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Predicting Falls and When to Intervene in Older People: A Multilevel Logistical Regression Model and Cost Analysis

Smith, MI, de Lusignan, S, Mullett, D, Correa, A, Tickner, J and Jones, S (2016) Predicting Falls and When to Intervene in Older People: A Multilevel Logistical Regression Model and Cost Analysis PLoS One, 11 (7).

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Abstract

Introduction Falls are the leading cause of injury in older people. Reducing falls could reduce financial pressures on health services. We carried out this research to develop a falls risk model, using routine primary care and hospital data to identify those at risk of falls, and apply a cost analysis to enable commissioners of health services to identify those in whom savings can be made through referral to a falls prevention service. Methods Multilevel logistical regression was performed on routinely collected general practice and hospital data from 74751 over 65’s, to produce a risk model for falls. Validation measures were carried out. A cost-analysis was performed to identify at which level of risk it would be cost-effective to refer patients to a falls prevention service. 95% confidence intervals were calculated using a Monte Carlo Model (MCM), allowing us to adjust for uncertainty in the estimates of these variables. Results A risk model for falls was produced with an area under the curve of the receiver operating characteristics curve of 0.87. The risk cut-off with the highest combination of sensitivity and specificity was at p = 0.07 (sensitivity of 81% and specificity of 78%). The risk cut-off at which savings outweigh costs was p = 0.27 and the risk cut-off with the maximum savings was p = 0.53, which would result in referral of 1.8% and 0.45% of the over 65’s population respectively. Above a risk cut-off of p = 0.27, costs do not exceed savings. Conclusions This model is the best performing falls predictive tool developed to date; it has been developed on a large UK city population; can be readily run from routine data; and can be implemented in a way that optimises the use of health service resources. Commissioners of health services should use this model to flag and refer patients at risk to their falls service and save resources

Item Type: Article
Subjects : Medical Science
Divisions : Faculty of Health and Medical Sciences > School of Biosciences and Medicine
Authors :
NameEmailORCID
Smith, MIUNSPECIFIEDUNSPECIFIED
de Lusignan, SUNSPECIFIEDUNSPECIFIED
Mullett, DUNSPECIFIEDUNSPECIFIED
Correa, AUNSPECIFIEDUNSPECIFIED
Tickner, JUNSPECIFIEDUNSPECIFIED
Jones, SUNSPECIFIEDUNSPECIFIED
Date : 22 July 2016
Identification Number : 10.1371/journal.pone.0159365
Copyright Disclaimer : © 2016 Smith et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Depositing User : Symplectic Elements
Date Deposited : 21 Oct 2016 15:08
Last Modified : 31 Oct 2017 18:49
URI: http://epubs.surrey.ac.uk/id/eprint/812550

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