New CMS Nursing Home Ownership Data: Major Gaps And Discrepancies.

Nursing home ownership has become increasingly complicated, partly because of the growth of facilities owned by institutional investors such as private equity (PE) firms and real estate investment trusts (REITs). Although the ownership transparency and accountability of nursing homes have historically been poor, the Biden administration's nursing home reform plans released in 2022 included a series of data releases on ownership. However, our evaluation of the newly released data identified several gaps: One-third of PE and fewer than one-fifth of REIT investments identified in the proprietary Irving Levin Associates and S&P Capital IQ investment data were present in Centers for Medicare and Medicaid Services (CMS) publicly available ownership data. Similarly, we obtained different results when searching for the ten top common owners of nursing homes using CMS data and facility survey reports of chain ownership. Finally, ownership percentages were missing in the CMS data for 82.40 percent of owners in the top ten chains and 55.21 percent of owners across all US facilities. Although the new data represent an important step forward, we highlight additional steps to ensure that the data are timely, accurate, and responsive. Transparent ownership data are fundamental to understanding the adequacy of public payments to provide patient care, enable policy makers to make timely decisions, and evaluate nursing home quality.

The relationship between Medicaid policy and realized access to home- and community-based services.

Medicaid funding for home- and community-based services (HCBS) has increased substantially in recent decades. Prior research has investigated the effects of this expansion on outcomes for individuals as well as costs to Medicaid, often using state policy as a proxy for access to HCBS or implicitly assuming that more generous policies affect outcomes through access, an assumption that may not hold. In this study, using survey data linked to Medicaid claims, we assess the extent to which common measures of state Medicaid HCBS generosity correspond to increased individual use of HCBS among older adults with potential needs. We find several measures to have strong predictive power, but only with relatively large changes in policy generosity. Our findings imply that increased funding of HCBS is not sufficient to ensure access to services and that researchers should be careful when using state policy generosity as a proxy for access.

Dementia, nurse staffing, and health outcomes in nursing homes.

OBJECTIVE: To estimate and contrast the relationships between nurse staffing and health outcomes in nursing homes with low and high dementia census, to understand the association of staffing hours with dementia care quality. DATA SOURCES AND STUDY SETTING: A national sample of nursing homes during 2017-2019 (pre-COVID). Data included the Payroll-Based Journal, Medicare Claims, Nursing Home Care Compare, and Long-Term Care Focus. STUDY DESIGN: Retrospective, regression analyses. We estimated separate linear models predicting six long-term facility-level outcomes. Independent variables included staffing hours per resident-day (HPRD) interacted with the facility percentage of dementia residents, controlling for other resident and facility characteristics. DATA COLLECTION/EXTRACTION METHODS: Hospital-based nursing homes, those with fewer than 30% dementia residents, and missing data were excluded. PRINCIPAL FINDINGS: We found that registered nurses and certified nurse assistants HPRDs were likely to exhibit positive returns in terms of outcomes throughout most of the range of HPRD for both high and low-census dementia facilities, although, high- and low-dementia facilities differed in most outcome rates at all staffing levels. Average predicted antipsychotics and activities of daily living as functions of HPRD were worse in higher dementia facilities, independent movement, and hospitalizations did not differ significantly, and Emergency Rooms and pressure sores were worse in lower dementia facilities. Average marginal effects were not statistically different [CI included zero] between the high and low dementia facilities for any outcome. CONCLUSIONS: These findings suggest that increasing staffing will improve outcomes by similar increments in both low- and high-dementia facilities for all outcomes. However, at any given level of staffing, absolute differences in outcomes between low- and high-dementia facilities remain, suggesting that additional staffing alone will not suffice to close these gaps. Further studies are required to identify opportunities for improvement in performance for both low- and high-dementia census facilities.

The health effects of nursing home specialization in post-acute care.

Nursing homes serve both long-term care and post-acute care (PAC) patients, two groups with distinct financing mechanisms and requirements for care. We examine empirically the effect of nursing home specialization in PAC using 2011-2018 data for Medicare patients admitted to nursing homes following a hospital stay. To address patient selection into specialized nursing homes, we use an instrumental variables approach that exploits variation over time in the distance from the patient's residential ZIP code to the closest nursing home with different levels of PAC specialization. We find that patients admitted to nursing homes more specialized in PAC have lower hospital readmissions and mortality, longer nursing home stays, and higher Medicare spending for the episode of care, suggesting that specialization improves patient outcomes but at higher costs.

Dementia Care Is Widespread In US Nursing Homes; Facilities With The Most Dementia Patients May Offer Better Care.

More than three million US nursing home residents were diagnosed with Alzheimer's disease and related dementias (ADRD) between 2017 and 2019. This number is expected to increase as the population ages and ADRD prevalence increases. People with ADRD require specialized care from trained staff. This study addressed two questions: Are residents with ADRD concentrated in nursing homes where they are the majority? If not, what are the implications for their quality of care and life? We answered the first question by determining the ADRD census for each nursing home in the country during the period 2017-19. Using the Minimum Data Set and Medicare claims, we compared characteristics of nursing homes with high and low ADRD census along several dimensions, including staffing, resident outcomes, and resident characteristics. We found that residents with ADRD were dispersed throughout all nursing homes, with fewer than half residing in nursing homes where residents with ADRD accounted for 60-90 percent of the census. Furthermore, only facilities exceeding 90 percent of residents with ADRD seemed to offer better care. These findings raise concerns about the quality of care and life for the majority of residents with ADRD, suggesting that current National Institutes of Health dementia research initiatives and the Biden administration's policies to improve nursing home care should be coordinated.

Effectiveness of a Bundled Payments for Care Improvement Program for Chronic Obstructive Pulmonary Disease.

BACKGROUND: The Medicare Bundled Payments for Care Improvement (BPCI) program reimburses 90-day care episodes post-hospitalization. COPD is a leading cause of early readmissions making it a target for value-based payment reform. OBJECTIVE: Evaluate the financial impact of a COPD BPCI program. DESIGN, PARTICIPANTS, INTERVENTIONS: A single-site retrospective observational study evaluated the impact of an evidence-based transitions of care program on episode costs and readmission rates, comparing patients hospitalized for COPD exacerbations who received versus those who did not receive the intervention. MAIN MEASURES: Mean episode costs and readmissions. KEY RESULTS: Between October 2015 and September 2018, 132 received and 161 did not receive the program, respectively. Mean episode costs were below target for six out of eleven quarters for the intervention group, as opposed to only one out of twelve quarters for the control group. Overall, there were non-significant mean savings of $2551 (95% CI: - $811 to $5795) in episode costs relative to target costs for the intervention group, though results varied by index admission diagnosis-related group (DRG); there were additional costs of $4184 per episode for the least-complicated cohort (DRG 192), but savings of $1897 and $1753 for the most complicated index admissions (DRGs 191 and 190, respectively). A significant mean decrease of 0.24 readmissions per episode was observed in 90-day readmission rates for intervention relative to control. Readmissions and hospital discharges to skilled nursing facilities were factors of higher costs (mean increases of $9098 and $17,095 per episode respectively). CONCLUSIONS: Our COPD BPCI program had a non-significant cost-saving effect, although sample size limited study power. The differential impact of the intervention by DRG suggests that targeting interventions to more clinically complex patients could increase the financial impact of the program. Further evaluations are needed to determine if our BPCI program decreased care variation and improved quality of care. PRIMARY SOURCE OF FUNDING: This research was supported by NIH NIA grant #5T35AG029795-12.

Accuracy of infection reporting in US nursing home ratings.

OBJECTIVE: To assess the accuracy of nursing home-reported data on urinary tract infections (UTIs), which are publicly reported on Nursing Home Care Compare, and pneumonia, which are not publicly reported. DATA SOURCES AND STUDY SETTING: We used secondary data for 100% of Medicare fee-for-service beneficiaries in the United States between 2011 and 2017. STUDY DESIGN: We identified Medicare fee-for-service beneficiaries who were nursing home residents between 2011 and 2017 and admitted to a hospital with a primary diagnosis of UTI or pneumonia. After linking these hospital claims to resident-level nursing home-reported assessment data in the Minimum Data Set, we calculated the percentages of infections that were appropriately reported and assessed variation by resident- and nursing home-level characteristics. We developed a claims-based nursing home-level measure of hospitalized infections and estimated correlations between this and publicly reported ratings. DATA EXTRACTION METHODS: Medicare fee-for-service beneficiaries who were nursing home residents and hospitalized for UTI or pneumonia during the study period were included. PRINCIPAL FINDINGS: Reporting rates were low for both infections (UTI: short-stay residents 29.1% and long-stay residents 19.2%; pneumonia: short-stay residents 66.0% and long-stay residents 70.6%). UTI reporting rates increased when counting additional assessments, but it is unclear whether these reports are for the same versus a newly developed UTI. Black residents had slightly lower reporting rates, as did nursing homes with more Black residents. Correlations between our claims-based measure and publicly reported ratings were poor. CONCLUSIONS: UTI and pneumonia were substantially underreported in data used for national public reporting. Alternative approaches are needed to improve surveillance of nursing home quality.

New Dimensions of Staffing Patterns in Nursing Homes and Nursing Home Quality: Comparing Staffing Instability to Staffing Turnover.

OBJECTIVE: This study examines how measures of staffing-turnover and instability-are associated with one another and how they independently contribute to quality of care in nursing homes. DESIGN: Cross-sectional analysis of 2021-2022 administrative data. Data included the Payroll Based Journal for daily staffing information, merged with Nursing Home Care Compare (NHCC) data for nursing home characteristics, total staffing turnover, and nursing home quality. SETTING AND PARTICIPANTS: A total of 11,840 nursing homes nationally reporting data on daily staffing and staffing turnover. METHODS: We explored correlations between measures of staffing and estimated facility-level regression models with robust standard errors. The dependent variables were indicators of nursing home quality included in the NHCC 5-star ratings. The independent variables of interest were average total staffing hours per resident-day, total staffing turnover, and total staffing instability. RESULTS: For the 11,840 nursing homes in the study, there was a weak positive correlation between turnover and instability, with some overlap between nursing homes with high instability and high turnover. Regression analysis revealed that staffing instability and turnover contributed independently to nursing home quality, with instability having a stronger association with some measures of quality and turnover with others. Staffing instability was positively and more strongly associated with long-stay residents' decline in activities of daily living levels and receipt of antipsychotic drugs and short-stay residents' functioning at discharge. Turnover was positively and more strongly associated with long-stay residents' prevalence of pressure ulcers and worsening mobility, and short-stay residents' hospitalizations. CONCLUSION AND IMPLICATIONS: Instability and turnover in total nursing home staffing independently contribute to nursing home quality. This suggests that adding measures of staffing instability to the existing measures of average staffing and staff turnover in NHCC may enhance the report card's value for providers engaged in quality improvement and consumers searching for high-quality nursing homes.

Caregiving for Older Adults With Dementia During the Time of COVID-19: A Multi-State Exploratory Qualitative Study.

This qualitative semi-structured interview study explores how 64 family caregivers for older adults with Alzheimer's Disease and related dementias across eight states experienced and executed caregiving decisions before and during the COVID-19 pandemic. First, caregivers experienced challenges communicating with loved ones and healthcare workers in all care settings. Second, caregivers displayed resilient coping strategies in adapting to pandemic restrictions, finding novel strategies to balance risks while preserving communication, oversight, and safety. Third, many caregivers modified care arrangements, with some avoiding and others embracing institutional care. Finally, caregivers reflected on the benefits and challenges of pandemic-related innovations. Certain policy changes reduced caregiver burden and could improve care access if made permanent. Telemedicine's increasing use highlights the need for reliable internet access and accommodations for individuals with cognitive deficits. Public policies must pay greater attention to challenges faced by family caregivers, whose labor is both essential and undervalued.

Association of Staffing Instability With Quality of Nursing Home Care.

Importance: Recent work suggests that instability in nursing home staffing levels may be an important marker of nursing home quality. Whether that association holds when controlling for average staffing levels is unknown. Objective: To examine whether staffing instability, defined as the percentage of days below average staffing levels, is associated with nursing home quality when controlling for average staffing levels. Design, Setting, and Participants: This quality improvement study of 14 717 nursing homes used the merged Centers for Medicare & Medicaid Services Payroll Based Journal, Minimum Data Set, Nursing Home Care Compare, and Long-Term-Care Focus data for fiscal years 2017 to 2019. Statistical analysis was performed from February 8 to November 14, 2022. Main Outcomes and Measures: Linear, random-effect models with state fixed effects and robust SEs were estimated for 12 quality indicators as dependent variables, percentage of below-average staffing days as independent variables, controlling for average staffing hours per resident-day for registered nurses, licensed practical nurses, and certified nurse aides. Below-average staffing days were defined as those 20% below the facility average, by staffing type. Quality indicators included deficiency citations; long-stay residents receiving an antipsychotic; percentage of high-risk long-stay residents with pressure ulcers (2 different measures for pressure ulcers were used); and percentage of long-stay residents with activities of daily living decline, mobility decline, emergency department visits, and hospitalizations; and short-stay residents with new antipsychotic medication, mobility decline, emergency department visits, and rehospitalizations. Results: For the 14 717 nursing homes in this study, the mean (SD) percentage of days with below-average staffing was 30.2% (12.0%) for registered nurses, 16.4% (11.3%) for licensed practical nurses, and 5.1% (5.3%) for certified nurse aides. Mean (SD) staffing hours per resident-day were 0.44 (0.40) for registered nurses, 0.80 (0.32) for licensed practical nurses, and 2.20 (0.50) for certified nurse aides. In regression models that included average staffing, a higher percentage of below-average staffing days was significantly associated with worse quality for licensed practical nurses in 10 of 12 models, with the largest association for decline of activities of daily living among long-stay residents (regression coefficient, 0.020; P < .001). A higher percentage of below-average staffing days was significantly associated with worse quality for certified nurse aides in 9 of 12 models, with the largest association for short-stay functioning (regression coefficient, 0.030; P = .01). Conclusions and Relevance: This study suggests that holding average staffing levels constant, day-to-day staffing stability, especially avoiding days with low staffing of licensed practical nurses and certified nurse aides, is a marker of better quality of nursing homes. Future research should investigate the causes and potential solutions for instability in staffing in all facilities, including those that may appear well-staffed on average.

Predicting Hospitalization among Medicaid Home- and Community-Based Services Users Using Machine Learning Methods.

We compare multiple machine learning algorithms and develop models to predict future hospitalization among Home- and Community-Based Services (HCBS) Users. Furthermore, we calculate feature importance, the score of input variables based on their importance to predict the outcome, to identify the most relevant variables to predict hospitalization. We use the 2012 national Medicaid Analytic eXtract data and Medicare Provider Analysis and Review data. Predicting any hospitalization, Random Forest appears to be the most robust approach, though XGBoost achieved similar predictive performance. While the importance of features varies by algorithm, chronic conditions, previous hospitalizations, as well as use of services for ambulance, personal care, and durable medical equipment were generally found to be important predictors of hospitalization. Utilizing prediction models to identify those who are prone to hospitalization could be useful in developing early interventions to improve outcomes among HCBS users.

Association of COVID-19 Vaccination Rates of Staff and COVID-19 Illness and Death Among Residents and Staff in US Nursing Homes.

Importance: It is important to understand the association between staff vaccination rates and adverse COVID-19 outcomes in nursing homes. Objective: To assess the extent to which staff vaccination was associated with preventing COVID-19 cases and deaths among residents and staff in nursing homes. Design, Setting, and Participants: This longitudinal cohort study used data on COVID-19 outcomes in Medicare- and Medicaid-certified nursing homes in the US between May 30, 2021, and January 30, 2022. Participants included the residents of 15 042 US nursing homes that reported COVID-19 data to the Centers for Disease Control and Prevention and passed Centers for Medicare & Medicaid Services data quality checks in the National Healthcare Safety Network. Exposures: Weekly staff vaccination rates. Main Outcomes and Measures: Main outcomes are weekly COVID-19 cases and deaths among residents and weekly COVID-19 cases among staff. The treatment variable is the primary 2-dose staff vaccination rate in each facility each week. Results: In the primary analysis of 15 042 nursing homes before the Omicron variant wave (May 30 to December 5, 2021) using fixed effects of facility and week, increasing weekly staff vaccination rates by 10 percentage points was associated with 0.13 (95% CI, -0.20 to -0.10) fewer weekly COVID-19 cases per 1000 residents, 0.02 (95% CI, -0.03 to -0.01) fewer weekly COVID-19 deaths per 1000 residents, and 0.03 (95% CI, -0.04 to -0.02) fewer weekly COVID-19 staff cases. In the secondary analysis of the Omicron wave (December 5, 2021, to January 30, 2022), increasing staff vaccination rates were not associated with lower rates of adverse COVID-19 outcomes in nursing homes. Conclusions and Relevance: The findings of this cohort study suggest that before the Omicron variant wave, increasing staff vaccination rates was associated with lower incidence of COVID-19 cases and deaths among residents and staff in US nursing homes. However, as newer, more infectious and transmissible variants of the virus emerged, the original 2-dose regimen of the COVID-19 vaccine as recommended in December 2020 was no longer associated with lower rates of adverse COVID-19 outcomes in nursing homes. Policy makers may want to consider longer-term policy options to increase the uptake of booster doses among staff in nursing homes.

Associations Between Daily Nurse Staffing Levels and Daily Hospitalizations and ED Visits in Nursing Homes.

OBJECTIVES: Although many prior studies have shown that high average levels of nurse staffing in nursing homes are associated with fewer hospitalizations, some studies have not, suggesting that the average nursing level may mask a more complex relationship. This study examines this issue by investigating the associations of daily staffing patterns and daily hospitalizations and emergency department (ED) visits. DESIGN: Retrospective analyses of national Payroll Based Journal (PBJ) staffing data merged with the Minimum Data Set. SETTING AND PARTICIPANTS: A total of 15,718 nursing homes nationally reporting PBJ data during 2017-2019, their staff, and residents. METHODS: We estimated facility-day-level models as conditional facility fixed-effect Poisson regressions with robust standard errors. The dependent variables were daily numbers of hospitalization and ED visits and the independent variables of interest were the number of registered nurse (RN), licensed practical nurse (LPN), and certified nurse assistant (CNA) hours on the same and prior days. RESULTS: The daily number of hospital transfers averaged 0.28 (SD 0.21). Daily total direct-care staffing hours averaged 288.7 (SD 188.2), with RNs accounting for 35.0, LPNs for 68.7, and CNAs for 185.0. Higher staffing was associated with more hospitalizations on the concurrent day. Higher staffing on the day prior was associated with fewer hospitalizations. The effect size was larger for RNs and LPNs (same day = ∼2%; prior day = approximately -0.7% to -0.9%) than for CNAs (same day <1%; prior day < -0.5%). ED visits not leading to hospitalizations, and analyses for subsamples exhibited similar findings. CONCLUSIONS AND IMPLICATIONS: Our findings suggest that staff can address developing problems and prevent admissions the next day and identify emergent problems and hospitalize the same day. They also underscore the complex array of nursing home factors involved in hospitalization and ED visits, including the influence of daily staffing variation, suggesting the need for further research to better understand the associations between staffing and appropriate resident transfers to the hospital or the ED, and the potential implications for quality metrics in these domains.

Trends In Medicaid Home And Community-Based Services Waivers For Older Adults.

During the past several decades, state Medicaid programs have expanded the use of home and community-based services, particularly through Section 1915(c) waivers and Section 1115 demonstration waivers. We document trends from the period 1997-2020 in waivers targeting older adults, focusing on services offered. Nearly every service category saw an increase in coverage and spending, especially support for self-direction and community transition.

Beyond Compliance: A More Integrated Public Health Approach To Outbreaks In Nursing Homes And Other Disasters.

Nursing home residents are vulnerable to disproportionate harm during disasters and health emergencies, as evidenced by the recent impact of COVID-19 and extreme weather events. Evaluation of these disasters shows that the nursing homes in question were often in compliance with relevant regulations around emergency planning. However, advance planning for disasters cannot anticipate every contingency, leading to the need for flexibility and quick adaptation on the part of nursing home leaders. In addition, disasters often involve larger, regional problems and situations that individual providers cannot solve. We suggest that policy makers need to prioritize development and expectations around leadership skills in nursing home management and better integrate the long-term care sector into local, state, and federal public health planning for future pandemics and other disasters.

Estimating the Impact of Medicaid Expansion and Federal Funding Cuts on FQHC Staffing and Patient Capacity.

Policy Points In the preexpansion period, federally qualified health centers (FQHCs) in Medicaid expansion states were significantly different from those in nonexpansion states. This gap widened as revenues in expansion states continued to grow at a faster rate after the expansion. If Medicaid expansion had occurred nationwide, FQHCs' revenue and capacity could have increased substantially. Over time, Medicaid could play a bigger role as it becomes a more stable funding source to allow for capital investments. Section 330 grants appear to have a larger impact on access to care. Given the varying levels of reliance on Medicaid, investing through federal grants might be more effective and equitable. CONTEXT: The Health Resources and Services Administration's Health Center Program (HCP) plays a critical role as the national ambulatory safety net, delivering services to patients in medically underserved areas, regardless of their ability to pay. As the program has grown, health policy initiatives may have altered access to care for the underserved population. Understanding how federally qualified health centers (FQHCs) have been affected by past policies is important for anticipating the effects of future policies. METHODS: By analyzing a national data set from the Uniform Data System, we examined, using two sets of random effects regressions, the potential impact of alternative policy actions affecting FQHCs. Our primary equation models the number of full-time equivalent staff, of patients served, and of visits provided in the subsequent year as a function of Medicaid revenues, Section 330 grants, and other revenues. Our secondary equation is a difference-in-differences analysis that models Medicaid revenues as a function of the states' status of Medicaid expansion. FINDINGS: The expansion of Medicaid in nonexpansion states could have increased Medicaid revenues by 138%, staffing by 25%, and patients' visits by 24% in 2017. Compared to the impact of a "repeal" of Medicaid expansion, the percentage of reductions in staffing would be similar to those predicted by a 50% cut in Medicaid revenues or in Section 330 grants. On a dollar-for-dollar basis, the effects of one dollar of Section 330 grants were more than double that of one dollar of Medicaid revenue. CONCLUSIONS: Both Medicaid eligibility and Section 330 funding support are important to the HCP, and Section 330 grants are particularly closely related to staffing and the provision of services. States' decisions not to participate in or to repeal Medicaid expansion, to reduce Medicaid payment rates, and federal funding cuts all could have a negative impact on FQHCs, resulting in thousands of low-income patients losing access to primary care.

Daily Variation in Nursing Home Staffing and Its Association With Quality Measures.

Importance: Average staffing measures are a focus of nursing homes' quality assessments and reporting. They may, however, mask daily variation in staffing, additional information that could be important for understanding nursing home quality and relative ranking. Objective: To examine daily variation in staffing, its association with quality, and whether daily variation provides information regarding quality ranking of nursing homes over and above the information provided by average staffing levels. Design, Setting, and Participants: This quality improvement study included registered nurses (RNs) and certified nurse aide (CNAs) at 13 339 certified nursing homes throughout the United States during 2017 to 2018. Retrospective analyses of the Payroll-Based Journal, Medicare Cost Reports, and Nursing Home Care Compare were conducted. Data were analyzed from January 2017 to December 2018. Main Outcomes and Measures: Three measures of daily variation, ie, coefficient of variation (COV), total outlier days (TOD), and low outlier days (LOD), were calculated for RNs and CNAs. The association between these measures and quality rankings and other facility characteristics were evaluated. Results: A total of 13 339 nursing homes were included in this study, with 9476 (71%) for-profit facilities. The mean (SD) hours-per-resident-day were 0.41 (0.29) for RNs and 2.16 (0.49) for CNAs, and a mean (SD) 55% (26%) of residents were Medicaid beneficiaries. Outcome measures were as follows: mean (SD) COV, 0.5 (0.6) for RNs and 0.1 (0.1) for CNAs; mean (SD) TOD, 220 (69) for RNs and 44 (45) for CNAs; and mean (SD) LOD, 116 (45) for RNs and 22 (24) for CNAs. All 3 variation measures, for both RNs and CNAs, were significantly associated with both the 5-Star Quality Measures (COV among RNs, -0.014 [95% CI, -0.021 to -0.007]; P < .001; COV among CNAs: -0.004 [95% CI, -0.006 to -0.003]; P < .001; TOD among RNs, -3.79 [95% CI, -4.59 to -2.99]; P < .001; TOD among CNAs, -2.52 [95% CI, -3.08 to -1.96]; P < .001; LOD among RNs, -2.46 [95% CI, -3.03 to -1.88]; P < .001; LOD among CNAs, -1.29 [95% CI, -1.58 to -0.99]; P < .001) and the 5-Star Survey rankings (COV among RNs,-0.026 [95% CI, -0.033 to -0.019]; P < .001; COV among CNAs: -0.006 [95% CI, -0.007 to -0.004]; P < .001; TOD among RNs, -5.10 [95% CI, -5.97 to -4.23]; P < .001; TOD among CNAs, -4.16 [95% CI, -4.77 to -3.55]; P < .001; LOD among RNs, -3.04 [95% CI, -3.65 to -2.44]; P < .001; LOD among CNAs, -1.97 [95% CI, -2.29 to -1.65]; P < .001) published in Nursing Home Care Compare. Low κ values, ranging from 0.23 to 0.63, indicated that the variation measures add information about ranking to the information provided by average staffing measure. Conclusions and Relevance: These findings highlight the importance of reporting daily variation in staffing to improve understanding of the relationship between staffing and quality. They suggest that 2 facilities with the same average staffing achieve different quality of resident care and survey ratings in association with on the day-to-day variation in staffing. Measures of daily staffing may enhance the value of Nursing Home Care Compare for nursing homes and others engaged in quality improvement and consumers searching for high quality nursing homes.

Consumer selection and home health agency quality and patient experience stars.

OBJECTIVE: To compare the impact of the introduction of two distinct sets of star ratings, quality of care, and patient experience, on home health agency (HHA) selection. DATA SOURCES: We utilized 2014-2016 home health Outcome and Assessment Information Set (OASIS) assessments, as well as publicly reported data from the Home Health Compare website. DATA COLLECTION/EXTRACTION METHODS: We identified a 5% random sample of admissions (186,498 admissions) for new Medicare Fee-for-Service home health users. STUDY DESIGN: This admission-level assessment compared HHA selection before (July 2014-June 2015) and after (February-December 2016) star ratings were published. We utilized a conditional logit, discrete choice model, which accounted for all HHAs that each patient could have selected (i.e., the choice set) based on ZIP codes. Our explanatory variables of interest were the interactions between star ratings and time period (pre/post stars). We stratified our analyses by race, admission source, and Medicaid eligibility. We adjusted for HHA characteristics and distance between patients' homes and HHAs. PRINCIPAL FINDINGS: The introduction of star ratings was associated with a 0.88-percentage-point increase in the probability of selecting a high-quality HHA and a 0.81-percentage-point increase in the probability of selecting a highly ranked patient experience HHA. Patients admitted from the community, and black and Medicare-Medicaid dual-eligible beneficiaries experienced larger increases in their likelihood of selecting high-rated agencies than inpatient, white, and nondual beneficiaries. CONCLUSIONS: The introduction of quality of care and patient experience stars were associated with changes in HHA selection; however, the strength of these relationships was weaker than observed in other health care settings where a single star rating was reported. The introduction of star ratings may mitigate disparities in HHA selection. Our findings highlight the importance of reporting information about quality and satisfaction separately and conducting research to understand the mechanisms driving HHA selection.