1168 infection control & hospital epidemiology october 2017, vol. 38, no. 10
choosing among these hospitals. The aim of this study was to determine whether nationwide HAI data published by CMS are useful in differentiating hospitals within a geographic area.
methods
hospital referral regions (HRRs) as specified by the Dartmouth Atlas.4 The HRRs are geographic areas based on “where patients were referred for major cardiovascular surgical pro- cedures and for neurosurgery.”4 Each hospital in the CMS
For our analysis, we used publicly available HAI data published by CMS in January 2015. These data cover calendar year 2013.3 We analyzed these data from the perspective of someone searching for a hospital. To do this, we compared HAI data among geographically proximate hospitals that would con- stitute a plausible set of choices for an actual person looking for a hospital in their geographic region. We defined geographic regions for grouping hospitals using
figure 1. The distribution of the number of hospitals within each hospital referral region.
data3 was assigned to a single HRR based on the “providerid” field that appeared in both datasets. Hospital referral regions were identified for 97% of hospitals in the CMS dataset based on “providerid,” and HRRs for the remainder were determined using zip codes. Several criteria were assessed for each geographic region
(HRR) as follows: Outcome 1: Data availability. We determined the proportion
of hospitals in each geographic region (HRR) where data for the HAI were available (ie, where data exist that can be used for comparing hospitals). If no HAI data are available for a high proportion of hospitals, then these data do not help differ- entiate hospitals. Outcome 2: HAI performance diversity. We determined the
percentage of hospitals in each HRR where at least 1 pair of hospitals had statistically different SIRs for the HAI, suggesting a difference in performance between the hospitals for that HAI. Then, SIR comparisons were performed using a method published by the CDC for comparing 2 SIRs, and a 2-tailed significance of σ=0.05 was used.5 If hospitals all perform similarly with in a HRR, then the HAI data are not useful in comparing hospitals in that geographic area. If this were true for many HRRs, then HAI data would not generally be useful for comparing hospitals nationwide.
results Descriptive Results
Publicly available CMS HAI data were analyzed for all 4,561 hospitals in 306 HRRs (geographic regions). These data represent all hospitals available in the CMS data and 88% of 5,187 registered community and federal government hospitals in the United States at the time of the analysis.6 The number of hospitals in each HRR ranged from 2 to 83, with a median of 11 (interquartile range, 6–17). Most HRRs included between 2 and 20 hospitals (Figure 1). Figure 2 shows the distribution of SIRs for each HAI within each HRR.
Outcome 1: Data availability. Table 1 shows the proportion of hospitals in each HRR with reported data, meaning that the hospital reports a number of infections rather than “not available” for a given HAI. Data that are “not available” indicate that either (1) the hospital did not report the data or (2) the denominator (eg, central line days for CLABSI) is below a CMS reporting threshold. (The threshold is not to our knowledge specified by CMS.3) For C. difficile, 250 of the 306 HRRs (82%) had at least 50% of member hospitals reporting data; 47% of HRRs had at least 50% of hospitals reporting CAUTI data; 43% of HRRs had at least 50% of hospitals reporting SSI (colon) data; and 38% of HRRs had at least 50% of hospitals reporting CLABSI and MRSA data. SSI (hysterectomy) data were reported much less frequently, with only 4% of HRRs having >50% of hospitals reporting these data. As shown in Table 1, percentages were lower for the more stringent reporting thresholds (ie, higher percentages of hospitals reporting data). Outcome 2: HAI performance diversity. Table 2 shows the percentage of HRRs where at least 1 pair of hospitals had statistically different SIRs. This is an indicator of the utility of HAI data within HRRs. If a HRR has at least 1 pair of hospitals with statistically different SIRs for a given HAI, then those data are useful for distinguishing between at least 2 hospitals within that HRR. CAUTI and C. difficile had the most diversity within HRRs, with 54% and 72% of HRRs having at least 1 pair of hospitals with statistically different SIRs for these HAIs, respectively. Lower percentages were observed for the other HAIs.
discussion
We analyzed publicly available HAI data to assess whether these data are (1) available among geographically proximate hospitals and (2) can be used to compare them.
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