Trends between PM2.5 and Ozone and Respiratory Illnesses

Asthma is a chronic respiratory condition that severely impacts the lives of those who suffer from it. Its impact on both health and productivity is dramatic, not just for the individuals impacted, but for society as a whole. In Baltimore, it’s estimated that over 12% of adults suffer from asthma - compared to 8.4% state-wide and 8.6% nationally. The socioeconomics of Baltimore pose a unique challenge for asthma treatment - the World Health Organization notes that asthma disproportionately impacts low and middle income communities. The Center for Disease Control and Prevention is responsible for tracking the incidence and spread of medical diseases nationwide. In their work, they create a detailed history over time of medical conditions and make that data available to the public. We correlate this health record data with air quality data provided by the Environmental Protection Agency over the same time periods to explore the influence of air pollution on lung related disease in Baltimore. Specifically we attempt to correlate weekly aggregate PM 2.5 levels to the weekly reporting of pneumonia and asthma hospitalizations. The hypothesis is that lung related issues would be more noticeable during times of high air pollution, and as a result, people would be more likely to be hospitalized shortly after (within the weekly reporting cadence). Significant correlations appear for both pneumonia and asthma over large periods of time (and thus significant sample sizes); we performed these calculations over the period of a full year. For pneumonia, we also saw the same correlations repeat at both the state (Maryland) and the city (Baltimore) over different years (2019 and 2016, respectively). We note that such a correlative test alone doesn’t show causality- other factors that varied over that same time period, such as local temperature and weather, could have influenced both variables simultaneously. We believe further work in this area is required to identify and isolate other potential compounding variables.