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Spatio-Temporal Dynamics of Tuberculosis Clusters in Indonesia
CONTEXT: Stratification of social determinants leads to clustering of low socioeconomic communities, which then leads to spatio-temporal tuberculosis (TB) clusters. While previous studies have investigated spatio-temporal TB clusters, few have reported on the dynamics of them and the characteristics...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Wolters Kluwer - Medknow
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6985962/ https://www.ncbi.nlm.nih.gov/pubmed/32029983 http://dx.doi.org/10.4103/ijcm.IJCM_182_19 |
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author | Rengganis Wardani, Dyah Wulan Sumekar Wahono, Endro Prasetyo |
author_facet | Rengganis Wardani, Dyah Wulan Sumekar Wahono, Endro Prasetyo |
author_sort | Rengganis Wardani, Dyah Wulan Sumekar |
collection | PubMed |
description | CONTEXT: Stratification of social determinants leads to clustering of low socioeconomic communities, which then leads to spatio-temporal tuberculosis (TB) clusters. While previous studies have investigated spatio-temporal TB clusters, few have reported on the dynamics of them and the characteristics of social determinants. AIMS: To investigate the spatio-temporal dynamics of TB clusters in Bandar Lampung, Indonesia, from 2015 to 2016, and to identify the characteristics of population density and percentage of poverty of the clusters. SETTINGS AND DESIGN: A cross-sectional study was performed to analyze the spatio-temporal dynamics of TB clusters. The sample consisted of 705 TB patients (2015) and 1134 TB patients (2016), registered in 30 community health centers in Bandar Lampung, Indonesia SUBJECTS AND METHODS: Geographical coordinates of the TB patients' residence were collected using Geographical Positioning System. Secondary data, consisting of population density and the percentage of poverty, were obtained from the subdistrict office in the region under investigation. STATISTICAL ANALYSIS: Data were analyzed with space–time permutation model using SaTScan software. RESULTS: Spatio-temporal dynamics of TB clusters were found in 2015 and 2016, including the number of significant clusters, TB cases within the clusters, as well as locations and sizes of the clusters. All the clusters were found to have similar social determinant characteristics: medium–high population density and low–medium percentage of poverty. CONCLUSIONS: TB control programs in countries with a high TB burden and low social determinants should consider the spatio-temporal dynamics of the TB cluster and its social determinant characteristics for a better TB's intervention. |
format | Online Article Text |
id | pubmed-6985962 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Wolters Kluwer - Medknow |
record_format | MEDLINE/PubMed |
spelling | pubmed-69859622020-02-06 Spatio-Temporal Dynamics of Tuberculosis Clusters in Indonesia Rengganis Wardani, Dyah Wulan Sumekar Wahono, Endro Prasetyo Indian J Community Med Original Article CONTEXT: Stratification of social determinants leads to clustering of low socioeconomic communities, which then leads to spatio-temporal tuberculosis (TB) clusters. While previous studies have investigated spatio-temporal TB clusters, few have reported on the dynamics of them and the characteristics of social determinants. AIMS: To investigate the spatio-temporal dynamics of TB clusters in Bandar Lampung, Indonesia, from 2015 to 2016, and to identify the characteristics of population density and percentage of poverty of the clusters. SETTINGS AND DESIGN: A cross-sectional study was performed to analyze the spatio-temporal dynamics of TB clusters. The sample consisted of 705 TB patients (2015) and 1134 TB patients (2016), registered in 30 community health centers in Bandar Lampung, Indonesia SUBJECTS AND METHODS: Geographical coordinates of the TB patients' residence were collected using Geographical Positioning System. Secondary data, consisting of population density and the percentage of poverty, were obtained from the subdistrict office in the region under investigation. STATISTICAL ANALYSIS: Data were analyzed with space–time permutation model using SaTScan software. RESULTS: Spatio-temporal dynamics of TB clusters were found in 2015 and 2016, including the number of significant clusters, TB cases within the clusters, as well as locations and sizes of the clusters. All the clusters were found to have similar social determinant characteristics: medium–high population density and low–medium percentage of poverty. CONCLUSIONS: TB control programs in countries with a high TB burden and low social determinants should consider the spatio-temporal dynamics of the TB cluster and its social determinant characteristics for a better TB's intervention. Wolters Kluwer - Medknow 2020 /pmc/articles/PMC6985962/ /pubmed/32029983 http://dx.doi.org/10.4103/ijcm.IJCM_182_19 Text en Copyright: © 2020 Indian Journal of Community Medicine http://creativecommons.org/licenses/by-nc-sa/4.0 This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms. |
spellingShingle | Original Article Rengganis Wardani, Dyah Wulan Sumekar Wahono, Endro Prasetyo Spatio-Temporal Dynamics of Tuberculosis Clusters in Indonesia |
title | Spatio-Temporal Dynamics of Tuberculosis Clusters in Indonesia |
title_full | Spatio-Temporal Dynamics of Tuberculosis Clusters in Indonesia |
title_fullStr | Spatio-Temporal Dynamics of Tuberculosis Clusters in Indonesia |
title_full_unstemmed | Spatio-Temporal Dynamics of Tuberculosis Clusters in Indonesia |
title_short | Spatio-Temporal Dynamics of Tuberculosis Clusters in Indonesia |
title_sort | spatio-temporal dynamics of tuberculosis clusters in indonesia |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6985962/ https://www.ncbi.nlm.nih.gov/pubmed/32029983 http://dx.doi.org/10.4103/ijcm.IJCM_182_19 |
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