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Verbal Autopsy: Evaluation of Methods to Certify Causes of Death in Uganda
To assess different methods for determining cause of death from verbal autopsy (VA) questionnaire data, the intra-rater reliability of Physician-Certified Verbal Autopsy (PCVA) and the accuracy of PCVA, expert-derived (non-hierarchical) and data-driven (hierarchal) algorithms were assessed for deter...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Public Library of Science
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4472780/ https://www.ncbi.nlm.nih.gov/pubmed/26086600 http://dx.doi.org/10.1371/journal.pone.0128801 |
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author | Mpimbaza, Arthur Filler, Scott Katureebe, Agaba Quick, Linda Chandramohan, Daniel Staedke, Sarah G. |
author_facet | Mpimbaza, Arthur Filler, Scott Katureebe, Agaba Quick, Linda Chandramohan, Daniel Staedke, Sarah G. |
author_sort | Mpimbaza, Arthur |
collection | PubMed |
description | To assess different methods for determining cause of death from verbal autopsy (VA) questionnaire data, the intra-rater reliability of Physician-Certified Verbal Autopsy (PCVA) and the accuracy of PCVA, expert-derived (non-hierarchical) and data-driven (hierarchal) algorithms were assessed for determining common causes of death in Ugandan children. A verbal autopsy validation study was conducted from 2008-2009 in three different sites in Uganda. The dataset included 104 neonatal deaths (0-27 days) and 615 childhood deaths (1-59 months) with the cause(s) of death classified by PCVA and physician review of hospital medical records (the ‘reference standard’). Of the original 719 questionnaires, 141 (20%) were selected for a second review by the same physicians; the repeat cause(s) of death were compared to the original,and agreement assessed using the Kappa statistic.Physician reviewers’ refined non-hierarchical algorithms for common causes of death from existing expert algorithms, from which, hierarchal algorithms were developed. The accuracy of PCVA, non-hierarchical, and hierarchical algorithms for determining cause(s) of death from all 719 VA questionnaires was determined using the reference standard. Overall, intra-rater repeatability was high (83% agreement, Kappa 0.79 [95% CI 0.76-0.82]). PCVA performed well, with high specificity for determining cause of neonatal (>67%), and childhood (>83%) deaths, resulting in fairly accurate cause-specific mortality fraction (CSMF) estimates. For most causes of death in children, non-hierarchical algorithms had higher sensitivity, but correspondingly lower specificity, than PCVA and hierarchical algorithms, resulting in inaccurate CSMF estimates. Hierarchical algorithms were specific for most causes of death, and CSMF estimates were comparable to the reference standard and PCVA. Inter-rater reliability of PCVA was high, and overall PCVA performed well. Hierarchical algorithms performed better than non-hierarchical algorithms due to higher specificity and more accurate CSMF estimates. Use of PCVA to determine cause of death from VA questionnaire data is reasonable while automated data-driven algorithms are improved. |
format | Online Article Text |
id | pubmed-4472780 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-44727802015-06-29 Verbal Autopsy: Evaluation of Methods to Certify Causes of Death in Uganda Mpimbaza, Arthur Filler, Scott Katureebe, Agaba Quick, Linda Chandramohan, Daniel Staedke, Sarah G. PLoS One Research Article To assess different methods for determining cause of death from verbal autopsy (VA) questionnaire data, the intra-rater reliability of Physician-Certified Verbal Autopsy (PCVA) and the accuracy of PCVA, expert-derived (non-hierarchical) and data-driven (hierarchal) algorithms were assessed for determining common causes of death in Ugandan children. A verbal autopsy validation study was conducted from 2008-2009 in three different sites in Uganda. The dataset included 104 neonatal deaths (0-27 days) and 615 childhood deaths (1-59 months) with the cause(s) of death classified by PCVA and physician review of hospital medical records (the ‘reference standard’). Of the original 719 questionnaires, 141 (20%) were selected for a second review by the same physicians; the repeat cause(s) of death were compared to the original,and agreement assessed using the Kappa statistic.Physician reviewers’ refined non-hierarchical algorithms for common causes of death from existing expert algorithms, from which, hierarchal algorithms were developed. The accuracy of PCVA, non-hierarchical, and hierarchical algorithms for determining cause(s) of death from all 719 VA questionnaires was determined using the reference standard. Overall, intra-rater repeatability was high (83% agreement, Kappa 0.79 [95% CI 0.76-0.82]). PCVA performed well, with high specificity for determining cause of neonatal (>67%), and childhood (>83%) deaths, resulting in fairly accurate cause-specific mortality fraction (CSMF) estimates. For most causes of death in children, non-hierarchical algorithms had higher sensitivity, but correspondingly lower specificity, than PCVA and hierarchical algorithms, resulting in inaccurate CSMF estimates. Hierarchical algorithms were specific for most causes of death, and CSMF estimates were comparable to the reference standard and PCVA. Inter-rater reliability of PCVA was high, and overall PCVA performed well. Hierarchical algorithms performed better than non-hierarchical algorithms due to higher specificity and more accurate CSMF estimates. Use of PCVA to determine cause of death from VA questionnaire data is reasonable while automated data-driven algorithms are improved. Public Library of Science 2015-06-18 /pmc/articles/PMC4472780/ /pubmed/26086600 http://dx.doi.org/10.1371/journal.pone.0128801 Text en https://creativecommons.org/publicdomain/zero/1.0/ This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration, which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. |
spellingShingle | Research Article Mpimbaza, Arthur Filler, Scott Katureebe, Agaba Quick, Linda Chandramohan, Daniel Staedke, Sarah G. Verbal Autopsy: Evaluation of Methods to Certify Causes of Death in Uganda |
title | Verbal Autopsy: Evaluation of Methods to Certify Causes of Death in Uganda |
title_full | Verbal Autopsy: Evaluation of Methods to Certify Causes of Death in Uganda |
title_fullStr | Verbal Autopsy: Evaluation of Methods to Certify Causes of Death in Uganda |
title_full_unstemmed | Verbal Autopsy: Evaluation of Methods to Certify Causes of Death in Uganda |
title_short | Verbal Autopsy: Evaluation of Methods to Certify Causes of Death in Uganda |
title_sort | verbal autopsy: evaluation of methods to certify causes of death in uganda |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4472780/ https://www.ncbi.nlm.nih.gov/pubmed/26086600 http://dx.doi.org/10.1371/journal.pone.0128801 |
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