Análise da morbimortalidade por covid-19 no Estado do Ceará, Brasil: magnitude, padrões espaciais e fatores associados.

Abstract

Epidemiological studies that explore risk factors associated with COVID-19 infections and mortality are needed to inform and guide health policies. The objective of this study was to

analyze the spatial patterns and factors associated with morbidity and mortality due to COVID- 19 in the state of Ceará, Brazil. This is an ecological study of data on Covid-19 cases made

available by the Ceará State Department of Health. Spatial autocorrelation analysis was performed and subsequently a Pearson correlation matrix was created with sociodemographic data. The data was organized retrospectively with information from 2020 to 2022 per semester. In 2020.1, Fortaleza, Sobral and Massapê presented the three highest mortality coefficients in Ceará, while Altaneira, Antonina do Norte and Baixio presented the lowest coefficients. A total of 52 municipalities showed a significance of p=0.05. 41 municipalities were found to have a high-high standard and 38 municipalities had a low-low standard. In 2020.2 Orós, Santana do Cariri and Fortaleza were the three municipalities with the highest mortality coefficients, while Aiuaba, Ipaporanga and Potengi had the three lowest coefficients. A total of 30 municipalities presented a significance of p=0.05. They were evidenced 19 municipalities with a high-high standard and 13 municipalities with a low-low standard. In 2020.1, São Gonçalo do Amarante, Orós and Antonina do Norte had the three highest mortality coefficients. Pontegi, Pires Ferreira and Granjeiro presented the lowest coefficients. A total of 22 municipalities presented a significance of p=0.05. 15 municipalities were identified with a high-high standard, 12 municipalities had a low-low standard. In 2021.2, São Gonçalo do Amarante, Orós and Fortaleza presented the three highest mortality coefficients, while Pontegi, Aiuaba and Pires Ferreira presented the lowest coefficients. A total of 23 municipalities presented a significance of p=0.05. A set of 11 municipalities presented a high-high pattern and 11 presented a low-low pattern. In 2022, the numbers were similar in both semesters. The significance and cluster maps were also similar. Regarding the regression analysis, the indicator that showed the greatest significance was the Average Population per household. Furthermore, the study reaffirms the importance of knowing the factors that are relevant to COVID-19 mortality to help design policies and strategies to combat other pandemics that may arise.