Turning up the volume to address biases in predicted healthcare-associated infections and enhance US hospital rankings

Abstract

Objectives: To examine potential biases in standardized infection ratio (SIR) metrics due to static U.S. Centers for Disease Control and Prevention (CDC) parameters and non-linearity of infection outcomes with volume. Correspondingly, to enhance the CDC predictions by incorporating additional information from volume met-rics and explore an alternative approach to more fairly rank hospitals to address the SIR=0 problem. Methods: This population-based study uses publicly available 2019 healthcare-associated infections (HAI) data from 3096 acute care U.S. hospitals. HAI-specific Poisson generalized additive models illustrate the recalibration of CDC predictions, using volume-based spline functions to adjust for biases. Implied cumula-tive distribution functions (CDF) were derived, and HAI-facility-specific probabilities were calculated. Hospi-tal rankings implied from these HAI-stratified probabilities were calculated. Results: Calibration plots demonstrate existing biases associated with CDC infection over-predictions. Volume-based spline functions were significant for all HAIs (P<.0004). CDF-based rankings resulted in larger discrimination across hospitals based on strength of evidence, especially among SIR=0 facilities. National maps depict ranking differences by HAI and state. Conclusion: Adjustment of SIR biases, which differ by facility volume, is needed to produce more accurate and fairer hospital rankings. (c) 2021 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.