Open Access

ARTICLE

From Local Large-Scale Health Signal Inflation to Stochastic Stationarity: A Multiple-Component Risk Recalibration Framework via Intelligent Difference-in-Differences Decomposition

Marco Roccetti^*

Department of Computer Science and Engineering, University of Bologna, Bologna, Italy

* Corresponding Author: Marco Roccetti. Email:

Computer Modeling in Engineering & Sciences 2026, 147(2), 40 https://doi.org/10.32604/cmes.2026.082258

Received 12 March 2026; Accepted 05 May 2026; Issue published 27 May 2026

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.

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Keywords

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