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.


OBJECTIVE
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%.


CONCLUSIONS
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.
Stalenhoef
P A
PA
Department of General Practice, Maastricht University, Postbox 616, 6200 MD Maastricht, The Netherlands.
Diederiks
J P
JP
Knottnerus
J A
JA
de Witte
L P
LP
Crebolder
H F
HF
D000058:Accidental Falls / Q000706:statistics & numerical data*; D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D015577:Geriatric Assessment; D006801:Humans; D015994:Incidence; D016015:Logistic Models; D008297:Male; D015233:Models, Statistical; D016017:Odds Ratio; D012306:Risk; D012307:Risk Factors