OBJETIVO: Apresentar a metodologia de estratificação das arboviroses urbanas (dengue, zika e chikungunya) pelo índice de receptividade territorial, instrumento de vigilância e controle dessas doenças que considera a heterogeneidade territorial intramunicipal. MÉTODOS: Estudo ecológico que utiliza como unidade de análise as áreas de abrangência dos centros de saúde de Belo Horizonte (MG). Para a construção do índice de receptividade territorial foram selecionados indicadores de determinação socioambiental das arboviroses urbanas a fim de integrar à análise de componentes principais. As componentes resultantes foram ponderadas por análise de processos hierárquicos (AHP) e agregadas por meio de álgebra de mapas. RESULTADOS: O índice de receptividade territorial evidenciou grande heterogeneidade das condições de infraestrutura urbana. As áreas classificadas como alta e muito alta receptividade correspondem a aproximadamente 33% da área ocupada e se concentram sobretudo nas regiões de planejamento administrativo Leste, Nordeste, Norte, Oeste e Barreiro, principalmente em áreas limítrofes do município. Quando sobrepostas à densidade de casos de dengue e de ovos de Aedes em 2016, a estratificação pelo índice de receptividade territorial às arboviroses urbanas demonstra que áreas de muito alta receptividade apresentam uma densidade de casos, bem como de ovos de Aedes superior àquela observada nas demais áreas da cidade, o que corresponde a um percentual bastante reduzido do território municipal (13,5%). CONCLUSÕES: As análises indicam a necessidade do desenvolvimento de ações de vigilância e controle adequadas para cada contexto, superando, assim, a lógica de alocação homogênea em todo o território.

Background: Urban arbovirus transmission is spatially and temporally heterogeneous. Estimating the risk of dengue through statistical models that consider simultaneous variability in space and time provides more realistic estimates of transmission dynamics, facilitating the identification of priority areas for intervention focused on surveillance and control. These models also enable predictions to support timely interventions for arboviruses like dengue, chikungunya, and Zika. Methodology/principal findings: We analyzed dengue case reports by epidemiological week and neighborhood in Natal, RN from 2015 to 2018. Temporal conditional autoregressive models were fitted using the Integrated Nested Laplace Approximation method. The predictors included a set of entomological, climatic and sociosanitary indicators with temporal lags, along with structures of temporal and spatial dependence. Additionally, we used an offset term to represent the expected number of dengue cases per neighborhood at each epidemiological week, under the hypothesis of homogeneity in the occurrence of cases across the municipality. We forecasted dengue case counts for the subsequent four weeks, addressing both zero occurrences and fluctuations during non-zero periods. Weekly risk dynamics were visualized through predictive maps, enabling the timely identification of neighborhoods with high and persistent dengue risk, that is, areas consistently exhibiting a high number of dengue cases that remained concentrated in the same location for several weeks. The optimal model revealed a significant rise in dengue occurrence probability during the observation week, associated with increased cases in the previous week, the Aedes egg positivity index from the prior four weeks, and the mean daytime temperature 6–8 weeks earlier. Dengue risk also rose with a one-standard-deviation increase in the density of the impoverished population per occupied area and the mean Aedes egg density index from the preceding 3–5 weeks. Conclusions/significance: The proposed Bayesian space-time analysis can contribute to the operational control of dengue and Aedes aegypti by identifying priority areas and forecasting dengue cases for the next four weeks. It also quantifies the effects of entomological, sociosanitary, climatic and demographic indicators on both the likelihood of dengue occurrence and the intensity of outbreaks.

Many techniques have been proposed to model space-varying observation processes with a nonstationary spatial covariance structure and/or anisotropy, usually on a geostatistical framework. Nevertheless, there is an increasing interest in point process applications, and methodologies that take nonstationarity into account are welcomed. In this sense, this work proposes an extension of a class of spatial Cox process using spatial deformation. The proposed method enables the deformation behavior to be data-driven, through a multivariate latent Gaussian process. Inference leads to intractable posterior distributions that are approximated via MCMC. The convergence of algorithms based on the Metropolis–Hastings steps proved to be slow, and the computational efficiency of the Bayesian updating scheme was improved by adopting Hamiltonian Monte Carlo (HMC) methods. Our proposal was also compared against an alternative anisotropic formulation. Studies based on synthetic data provided empirical evidence of the benefit brought by the adoption of nonstationarity through our anisotropic structure. A real data application was conducted on the spatial spread of the Spodoptera frugiperda pest in a corn-producing agricultural area in southern Brazil. Once again, the proposed method demonstrated its benefit over alternatives.

Este artigo apresenta as tábuas de vida do mercado de seguros privados brasileiro, BR-EMS 2021. Os dados obtidos de 23 grupos seguradores ao longo de 15 anos, totalizando 3,5 bilhões de registros, foram corrigidos por meio de rede neural com duas camadas ocultas. Usando a inferência bayesiana para estimar os parâmetros sob a lei de mortalidade Heligman-Pollard, as tábuas obtidas mostram que a população segurada apresenta probabilidades de morte mais baixas do que a população brasileira em geral e até mesmo em relação a populações nacionais de países desenvolvidos, como os EUA. Além da esperada diferença de gênero nas taxas de mortalidade, há uma clara distância entre as probabilidades de morte dos grupos de cobertura de risco e cobertura de sobrevivência. Da mesma forma, é demonstrado que as tábuas regionais da população segurada não apresentam as discrepâncias regionais conhecidas no Brasil, indicando que fazer parte da população selecionada de segurados está associado a um fator de proteção mais eficaz do que outros fatores, como a região geográfica de residência.

Background Air pollution by fine particulate matter with an aerodynamic diameter ≤ 2.5 μm (PM2.5) is the main environmental risk factor associated with diseases of the circulatory system (DCS), ischemic heart disease (IHD), and cerebrovascular diseases (CBVD). Objective To estimate mortality rates from DCS, IHD, and CBVD (2000-2019) among residents of the 164 neighborhoods of Rio de Janeiro, according to PM2.5 levels. Methods This retrospective ecological study used georeferenced satellite data classified into three PM2.5 levels and mortality records from the Department of Information and Informatics of the Unified Health System for DCS, IHD, and CBVD among individuals of both sexes aged ≥ 20 years from 2000 to 2019. Age-adjusted mortality rates per 1,000 inhabitants were calculated, and comparative statistical analyses were performed by sex, PM2.5 level, and age group (5% significance). Results Approximately 91% of the 4.7 million residents (≥ 20 years) live in areas with high or extreme PM2.5 pollution. Deaths occurred up to 3.4 years earlier among men living in highly polluted areas compared with those in moderately polluted areas. The highest DCS mortality rates were observed in neighborhoods with high and extreme pollution (female = 3.9 ± 1.7; 95% CI = 3.5-4.2; male = 4.6 ± 2.1; 95% CI = 4.1-4.9), particularly in individuals aged ≥ 70 years. Significant associations were found between mortality rates and pollution levels for DCS (p = 0.019), IHD (p = 0.025), and CBVD (p = 0.002) in the 50-69-year age group when comparing moderately and extremely polluted areas. Intermediate/high social vulnerability was identified in 71% of neighborhoods, with an increasing socioenvironmental gradient linking higher vulnerability to higher PM2.5 concentrations (R = 0.354; p = 0.001). Conclusion Mean PM2.5 concentrations in the neighborhoods of Rio de Janeiro exceeded the World Health Organization’s recommended standard by a factor of four. Mortality from DCS is significantly higher and occurs earlier in areas with high or extreme levels of pollution.