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Aims: Fruit and vegetable consumption is essential in disease prevention. Socioeconomic differences in consumption have been observed but evidence from longitudinal studies incorporating multiple socioeconomic indicators is lacking. We examined long-term fruit and vegetable consumption trajectories and multiple socioeconomic circumstances as their determinants., Methods: We used survey data from the Helsinki Health Study (phase 1 in 2000-2002, N =8960, response rate 67%; phases 2-4 in 2007, 2012 and 2017) among initially 40- to 60-year-old employees of City of Helsinki, Finland. Fruit and vegetable consumption was measured by a food frequency questionnaire and consumption times per month were calculated. Childhood (parental education, economic difficulties), conventional (own education, occupational class, household income) and material (housing tenure, wealth, current economic difficulties) socioeconomic circumstances were included. We used group-based trajectory modelling for identifying fruit and vegetable consumption trajectories and multinomial logistic regression for examining associations between socioeconomic circumstances and the trajectories., Results: Four fruit and vegetable consumption trajectories were identified: increasing higher (12%), decreasing higher (10%), stable moderate (43%) and stable low (35% of participants). Childhood, conventional and material socioeconomic circumstances were all associated with the trajectories: belonging to a lower socioeconomic group was associated with belonging to the stable low and moderate trajectories. In multivariate models, the strongest associations were found for occupational class and household wealth., Conclusions: Disadvantageous childhood and adulthood socioeconomic circumstances were associated with lower long-term fruit and vegetable consumption. Socioeconomic circumstances should be considered in attempts to promote fruit and vegetable consumption, and people with disadvantageous circumstances need to be targeted in future interventions., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Aims: The aim was to examine whether the contribution of physical work exposures to the risk of sickness absence (SA) is different between those with and without common mental disorders (CMD). Methods: We used questionnaire data on four work exposures and CMD from 6159 participants of the Helsinki Health Study cohort with 12,458 observations from three surveys (2000-2002, 2007 and 2012). We formed combination exposures for the work exposures (hazardous exposures, physical workload, computer and shift work) with CMD. Associations with SA of different length were examined with negative binomial regression models. Results: We observed stronger associations for CMD with SA than for the individual work exposures. The strength of the associations for hazardous exposures and physical workload increased with length of SA, especially when the participant also had CMD. The strongest associations for the combined exposures were observed for SA ⩾15 days, the rate ratios being 2.63 (95% CI 2.27-3.05) among those with hazardous exposure and CMD, and 3.37 (95% CI 2.93-3.88) among those with heavy physical workload and CMD. Conclusions: Employees with hazardous exposures or physical workload combined with CMD were at the highest risk of SA compared with those without these exposures or with only one exposure.

Aims: Factors that contribute to the development of overweight are numerous and form a complex structure with many unknown interactions and associations. We aimed to explore this structure (i.e. the mutual importance or hierarchy of sociodemographic and lifestyle-related risk factors of being overweight) using a machine-learning technique called random forest (RF). The results were compared with traditional logistic regression (LR) analysis., Methods: The cross-sectional FINRISK 2007 Study included 4757 Finns (aged 25-74 years). Information on participants' lifestyle and sociodemographic characteristics were collected with questionnaires. Diet was assessed, using a validated food-frequency questionnaire. Height and weight were measured. Participants with a body mass index (BMI) ≥25 kg/m 2 were classified as overweight. R-statistical software was used to run RF analysis ('randomForest') to derive estimates for variable importance and out-of-bag error, which were compared to a LR model., Results: In total, 704 (32%) men and 1119 (44%) women had normal BMI, whereas 1502 (69%) men and 1432 (57%) women had BMI ≥25. Estimated error rates for the models were similar (RF vs. LR: 42% vs. 40% for men, 38% vs. 35% for women). Both models ranked age, education and physical activity as the most important risk factors for being overweight, but RF ranked macronutrients (carbohydrates and protein) as more important compared to LR., Conclusions: RF did not demonstrate higher power in variable selection compared to LR in our study. The features of RF are more likely to appear beneficial in settings with a larger number of predictors.