BACKGROUND: Several states offer publicly funded-care management programs to prevent long-term care placement of high-risk Medicaid beneficiaries. Understanding participant risk factors and services that may prevent long-term care placement can facilitate efficient allocation of program resources. OBJECTIVES: To develop a practical prediction model to identify participants in a home- and community-based services program who are at highest risk for long-term nursing home placement, and to examine participant-level and program-level predictors of nursing home placement. STUDY DESIGN: In a retrospective observational study, we used deidentified data for participants in the Connecticut Home Care Program for Elders who completed an annual assessment survey between 2005 and 2010. METHODS: We analyzed data on patient characteristics, use of program services, and short-term facility admissions in the previous year. We used logistic regression models with random effects to predict nursing home placement. The main outcome measures were long-term nursing home placement within 180 days or 1 year of assessment. RESULTS: Among 10,975 study participants, 1249 (11.4%) had nursing home placement within 1 year of annual assessment. Risk factors included Alzheimer's disease (odds ratio [OR], 1.30; 95% CI, 1.18-1.43), money management dependency (OR, 1.33; 95% CI, 1.18-1.51), living alone (OR, 1.53; 95% CI, 1.31-1.80), and number of prior short-term skilled nursing facility stays (OR, 1.46; 95% CI, 1.31-1.62). Use of a personal care assistance service was associated with 46% lower odds of nursing home placement. The model C statistic was 0.76 in the validation cohort. CONCLUSIONS: A model using information from a home- and community-based service program had strong discrimination to predict risk of long-term nursing home placement and can be used to identify high-risk participants for targeted interventions.
BACKGROUND: Several states offer publicly funded-care management programs to prevent long-term care placement of high-risk Medicaid beneficiaries. Understanding participant risk factors and services that may prevent long-term care placement can facilitate efficient allocation of program resources. OBJECTIVES: To develop a practical prediction model to identify participants in a home- and community-based services program who are at highest risk for long-term nursing home placement, and to examine participant-level and program-level predictors of nursing home placement. STUDY DESIGN: In a retrospective observational study, we used deidentified data for participants in the Connecticut Home Care Program for Elders who completed an annual assessment survey between 2005 and 2010. METHODS: We analyzed data on patient characteristics, use of program services, and short-term facility admissions in the previous year. We used logistic regression models with random effects to predict nursing home placement. The main outcome measures were long-term nursing home placement within 180 days or 1 year of assessment. RESULTS: Among 10,975 study participants, 1249 (11.4%) had nursing home placement within 1 year of annual assessment. Risk factors included Alzheimer's disease (odds ratio [OR], 1.30; 95% CI, 1.18-1.43), money management dependency (OR, 1.33; 95% CI, 1.18-1.51), living alone (OR, 1.53; 95% CI, 1.31-1.80), and number of prior short-term skilled nursing facility stays (OR, 1.46; 95% CI, 1.31-1.62). Use of a personal care assistance service was associated with 46% lower odds of nursing home placement. The model C statistic was 0.76 in the validation cohort. CONCLUSIONS: A model using information from a home- and community-based service program had strong discrimination to predict risk of long-term nursing home placement and can be used to identify high-risk participants for targeted interventions.
Authors: Nicole Williams; Natalie A Phillips; Walter Wittich; Jennifer L Campos; Paul Mick; Joseph B Orange; M Kathleen Pichora-Fuller; Marie Y Savundranayagam; Dawn M Guthrie Journal: Innov Aging Date: 2020-01-01
Authors: Colleen J Maxwell; Michael A Campitelli; Christina Diong; Luke Mondor; David B Hogan; Joseph E Amuah; Sarah Leslie; Dallas Seitz; Sudeep Gill; Kednapa Thavorn; Walter P Wodchis; Andrea Gruneir; Gary Teare; Susan E Bronskill Journal: BMC Geriatr Date: 2018-09-12 Impact factor: 3.921
Authors: Maria C Inacio; Azmeraw T Amare; Craig Whitehead; Sarah C E Bray; Megan Corlis; Renuka Visvanathan; Steve Wesselingh Journal: Australas J Ageing Date: 2020-01-23 Impact factor: 2.111