Beyond time-series counts: reframing the short-term association between exposure to air pollution and mortality using the novel repeated acute exposure (RAE) design.

Assessing the robustness of epidemiological associations requires diverse methodological approaches. This study introduces the Repeated Acute Exposure (RAE) design, a survival-based framework utilising an extended Cox model with time-varying coefficients to evaluate the short-term association between PM10 and daily mortality in Valencia, Spain. By incorporating a time-varying coefficient to capture the dynamic intensity of the risk across a 7-day window, this approach conceptualises every exposure increment as contributory to the mortality risk. We adjusted for temperature, relative humidity, and day of the week, incorporating a Ridge penalty for the date to account for the structural dependency and redundancy of the lag-stratified data. The analysis yielded a Hazard Ratio of 1.025 (95% CI: 1.009-1.040) per 10 µg/m3 increase in PM10. While numerically analogous to the Relative Risk (RR), the HR specifically reflects the instantaneous risk profile. The estimated effect size was consistent in direction and significance of the association with the one obtained using a Generalised Additive Model (GAM)-based distributed lag non-linear model (RR: 1.035; 95% CI: 1.005-1.066), and other time-series methods. The RAE design offers a rigorous sensitivity tool for validating associations through methodological triangulation. The dataset and R script for reproducibility are available on Zenodo at https://doi.org/10.5281/zenodo.19115821.