A case-based complexity approach to health inequality: Understanding and tracing place-based differences to enhance policy calibration

, offering a basis for investigating how socio-spatial processes shape health inequalities over time. We argue that reducing health inequalities requires a shift away from narrowly targeted interventions toward configurational-informed, multi-level governance. This includes recognising the interdependence of places, anticipating cross-cluster effects, and embedding adaptive feedback mechanisms in policy design. The paper also develops a CBC rubric to build on and enhance the analysis provided here. In so doing, our framework also supports a proportionate universalism that is locally calibrated while systemically coherent. By combining a complexity-informed, configurational-based, machine learning set of methods, this paper demonstrates how CBC is a conceptual and methodological advance on policy-relevant approaches for addressing persistent and embedded health inequalities across place.