Spatial patterns and factors associated with the incidence of tuberculosis-diabetes in Brazil.

OBJECTIVES: The aim of this study was to analyse the spatial patterns and factors associated with the incidence of tuberculosis-diabetes (TB-DM) in Brazil, from 2001 to 2019. STUDY DESIGN: Ecological study. METHODS: Brazilian municipalities were used as the units of analysis. The local empirical Bayesian rate and the spatial autocorrelation test were calculated. Moran and Getis-Ord Gi∗ were used to identify spatial clusters, and spatially weighted regression was conducted. RESULTS: In total, 75,021 new cases of TB associated with DM were reported in Brazil during the study period. Most Brazilian municipalities had an average TB-DM incidence of 1.0-2.0/100,000 inhabitants. The regression showed that the Gini index (ß = 0.85) and family health strategy coverage (ß = -0.26) were the two indicators that had the most influence on TB-DM incidence in Brazil. CONCLUSIONS: This study identified spatial clusters of TB-DM in Brazil. The results also indicated that social inequalities played a key role in the incidence of TB.

Spatial dynamics of the COVID-19 pandemic in Brazil.

The objective of this study was to analyse the dynamics of spatial dispersion of the coronavirus disease 2019 (COVID-19) in Brazil by correlating them to socioeconomic indicators. This is an ecological study of COVID-19 cases and deaths between 26 February and 31 July 2020. All Brazilian counties were used as units of analysis. The incidence, mortality, Bayesian incidence and mortality rates, global and local Moran indices were calculated. A geographic weighted regression analysis was conducted to assess the relationship between incidence and mortality due to COVID-19 and socioeconomic indicators (independent variables). There were confirmed 2 662 485 cases of COVID-19 reported in Brazil from February to July 2020 with higher rates of incidence in the north and northeast. The Moran global index of incidence rate (0.50, P = 0.01) and mortality (0.45 with P = 0.01) indicate a positive spatial autocorrelation with high standards in the north, northeast and in the largest urban centres between cities in the southeast region. In the same period, there were 92 475 deaths from COVID-19, with higher mortality rates in the northern states of Brazil, mainly Amazonas, Pará and Amapá. The results show that there is a geospatial correlation of COVID-19 in large urban centres and regions with the lowest human development index in the country. In the geographic weighted regression, it was possible to identify that the percentage of people living in residences with density higher than 2 per dormitory, the municipality human development index (MHDI) and the social vulnerability index were the indicators that most contributed to explaining incidence, social development index and the municipality human development index contributed the most to the mortality model. We hope that the findings will contribute to reorienting public health responses to combat COVID-19 in Brazil, the new epicentre of the disease in South America, as well as in other countries that have similar epidemiological and health characteristics to those in Brazil.

Spatiotemporal trends of Chagas disease-related mortality in the Northeast of Brazil, 2007-2017.

Chagas disease (CD) is a neglected disease and endemic in Brazil. In the Brazilian Northeast Region, it affects millions of people. Therefore, it is necessary to identify the spatiotemporal trends of CD mortality in the Northeast of Brazil. This ecological study was designed, in which the unit of analysis was the municipality of the Brazilian northeast. The data source was the Information System of Mortality. It was calculated relative risk from socioeconomic characteristics. Mortality rates were smoothed by the Local Empirical Bayes method. Spatial dependency was analysed by the Global and Local Moran Index. Scan spatial statistics were also used. A total of 11 287 deaths by CD were notified in the study. An expressive parcel of this number was observed among 70-year-olds or more (n = 4381; 38.8%), no schooling (n = 4381; 38.8%), mixed-race (n = 4381; 62.3%), male (n = 6875; 60.9%). It was observed positive spatial autocorrelation, mostly in municipalities of the state of Bahia, Piauí (with high-high clusters), and Maranhão (with low-low clusters). The spatial scan statistics has presented a risk of mortality in 24 purely spatial clusters (P < 0.05). The study has identified the spatial pattern of CD mortality mostly in Bahia and Piauí, highlighting priority areas in planning and control strategies of the health services.

Mortality and survival of COVID-19.

This study aims to identify the risk factors associated with mortality and survival of COVID-19 cases in a state of the Brazilian Northeast. It is a historical cohort with a secondary database of 2070 people that presented flu-like symptoms, sought health assistance in the state and tested positive to COVID-19 until 14 April 2020, only moderate and severe cases were hospitalised. The main outcome was death as a binary variable (yes/no). It also investigated the main factors related to mortality and survival of the disease. Time since the beginning of symptoms until death/end of the survey (14 April 2020) was the time variable of this study. Mortality was analysed by robust Poisson regression, and survival by Kaplan-Meier and Cox regression. From the 2070 people that tested positive to COVID-19, 131 (6.3%) died and 1939 (93.7%) survived, the overall survival probability was 87.7% from the 24th day of infection. Mortality was enhanced by the variables: elderly (HR 3.6; 95% CI 2.3-5.8; P < 0.001), neurological diseases (HR 3.9; 95% CI 1.9-7.8; P < 0.001), pneumopathies (HR 2.6; 95% CI 1.4-4.7; P < 0.001) and cardiovascular diseases (HR 8.9; 95% CI 5.4-14.5; P < 0.001). In conclusion, mortality by COVID-19 in Ceará is similar to countries with a large number of cases of the disease, although deaths occur later. Elderly people and comorbidities presented a greater risk of death.