Estimated Mortality and Morbidity Attributable to Smoke Plumes in the United States: Not Just a Western US Problem

1 Introduction and Background

Smoke from landscape (wild, prescribed, and agricultural) fires significantly degrades air quality across the United States (US) (Brey, Barnes, et al., 2018; Brey & Fischer, 2016; Buysse et al., 2019; Ford et al., 2017; Kaulfus et al., 2017; Val Martin et al., 2015). Landscape-fire smoke, hereafter simply “smoke,” contributes over 40% of primary emissions of particulate matter with diameters smaller than 2.5 microns (PM2.5) in the US (US EPA, 2017) and is responsible for a majority of non-anthropogenic exceedances in National Ambient Air Quality Standards (NAAQS) for PM2.5 (David et al., 2021). In heavily fire-impacted parts of the western US, fires dominate interannual variability in PM (Spracklen et al., 2007), have led to observed increases in the intensity of extreme PM2.5 events (McClure & Jaffe, 2018), and possible increases in summer mean PM2.5, despite decreasing anthropogenic emissions (O’Dell et al., 2019). As anthropogenic emissions of PM2.5 continue to decline (Lam et al., 2011; Leibensperger et al., 2012; Tagaris et al., 2007; Val Martin et al., 2015) and smoke PM2.5 increases (Ford et al., 2018; Li et al., 2020; Liu et al., 2016; Neumann et al., 2021; Yue et al., 2013), the relative importance of smoke PM2.5 for US air quality will likely increase.

Acute exposure to smoke has negative impacts on human health (Cascio, 2018; Liu et al., 2015; Reid, Brauer, et al., 2016, and references within), which may differ from the health effects of anthropogenic PM2.5 due to differences in composition and exposure. Many epidemiological studies of acute exposure to smoke PM2.5 have observed impacts on respiratory morbidity (e.g., Aguilera et al., 2021; DeFlorio-Barker et al., 2019; Gan et al., 2020; Hutchinson et al., 2018; Magzamen et al., 2021; Rappold et al., 2012; Reid, Jerrett, et al., 2016). Impacts on mortality and cardiovascular morbidity are less certain (e.g., Reid, Brauer, et al., 2016), but evidence for these outcomes of acute smoke exposure is growing (e.g., Doubleday et al., 2020; Magzamen et al., 2021; Wettstein et al., 2018). Several recent works have investigated differences in asthma-related and respiratory hospital admissions on smoke-impacted days compared to non-smoke-impacted days and found larger concentration response functions for PM2.5 on smoke-impacted days (Aguilera et al., 2021; DeFlorio-Barker et al., 2019; Kiser et al., 2020). A potentially different impact of smoke PM2.5 versus anthropogenic PM2.5 on health is also supported by evidence from toxicological studies that suggest that smoke-sourced PM2.5 may be more harmful than other sources of PM2.5 due to compositional differences (Wegesser et al., 2009). In addition to differences in particle composition, differences in exposure may also differentially impact health. While there are seasonal differences in PM2.5 abundance and composition driven by modest variability in anthropogenic sources and atmospheric chemistry (Bell et al., 2007), emissions of PM2.5 from landscape fires are highly episodic and have distinct seasonal cycles. The seasonality of fires and smoke events varies by US region due to both climate and human factors (Balch et al., 2017; Brey, Barnes, et al., 2018; McCarty et al., 2009; Westerling et al., 2003). The implications of the unique composition and exposure timing of smoke-specific PM2.5 on the US healthcare system are not well understood.

Repeated acute smoke events from landscape fires contribute to the overall long-term exposure to multiple health-relevant pollutants. Health effects of chronic exposure to smoke-specific PM2.5 have yet to be quantified. However, chronic exposure to anthropogenic PM2.5 has been associated with all-cause mortality, cardiopulmonary mortality, and lung cancer (Crouse et al., 2019; Krewski et al., 2009; Pope et al., 2009). In addition to PM2.5, wildfire smoke also contains many hazardous air pollutants (HAPs; Andreae, 2019; O’Dell et al., 2020; US EPA, 2015) which are compounds known or suspected to lead to serious health impacts (US EPA, 2015). The relative contribution of these different pollutants to potential health impacts of chronic smoke exposure is currently understudied.

This work leverages a growing knowledge of smoke concentrations and health responses to use a health impact assessment (HIA) to quantify: (a) the seasonal and spatial distribution of US asthma hospital admissions and emergency department (ED) visits attributable to acute smoke PM2.5 exposure, (b) the mortality from chronic smoke PM2.5 exposure by state, and (c) the relative contribution of HAPs to health impacts of chronic smoke exposure. We build upon previous US smoke HIAs and leverage new knowledge of smoke in several ways. In this HIA, we use observation-based smoke PM2.5 estimates (O’Dell et al., 2019), as opposed to previous model-based estimates (Fann et al., 2018; Ford et al., 2018; Neumann et al., 2021). In addition, we apply a recent meta-analysis of the impacts of smoke PM2.5 exposure on asthma morbidity (Borchers Arriagada et al., 2019) to estimate the asthma hospital admissions and asthma ED visits attributable to acute smoke PM2.5 exposure. Finally, we incorporate observation-based estimates of HAPs in smoke (O’Dell et al., 2020) into our HIA. To our knowledge, this is the first time HAPs have been included in a smoke HIA. The results of this HIA will be beneficial for individual, state, and regional awareness and preparedness for the health burdens posed by smoke exposure.

2 Materials and Methods 2.1 Smoke PM2.5 and HAPs Concentration Estimates

To conduct this HIA on smoke in the US, we estimated observation-based daily smoke PM2.5 concentrations by combining surface observations and satellite-based smoke plume estimates. This method was developed by O’Dell et al. (2019), and the data are available from 2006 to 2018. Here, we provide a brief description of the data. For a full description, please refer to O’Dell et al. (2019) or the metadata available in the data repository linked in the data availability statement. Daily average PM2.5 observations from the surface monitors in the US EPA Air Quality System (AQS) were interpolated to a 15 × 15 km grid using ordinary kriging. Daily smoke plume information was obtained from NOAA Hazard Mapping System (HMS) smoke plume polygons (Brey, Ruminski, et al., 2018; Ruminski et al., 2006). These polygons indicate where smoke is likely present somewhere in the daytime atmospheric column according to visible satellite imagery. Combining the daily HMS smoke plume polygons with the gridded daily average PM2.5 concentrations, we estimated a non-smoke background PM2.5 as the seasonal median of the gridded PM2.5 on days without an overlapping HMS smoke plume. We also conduct our analysis using the seasonal mean of the gridded PM2.5 on days without an overlapping HMS smoke plume (results provided in Figures S1 and S2 and Table S1) and find this choice does not impact our main conclusions. The smoke PM2.5 was then calculated as the difference between the kriged PM2.5 and the non-smoke background PM2.5 on smoke-impacted days. On non-smoke-impacted days, smoke PM2.5 was set to zero. These data have been previously used in atmospheric science, epidemiological, and economic studies of US smoke PM2.5 (Abdo et al., 2019; Burkhardt et al., 20192020; Gan et al., 2020; Lipner et al., 2019; Magzamen et al., 2021; O’Dell et al., 20192020).

We estimated gas-phase HAPs enhancements in smoke (hereafter “smoke HAPs”) using a previously published method from O’Dell et al. (2020). Briefly, ratios of smoke HAPs to PM1 (particulate matter with aerodynamic diameters smaller than 1 µm) were developed using observations from the Western Wildfire Experiment on Cloud Chemistry, Aerosol Absorption, and Nitrogen (WE-CAN). WE-CAN was an aircraft-based field campaign which sampled lofted smoke plumes from large western US wildfires in summer 2018. Ratios of smoke HAPs to smoke PM1 were developed for “young," “medium," and “old” smoke with approximate chemical ages of <1 day, 1–3 days, and >3 days, respectively. Here, we used the “young” ratios for an upper-estimate of smoke HAPs concentrations. We multiplied these ratios by 2006–2018 mean kriged smoke PM2.5 by grid cell for a gridded estimate of chronic smoke HAPs exposures. To perform this calculation, we made several assumptions. First, the WE-CAN ratios of HAPs to PM relied on smoke PM1 mass concentrations, however, our kriged estimates were of smoke PM2.5 mass concentrations. Thus, in order to use these HAP to PM1 ratios with our krigged smoke PM2.5 estimates, we assumed the mass concentration of particles with diameters between 1 and 2.5 µm was negligible. Volume size distributions of smoke aerosol from Bian et al. (2020) indicate that <5% of PM2.5 volume (and hence mass) exists in the diameter range of 1–2.5 µm, thus errors due to this assumption are <5%, smaller than the relative uncertainty from the concentration response function. Further, we assumed that the WE-CAN HAPs to PM1 ratios are representative of all US smoke plumes, but smoke HAPs concentrations may vary by fuel type (e.g., Gilman et al., 2015), burn conditions (Sekimoto et al., 2018), and smoke age (O’Dell et al., 2020). However, as we show in the results, the estimated health impacts of smoke HAPs are much smaller than that of smoke PM2.5, such that the overall health estimates of smoke are not greatly influenced by our assumptions in the HAPs calculation.

2.2 HIA of Acute Smoke Exposure We focused the present HIA of acute smoke exposure on asthma hospitalizations and asthma ED visits as these outcomes are consistently associated with smoke exposure (e.g., Reid, Brauer, et al., 2016) and have been included in a meta-analysis of acute smoke PM2.5 exposure (Borchers Arriagada et al., 2019). We estimated asthma hospitalizations and ED visits attributable to acute smoke PM2.5 exposure with the following health impact function, urn:x-wiley:24711403:media:gh2271:gh2271-math-0001(1)from Anenberg et al. (20142010) for chronic PM2.5 exposure. We assumed the acute smoke health impact function follows the same functional form (e.g., Pratt et al., 2019). In Equation 1, Y0 is the annual baseline asthma hospital admission rate or asthma ED visit rate, ΔPM2.5 is the daily smoke PM2.5 concentration, and urn:x-wiley:24711403:media:gh2271:gh2271-math-0002 is defined, urn:x-wiley:24711403:media:gh2271:gh2271-math-0003(2)where RR is the relative risk per ΔX increase in smoke PM2.5. We used smoke-specific RRs for asthma hospital admissions and asthma ED visits from a meta-analysis of smoke PM2.5 exposure in the US (Borchers Arriagada et al., 2019). The meta-analysis RRs for the US are provided in the supplement of Borchers Arriagada et al. (2019) and incorporate RRs from several different time lags (i.e., admissions/visits at different numbers of days after smoke PM2.5 exposure) but is similar in magnitude to the meta-analysis of lag-0-specific RRs for both asthma hospital admissions and asthma ED visits. We also calculated a pooled smoke-specific RR using the US studies from Borchers Arriagada et al. (2019) (Alman et al., 2016; Delfino et al., 2009; Gan et al., 2017; Hutchinson et al., 2018; Le et al., 2014; Reid, Jerrett, et al., 2016; Resnick et al., 2015) and additional RRs from eastern US fires (Rappold et al., 2012; Tinling et al., 2016) as well as two recently published RRs based on smoke PM2.5 from western US fires (Gan et al., 2020; Magzamen et al., 2021). RRs from these individual studies, the pooled RR, and meta-analysis RR are plotted in Figure S3. As shown in Figure S3, we found the meta-analysis central estimate and 95th percent confidence interval (CI) lies within the much wider 95th percent CI of our pooled RRs estimate. Thus, despite our addition of several RRs from both eastern and western US fires, our pooled RR and the US-specific meta-analysis RR from Borchers Arriagada et al. (2019) are not statistically different. We used the US-specific meta-analysis RR in our calculations due to its tighter CI.

Values of the smoke-specific RRs and baseline rates used in Equations 1 and 2 are provided in Table S2. National annual baseline rates for the year 2010 for asthma (ICD9-493) hospital admissions and ED visits were obtained from the Healthcare Cost and Utilization Project (HCUP). We used national estimates of baseline rates from the National Emergency Department Sample (NEDS) and National Inpatient Sample (NIS), which are weighted national estimates based on state-provided data (AHRQ, 2006). Although asthma prevalence varies by state (BRFSS/CDC, 2019) and asthma hospitalization and ED visit rates vary by season (Verdier et al., 2017), to our knowledge, a complete national database of sub-national or sub-annual asthma ED/hospitalization rates is currently unavailable. Gridded population estimates for 2010 were obtained from the National Space Administration's Socioeconomic Data and Applications Center (NASA SEDAC, 2018), which we regridded from the original 2.5 arc-minute grid resolution to our 15 × 15 km kriged PM2.5 grid. Daily, krigged smoke PM2.5 estimates, described previously, are used as the smoke PM2.5 input in Equation 1. These data were applied in Equation 1 to estimate daily, gridded asthma hospital admissions and asthma ED visits attributable to smoke PM2.5 at lag day 0. The largest contributors of uncertainty in US smoke PM2.5 HIAs are uncertainty in the shape of the concentration response function and smoke-specific PM2.5 concentrations (Cleland et al., 2021). We represent uncertainty in the asthma morbidity attributable to smoke PM2.5 as the range of asthma ED visits and asthma hospitalizations estimated by calculating asthma ED visits and asthma hospitalizations using the lower and upper bounds of the 95% CI in the smoke-specific RRs.

We defined nine US regions following Brey, Ruminski, et al. (2018). The regions are roughly the 10 EPA regions; however, only contiguous US states are included and several regions are combined/altered to follow likely fire and smoke patterns. The list of states in each region are provided in Table S3. Seasons were defined as follows: Winter: January, February, March; Spring: April, May June; Summer: July, August, September; Fall: October, November, December. We chose this less conventional seasonal categorization so that we better group regional US wildfire activity into a single season category where possible (results using a more standard seasonal categorization are provided in Figures S4 and S5). Gridded asthma ED visits and hospital admissions attributable to smoke PM2.5 were summed by each region and season. We sum by region for asthma morbidity, as opposed to by state as was done for mortality (described in the next section), because we found seasonal, by-state totals for each year to be too cumbersome for the main text. We present the seasonal fraction of asthma ED visits attributable to smoke PM2.5 by state in Figures S6–S9.

2.3 HIA for Chronic Exposure to Smoke PM2.5 As no concentration response function for mortality specific to chronic exposure to smoke PM2.5 currently exists, we used the Global Exposure Mortality Model (GEMM, Burnett et al., 2018) to estimate premature mortality and disability-adjusted life years (DALYs) attributable to chronic exposure to both all-source and smoke PM2.5. We note excess risk of mortality from chronic exposure to smoke PM2.5 may differ from all-source PM2.5 due to differences in PM2.5 composition, toxicity, and exposure type (e.g., episodic vs. consistent). However, at present, there are no studies of increased mortality risk from chronic exposure to smoke PM2.5, thus we assumed the GEMM is applicable to smoke PM2.5. The GEMM was developed from 41 cohort studies in 16 different countries on the increased mortality risk from chronic exposure to all-source ambient PM2.5. We estimated mortality and DALYs attributable to all-source PM2.5 from the GEMM following, urn:x-wiley:24711403:media:gh2271:gh2271-math-0004(3)where Events is mortalities or DALYs attributable to PM2.5, Population is the regridded 2010 population from SEDAC described in Section 2.2, Y0 is the sum of baseline mortality or DALY rates for non-communicable diseases and lower respiratory infections, and HR is the hazard ratio from Burnett et al. (2018). Although the HR from Burnett et al. (2018) was developed specifically for mortality, we assume it can be applied to estimate DALYs (the sum of years of life lost and years of living with disability), as with prior PM2.5 mortality HRs (Burnett et al., 2014; Cohen et al., 2017). We used the all-cause mortality HR function with all countries included, which includes all non-communicable diseases and lower respiratory infections, with a threshold concentration of 2.4 µg m−3, the lowest observed concentration in the cohort studies used to develop the GEMM. Baeline all-cause (sum of non-communicable diseases and lower respiratory infections) mortality and DALY rates for 2010 were obtained from the GBD (GBD, 2019) and are provided in Table S2. In Table S1, we provide estimated mortalities and DALYs for smoke PM2.5 using the five leading causes of death HRs from the GEMM.

The mortality and DALYs attributable to smoke PM2.5 were estimated by multiplying the mortality (or DALYs) attributable to all-source PM2.5 from Equation 3 by the smoke PM2.5 fraction of 2006–2018 mean PM2.5 at each grid cell. We follow this approach, as opposed to applying the GEMM directly to smoke PM2.5 concentrations, due to the non-linearity of the concentration response function (the "attribution method" from Bilsback et al., 2020 and Kodros et al., 2016). With this method, we estimate excess mortalities and DALYs attributable to all-source and smoke PM2.5 for each grid cell and summed the excess mortality across each US state. We represent an uncertainty range in mortality and DALYs attributable to all-source PM2.5 and smoke PM2.5 as the range of deaths (or DALYs) estimated by calculating mortality using the upper and lower bounds of the uncertainty range (urn:x-wiley:24711403:media:gh2271:gh2271-math-00052 standard error) in the GEMM concentration response function coefficients.

2.4 HIA for Chronic Exposure to Smoke HAPs

To estimate DALYs attributable to smoke HAPs, we took a different approach than that used to estimate DALYs attributable to smoke PM2.5. The method used to estimate DALYs from smoke PM2.5, described previously in Section 2.3, relies on epidemiological concentration response functions relating exposure with specific diseases (e.g., Burnett et al., 2018), which are subsequently associated with an estimated number of DALYs (GBD, 2019; see Table S2). There are currently no concentration response functions associating the speciated smoke HAPs studied in this work with incidence of certain diseases in humans. Therefore, to estimate the DALYs attributable to smoke HAPs, we used estimates of human damage factors, expressed as DALYs, per annual intake of HAPs from Huijbregts et al. (2005). A full description of the calculation of DALYs per pollutant intake can be found in Huijbregts et al. (2005). Briefly, the DALY per intake factors were estimated through extrapolation of animal toxicity literature to estimate pollutant toxicity and subsequent disease incidence in humans. Disease incidence per pollutant exposure estimates were then combined with an estimated number of DALYs per disease. The disease per intake and DALY per disease factors were then combined to determine a final DALYs per pollutant intake factor. With this method, Huijbregts et al. (2005) estimated DALYs per year due to cancer and noncancer effects per mass intake of 1,192 pollutants. These DALY factors have been previously applied to estimate DALYs from HAPs in third-hand cigarette smoke exposure (Sleiman et al., 2014) and indoor exposure to HAPs (Logue et al., 2012). While these DALY factors allow us to compare health impacts of smoke PM2.5 and HAPs with the same metric (DALYs), we note the two methods used to estimate DALYs are very different. Although the approach based on concentration response functions in humans is the more precise method, it is not possible to apply such an approach to estimate DALYs from speciated HAPs, thus we rely on the DALY factor method.

We applied the DALY factors to estimate DALYs per person attributable to chronic exposure to smoke HAPs by, urn:x-wiley:24711403:media:gh2271:gh2271-math-0006(4)

similar to Logue et al., (2012). In Equation 4, Ci is the concentration of smoke HAP i described in Section 2.1, V is 14.4 m3 day−1, an estimated population-mean volume of air inhaled per day from Logue et al. (2012), and (∂DALYcancer/∂intake)i is the estimated DALYs due to cancer effects, and (∂DALYnoncancer/∂intake)i is the estimated DALYs due to noncancer effects, per intake of pollutant i from Huijbregts et al. (2005). Unlike the smoke PM2.5 DALYs calculation, there is no threshold concentration applied for smoke HAPs. Implications of this are discussed in the results. Of the 32 smoke HAPs with estimated concentrations from Section 2.1, 25 have DALY factors for cancer and/or noncancer effects reported in Huijbregts et al. (2005). Huijbregts et al. (2005) reports the median estimate of DALY factors and provides an uncertainty estimate, ki, expressed as the square root of the ratio of the 97.5th and 2.5th percentiles. This value is defined such that 95% of the distribution of DALY factors lie within a factor of ki of the reported median estimate. We thus represent the 95% CI around the cancer and noncancer DALY factors as (∂DALY/∂intake)iki−1 to (∂DALY/∂intake)iki. The 95% CI around the DALY factors is large, spanning several orders of magnitude, driven by large uncertainties in extrapolating animal toxicity studies to humans and uncertainty in noncancer disease incidence and human impact (Huijbregts et al., 2005). We apply these upper and lower bounds on the 95% CI into Equation 4 to estimate the uncertainty bounds for our DALY estimates.

3 Results 3.1 Landscape-Fire Smoke PM2.5

Observation-based smoke PM2.5 estimates across the study period are presented in Figures 1a and 1b. Mean total PM2.5 from 2006 to 2018 and the long-term smoke PM2.5 fraction are shown in Figure S10. The 2006–2018 mean smoke PM2.5, Figure 1a, reaches over 2 µg m−3 in heavily fire-impacted regions of the western US. The box plots in Figure 1b show the distribution of annual average smoke PM2.5 across all 15 × 15 km grid cells in the US for each year in our study period. In 2017, in several grid cells in Montana, the annual average smoke PM2.5 exceeded 10 µg m−3. Across all US grid cells, the area-weighted mean annual smoke PM2.5 (black points in Figure 1b) is much lower ranging from 0.11 µg m−3 in 2009 to 0.73 µg m−3 in 2018. In Figure 1b, we also provide population-weighted mean smoke PM2.5 estimates for the eastern and western US (orange and red points, respectively). These values were calculated as the sum of each western (eastern) US grid-cell annual mean PM2.5 multiplied by the fraction of the total western (eastern) US population in that grid cell. We find the area-weighted mean smoke PM2.5 across the US is often similar to the population-weighted mean smoke PM2.5 in the eastern and western US. The mean population-weighted smoke PM2.5 across the full time period is higher in the western US (0.33 µg m−3) than the eastern US (0.26 µg m−3), however there is high inter-annual variability in both the western and eastern US population-weighted mean smoke PM2.5. Our US-wide annual mean observation-based smoke PM2.5 is a factor of 2–6 lower than model-based estimates used in Fann et al. (2018), depending on the year. In addition, our long-term average smoke PM2.5 is lower than Ford et al. (2018) but of similar magnitude to Neumann et al. (2021), both model-based estimates. A direct, quantitative national or regional comparison between our chronic PM2.5 exposure estimates and these previous works is not possible due to a difference in time periods and regional definitions (or a lack of regional-level estimates). There are limitations to both model-based estimates (e.g., smoke dispersion in complex topography (Gan et al., 2017), determining plume injection height (Paugam et al., 2016), estimating fuel burned) and our observation-based estimates (e.g., lack of information on vertical smoke distribution (Brey, Ruminski, et al., 2018), sparse surface monitoring) that lead to uncertainties in total smoke PM2.5 concentrations.

image

2006–2018 mean smoke PM2.5 on a 15 × 15 km grid (panel a) and the distribution of annual average smoke PM2.5 across all grid cells for each year (panel b). Boxes in panel (b) extend from the 25th to 75th percentile, with a bar across the box indicating the median value. Box whiskers extend from the minimum to maximum value. Black points represent the area-weighted mean smoke PM2.5 and orange and red points indicate the population-weighted mean PM2.5 in the eastern and western US states, respectively.

3.2 Spatial Distribution of Asthma Morbidity Attributable to Smoke PM2.5

In Figure 2, we show the contribution of each region to the total number and percent of asthma ED visits (Figure 2a) and asthma hospital admissions (Figure 2b) attributable to smoke PM2.5 in the US by year from 2006 to 2018. There is high inter-annual variability in the total amount of asthma morbidity attributable to smoke PM2.5 in the US over this time period. There is similarly high inter-annual variability in smoke PM2.5 concentrations over this same time period (O’Dell et al., 2019). Asthma ED visits attributable to smoke PM2.5 in the US range from approximately 1,300 to 5,900 visits per year, or 0.07%–0.33% of all asthma ED visits. The asthma hospital admissions attributable to smoke PM2.5 contribute a similar percent (0.08%–0.37%) of total annual asthma hospital admissions, compared to the asthma ED visits. We find a lower total number (300–1,400) of smoke PM2.5 attributable asthma hospital admissions than ED visits, due to a lower baseline rate in the former.

image

Asthma emergency department (ED, panel a) and asthma hospital admissions (panel b) attributable to smoke PM2.5 across the contiguous US for each year from 2006 to 2018. Colors represent different US regions, as indicated by the map, where darker colors represent regions in the western US and lighter colors represent regions outside the western US. The left y-axis on each panel represents the total number of events attributable to smoke PM2.5 and the right y-axis represents the percent of all asthma ED visits (panel a) or asthma hospital admissions (panel b) in the US that are attributable to smoke PM2.5. Errors bars represent the range of morbidities estimated using the upper and lower 95% CI bounds on smoke-specific relative risk.

Total numbers and percent of asthma ED visits and hospital admissions attributable to smoke PM2.5 by year are given in Table 1 alongside previous estimates of respiratory morbidity attributable to smoke PM2.5 in the US from Fann et al. (2018) and Neumann et al. (2021). Our estimated asthma ED visits and hospital admissions are considerably higher than those from Neumann et al. (2021) of 400 and 68 per year, respectively. However, the estimates from Neumann et al. (2021) only account for smoke originating from fires in the western US and rely on different smoke-estimation methods and health impact functions than this work. In contrast, our estimates of asthma hospital admissions attributable to smoke PM2.5 are a factor of 6–8 lower than the all respiratory hospital admissions attributable to smoke PM2.5 in Fann et al. (2018). A lower number of asthma hospital admissions compared to all respiratory hospital admissions attributable to smoke PM2.5 is expected, due to a lower baseline rate in the former (AHRQ, 2006).

Table 1. Total Asthma Hospital Admissions, Asthma Emergency Department (ED) Visits, and Mortality Attributable to Smoke PM2.5 Exposure and Mortality Attributable to Total PM2.5 Exposure Across the Contiguous US From This Work Compared Results From Three Previous US Smoke Health Impact Assessments Outcome This work Ford et al. (2018a Fann et al. (2018b Neumann et al. (2021c All respiratory hospital admissions - - 3,900–8,500 350 Asthma hospital admissions 300–1,400d - - 68 Asthma ED visits 1,300–5,900d - - 400 Annual mortality attributable to chronic smoke PM2.5 exposure 6,300 (CI: 4,800–7,800) 7,000–28,000 8,700–32,000 720–1,600 Annual mortality attributable to chronic PM2.5 exposure 216,000 (CI: 163,000–266,000) 69,000–222,000 - - a Ford et al.(2018) results for the decade centered around 2000. Range given represents the range across multiple concentration response functions used in the study. b Fann et al.(2018) results were presented annually with two concentration response functions for mortality and two odds ratios for respiratory hospital admissions. The given range is the full range across all years and both concentration response functions. c Neumann et al. (2021) results for health impacts of western wildfires on the full US (i.e., non-western wildfires, prescribed burning, and agricultural burning were not included). The given range for mortality is the range across both concentration response functions used. d Range across all years in this analysis (2006–2018). US, United States.

In most years, the majority of asthma ED visits and asthma hospital admissions attributable to smoke PM2.5 occur in non-western states (lighter colors in Figure 2, including the Midwest (MW), Great Plains (GP), Southern Plains (SP), Northeast (NE), Mid Atlantic (MA), and Southeast (SE) regions). There are only 2 years during our 13-year study period when over 50% of asthma morbidity attributable to smoke PM2.5 occurs in the western US (darker colors in Figure 2, including the Northwest (NW), Southwest (SW), and Rocky Mountain (RM) regions). In 2017 and 2018, 64%, and 52%, respectively, of all US asthma morbidity attributable to smoke PM2.5 occurred in the western states. In all other years during our study period, the western regions contributed on average 19% of US asthma morbidity attributable to smoke PM2.5. The high inter-annual variability in the total amount of asthma morbidity attributable to smoke PM2.5 is also not exclusively driven by the western states. In fact, in the year with the most asthma morbidity attributable to smoke PM2.5, 2011, less than 5% occurred in all the western states combined. This is largely driven by higher population densities in the East. As mentioned previously, we find the 2006–2018 population-weighted mean smoke PM2.5 concentration is higher in the western states (0.33 µg m−3) than the eastern states (0.26 µg m−3). However, the population is much higher in the East (around 226 million people) than the West (around 64 million people) overall. Thus, locations typically not considered to be heavily smoke impacted due to lower average concentrations of smoke PM2.5, but with large population den

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