Nomogram for Predicting the Risk Factors for Falls in Older People: A Secondary Data Analysis Based on the 2021 Community Health Survey

Abstract

This study aimed to identify the risk factors for falls among older individuals living at home in a community and develop a nomogram to predict falls. This study included 74 492 people aged 65 years or older who participated in the 2021 Community Health Survey conducted in Korea. The data analysis methods used included the Rao-Scott χ2 test, a complex sample t-test, and complex binary logistic regression using SPSS 26.0. Using logistic regression analysis, a fall-risk prediction nomogram was created based on regression coefficients, and the reliability of the nomogram was calculated using a receiver operating characteristic (ROC) curve and values of the area under the curve (AUC). The fall incidence rate among older adults was 16.4%. Factors affecting the subject’s fall experience included being more than 85 years old (OR = 1.40); living alone (OR = 1.13); receiving basic welfare (OR = 1.18); subjective health status (OR = 1.72); number of days spent walking (OR = 0.98); obesity (OR = 1.08); severe depression (OR = 2.84); sleep duration time (OR = 1.11); experiencing cognitive decline (OR = 1.34); and diabetes (OR = 1.12). In the nomogram, the depression score exhibited the greatest discriminatory power, followed by subjective health status, gender, experience of cognitive decline, age, basic livelihood security, adequacy of sleep, living alone, diabetes, and number of days of walking. The AUC value was 0.66. An intervention plan that comprehensively considers physical, psychological, and social factors is required to prevent falls in older adults. The nomogram developed in this study will help local health institutions assess all these risk factors for falling and create and implement systematic education and intervention programs to prevent falls and fall-related injuries among older individuals.