Cargando…

Spatial and temporal patterns of dengue infections in Timor-Leste, 2005–2013

BACKGROUND: Dengue remains an important public health problem in Timor-Leste, with several major epidemics occurring over the last 10 years. The aim of this study was to identify dengue clusters at high geographical resolution and to determine the association between local environmental characterist...

Descripción completa

Detalles Bibliográficos
Autores principales: Wangdi, Kinley, Clements, Archie C. A., Du, Tai, Nery, Susana Vaz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5755460/
https://www.ncbi.nlm.nih.gov/pubmed/29301546
http://dx.doi.org/10.1186/s13071-017-2588-4
_version_ 1783290590563139584
author Wangdi, Kinley
Clements, Archie C. A.
Du, Tai
Nery, Susana Vaz
author_facet Wangdi, Kinley
Clements, Archie C. A.
Du, Tai
Nery, Susana Vaz
author_sort Wangdi, Kinley
collection PubMed
description BACKGROUND: Dengue remains an important public health problem in Timor-Leste, with several major epidemics occurring over the last 10 years. The aim of this study was to identify dengue clusters at high geographical resolution and to determine the association between local environmental characteristics and the distribution and transmission of the disease. METHODS: Notifications of dengue cases that occurred from January 2005 to December 2013 were obtained from the Ministry of Health, Timor-Leste. The population of each suco (the third-level administrative subdivision) was obtained from the Population and Housing Census 2010. Spatial autocorrelation in dengue incidence was explored using Moran’s I statistic, Local Indicators of Spatial Association (LISA), and the Getis-Ord statistics. A multivariate, Zero-Inflated, Poisson (ZIP) regression model was developed with a conditional autoregressive (CAR) prior structure, and with posterior parameters estimated using Bayesian Markov chain Monte Carlo (MCMC) simulation with Gibbs sampling. RESULTS: The analysis used data from 3206 cases. Dengue incidence was highly seasonal with a large peak in January. Patients ≥ 14 years were found to be 74% [95% credible interval (CrI): 72–76%] less likely to be infected than those < 14 years, and females were 12% (95% CrI: 4–21%) more likely to suffer from dengue as compared to males. Dengue incidence increased by 0.7% (95% CrI: 0.6–0.8%) for a 1 °C increase in mean temperature; and 47% (95% CrI: 29–59%) for a 1 mm increase in precipitation. There was no significant residual spatial clustering after accounting for climate and demographic variables. CONCLUSIONS: Dengue incidence was highly seasonal and spatially clustered, with positive associations with temperature, precipitation and demographic factors. These factors explained the observed spatial heterogeneity of infection. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13071-017-2588-4) contains supplementary material, which is available to authorized users.
format Online
Article
Text
id pubmed-5755460
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-57554602018-01-08 Spatial and temporal patterns of dengue infections in Timor-Leste, 2005–2013 Wangdi, Kinley Clements, Archie C. A. Du, Tai Nery, Susana Vaz Parasit Vectors Research BACKGROUND: Dengue remains an important public health problem in Timor-Leste, with several major epidemics occurring over the last 10 years. The aim of this study was to identify dengue clusters at high geographical resolution and to determine the association between local environmental characteristics and the distribution and transmission of the disease. METHODS: Notifications of dengue cases that occurred from January 2005 to December 2013 were obtained from the Ministry of Health, Timor-Leste. The population of each suco (the third-level administrative subdivision) was obtained from the Population and Housing Census 2010. Spatial autocorrelation in dengue incidence was explored using Moran’s I statistic, Local Indicators of Spatial Association (LISA), and the Getis-Ord statistics. A multivariate, Zero-Inflated, Poisson (ZIP) regression model was developed with a conditional autoregressive (CAR) prior structure, and with posterior parameters estimated using Bayesian Markov chain Monte Carlo (MCMC) simulation with Gibbs sampling. RESULTS: The analysis used data from 3206 cases. Dengue incidence was highly seasonal with a large peak in January. Patients ≥ 14 years were found to be 74% [95% credible interval (CrI): 72–76%] less likely to be infected than those < 14 years, and females were 12% (95% CrI: 4–21%) more likely to suffer from dengue as compared to males. Dengue incidence increased by 0.7% (95% CrI: 0.6–0.8%) for a 1 °C increase in mean temperature; and 47% (95% CrI: 29–59%) for a 1 mm increase in precipitation. There was no significant residual spatial clustering after accounting for climate and demographic variables. CONCLUSIONS: Dengue incidence was highly seasonal and spatially clustered, with positive associations with temperature, precipitation and demographic factors. These factors explained the observed spatial heterogeneity of infection. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13071-017-2588-4) contains supplementary material, which is available to authorized users. BioMed Central 2018-01-04 /pmc/articles/PMC5755460/ /pubmed/29301546 http://dx.doi.org/10.1186/s13071-017-2588-4 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Wangdi, Kinley
Clements, Archie C. A.
Du, Tai
Nery, Susana Vaz
Spatial and temporal patterns of dengue infections in Timor-Leste, 2005–2013
title Spatial and temporal patterns of dengue infections in Timor-Leste, 2005–2013
title_full Spatial and temporal patterns of dengue infections in Timor-Leste, 2005–2013
title_fullStr Spatial and temporal patterns of dengue infections in Timor-Leste, 2005–2013
title_full_unstemmed Spatial and temporal patterns of dengue infections in Timor-Leste, 2005–2013
title_short Spatial and temporal patterns of dengue infections in Timor-Leste, 2005–2013
title_sort spatial and temporal patterns of dengue infections in timor-leste, 2005–2013
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5755460/
https://www.ncbi.nlm.nih.gov/pubmed/29301546
http://dx.doi.org/10.1186/s13071-017-2588-4
work_keys_str_mv AT wangdikinley spatialandtemporalpatternsofdengueinfectionsintimorleste20052013
AT clementsarchieca spatialandtemporalpatternsofdengueinfectionsintimorleste20052013
AT dutai spatialandtemporalpatternsofdengueinfectionsintimorleste20052013
AT nerysusanavaz spatialandtemporalpatternsofdengueinfectionsintimorleste20052013