Incidence of excess body weight and annual weight gain in women and men: Results from the ELSA-Brasil cohort.

OBJECTIVE: To estimate annual weight gain and the incidence of overweight and obesity, stratified according to gender and socioeconomic factors. METHODS: From the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), we included 13 625 women and men aged 35-74 (2008-2010) who attended a follow-up visit after a mean 3.8-years. Standardized questionnaires were used to record sociodemographic data, and height and weight were measured on all participants during in-person visits at research centers. The incidence rate to overweight was calculated among those not having excess weight at baseline, and incident obesity among those not having this condition at baseline. We evaluated the incidence of overweight and obesity in men and women, adjusted by age, through Poisson regression with robust variance. Large annual weight gain by gender was being defined as ≥90th percentile in the cohort. RESULTS: A global incidence of 7.7% for overweight and 10.6% for obesity was observed, with higher levels seen among black woman (28.5%), young men (21.1%) and woman with low educational level (35.0%). The proportions of overweight and obesity increased with age at both time points, more commonly among those with the lowest levels of per capita income and fewer years of schooling. Large annual weight gain was greater among participants with an intermediate level of education and those who self-identified as black. CONCLUSIONS: A high overall risk of becoming overweight/obese was found, especially among women. The roles of race and education level are fundamental to understanding the effects produced by social inequalities in rates of excess weight.

Brazilian Longitudinal Study of Adult Health (ELSA-Brasil): socio-occupational class as an effect modifier for the relationship between adiposity measures and self-rated health.

BACKGROUND: Little is known about the role of social class in the association between adiposity measures and self-rated health, and several studies have evaluated its influence as a confounder. The aim of the study is to investigate whether social class is an effect modifier in the association between adiposity measures and self-rated health in participants in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). METHOD: Cross-sectional design, including 6453 men and 7686 women. Body mass index (kg/m2) and waist circumference (cms) were assessed. Self-rated health was categorized as good, fair and poor. Socio-occupational class was based on the participants' occupation, education and per capita income. Multicovariate ordinal logistic model was used to evaluate the association between adiposity measures and self-rated health. RESULTS: For women, the low and medium socio-occupational class effects were higher for those with waist circumference between 80 and 88 cm or overweight. For men, the low and medium socio-occupational class effects were higher for those with adequate waist circumference or normal body mass index. CONCLUSIONS: Social class is an effect modifier in the association between body mass index or waist circumference and self-rated health.

Comparing machine learning algorithms for multimorbidity prediction: An example from the Elsa-Brasil study.

BACKGROUND: Multimorbidity is a worldwide concern related to greater disability, worse quality of life, and mortality. The early prediction is crucial for preventive strategies design and integrative medical practice. However, knowledge about how to predict multimorbidity is limited, possibly due to the complexity involved in predicting multiple chronic diseases. METHODS: In this study, we present the use of a machine learning approach to build cost-effective multimorbidity prediction models. Based on predictors easily obtainable in clinical practice (sociodemographic, clinical, family disease history and lifestyle), we build and compared the performance of seven multilabel classifiers (multivariate random forest, and classifier chain, binary relevance and binary dependence, with random forest and support vector machine as base classifiers), using a sample of 15105 participants from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). We developed a web application for the building and use of prediction models. RESULTS: Classifier chain with random forest as base classifier performed better (accuracy = 0.34, subset accuracy = 0.15, and Hamming Loss = 0.16). For different feature sets, random forest based classifiers outperformed those based on support vector machine. BMI, blood pressure, sex, and age were the features most relevant to multimorbidity prediction. CONCLUSIONS: Our results support the choice of random forest based classifiers for multimorbidity prediction.

Lifetime night work exposure and the risk of type 2 diabetes: results from the longitudinal study of adult health (ELSA-Brasil).

The aim of this research project was to test the effects of lifetime night work exposure on type 2 diabetes (T2DM) risk. The Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) is a prospective cohort study of chronic conditions, such as diabetes and cardiovascular disease. The participants (N = 15105) were recruited (2008-2010) at five public universities and one research institute in six cities in Brazil. Participants from the first wave (2008-2010) were followed up for a mean of 3.8 years. Current analyses comprise 4671 women and 3965 men. Hazard ratios (HR) were estimated using Cox regression models. Crude T2DM incidence rates were 2.26 and 1.44 per 100 person-years, respectively, for women and men who reported ≥ 10 years' working nights. In women, ≥ 10 years of night work was associated with a higher risk of T2DM (HR 1.46 [95% CI: 1.03; 2.08]), after adjusting for age, education, work hours, and BMI. The additional adjustment for physical activity attenuated the association (HR 1.36 [95% CI: 0.94; 1.96]). In men, the results were not statistically significant (HR = 0.65 [95% CI: 0.40; 1.07]). The findings indicate the effects of lifetime night work on T2DM incidence seem to be greater among women than men.

Combined Use of Job Stress Models and the Incidence of Glycemic Alterations (Prediabetes and Diabetes): Results from ELSA-Brasil Study.

Evidence of psychosocial stress at work as a risk factor for diabetes and prediabetes is restricted. OBJECTIVES: Analyze the independent and combined association of the models, demand-control and social support (DC-SS) and the effort-reward imbalance and overcommitment (ERI-OC), and the incidence of glycemic alterations (prediabetes and diabetes). METHODS: A prospective study was carried out with data from 7503 active workers from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) study in the period 2008-2014. Work stress was measured by two stress models. Glycemic levels were evaluated by glycated hemoglobin (HbA1c) in two moments and classified in four groups: normal, maintenance of prediabetes, incident prediabetes, and incident diabetes. Multinomial logistic regression was analyzed with 5% significance levels stratified by sex, and multiplicative interactions were investigated. RESULTS: Work stress and glycemic alterations were more frequent in women. Psychosocial stress at work was shown to be associated to the risk of prediabetes and diabetes only among women. For women, the combination of models enlarged the magnitude of the association: prediabetes (DC-ERI = OR 1.51, 95% CI 1.15-1.99) and diabetes (DC-ERI = OR 2.10, 95% CI 1.20-3.65). Highly-educated women exposed to ERI-OC were four times more likely to have diabetes. CONCLUSION: Both models may contribute to explaining the psychosocial stress load according to each pattern of glycemic alteration among women.

The association between the neighbourhood social environment and obesity in Brazil: a cross-sectional analysis of the ELSA-Brasil study.

OBJECTIVE: To investigate the association between the neighbourhood social environment, including social cohesion, perceived neighbourhood safety, perceived neighbourhood violence, and obesity in Brazil. DESIGN: Cross-sectional study. SETTING: 6 state capitals in Brazil (Salvador, Vitoria, Belo Horizonte, Porto Alegre, Sao Paulo and Rio de Janeiro) PARTICIPANTS: Current or former employees of five federal universities and one research centre in each of the six Brazilian state capitals who were participants of the baseline wave (2008-2010) of the Brazilian Longitudinal Study of Adult Health (n=11 456; 56% women; 56% White, 28% Brown, and 16% Black). PRIMARY OUTCOME MEASURE: Obesity, based on measured weight and height, and defined as having a body mass index ≥30 kg/m2. RESULTS: No associations were found between the neighbourhood social environment and obesity among men. In multilevel logistic regression models adjusted for age, education, skin colour, state of residence, and individual-level social cohesion and perceived violence scores, respectively, women living in the least socially cohesive neighbourhoods and in those perceived as most violent had higher odds of obesity compared with their counterparts (OR=1.25, 95% CI=1.02-1.53; OR=1.28, 95% CI=1.04-1.56, respectively). When stratified by neighbourhood socioeconomic status (SES)-defined based on number of people per household, proportion of children 0-4 years, median income and per cent of white residents at the neighbourhood level-results for social cohesion and for violence remained only for women residing in high SES and low SES neighbourhoods, respectively. CONCLUSIONS: In this civil-servant sample in six large cities in Brazil, the neighbourhood social environment was associated with obesity among women, but not men. Neighbourhood-level interventions to increase social cohesion and reduce violence may help in the prevention of obesity among women in Brazil.

Job strain and binge eating among Brazilian workers participating in the ELSA-Brasil study: does BMI matter?

OBJECTIVE: To assess the association between job strain and binge eating as well as the effect-modifying influence of body mass index (BMI) on this association. METHODS: A total of 11,951 active civil servants from the multicenter Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) was included in this cross-sectional analysis. Job strain was assessed using the Demand-Control-Support Questionnaire. Binge eating was defined as eating a large amount of food with a sense of lack of control over what and how much is eaten in less than 2 hours at least twice a week. Multiple logistic regression was used to determine the association between binge eating and job strain as well as its interaction with BMI. RESULTS: After adjustment, and using low-strain job as the reference category, binge eating was associated with high-strain job (high demand/low control: odds ratio [OR]=1.58, 95% confidence interval [CI] 1.26-1.98), active job (high demand/high control: OR=1.35, 95% CI 1.07-1.70), and passive job (low demand/low control: OR=1.24, 95% CI 1.01-1.53). Psychological job demands were positively associated with binge eating (OR=1.04, 95% CI 1.01-1.07), while greater job control and social support at work were each inversely associated with binge eating (OR=0.95, 95% CI 0.92-0.97 and OR=0.96, 95% CI 0.94-0.98, respectively). BMI modified the association between job strain and binge eating: Heavier psychological job demands were associated with higher odds of binge eating among obese participants, while a stronger inverse association between job control and binge eating was seen among slimmer participants. CONCLUSIONS: Job strain increases the odds of binge eating and this association is modified by BMI.

Weight gain and body mass index following change from daytime to night shift - a panel study with nursing professionals.

We analyzed the association between change in work schedule and modifications in nutritional status. We performed a panel study with nursing professionals based on two surveys seven year apart (n = 372). Groups with no change in work schedule, change from daytime to night shift, and change from night to daytime shift were analyzed. Outcomes were weight gain and body mass index (BMI) category increase. Participants who changed from daytime to night shift showed about twice increase in odds of more than 5 kg weight gain and BMI category increase. Changing from daytime to night work seems to influence weight and BMI.