Analysis of influencing factors of falls among rural elderly in China and construction of nomogram model

Objective To explore the factors influencing falls among elderly individuals in rural China and to construct a nomogram model. This study aims to provide a scientific basis for identifying high-risk populations and implementing fall prevention interventions. Methods A multi-stage random sampling method was employed, selecting one city each from the northern, central, and southern regions of Anhui Province—Suzhou, Hefei, and Anqing, respectively. From each city, one county was randomly selected, and within these counties, a total of 18 villages were randomly chosen as survey sites. Elderly individuals from these villages constituted the study population, with a total of 1546 participants. These participants were randomly divided into a training set (1208 individuals) and a validation set (338 individuals) in an 8:2 ratio. Univariate analysis was conducted using the Mann-Whitney U test and Kruskal-Wallis H test, while multivariate analysis employed binary logistic regression to identify influencing factors of falls in the training set of rural elderly. A nomogram model was subsequently developed based on these factors. Results From the univariate and multivariate analyses of the training set, five variables were identified: age, anxiety, frailty, living style, and frequency of coarse grain consumption. These variables were incorporated into the nomogram model, which exhibited an area under the ROC curve (AUC) of 0.722, indicating good discriminative ability. The calibration curve demonstrated high calibration accuracy. Internal validation of the nomogram model using the validation set yielded an AUC of 0.703, reflecting high discriminative ability, and the Hosmer-Lemeshow test result of P=0.08 indicated high calibration accuracy. Conclusion Falls among the elderly in rural China are influenced by age, anxiety, frailty, living style, and coarse grain consumption frequency. The nomogram model can predict the probability of falls among rural elderly individuals based on these factors, providing significant value for identifying high-risk populations and offering targeted interventions to reduce the occurrence of falls among older adults, which can ultimately enhance the quality of life and well-being of elderly individuals in their later years.