surgical site infections and warm weather 811


comorbidity index (29 categories),27 admission month, and admission year. In addition, at the hospital level, our firstmodel controls for region (ie, northeast, midwest, west, and south), longitude, and latitude. Our second model is a “weather model” that controls for the same covariates as the demographicsmodel, and it adds the average monthly temperature (in 2.8°C [5°F] steps from <4.4°C [<40°F] to >32.2°C [>90°F]). The other weather covariates were very highly correlated with average monthly temperature in the model and were not included.


results


The NIS contains 108,595,896 hospitalizations from 4,532 hospitals over the course of our study (0.368% with a primary SSI). We observed 9,474,937 discharges with surgeries that could potentially lead to an SSI. In the time series models,


table 1. Sample Size Filter None


Nonmissing Admission Month Sex


Length of Stay Payer Age


Age ≥18 y Address listed


we excluded 65,485 SSIs and 850,510 surgeries due to missing admissionmonth or discharge weight. For our logistic regression models, the sample sizewas 55,665,828 (2,512 unique hospitals). Exclusion criteria are summarized in Table 1. In Figure 1, we show the monthly incidence of SSI hospitali-


Sample Size 108,595,896


98,435,410 98,252,484 98,246,157 97,971,752 97,957,295 81,174,170 55,665,828


Initial Sample, % 100


90.64 90.48 90.47 90.22 90.20 74.75 51.26


zations. The nadir month for SSIs was January and the peak month was August. After controlling for a linear time trend, the average seasonal increase (between January and August) was 2,312 infections (95% CI, 2,071–2,553). This corresponds to an increase of 26.5% (95% CI, 23.3%–29.7%). The overall test for seasonality was statistically significant (P<.001). Adjusting for seasonality, the number of SSIs increased by 4,274 cases per year (95% CI, 3,541–5,007),which corresponds to an increase of 3.9% peryear(95%CI, 3.0%–4.8%). After adding the loggedmonthly series of SSI-prone surgeries into themodel as a covariate,we first noted that the seasonality lessened slightly to 23.56% (95% CI, 20.6%–26.6%), and the trend became less prominent at 0.16% growth peryear(95%CI, −0.52% to 0.85%). Using this model, we then estimated that a 25% reduction in the average number of at-risk surgeries in themonths of August and July was associated with a decrease of ~1,690 SSI cases for the year (a decrease of 20.6% from the observed SSI rate). The annual trend and the average increase in the peak


month for each subgroup considered are presented in Table 2. Seasonality and incidence were similar across all regions, age groups, genders, and hospital teaching categories. Seasonality was greatest among patients aged in their 40s and 50s. In addition, the seasonality of SSIs was very prominent for both teaching and nonteaching hospitals, and there was no significant difference between the 2 groups of hospitals:


figure 1. Hospitalizations with a primary or a secondary diagnosis of a surgical-site infection: absolute-scale model (top) and relative- scale (log-transformed) model (bottom). We controlled for the error structure in each model using an AR(2) error structure.


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