availability of data and materials

The data that support the findings of this study are available from Statistics Belgium but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available.

Authors’ contributions

PD and LVdB designed the study. LVdB conducted the study and wrote the first draft of the manuscript. Both authors edited the draft, discussed the interpretation and approved the final version of the manuscript.

Ethics approval and consent to participate

Permission for analyses were granted after verification of the research goals by the Belgian Commission for the protection of privacy.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no conflict of interest.

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Authors’ Affiliations

(1)
Interface Demography, Department of Sociology, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium

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