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Spatial and temporal analysis of hospitalized dengue patients in Bandung: demographics and risk
BACKGROUND: Bandung, the fourth largest city in Indonesia and capital of West Java province, has been considered a major endemic area of dengue, and studies show that the incidence in this city could increase and spread rapidly. At the same time, estimation of incidence could be inaccurate due to a...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8152360/ https://www.ncbi.nlm.nih.gov/pubmed/34039439 http://dx.doi.org/10.1186/s41182-021-00329-9 |
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author | Faridah, Lia Mindra, I. Gede Nyoman Putra, Ramadhani Eka Fauziah, Nisa Agustian, Dwi Natalia, Yessika Adelwin Watanabe, Kozo |
author_facet | Faridah, Lia Mindra, I. Gede Nyoman Putra, Ramadhani Eka Fauziah, Nisa Agustian, Dwi Natalia, Yessika Adelwin Watanabe, Kozo |
author_sort | Faridah, Lia |
collection | PubMed |
description | BACKGROUND: Bandung, the fourth largest city in Indonesia and capital of West Java province, has been considered a major endemic area of dengue, and studies show that the incidence in this city could increase and spread rapidly. At the same time, estimation of incidence could be inaccurate due to a lack of reliable surveillance systems. To provide strategic information for the dengue control program in the face of limited capacity, this study used spatial pattern analysis of a possible outbreak of dengue cases, through the Geographic Information System (GIS). To further enhance the information needed for effective policymaking, we also analyzed the demographic pattern of dengue cases. METHODS: Monthly reports of dengue cases from January 2014 to December 2016 from 16 hospitals in Bandung were collected as the database, which consisted of address, sex, age, and code to anonymize the patients. The address was then transformed into geocoding and used to estimate the relative risk of a particular area’s developing a cluster of dengue cases. We used the kernel density estimation method to analyze the dynamics of change of dengue cases. RESULTS: The model showed that the spatial cluster of the relative risk of dengue incidence was relatively unchanged for 3 years. Dengue high-risk areas predominated in the southern and southeastern parts of Bandung, while low-risk areas were found mostly in its western and northeastern regions. The kernel density estimation showed strong cluster groups of dengue cases in the city. CONCLUSIONS: This study demonstrated a strong pattern of reported cases related to specific demographic groups (males and children). Furthermore, spatial analysis using GIS also visualized the dynamic development of the aggregation of disease incidence (hotspots) for dengue cases in Bandung. These data may provide strategic information for the planning and design of dengue control programs. |
format | Online Article Text |
id | pubmed-8152360 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-81523602021-05-26 Spatial and temporal analysis of hospitalized dengue patients in Bandung: demographics and risk Faridah, Lia Mindra, I. Gede Nyoman Putra, Ramadhani Eka Fauziah, Nisa Agustian, Dwi Natalia, Yessika Adelwin Watanabe, Kozo Trop Med Health Research BACKGROUND: Bandung, the fourth largest city in Indonesia and capital of West Java province, has been considered a major endemic area of dengue, and studies show that the incidence in this city could increase and spread rapidly. At the same time, estimation of incidence could be inaccurate due to a lack of reliable surveillance systems. To provide strategic information for the dengue control program in the face of limited capacity, this study used spatial pattern analysis of a possible outbreak of dengue cases, through the Geographic Information System (GIS). To further enhance the information needed for effective policymaking, we also analyzed the demographic pattern of dengue cases. METHODS: Monthly reports of dengue cases from January 2014 to December 2016 from 16 hospitals in Bandung were collected as the database, which consisted of address, sex, age, and code to anonymize the patients. The address was then transformed into geocoding and used to estimate the relative risk of a particular area’s developing a cluster of dengue cases. We used the kernel density estimation method to analyze the dynamics of change of dengue cases. RESULTS: The model showed that the spatial cluster of the relative risk of dengue incidence was relatively unchanged for 3 years. Dengue high-risk areas predominated in the southern and southeastern parts of Bandung, while low-risk areas were found mostly in its western and northeastern regions. The kernel density estimation showed strong cluster groups of dengue cases in the city. CONCLUSIONS: This study demonstrated a strong pattern of reported cases related to specific demographic groups (males and children). Furthermore, spatial analysis using GIS also visualized the dynamic development of the aggregation of disease incidence (hotspots) for dengue cases in Bandung. These data may provide strategic information for the planning and design of dengue control programs. BioMed Central 2021-05-26 /pmc/articles/PMC8152360/ /pubmed/34039439 http://dx.doi.org/10.1186/s41182-021-00329-9 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Faridah, Lia Mindra, I. Gede Nyoman Putra, Ramadhani Eka Fauziah, Nisa Agustian, Dwi Natalia, Yessika Adelwin Watanabe, Kozo Spatial and temporal analysis of hospitalized dengue patients in Bandung: demographics and risk |
title | Spatial and temporal analysis of hospitalized dengue patients in Bandung: demographics and risk |
title_full | Spatial and temporal analysis of hospitalized dengue patients in Bandung: demographics and risk |
title_fullStr | Spatial and temporal analysis of hospitalized dengue patients in Bandung: demographics and risk |
title_full_unstemmed | Spatial and temporal analysis of hospitalized dengue patients in Bandung: demographics and risk |
title_short | Spatial and temporal analysis of hospitalized dengue patients in Bandung: demographics and risk |
title_sort | spatial and temporal analysis of hospitalized dengue patients in bandung: demographics and risk |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8152360/ https://www.ncbi.nlm.nih.gov/pubmed/34039439 http://dx.doi.org/10.1186/s41182-021-00329-9 |
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