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

Abstract

Background: The association between fine particulate matter (PM2.5) and cardiovascular outcomes is well established. To evaluate whether source-specific PM2.5 is differentially associated with cardiovascular disease in New York City (NYC), we identified PM2.5 sources and examined the association between source-specific PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 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 PM2.5, a 0.44% (95% CI: –0.04, 0.93%) increase per 1 μg/m3 increase in crustal PM2.5, and a 1.34% (95% CI: –0.46, 3.17%) increase per 1 μg/m3 increase in chromium-related PM2.5, on average. Conclusions: In our NYC study, we identified traffic, crustal dust, and chromium PM2.5 as potentially relevant sources for cardiovascular disease. We also demonstrated the potential utility of PCP as a pattern recognition method for environmental mixtures.

Exposome Profiling of Environmental Pollutants in Seminal Plasma and Novel Associations with Semen Parameters.

“…Dimension Reduction via Unsupervised Machine Learning Pattern Recognition: Principal Component Pursuit (PCP) is a robust method for dimensionality reduction and pattern recognition 49,50 . Its theory and application to environmental health research has been described previously 20,21 . In brief, we can consider any set of exposure data to comprise of two underlying matrices -the low-rank L-matrix and a sparse S-matrix.…”

“…In brief, we leveraged state of the art technological advances in high-resolution mass spectrometry 17 to simultaneously pro le thousands of potential environmental chemicals in seminal plasma, which is more proximal and relevant for male reproductive health compared to measures of chemicals in systemic circulation 18,19 . We then combined a novel machine learning pattern recognition approach, principal component pursuit (PCP) 20,21 , with modern statistical mixtures analyses 22 to e ciently detect associations of environmental chemicals with male reproductive health. Typically, studies model one feature (e.g.…”