Background: Ultra-processed foods are industrial formulations made from food extracts or constituents with little or no intact food and often containing additives that confer hyper-palatability. The consumption of these products increases the risk of chronic non-communicable diseases. Stressed people may engage in unhealthy eating as a way to cope. This study aimed to verify whether ultra-processed food consumption was associated with perceived stress levels in industrial and retail workers from Vitoria da Conquista, Brazil. Methods: This was a cross-sectional study carried out between July 2017 and August 2018. During the study period, 1270 participants completed a survey administered by an interviewer. Stress levels were assessed using the Perceived Stress Scale. Information regarding weekly ultra-processed food consumption was collected. Ultra-processed foods were classified into four groups: sugary drinks; sugary foods; fast foods; and canned foods, frozen foods, or processed meat. The Student’s t-test or one-way analysis of variance was used to assess the differences in stress levels and ultra-processed food consumption. Ordinal regression was used to determine the association between the degrees of stress and ultra-processed food consumption levels. Results: Factors such as a young age, being unmarried, smoking, high-risk alcohol consumption, negative health perception, and high perceived stress level indicated higher rates of ultra-processed food consumption. Ordinal regression analysis showed that high stress levels were associated with increased odds of higher ultra-processed food consumption (odds ratio: 1.94; 95% CI: 1.54–2.45). Conclusions: These findings could help identify appropriate target areas for interventions aimed at mental health promotion and healthier food consumption.

Introduction As non-communicable disease (NCD) burden rises worldwide, community-based programmes are a promising strategy to bridge gaps in NCD care. The HealthRise programme sought to improve hypertension and diabetes management for underserved communities in nine sites across Brazil, India, South Africa and the USA between 2016 and 2018. This study presents findings from the programme’s endline evaluation. Methods The evaluation utilises a mixed-methods quasi-experimental design. Process indicators assess programme implementation; quantitative data examine patients’ biometric measures and qualitative data characterise programme successes and challenges. Programme impact was assessed using the percentage of patients meeting blood pressure and A1c treatment targets and tracking changes in these measures over time. Results Almost 60 000 screenings, most of them in India, resulted in 1464 new hypertension and 295 new diabetes cases across sites. In Brazil, patients exhibited statistically significant reductions in blood pressure and A1c. In Shimla, India, and in South Africa, country with the shortest implementation period, there were no differences between patients served by facilities in HealthRise areas relative to comparison areas. Among participating patients with diabetes in Hennepin and Ramsey counties and hypertension patients in Hennepin County, the percentage of HealthRise patients meeting treatment targets at endline was significantly higher relative to comparison group patients. Qualitative analysis identified linking different providers, services, communities and information systems as positive HealthRise attributes. Gaps in health system capacities and sociodemographic factors, including poverty, low levels of health education and limited access to nutritious food, are remaining challenges. Conclusions Findings from Brazil and the USA indicate that the HealthRise model has the potential to improve patient outcomes. Short implementation periods and strong emphasis on screening may have contributed to the lack of detectable differences in other sites. Community-based care cannot deliver its full potential if sociodemographic and health system barriers are not addressed in tandem.

Background: This study aimed to assess the quality of life associated with gender inequalities in formal workers and to determine the effect of sociodemographic, clinical, and behavioral factors on the quality of life (QOL). Methods: This cross-sectional study involved 1270 workers. Quality of life was measured using the EUROHIS-QOL 8-Item and assessed in terms of psychological, environmental, social, and physical domains, while demographic, socioeconomic, behavioral, and clinical variables served as explanatory variables. Analyses were performed using an ordinal logistic regression model whose significance level was 5%. Results: Of the participants, 80.2% were men, and 19.8% were women; the mean age was 34 (standard deviation: ±10) and 32 (±9) years, respectively. In all prediction scenarios, men were more likely to have a higher quality of life, especially in the physical (odds ratio: 2.16; 95% confidence interval: 1.60–2.93) and psychological (odds ratio: 2.09; 95% confidence interval: 1.51–2.91) domains. Conclusions: Men and women had significantly different levels of quality of life, and sociodemographic, clinical, and behavioral variables partially clarified these differences, which were possibly established by a socio-historical process of construction of the work role determined by gender issues.

Objective: We evaluated the cost-effectiveness of the point-of-care A1c (POC-A1c) test device vs. the traditional laboratory dosage in a primary care setting for people living with type 2 diabetes.Materials and Methods: The Markov model with a 10-year time horizon was based on data from the HealthRise project, in which a group of interventions was implemented to improve diabetes and hypertension control in the primary care network of the urban area of a Brazilian municipality. A POC-A1c device was provided to be used directly in a primary care unit, and for a period of 18 months, 288 patients were included in the point-of-care group, and 1,102 were included in the comparison group. Sensitivity analysis was performed via Monte Carlo simulation and tornado diagram.Results: The results indicated that the POC-A1c device used in the primary care unit was a cost-effective alternative, which improved access to A1c tests and resulted in an increased rate of early control of blood glucose. In the 10-year period, POC-A1c group presented a mean cost of US$10,503.48 per patient and an effectiveness of 0.35 vs. US$9,992.35 and 0.09 for the traditional laboratory test, respectively. The incremental cost was US$511.13 and the incremental effectiveness was 0.26, resulting in an incremental cost-effectiveness ratio of 1,947.10. In Monte Carlo simulation, costs and effectiveness ranged between $9,663.20–$10,683.53 and 0.33–0.37 for POC-A1c test group, and $9,288.28–$10,413.99 and 0.08–0.10 for traditional laboratory test group, at 2.5 and 97.5 percentiles. The costs for nephropathy, retinopathy, and cardiovascular disease and the probability of being hospitalized due to diabetes presented the greatest impact on the model’s result.Conclusion: This study showed that using POC-A1c devices in primary care settings is a cost-effective alternative for monitoring glycated hemoglobin A1c as a marker of blood glucose control in people living with type 2 diabetes. According to our model, the use of POC-A1c device in a healthcare unit increased the early control of type 2 diabetes and, consequently, reduced the costs of diabetes-related outcomes, in comparison with a centralized laboratory test.