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Monitoring progress with national and subnational health goals by integrating verbal autopsy and medically certified cause of death data

INTRODUCTION: The measurement of progress towards many Sustainable Development Goals (SDG) and other health goals requires accurate and timely all-cause and cause of death (COD) data. However, existing guidance to countries to calculate these indicators is inadequate for populations with incomplete...

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Autores principales: Adair, Tim, Firth, Sonja, Phyo, Tint Pa Pa, Bo, Khin Sandar, Lopez, Alan D
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8169488/
https://www.ncbi.nlm.nih.gov/pubmed/34059494
http://dx.doi.org/10.1136/bmjgh-2021-005387
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author Adair, Tim
Firth, Sonja
Phyo, Tint Pa Pa
Bo, Khin Sandar
Lopez, Alan D
author_facet Adair, Tim
Firth, Sonja
Phyo, Tint Pa Pa
Bo, Khin Sandar
Lopez, Alan D
author_sort Adair, Tim
collection PubMed
description INTRODUCTION: The measurement of progress towards many Sustainable Development Goals (SDG) and other health goals requires accurate and timely all-cause and cause of death (COD) data. However, existing guidance to countries to calculate these indicators is inadequate for populations with incomplete death registration and poor-quality COD data. We introduce a replicable method to estimate national and subnational cause-specific mortality rates (and hence many such indicators) where death registration is incomplete by integrating data from Medical Certificates of Cause of Death (MCCOD) for hospital deaths with routine verbal autopsy (VA) for community deaths. METHODS: The integration method calculates population-level cause-specific mortality fractions (CSMFs) from the CSMFs of MCCODs and VAs weighted by estimated deaths in hospitals and the community. Estimated deaths are calculated by applying the empirical completeness method to incomplete death registration/reporting. The resultant cause-specific mortality rates are used to estimate SDG Indicator 23: mortality between ages 30 and 70 years from cardiovascular diseases, cancers, chronic respiratory diseases and diabetes. We demonstrate the method using nationally representative data in Myanmar, comprising over 42 000 VAs and 7600 MCCODs. RESULTS: In Myanmar in 2019, 89% of deaths were estimated to occur in the community. VAs comprised an estimated 70% of community deaths. Both the proportion of deaths in the community and CSMFs for the four causes increased with older age. We estimated that the probability of dying from any of the four causes between 30 and 70 years was 0.265 for men and 0.216 for women. This indicator is 50% higher if based on CSMFs from the integration of data sources than on MCCOD data from hospitals. CONCLUSION: This integration method facilitates country authorities to use their data to monitor progress with national and subnational health goals, rather than rely on estimates made by external organisations. The method is particularly relevant given the increasing application of routine VA in country Civil Registration and Vital Statistics systems.
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spelling pubmed-81694882021-06-17 Monitoring progress with national and subnational health goals by integrating verbal autopsy and medically certified cause of death data Adair, Tim Firth, Sonja Phyo, Tint Pa Pa Bo, Khin Sandar Lopez, Alan D BMJ Glob Health Original Research INTRODUCTION: The measurement of progress towards many Sustainable Development Goals (SDG) and other health goals requires accurate and timely all-cause and cause of death (COD) data. However, existing guidance to countries to calculate these indicators is inadequate for populations with incomplete death registration and poor-quality COD data. We introduce a replicable method to estimate national and subnational cause-specific mortality rates (and hence many such indicators) where death registration is incomplete by integrating data from Medical Certificates of Cause of Death (MCCOD) for hospital deaths with routine verbal autopsy (VA) for community deaths. METHODS: The integration method calculates population-level cause-specific mortality fractions (CSMFs) from the CSMFs of MCCODs and VAs weighted by estimated deaths in hospitals and the community. Estimated deaths are calculated by applying the empirical completeness method to incomplete death registration/reporting. The resultant cause-specific mortality rates are used to estimate SDG Indicator 23: mortality between ages 30 and 70 years from cardiovascular diseases, cancers, chronic respiratory diseases and diabetes. We demonstrate the method using nationally representative data in Myanmar, comprising over 42 000 VAs and 7600 MCCODs. RESULTS: In Myanmar in 2019, 89% of deaths were estimated to occur in the community. VAs comprised an estimated 70% of community deaths. Both the proportion of deaths in the community and CSMFs for the four causes increased with older age. We estimated that the probability of dying from any of the four causes between 30 and 70 years was 0.265 for men and 0.216 for women. This indicator is 50% higher if based on CSMFs from the integration of data sources than on MCCOD data from hospitals. CONCLUSION: This integration method facilitates country authorities to use their data to monitor progress with national and subnational health goals, rather than rely on estimates made by external organisations. The method is particularly relevant given the increasing application of routine VA in country Civil Registration and Vital Statistics systems. BMJ Publishing Group 2021-05-31 /pmc/articles/PMC8169488/ /pubmed/34059494 http://dx.doi.org/10.1136/bmjgh-2021-005387 Text en © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Original Research
Adair, Tim
Firth, Sonja
Phyo, Tint Pa Pa
Bo, Khin Sandar
Lopez, Alan D
Monitoring progress with national and subnational health goals by integrating verbal autopsy and medically certified cause of death data
title Monitoring progress with national and subnational health goals by integrating verbal autopsy and medically certified cause of death data
title_full Monitoring progress with national and subnational health goals by integrating verbal autopsy and medically certified cause of death data
title_fullStr Monitoring progress with national and subnational health goals by integrating verbal autopsy and medically certified cause of death data
title_full_unstemmed Monitoring progress with national and subnational health goals by integrating verbal autopsy and medically certified cause of death data
title_short Monitoring progress with national and subnational health goals by integrating verbal autopsy and medically certified cause of death data
title_sort monitoring progress with national and subnational health goals by integrating verbal autopsy and medically certified cause of death data
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8169488/
https://www.ncbi.nlm.nih.gov/pubmed/34059494
http://dx.doi.org/10.1136/bmjgh-2021-005387
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