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


sample of hospitalizations. The second objective was to determine the extent to which seasonality in the incidence of SSIs can be explained by local weather conditions.


methods Data Extraction


All discharge data were extracted from the Nationwide Inpatient Sample (NIS), the largest all-payer database of hospital discharges in the United States. The database is maintained as part of the Healthcare Cost and Utilization Project (HCUP) by the Agency for Healthcare Research and Quality, and it contains data from a 20% stratified sample of nonfederal acute-care hospitals. Observational studies using deidentified data, such as this one, are deemed exempt by our institutional review board. We identified every adult hospitalization with a primary


diagnosis of SSI from January 1998 to November 2011. For case ascertainment, we used the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes 998.51 and 998.59. To estimate a monthly SSI incidence series, we aggregated the number of primary SSI discharges by admission month and year. We applied discharge weights to account for yearly changes in the sampling design, and we applied additional weights to account for changes in the number of days per month. The NIS does not include unique identifiers to allow the


tracking of patients across visits, for example, to determine whether a surgery in one visit resulted in a readmission in a subsequent visit. Thus, we also extracted adult hospitalizations with a primary or secondary procedure likely to be associated with an SSI to estimate a population “at-risk” for SSIs. We used this series to ensure that any findings on the seasonality of SSI were not merely a reflection of a lower surgical volume concurrently or in the month prior. Hospitalizations were identified using Clinical Classification software (CCS) codes developed byHCUP.Weincluded the following codes: 152 (knee arthroplasty), 153 (hip replacement, total and partial), 158 (spinal fusion), 147 (treatment of fracture or dislocation of lower extremity), 78 (colorectal resection), 75 (small bowel resection), 134 (cesarian section), 85 (inguinal and femoral hernia repair), 86 (another hernia repair), and 87 (exploratory laparotomy). To estimate this monthly surgery incidence series (ie, the at-risk series), we aggregated cases by admission month and year and applied discharge and days-per-monthweights.Finally,we calculated the number of patients at risk for an SSI in a given month by taking an average of the number surgeries in that month and the number of surgeries in the prior month.


Time Series Analysis


The adjusted SSI incidence series was fit with a linear time trend and a collection of fixed effects (ie, indicator variables) that represent monthly mean deviations from the overall trend. The cyclic nature of the series was captured by the monthly


fixed effects. We also explored adding a covariate to this model for the log of the at-risk series. To account for temporal correlation in the residuals, we investigated autoregressive structures of orders 1 through 4.Weselected the order for each series based on the Bayesian information criterion (BIC) and upon inspection of the autocorrelation function and the partial autocorrelation function plots. In the regression equation, the coefficient for the peakmonth can be interpreted as the “average amplitude of seasonality” adjusted for the other covariates. Similar analyses were performed on the log-transformed series, which facilitated a percentage interpretation of model coefficients. An overall test for seasonality was computed using a likelihood ratio test on the 11 monthly fixed effects. All analyses were performed using R 3.1.2 and SAS 9.4 (SAS Institute, Cary, NC).


Subgroup Time Series Analysis


We performed subgroup analyses stratified by region (north, south, east, and west), gender, age (grouped by decade), institutional teaching status (teaching/nonteaching), and institutional location (urban/rural). For each subgroup, we calculated the average amplitude of seasonality and the annual trend on the log-transformed count series to allow for easy comparison. The autoregressive structures for all subgroups were individually selected based on BIC.


Weather Data


Hospitals in the study were geolocated using the Google Maps Geocoding application program interface and the American Hospital Association address.25 Weather data were obtained from unedited local climatological data (1998–2004) and quality-controlled local climatological data (2005–2011). Both datasets were reported by the National Climatic Data Center of the National Oceanic and Atmospheric Administration. Using each hospital’s longitude and latitude, we identified


all weather stations within 100 km of the hospital, then we extracted the following monthly summary statistics from these stations: average temperature, minimum temperature, max- imum temperature, total precipitation, average dew point, average wet bulb temperature, average heating degree days, average cooling degree days, resultant wind speed, and total monthly precipitation. The summary statistics for hospitals with multiple nearby stations were averaged across stations, whereas the summary statistics for hospitals with no nearby stations (1.9%) were imputed using k nearest neighbors (k=5) and the caret package in R.26


Logistic Regression Models


We used logistic regression to estimate the odds of a hospital discharge having a primary diagnosis of SSI using 2 different models. Our first model is a “demographics-only model” that controls for the following patient-level covariates: age (grouped by decade), sex, primary payer, length of stay, Elixhauser


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