Cardiopulmonary Effects of Fine Particulate Matter Exposure among Older Adults, during Wildfire and Non-Wildfire Periods, in the United States 2008-2010

Abstract

Background: The effects of exposure to fine particulate matter ([Formula: see text]) during wildland fires are not well understood in comparison with [Formula: see text] exposures from other sources.

Objectives: We examined the cardiopulmonary effects of short-term exposure to [Formula: see text] on smoke days in the United States to evaluate whether health effects are consistent with those during non-smoke days.

Methods: We examined cardiopulmonary hospitalizations among adults [Formula: see text] y of age, in U.S. counties ([Formula: see text]) within [Formula: see text] of 123 large wildfires during 2008-2010. We evaluated associations during smoke and non-smoke days and examined variability with respect to modeled and observed exposure metrics. Poisson regression was used to estimate county-specific effects at lag days 0-6 (L0-6), adjusted for day of week, temperature, humidity, and seasonal trend. We used meta-analyses to combine county-specific effects and estimate overall percentage differences in hospitalizations expressed per [Formula: see text] increase in [Formula: see text].

Results: Exposure to [Formula: see text], on all days and locations, was associated with increased hospitalizations on smoke and non-smoke days using modeled exposure metrics. The estimated effects persisted across multiple lags, with a percentage increase of 1.08% [95% confidence interval (CI): 0.28, 1.89] on smoke days and 0.67% (95% CI: [Formula: see text], 1.44) on non-smoke days for respiratory and 0.61% (95% CI: 0.09, 1.14) on smoke days and 0.69% (95% CI: 0.19, 1.2) on non-smoke days for cardiovascular outcomes on L1. For asthma-related hospitalizations, the percentage increase was greater on smoke days [6.9% (95% CI: 3.71, 10.11)] than non-smoke days [1.34% (95% CI: [Formula: see text], 3.77)] on L1.

Conclusions: The increased risk of [Formula: see text]-related cardiopulmonary hospitalizations was similar on smoke and non-smoke days across multiple lags and exposure metrics, whereas risk for asthma-related hospitalizations was higher during smoke days. https://doi.org/10.1289/EHP3860.

Figure 1.

Counties included in analysis, 2008–2010. The centroid of all highlighted counties ($n=692$) were located within $200\mathrm{km}$ of a wildfire point of origin ($n=123$, denoted by a triangle), of which 178 counties (25.7%) had monitoring sites.

Figure 2.

Percentage difference and 95% confidence intervals in hospitalizations during 2008–2010, among U.S. Medicare recipients $\ge 65$ y of age per $10{\text{-}\mathrm{\mu }\mathrm{g}/\mathrm{m}}^3$ increase in ${\mathrm{PM}}_{2.5}$, lag day 0 to lag day 6. Smoke days are defined as having a wildfire-specific contribution $>5{\mathrm{\mu }\mathrm{g}/\mathrm{m}}^3$, and non-smoke days as wildfire-specific contribution $\le 5{\mathrm{\mu }\mathrm{g}/\mathrm{m}}^3$. Associations are estimated using a single lag model for the interaction between ${\mathrm{PM}}_{2.5}$ (${{\mathrm{PM}}_{2.5}}^{\text{Tot}\text{CMAQ}}$, ${{\mathrm{PM}}_{2.5}}^{\text{Tot}\text{CMAQ}\text{-}\mathrm{M}}$, or ${{\mathrm{PM}}_{2.5}}^{\text{Tot}}$) and SmokeDay adjusting for day of the week, day [natural spline with 6 degrees of freedom (df) per year], temperature (natural spline with 3 df), and relative humidity (natural spline with 3 df) for each county, followed by a meta-analysis. Using ${{\mathrm{PM}}_{2.5}}^{\text{Tot}\text{CMAQ}}$ 595 counties, 341 counties, and 607 counties were used in the meta-analyses for (A) respiratory; (B) asthma, bronchitis, and wheezing; (C) and cardiovascular hospitalizations, respectively. Using the other metrics (${{\mathrm{PM}}_{2.5}}^{\text{Tot}\text{CMAQ}\text{-}\mathrm{M}}$ or ${{\mathrm{PM}}_{2.5}}^{\text{Tot}}$) 134 counties, 92 counties, and 137 counties were used in the meta-analyses for (A) respiratory; (B) asthma, bronchitis, and wheezing; (C) and cardiovascular hospitalizations, respectively. The y-axes limits differ between hospitalization types. (See Table S1 for corresponding numeric data.)

Figure 3.

Summary of ${\mathrm{PM}}_{2.5}$ concentrations (${\mathrm{\mu }\mathrm{g}/\mathrm{m}}^3$), overall and by smoke and non-smoke days. ${{\mathrm{PM}}_{2.5}}^{\text{Tot}\text{CMAQ}}$: CMAQ estimated ${\mathrm{PM}}_{2.5}$ concentrations on all days and in all counties, ${{\mathrm{PM}}_{2.5}}^{\text{Tot}\text{CMAQ}\text{-}\mathrm{M}}$: CMAQ estimated ${\mathrm{PM}}_{2.5}$ concentrations on days and in counties with corresponding monitored data, ${{\mathrm{PM}}_{2.5}}^{\text{Tot}}$: Monitored data alone. Smoke days defined as wildfire-specific contribution $>5{\mathrm{\mu }\mathrm{g}/\mathrm{m}}^3$ and on-smoke days defined as wildfire-specific contribution $\le 5\mathrm{\mu }\mathrm{g}/\mathrm{m}$. The horizontal line within each box represents the median, whereas the ends of the box correspond to the 25th and 75th percentiles. The lines extending from the box correspond to the minimum and maximum. (See Table S2 for corresponding numeric data.)