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

Perceived Factors Influencing Blue-Collar Workers' Participation in Worksite Health Promotion Programs in Freight Transport: A Qualitative Investigation Using the TDF and COM-B

Blue-collar workers in the freight transport industry report a high risk of developing chronic diseases, partly due to an unhealthy lifestyle. Worksite health promotion programs (WHPPs) may be able to promote a healthier lifestyle, but participation among blue-collar workers in these programs is generally lower than among other workers. The current study aimed to further examine factors that can explain participation of blue-collar workers in these programs. A pragmatic, qualitative study was conducted, and semi-structured interviews were held with 32 blue-collar workers in freight transport in the Netherlands (94% men, 81% driver, mean age 48 (SD = 11)). The interview guide was based on the Theoretical Domains Framework (TDF) and Capability-Opportunity-Motivation-Behavior (COM-B) model, and was used to assess perceived determinants that influence participation. A combination of framework analysis and thematic analysis was conducted, which yielded the following nine main themes: (i) not being aware of WHPPs on offer, (ii) no clear picture of what to expect, (iii) (not) giving priority to health, (iv) expecting feedback and practical support, (v) being open and ready to change, (vi) preferring to be self-dependent, (vii) being offered a practical, fun and joint WHPP, (viii) having an employer who cares, thinks along and facilitates participation, and (ix) working and living in an environment in which a healthy lifestyle is not the norm. With these insights we were able to formulate recommendations to enhance the participation of blue-collar workers in WHPPs.

Factors associated with blue-collar workers' participation in Worksite Health Promotion Programs: a scoping literature review

A growing number of employers implement worksite health promotion programs (WHPPs). In particular, blue-collar workers may benefit from these WHPPs. However, they are less likely than other workers to participate and little is known about which factors affect their participation. The aim of this scoping literature review is to produce an overview of studies on factors associated with blue-collar workers' participation in WHPPs. Five databases were searched: BSU, PsycINFO, Medline, Web of Science and CINAHL. The review included peer-reviewed empirical studies on determinants associated with blue-collar workers' participation in WHPPs. Factors were extracted and categorized. Similar determinants were clustered and the direction of the associations was further examined. Nineteen papers describing 11 qualitative and 4 quantitative studies met the eligibility criteria. Seventy-seven determinants were analyzed (in quantitative studies) or reported (in qualitative studies). In most studies, only participant characteristics were investigated. Participation may be enhanced by addressing needs, tailoring from a broad range of activities, offering group activities, requiring little effort and commitment at the start, using incentives, leading by example and combining WHPPs with occupational safety interventions. WHPPs seem to be able to reach blue-collar workers, but it remains particularly challenging to reach shift workers and those who do not yet experience health complaints.

Use of outpatient and inpatient health care services by occupation-a register study of employees in Oulu, Finland

Background

The aim of this study was to examine how the use of outpatient and inpatient health services differs by occupational groups, and whether the differences are explained by sociodemographic factors and health status.

Methods

We used register-based data on 25–64-year-old employees living in the city of Oulu, Finland, in 2018 (N = 61,848). Use of outpatient health care services (public, private and occupational health care) among men and women was analysed with negative binomial regression models, and use of inpatient health care with logistic regression models, using two occupational classifications: occupational group (1-digit level) and more detailed occupation (2-digit level). Adjusted covariates were age, education, income, marital status, special reimbursement entitlements for medicines, and sickness absence.

Results

Examined at the level of larger occupational groups, the use of outpatient and inpatient health care was less common than average among managers, professionals and skilled agricultural, forestry and fishery workers; in women also among craft and related trades workers. Controlling for covariates explained only part of the differences, more among women than among men. Analysed at the level of more detailed occupations, the adjusted use of outpatient and inpatient care was more common among health associate professionals and stationary plant and machine operators, both among men and women. Furthermore, the use of outpatient care was common among male personal care workers, protective service workers and metal, machinery and related trades workers as well as among labourers in mining, construction, manufacturing and transport, and female customer services clerks and sales workers.

Conclusion

The use of health care services differs by occupation, and the differences are not fully explained by sociodemographic factors and health status. High occupational risks, attitudes and knowledge may explain the more frequent use of health services. Furthermore, explanations may be sought from lack of access to occupational health care or healthier working conditions and behavior.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-022-07970-y.

Manual occupations with high all-cause mortality: The contribution of socioeconomic and occupational characteristics

AIMS

Most high mortality-risk occupations are manual occupations. We examined to what extent high mortality of such occupations could be explained by education, income, unemployment or industry and whether there were differences in these effects among different manual occupations.

METHODS

We used longitudinal individual-level register-based data, the study population consisting of employees aged 30-64 at the end of the year 2000 with the follow-up period 2001-2015. We used Cox proportional hazard regression models in 31 male and 11 female occupations with high mortality.

RESULTS

There were considerable differences between manual occupations in how much adjusting for education, income, unemployment and industry explained the excess mortality. The variation was especially large among men: controlling for these variables explained over 50% of the excess mortality in 23 occupations. However, in some occupations the excess mortality even increased in relation to unadjusted mortality. Among women, these variables explained a varying proportion of the excess mortality in every occupation. After adjustment of all variables, mortality was no more statistically significantly higher than average in 14 occupations among men and 2 occupations among women.

CONCLUSIONS

The high mortality in manual occupations was mainly explained by education, income, unemployment and industry. However, the degree of explanation varied widely between occupations, and considerable variation in mortality existed between manual occupations after controlling for these variables. More research is needed on other determinants of mortality in specific high-risk occupations.

Social Inequalities in Cancer Survival in Belgium: A Population-Based Cohort Study

BACKGROUND

Socioeconomic status (SES) is an important factor in cancer survival; however, results are heterogeneous and linked to characteristics of the study population and health care system. This population-based cohort study evaluates the association between individual-level socioeconomic and demographic factors and cancer survival for the first time in Belgium.

METHODS

From the Belgian Cancer Registry, we identified 109,591 patients diagnosed between 2006 and 2013 with one of eight common cancer types. Information on treatment, socioeconomic parameters, and vital status were retrieved from multiple data sources and linked using a unique personal identification number. The outcome was 5-year observed survival. Associations between survival and socioeconomic and demographic factors were assessed using multivariable Cox proportional-hazard regression models.

RESULTS

Lower income, unemployment, and living alone were all associated with worse cancer survival. These associations were most pronounced for certain lifestyle-related cancer types (e.g., head and neck cancers) and those with good to moderate prognosis (e.g., colorectal and female breast cancer).

CONCLUSIONS

These results indicate that, despite a comprehensive and nationwide health insurance program in which equity in rights and access to health care are pursued, SES is associated with disparities in cancer survival in Belgium.

IMPACT

This population-based study with individual-level socioeconomic information of more than 100,000 patients with cancer identifies patient groups that may be at highest risk for socioeconomic disparities in cancer survival. Reasons behind the observed disparities are multiple and complex and should be further examined. Health policy interventions should consider the observed deprivation gap to plan targeted actions.