Temporal Variations in the Association between Short-Term Exposure to PM 2.5 , Its Components, and Ischemic Stroke Onset: A Nationwide Case-Crossover Study of 1.62 Million Patients

Approximately 16% of stroke-related deaths can be attributed to PM2.5, and extensive evidence has consistently linked short-term exposure to PM2.5 to an increased risk of ischemic stroke onset. However, most previous studies rely on the assumption of a constant impact of PM2.5 over time, despite emerging evidence on the time-variant nature of PM2.5-associated health effects. This study aims to assess the temporal changes in the association between PM2.5 and its components and the risk of ischemic stroke. We employed the case-crossover design and included 1,625,763 participants from the Chinese Stroke Center Alliance (CSCA) program. Conditional logistic models were used to estimate the acute effect of PM2.5 and its components on the ischemic stroke onset. Temporal changes in PM2.5 and its components were evaluated across two periods (2015–2017 and 2018–2022) and annually. A 10 μg/m3 increase in PM2.5 at lag 0 day demonstrated a stronger association with ischemic stroke during Period 1 (percent change in risk: 0.54% [0.42 to 0.65%]) compared to Period 2 (0.29% [0.19 to 0.38%]), indicating a significant decline of −0.25% (−0.40 to −0.10%). This decreasing trend was consistent across five PM2.5 components, particularly for black carbon (BC), which exhibited the largest reduction (−4.47% [−7.47, −1.38%]). Nonlinear exposure-response curves for PM2.5 and its components also exhibited a steeper trend in Period 1. By integrating individual ischemic stroke onset data from over 2,600 hospitals across China, our study demonstrates a declining trend in the health effects of PM2.5 and its chemical components on ischemic stroke onset. These findings highlight the importance of accounting for temporal variations in PM2.5-related health effects when estimating disease burden, designing and evaluating air pollution control policies, and informing public health decision-making.