Identifying Distinct Risk Profiles to Predict Adverse Events among Community-Dwelling Older Adults
Penn collection
Degree type
Discipline
Subject
Emergency Service, Hospital
Female
Hospitalization
Humans
Independent Living
Male
Medicare
Outcome Assessment (Health Care)
Risk Assessment
United States
Chronic Disease
Emergency Service
Hospital
Female
Hospitalization
Humans
Independent Living
Male
Medicare
Outcome Assessment (Health Care)
Risk Assessment
United States
Geriatric Nursing
Geriatrics
Health and Medical Administration
Health Services Administration
Health Services Research
Medical Humanities
Medicine and Health Sciences
Nursing
Preventive Medicine
Public Health and Community Nursing
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Abstract
Preventing adverse events among chronically ill older adults living in the community is a national health priority. The purpose of this study was to generate distinct risk profiles and compare these profiles in time to: hospitalization, emergency department (ED) visit or death in 371 community-dwelling older adults enrolled in a Medicare demonstration project. Guided by the Behavioral Model of Health Service Use, a secondary analysis was conducted using Latent Class Analysis to generate the risk profiles with Kaplan Meier methodology and log rank statistics to compare risk profiles. The Vuong-Lo-Mendell-Rubin Likelihood Ratio Test demonstrated optimal fit for three risk profiles (High, Medium, and Low Risk). The High Risk profile had significantly shorter time to hospitalization, ED visit, and death (p < 0.001 for each). These findings provide a road map for generating risk profiles that could enable more effective targeting of interventions and be instrumental in reducing health care costs for subgroups of chronically ill community-dwelling older adults.