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Submitted URL: https://doi.org/10.1017/ice.2022.288
Effective URL: https://www.cambridge.org/core/journals/infection-control-and-hospital-epidemiology/article/abs/random-variation-drives-a-...
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Effective URL: https://www.cambridge.org/core/journals/infection-control-and-hospital-epidemiology/article/abs/random-variation-drives-a-...
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Infection Control & Hospital Epidemiology
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ARTICLE CONTENTS
* Abstract
* References
RANDOM VARIATION DRIVES A CRITICAL BIAS IN THE COMPARISON OF
HEALTHCARE-ASSOCIATED INFECTIONS
Published online by Cambridge University Press: 10 March 2023
Kenneth J. Locey [Opens in a new window] ,
Thomas A. Webb [Opens in a new window] ,
Robert A. Weinstein [Opens in a new window] ,
Bala Hota [Opens in a new window] and
Brian D. Stein
Show author details
--------------------------------------------------------------------------------
Kenneth J. Locey* Affiliation:
Center for Quality, Safety and Value Analytics, Rush University Medical Center,
Chicago, Illinois
Thomas A. Webb Affiliation:
Center for Quality, Safety and Value Analytics, Rush University Medical Center,
Chicago, Illinois
Robert A. Weinstein Affiliation:
Division of Infectious Diseases, Rush Medical College, Chicago, Illinois
Bala Hota Affiliation:
Tendo Systems, Inc, Hinsdale, Illinois
Brian D. Stein Affiliation:
Center for Quality, Safety and Value Analytics, Rush University Medical Center,
Chicago, Illinois
*
Author for correspondence: Kenneth J. Locey, E-mail: Kenneth_J_Locey@rush.edu
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ABSTRACT
Objective:
To evaluate random effects of volume (patient days or device days) on
healthcare-associated infections (HAIs) and the standardized infection ratio
(SIR) used to compare hospitals.
Design:
A longitudinal comparison between publicly reported quarterly data (2014–2020)
and volume-based random sampling using 4 HAI types: central-line–associated
bloodstream infections, catheter-associated urinary tract infections,
Clostridioides difficile infections, methicillin-resistant Staphylococcus aureus
infections.
Methods:
Using 4,268 hospitals with reported SIRs, we examined relationships of SIRs to
volume and compared distributions of SIRs and numbers of reported HAIs to the
outcomes of simulated random sampling. We included random expectations into SIR
calculations to produce a standardized infection score (SIS).
Results:
Among hospitals with volumes less than the median, 20%–33% had SIRs of 0,
compared to 0.3%–5% for hospitals with volumes higher than the median.
Distributions of SIRs were 86%–92% similar to those based on random sampling.
Random expectations explained 54%–84% of variation in numbers of HAIs. The use
of SIRs led hundreds of hospitals with more infections than either expected at
random or predicted by risk-adjusted models to rank better than other hospitals.
The SIS mitigated this effect and allowed hospitals of disparate volumes to
achieve better scores while decreasing the number of hospitals tied for the best
score.
Conclusions:
SIRs and numbers of HAIs are strongly influenced by random effects of volume.
Mitigating these effects drastically alters rankings for HAI types and may
further alter penalty assignments in programs that aim to reduce HAIs and
improve quality of care.
--------------------------------------------------------------------------------
Type Original Article
Information
Infection Control & Hospital Epidemiology , Volume 44 , Issue 9 , September 2023
, pp. 1396 - 1402
DOI: https://doi.org/10.1017/ice.2022.288 [Opens in a new window]
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of The
Society for Healthcare Epidemiology of America
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