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BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 1/2021

Open Access 01-12-2021 | Public Health | Research article

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

Authors: Sarah Charlotte Johnson, Matthew Cunningham, Ilse N. Dippenaar, Fablina Sharara, Eve E. Wool, Kareha M. Agesa, Chieh Han, Molly K. Miller-Petrie, Shadrach Wilson, John E. Fuller, Shelly Balassyano, Gregory J. Bertolacci, Nicole Davis Weaver, Alan D. Lopez, Christopher J. L. Murray, Mohsen Naghavi, GBD Cause of Death Collaborators

Published in: BMC Medical Informatics and Decision Making | Issue 1/2021

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Abstract

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.
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Metadata
Title
Public health utility of cause of death data: applying empirical algorithms to improve data quality
Authors
Sarah Charlotte Johnson
Matthew Cunningham
Ilse N. Dippenaar
Fablina Sharara
Eve E. Wool
Kareha M. Agesa
Chieh Han
Molly K. Miller-Petrie
Shadrach Wilson
John E. Fuller
Shelly Balassyano
Gregory J. Bertolacci
Nicole Davis Weaver
Alan D. Lopez
Christopher J. L. Murray
Mohsen Naghavi
GBD Cause of Death Collaborators
Publication date
01-12-2021
Publisher
BioMed Central
Keyword
Public Health
Published in
BMC Medical Informatics and Decision Making / Issue 1/2021
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-021-01501-1

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