Muni, Kennedy MaringNingwa, AlbertOsuret, JimmyBayiga Zziwa, EstherNamatovu, StellahBiribawa, ClaireNakafeero, MaryMutto, MiltonGuwatudde, DavidKyamanywa, PatrickKobusingye, Olive2022-02-112022-02-112021: Muni KM, Ningwa A, Osuret J, et al. Inj Prev Epub ahead of print: [please include Day Month Year]. doi:10.1136/ injuryprev-2020-04365410.1136/ injuryprev-2020-043654https://nru.uncst.go.ug/xmlui/handle/123456789/2074d In many low-income countries, estimates of road injury burden are derived from police reports, and may not represent the complete picture of the burden in these countries. As a result, WHO and the Global Burden of Diseases, Injuries and Risk Factors Project often use complex models to generate country-specific estimates. Although such estimates inform prevention targets, they may be limited by the incompleteness of the data and the assumptions used in the models. In this crosssectional study, we provide an alternative approach to estimating road traffic injury burden for Uganda for the year 2016 using data from multiple data sources (the police, health facilities and mortuaries). Methods A digitised data collection tool was used to extract crash and injury information from files in 32 police stations, 31 health facilities and 4 mortuaries in Uganda. We estimated crash and injury burden using weights generated as inverse of the product of the probabilities of selection of police regions and stations. Results We estimated that 25 729 crashes occurred on Ugandan roads in 2016, involving 59 077 individuals with 7558 fatalities. This is more than twice the number of fatalities reported by the police for 2016 (3502) but lower than the estimate from the 2018 Global Status Report (12 036). Pedestrians accounted for the greatest proportion of the fatalities 2455 (32.5%), followed by motorcyclists 1357 (18%). Conclusions Using both police and health sector data gives more robust estimates for the road traffic burden in Uganda than using either source alone.enBurdenRoad traffic crashesUgandaPoliceHealth sector data sourcesArticle