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Background: The pandemic profoundly stressed practicing nurses and could have thereby affected trends in nursing-sensitive quality indicators (NSIs), measures that detect changes in patient health status directly affected by nursing care., Objectives: The aim of the study was to determine if NSIs have worsened in response to the pandemic and then returned to prepandemic levels using data from 2019 through 2022., Methods: We conducted a cross-sectional descriptive study of annual trends, examining unit data from the National Database of Nursing Quality Indicators (NDNQI) from 2019 through 2022 for five indicators: rates of falls, central line-associated bloodstream infections (CLABSI), catheter-associated urinary tract infections (CAUTI), hospital-acquired pressure injuries (HAPI), and ventilator-associated events (VAE). The NDNQI is the largest repository of nursing quality indicators, which are derived from patient-level events, reported at the nursing unit level, and submitted quarterly by over 2,000 member hospitals. Adult medical-surgical or critical care inpatient nursing units with complete data for the 4 years were included, with samples ranging from 456 to 5,818 nursing units in 2,346 hospitals. Analysis of variance was conducted by comparing the 2019 rates to each subsequent year., Results: In decreasing order of prevalence, the mean prepandemic rates were 6.58 VAE per 1,000 ventilator days (critical care only), 2.41 HAPI per 1,000 device days, 2.20 falls per 1,000 patient days, 0.96 CAUTI per 1,000 catheter days, and 0.68 CLABSI per 1,000 central line days for medical-surgical and critical care units combined. The rates for all five nurse-sensitive indicators increased significantly beginning in 2020 and have begun to decline but have not returned to baseline by 2022. The maximum rate was observed in 2020 for falls and 2021 for the remaining indicators. These increases to the maximum ranged from a 12% increase in CAUTI to 49% for CLABSI., Discussion: NSIs increased during the pandemic and are now returning to baseline. The pandemic underscored the importance of nursing practice. The pandemic's enduring negative effects on the nursing workforce must be addressed to preserve patient safety., Competing Interests: Drs. Pascal and Warshawsky are employed by Press Ganey but did not receive any special incentives for this project. The remaining authors have no conflicts of interest to report., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Background: Multilevel models were designed to analyze data generated from a nested structure (e.g., nurses within hospitals) because conventional linear regression models underestimate standard errors and, in turn, overestimate test statistics., Objectives: To introduce 2 types of multilevel models, the random intercept model and the random coefficient model, to describe the correlation among observations within a cluster, and to demonstrate how to identify the superior model., Method: The conceptual and mathematical bases for the 2 multilevel model types are presented. Intraclass correlation is defined and assessment of model fit is detailed. An empirical example is presented in which average work hours per week and burnout are analyzed using data from 4,320 staff nurses clustered in 19 hospitals., Results: Average work hours were positively associated with nurse burnout. The multilevel models corrected the problem of underestimated standard errors in conventional linear regression models. Graphs displaying the hospital-level differences illustrated the 2 multilevel model types. Although the multilevel models corrected the underestimation of standard errors, the results did not differ substantively for the conventional or the 2 multilevel models. The intraclass correlation coefficient was .044, indicating that the extent of shared variance among nurses in a hospital was low. The random intercept model fit the data better than did the random coefficient model., Conclusions: Multilevel models provide a more accurate and comprehensive description of relationships in clustered data than do conventional models, by correcting underestimated standard errors, by estimating components of variance at several levels, and by estimating cluster-specific intercepts and slopes.