The Role of Assisted Living Capacity on Nursing Home Financial Performance

COVID-19 Pandemic Impact on Nursing Homes Financial Performance

Nursing homes expressed concern about potential severe adverse financial outcomes of COVID-19, with worries extending to the possibility of some facilities facing closure. Maintaining a strong financial well-being is crucial, and there were concerns that the pandemic might have significantly impacted both expenses and income. This longitudinal study aimed to analyze the financial performance of nursing homes during COVID-19 pandemic. Specifically, we examined the impact of the pandemic on nursing home operating margins, operating revenue per resident day, and operating cost per resident day. The study utilized secondary data from various sources, including CMS Medicare cost reports, Brown University's Long Term Care Focus (LTCFocus), CMS Payroll-Based Journal, CMS Care Compare, Area Health Resource File, Provider Relief Fund distribution data, and CDC's NH COVID-19 public file. The sample consisted of 45 833 nursing home-year observations from 2018 to 2021. Fixed-effects regression analysis was employed to assess the impact of the pandemic on financial performance while controlling for various organizational and market characteristics. The study found that nursing homes' financial performance deteriorated during the COVID-19 pandemic. Operating margins decreased by approximately 4.3%, while operating costs per resident day increased by $26.51, outweighing the increase in operating revenue per resident day by about $17. Occupancy rates, payer mix, and staffing intensity were found to impact financial performance. The study highlights the significant financial impact of the COVID-19 pandemic on nursing homes. While nursing homes faced substantial financial strains, the findings offered lessons for the future, underscoring the need for nursing homes to improve the accuracy of their cost reports and enhance financial transparency and accountability.

Changing landscape of nursing homes serving residents with dementia and mental illnesses

OBJECTIVE

Nursing homes (NHs) are serving an increasing proportion of residents with cognitive issues (e.g., dementia) and mental health conditions. This study aims to: (1) implement unsupervised machine learning to cluster NHs based on residents' dementia and mental health conditions; (2) examine NH staffing related to the clusters; and (3) investigate the association of staffing and NH quality (measured by the number of deficiencies and deficiency scores) in each cluster.

DATA SOURCES

2009-2017 Certification and Survey Provider Enhanced Reporting (CASPER) were merged with LTCFocUS.org data on NHs in the United States.

STUDY DESIGN

Unsupervised machine learning algorithm (K-means) clustered NHs based on percent residents with dementia, depression, and serious mental illness (SMI, e.g., schizophrenia, anxiety). Panel fixed-effects regressions on deficiency outcomes with staffing-cluster interactions were conducted to examine the effects of staffing on deficiency outcomes in each cluster.

DATA EXTRACTION METHODS

We identified 110,463 NH-year observations from 14,671 unique NHs using CASPER data.

PRINCIPAL FINDINGS

Three clusters were identified: low dementia and mental illnesses (Postacute Cluster); high dementia and depression, but low SMI (Long-stay Cluster); and high dementia and mental illnesses (Cognitive-mental Cluster). From 2009 to 2017, the number of Postacute Cluster NHs increased from 3074 to 5719, while the number of Long-stay Cluster NHs decreased from 6745 to 3058. NHs in Long-stay/Cognitive-mental Clusters reported slightly lower nursing staff hours in 2017. Regressions suggested the effect of increasing staffing on reducing deficiencies is statistically similar across NH clusters. For example, 1 hour increase in registered nurse hours per resident day was associated with -0.67 (standard error [SE] = 0.11), -0.88 (SE = 0.12), and -0.97 (SE = 0.15) deficiencies in Postacute Cluster, Long-stay Cluster, and Cognitive-mental Cluster, respectively.

CONCLUSIONS

Unsupervised machine learning detected a changing landscape of NH serving residents with dementia and mental illnesses, which requires assuring staffing levels and trainings are suited to residents' needs.

Changes in Long-Term Care Markets: Assisted Living Supply and the Prevalence of Low-Care Residents in Nursing Homes

OBJECTIVES

To assess the effect of changes in assisted living (AL) capacity within a market on prevalence of residents with low care needs in nursing homes.

DESIGN

Retrospective, longitudinal analysis of nursing home markets.

SETTING AND PARTICIPANTS

Twelve thousand two hundred fifity-one nursing homes in operation during 2007 and 2014.

MEASUREMENTS

We analyzed the percentage of residents in a nursing home who qualified as low-care. For each nursing home, we constructed a market consisting of AL communities, Medicare beneficiaries, and competing nursing homes within a 15-mile radius. We estimated the effect of change in AL beds on prevalence of low-care residents using multivariate linear models with year and nursing home fixed effects.

RESULTS

The supply of AL beds increased by an average 258 beds per nursing home market (standard deviation = 591) during the study period. The prevalence of low-care residents decreased from an average of 13.0% (median 10.5%) to 12.2% (median 9.5%). In adjusted models, a 100-bed increase in AL supply was associated with a decrease in low-care residents of 0.041 percentage points (P = .026), controlling for changes in market and nursing home characteristics, county demographics, and year and nursing home fixed effects. In markets with a high percentage of its Medicare beneficiaries (≥14%) dual eligible for Medicaid, the effect of AL is stronger, with a 0.066-percentage point decrease per 100 AL beds (P = .026) vs a 0.016-percentage point decrease in low-duals markets (P = .48).

CONCLUSIONS AND IMPLICATIONS

Our analysis suggests that some of the growth in AL capacity serves as a substitute for nursing homes for patients with low care needs. Furthermore, the effects are concentrated in markets with an above-average proportion of beneficiaries with dual Medicaid eligibility.

Variation in Licensed Nurse Staffing Characteristics by State Requirements in Residential Care

Research on licensed nurses in assisted living and residential care communities (RCCs) is sparse compared to that on licensed nurses in nursing homes. RCCs are state-regulated; thus, staffing requirements vary considerably. The current study analyzed variation in characteristics of licensed nurses by state-specific requirements for licensed nurses in RCCs. A significantly higher percentage of RCCs with one or more RNs (68.87%) and licensed practical nurses (LPNs) or licensed vocational nurses (LVNs) (56.85%) were found among states with licensed nurse requirements compared to states with no such requirements (37.35% and 29.08%, respectively; p < 0.05). LPN/LVN hours were higher among RCCs in states with licensed nurse requirements compared to RCCs in states with no such requirements (17 minutes and 8 minutes, respectively; p < 0.05). The findings provide the first evidence of variation in characteristics of licensed nurses by state-specific requirements for licensed nurses. [Res Gerontol Nurs. 2019; 12(1):27-33.].

Nursing Home Quality and Financial Performance: Is There a Business Case for Quality?

This study examines the relationship between nursing home quality and financial performance to assess whether there is a business case for quality. Secondary data sources included the Online Survey Certification and Reporting (OSCAR), Certification and Survey Provider Enhanced Reporting (CASPER), Medicare Cost Reports, Minimum Data Set (MDS 2.0), Area Resource File (ARF), and LTCFocus for all free-standing, nongovernment nursing homes for 2000 to 2014. Data were analyzed using panel data linear regression with facility and year fixed effects. The dependent variable, financial performance, consisted of the operating margin. The independent variables comprised nursing home quality measures that capture the three dimensions of Donabedian's structure-process-outcomes framework: structure Registered Nurse (RN) hours per resident day, Licensed Practical Nurse (LPN) hours per resident day, Certified Nursing Assistant (CNA) hours per resident day, RN skill mix), process (facility-acquired restraints, facility-acquired catheters, pressure ulcer prevention, and restorative ambulation), and outcomes (facility-acquired contractures, facility-acquired pressure ulcers, hospitalizations per resident, rehospitalizations, and health deficiencies). Control variables included size, average acuity index, market competition, per capita income, and Medicare Advantage penetration rate. This study found that the operating margin was lower in nursing homes that reported higher LPN hours per resident day and higher RN skill mix (structure); higher use of catheters, lower pressure ulcer prevention, and lower restorative ambulation (process); and more residents with contractures, pressure ulcers, hospitalizations and health deficiencies (outcomes). The results suggest that there is a business case for quality, whereas nursing homes that have better processes and outcomes of care perform better financially.