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Enviromental Determinants

Outdoor air pollution and the risk of asthma exacerbations in single lag0 and lag1 exposure patterns: a systematic review and meta-analysis

Junjun Huang,a Department of Respiratory and Critical Care Medicine, Peking University First Hospital, Beijing, ChinaORCID Iconhttps://orcid.org/0000-0001-5246-0293View further author information
, MDORCID Icon,
Xiaoyu Yang,a Department of Respiratory and Critical Care Medicine, Peking University First Hospital, Beijing, ChinaORCID Iconhttps://orcid.org/0000-0002-6792-1870View further author information
, MMORCID Icon,
Fangfang Fanb Department of Cardiology, Peking University First Hospital, Beijing, ChinaView further author information
, MD,
Yan Hu,a Department of Respiratory and Critical Care Medicine, Peking University First Hospital, Beijing, ChinaCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0003-1908-4892View further author information
, MDORCID Icon,
Xi Wang,a Department of Respiratory and Critical Care Medicine, Peking University First Hospital, Beijing, ChinaView further author information
, MD,
Sainan Zhu,c Department of Biostatistics, Peking University First Hospital, Beijing, ChinaView further author information
, MM,
Guanhua Ren,d Department of Library, Peking University First Hospital, Beijing, ChinaView further author information
, MM &
Guangfa Wang,a Department of Respiratory and Critical Care Medicine, Peking University First Hospital, Beijing, ChinaView further author information
, MD show all
Pages 2322-2339 | Received 14 Oct 2021, Accepted 16 Nov 2021, Published online: 14 Dec 2021

Abstract

Objective: To synthesize evidence regarding the relationship between outdoor air pollution and risk of asthma exacerbations in single lag0 and lag1 exposure patterns.

Methods: We performed a systematic literature search using PubMed, Embase, Cochrane Library, Web of Science, ClinicalTrials, China National Knowledge Internet, Chinese BioMedical, and Wanfang databases. Articles published until August 1, 2020 and the reference lists of the relevant articles were reviewed. Two authors independently evaluated the eligible articles and performed structured extraction of the relevant information. Pooled relative risks (RRs) and 95% confidence intervals (CIs) of lag0 and lag1 exposure patterns were estimated using random-effect models.

Results: Eighty-four studies met the eligibility criteria and provided sufficient information for meta-analysis. Outdoor air pollutants were associated with increased risk of asthma exacerbations in both single lag0 and lag1 exposure patterns [lag0: RR (95% CI) (pollutants), 1.057(1.011, 1.103) (air quality index, AQI), 1.007 (1.005, 1.010) (particulate matter of diameter ≤ 2.5 μm, PM2.5), 1.009 (1.005, 1.012) (particulate matter of diameter, PM10), 1.010 (1.006, 1.014) (NO2), 1.030 (1.011, 1.048) (CO), 1.005 (1.002, 1.009) (O3); lag1:1.064(1.022, 1.106) (AQI), 1.005 (1.002, 1.008) (PM2.5), 1.007 (1.004, 1.011) (PM10), 1.008 (1.004, 1.012) (NO2), 1.025 (1.007, 1.042) (CO), 1.010 (1.006, 1.013) (O3)], except SO2 [lag0: RR (95% CI), 1.004 (1.000, 1.007); lag1: RR (95% CI), 1.003 (0.999, 1.006)]. Subgroup analyses revealed stronger effects in children and asthma exacerbations associated with other events (including symptoms, lung function changes, and medication use).

Conclusion: Outdoor air pollution increases the asthma exacerbation risk in single lag0 and lag1 exposure patterns.

Trial registration: PROSPERO, CRD42020204097. https://www.crd.york.ac.uk/.

Supplemental data for this article is available online at https://doi.org/10.1080/02770903.2021.2008429 .

Introduction

Asthma is a common chronic airway inflammatory disease that affects > 300 million people worldwide (Citation1). The global prevalence of clinical/treated asthma in adults is 4.5%; moreover, it varies among 70 countries by as much as 21-fold (Citation2). Epidemiologic studies have revealed an increasing prevalence of asthma; in addition, asthma contributes to functional loss, increased healthcare costs, and severe medical complications (Citation3). Exacerbation of asthma accelerates disease progression and increases the incidence of hospitalization and death (Citation4).

Outdoor air pollution has received increasing attention owing to its serious consequences. Air pollution can cause critical public health problems. A retrospective study of 80 515 deaths in Beijing during 2004–2008 found that a reduction in life expectancy was associated with increased air pollution (Citation5). Exposure to air pollution also increases the risk of asthma exacerbations (Citation6–8). The air pollution components are complex, including particulate matter of diameters ≤ 2.5 μm (PM2.5) and ≤ 10 μm (PM10), sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), and ozone (O3), among others. Martinez-Rivera et al. (Citation6) reported a positive association of the levels of NO2, but not of SO2, and CO, with the number of accident and emergency room (ER) visits and hospitalizations for asthma exacerbations. However, another study reported a positive association of SO2 levels, but not of NO2 levels, with pediatric asthma exacerbations (Citation7). Additionally, Ostro et al. (Citation8) reported that new coughing episodes were associated with exposure to PM10, PM2.5, and NO2, but not O3. Studies on different pollutants have reported inconsistent findings.

A meta-analysis confirmed the association between the aforementioned six air pollutants and increased risk of ER visits and hospitalizations for asthma (Citation9). Nonetheless, there are numerous pollutant types to be monitored, and each pollutant has different effects on asthma exacerbation. Therefore, there is a need for a comprehensive pollution index that can represent the various pollutant effects and facilitate estimation of the air pollution level by the general public. The air quality index (AQI) is a useful comprehensive index that was adopted by the US Environmental Pollution Administration for daily air quality reporting to the general public in 1999 (Citation10). The AQI includes sub-indices for PM2.5, PM10, SO2, NO2, CO, and O3 (Citation11). Pan et al. (Citation12) reported an association between AQI and increased risk of hospitalization for childhood asthma. In contrast, Letz et al. (Citation13) reported no significant correlation between AQI and ER visits for asthma in the basic military trainee population. There have been inconsistent findings regarding AQI and the risk of asthma exacerbation. In addition, multiple lag estimates, including single and cumulative lags, were selected for the overall analysis of the aforementioned meta-analysis. Lag exposure sensitivity analyses have employed different single-lag patterns for different pollutants (Citation9). However, there is a need to understand if pollutants contribute to asthma exacerbation on the same day or have lag effects. Furthermore, other asthma exacerbation outcomes (e.g. symptoms) should be considered.

This systematic review and meta-analysis of time-series and case-crossover studies aimed to summarize existing evidence regarding the relationships of various pollutants (AQI, PM2.5, PM10, SO2, NO2, CO, and O3) with the risk of asthma exacerbation (exacerbation-associated outpatient visits; ER visits; hospitalizations; deaths, and other events, including symptoms, lung function changes, and medication use as needed) in single lag0 and lag1 exposure patterns.

Methods

We followed the standardized Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (Citation14). There is no review protocol for this study.

Eligibility criteria and search strategy

The inclusion criteria were as follows: 1) a time-series or case-crossover study with original data; 2) asthma exacerbations associated with outpatient visits, ER visits, hospitalizations, deaths, or other events (symptoms, lung function changes, and medication use as needed) as outcomes; 3) inclusion of a study population of children, adults, or both; 4) use of AQI, PM2.5, PM10, SO2, NO2, CO, or O3 as measures of outdoor air pollution; and 5) report of the relationship between outdoor air pollution and asthma exacerbations.

The exclusion criteria were as follows: 1) no single-pollutant model; 2) no data regarding single lag0 or lag1 exposure patterns; and 3) data that could not be recalculated into study-specific relative risks (RRs) and 95% confidence intervals (CIs) to a 100-unit increase in AQI, a 1 mg/m3 increase in CO, and a 10 μg/m3 increase in the other pollutants (PM2.5, PM10, SO2, NO2, and O3) by assuming a linear relationship of all pollutants.

We conducted a systematic literature search of the PubMed, Embase, Cochrane Library, Web of Science, ClinicalTrials, China National Knowledge Internet, Chinese BioMedical, and Wanfang databases until August 1, 2020 (no start date specified and no language limitation). We used a combination of keywords associated with the exposure types (e.g. “AQI”) and asthma exacerbation outcomes (e.g. “symptom increase”)

We excluded irrelevant articles by screening their title. Articles meeting the inclusion criteria were included in abstract reading. Moreover, we included the references of articles that met the inclusion criteria. Articles that did not meet the exclusion criteria were included in this systematic review and meta-analysis through full-text reading.

Quality assessment and data extraction

Quality assessment was conducted based on Mustafic’s study with a maximum score of 5 (Citation15). One point was assigned if asthma exacerbations were coded by the International Classification of Diseases, American Thoracic Society, National Asthma Education or Prevention Program, or International Classification of Primary Care 2. One point was assigned if pollutant measurements were performed daily with < 25% of missing data. One point was assigned if long-term trends, seasonality, and temperature were all adjusted. One point was assigned if the humidity or the day of the week was adjusted. One point was assigned if influenza epidemics or holidays were adjusted.

Data extraction was performed using a standardized form, including the main characteristics (author, publication year, location, subgroup, study population, sample size, period, lag pattern, and quality score), outcome measures (asthma exacerbations associated with outpatient visits; ER visits; hospitalizations; deaths; and other events, including symptoms, lung function changes, and medication use as needed), exposure measures (AQI, PM2.5, PM10, SO2, NO2, CO, and O3), and study-specific RRs (95%CIs) of the single lag0 or lag1 exposure patterns. Eligible studies were examined and their relevant characteristics were independently recorded by two authors (XW and JH) in this standardized form. Any disagreements were resolved by consensus after discussion with an additional author (YH). When the same population was used in several publications, we included only the largest and most complete study.

Statistical analysis and data synthesis

Standardized effect estimates are expressed as RRs and 95% CIs. The study-specific RRs were derived from single-pollutant models reporting RRs or percentage changes. We recalculated the study-specific RRs to a 100-unit increase in AQI, a 1 mg/m3 increase in CO, and a 10 μg/m3 increase in the other pollutants (PM2.5, PM10, SO2, NO2, and O3), by assuming a linear relationship of all pollutants. We calculated the summary RRs of the single lag0 and lag1 exposure patterns. Heterogeneity was evaluated using Q and I2-statistics through the random-effect model. An I2 statistic > 50%, 25%-50%, and < 25% indicate high, moderate, and low heterogeneity, respectively (Citation16). Publication bias was assessed using Begg’s and Egger’s tests (Citation17,Citation18). To explore the heterogeneity in our pooled analysis, we applied sensitivity analyses based on studies with a quality score of 4–5. Various outcome analyses were conducted to combine the effects of evaluating differences in outpatient visits, ER visits, hospitalizations, deaths, and other events. Moreover, age-based subgroup analyses were conducted to evaluate the differences between children (0–14 years) and adults (> 14 years). Statistical analyses were conducted using Stata/MP 14.0 (Stata, College Station, TX, USA). All tests were two-sided, and statistical significance was defined as P < 0.05.

Results

Study selection and eligible study characteristics

We initially identified 1169 articles. After reading the title, abstract, and full text, we included 84 articles. presents the approach to study selection.

Figure 1. The flow chart of article selection.

Figure 1. The flow chart of article selection.

Supplementary Table S2 presents the quality scores and main characteristics of the eligible studies (Citation12,Citation19–101). The outcomes were asthma exacerbations associated with outpatient visits (9 studies), ER visits (44 studies), hospitalizations (29 studies), deaths (2 studies), and other events (7 studies). Additionally, one study did not specify separate data regarding ER visits and hospitalizations (Citation96); consequently, it was classified as ER visits or hospitalization outcomes. Regarding the single lag exposure pattern, 68 and 63 studies reported on lag0 and lag1 exposure patterns, respectively. Regarding the age subgroups, 34 and 21 studies investigated children and adults, respectively.

Overall and quality sensitivity analyses

In overall analyses, air pollutants were associated with increased risks of asthma exacerbations in both of the single lag0 and lag1 exposure patterns [lag0: RR (95% CI) (pollutants), 1.057 (1.011, 1.103) (AQI), 1.007 (1.005, 1.010) (PM2.5), 1.009 (1.005, 1.012) (PM10), 1.010 (1.006, 1.014) (NO2), 1.030 (1.011, 1.048) (CO), 1.005 (1.002, 1.009) (O3); lag1: RR (95% CI) (pollutants), 1.064 (1.022, 1.106) (AQI), 1.005 (1.002, 1.008) (PM2.5), 1.007 (1.004, 1.011) (PM10), 1.008 (1.004, 1.012) (NO2), 1.025 (1.007, 1.042) (CO), 1.010 (1.006, 1.013) (O3)], except for SO2 [lag0: RR (95% CI), 1.004 (1.000, 1.007); lag1: RR (95% CI), 1.003 (0.999, 1.006)] (). The study-specific RRs presented high heterogeneity for all pollutants except for CO in the single lag1 exposure pattern. shows the p values of the Begg’s and Egger’s tests. There was no publication bias for AQI, PM2.5, NO2, and CO in the lag0 pattern, as well as AQI, PM2.5, SO2, and NO2 in the lag1 pattern. shows the forest plot for the association between air pollutants and asthma exacerbations, while shows the Begg’s funnel plot.

Figure 2. Forest plot for relationships between air pollutants ((A) AQI, (B) PM2.5, (C) PM10, (D) SO2, (E) NO2, (F) CO, (G) O3) and asthma exacerbations with lag0 exposure in age subgroup analyses.

Table 1. Relationships between air pollutants and asthma exacerbations in overall and quality sensitivity analyses.

Moreover, quality sensitivity analyses revealed a significant positive association of PM2.5, PM10, NO2, CO, and O3 with risk of asthma exacerbation in both single lag0 and lag1 exposure patterns (). The study-specific RRs showed high heterogeneity for all pollutants, except for CO. Publication bias was detected using Begg’s test for O3 in the lag0 pattern and Egger’s test for PM10 and O3 in the lag1 pattern (). shows the forest plot for relationships between air pollutants and asthma exacerbations, while Supplementary Figure S4 shows the Begg’s funnel plot.

Figure 3. Forest plot for relationships between air pollutants ((A) AQI, (B) PM2.5, (C) PM10, (D) SO2, (E) NO2, (F) CO, (G) O3) and asthma exacerbations with lag1 exposure in age subgroup analyses.

Various outcome and age subgroup analyses

Various outcome analyses revealed more pronounced relationships with other event outcomes [lag0: RR (95% CI) (pollutants), 1.201 (1.155, 1.247) (AQI), 1.047 (1.024, 1.069) (PM2.5), 1.119 (1.018, 1.220) (PM10), 1.033 (1.000, 1.066) (NO2), 1.124(1.051, 1.197) (CO); lag1: RR (95% CI) (pollutants), 1.204 (1.158, 1.251) (AQI), 1.080 (1.010, 1.150) (PM2.5), 1.122 (1.015, 1.230) (PM10), 1.046 (1.013, 1.079) (NO2), 1.155 (1.037, 1.274) (CO)], except for SO2 and O3. Supplementary Table S3 presents details regarding the relationship between air pollutants and asthma exacerbations in various outcome analyses. shows the forest plot.

Age subgroup analyses revealed stronger relations in children than in adults, except for NO2 in the lag1 pattern and O3 in both patterns. There was no tendency toward a stronger relation for AQI, given that the AQI adult subgroup lacked eligible studies. Supplementary Table S3 presents the details regarding the relationship between air pollutants and asthma exacerbations in various outcomes and age subgroup analyses. and show the forest plots.

Discussion

Summary of evidence

Our study provides novel evidence that air pollution exposure increases the risk of asthma exacerbation; specifically, AQI, PM2.5, PM10, NO2, CO, and O3 contribute to asthma exacerbations with single lag0 and lag1 exposure patterns. This suggests that these pollutants cause asthma exacerbation on the day of air pollution onset. Furthermore, AQI may be a good indicator of asthma exacerbation risk during air pollution. Mechanisms underlying the association between air pollution and asthma exacerbations include stimulation of airway epithelium and inflammatory cells, oxidative stress responses, respiratory cells, respiratory reflex responses, and epigenetic modifications (Citation102). Exposure to PM modulates the airway epithelium and promotes the production of several cytokines, including IL-1, IL-6, IL-8, IL-25, IL-33, TNF-α, and GM-CSF (Citation103). In a mouse model, there was an ozone-induced increase in bronchoalveolar lavage (BAL) levels of IL-23, which is an important cytokine for IL-17A + cell recruitment and activation, 24 h after ozone exposure (2 ppm for 3 h) (Citation104). Willart et al. (Citation105) reported an increase in lung IL-1α levels in naïve mice at 2 and 24 h after exposure. Another study reported an increase in IL-1α levels within the first 24 h after ozone exposure (1 ppm for 1 h) (Citation106). One study assessed sensitized mice before (0 h) and 1, 6, 12, 24, and 72 h after exposure and reported that the 12- and 24-h groups had the highest cytokine levels and cell counts in BAL fluid (Citation107). Diesel exhaust particles were found to induce an increase in IL-17A levels in primary bronchial epithelial cells of patients with asthma at 2 h post-exposure (Citation108). In addition, Zhang et al. (Citation109) reported that diesel exhaust particles induced increased TET1 expression in human bronchial epithelial cells at 1 h post-exposure. Therefore, if air pollution exposure cannot be avoided, patients with asthma should employ adequate strategies (including asthma medications) to reduce damage on the same day.

In lag0 and lag1 exposure patterns, we found that the RRs of air pollution and asthma exacerbations associated with other events (including symptoms, lung function changes, and medication use as needed) were much higher than those of outpatient visits, ER visits, hospitalizations, and deaths. There have been no studies on the relationship between outdoor air pollution and other events, as well as outpatient visits, ER visits, hospitalizations, or deaths within the same population. However, a prospective case-control study on children reported an association of AQI, PM2.5, NO2, and O3 with increased expression of multiple inflammatory airway epithelial responses, as well as a decline in FEV1%predicted in the lead up to clinical asthma exacerbations on the exacerbation-onset day without a viral trigger (Citation110). Pan et al. (Citation12) reported a positive correlation between AQI and childhood asthma hospitalizations that appeared and peaked on lag3 and lag9 days, respectively. Additionally, there were positive correlations between asthma deaths and PM2.5 on lag3 day (Citation32) or SO2 and NO2 on lag2 day (Citation79). This could be attributed to changes in symptoms and lung function, which are the initial manifestations of airway inflammation caused by air pollution. Subsequently, patients use relief medications for these changes, and if they do not improve, they seek medical help and are recorded as outpatient visits, ER visits, or hospitalizations. Therefore, when patients with asthma present with symptoms or lung function changes during air pollution, they should receive active treatment to block asthma exacerbation progression.

It has been confirmed that children with asthma are more susceptible to outdoor air pollution. This could be attributed to immature lung growth and host defense capacity in children (Citation9). Since children spend more time outdoors than adults, they inhale more air pollutants per pound of body weight (Citation111). Moreover, children may have low symptom tolerance, and parents may be more active in taking their children to hospitals.

Validity of results

This study has several strengths, including the identification of potential articles based on a systematic literature search of six databases (no start date specified and no language limitation) and inclusion of secondary references. Furthermore, two authors examined the eligible studies based on the inclusion and exclusion criteria, followed by independent data extraction using a priori set method in the standardized form. Finally, disagreements were discussed with a third author.

Limitations

This study has several limitations. First, there are few available studies on AQI, other events, and death analyses. Therefore, findings regarding the AQI, other events, and death analyses should be cautiously interpreted. Second, there was a high degree of heterogeneity in most of the analyses. This may be associated with varying study outcomes and design qualities. Nonetheless, we reduced the degree of heterogeneity in the quality sensitivity and various outcome analyses.

Conclusions

This systematic review and meta-analysis provides new evidence indicating that air pollution exposure increases the risk of asthma exacerbation. Specifically, AQI, PM2.5, PM10, NO2, CO, and O3 contribute to asthma exacerbations with single lag0 and lag1 exposure patterns. If air pollution exposure cannot be avoided, patients with asthma should employ adequate strategies (including asthma medications) to reduce same-day damage.

Authors’ contributions

JH and XY are joint first authors. JH obtained funding. JH, YH, and GW conceived and designed the study. JH and XY performed a systematic literature search under the supervision of GR and YH. JH, YH, and XW reviewed the articles and performed data extraction. JH and FF analyzed the data under the supervision of SZ, YH, and GW. JH, YH, XY, and FF wrote the manuscript. All authors read and approved the final manuscript and were responsible for their contributions.

The authors thank Dr. Pengkang He from Peking University First Hospital for his help.

Abbreviations
AQI=

air quality index

BAL=

bronchoalveolar lavage

CIs=

confidence intervals

CO=

carbon monoxide

ER=

emergency room

NO2=

nitrogen dioxide

O3=

ozone

PM2.5=

particulate matter diameter ≤ 2.5 μm

PM10=

particulate matter diameter ≤ 10 μm

RRs=

relative risks

SO2=

sulfur dioxide

Supplemental material

Figure_S4._Begg_s_funnel_plot_for_relationships_between_air_pollutants_and_asthma_exacerbations_in_quality_sensitivity_analyses.pdf

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Figure_S3._Forest_plot_for_relationships_between_air_pollutants_and_asthma_exacerbations_in_quality_sensitivity_analyses.pdf

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Figure_S2._Begg_s_funnel_plot_for_relationships_between_air_pollutants_and_asthma_exacerbations_in_overall_analyses.pdf

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Figure_S1._Forest_plot_for_relationships_between_air_pollutants_and_asthma_exacerbations_in_overall_and_various_outcomes_analyses.pdf

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Table_S3._Relationships_between_air_pollutants_and_asthma_exacerbations_in__various_outcomes_and_age_subgroup_analyses.pdf

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Table_S2.__The_quality_scores_and_the_main_characteristics_of_the_eligible_studies.xlsx

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Table_S1._Search_strategies.pdf

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Data availability

Data are available on reasonable request (contact Yan Hu, [email protected]).

Declaration of interest

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of this article.

Additional information

Funding

This study is supported by grant 2018CR02 from the Youth Clinical Research Project of Peking University First Hospital. The funding bodies did not participate in the study.

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