ABSTRACT Of the more than one million Americans that sustain an extremity fracture requiring surgical treatment each year, over 40,000 patients will endure a subsequent surgical site infection (SSI). The impact of these avoidable healthcare-associated infections (HAI) on patient income is unknown. The overarching goal of this research is to determine the effect of a surgical site infection (SSI) on patient income. By linking hospital data with State tax data, this study will 1) determine the loss in patient income associated with a deep SSI following a traumatic extremity fracture and 2) describe the heterogeneity in effect of short- and long-term patient income loss after a post-fracture SSI based on policy-relevant subgroups. Propensity scores will be used to balance key covariates in patients exposed to an SSI with unexposed patients. Random-effects models will be used for a longitudinal analysis to estimate the effect of a deep SSI on patient income. Subgroup analyses will be used to identify patient attributes, based on AHRQ's Priorities Populations, that are associated with an increased risk of economic loss. Secondary models will censor outcomes at one-, two-, and five-years post- injury to determine the average economic effect at these time points. The study team has substantial experience assessing the economic impact of injury and previous experience analyzing the clinical data in this proposed study. Understanding the full breadth of the economic impact of these adverse events is necessary to build the case for commensurate patient safety resources and the design of incentives within reimbursement models. Determining the subgroups that suffer the greatest economic impact of an avoidable HAIs is critical to the design of specific strategies that mitigate these consequences, such as direct provider reimbursement for social interventions. Considering the high incidence of fractures in the United States and the increasing number of surgical procedures performed each year, this study will be critical to benchmarking improvements in patient safety based on patient economic measures.