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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 ([email protected]).
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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.

References

REFERENCES

1. State-based HAI prevention. Tracking. Centers for Disease Control and Prevention Web site. http://www.cdc.gov/hai/state-based/tracking.html. Accessed February 17, 2015.Google Scholar
2. National Healthcare Safety Network. CMS Quality Reporting Programs Frequently Asked Questions. Centers for Disease Control and Prevention Web site. http://www.cdc.gov/nhsn/faqs/FAQ_CMS_HAI.html. Accessed February 27, 2015.Google Scholar
3. Claims-based Measures. Quality Net Web site. http://www.qualitynet.org/dcs/ContentServer?c=Page&pagename=QnetPublic%2FPage%2FQnetTier2&cid=1228763452133. Accessed July 20, 2015.Google Scholar
4. Sax, H, Pittet, D, the Wsiaa-NOSO Networks. Interhospital differences in nosocomial infection rates: Importance of case-mix adjustment. Arch Intern Med 2002;162:2437–2442.Google Scholar
5. McKibben, L, Horan, T, Tokars, JI, Fowler, G, Cardo, DM, the Healthcare Infection Control Practices Advisory Committee. Guidance on Public Reporting of Healthcare-Associated Infections: Recommendations of the Healthcare Infection Control Practices Advisory Committee. Am J Infect Control 2005;33:217–226.Google Scholar
6. Anderson, DJ, Chen, LF, Sexton, DJ, Kaye, KS. Complex surgical site infections and the devilish details of risk adjustment: important implications for public reporting. Infect Control Hosp Epidemiol 2008;29:941–946.Google Scholar
7. Tong, ENC, Clements, ACA, Haynes, MA, Jones, MA, Morton, AP, Whitby, M. Improved hospital-level risk adjustment for surveillance of healthcare-associated bloodstream infections: a retrospective cohort study. BMC Infect Dis 2009;9:145.Google Scholar
8. Mu, Y, Edwards, JR, Horan, TC, Berrios, SI, Fridkin, SF. Improving risk-adjusted measures of Surgical Site Infection for the National Healthcare Safety Network. Infect Control Hosp Epidemiol 2011;32:970–986.Google Scholar
9. Haley, VB, DiRienzo, AG, Lutterloh, EC, Stricof, RL. Quantifying sources of bias in the National Healthcare Safety Network laboratory-identified Clostridium difficile infection rates. Infect Control Hosp Epiemiol 2014;35:1–7.Google Scholar
10. McGregor, JC, Harros, AD. The need for advancements in the field of risk adjustment for healthcare associated infections. Infect Control Hosp Epidemiol 2014;35:8–9.Google Scholar
11. Dudeck, MA, Weiner, LM, Allen-Bridson, K, Malpiedi, PJ, Peterson, KD, et al. National Healthcare Safety Network report, Data Summary for 2012, Device-associated module. Am J Infect Control 2013;41:1148–1166.Google Scholar
12. Iezzoni, LI. Using risk-adjusted outcome to assess clinical practice: an overview of issues pertaining to risk adjustment. Ann Thorac Surg 1994;58:1822–1826.Google Scholar
13. Tenrani, DM, Phelan, MJ, Cao, C, Billilek, J, Datta, R, et al. Substantial shifts in ranking of California hospitals by hospital-associated methicillin-resistant Staphylococcus aureus infection following adjustment for hospital characteristics and case mix. Infect Control Hosp Epiemiol 2014;35:1263–1270.Google Scholar
14. Mendez, CM, Harrington, DQ, Christenson, P, Spellberg, B. Impact of hospital variables on case mix index as a marker of disease severity. Popul Health Manag 2014;17:28–34.Google Scholar
15. Rosenbaum, BP, Lorenz, RR, Luter, RB, Knowles-Ward, L, Kelly, DL, Weil, RJ. Improving and measuring inpatient documentation of medical care within the MS-DRG System: education, monitoring, and normalized case mix index. Perspect Health Info Manag 2014, Summer 1–11.Google Scholar
16. Kuster, SP, Ruef, C, Bollinger, AK, et al. Correlation between case mix index and antibiotic use in hospitals. J Antimicrob Chemother 2008;62:837–842.Google Scholar
17. Steinwald, B, Dummit, LA. Hospital case-mix change: sicker patients or DRG creep? Health Affairs 1989, Summer 35–47.Google Scholar
18. NHSN Multidrug-Resistant Organism and Clostridium difficile Infection (MDRO/CDI) Module. Centers for Disease Control and Prevention Web site. http://www.cdc.gov/nhsn/PDFs/pscManual/12pscMDRO_CDADcurrent.pdf. Accessed February 6, 2015.Google Scholar
19. Risk Adjustment for Healthcare Facility-Onset C. difficile and MRSA Bacteremia Laboratory-Identified Event Reporting in NHSN. Centers for Disease Control and Prevention Web site. http://www.cdc.gov/nhsn/PDFs/mrsa-cdi/RiskAdjustment-MRSA-CDI.pdf. Accessed January 26, 2015.Google Scholar
20. Acute Inpatient PPS. Details for Title: Case Mix Index. Centers for Medicare and Medicaid Services Web site. http://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/Acute-Inpatient-Files-for-Download-Items/CMS022630.html Accessed February 2, 2015.Google Scholar
21. Hilbe, JM. Negative Binomial Regression (2nd ed). London: Cambridge University Press; 2011.Google Scholar
22. Miaou, S-P. Measuring the Goodness-of-Fit of Accident Prediction Models. FHWA-RD-96-040, Federal Highway Administration, Washington, DC; 1996.Google Scholar
23. UCLA: Statistical Consulting Group. FAQ: What are pseudo R-squareds? Institute for Digital Research and Education Web site. www.ats.ucla.edu/stat/mult_pkg/faq/general/Psuedo_RSquareds.htm. Accessed February 6, 2015.Google Scholar
24. Yang, CM, Reinke, W. Feasibility and validity of International Classification of Diseases based case mix indices. BMC Health Serv Res 2006;6:125.Google Scholar
25. Munoz-Price, LS, Hota, B, Stemer, A, Weinstein, RA. Prevention of bloodstream infections by use of daily chlorhexidine baths for patients at a long-term acute care hospital. Infect Control Hosp Epidemiol 2009;30:1031–1035.CrossRefGoogle Scholar
26. Polk, RE, Hohmann, SF, Medvedev, M, Ibrahim, O. Benchmarking risk-adjusted adult antibacterial drug use in 70 US academic medical center hospitals. Clin Infect Dis 2011;53:1100–1110.Google Scholar
27. Methicillin resistant Staphylococcus aureus and vancomycin resistant Enterococcus bloodstream infections in California general acute care hospitals, April 2010 through March 2011. California Department of Public Health Web site. http://www.cdph.ca.gov/programs/hai/Documents/MRSA-and-VRE-BSI-April-2010%E2%80%94March-2011.pdf. Published 2011. Accessed February 19, 2015.Google Scholar
28. Lagman, RL, Walsh, D, Davis, MP, Young, B. All patient refined-diagnostic related group and case mix index in acute care palliative medicine. J Support Oncol 2007;5:145–149.Google Scholar
29. All-Patient-Refined Diagnosis-Related Group (APR DRG) Software. 3M Web site. http://solutions.3m.com/wps/portal/3M/en_US/Health-Information-Systems/HIS/Products-and-Services/Products-List-A-Z/APR-DRG-Software/. Accessed April 13, 2015.Google Scholar
30. Lipstein, SH, Dunagan, WC. The risks of not adjusting performance measures for sociodemographic factors. Ann Internal Med 2014;161:594–597.Google Scholar
31. Fiscella, K, Burstin, HR, Nerenz, DR. Quality measures and sociodemographic risk factors: to adjust or not adjust. JAMA 2014;312:2615–2616.Google Scholar
32. National Healthcare Safety Network. Centers for Disease Control and Prevention Web site. http://www.cdc.gov/nhsn/CDA/. Accessed February 27, 2015.Google Scholar