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Updated: Oct 2, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Another look at aggregate changes in severe cognitive impairment: further investigation into the cumulative effects
Vicki A Freedman1, Hakan Aykan, Linda G Martin
1Polisher Research Institute, Madlyn and Leonard Abramson Center for Jewish Life (formerly Philadelphia Geriatric Center), North Wales, Pennsylvania 19454, USA. vfreedman@abramsoncenter.org
Objectives:
This study explored whether previously reported declines in severe cognitive impairment were robust to cumulative effects of potentially confounding survey design issues.
Methods:
Using the 1993 Asset and Health Dynamics of the Oldest Old study (n = 7,443) and 1998 Health and Retirement Survey (HRS; n = 7,624) the proportion of persons ages 70 and older with severe cognitive impairment was calculated under various assumptions about item nonresponse, differential loss to follow-up, and the size and composition of the nursing home population. Impairment was measured for self-respondents using a modified version of the Telephone Interview Cognitive Screen; for proxy respondents ratings of memory and judgment were used. Chi-square tests were adjusted to account for complex survey designs.
Results:
Ignoring loss to follow-up, excluding nursing home residents, and assigning a low score to those refusing subscales yielded a statistically significant decline in severe cognitive impairment from 5.8% in 1993 to 3.8% in 1998, or an average annual decline of 6.9%. When cumulative effects of survey design issues were considered and design effects incorporated into statistical tests, statistically significant declines persisted, albeit at a reduced average annual rate, ranging from 2.5% to 6.9% per year.
Discussion:
Previously reported improvements in severe cognitive impairment appear to be robust to a variety of specifications. Replication with future waves of the HRS and other data is warranted.
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