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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 1(st) Health Region of Pernambuco (1(st) HRP). METHODS: This ecological study used a spatial analysis of Mean Incidence Rates (MIR) of probabl...

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Autores principales: Aguiar-Santos, Maísa, Mendes, Liana Gabriele da Cruz, dos Passos, Diogenes Ferreira, Santos, Tamyris Gomes da Silva, Lins, Rosanny Holanda Freitas Benevides, do Monte, Ana Cristina Pedrosa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Associação Brasileira de Saúde Coletiva 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9949488/
https://www.ncbi.nlm.nih.gov/pubmed/36820755
http://dx.doi.org/10.1590/1980-549720230018
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author Aguiar-Santos, Maísa
Mendes, Liana Gabriele da Cruz
dos Passos, Diogenes Ferreira
Santos, Tamyris Gomes da Silva
Lins, Rosanny Holanda Freitas Benevides
do Monte, Ana Cristina Pedrosa
author_facet Aguiar-Santos, Maísa
Mendes, Liana Gabriele da Cruz
dos Passos, Diogenes Ferreira
Santos, Tamyris Gomes da Silva
Lins, Rosanny Holanda Freitas Benevides
do Monte, Ana Cristina Pedrosa
author_sort Aguiar-Santos, Maísa
collection PubMed
description OBJECTIVE: To identify the spatial patterns of chikungunya fever (CHIKF) and the associated socioeconomic, demographic, and vector infestation factors in the 1(st) Health Region of Pernambuco (1(st) 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 1(st) 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 1(st) 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.
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spelling pubmed-99494882023-02-24 Spatial analysis of Chikungunya fever incidence and the associated socioeconomic, demographic, and vector infestation factors in municipalities of Pernambuco, Brazil, 2015–2021 Aguiar-Santos, Maísa Mendes, Liana Gabriele da Cruz dos Passos, Diogenes Ferreira Santos, Tamyris Gomes da Silva Lins, Rosanny Holanda Freitas Benevides do Monte, Ana Cristina Pedrosa Rev Bras Epidemiol Original Article OBJECTIVE: To identify the spatial patterns of chikungunya fever (CHIKF) and the associated socioeconomic, demographic, and vector infestation factors in the 1(st) Health Region of Pernambuco (1(st) 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 1(st) 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 1(st) 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. Associação Brasileira de Saúde Coletiva 2023-02-20 /pmc/articles/PMC9949488/ /pubmed/36820755 http://dx.doi.org/10.1590/1980-549720230018 Text en https://creativecommons.org/licenses/by/4.0/All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License
spellingShingle Original Article
Aguiar-Santos, Maísa
Mendes, Liana Gabriele da Cruz
dos Passos, Diogenes Ferreira
Santos, Tamyris Gomes da Silva
Lins, Rosanny Holanda Freitas Benevides
do Monte, Ana Cristina Pedrosa
Spatial analysis of Chikungunya fever incidence and the associated socioeconomic, demographic, and vector infestation factors in municipalities of Pernambuco, Brazil, 2015–2021
title Spatial analysis of Chikungunya fever incidence and the associated socioeconomic, demographic, and vector infestation factors in municipalities of Pernambuco, Brazil, 2015–2021
title_full Spatial analysis of Chikungunya fever incidence and the associated socioeconomic, demographic, and vector infestation factors in municipalities of Pernambuco, Brazil, 2015–2021
title_fullStr Spatial analysis of Chikungunya fever incidence and the associated socioeconomic, demographic, and vector infestation factors in municipalities of Pernambuco, Brazil, 2015–2021
title_full_unstemmed Spatial analysis of Chikungunya fever incidence and the associated socioeconomic, demographic, and vector infestation factors in municipalities of Pernambuco, Brazil, 2015–2021
title_short Spatial analysis of Chikungunya fever incidence and the associated socioeconomic, demographic, and vector infestation factors in municipalities of Pernambuco, Brazil, 2015–2021
title_sort spatial analysis of chikungunya fever incidence and the associated socioeconomic, demographic, and vector infestation factors in municipalities of pernambuco, brazil, 2015–2021
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9949488/
https://www.ncbi.nlm.nih.gov/pubmed/36820755
http://dx.doi.org/10.1590/1980-549720230018
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