Nearly half of hospitalized Medicare patients in 2018 were discharged to post-acute care (PAC), accounting for approximately $60 billion in annual spending. There are four PAC settings, and these vary in the intensity and complexity of medical, skilled nursing, and rehabilitative services provided; each setting uses a separate payment system. Due to considerable variation in PAC use, with concerns that similar patients can be treated in different PAC settings, the Centers for Medicare & Medicaid Services (CMS) recently introduced several major policy changes. For home health agencies (HHAs) and skilled nursing facilities (SNFs), CMS implemented new payment models to better align payment with patients' care needs rather than the provision of rehabilitation. For long-term acute care hospitals, CMS will now decrease payment for less medically ill patients. To choose PAC wisely, hospitalists and hospital leaders must understand how these new policies will change where patients can be discharged and the services these patients receive at these PAC settings.

Nursing staff turnover has long been considered an important indicator of nursing home quality. However, turnover has never been reported on the Nursing Home Compare website, likely because of the lack of adequate data. On July 1, 2016, the Centers for Medicare and Medicaid Services began collecting auditable payroll-based daily staffing data for US nursing homes. We used 492 million nurse shifts from these data to calculate a novel turnover metric representing the percentage of hours of nursing staff care that turned over annually at each of 15,645 facilities. Mean and median annual turnover rates for total nursing staff were roughly 128 percent and 94 percent, respectively. Turnover rates were correlated with facility location, for-profit status, chain ownership, Medicaid patient census, and star ratings. Disseminating facilities' nursing staff turnover rates on Nursing Home Compare could provide important quality information for policy makers, payers, and consumers, and it may incentivize efforts to reduce turnover.

Background/objectives

The 2019 coronavirus disease (COVID-19) has been documented in a large share of nursing homes throughout the United States, leading to high rates of mortality for residents. To understand how to prevent and mitigate future outbreaks, it is imperative that we understand which nursing homes are more likely to experience COVID-19 cases. Our aim was to examine the characteristics of nursing homes with documented COVID-19 cases in the 30 states reporting the individual facilities affected.

Design

We constructed a database of nursing homes with verified COVID-19 cases as of May 11, 2020, via correspondence with and publicly available reports from state departments of health. We linked this information to nursing home characteristics and used regression analysis to examine the association between these characteristics and the likelihood of having a documented COVID-19 case.

Setting

All nursing homes from 30 states that reported COVID-19 cases at the facility-level.

Participants

Nursing home residents in states reporting data.

Measurements

Whether a nursing home had a reported COVID-19 case (yes/no), and conditional on having a case, the number of cases at a nursing home.

Results

Of 9,395 nursing homes in our sample, 2,949 (31.4%) had a documented COVID-19 case. Larger facility size, urban location, greater percentage of African American residents, non-chain status, and state were significantly (P < .05) related to the increased probability of having a COVID-19 case. Five-star rating, prior infection violation, Medicaid dependency, and ownership were not significantly related.

Conclusion

COVID-19 cases in nursing homes are related to facility location and size and not traditional quality metrics such as star rating and prior infection control citations. J Am Geriatr Soc 68:1653-1656, 2020.

Nationwide nursing home private-pay prices at the facility-level have not been available for researchers interested in studying this unique health care market. This study presents a new data source, Caregiverlist, for private-pay prices for private and semiprivate rooms for 12,000 nursing homes nationwide collected between 2008 and 2010. We link these data to publicly available national nursing home-level data sets to examine the relationship between price and nursing home characteristics. We also compare private-pay prices with average private-pay revenues per day for California nursing homes obtained from facilities' financial filings. On average, private-pay prices were $224 per day for private rooms compared with $197 per day for semiprivate rooms. We find that nursing homes that are nonprofit, urban, hospital-based, have a special care unit, chain-owned, and have higher quality ratings have higher prices. We find average revenues per day in California to be moderately correlated with prices reported by Caregiverlist.

Objective

To describe the association between nursing home staff turnover and the presence and scope of infection control citations.

Data sources

Secondary data for all US nursing homes between March 31, 2017, through December 31, 2019 were obtained from Payroll-Based Journal (PBJ), Nursing Home Compare, and Long-Term Care: Facts on Care in the US (LTC Focus).

Study design

We estimated the association between nurse turnover and the probability of an infection control citation and the scope of the citation while controlling for nursing home fixed effects. Our turnover measure is the percent of the facility's nursing staff hours that were provided by new staff (less than 60 days of experience in the last 180 days) during the 2 weeks prior to the health inspection. We calculated turnover for all staff together and separately for registered nurses, licensed practical nurses (LPNs), and certified nursing assistants.

Data collection/extraction methods

We linked nursing homes standard inspection surveys to 650 million shifts from the PBJ data. We excluded any nursing home with incomplete or missing staffing data. Our final analytic sample included 12,550 nursing homes with 30,536 surveys.

Principal findings

Staff turnover was associated with an increased likelihood of an infection control citation (average marginal effect [AME] = 0.12 percentage points [pp]; 95% confidence interval [CI]: 0.05, 0.18). LPN (AME = 0.06 pp; 95% CI: 0.01, 0.11) turnover was conditionally associated with an infection control citation. Conditional on having at least an isolated citation for infection control, staff turnover was positively associated with receiving a citation coded as a "pattern" (AME = 0.21 pp; 95% CI: 0.10, 0.32). Conditional of having at least a pattern citation, staff turnover was positively associated with receiving a widespread citation (AME = 0.21 pp; 95% CI: 0.10, 0.32).

Conclusions

Turnover was positively associated with the probability of an infection control citation. Staff turnover should be considered an important factor related to the spread of infections within nursing homes.

Medicare continues to implement payment reforms that shift reimbursement from fee-for-service toward episode-based payment, affecting average and marginal payment. We contrast the effects of two reforms for home health agencies. The home health interim payment system in 1997 lowered both types of payment; our conceptual model predicts a decline in the likelihood of use and costs, both of which we find. The home health prospective payment system in 2000 raised average but lowered marginal payment with theoretically ambiguous effects; we find a modest increase in use and costs. We find little substantive effect of either policy on readmissions or mortality.

Staff in skilled nursing facilities (SNFs) are essential health care workers, yet they can also be a source of COVID-19 transmission. We used detailed staffing data to examine the relationship between a novel measure of staff size (that is, the number of unique employees working daily), conventional measures of staffing quality, and COVID-19 outcomes among SNFs in the United States without confirmed COVID-19 cases by June 2020. By the end of September 2020, sample SNFs in the lowest quartile of staff size had 6.2 resident cases and 0.9 deaths per 100 beds, compared with 11.9 resident cases and 2.1 deaths per 100 beds among facilities in the highest quartile. Staff size, including staff members not involved in resident care, was strongly associated with SNFs' COVID-19 outcomes, even after facility size was accounted for. Conventional staffing quality measures, including direct care staff-to-resident ratios and skill mix, were not significant predictors of COVID-19 cases or deaths. Reducing the number of unique staff members without decreasing direct care hours, such as by relying on full-time rather than part-time staff, could help prevent outbreaks.