SCH: Pennsylvania Asthma-COPD Syndromic Surveillance (PASS) - Outdoor particulate matter, ozone, and first- or second-hand cigarette smoke collectively afflict over 545 million people globally, with approximately equal distribution between chronic obstructive pulmonary disease (COPD) and asthma cases [1]. As of 2020, Pennsylvania exhibited the highest excess mortality due to air pollution nationwide [2]. Lehigh Valley in eastern Pennsylvania, particularly Allentown, represents one of the most significantly impacted metropolitan regions in the nation, with one of the highest asthma risk profiles in the US [2]. However, there is a notable absence of an early-warning system for environmentally attributable risks for lower respiratory infection, asthma, COPD, and the co- occurrence of asthma and COPD, referred to herein as chronic respiratory syndrome (CRS) for at-risk populations. Furthermore, differentiating between emergent risks (i.e., air pollution-attributable) and baseline risks (i.e., built environment, access, and economic factors) represents a critical advancement in addressing respiratory health needs for vulnerable populations. Our overarching goals focus on developing an intelligent and agile early-warning system for two primary stakeholders: general citizens who can visualize CRS risks through geospatial mapping interfaces, and clinical providers in local healthcare settings who can optimize patient flow management through advanced outbreak prediction algorithms. Our proposed Pennsylvania Asthma- COPD Syndromic Surveillance (PASS) establishes a responsive data analytics infrastructure capable of distinguishing emergent environmental risks (e.g., outdoor air pollution) from underlying health vulnerabilities in geographically susceptible areas, integrating multiple publicly available secondary data streams to enhance public health protection measures. This study represents the first comprehensive analysis of how Pennsylvania and neighboring states' recurrent air pollution episodes and variable weather patterns contribute to outbreaks of CRS-related in-/outpatient visits. Accordingly, we will develop a novel framework for identifying CRS exacerbation-prone regions through data-driven geospatial models (AIM 1). We will quantify the burden of in- /out-patient CRS visits attributable to poor air quality after accounting for other confounding variables. (AIM 2). Finally, we will develop advanced syndromic surveillance systems, capable of detecting the onset of CRS outbreaks across zip code tabulation areas by integrating real-time air quality monitoring data with population susceptibility indicators, thereby enabling more targeted public health prevention, interventions, and resource allocation (AIM 3).
