Spatial analysis of hospital-related mortality due to COVID-19 among children and adolescents in Brazil

The objective was to perform a spatial analysis of the hospital mortality rate (HMR) due to severe acute respiratory syndrome (SARS) attributed to COVID-19 among children and adolescents in Brazil from 2020 to 2021. A cluster method was used to group federal units (FUs) based on HMR. In 2020, clusters with high HMRs were formed by north/northeast FUs. In 2021, there was a reduction in HMR. Clusters with higher rates remained in the N/NE region. Regional differences were observed in the HMR. The findings may reflect social inequalities and access to hospital care, especially in the under 1-year-old age group due to the severity of the disease in this group.

Spatio-temporal analysis of leptospirosis in Brazil and its relationship with flooding

Leptospirosis is a serious public health problem in Brazil, which can be observed after flooding events. Using an exploratory mixed clustering method, this ecological study analyzes whether spatial-temporal clustering patterns of leptospirosis occur in Brazil. Data from the Brazilian Unified Health System (SUS) were used to calculate the prevalence of leptospirosis between 2007 and 2017 in all counties of the country. Clustering techniques, including spatial association indicators, were used for analysis and evaluation of disease yearly spatial distribution. Based on Local Indicators of Spatial Association (LISA) with Empirical Bayesian rates detected spatial patterns of leptospirosis ranging from 0.137 (p = 0.001 in 2009) to 0.293 (p = 0.001 in 2008). Over the whole period, the rate was 0.388 (p = 0.001). The main pattern showed permanence of leptospirosis clusters in the South and emergence and permanence of such clusters in northern Brazil. The municipalities with leptospirosis cases and at least one flood occurrence registered in the Brazilian Integrated Disaster Information System were incorporated into the LISA cluster map with Empirical Bayesian rates. These counties were expected to exhibit clustering, not all did. The results of the cluster analysis suggest allocation of health resources in areas with leptospirosis clustering.

[Tuberculosis and diabetes comorbidity in Brazil, 2012-2018: exploratory spatial data analysis and statistical modelingLa comorbilidad tuberculosis-diabetes en Brasil, 2012-2018: análisis exploratorio de la distribución espacial y modelización estadística]

Objetivo.

Descrever a distribuição espacial da comorbidade tuberculose-diabetes e identificar os determinantes sociais da dupla carga no período de 2012 a 2018 no Brasil.

Métodos.

Este estudo ecológico utilizou os municípios como unidade de análise. Incluíram-se todos os casos de tuberculose notificados de 2012 a 2018 ao Sistema de Informação de Agravos de Notificação. Como determinantes, foram analisadas variáveis socioeconômicas de emprego, renda e desenvolvimento e o indicador de cobertura da atenção básica. O índice de Moran global foi calculado para verificar a existência de autocorrelação espacial da comorbidade. O índice de Moran local foi utilizado para delimitar clusters de tuberculose-diabetes: alto/alto (municípios com alta proporção de tuberculose-diabetes cujos vizinhos também apresentaram altas proporções) e baixo/baixo (municípios com proporção abaixo da média, cercados por municípios com baixas proporções).

Resultados.

Observou-se elevação na proporção de tuberculose-diabetes na maioria das regiões brasileiras. Constatou-se autocorrelação espacial da comorbidade tuberculose-diabetes e identificou-se um cluster alto-alto de tuberculose-diabetes reunindo 88 municípios, pertencentes principalmente às regiões Nordeste, Sudeste e Sul, com média de prevalência da comorbidade de 28,04%. As variáveis “percentual da população que vive em domicílios com densidade superior a duas pessoas por dormitório”, “percentual de desemprego de pessoas maiores de 18 anos” e “renda per capita” relacionaram-se à ocorrência da comorbidade.

Conclusão.

Os resultados mostraram uma distribuição não aleatória da comorbidade tuberculose-diabetes, com áreas de alto risco e variáveis explicativas de sua ocorrência. Esses achado reforçam a necessidade de operacionalizar a colaboração entre programas de tuberculose e diabetes, com vistas ao controle tanto de cada agravo isoladamente quanto da sindemia.

Autoregressive spatial modeling of possible cases of dengue, chikungunya, and Zika in the capital of Northeastern Brazil

INTRODUCTION:

Dengue, chikungunya, and Zika are a growing global health problem. This study analyzed the spatial distribution of dengue, chikungunya, and Zika cases in São Luís, Maranhão, from 2015 to 2016 and investigated the association between socio-environmental and economic factors and hotspots for mosquito proliferation.

METHODS:

This was a socio-ecological study using data from the National Information System of Notifiable Diseases. The spatial units of analysis were census tracts. The incidence rates of the combined cases of the three diseases were calculated and smoothed using empirical local Bayes estimates. The spatial autocorrelation of the smoothed incidence rate was measured using Local Moran's I and Global Moran's I. Multiple linear regression and spatial autoregressive models were fitted using the log of the smoothed disease incidence rate as the dependent variable and socio-environmental factors, demographics, and mosquito hotspots as independent variables.

RESULTS:

The findings showed a significant spatial autocorrelation of the smoothed incidence rate. The model that best fit the data was the spatial lag model, revealing a positive association between disease incidence and the proportion of households with surrounding garbage accumulation.

CONCLUSIONS:

The distribution of dengue, chikungunya, and Zika cases showed a significant spatial pattern, in which the high-risk areas for the three diseases were explained by the variable "garbage accumulated in the surrounding environment,” demonstrating the need for an intersectoral approach for vector control and prevention that goes beyond health actions.

Spatial analysis of probable cases of dengue fever, chikungunya fever and zika virus infections in Maranhao State, Brazil

Dengue fever, chikungunya fever, and zika virus infections are increasing public health problems in the world, the last two diseases having recently emerged in Brazil. This ecological study employed spatial analysis of probable cases of dengue fever, chikungunya fever, and zika virus infections reported to the National Mandatory Reporting System (SINAN) in Maranhao State from 2015 to 2016. The software GeoDa version 1.10 was used for calculating global and local Moran indices. The global Moran index identified a significant autocorrelation of incidence rates of dengue (I=0.10; p=0.009) and zika (I=0.07; p=0.03). The study found a positive spatial correlation between dengue and the population density (I=0.31; p<0.001) and a negative correlation with the Performance Index of Unified Health System (PIUHS) by basic care coverage (I=-0.08; p=0.01). Regarding chikungunya fever, there were positive spatial correlations with the population density (I=0.06; p=0.03) and the Municipal Human Development Index (MHDI) (I=0.10; p=0.002), and a negative correlation with the Gini index (I=-0.01; p<0.001) and the PIUHS by basic care coverage (I=-0.18; p<0.001). Lastly, we found positive spatial correlations between Zika virus infections and the population density (I=0.13; p=0.005) and the MHDI (I=0.12; p<0.001), as well as a negative correlation with the Gini index (I=-0.11; p<0.001) and the PIUHS by basic care coverage (I=-0.05; p=0.03). Our results suggest that several socio-demographic factors influenced the occurrence of dengue fever, chikungunya fever, and zika virus infections in Maranhao State.

Principal axes for stochastic dynamics

We introduce a general procedure for directly ascertaining how many independent stochastic sources exist in a complex system modeled through a set of coupled Langevin equations of arbitrary dimension. The procedure is based on the computation of the eigenvalues and the corresponding eigenvectors of local diffusion matrices. We demonstrate our algorithm by applying it to two examples of systems showing Hopf bifurcation. We argue that computing the eigenvectors associated to the eigenvalues of the diffusion matrix at local mesh points in the phase space enables one to define vector fields of stochastic eigendirections. In particular, the eigenvector associated to the lowest eigenvalue defines the path of minimum stochastic forcing in phase space, and a transform to a new coordinate system aligned with the eigenvectors can increase the predictability of the system.