Educational differences in the compression of disability incidence in the United States.
Abstract
<h4>Objective</h4>To examine educational differences in the compression of disability
incidence in the United States.<h4>Method</h4>We use the Health and Retirement Study
and techniques of microsimulation and bootstrap to estimate the distribution of mortality
and disability incidence for major education groups.<h4>Results</h4>Higher education
is associated with a right shift in the age distributions of both mortality and disability
incidence, and more compressed distributions above the modal ages (p<0.05). Our study
also points to gender differences in the association between education and compression
of mortality and disability incidence (p<0.05).<h4>Discussion</h4>To our knowledge,
no prior studies have examined educational difference in compression of disability
incidence and conducted formal tests for statistical significance. Educational differences
in life span variation in mortality correspond closely with life span variation in
disability incidence. One long-range implication of this work is growing inequality
in life-span variation in disability incidence given trends in educational differences
in life-span variation in mortality.
Type
Journal articlePermalink
https://hdl.handle.net/10161/22787Published Version (Please cite this version)
10.1016/j.ssmph.2018.100347Publication Info
Chiu, Chi-Tsun; Hayward, Mark D; Chan, Angelique; & Matchar, David B (2019). Educational differences in the compression of disability incidence in the United States.
SSM - population health, 7. pp. 100347. 10.1016/j.ssmph.2018.100347. Retrieved from https://hdl.handle.net/10161/22787.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
David Bruce Matchar
Professor of Medicine
My research relates to clinical practice improvement - from the development of clinical
policies to their implementation in real world clinical settings. Most recently my
major content focus has been cerebrovascular disease. Other major clinical areas in
which I work include the range of disabling neurological conditions, cardiovascular
disease, and cancer prevention. Notable features of my work are: (1) reliance on
analytic strategies such as meta-analysis, simulation, decision analy

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