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Models for Predicting Risk of Dementia: Predictive Accuracy and Model Complexity

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Book cover Health and Cognition in Old Age

Part of the book series: International Perspectives on Aging ((Int. Perspect. Aging,volume 10))

Abstract

As strategies to prevent dementia or delay disease progression are developed, it will be important to have risk prediction models to prioritize and target intervention to high-risk individuals. Targeting whole populations is not always cost-effective, particularly when intervention strategies are expensive or adherence rates are low. A complementary approach may be to develop a prediction algorithm to identify individuals at highest risk of dementia as early as possible without being too broad in risk selection. This chapter will present an overview of existing dementia risk prediction models with a focus on their predictive accuracy (e.g., sensitivity, specificity and discrimination), the types of variables incorporated (e.g., cognitive, neuroimaging, genetics and health assessment) and the cost of attaining the risk score (e.g., in terms of equipment and the need for specialist training for risk score attainment). A better understanding of available dementia risk models and their predictive accuracy has implications for improving diagnostics, targeting services and undertaking of more focused risk factor reduction in older aged populations.

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Acknowledgments

Part of the work in this chapter was supported by a European Research Area in Ageing (ERA-Age) Future Leaders of Ageing Research in Europe Postdoctoral Fellowship (FLARE Fellowship; awarded through the Medical Research Council: MRC, UK), awarded to Dr. Blossom C.M. Stephan.

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Authors and Affiliations

  1. Institute of Health and Society, Newcastle University, Baddiley Clark Building, Richardson Road, Newcastle upon Tyne, NE2 4AX, UK

    Blossom Christa Maree Stephan Ph.D.

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  1. Blossom Christa Maree Stephan Ph.D.
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Correspondence to Blossom Christa Maree Stephan Ph.D. .

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Editors and Affiliations

  1. Faculty of Language and Literature, Humanities, Arts and Education, University of Luxembourg, Walferdange, Luxembourg

    Anja K. Leist

  2. Gerontology Research Center, University of Jyväskylä, Jyväskylä, Finland

    Jenni Kulmala

  3. Mental Health Promotion Unit, National Institute for Health and Welfare, THL, Vaasa, Finland

    Fredrica Nyqvist

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Stephan, B.C.M. (2014). Models for Predicting Risk of Dementia: Predictive Accuracy and Model Complexity. In: Leist, A., Kulmala, J., Nyqvist, F. (eds) Health and Cognition in Old Age. International Perspectives on Aging, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-319-06650-9_10

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