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CHARACTERIZING DISABILITY AND ITS TRENDS AMONG OLDER AMERICANS

$198,224P01FY2011AGNIH

National Bureau Of Economic Research, Cambridge MA

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Abstract

PROJECT SUMIVIARY (See instructions): SUBPROJECT 1: CHARACTERIZING DISABILITY AND ITS TRENDS AMONG OLDER AMERICANS This subproject explores the multiple dimensions of health and functional ability, how they are changing, and the factors that have been most important in improving health. Understanding trends in the health of the elderly population is important for forecasting health and work capacity in old age, for evaluating proposals to change Social Security, and for designing health policy. Disability in an elderly population is typically defined as the need for assistance with one or more self care tasks, such as bathing or eating, called activities of daily living (ADLs), or tasks required to live independently, such as grocery shopping or preparing meals, called instrumental activities of daily living (lADLs). Using this definition, literature over the past decade has shown improved health of the elderly, with disability rates declining 1-2 percent per year for several decades. However, there are often discrepancies in the types of limitations the elderly report, and a need to more rigorously characterize the multiple dimensions of health. The aims of this subproject are (1) to combine survey measures of physical and mental functioning and social performance into several summary health measures that describe the multiple dimensions of elderly health;(2) to document individual-specific trajectories of health as people age, as well as health trends for the population as a whole;(3) to link health changes to the onset of medical conditions;and (4) to relate disease onset and subsequent disability outcomes of disease to medical care. We seek to understand why health has changed, and the importance of medical care in improving health outcomes over time. We focus on the older population because health improvements have been largest, medical spending is highest, and the data are best.

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