Dataset Information

Population-Based Screening for Functional Disability in Older Adults.

ABSTRACT:

Background and objectives

Screening for functional disability is a promising strategy to identify high-need older adults. We compare 2 disability measures, activities of daily living (ADLs), and life space constriction (LSC), in predicting hospitalization and mortality in older adults.

Research design and methods

We used the nationally representative National Health and Aging Trends Study of 30,885 observations of adults aged 65 years and older. Outcomes were 1-year mortality and hospitalization. Predictors were ADLs (receiving help with bathing, eating, dressing, toileting, getting out of bed, walking inside) and LSC (frequency of leaving home).

Results

Of respondents, 12.4% reported 3 or more ADLs and 10.8% reported rarely/never leaving home. ADL disability and LSC predicted high rates of 1-year mortality and hospitalization: of those with 3 or more ADLs, 46.4% died and 41.0% were hospitalized; of those who never/rarely left home, 40.7% died and 37.0% were hospitalized. Of those with both 3 or more ADLs and who never/rarely left home, 58.4% died. ADL and LSC disability combined was more predictive of 1-year mortality and hospitalization than either measure alone. ADL disability and LSC screens identified overlapping but distinct populations. LSC identified more women (72.6% vs 63.8% with ADL disability), more people who live alone (40.7% vs 30.7%), fewer who were White (71.7% vs 76.2%) with cancer (27.6% vs 32.4), and reported pain (67.1% vs 70.0%).

Discussion and implications

LSC and ADLs both independently predicted mortality and hospitalization but using both screens was most predictive. Routine screening for ADLs and LSC could help health systems identify those at high risk for mortality and health care use.

SUBMITTER: Ankuda CK

PROVIDER: S-EPMC7817111 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Publications

Population-Based Screening for Functional Disability in Older Adults.

Ankuda Claire K CK Freedman Vicki A VA Covinsky Kenneth E KE Kelley Amy S AS

Innovation in aging 20201222 1

<h4>Background and objectives</h4>Screening for functional disability is a promising strategy to identify high-need older adults. We compare 2 disability measures, activities of daily living (ADLs), and life space constriction (LSC), in predicting hospitalization and mortality in older adults.<h4>Research design and methods</h4>We used the nationally representative National Health and Aging Trends Study of 30,885 observations of adults aged 65 years and older. Outcomes were 1-year mortality and ...[more]

PMID: 33506111

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Project description:BackgroundIt is important to identify older adults at high risk of functional disability and to take preventive measures for them at an early stage. To our knowledge, there are no studies that predict functional disability among community-dwelling older adults using machine learning algorithms.ObjectiveTo construct a model that can predict functional disability over 5 years using basic machine learning algorithms.DesignA cohort study with a mean follow-up of 5.4 years.ParticipantsWe used data from the Japan Gerontological Evaluation Study, which involved 73,262 people aged ≥ 65 years who were not certified as requiring long-term care. The baseline survey was conducted in 2013 in 19 municipalities.Main measuresWe defined the onset of functional disability as the new certification of needing long-term care that was ascertained by linking participants to public registries of long-term care insurance. All 183 candidate predictors were measured by self-report questionnaires.Key resultsDuring the study period, 16,361 (22.3%) participants experienced the onset of functional disability. Among machine learning-based models, ridge regression (C statistic = 0.818) and gradient boosting (0.817) effectively predicted functional disability. In both models, we identified age, self-rated health, variables related to falls and posture stabilization, and diagnoses of Parkinson's disease and dementia as important features. Additionally, the ridge regression model identified the household characteristics such as the number of members, income, and receiving public assistance as important predictors, while the gradient boosting model selected moderate physical activity and driving. Based on the ridge regression model, we developed a simplified risk score for functional disability, and it also indicated good performance at the cut-off of 6/7 points.ConclusionsMachine learning-based models showed effective performance prediction over 5 years. Our findings suggest that measuring and adding the variables identified as important features can improve the prediction of functional disability.

Project description:ObjectivesTo examine associations between weight change, body composition, risk of mobility disability, and mortality in older adults.DesignProspective, longitudinal, population-based cohort.SettingThe Health, Aging, and Body Composition Study.ParticipantsWomen (n = 1,044) and men (n = 931) aged 70 to 79.MeasurementsWeight and lean and fat mass from dual-energy X-ray absorptiometry measured annually over 5 years. Weight was defined as stable (n = 664, reference), loss (n = 662), gain (n = 321), or cycling (gain and loss, n = 328) using change of 5% from year to year or from Year 1 to 6. Mobility disability (two consecutive reports of difficulty walking one-quarter mile or climbing 10 steps) and mortality were determined for 8 years after the weight change period. Associations were analyzed using Cox proportional hazards regression adjusted for covariates.ResultsDuring follow-up, 313 women and 375 men developed mobility disability, and 322 women and 378 men died. There was no risk of mobility disability or mortality with weight gain. Weight loss (hazard ratio (HR) = 1.88, 95% confidence interval (CI) = 1.40-2.53) and weight cycling (HR = 1.59, 95% CI = 1.11-2.29) were associated with mobility disability in women, and weight loss was associated with mobility disability in men (HR = 1.30, 95% CI = 1.01-1.69). Weight loss and weight cycling were associated with mortality risk in women (weight loss: HR = 1.47, 95% CI = 1.07-2.01; weight cycling: HR = 1.62, 95% CI = 1.15-2.30) and in men (weight loss: HR = 1.41, 95% CI = 1.09-1.83; weight cycling: HR = 1.50, 95% CI = 1.08-2.08). Adjustment for lean and fat mass and change in lean and fat mass from Year 1 to 6 attenuated the relationships between weight loss and mobility disability in men and between weight loss and mortality in men and women.ConclusionWeight cycling and weight loss predict impending mobility disability and mortality in old age, underscoring the prognostic importance of weight history.

Dataset Information

Population-Based Screening for Functional Disability in Older Adults.

Background and objectives

Research design and methods

Results

Discussion and implications

Publications

Population-Based Screening for Functional Disability in Older Adults.

Similar Datasets

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