Video Abstract

Video Abstract

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BACKGROUND AND OBJECTIVES:

Exposure to airborne fine particles with diameters ≤2.5 μm (PM2.5) pollution is a well-established cause of respiratory diseases in children; whether wildfire-specific PM2.5 causes more damage, however, remains uncertain. We examine the associations between wildfire-specific PM2.5 and pediatric respiratory health during the period 2011–2017 in San Diego County, California, and compare these results with other sources of PM2.5.

METHODS:

Visits to emergency and urgent care facilities of Rady’s Children Hospital network in San Diego County, California, by individuals (aged ≤19 years) with ≥1 of the following respiratory conditions: difficulty breathing, respiratory distress, wheezing, asthma, or cough were regressed on daily, community-level exposure to wildfire-specific PM2.5 and PM2.5 from ambient sources (eg, traffic emissions).

RESULTS:

A 10-unit increase in PM2.5 (from nonsmoke sources) was estimated to increase the number of admissions by 3.7% (95% confidence interval: 1.2% to 6.1%). In contrast, the effect of PM2.5 attributable to wildfire was estimated to be a 30.0% (95% confidence interval: 26.6% to 33.4%) increase in visits.

CONCLUSIONS:

Wildfire-specific PM2.5 was found to be ∼10 times more harmful on children’s respiratory health than PM2.5 from other sources, particularly for children aged 0 to 5 years. Even relatively modest wildfires and associated PM2.5 resolved on our record produced major health impacts, particularly for younger children, in comparison with ambient PM2.5.

What’s Known on This Subject:

Although children are considered a vulnerable group in terms of exposure to airborne fine particles with diameters ≤2.5 μm (PM2.5), few studies have demonstrated increased vulnerability in children exposed to PM2.5 from wildfire smoke. Recent animal toxicological studies suggest that wildfire PM2.5 may be more harmful.

What This Study Adds:

We estimated wildfire-specific PM2.5 and found that it can cause a greater impact on pediatric respiratory health than PM2.5 ambient concentrations.

Airborne fine particles with diameters ≤2.5 μm (PM2.5) from wildfire smoke adversely impact public health, notably through respiratory diseases15  because PM2.5 can deposit in the respiratory tract6  and affect vulnerable populations.79  Although PM2.5 in the United States has decreased in past decades because of environmental regulations,10,11  wildfire PM2.5 and the associated health impacts are projected to increase with global change.1214 

Current air quality regulations from the Clean Air Act Amendments assume that the toxicity of PM2.5 does not vary across different sources of emission. Recent toxicological studies have, however, found that the toxicity level of PM2.5 in wildfires smoke is greater than that of an equivalent dose of PM2.5 in ambient air,15,16  which suggests current PM2.5 standards may need reconsideration. Characterizing specific vulnerabilities to wildfire smoke and other sources of PM2.5 helps targeting population groups for preventive measures and health monitoring.

In children, high levels of PM2.5 affect lung development and impair lung function,17,18  regardless of any preexisting illness or chronic condition.19  Although children are considered a vulnerable group in terms of exposure to PM2.5,20  few studies have demonstrated increased vulnerability in children exposed to PM2.5 from wildfire smoke.2126  In addition, existing literature has mainly addressed the effects of single wildfires over a short period of time and has not considered any differential effects of PM2.5 on health based on sources of emission.

In coastal Southern California (SoCal), wildfires driven by the warm, dry, northeasterly Santa Ana winds (SAWs) in early fall27,28  have the greatest effect on human health and the economy.2931  Wildfire severity is expected to increase in this region because of a warming and drying climate32,33  despite a projected decrease in SAW activity.34,35  Two studies examining the impacts of the 2003 SAW-driven firestorm on pediatric health revealed higher prevalence of self-reported respiratory symptoms in school-aged children living in communities exposed to smoke.22,36  Hutchinson et al25  found a 70% increase in emergency department visits for respiratory diagnoses in San Diego County for children aged 0 to 4 years during a similar firestorm in 2007. Leibel et al26  found an increase in pediatric respiratory visits, particularly for younger children, after a SAW-driven wildfire in 2017. Wildfire health impacts are also associated with a substantial economic burden, such as the estimated $3.4 million in health care costs during the October 2007 firestorm in SoCal.37 

Here, we examine the associations between wildfire-specific PM2.5 and pediatric visits for respiratory diseases during the period 2011–2017 in San Diego County and compare these results with other sources of PM2.5. Our analytical approach to estimate human exposure to wildfire-specific PM2.5 allows for the examination of multiple wildfire events, which is uncommon in the existing literature. Specifically, our spatiotemporal multiple imputation approach to isolate wildfire-specific PM2.5 offers the advantage of modeling daily concentrations at the zip code level over a large study period (7 years) and area.

The study area covered 86 zip codes in San Diego County with PM2.5 data availability (Fig 1), which mainly involved coastal zip codes with the largest population in the county (Supplemental Fig 3). Between 2011 and 2017, there were 45 wildfires, of which 14 were SAW-related (Supplemental Fig 3, Supplemental Table 4), with a total of 108 000 acres burned over the 7-year period, according to data from the California Department of Forestry and Fire Protection (CalFire; http://frap.fire.ca.gov/). This period did not contain extreme wildfires; for comparison, the largest single wildfire in the county, the Witch Fire, burned 198 000 acres in October 2007. SAW-driven wildfires burn in the backcountry foothills typically in fall (the beginning of the SAW season before the first rains of winter) transporting smoke toward coastal areas. Conversely, most summer fires in SoCal are quickly contained and typically cause little damage to population.38 

FIGURE 1

Mean daily PM2.5 during the 7-year period (2011–2017) in the 86 zip codes with available concentrations after interpolation from EPA Air Quality System air quality monitors (shown here as black triangles). Inset is the situation of San Diego County (in red) in California.

FIGURE 1

Mean daily PM2.5 during the 7-year period (2011–2017) in the 86 zip codes with available concentrations after interpolation from EPA Air Quality System air quality monitors (shown here as black triangles). Inset is the situation of San Diego County (in red) in California.

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Within our 7-year study period, there were 884 471 emergency department and urgent care visits in the Rady’s Children Hospital network, which provides medical care for 91% of San Diego County’s hospitalized children.26  The specific data fields analyzed included date of visit, date of birth, zip code of patient’s residence, and respiratory or nonrespiratory visit. Respiratory visits were defined by the following specific data field chief complaints: difficulty breathing, respiratory distress, wheezing, asthma, or cough. Using the visit’s chief complaint, a single specific data field indicating the intent of the visit captured a greater number of visits with an indication of a respiratory condition compared to using International Classification of Diseases, Ninth Revision and International Classification of Diseases, 10th Revision diagnostic codes.25,29  Respiratory visits accounted for 20% of total visits (n = 174 766). In addition, we also considered the above 5 chief complaints individually in our statistical analyses related to the effects of wildfire PM2.5 on pediatric health.

Daily zip code-specific concentrations of PM2.5 were estimated from 2011 to 2017 by using 24-hour daily means sampled and analyzed by the US Environmental Protection Agency (EPA) Air Quality System (https://www.epa.gov/aqs) at ground monitoring stations (Fig 1). Values were interpolated by using an inverse distance weighting approach,39  which gives greater importance to monitoring stations closer to the point of interest, with monitored PM2.5 data within a 20-km radius of each population-weighted zip code centroid. The resulting zip code level values of daily PM2.5 represent fine particulate matter from all sources (ambient levels from traffic and industrial emissions and wildfire smoke).

We used available smoke plume data sets from the National Oceanographic and Atmospheric Administration’s Hazard Mapping System (HMS) to identify zip code days exposed to wildfire smoke. HMS uses visible satellite imagery and trained satellite analyst skills to estimate the spatial extent of smoke, although it cannot discern whether a given plume is at ground level (where PM2.5 samples are taken for monitoring) or higher in the atmosphere.40  The HMS smoke products represent the spatial extent of daily smoke plumes.41  A simple smoke binary variable was created by intersecting zip code polygons with smoke polygons, which was then used as an indication of daily exposure to wildfire smoke PM2.5. In other words, a value of 1 (exposed) was assigned to a zip code on a given day if its boundaries were within a smoke plume polygon from the HMS data set.

We used a cubic spline interpolation to impute the PM2.5 concentrations attributable to nonsmoke sources in zip code days previously identified as exposed to wildfire smoke. Cubic splines are an extension of polynomial regression in which times t are divided into k intervals called knots. For each interval, a regression is fit with 3 parameters. This method has been found to allow the inclusion of local characteristics of a trend without prejudicing its global characteristics. More specifically, we followed the steps below:

  1. Using the exposure definition based on available HMS smoke plumes, we identified the zip code days exposed to wildfire smoke in our original PM2.5 data set.

  2. We used a spline interpolation approach to impute the values of nonsmoke PM2.5 on all zip code days categorized as exposed to smoke and where PM2.5 data were originally available (ie, we did not impute missing values in the original data set). Cubic spline interpolation was implemented by means of the imputeTS R package.42  This step provided estimates of ambient PM2.5 unrelated to wildfire smoke.

  3. We then subtracted all nonsmoke PM2.5 values from the original daily PM2.5 concentrations to obtain the levels of PM2.5 attributable to wildfire smoke in zip code days previously categorized as exposed.

  4. Lastly, rates of pediatric visits (per 100 000 individuals) were regressed on the wildfire-specific PM2.5 concentrations (Equation 1 below), including controls for day-of-week effects, month-of-year effects, a linear time trend, and zip code fixed effects. Ordinary Least Squares regressions were implemented with the Linear Models for Panel Data R package.43  The same analysis was performed for aggregated and nonsmoke concentrations of PM2.5. To summarize, our analysis of impacts on rates (ie, population in the denominator) of respiratory visits controls for time-fixed confounders at the zip code level, which, in turn, include baseline risk (intercept) and population composition:

formula
  • 5. Subsequent strata-specific analyses of pediatric visits by age categories of <6, 6 to 12, and 13 to 19 years were performed. In addition, we conducted a sensitivity analysis by removing the zip code fixed-effects term. Finally, we also evaluated the impacts on health with lag effects (lags 1–3).

Overall mean wildfire-specific PM2.5 for the 7-year period was 2.46 μg m−3, varying spatially from 0.06 to 7 μg m−3 (Fig 2). Maximum wildfire-specific PM2.5 was 18.2 μg m−3. Interpolated mean PM2.5 derived from the EPA monitoring stations (Fig 1) was 9.7 μg m−3 (interquartile range [IQR] = 4.7).

FIGURE 2

Mean wildfire-specific PM2.5 within 2011–2017 in San Diego County zip codes. Wildfire perimeters are shown in red.

FIGURE 2

Mean wildfire-specific PM2.5 within 2011–2017 in San Diego County zip codes. Wildfire perimeters are shown in red.

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Table 1 summarizes our results for the effects of wildfire-PM2.5 on pediatric respiratory visits in San Diego County over the period 2011–2017. On the basis of the mean number (2.5) of daily respiratory visits per 100 000 individuals, a 10-U increase in PM2.5 (from aggregated sources) was estimated to increase the number of admissions by 3.9% (95% confidence interval [CI]: 1.5% to 6.3%). A similar increase was found for nonsmoke concentrations of PM2.5 (3.7%; CI: 1.2% to 6.1%). In contrast, the effect of PM2.5 attributable to wildfire smoke was estimated to be a 30.0% (95% CI: 26.6% to 33.4%) increase in visits associated with a 10-unit increase in PM2.5. Comparable results were observed when expressing the differential impacts with an IQR increase in PM2.5 (Supplemental Table 5) Wildfire-specific PM2.5 was thus found to be ∼10 times more harmful on children’s respiratory health than PM2.5 from other sources (eg, nonsmoke). A sensitivity analysis in which the zip code fixed-effects term was removed resulted in similar findings (see Supplemental Table 6). Results of lag effects (lags 1–3; presented in Supplemental Table 7), although highly imprecise at some instances, yielded greater coefficient values (of at least one order of magnitude) for wildfire-specific PM2.5, which translates to greater impacts on health.

TABLE 1

Fixed-Effects Regression Model for Pediatric Respiratory Visits (Rate per 100 000 People)

Model CoefficientAggregated PM2.5 (Smoke and Nonsmoke)Specific PM2.5 (Nonsmoke)Specific PM2.5 (Wildfire Smoke)
PM2.5 (95% CI) 9.7×10−3 (3.7×10−3 to 1.6×10−29.2×10−3 (3.0×10−3 to 1.5×10−27.5×10−2 (6.7×10−3 to 8.4×10−2
% change with an increase of 10 μg m−3 PM2.5 (95% CI) 3.9 (1.5 to 6.3) 3.7 (1.2 to 6.1) 30.0 (26.6 to 33.4) 
Model CoefficientAggregated PM2.5 (Smoke and Nonsmoke)Specific PM2.5 (Nonsmoke)Specific PM2.5 (Wildfire Smoke)
PM2.5 (95% CI) 9.7×10−3 (3.7×10−3 to 1.6×10−29.2×10−3 (3.0×10−3 to 1.5×10−27.5×10−2 (6.7×10−3 to 8.4×10−2
% change with an increase of 10 μg m−3 PM2.5 (95% CI) 3.9 (1.5 to 6.3) 3.7 (1.2 to 6.1) 30.0 (26.6 to 33.4) 

All regressions include controls: day-of-week effects, month-of-year effects, zip code fixed effects, and a time trend.

When looking at the association between wildfire PM2.5 and respiratory visits by age categories (Table 2), children from 0 to 5 years old were most affected. For this age range, wildfire-specific PM2.5 was ∼10 times more harmful (34.5% increased visits; Table 2) than PM2.5 from other sources (eg, 3.1% increase for nonsmoke PM2.5). Results for the remaining age categories were rather imprecise, although the coefficient for wildfire-specific PM2.5 was 2 orders of magnitude larger than those for aggregated and nonsmoke PM2.5 for children aged 6 to 12 years. The negative coefficient (−0.010; 95% CI: −0.041 to 0.020) for children between 13 and 19 years old would indicate a predominant protective effect under wildfire smoke conditions, although results were also imprecise for this group.

TABLE 2

Fixed-Effects Regression Models for Pediatric Respiratory Visits (Rate per 100 000 People) by Age

Age Group in y (n = Zip Code Days) and Model CoefficientAggregated PM2.5 (Smoke and Nonsmoke)Specific PM2.5 (Nonsmoke)Specific PM2.5 (Wildfire Smoke)
0–5 (n = 104 481)    
 PM2.5 (95% CI)  8.7×10−3 (3.0×10−3 to 1.4×10−2 7.7×10−3 (1.9×10−3 to 1.4×10−28.6×10−2 (0.037 to 0.14) 
 % change with an increase of 10 μg m−3 PM2.5 (95% CI) 3.5 (1.2 to 5.7) 3.1 (0.8 to 5.4) 34.5 (14.8 to 54.3) 
6–12 (n = 79 152)    
 PM2.5 (95% CI) 2.5×10−5 (−3.9×10−3 to 4.0×10−31.0×10−4 (−3.9×10−3 to 4.1×10−31.6×10−2 (−1.8×10−2 to 5.0×10−2
 % change with an increase of 10 μg m−3 PM2.5 (95% CI) 0.01 (−1.6 to 1.6) 0.04 (−1.6 to 1.7) 6.5 (−7.1 to 20.2) 
13–19 (n = 56 225)    
 PM2.5 (95% CI) 2.5×10−3 (−1.0×10−3 to 6.1×10−33.0×10−3 (−7.1×10−4 to 6.6×10−3−0.01 (−0.041 to 0.02) 
 % change with an increase of 10 μg m−3 PM2.5 (95% CI) 1.0 (−0.4 to 2.4) 1.2 (−0.3 to 2.7) −4.2 (−16.4 to 8.1) 
Age Group in y (n = Zip Code Days) and Model CoefficientAggregated PM2.5 (Smoke and Nonsmoke)Specific PM2.5 (Nonsmoke)Specific PM2.5 (Wildfire Smoke)
0–5 (n = 104 481)    
 PM2.5 (95% CI)  8.7×10−3 (3.0×10−3 to 1.4×10−2 7.7×10−3 (1.9×10−3 to 1.4×10−28.6×10−2 (0.037 to 0.14) 
 % change with an increase of 10 μg m−3 PM2.5 (95% CI) 3.5 (1.2 to 5.7) 3.1 (0.8 to 5.4) 34.5 (14.8 to 54.3) 
6–12 (n = 79 152)    
 PM2.5 (95% CI) 2.5×10−5 (−3.9×10−3 to 4.0×10−31.0×10−4 (−3.9×10−3 to 4.1×10−31.6×10−2 (−1.8×10−2 to 5.0×10−2
 % change with an increase of 10 μg m−3 PM2.5 (95% CI) 0.01 (−1.6 to 1.6) 0.04 (−1.6 to 1.7) 6.5 (−7.1 to 20.2) 
13–19 (n = 56 225)    
 PM2.5 (95% CI) 2.5×10−3 (−1.0×10−3 to 6.1×10−33.0×10−3 (−7.1×10−4 to 6.6×10−3−0.01 (−0.041 to 0.02) 
 % change with an increase of 10 μg m−3 PM2.5 (95% CI) 1.0 (−0.4 to 2.4) 1.2 (−0.3 to 2.7) −4.2 (−16.4 to 8.1) 

All regressions include controls: day-of-week effects, month-of-year effects, zip code fixed effects, and a time trend.

Among the 5 specific conditions, cough was present in most visits categorized as respiratory (Supplemental Table 8, Supplemental Fig 4). A 10-U increase in wildfire-specific PM2.5 was estimated to cause a 21.8% (95% CI: 3.6% to 40.1%) increase in visits reporting cough (Table 3). Models for aggregated and nonsmoke PM2.5 reported a 3% increase in respiratory visits associated with cough. For the remaining 4 conditions, results from our models consistently revealed at least 1 order of magnitude increase for visits associated with wildfire-specific PM2.5 in relation to other sources, although results lacked precision (Table 3).

TABLE 3

Fixed-Effects Regression Models for Pediatric Respiratory Visits (Rate per 100 000 People)

Respiratory ConditionAggregated PM2.5 (Smoke and Nonsmoke)Specific PM2.5 (Nonsmoke)Specific PM2.5 (Wildfire Smoke)
Asthma    
 PM2.5 (95% CI) −1.6×10−7 (−6.8×10−4 to 6.8×10−4−1.0×10−5 (−7.1×10−4 to 6.9×10−45.3×10−4 (−5.3×10−3 to 6.3×10−4
 % change with an increase of 10 μg m−3 PM2.5 (95% CI) −0.01 (−0.27 to 0.27) −0.0041 (−0.28 to 0.27) 0.21 (−2.11 to 2.53) 
Cough    
 PM2.5 (95% CI) 7.4×10−3 (2.0×10−3 to 0.013) 7.1×10−3 (1.6×10−3 to 0.013) 5.5×10−2 (8.9×10−3 to 0.10) 
 % change with an increase of 10 μg m−3 PM2.5 (95% CI) 3.0 (0.81 to 5.1) 2.8 (0.65 to 5.0) 21.8 (3.6 to 40.1) 
Difficulty breathing    
 PM2.5 (95% CI) −3.1×10−3 (−1.3×10−4 to 6.4×10−3−2.9×10−3 (4.3×10−4 to 6.2×10−31.8×10−2 (−9.4×10−3 to 4.6×10−2
 % change with an increase of 10 μg m−3 PM2.5 (95% CI) 1.3 (−0.052 to 2.6) 1.2 (−0.17 to 2.5) 7.3 (−3.8 to 18.4) 
Respiratory distress    
 PM2.5 (95% CI) −3.0×10−4 (−7.9×10−4 to 2.4×10−4−3.0×10−4 (−7.9×10−4 to 2.7×10−41.1×10−3 (−3.3×10−3 to 5.5×10−3
 % change with an increase of 10 μg m−3 PM2.5 (95% CI) −0.11 (−0.32 to 0.094) −0.10 (−0.31 to 0.11) 0.44 (−1.3 to 2.2) 
Wheezing    
 PM2.5 (95% CI) −1.2×10−4 (−1.2×10−3 to 9.6×10−4−1.7×10−4 (−1.3×10−3 to 9.3×10−4−3.5×10−4 (−9.5×10−3 to 8.8×10−3
 % change with an increase of 10 μg m−3 PM2.5 (95% CI) −0.047 (−0.48 to 0.38) −0.068 (−0.51 to 0.37) −0.14 (−3.8 to 3.5) 
Respiratory ConditionAggregated PM2.5 (Smoke and Nonsmoke)Specific PM2.5 (Nonsmoke)Specific PM2.5 (Wildfire Smoke)
Asthma    
 PM2.5 (95% CI) −1.6×10−7 (−6.8×10−4 to 6.8×10−4−1.0×10−5 (−7.1×10−4 to 6.9×10−45.3×10−4 (−5.3×10−3 to 6.3×10−4
 % change with an increase of 10 μg m−3 PM2.5 (95% CI) −0.01 (−0.27 to 0.27) −0.0041 (−0.28 to 0.27) 0.21 (−2.11 to 2.53) 
Cough    
 PM2.5 (95% CI) 7.4×10−3 (2.0×10−3 to 0.013) 7.1×10−3 (1.6×10−3 to 0.013) 5.5×10−2 (8.9×10−3 to 0.10) 
 % change with an increase of 10 μg m−3 PM2.5 (95% CI) 3.0 (0.81 to 5.1) 2.8 (0.65 to 5.0) 21.8 (3.6 to 40.1) 
Difficulty breathing    
 PM2.5 (95% CI) −3.1×10−3 (−1.3×10−4 to 6.4×10−3−2.9×10−3 (4.3×10−4 to 6.2×10−31.8×10−2 (−9.4×10−3 to 4.6×10−2
 % change with an increase of 10 μg m−3 PM2.5 (95% CI) 1.3 (−0.052 to 2.6) 1.2 (−0.17 to 2.5) 7.3 (−3.8 to 18.4) 
Respiratory distress    
 PM2.5 (95% CI) −3.0×10−4 (−7.9×10−4 to 2.4×10−4−3.0×10−4 (−7.9×10−4 to 2.7×10−41.1×10−3 (−3.3×10−3 to 5.5×10−3
 % change with an increase of 10 μg m−3 PM2.5 (95% CI) −0.11 (−0.32 to 0.094) −0.10 (−0.31 to 0.11) 0.44 (−1.3 to 2.2) 
Wheezing    
 PM2.5 (95% CI) −1.2×10−4 (−1.2×10−3 to 9.6×10−4−1.7×10−4 (−1.3×10−3 to 9.3×10−4−3.5×10−4 (−9.5×10−3 to 8.8×10−3
 % change with an increase of 10 μg m−3 PM2.5 (95% CI) −0.047 (−0.48 to 0.38) −0.068 (−0.51 to 0.37) −0.14 (−3.8 to 3.5) 

All regressions include controls: day-of-week effects, month-of-year effects, zip code fixed effects, and a time trend.

We found that wildfire-specific PM2.5 cause a greater impact on pediatric respiratory health than PM2.5 from other sources, such as ambient concentrations from traffic emissions. The differential toxicity of wildfire PM2.5 as compared with other sources of PM2.5 has been debated,44,45  but recent animal toxicological studies suggest that wildfire particulate matter may be more toxic than equal doses of ambient PM2.5.15,16  Previous research suggests that wildfire-specific particulate matter is mainly constituted of elemental carbon, although heat-labile organic compounds (eg, volatile organic compounds such as benzene) may also play a significant role in explaining higher toxicity. As fires reach the wildland-urban interface, other toxic chemicals may be released from the burning of residential or industrial infrastructure. In epidemiological studies, it has been revealed that exposure to PM2.5 from wildfire smoke leads to acute exacerbations of respiratory symptoms and increased health care use1,8,9,25  and particularly affects children.26 

Because children breathe more air per minute than adults and have lungs that are still developing, they are especially vulnerable to health effects during wildfires, particularly in the case of children who are very young or who already have respiratory diseases. Our findings also indicate that children between 0 and 5 years old were more affected by PM2.5 in wildfire smoke than older children, which is consistent with recent evidence in our study region.25,26  Acute effects of smoke exposure observed on children’s respiratory systems could also lead to chronic health issues. For instance, Black et al46  observed a decrease in lung function in a cohort of 3-year-old (adolescent) Macaque monkeys who were infants during the 2008 wildfires in Northern California that was not observed in an unexposed cohort (born a year later). Research on long-term effects of wildfire smoke exposure during childhood is, however, scarce and deserves further attention.3 

By understanding the impacts of wildfire smoke exposure on vulnerable pediatric populations, researchers and policy makers can develop evidence-based guidelines for mitigating risk and increasing public awareness of wildfire impacts. Public health strategies to protect susceptible individuals could involve early warning systems and community coordination with schools and health care providers.47  A larger burden on the health care system beyond inpatient hospitalizations would also increase awareness among public health care practitioners and providers of the possible increase in care use after a wildfire.9  From a land management perspective, prescribed fires during appropriate fuel and weather conditions can help reduce the likelihood of severe damage caused by large, devastating wildfires.48  Although prescribed fires raise public concerns regarding pollutant exposure, a recent study in California has suggested that wildfire smoke may be more harmful to children’s health than smoke from a prescribed fire.49 

Understanding the impacts of wildfire on public health is of vital importance and is particularly relevant in a wildfire-prone region like SoCal that is densely populated downwind of wildfire-risk areas. Warming is continually increasing wildfire frequency and severity in California.33,50  A later start to the wet season, together with an expected decrease in SAW activity in the fall,35  will gradually shift the SoCal wildfire season from fall to winter.33,35  Moreover, the region is continuously experiencing spatial patterns of population expansion from coastal areas inland, leading to the expansion of the wildland-urban interface,51  which can increase sources of wildfire ignition, leading to a greater probability of large and destructive wildfires in the sloping backcountry where SAWs are maximized.

Although extreme wildfires have occurred elsewhere in SoCal during our 2011–2017 period of record, San Diego County, specifically, has been spared from devastating extremes certainly due in part to lessons learned from the 2003 and 2007 catastrophic wildfires.52  Even relatively modest wildfires resolved on our record produced major health impacts, as revealed here.

Study limitations include the use of patient home address (zip code) to estimate exposures and using community-level PM2.5 to assess and quantify individual wildfire PM2.5 exposures. In addition, it is possible that some visits are from the same affected individuals; although it is unfeasible to perform a sensitivity analysis because of lack of information on individuals, we expect that it would not significantly affect the outcome and conclusions of our study. The number and extent of smoke plumes used to categorize exposed zip code days represent a conservative estimate because of the limitations of visible satellite data and geospatial relationships. All the above may have led to the misclassification of some of the smoke PM2.5 as nonsmoke PM2.5 and vice versa. We did not specifically evaluate weather-related confounders, although we assume that seasonal controls in our regression models capture the variability of regional and seasonal factors such as temperature and humidity. Lastly, although not considered in this study, it would be interesting to assess the epidemiological response in the pediatric population in relation to health outcomes beyond respiratory conditions.

Overall, our results underscore the need for a better understanding of the differential effects of wildfire-specific PM2.5 on vulnerable populations. Broader regional trends in extreme wildfire occurrences associated with global warming33  and population expansion51  as well as compounding effects on the respiratory system from the current global pandemic (coronavirus disease 2019) and regional epidemics (eg, seasonal flu) provide urgency to quantifying impacts and develop vulnerability-targeted early warning systems and adaptation plans expanding from county to broader scales.

Dr Aguilera collected data, conducted the initial analyses, drafted the initial manuscript, and reviewed and revised the manuscript; Dr Corringham collected data, conducted the initial analyses, and reviewed the manuscript; Dr Gershunov conceptualized and designed the study, coordinated and supervised data collection, and critically reviewed the manuscript for important intellectual content; Dr Leibel coordinated data collection and critically reviewed the manuscript for important intellectual content; Dr Benmarhnia conceptualized and designed the study, coordinated and supervised data collection, directed the implementation of the study, and critically reviewed the manuscript for important intellectual content; and all authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

FUNDING: Supported by the University of California Office of the President via Multicampus Research Programs and Initiatives (MRP-17-446315) and the National Oceanic and Atmospheric Administration’s Regional Integrated Sciences and Assessments California–Nevada Climate Applications Program award NA17OAR4310284.

     
  • CI

    confidence interval

  •  
  • EPA

    Environmental Protection Agency

  •  
  • HMS

    Hazard Mapping System

  •  
  • IQR

    interquartile range

  •  
  • PM2.5

    airborne fine particles with diameters ≤2.5 μm

  •  
  • SAW

    Santa Ana wind

  •  
  • SoCal

    Southern California

1
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Competing Interests

POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose.

FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.

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