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Can National Healthcare-Associated Infections (HAIs) Data Differentiate Hospitals in the United States?

Published online by Cambridge University Press:  14 September 2017

Max Masnick ,
Daniel J. Morgan ,
John D. Sorkin ,
Mark D. Macek ,
Jessica P. Brown ,
Penny Rheingans  and
Anthony D. Harris
Max Masnick*
Affiliation:
Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland
Daniel J. Morgan
Affiliation:
Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland Veterans Affairs Maryland Healthcare System, Baltimore, Maryland
John D. Sorkin
Affiliation:
Veterans Affairs Maryland Healthcare System Geriatrics Research, Education, and Clinical Center, Baltimore, Maryland
Mark D. Macek
Affiliation:
University of Maryland School of Dentistry, Baltimore, Maryland
Jessica P. Brown
Affiliation:
Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland
Penny Rheingans
Affiliation:
Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, Maryland
Anthony D. Harris
Affiliation:
Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland
*
Address correspondence to Max Masnick, 10 S. Pine Street, MSTF 330, Baltimore, MD 21201 ([email protected]).
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Abstract

OBJECTIVE

To determine whether patients using the Centers for Medicare and Medicaid Services (CMS) Hospital Compare website (http://medicare.gov/hospitalcompare) can use nationally reported healthcare-associated infection (HAI) data to differentiate hospitals.

DESIGN

Secondary analysis of publicly available HAI data for calendar year 2013.

METHODS

We assessed the availability of HAI data for geographically proximate hospitals (ie, hospitals within the same referral region) and then analyzed these data to determine whether they are useful to differentiate hospitals. We assessed data for the 6 HAIs reported by hospitals to the Centers for Disease Control and Prevention (CDC).

RESULTS

Data were analyzed for 4,561 hospitals representing 88% of registered community and federal government hospitals in the United States. Healthcare-associated infection data are only useful for comparing hospitals if they are available for multiple hospitals within a geographic region. We found that data availability differed by HAI. Clostridium difficile infections (CDI) data were most available, with 82% of geographic regions (ie, hospital referral regions) having >50% of hospitals reporting them. In contrast, 4% of geographic regions had >50% of member hospitals reporting surgical site infections (SSI) for hysterectomies, which had the lowest availability. The ability of HAI data to differentiate hospitals differed by HAI: 72% of hospital referral regions had at least 1 pair of hospitals with statistically different risk-adjusted CDI rates (SIRs), compared to 9% for SSI (hysterectomy).

CONCLUSIONS

HAI data generally are reported by enough hospitals to meet minimal criteria for useful comparisons in many geographic locations, though this varies by type of HAI. CDI and catheter-associated urinary tract infection (CAUTI) are more likely to differentiate hospitals than the other publicly reported HAIs.

Infect Control Hosp Epidemiol 2017;38:1167–1171

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 38 , Issue 10 , October 2017 , pp. 1167 - 1171
Copyright
© 2017 by The Society for Healthcare Epidemiology of America. All rights reserved 

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References

REFERENCES

1. Healthcare-associated infection progress report: questions and answers. Centers for Disease Control and Prevention website. http://www.cdc.gov/HAI/surveillance/QA_stateSummary.html#b9. Updated 2016. Accessed July 28, 2017.Google Scholar
2. Rock, C, Thom, KA, Harris, AD, et al. A multicenter longitudinal study of hospital-onset bacteremia: time for a new quality outcome measure? Infect Control Hosp Epidemiol 2016;37:143–148.CrossRefGoogle ScholarPubMed
3. Healthcare-associated infections hospital data. US Medicare Services website. https://data.medicare.gov/Hospital-Compare/Healthcare-Associated-Infections-Hospital/77hc-ibv8/about. Accessed July 28, 2017.Google Scholar
4. Data by region. Dartmouth Atlas of Health Care website. http://www.dartmouthatlas.org/data/region/. Updated 2017. Accessed July 28, 2017.Google Scholar
5. Soe MM. Compare 2 SIRs by exact binomial test and mid-P, 95% CI. Centers for Disease Control and Prevention website. http://www.cdc.gov/nhsn/sas/binom.sas. Accessed October 24, 2016.Google Scholar
6. Fast Facts on US hospitals. American Hospital Association website. http://www.aha.org/research/rc/stat-studies/fast-facts.shtml. Published 2013. Accessed July 28, 2017.Google Scholar
7. Safavi, KC, Dai, F, Gilbertsen, TA, Schonberger, RB. Variation in surgical quality measure adherence within hospital referral regions: do publicly reported surgical quality measures distinguish among hospitals that patients are likely to compare? Health Serv Res 2014;49:1108–1120.Google Scholar
8. Rothman, KJ, Greenland, S, Lash, TL. Modern Epidemiology. Philadelphia: Wolters Kluwer/Lippincott Williams & Wilkins; 2008.Google Scholar
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