Identifying risk factors of overall injury and cause-specific injuries
Overall injury and cause-specific unintentional injuries are important public health issues globally. Identifying the characteristics and specific risk factors of unintentional injuries is critical to the design of evidence-based interventions. It has been shown that unintentional injuries are associated with individual lifestyle and health status, demographics and socio-economic status, as well as environment exposure. However, many important associations have not been examined rigorously through large-sample cohort study. Valuable predictive models have not been developed for specific types of injury.
In this 3-year project, we aim to identify factors that associated with overall injury and cause-specific injuries (falls, road traffic injuries, et al) by using the UK Biobank data. With the development of techniques on big data analytics applied in biomedical community, we are able to use large sample size and large scale of variables to evaluate the morbidity and mortality risk of overall injury and specific injuries with higher accuracy. We propose to use cox regression models for risk factors and use Mendelian randomization study for etiological inferences of specific injury causes. This can improve the accuracy and precision of preventing unintentional injuries. Our results will be free, public access to the research community.