Predicting nursing home placement among home- and community-based services program participants.
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.


OBJECTIVE
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.


METHODS
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.
Melissa A
MA
Greiner
Laura G
LG
Qualls
Isao
I
Iwata
Heidi K
HK
White
Sheila L
SL
Molony
M Terry
MT
Sullivan
Bonnie
B
Burke
Kevin A
KA
Schulman
Duke Clinical Research Institute, PO Box 17969, Durham, NC 27715. E-mail: soko@post.harvard.edu.
Soko
S
Setoguchi
D000368:Aged; D000544:Alzheimer Disease / Q000628:therapy; D003153:Community Health Services / Q000706:statistics & numerical data*; D003237:Connecticut; D005260:Female; D006299:Health Services for the Aged / Q000706:statistics & numerical data*; D006699:Home Care Services / Q000706:statistics & numerical data*; D006801:Humans; D007326:Institutionalization / Q000706:statistics & numerical data*; D008297:Male; D009735:Nursing Homes / Q000706:statistics & numerical data*; D012307:Risk Factors; D012847:Single Person / Q000706:statistics & numerical data