%0 Journal Article %A Bidhya Pandey %A Julie Mytton %A Isabelle Bray %A Sunil Joshi %T 5E.003 Epidemiology of injuries among in-patients in Nepal: a secondary data analysis %D 2021 %R 10.1136/injuryprev-2021-safety.136 %J Injury Prevention %P A45-A45 %V 27 %N Suppl 2 %X Background Injuries are an important public health issue in Nepal, contributing significantly to the burden of morbidity and mortality. There is no injury surveillance system available, however healthcare service use is routinely reported to central government using the Health Management Information System (HMIS).Methods To explore the epidemiology of injuries in Nepal we used published national HMIS data on inpatients with injuries from 2009/10 to 2016/17. International Classification of Disease codes were used to classify injury type.Results Trends varied by injury type. Road Traffic Injuries (RTI) increased from 4.28 (95% CI 4.03–4.52) per 100,000 in 2009/10 to 10.55 (10.17–10.92) in 2016/17, while injuries from poisoning almost halved over the same period (from 8.71 (8.36–9.06) to 4.46 (4.22–4.71) per 100,000). Inequalities by age and gender were noted; in 2016/17, RTI was the most common unintentional injury affecting adults aged 15–59 years (14.26 (13.70–14.82) per 100,000), while RTIs were almost twice as common in men (13.76 (13.14–14.48) per 100,000) than women (7.66 (7.21–8.11) per 100,000). In contrast, trends in intentional injuries appear to have fallen over the same time period.Conclusion In the absence of surveillance data, routine inpatient data can provide evidence of injury epidemiology though underestimates the true burden of disease. Such data may provide evidence to monitor progress towards Sustainable Development Goals (SDG 3.6).Learning Outcomes HMIS data have not previously been used for injury research in Nepal. The established reporting system offers the potential for basic epidemiological analysis, though the available data fields are limited. %U https://injuryprevention.bmj.com/content/injuryprev/27/Suppl_2/A45.1.full.pdf