A Conceptual Framework Integrating Epidemiologic Measurement, Digital Detection, and Equitable Delivery to Improve Early Detection, Reduce Health Disparities, and Strengthen Population Health Outcomes

Vollständiger Abstract

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Cardiovascular disease remains the foremost cause of death nationally and globally, and its burden is widening rather than narrowing as the population ages and as the prevalence of obesity, hypertension, and diabetes continues to climb. This paper develops a conceptual framework for equitable cardiovascular prevention, termed the Measurement, Detection, and Delivery framework, that organizes three functions too often pursued in isolation into a single integrated structure. The framework rests on a synthesis of the contemporary prevention landscape across three domains. The measurement construct draws on epidemiology to locate cardiovascular risk precisely in populations and settings, with attention to the metabolic and renal pathways that now drive much of the residual burden and to the demographic, geographic, and socioeconomic concentration of that burden. The detection construct draws on translational digital-health innovation, including wearable biosensors, artificial intelligence applied to the electrocardiogram, and remote monitoring, to move the point of identification earlier in the disease course and to extend reach beyond conventional clinical pathways. The delivery construct draws on implementation science, including established frameworks for reach, adoption, fidelity, and sustainment and community-anchored delivery models, to convert detection into effective and equitable action. The framework specifies the relationships among these constructs as a sequence linked by feedback, in which measurement directs detection, detection informs delivery, and outcomes and newly generated data return to refine measurement and detection, with equity treated as a cross-cutting construct rather than an afterthought. From this structure the paper derives a set of testable propositions concerning integration, the value of earlier detection, the primacy of reach, the role of feedback, the dependence of aggregate gains on equitable performance, and the bounding effect of trust on participation. The paper then sets out how the framework can be operationalized and empirically evaluated, and it specifies the assumptions, boundary conditions, and limitations that govern its use. The central contribution is a defensible and verifiable conceptual structure that links measurement, detection, and delivery into a single model capable of guiding research, policy, and practice toward earlier identification, narrowed disparities, and stronger population health outcomes at scale.