Time-series analysis of vitiligo-related online search behavior in response to ambient air pollutants

Ambient air pollutants are hazardous materials posing significant risks to global public health. While their impacts on respiratory and cardiovascular systems are well-established, their role in triggering specific autoimmune skin diseases remains under-explored. This study investigates the temporally resolved public health response to air pollutant exposure, using the autoimmune condition vitiligo as a sensitive health endpoint. We aimed to evaluate the differential public health responses to six major air pollutants by analyzing their lagged effects on vitiligo-related online search behavior, using the Baidu Search Index (BSI) as a novel proxy for public concern and health-seeking activity. In this ecological time-series study, we applied a Distributed Lag Non-linear Model (DLNM) to daily data on BSI, air pollutants, and meteorological factors in Changsha, China (2019-2021). The findings reveal distinct, pollutant-specific temporal patterns of public response, suggesting different toxicological pathways. Exposure to industrial pollutants like sulfur dioxide (SO₂) prompted an immediate, acute increase in search behavior. In contrast, traffic-related pollutants such as carbon monoxide (CO) were associated with a more persistent, long-term response. These associations remained robust in two-pollutant models. Short-term exposure to hazardous air pollutants is associated with significant changes in public health-seeking behavior. The study highlights the value of infodemiology as a near real-time surveillance tool for monitoring population-level responses to environmental hazards and provides new evidence on the differential health impacts of specific pollutants on autoimmune skin conditions.