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 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.

Accounting for misclassified and unknown cause of death data in vital registration systems for estimating trends in HIV mortality.

INTRODUCTION: Misclassification of HIV deaths can substantially diminish the usefulness of cause of death data for decision-making. In this study, we describe the methods developed by the Global Burden of Disease Study to account for the misclassified cause of death data from vital registration systems for estimating HIV mortality in 132 countries and territories. METHODS: The cause of death data were obtained from the World Health Organization Mortality Database and official country-specific mortality databases. We implemented two steps to adjust the raw cause of death data: (1) redistributing garbage codes to underlying causes of death, including HIV/AIDS by applying methods, such as analysis of multiple cause data and proportional redistribution, and (2) reassigning HIV deaths misclassified as other causes to HIV/AIDS by examining the age patterns of underlying causes in location and years with and without HIV epidemics. RESULTS: In 132 countries, during the period from 1990 to 2018, 1,848,761 deaths were reported as caused by HIV/AIDS. After garbage code redistribution in these 132 countries, this number increased to 4,165,015 deaths. An additional 1,944,291 deaths were added through correction of HIV deaths misclassified as other causes in 44 countries. The proportion of HIV deaths derived from garbage code redistribution decreased over time, from 0.4 in 1990 to 0.1 in 2018. The proportion of deaths derived from HIV misclassification correction peaked at 0.4 in 2006 and declined afterwards to 0.08 in 2018. The greatest contributors to garbage code redistribution were "immunodeficiency antibody" (ICD 9: 279-279.1; ICD 10: D80-D80.9) and "immunodeficiency other" (ICD 9: 279, 279.5-279.9; ICD 10: D83-D84.9, D89, D89.8-D89.9), which together contributed 77% of all redistributed deaths at their peak in 1995. Respiratory tuberculosis (ICD 9: 010-012.9; ICD 10: A10-A14, A15-A16.9) contributed the greatest proportion of all HIV misclassified deaths (25-62% per year) over the most years. CONCLUSIONS: Correcting for miscoding and misclassification of cause of death data can enhance the utility of the data for analyzing trends in HIV mortality and tracking progress toward the Sustainable Development Goal targets.