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A New Surveillance Model of Tuberculosis Transmission Control Based on Geographic Information System in the Primary Health Care

BACKGROUND: Tuberculosis can cause a substantial challenge against public health, especially in the developing countries which have low level of socio-economic condition that does not support the control over tuberculosis transmission and infection. OBJECTIVE: To find a new surveillance model of tub...

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Autores principales: Purba, Bahtera Bindavid, Siagian, Albiner, Juanita, Aritonang, Evawany, Purba, Dahlia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Academy of Medical sciences 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9665415/
https://www.ncbi.nlm.nih.gov/pubmed/36467318
http://dx.doi.org/10.5455/aim.2022.30.287-294
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author Purba, Bahtera Bindavid
Siagian, Albiner
Juanita,
Aritonang, Evawany
Purba, Dahlia
author_facet Purba, Bahtera Bindavid
Siagian, Albiner
Juanita,
Aritonang, Evawany
Purba, Dahlia
author_sort Purba, Bahtera Bindavid
collection PubMed
description BACKGROUND: Tuberculosis can cause a substantial challenge against public health, especially in the developing countries which have low level of socio-economic condition that does not support the control over tuberculosis transmission and infection. OBJECTIVE: To find a new surveillance model of tuberculosis transmission control based on geographic information system data in improving the tuberculosis transmission control and treatment outcome. METHODS: Effectiveness test of the self-monitoring of calendar documentation on the tuberculosis transmission control and outcome treatment with quasi-experimental post-test only with control group design. The analytical unit consisted of 96 tuberculosis cases as the self-monitoring group of calendar documentation (intervention) and 87 tuberculosis cases as the control group sourced of two different primary health care. RESULTS: The self-monitoring intervention of calendar documentation increased the average treatment effect on medicines intake control 0.11 (95% CI: 0.01-0.21), environment control 0.32 (95% CI: 0.19-46), droplets nuclei control 0.49 (95% CI: 0.36-0.61), cured 0.22 (95% CI: 0.09-0.36), completed 0.18 (95% CI: 0.09-0.26), drop out 0.09 (95% CI: 0.01-0.16), and failure of treatment 0.18 (95% CI: 0.09-0.26) of the control group (baseline). CONCLUSION: The final result of this research found a new surveillance model of tuberculosis transmission control in google earth mapping aplication based on Geographic Information system.
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spelling pubmed-96654152022-12-01 A New Surveillance Model of Tuberculosis Transmission Control Based on Geographic Information System in the Primary Health Care Purba, Bahtera Bindavid Siagian, Albiner Juanita, Aritonang, Evawany Purba, Dahlia Acta Inform Med Original Paper BACKGROUND: Tuberculosis can cause a substantial challenge against public health, especially in the developing countries which have low level of socio-economic condition that does not support the control over tuberculosis transmission and infection. OBJECTIVE: To find a new surveillance model of tuberculosis transmission control based on geographic information system data in improving the tuberculosis transmission control and treatment outcome. METHODS: Effectiveness test of the self-monitoring of calendar documentation on the tuberculosis transmission control and outcome treatment with quasi-experimental post-test only with control group design. The analytical unit consisted of 96 tuberculosis cases as the self-monitoring group of calendar documentation (intervention) and 87 tuberculosis cases as the control group sourced of two different primary health care. RESULTS: The self-monitoring intervention of calendar documentation increased the average treatment effect on medicines intake control 0.11 (95% CI: 0.01-0.21), environment control 0.32 (95% CI: 0.19-46), droplets nuclei control 0.49 (95% CI: 0.36-0.61), cured 0.22 (95% CI: 0.09-0.36), completed 0.18 (95% CI: 0.09-0.26), drop out 0.09 (95% CI: 0.01-0.16), and failure of treatment 0.18 (95% CI: 0.09-0.26) of the control group (baseline). CONCLUSION: The final result of this research found a new surveillance model of tuberculosis transmission control in google earth mapping aplication based on Geographic Information system. Academy of Medical sciences 2022-12 /pmc/articles/PMC9665415/ /pubmed/36467318 http://dx.doi.org/10.5455/aim.2022.30.287-294 Text en © 2022 Bahtera Bindavid Purba, Albiner Siagian, Juanita, Evawany Aritonang, Dahlia Purba https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Paper
Purba, Bahtera Bindavid
Siagian, Albiner
Juanita,
Aritonang, Evawany
Purba, Dahlia
A New Surveillance Model of Tuberculosis Transmission Control Based on Geographic Information System in the Primary Health Care
title A New Surveillance Model of Tuberculosis Transmission Control Based on Geographic Information System in the Primary Health Care
title_full A New Surveillance Model of Tuberculosis Transmission Control Based on Geographic Information System in the Primary Health Care
title_fullStr A New Surveillance Model of Tuberculosis Transmission Control Based on Geographic Information System in the Primary Health Care
title_full_unstemmed A New Surveillance Model of Tuberculosis Transmission Control Based on Geographic Information System in the Primary Health Care
title_short A New Surveillance Model of Tuberculosis Transmission Control Based on Geographic Information System in the Primary Health Care
title_sort new surveillance model of tuberculosis transmission control based on geographic information system in the primary health care
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9665415/
https://www.ncbi.nlm.nih.gov/pubmed/36467318
http://dx.doi.org/10.5455/aim.2022.30.287-294
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