OPTIMIZATION MODELS FOR EQUITABLE RESOURCE ALLOCATION IN PUBLIC HEALTH SYSTEMS: A SYSTEMATIC REVIEW OF METHODS AND OUTCOMES

Public health resource allocation often relies on optimization models that prioritize efficiency, yet these approaches may reproduce existing inequities if fairness is not formally incorporated. This review examined how equity and fairness have been embedded within mathematical optimization models used for public health resource allocation in the United States and other high-income settings. We conducted a systematic review of peer-reviewed studies published between January 2021 and June 2026. Eligible studies applied formal optimization methods and embedded a quantifiable equity or fairness metric directly into the model structure. Because of substantial heterogeneity in model types, equity definitions, and public health domains, findings were synthesized narratively. Twenty-nine studies met inclusion criteria. Mixed-integer linear programming and stochastic programming were the most frequently used architectures, followed by linear, dynamic, nonlinear, and hybrid predictive-prescriptive models. Equity was operationalized in three main ways: as a structural constraint, as a co-objective, or as a parameter within the objective function. Across studies, equity constraints often improved access for underserved groups and, in many cases, produced modest or no efficiency penalties. Trade-offs were most apparent in severely scarce pandemic settings, while several chronic disease, emergency care, and infrastructure allocation studies found that equity and efficiency could be mutually reinforcing. Equity-explicit optimization is technically feasible across multiple public health applications and can often improve fairness without substantial operational compromise. The findings support broader use of optimization models that incorporate fairness directly into allocation decisions, particularly when historical inequities have shaped existing systems.