1174 infection control & hospital epidemiology october 2017, vol. 38, no. 10 The quality of outcome reporting dataset comprising all


randomly drawn cases (ie, cases with and without infection, as classified by the hospital) from each visited hospital yielded cases that fell into 4 categories: (1) cases reported by hospital and identified by Swissnoso validation staff as SSI cases (true positives); (2) cases not reported by hospital and ruled out as SSI cases by Swissnoso validation staff (true negatives); (3) cases reported by hospital but ruled out as SSI cases by Swissnoso validation staff (false positives); and (4) cases not reported by the hospital but identified as SSI cases by Swissnoso validation staff (false negatives). From these num- bers, sensitivity, specificity, positive predictive value, and negative predictive value with 95% confidence intervals were calculated for the overall data set, with the exception of cases with incomplete information at the time of the validation visit. All statistical analyses were performed using Stata 14.2


software (StataCorp, College Station, TX), and 2-sided P values <.05 were considered statistically significant.


Sample Size Estimation


In a preceding sample-size estimation, a randomsample of 913 patient records was considered necessary to achieve a 95% confidence interval for sensitivity of 5% when an overall SSI prevalence of 8% and a sensitivity of 95% were assumed.25


results


Between October 1, 2012, and June 26, 2016, all 147 hospitals or hospital units that participated in the surveillance and had sub- mitted cases by October 31, 2015, were visited and audited in 25 of 26 Swiss cantons.Overall, 107 hospitals (72.8%)were from the German-speaking region of Switzerland; 31 (21.1%) were from the French-speaking region, and 9 (6.1%) were from the Italian- speaking region. 96 (65.3%) werepublichospitals or hospital units, and 9 (6.1%) were university affiliated. Furthermore, 87 (59.2%) hospitals participated in the surveillance for more than 3 years at the time of validation (median time of participation: 3.4years;range,0.8–15.8 years).


Structure and Process Validation


The characteristics of the 147 surveillance teams are shown in Table 1. The 2 surgical procedures that are most strongly represented are colon surgery (followed by 70.8% of validated hospitals) and hip prosthesis surgery (69.4%). Understaffing was noted in 34.7% of the surveillance teams, and 35.4% of medical supervisors had not undergone the required struc- tured training in the surveillance methodology. Conflicts of interest (ie, surveillance supervised by a member of the surgical team) were detected among 11.6% of medical supervisors. Table 2 depicts the 9 domains assessed in the process


validation score, their individual scores and weights, and the unweighted mean score per domain among the 147 hospitals or hospital units. The overall mean score was 34.85 points


table 1. Surgical Interventions Included in the Validation and Followed by the147 Audited Surveillance Teams, and Team Characteri- stics, October 1, 2012, to June 26, 2016


Variable


Surgical procedures followed, No. (%) Appendectomy Colon surgery Rectum surgery Cholecystectomy Herniorrhaphy


Gastric bypass surgery Caesarian section Hip prostheses Knee prostheses Cardiac surgery


Understaffing compared to volume of operations included, No. (%)


Specialization of medical supervisor Internal medicine alone, No. (%)


Infectious diseases with or without internal medicine, No. (%) Surgery, No. (%)


Anesthesiology, No. (%) Other, No. (%)


Conflict of interest of medical supervisor, No. (%) 17 (11.6)


Inadequate training of medical supervisor in the surveillance methodology, No. (%)


(standard deviation [SD], 6.95 points), with a median of 35.5 points (range, 16.25–48.5 points) for a maximum of 50 points (Figure 1). The 2 domains that contributedmost to lower scores were ‘follow-up during hospitalization’ (weighted mean differ- ence from maximum score, 3.97 points; SD, 2.30 points) and ‘data quality of eCRF compared to original data’ (weighted mean difference from maximum score: 3.22 points; SD, 1.64 points). The associations between hospital status, language


region, duration of participation in the surveillance program, and hospital size with scores within individual domains are depicted in Table 3. In multivariate linear regression analysis, public hospital status (P<.001), Italian-speaking region (P=.021) and duration of participation in the surveillance programs (P=.018) were associated with higher validation scores, whereas hospital size was not. The number of full-time equivalents dedicated to surveillance was neither associated with the overall score nor with scores of individual domains.


SSI Outcome Validation


A total of 1,110 randomly selected clinical cases (Dataset A, ie, irrespective of the presence or absence of SSI) with complete


Value


62 (42.2) 104 (70.8) 15 (10.2) 49 (33.3) 49 (33.3) 10 (6.8) 46 (31.3) 102 (69.4) 66 (44.9) 12 (8.2)


No. of procedures included per year, median (range) 300 (15–2,000) Full-time equivalents dedicated to surveillance, median (range)


Adequate professional background of persons performing surveillance, No. (%)


0.2 (0.01–1.3) 51 (34.7) 134 (91.2)


32 (21.8) 84 (57.1)


9 (6.1)


17 (11.6) 5 (3.4)


52 (35.4)


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