Self-reported resilience of North Carolina long-term care organizations and public health agencies in the midst of the COVID-19 pandemic

The objective of this study was to determine the self-reported resilience of North Carolina long-term care (LTC) and public health organizations while in the midst of a pandemic. Further, we correlate resilience with several organizational attributes for some insight into the types of organizations that are more resilient. Through collaboration with four LTC professional associations in North Carolina, the Benchmark Resilience Tool-13 (BRT-13) survey was disseminated to LTC leaders across North Carolina, and emailed to Public Health leaders during April 2021. The 13 survey items were divided into adaptive capacity and planning factors. Analysis of variance was used to determine whether organizational factors contributed to resiliency. A total of 142 completed surveys were received, 20 from assisted living administrators, 67 nursing home administrators, 14 from continuing care retirement community leaders, and 41 from public health officials. Average resilience, adaptive capacity, and planning were measured and analyzed against type of facility, RUCA area designation, owner type, level of debt, level of profitability, and employee satisfaction. Average resilience scores were 3.96 (SD = 0.774) for public health, 4.19 (SD = 0.725) for nursing homes, 4.22 (SD = 0.509) for assisted living, and 4.46 (SD = 0.407) for continuing care retirement communities. Results show a significant trend of higher employee satisfaction related to higher adaptive capacity, planning, and overall resilience. A key to organizational resilience in the pandemic was how well an organization cared for their employees. We must prepare before a crisis by being attentive to employee satisfaction and not lose track of employee satisfaction during the crisis. Public health agencies and LTC organizations who maintain high employee satisfaction will be more resilient.

Southeastern United States Predictors of COVID-19 in Nursing Homes.

This study's aim was to determine nursing home (NH) and county-level predictors of COVID-19 outbreaks in nursing homes (NHs) in the southeastern region of the United States across three time periods. NH-level data compiled from census data and from NH compare and NH COVID-19 infection datasets provided by the Center for Medicare and Medicaid Services cover 2951 NHs located in 836 counties in nine states. A generalized linear mixed-effect model with a random effect was applied to significant factors identified in the final stepwise regression. County-level COVID-19 estimates and NHs with more certified beds were predictors of COVID-19 outbreaks in NHs across all time periods. Predictors of NH cases varied across the time periods with fewer community and NH variables predicting COVID-19 in NH during the late period. Future research should investigate predictors of COVID-19 in NH in other regions of the US from the early periods through March 2021.

County-Level Social Determinants of Health and COVID-19 in Nursing Homes, United States, June 1, 2020-January 31, 2021.

OBJECTIVES: Nursing homes are a primary setting of COVID-19 transmission and death, but research has primarily focused only on factors within nursing homes. We investigated the relationship between US nursing home-associated COVID-19 infection rates and county-level and nursing home attributes. METHODS: We constructed panel data from the Centers for Medicare & Medicaid Services (CMS) minimum dataset, CMS nursing home data, 2010 US Census data, 5-year (2012-2016) American Community Survey estimates, and county COVID-19 infection rates. We analyzed COVID-19 data from June 1, 2020, through January 31, 2021, during 7 five-week periods. We used a maximum likelihood estimator, including an autoregressive term, to estimate effects and changes over time. We performed 3 model forms (basic, partial, and full) for analysis. RESULTS: Nursing homes with nursing (0.005) and staff (0.002) shortages had high COVID-19 infection rates, and locally owned (-0.007) or state-owned (-0.025) and nonprofit (-0.011) agencies had lower COVID-19 infection rates than privately owned agencies. County-level COVID-19 infection rates corresponded with COVID-19 infection rates in nursing homes. Racial and ethnic minority groups had high nursing home-associated COVID-19 infection rates early in the study. High median annual personal income (-0.002) at the county level correlated with lower nursing home-associated COVID-19 infection rates. CONCLUSIONS: Communities with low rates of nursing home infections had access to more resources (eg, financial resources, staffing) and likely had better mitigation efforts in place earlier in the pandemic than nursing homes that had access to few resources and poor mitigation efforts. Future research should address the social and structural determinants of health that are leaving racial and ethnic minority populations and institutions such as nursing homes vulnerable during times of crises.

Mapping community-level determinants of COVID-19 transmission in nursing homes: A multi-scale approach.

Deaths from the COVID-19 pandemic have disproportionately affected older adults and residents in nursing homes. Although emerging research has identified place-based risk factors for the general population, little research has been conducted for nursing home populations. This GIS-based spatial modeling study aimed to determine the association between nursing home-level metrics and county-level, place-based variables with COVID-19 confirmed cases in nursing homes across the United States. A cross-sectional research design linked data from Centers for Medicare & Medicaid Services, American Community Survey, the 2010 Census, and COVID-19 cases among the general population and nursing homes. Spatial cluster analysis identified specific regions with statistically higher COVID-19 cases and deaths among residents. Multivariate analysis identified risk factors at the nursing home level including, total count of fines, total staffing levels, and LPN staffing levels. County-level or place-based factors like per-capita income, average household size, population density, and minority composition were significant predictors of COVID-19 cases in the nursing home. These results provide a framework for examining further COVID-19 cases in nursing homes and highlight the need to include other community-level variables when considering risk of COVID-19 transmission and outbreaks in nursing homes.