A Prediction Algorithm For Fall Risk Assessment Among Community-Dwelling Elderly People

Abstract

Worldwide, life expectancy has steadily increased over the years. However, the integrity and the functionality of physiological systems reduce over time making older people more vulnerable to adverse events such as falls. Consequences of falls can include physical injuries (hip fractures), psychological issues (fear of falling) and social isolation (need for assistance) increasing the demand for healthcare services (hospitalization, rehabilitation, institutionalization). Despite many studies investigated the most predisposing factors to a fall risk, this study aimed at identifying those factors through a multidimensional health assessment of elderly people crossing over multiple health domains such as nutritional, clinical, psychological, social, and functional. The acquired variables were processed in terms of data cleaning and coding, to make them ready for subsequent statistical evaluation. Correlation and regression analyses were performed to find out the relevance of the variables with respect to the fall event. The most significant variables were accounted for the development of the predictive index. Each predictor was associated with a score according to specific weights so that the sum of the answers to each question of the index gave the final fall risk index. Its validation was assessed over the sample of the study by comparing the index output to that of the multidimensional evaluation and the one of a common fall risk test. Clinicians will benefit from this tool for a fast and easy screening of fall risk among community-dwelling seniors to act promptly on those subjects at increased risk, and preventively on low-risk subjects. This way, it is possible to optimize time, costs, and resources for sustainable and effective management of patient care.