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

Melissa A. Greiner, Laura G. Qualls, Isao Iwata, Heidi K. White, Sheila L. Molony, M. Terry Sullivan, Bonnie Burke, Kevin A. Schulman, Soko Setoguchi Iwata

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)e535-e536
JournalThe American journal of managed care
Volume20
Issue number12
StatePublished - Jan 1 2014
Externally publishedYes

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Social Welfare
Nursing Homes
Odds Ratio
Long-Term Care
Logistic Models
Skilled Nursing Facilities
Resource Allocation
Medicaid
Home Care Services
Observational Studies
Alzheimer Disease
Retrospective Studies
Outcome Assessment (Health Care)

All Science Journal Classification (ASJC) codes

  • Health Policy

Cite this

Greiner, M. A., Qualls, L. G., Iwata, I., White, H. K., Molony, S. L., Sullivan, M. T., ... Setoguchi Iwata, S. (2014). Predicting nursing home placement among home- and community-based services program participants. The American journal of managed care, 20(12), e535-e536.
Greiner, Melissa A. ; Qualls, Laura G. ; Iwata, Isao ; White, Heidi K. ; Molony, Sheila L. ; Sullivan, M. Terry ; Burke, Bonnie ; Schulman, Kevin A. ; Setoguchi Iwata, Soko. / Predicting nursing home placement among home- and community-based services program participants. In: The American journal of managed care. 2014 ; Vol. 20, No. 12. pp. e535-e536.
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Greiner, MA, Qualls, LG, Iwata, I, White, HK, Molony, SL, Sullivan, MT, Burke, B, Schulman, KA & Setoguchi Iwata, S 2014, 'Predicting nursing home placement among home- and community-based services program participants', The American journal of managed care, vol. 20, no. 12, pp. e535-e536.

Predicting nursing home placement among home- and community-based services program participants. / Greiner, Melissa A.; Qualls, Laura G.; Iwata, Isao; White, Heidi K.; Molony, Sheila L.; Sullivan, M. Terry; Burke, Bonnie; Schulman, Kevin A.; Setoguchi Iwata, Soko.

In: The American journal of managed care, Vol. 20, No. 12, 01.01.2014, p. e535-e536.

Research output: Contribution to journalArticle

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T1 - Predicting nursing home placement among home- and community-based services program participants

AU - Greiner, Melissa A.

AU - Qualls, Laura G.

AU - Iwata, Isao

AU - White, Heidi K.

AU - Molony, Sheila L.

AU - Sullivan, M. Terry

AU - Burke, Bonnie

AU - Schulman, Kevin A.

AU - Setoguchi Iwata, Soko

PY - 2014/1/1

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N2 - 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.

AB - 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.

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Greiner MA, Qualls LG, Iwata I, White HK, Molony SL, Sullivan MT et al. Predicting nursing home placement among home- and community-based services program participants. The American journal of managed care. 2014 Jan 1;20(12):e535-e536.