Road traffic death coding quality in the WHO Mortality Database

BULLETIN OF THE WORLD HEALTH ORGANIZATION(2023)

Cited 0|Views13
No score
Abstract
Objective To evaluate the precision and dependability of road traffic mortality data recorded in the World Health Organization Mortality Database and investigate how uncorrected data influence vital mortality statistics used in traffic safety programmes worldwide. Methods We assessed country and territory-specific data quality from 2015 to 2020 by calculating the proportions of five types of nonspecific cause of death codes related to road traffic mortality. We compared age-adjusted road traffic mortality and changes in the average annual mortality rate before and after correcting the deaths with nonspecific codes. We generated road traffic mortality projections with both corrected and uncorrected codes, and redistributed the data using the proportionate method. Findings We analysed data from 124 countries and territories with at least one year of mortality data from 2015 to 2020. The number of countries and territories reporting more than 20% of deaths with ill-defined or unknown cause was 2; countries reporting injury deaths with undetermined intent was 3; countries reporting unspecified unintentional injury deaths was 21; countries reporting unspecified transport crash deaths was 3; and countries reporting unspecified unintentional road traffic deaths was 30. After redistributing deaths with nonspecific codes, road traffic mortality changed by greater than 50% in 7% (5/73) to 18% (9/51) of countries and territories. Conclusion Nonspecific codes led to inaccurate mortality estimates in many countries. We recommend that injury researchers and policymakers acknowledge the potential pitfalls of relying on raw or uncorrected road traffic mortality data and instead use corrected data to ensure more accurate estimates when improving road traffic safety programmes.
More
Translated text
Key words
road traffic death,who mortality database,coding
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined