1170 infection control & hospital epidemiology october 2017, vol. 38, no. 10
table 1. Percentage of Hospital Referral Regions (HRRs) That Have at Least 50%, 70%, and 90% of Its Hospitals Reporting Data for Each Hospital-Associated Infection (HAI)
>50%
Reporting Data
HAI CAUTI
Clostridium difficile CLABSI MRSA
>70%
Reporting Data
>90%
Reporting Data
No. of HRRs No. % No. % No. %
SSI (hysterectomy) 306 SSI (colon)
306 144 47 92 30 47 15 306 250 82 195 64 104 34 306 115 38 75 25 33 11 306 117 38 65 21 25 8 12 4 1 0 0 0
306 131 43 67 22 24 8
NOTE. CAUTI, catheter-associated urinary tract infection; CLABSI, central-line–associated bloodstream infection; MRSA, methicillin- resistant Staphylococcus aureus; SSI, surgical site infection.
table 2. Percentage of Hospital Referral Regions (HRRs) That Have at Least 1 Pair of Hospitals With a Statistically Significant (P<.05) Standardized Infection Ratio (SIR)
HRRs with ≥1 Pair of
Hospitals With Overlapping SIRs
CAUTI
Clostridium difficile CLABSI MRSA
SSI (hysterectomy) SSI (colon)
Total No. of HRRs No. % 303
306 303 293 228 300
162 220 99 87 21
106
53.5 71.9 32.7 29.7 9.2
35.3
NOTE. CAUTI, catheter-associated urinary tract infection; CLABSI, central-line–associated bloodstream infection; MRSA, methicillin- resistant Staphylococcus aureus; SSI, surgical site infection.
In a substantial number of geographic regions (HRRs),
>50% of the hospitals in each group report data (Outcome 1, data availability). Clostridium difficile had the highest percen- tage of reported data, with 82% of HRRs have at least 50% of hospitals reporting C. difficile. For CAUTI, 47% of HRRs have at least 50% of hospitals reporting this HAI, compared with SSI (colon) (43% of HRRs with at least 50% of hospitals reporting), CLABSI, and MRSA (38% of HRRs with at least 50% of hospitals reporting). Far fewer hospitals report SSI (hysterectomy) HAI data. Thus, HAI data generally are reported by enough hospitals for it to be useful in comparisons in many geographic locations, though this varies by HAI. Outcome 2 assessed performance differences between hos-
pitals within a geographic region. For each HAI, we calculated the percentage of hospitals in each geographic region where at least 1 pair of hospitals had statistically different SIRs. Results varied by HAI, ranging from 72% of geographic regions with at
least 1 pair of hospitals with statistically different performance for C. difficile, to 9% for SSI (hysterectomy). These results indicate that CAUTI, C. difficile, and CLABSI data are more useful to differentiate hospitals than data for the other publicly reported HAIs. To our knowledge, ours is the first in-depth assessment of
its kind for publicly reported HAI data. Safavi et al7 conducted a somewhat similar analysis for SSI process data and found that it was not useful in differentiating the “vast majority” of hospitals.7 In contrast, our findings indicate that HAI outcome data are useful to differentiate some hospitals in many, though not all, geographic areas (corresponding to the areas covered by HRRs). Strengths of this study include the use of nationwide data
(as opposed to regional data) and including analysis of all 6 HAIs reported by the CMS. As described above, CMS HAI data are reported using SIRs, a method of indirect standardi- zation to adjust for risk among hospitals. A major limitation of indirect standardization is that comparison of indirectly standardized rates such as SIRs can suffer from residual confounding (see Online Appendix for further discussion).8 Despite potential bias from residual confounding inherent
in using SIRs, patients and other users of the CMS Hospital Compare website do compare SIRs between hospitals. This is the only available risk-adjusted HAI measure of hospital performance, and the primary purpose of the CMS Hospital Compare website is to compare hospitals. Outcome 2 of our analysis examines this usage of public HAI data, but it is limited by potential biases inherent in comparing SIRs among hospitals. The CMS does not publish the data necessary to determine the extent of these biases; it would be helpful if CMS made these data available. In conclusion, HAI data currently available on the CMS
Hospital Compare website are useful to differentiate hospitals. Unfortunately, the utility of these data is reduced by the number of hospitals reporting “not available” for HAIs (either the hospital does not report the data, or because of denomi- nators below the CMS reporting threshold), and because comparing SIRs can be subject to bias. The current reporting of HAI data to the public via the CMS Hospital Compare website is a good first step, but it could be further improved with additional research on which outcome measures should be reported by hospitals to the CDC, with the addition of risk adjustment methodology for these measures.
acknowledgments
Financial support: This work was supported by the Agency for Healthcare Research and Quality (grant no. HS18111 to D.J.M.), the Veterans Adminis- tration Health Services Research and Development (grant no. CRE 12-289 to D.J.M.), the Baltimore VA Medical Center Geriatrics Research, Education, and Clinical Center (J.D.S.), the National Institute on Aging at the National Institutes of Health (grant no. NIA 5 P30 AG028747 to J.D.S.), the National Institute of Diabetes and Digestive and Kidney Diseases at the National Insti- tutes of Health (grant no NIDDK 5 P30 DK072488 to J.D.S.), and the National Institutes of Health (grant no. 5 K24AI079040-05 to A.D.H.).
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