Hence, for a clearer picture, further evaluation of differences between occupational groups regarding lifestyle-associated health risks for disease occurrence in high-powered cohorts are needed. The authors pointed at variations in underlying lifestyle related factors to possibly influence the variation in diabetes incidence between occupational groups, however had no data to study this. A recent Swedish study reported large differences in diabetes type 2 incidence between the 30 most common occupations. For instance, previous reports have indicated variations in overweight, smoking as well as occupational and leisure time physical activity between a larger range of occupational groups, with conflicting results of differences in cardiorespiratory fitness. The sub-categorisation into white- and blue-collar occupations may mislead as these are heterogeneous groups of occupations with a diversity of work situations that could have an effect on health outcomes. Moreover, risk factors such as smoking, obesity and hypertension are commonly prevalent in blue-collar occupations, at the same time as social status and benefits are lower in blue collar occupations. For example, white-collar occupations are reported to sit more at work and be more physically active in leisure time, while blue-collar occupations have a higher total amount of daily physical activity. Physical activity, other lifestyle habits, physiological characteristic and social factors explains a large part of the variation between different occupational groups. Recent research have implied lower risk of cardiovascular disease and mortality in white collar occupations compared to blue collar occupations. Future health interventions should target the occupational groups identified with the highest risk for effective disease prevention.
There were large differences in health risk indicators across occupational groups, mainly between high-skilled white-collar occupations and the other occupations, with important variations also between major and sub-major occupational groups. Compared to high-skilled white-collar workers, low-skilled white-collar workers had similar OR (2.00 1.88–2.13) as high-skilled blue-collar workers (1.98 1.86–2.12), with low-skilled blue-collar workers having the highest clustered risk (2.32 2.17–2.48).
For clustering of health risk indicators, blue-collar workers had significantly higher clustering of health risks (OR: 1.80 95% CI 1.71–1.90) compared to white-collar workers (reference). The greatest variation in OR across sub-major and major occupational groups were seen for daily smoking (OR = 0.68 to OR = 5.12), physically demanding work (OR = 0.55 to OR = 45.74) and high sitting at work (OR = 0.04 to OR = 1.86). These were further dichotomized (yes/no) and as clustering of risk indicators (≥3 vs. Seven health risk indicators were self-reported: exercise, physical work situation, sitting at work and leisure, smoking, diet, and perceived health, whereas cardiorespiratory fitness, BMI and blood pressure were measured. These were analysed separately, as white- and blue-collar occupations and as low- and high-skilled occupations.
Occupation was defined by the Swedish Standard Classification of Occupation, and divided into nine major and additionally eight sub-major groups. MethodsĪ total of 72,855 participants (41% women) participating in an occupational health service screening in 2014–2019 were included.
Identify and compare health risk indicators for common chronic diseases between different occupational groups.
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