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Refining circumstances of mortality categories (COMCAT): a verbal autopsy model connecting circumstances of deaths with outcomes for public health decision-making
BACKGROUND: Recognising that the causes of over half the world’s deaths pass unrecorded, the World Health Organization (WHO) leads development of Verbal Autopsy (VA): a method to understand causes of death in otherwise unregistered populations. Recently, VA has been developed for use outside researc...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Taylor & Francis
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8986216/ https://www.ncbi.nlm.nih.gov/pubmed/35377291 http://dx.doi.org/10.1080/16549716.2021.2000091 |
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author | D’Ambruoso, Lucia Price, Jessica Cowan, Eilidh Goosen, Gerhard Fottrell, Edward Herbst, Kobus van der Merwe, Maria Sigudla, Jerry Davies, Justine Kahn, Kathleen |
author_facet | D’Ambruoso, Lucia Price, Jessica Cowan, Eilidh Goosen, Gerhard Fottrell, Edward Herbst, Kobus van der Merwe, Maria Sigudla, Jerry Davies, Justine Kahn, Kathleen |
author_sort | D’Ambruoso, Lucia |
collection | PubMed |
description | BACKGROUND: Recognising that the causes of over half the world’s deaths pass unrecorded, the World Health Organization (WHO) leads development of Verbal Autopsy (VA): a method to understand causes of death in otherwise unregistered populations. Recently, VA has been developed for use outside research environments, supporting countries and communities to recognise and act on their own health priorities. We developed the Circumstances of Mortality Categories (COMCATs) system within VA to provide complementary circumstantial categorisations of deaths. OBJECTIVES: Refine the COMCAT system to (a) support large-scale population assessment and (b) inform public health decision-making. METHODS: We analysed VA data for 7,980 deaths from two South African Health and Socio-Demographic Surveillance Systems (HDSS) from 2012 to 2019: the Agincourt HDSS in Mpumalanga and the Africa Health Research Institute HDSS in KwaZulu-Natal. We assessed the COMCAT system’s reliability (consistency over time and similar conditions), validity (the extent to which COMCATs capture a sufficient range of key circumstances and events at and around time of death) and relevance (for public health decision-making). RESULTS: Plausible results were reliably produced, with ‘emergencies’, ‘recognition, ‘accessing care’ and ‘perceived quality’ characterising the majority of avoidable deaths. We identified gaps and developed an additional COMCAT ‘referral’, which accounted for a significant proportion of deaths in sub-group analysis. To support decision-making, data that establish an impetus for action, that can be operationalised into interventions and that capture deaths outside facilities are important. CONCLUSIONS: COMCAT is a pragmatic, scalable approach enhancing functionality of VA providing basic information, not available from other sources, on care seeking and utilisation at and around time of death. Continued development with stakeholders in health systems, civil registration, community and research environments will further strengthen the tool to capture social and health systems drivers of avoidable deaths and promote use in practice settings. |
format | Online Article Text |
id | pubmed-8986216 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Taylor & Francis |
record_format | MEDLINE/PubMed |
spelling | pubmed-89862162022-04-07 Refining circumstances of mortality categories (COMCAT): a verbal autopsy model connecting circumstances of deaths with outcomes for public health decision-making D’Ambruoso, Lucia Price, Jessica Cowan, Eilidh Goosen, Gerhard Fottrell, Edward Herbst, Kobus van der Merwe, Maria Sigudla, Jerry Davies, Justine Kahn, Kathleen Glob Health Action Research Article BACKGROUND: Recognising that the causes of over half the world’s deaths pass unrecorded, the World Health Organization (WHO) leads development of Verbal Autopsy (VA): a method to understand causes of death in otherwise unregistered populations. Recently, VA has been developed for use outside research environments, supporting countries and communities to recognise and act on their own health priorities. We developed the Circumstances of Mortality Categories (COMCATs) system within VA to provide complementary circumstantial categorisations of deaths. OBJECTIVES: Refine the COMCAT system to (a) support large-scale population assessment and (b) inform public health decision-making. METHODS: We analysed VA data for 7,980 deaths from two South African Health and Socio-Demographic Surveillance Systems (HDSS) from 2012 to 2019: the Agincourt HDSS in Mpumalanga and the Africa Health Research Institute HDSS in KwaZulu-Natal. We assessed the COMCAT system’s reliability (consistency over time and similar conditions), validity (the extent to which COMCATs capture a sufficient range of key circumstances and events at and around time of death) and relevance (for public health decision-making). RESULTS: Plausible results were reliably produced, with ‘emergencies’, ‘recognition, ‘accessing care’ and ‘perceived quality’ characterising the majority of avoidable deaths. We identified gaps and developed an additional COMCAT ‘referral’, which accounted for a significant proportion of deaths in sub-group analysis. To support decision-making, data that establish an impetus for action, that can be operationalised into interventions and that capture deaths outside facilities are important. CONCLUSIONS: COMCAT is a pragmatic, scalable approach enhancing functionality of VA providing basic information, not available from other sources, on care seeking and utilisation at and around time of death. Continued development with stakeholders in health systems, civil registration, community and research environments will further strengthen the tool to capture social and health systems drivers of avoidable deaths and promote use in practice settings. Taylor & Francis 2022-04-04 /pmc/articles/PMC8986216/ /pubmed/35377291 http://dx.doi.org/10.1080/16549716.2021.2000091 Text en © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article D’Ambruoso, Lucia Price, Jessica Cowan, Eilidh Goosen, Gerhard Fottrell, Edward Herbst, Kobus van der Merwe, Maria Sigudla, Jerry Davies, Justine Kahn, Kathleen Refining circumstances of mortality categories (COMCAT): a verbal autopsy model connecting circumstances of deaths with outcomes for public health decision-making |
title | Refining circumstances of mortality categories (COMCAT): a verbal autopsy model connecting circumstances of deaths with outcomes for public health decision-making |
title_full | Refining circumstances of mortality categories (COMCAT): a verbal autopsy model connecting circumstances of deaths with outcomes for public health decision-making |
title_fullStr | Refining circumstances of mortality categories (COMCAT): a verbal autopsy model connecting circumstances of deaths with outcomes for public health decision-making |
title_full_unstemmed | Refining circumstances of mortality categories (COMCAT): a verbal autopsy model connecting circumstances of deaths with outcomes for public health decision-making |
title_short | Refining circumstances of mortality categories (COMCAT): a verbal autopsy model connecting circumstances of deaths with outcomes for public health decision-making |
title_sort | refining circumstances of mortality categories (comcat): a verbal autopsy model connecting circumstances of deaths with outcomes for public health decision-making |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8986216/ https://www.ncbi.nlm.nih.gov/pubmed/35377291 http://dx.doi.org/10.1080/16549716.2021.2000091 |
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