Modelagem espacial da hanseníase no estado da Bahia, Brasil, (2001-2015) e determinantes sociais da saúde

Abstract

Resumo O trabalho analisa a distribuição espacial da hanseníase na Bahia e os determinantes sociais relacionados. Estudo ecológico com dados de hanseníase do período 2001-2015. Três indicadores epidemiológicos foram selecionados: coeficiente de detecção na população geral e em menores de 15 anos e a taxa de casos novos com grau II de incapacidade. Os indicadores foram suavizados pelo Modelo Bayesiano Empírico Local e aplicou-se estatística de Moran Global e Local. As variáveis independentes foram selecionadas a partir do Censo IBGE-2010. Regressões multivariadas foram empregadas, seguidas de regressão espacial. Observou-se distribuição heterogênea no estado, com concentração no eixo norte-oeste e região sul. Para o coeficiente de detecção geral, cinco variáveis compuseram o modelo: densidade demográfica, proporção da população urbana, renda per capita, proporção de extremamente pobres e domicílios com mais de três pessoas por dormitório. A proporção de analfabetismo compôs o modelo final para a taxa de grau II de incapacidade física. Não foram identificados determinantes da ocorrência da doença em menores de 15 anos. A modelagem utilizada contribuiu para demonstrar a heterogeneidade espacial e os determinantes sociais da doença na Bahia, colocando em evidência a complexidade do problema.

Spatial dynamics of the COVID-19 pandemic in Brazil

“…The presence of agglomerates in Manaus and the metropolitan region is linked to a social scenario in which social inequality, competitiveness and fragile relations between municipalities prevail, resulting in an insufficient network of health services and a quantitative deficiency of human resources [21, 30]. The direct relationship of social vulnerability, expressed by the SVI, which verifies population conditions in addition to economic aspects, including housing and education conditions, corroborates to explain the existence of spatial clusters in these regions, since factors that express poverty increase the risk of transmission by infectious diseases [16].…”

“…It is assumed that the influence of income per capita on the outcomes studied is related to the presence of a better network of health services, expanding the population's access to carrying out diagnostic tests in the municipalities with the highest per capita income. Corroborating this finding, GWR analysis revealed that coverage of primary health care is directly related to the incidence of COVID-19, since expanding access increases the number of diagnosed cases and reduces underreporting, contributing to increasing the coefficients incidence and mortality [ 16 ].…”

“…After this stage, the analysis of the regression residuals was performed to verify the presence of spatial dependence. Once verified, the geographically weighted regression (GWR) model was used as it considers a spatial component as an important factor in the model [ 16 ].…”

Can the municipal social deprivation index influence the time trend of the leprosy detection rate?

“…The SNI involved four variables: 1) Economic and Social Performance-Economy and Finance Index (IPESE-EF), (2) average monthly value of per capita income (RENDAPERCAPIT), (3) proportion of extremely poor (%EXTRPOBRES), and (4) number of households with density larger than three persons per room (DOM3PPDOR). These variables were selected from previous studies in which these indicators were associated with the dynamics of leprosy transmission in Bahia state 4 , 7 . After the calculation, the municipalities were classified into quartiles: low SNI (0.142 to 0.259), medium SNI (0.260 to 0.369), high SNI (0.370 to 0.479), and very high SNI (0.480 to 0.699).…”

“…Several investigations have shown that the risk of leprosy is high among people living in poor living conditions 4 , 5 , 7 , 9 . Income inequality, social vulnerability, poverty, poor housing conditions, poor diet, and low educational level are considered social determinants related to disease transmission 4 , 5 , 7 , 9 . However, poor conditions may inhibit the early diagnosis, thus increasing the hidden prevalence, which justifies the low coefficients observed in the strata of higher SNI.…”