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The impact of nurse staffing levels on nursing-sensitive patient outcomes: a multilevel regression approach

Author

Listed:
  • Karina Dietermann

    (University of Hamburg)

  • Vera Winter

    (University of Wuppertal)

  • Udo Schneider

    (Health Care Management at Techniker Krankenkasse)

  • Jonas Schreyögg

    (University of Hamburg)

Abstract

The goal of this study is to provide empirical evidence of the impact of nurse staffing levels on seven nursing-sensitive patient outcomes (NSPOs) at the hospital unit level. Combining a very large set of claims data from a German health insurer with mandatory quality reports published by every hospital in Germany, our data set comprises approximately 3.2 million hospital stays in more than 900 hospitals over a period of 5 years. Accounting for the grouping structure of our data (i.e., patients grouped in unit types), we estimate cross-sectional, two-level generalized linear mixed models (GLMMs) with inpatient cases at level 1 and units types (e.g., internal medicine, geriatrics) at level 2. Our regressions yield 32 significant results in the expected direction. We find that differentiating between unit types using a multilevel regression approach and including postdischarge NSPOs adds important insights to our understanding of the relationship between nurse staffing levels and NSPOs. Extending our main model by categorizing inpatient cases according to their clinical complexity, we are able to rule out hidden effects beyond the level of unit types.

Suggested Citation

  • Karina Dietermann & Vera Winter & Udo Schneider & Jonas Schreyögg, 2021. "The impact of nurse staffing levels on nursing-sensitive patient outcomes: a multilevel regression approach," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 22(5), pages 833-846, July.
    • Handle: RePEc:spr:eujhec:v:22:y:2021:i:5:d:10.1007_s10198-021-01292-2
    DOI: 10.1007/s10198-021-01292-2
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    References listed on IDEAS

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    1. Barbara A. Mark & David W. Harless & Michael McCue, 2005. "The impact of HMO penetration on the relationship between nurse staffing and quality," Health Economics, John Wiley & Sons, Ltd., vol. 14(7), pages 737-753, July.
    2. Andrew Bell & Malcolm Fairbrother & Kelvyn Jones, 2019. "Fixed and random effects models: making an informed choice," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(2), pages 1051-1074, March.
    3. Cook, Andrew & Gaynor, Martin & Stephens Jr, Melvin & Taylor, Lowell, 2012. "The effect of a hospital nurse staffing mandate on patient health outcomes: Evidence from California's minimum staffing regulation," Journal of Health Economics, Elsevier, vol. 31(2), pages 340-348.
    4. Mareike Heimeshoff & Jonas Schreyögg & Oliver Tiemann, 2014. "Employment effects of hospital privatization in Germany," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 15(7), pages 747-757, September.
    5. Augurzky, Boris & Bünnings, Christian & Wübker, Ansgar, 2017. "The relationship between nurse staffing levels and objective and subjective quality of care: A panel data approach for Germany," Ruhr Economic Papers 724, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    6. Milstein, Ricarda & Schreyoegg, Jonas, 2020. "The relationship between nurse staffing levels and nursing-sensitive outcomes in hospitals: Assessing heterogeneity among unit and outcome types," Health Policy, Elsevier, vol. 124(10), pages 1056-1063.
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