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Evaluating the Use of the Case Mix Index for Risk Adjustment of Healthcare-Associated Infection Data: An Illustration using Clostridium difficile Infection Data from the National Healthcare Safety Network

Published online by Cambridge University Press:  21 October 2015

Nicola D. Thompson ,
Jonathan R. Edwards ,
Margaret A. Dudeck ,
Scott K. Fridkin  and
Shelley S. Magill
Nicola D. Thompson*
Affiliation:
Division of Healthcare Quality Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
Jonathan R. Edwards
Affiliation:
Division of Healthcare Quality Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
Margaret A. Dudeck
Affiliation:
Division of Healthcare Quality Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
Scott K. Fridkin
Affiliation:
Division of Healthcare Quality Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
Shelley S. Magill
Affiliation:
Division of Healthcare Quality Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
*
Address correspondence to Nicola D. Thompson, PhD, MS, Division of Healthcare Quality Promotion, Centers for Disease Control and Prevention, 1600 Clifton Road MS A-16, Atlanta, GA 30333 (ndthompson@cdc.gov).
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Abstract

BACKGROUND

Case mix index (CMI) has been used as a facility-level indicator of patient disease severity. We sought to evaluate the potential for CMI to be used for risk adjustment of National Healthcare Safety Network (NHSN) healthcare-associated infection (HAI) data.

METHODS

NHSN facility-wide laboratory-identified Clostridium difficile infection event data from 2012 were merged with the fiscal year 2012 Inpatient Prospective Payment System (IPPS) Impact file by CMS certification number (CCN) to obtain a CMI value for hospitals reporting to NHSN. Negative binomial regression was used to evaluate whether CMI was significantly associated with healthcare facility-onset (HO) CDI in univariate and multivariate analysis.

RESULTS

Among 1,468 acute care hospitals reporting CDI data to NHSN in 2012, 1,429 matched by CCN to a CMI value in the Impact file. CMI (median, 1.49; interquartile range, 1.36–1.66) was a significant predictor of HO CDI in univariate analysis (P<.0001). After controlling for community onset CDI prevalence rate, medical school affiliation, hospital size, and CDI test type use, CMI remained highly significant (P<.0001), with an increase of 0.1 point in CMI associated with a 3.4% increase in the HO CDI incidence rate.

CONCLUSIONS

CMI was a significant predictor of NHSN HO CDI incidence. Additional work to explore the feasibility of using CMI for risk adjustment of NHSN data is necessary.

Infect. Control Hosp. Epidemiol. 2015;37(1):19–25

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 37 , Issue 1 , January 2016 , pp. 19 - 25
Copyright
© 2015 by The Society for Healthcare Epidemiology of America. All rights reserved 

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Footnotes

PREVIOUS PRESENTATION. Data were presented in part at ID Week 2014: Joint Meeting of IDSA, SHEA, HIVMA, and PIDS in Philadelphia, Pennsylvania on October 9, 2014. Abstract #114.

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