Análise espacial da distribuição dos casos de dengue e sua relação com fatores socioambientais no estado da Paraíba, Brasil, 2007-2016

Spatial analysis of Chikungunya fever incidence and the associated socioeconomic, demographic, and vector infestation factors in municipalities of Pernambuco, Brazil, 2015-2021

OBJECTIVE

To identify the spatial patterns of chikungunya fever (CHIKF) and the associated socioeconomic, demographic, and vector infestation factors in the 1st Health Region of Pernambuco (1st HRP).

METHODS

This ecological study used a spatial analysis of Mean Incidence Rates (MIR) of probable cases of CHIKF reported among residents of the 19 municipalities of the 1st HRP, in 2015-2021. The univariate and bivariate global Moran indexes (I) were estimated. From the significant associations (p<0.05), clusters were identified using the local Moran index and maps.

RESULTS

A predominance of the largest CHIKF rates was identified in the east. However, there was a heterogeneous distribution of rates across municipalities, which may have contributed to the absence of spatial autocorrelation of CHIKF (I=0.03; p=0.294) in univariate I. The bivariate I revealed a positive spatial correlation between CHIKF and the Municipal Human Development Index (MHDI) (I=0.245; p=0.038), but with a cluster of cities with low incidences and low MHDI in the west. There was no spatial correlation between CHIKF and the other variables analyzed: population density, Gini index, social vulnerability index, and building infestation index for Aedes aegypti.

CONCLUSIONS

The results suggest that only the MHDI influenced the occurrence of CHIKF in the 1st HRP, so that municipalities in the west demonstrated spatial dependence between lower values of MHDI and MIR. However, this spatial correlation may have occurred due to possible underreporting in the area. These findings can assist in the (re)orientation of resources for surveillance and health care services.

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

RESUMO Objetivo: Identificar, na Iᵃ Região de Saúde de Pernambuco (Iᵃ RSP), os padrões espaciais da febre de Chikungunya (CHIKF) e os fatores socioeconômicos, demográficos e de infestação vetorial associados. Métodos: Este estudo ecológico utilizou a análise espacial das Taxas Médias de Incidência (TMI) de casos prováveis da CHIKF notificados entre os residentes dos 19 municípios da Iᵃ RSP no período de 2015–2021. Os índices de Moran global (I) univariados e bivariados foram estimados. Das associações significativas (p<0,05), clusters foram localizados por meio do Índice de Moran Local e de mapas. Resultados: Identificou-se predominância das maiores TMI da CHIKF no leste. Entretanto, houve distribuição heterogênea das taxas dos municípios, o que pode ter contribuído para a ausência de autocorrelação espacial da CHIKF (I=0,03; p=0,294) no I univariado. O I bivariado revelou correlação espacial positiva entre a CHIKF e o Índice de Desenvolvimento Humano Municipal (IDHM) (I=0,245; p=0,038), porém com um cluster de cidades com baixas incidências e baixo IDHM no oeste. Não houve correlação espacial entre a CHIKF e as demais variáveis analisadas: densidade demográfica, Índice de Gini, Índice de Vulnerabilidade Social e Índice de Infestação Predial de Aedes aegypti. Conclusões: Os resultados sugerem que somente o IDHM influenciou na ocorrência da CHIKF na Iᵃ RSP, de forma que municípios do oeste demonstraram dependência espacial entre menores valores de IDHM e TMI. No entanto, essa correlação espacial pode ter ocorrido devido às possíveis subnotificações na área. Tais achados podem auxiliar na (re)orientação de recursos dos serviços de vigilância e assistência à saúde.

How heterogeneous is the dengue transmission profile in Brazil? A study in six Brazilian states

Dengue is a vector-borne disease present in most tropical countries, infecting an average of 50 to 100 million people per year. Socioeconomic, demographic, and environmental factors directly influence the transmission cycle of the dengue virus (DENV). In Brazil, these factors vary between regions producing different profiles of dengue transmission and challenging the epidemiological surveillance of the disease. In this article, we aimed at classifying the profiles of dengue transmission in 1,823 Brazilian municipalities, covering different climates, from 2010 to 2019. Time series data of dengue cases were obtained from six states: Ceará and Maranhão in the semiarid Northeast, Minas Gerais in the countryside, Espírito Santo and Rio de Janeiro in the tropical Atlantic coast, and Paraná in the subtropical region. To describe the time series, we proposed a set of epi-features of the magnitude and duration of the dengue epidemic cycles, totaling 13 indicators. Using these epi-features as inputs, a multivariate cluster algorithm was employed to classify the municipalities according to their dengue transmission profile. Municipalities were classified into four distinct dengue transmission profiles: persistent transmission (7.8%), epidemic (21.3%), episodic/epidemic (43.2%), and episodic transmission (27.6%). Different profiles were associated with the municipality’s population size and climate. Municipalities with higher incidence and larger populations tended to be classified as persistent transmission, suggesting the existence of critical community size. This association, however, varies depending on the state, indicating the importance of other factors. The proposed classification is useful for developing more specific and precise surveillance protocols for regions with different dengue transmission profiles, as well as more precise public policies for dengue prevention.

Dengue is one of the fastest-growing vector-borne diseases in the world. Currently, vaccines are experimental and are not very effective, so prevention depends on the control of the mosquito Aedes aegypti. Health promotion campaigns aimed at encouraging people to reduce mosquito breeding sites have limited effect. In addition, the heterogeneity of the territories that have dengue becomes a major challenge for the epidemiological surveillance of the disease. Brazil has a territory of continental size, and single standardized surveillance is not very effective for monitoring this arbovirus. Classifying types of dengue dynamics based on features of the epidemiological cycle in each location has the potential to increase the precision of surveillance and control strategies. In our study, we were able to classify areas according to different dengue transmission profiles, ranging from episodic to persistent transmission. These results can provide tools to guide actions aimed at achieving the World Health Organization’s goals of eliminating neglected tropical diseases in countries that have the virus.