Predicting Nursing Home Placement Among Home- and Community-Based Services Program Participants

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.

Am J Manag Care. 2014;20(12):e535-e546A 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.

• Approximately 11% of participants in a home- and community-based service program were placed in a nursing home within 1 year.
• Risk factors for long-term nursing home placement include Alzheimer's disease, money management dependency, living alone, and the number of prior short-term skilled nursing facility stays.
• Use of a personal care assistance service is associated with significantly lower odds of nursing home placement.

Lifetime risk of nursing home use is estimated at more than 40% and is projected to increase with greater life expectancy among Baby Boomer retirees.¹ Medicaid is the primary payer of nursing home services in the United States at an average annual cost of $84,000 per beneficiary.² In 2010, long-term care services for older patients accounted for more than one-third of state Medicaid spending.³ At a total annual cost of over $140 billion, Medicaid costs for long-term care will likely be part of ongoing discussions about state and federal deficit reduction.^2,3

In efforts to curb these costs, many states have moved towards home- and community-based services (HCBS)—programs that aim to prevent placement of high-risk Medicaid beneficiaries into long-term nursing facilities. These programs account for 45% of Medicaid long-term care spending,³ and research has shown that such programs may be effective.^4-8 The Patient Protection and Affordable Care Act (PPACA) provides monetary incentives to states that implement HCBS programs as alternatives to nursing homes.⁹

Although a fair amount is known about the predictors of nursing home use,^10-19 validated prediction models for long-term nursing home placement in high-risk, HCBS populations have not been studied.^20,21 Understanding the risk factors for nursing home placement and identifying services that may prevent such placement can facilitate efficient allocation of resources among HCBS program participants. We used clinical and administrative databases for elderly participants in a state- and waiver-funded HCBS program to develop a practical model to predict the risk of long-term nursing home placement and to examine associated participant characteristics and program services.

METHODSData Sources

We used clinical and administrative data from the Connecticut Home Care Program for Elders (CHCPE) provided by Connecticut Community Care, Inc (CCCI). CHCPE is a publicly funded care-management program that provides preventive home care services to older Connecticut residents who are at risk for permanent nursing home placement. CCCI provided deidentified data for all clients from 2005 through 2011. The data included baseline eligibility evaluations and annual reassessments conducted by CCCI primary care managers, which contain demographic characteristics, medical history, functional ability, social support, and financial assistance data elements in the “Modified Community Assessment Tool” published by the Connecticut Department of Social Services (CDSS).²² Additional data included information on program status, funded and unpaid program services, hospital visits, short-term skilled nursing facility stays, and medications.

Study Population

The study population included at-risk residents who were referred to and deemed eligible for the CHCPE program. Program eligibility is based on the number of critical needs, income, and total assets. The state defines critical needs as functional dependencies in specific activities of daily living (ADLs) and instrumental activities of daily living (IADLs) and/or cognitive impairment requiring supervision. ²³ We included CHCPE participants 65 years and older who completed an annual assessment between January 1, 2005, and December 31, 2010. If a participant had multiple assessments, we used the earliest reassessment for analysis.

Potential Predictors of Long-Term Nursing HomePlacement

Potential predictors included demographic characteristics, clinical characteristics, social support, living arrangements, financial assistance, and program-level variables. Demographic information included age, sex, race, marital status, and primary spoken language. We categorized participants’ race/ethnicity as black, Hispanic, white, or other. Clinical information included medical diagnoses (coded 0 [none], 1 [secondary], or 2 [major]); ADLs and IADLs (coded 0 [independent], 1 [requires assistance], or 2 [total dependence]); mental status quotient (0 to 10 errors); behavioral and psychological issues; vision and hearing assessments; and medications. A dichotomous variable for “meets nursing home level of care” was based on 3 or more critical care needs as defined by the CDSS.²³

Program-level variables included healthcare utilization, program services, and the patient’s primary care manager and team. We assessed hospital admissions, emergency department visits, and short-term skilled nursing facility stays during the year before the assessment. We grouped services into categories (eAppendix A, available at www.ajmc.com); identified the services in place at the time of and in the 12 months before the assessment; and calculated average monthly total costs, medical costs, and social service costs. The personal care assistant service pilot offered during the period of our study gave participants authority to hire a single person, including a family member, to perform services that might otherwise be provided by multiple persons. The assessment year, time since the initial assessment, and time since program activation were used to account for variation in subjects’ program participation time.

Most variables had low rates of missingness (ie, less than 2%). We imputed missing values as follows: “no” for dichotomous variables, the most frequent level or category for multichotomous variables, and median values for continuous variables.²⁴ Since missing values for the mental status quotient (3%) were likely attributable to pronounced cognitive or communication impairments, we imputed missing values to 11.

Outcomes

The primary outcome of interest was placement in a long-term nursing home within 180 or 365 days after the assessment. We calculated the days from assessment to nursing home placement based on a termination record in the program status file.

Statistical Analysis

We present patient characteristics at the time of the annual assessment, using proportions for categorical variables and using means with standard deviations or medians with interquartile ranges for continuous variables. We calculated the frequency and Kaplan-Meier estimates of 1-year or 6-month nursing home placement.

Since the goal of this study was to develop a prediction model that would be useful in practice, we carefully preselected potential predictors based on clinical knowledge and previous literature,^{10,13,15,25,26} and adhered to the rule of 10 events per examined variable to avoid overfitting.^27-30 We used logistic regression models to predict 1-year and 6-month nursing home placement. We chose logistic regression to facilitate both internal- and external-model validation using well-established methods and practical implementation of the prediction model scoring mechanism in the electronic data systems of 2 HCBS programs. In all models, we incorporated random effects to account for variance in nursing home placement among primary care-manager teams. As a sensitivity analysis, we used Cox proportional hazards models with robust standard errors to account for clustering of participants within primary care-management teams. For the Kaplan-Meier and Cox survival analyses, we censored data for participants if they terminated the program and at the time of death.

We randomly selected a derivation sample consisting of 66% of the study cohort and a validation sample consisting of the remaining 34% of the study cohort. We developed the logistic regression models in the derivation sample and then applied the results from these models to the validation sample. We evaluated the calibration and discrimination of all models in both samples and refit the models for the entire study cohort.^31,32 Using the derivation model estimates, we generated predicted probabilities of nursing home placement in the derivation and validation samples. To assess the clinical usefulness of the prediction models, we considered patients who were in the top 10%, 15%, 25%, or 50% of the predicted probabilities to be those whom the model predicted would have nursing home placement.³³ At each threshold, we calculated the model’s sensitivity, false-negative rate, specificity, false-positive rate, and positive predictive value. We calculated the area under the curve (C statistic) to assess overall model discrimination. To assess model calibration, we plotted percent of predicted nursing home placement versus percent of observed nursing home placement by decile of predicted probability and calculated E_avg.³⁰ Additional details about methods are provided in eAppendix B.

RESULTS

Among 10,975 CHCPE participants who completed an annual assessment between 2005 and 2010, the mean time since program activation was 425 days. The mean available follow-up for the study outcome was 320 days (range, 1-365). The median age was 75 to 79 years, 74% of participants were women, and 74% were white (Table 1). More than two-thirds of participants were eligible for Medicaid. Common medical diagnoses included hypertension (59%), diabetes (33%), and Alzheimer's disease (20%). Common functional dependencies included bathing (80%), meal preparation (92%), housework (97%), and money management (75%). Most participants were able to perform eating/feeding (89%) and toileting (86%) activities without assistance. Over half of participants lived alone, and most had regular contact with their support system.

The most common services received were: housekeeping or shopping (95%); skilled nursing care or medication administration (80%); emergency response system (72%); and adult care, companionship, or supervision (62%) (Table 2). In addition to formal services paid for through the program, most participants also received unpaid services typically performed by someone in their support system, including household/shopping, safety checks, and hands-on care. The pilot personal care assistance service was used by 160 (1.5%) participants, for an average of about 90 hours per month. The mean total monthly cost for paid program services was $2296, the mean non-Medicare medical service cost was $714, and the social service cost was $1549.

Within 1 year of assessment, 1249 (11.4%) participants had nursing home placement, 836 (7.6%) died, and 521 (4.7%) terminated the program for other reasons. The Kaplan-Meier 1-year nursing home rate was 12.2%. Among participants with nursing home placement within 1 year, the median time to placement was 170 days.

Significant risk factors for 1-year nursing home placement included age (OR, 1.19; 95% CI, 1.13-1.25), Alzheimer's disease (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), meeting a nursing home level of care (OR, 1.30; 95% CI, 1.04-1.63), and English as primary language (OR, 2.22; 95% CI, 1.66-2.97) (Table 3). Each additional short-term skilled nursing facility stay in the previous 12 months was associated with 46% higher odds of nursing home placement (95% CI, 1.31-1.62). Women, black patients, and patients with regular contact with a support system had lower risks of nursing home placement than other patients. Participants with monthly medical service costs above the median (>$511) had on average 7% higher odds of nursing home placement than other patients. Use of a personal care assistance service was associated with 46% lower odds of nursing home placement. However, none of the other service or cost variables improved model performance, so they were not included in the final models. Results for the 6-month models were similar (eAppendix C).

The prediction models were well calibrated for predicting nursing home placement. In the 1-year model, the E_avg was 0.01 for the derivation sample and 0.57 for the validation sample, indicating overall consistency between observed and predicted outcome rates. The predicted probability distributions were highly consistent, and the percentages of predicted and observed nursing home placement were similar within deciles (Figure). Table 4 summarizes the performance of the model for predicting 1-year nursing home placement after assessment. Using the 10% threshold for predicted probabilities in the validation sample, 29% of the participants who actually had nursing home placement to have nursing home placement. The sensitivity of the model improved as the threshold became less stringent, reaching 83% at the 50% threshold. On the other hand, using the 10% threshold, 92% of the participants who actually had no nursing home placement were predicted to have no nursing home placement. The specificity of the model declined as the threshold became less stringent. The C statistic was 0.79 in the the derivation cohort and 0.76 in the validation cohort. In the 6-month models, the calibration and performance measures improved slightly (eAppendices D and E). In the sensitivity analysis using Cox regression models, the discrimination, calibration, and model predictor variables were similar (eAppendices F-I).

DISCUSSION

We used clinical and administrative data for 10,975 participants in an HCBS program to derive and validate models to predict risks for nursing home placement within 6 months and 1 year after an annual assessment. The models were well calibrated and had good discriminatory power. After adjustment for multiple participant-level predictors, the personal care assistance service was associated with lower risk of nursing home placement, and higher monthly medical service cost was associated with higher risk.

To our knowledge, ours is the first study to develop a practical, implementable, and validated nursing home prediction model using data from participants in an HCBS program. Several studies have examined predictors of nursing home placement, but only 1 published discrimination measurements for a validated prediction model for use in identifying the highest-risk patients with dementia.³⁴ That study’s model for 3-year nursing home placement had a C statistic of 0.63 in the validation cohort, a low discriminatory power likely attributable to the fact that all study participants had dementia, which itself is a significant risk factor for nursing home placement. Furthermore, the generalizability of the model was limited to patients with dementia. Our population of community-dwelling older participants in an HCBS program is heterogeneous, including participants with a variety of functional dependencies and those with and without cognitive impairment. Therefore, our results are likely to have broader generalizability.

Consistent with studies of nursing home predictors in the general population, we found that higher risk was associated with greater age, Alzheimer's disease, being white, living alone, previous skilled nursing facility stays, and receiving a nursing home—level of care.^13,15 Even after adjustment for race/ethnicity, we also found that participants whose primary spoken language was English had twice the risk of nursing home placement. It is possible that language serves as a proxy for cultural and/or socioeconomic factors that influence use of nursing homes.

Although other studies have found associations between nursing home risk and multiple ADL dependencies, we identified dependence on money management as an independent predictor. Difficulty with money management is likely a marker of worse cognitive function, and a community-based money management program may be effective in preventing nursing home placement.³⁵ Such assistance may prevent loss of home or other assets, eviction, and financial abuse—events that often precipitate nursing home placement. Further research is needed to understand the effectiveness of money management interventions. Use of a personal care assistance service was associated with 46% lower odds of nursing home placement. Based on the success of the pilot and recommendations from a study investigating reasons for CHCPE nursing home placements,³⁶ CMS approved personal care assistance as a waiver service and extended it to new enrollees starting in 2011. In contrast to the traditional agency-directed HCBS program model, recent studies have shown that clients reported greater satisfaction, greater sense of security, and fewer unmet IADLs in a consumer-directed program.^37,38 In a study of the Indiana Medicaid Aged and Disabled Waiver program, use of a similar personal care assistance service lowered the risk of nursing home placement by 5% per each 5-hour increment per month.³⁹ We also found that higher medical service cost, which may be a proxy for intensity of services, was generally associated with greater risk of nursing home placement. In a study of disabled older adults, greater use of formal services was associated with higher risk of nursing home use, except in a subset of patients with cognitive impairment.⁴⁰

The CMS Office of the Actuary estimated that, as a result of the PPACA expansion, 26 million Medicaid enrollees will be added in 2020.⁴¹ With expanding Medicaid rolls, shrinking revenues, and PPACA incentives, states are expected to continue to shift long-term care costs away from institutional care and toward HCBS programs.^42,43 Many have argued that HCBS waiver programs increase overall Medicaid spending because of the so-called woodwork effect, whereby new applicants are attracted to Medicaid, and because of cost-shifting from higher inpatient and emergency department use by HCBS participants.^37,44,45 CMS requires only that HCBS waiver programs demonstrate cost neutrality compared with institutional care. This flexibility has led to much variation in states’ program implementation and cost-containment strategies.⁴³ Kaye and colleagues⁴⁶ observed cost savings in states with expanded HCBS programs, although it generally took several years to reduce the number of Medicaid-funded nursing home residents and recoup program start-up costs.

There is growing consensus that targeting patients with the highest risk of nursing home placement through stringent preadmission screening is paramount for cost containment.^6,36,45 As an alternative to traditional targeting, Weissert and colleagues⁴⁷ have argued for titration of care where resources are allocated based on risk measurement, attributable effectiveness of HCBS, and the monetary value of avoiding nursing home placement. Our model could be used as a risk-measurement tool to identify the highest-risk participants and develop targeted care management strategies to improve the effectiveness of HCBS programs. For example, some evidence points to the need for more intensive case management or multidisciplinary team models for high-maintenance participants, particularly those with behavioral issues related to dementia, depression, or mental illness.^48,49 Annual assessment could be an ideal time for case managers to develop tailored interventions, including management of psychological and behavioral problems, to successfully maintain high-risk participants in the community. Furthermore, for participants who are severely frail or disabled, who incur higher levels of medical service and care than other participants, HCBS programs may be inadequate—nursing homes may be better suited to meeting their care needs.⁵⁰

Limitations

First, data on age were limited to 5-year categories and collected during the initial assessment. Although our models may not have fully adjusted for risk associated with greater age, we included multiple factors representing functional age. Second, we were unable to account for patient preferences and the availability of nursing home beds, which may be associated with nursing home placement. Third, although we adjusted for participant risk factors, the observed protective effect of the personal care assistant service may have been related to unmeasured differences between pilot and nonpilot participants.³⁸ Fourth, assessment of comorbidity severity was limited due to the nature of the data. Nonetheless, we obtained complementary data on medical costs and medications that likely offset this limitation. Finally, the analysis was restricted to CHCPE participants, so the results may not be generalizable to older adults in other regions or thosewho were not enrolled in a similar HCBS programs.

CONCLUSIONS

A model using information from an HCBS program had strong discrimination to predict the risk of long-term nursing home placement and can be implemented to identify high-risk participants for targeted interventions and tailored services. A consumer-driven personal care assistance service seemed to be effective in preventing nursing home placement. Future studies should explore the costs and effectiveness of targeting high-risk participants and tailoring services to prevent nursing home placement.

Acknowledgment

Damon M. Seils, MA, Duke University, assisted with manuscript preparation. Mr Seils did not receive compensation for his assistance apart from his employment at the institution where the study was conducted.Author Affiliations: Duke Clinical Research Institute (MAG, LGQ, KAS, SS) and Department of Medicine (HKW, KAS, SS), Duke University School of Medicine, Durham, NC; Department of Medicine, University of North Carolina School of Medicine (II), Chapel Hill, NC; Connecticut Community Care, Inc (SLM, MTS), Bristol, CT; Anthem, Inc (BB), Virginia Beach, VA.

Source of Funding: Supported by a research agreement between Amerigroup Corporation and Duke University.

Author Disclosures: Ms Burke is employed by Anthem, Inc, who has licensed the predictive model developed from this work, and owns stock in Anthem, Inc. Dr Schulman is a board member for the Kaiser Permanente Institute for Health Policy and is a consultant with Blue Cross and Blue Shield of North Carolina. His compete disclosure can be found at www.dcri.duke.edu/about-us/conflict-of-interest. Drs Iwata, White, Molony, and Setoguchi, and Mses Greiner, Qualls, and Sullivan report no conflicts of interest.

Authorship Information: Concept and design (MAG, LGQ, II, SS); acquisition of data (MAG, LGQ, SLM, MTS, BB, SS); analysis and interpretation of data (MAG, LGQ, II, HKW, SLM, BB, KAS); drafting of the manuscript (MAG, LGQ, SLM, SS); critical revision of the manuscript for important intellectual content (MAG, II, HKW, SLM, BB, KAS, SS); statistical analysis (MAG); provision of study materials or patients (MTS); obtaining funding (BB, SS); administrative, technical, or logistic support (LGQ, MTS); and supervision (BB, KAS, SS).

Address correspondence to: Soko Setoguchi, MD, DrPH, Duke Clinical Research Institute, PO Box 17969, Durham, NC 27715. E-mail: soko@post.harvard.edu.REFERENCES

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