Análise espacial dos casos e óbitos da covid-19 e sua relação com indicadores socioeconômicos e de saúde no estado do Maranhão.

• Autor(a) principal: ALENCAR, Larissa Karla Barros de
• Data de Publicação: 2023
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações da UFMA
• Texto Completo: https://tedebc.ufma.br/jspui/handle/tede/tede/4753
• topic: análise espacial;; covid-19;; indicadores socioeconômicos;; indicadores de saúde;; spatial analysis;; COVID-19;; Socioeconomic indicators;; health indicators.; Enfermagem

Introduction: COVID-19 is an infectious disease, caused by the SARS-CoV-2 virus, with high transmissibility and global distribution. Since it was first reported in the city of Wuhan, China, in December 2019, the world has been following the exponential growth of cases of the disease. Objective: To analyze the spatial distribution of COVID-19 cases and deaths in Maranhão and its relationship with socioeconomic and health indicators. Methodology: Ecological study of all cases and deaths of COVID-19 in the state of Maranhão notified until August 2022 at the Secretary of State for Health. Socioeconomic and health indicators were collected from the online sites of the Brazilian Institute of Geography and Statistics (IBGE), Institute of Applied Economic Research (IPEA) and e-Gestor Assistência Básica. The dependent variables used were: incidence, mortality and lethality of COVID-19, and the independent ones were: resident population of the municipalities of Maranhão, Gini Index, Municipal Human Development Index (MHDI), Social Vulnerability Index (SVI), income per capita, proportion of poor people, household crowding, illiteracy rate of people aged 15 years or over, proportion of households with a general water network, unemployment rate of the population aged 18 years or over and coverage of Primary Care (AB). The incidence, mortality and lethality rates of the 217 municipalities in Maranhão were estimated. The Global Moran Index (I) was used to assess the existence of spatial autocorrelation with the dependent variables, and the Local Moran Index to identify high and low risk areas (clusters). The maps were made using the QGIS software version 3.12.0. To calculate the global spatial autocorrelation indices, as well as the regression models, the GeoDa software, version 1.14, was used. Result: Until August 31, 2022, 468,943 cases and 11,524 deaths from COVID-19 were reported in Maranhão. The municipality of São Luís registered the highest number of cases and deaths, with 73,218 (15.61%) and 2,873 (24.93%), respectively, and the municipality of Boa Vista do Gurupi registered the lowest number, 16 cases (0.003 %). The municipality of São Francisco do Brejão did not record a death from COVID-19. The highest incidence rate was recorded in the municipality of Lagoa do Mato (25,957.44/100,000 inhab.) and the lowest rate was in Boa Vista do Gurupi (188.36/100,000 inhab.). The highest mortality rate was recorded in Imperatriz (374.25/100,000 inhab.) and the highest lethality rate was in Boa Vista do Gurupi (31.25%). The Moran I Index showed positive spatial autocorrelation for incidence, mortality and lethality in the studied period, making it possible to identify clusters of high and low risk for the dependent variables. The Ordinary Least Squares Estimation (OLS) regression model confirmed spatial autocorrelation with the dependent variables. Incidence showed a positive association with the Gini Index and AB coverage, and a negative association with IVS, MHDI and proportion of poor people. Mortality was positively associated with the Gini Index and illiteracy rate and negatively associated with the proportion of poor people and IVS. Regarding lethality, there was a positive correlation with household crowding and a negative correlation with primary care coverage and illiteracy rate. Conclusion: The spread of COVID-19 occurred heterogeneously, with wide variation between the municipalities of Maranhão, making it possible to identify areas of greater and lesser risk for the disease. Socioeconomic and health indicators influenced the evolution of the pandemic, and that such characteristics should be considered in the formulation of public policies to control the disease, as well as to reduce existing inequalities in the State.