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

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).

Weather effects on hand, foot, and mouth disease at individual level: a case-crossover study

“…To estimate the associations between HFMD and temperature and relative humidity, we used a case-crossover design and conditional logistic regression models [17]. The fixed 28-day-window case-crossover approach was used in this study and was recently used in previous studies of environmental health [18, 19]. Each HFMD case was defined as “case” in the matched case-control study.…”

“…We assigned the measurements of each monitoring site to the cases within 10, 20, and 30 km. Consistent with previous studies [5, 7, 18, 22], we calculated the mean of weather factors for the onset day (lag day 0), and previous 2 to 14 days (lag days 0–1 to 0–13) to estimate the cumulative lagged effects. Another reasoning for setting lag days is HFMD has an incubation period of 3–7 days [23].…”

Cardiovascular health impacts of wildfire smoke exposure

“…For example, the peak levels of PM 2.5 and PM 10 during wildfire days were approximately 10 times higher than those in non-fire days in Sydney, Australia [ 94 ]. Efforts have also been made specifically to address the role of organic compounds in epidemiology by using environmental markers of PAHs such as delta-C [ 18 , 19 , 38 , 45 , 127 ]. Delta-C is based on a calculation of the difference between light absorption of ultraviolet black carbon (BC), measured at 370 nm and BC 880 nm with a two-wavelength aethalometer [ 38 ], and was used for source apportionment for wood smoke [ 159 ].…”

Assessing the Impact of Wildfires on the Use of Black Carbon as an Indicator of Traffic Exposures in Environmental Epidemiology Studies

“…Subtracting brown carbon mass associated with biomass burning (which has an absorbance at 375 nm) from all mass absorbing at 880 nm may better reflect BC from traffic sources. The Delta‐C metric has recently been used in epidemiology studies to better differentiate the impacts of traffic and wood smoke exposures on health (Assibey‐Mensah et al., 2020; Rich et al., 2018). Alternatively, specific elements (e.g., sulfur and potassium) or organic tracers such as levoglucosan may also differentiate biomass emissions from TRAP in particulate matter samples (Bhattarai et al., 2019; Chen et al., 2017; Li et al., 2003).…”