Incident disability in older adults: prediction models based on two British prospective cohort studies.

Nüesch, E; Pablo, P; Dale, CE; Prieto-Merino, D; Kumari, M; Bowling, A; Ebrahim, S; Casas, JP; (2014) Incident disability in older adults: prediction models based on two British prospective cohort studies. Age and ageing. ISSN 0002-0729 DOI:

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to develop and validate a prediction model for incident locomotor disability after 7 years in older adults. prospective British cohort studies: British Women's Heart and Health Study (BWHHS) for development and the English Longitudinal Study of Ageing (ELSA) for validation. community-dwelling older adults. multivariable logistic regression models after selection of predictors with backward elimination. Model performance was assessed using metrics of discrimination and calibration. Models were internally and externally validated. locomotor disability was reported in BWHHS by 861 of 1,786 (48%) women after 7 years. Age, a history of arthritis and low physical activity levels were the most important predictors of locomotor disability. Models using routine measures as predictors had satisfactory calibration and discrimination (c-index 0.73). Addition of 31 blood markers did not increase the predictive performance. External validation in ELSA showed reduced discrimination (c-index 0.65) and an underestimation of disability risks. A web-based calculator for locomotor disability is available ( we developed and externally validated a prediction model for incident locomotor disability in older adults based on routine measures available to general practitioners, patients and public health workers, and showed an adequate discrimination. Addition of blood markers from major biological pathways did not improve the performance of the model. Further replication in additional data sets may lead to further enhancement of the current model.

Item Type: Article
Faculty and Department: Faculty of Epidemiology and Population Health > Dept of Non-Communicable Disease Epidemiology
Research Centre: Centre for Statistical Methodology
PubMed ID: 25349151
Web of Science ID: 352159500018


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