Contrasting seasonality of the incidence of incisional surgical site infection after general and gastroenterological surgery: an analysis of 8436 patients in a single institute

Background: While seasonality of hospital-acquired infections, including incisional SSI after orthopaedic surgery, is recognized, the seasonality of incisional SSI after general and gastroenterological surgeries remains unclear. Aim: To analyse the seasonality and risk factors of incisional SSI after general and gastroenterological surgeries. Methods: This was a retrospective, single-institute, observational study using univariate and multivariate analyses. The evaluated variables included age, sex, surgical approach, surgical urgency, operation time, wound classification, and the American Society of Anesthesiologists physical status (ASA-PS). Findings: A total of 8436 patients were enrolled. General surgeries (N = 2241) showed a pronounced SSI incidence in summer (3.9%; odds ratio (OR): 1.87; 95% confidence interval (CI): 1.05-3.27; P = 0.025) compared to other seasons (2.1%). Conversely, gastroenterological surgeries (N = 6195) showed a higher incidence in winter (8.3%; OR: 1.38; 95% CI: 1.10-1.73; P = 0.005) than in other seasons (6.1%). Summer for general surgery (OR: 1.90; 95% CI: 1.12-3.24; P = 0.018) and winter for gastroenterological surgery (1.46; 1.17-1.82; P = 0.001) emerged as independent risk factors for incisional SSI. Open surgery (OR: 2.72; 95% CI: 1.73-4.29; P < 0.001) and an ASA-PS score >= 3 (1.64; 1.08-2.50; P = 0.021) were independent risk factors for incisional SSI in patients undergoing gastroenterological surgery during winter. Conclusion: Seasonality exists in the incisional SSI incidence following general and gastroenterological surgeries. Recognizing these trends may help enhance preventive strategies, highlighting the elevated risk in summer for general surgery and in winter for gastroenterological surgery. (c) 2024 The Healthcare Infection Society. Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.