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@article{Johnson:2021:10.1186/s12911-021-01501-1,
author = {Johnson, SC and Cunningham, M and Dippenaar, IN and Sharara, F and Wool, EE and Agesa, KM and Han, C and Miller-Petrie, MK and Wilson, S and Fuller, JE and Balassyano, S and Bertolacci, GJ and Davis, Weaver N and Lopez, AD and Murray, CJL and Naghavi, M},
doi = {10.1186/s12911-021-01501-1},
journal = {BMC Medical Informatics and Decision Making},
pages = {1--20},
title = {Public health utility of cause of death data: applying empirical algorithms to improve data quality},
url = {http://dx.doi.org/10.1186/s12911-021-01501-1},
volume = {21},
year = {2021}
}

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TY  - JOUR
AB  - BackgroundAccurate, 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.MethodsWe 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.ResultsThe 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
AU  - Johnson,SC
AU  - Cunningham,M
AU  - Dippenaar,IN
AU  - Sharara,F
AU  - Wool,EE
AU  - Agesa,KM
AU  - Han,C
AU  - Miller-Petrie,MK
AU  - Wilson,S
AU  - Fuller,JE
AU  - Balassyano,S
AU  - Bertolacci,GJ
AU  - Davis,Weaver N
AU  - Lopez,AD
AU  - Murray,CJL
AU  - Naghavi,M
DO  - 10.1186/s12911-021-01501-1
EP  - 20
PY  - 2021///
SN  - 1472-6947
SP  - 1
TI  - Public health utility of cause of death data: applying empirical algorithms to improve data quality
T2  - BMC Medical Informatics and Decision Making
UR  - http://dx.doi.org/10.1186/s12911-021-01501-1
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000660772600002&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-021-01501-1
UR  - http://hdl.handle.net/10044/1/92071
VL  - 21
ER  -

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