RESUMEN
Background. Antimicrobial resistance (AMR) is a global public health threat. The wider implications of AMR, such as the impact of antibiotic resistance (ABR) on surgical procedures, are yet to be quantified. The objective of this study was to produce a conceptual modeling framework to provide a basis for estimating the current and potential future consequences of ABR for surgical procedures in England. Design. A framework was developed using literature-based evidence and structured expert elicitation. This was applied to populations undergoing emergency repair of the neck of the femur and elective colorectal resection surgery. Results. The framework captures the implications of increasing ABR by allowing for higher rates of surgical site infection (SSI) as the effectiveness of antibiotic prophylaxis wanes and worsened outcomes following SSIs to reflect reduced antibiotic treatment effectiveness. The expert elicitation highlights the uncertainty in quantifying the impact of ABR, reflected in the results. A hypothetical SSI rate increase of 14% in a person undergoing emergency repair of the femur could increase costs by 39% (-2% to 108% credible interval [CI]) and decrease quality-adjusted life-years by 11% (0.4% to 62% CI) over 15 y. Conclusions. The modeling framework is a starting point for addressing the implication of ABR on the outcomes and costs of surgeries. Due to clinical uncertainty highlighted in the expert elicitation process, the numerical outputs of the case studies should not be focused on but rather the framework itself, illustration of the evidence gaps, the benefit of expert elicitation in quantifying parameters with limited data, and the potential magnitude of the impact of ABR on surgical procedures. Implications. The framework can be used to support research surrounding the health and cost burden of ABR in England. Highlights: The modeling framework is a starting point for assessing the health and cost impacts of antibiotic resistance on surgeries in England.Formulating a framework and synthesizing evidence to parameterize data gaps provides targets for future research.Once data gaps are addressed, this modeling framework can be used to feed into overall estimates of the health and cost burden of antibiotic resistance and evaluate control policies.