Symptoms reported by young adults with asthma during wildfire smoke season

Abstract

Introduction:

The purpose of this paper is to evaluate the relationship between exposure to poor air quality (AQ) and self-reported symptoms among young adults with asthma during wildfire smoke season.

Methods:

Sixty-seven young adults (18-26 years old) completed the Asthma Control Test (ACT) and reported asthma symptoms at three timepoints (baseline, 4-weeks, 8-weeks) during wildfire season as part of a clinical trial. Bivariate correlations between ACT and AQ measures were examined followed by predictive linear regression. Multiple symptoms were compared between participants that experienced poor AQ and those who did not.

Results:

Asthma control was inversely related to AQ with increased exposure to poor AQ tied to poor asthma control. A significantly greater proportion of participants reported critical respiratory symptoms when exposed to poor AQ than those who were not.

Conclusion:

Respiratory symptoms are key indicators that young adults can monitor to optimize their asthma management during wildfire smoke season.

Introduction

Climate change has increased the duration and intensity of the wildfire season, particularly in the Western United States (US).^1,2 Individuals with asthma are vulnerable to health effects from wildfire smoke exposure.³ Robust epidemiological evidence links short term exposures to fine particulate matter (PM_2.5), a primary pollutant in wildfire smoke, and emergency room admissions and hospitalizations for asthma exacerbations.⁴

Young adults with asthma constitute a high-risk population. Most (57.6%) young adults (18-34 years) with asthma are uncontrolled⁵ and adherence to inhaled corticosteroids is sub-optimal during the transition to adulthood.⁶ Younger adults are less likely to adhere to health advice accompanying air quality (AQ) warnings than older adults ^7,8 and take fewer measures to avoid exposure.⁹ The purpose of this paper was to evaluate the relationship between exposure to poor AQ and symptoms in young adults with asthma during wildfire smoke season.

Methods

Study data were collected during a feasibility study that tested interventions to minimize smoke exposure among a sample of young adults (18–26 years) with self-reported asthma. Full methods have been reported elsewhere.^10,11 Briefly, 67 young adults were enrolled in an 8-week clinical trial that took place during wildfire season¹² with assessments conducted at baseline, 4-weeks, and 8-weeks. The study was reviewed and approved by the university’s institutional review board. All participants provided informed consent.

Exposure to poor AQ was measured by collecting the daily average PM_2.5 from www.airnowtech.org, based on the participants’ reported zip code. Poor AQ was defined using the level of concern of PM_2.5 for sensitive groups, including those with asthma, which is 35.5μg/m³ or above, per the Air Quality Index.¹³ Asthma control was measured using the asthma control test (ACT). The ACT measures shortness of breath, asthma symptoms, use of rescue medications, daily functioning, and an overall self-assessment of asthma control via self-report. It has 5 items, uses a 4-week recall and a response scale that ranges from 5 (poor control) to 25 (complete control). An ACT score >19 indicates well-controlled (≤19 poorly controlled) asthma.¹⁴ It is reliable, valid, and correlates with specialists’ ratings of asthma control based on history, examination and forced expiratory volume in one second (FEV₁). ^14,15 Multiple symptoms were also assessed via self-report at baseline, 4 and 8 weeks using a questionnaire developed by the Environmental Protection Agency. ¹⁶ Respondents were asked to identify if, over the past month, they experienced any of the 14 symptoms (yes/no).

Pearson’s correlation was used to assess the relationship between participants’ asthma control and PM_2.5 exposure over the previous 30-days. Based on the linear relationship between the two variables, further investigation was conducted via scatter plot (x-axis: ACT score; y-axis: percent of days over the previous 30-days, with a PM_2.5 at or above 35.5μg/m³) and analysis of variance (ANOVA). Using the best-fit linear equation, the percentage of days spent in poor AQ that impacts participants’ ACT score was calculated using an ACT score of 19 as an indicator of uncontrolled asthma over the previous 4 weeks. Participants were then split into 2 groups: 1) those that spent the calculated percentage of days over the prior 30 days in poor AQ, and 2) those who did not. A two-by-two contingency table was constructed for each of the self-reported symptoms based on whether participants per group experienced them. An adjusted chi-square test was used to examine differences in symptoms between the two groups.

Results

Most participants were non-Hispanic (88%) and white (78%).¹⁰ Over the study period, participants provided 182 valid ACT measurements (Baseline: 65; 4-Weeks: 59; 8-Weeks: 58). At baseline, 52% of the participants had mild asthma, 41% moderate asthma and 7% severe asthma based on their reported medication therapy and whether they were well-controlled or not per their ACT score at study entry.¹⁷ Mean ACT score was 20.8 ± 2.7 (Baseline: 20.4 ± 2.5; 4-Weeks: 20.7 ± 3; 8-Weeks: 21.3 ± 2.6) while mean PM_2.5 was 21.5 ± 26.3 (Baseline: 32.7 ± 36.5; 4-Weeks: 20.8 ± 19.3; 8-Weeks: 9.5 ± 5.5).

Bivariate correlation testing indicated participants’ ACT score was significantly inversely related to the average PM_2.5 over the previous 30 days (r=−0.175; p=0.018) and remained after using regression to control for participants’ repeated measures (r=−0.178; p=0.016). Additionally, a significant inverse linear relationship between ACT score and the percentage of days, over the previous 30-days, with PM_2.5 at least 35.5 μg/m³ (r=−0.238; p=0.001) was found. A one-way ANOVA indicated that there was no effect of time on participants’ ACT score (f[2, 58]=1.267; p=0.285).

The regression was significant (R²=0.057; f[1, 180]=10.853; p=0.001) with the model equation: y = 29.48 – 1.04 * x. Based on this analysis, exposure to poor AQ on at least 9.72% of days over the prior 30-days was predicted to lead to poor asthma control (Figure 1).

Figure 1.

Scatter Plot of Percent of Days Over the Previous 30-days, with a PM_2.5 at or above 35.5μg/m3 by Asthma Control Test Score

After removal of 11 data measures due to incomplete symptom reporting, remaining records (n=171) were grouped by those that had been exposed to a PM_2.5 of 35.5 μg/m³ or above for at least 9.72% of the previous 30 days (n=57) and those who were not (n=114). Participants in the poor AQ group reported significantly more respiratory symptoms (adjusted chi square: 7.3274; p-value: 0.007), specifically coughing, trouble breathing normally and shortness of breath, than the other group (adjusted chi square: 4.6194; p-value: 0.032). Table 1 provides a full summary of symptoms assessed.

Table 1.

Reported Symptoms by Air Quality

	Not Poor AQ (n=114*)	Poor AQ (n=57*)	Adjusted Chi Square	p-value
Respiratory†
Yes	177 (38.8%)	114 (50.0%)	7.3274	0.006791
No	279 (61.2%)	114 (50.0%)
Scratchy throat
Yes	53 (46.5%)	31 (54.4%)	0.6581	0.417232
No	61 (53.5%)	26 (45.6%)
Asthma attack
Yes	21 (18.4%)	15 (26.3%)	0.9896	0.319844
No	93 (81.6%)	42 (73.7%)
Coughing, trouble breathing normally, or shortness of breath†
Yes	61 (53.5%)	41 (71.9%)	4.6194	0.031612
No	53 (46.5%)	16 (28.1%)
Wheezing
Yes	42 (36.8%)	27 (47.4%)	1.3394	0.247148
No	72 (63.2%)	30 (52.6%)
Ears, nose, and throat
Yes	192 (42.1%)	106 (46.5%)	1.0176	0.313095
No	264 (57.9%)	122 (53.5%)
Stinging, itching, or watery eye(s)
Yes	60 (52.6%)	35 (61.4%)	0.8556	0.354975
No	54 (47.4%)	22 (38.6%)
Ear or other viral infections
Yes	3 (2.6%)	1 (1.8%)	0.032	0.858031
No	111 (97.4%)	56 (98.2%)
Runny or stuffy nose
Yes	73 (64.0%)	39 (68.4%)	0.1585	0.69054
No	41 (36.0%)	18 (31.6%)
Irritated sinuses or congestion
Yes	56 (49.1%)	31 (54.4%)	0.2369	0.626443
No	58 (50.9%)	26 (45.6%)
Cardiovascular
Yes	107(23.5%)	47 (20.6%)	0.5541	0.456628
No	349 (76.5%)	181 (79.4%)
Tiredness, dizziness, or similar
Yes	59 (51.8%)	27 (47.4%)	0.1433	0.705042
No	55 (48.2%)	30 (52.6%)
Fast or irregular heartbeat
Yes	25 (21.9%)	7 (12.3%)	1.7348	0.187799
No	89 (78.1%)	50 (87.7%)
Chest pain
Yes	20 (17.5%)	12 (21.1%)	0.1201	0.728885
No	94 (82.5%)	45 (78.9%)
High blood pressure
Yes	3 (2.6%)	1 (1.8%)	0.032	0.858031
No	111 (97.4%)	56 (98.2%)
Other
Yes	126 (55.3%)	61 (53.5%)	0.0369	0.847725
No	102 (44.7%)	53 (46.5%)
Anxiety
Yes	73 (64%)	37 (64.9%)	0.0032	0.954991
No	41 (36%)	20 (35.1%)
Trouble sleeping
Yes	53 (46.5%)	24 (42.1%)	0.1447	0.703648
No	61 (53.5%)	33 (57.9%)

Note: 11 data records were not included as participants did not report symptoms.

^*Respiratory, Ears, Nose and Throat, and Cardiovascular groups were all made up of four symptom categories (Not poor AQ n: 456; Poor AQ n: 228) while Other was made up of two symptom categories (Not poor AQ n: 228; Poor AQ n: 114).

^†Significant between-group difference.

Implications and Contribution

Findings offer a nuanced, novel view of patients’ symptoms during wildfire smoke season and complement epidemiological data. ¹⁸ AQ only explained 5.7% of the variance in asthma control. Although significant, lack of a stronger association may be related to subjective analysis of asthma symptoms,¹⁹ the lag time between exposure and symptom measurement, and the limitation that participants had self-reported asthma. That said, respiratory symptoms are key indicators that young adults can monitor to optimize their asthma management during smoke season. Although reported symptoms may also be related to other sources of air pollution, wildfires are increasing due to climate change. Providers can act on these findings by discussing symptoms, such as coughing, that reflect their patients’ daily experience which, in turn, may help motivate asthma self-management. ²⁰

Implications and Contribution:

Poor AQ from wildfire smoke will continue to increase as our climate changes. Monitoring coughing, trouble breathing normally, and shortness of breath will facilitate asthma self-management during smoke season. Providers can act on these findings by discussing symptoms that reflect their patients’ daily experience.

Abbreviations

AQ

Air Quality

ANOVA

Analysis of Variance

ACT

Asthma Control Test

FEV₁

Forced Expiratory Volume in One Second

GINA

Global Initiative for Asthma

PM_2.5

Particulate Matter

US

United States