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Respiratory Outcomes of Firefighter Exposures in the Fort McMurray Fire

A Cohort Study From Alberta Canada

Cherry, Nicola MD, PhD; Barrie, James R. MD; Beach, Jeremy MD; Galarneau, Jean-Michel PhD; Mhonde, Trish MBChB; Wong, Eric MD

Author Information
Journal of Occupational and Environmental Medicine: September 2021 - Volume 63 - Issue 9 - p 779-786
doi: 10.1097/JOM.0000000000002286


In May 2016 the municipal region of Fort McMurray in the north of Alberta, Canada was overcome by a wildland fire that escaped control and continued to burn for many weeks.1 Firefighters based in the Fort McMurray area, including in first nation and industrial communities, were immediately deployed and joined by structural and wildland firefighters from across the province. Firefighting during the first days of the fire entailed prolonged heavy work in dense smoke often without respiratory protection.

There is little conclusive evidence that firefighting under more controlled conditions results in respiratory damage. Studies do not suggest an excess of lung cancer or mortality from non-malignant respiratory disease.2–6 A cohort study of Danish full-time firefighters, compared with military personnel, found an excess of asthma but not chronic obstructive pulmonary disease (COPD).7 There are few data on long term effects on respiratory health of those in wildland firefighting, where respiratory protection is not worn.8,9 A systematic review of the effects of urban firefighting on change in lung function suggested that long term effects were confined to those who had been exposed to catastrophic events,10 including a chemical fire11 and the collapse of the World Trade Center (WTC).12–14 Clinical findings from WTC firefighters suggested an increase in hyperreactivity and airways injury with bronchial wall thickening.15 The present study was designed to document the nature and extent of respiratory ill-health in firefighters deployed to the Fort McMurray fire using data from administrative health records, spirometry (both routine and in response to the fire), and a clinical assessment parallel to the WTC study.15 A particular focus was the relation of health measures post-fire to estimated particulate exposure during the conflagration.


Assembly of the Cohort and Data Collection from Firefighters

In May to September 2016, firefighters were recruited from 12 structural fire services. Spirometry, following American Thoracic Society guidelines,16 was carried out in a mobile laboratory at each fire station. The cohort was then expanded to recruit industrial, structural, and wildland firefighters from throughout Alberta who had attended the Fort McMurray fire. All participant firefighters completed a recruitment questionnaire giving details of dates, work hours, and location of their firefighting, together with tasks carried out and use of respiratory protective equipment (RPE). In an online follow-up in October 2018 to January 2019, participants completed the European Community Respiratory Health Survey (ECRHS),17 together with a self-report of “any current lung or breathing problem related to the Fort McMurray fire” with a binary response (yes/no).

Establishment of a Community-Based Case–Control Study Group

At the time of recruitment firefighters were asked to consent to linkage to the Alberta Administrative Health Database (AHDB): in Alberta, physicians, to claim for the service provided, are required to report at least one diagnosis. Alberta Health (AH) uses these data, inter-alia, to determine whether an Alberta resident has developed any of a number of chronic conditions, including asthma and COPD. For this report we used these data in two ways. First, we asked the AHDB to match each firefighter with five population controls (referents) on age (±3 years), sex, geographic area (first three digits of postal code or, failing that, local government area), and number of physician claims (0, 1 to 2, 3 to 7, 8+) for all conditions in the 12 months to March 31st 2016. This aimed to reflect equivalent accessibility of health care in cases and controls. Claims data were then extracted for 3 years pre-fire (April 1st 2013 to March 31st 2016) and 2 years including and post-fire (April 1st 2016 to March 31st 2018). We coded physician visits for asthma (ICD-9 493, ICD-10 J45–46). Second, we identified any firefighter who met, pre-fire, the AH criteria for chronic asthma or COPD. These were, for asthma: any hospital admission with asthma code ICD-9 493; ICD-10 J45–46 or two physician visits within 2 years with ICD-9 493 or ICD-10 J45–46 as the primary diagnosis and for COPD: for individuals age 35 or older any hospital admission with ICD-9 code 491 to 492, 496 or ICD-10 J41 to 44 or two physician visits with a diagnosis of COPD in the first diagnostic position within 2 years.

Collection of Spirometry Reports from Occupational Health Monitoring Records

Spirometry for occupational health monitoring of firefighters is voluntary in Alberta and offered only by larger fire services. We asked all firefighters whether they had had such tests and for consent to obtain reports. Where the firefighter gave consent, we attempted to collect as many spirometry records as possible, asking for the closest record before the fire (2nd May 2016) and the first post-fire. Not all fire services were able to supply records.

Clinical Assessment of a Stratified Random Sample from Within the Firefighter Cohort

Firefighters who reported at the 2018 to 2019 follow-up that they “did not smoke/use tobacco before the fire or now,” had completed the ECRHS in 2018 to 2019 and were not identified as having chronic asthma or COPD pre-fire were considered for a clinical respiratory assessment. This comprised full pulmonary function tests (spirometry, plethysmography, diffusion capacity), methacholine challenge testing (MCT), and high-resolution chest computerized tomography (CT). Assessments were carried out July 2019 through February 2020. A stratified random sample was selected for assessment using a wheeze scale extracted from responses to the ECRHS questionnaire using the approach of Sunyer et al18 (Supplemental Digital Content 1, Table showing extraction of a wheeze factor from the ECRHS). We randomly selected 50% from the top wheeze quartile, 25% of those in the next quartile, and 10% of those below the median. Any firefighter who had not been randomly selected but met the selection criteria (on smoking, prior lung disease, and ECRHS completion) and who reported current fire-related respiratory problems in 2018 to 2019 was also invited to attend (Fig. 1). Assessments were arranged at multiple facilities as close as possible to the place of residence. The MCT was offered only in major centres but, because of high exposures in firefighters based in Fort McMurray, a mobile team conducted the MCT in that area. The delivered (provocative) dose of methacholine eliciting a drop of 20% in FEV1 (PD20) was recorded.19 High-resolution CT was carried out by private or provincial facilities following a standard protocol (Supplemental Digital Content 2, Protocol for the CT Chest). All CTs were read, blind to exposure and symptoms, by a single radiologist (JRB) who recorded the presence of bronchial wall thickening (BWT), nodules, local or diffuse fibrosis, and findings suggestive of sarcoidosis or emphysema (Supplemental Digital Content 3, Record sheet for reading Chest CT). Pulmonary function testing, again by multiple facilities, included FEV1, FVC, FEV1/FVC, FEF25–75 (forced expiratory flow at 25% to 75% of FVC), PEF (peak expiratory flow), VC (vital capacity), DLCO (diffusing capacity of the lungs to carbon monoxide), and VA (alveolar volume). The DLCO was performed via the single breath-hold technique following the ATS/ERS guidelines.20 The results of the pulmonary function tests and methacholine challenge were sent to a physician nominated by the participant and to the participant themselves. Where an abnormality was detected on the CT, the radiologist determined whether a referral was appropriate and the nominated physician was advised of this recommendation.

Flow diagram for clinical follow-up.

Estimation of Exposure to PM2.5 During the Fire

Exposure to fire-related particles (PM2.5) was estimated combining firefighter reports on dates and hours firefighting in each location with estimates of particulate matter from Alberta Environment (using results from monitoring stations and satellite imagery (Supplemental Digital Content 4, Estimation of exposure to smoke particulates). For the first deployment these were weighted by factors representing task-specific smoke exposures and the mitigating effect of any RPE. Exposure during subsequent rotations was represented only by dates, hours, and estimates of particulate exposure. Exposures to wildland firefighters, who were deployed over a wider area and for longer periods, were estimated separately, using the same approach but without the factor for RPE.

Statistical Methods

The analysis of the matched case-referent data from the AHDB used conditional logistic regression to estimate the odds, for a firefighter, of any physician-recorded diagnosis of asthma pre-fire and post-fire, of any (new onset) asthma diagnosis post-fire in someone with no asthma diagnosis pre-fire, and of meeting the criteria for chronic asthma post-fire. Community controls had been matched to cases on potential confounders: these were not entered into the analysis.

For internal analysis within the firefighter cohort, we took the log of the exposure estimate to reduce effects of skew and used this both as a continuous variable and, to aid presentation of trends, grouped into quartiles.

For analysis of the first post-fire spirometry, predicted values were calculated from linear regressions that included age, sex, height, smoking, the source of record (research team or routine monitoring), a history of chronic asthma before the fire and time to spirometry since first deployment. The relation of means observed (O), predicted (P), and their ratio ([O/P]%) to exposure quartile were tested for linearity. Supplementary regression analyses examined the relation to post-fire spirometry of log exposure as a continuous variable. Predicted values for pulmonary function tests in the clinical sample were also calculated from linear regressions that included age at testing, sex, height, and body mass index. Outcomes from the clinical assessments were examined for trend across exposure quartiles, overall, and stratified by a report of ongoing respiratory problems at the 2018 to 2019 follow-up. Prevalence was estimated using weights reflecting the reciprocal probability of assessment (Supplemental Digital Content 5, Estimation of prevalence in the base population) in a Poisson regression model.


Asthma in Firefighters Matched to Community Controls

In the final cohort of 1234 firefighters, 955 were matched to the Alberta AHDB and to five database controls. Among the firefighters, 6.9% had a physician consultation for asthma in the 37 months before the fire and 7.2% % in the 23 months including and since the fire (Table 1). The odds ratios (OR = 0.91; 95%CI 0.69 to 1.20) showed no difference between firefighters and controls pre-fire, but an increased risk for firefighters post-fire (OR = 1.45; 95%CI 1.10 to 1.92). When only new onset asthma was considered (with 4.7% in firefighters and 1.9% in controls), the odds ratio increased to 2.56 (95%CI 1.75 to 3.74). The number of firefighters and controls meeting the criteria for chronic asthma (with a median date of August 1997) was 703 pre-fire but only 38 post-fire. Firefighters had a higher risk than community controls of chronic asthma both pre-fire (OR = 1.25; 95%CI 1.01 to 1.53) and, with a higher odds ratio (2.11; 95%CI 1.04 to 4.27), post-fire.

TABLE 1 - Asthma in Firefighters Deployed to the Fort McMurray Fire Matched to Community Controls from the Alberta Administrative Health Database (AHDB)
Firefighter Community Controls
Asthma Diagnosis in AHDB n N % n N % ORFirefighter 95%CI
1) Any physician diagnosis
 1st April 2013 to 1st May 2016 66 955 6.9 358 4775 7.5 0.91 0.69–1.20
 2nd May 2016 to 31st March 2018 69 955 7.2 243 4775 5.1 1.45 1.10–1.92
2) New onset physician diagnosis
 2nd May 2016 to 31st March 2018 42 889 4.7 84 4417 1.9 2.56 1.75–3.74
3) Met chronic asthma criteria
 Before 2nd May 2016 136 995 14.2 567 4775 11.9 1.25 1.01–1.53
 2nd May 2016 to 30th May 2018 11 819 1.2 27 4208 0.6 2.11 1.04–4.27
Asthma diagnostic code recorded (primary or secondary) ICD-9 493; ICD-10 J45–46.
Asthma diagnostic code recorded (primary or secondary) ICD-9 493; ICD-10 J45–46 with no asthma coded April 2013 through 1st May 2016.
Any hospital admission with asthma code ICD-9 493; ICD-10 J45–46 or two physician visits within 2 years with ICD-9 493 or ICD-10 J45–46 as the primary diagnosis.

Spirometry Post-fire

Spirometry was obtained post-fire for 47% (582/1234) of firefighters from 17 fire services. The record closest to (after) the fire was selected for analysis. This was either from testing by the research team (N = 271) or from routine spirometry carried out by, or for, the employing fire service (N = 311). Overall, external records post-fire were available for 376 firefighters, of whom 237 (19% of the cohort) also had a pre-fire record. Spirometry carried out by the team was completed on average 8 weeks after the start of the fire, with those carried out by the employing fire service a mean of 33 weeks post-fire. The relation of exposure during the fire to factors included in the analysis of spirometry results is shown in Table 2 for the 582 firefighters with post-fire spirometry. Those with higher exposure were younger, more likely to be female and to have been a cigarette smoker. There were differences, but no linear trend, with mean time from start of deployment to testing and the proportion tested by the research team. The height of the firefighter and a history of asthma (not shown) were unrelated to exposure.

TABLE 2 - Distribution by Exposure Quartile of Factors Potentially Related to Spirometry Values (N = 582)
Age Height Weeks Since Deployment Sex Female Smoke Ever Testing by: Research Team
Exposure Quartile Mean SD Mean SD Mean SD n % n % n % N
Low 39.5 9.5 177.1 10.2 19.1 30.9 6 4.1 21 14.5 65 44.8 145
Below median 39.6 9.2 178.5 7.5 16.2 31.4 5 3.4 23 15.8 73 50.0 146
Above median 38.1 9.6 178.8 9.8 32.2 45.7 9 6.2 39 26.7 53 36.3 146
High 35.6 9.8 177.3 9.1 18.2 21.0 16 11.0 34 23.4 80 55.2 145
Overall 38.2 9.6 177.9 9.2 21.4 34.0 36 6.2 117 20.1 271 46.6 582
Difference between
 Groups, P= 0.001 0.281 0.001 0.031 0.023 0.010
Linearity, P= <0.001 0.784 0.281 0.009 0.011 0.353
Log PM2.5 μg/m3∗h low ≤ 9.23; below median > 9.23 ≤ 10.90; above median > 10.90 ≤ 11.80; high > 11.80.
Analysis of variance for difference between means: chi-square for difference between proportions.

Predicted values for each spirometry parameter were computed (Table 3) and percent predicted values calculated. Table 4 shows that the percent predicted FEV1 and FVC decreased with increasing exposure quartiles, with those in the lowest quartile having markedly better than predicted values. The percent predicted FEV1/FVC was unrelated to exposure.

TABLE 3 - Regression Analyses to Estimated Predicted Values of Spirometry Parameters at First Test Post-fire (N = 582)
ß 95%CI P= ß 95%CI P= ß 95%CI P=
Age (years) at testing (continuous) −0.03 −0.04 −0.03 <0.001 −0.03 −0.04 −0.03 <0.001 −0.12 −0.17 −0.06 <0.001
Sex (male) 0.52 0.33 0.71 <0.001 0.72 0.50 0.95 <0.001 −1.15 −3.32 1.03 0.301
Height (cm) continuous 0.03 0.03 0.04 <0.001 0.04 0.04 0.05 <0.001 −0.06 −0.12 0.00 0.034
Asthma before the fire −0.15 −0.26 −0.04 0.008 −0.04 −0.17 0.09 0.552 −2.14 −3.38 −0.90 0.001
Ever smoked (yes) −0.20 −0.31 −0.09 <0.001 −0.19 −0.32 −0.06 0.004 −1.06 −2.31 0.19 0.095
Testing external to research team 0.02 −0.07 0.10 0.719 −0.10 −0.20 0.01 0.069 1.62 0.61 2.62 0.002
Constant −0.63 −1.51 0.26 0.167 −1.91 −2.96 −0.86 <0.001 94.34 84.23 104.45 <0.001
Forced Expiratory Volume in 1 second.
Forced Vital Capacity.

TABLE 4 - Means of Observed (O), Predicted (P), and Percentage O/P Ratio for Spirometry Parameters by Exposure Quartile (N = 582)
Exposure Quartiles N FEV1 (O) FEV1 (P) FEV1% (O/P) FVC (O) FVC (P) FVC % (O/P) FEV1/FVC (O) FEV1/FVC (P) FEV1/FVC% (O/P)
Low 145 4.26 4.16 102.63 5.50 5.35 103.19 77.65 77.97 99.59
Below median 146 4.20 4.20 99.76 5.38 5.42 99.11 78.11 77.76 100.46
Above median 146 4.21 4.23 99.59 5.38 5.42 99.20 78.45 78.11 100.42
High 145 4.16 4.23 98.20 5.36 5.42 98.72 77.83 78.21 99.52
Overall 582 4.21 5.40 100.04 5.40 5.40 100.05 78.01 78.01 100.00
Difference between means, P= 0.697 0.585 0.023 0.517 0.666 0.003 0.711 0.163 0.591
Linearity, P= 0.288 0.190 0.004 0.202 0.303 0.002 0.698 0.109 0.928
Forced Expiratory Volume in 1 second.
Forced Vital Capacity.
Analysis of variance for difference between means.

The analysis was then repeated with log exposure as a continuous variable, first for all 582 with any spirometry results post fire and then for the smaller group of 237 with external testing both pre- and post-fire. In the analysis of the whole sample (Supplemental Digital Content 6, Regression of spirometry parameters Table A1), higher exposure was strongly related to a decrease in FEV1 and FVC but not to FEV1/FVC. In the 237 with external spirometry both pre- and post-fire (Supplemental Digital Content 6, Regression of spirometry parameters Table A2), the relation of exposure to FEV1 and FVC remained significant, but was less marked.

Clinical Assessment

Clinical assessments took place up to 46 months after the start of the fire (median 43.1 months). Among the 995 who had completed the ECRHS there were 703 non-smokers not identified in the AHDB as having chronic asthma (N = 106) or COPD (N = 2) pre-fire. A stratified random sample of 166 was selected, with increasing probability of selection with higher wheeze score. The random selection included 43 of the “ongoing respiratory problems” group. The remaining 57 of that group were also invited to be assessed regardless of their wheeze score. The sample selected is shown in Table 5 and Figure 1. Completion of the assessment was highest (169/223: 75.8%) for the CT scan (available at many smaller hospitals) and lowest for the MCT (149/223: 66.8%), less easily accessed. On the CT scan, BWT was recorded for 36 (21.3% of those assessed), with extensive thickening in three. One or more nodule was noted in the scan for 51 firefighters (31.1%) and local, non-nodular fibrosis in 24 (14.6%). Possible sarcoidosis was noted for three. No diffuse fibrosis was reported. Overall there were 16 recommendations made for referrals. Other than a weak trend for BWT, no CT finding was related to exposure. Of the 149 completing the MCT 29 (19.5%) had a drop in FEV1 of 20% or greater consistent with hyperresponsiveness. Predicted values were computed internally for PFT parameters, using regressions given in Table A3 in supplemental digital content 6,, which predicted values from age, sex, height, and body mass index. The observed and predicted values, and their ratio are given by exposure quartile in Table A4 in supplemental digital content 6, No relation to exposure was seen, within the clinical sample, for FEV1, FVC or FEV1/FVC, or alveolar volume but a strong trend was seen with diffusion capacity. The relation of clinical outcomes to exposure quartile is shown in Table 6 for parameters showing a linear trend with exposure. The observed trends were unlikely to have arisen by chance for DLCO, DLCO/VA and for those with both a positive MCT and BWT.

TABLE 5 - Respiratory Assessment: Selection of Firefighters and Numbers of Assessments Completed
Respiratory Problems From the Fire in 2018–2019 Selected for Respiratory Assessment Assessment Completed
Grouped Wheeze Score From ECRHS N n % n % MCT CT PFT
Low (no wheeze) 257 Yes 1 0.4 Yes 24 9.3 Yes 16 17 16
No 256 99.6 No 233 91.7 No 8 7 8
Below median 92 Yes 4 4.3 Yes 15 16.3 Yes 12 14 13
No 88 95.7 No 77 83.7 No 3 1 2
Above median 180 Yes 27 15.0 Yes 65 36.1 Yes 44 48 44
No 153 85.0 No 115 63.9 No 21 17 21
High 174 Yes 68 39.1 Yes 119 68.4 Yes 77 90 80
No 106 60.9 No 55 31.6 No 42 29 39
All 703 Yes 100 14.2 Yes 223 31.7 Yes 149 169 153
No 603 85.8 No 480 68.3 No 74 54 70
Total 703 100.0 703 100.0 223 223 223
ECRHS, European Community Respiratory Health Survey; MCT, methacholine challenge test.
As self-reported in the 2018–19 follow-up questionnaire.
Selected if non-smoker without pre-fire chronic lung disease and randomly selected within wheeze stratum and/or self-reported respiratory problems from the fire in 2018–19 follow-up.

TABLE 6 - Clinical Signs (Diffusion Capacity, Positive Methacholine Challenge Test Concurrent with Bronchial Wall Thickening) Related to Exposure Quartile: Clinical Sample
Diffusing Capacity of the Lungs to Carbon Monoxide (DLCO), Alveolar Volume (VA), and Their Ratio: Observe and Observed (O)/Predicted (P) % Methacholine Challenge (MCT) Positive CT ChestBronchial Wall Thickening (BWT) MCT and CTPositive MCT and BWT
Exposure Quartile (Clinical) N DLCO (mL/mm Hg/minute) DLCO/VA (mL/mm Hg/minute/L) DLCO O/P % DLCO/VA O/P % N n % N n % N n %
Mean SD Mean SD Mean SD Mean SD
Low 39 37.0 7.4 5.5 0.7 105.0 14.4 103.0 12.0 38 5 13.2 42 7 16.7 38 1 2.6
Below median 38 33.8 7.0 5.3 1.0 100.8 16.4 101.7 16.3 36 6 16.7 42 6 14.3 35 2 5.7
Above median 40 34.8 5.3 5.4 0.7 100.1 10.6 101.3 11.6 40 7 17.6 43 12 27.9 40 5 12.5
High 36 33.4 6.2 5.0 0.7 94.0 14.1 93.4 12.6 35 11 31.4 42 11 26.2 35 6 17.1
Overall 153 34.8 6.6 530 0.8 100.1 14.4 100.0 13.6 149 29 19.6 169 36 21.3 148 14 9.5
Between groups, P= 0.073 0.048 0.010 0.009 0.218 0.327 0.139
Linearity, P= 0.038 0.013 0.001 0.004 0.063 0.151 0.021
CT, computerized tomography.
Prediction equations are given in Table A3, Supplemental Digit Content 6.
N = number of people in the quartile tested: n = number of people positive: % = n/N × 100.
Analysis of variance for difference between means: chi-square for difference between proportions.

Table 7 shows the estimated effect size with exposure quartile. As suggested in Table 6, there was an estimated 4 mL/mm Hg/minute difference between highest and lowest quartiles for DLCO. The odds ratios for BWT and a positive MCT increased with exposure quartile, particularly for those with both a positive methacholine challenge test and bronchial wall thickening. With small numbers of cases with both outcomes (14/148) the confidence intervals were wide. When log exposure was entered as a continuous variable, rather than as quartiles, the estimated odds ratio (OR = 1.67; 95%CI 1.04 to 2.68; P = 0.032) supported the observed relationship between exposure and this composite outcome.

TABLE 7 - Regression Analysis, Adjusted for Age, Sex, Height, and Body Mass Index, of Diffusion Capacity, Bronchial Wall Thickening, and Positive Methacholine Challenge Test in Those with High Exposure or with Ongoing Respiratory Issues
Exposure Quartile
N Low Below Median Above Median High Respiratory Issues in 2018–2019
Diffusion capacity (DLCO)
 β −1.65 −1.87 −4.11 −0.58
 95%CI −3.87 to 0.56 −4.11 to 0.37 −6.34 to −1.89 −2.20 to 1.04
P 0.142 0.102 <0.001 0.482
N 153 39 38 40 36 73
Bronchial wall thickening (BWT)
 OR 1.00 0.67 1.65 1.82 1.17
 95%CI 0.20 to 2.28 0.56 to 4.86 0.61 to 5.41 0.55 to 2.49
P 0.519 0.367 0.280 0.682
N 169 42 42 43 42 78
Methacholine challenge test (MCT) positive
 OR 1.00 1.41 1.45 3.01 3.21
 95%CI 0.38 to 5.17 0.40 to 5.24 0.91 to 9.88 1.33 to 7.72
P 0.607 0.542 0.070 0.009
N 149 38 36 40 35 70
Both MCT positive and BWT
 OR 1.00 2.55 4.56 6.82 4.35
 95%CI 0.20 to 32.55 0.47 to 44.35 0.73 to 63.49 1.11 to 17.12
P 0.470 0.122 0.092 0.035
N 148 38 35 40 35 69
Firefighters in the clinical sample in the highest exposure quartile compared with firefighters in the lowest exposure quartile.
Firefighters self-reporting ‘any lung or breathing problem related to the Fort McMurray fire’ on the 2018–19 follow-up questionnaire compared with all others in the clinical sample who did not report such a problem.

The final analysis was to compare those who complained of a lung or breathing problem related to the fire at the time of the 2018 to 2019 questionnaire with all others who had undergone assessment. Of the 100 with ongoing respiratory problems, 79 completed at least one element of the clinical assessment as had 91 without such a response (Fig. 1). Those with ongoing problems were found to have higher estimated exposure during the fire (mean 10.8 [SD] 1.4 log PM2.5 μg/m3∗h compared with 10.1 SD 1.8 log PM2.5 μg/m3∗h: P = 0.005). Those reporting problems were more likely to have a positive MCT (28.6% [20/70] with on-going problems, 8.9% [7/79] without), and the combination of positive MCT and BWT (15.9% [11/69] with on-going problems, 3.8% [3/79] without). The final column of Table 7 estimates the effect size associated with reporting an ongoing respiratory complaint related to the fire and confirms the higher risk of positive MCT and MCT + BWT. The odds ratios shown in the final column of Table 7 were reduced only slightly if adjustment was made also for estimated exposures, with that for a positive MCT becoming OR = 2.77; 95%CI = 1.18 to 6.49; P = 0.019 and for concurrent positive MCT and BWT 3.94; 95%CI 1.06 to 14.68; P = 0.040.

Prevalence estimates, reweighted for sampling (Supplemental Digital Content 5, Estimation of prevalence), were 12.5% (95%CI 7.81 to 20.11) for a positive MCT, 18.5% (95%CI 12.60 to 27.12) for BWT, and 5.7% (95%CI 2.73 to 12.08) for the concurrence of the two, in the subgroup of 703 non-smokers without chronic respiratory disease prior to the fire.


The examination of respiratory ill-health in this cohort of firefighters showed an increase in asthma consultation post-fire in relation to community controls, a decrease in FEV1 and FVC related to estimated exposure and, in the clinical sample, an exposure-related decrease in DLCO and increase in hyperreactivity with bronchial wall thickening. Reweighting to the population from which the sample was drawn suggested an overall prevalence of 12.5% for hyperreactivity. Those reporting ongoing respiratory issues from the fire were more likely than others to demonstrate hyperreactivity both alone and in combination with bronchial wall thickening.

This appears to be the first study of respiratory heath of firefighters in which the catastrophic event resulted in exposures to typical combustion products rather than to dust (as in the WTC collapse13) or to chemicals plus smoke, as in the Texas warehouse fire.11 In the Fort McMurray fire, structural and industrial firefighters largely tackled fires in buildings, infrastructure and vehicles and wildland firefighters the burning biomass, but much of the work was at the interface, particularly during the chaotic early days of the fire. Strengths of the study were that we were able to make estimates of particulate exposure, and to examine whether greater exposure was associated with poorer respiratory health. We were also able to use results from the clinical sample to estimate for the whole population and to use administrative health data, rather than simply self-report, to examine change in physician contacts for asthma before and after the fire. The increased rate of asthma, as seen by data linkage, is consistent with Pederson et al7 using a similar approach.

Each part of the study had limitations. Only 995 of the 1234 firefighters (81.1%) were included in the record linkage analysis. Those excluded, because of non-consent, not found in the administrative record or not resident for the 5 years of interest, may have differed from those included. The analysis of spirometry was limited by the absence of prior spirometry record for many whose spirometry was carried out by the research team, and the low particulate exposures in those from the large, unrepresentative, fire services that could provide both pre- and post-fire records. Health surveillance programs used different algorithms to estimate expected values, necessitating the use of predicted values from internal data. All the firefighters will have attended other fires since that in Fort McMurray. In excluding this as a factor in the analysis we assume the effect of these to be non-differential (ie, unrelated to intensity of exposure during the Fort McMurray fire or any respiratory condition resulting from it): this assumption may not be valid. The clinical assessment used facilities as close as possible to the firefighter's home: although protocols were agreed for each assessment, equipment, staff, and prediction equations differed. Again, we used internal standardization rather than predictions from different algorithms. Not all those selected were able and willing to attend assessments. The CT images were read by a single radiologist, blind to exposure, and symptoms.

We included all those who reported respiratory issues related to the fire (even if they were not randomly selected for the stratified sample) because we wanted to be sure we were not missing any atypical presentation: the stratification was on a wheeze factor that might have omitted some other cluster of symptoms. It was also important, if the study results were to be seen as valid by the firefighters, that all who felt their respiratory health had been damaged had the opportunity to be assessed (if they were non-smokers without prior chronic lung disease). This did not introduce bias into the prevalence estimates, which allowed for the dual selection criteria, but did give us the numbers to determine whether those self-presenting with fire-related symptoms did indeed have poorer respiratory health using objective criteria.

The clinical assessment was based on that used with the WTC cohort15 and produced broadly similar results where comparison was possible. The prevalence of BWT in the WTC clinical assessment sample was 26% compared to 21% in the clinical sample in the present study. Estimate of exposure in the WTC cohort was limited to time of arrival (with early arrivals more exposed to the dust plume), but a dose response was clearly demonstrated for FEV114 and a recent paper suggests a diverging cumulative incidence of BWT, with firefighters arriving early more likely to develop this.12 Given the differences in the nature of particles between the two cohort and the inclusion of only non-smokers without prior chronic lung disease in the Alberta clinical assessment, it would be unrealistic to expect exactly parallel results. In the Alberta cohort we found no excess of “sarcoid-like” pulmonary disease21 and, unlike the WTC assessment, we found a decrease in DLCO with increased exposure. This might, perhaps, be a chance finding. Alternatively, it might suggest damage to the alveolar region of the lung not evident on CT, but perhaps linked to the BWT. Members of the cohort had evidence of inflammation related to months post-fire22 which may have resulted in damage evident through DLCO changes and lower rates of diffusion. As with the WTC cohort, examination of the chest CT did not show interstitial disease. In both cohorts the data suggested airway injury that manifested as hyperreactivity and BWT together with, in the Alberta cohort only, a decreased diffusing capacity. It should be noted that the decrease in diffusion capacity in the Alberta cohort was very small and unlikely to be of clinical significance, but it may be appropriate to investigate this further in future studies of firefighters exposed in major wildfires.

In this cohort, 14.3% of non-smokers without prior chronic respiratory disease reported, many months after their deployment, that they had respiratory symptoms they attributed to the fire. One aim of the clinical assessment was to examine whether there were features of their respiratory disease that would help determine whether their condition was fire-related. Among those with ongoing symptoms, 16% of those assessed had both a positive MCT and BWT with an elevated odds ratio of 4.35. While it is not easy to fit all the observed changes into a single clinical syndrome, such a combination of MCT and BWT, in someone with non-resolving respiratory issues after a catastrophic fire, and without a history of smoking or pre-existing asthma, might suggest, on the balance of probabilities, that the condition was caused by the fire.

In conclusion, it appears that exposures to very high concentrations of particulates during the Fort McMurray fire resulted in ongoing respiratory issues with reduced diffusing capacity, new onset asthma, and hyperreactivity with bronchial wall thickening in those deployed.


David Pawluski, Physiological Laboratory Technologist at the University of Alberta Hospital Pulmonary Function Laboratory provided exceptional support in arranging out-of-hours testing for firefighters during the clinical assessments.


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bronchial wall thickening; firefighters; hyperreactivity; lung function; wildfire

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