Global Repository for Injury Data Discrepancies Across Low- and Middle-Income Countries and Harmonization Strategies

Introduction: There is a high burden of injuries in low- and middle-income countries (LMICs). Data management and storage systems are suboptimal, making it hard to share data. Also, small data sizes hinder the application of modern data science methods to draw insight. Methods: We performed a document analysis of injury data dictionaries from 4 institutions located in 4 different LMICs for discrepancies among data elements. We also compared each of the dictionaries with the World Health Organization (WHO) data set for injury (DSI) and then explored harmonization strategies of injury data, given the discrepancies. Results: Of the 949 data elements across the dictionaries, there were 6 (0.72%) shared data elements when considered by presence and 4 (0.45%) when considered by equality across the 4 dictionaries. The number of shared data elements varied when the dictionaries were compared in pairs and triads. We identified four methods of ensuring harmonization of injury data; 1) using the WHO DSI common data elements, 2) using the National Institute of Neurologic Disorders and Stroke (NINDS) common data elements, 3) using functions written in software like R and python, and 4) adopting a prospective and somewhat promising development of a framework that includes common data elements specific to injuries. We successfully harmonized three injury data sets from the Kilimanjaro Christian Medical Centre (KCMC). Conclusion: There are huge discrepancies across injury data dictionaries in LMICs. Harmonization of injury data from LMICs is however achievable and can result in a larger dataset. More research is needed in this area for the development of tools that facilitate injury data harmonization.