Risk factors for falls and fall-related injuries associated with mild neurocognitive disorders in the older Canadian population: a population-based, prospective, longitudinal, observational cohort study

Falls are frequent events in adults over age 65. They are a major Canadian public health concern, which negatively impacts health and quality of life of fallers, and health care system. Major (i.e., moderate to severe dementia) neurocognitive disorders are strongly associated with falls and fall-related injuries. However, little is known about the association of mild cognitive impairment (MCI) and mild dementia with falls and fall-related injuries. Emerging modeling methods such as artificial neural networks (ANNs) improve the performance criteria of fall prediction compared to classical linear models. Other methods such as Factor Mixture Models (FMMs) could also be helpful but have not been used in this field of research. This project will use the database of the Canadian Longitudinal Study on Aging (CLSA) to characterize, compare and model the risk for incident falls and fall-related injuries at the onset of neurocognitive disorders (i.e., MCI and mild dementia).