Scottish Executive Health Department Chief Scientist Office
Development of a Risk Prediction tool for entering a Nursing Home in those aged 65 and over in a Scottish Population Researchers Prof Peter T Donnan, Dr Karen Barnett, Prof Frank Sullivan, Dr Colin McCowan, Dr Michael Norbury, Dr Pauline Lockhart, Dr Wendy Gidman, Prof Bruce Guthrie
prescriptions were all strongly associated with entry to a Care Home.
Aims The aims of the study were to examine risk factors associated with entry to a Nursing Home in those aged 65 or over in Tayside, Scotland, derive a risk equation for entry to a Nursing Home and then test its predictive ability and accuracy and finally, to create a simplified scoring rule for entry to a Nursing Home for clinical use.
Project Outline/Methodology This record-linkage study of Tayside population health databases assessed the risk of entering a Care Home in the whole population aged 65 or over in the period 2005 – 2009. A baseline was ascertained for each individual in the cohort who had never been in a Care Home and their history over the previous year of prescriptions received, hospitalisations, deprivation decile based on last community residence, and date of death were extracted. The binary outcome was defined as entry to either a Nursing Home, or Mixed Nursing Home and residential care, or unknown type coded as entry to a ‘Care Home’ (Yes, No). Followup to entry to a Care Home or not was analysed with logistic regression and Cox regression models and predictive models were derived. The discriminative performance of the models were assessed by calculating the c-statistic (AUROC for logistic) and calibration assessed with the Hosmer-Lemeshow test.
A number of medications were associated with lower risk; namely, receipt of NSAIDS, statins, ulcerhealing drugs and beta-blockers. A final model with logistic regression was derived that included 20 factors which gave an AUROC = 0.89 and good calibration (p = 0.210). A similar model was derived from the Cox regression but with further factors added as power was greater.
Key Results Drugs for dementia, older age, being female and previous hospitalisations were powerful predictors.
Conclusions Data-linkage of population databases facilitated the derivation of predictive models for entry to a Care Home.
Results
What does this study add to the field?
A total of 84,432 people aged 65 or over were identified in the cohort who had never entered a Care Home previously. The rate of entry was 5.6% over a median follow-up of 5 years. Strong predictors of entry were older age as a quadratic function, with an increasing rate for older women relative to men. Those living in deprived areas were less likely to enter a Care Home, with affluent women having the highest rate of entry. A high number of days in hospital in the previous year was associated with higher risk as was previous admissions for cerebrovascular disease, falls, and diabetes. Receipt of drugs for dementia, antipsychotics, and drugs for Parkinson’s disease as well as the total number of
This is the first large population study to derive a predictive model for entry to a Care Home in the UK.
Implications for Practice or Policy The predictive model could be used to identify those at high risk and interventions to maintain autonomy implemented.
Where to next? Further work could develop user-friendly Software for use in GP systems. Further details from: Prof Peter T. Donnan Dundee Epidemiology and Biostatistics Unit, Quality, Safety and Informatics University of Dundee DD2 4BF p.t.donnan@cpse.dundee.ac.uk
Chief Scientist Office, St Andrews House, Regent Road, Edinburgh, EH1 3DG Tel:0131 244 2248
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