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Performance of the Tariff Method: validation of a simple additive algorithm for analysis of verbal autopsies

BACKGROUND: Verbal autopsies provide valuable information for studying mortality patterns in populations that lack reliable vital registration data. Methods for transforming verbal autopsy results into meaningful information for health workers and policymakers, however, are often costly or complicat...

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Autores principales: James, Spencer L, Flaxman, Abraham D, Murray, Christopher JL
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3160924/
https://www.ncbi.nlm.nih.gov/pubmed/21816107
http://dx.doi.org/10.1186/1478-7954-9-31
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author James, Spencer L
Flaxman, Abraham D
Murray, Christopher JL
author_facet James, Spencer L
Flaxman, Abraham D
Murray, Christopher JL
author_sort James, Spencer L
collection PubMed
description BACKGROUND: Verbal autopsies provide valuable information for studying mortality patterns in populations that lack reliable vital registration data. Methods for transforming verbal autopsy results into meaningful information for health workers and policymakers, however, are often costly or complicated to use. We present a simple additive algorithm, the Tariff Method (termed Tariff), which can be used for assigning individual cause of death and for determining cause-specific mortality fractions (CSMFs) from verbal autopsy data. METHODS: Tariff calculates a score, or "tariff," for each cause, for each sign/symptom, across a pool of validated verbal autopsy data. The tariffs are summed for a given response pattern in a verbal autopsy, and this sum (score) provides the basis for predicting the cause of death in a dataset. We implemented this algorithm and evaluated the method's predictive ability, both in terms of chance-corrected concordance at the individual cause assignment level and in terms of CSMF accuracy at the population level. The analysis was conducted separately for adult, child, and neonatal verbal autopsies across 500 pairs of train-test validation verbal autopsy data. RESULTS: Tariff is capable of outperforming physician-certified verbal autopsy in most cases. In terms of chance-corrected concordance, the method achieves 44.5% in adults, 39% in children, and 23.9% in neonates. CSMF accuracy was 0.745 in adults, 0.709 in children, and 0.679 in neonates. CONCLUSIONS: Verbal autopsies can be an efficient means of obtaining cause of death data, and Tariff provides an intuitive, reliable method for generating individual cause assignment and CSMFs. The method is transparent and flexible and can be readily implemented by users without training in statistics or computer science.
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spelling pubmed-31609242011-08-25 Performance of the Tariff Method: validation of a simple additive algorithm for analysis of verbal autopsies James, Spencer L Flaxman, Abraham D Murray, Christopher JL Popul Health Metr Research BACKGROUND: Verbal autopsies provide valuable information for studying mortality patterns in populations that lack reliable vital registration data. Methods for transforming verbal autopsy results into meaningful information for health workers and policymakers, however, are often costly or complicated to use. We present a simple additive algorithm, the Tariff Method (termed Tariff), which can be used for assigning individual cause of death and for determining cause-specific mortality fractions (CSMFs) from verbal autopsy data. METHODS: Tariff calculates a score, or "tariff," for each cause, for each sign/symptom, across a pool of validated verbal autopsy data. The tariffs are summed for a given response pattern in a verbal autopsy, and this sum (score) provides the basis for predicting the cause of death in a dataset. We implemented this algorithm and evaluated the method's predictive ability, both in terms of chance-corrected concordance at the individual cause assignment level and in terms of CSMF accuracy at the population level. The analysis was conducted separately for adult, child, and neonatal verbal autopsies across 500 pairs of train-test validation verbal autopsy data. RESULTS: Tariff is capable of outperforming physician-certified verbal autopsy in most cases. In terms of chance-corrected concordance, the method achieves 44.5% in adults, 39% in children, and 23.9% in neonates. CSMF accuracy was 0.745 in adults, 0.709 in children, and 0.679 in neonates. CONCLUSIONS: Verbal autopsies can be an efficient means of obtaining cause of death data, and Tariff provides an intuitive, reliable method for generating individual cause assignment and CSMFs. The method is transparent and flexible and can be readily implemented by users without training in statistics or computer science. BioMed Central 2011-08-04 /pmc/articles/PMC3160924/ /pubmed/21816107 http://dx.doi.org/10.1186/1478-7954-9-31 Text en Copyright ©2011 James et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
James, Spencer L
Flaxman, Abraham D
Murray, Christopher JL
Performance of the Tariff Method: validation of a simple additive algorithm for analysis of verbal autopsies
title Performance of the Tariff Method: validation of a simple additive algorithm for analysis of verbal autopsies
title_full Performance of the Tariff Method: validation of a simple additive algorithm for analysis of verbal autopsies
title_fullStr Performance of the Tariff Method: validation of a simple additive algorithm for analysis of verbal autopsies
title_full_unstemmed Performance of the Tariff Method: validation of a simple additive algorithm for analysis of verbal autopsies
title_short Performance of the Tariff Method: validation of a simple additive algorithm for analysis of verbal autopsies
title_sort performance of the tariff method: validation of a simple additive algorithm for analysis of verbal autopsies
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3160924/
https://www.ncbi.nlm.nih.gov/pubmed/21816107
http://dx.doi.org/10.1186/1478-7954-9-31
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