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Nursing home staff networks and COVID-19

Author

Listed:
  • M. Keith Chen

    (Anderson School of Management, University of California, Los Angeles, CA 90095)

  • Judith A. Chevalier

    (Yale School of Management, Yale University, New Haven, CT 06511; National Bureau of Economic Research, Cambridge, MA 02138)

  • Elisa F. Long

    (Anderson School of Management, University of California, Los Angeles, CA 90095)

Abstract

Nursing homes and other long-term care facilities account for a disproportionate share of COVID-19 cases and fatalities worldwide. Outbreaks in US nursing homes have persisted despite nationwide visitor restrictions beginning in mid-March. An early report issued by the Centers for Disease Control and Prevention identified staff members working in multiple nursing homes as a likely source of spread from the Life Care Center in Kirkland, WA, to other skilled nursing facilities. The full extent of staff connections between nursing homes—and the role these connections serve in spreading a highly contagious respiratory infection—is currently unknown given the lack of centralized data on cross-facility employment. We perform a large-scale analysis of nursing home connections via shared staff and contractors using device-level geolocation data from 50 million smartphones, and find that 5.1% of smartphone users who visited a nursing home for at least 1 h also visited another facility during our 11-wk study period—even after visitor restrictions were imposed. We construct network measures of connectedness and estimate that nursing homes, on average, share connections with 7.1 other facilities. Traditional federal regulatory metrics of nursing home quality are unimportant in predicting outbreaks, consistent with recent research. Controlling for demographic and other factors, a home’s staff network connections and its centrality within the greater network strongly predict COVID-19 cases.

Suggested Citation

  • M. Keith Chen & Judith A. Chevalier & Elisa F. Long, 2021. "Nursing home staff networks and COVID-19," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 118(1), pages 2015455118-, January.
    • Handle: RePEc:nas:journl:v:118:y:2021:p:e2015455118
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    References listed on IDEAS

    as
    1. Susan Feng Lu & Lauren Xiaoyuan Lu, 2017. "Do Mandatory Overtime Laws Improve Quality? Staffing Decisions and Operational Flexibility of Nursing Homes," Management Science, INFORMS, vol. 63(11), pages 3566-3585, November.
    2. Vincent W. Slaugh & Alan A. Scheller‐Wolf & Sridhar R. Tayur, 2018. "Consistent Staffing for Long‐Term Care through On‐Call Pools," Production and Operations Management, Production and Operations Management Society, vol. 27(12), pages 2144-2161, December.
    3. M. Keith Chen & Ryne Rohla, 2017. "The Effect of Partisanship and Political Advertising on Close Family Ties," Papers 1711.10602, arXiv.org, revised Jun 2018.
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    Cited by:

    1. Antoci, Angelo & Sabatini, Fabio & Sacco, Pier Luigi & Sodini, Mauro, 2022. "Experts vs. policymakers in the COVID-19 policy response," Journal of Economic Behavior & Organization, Elsevier, vol. 201(C), pages 22-39.
    2. Matthew Spiegel & Heather Tookes, 2021. "Business Restrictions and COVID-19 Fatalities [The immediate effect of COVID-19 policies on social distancing behavior in the United States]," The Review of Financial Studies, Society for Financial Studies, vol. 34(11), pages 5266-5308.
    3. Pongou, Roland & Tchuente, Guy & Tondji, Jean-Baptiste, 2021. "Optimally Targeting Interventions in Networks during a Pandemic: Theory and Evidence from the Networks of Nursing Homes in the United States," GLO Discussion Paper Series 957, Global Labor Organization (GLO).
    4. A. James O'Malley & Thomas A. Bubolz & Jonathan S. Skinner, 2021. "The Diffusion of Health Care Fraud: A Network Analysis," NBER Working Papers 28560, National Bureau of Economic Research, Inc.
    5. Xavier Flawinne & Mathieu Lefebvre & Sergio Perelman & Pierre Pestieau & Jérôme Schoenmaeckers, 2023. "Nursing homes and mortality in Europe: Uncertain causality," Health Economics, John Wiley & Sons, Ltd., vol. 32(1), pages 134-154, January.
    6. Abe Dunn & Joshua D. Gottlieb & Adam Shapiro & Daniel J. Sonnenstuhl & Pietro Tebaldi, 2021. "A Denial a Day Keeps the Doctor Away," NBER Working Papers 29010, National Bureau of Economic Research, Inc.
    7. Taylor A Begley & Daniel Weagley, 2023. "Firm Finances and the Spread of COVID-19: Evidence from Nursing Homes," The Review of Corporate Finance Studies, Society for Financial Studies, vol. 12(1), pages 1-35.
    8. Drost, Alyssa & Alam, M Injamam & Boamah, Sheila & Kralj, Boris & Costa, Andrew & Sweetman, Arthur, 2023. "Multiple jobholding and part-time work among nurses in long-term care homes compared to other healthcare sectors: Evidence from Ontario," Health Policy, Elsevier, vol. 130(C).
    9. Paul L. Joskow, 2022. "From Scarcity to Abundance: Government and Private Initiatives to Manage the Allocation of N95 Masks in the U.S. During the COVID-19 Pandemic," NBER Working Papers 29876, National Bureau of Economic Research, Inc.
    10. Cronin, Christopher J. & Evans, William N., 2022. "Nursing home quality, COVID-19 deaths, and excess mortality," Journal of Health Economics, Elsevier, vol. 82(C).
    11. Casey B. Mulligan, 2021. "The Backward Art of Slowing the Spread? Congregation Efficiencies during COVID-19," NBER Working Papers 28737, National Bureau of Economic Research, Inc.
    12. Eric Budish, 2020. "Maximize Utility subject to R≤1: A Simple Price-Theory Approach to Covid-19 Lockdown and Reopening Policy," NBER Working Papers 28093, National Bureau of Economic Research, Inc.
    13. Coven, Joshua & Gupta, Arpit & Yao, Iris, 2023. "JUE Insight: Urban flight seeded the COVID-19 pandemic across the United States," Journal of Urban Economics, Elsevier, vol. 133(C).
    14. Pongou, Roland & Sidie, Ghislain Junior & Tchuente, Guy & Tondji, Jean-Baptiste, 2022. "Profits, Pandemics, and Lockdown Effectiveness in Nursing Home Networks," GLO Discussion Paper Series 1131, Global Labor Organization (GLO).
    15. Theresa Kuchler & Johannes Stroebel, 2022. "Social Interactions, Resilience, and Access to Economic Opportunity: A Research Agenda for the Field of Computational Social Science," CESifo Working Paper Series 9606, CESifo.
    16. Almagro, Milena & Coven, Joshua & Gupta, Arpit & Orane-Hutchinson, Angelo, 2023. "Disparities in COVID-19 risk exposure: Evidence from geolocation data," Regional Science and Urban Economics, Elsevier, vol. 102(C).
    17. Roland Pongou & Guy Tchuente & Jean-Baptiste Tondji, 2021. "Optimally Targeting Interventions in Networks during a Pandemic: Theory and Evidence from the Networks of Nursing Homes in the United States," Papers 2110.10230, arXiv.org.
    18. Christopher Avery & William Bossert & Adam Clark & Glenn Ellison & Sara Fisher Ellison, 2020. "An Economist's Guide to Epidemiology Models of Infectious Disease," Journal of Economic Perspectives, American Economic Association, vol. 34(4), pages 79-104, Fall.

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    More about this item

    Keywords

    nursing homes; COVID-19; complex networks; smartphone data;
    All these keywords.

    JEL classification:

    • I10 - Health, Education, and Welfare - - Health - - - General
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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