Nursing Home Characteristics Associated With Resident COVID-19 Morbidity in Communities With High Infection Rates | Geriatrics | JAMA Network Open | JAMA Network
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Table.  Characteristics of 3008 NHs in Communities With the Highest COVID-19 Prevalencea
Characteristics of 3008 NHs in Communities With the Highest COVID-19 Prevalencea
1.
White  EM, Kosar  CM, Feifer  RA,  et al.  Variation in SARS-CoV-2 prevalence in U.S. skilled nursing facilities.   J Am Geriatr Soc. 2020;68(10):2167-2173. doi:10.1111/jgs.16752 PubMedGoogle ScholarCrossref
2.
Centers for Medicare and Medicaid. COVID-19 nursing home data. 2020. Accessed October 20, 2020. https://data.cms.gov/stories/s/COVID-19-Nursing-Home-Data/bkwz-xpvg/
3.
Brown School of Public Health. Long-term care: facts on care in the US. 2020. Accessed February 10, 2021. http://ltcfocus.org/2/faq
4.
USAFacts. Coronavirus locations: COVID-19 map by county and state. 2020. Accessed October 20, 2020. https://usafacts.org/visualizations/coronavirus-covid-19-spread-map/
5.
US Census Bureau. American Community Survey (ACS). 2017. Accessed October 20, 2020. https://www.census.gov/programs-surveys/acs
6.
Morris  JN, Fries  BE, Morris  SA.  Scaling ADLs within the MDS.   J Gerontol A Biol Sci Med Sci. 1999;54(11):M546-M553. doi:10.1093/gerona/54.11.M546PubMedGoogle ScholarCrossref
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    Research Letter
    Geriatrics
    March 16, 2021

    Nursing Home Characteristics Associated With Resident COVID-19 Morbidity in Communities With High Infection Rates

    Author Affiliations
    • 1Perelman School of Medicine, University of Pennsylvania, Philadelphia
    • 2Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia
    • 3Department of Population Health Sciences, Weill Cornell Medical College, Cornell University, New York, New York
    • 4Division of General Internal Medicine, Perelman School of Medicine, Department of Medicine, University of Pennsylvania, Philadelphia
    JAMA Netw Open. 2021;4(3):e211555. doi:10.1001/jamanetworkopen.2021.1555
    Introduction

    Nursing home (NH) residents have been disproportionately affected by the coronavirus disease 2019 (COVID-19) pandemic. Transmission rates in an NH’s surrounding community have been identified as a key risk factor associated with NH COVID-19 outbreaks.1 It is not known whether some NHs within communities are more successful at mitigating outbreaks among residents than others. We examined NHs in communities with the highest COVID-19 prevalence to identify characteristics associated with resident infection rates.

    Methods

    This cross-sectional analysis used data on COVID-19 cases in US NHs reported through October 11, 2020, in the Centers for Medicare and Medicaid Services Nursing Home COVID-19 Public File.2 The first week of COVID-19 data reporting from the Centers for Medicare and Medicaid ended May 24, 2020. However, NHs could opt to report data retrospectively back to January 1, 2020. We merged these data with the 2017 Long-term Care: Facts on Care in the US (LTCFocus) database3 to obtain NH characteristics. We also used the USAFacts website4 to obtain county-level infection rates and the 2017 American Community Survey5 to obtain community characteristics. The Weill Cornell Medical College Institutional Review Board determined this study to be exempt from review because it did not involve human participants. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.

    Our analysis was restricted to counties in the top quartile of COVID-19 prevalence (mean, 36.0; range, 28.3-164.9 per 1000 population) nationwide. Within these counties, we compared facility characteristics by quartile of COVID-19 prevalence (cases per 1000 NH residents), including resident demographic characteristics (age, sex, and race), and activities of daily living score.6 The analysis also includes the number of NH beds, occupancy rate, for-profit status, chain membership, direct care staff hours, presence of an advanced practitioner (nurse practitioner or physician’s assistant), Alzheimer disease specialty unit presence, and the shares of residents covered by Medicare and Medicaid. County characteristics included median household income, percentage of individuals 75 years or older, and rural location. We used 1-way analysis of variance for continuous variables and χ2 tests for categorical variables to test for statistical significance (2-sided P < .05) of differences in NH characteristics. Multivariable linear regression was used to examine characteristics associated with NH COVID-19 prevalence. We included hospital referral region fixed effects to account for unobserved regional factors that may affect COVID-19 spread. Standard errors were adjusted for clustering at the state level. Stata/IC version 16.0 (StataCorp LLC) was used for analysis.

    Results

    Our sample included 3008 NHs (255 923 occupied beds). The full cohort had a mean (SD) age of 78.4 (7.3) years, 165 582 residents (64.7%) were female, 90 341 residents (35.3%) were male, and 158 160 residents (61.8%) were insured by Medicaid. The NHs had a mean (SD) of 6.7 (9.5) COVID-19 cases per 1000 residents in the lowest quartile (755 NHs) and a mean (SD) of 677.1 (146.2) cases per 1000 residents in the highest quartile (752 NHs).

    Adjusted estimates indicate that residents in NHs with more COVID-19 cases were older (regression coefficient, 2.2; 95% CI, 0.4-4.0; P = .02), the NHs had a lower proportion of White residents (−1.0; 95% CI, −1.7 to −0.2; P = .02), and residents had higher activities of daily living scores (7.1; 95% CI, 1.9-12.3; P = .009) (Table). In addition, a higher proportion of residents were insured by Medicaid (0.9; 95% CI, 0.1-1.7; P = .03), and the NHs had lower occupancy rates (−4.1; 95% CI, −5.1 to −3.0; P < .001) and fewer direct care hours per patient per day (−21.9; 95% CI, −32.7 to −11.0; P < .001). Nursing homes with more COVID-19 cases were more likely to have an advanced practitioner (33.7; 95% CI, 9.8-57.6; P = .007) compared with NHs with fewer COVID-19 cases among residents.

    Discussion

    In communities with high COVID-19 prevalence, we found significant inequities in infection rates among NHs with larger proportions of racial minority residents and Medicaid participants. In addition, facilities with fewer direct patient care hours were more susceptible to virus spread. Study limitations stem from the use of COVID-19 data reported by facilities and NH characteristics that may not reflect changes in patient demographic characteristics during the pandemic. Nevertheless, these data sets represent the most comprehensive accounting of NH COVID-19 cases and characteristics available as of October 11, 2020. Interventions such as increasing staff support and directing more resources toward NHs with disproportionate shares of racial minorities and Medicaid participants may reduce disparities in COVID-19 morbidity among NH residents.

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    Article Information

    Accepted for Publication: January 22, 2021.

    Published: March 16, 2021. doi:10.1001/jamanetworkopen.2021.1555

    Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Chen AT et al. JAMA Network Open.

    Corresponding Author: Hye-Young Jung, PhD, Department of Population Health Sciences, Weill Cornell Medical College, 402 E 67th St, New York, NY 10065 (arj2005@med.cornell.edu).

    Author Contributions: Ms Yun and Dr Jung had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Chen, Ryskina, Jung.

    Acquisition, analysis, or interpretation of data: Chen, Yun, Jung.

    Drafting of the manuscript: Chen, Jung.

    Critical revision of the manuscript for important intellectual content: All authors.

    Statistical analysis: Chen, Yun, Jung.

    Obtained funding: Jung.

    Administrative, technical, or material support: Ryskina, Jung.

    Supervision: Ryskina, Jung.

    Conflict of Interest Disclosures: None reported.

    Funding/Support: Ms Chen received support from Training in Healthcare Financing, Organization and Delivery for Aging Populations grant T32 AG051090 from the National Institute on Aging (NIA), National Institutes of Health (NIH), awarded to the University of Pennsylvania. Dr Ryskina’s work on this study was supported by NIA Career Development Award K08-AG052572. Dr Jung’s work on this study was supported by Mentored Research Scientist Development Award K01AG057824 from the NIA).

    Role of the Funder/Sponsor: The NIA and NIH had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

    References
    1.
    White  EM, Kosar  CM, Feifer  RA,  et al.  Variation in SARS-CoV-2 prevalence in U.S. skilled nursing facilities.   J Am Geriatr Soc. 2020;68(10):2167-2173. doi:10.1111/jgs.16752 PubMedGoogle ScholarCrossref
    2.
    Centers for Medicare and Medicaid. COVID-19 nursing home data. 2020. Accessed October 20, 2020. https://data.cms.gov/stories/s/COVID-19-Nursing-Home-Data/bkwz-xpvg/
    3.
    Brown School of Public Health. Long-term care: facts on care in the US. 2020. Accessed February 10, 2021. http://ltcfocus.org/2/faq
    4.
    USAFacts. Coronavirus locations: COVID-19 map by county and state. 2020. Accessed October 20, 2020. https://usafacts.org/visualizations/coronavirus-covid-19-spread-map/
    5.
    US Census Bureau. American Community Survey (ACS). 2017. Accessed October 20, 2020. https://www.census.gov/programs-surveys/acs
    6.
    Morris  JN, Fries  BE, Morris  SA.  Scaling ADLs within the MDS.   J Gerontol A Biol Sci Med Sci. 1999;54(11):M546-M553. doi:10.1093/gerona/54.11.M546PubMedGoogle ScholarCrossref
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