Background: Health studies of air pollution are increasingly aiming to study associations between air pollutant mixtures and health.
Objective: Estimate associations between observed combinations of ambient air pollutants and select cardiorespiratory outcomes in Columbia, SC during 2002 to 2013.
Methods: We estimate associations using a two-stage approach. First, we identified a collection of observed pollutant combinations, which we define as multipollutant day types (MDTs), by applying a self-organizing map (SOM) to daily measures of nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and particulate matter ≤ 2.5 microns (PM2.5). Then, overdispersed Poisson time-series models were used to estimate associations between MDTs and each outcome using a 'clean' MDT referent and controlling for long-term, seasonal, and day-of-the-week trends and meteorology. Outcomes included daily emergency department visits for asthma and upper respiratory infection (URI), and hospital admissions for congestive heart failure (CHF) and ischemic heart disease (IHD).
Results: We found that a number of MDTs were significantly and positively associated (point estimates ranged from~2-5%) with cardiorespiratory outcomes in Columbia when compared to days with low pollution. Estimated associations revealed that outcomes for asthma, URIs, and IHD increased 2-4% on warm, dry days experiencing elevated levels of O3 and PM2.5. We also found that cooler days with higher NO2 pollution associated with increased asthma, CHF, and IHD outcomes (2-5%).
Conclusion: Our analysis continues support for using self-organizing maps to develop multipollutant exposure metrics and further illustrates how such metrics can be applied to explore associations between pertinent pollutant combinations and health.
Keywords: Asthma; Cardiovascular; Columbia; Kohonen; Mixtures; Multipollutant; Pulmonary; Self-organizing maps.