Dependent variables believed to contribute to a likelihood of falling are analyzed using a latent class analysis. The dependent variables are biomedical factors, which may include, for example, arthritis, high blood pressure, diabetes, foot disorders, Parkinson's Disease, stroke, eye disorder, limb disorder, or proprioceptive disorder. Data pertaining to the biomedical factors is gathered from a population of individuals at risk of falling. Covariate data, including for example age and the number of prescriptions taken, is further analyzed against latent class data. For a particular group of at risk individuals, a set of five classes produced useful results broadly corresponding to groups representing individuals who have: good health; a range of diseases; Parkinson's Disease; arthritis; and high blood pressure. A probability of falling is determined, relative to the group of individuals with good health.
Statistical model for predicting falling in humans