814 infection control & hospital epidemiology july 2017, vol. 38, no. 7


figure 3. Monthly odds ratios for a primary surgical-site infection for both our demographics-only model and our weather model. Weather explains a portion of the seasonality in primary SSI admissions.


By incorporating weather into our analysis, we have demon- strated that the average temperature in the month of a hospi- talization is an important risk factor for SSIs and that higher temperatures are associated with higher odds of SSI. We observed a slight annual increase in the number of SSIs, though this became insignificant after controlling for the volume of procedures. The incidence of many infections is seasonal.28 Respiratory


infections peak during winter months and tick- and mosquito- borne infections peak during the summer. Less attention has been focused on the seasonality of healthcare-associated infections. However, reports show evidence of seasonality in the incidence of Clostridium difficile infections, with cases peaking during winter and spring,29–31 and in the incidence of catheter-related bloodstream infections peaking during sum- mer months32,33 along with urinary tract infections34,35 and cellulitis.36 A few reports of seasonal SSIs exist, but most of these were either in single centers over short time periods or were focused on a specific geographic region, and few incor-


poratedweather data into their analysis. Nevertheless, previous findings are similar to ours. Kane et al23 found the highest incidence of SSIs following total joint arthroplasties in August, with the majority occurring during July–September. Both Durkin et al22 and Gruskay et al37 found an increased rate of infection after elective spine surgery during the summer months. Assessing a more generalized group of patients who underwent various procedures, Durkin et al12 also reported a seasonal effect on SSI with summer months demonstrating higher SSI rates. Unlike prior studies, we included a large population: 20% of all hospital discharges over a long period of time and across different geographic regions. In addition to establishing statistical significance in the seasonality of primary admissions for SSI, our results also demonstrate the potential clinical significance of this seasonality. For example, in our multivariate model controlling for patient demographics, severity, and hospital location, the increase in odds of an SSI admission during an especially warm August relative to a cold


January reaches a peak of 55.6%, double the effect of diabetes (26.9%). Our results also demonstrate the clinical impact of this seasonality. For example, a 25% reduction in surgical cases in the peak months is associated with a >20% reduction in SSIs. Thus, if some elective surgeries are moved from the very warm summer months to other months, we may be able to reduce both infections and healthcare costs. The reason that SSIs peak in the summer is unclear. However, the incidence of other skin and soft-tissue infections have been reported to be seasonal.38–40 Elevated levels of bac- teria may be found in certain anatomic locations with higher temperatures.41 Regardless of the specific mechanism, we believe that the seasonality of SSIs is, in large part, driven by weather conditions. In a logistic regression model of the incidence of SSIs, we explained approximately 35% of the seasonal variation by including average monthly temperature data. By including more granular data regarding the incidence of SSIs and weather, we may be able to explain an even larger amount of the seasonality. Some reports suggest that surgical complications such as SSI


could be due to a “July effect,” explained as staff turnover at teaching institutions.42 However, previous authors identified an increase in SSI in patients undergoing spine procedures during the summer months at a regional collection of non- teaching hospitals.22 Similarly, we found no significant differ- ences in the amplitude of seasonality of SSIs between teaching and nonteaching institutions. In addition, we added an inter- action between hospital teaching status and month to our logistic regression model, and the result was nonsignificant (data not shown.) Thus, the August peak incidence of SSI we report is not likely to be attributable to trainees involved with surgical procedures. Finally, it is possible that the seasonal incidence of SSIs could be due to seasonal variations in surgical volume because most SSIs occur within 30 days of the surgery. However, in our time-series model, we controlled for the number surgeries performed in the current and prior months to adjust for surgical volume as a confounding factor in the seasonality of SSIs, yet the seasonality in the series was still highly significant. Our results are subject to several limitations. First, our


analyses were based on the month of the primary admission for SSI, not the procedure that precipitated the SSI. We cannot link admissions for SSIs to admissions for specific procedures because the NIS data do not provide a unique identifier to link patient visits across hospitalizations. Thus, our analysis considers all SSIs together, and we were unable to determine the SSI seasonality for different procedures. Secondary admissions for SSI are also seasonal (data not shown), and some secondary admissions may have occurred during the surgical admission. Second, we used administrative data, (eg, ICD-9 codes) to identify SSIs, and we were unable to do chart reviews. Our data do not include microbiology or medication- administration data. Comparisons of SSI codes to traditional forms of SSI highlight the limitations of using ICD-9 codes.43 However, the sensitivity and specificity of these codes have


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