What question did this study set out to answer?

Describe empirical redistribution algorithms developed for the Global Burden of Disease study to systematically reallocate vaguely or incorrectly coded deaths to plausible underlying causes.

June 2, 2021BMC Medical Informatics and Decision MakingOpen Access

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

View Full Paper

Ask AI

Bookmark

Authors

SJSarah Charlotte JohnsonNew York City Department of Health and Mental Hygiene MCMatthew CunninghamInstitute for Health Metrics and Evaluation IDIlse N DippenaarUniversity of Washington

Discussion

Member takes

Implication

Methodological study finds wide variation in garbage-coded death records globally, highlighting the necessity of empirical reallocation algorithms for accurate mortality tracking.

Key Points

Describe empirical redistribution algorithms developed for the Global Burden of Disease study to systematically reallocate vaguely or incorrectly coded deaths to plausible underlying causes.
Classified garbage codes based on diagnostic specificity and applied empirical methods, including multiple-cause data analysis, negative correlation, impairment analysis, and proportional redistribution, to global vital registration datasets.
Assessed global trends across Socio-Demographic Index levels and evaluated redistribution impacts across age, sex, and four reference countries: Brazil, the United States, Japan, and France.
Least-specific (class 1 and 2) garbage codes accounted for between 3.7% and 67.3% of all vital registration deaths in 2015, showing an inverse age-standardized association with the Socio-Demographic Index.
Unspecified lower respiratory infections represented nearly 30% of all garbage-coded deaths in infants under 1 year of age across both sexes, comprising the largest single contributor in this age bracket.
Empirical reallocation substantially altered the overall cause-of-death rankings and total death counts across Brazil, the United States, Japan, and France.

Cite This Study

Johnson et al. (2021) studied this question.

synapsesocial.com/papers/6a99b93509e2beb8af95b916 https://doi.org/10.1186/s12911-021-01501-1

View Full Paper

Ask AI

Bookmark

Implication

Methodological study finds wide variation in garbage-coded death records globally, highlighting the necessity of empirical reallocation algorithms for accurate mortality tracking.

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

Discussion

Member takes

Implication

Key Points

Cite This Study

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

Discussion

Member takes

Implication

Key Points

Cite This Study

Also Consider

Also Consider