Mixture-Aware Air-Pollution Epidemiology and Suicide Risk

Abstract

What evidence is needed to determine how individual pollutants and their mixture should be analyzed when exposure is correlated and the outcome is acute? This review answers by treating mixture-aware case-crossover inference as a property of a sociotechnical workflow rather than a feature that can be read from average accuracy. The focal setting is short-term ambient pollution exposure and suicide mortality in a population surveillance system, where correlated exposures, model flexibility, and residual confounding can turn an association into an unjustifiably specific causal story. Evidence from the assigned publications is synthesized with foundational studies of calibration, distribution shift, causal structure, and responsible deployment. Four requirements follow: preserve the lineage of exposure measurements, health outcomes, temporal or cohort structure, covariates, mixture models, and sensitivity analyses; measure stability across relevant perturbations; connect confidence to a specific action; and maintain a route for human challenge and correction. The framework distinguishes descriptive performance from decision utility and separates uncertainty about the world from uncertainty created by the model and its evaluator. It also shows why faster inference or richer reasoning is valuable only when it improves a defined decision under a transparent resource budget. The article is a literature review and research agenda, not a report of a newly completed trial.