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

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 socioenvironmental 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 socioenvironmental 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 Chikungunya fever incidence and the associated socioeconomic, demographic, and vector infestation factors in municipalities of Pernambuco, Brazil, 2015–2021

“…In contrast, studies conducted in the cities of Rio de Janeiro 9 and São Luís 8 demonstrated positive and significant global spatial autocorrelation for the incidence rates of suspected and probable cases of this arbovirus, respectively, indicating that adjacent neighborhoods have similar rates. Freitas et al 28 showed spatial dependence in municipalities with high incidences of CHIKF in the MRR, Pernambuco, in 2018.…”

“…Characterized by intense arthralgia and possible atypical manifestations (such as neurological manifestations) that can persist for years 7 , CHIKF has the potential to cause overload of health services and work absenteeism 7 . Both the magnitude and dissemination of this disease are influenced by the presence and abundance of the A. aegypti vector 8 , whose proliferation depends on socioeconomic, demographic, and environmental factors 8 – 10 . Studies show that urban areas with precarious sanitation conditions and high population density provide ideal breeding grounds for these vectors, favoring an increase in the incidence of arboviruses 10 , 11 .…”

“…In view of the above, it is essential to know which determinants interfere in the health-disease process of a territory, so that there is guidance for effective control and prevention actions. That said, spatial analysis techniques become extremely useful instruments, as they have been used to investigate the spatial dynamics of diseases such as arboviruses in the social context in different locations, addressing possible associated factors and detecting vulnerable areas 8 , 9 , 16 – 18 . However, there are gaps in the knowledge of these factors in the context of CHIKF in Pernambuco.…”

Análise espacial da incidência da febre de Chikungunya e dos fatores socioeconômicos, demográficos e de infestação vetorial associados, em municípios de Pernambuco, Brasil, 2015–2021

“…Caracterizada por intensa artralgia e por possíveis manifestações atípicas (como as neurológicas) que podem persistir durante anos 7 , a CHIKF tem o potencial de acarretar sobrecarga dos serviços de saúde e absenteísmo laboral 7 . Tanto a magnitude quanto a disseminação dessa doença são influenciadas pela presença e abundância do vetor A. aegypti 8 , cuja proliferação depende de um complexo de fatores socioeconômicos, demográficos e ambientais [8][9][10] . Estudos evidenciam que áreas urbanas com condições precárias de saneamento e alta densidade populacional possibilitam criadouros ideais para esses vetores, favorecendo o aumento da incidência de arboviroses 10,11 .…”

Uso da análise espacial como intervenção de gestão em enfermagem para o controle do estado de saúde da comunidade com risco para arboviroses: um protocolo de revisão de escopo

“…No planejamento para estratégias de vigilância e controle das arboviroses, o enfermeiro pode utilizar a análise espacial como intervenção (Lopes et al, 2021). Tendo como benefício o apontamento de novos subsídios para o planejamento e a avaliação das ações baseadas na análise da distribuição espacial das doenças, localização dos serviços de saúde e dos riscos ambientais, a análise de dados distribuídos pelo espaço geográfico vem sendo cada vez mais valorizada na gestão de saúde (Cardoso et al, 2020;Costa et al, 2021).…”