Association between staff turnover and nursing home quality - evidence from payroll-based journal data.

BACKGROUND: Staff turnover is considered an important indicator of nursing home quality. We used auditable staffing data from the Centers for Medicare & Medicaid Services (CMS) Payroll-Based Journal (PBJ) system to calculate turnover measures for nurse staff and administrators and examined the relationship between turnover and nursing home quality. METHODS: Our analyses included data from 13,631 nursing homes that submitted complete staffing data through PBJ for 2018Q3 - 2019Q4. We identified turnover based on gaps in days worked by eligible employees, allowing us to calculate turnover measures that do not depend on termination dates reported by nursing homes, which are not captured in PBJ. We linked staff turnover measures to nursing home quality measures and star ratings published on CMS' Care Compare website in January 2020 and examined the relationship between turnover and quality of care. We used ordinary least squared models for continuous outcomes and ordered logit models for categorical outcomes, controlling for facility, and county characteristics. RESULTS: Mean annual turnover rates were about 44% for RNs and 46% for total nurse staff. On average, there was one administrator leaving each nursing home during this period although about half of nursing homes had no administrator turnover. Turnover rates varied greatly across nursing homes. For-profit and larger nursing homes had higher turnover rates. Higher turnover was consistently associated with lower quality of care. CONCLUSIONS: Our study highlights the importance of staff turnover due to its relationship to nursing home quality. In January 2022, CMS started posting turnover measures on Care Compare to allow consumers to use this information in their assessment of nursing home quality and to motivate nursing homes to implement innovative strategies to retain staff. While these actions are challenging, they are nonetheless warranted for improving the quality of care for nursing home residents.

The association of nursing home quality ratings and spread of COVID-19.

OBJECTIVES: The coronavirus disease 2019 (COVID-19) pandemic has severely affected nursing home residents. Given the continued high incidence of COVID-19, and the likelihood that new variants and other infectious agents may cause future outbreaks, we sought to understand the relationship of nursing home quality ratings and measures of COVID-19 outbreak severity and persistence. DESIGN: We analyzed nursing home facility-level data on COVID-19 cases and deaths, county-level COVID-19 rates, and nursing home data from the Centers for Medicare & Medicaid Services (CMS), including ratings from the CMS Nursing Home Five-Star Quality Rating System. We used regression analysis to examine the association between star ratings and cumulative COVID-19 incidence and mortality as well as persistent high resident incidence. SETTING: All nursing homes in the CMS COVID-19 Nursing Home Dataset reporting data that passed quality assurance checks for at least 20 weeks and that were included in the January 2021 Nursing Home Care Compare update. PARTICIPANTS: Residents of the included nursing homes. MEASUREMENTS: Cumulative resident COVID-19 incidence and mortality through January 10, 2021; number of weeks with weekly resident incidence of COVID-19 in the top decile nationally. RESULTS: As of January 10, 2021, nearly all nursing homes (93.6%) had reported at least one case of COVID-19 among their residents, more than three-quarters (76.9%) had reported at least one resident death, and most (83.5%) had experienced at least 1 week in the top decile of weekly incidence. In analyses adjusted for facility and county-level characteristics, we found generally consistent relationships between higher nursing home quality ratings and lower COVID-19 incidence and mortality, as well as with fewer high-incidence weeks. CONCLUSION: Nursing home quality ratings are associated with COVID-19 incidence, mortality, and persistence. Nursing homes receiving five-star ratings, for overall quality as well as for each domain, had lower COVID-19 rates among their residents.

How well are infant and young child World Health Organization (WHO) feeding indicators associated with growth outcomes? An example from Cambodia.

We assessed eight World Health Organization (WHO) core child feeding indicators for their association with stunting and underweight in Cambodia in 2000 and 2005. We compared the feeding data from the Cambodian Demographic and Health Surveys for 2000 with 2005 for 0-24 months children using the WHO feeding indicators, with stunting and underweight as outcomes. Prevalence of stunting and underweight was significantly less in 2005 than in 2000 among children aged 0-5 and 6-11 months, but stunting among children 18-23 months remained >50%. Prevalence of compliance with seven of the eight core healthy feeding indicators was higher in 2005. Exclusive breastfeeding among 0-5 months infants increased more than fivefold; among 6-11 and 12-17 months children, prevalence of feeding diversity and meeting a minimally acceptable diet, while improved, remained ≈25%. Modelling showed compliance with breastfeeding indicators was associated with reduced risk of underweight in 0-5 months infants, no association between compliance with feeding indicators and growth outcomes in other ages, and a significant association of higher relative wealth with growth outcomes overall. Between 2000 and 2005, Cambodia stabilized and focused resources on infant feeding. Prevalence of meeting the WHO feeding indicators improved, but modelling indicated that, in general, relative wealth, not feeding practices, was associated with improved growth outcomes. Yet, over 50% of children 18-23 months were stunted in 2005. Similar to the success with breastfeeding, focus on complementary feeding of 6-23 months children may reduce the risk of stunting in Cambodia.

Why are hospital-based nursing homes so costly? Relative importance of acuity and treatment setting.

OBJECTIVES: To determine the extent to which higher costs in hospital-based skilled nursing facilities (HBSNF) can be explained by observable resident characteristics and unobservable selection effects, implying a design shortcoming of the skilled nursing facility prospective payment system (SNF PPS) implemented for Medicare-covered stays by the Balanced Budget Act of 1997 (BBA 1997). RESEARCH DESIGN: Data on resident characteristics from the Minimum Data Set (MDS) are combined with staff time costs from the Centers for Medicare and Medicaid Services' (CMS, formerly HCFA) 1995 and 1997 SNF Staff Time Measurement (STM) studies and nontherapy ancillary claim costs extracted from CMS SNF claim records. An endogenous switching model was estimated to measure the effect on costs of the relatively high acuity of HBSNF residents, net of differences purely attributable to the treatment setting. RESULTS: It was found that virtually the entire HBSNF differential is attributable to setting effects with resident characteristics and selection effects playing a negligible role. In addition, it was found that marginal costs associated with particular services and conditions are often lower in hospital-based than in freestanding facilities. CONCLUSIONS: HBSNFs incur high costs regardless of the characteristics of their residents. Their high fixed costs accompany relatively low marginal costs associated with admitting high-acuity residents. Consequently, a PPS casemix system that depends on resident characteristics and excludes consideration of facility characteristics (as mandated by BBA 1997) need not unfairly penalize HBSNFs, provided a powerful casemix system is used and HBSNFs specialize in the care of high-acuity residents.

Assessing the RUG-III resident classification system for skilled nursing facilities.

Resource utilization groups, version III (RUG-III) is used by CMS to classify skilled nursing facility (SNF) residents into Medicare payment groups. Using a sample of 1,304 SNF residents with Medicare-covered stays, we find that RUG-III only explains 10.4 percent of the variance in total per diem costs. RUG-III explains variance in staff-time costs fairly well, but does not explain variance in non-therapy ancillary costs. Receipt of special treatments such as intravenous medications and respiratory therapy is strongly associated with high residual costs (p < 0.01). Modifications to the RUG-III system can increase its variance explanation.