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Routine mortality surveillance to identify the cause of death pattern for out-of-hospital adult (aged 12+ years) deaths in Bangladesh: introduction of automated verbal autopsy

BACKGROUND: In Bangladesh, a poorly functioning national system of registering deaths and determining their causes leaves the country without important information on which to inform health programming, particularly for the 85% of deaths that occur in the community. In 2017, an improved death regist...

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Autores principales: Shawon, Md. Toufiq Hassan, Ashrafi, Shah Ali Akbar, Azad, Abul Kalam, Firth, Sonja M., Chowdhury, Hafizur, Mswia, Robert G., Adair, Tim, Riley, Ian, Abouzahr, Carla, Lopez, Alan D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7952220/
https://www.ncbi.nlm.nih.gov/pubmed/33706739
http://dx.doi.org/10.1186/s12889-021-10468-7
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author Shawon, Md. Toufiq Hassan
Ashrafi, Shah Ali Akbar
Azad, Abul Kalam
Firth, Sonja M.
Chowdhury, Hafizur
Mswia, Robert G.
Adair, Tim
Riley, Ian
Abouzahr, Carla
Lopez, Alan D.
author_facet Shawon, Md. Toufiq Hassan
Ashrafi, Shah Ali Akbar
Azad, Abul Kalam
Firth, Sonja M.
Chowdhury, Hafizur
Mswia, Robert G.
Adair, Tim
Riley, Ian
Abouzahr, Carla
Lopez, Alan D.
author_sort Shawon, Md. Toufiq Hassan
collection PubMed
description BACKGROUND: In Bangladesh, a poorly functioning national system of registering deaths and determining their causes leaves the country without important information on which to inform health programming, particularly for the 85% of deaths that occur in the community. In 2017, an improved death registration system and automated verbal autopsy (VA) were introduced to 13 upazilas to assess the utility of VA as a routine source of policy-relevant information and to identify leading causes of deaths (COD) in rural Bangladesh. METHODS: Data from 22,535 VAs, collected in 12 upazilas between October 2017 and August 2019, were assigned a COD using the SmartVA Analyze 2.0 computer algorithm. The plausibility of the VA results was assessed using a series of demographic and epidemiological checks in the Verbal Autopsy Interpretation, Performance and Evaluation Resource (VIPER) software tool. RESULTS: Completeness of community death reporting was 65%. The vast majority (85%) of adult deaths were due to non-communicable diseases, with ischemic heart disease, stroke and chronic respiratory disease comprising about 60% alone. Leading COD were broadly consistent with Global Burden of Disease study estimates. CONCLUSIONS: Routine VA collection using automated methods is feasible, can produce plausible results and provides critical information on community COD in Bangladesh. Routine VA and VIPER have potential application to countries with weak death registration systems. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-021-10468-7.
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spelling pubmed-79522202021-03-12 Routine mortality surveillance to identify the cause of death pattern for out-of-hospital adult (aged 12+ years) deaths in Bangladesh: introduction of automated verbal autopsy Shawon, Md. Toufiq Hassan Ashrafi, Shah Ali Akbar Azad, Abul Kalam Firth, Sonja M. Chowdhury, Hafizur Mswia, Robert G. Adair, Tim Riley, Ian Abouzahr, Carla Lopez, Alan D. BMC Public Health Research Article BACKGROUND: In Bangladesh, a poorly functioning national system of registering deaths and determining their causes leaves the country without important information on which to inform health programming, particularly for the 85% of deaths that occur in the community. In 2017, an improved death registration system and automated verbal autopsy (VA) were introduced to 13 upazilas to assess the utility of VA as a routine source of policy-relevant information and to identify leading causes of deaths (COD) in rural Bangladesh. METHODS: Data from 22,535 VAs, collected in 12 upazilas between October 2017 and August 2019, were assigned a COD using the SmartVA Analyze 2.0 computer algorithm. The plausibility of the VA results was assessed using a series of demographic and epidemiological checks in the Verbal Autopsy Interpretation, Performance and Evaluation Resource (VIPER) software tool. RESULTS: Completeness of community death reporting was 65%. The vast majority (85%) of adult deaths were due to non-communicable diseases, with ischemic heart disease, stroke and chronic respiratory disease comprising about 60% alone. Leading COD were broadly consistent with Global Burden of Disease study estimates. CONCLUSIONS: Routine VA collection using automated methods is feasible, can produce plausible results and provides critical information on community COD in Bangladesh. Routine VA and VIPER have potential application to countries with weak death registration systems. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-021-10468-7. BioMed Central 2021-03-12 /pmc/articles/PMC7952220/ /pubmed/33706739 http://dx.doi.org/10.1186/s12889-021-10468-7 Text en © The Author(s) 2021 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/. 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 in a credit line to the data.
spellingShingle Research Article
Shawon, Md. Toufiq Hassan
Ashrafi, Shah Ali Akbar
Azad, Abul Kalam
Firth, Sonja M.
Chowdhury, Hafizur
Mswia, Robert G.
Adair, Tim
Riley, Ian
Abouzahr, Carla
Lopez, Alan D.
Routine mortality surveillance to identify the cause of death pattern for out-of-hospital adult (aged 12+ years) deaths in Bangladesh: introduction of automated verbal autopsy
title Routine mortality surveillance to identify the cause of death pattern for out-of-hospital adult (aged 12+ years) deaths in Bangladesh: introduction of automated verbal autopsy
title_full Routine mortality surveillance to identify the cause of death pattern for out-of-hospital adult (aged 12+ years) deaths in Bangladesh: introduction of automated verbal autopsy
title_fullStr Routine mortality surveillance to identify the cause of death pattern for out-of-hospital adult (aged 12+ years) deaths in Bangladesh: introduction of automated verbal autopsy
title_full_unstemmed Routine mortality surveillance to identify the cause of death pattern for out-of-hospital adult (aged 12+ years) deaths in Bangladesh: introduction of automated verbal autopsy
title_short Routine mortality surveillance to identify the cause of death pattern for out-of-hospital adult (aged 12+ years) deaths in Bangladesh: introduction of automated verbal autopsy
title_sort routine mortality surveillance to identify the cause of death pattern for out-of-hospital adult (aged 12+ years) deaths in bangladesh: introduction of automated verbal autopsy
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7952220/
https://www.ncbi.nlm.nih.gov/pubmed/33706739
http://dx.doi.org/10.1186/s12889-021-10468-7
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