The Impact of Wildfire Smoke on Acute Cardiovascular and Respiratory Illness in the US

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To evaluate the association between wildfire-specific particulate matter (PM 2.5 ) and acute health impacts a retrospective cohort study was conducted utilizing National COVID Cohort Collaborative health records from 109,012 patients across 58 US health systems from 2020 to 2021. County-level wildfire-specific PM 2.5 concentrations were estimated using a fire emissions database and chemical transport modeling. Generalized linear mixed-effects models were used to analyze the association between weekly county-level wildfire-specific PM 2.5 exposure (up to 50 µg/m³) and hospital encounters for a series of cardiac, pulmonary, obstetric and neonatal outcomes. Statistically significant increases in weekly encounters per county of residence were observed for every 10 µg/m³ rise in weekly maximum wildfire-specific PM 2.5 for acute myocardial infarction (0.084, 95% CI, 0.023–0.146), cardiac arrest (0.011, 95% CI, 0.001–0.021), heart failure (0.083, 95% CI, 0.024–0.142), atrial fibrillation (0.115, 95% CI, 0.014–0.216), COPD exacerbation (0.034, 95% CI, 0.001–0.066) and pulmonary embolism (0.048, 95% CI, 0.003–0.094). COVID infection status was not found to have a modifying effect on these relationships. There was no significant increase in COVID pneumonia admissions in response to increasing wildfire smoke. These findings demonstrate quantifiable increases in acute cardiorespiratory morbidity associated with wildfire smoke exposure. Health sciences/Cardiology Health sciences/Diseases Earth and environmental sciences/Environmental sciences Health sciences/Medical research Health sciences/Risk factors wildfire particulate matter cardiovascular health respiratory health public health Figures Figure 1 Figure 2 INTRODUCTION Climate change, recognized as the greatest health threat of the 21st century [ 1 ] , is driving an increase in wildfire frequency [ 2 – 4 ] , and thus human exposure to wildfire smoke. From 2001–2004 to 2018–2021, 61% of countries experienced an increase in population exposure to wildfires [ 5 ] . Inhalation of particulate matter (PM) from wildfire smoke triggers a cascade of harmful events, including inflammation, oxidative stress, endothelial dysfunction, sympathetic activation and platelet activation, which can result in end organ damage and lead to a variety of acute health issues [ 6 ] . Direct contact of the respiratory system with PM increases the incidence of asthma and COPD exacerbation [ 7 ] . Particulate matter with a diameter of 2.5 microns (PM 2.5 ) and less is a particular focus in health research compared to larger particle sizes, as it penetrates deep into the lung and enters the circulation causing systemic effects [ 8 ] . Systemic effects (above) can have a broad range of cardiovascular effects [ 9 ] . Arrhythmias, including atrial fibrillation, are driven by inflammation and sympathetic stimulation [ 10 ] . PM-induced hypertension increases cardiac afterload. Both can exacerbate heart failure [ 11 ] . Platelet activation and endothelial dysfunction can precipitate acute coronary syndromes, myocardial infarction, [ 12 ] cerebral infarction and pulmonary embolism [ 13 , 14 ] . Ultimately, increased incidence of cardiac arrests has also been observed in response to PM exposure [ 15 ] . Additionally, impacts on both the respiratory system and the endothelium are central to the pathophysiology of COVID-19 pneumonia, which was highly prevalent during the study period, and there appears to be a detrimental interaction between the effects of COVID-19 infection and PM exposure [ 16 ] . The placental-fetal unit is also highly sensitive to endothelial dysfunction, inflammation and oxidative stress. Vascular injury and inflammation here can lead to pre-eclampsia, intrauterine growth restriction, premature labor and low birth weight [ 17 , 18 ] . Wildfire derived particulate matter appears to be more harmful than other sources of PM 2.5 , due to the presence of polycyclic aromatic hydrocarbons and aldehydes in the smoke [ 19 ] . Despite the increasing exposure of human populations to wildfire smoke, effective methods to predict and mitigate the health impact of this exposure remain inadequate. Although the mechanisms of smoke impact have been well described, little is known about the magnitude of the health impacts caused by exposure to given concentrations of wildfire smoke. Many existing studies have focused on the effects of individual wildfires in specific regions over brief time periods, or focused on the effects of isolated fire events on confined populations [ 20 , 21 ] . Many previous studies have tended to combine all PM sources, rather than isolating the effects of wildfire-specific PM 2.5 . Therefore, the differences between the health impacts of wildfire-specific (compared to anthropogenic) PM 2.5 are not yet fully understood. A number of contemporary studies have examined the impact of wildfire-specific PM 2.5 , on a range of outcomes including cardiovascular and respiratory mortality globally [ 22 ] , as well as in the US [ 23 ] and Brazil [ 24 ] . However, impacts on morbidity (hospital admissions for specific diagnoses), and especially that relating to low-level exposure (such as that deriving from far distant fires) across large populations, are less well defined. While there are national level studies that estimate population-level respiratory impacts of PM exposure, they employ risk estimates from one or more smaller studies and extrapolate the risk estimates to a national level [ 25 , 26 ] . Our study improves on this methodology by measuring these outcomes directly. It is possible that significant unrecognized acute morbidity results from low-level wildfire smoke exposure. We sought to address this gap in knowledge, by studying the relationship between a range of cardiovascular, respiratory and obstetric/neonatal conditions and wildfire-related PM 2.5 exposure (at the low-moderate concentrations, < 50 µg/m³, experienced by large populations) across the entire continental US (CONUS). RESULTS Figure 1 depicts a flowchart of the data selection process, utilizing the N3C database initially comprising a total of 23,155,003 patients. The analysis continued with restriction to 3,908,545 patients diagnosed with COVID-19, cardiac, pulmonary, obstetric, or neonatal conditions during the 2020–2021 period. Further exclusions were made for patients located outside CONUS, counties without available total population data, counties with annual admission rates and annual PM 2.5 levels below the 66th percentile (Fig. 2 ), and dates outside of the wildfire season resulting in 124,362 patients. Finally, after removing days with county level wildfire-specific PM 2.5 > 50 µg/m³, 109,012 patients remained in the dataset for analysis. Each of these values have been skewed by ± 5 to enhance deidentification. Among patients treated at hospitals in the N3C database in areas affected by wildfire smoke, increasing wildfire-specific PM 2.5 is associated with an increase in the incidence of major cardiovascular and respiratory diseases (Table 2 ). For each 10 µg/m³ increase in weekly maximum wildfire-specific PM 2.5 there are statistically significant increases in the weekly incidence per county for acute myocardial infarction (0.084, 95% CI, 0.023–0.146), cardiac arrest (0.011, 95% CI, 0.001–0.021), heart failure (0.083, 95% CI, 0.024–0.142), atrial fibrillation (0.115, 95% CI, 0.014–0.216), COPD exacerbation (0.034, 95% CI, 0.001–0.066) and pulmonary embolism (0.048, 95% CI, 0.003–0.094). While the increased incidences of acute MI and heart failure exacerbations were consistent across all metrics of wildfire specific PM 2.5 exposure, cerebral infarction only demonstrated an increased risk in relation to weekly mean wildfire specific PM 2.5 , and cardiac arrest correlated only with the weekly maximum concentration. There were no statistically significant increases in the incidence of obstetric or neonatal disease seen in this analysis. These results represent the mean increase in weekly hospital admissions and emergency department visits per county in response for each 10 µg/m³ increase in wildfire specific PM 2.5 . Table 1 Characteristics of the study sample stratified by year (2020–2021) n Overall 2020 2021 109,012¥ 54,272¥ 54,740¥ Length of stay (mean (SD)) 2.31 (8.44) 2.29 (8.26) 2.34 (8.63) Gender (%) - Male 51,694 (47.4) 25,828 (47.6) 25,866 (47.3) - Female 57,299 (52.6) 28,435 (52.4) 28,864 (52.7) - Other/Unknown < 20* (0.0) < 20* (0.0) < 20* (0.0) Race (%) - White 70,397 (64.6) 35,344 (65.1) 35,053 (64.0) - Black or African American 17,136 (15.7) 8,266 (15.2) 8,870 (16.2) - Asian 4,297 (3.9) 2,053 (3.8) 2,244 (4.1) - Other/Unknown 17,182 (15.8) 8,609 (15.9) 8,573 (15.7) Age (mean (SD)) 51.53 (25.11) 52.44 (23.82) 50.63 (26.30) Death (%) 13,226 (12.1) 7,401 (13.6) 5,825 (10.6) Quan-Charlson comorbidity score (mean (SD)) 1.53 (2.00) 1.51 (1.97) 1.56 (2.03) Covid status = Positive (%) 14,204 (13.0) 6,745 (12.4) 7,459 (13.6) Estimated percent poverty (mean (SD)) 12.57 (4.21) 12.57 (4.22) 12.56 (4.21) Estimated percent unemployment (mean (SD)) 5.37 (1.84) 5.38 (1.85) 5.35 (1.83) Estimated per capita income (mean (SD)) 34,590.71 (7,385.32) 34,506.52 (7,217.05) 34,674.19 (7,547.59) Estimated percent no high school diploma (mean (SD)) 10.74 (4.64) 10.74 (4.54) 10.73 (4.74) Acute myocardial infarction (%) 11,744 (10.8) 5,946 (11.0) 5,798 (10.6) Atrial fibrillation (%) 22,534 (20.7) 11,721 (21.6) 10,813 (19.8) Cardiac arrest (%) 2,306 (2.1) 1,142 (2.1) 1,164 (2.1) Heart failure (%) 8,319 (7.6) 4,334 (8.0) 3,985 (7.3) Chronic obstructive pulmonary disease (%) 4,647 (4.3) 2,186 (4.0) 2,461 (4.5) Asthma (%) 26,379 (24.2) 12,499 (23.0) 13,880 (25.4) Pulmonary embolism (%) 7,521 (6.9) 3,673 (6.8) 3,848 (7.0) Cerebral infarction (%) 10,626 (9.7) 5,364 (9.9) 5,262 (9.6) Neonatal cases (%) 5,498 (5.0) 2,839 (5.2) 2,659 (4.9) Obstetric cases (%) 7,960 (7.3) 4,215 (7.8) 3,745 (6.8) * Counts less than 20 were masked for data privacy purposes. ¥ Values were skewed by up to 5 to obscure precise counts. Table 2 Results of generalized linear mixed effects model, with three different exposure metrics for weekly particulate matter exposure as predictive variables. Columns show the weekly county level change in incidence per 10 µg/m³ increase in wildfire-specific PM 2.5 . Confounders include month, the difference in weekly temperature from monthly mean, total PM 2.5 , and county level COVID-19 incidence. *When measuring the rate of COVID admissions, a separate model was run that did not include county level COVID-19 incidence as a confounding variable. Outcome Mean of maximum 2 values of PM2.5 Weekly maximum PM2.5 Weekly mean PM2.5 Change in incidence per county per week /10 µg/m³ CI P value Change in incidence per county per week /10 µg/m³ CI P value Change in incidence per county per week /10 µg/m³ CI P value Acute myocardial infarction 0.095 [0.012–0.177] 0.025 0.084 [0.023–0.146] 0.007 0.134 [0.017–0.251] 0.025 Cardiac arrest 0.011 [-0.003–0.024] 0.13 0.011 [0.001–0.021] 0.036 0.018 [-0.002–0.038] 0.074 Heart failure 0.095 [0.015–0.174] 0.019 0.083 [0.024–0.142] 0.006 0.135 [0.022–0.248] 0.019 Atrial fibrillation 0.111 [-0.023–0.246] 0.104 0.115 [0.014–0.216] 0.026 0.186 [-0.005–0.378] 0.057 Cerebral infarction 0.068 [-0.009–0.145] 0.082 0.043 [-0.015–0.100] 0.147 0.189 [0.079–0.298] < 0.001 COPD 0.05 [0.007–0.093] 0.024 0.034 [0.001–0.066] 0.041 0.049 [-0.013–0.111] 0.122 Asthma 0.036 [-0.356–0.429] 0.857 0.067 [-0.229–0.362] 0.659 0.054 [-0.492–0.601] 0.845 Pulmonary embolism 0.077 [0.016–0.139] 0.014 0.048 [0.003–0.094] 0.038 0.076 [-0.013–0.164] 0.095 *COVID hospitalization 0.045 [-0.268–0.359] 0.776 0.026 [-0.147 - 0.2] 0.767 0.742 [-0.046 -1.529] 0.065 Neonatal outcomes 0.027 [-0.031–0.085] 0.356 0.03 [-0.014–0.073] 0.179 0.059 [-0.023–0.14] 0.157 Obstetric outcomes 0.023 [-0.052–0.099] 0.545 0.038 [-0.018–0.094] 0.459 0.04 [-0.068–0.148] 0.468 No significant association was found between wildfire smoke exposure and hospital admission for COVID pneumonia or COVID-related respiratory distress. Similarly, our specificity analysis including 14,204 COVID positive patients did not demonstrate that COVID infection has a modifying effect on the impact of wildfire smoke exposure on hospital admissions for the measured acute medical admissions (Table 3 ). Table 3 Specificity analysis comparing COVID positive and COVID negative patients, using weekly maximum wildfire specific PM 2.5 as the predictive variable. Columns show the weekly county level change in incidence per 10 µg/m³ increase in wildfire-specific PM 2.5 . Confounders include month, the difference in weekly temperature from monthly mean, and total PM 2.5 . Outcome COVID + COVID - Change in incidence per county per week /10 µg/m³ CI P value Change in incidence per county per week/ 10 µg/m³ CI P Value Acute myocardial infarction 0.016 [-0.036- 0.068] p = 0.553 0.092 [-0.066- 0.251] p = 0.252 Cardiac arrest 0.001 [-0.023- 0.025] p = 0.942 -0.031 [-0.097- 0.036] p = 0.367 Heart failure -0.016 [-0.054- 0.022] p = 0.416 -0.009 [-0.137- 0.119] p = 0.885 Atrial fibrillation 0.056 [-0.020- 0.131] p = 0.148 -0.115 [-0.302- 0.071] p = 0.226 Cerebral infarction -0.013 [-0.053- 0.026] p = 0.504 -0.104 [-0.241- 0.033] p = 0.136 COPD 0.044 [-0.014- 0.103] p = 0.136 0.063 [-0.038- 0.164] p = 0.222 Asthma -0.002 [-0.077- 0.072] p = 0.948 0.018 [-0.142- 0.178] p = 0.827 Pulmonary embolism 0.032 [-0.026- 0.090] p = 0.281 -0.035 [-0.138- 0.068] p = 0.506 Neonatal outcomes -0.011 [-0.039- 0.016] p = 0.422 0.021 [-0.040- 0.082] p = 0.498 Obstetric outcomes -0.001 [-0.026- 0.025] p = 0.958 -0.049 [-0.165- 0.067] p = 0.408 The analyses were repeated, investigating the effects of total PM 2.5 , rather than wildfire specific PM on the same outcomes. In these analyses, very few of the same effects were observed as had been seen when studying the effects of wildfire PM 2.5 . When studying the effects of total PM exposure, acute myocardial infarction only showed an increase in response to the mean of the two weekly maximum values. Atrial fibrillation risk increased in response to both weekly maximum total PM and mean of the two weekly maximums. There was an increase in encounters for asthma in response to the mean of two weekly maximum values, which was not identified in wildfire specific analyses (Table 4 ). Table 4 Results of generalized linear mixed effects model, with three different exposure metrics for weekly particulate matter exposure as predictive variables. Columns show the weekly county level change in incidence per 10 µg/m³ increase in total PM 2.5 . Confounders include month, the difference in weekly temperature from monthly mean, total PM 2.5 , and county level COVID-19 incidence. Outcome Mean of maximum 2 values of PM 2.5 Weekly maximum PM 2.5 Weekly mean PM 2.5 Change in incidence per county per week /10 µg/m³ CI P value Change in incidence per county per week /10 µg/m³ CI P value Change in incidence per county per week /10 µg/m³ CI P value Acute myocardial infarction 0.181 [0.001–0.361] 0.049 0.085 [-0.117–0.287] 0.41 0.402 [-0.106–0.911] 0.121 Cardiac arrest 0.018 [-0.051–0.087] 0.612 0.048 [-0.030–0.125] 0.232 -0.016 [-0.212–0.180] 0.871 Heart failure 0.103 [-0.038–0.244] 0.151 -0.019 [-0.177–0.138] 0.809 0.131 [-0.266–0.528] 0.517 Atrial fibrillation 0.355 [0.100–0.611] 0.006 0.222 [-0.065–0.509] 0.13 0.89 [0.168–1.611] 0.016 Cerebral infarction 0.111 [-0.056–0.278] 0.193 0.069 [-0.119–0.256] 0.474 0.408 [-0.063–0.880] 0.09 COPD 0.055 [-0.073–0.184] 0.4 0.017 [-0.128–0.161] 0.822 0.153 [-0.210–0.516] 0.408 Asthma 0.302 [0.064–0.540] 0.013 0.188 [-0.079–0.455] 0.168 0.577 [-0.095–1.249] 0.093 Pulmonary embolism -0.049 [-0.188–0.090] 0.487 -0.015 [-0.171–0.141] 0.853 -0.248 [-0.641–0.144] 0.215 Neonatal outcomes 0.014 [-0.100–0.127] 0.814 -0.032 [-0.159–0.096] 0.628 0.104 [-0.216 -0.424] 0.524 Obstetric outcomes 0.063 [-0.074–0.199] 0.368 0.112 [-0.042–0.265] 0.153 0.2 [-0.185–0.585] 0.308 Discussion Our findings suggest that each 10 µg/m³ rise in wildfire smoke exposure is associated with an increase in weekly county level hospital admission or emergency department presentation for acute myocardial infarction, cardiac arrest, heart failure, atrial fibrillation and pulmonary embolism. Weekly maximum PM 2.5 concentrations most closely correlated with rates of admission. Such findings are significant as they demonstrate relatively low concentrations of wildfire smoke, in the range of 10–50 µg/m³, are associated with acute health impacts across a wide range of diagnoses impacting multiple organ systems. We specifically sought to address the impact of lower-concentration and smoke-specific PM 2.5 exposure and, as such, the magnitudes of the mean differences reported here are modest. However, our data suggest that beyond the immediate local effects of wildfires, large populations across a wide area may suffer associated acute health impacts, as smoke has been reported to travel hundreds of miles from large wildfires. As the population impacted by these low levels of smoke is large, so too are the associated economic impacts and burden to health systems. Importantly, the associations demonstrated in this work are specifically attributable to wildfire smoke at a national level, and are not limited to an individual fire, institution, or state. (Fig. 2 ) Hence, we propose that this approach makes these results more generalizable. The lack of interaction between COVID infection and wildfire smoke may be explained by differences in the strength of each association, or a relatively small sample size of COVID positive patients. There is a shared mechanism of systemic inflammation and endothelial dysfunction between both COVID and wildfire smoke exposure. However, the acuity and severity of COVID infection requiring hospitalization may mask the effects of smoke exposure. There are several notable contemporary studies that employ similar methodology to isolate wildfire specific PM using chemical atmospheric transport models. Applying a 14-day time series approach to national Brazilian mortality data demonstrated that wildfire specific PM exposure significantly increased risk of cardiovascular (2.6%) and respiratory (7.7%) mortality as well as all-cause mortality (2.4%). The outcome metric reported was the mean increase in mortality per 10 µg/m³ increase in wildfire specific PM 2.5 over the two-week period. Significant geographic heterogeneity was observed in the results, with stronger relationships seen in the Southeast of Brazil, closer to the major population centers [ 24 ] . A similar global time series, spanning 749 cities in 43 countries, was used to calculate an attributable fraction and relative risk of annual mortality from wildfire specific PM. For each 10 µg/m³ increase in wildfire specific PM 2.5 across a moving 3 day average, relative risks were found to be 1.019, 1.017 and 1.019 for all-cause, cardiovascular and respiratory mortality, respectively [ 22 ] . When studying the effect of smoke waves, defined as days with wildfire specific PM 2.5 greater than 37 µg/m³, a 7.2% increase in all-cause respiratory admissions was observed among patients over the age of 65 in the Western United States. No significant increase was observed in cardiovascular admissions in response to these smoke waves [ 27 ] . In Southern California, multiple derivations of wildfire-specific PM 2.5 isolation and attributions demonstrate increased association with all-cause respiratory admissions compared to the association with total PM 2.5 . The effect of a 10 µg/m³ increase in daily ZIP code level wildfire specific PM 2.5 ranged from 1.28% to 10% increase in respiratory admissions, depending on the approach used to isolate wildfire smoke [ 19 ] . Our study is unique as it combines a large geographic area, long study period and diagnostic code level outcomes data with this contemporary approach to wildfire-specific PM 2.5 isolation. Our cohort was limited, as it did not capture every individual or admission within the regions studied. Due to the nature of the N3C dataset, only patients who were tested for COVID-19 were included. However, in the years 2020 and 2021, testing for COVID-19 became routine at many institutions, so it is unlikely that this induces significant selection bias. Moreover, the dataset includes data from only 58 hospital systems nationwide, meaning not all hospitals in the regions of interest were represented. As a result, the data are limited for California and the West Coast, areas of highest smoke exposure. Here, such sparse data create challenges, such as near-zero incidence rates when attempting to model the data at a daily temporal resolution despite use of zero-inflated models, which requiring a shift to weekly data. It is likely that the lack of daily analysis obscured the detection of respiratory presentations in this study. Furthermore, each hospital encounter is marked with the patient’s home ZIP code, which is not necessarily the area in which they experience smoke exposure. Additionally, as the exposure data were significantly skewed towards lower concentrations, the analysis was limited to concentrations of 50 µg/m³ and below. As a result of this narrower exposure range, a linear model was applied. While this limits our investigation of health impacts at higher smoke concentrations, it does make our model more representative of the exposure that much of the population likely experiences. Future studies should aim to incorporate higher-fidelity health data with a more comprehensive spatial distribution. Capturing greater numbers of cases in regions of high wildfire smoke would allow for nonlinear modelling of the impacts of high concentration smoke exposure. Increased access to high quality health data will allow for improved modeling of the effects that have been seen in this study. There is a significant increase in many cardiovascular and respiratory diseases in response to low concentration exposure to wildfire specific particulate matter. This observation at a population level demonstrates that even low concentrations of wildfire smoke, may cause acute health impacts. Methods Study Design A retrospective cohort study was conducted to identify associations between exposure to wildfire-specific PM 2.5 and hospital presentation with COVID-19 or acute cardiac and respiratory encounters, as well as two composite outcome measures of obstetric and neonatal pathology. This study was conducted using data from the National COVID Cohort Collaborative Secure Enclave, which were collected under Johns Hopkins University School of Medicine Central IRB (IRB00249128). Based on 45 CRF 46.116 guidelines, a waiver of informed consent was granted by the John Hopkins University IRB. The Institutional Review Board of Duke University determined that our study was exempt as the work deals exclusively with deidentified data (Pro00109375). All methods were carried out in accordance with relevant guidelines and regulations. All experimental protocols were approved by the above named institutional committees. Database Electronic health record data during US wildfire season (May 1st to October 31st) from the National COVID Cohort Collaborative (N3C) for 2020 and 2021 were evaluated. N3C is an extensive centralized repository of all patients who underwent COVID-19 testing in 58 different healthcare systems across the United States [ 28 ] . This work took place under an N3C Data Use Request [DUR-5477342]. Importantly, COVID testing was performed routinely in these hospital systems during the study period. This includes patients who presented to emergency departments or were admitted inpatient at any of the hospitals that report to N3C. The N3C limited dataset uses diagnostic codes in the form of Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM). The database contains daily counts of encounters, labelled with OMOP-CDM diagnostic codes and associated ZIP codes for the patients’ home addresses. Our function selects the first relevant diagnostic code to determine the cause of the encounter. To preserve temporal accuracy, we filtered the dataset to include only records without date shifting, which is a practice employed in some instances to deidentify records. Population Inclusion criteria: All Adult ( ≥ 18 years) and neonatal (< 1 month) patients in the N3C database, where the visit was coded with one or more of the diagnoses specified for investigation (Supplemental Matieral 1). Exclusion criteria: Encounters that lacked localization or visit information data, or patients outside the continental US (Hawaii and Alaska states) were excluded from the study. Additionally, days falling outside the wildfire season (above) and outlier days with county-level wildfire-specific PM 2.5 concentrations > 50 µg/m³ were excluded. A threshold of 50 µg/m³ was chosen, as above this concentration our models became unreliable due to sparse data. To avoid modelling areas with minimal smoke exposure, patients residing in counties falling in the bottom 66% for annual mean wildfire PM 2.5 concentrations and annual mean admissions each year were excluded. This was required to overcome problems of near-zero incidence, in counties with sparse population and few admissions, and to exclude areas with minimal smoke exposure throughout the study period. Data Processing Patient outcomes were aggregated into counts per day by patient home ZIP Code. The outcome data were then merged with the PM 2.5 exposure data from CAMS and GeosCHEM (below) using the day and ZIP Code as standard identifiers. Finally, to overcome modelling limitations in cases of near zero incidence when using daily admission data, the data were aggregated at the county level and compiled into weekly counts. The cohort construction process is illustrated in the flowchart in Fig. 1 . Exposure Daily total PM 2.5 in micrograms per cubic meter (µg/m³) was evaluated using data from the Copernicus atmosphere monitoring service (CAMS) [ 29 ] . To isolate the component of total PM concentrations that is attributable to wildfire smoke, a chemical transport model was employed. Specifically, the GEOS-Chem atmospheric model [ 30 ] was run both with and without satellite fire emissions data from NASA’s Global Fire Emissions Database v4. Initially, a global model was run at 4º x 5º spatial resolution. With these boundary conditions, 0.5° x 0.625° models were run for the continental US. From this modelling, a ‘fire fraction’ was calculated at each geographical and temporal data point to represent the proportion of total PM 2.5 that was specifically attributable to wildfires. The fire-fraction data from GEOS-Chem were then used to apportion the original CAMS data, with the output taken to represent wildfire specific PM 2.5 concentrations, as previously described [ 27 ] . Values for wildfire-specific PM 2.5 were calculated from 2020 through 2021 for each CONUS ZIP Code and were then averaged at the US county level. Three separate exposure metrics were used in our analysis. First, the mean of the two maximum weekly wildfire-specific PM 2.5 values was investigated with a one-day lag. This metric, similar to that used in other studies, captures periods of ongoing smoke exposure while accounting for the delay between exposure and disease onset or hospitalization [ 31 ] . Addition exposure variables were of weekly maximum and weekly mean wildfire-specific PM 2.5 concentrations. Outcomes The outcomes examined were the weekly incidence of acute myocardial infarction, acute heart failure exacerbation, atrial fibrillation, cerebral infarction, pulmonary embolism, cardiac arrest, asthma, and acute exacerbation of COPD at a county level. To investigate the sensitivity of the fetal-placental vasculature to wildfire smoke exposure, composite measures for adverse neonatal and obstetric outcomes were also included. The complete list of OMOP-CDM concepts associated with these categories is presented in Supplemental Matieral 1. Covariates Covariates included the month of the year, the total (non-wildfire-specific) PM 2.5 exposure, county level COVID-19 incidence, and temperature difference from monthly means. Statistical Methods Generalized linear mixed-effects models, adjusting for previously identified covariates, were used. The results are expressed as fixed-effect coefficients describing the change in weekly county level incidence of each condition with each 10 µg/m³ increase in wildfire specific PM 2.5 , concentrations, along with 95% confidence intervals. Results were considered statistically significant when p-values were below 0.05 and when the confidence intervals did not include zero. The analyses were then repeated, investigating the effects of total PM 2.5 , rather than wildfire specific PM on the same outcomes. COVID interaction A separate generalized linear mixed effects model was run to investigate the impact of wildfire smoke on COVID-positive admissions, defined as those with a COVID diagnosis within ± 7 days of admission. This model did not include COVID incidence as a confounder and instead measured the rate of admission for COVID associated pneumonia, respiratory distress or hypoxia. Additionally, a sensitivity analysis was performed to investigate differences in the association between wildfire smoke and admission rates among patients with and without comorbid COVID infection. All analyses were conducted using the R programming language, including figures, which were created with the R ggplot 2, and biscale packages [ 32 ] . Declarations Authorship F.H., K.R., V.K. and H.M. conceived the study. L.P., P.K., and D.S. developed the atmospheric models and exposure metrics. F.H., B.A., T.O. and V.K. directed the statistical analysis. F.H. and J.S. wrote the initial draft. All authors reviewed, edited and approved the manuscript. Competing interests The authors declare no competing interests HM is partly supported by the National Institute for Health Research’s (NIHR’s) Comprehensive Biomedical Research Centre at University College London Hospitals. Data availability The health outcomes data used in this study are available through the National COVID Cohort Collaborative (N3C) Secure Enclave, maintained by NCATS. Researchers may access the data by submitting a Data Use Request (DUR) via the N3C portal (covid.cd2h.org).Atmospheric data supporting the exposure metrics are publicly available. Total PM2.5 data were obtained from the Copernicus Atmosphere Monitoring Service (CAMS) (https://ads.atmosphere.copernicus.eu/datasets/cams-global-reanalysis-eac4). Wildfire emission data were sourced from the NASA Global Fire Emissions Database (GFEDv4) (https://globalfiredata.org/related.html#gfed4). The GEOS-Chem model code used for chemical transport modeling is open-source and available at geos-chem.org. Processed wildfire-specific PM2.5fractions generated during this study are available from the corresponding author upon request Funding: This work was possible due to seed funding from the OAK Foundation, and intramural funding at Duke University (Climate + Health grant funding). Acknowledgements Data science support was provided by SporeData (sporedata.com) The analyses described in this publication were conducted with data or tools accessed through the NCATS N3C Data Enclave https://covid.cd2h.org and N3C Attribution & Publication Policy v 1.2-2020-08-25b supported by NCATS Contract No. 75N95023D00001, Axle Informatics Subcontract: NCATS-P00438-B, and Duke University. This research was possible because of the patients whose information is included within the data and the organizations (https://ncats.nih.gov/n3c/resources/data-contribution/data-transfer-agreement-signatories) and scientists who have contributed to the on-going development of this community resource [https://doi.org/10.1093/jamia/ocaa196]. Disclaimer The N3C Publication committee confirmed that this manuscript in accordance with N3C data use and attribution policies; however, this content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the N3C program. The project described was supported by the National Institute of General Medical Sciences, 5U54GM104942-04. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The N3C data transfer to NCATS is performed under a Johns Hopkins University Reliance Protocol # IRB00249128 or individual site agreements with NIH. The N3C Data Enclave is managed under the authority of the NIH; information can be found at https://ncats.nih.gov/n3c/resources. We gratefully acknowledge the following core contributors to N3C:Adam B. Wilcox, Adam M. Lee, Alexis Graves, Alfred (Jerrod) Anzalone, Amin Manna, Amit Saha, Amy Olex, Andrea Zhou, Andrew E. Williams, Andrew M. Southerland, Andrew T. Girvin, Anita Walden, Anjali Sharathkumar, Benjamin Amor, Benjamin Bates, Brian Hendricks, Brijesh Patel, G. Caleb Alexander, Carolyn T. Bramante, Cavin Ward-Caviness, Charisse Madlock-Brown, Christine Suver, Christopher G. Chute, Christopher Dillon, Chunlei Wu, Clare Schmitt, Cliff Takemoto, Dan Housman, Davera Gabriel, David A. Eichmann, Diego Mazzotti, Donald E. Brown, Eilis Boudreau, Elaine L. Hill, Emily Carlson Marti, Emily R. Pfaff, Evan French, Farrukh M Koraishy, Federico Mariona, Fred Prior, George Sokos, Greg Martin, Harold P. Lehmann, Heidi Spratt, Hemalkumar B. Mehta, J.W. Awori Hayanga, Jami Pincavitch, Jaylyn Clark, Jeremy Richard Harper, Jessica Yasmine Islam, Jin Ge, Joel Gagnier, Johanna J. Loomba, John B. Buse, Jomol Mathew, Joni L. Rutter, Julie A. McMurry, Justin Guinney, Justin Starren, Karen Crowley, Katie Rebecca Bradwell, Kellie M. Walters, Ken Wilkins, Kenneth R. Gersing, Kenrick Cato, Kimberly Murray, Kristin Kostka, Lavance Northington, Lee Pyles, Lesley Cottrell, Lili M. Portilla, Mariam Deacy, Mark M. Bissell, Marshall Clark, Mary Emmett, Matvey B. Palchuk, Melissa A. Haendel, Meredith Adams, Meredith Temple-O'Connor, Michael G. Kurilla, Michele Morris, Nasia Safdar, Nicole Garbarini, Noha Sharafeldin, Ofer Sadan, Patricia A. Francis, Penny Wung Burgoon, Philip R.O. Payne, Randeep Jawa, Rebecca Erwin-Cohen, Rena C. Patel, Richard A. Moffitt, Richard L. Zhu, Rishikesan Kamaleswaran, Robert Hurley, Robert T. Miller, Saiju Pyarajan, Sam G. Michael, Samuel Bozzette, Sandeep K. Mallipattu, Satyanarayana Vedula, Scott Chapman, Shawn T. O'Neil, Soko Setoguchi, Stephanie S. Hong, Steven G. Johnson, Tellen D. Bennett, Tiffany J. Callahan, Umit Topaloglu, Valery Gordon, Vignesh Subbian, Warren A. Kibbe, Wenndy Hernandez, Will Beasley, Will Cooper, William Hillegass, Xiaohan Tanner Zhang. Details of contributions available at covid.cd2h.org/core-contributors References Costello, A. et al. Managing the health effects of climate change: Lancet and University College London Institute for Global Health Commission. Lancet 373 , 1693–1733 (2009). Marlon, J. R. et al. Wildfire responses to abrupt climate change in North America. Proc. Natl. Acad. Sci. U S A . 106 , 2519–2524 (2009). Mansoor, S. et al. Elevation in wildfire frequencies with respect to the climate change. J. Environ. Manage. 301 , 113769 (2022). Jones, M. W. et al. Global and regional trends and drivers of fire under climate change. Rev Geophys 60 , (2022). Romanello, M. et al. The 2024 report of the Lancet Countdown on health and climate change: facing record-breaking threats from delayed action. Lancet 404 , 1847–1896 (2024). Hughes, F. et al. Impact of Wildfire Smoke on Acute Illness. Anesthesiology 141 , 779–789 (2024). Reid, C. E. et al. Critical Review of Health Impacts of Wildfire Smoke Exposure. Environ. Health Perspect. 124 , 1334–1343 (2016). Thangavel, P., Park, D. & Lee, Y. C. Recent insights into particulate matter (PM2.5)-mediated toxicity in humans: An overview. Int. J. Environ. Res. Public. Health . 19 , 7511 (2022). Wettstein, Z. S. et al. Cardiovascular and Cerebrovascular Emergency Department Visits Associated With Wildfire Smoke Exposure in California in 2015. J Am. Heart Assoc 7 , (2018). Mandaglio-Collados, D. et al. Impact of particulate matter on the incidence of atrial fibrillation and the risk of adverse clinical outcomes: A review. Sci. Total Environ. 880 , 163352 (2023). Shah, A. S. V. et al. Global association of air pollution and heart failure: a systematic review and meta-analysis. Lancet 382 , 1039–1048 (2013). Farhadi, Z., Abulghasem Gorgi, H., Shabaninejad, H., Aghajani Delavar, M. & Torani, S. Association between PM2.5 and risk of hospitalization for myocardial infarction: a systematic review and a meta-analysis. BMC Public. Health . 20 , 314 (2020). Chen, Z., Liu, P., Xia, X., Wang, L. & Li, X. The underlying mechanism of PM2.5-induced ischemic stroke. Environ. Pollut . 310 , 119827 (2022). Kloog, I. et al. Effects of airborne fine particles (PM2.5) on deep vein thrombosis admissions in the northeastern United States. J. Thromb. Haemost . 13 , 768–774 (2015). Kojima, S. et al. Association of Fine Particulate Matter Exposure With Bystander-Witnessed Out-of-Hospital Cardiac Arrest of Cardiac Origin in Japan. JAMA Netw. Open. 3 , e203043 (2020). Sheppard, N., Carroll, M., Gao, C. & Lane, T. Particulate matter air pollution and COVID-19 infection, severity, and mortality: A systematic review and meta-analysis. Sci. Total Environ. 880 , 163272 (2023). Amjad, S., Chojecki, D., Osornio-Vargas, A. & Ospina, M. B. Wildfire exposure during pregnancy and the risk of adverse birth outcomes: A systematic review. Environ. Int. 156 , 106644 (2021). Evans, J. et al. Birth Outcomes, Health, and Health Care Needs of Childbearing Women following Wildfire Disasters: An Integrative, State-of-the-Science Review. Environ. Health Perspect. 130 , 86001 (2022). Aguilera, R., Corringham, T., Gershunov, A. & Benmarhnia, T. Wildfire smoke impacts respiratory health more than fine particles from other sources: observational evidence from Southern California. Nat. Commun. 12 , 1493 (2021). Morgan, G. et al. Effects of bushfire smoke on daily mortality and hospital admissions in Sydney, Australia. Epidemiology 21 , 47–55 (2010). Salimi, F., Henderson, S. B., Morgan, G. G., Jalaludin, B. & Johnston, F. H. Ambient particulate matter, landscape fire smoke, and emergency ambulance dispatches in Sydney, Australia. Environ. Int. 99 , 208–212 (2017). Chen, G. et al. Mortality risk attributable to wildfire-related PM pollution: a global time series study in 749 locations. Lancet Planet. Health . 5 , e579–e587 (2021). Ma, Y. et al. Long-term exposure to wildland fire smoke PM and mortality in the contiguous United States. Proc. Natl. Acad. Sci. U S A . 121 , e2403960121 (2024). Ye, T. et al. Short-term exposure to wildfire-related PM increases mortality risks and burdens in Brazil. Nat. Commun. 13 , 7651 (2022). Fann, N. et al. The health impacts and economic value of wildland fire episodes in the U.S.: 2008–2012. Sci. Total Environ. 610–611 , 802–809 (2018). O’Dell, K. et al. Estimated mortality and morbidity attributable to smoke plumes in the United States: Not just a western US problem. GeoHealth 5, e2021GH000457 (2021). Liu, J. C. et al. Wildfire-specific Fine Particulate Matter and Risk of Hospital Admissions in Urban and Rural Counties. Epidemiology 28 , 77–85 (2017). Haendel, M. A. et al. The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment. J. Am. Med. Inf. Assoc. 28 , 427–443 (2021). Validation report of. the CAMS global reanalysis of aerosols and reactive trace gases, period 2003–2022. https://atmosphere.copernicus.eu/node/1011 The International GEOS-Chem User Community. geoschem/GCClassic: GEOS-Chem Classic 14.1.0. Zenodo, (2023). 10.5281/ZENODO.7600579 Heaney, A. et al. Impacts of fine particulate matter from wildfire smoke on respiratory and cardiovascular health in California. GeoHealth 6, eGH000578 (2022). (2021). R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing,Vienna, Austria. (2017). https://www.R-project.org/ Additional Declarations No competing interests reported. Supplementary Files SupplementalMaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 10 May, 2026 Reviewers agreed at journal 08 May, 2026 Reviewers agreed at journal 07 May, 2026 Reviewers invited by journal 03 Apr, 2026 Editor assigned by journal 26 Mar, 2026 Submission checks completed at journal 24 Mar, 2026 First submitted to journal 24 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9012485","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":617168386,"identity":"15ef6454-42fc-4f19-8b91-7869eb4085b6","order_by":0,"name":"Fintan Hughes","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAq0lEQVRIiWNgGAWjYBAC9mYg8YHhAAODBLFaeA4zMDbOIE3LAQbGZh7StLDzHn9su+NO4vzoBsaHP4jSwsyX2Jx75lnixjsHmI15iNFiz8xj2Jzbdjhx44wENmniHAbSYgnRwv6TSIcBtTACtcyXSGBjIMphIC0ze9sOG2+QSGyWJk4L/xmDDz/bDsvOn5F88CNRDoMDgwOMDSRpYGCQJ1XDKBgFo2AUjBwAAIXeMvytK06zAAAAAElFTkSuQmCC","orcid":"","institution":"University College London","correspondingAuthor":true,"prefix":"","firstName":"Fintan","middleName":"","lastName":"Hughes","suffix":""},{"id":617168387,"identity":"ebe35e18-5e2c-4cc0-913e-97a7e2d85124","order_by":1,"name":"Luke Parsons","email":"","orcid":"","institution":"The Nature Conservancy","correspondingAuthor":false,"prefix":"","firstName":"Luke","middleName":"","lastName":"Parsons","suffix":""},{"id":617168388,"identity":"830edd1a-5a62-49a0-a6e6-84782632fe10","order_by":2,"name":"Brooke Alhanti","email":"","orcid":"","institution":"Duke University","correspondingAuthor":false,"prefix":"","firstName":"Brooke","middleName":"","lastName":"Alhanti","suffix":""},{"id":617168389,"identity":"16bd8aff-c354-497e-9fa2-59d7c7cbb271","order_by":3,"name":"Jamarc Simon","email":"","orcid":"","institution":"Duke University","correspondingAuthor":false,"prefix":"","firstName":"Jamarc","middleName":"","lastName":"Simon","suffix":""},{"id":617168390,"identity":"50da4eac-b44f-40da-b0cb-977451c7c479","order_by":4,"name":"Prasad Kasibhatla","email":"","orcid":"","institution":"Duke University","correspondingAuthor":false,"prefix":"","firstName":"Prasad","middleName":"","lastName":"Kasibhatla","suffix":""},{"id":617168391,"identity":"d76f4ddb-54c8-45fa-9fdf-c7ca1afc292b","order_by":5,"name":"Drew Shindell","email":"","orcid":"","institution":"Duke University","correspondingAuthor":false,"prefix":"","firstName":"Drew","middleName":"","lastName":"Shindell","suffix":""},{"id":617168392,"identity":"61175008-fd4d-4e44-a9ff-3d5895f0bcd2","order_by":6,"name":"Tetsu Ohnuma","email":"","orcid":"","institution":"Duke University","correspondingAuthor":false,"prefix":"","firstName":"Tetsu","middleName":"","lastName":"Ohnuma","suffix":""},{"id":617168393,"identity":"c81ac005-a4c7-4e95-b55a-d103bdbde1d6","order_by":7,"name":"Karthik Ragunathan","email":"","orcid":"","institution":"Duke University","correspondingAuthor":false,"prefix":"","firstName":"Karthik","middleName":"","lastName":"Ragunathan","suffix":""},{"id":617168394,"identity":"f8c48720-dfc2-43a7-8088-36415348f9b0","order_by":8,"name":"Hugh Montgomery","email":"","orcid":"","institution":"University College London","correspondingAuthor":false,"prefix":"","firstName":"Hugh","middleName":"","lastName":"Montgomery","suffix":""},{"id":617168395,"identity":"69cd8205-e4e8-455d-9d21-6d05b042ab4d","order_by":9,"name":"Vijay Krishnamoorthy","email":"","orcid":"","institution":"Duke University","correspondingAuthor":false,"prefix":"","firstName":"Vijay","middleName":"","lastName":"Krishnamoorthy","suffix":""}],"badges":[],"createdAt":"2026-03-02 16:54:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9012485/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9012485/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106540796,"identity":"18b2f1f5-e819-436e-8a6e-c2172e9058d4","added_by":"auto","created_at":"2026-04-09 16:02:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":329418,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart illustrating data inclusion and exclusion criteria in the N3C study. Each of these values have been skewed by ±5 to enhance deidentification.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9012485/v1/e7d04eb08a4134c18fd3761d.png"},{"id":106540798,"identity":"8adf3f33-b85a-48e3-ae89-987ac511e2c6","added_by":"auto","created_at":"2026-04-09 16:02:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1474497,"visible":true,"origin":"","legend":"\u003cp\u003eBivariate maps of average adjusted PM\u003csub\u003e2.5\u003c/sub\u003e concentration and hospital admissions in the United States (2020 above, 2021 below). The red gradient represents tertiles of mean weekly admission, and the blue gradient represents mean annual wildfire specific PM\u003csub\u003e2.5\u003c/sub\u003e. Only counties in the highest tertile of weekly admissions and wildfire specific PM\u003csub\u003e2.5\u003c/sub\u003e were included. In 2020 this included counties with a mean annual wildfire specific PM\u003csub\u003e2.5\u003c/sub\u003e concentration \u0026gt;0.86 µg/m³ and mean weekly admissions \u0026gt;35 patients. In 2021 included counties had a mean annual wildfire specific PM\u003csub\u003e2.5\u003c/sub\u003e concentration \u0026gt;2.8 µg/m³ and mean weekly admissions \u0026gt;38 patients.\u0026nbsp;\u0026nbsp;\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9012485/v1/f8f5742b66eba448e46f5b77.png"},{"id":106724894,"identity":"0da89eb7-ba3b-41c1-8f50-ede6c05ecd7f","added_by":"auto","created_at":"2026-04-12 18:30:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3451009,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9012485/v1/c8d231ee-e20d-4aeb-a111-a7b103f2209a.pdf"},{"id":106540797,"identity":"42106922-01eb-448c-9b3e-e0df8ed63324","added_by":"auto","created_at":"2026-04-09 16:02:26","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":4234419,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-9012485/v1/3ec3e5e149a1b84a74342179.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Impact of Wildfire Smoke on Acute Cardiovascular and Respiratory Illness in the US","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eClimate change, recognized as the greatest health threat of the 21st century \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e, is driving an increase in wildfire frequency \u003csup\u003e[\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e, and thus human exposure to wildfire smoke. From 2001\u0026ndash;2004 to 2018\u0026ndash;2021, 61% of countries experienced an increase in population exposure to wildfires \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Inhalation of particulate matter (PM) from wildfire smoke triggers a cascade of harmful events, including inflammation, oxidative stress, endothelial dysfunction, sympathetic activation and platelet activation, which can result in end organ damage and lead to a variety of acute health issues \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Direct contact of the respiratory system with PM increases the incidence of asthma and COPD exacerbation \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Particulate matter with a diameter of 2.5 microns (PM\u003csub\u003e2.5\u003c/sub\u003e) and less is a particular focus in health research compared to larger particle sizes, as it penetrates deep into the lung and enters the circulation causing systemic effects \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Systemic effects (above) can have a broad range of cardiovascular effects \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Arrhythmias, including atrial fibrillation, are driven by inflammation and sympathetic stimulation \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. PM-induced hypertension increases cardiac afterload. Both can exacerbate heart failure \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Platelet activation and endothelial dysfunction can precipitate acute coronary syndromes, myocardial infarction, \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e cerebral infarction and pulmonary embolism \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Ultimately, increased incidence of cardiac arrests has also been observed in response to PM exposure \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Additionally, impacts on both the respiratory system and the endothelium are central to the pathophysiology of COVID-19 pneumonia, which was highly prevalent during the study period, and there appears to be a detrimental interaction between the effects of COVID-19 infection and PM exposure \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. The placental-fetal unit is also highly sensitive to endothelial dysfunction, inflammation and oxidative stress. Vascular injury and inflammation here can lead to pre-eclampsia, intrauterine growth restriction, premature labor and low birth weight \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Wildfire derived particulate matter appears to be more harmful than other sources of PM\u003csub\u003e2.5\u003c/sub\u003e, due to the presence of polycyclic aromatic hydrocarbons and aldehydes in the smoke \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite the increasing exposure of human populations to wildfire smoke, effective methods to predict and mitigate the health impact of this exposure remain inadequate. Although the mechanisms of smoke impact have been well described, little is known about the magnitude of the health impacts caused by exposure to given concentrations of wildfire smoke. Many existing studies have focused on the effects of individual wildfires in specific regions over brief time periods, or focused on the effects of isolated fire events on confined populations \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Many previous studies have tended to combine all PM sources, rather than isolating the effects of wildfire-specific PM\u003csub\u003e2.5\u003c/sub\u003e. Therefore, the differences between the health impacts of wildfire-specific (compared to anthropogenic) PM\u003csub\u003e2.5\u003c/sub\u003e are not yet fully understood. A number of contemporary studies have examined the impact of wildfire-specific PM\u003csub\u003e2.5\u003c/sub\u003e, on a range of outcomes including cardiovascular and respiratory \u003cem\u003emortality\u003c/em\u003e globally \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e, as well as in the US \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e and Brazil \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. However, impacts on \u003cem\u003emorbidity\u003c/em\u003e (hospital admissions for specific diagnoses), and especially that relating to low-level exposure (such as that deriving from far distant fires) across large populations, are less well defined. While there are national level studies that estimate population-level respiratory impacts of PM exposure, they employ risk estimates from one or more smaller studies and extrapolate the risk estimates to a national level \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Our study improves on this methodology by measuring these outcomes directly. It is possible that significant unrecognized acute morbidity results from low-level wildfire smoke exposure. We sought to address this gap in knowledge, by studying the relationship between a range of cardiovascular, respiratory and obstetric/neonatal conditions and wildfire-related PM\u003csub\u003e2.5\u003c/sub\u003e exposure (at the low-moderate concentrations, \u0026lt;\u0026thinsp;50 \u0026micro;g/m\u0026sup3;, experienced by large populations) across the entire continental US (CONUS).\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e depicts a flowchart of the data selection process, utilizing the N3C database initially comprising a total of 23,155,003 patients. The analysis continued with restriction to 3,908,545 patients diagnosed with COVID-19, cardiac, pulmonary, obstetric, or neonatal conditions during the 2020\u0026ndash;2021 period. Further exclusions were made for patients located outside CONUS, counties without available total population data, counties with annual admission rates and annual PM\u003csub\u003e2.5\u003c/sub\u003e levels below the 66th percentile (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), and dates outside of the wildfire season resulting in 124,362 patients. Finally, after removing days with county level wildfire-specific PM\u003csub\u003e2.5\u003c/sub\u003e \u0026gt; 50 \u0026micro;g/m\u0026sup3;, 109,012 patients remained in the dataset for analysis. Each of these values have been skewed by \u0026plusmn;\u0026thinsp;5 to enhance deidentification.\u003c/p\u003e \u003cp\u003eAmong patients treated at hospitals in the N3C database in areas affected by wildfire smoke, increasing wildfire-specific PM\u003csub\u003e2.5\u003c/sub\u003e is associated with an increase in the incidence of major cardiovascular and respiratory diseases (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For each 10 \u0026micro;g/m\u0026sup3; increase in weekly maximum wildfire-specific PM\u003csub\u003e2.5\u003c/sub\u003e there are statistically significant increases in the weekly incidence per county for acute myocardial infarction (0.084, 95% CI, 0.023\u0026ndash;0.146), cardiac arrest (0.011, 95% CI, 0.001\u0026ndash;0.021), heart failure (0.083, 95% CI, 0.024\u0026ndash;0.142), atrial fibrillation (0.115, 95% CI, 0.014\u0026ndash;0.216), COPD exacerbation (0.034, 95% CI, 0.001\u0026ndash;0.066) and pulmonary embolism (0.048, 95% CI, 0.003\u0026ndash;0.094). While the increased incidences of acute MI and heart failure exacerbations were consistent across all metrics of wildfire specific PM\u003csub\u003e2.5\u003c/sub\u003e exposure, cerebral infarction only demonstrated an increased risk in relation to weekly mean wildfire specific PM\u003csub\u003e2.5\u003c/sub\u003e, and cardiac arrest correlated only with the weekly maximum concentration. There were no statistically significant increases in the incidence of obstetric or neonatal disease seen in this analysis. These results represent the mean increase in weekly hospital admissions and emergency department visits per county in response for each 10 \u0026micro;g/m\u0026sup3; increase in wildfire specific PM\u003csub\u003e2.5\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the study sample stratified by year (2020\u0026ndash;2021)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109,012\u0026yen;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54,272\u0026yen;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54,740\u0026yen;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of stay (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.31 (8.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.29 (8.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.34 (8.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- Male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51,694 (47.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25,828 (47.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25,866 (47.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57,299 (52.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28,435 (52.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28,864 (52.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- Other/Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20* (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20* (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20* (0.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70,397 (64.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35,344 (65.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35,053 (64.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- Black or African American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17,136 (15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8,266 (15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,870 (16.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- Asian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,297 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,053 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,244 (4.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- Other/Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17,182 (15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8,609 (15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,573 (15.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51.53 (25.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.44 (23.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.63 (26.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeath (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13,226 (12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,401 (13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5,825 (10.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuan-Charlson comorbidity score (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.53 (2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.51 (1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.56 (2.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCovid status\u0026thinsp;=\u0026thinsp;Positive (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14,204 (13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,745 (12.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7,459 (13.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstimated percent poverty (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.57 (4.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.57 (4.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.56 (4.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstimated percent unemployment (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.37 (1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.38 (1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.35 (1.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstimated per capita income (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34,590.71 (7,385.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34,506.52 (7,217.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34,674.19 (7,547.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstimated percent no high school diploma (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.74 (4.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.74 (4.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.73 (4.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute myocardial infarction (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11,744 (10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5,946 (11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5,798 (10.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial fibrillation (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,534 (20.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11,721 (21.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10,813 (19.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac arrest (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,306 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,142 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,164 (2.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart failure (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8,319 (7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,334 (8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3,985 (7.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic obstructive pulmonary disease (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,647 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,186 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,461 (4.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsthma (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26,379 (24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12,499 (23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13,880 (25.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulmonary embolism (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7,521 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,673 (6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3,848 (7.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebral infarction (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,626 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5,364 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5,262 (9.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeonatal cases (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,498 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,839 (5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,659 (4.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObstetric cases (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7,960 (7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,215 (7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3,745 (6.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e* Counts less than 20 were masked for data privacy purposes.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u0026yen; Values were skewed by up to 5 to obscure precise counts.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of generalized linear mixed effects model, with three different exposure metrics for weekly particulate matter exposure as predictive variables. Columns show the weekly county level change in incidence per 10 \u0026micro;g/m\u0026sup3; increase in \u003cb\u003ewildfire-specific\u003c/b\u003e PM\u003csub\u003e2.5\u003c/sub\u003e. Confounders include month, the difference in weekly temperature from monthly mean, total PM\u003csub\u003e2.5\u003c/sub\u003e, and county level COVID-19 incidence. *When measuring the rate of COVID admissions, a separate model was run that did not include county level COVID-19 incidence as a confounding variable.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eMean of maximum 2 values of PM2.5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eWeekly maximum PM2.5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eWeekly mean PM2.5\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChange in incidence per county per week /10 \u0026micro;g/m\u0026sup3;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChange in incidence per county per week /10 \u0026micro;g/m\u0026sup3;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eChange in incidence per county per week /10 \u0026micro;g/m\u0026sup3;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute myocardial infarction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.095\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e[0.012\u0026ndash;0.177]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.084\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e[0.023\u0026ndash;0.146]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.134\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e[0.017\u0026ndash;0.251]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac arrest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[-0.003\u0026ndash;0.024]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e[0.001\u0026ndash;0.021]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.036\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e[-0.002\u0026ndash;0.038]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.095\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e[0.015\u0026ndash;0.174]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.083\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e[0.024\u0026ndash;0.142]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.135\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e[0.022\u0026ndash;0.248]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial fibrillation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[-0.023\u0026ndash;0.246]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.115\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e[0.014\u0026ndash;0.216]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.026\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e[-0.005\u0026ndash;0.378]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebral infarction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[-0.009\u0026ndash;0.145]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[-0.015\u0026ndash;0.100]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.189\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e[0.079\u0026ndash;0.298]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e[0.007\u0026ndash;0.093]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.034\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e[0.001\u0026ndash;0.066]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.041\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e[-0.013\u0026ndash;0.111]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsthma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[-0.356\u0026ndash;0.429]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[-0.229\u0026ndash;0.362]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e[-0.492\u0026ndash;0.601]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulmonary embolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.077\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e[0.016\u0026ndash;0.139]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.048\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e[0.003\u0026ndash;0.094]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.038\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e[-0.013\u0026ndash;0.164]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e*COVID hospitalization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[-0.268\u0026ndash;0.359]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[-0.147 - 0.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e[-0.046 -1.529]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeonatal outcomes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[-0.031\u0026ndash;0.085]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[-0.014\u0026ndash;0.073]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e[-0.023\u0026ndash;0.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObstetric outcomes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[-0.052\u0026ndash;0.099]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[-0.018\u0026ndash;0.094]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e[-0.068\u0026ndash;0.148]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNo significant association was found between wildfire smoke exposure and hospital admission for COVID pneumonia or COVID-related respiratory distress. Similarly, our specificity analysis including 14,204 COVID positive patients did not demonstrate that COVID infection has a modifying effect on the impact of wildfire smoke exposure on hospital admissions for the measured acute medical admissions (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpecificity analysis comparing COVID positive and COVID negative patients, using weekly maximum wildfire specific PM\u003csub\u003e2.5\u003c/sub\u003e as the predictive variable. Columns show the weekly county level change in incidence per 10 \u0026micro;g/m\u0026sup3; increase in \u003cb\u003ewildfire-specific\u003c/b\u003e PM\u003csub\u003e2.5\u003c/sub\u003e. Confounders include month, the difference in weekly temperature from monthly mean, and total PM\u003csub\u003e2.5\u003c/sub\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eCOVID +\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eCOVID -\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChange in incidence per county per week /10 \u0026micro;g/m\u0026sup3;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChange in incidence per county per week/ 10 \u0026micro;g/m\u0026sup3;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute myocardial infarction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[-0.036- 0.068]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e[-0.066- 0.251]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac arrest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[-0.023- 0.025]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e[-0.097- 0.036]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.367\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[-0.054- 0.022]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e[-0.137- 0.119]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.885\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial fibrillation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[-0.020- 0.131]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e[-0.302- 0.071]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.226\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebral infarction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[-0.053- 0.026]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e[-0.241- 0.033]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[-0.014- 0.103]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e[-0.038- 0.164]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsthma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[-0.077- 0.072]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e[-0.142- 0.178]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.827\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulmonary embolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[-0.026- 0.090]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e[-0.138- 0.068]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.506\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeonatal outcomes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[-0.039- 0.016]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e[-0.040- 0.082]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.498\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObstetric outcomes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[-0.026- 0.025]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e[-0.165- 0.067]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.408\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe analyses were repeated, investigating the effects of total PM\u003csub\u003e2.5\u003c/sub\u003e, rather than wildfire specific PM on the same outcomes. In these analyses, very few of the same effects were observed as had been seen when studying the effects of wildfire PM\u003csub\u003e2.5\u003c/sub\u003e. When studying the effects of total PM exposure, acute myocardial infarction only showed an increase in response to the mean of the two weekly maximum values. Atrial fibrillation risk increased in response to both weekly maximum total PM and mean of the two weekly maximums. There was an increase in encounters for asthma in response to the mean of two weekly maximum values, which was not identified in wildfire specific analyses (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of generalized linear mixed effects model, with three different exposure metrics for weekly particulate matter exposure as predictive variables. Columns show the weekly county level change in incidence per 10 \u0026micro;g/m\u0026sup3; increase in \u003cb\u003etotal\u003c/b\u003e PM\u003csub\u003e2.5\u003c/sub\u003e. Confounders include month, the difference in weekly temperature from monthly mean, total PM\u003csub\u003e2.5\u003c/sub\u003e, and county level COVID-19 incidence.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eMean of maximum 2 values of PM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eWeekly maximum PM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eWeekly mean PM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChange in incidence per county per week /10 \u0026micro;g/m\u0026sup3;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChange in incidence per county per week /10 \u0026micro;g/m\u0026sup3;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eChange in incidence per county per week /10 \u0026micro;g/m\u0026sup3;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute myocardial infarction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.001\u0026ndash;0.361]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e[-0.117\u0026ndash;0.287]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[-0.106\u0026ndash;0.911]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac arrest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[-0.051\u0026ndash;0.087]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e[-0.030\u0026ndash;0.125]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[-0.212\u0026ndash;0.180]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[-0.038\u0026ndash;0.244]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e[-0.177\u0026ndash;0.138]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[-0.266\u0026ndash;0.528]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.517\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial fibrillation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.355\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e[0.100\u0026ndash;0.611]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e[-0.065\u0026ndash;0.509]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[0.168\u0026ndash;1.611]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebral infarction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[-0.056\u0026ndash;0.278]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e[-0.119\u0026ndash;0.256]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[-0.063\u0026ndash;0.880]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[-0.073\u0026ndash;0.184]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e[-0.128\u0026ndash;0.161]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[-0.210\u0026ndash;0.516]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.408\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsthma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.302\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e[0.064\u0026ndash;0.540]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e[-0.079\u0026ndash;0.455]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[-0.095\u0026ndash;1.249]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulmonary embolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[-0.188\u0026ndash;0.090]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e[-0.171\u0026ndash;0.141]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[-0.641\u0026ndash;0.144]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeonatal outcomes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[-0.100\u0026ndash;0.127]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e[-0.159\u0026ndash;0.096]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[-0.216 -0.424]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObstetric outcomes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[-0.074\u0026ndash;0.199]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e[-0.042\u0026ndash;0.265]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[-0.185\u0026ndash;0.585]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur findings suggest that each 10 \u0026micro;g/m\u0026sup3; rise in wildfire smoke exposure is associated with an increase in weekly county level hospital admission or emergency department presentation for acute myocardial infarction, cardiac arrest, heart failure, atrial fibrillation and pulmonary embolism. Weekly maximum PM\u003csub\u003e2.5\u003c/sub\u003e concentrations most closely correlated with rates of admission. Such findings are significant as they demonstrate relatively low concentrations of wildfire smoke, in the range of 10\u0026ndash;50 \u0026micro;g/m\u0026sup3;, are associated with acute health impacts across a wide range of diagnoses impacting multiple organ systems.\u003c/p\u003e \u003cp\u003eWe specifically sought to address the impact of lower-concentration and smoke-specific PM\u003csub\u003e2.5\u003c/sub\u003e exposure and, as such, the magnitudes of the mean differences reported here are modest. However, our data suggest that beyond the immediate local effects of wildfires, large populations across a wide area may suffer associated acute health impacts, as smoke has been reported to travel hundreds of miles from large wildfires. As the population impacted by these low levels of smoke is large, so too are the associated economic impacts and burden to health systems. Importantly, the associations demonstrated in this work are specifically attributable to wildfire smoke at a national level, and are not limited to an individual fire, institution, or state. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) Hence, we propose that this approach makes these results more generalizable.\u003c/p\u003e \u003cp\u003eThe lack of interaction between COVID infection and wildfire smoke may be explained by differences in the strength of each association, or a relatively small sample size of COVID positive patients. There is a shared mechanism of systemic inflammation and endothelial dysfunction between both COVID and wildfire smoke exposure. However, the acuity and severity of COVID infection requiring hospitalization may mask the effects of smoke exposure.\u003c/p\u003e \u003cp\u003eThere are several notable contemporary studies that employ similar methodology to isolate wildfire specific PM using chemical atmospheric transport models. Applying a 14-day time series approach to national Brazilian mortality data demonstrated that wildfire specific PM exposure significantly increased risk of cardiovascular (2.6%) and respiratory (7.7%) mortality as well as all-cause mortality (2.4%). The outcome metric reported was the mean increase in mortality per 10 \u0026micro;g/m\u0026sup3; increase in wildfire specific PM\u003csub\u003e2.5\u003c/sub\u003e over the two-week period. Significant geographic heterogeneity was observed in the results, with stronger relationships seen in the Southeast of Brazil, closer to the major population centers \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. A similar global time series, spanning 749 cities in 43 countries, was used to calculate an attributable fraction and relative risk of annual mortality from wildfire specific PM. For each 10 \u0026micro;g/m\u0026sup3; increase in wildfire specific PM\u003csub\u003e2.5\u003c/sub\u003e across a moving 3 day average, relative risks were found to be 1.019, 1.017 and 1.019 for all-cause, cardiovascular and respiratory mortality, respectively \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. When studying the effect of smoke waves, defined as days with wildfire specific PM\u003csub\u003e2.5\u003c/sub\u003e greater than 37 \u0026micro;g/m\u0026sup3;, a 7.2% increase in all-cause respiratory admissions was observed among patients over the age of 65 in the Western United States. No significant increase was observed in cardiovascular admissions in response to these smoke waves \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. In Southern California, multiple derivations of wildfire-specific PM\u003csub\u003e2.5\u003c/sub\u003e isolation and attributions demonstrate increased association with all-cause respiratory admissions compared to the association with total PM\u003csub\u003e2.5\u003c/sub\u003e. The effect of a 10 \u0026micro;g/m\u0026sup3; increase in daily ZIP code level wildfire specific PM\u003csub\u003e2.5\u003c/sub\u003e ranged from 1.28% to 10% increase in respiratory admissions, depending on the approach used to isolate wildfire smoke \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Our study is unique as it combines a large geographic area, long study period and diagnostic code level outcomes data with this contemporary approach to wildfire-specific PM\u003csub\u003e2.5\u003c/sub\u003e isolation.\u003c/p\u003e \u003cp\u003eOur cohort was limited, as it did not capture every individual or admission within the regions studied. Due to the nature of the N3C dataset, only patients who were tested for COVID-19 were included. However, in the years 2020 and 2021, testing for COVID-19 became routine at many institutions, so it is unlikely that this induces significant selection bias. Moreover, the dataset includes data from only 58 hospital systems nationwide, meaning not all hospitals in the regions of interest were represented. As a result, the data are limited for California and the West Coast, areas of highest smoke exposure. Here, such sparse data create challenges, such as near-zero incidence rates when attempting to model the data at a daily temporal resolution despite use of zero-inflated models, which requiring a shift to weekly data. It is likely that the lack of daily analysis obscured the detection of respiratory presentations in this study. Furthermore, each hospital encounter is marked with the patient\u0026rsquo;s home ZIP code, which is not necessarily the area in which they experience smoke exposure. Additionally, as the exposure data were significantly skewed towards lower concentrations, the analysis was limited to concentrations of 50 \u0026micro;g/m\u0026sup3; and below. As a result of this narrower exposure range, a linear model was applied. While this limits our investigation of health impacts at higher smoke concentrations, it does make our model more representative of the exposure that much of the population likely experiences.\u003c/p\u003e \u003cp\u003eFuture studies should aim to incorporate higher-fidelity health data with a more comprehensive spatial distribution. Capturing greater numbers of cases in regions of high wildfire smoke would allow for nonlinear modelling of the impacts of high concentration smoke exposure. Increased access to high quality health data will allow for improved modeling of the effects that have been seen in this study.\u003c/p\u003e \u003cp\u003eThere is a significant increase in many cardiovascular and respiratory diseases in response to low concentration exposure to wildfire specific particulate matter. This observation at a population level demonstrates that even low concentrations of wildfire smoke, may cause acute health impacts.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eA retrospective cohort study was conducted to identify associations between exposure to wildfire-specific PM\u003csub\u003e2.5\u003c/sub\u003e and hospital presentation with COVID-19 or acute cardiac and respiratory encounters, as well as two composite outcome measures of obstetric and neonatal pathology. This study was conducted using data from the National COVID Cohort Collaborative Secure Enclave, which were collected under Johns Hopkins University School of Medicine Central IRB (IRB00249128). Based on 45 CRF 46.116 guidelines, a waiver of informed consent was granted by the John Hopkins University IRB. The Institutional Review Board of Duke University determined that our study was exempt as the work deals exclusively with deidentified data (Pro00109375). All methods were carried out in accordance with relevant guidelines and regulations. All experimental protocols were approved by the above named institutional committees.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDatabase\u003c/h3\u003e\n\u003cp\u003eElectronic health record data during US wildfire season (May 1st to October 31st) from the National COVID Cohort Collaborative (N3C) for 2020 and 2021 were evaluated. N3C is an extensive centralized repository of all patients who underwent COVID-19 testing in 58 different healthcare systems across the United States \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. This work took place under an N3C Data Use Request [DUR-5477342]. Importantly, COVID testing was performed routinely in these hospital systems during the study period. This includes patients who presented to emergency departments or were admitted inpatient at any of the hospitals that report to N3C. The N3C limited dataset uses diagnostic codes in the form of Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM). The database contains daily counts of encounters, labelled with OMOP-CDM diagnostic codes and associated ZIP codes for the patients\u0026rsquo; home addresses. Our function selects the first relevant diagnostic code to determine the cause of the encounter. To preserve temporal accuracy, we filtered the dataset to include only records without date shifting, which is a practice employed in some instances to deidentify records.\u003c/p\u003e\n\u003ch3\u003ePopulation\u003c/h3\u003e\n\u003cp\u003eInclusion criteria: All Adult (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;18 years) and neonatal (\u0026lt;\u0026thinsp;1 month) patients in the N3C database, where the visit was coded with one or more of the diagnoses specified for investigation (Supplemental Matieral 1).\u003c/p\u003e \u003cp\u003eExclusion criteria: Encounters that lacked localization or visit information data, or patients outside the continental US (Hawaii and Alaska states) were excluded from the study. Additionally, days falling outside the wildfire season (above) and outlier days with county-level wildfire-specific PM\u003csub\u003e2.5\u003c/sub\u003e concentrations\u0026thinsp;\u0026gt;\u0026thinsp;50 \u0026micro;g/m\u0026sup3; were excluded. A threshold of 50 \u0026micro;g/m\u0026sup3; was chosen, as above this concentration our models became unreliable due to sparse data.\u003c/p\u003e \u003cp\u003eTo avoid modelling areas with minimal smoke exposure, patients residing in counties falling in the bottom 66% for annual mean wildfire PM\u003csub\u003e2.5\u003c/sub\u003e concentrations and annual mean admissions each year were excluded. This was required to overcome problems of near-zero incidence, in counties with sparse population and few admissions, and to exclude areas with minimal smoke exposure throughout the study period.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData Processing\u003c/h2\u003e \u003cp\u003ePatient outcomes were aggregated into counts per day by patient home ZIP Code. The outcome data were then merged with the PM\u003csub\u003e2.5\u003c/sub\u003e exposure data from CAMS and GeosCHEM (below) using the day and ZIP Code as standard identifiers. Finally, to overcome modelling limitations in cases of near zero incidence when using daily admission data, the data were aggregated at the county level and compiled into weekly counts. The cohort construction process is illustrated in the flowchart in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExposure\u003c/h3\u003e\n\u003cp\u003eDaily total PM\u003csub\u003e2.5\u003c/sub\u003e in micrograms per cubic meter (\u0026micro;g/m\u0026sup3;) was evaluated using data from the Copernicus atmosphere monitoring service (CAMS) \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. To isolate the component of total PM concentrations that is attributable to wildfire smoke, a chemical transport model was employed. Specifically, the GEOS-Chem atmospheric model \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e was run both with and without satellite fire emissions data from NASA\u0026rsquo;s Global Fire Emissions Database v4. Initially, a global model was run at 4\u0026ordm; x 5\u0026ordm; spatial resolution. With these boundary conditions, 0.5\u0026deg; x 0.625\u0026deg; models were run for the continental US. From this modelling, a \u0026lsquo;fire fraction\u0026rsquo; was calculated at each geographical and temporal data point to represent the proportion of total PM\u003csub\u003e2.5\u003c/sub\u003e that was specifically attributable to wildfires. The fire-fraction data from GEOS-Chem were then used to apportion the original CAMS data, with the output taken to represent wildfire specific PM\u003csub\u003e2.5\u003c/sub\u003e concentrations, as previously described \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Values for wildfire-specific PM\u003csub\u003e2.5\u003c/sub\u003e were calculated from 2020 through 2021 for each CONUS ZIP Code and were then averaged at the US county level. Three separate exposure metrics were used in our analysis. First, the mean of the two maximum weekly wildfire-specific PM\u003csub\u003e2.5\u003c/sub\u003e values was investigated with a one-day lag. This metric, similar to that used in other studies, captures periods of ongoing smoke exposure while accounting for the delay between exposure and disease onset or hospitalization \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Addition exposure variables were of weekly maximum and weekly mean wildfire-specific PM\u003csub\u003e2.5\u003c/sub\u003e concentrations.\u003c/p\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003eThe outcomes examined were the weekly incidence of acute myocardial infarction, acute heart failure exacerbation, atrial fibrillation, cerebral infarction, pulmonary embolism, cardiac arrest, asthma, and acute exacerbation of COPD at a county level. To investigate the sensitivity of the fetal-placental vasculature to wildfire smoke exposure, composite measures for adverse neonatal and obstetric outcomes were also included. The complete list of OMOP-CDM concepts associated with these categories is presented in Supplemental Matieral 1.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCovariates\u003c/h2\u003e \u003cp\u003eCovariates included the month of the year, the total (non-wildfire-specific) PM\u003csub\u003e2.5\u003c/sub\u003e exposure, county level COVID-19 incidence, and temperature difference from monthly means.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Methods\u003c/h2\u003e \u003cp\u003eGeneralized linear mixed-effects models, adjusting for previously identified covariates, were used. The results are expressed as fixed-effect coefficients describing the change in weekly county level incidence of each condition with each 10 \u0026micro;g/m\u0026sup3; increase in wildfire specific PM\u003csub\u003e2.5\u003c/sub\u003e, concentrations, along with 95% confidence intervals. Results were considered statistically significant when p-values were below 0.05 and when the confidence intervals did not include zero. The analyses were then repeated, investigating the effects of total PM\u003csub\u003e2.5\u003c/sub\u003e, rather than wildfire specific PM on the same outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCOVID interaction\u003c/h2\u003e \u003cp\u003eA separate generalized linear mixed effects model was run to investigate the impact of wildfire smoke on COVID-positive admissions, defined as those with a COVID diagnosis within \u0026plusmn;\u0026thinsp;7 days of admission. This model did not include COVID incidence as a confounder and instead measured the rate of admission for COVID associated pneumonia, respiratory distress or hypoxia. Additionally, a sensitivity analysis was performed to investigate differences in the association between wildfire smoke and admission rates among patients with and without comorbid COVID infection. All analyses were conducted using the R programming language, including figures, which were created with the R ggplot 2, and biscale packages \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eAuthorship \u003c/p\u003e\n\u003cp\u003eF.H., K.R., V.K. and H.M. conceived the study. \u003c/p\u003e\n\u003cp\u003eL.P., P.K., and D.S. developed the atmospheric models and exposure metrics. \u003c/p\u003e\n\u003cp\u003eF.H., B.A., T.O. and V.K. directed the statistical analysis. \u003c/p\u003e\n\u003cp\u003eF.H. and J.S. wrote the initial draft. All authors reviewed, edited and approved the manuscript.\u003c/p\u003e\n\n\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e\n\u003cp\u003eHM is partly supported by the National Institute for Health Research\u0026rsquo;s (NIHR\u0026rsquo;s) Comprehensive Biomedical Research Centre at University College London Hospitals.\u003c/p\u003e\n\n\n\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eThe health outcomes data used in this study are available through the National COVID Cohort Collaborative (N3C) Secure Enclave, maintained by NCATS. Researchers may access the data by submitting a Data Use Request (DUR) via the N3C portal (covid.cd2h.org).Atmospheric data supporting the exposure metrics are publicly available. Total PM2.5 data were obtained from the Copernicus Atmosphere Monitoring Service (CAMS) (https://ads.atmosphere.copernicus.eu/datasets/cams-global-reanalysis-eac4). Wildfire emission data were sourced from the NASA Global Fire Emissions Database (GFEDv4) (https://globalfiredata.org/related.html#gfed4). The GEOS-Chem model code used for chemical transport modeling is open-source and available at geos-chem.org. Processed wildfire-specific PM2.5fractions generated during this study are available from the corresponding author upon request\u003c/p\u003e\n\n\u003cp\u003eFunding:\u003c/p\u003e\n\u003cp\u003eThis work was possible due to seed funding from the OAK Foundation, and intramural funding at Duke University (Climate + Health grant funding).\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eData science support was provided by SporeData (sporedata.com)\u003c/p\u003e\n\n\u003cp\u003eThe analyses described in this publication were conducted with data or tools accessed through the NCATS N3C Data Enclave https://covid.cd2h.org and N3C Attribution \u0026amp; Publication Policy v 1.2-2020-08-25b supported by NCATS Contract No. 75N95023D00001, Axle Informatics Subcontract: NCATS-P00438-B, and Duke University. This research was possible because of the patients whose information is included within the data and the organizations (https://ncats.nih.gov/n3c/resources/data-contribution/data-transfer-agreement-signatories) and scientists who have contributed to the on-going development of this community resource [https://doi.org/10.1093/jamia/ocaa196].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclaimer\u003c/strong\u003e The N3C Publication committee confirmed that this manuscript in accordance with N3C data use and attribution policies; however, this content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the N3C program.\u003c/p\u003e\n\u003cp\u003eThe project described was supported by the National Institute of General Medical Sciences, 5U54GM104942-04. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The N3C data transfer to NCATS is performed under a Johns Hopkins University Reliance Protocol # IRB00249128 or individual site agreements with NIH. The N3C Data Enclave is managed under the authority of the NIH; information can be found at https://ncats.nih.gov/n3c/resources.\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge the following core contributors to N3C:Adam B. Wilcox, Adam M. Lee, Alexis Graves, Alfred (Jerrod) Anzalone, Amin Manna, Amit Saha, Amy Olex, Andrea Zhou, Andrew E. Williams, Andrew M. Southerland, Andrew T. Girvin, Anita Walden, Anjali Sharathkumar, Benjamin Amor, Benjamin Bates, Brian Hendricks, Brijesh Patel, G. Caleb Alexander, Carolyn T. Bramante, Cavin Ward-Caviness, Charisse Madlock-Brown, Christine Suver, Christopher G. Chute, Christopher Dillon, Chunlei Wu, Clare Schmitt, Cliff Takemoto, Dan Housman, Davera Gabriel, David A. Eichmann, Diego Mazzotti, Donald E. Brown, Eilis Boudreau, Elaine L. Hill, Emily Carlson Marti, Emily R. Pfaff, Evan French, Farrukh M Koraishy, Federico Mariona, Fred Prior, George Sokos, Greg Martin, Harold P. Lehmann, Heidi Spratt, Hemalkumar B. Mehta, J.W. Awori Hayanga, Jami Pincavitch, Jaylyn Clark, Jeremy Richard Harper, Jessica Yasmine Islam, Jin Ge, Joel Gagnier, Johanna J. Loomba, John B. Buse, Jomol Mathew, Joni L. Rutter, Julie A. McMurry, Justin Guinney, Justin Starren, Karen Crowley, Katie Rebecca Bradwell, Kellie M. Walters, Ken Wilkins, Kenneth R. Gersing, Kenrick Cato, Kimberly Murray, Kristin Kostka, Lavance Northington, Lee Pyles, Lesley Cottrell, Lili M. Portilla, Mariam Deacy, Mark M. Bissell, Marshall Clark, Mary Emmett, Matvey B. Palchuk, Melissa A. Haendel, Meredith Adams, Meredith Temple-O\u0026apos;Connor, Michael G. Kurilla, Michele Morris, Nasia Safdar, Nicole Garbarini, Noha Sharafeldin, Ofer Sadan, Patricia A. Francis, Penny Wung Burgoon, Philip R.O. Payne, Randeep Jawa, Rebecca Erwin-Cohen, Rena C. Patel, Richard A. Moffitt, Richard L. Zhu, Rishikesan Kamaleswaran, Robert Hurley, Robert T. Miller, Saiju Pyarajan, Sam G. Michael, Samuel Bozzette, Sandeep K. Mallipattu, Satyanarayana Vedula, Scott Chapman, Shawn T. O\u0026apos;Neil, Soko Setoguchi, Stephanie S. Hong, Steven G. Johnson, Tellen D. Bennett, Tiffany J. Callahan, Umit Topaloglu, Valery Gordon, Vignesh Subbian, Warren A. Kibbe, Wenndy Hernandez, Will Beasley, Will Cooper, William Hillegass, Xiaohan Tanner Zhang. Details of contributions available at covid.cd2h.org/core-contributors\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCostello, A. et al. Managing the health effects of climate change: Lancet and University College London Institute for Global Health Commission. \u003cem\u003eLancet\u003c/em\u003e \u003cb\u003e373\u003c/b\u003e, 1693\u0026ndash;1733 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarlon, J. R. et al. 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Zenodo, (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5281/ZENODO.7600579\u003c/span\u003e\u003cspan address=\"10.5281/ZENODO.7600579\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeaney, A. et al. Impacts of fine particulate matter from wildfire smoke on respiratory and cardiovascular health in California. \u003cem\u003eGeoHealth\u003c/em\u003e 6, eGH000578 (2022). (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing,Vienna, Austria. (2017). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org/\u003c/span\u003e\u003cspan address=\"https://www.R-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"wildfire, particulate matter, cardiovascular health, respiratory health, public health","lastPublishedDoi":"10.21203/rs.3.rs-9012485/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9012485/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEscalating wildfire frequency increases population exposure to wildfire smoke. To evaluate the association between wildfire-specific particulate matter (PM\u003csub\u003e2.5\u003c/sub\u003e) and acute health impacts a retrospective cohort study was conducted utilizing National COVID Cohort Collaborative health records from 109,012 patients across 58 US health systems from 2020 to 2021. County-level wildfire-specific PM\u003csub\u003e2.5\u003c/sub\u003e concentrations were estimated using a fire emissions database and chemical transport modeling. Generalized linear mixed-effects models were used to analyze the association between weekly county-level wildfire-specific PM\u003csub\u003e2.5\u003c/sub\u003e exposure (up to 50 \u0026micro;g/m\u0026sup3;) and hospital encounters for a series of cardiac, pulmonary, obstetric and neonatal outcomes. Statistically significant increases in weekly encounters per county of residence were observed for every 10 \u0026micro;g/m\u0026sup3; rise in weekly maximum wildfire-specific PM\u003csub\u003e2.5\u003c/sub\u003e for acute myocardial infarction (0.084, 95% CI, 0.023\u0026ndash;0.146), cardiac arrest (0.011, 95% CI, 0.001\u0026ndash;0.021), heart failure (0.083, 95% CI, 0.024\u0026ndash;0.142), atrial fibrillation (0.115, 95% CI, 0.014\u0026ndash;0.216), COPD exacerbation (0.034, 95% CI, 0.001\u0026ndash;0.066) and pulmonary embolism (0.048, 95% CI, 0.003\u0026ndash;0.094). COVID infection status was not found to have a modifying effect on these relationships. There was no significant increase in COVID pneumonia admissions in response to increasing wildfire smoke. These findings demonstrate quantifiable increases in acute cardiorespiratory morbidity associated with wildfire smoke exposure.\u003c/p\u003e","manuscriptTitle":"The Impact of Wildfire Smoke on Acute Cardiovascular and Respiratory Illness in the US","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-09 16:02:18","doi":"10.21203/rs.3.rs-9012485/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"295772295286055673143043709949948171301","date":"2026-05-10T23:48:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"267331356532268915260234159920963254889","date":"2026-05-08T11:48:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"293516564247811296452055057585040663542","date":"2026-05-08T00:11:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-03T04:18:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-26T04:15:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-24T17:08:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-24T17:04:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cb07718e-9190-4d36-b4fe-0a776b8da813","owner":[],"postedDate":"April 9th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"295772295286055673143043709949948171301","date":"2026-05-10T23:48:10+00:00","index":88,"fulltext":""},{"type":"reviewerAgreed","content":"267331356532268915260234159920963254889","date":"2026-05-08T11:48:28+00:00","index":87,"fulltext":""},{"type":"reviewerAgreed","content":"293516564247811296452055057585040663542","date":"2026-05-08T00:11:17+00:00","index":86,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":65681494,"name":"Health sciences/Cardiology"},{"id":65681495,"name":"Health sciences/Diseases"},{"id":65681496,"name":"Earth and environmental sciences/Environmental sciences"},{"id":65681497,"name":"Health sciences/Medical research"},{"id":65681498,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-04-09T16:02:18+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-09 16:02:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9012485","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9012485","identity":"rs-9012485","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}