Optimizing Signal Integrity and External Robustness in High- Dimensional Health Registries: A Two-Tiered Computational Auditing Approach

Ensuring the stochastic stability and external robustness of medical data-driven solutions under baseline uncertainty remains a critical challenge in high-dimensional health systems. Tracking macroscopic trends offers an immediate, high-impact approach to evaluating the real-world evolution of epidemiological phenomena over time via parameters of primary importance (e.g., deaths count). However, modern predictive monitoring platforms require more sophisticated, optimized computational instruments to determine at a micro-scale whether an initial temporal or localized spatial signal is destined to amplify, dissipate, or undergo structural transitions as it progresses across different populations and spaces. To address this operational need, this paper introduces the second tier of a novel, two-tiered computational auditing framework designed to optimize the integrity of health risk associations extracted from N-size administrative health repositories. Building upon a preliminary experience in developing a first macro-scale tier able to identify biphasic COVID-19 mortality trends in Italy in the 2021–2024 period, we then focused on an advanced second-tier micro-audit mechanism. This mechanism has been designed to evaluate whether an emergent health signal represents a genuine epidemiological alert or a mathematical artifact, repurposing the econometric Blinder-Oaxaca decomposition into a log- linear diagnostic engine. This tier operates under uncertainty to evaluate signal propagation and stability, explicitly testing whether closed algorithmic optimizations (e.g., Propensity Score Matching) mask underlying baseline non-stationarity or cohort deflations. We put this framework to the test using a massive nationwide high profile health registry. In this application, our micro-audit successfully unmasked a critical baseline incidence deficit (- 45.1% of cancer cases in the reference cohort), demonstrating how an ostensibly significant initial risk signal can be deconstructed and dynamically re-calibrated against national gold standards to reveal its true, counterfactual trajectory. This scalable framework has the potential to establish a new paradigm for cross-disciplinary health data integrity and robust medical signal verification.