Daily land use regression estimated woodsmoke and traffic pollution concentrations and the triggering of ST-elevation myocardial infarction: a case-crossover study
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Updated: Feb 12, 2026

Postconditioning with Lactate-enriched Blood for Cardioprotection in ST-segment Elevation Myocardial Infarction
Published on: May 28, 2019
David Q Rich1,2, Mark J Utell2,3, Daniel P Croft3
11Department of Public Health Sciences, University of Rochester School of Medicine and Dentistry, 265 Crittenden Boulevard CU 420644, Rochester, NY 14642 USA.
Abstract:
Prior work has reported acute associations between ST-elevation myocardial infarction (STEMI) and short-term increases in airborne particulate matter. Subsequently, the association between STEMI and hourly measures of Delta-C (marker of woodsmoke) and black carbon (marker of traffic pollution) measured at a central site in Rochester, NY, were examined, but no association was found. Therefore, land use regression estimates of Delta-C and black carbon concentrations at each patient's residence were developed for 246 STEMI patients treated at the University of Rochester Medical Center during the winters of 2008-2012. Using case-crossover methods, the rate of STEMI associated with increased Delta-C and BC concentration on the same and previous 3 days was estimated after adjusting for 3-day mean temperature and relative humidity. Non-statistically significant increased rates of STEMI associated with interquartile range increases in concentrations of BC in the previous 2 days (1.10 μg/m3; OR = 1.12; 95% CI 0.93, 1.35) and Delta-C in the previous 3 days (0.43 μg/m3; OR = 1.16; 95% CI 0.96, 1.40) were found. Significantly increased rates of STEMI associated with interquartile range increases in concentrations of BC (1.23 μg/m3; OR = 1.04; 95% CI = 0.87, 1.24) or Delta-C (0.40 μg/m3; OR = 0.94; 95% CI = 0.85, 1.09) on the same day were not observed likely due, in part, to temporal misalignment. Therefore, sophisticated spatial-temporal models will be needed to minimize exposure error and bias by better predicting concentrations at individual locations for individual hours, especially for outcomes with short-term responses to air pollution (< 24 h).
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