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Public health utility of cause of death data: applying empirical algorithms to improve data quality

BACKGROUND: Accurate, comprehensive, cause-specific mortality estimates are crucial for informing public health decision making worldwide. Incorrectly or vaguely assigned deaths, defined as garbage-coded deaths, mask the true cause distribution. The Global Burden of Disease (GBD) study has developed...

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Autores principales: Johnson, Sarah Charlotte, Cunningham, Matthew, Dippenaar, Ilse N., Sharara, Fablina, Wool, Eve E., Agesa, Kareha M., Han, Chieh, Miller-Petrie, Molly K., Wilson, Shadrach, Fuller, John E., Balassyano, Shelly, Bertolacci, Gregory J., Davis Weaver, Nicole, Lopez, Alan D., Murray, Christopher J. L., Naghavi, Mohsen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8170729/
https://www.ncbi.nlm.nih.gov/pubmed/34078366
http://dx.doi.org/10.1186/s12911-021-01501-1
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author Johnson, Sarah Charlotte
Cunningham, Matthew
Dippenaar, Ilse N.
Sharara, Fablina
Wool, Eve E.
Agesa, Kareha M.
Han, Chieh
Miller-Petrie, Molly K.
Wilson, Shadrach
Fuller, John E.
Balassyano, Shelly
Bertolacci, Gregory J.
Davis Weaver, Nicole
Lopez, Alan D.
Murray, Christopher J. L.
Naghavi, Mohsen
author_facet Johnson, Sarah Charlotte
Cunningham, Matthew
Dippenaar, Ilse N.
Sharara, Fablina
Wool, Eve E.
Agesa, Kareha M.
Han, Chieh
Miller-Petrie, Molly K.
Wilson, Shadrach
Fuller, John E.
Balassyano, Shelly
Bertolacci, Gregory J.
Davis Weaver, Nicole
Lopez, Alan D.
Murray, Christopher J. L.
Naghavi, Mohsen
author_sort Johnson, Sarah Charlotte
collection PubMed
description BACKGROUND: Accurate, comprehensive, cause-specific mortality estimates are crucial for informing public health decision making worldwide. Incorrectly or vaguely assigned deaths, defined as garbage-coded deaths, mask the true cause distribution. The Global Burden of Disease (GBD) study has developed methods to create comparable, timely, cause-specific mortality estimates; an impactful data processing method is the reallocation of garbage-coded deaths to a plausible underlying cause of death. We identify the pattern of garbage-coded deaths in the world and present the methods used to determine their redistribution to generate more plausible cause of death assignments. METHODS: We describe the methods developed for the GBD 2019 study and subsequent iterations to redistribute garbage-coded deaths in vital registration data to plausible underlying causes. These methods include analysis of multiple cause data, negative correlation, impairment, and proportional redistribution. We classify garbage codes into classes according to the level of specificity of the reported cause of death (CoD) and capture trends in the global pattern of proportion of garbage-coded deaths, disaggregated by these classes, and the relationship between this proportion and the Socio-Demographic Index. We examine the relative importance of the top four garbage codes by age and sex and demonstrate the impact of redistribution on the annual GBD CoD rankings. RESULTS: The proportion of least-specific (class 1 and 2) garbage-coded deaths ranged from 3.7% of all vital registration deaths to 67.3% in 2015, and the age-standardized proportion had an overall negative association with the Socio-Demographic Index. When broken down by age and sex, the category for unspecified lower respiratory infections was responsible for nearly 30% of garbage-coded deaths in those under 1 year of age for both sexes, representing the largest proportion of garbage codes for that age group. We show how the cause distribution by number of deaths changes before and after redistribution for four countries: Brazil, the United States, Japan, and France, highlighting the necessity of accounting for garbage-coded deaths in the GBD. CONCLUSIONS: We provide a detailed description of redistribution methods developed for CoD data in the GBD; these methods represent an overall improvement in empiricism compared to past reliance on a priori knowledge. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12911-021-01501-1.
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spelling pubmed-81707292021-06-02 Public health utility of cause of death data: applying empirical algorithms to improve data quality Johnson, Sarah Charlotte Cunningham, Matthew Dippenaar, Ilse N. Sharara, Fablina Wool, Eve E. Agesa, Kareha M. Han, Chieh Miller-Petrie, Molly K. Wilson, Shadrach Fuller, John E. Balassyano, Shelly Bertolacci, Gregory J. Davis Weaver, Nicole Lopez, Alan D. Murray, Christopher J. L. Naghavi, Mohsen BMC Med Inform Decis Mak Research Article BACKGROUND: Accurate, comprehensive, cause-specific mortality estimates are crucial for informing public health decision making worldwide. Incorrectly or vaguely assigned deaths, defined as garbage-coded deaths, mask the true cause distribution. The Global Burden of Disease (GBD) study has developed methods to create comparable, timely, cause-specific mortality estimates; an impactful data processing method is the reallocation of garbage-coded deaths to a plausible underlying cause of death. We identify the pattern of garbage-coded deaths in the world and present the methods used to determine their redistribution to generate more plausible cause of death assignments. METHODS: We describe the methods developed for the GBD 2019 study and subsequent iterations to redistribute garbage-coded deaths in vital registration data to plausible underlying causes. These methods include analysis of multiple cause data, negative correlation, impairment, and proportional redistribution. We classify garbage codes into classes according to the level of specificity of the reported cause of death (CoD) and capture trends in the global pattern of proportion of garbage-coded deaths, disaggregated by these classes, and the relationship between this proportion and the Socio-Demographic Index. We examine the relative importance of the top four garbage codes by age and sex and demonstrate the impact of redistribution on the annual GBD CoD rankings. RESULTS: The proportion of least-specific (class 1 and 2) garbage-coded deaths ranged from 3.7% of all vital registration deaths to 67.3% in 2015, and the age-standardized proportion had an overall negative association with the Socio-Demographic Index. When broken down by age and sex, the category for unspecified lower respiratory infections was responsible for nearly 30% of garbage-coded deaths in those under 1 year of age for both sexes, representing the largest proportion of garbage codes for that age group. We show how the cause distribution by number of deaths changes before and after redistribution for four countries: Brazil, the United States, Japan, and France, highlighting the necessity of accounting for garbage-coded deaths in the GBD. CONCLUSIONS: We provide a detailed description of redistribution methods developed for CoD data in the GBD; these methods represent an overall improvement in empiricism compared to past reliance on a priori knowledge. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12911-021-01501-1. BioMed Central 2021-06-02 /pmc/articles/PMC8170729/ /pubmed/34078366 http://dx.doi.org/10.1186/s12911-021-01501-1 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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
Johnson, Sarah Charlotte
Cunningham, Matthew
Dippenaar, Ilse N.
Sharara, Fablina
Wool, Eve E.
Agesa, Kareha M.
Han, Chieh
Miller-Petrie, Molly K.
Wilson, Shadrach
Fuller, John E.
Balassyano, Shelly
Bertolacci, Gregory J.
Davis Weaver, Nicole
Lopez, Alan D.
Murray, Christopher J. L.
Naghavi, Mohsen
Public health utility of cause of death data: applying empirical algorithms to improve data quality
title Public health utility of cause of death data: applying empirical algorithms to improve data quality
title_full Public health utility of cause of death data: applying empirical algorithms to improve data quality
title_fullStr Public health utility of cause of death data: applying empirical algorithms to improve data quality
title_full_unstemmed Public health utility of cause of death data: applying empirical algorithms to improve data quality
title_short Public health utility of cause of death data: applying empirical algorithms to improve data quality
title_sort public health utility of cause of death data: applying empirical algorithms to improve data quality
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8170729/
https://www.ncbi.nlm.nih.gov/pubmed/34078366
http://dx.doi.org/10.1186/s12911-021-01501-1
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