This study aimed to analyse the trend and spatial–temporal clusters of risk of transmission of COVID-19 in northeastern Brazil. We conducted an ecological study using spatial and temporal trend analysis. All confirmed cases of COVID-19 in the Northeast region of Brazil were included, from 7 March to 22 May 2020. We used the segmented log-linear regression model to assess time trends, and the local empirical Bayesian estimator, the global and local Moran indexes for spatial analysis. The prospective space–time scan statistic was performed using the Poisson probability distribution model. There were 113 951 confirmed cases of COVID-19. The average incidence rate was 199.73 cases/100 000 inhabitants. We observed an increasing trend in the incidence rate in all states. Spatial autocorrelation was reported in metropolitan areas, and 178 municipalities were considered a priority, especially in the states of Ceará and Maranhão. We identified 11 spatiotemporal clusters of COVID-19 cases; the primary cluster included 70 municipalities from Ceará state. COVID-19 epidemic is increasing rapidly throughout the Northeast region of Brazil, with dispersion towards countryside. It was identified high risk clusters for COVID-19, especially in the coastal side.

Tuberculosis is an infectious disease caused by Mycobacterium tuberculosis. It is a disease known worldwide for its vulnerability factors, magnitude and mortality. The objective of the study was to analyze the spatial and temporal dynamics of TB in the area of social inequality in northeast Brazil between the years 2001 and 2016. An ecological time series study with the use of spatial analysis techniques was carried out from 2001 to 2016. The units of analysis were the 75 municipalities in the state of Sergipe. Data from the Notification of Injury Information System were used. For the construction of the maps, the cartographic base of the state of Sergipe, obtained at the Instituto Brasileiro de Geografia e Estatística, was used. Georeferenced data were analysed using TerraView 4.2.2 software (Instituto Nacional de Pesquisas Espaciais) and QGis 2.18.2 (Open Source Geospatial Foundation). Spatial analyses included the empirical Bayesian model and the global and local Moran indices. The time trend analyses were performed by the software Joinpoint Regression, Version 4.5.0.1, with the variables of sex, age, cure and abandonment. There was an increasing trend of tuberculosis cases in patients under 20 years old and 20–39 years old, especially in males. Cured cases showed a decreasing trend, and cases of treatment withdrawal were stationary. A spatial dependence was observed in almost all analysed territories but with different concentrations. Significant spatial correlations with the formation of clusters in the southeast and northeast of the state were observed. The probability of illness among municipalities was determined not to occur in a random way. The identification of risk areas and priority groups can help health planning by refining the focus of attention to tuberculosis control. Understanding the epidemiological, spatial and temporal dynamics of tuberculosis can allow for improved targeting of strategies for disease prevention and control.

Dengue is a global public health problem. The Dengue Virus (DENV) serotypes are transmitted by an Aedes aegypti mosquito. Vector control is among the primary methods to prevent the disease, especially in tropical countries. This study aimed to analyze the spatial distribution of dengue and its relationship with social inequalities using spatial modelling. An ecological study with temporal and spatial analysis was conducted in the state of Sergipe, Northeast Brazil, over a period of 18 years. Spatial modelling was used to determine the influence of space on dengue incidence and social inequalities. The epidemic rates in 2008, 2012, and 2015 were identified. Spatial modelling explained 40% of the influence of social inequalities on dengue incidence in the state. The main social inequalities related to the occurrence of dengue were the percentage of people living in extreme poverty and inadequate sanitation. The epidemic situation even increased the risk of dengue in the population of the state of Sergipe. These results demonstrate the potential of spatial modelling in determining the factors associated with dengue epidemics and are useful in planning the intersectoral public health policies.

Introduction: Coronavirus disease 2019 (COVID-19) has become a global public health emergency with lethality ranging from 1% to 5%. This study aimed to identify active high-risk transmission clusters of COVID-19 in Sergipe. Methods: We performed a prospective space-time analysis using confirmed cases of COVID-19 during the first 7 weeks of the outbreak in Sergipe. Results: The prospective space-time statistic detected "active" and emerging spatio-temporal clusters comprising six municipalities in the south-central region of the state. Conclusions: The Geographic Information System (GIS) associated with spatio-temporal scan statistics can provide timely support for surveillance and assist in decision-making.

Brazil ranks second in the number of confirmed cases of COVID-19 worldwide. In spite of this, coping measures differ throughout the national territory, as does the disease's impact on the population. This cross-sectional observational study, with 59 695 cases of COVID-19 registered in the state of Alagoas between March and August 2020, analysed clinical-epidemiological variables, incidence rate, mortality rate, case fatality rate (CFR) and the social indicators municipal human development index (MHDI) and social vulnerability index (SVI). Moran statistics and regression models were applied. Logistic regression analysis was applied to determine the predictors of death. The incidence rate was 1788.7/100 000 inhabitants; mortality rate was 48.0/100 000 and CFR was 2.7%. The highest incidence rates were observed in municipalities with better human development (overall MHDI (I = 0.1668; p = 0.002), education MHDI (I = 0.1649; p = 0.002) and income MHDI (I = 0.1880; p = 0.005)) and higher social vulnerability (overall SVI (I = 0.0599; p = 0.033)). CFR was associated with higher social vulnerability (SVI human capital (I = 0.0858; p = 0.004) and SVI urban infrastructure (I = 0.0985; p = 0.040)). Of the analysed cases, 55.4% were female; 2/3 were Black or Brown and the median age was 41 years. Among deaths, most were male (919; 57.4%) and elderly (1171; 73.1%). The predictors of death were male sex, advanced age and the presence of comorbidities. In Alagoas, Brazil, the disease has undergone a process of interiorisation and caused more deaths in poorer municipalities. The presence of comorbidities and advanced age were predictors of death.

The process of population aging is a worldwide reality becoming a global public health challenge. Although population aging is especially noticeable in more developed regions, there has also been a significant advance in the quantity of elderly people in areas with unfavourable socioeconomic indicators, and a rapid growth in countries with a low level of economic development. This article presents an analysis based on spatial autocorrelation of the relationship between life expectancy and social determinants in a north-eastern region of Brazil. An ecological study was conducted using the secondary data of social, demographic, and health indicators of elderly people collected in the Brazilian Demographic Census of the 75 municipalities of the state of Sergipe. Spatial autocorrelation was evaluated using the Moran global index and the local indicators of space association. Multiple linear regression models were used to identify the relationship between life expectancy and social determinants. The South-eastern region of the state presented clusters with all indicators pointing to acceptable lifestyles, whereas the municipalities of the north-western and far-eastern regions were characterized by values demonstrating precarious living conditions. The high dependency ratio, illiteracy rate, and unemployment rate among elderly people had a negative impact on life expectancy. The evidence confirms that there is an autocorrelation between social determinants and life expectancy, indicating that the worse the social, economic, and health indicators are, the lower the life expectancy. This finding indicates the need to redirect public policies and formulate strategies aimed at reducing social and health inequalities.