1. Introduction
Forest fires are known to be a major source of air pollutants [
1] on a local and a global scale [
2,
3,
4,
5,
6]. Each year, combustion products from local and distant wildfires impact large populations worldwide [
5,
7,
8,
9,
10,
11,
12,
13]. The atmospheric pollutant that most consistently increases with biomass smoke from wildfires is suspended fine particulate matter (PM), which is commonly associated with increased mortality and morbidity [
1,
4,
14,
15,
16,
17]. The PM in biomass smoke consists mainly of black carbon (soot and charcoal particles), organic carbon, sulfates and/or nitrates, potassium carbonate and silica [
12,
18].
Short-term exposures to fine particulate matter, PM
2.5 (airborne particulate matter with aerodynamic diameter of ≤2.5 µm) have been associated with increased mortality and morbidity in various communities around the world and in the United States [
19,
20,
21,
22,
23,
24,
25,
26,
27,
28]. Most studies addressing health impacts of short-term exposures are related to anthropogenically generated PM, which are commonly associated with automobile combustion and industrial practices. There has been a limited but growing body of literature addressing the impact of shorter-term exposure to smoke from forest and bush fires (referred to as wildfires in this paper) [
8,
9,
11,
17,
29,
30,
31,
32,
33]. The majority of these studies have examined the impact on nearby local communities of exposures to wildfire aerosols. Associated health effects include increased emergency department visits and hospital admissions for chronic obstructive pulmonary disease (COPD), bronchitis, asthma and chest pain [
7,
13,
15,
34,
35,
36,
37]. For example, the San Diego wildfire in October 2003 caused the daily 24 h average PM
2.5 concentrations to exceed 150 µg/m
3, and was associated with significant increases in hospital room emergency room visits for asthma, respiratory problems, eye irritation, and smoke inhalation [
38], and increased eye and respiratory symptoms, medication use and physician visits in children living in the San Diego area [
30]. In Canada, Moore
et al. estimated that forest fire smoke in 2003 was associated with excess respiratory complaints in Kelowna (Kelowna, BC, Canada) area residents [
31].
The wildfire aerosols have lifetimes on the order of many days [
12], which allows transport over large distances [
4,
8,
11,
39]. While it is clear that local populations are affected by wildfire events, a growing concern is the potential health impact on geographically distant populations, specifically in susceptible groups such as the elderly. Epidemiologic research has identified the elderly, who are more likely to have pre-existing lung and heart diseases, as a population vulnerable to the effects of short-term exposures to air pollution including fine particles [
24,
40,
41,
42,
43,
44,
45,
46,
47,
48,
49,
50].
In July 2002, a dramatic increase in forest fire activity was registered in the province of Quebec, Canada [
5]. Specifically, on 7 July 2002 at least 85 fires were burning out of control and destroyed approximately one million hectares of forest that month. The Canadian Forest Services indicated that the major causal factors contributing to these fires were a long period without precipitation and strong winds coming from the north. Lightning and dry conditions sparked fires on 2 July 2002 in two separate regions southeast of James Bay, which is between 200 and 400 miles north of the U.S. border.
The smoke plume generated from these forest fires had a major impact on air quality across the east coast of the United States during the first week in July 2002 [
5,
51]. The plume was carried by strong northerly winds from Quebec across the U.S. border covering a distance that extended from north of Montreal to northern Virginia and Maryland [
5,
51]. Satellite images show the plume on 7 July covering parts of New Hampshire, Vermont, and New York, and diffuse and patchy over the eastern seaboard down to Washington, DC (
Figure 1: MODIS satellite image on 7 July 2002 [
52]. An air quality study being conducted in Baltimore, MD at that time reported that the 24-hour PM
2.5 concentrations reached 86 µg/m
3 on 7 July, resulting in as much as a 30-fold increase in the daily ambient PM
2.5 concentrations [
5] during the peak period of haze over the city. On that day, the highest PM
2.5 concentration (338 µg/m
3) was reported in New Hampshire [
51]. In many cities in the region (such as New York City and Philadelphia), health advisories were issued for residents with espiratory conditions [
53].
Figure 1.
MODIS satellite image taken on 7 July 2002, 10:35 EDT. The red dots mark areas of high forest fire activity. The black dots represent the centroids of counties used in our analysis.
Figure 1.
MODIS satellite image taken on 7 July 2002, 10:35 EDT. The red dots mark areas of high forest fire activity. The black dots represent the centroids of counties used in our analysis.
This transboundary wildfire smoke episode offered a unique opportunity to evaluate the vulnerability of a susceptible population in the United States exposed to smoke generated from a large-scale wildfires more than a thousand kilometers away. In this paper, we assess the relationship between hospital admissions for individuals 65 years and older and PM2.5 concentrations during July 2002 for U.S. states impacted by the Canadian wildfire plume. Unlike previous research that focused on health impacts on populations living near or relatively close to wildfire events, this paper presents one of the first analyses of wildfire smoke health impacts to populations living at great distances, particularly within the U.S., from fires and not previously identified to be at significant risk.
3. Results
Smoke from wildfires in the Quebec region of Canada drifted over the Northeastern and Mid-Atlantic region of the United States on 6–8 July 2002 blanketing much of the area (
Figure 1).
Figure 2 shows the region wide average daily PM
2.5 concentrations for the two months surrounding this event from both the affected area and unaffected reference area of Illinois, which are shown to have similar ambient PM
2.5 concentrations. However, during the identified haze period we observe a substantial spike in PM
2.5 only in the affected region. The daily averages shown
Figure 2 were obtained using 10% trimmed mean to average across monitors after correcting for yearly averages for each monitor; an accepted approach for summarizing longer times series and larger geographic areas of air pollution data [
55]. Missing concentrations were imputed using a natural spline interpolation method that accounts for the daily seasonality in PM
2.5.
Figure 2.
Countywide daily mean PM2.5 (µg/m3) in the affected and unaffected regions from 1 June to 31 Jul 2002.
Figure 2.
Countywide daily mean PM2.5 (µg/m3) in the affected and unaffected regions from 1 June to 31 Jul 2002.
Figure 3 displays for the affected region the spatial distribution in county specific average PM
2.5 concentrations (estimated via block kriging) as being substantially higher for days in the haze period compared to the same days in the preceding non-haze period.
Table 2 presents summary statistics for these estimated county specific PM
2.5 concentrations in the affected states and the chosen Illinois reference area during the haze and non-haze periods. The average countywide PM
2.5 concentrations for 6–8 July 2002 were significantly higher (
p-value < 0.001) in the haze period (mean 53.0 µg/m
3, standard deviation SD = 25.0), compared to non-haze period (mean 21.5 µg/m
3, SD = 10.3 for the affected states. No significant difference in average countywide PM
2.5 was found in the unaffected reference area of Illinois between the same haze and non-haze periods.
Figure 3.
County level block kriged estimated mean PM2.5 (µg/m3) for the 81 affected counties in non-haze and haze periods. Top Row: Control period between 29 June–1 July designated as non-haze period. Bottom Row: Haze period between 6–8 July.
Figure 3.
County level block kriged estimated mean PM2.5 (µg/m3) for the 81 affected counties in non-haze and haze periods. Top Row: Control period between 29 June–1 July designated as non-haze period. Bottom Row: Haze period between 6–8 July.
Table 2.
Summary statistics for the block kriged estimated county average PM2.5 (µg/m3) for the plume affected area and Illinois reference area both stratified by the haze and non-haze periods. Data were pooled over the three days comprising both the haze and non-haze periods.
Table 2.
Summary statistics for the block kriged estimated county average PM2.5 (µg/m3) for the plume affected area and Illinois reference area both stratified by the haze and non-haze periods. Data were pooled over the three days comprising both the haze and non-haze periods.
Summary Statistics | Affected Region | Unaffected Region (IL) |
---|
Haze | Non-Haze | Haze | Non-Haze |
---|
Min | 17.6 | 7.0 | 12.2 | 15.2 |
25th %tile | 35.6 | 13.7 | 16.0 | 19.0 |
Median | 43.1 | 22.6 | 20.7 | 20.7 |
Mean | 53.0 | 21.5 | 22.4 | 20.8 |
75th %tile | 69.1 | 25.3 | 29.7 | 22.1 |
Max | 127.7 | 43.7 | 33.0 | 28.4 |
SD | 25.0 | 10.3 | 7.2 | 5.0 |
Regression results for the parameter of interest,
β2 from model (1) representing the increase in log relative rate of hospitalization comparing the haze to non-haze period, are presented in
Table 3. Results listed in
Table 3 are for (exp(
β2) − 1) × 100% representing the percent change in admissions for the three primary outcome categories, all respiratory, all cardiovascular, and the selected control outcome injury. Compared to the non-haze period, this Medicare population had a 49.55% (95% confidence interval (CI): 29.82–72.29) significantly increased rate for respiratory related hospitalizations and a 64.93% (CI: 44.30–88.51) significantly increased rate for cardiovascular related hospitalizations during the haze period compared to the non-haze period, adjusting for weather and PM
2.5 on the same day (lag 0 model). For the chosen control outcome there was no significant increase in the rate of injury related hospitalizations between the haze and non-haze periods. Single and distributed lag model results show similar significant increases in respiratory and cardiovascular related hospitalizations although not as high as the lag 0 models.
Table 3.
Percent change in hospital admissions for the haze period compared to the non-haze period in the affected region controlling for PM2.5, temperature, and dew point. Bolded model lag types denote significant change in hospital admissions at the 0.05 level.
Table 3.
Percent change in hospital admissions for the haze period compared to the non-haze period in the affected region controlling for PM2.5, temperature, and dew point. Bolded model lag types denote significant change in hospital admissions at the 0.05 level.
Hospitalization Codes | PM2.5 Model Lag | Percent Change | 95% Confidence Interval |
---|
All Respiratory | Lag 0 | 49.55 | (29.82, 72.29) |
Lag 1 | 21.14 | (2.69, 42.90) |
Lag 2 | 23.36 | (10.20, 38.10) |
Dlag1 | 30.20 | (7.66, 57.46) |
Dlag2 | 6.48 | (−14.63, 32.81) |
All Cardiovascular | Lag 0 | 64.93 | (44.30, 88.51) |
Lag 1 | 36.06 | (18.43, 56.32) |
Lag 2 | 49.28 | (34.39, 65.81) |
Dlag1 | 33.85 | (12.45, 59.33) |
Dlag2 | 8.31 | (−13.89, 36.24) |
Injury | Lag 0 | 3.04 | (−21.31, 34.92) |
Lag 1 | 10.97 | (−14.04, 43.27) |
Lag 2 | 13.23 | (−5.64, 35.89) |
Dlag1 | 0.37 | (−25.69, 35.55) |
Dlag2 | −6.04 | (−30.50, 27.02) |
The effect of the forest fire seemed to have a slightly greater impact on cardiovascular than respiratory admissions, a finding contrary to other studies [
20,
21], which found a higher impact on respiratory admissions than cardiovascular admissions. This may be because of over classification with the use of both primary and secondary discharge codes. However, an increase in cardiovascular and respiratory hospitalizations is consistent with the literature [
8,
16,
24,
50,
66], though some literature has shown no increase in cardiovascular hospitalizations [
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21] or mortality [
67].
Figure 4 displays the percent increase in hospital admissions for the haze period compared to the non-haze period in the affected region for all specific diagnoses of interest with single and distributed lag models. Percent increases in hospitalizations were significantly higher for same day models in all diagnosis groups except for respiratory tract infections, cerebrovascular disease, stroke, and myocardial infarction. For one-day lag models, COPD, heart rhythm disturbances, other heart disease, and hypertension were all significant. The largest change in hospitalization was observed on same day lag models; the magnitude of the effect decreases with increasing inclusion of lag effects. Some of the diagnostic codes, such as asthma, have a low prevalence in hospitalizations and may be more difficult to detect a change in rates of hospitalizations.
Figure 4.
Percent change and 95% CI in hospital related admissions for the haze period compared to the non-haze period in the affected region controlling for PM2.5, temperature, and dew point.
Figure 4.
Percent change and 95% CI in hospital related admissions for the haze period compared to the non-haze period in the affected region controlling for PM2.5, temperature, and dew point.
Note: Percent increase in admissions for the haze period compared to the non-haze period.
Models with the additional PM
2.5 by haze period interaction term did not reveal consistent or significant results for this interaction effect across many of the outcomes considered (results not shown) suggesting the PM
2.5 effect not to be the statistically different during the haze and non-haze periods. Results did however continue to reveal the strong significant increase in hospitalizations for the haze period compared to the non-haze period. The lack of evidence supporting a change in PM
2.5 associated relative risk of hospitalizations during the haze period compared to the non-haze period (PM
2.5 by haze period interaction) could be due to a combination of several factors including; PM
2.5 from wildfire sources not any more toxic than non-wildfire sources, the fact that the haze period happened to include both a weekday and the weekend possibly confounding exposure or that there are other drivers of hospitalizations during the haze period that is not entirely explained by PM
2.5 [
68]. Although other studies have shown that wildfire PM is at least as toxic as urban PM [
66,
68]. Sensitivity analysis was performed increasing the haze period to five, seven, and nine days surrounding 7 July 2002 as well as lagging the haze period when considering models with a lagged PM
2.5 exposure. Results differed quantitatively, however the overall qualitative interpretations remained consistent. As such all reported and interpreted regressions results were based on model (1) with the predefined three-day haze period.
4. Discussion
With the Medicare National Claims History, National Climatic Data Center’s weather data, and EPA’s National Monitoring Network, we conducted an opportunistic study of the effects of wildfire air pollution from the Province of Quebec on the health of the elderly population stretching between New York and the District of Columbia.
The selection of our outcome categories was informed by several considerations. Acute exposure to PM may exacerbate existing pulmonary disease [
69,
70,
71,
72]. Because COPD is a substantial risk factor for cardiovascular mortality and morbidity [
73,
74,
75,
76], air pollution exposure may also contribute to cardiovascular risk through exacerbation of COPD symptoms.
We considered several specific cardiovascular and respiratory outcomes that are impacted by inflammatory processes, such as myocardial infarction, stroke, and asthma. The short term effects of exposure to high levels of air pollution are likely to cause inflammatory responses in the lung and release of cytokines with local and systemic consequences [
24,
77]. Acute effects of PM exposure have also been shown to increase plasma viscosity [
25,
78].
The log-linear model we used to estimate associations between day-to-day variations in PM
2.5 (at various lags) and day-to-day variations in the county-level hospitalization rates is typical of time-series analysis [
79]. The advantage of the time-series approach is that confounding by individual-level covariates, such as smoking, is not an issue. However, factors that vary with daily pollution exposure, such as weather and co-pollutants, are likely to be confounders in these studies. Time-series analyses typically include nonlinear terms for weather and season [
60,
63]. One advantage of examining the effects of abrupt increases in PM
2.5 concentration over a short period of time is that it is unlikely that our analyses were confounded by any seasonal pattern unaccounted for in the model.
Our study focused on a population of interest, the elderly, using a nationally available database of health claims. In interpreting the findings of our analysis, considerations need to be given to the inherent limitations of the data analyzed. Information in the Medicare database is prone to bias due to inaccuracy of claims coding for specific diagnoses [
80,
81,
82,
83]. In an attempt to reduce misclassification for outcomes of interest, we used primary and secondary diagnosis codes to identify records for inclusion.
In addition, the ambient air pollution data from administrative databases such as EPA’s National Monitoring Network, which have been created for regulatory purposes, only provide limited spatial and temporal coverage. This is an issue typical of air pollution studies relying on publicly available datasets. Also, during the short period when the plume affected the northeast U.S. (three peak days) the number of hospitalizations recorded was small compared to that observed in larger scale time-series studies. The small numbers of hospitalization counts and the limited exposure data reduces the power of our study.
In common with all studies examining the relationship between exposure to air pollution and health that depend on ambient air quality data, our study finding may be biased because of exposure misclassification. In our study all individuals were assumed to be exposed similarly to the corresponding ambient PM measured at EPA monitoring sites. However, some people may have listened to the health advisories (like the ones in New York and Pennsylvania, USA) and retreated indoors during the event. Although, as shown by Sapkota
et al., some indoor environments were substantially impacted by the elevated ambient PM
2.5 due to this event [
5], the use air conditioning may also have ameliorated the indoor exposure [
84].
Wildfires have rapid and substantial impacts on local air quality that elevate ambient PM concentrations well above the norm. The impact of this increased pollution on the health of local populations has been well documented [
9,
11,
13,
17,
29,
30,
85,
86,
87]. For example, Duclos
et al. showed a 40% and 30% increase in number of local emergency room visits for asthma and COPD respectively, during 1987 forest fires in northern California [
13]. While composition of wildfire smoke has been shown to influence smoke related health outcomes [
16], reliance on PM concentrations is common for regional studies because of the fire plume components it is most consistently elevated during smoke events, as opposed to other attributed components like carbon monoxide or nitric oxide [
5,
32].
Increases in wildfire activity have been linked to warmer spring and summer temperatures resulting from climate change and long-accumulated stocks of combustible vegetation [
88,
89]. In addition, climate models predict an increase in rising temperatures and regional drying will result in an increase in wildfire activity. Regions like the western U.S. can expect to see considerable increase in wildfire activity [
90]. These wildfires may have significant impacts on future air quality and on the health of populations susceptible to the effects of air pollution even those living at a great distance. Our approach is replicable on a larger scale for estimating the health effects of large pollution events resulting from biomass burning over large distances.