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.

Unintentional injuries in Mexico, 1990-2017: findings from the Global Burden of Disease Study 2017.

BACKGROUND: To date, the burden of injury in Mexico has not been comprehensively assessed using recent advances in population health research, including those in the Global Burden of Disease Study 2017 (GBD 2017). METHODS: We used GBD 2017 for burden of unintentional injury estimates, including transport injuries, for Mexico and each state in Mexico from 1990 to 2017. We examined subnational variation, age patterns, sex differences and time trends for all injury burden metrics. RESULTS: Unintentional injury deaths in Mexico decreased from 45 363 deaths (44 662 to 46 038) in 1990 to 42 702 (41 439 to 43 745) in 2017, while age-standardised mortality rates decreased from 65.2 (64.4 to 66.1) in 1990 to 35.1 (34.1 to 36.0) per 100 000 in 2017. In terms of non-fatal outcomes, there were 3 120 211 (2 879 993 to 3 377 945) new injury cases in 1990, which increased to 5 234 214 (4 812 615 to 5 701 669) new cases of injury in 2017. We estimated 2 761 957 (2 676 267 to 2 859 777) disability-adjusted life years (DALYs) due to injuries in Mexico in 1990 compared with 2 376 952 (2 224 588 to 2 551 004) DALYs in 2017. We found subnational variation in health loss across Mexico's states, including concentrated burden in Tabasco, Chihuahua and Zacatecas. CONCLUSIONS: In Mexico, from 1990 to 2017, mortality due to unintentional injuries has decreased, while non-fatal incident cases have increased. However, unintentional injuries continue to cause considerable mortality and morbidity, with patterns that vary by state, age, sex and year. Future research should focus on targeted interventions to decrease injury burden in high-risk populations.

Estimating global injuries morbidity and mortality: methods and data used in the Global Burden of Disease 2017 study.

BACKGROUND: While there is a long history of measuring death and disability from injuries, modern research methods must account for the wide spectrum of disability that can occur in an injury, and must provide estimates with sufficient demographic, geographical and temporal detail to be useful for policy makers. The Global Burden of Disease (GBD) 2017 study used methods to provide highly detailed estimates of global injury burden that meet these criteria. METHODS: In this study, we report and discuss the methods used in GBD 2017 for injury morbidity and mortality burden estimation. In summary, these methods included estimating cause-specific mortality for every cause of injury, and then estimating incidence for every cause of injury. Non-fatal disability for each cause is then calculated based on the probabilities of suffering from different types of bodily injury experienced. RESULTS: GBD 2017 produced morbidity and mortality estimates for 38 causes of injury. Estimates were produced in terms of incidence, prevalence, years lived with disability, cause-specific mortality, years of life lost and disability-adjusted life-years for a 28-year period for 22 age groups, 195 countries and both sexes. CONCLUSIONS: GBD 2017 demonstrated a complex and sophisticated series of analytical steps using the largest known database of morbidity and mortality data on injuries. GBD 2017 results should be used to help inform injury prevention policy making and resource allocation. We also identify important avenues for improving injury burden estimation in the future.