An evaluation study of direct economic losses from surgical site infections in adults: structural equation modeling.

INTRODUCTION

Surgical site infection (SSI) represents a significant postoperative complication, resulting in extended hospital stays and substantial economic burdens. Previous research on the direct economic impact of SSIs using recursive systems modeling is limited. This study aims to quantify the direct economic losses attributable to SSIs and to dissect the various factors to these losses.

METHODS

A retrospective 1:1 matched case-control study was conducted from January 2023 to March 2024 in three tertiary hospitals in Xinjiang, China. Patients with SSIs were matched on a 1:1 basis by hospital, department, age (±5 years), sex, primary diagnosis, and procedure with controls to form case and control groups. Wilcoxon Signed Ranks Test was utilized to quantify the direct economic loss from SSIs. Influencing factors were analyzed using a recursive system model.

RESULTS

Among the 74,258 patients surveyed, 226 developed SSIs, resulting in an infection rate of 0.3%. The total direct economic loss from SSIs at three hospitals was $467,867, with an average loss of $1,364.37 per SSI patient. SSI patients experienced hospital stays 11 days longer than uninfected patients. Multivariate linear regression identified the duration of hospital stay, catheter and ventilator usage, age, number of surgeries, and duration of antibiotic treatment as influencing factors. Recursive system modeling revealed the indirect contributions of the number of surgeries (indirect effect: 0.074), antibiotic use for 17-36 days (indirect effect: 0.063) and ≥ 37 days (indirect effect: 0.045), and debridement procedures (indirect effect: 0.054), as well as the direct contributions of hospital days (direct effect: 0.276), indwelling catheter days (direct effect: 0.260), ventilator days (direct effect: 0.221), and age (direct effect: 0.182).

CONCLUSION

Recursive system modeling helped identify the key factors influencing the economic losses from SSIs. These findings provide a theoretical basis for healthcare departments to develop targeted policies.