Introduction Healthy lifestyles are relevant to several diseases and to maintain individuals’ mental health. Exposure to epidemics and confinement have been consistently associated with psychological consequences, but changes on lifestyle behaviours remain under-researched. Materials and Methods An online survey was conducted among the general population living in Spain during the COVID-19 home-isolation. In addition to demographic and clinical data, participants self-reported changes in seven lifestyle domains. The Short Multidimensional Inventory Lifestyle Evaluation was developed specifically to evaluate changes during the confinement (SMILE-C). Results A total of 1254 individuals completed the survey over the first week of data collection. The internal consistency of the SMILE-C to assess lifestyles during confinement was shown (Cronbach's Alpha = 0.747). Most participants reported substantial changes on outdoor time (93.6%) and physical activity (70.2%). Moreover, about one third of subjects reported significant changes on stress management, social support, and restorative sleep. Several demographic and clinical factors were associated to lifestyle scores. In the multivariate model, those independently associated with a healthier lifestyle included substantial changes on stress management ( p < 0.001), social support ( p = 0.001) and outdoor time ( p < 0.001), amongst others. In contrast, being an essential worker ( p = 0.001), worse self-rated health ( p < 0.001), a positive screening for depression/anxiety ( p < 0.001), and substantial changes on diet/nutrition ( p < 0.001) and sleep ( p < 0.001) were all associated with poorer lifestyles. Conclusions In this study, sizable proportions of participants reported meaningful changes in lifestyle behaviours during the COVID-19 pandemic in Spain. Moreover, the SMILE-C was sensitive to detect these changes and presented good initial psychometric properties. Further follow-up studies should collect relevant data to promote healthy lifestyles in pandemic times.

Background Essential workers have been shown to present a higher prevalence of positive screenings for anxiety and depression during the COVID-19 pandemic. Individuals from countries with socioeconomic inequalities may be at increased risk for mental health disorders. Objective We aimed to assess the prevalence and predictors of depression, anxiety, and their comorbidity among essential workers in Brazil and Spain during the COVID-19 pandemic. Methods A web survey was conducted between April and May 2020 in both countries. The main outcome was a positive screening for depression only, anxiety only, or both. Lifestyle was measured using a lifestyle multidimensional scale adapted for the COVID-19 pandemic (Short Multidimensional Inventory Lifestyle Evaluation–Confinement). A multinomial logistic regression model was performed to evaluate the factors associated with depression, anxiety, and the presence of both conditions. Results From the 22,786 individuals included in the web survey, 3745 self-reported to be essential workers. Overall, 8.3% (n=311), 11.6% (n=434), and 27.4% (n=1027) presented positive screenings for depression, anxiety, and both, respectively. After adjusting for confounding factors, the multinomial model showed that an unhealthy lifestyle increased the likelihood of depression (adjusted odds ratio [AOR] 4.00, 95% CI 2.72-5.87), anxiety (AOR 2.39, 95% CI 1.80-3.20), and both anxiety and depression (AOR 8.30, 95% CI 5.90-11.7). Living in Brazil was associated with increased odds of depression (AOR 2.89, 95% CI 2.07-4.06), anxiety (AOR 2.81, 95%CI 2.11-3.74), and both conditions (AOR 5.99, 95% CI 4.53-7.91). Conclusions Interventions addressing lifestyle may be useful in dealing with symptoms of common mental disorders during the strain imposed among essential workers by the COVID-19 pandemic. Essential workers who live in middle-income countries with higher rates of inequality may face additional challenges. Ensuring equitable treatment and support may be an important challenge ahead, considering the possible syndemic effect of the social determinants of health.

The temporal and spatial evolution of malaria was described for the postfrontier phase of the Brazilian Amazon in 2003–2013. The current ecological study aimed to understand the relationship between spatial population mobility and the distribution of malaria cases. The study identified epidemiologically relevant areas using regional statistical modeling and spatial analyses that considered differential infections and types of work activities. Annual parasite incidence (API) in the region was highest in hotspots along the Amazon River and in the south and west settlement zone of Hiléia, with concentrations in environmental protection areas and açaí and Brazil nut extraction areas. The dispersal force decreased in the Central Amazon due to rapid urbanization and improved socioeconomic conditions for workers in consolidated settlement areas. The study characterized the spatial patterns of disease transmission according to the economic activity and regionalization of geographic areas, confirming that the incidence of infection by work activity and labor flow is linked to extractive activities and agricultural settlements.

No presente estudo, o DALY (anos de vida perdidos ajustados por incapacidade), indicador de estudos de carga de doença, foi estimado para o Brasil em 2008. Entre os principais resultados, observam-se maior carga de doença no Norte e Nordeste e preponderância das doenças crônicas não transmissíveis em todas as regiões do país, em particular as doenças cardiovasculares, os transtornos mentais, com destaque para a depressão, o diabetes e a doença pulmonar obstrutiva crônica. Também chama a atenção a elevada carga dos homicídios e dos acidentes de trânsito. O perfil epidemiológico apresenta-se ainda mais complexo quando se considera a carga não desprezível das doenças transmissíveis, das condições maternas, das condições perinatais e das deficiências nutricionais. As análises empreendidas ao longo do estudo possibilitaram conhecer de forma mais detalhada o status de saúde da população, evidenciando a demanda por ações transversais, que vão além de políticas específicas circunscritas à área de saúde, bem como a necessidade de ampliar o escopo de preocupação com a qualidade das informações sobre morbimortalidade no Brasil.

Different sampling strategies, analytic alternatives, and estimators have been proposed to better assess the characteristics of different hard-to-reach populations and their respective infection rates (as well as their sociodemographic characteristics, associated harms, and needs) in the context of studies based on respondent-driven sampling (RDS). Despite several methodological advances and hundreds of empirical studies implemented worldwide, some inchoate findings and methodological challenges remain. The in-depth assessment of the local structure of networks and the performance of the available estimators are particularly relevant when the target populations are sparse and highly stigmatized. In such populations, bottlenecks as well as other sources of biases (for instance, due to homophily and/or too sparse or fragmented groups of individuals) may be frequent, affecting the estimates.In the present study, data were derived from a cross-sectional, multicity RDS study, carried out in 12 Brazilian cities with transgender women (TGW). Overall, infection rates for HIV and syphilis were very high, with some variation between different cities. Notwithstanding, findings are of great concern, considering the fact that female TGW are not only very hard-to-reach but also face deeply-entrenched prejudice and have been out of the reach of most therapeutic and preventive programs and projects.We cross-compared findings adjusted using 2 estimators (the classic estimator usually known as estimator II, originally proposed by Volz and Heckathorn) and a brand new strategy to adjust data generated by RDS, partially based on Bayesian statistics, called for the sake of this paper, the RDS-B estimator. Adjusted prevalence was cross-compared with estimates generated by non-weighted analyses, using what has been called by us a naïve estimator or rough estimates.