Predicting hospitalization in community-living older people: a prognostic study
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Background: For many older people, hospitalization results in functional decline, what leads to a greater dependence in daily life and reduced self-reliance. To prevent loss of function after hospitalization, insight into the predictors of hospitalization in older people is needed. Aim: To determine predictors of hospitalization in community-living older people (65+). Methods: This was a secondary data analysis with a prognostic design. Data was obtained by a self-reported questionnaire, supplemented with information from electronic medical record of the general practitioner. The primary outcome of this study was a self-reported hospitalization. Candidate predictors consisted of demographics, medical conditions, daily functioning, general health status and health utilisation. Data were analysed by univariate and multivariate logistic regressions. To evaluate the performance of the model, the discrimination and calibration of the model were determined. Results: In the total study population (n=1964), 1123 participants (57.2%) were female and the mean age of the participants was 75.9 year. The rate of hospitalizations was 24.7% (n=486). The multivariate analysis showed that a previous hospitalization (Odds Ratio (OR) 2.26; Confidence Interval (CI) 1.77 – 2.87) was the strongest predictor of hospitalization. Other predictors were: male gender, married status, living alone, living with home care, a higher frailty index, poorer physical function, better mental health, poorer vitality, better social functioning, lower quality of life, problems with selfcare, pain and visits to the GP. The final model had a moderate predicting power (Area Under the Curve 0.68; 95% CI 0.65 – 0.71) and an acceptable calibration (p-value 0.697) Conclusion and recommendations: This study found thirteen predictors of hospitalization in older people. Identification of those predictors gives direction for potential interventions to prevent hospitalization. Particular attention should be paid to individuals with a previous hospitalization. Further research toward reducing avoidable hospitalizations is required.