Structural Reliability in Large-Scale Health Data: A Difference-in-Differences Framework for Local-to-Global Calibration

This study proposes a framework for analyzing the structural reliability of geospatial health data by measuring the integrity of the link between local observations and global contexts. We define a health system state through a tuple T = < L, C, D, E, NE, OBS, RF, RRF >, where Large-Scale Local observations (L) must be calibrated against a global national baseline (C) to ensure inferential integrity. In this architecture, we contrast an exposed experimental population (E) against a non-exposed (NE) group within a localized domain. By operationalizing four mathematical operators: a Total System Gap, a Relative Weighting Gap, a Difference-in-Differences function, and a Recentered Influence Function, we evaluate the structural stability of risk signals. Our framework demonstrates its efficacy by identifying a critical 32.5% weighting deficit in the local elderly stratum compared to the global standard for a very exemplar case. Significantly, the application of our spatial Difference-in-Differences function uncovered an 18.2% asymmetric divergence in disease incidence between the local NE and E groups relative to national benchmarks. This systemic decoupled state proves that the initially reported risk of the investigated example was a byproduct of internal structural fractures, specifically a -45.1% case deficit in the local NE arm, rather than a signal extensible to the global context. We demonstrate that without local- to-global calibration, massive geospatial health data can produce phantom signals indistinguishable from structural errors.