@Article{cmes.2026.082258,
AUTHOR = {Marco Roccetti},
TITLE = {From Local Large-Scale Health Signal Inflation to Stochastic Stationarity: A Multiple-Component Risk Recalibration Framework via Intelligent Difference-in-Differences Decomposition},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/CMES/online/detail/26872},
ISSN = {1526-1506},
ABSTRACT = {Geospatial health risk signals, characterized by associations with high magnitude statistical significance, may frequently originate from circumscribed observational data streams. When these signals are fueled by massive N-size datasets, the large dimensional scale of the sample can induce a misleading interpretation of local evidence as a statistically significant risk inflation. The objective of this study is to verify whether such health risk configurations constitute geospatial structural artifacts: namely, stochastic distortions generated by the spatial information of local health repositories that, despite their massive scale, may remain fundamentally distant from broader contextual realities. To this aim, we present a mathematical framework designed to evaluate geospatial health systemic resilience by resolving local signal inflation through a structural remodelling procedure. By integrating a Recentered Influence Function (RIF) regression and a state-tuple formalization, the model expands the analytical context beyond the boundaries of the local health data, systematically hooking into broader reference frameworks (e.g., historical metrics and national-level standards). Through the application of a spatial Difference-in-Differences (DiD) decomposition, the local risk signal is partitioned into an explained component, justified by local covariates, and a structural unexplained component. This decomposition isolates the divergence between the observed phenomena and a broader baseline, revealing how the magnitude of a local health alarm can often be a function of a measurement bias internal to the data-generating process. To prove the framework’s efficacy, we conducted our analysis on a large geospatial health dataset. The analysis revealed that the initial reported alarm was the byproduct of a 32.5% structural weight deficit in the high-risk stratum of the local experimental population and, most critically, a 45.1% deficit in disease occurrences within the non-exposed local baseline compared to the national reference. This exemplar modeling has demonstrated that our proposed diagnostic and corrective framework can be a useful diagnostic tool to validate the identification of a health risk, having quantified the inversion of the original signal from an initial risk factor of 1.27 to a recalibrated 0.77 value. By isolating the structural difference between local observations and extra-local references, this methodology ensures consistency between verifiable health reality and dataset-specific outcomes, detecting and mitigating structurally inflated risk signals.},
DOI = {10.32604/cmes.2026.082258}
}