Applying principal component pursuit to investigate the association between source-specific fine particulate matter and myocardial infarction hospitalizations in New York City

The association between fine particulate matter (PM(2.5)) and cardiovascular outcomes is well established. To evaluate whether source-specific PM(2.5) is differentially associated with cardiovascular disease in New York City (NYC), we identified PM(2.5) sources and examined the association between source-specific PM(2.5) exposure and risk of hospitalization for myocardial infarction (MI). METHODS: We adapted principal component pursuit (PCP), a dimensionality-reduction technique previously used in computer vision, as a novel pattern recognition method for environmental mixtures to apportion speciated PM(2.5) to its sources. We used data from the NY Department of Health Statewide Planning and Research Cooperative System of daily city-wide counts of MI admissions (2007–2015). We examined associations between same-day, lag 1, and lag 2 source-specific PM(2.5) exposure and MI admissions in a time-series analysis, using a quasi-Poisson regression model adjusting for potential confounders. RESULTS: We identified four sources of PM(2.5) pollution: crustal, salt, traffic, and regional and detected three single-species factors: cadmium, chromium, and barium. In adjusted models, we observed a 0.40% (95% confidence interval [CI]: –0.21, 1.01%) increase in MI admission rates per 1 μg/m(3) increase in traffic PM(2.5), a 0.44% (95% CI: –0.04, 0.93%) increase per 1 μg/m(3) increase in crustal PM(2.5), and a 1.34% (95% CI: –0.46, 3.17%) increase per 1 μg/m(3) increase in chromium-related PM(2.5), on average. CONCLUSIONS: In our NYC study, we identified traffic, crustal dust, and chromium PM(2.5) as potentially relevant sources for cardiovascular disease. We also demonstrated the potential utility of PCP as a pattern recognition method for environmental mixtures.