Structural Risk Recalibration and Stochastic Stationarity in Localized Large-Scale Health Data: An Intelligent Difference-in-Differences Framework

This study proposes a framework for analyzing the reliability of spatial health data through structural system integrity. We define a health system state through a tuple S = , where local observations (L) must be calibrated against a national contextual baseline (C) to ensure inferential integrity. In this architecture, a portion of the experimental population (E) is exposed to a specific determinant (D), compared to a non-exposed (NE) group. By applying four mathematical operators, a total system gap (Delta), a relative weighting gap (G_w), a structural difference-in-differences function (DiD), and a recentered influence function (RIF), we executed our context-aware framework on a large-scale health-disease incidence local dataset relative to its broader spatial context. Our framework demonstrated its efficacy by extrapolating, in the example of interest, a critical 32.5% G_w, indicating an under-representation of the older population within the local space compared to the national context. Significantly, the application of DiD uncovered a 18.2% asymmetric divergence of the observed disease incidence in the NE (non-exposed) group compared to E, relative to national benchmarks. The subsequent RIF recalibration failed to reach a contextual leverage, proving that the initially calculated risk factor (RF) was an outcome of internal fractures rather than a signal emitted by the health system. The use of our framework proved that an unbalanced choice of the experimental population (marked by a -45.1% deficit in the older NE arm) had rendered the initial calculation of risk structurally unstable. We demonstrate that without contextual calibration, geospatial large scale health data can produce phantom signals indistinguishable from systemic errors.