Random variation drives a critical bias in the comparison of healthcare-associated infections

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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.

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