Characteristics of Nursing Homes by COVID-19 Cases Among Staff: March to August 2020

ObjectiveTo measure the association between nursing home (NH) characteristics and Coronavirus Disease 2019 (COVID-19) prevalence among NH staff.DesignRetrospective cross-sectional study.Setting and ParticipantsCenters for Disease Control and Prevention COVID-19 database for US NHs between March and August 2020, linked to NH facility characteristics (LTCFocus database) and local COVID-19 prevalence (USA Facts).MethodsWe estimated the associations between NH characteristics, local infection rates, and other regional characteristics and COVID-19 cases among NH staff (nursing staff, clinical staff, aides, and other facility personnel) measured per 100 beds, controlling for the hospital referral regions in which NHs were located to account for local infection control practices and other unobserved characteristics.ResultsOf the 11,858 NHs in our sample, 78.6% reported at least 1 staff case of COVID-19. After accounting for local COVID-19 prevalence, NHs in the highest quartile of confirmed resident cases (413.5 to 920.0 cases per 1000 residents) reported 18.9 more staff cases per 100 beds compared with NHs that had no resident cases. Large NHs (150 or more beds) reported 2.6 fewer staff cases per 100 beds compared with small NHs (<50 beds) and for-profit NHs reported 0.8 fewer staff cases per 100 beds compared with nonprofit NHs. Higher occupancy and more direct-care hours per day were associated with more staff cases (0.4 more cases per 100 beds for a 10% increase in occupancy, and 0.7 more cases per 100 beds for an increase in direct-care staffing of 1 hour per resident day, respectively). Estimates associated with resident demographics, payer mix, or regional socioeconomic characteristics were not statistically significant.Conclusions and ImplicationsThese findings highlight the urgent need to support facilities with emergency resources such as back-up staff and protocols to reduce resident density within the facility, which may help stem outbreaks.     

U.S. Nursing Home Quality Ratings Associated with COVID-19 Cases and Deaths

ObjectivesTo inform future policies and disaster preparedness plans in the vulnerable nursing home setting, we need greater insight into the relationship between nursing homes’ (NHs’) quality and the spread and severity of COVID-19 in NH facilities. We therefore extend current evidence on the relationships between NH quality and resident COVID-19 infection rates and deaths, taking into account NH structural characteristics and community characteristics.DesignCross-sectional study.Setting and Participants15,390 Medicaid- and Medicare-certified NHs.MethodsWe obtained and merged the following data sets: (1) COVID-19 weekly data reported by each nursing home to the Centers for Disease Control and Prevention’s National Healthcare Safety Network, (2) Centers for Medicare & Medicaid Services Five Star Quality Rating System, (3) county-level COVID-19 case counts, (4) county-level population data, and (5) county-level sociodemographic data.ResultsAmong 1-star NHs, there were an average of 13.19 cases and 2.42 deaths per 1000 residents per week between May 25 and December 20, 2020. Among 5-star NHs, there were an average of 9.99 cases and 1.83 deaths per 1000 residents per week. The rate of confirmed cases of COVID-19 was 31% higher among 1-star NHs compared with 5-star NHs [model 1: incidence rate ratio (IRR) 1.31, 95% confidence interval (CI) 1.23-1.39], and the rate of COVID-19 deaths was 30% higher (IRR 1.30, 95% CI 1.20, 1.41). These associations were only partially explained by differences in community spread of COVID-19, case mix, and the for-profit status and size of NHs.Conclusions and ImplicationsWe found that COVID-19 case and death rates were substantially higher among NHs with lower star ratings, suggesting that NHs with quality much below average are more susceptible to the spread of COVID-19. This relationship, particularly with regard to case rates, can be partially attributed to external factors: lower-rated NHs are often located in areas with greater COVID-19 community spread and serve more socioeconomically vulnerable residents than higher-rated NHs.     

Assessment of Coronavirus Disease 2019 Infection and Mortality Rates Among Nursing Homes With Different Proportions of Black Residents

ObjectiveCoronavirus disease 2019 (COVID-19) has disproportionately impacted nursing homes (NHs) with large shares of Black residents. We examined the associations between the proportion of Black residents in NHs and COVID-19 infections and deaths, accounting for structural bias (operationalized as county-level factors) and stratifying by urbanicity/rurality.DesignThis was a cross-sectional observational cohort study using publicly available data from the LTCfocus, Centers for Disease Control and Prevention Long-Term Care Facility COVID-19 Module, and the NYTimes county-level COVID-19 database. Four multivariable linear regression models omitting and including facility characteristics, COVID-19 burden, and county-level fixed effects were estimated.Setting and ParticipantsIn total, 11,587 US NHs that reported data on COVID-19 to the Centers for Disease Control and Prevention and had data in LTCfocus and NYTimes from January 20, 2020 through July 19, 2020.MeasuresProportion of Black residents in NHs (exposure); COVID-19 infections and deaths (main outcomes).ResultsThe proportion of Black residents in NHs were as follows: none= 3639 (31.4%), <20% = 1020 (8.8%), 20%-49.9% = 1586 (13.7%), ≥50% = 681 (5.9%), not reported = 4661 (40.2%). NHs with any Black residents showed significantly more COVID-19 infections and deaths than NHs with no Black residents. There were 13.6 percentage points more infections and 3.5 percentage points more deaths in NHs with ≥50% Black residents than in NHs with no Black residents (P < .001). Although facility characteristics explained some of the differences found in multivariable analyses, county-level factors and rurality explained more of the differences.Conclusions and ImplicationsIt is likely that attributes of place, such as resources, services, and providers, important to equitable care and health outcomes are not readily available to counties where NHs have greater proportions of Black residents. Structural bias may underlie these inequities. It is imperative that support be provided to NHs that serve greater proportions of Black residents while considering the rurality of the NH setting.     

Predicting Coronavirus Disease 2019 Infection Risk and Related Risk Drivers in Nursing Homes: A Machine Learning Approach

ObjectiveInform coronavirus disease 2019 (COVID-19) infection prevention measures by identifying and assessing risk and possible vectors of infection in nursing homes (NHs) using a machine-learning approach.DesignThis retrospective cohort study used a gradient boosting algorithm to evaluate risk of COVID-19 infection (ie, presence of at least 1 confirmed COVID-19 resident) in NHs.Setting and ParticipantsThe model was trained on outcomes from 1146 NHs in Massachusetts, Georgia, and New Jersey, reporting COVID-19 case data on April 20, 2020. Risk indices generated from the model using data from May 4 were prospectively validated against outcomes reported on May 11 from 1021 NHs in California.MethodsModel features, pertaining to facility and community characteristics, were obtained from a self-constructed dataset based on multiple public and private sources. The model was assessed via out-of-sample area under the receiver operating characteristic curve (AUC), sensitivity, and specificity in the training (via 10-fold cross-validation) and validation datasets.ResultsThe mean AUC, sensitivity, and specificity of the model over 10-fold cross-validation were 0.729 [95% confidence interval (CI) 0.690‒0.767], 0.670 (95% CI 0.477‒0.862), and 0.611 (95% CI 0.412‒0.809), respectively. Prospective out-of-sample validation yielded similar performance measures (AUC 0.721; sensitivity 0.622; specificity 0.713). The strongest predictors of COVID-19 infection were identified as the NH's county's infection rate and the number of separate units in the NH; other predictors included the county's population density, historical Centers of Medicare and Medicaid Services cited health deficiencies, and the NH's resident density (in persons per 1000 square feet). In addition, the NH's historical percentage of non-Hispanic white residents was identified as a protective factor.Conclusions and ImplicationsA machine-learning model can help quantify and predict NH infection risk. The identified risk factors support the early identification and management of presymptomatic and asymptomatic individuals (eg, staff) entering the NH from the surrounding community and the development of financially sustainable staff testing initiatives in preventing COVID-19 infection.     

Remote Infection Control Assessments of US Nursing Homes During the COVID-19 Pandemic,April to June 2020

BackgroundNursing homes (NHs) provide care in a congregate setting for residents at high risk of severe outcomes from SARS-CoV-2 infection. In spring 2020, NHs were implementing new guidance to minimize SARS-CoV-2 spread among residents and staff.ObjectiveTo assess whether telephone and video-based infection control assessment and response (TeleICAR) strategies could efficiently assess NH preparedness and help resolve gaps.DesignWe incorporated Centers for Disease Control and Prevention COVID-19 guidance for NH into an assessment tool covering 6 domains: visitor restrictions; health care personnel COVID-19 training; resident education, monitoring, screening, and cohorting; personal protective equipment supply; core infection prevention and control (IPC); and communication to public health. We performed TeleICAR consultations on behalf of health departments. Adherence to each element was documented and recommendations provided to the facility.Setting and ParticipantsHealth department–referred NHs that agreed to TeleICAR consultation.MethodsWe assessed overall numbers and proportions of NH that had not implemented each infection control element (gap) and proportion of NH that reported making ≥1 change in practice following the assessment.ResultsDuring April 13 to June 12, 2020, we completed TeleICAR consultations in 629 NHs across 19 states. Overall, 524 (83%) had ≥1 implementation gap identified; the median number of gaps was 2 (interquartile range: 1-4). The domains with the greatest number of facilities with gaps were core IPC practices (428/625; 68%) and COVID-19 education, monitoring, screening, and cohorting of residents (291/620; 47%).Conclusions and ImplicationsTeleICAR was an alternative to onsite infection control assessments that enabled public health to efficiently reach NHs across the United States early in the COVID-19 pandemic. Assessments identified widespread gaps in core IPC practices that put residents and staff at risk of infection. TeleICAR is an important strategy that leverages infection control expertise and can be useful in future efforts to improve NH IPC.     

COVID-19 Cases and Death in Nursing Homes: The Role of Racial and Ethnic Composition of Facilities and Their Communities

ObjectivesTo examine the extent to which the racial and ethnic composition of nursing homes (NHs) and their communities affects the likelihood of COVID-19 cases and death in NHs, and whether and how the relationship between NH characteristics and COVID-19 cases and death varies with the racial and ethnic composition of the community in which an NH is located.Methods and DesignCenters for Medicare & Medicare Services Nursing Home COVID-19 data were linked with other NH- or community-level data (eg, Certification and Survey Provider Enhanced Reporting, Minimum Data Set, Nursing Home Compare, and the American Community Survey). Setting and Participants: NHs with more than 30 occupied beds (N=13,123) with weekly reported NH COVID-19 records between the weeks of June 7, 2020, and August 23, 2020. Measurements and model: Weekly indicators of any new COVID-19 cases and any new deaths (outcome variables) were regressed on the percentage of black and Hispanic residents in an NH, stratified by the percentage of blacks and Hispanics in the community in which the NH was located. A set of linear probability models with NH random effects and robust standard errors were estimated, accounting for other covariates.ResultsThe racial and ethnic composition of NHs and their communities were both associated with the likelihood of having COVID-19 cases and death in NHs. The racial and ethnic composition of the community played an independent role in the likelihood of COVID-19 cases and death in NHs, even after accounting for the COVID-19 infection rate in the community (ie, daily cases per 1000 people in the county). Moreover, the racial and ethnic composition of a community modified the relationship between NH characteristics (eg, staffing) and the likelihoods of COVID-19 cases and death.Conclusions and ImplicationsTo curb the COVID-19 outbreaks in NHs and protect vulnerable populations, efforts may be especially needed in communities with a higher concentration of racial and ethnic minorities. Efforts may also be needed to reduce structural racism and address social risk factors to improve quality of care and population health in communities of color.     

Factors Associated With the Quality of Life of Nursing Home Residents During the COVID-19 Pandemic: A Cross-Sectional Study

ObjectivesQuality of life (QoL) of nursing home (NH) residents is critical, yet understudied, particularly during the COVID-19 pandemic. Our objective was to examine whether COVID-19 outbreaks, lack of access to geriatric professionals, and care aide burnout were associated with NH residents' QoL.DesignCross-sectional study (July to December 2021).Setting and ParticipantsWe purposefully selected 9 NHs in Alberta, Canada, based on their COVID-19 exposure (no or minor/short outbreaks vs repeated or extensive outbreaks). We included data for 689 residents from 18 care units.MethodsWe used the DEMQOL-CH to assess resident QoL through video-based care aide interviews. Independent variables included a COVID-19 outbreak in the NH in the past 2 weeks (health authority records), care unit-levels of care aide burnout (9-item short-form Maslach Burnout Inventory), and resident access to geriatric professionals (validated facility survey). We ran mixed-effects regression models, adjusted for facility and care unit (validated surveys), and resident covariates (Resident Assessment Instrument–Minimum Data Set 2.0).ResultsRecent COVID-19 outbreaks (β = 0.189; 95% CI: 0.058–0.320), higher proportions of emotionally exhausted care aides on a care unit (β = 0.681; 95% CI: 0.246–1.115), and lack of access to geriatric professionals (β = 0.216; 95% CI: 0.003–0.428) were significantly associated with poorer resident QoL.Conclusions and ImplicationsPolicies aimed at reducing infection outbreaks, better supporting staff, and increasing access to specialist providers may help to mitigate how COVID-19 has negatively affected NH resident QoL.     

Shifting US Patterns of COVID-19 Mortality by Race and Ethnicity From June–December 2020

ObjectivesThe COVID-19 pandemic has disproportionately affected racial and ethnic minorities in the United States and has been devastating for residents of nursing homes (NHs). However, evidence on racial and ethnic disparities in COVID-19–related mortality rates within NHs and how that has changed over time has been limited. This study examines the impact of a high proportion of minority residents in NHs on COVID-19–related mortality rates over a 30-week period.DesignLongitudinal study.Setting and ParticipantsCenters for Medicare & Medicaid Services Nursing Home COVID-19 Public Use File data from 50 states from June 1, 2020, to December 27, 2020.MethodsWe linked data from 11,718 NHs to (1) Nursing Home Compare data, (2) the Long-Term Care: Facts on Care in the U.S., and (3) US county-level data on COVID cases and deaths. Our primary independent variable was proportion of minority residents (blacks and Hispanics) in NHs and its association with mortality rate over time.ResultsDuring the first 6 weeks from June 1, 2020, NHs with a higher proportion of black residents reported more COVID-19 deaths per 1000 followed by NHs with a higher proportion of Hispanic residents. Between 7 and 12 weeks, NHs with a higher proportion of Hispanic residents reported more deaths per 1000, followed by NHs with a higher proportion of black residents. However, after 23 weeks (mid-November 2020), NHs serving a higher proportion of white residents reported more deaths per 1000 than NHs serving a high proportion of black and Hispanic residents.Conclusions and ImplicationsThe disparities in COVID-19–related mortality for nursing homes serving minority residents is evident for the first 12 weeks of our study period. Policy interventions and the equitable distribution of vaccine are required to mitigate the impact of systemic racial injustice on health outcomes of people of color residing in NHs.     

High Prevalence of Multidrug-Resistant Organism Colonization in 28 Nursing Homes: An “Iceberg Effect”

ObjectiveDetermine the prevalence of methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus spp. (VRE), extended-spectrum beta-lactamase producing organisms (ESBLs), and carbapenem-resistant Enterobacteriaceae (CRE) among residents and in the environment of nursing homes (NHs).DesignPoint prevalence sampling of residents and environmental sampling of high-touch objects in resident rooms and common areas.SettingTwenty-eight NHs in Southern California from 2016 to 2017.ParticipantsNH participants in Project PROTECT, a cluster-randomized trial of enhanced bathing and decolonization vs routine care.MethodsFifty residents were randomly sampled per NH. Twenty objects were sampled, including 5 common room objects plus 5 objects in each of 3 rooms (ambulatory, total care, and dementia care residents).ResultsA total of 2797 swabs were obtained from 1400 residents in 28 NHs. Median prevalence of multidrug-resistant organism (MDRO) carriage per NH was 50% (range: 24%-70%). Median prevalence of specific MDROs were as follows: MRSA, 36% (range: 20%-54%); ESBL, 16% (range: 2%-34%); VRE, 5% (range: 0%-30%); and CRE, 0% (range: 0%-8%). A median of 45% of residents (range: 24%-67%) harbored an MDRO without a known MDRO history. Environmental MDRO contamination was found in 74% of resident rooms and 93% of common areas.Conclusions and ImplicationsIn more than half of the NHs, more than 50% of residents were colonized with MDROs of clinical and public health significance, most commonly MRSA and ESBL. Additionally, the vast majority of resident rooms and common areas were MDRO contaminated. The unknown submerged portion of the iceberg of MDRO carriers in NHs may warrant changes to infection prevention and control practices, particularly high-fidelity adoption of universal strategies such as hand hygiene, environmental cleaning, and decolonization.     

Shortages of Staff in Nursing Homes During the COVID-19 Pandemic: What are the Driving Factors?

ObjectivesDuring the Coronavirus Disease 2019 (COVID-19) pandemic, US nursing homes (NHs) have been under pressure to maintain staff levels with limited access to personal protection equipment (PPE). This study examines the prevalence and factors associated with shortages of NH staff during the COVID-19 pandemic.DesignWe obtained self-reported information on staff shortages, resident and staff exposure to COVID-19, and PPE availability from a survey conducted by the Centers for Medicare and Medicaid Services in May 2020. Multivariate logistic regressions of staff shortages with state fixed-effects were conducted to examine the effect of COVID-19 factors in NHs.Setting and Participants11,920 free-standing NHs.MeasuresThe dependent variables were self-reported shortages of licensed nurse staff, nurse aides, clinical staff, and other ancillary staff. We controlled for NH characteristics from the most recent Nursing Home Compare and Certification and Survey Provider Enhanced Reporting, market characteristics from Area Health Resources File, and state Medicaid reimbursement calculated from Truven data.ResultsOf the 11,920 NHs, 15.9%, 18.4%, 2.5%, and 9.8% reported shortages of licensed nurse staff, nurse aides, clinical staff, and other staff, respectively. Georgia and Minnesota reported the highest rates of shortages in licensed nurse and nurse aides (both >25%). Multivariate regressions suggest that shortages in licensed nurses and nurse aides were more likely in NHs having any resident with COVID-19 (adjusted odds ratio [AOR] = 1.44, 1.60, respectively) and any staff with COVID-19 (AOR = 1.37, 1.34, respectively). Having 1-week supply of PPE was associated with lower probability of staff shortages. NHs with a higher proportion of Medicare residents were less likely to experience shortages.Conclusions/ImplicationsAbundant staff shortages were reported by NHs and were mainly driven by COVID-19 factors. In the absence of appropriate staff, NHs may be unable to fulfill the requirement of infection control even under the risk of increased monetary penalties.     

The Metrics Matter: Improving Comparisons of COVID-19 Outbreaks in Nursing Homes

ObjectivesIn the United States, nursing facility residents comprise fewer than 1% of the population but more than 40% of deaths due to Coronavirus Disease 2019 (COVID-19). Mitigating the enormous risk of COVID-19 to nursing home residents requires adequate data. The widely used Centers for Medicare & Medicaid Services (CMS) COVID-19 Nursing Home Dataset contains 2 derived statistics: Total Resident Confirmed COVID-19 Cases per 1000 Residents and Total Resident COVID-19 Deaths per 1000 Residents. These metrics provide a misleading picture, as facilities report cumulative counts of cases and deaths over different time periods but use a point-in-time measure as proxy for number of residents (number of occupied beds in a week), resulting in inflated statistics. We propose an alternative statistic to better illustrate the burden of COVID-19 cases and deaths across nursing facilities.DesignRetrospective cohort study.Setting and ParticipantsUsing the CMS Nursing Home Compare and COVID-19 Nursing Home Datasets, we examined facilities with star ratings and COVID-19 data passing quality assurance checks for each reporting period from May 31 to August 16, 2020 (n = 11,115).MethodsWe derived an alternative measure of the number of COVID-19 cases per 1000 residents using the net change in weekly census. For each measure, we compared predicted number of cases/deaths by overall star rating using negative binomial regression with constant dispersion, controlling for county-level cases per capita and nursing home characteristics.ResultsThe average number of cases per 1000 estimated residents using our method is lower compared with the metric using occupied beds as proxy for number of residents (44.8 compared with 66.6). We find similar results when examining number of COVID-19 deaths per 1000 residents.Conclusions and ImplicationsFuture research should estimate the number of residents served in nursing facilities when comparing COVID-19 cases/deaths in nursing facilities. Identifying appropriate metrics for facility-level comparisons is critical to protecting nursing home residents as the pandemic continues.     

Risk of Death in Nursing Home Residents After COVID-19 Vaccination

ObjectivesIn the first months of 2021, the Dutch COVID-19 vaccination campaign was disturbed by reports of death in Norwegian nursing homes (NHs) after vaccination. Reports predominantly concerned persons >65 years of age with 1 or more comorbidities. Also, in the Netherlands adverse events were reported after COVID-19 vaccination in this vulnerable group. Yet, it was unclear whether a causal link between vaccination and death existed. Therefore, we investigated the risk of death after COVID-19 vaccination in Dutch NH residents compared with the risk of death in NH residents prior to the COVID-19 pandemic.DesignPopulation-based longitudinal cohort study with electronic health record data.Setting and ParticipantsWe studied Dutch NH residents from 73 NHs who received 1 or 2 COVID-19 vaccination(s) between January 13 and April 16, 2021 (n = 21,762). As a historical comparison group, we included Dutch NH residents who were registered in the same period in 2019 (n = 27,591).MethodsData on vaccination status, age, gender, type of care, comorbidities, and date of NH entry and (if applicable) discharge or date of death were extracted from electronic health records. Risk of death after 30 days was evaluated and compared between vaccinated residents and historical comparison residents with Kaplan-Meier and Cox regression analyses. Regression analyses were adjusted for age, gender, comorbidities, and length of stay.ResultsRisk of death in NH residents after one COVID-19 vaccination (regardless of whether a second vaccination was given) was decreased compared with historical comparison residents from 2019 (adjusted HR 0.77, 95% CI 0.69-0.86). The risk of death further decreased after 2 vaccinations compared with the historical comparison group (adjusted HR 0.57, 95% CI 0.50-0.64).Conclusions and ImplicationsWe found no indication that risk of death in NH residents is increased after COVID-19 vaccination. These results indicate that COVID-19 vaccination in NH residents is safe and could reduce fear and resistance toward vaccination.     

A Longitudinal,Clinical, and Spatial Epidemiologic Analysis of a Large COVID-19 Long-Term Care Home Outbreak

ObjectivesCOVID-19 has had devastating effects on long-term care homes across much of the world, and especially within Canada, with more than 50% of the mortality from COVID-19 in 2020 in these homes. Understanding the way in which the virus spreads within these homes is critical to preventing further outbreaks.DesignRetrospective chart review.Settings and ParticipantsLong-term care home residents and staff in Ontario, Canada.MethodsWe conducted a longitudinal study of a large long-term care home COVID-19 outbreak in Ontario, Canada, using electronic medical records, public health records, staff assignments, and resident room locations to spatially map the outbreak through the facility.ResultsBy analyzing the outbreak longitudinally, we were able to draw 3 important conclusions: (1) 84.5% had typical COVID-19 symptoms and only 15.5% of residents had asymptomatic infection; (2) there was a high attack rate of 85.8%, which appeared to be explained by a high degree of interconnectedness within the home exacerbated by staffing shortages; and (3) clustering of infections within multibedded rooms was common.Conclusion and ImplicationsLow rates of asymptomatic infection suggest that symptom-based screening in residents remains very important for detecting outbreaks, a high degree of interconnectedness explains the high attack rate, and there is a need for improved guidance for homes with multibedded rooms on optimizing resident room movement to mitigate spread of COVID-19 in long-term care homes.     

A spatial analysis of the association between social vulnerability and the cumulative number of confirmed deaths from COVID-19 in United States counties through November 14, 2020

ObjectivesCoronavirus disease 2019 (COVID-19) is classified as a natural hazard, and social vulnerability describes the susceptibility of social groups to potential damages from natural hazards. Therefore, the objective of this study was to examine the association between social vulnerability and the cumulative number of confirmed COVID-19 deaths (per 100,000) in 3,141 United States counties.MethodsThe cumulative number of COVID-19 deaths was obtained from USA Facts. Variables related to social vulnerability were obtained from the Centers for Disease Control and Prevention Social Vulnerability Index and the 2018 5-Year American Community Survey. Data were analyzed using spatial autoregression models.ResultsLowest income and educational level, as well as high proportions of single parent households, mobile home residents, and people without health insurance were positively associated with a high cumulative number of COVID-19 deaths.ConclusionIn conclusion, there are regional differences in the cumulative number of COVID-19 deaths in United States counties, which are affected by various social vulnerabilities. Hence, these findings underscore the need to take social vulnerability into account when planning interventions to reduce COVID-19 deaths.     

Nursing Home Characteristics Associated With Implementation of an Advance Care Planning Video Intervention

ObjectivesAdvance care planning (ACP) is important to ensure that nursing home (NH) residents receive care concordant with their goals. Video interventions have been developed to improve the process of ACP. Yet, little is known about which NH characteristics are associated with implementation of ACP video interventions in clinical practice. Our objective was to examine NH-level characteristics associated with the implementation of an ACP video intervention as part of the Pragmatic trial of Video Education in Nursing Homes (PROVEN) trial.DesignCross-sectional study of NHs in PROVEN.Setting and participants119 NHs randomized to receive the ACP video intervention.MeasurementsThe outcomes were the proportion of short- (<100 days) and long-stay (≥100 days) NH residents who were (1) offered to watch a video and (2) shown a video, aggregated to the NH-level, and measured using electronic forms of video offers. The association between outcomes and NH facility characteristics (eg, staffing, resident acuity) and participation in other aspects of the PROVEN trial (eg, monthly check-in calls) were estimated using multivariate linear regression models. NH characteristics were measured using data from Online Survey Certification and Reporting data, Long-term Care: Facts on Care in the US and NH Compare.ResultsOffer rates were 69% [standard deviation (SD): 28] for short-stay and 56% (SD: 20) for long-stay residents. Show rates were 19% (SD: 21) for short-stay and 17% (SD: 17) for long-stay residents. After adjusting for NH characteristics, compared to 1-star NHs, higher star-rated NHs had higher offer rates. Champions' participation in check-in calls was positively associated with both outcomes for long-stay residents.Conclusions/implicationsLower-quality NHs seem unable to integrate a novel ACP video education program into routine care processes. Ongoing support for and engagement with NH staff to champion the intervention throughout implementation is important for the success of a pragmatic trial within NHs.     

Thirty-Year Trends in Nursing Home Composition and Quality Since the Passage of the Omnibus Reconciliation Act

ObjectiveIn 1987, the Omnibus Reconciliation Act (OBRA) called for a dramatic overhaul of the nursing home (NH) quality assurance system. This study examines trends in facility, resident, and quality characteristics since passage of that legislation.MethodsWe conducted univariate analyses of national data on US NHs from 3 sources: (1) the 1985 National Nursing Home Survey (NNHS), (2) the 1992-2015 Online Survey Certification and Reporting (OSCAR) Data, and (3) LTCfocUS data for 2000-2015. We examined changes in NH characteristics, resident composition, and quality.Setting and participantsUS NH facilities and residents between 1985 and 2015.ResultsThe proportion of NHs that are Medicare and Medicaid certified, members of chains, and operating not-for-profit has increased over the past 30 years. There have also been reductions in occupancy and increases in the share of residents who are racial or ethnic minorities, admitted for post-acute care, in need of physical assistance with daily activities, primarily supported by Medicare, and diagnosed with a psychiatric condition such as schizophrenia. With regard to NH quality, direct care staffing levels have increased. The proportion of residents physically restrained has decreased dramatically, coupled with changes in inappropriate antipsychotic (chemical restraint) use.Conclusions and implicationsTogether with changes in the long-term care market, the NHs of today look very different from NHs 30 years ago. The 30th anniversary of OBRA provides a unique opportunity to reflect, consider what we have learned, and think about the future of this and other sectors of long-term care.