The Construction of a Patient Record-based Risk Model for Recurrent Falls Among Elderly People Living in the Community

Background: Predictive models of fall risk in the elderly living in the community may contribute to the identification of elderly at risk for recurrent falling.

Objectives: Our aim was to investigate occurrence, determinants and health consequences of falls in a community-dwelling elderly population and the contribution of data from patient records to a risk model of recurrent falls.

Methods: A population survey was carried out using a postal questionnaire. The questionnaire on occurrence, determinants and health consequences of falls was sent to 2744 elderly persons of 70 years and over, registered in four general practices (n = 27 000). Data were analysed by bivariate techniques and logistic regression.

Results: A total of 1660 (60%) responded. Falls (> or =1 fall) in the previous year were reported by 44%: one-off falls by 25% and recurrent falls (> or =2 falls) by 19%. Women had significantly more falls than men. Major injury was reported by 8% of the fallers; minor injury by 49%. Treatment of injuries was by the GP in 67% of cases. From logistic regression, a risk model for recurrent falls, consisting of the risk factors female gender, age 80 years or over, presence of a chronic neurological disorder, use of antidepressants, problems of balance and sense organs and complaints of muscles and joints was developed. The model predicted recurrent falls with a sensitivity of 64%, a specificity of 71%, a positive predictive value of 42% and a negative predictive value of 86%.

Conclusion: A risk model consisting of six variables usually known to the GP from the patient records may be a useful tool in the identification of elderly people living in the community at risk for recurrent falls.