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/m³ increase in traffic PM_2.5, a 0.44% (95% CI: -0.04, 0.93%) increase per 1 μg/m³ increase in crustal PM_2.5, and a 1.34% (95% CI: -0.46, 3.17%) increase per 1 μg/m³ 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.