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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Associação Brasileira de Saúde Coletiva
2023
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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. |
format | Online Article Text |
id | pubmed-9949488 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Associação Brasileira de Saúde Coletiva |
record_format | MEDLINE/PubMed |
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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