Background Limited evidence exists about associations between road crash injury and economic status in sub-Saharan Africa from large, population-based data sets. Existing studies generally do not incorporate fatal crashes. This study aims to understand the relationship between relative wealth and road crash injury and severity using population-representative cross-sectional data from Uganda’s 2016 Demographic and Health Survey .
Methods One-year road crash risk was flexibly modelled as a function of wealth using fractional polynomial models, stratified by sex and rural/urban residence. Wealth was operationalised as 1/20th quantiles of the first principal component from a polychoric principal component analysis. Injury severity was coded as a three-level ordinal variable; associations with wealth were modelled with ordinal logistic regression on quintiles of relative wealth, stratified by residence.
Results Overall, injury risk peaked in the upper middle of the wealth distribution. Rural resident injury risk increased monotonically with wealth. Urban resident risk had an upside-down U shape. Risk peaked in the distribution’s middle at about double the lowest levels. Only urban men had higher risk among the least wealthy than most wealthy (3.2% vs 1.7%; difference=1.5 percentage points, 95% CI 0.2 to 2.7). Among those with road crash injuries, greater relative wealth was associated with decreased likelihood of more severe injury (33.2 percentage points lower in the highest category than lowest, 95% CI 18.4 to 48.1) or death (5.9 percentage points, 95% CI −0.1 to 11.8) for urban residents but not rural residents.
Conclusion Relationships between relative wealth and injury risk and severity are complex and different for urban and rural Ugandans.
- public health
- descriptive epidemiology
- low-middle income country
Data availability statement
Data are available in a public, open access repository. Data are freely available from the Demographic and Health Survey programme at https://dhsprogram.com/data/. Statistical code to prepare data sets for analysis and replicate all analyses reported in this paper is provided as an online supplement.
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