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

A snake handler suffers ophthalmic envenomation by an Ashe’s spitting cobra.

(A) the offending snake was identified as an Ashe’s spitting cobra (Naja ashei) by a trained herpetologist and the snake handler. (B) the eye of the victim after the ophthalmic envenomation occurred, highlighting the irritation of the eye. (C) the affected eye of the victim the morning after the incident occurred, highlighting the thick mucus build-up. (D) the affected eye at 2 pm on the day after the incident occurred, following Diclogenta treatment.</p

Symptoms and main events with appropriate timelines illustrating the impact and recovery following the ophthalmic envenoming by an Ashes spitting cobra.

Symptoms and main events with appropriate timelines illustrating the impact and recovery following the ophthalmic envenoming by an Ashes spitting cobra.</p

Average treatment costs of different categories of total treatment costs for each snake.

All the values are shown in percentages of total treatment costs for each snake.</p

The relationship between the gender and age of snakebite patients and their total treatment costs.

(A) The relationship between the gender of the patient and their total treatment costs for different snake species. (B) The patient’s age and the total treatment costs for different snakebites.</p

The breakdown of total treatment costs based on the type of snake species and nature of expenses.

(A) Total treatment costs of patients bitten by different snake species. The total costs for the hospital (B), pharmacy (C), laboratory (D) and investigation (E) were also analysed individually.</p

Impact of the number of vials of antivenom used, and the length of hospital stay in snakebite treatment costs.

(A) The relationship between the number of antivenom vials received by snakebite patients and their corresponding total treatment costs for the species in question. (B) The length of hospital stay in days for snakebite patients and the corresponding total treatment costs. NA—‘not available’ indicates that for a small number of patients, accurate details are not available. (C) The number of patients arrived at the hospital at different time points following the snakebites.</p

A Sankey plot showing the relationships between treatment costs and various parameters analysed in this study.

RV—Russell’s viper, CO—cobra, KR—common krait, UN—unknown, NV—non-venomous snakes and SSV—saw-scaled viper.</p

Age and gender profile of snakebite patients.

(A) A graph showing the age and gender distribution of the 913 total snakebite patients included in this study. (B) The age and gender distribution of 355 Russell’s viper bite patients.</p

Total average treatment costs for snakebite patients who were bitten at different times of the day/night.

The treatment costs are shown in INR and the number of patients is shown in brackets.</p