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

• Published in: Revista da Sociedade Brasileira de Medicina Tropical
• Main Authors: Silmery da Silva Brito Costa, Maria dos Remédios Freitas Carvalho Branco, Vitor Vieira Vasconcelos, Rejane Christine de Sousa Queiroz, Adriana Soraya Araujo, Ana Patrícia Barros Câmara, Angela Terumi Fushita, Maria do Socorro da Silva, Antônio Augusto Moura da Silva, Alcione Miranda dos Santos
• Format: Article in Journal/Newspaper
• Language: English
• Published: Sociedade Brasileira de Medicina Tropical (SBMT) 2021
• Subjects: Dengue; Chikungunya; Zika; Spatial analysis; Socio-environmental factors; Economic factors; Arctic medicine. Tropical medicine; RC955-962; Arctic
• Online Access: https://doi.org/10.1590/0037-8682-0223-2021; https://doaj.org/article/aa2bab86e5fb45969da40a7f7382ca53

Abstract 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.