Background: The associations of long-term exposure to air pollutants in the presence of asthmatic symptoms remain inconclusive and the joint effects of air pollutants as a mixture are unclear.
Objective: We aimed to investigate the individual and joint associations of long-term exposure to ambient fine particulate matter (PM2.5) and daily 8-hour maximum ozone concentrations (MDA8 O3) in the presence of asthmatic symptoms in Chinese adults.
Methods: Data were derived from the World Health Organization Study on Global Ageing and Adult Health (WHO SAGE) cohort study among adults aged 50 years or older, which was implemented in 1 municipality and 7 provinces across China during 2007-2018. Annual average MDA8 O3 and PM2.5 at individual residential addresses were estimated by an iterative random forest model and a satellite-based spatiotemporal model, respectively. Participants who were diagnosed with asthma by a doctor or taking asthma-related therapies or experiencing related conditions within the past 12 months were recorded as having asthmatic symptoms. The individual associations of PM2.5 and MDA8 O3 with asthmatic symptoms were estimated by a Cox proportional hazards regression model, and the joint association was estimated by a quantile g-computation model. A series of subgroup analyses was applied to examine the potential modifications of some characteristics. We also calculated the population-attributable fraction (PAF) of asthmatic symptoms attributed to PM2.5 and MDA8 O3.
Results: A total of 8490 adults older than 50 years were included, and the average follow-up duration was 6.9 years. During the follow-up periods, 586 (6.9%) participants reported asthmatic symptoms. Individual effect analyses showed that the risk of asthmatic symptoms was positively associated with MDA8 O3 (hazard ratio [HR] 1.12, 95% CI 1.01-1.24, for per quantile) and PM2.5 (HR 1.18, 95% CI 1.05-1.31, for per quantile). Joint effect analyses showed that per equal quantile increment of MDA8 O3 and PM2.5 was associated with an 18% (HR 1.18, 95% CI 1.05-1.33) increase in the risk of asthmatic symptoms, and PM2.5 contributed more (68%) in the joint effects. The individual PAFs of asthmatic symptoms attributable to PM2.5 and MDA8 O3 were 2.86% (95% CI 0.17%-5.50%) and 4.83% (95% CI 1.42%-7.25%), respectively, while the joint PAF of asthmatic symptoms attributable to exposure mixture was 4.32% (95% CI 1.10%-7.46%). The joint associations were greater in participants with obesity, in urban areas, with lower family income, and who used unclean household cooking fuel.
Conclusions: Long-term exposure to PM2.5 and MDA8 O3 may individually and jointly increase the risk of asthmatic symptoms, and the joint effects were smaller than the sum of individual effects. These findings informed the importance of joint associations of long-term exposure to air pollutants with asthma.
Asthma is a heterogeneous disease defined by a history of respiratory symptoms (eg, wheezing, shortness of breath, chest tightness, and cough) that vary over time and in intensity, accompanied by variable expiratory airflow limitation . Asthma could affect people of all ages worldwide. The Global Burden of Disease (GBD) study in 2019 estimated that there were 262 million people worldwide affected by asthma, with a 15.7% increase in prevalence since 2010 [ ].
The complexity of asthma is particularly challenging, and increasing evidence has linked air pollution exposures to the incidence of asthma. There is ample evidence that supports the association of short-term exposure to air pollution and morbidity of asthma [, ]. However, the associations between long-term exposure to air pollution and asthma remain inconclusive [ ]. Some studies reported positive associations of long-term exposure to air pollutants with the incidence of asthma [ - ], while others reported null or negative associations [ - ]. This inconsistency may be related to different populations, exposure assessment approaches for air pollution, and study designs among previous studies. For example, several studies were conducted in children, in which the role of chronic air pollution exposure in the development of childhood asthma has been well demonstrated [ , ], while in some studies conducted in adults, the role of air pollution in adult-onset asthma was inconclusive [ , , , ]. It was suggested that childhood-onset and adult-onset asthma are 2 distinct asthma phenotypes that have different clinical, biological, and genetic characteristics [ ]. The associations of air pollution with adult-onset asthma should not be extrapolated from studies in children and should be particularly investigated. In addition, more studies that investigated the chronic effects of air pollution on asthma were cross-sectional studies, and few were prospective studies [ ]. Those results from cross-sectional studies may be biased due to recall bias and confounding bias. Therefore, we believe more prospective studies on adults are needed.
Although studies on the long-term effects of air pollution on asthma in adults are few and inconsistent, several previous studies showed a positive association between air pollution and other chronic respiratory diseases such as chronic obstructive pulmonary disease (COPD). For example, a 15-year population cohort study in Canada found positive associations of COPD with ambient fine particulate matter (PM2.5) and O3 . A systemic review and meta-analysis showed that per 10 μg/m3 increase in PM2.5 is associated with an increased incidence of COPD (hazard ratio [HR] 1.18, 95% CI 1.13-1.23) [ ]. These studies suggested the plausibility of the association between air pollution and asthma.
It is increasingly recognized that people in their daily lives do not get exposed to a single air pollutant but rather to multiple air pollutants as a mixture that altogether may impact health. The idea of coexposures to multiple pollutants has been articulated as part of the exposome concept , and the scientific community and the US Environmental Protection Agency are moving toward a multipollutant approach to quantify the health consequences of air pollution mixtures as a whole [ ]. A prospective cohort study from Australia reported a significant joint association of 5 air pollutants (PM10, PM2.5, CO, NOx, and SO2) with COPD, although they did not find significant associations of COPD with several pollutants in the multiple pollutant model [ ], suggesting the importance of assessing joint effects of air pollutants as a whole. However, to date, very few studies have estimated the joint effects of air pollutants on the risk of asthma.
China is one of the most popular countries with a large number of patients with asthma and has serious air pollution. The GBD 2019 study estimated that there were about 24.8 million patients with asthma across China in 2019 . The annual average PM2.5 concentration across China was as high as 77.05 μg/m3 in 2013 [ ]. To tackle severe air pollution, Chinese governments have implemented a series of rigorous air pollution control policies in the past decade [ ]. Although the national average PM2.5 concentration across China in 2021 has been significantly reduced to 30 μg/m3, it is still 6 times higher than the recommended air quality guidelines (5 μg/m3) by World Health Organization (WHO) (2021). In addition, these control policies in air pollution inappropriately reduce NOx rather than the volatile organic compounds, which may lead to an increase in the ambient ozone (O3) [ ]. The national population-weighted mean concentrations of maximum daily 8-hour average ozone (MDA8 O3) increased from 89.34 μg/m3 in 2013 to 100.96 μg/m3 in 2019 [ ]. Currently, both ambient PM2.5 and O3 have become the major air pollutants across China. However, the individual and joint associations of long-term exposure to PM2.5 and O3 with asthma in adults remain unknown.
To fill in these research gaps, this study implemented a national cohort study in China to estimate both individual and joint associations of long-term exposure to PM2.5 and O3 with risk of asthmatic symptoms and calculated the population-attributable fraction (PAF) of asthmatic symptoms attributable to PM2.5 and O3 in Chinese adults aged older than 50 years.
Study Design and Population
The World Health Organization Study on Global Ageing and Adult Health (WHO SAGE) is a cohort study that was conducted in 1 municipality (Shanghai) and 7 provinces (Guangdong, Hubei, Jilin, Shaanxi, Shandong, Yunnan, and Zhejiang) in China by using a stratified multistage random cluster sampling among adults aged older than 18 years and older from 2007 to 2018 [, ]. A total of 18,673 participants were recruited at baseline in the first wave (2007-2010) conducted through a face-to-face household interview and were followed up during 2014-2015 and 2017-2018, respectively. In each round of follow-up investigation, new participants were added to enlarge the sample size. Eventually, a total of 18,673 participants were included in the research. Participants were excluded from this study if they were lost to follow-up (n=8053), reported asthmatic symptoms at baseline (n=420), unavailability of data for follow-up (n=33), without information about asthma (n=676), were younger than 50 years (n=728), and missing information on key covariates (n=273). Finally, a total of 8490 participants were included in the analysis (Figure S1 in ).
This study was approved by the Ethics Committee of the Chinese Centre for Disease Control and Prevention. Informed consent was obtained from each participant before the interview. Individually identifiable information was anonymous. WHO SAGE was approved by the World Health Organization’s Ethical Review Board (RPC146), in China, SAGE wave1 was approved by the Chinese Center for Disease Control and Prevention (200601), and SAGE wave 2-3 was approved by Shanghai Municipal Center for Disease Control and Prevention (2014-8, 2018-1).
Assessment of Environmental Exposures
Daily 8-hour maximum ozone concentrations (MDA8 O3) with a spatial resolution of 7×7 km across China during 2008-2018 were estimated by an iterative random forest model, which were derived from ground monitoring ozone data, satellite-derived ozone column amount, various meteorological information, normalized difference vegetation index, fractional urban cover, and elevation data. The random forest model showed sample-based and site-based cross-validation R2 of 0.84 and 0.79, respectively, indicating the high accuracy of estimation of daily MDA8 O3 . The annual average MDA8 O3 was calculated using the daily data in each grid. Annual mean PM2.5 concentration was measured by a satellite-based spatiotemporal model with a spatial resolution of 1×1 km across China, which was established by the Atmospheric Composition Analysis Group. The R2 value for the spatiotemporal model was 0.81 [ ]. The annual average temperature was obtained from ERA5 [ ].
We first calculated the annual mean exposure to MDA8 O3, PM2.5, and temperature in each calendar year (from the first day to the last day of a year) at a spatial grid across China. Then, the environment exposure data were assigned to each participant based on latitude and longitude of residence address, which ensured the accuracy of exposure information if the participant changed address. One-year exposure was regarded as an indicator of long-term exposure level and treated as a time-varying variable [, ].
Participants who were diagnosed with asthma by a doctor prior to the interview or taking asthma-related therapies within the past 12 months prior to the interview were recorded as having asthmatic symptoms. Participants were also defined as cases with asthmatic symptoms based on an algorithm about self-reported symptom-based questions within the past 12 months prior to the survey that is listed in Table S1 in[ ]. The outcome was collected at baseline and at each round of follow-up, and the new asthmatic symptom was defined as the first ever met the condition listed above during follow-up.
Covariates in this study included age (50-64 years and ≥65 years), sex (male and female), urbanicity (urban and rural), region (southern and northern), smoking status (ever and never), drinking status (ever, never), BMI (kg/m2) (normal [≤23.9], overweight [24.0-27.9], and obese [≥28]), marriage status (married and unmarried), family income (low and high), major type of household cooking fuel (clean fuel and unclean fuel), educational level (no formal education, primary school, and middle school or higher), physical activity (low, middle, and high), fruit intake (sufficient and insufficient), vegetable intake (sufficient and insufficient), air pollution–related occupations (related and not related), and ambient temperature (Table S1 in).
Individual Associations of Air Pollutants With the Presence of Asthmatic Symptoms
A Cox proportional hazards model using time-varying covariates and coefficients  was used to estimate the HRs and 95% CI on the presence of asthmatic symptoms for each quartile increment in the pollutant concentration in the single-pollutant model. We also explored the nonlinear effects by applying a natural spline with 3 degrees of freedom in the model. We applied a series of subgroups analysis to examine the potential modifications of some characteristics.
Based on the above estimates, we used the following formula (equation 1) to calculate the individual PAF of asthmatic symptoms attributable to PM2.5 and MDA8 O3 :
where Pi indicates the proportion of the population in exposure category “i” (using each PM2.5 and MDA8 O3 concentration quantile as the category) and RRi represents HR of exposure category “i.”
Joint Associations of Air Pollutants With the Presence of Asthmatic Symptoms
We first tested the collinearity among air pollutants and covariates using the Variance Inflation Score, and the results illustrated that the low collinearity with the Variance Inflation Scores of the multipollutant model among variables was less than 10 (Table S2 in).
A quantile g-computation model was used by using a Cox proportional hazards model as an underlying model to estimate the joint effects of PM2.5 and MDA8 O3. We estimated the joint effect of air pollutants for an equal quartile increment of each pollutant in the model. The quantile g-computation model first transformed each pollutant into a categorical variable, coded as 0, 1, 2, 3, and then estimated associations with health outcomes when all pollutants change 1 unit. The model also calculated the weight of each pollutant, if all the exposure pollutants have the same direction, interpreting as the proportion of the effect and sum to 1. If the exposure pollutants have different directions of effect, the weights are interpreted as a proportion of the positive or negative effect, and sum to 2 . Moreover, the quantile g-computation model was also conducted in subgroups. The between-group variability was tested using a 2-sample z test [ ], and P values calculated by 2-by-2 comparisons between multiple groups were corrected using the Bonferroni method. The formula (equation 2) for the between-group variability test is as follows:
where b1 and b2 are the estimated effects of 2 groups, and SE12 and SE22 are the standard errors of the estimated effects of 2 groups .
Based on the above estimates, we calculated the individual and joint PAFs of asthmatic symptoms attributable to PM2.5 and MDA8 O3 . We used the following formula (equation 3) to calculate the joint PAF:
where PAFoverall represents the effect values of all risk factors, PAFi is the PAF for exposure category “i.” A variance-covariance matrix of the parameters from the regression model was used to estimate the 95% CI of joint PAF by randomly generating 10,000 PAFs from the normal distribution.
Sensitivity analysis adjustment for different confounders was used to test the robustness of our findings. For some confounders with serious missing such as physical activity, fruit intake, and vegetable intake, sensitivity analyses were conducted only in participants with completed data.
All analyses were conducted by R software (version 4.1.3; R Development Core Team). All tests were 2-tailed, and P<.05 was considered statistically significant.
Characteristics of Study Participants
A total of 8490 participants were finally included in this analysis, with a mean of 6.9 (SD 2.3) years of follow-up and an overall 58,737 person-years of follow-up. Among the total participants, 2950 (34.75%) were older than 64 years, 4066 were (47.89%) males, 4867 (57.43%) resided in rural areas, 5259 (61.94%) lived in Southern China, and 4641(54.66%) used unclean energy as the major household cooking fuel. At the follow-up, 586 (6.9%) participants were diagnosed with asthmatic symptoms ().
The mean PM2.5 concentration was 42.85 (SD 12.74) μg/m3, and the 25%, 50%, and 75% quantiles were 31.70 μg/m3, 41.30 μg/m3, and 53.10 μg/m3, respectively. The mean MDA8 O3 concentration was 94.19 (SD 5.62) μg/m3, and the 25%, 50%, and 75% quantiles were 89.12 μg/m3, 93.73 μg/m3, and 97.29 μg/m3, respectively. PM2.5 was positively correlated with ambient MDA8 O3 (r=0.663, P<.001) and ambient temperature (r=0.090, P<.001). Ambient MDA8 O3 was also positively correlated with ambient temperature (r=0.428, P<.001; Table S3 in).
|Categories||Total participants (N=8490)||Participants with asthmatic symptoms during the follow-up|
|No (n=7904)||Yes (n=586)|
|MDA8 O3 (μg/m3), mean (SD)||94.19 (5.62)||94.18 (5.64)||94.12 (5.46)|
|PM2.5 (μg/m3), mean (SD)||46.20 (12.74)||46.02 (12.64)||46.64 (14.25)|
|Age (years), n (%)||61.89 (8.57)||61.71 (8.52)||64.31 (8.9)|
|50-64||5540 (65.25)||5225 (66.11)||315 (53.8)|
|≥65||2950 (34.75)||2679 (33.89)||271 (46.2)|
|Gender, n (%)|
|Male||4066 (47.89)||3780 (47.82)||286 (48.8)|
|Female||4424 (52.11)||4124 (52.18)||300 (51.2)|
|Urbanicity, n (%)|
|Rural||4876 (57.43)||4606 (58.27)||270 (46.1)|
|Urban||3614 (42.57)||3298 (41.73)||316 (53.9)|
|Region, n (%)|
|Southern China||5259 (61.94)||4943 (62.54)||316 (53.9)|
|Northern China||3231 (38.06)||2961 (37.46)||270 (46.1)|
|Marital status, n (%)|
|Married||7340 (86.45)||6861 (86.80)||479 (81.7)|
|Unmarried||1150 (13.55)||1043 (13.20)||107 (18.3)|
|BMI (kg/m2), n (%)|
|Normal weight (≤23.9)||4683 (55.16)||4359 (55.2)||324 (55.3)|
|Overweight (24-27.9)||2772 (32.65)||2587 (32.73)||185 (31.6)|
|Obese (≥28)||1035 (12.19)||958 (12.12)||77 (13.1)|
|Major type of household cooking fuel, n (%)|
|Clean||4641 (54.66)||4308 (54.50)||333 (56.8)|
|Unclean||3849 (45.34)||3596 (45.50)||253 (43.2)|
|Smoking status, n (%)|
|Ever||2819 (33.20)||2609 (33.01)||210 (35.8)|
|Never||5671 (66.80)||5295 (66.99)||376 (64.2)|
|Drinking status, n (%)|
|Ever||2716 (31.99)||2526 (31.96)||190 (32.4)|
|Never||5774 (68.01)||5378 (68.04)||396 (67.6)|
|Family income, n (%)|
|High||4306 (50.72)||4008 (50.71)||298 (50.8)|
|Low||4184 (49.28)||3896 (49.29)||288 (49.2)|
|Educational level, n (%)|
|No formal education||3393 (39.97)||3154 (39.90)||239 (40.8)|
|Primary school||1802 (21.22)||1662 (21.03)||140 (23.9)|
|Middle school or higher||3295 (38.81)||3088 (39.07)||207 (35.3)|
|Fruit intake, n (%)|
|Sufficient||1259 (14.83)||1151 (14.56)||108 (18.4)|
|Insufficient||6699 (78.90)||6253 (79.11)||446 (76.1)|
|Missing||532 (6.27)||500 (6.33)||32 (5.5)|
|Vegetable intake, n (%)|
|Sufficient||5828 (68.64)||5407 (68.41)||421 (71.8)|
|Insufficient||2417 (28.47)||2266 (28.67)||151 (25.8)|
|Missing||245 (2.89)||231 (2.92)||14 (2.4)|
|Air pollution–related occupations, n (%)|
|Related||1045 (12.31)||950 (12.02)||95 (16.2)|
|Not related||6358 (74.89)||5945 (75.21)||413 (70.5)|
|Missing||1087 (12.80)||1009 (12.77)||78 (13.3)|
|Physical activity, n (%)|
|High||4025 (47.41)||3749 (47.43)||276 (47.1)|
|Middle||1737 (20.46)||1625 (20.56)||112 (19.1)|
|Low||1611 (18.98)||1495 (18.91)||116 (19.8)|
|Missing||1117 (13.15)||1035 (13.10)||82 (14)|
Individual Effects of Long-Term Exposure to MDA8 O3 and PM2.5 in the Presence of Asthmatic Symptoms
We observed positive nonlinear exposure-response curves of MDA8 O3 and PM2.5 in the presence of asthmatic symptoms (and ). Linear analyses showed that per quantile increment of MDA8 O3 (HR 1.12, 95% CI 1.01-1.24) and PM2.5 (HR 1.18, 95% CI 1.05-1.31) were positively associated with the risk of asthmatic symptoms after adjusted for confounders. Subgroup analyses suggested that the associations were modified by several individual characteristics. For example, the associations between PM2.5 and asthmatic symptoms were stronger in rural individuals (HR 1.54, 95% CI 1.30-1.83) than in urban individuals (HR 0.89, 95% CI 0.76-1.05), in individuals with low family income (HR 1.43, 95% CI 1.21-1.69) than in individual with high family income (HR 0.98, 95% CI 0.84-1.14), in individuals using unclean household cooking fuel (HR 1.66, 95% CI 1.38-1.99) than individuals using clean fuel (HR 0.90, 95% CI 0.78-1.04), and in individuals exposed to higher temperatures (HR 1.73, 95% CI 1.27-2.37) than in individuals exposed to lower temperatures (HR 0.92, 95% CI 0.75-1.12; ).
|Characteristics||Participants (n)||MDA8 O3, HR (95% CI)||P for difference test||PM2.5, HR (95% CI)||P for difference test||Joint association, HR (95% CI)||P for difference test|
|Total||8490||1.12 (1.01-1.24)||—b||1.18 (1.05-1.31)||—||1.18 (1.05-1.33)||—|
|50-64||5540||1.09 (0.94-1.26)||—||1.24 (1.06-1.45)||—||1.28 (1.07-1.54)||—|
|≥65||2950||1.16 (0.99-1.35)||.58||1.11 (0.95-1.29)||.30||1.04 (0.84-1.27)||.13|
|Male||4066||1.06 (0.92-1.23)||—||1.08 (0.93-1.27)||—||1.19 (1.00-1.42)||—|
|Female||4424||1.18 (1.01-1.37)||.35||1.27 (1.08-1.48)||.17||1.17 (0.90-1.51)||.88|
|Normal weight||4683||1.07 (0.94-1.23)||—||1.19 (1.03-1.38)||—||1.14 (0.95-1.37)||—|
|Overweight||2772||1.09 (0.90-1.33)||.87||0.98 (0.81-1.19)||.95||1.02 (0.79-1.30)||.37|
|Obese||1035||1.87 (1.30-2.69)||.005||1.94 (1.33-2.82)||.20||2.05 (1.32-3.19)||.017|
|Rural||4876||1.15 (1.00-1.32)||—||1.54 (1.30-1.83)||—||1.47 (1.21-1.79)||—|
|Urban||3614||1.10 (0.91-1.29)||.69||0.89 (0.76-1.05)||<.001||0.87 (0.70-1.09)||<.001|
|High||4306||1.12 (0.96-1.30)||—||0.98 (0.84-1.14)||—||0.98 (0.80-1.21)||—|
|Low||4184||1.14 (0.98-1.32)||.87||1.43 (1.21-1.69)||.001||1.39 (1.13-1.72)||.02|
|Major type of household cooking fuel|
|Clean||4641||1.07 (0.93-1.24)||—||0.90 (0.78-1.04)||—||0.91 (0.77-1.09)||—|
|Unclean||3849||1.16 (0.99-1.36)||.47||1.66 (1.38-1.99)||<.001||1.65 (1.29-2.11)||<.001|
|Qc1 (4.41-12.68)||1931||1.16 (0.95-1.42)||—||1.11 (0.81-1.51)||—||1.16 (0.91-1.48)||—|
|Q2 (12.69-15.80)||2140||0.98 (0.81-1.20)||.26||1.18 (0.97-1.44)||.73||1.06 (0.82-1.38)||.49|
|Q3 (15.81-16.41)||2071||0.79 (0.67-0.93)||.004||0.92 (0.75-1.12)||.31||0.86 (0.68-1.08)||.10|
|Q4 (16.42-22.94)||2348||1.18 (0.87-1.58)||.94||1.73 (1.27-2.37)||.05||1.71 (1.03-2.86)||.30|
aAdjustment for age, sex, urbanicity, region, smoking status, drinking status, BMI, marriage status, educational level, household income, indoor fuel type, and temperature. In subgroup analyses, other confounders except for the subgroup category variable, analyzed as an independent variable, were adjusted for.
Joint Effects of Long-Term Exposure to MDA8 O3 and PM2.5 With Asthmatic Symptoms
Joint effect analyses showed that per quantile increment of MDA8 O3 and PM2.5 were associated with an 18% (HR 1.18, 95% CI 1.05-1.33) increase in the risk of asthmatic symptoms. In the joint effect, the contribution of PM2.5 and MDA8 O3 was 68% and 32%, respectively. We also found greater joint HRs estimated by subgroup analyses in rural residents (HR 1.47, 95% CI 1.21-1.79) than in urban residents (HR 0.87, 95% CI 0.70-1.09), in low-income level (HR 1.39, 95% CI 1.13-1.72) than high-income level (HR 0.98, 95% CI 0.80-1.21), in unclean indoor fuel type (HR 1.65, 95% CI 1.29-2.11) than clean indoor fuel type (HR 0.91, 95% CI 0.77-1.09). Moreover, the highest HR was found in participants with obesity (HR 2.05, 95% CI 1.32-3.19;).
PAFs of Asthmatic Symptoms Attributable to MDA8 O3 and PM2.5
shows the PAFs of asthmatic symptoms attributable to MDA8 O3 and PM2.5, estimated by the individual and joint associations. In the total participants, the individual PAFs caused by MDA8 O3 and PM2.5 were 2.86% (95% CI 0.17%-5.50%) and 4.38% (95% CI 1.42%-7.25%), and the joint PAFs by MDA8 O3 and PM2.5 were 4.32% (95% CI 1.10%-7.46%). Subgroup analyses showed a large variation of PAFs among different groups. For example, the PAFs of asthmatic symptoms were the largest in obese individuals. The individual PAF caused by MDA8 O3 and PM2.5 was 17.29% (95% CI 7.62%-26.07%) and 18.57% (95% CI 8.40%-27.70%), and the joint PAFs by MDA8 O3 and PM2.5 was 18.63% (95% CI 7.73%-28.83%). More detailed information is shown in .
Results indicated that HRs did not substantially change after adjustment for different confounders (Table S4 in).
In this national cohort study, we found a positive correlation between individual and joint associations on long-term exposure to ambient PM2.5 and MDA8 O3 with the risk of asthmatic symptoms in a Chinese population older than 50 years. Based on the joint associations, 4.32% of asthmatic symptoms could be attributable to PM2.5 and MDA8 O3 exposures, and ambient PM2.5 contributed much more to the joint effect. In addition, the associations were modified by obesity, urbanicity, family income level, household cooking fuel, and ambient temperature. Our findings are significant for estimating the disease burden of air pollution and making policies for air pollution control.
In the literature, relatively fewer studies have investigated the associations of long-term exposure to PM2.5 and MDA8 O3 with asthma incidence in adults compared to those studies on short-term exposures and on childhood asthma. Some studies reported consistent results with this study that long-term exposure to PM2.5 and MDA8 O3 were separately associated with an increased risk of asthma incidence [- ]. For example, the Health Effects Institute reported that long-term exposure to higher PM2.5 was associated with an increased risk of asthma, which was from the European Cohorts in the ELAPSE Project [ ]. The Epidemiological Study of the Genetic and Environmental Factors of Asthma reported that the risk of asthma increased with O3 exposure [ ]. McDonnell et al [ ] also reported that long-term exposure to O3 was positively associated with new-onset asthma in adult males. However, several previous studies did not find positive associations of PM2.5 and MDA8 O3 with the incidence of asthma [ , , ]. For example, a prospective cohort from Australia reported a null association between long-term exposure to PM2.5 with adult-onset asthma [ ]. The results from the ELAPSE project reported a negative association (HR 0.90, 95% CI 0.81-0.99 for each 10 μg/m3) between long-term exposure to O3 and the incidence of asthma [ ]. Although it may be difficult to directly compare the results among these studies due to different study populations, this interstudy variation of associations suggests that the associations of long-term exposure to PM2.5 and O3 with asthma incidence remain inconclusive, and more studies are needed particularly in low-and middle-income countries. For example, we did not find a prospective cohort that has investigated the associations of long-term exposure to PM2.5 and O3 with asthma in China.
Exposure to air pollution can trigger inflammatory and immune responses, oxidative stress, and airway remodeling in the development and exacerbation of asthma. Exposure to PM2.5 induces the release of inflammatory mediators from alveolar macrophages, which is an important pathogenesis of pulmonary inflammation in the development of asthma. PM2.5 induces inflammatory responses, associated with Th1/Th2 pathway imbalance and leads to Th2-oriented inflammation . Apart from that, PM2.5 induces the production of reactive oxygen species. Reactive oxygen species will further enhance the oxidative stress response, resulting in DNA, protein, lipid, and other cellular and molecular damage, leading to respiratory disease [ , ]. In this study, PM2.5 also induced a significant upregulation of vascular endothelial growth factor A production, a signaling event that controls vascular remodeling [ ]. Furthermore, exposure to O3 is associated with an increase in free radicals and biomarkers associated with oxidative stress [ ]. Ozone exposure also induced airway inflammation with increased numbers of neutrophils [ ].
More importantly, we quantified the joint associations of long-term exposure to PM2.5 and MDA8 O3 in the presence of asthmatic symptoms in a Chinese population. Our findings had 2 major implications. First, PM2.5 (68%) contributed more to the joint associations than O3 (32%), suggesting the more important role of PM2.5 in the development of asthmatic symptoms. Second, the PAFs of asthmatic symptoms based on the joint associations were lower than the sum of PAFs based on the individual associations, which suggests that the calculation based on a single-pollutant model might overestimate the effects of air pollutants due to the potential mutual confounding . Several studies reported similar findings to this study [ - ]. For example, Winquist et al [ ] reported smaller joint effect estimates from multipollutant models than estimates from single-pollutant models. In practice, people are usually exposed to multiple pollutants as air pollution complex mixture. Therefore, our findings combined with previous studies suggested that estimating the disease burden of air pollution calculated using a joint effect model may be more appealing because it transparently and explicitly specifies the contribution of various pollutants of mixtures.
PAF is a statistical indicator that quantitatively describes the health impact of a certain risk factor on a population. Specifically, it represents the proportion of total disease (or mortality) in the population that can be attributed to a certain factor, so we describe that a total of 4.32% of the presence of asthmatic symptoms could be attributable to joint PM2.5 and MDA8 O3 exposures. Although this figure is not very large, air pollution exposure is ubiquitous, and the large number of patients with asthmatic symptoms in China may indicate important public health issues. It was estimated that there were about 848,000 incident cases with asthmatic symptoms in 2019 in Chinese people older than 50 years . As a result, there were about 37,000 cases with asthmatic symptoms, which may have been caused by exposure to PM2.5 and O3.
The results of stratified analyses showed greater effects of PM2.5 and O3 on the risk of asthmatic symptoms in participants in rural areas than in urban areas. The stronger associations in rural areas may be because the rural population is more likely to smoke and use unclean cooking fuel. Usage of unclean cooking fuel could also increase the risk of asthmatic symptoms through similar mechanisms of air pollution [, ], and hence synergize the effects of ambient air pollutants. Compared with urban residents, rural residents usually have harder physical activities, which may increase their exposure to air pollution. Moreover, the rural population may be more susceptible to air pollution due to the disadvantaged sanitation and health care systems, dwelling environments, poor health status, and so forth [ ]. These potential mechanisms were also confirmed by our findings of greater associations in participants with lower family income and using unclean energy as the major household cooking fuel.
The stratified analyses also showed greater associations in obese participants than in lean individuals. The modification of obesity on the respiratory health impacts of air pollution has been reported in other studies [, ]. For example, a cross-sectional study in northeastern Chinese cities reported that obesity enhanced the respiratory health effects caused by air pollution in children [ ]. Obesity is a proinflammatory state, and the adipose tissue propagates inflammation by recruitment of macrophages via chemokines such as monocyte chemoattractant protein-1, and via elaboration of cytokines and chemokines such as leptin, interleukin-6, and tumor necrosis factor α. Meanwhile, inhaled air pollutants could also produce these cytokines and chemokines, and hence increase inflammation [ , ]. As a result, obesity may amplify the inflammatory response induced by air pollution and increase the vulnerability of obese participants to harmful respiratory effects caused by air pollution exposures.
There are a few limitations in this study. First, many participants were lost to follow-up due to reasons such as immigration, urbanization, and so forth, which may lead to selection bias. Second, the presence of asthmatic symptoms was based on self-reported questionnaires, which may lead to misclassification bias. However, all reported patients must be diagnosed by a doctor or by clinical symptoms, which could partially reduce the bias. Third, some potential confounders such as physical activity, occupational exposure to air pollution, vegetable and fruit intake, and the higher rate of missing information were not adjusted for in the main models. However, the results of sensitivity analyses conducted only in those participants with complete information showed that the results are robust to these factors (Table S4 in). Fourth, we did not include other air pollutants such as NO2, SO2, and CO due to the unavailability of data in the earlier years. In particular, NO2 was involved in the production of atmospheric ozone, and the effects of MDA8 O3 were biased due to the unavailability of NO2. Therefore, more studies are needed in the future. Fifth, 2 different methods were applied to assess the participants’ exposure to ambient MDA8 O3 and PM2.5, which may affect the result. However, the 2 methods are commonly adopted at present in air pollution exposure assessment, and the prediction results of the 2 models have been verified with high accuracy, which suggests that the impacts of using different assessment methods on our findings may be limited.
In conclusion, this study provided novel evidence that long-term exposures to ambient PM2.5 and O3 were individually and jointly associated with a higher presence of asthmatic symptoms, and ambient PM2.5 contributed more to the joint effects. The combined effects of air pollutants based on a single-pollutant model might be overestimated. The joint effects were more pronounced in participants with obesity, from rural areas, with lower income levels, and who use unclean household cooking fuel.
We thank all participants who took part in this program, and those investigators who contributed to this program. This work was supported by the National Natural Science Foundation of China (42075173, 42175181, and 42275187) and the National Foreign Expert Project (G2022199006L).
The data that support the findings of this study are available on request from the corresponding author.
TL and FW conceptualized and design this study. YG and YS contributed to data collection. JX, GC, and SL contributed to data interpretation and methodology; JX, YS, and GC contributed to data analysis and writing of the original draft. TL, FW, WM, GH, XD, PY, and ZL reviewed and edited the manuscript. All authors had full access to all data and approved the final manuscript as submitted. FW and TL are co-corresponding authors and have contributed equally to this article.
Conflicts of Interest
The joint effects of long-term exposure to ambient PM2.5 and ozone on incident asthma: prospective cohort study.DOCX File , 107 KB
- Reddel HK, Bacharier LB, Bateman ED, Brightling CE, Brusselle GG, Buhl R, et al. Global initiative for asthma strategy 2021: executive summary and rationale for key changes. J Allergy Clin Immunol Pract 2022;10(1S):S1-S18 [https://www.jaci-inpractice.org/article/S2213-2198(21)01064-3/fulltext] [CrossRef] [Medline]
- GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 2020;396(10258):1204-1222 [https://linkinghub.elsevier.com/retrieve/pii/S0140-6736(20)30925-9] [CrossRef] [Medline]
- Zhang S, Li G, Tian L, Guo Q, Pan X. Short-term exposure to air pollution and morbidity of COPD and asthma in East Asian area: a systematic review and meta-analysis. Environ Res 2016;148:15-23 [CrossRef] [Medline]
- Guarnieri M, Balmes JR. Outdoor air pollution and asthma. Lancet 2014;383(9928):1581-1592 [https://europepmc.org/abstract/MED/24792855] [CrossRef] [Medline]
- Anderson HR, Favarato G, Atkinson RW. Long-term exposure to outdoor air pollution and the prevalence of asthma: meta-analysis of multi-community prevalence studies. Air Qual Atmos Health 2011;6(1):57-68 [CrossRef]
- Brunekreef B, Strak M, Chen J, Andersen ZJ, Atkinson R, Bauwelinck M, et al. Mortality and morbidity effects of long-term exposure to low-level PM, BC, NO, and O: an analysis of European cohorts in the ELAPSE project. Res Rep Health Eff Inst 2021(208):1-127 [Medline]
- Havet A, Zerimech F, Sanchez M, Siroux V, Le Moual N, Brunekreef B, et al. Outdoor air pollution, exhaled 8-isoprostane and current asthma in adults: the EGEA study. Eur Respir J 2018;51(4):1702036 [https://hal.archives-ouvertes.fr/inserm-01799461] [CrossRef] [Medline]
- Hwang BF, Lee YL, Lin YC, Jaakkola JJK, Guo YL. Traffic related air pollution as a determinant of asthma among Taiwanese school children. Thorax 2005;60(6):467-473 [https://thorax.bmj.com/lookup/pmidlookup?view=long&pmid=15923246] [CrossRef] [Medline]
- Knibbs LD, Cortés de Waterman AM, Toelle BG, Guo Y, Denison L, Jalaludin B, et al. The Australian Child Health and Air Pollution Study (ACHAPS): a national population-based cross-sectional study of long-term exposure to outdoor air pollution, asthma, and lung function. Environ Int 2018;120:394-403 [CrossRef] [Medline]
- Ai S, Qian ZM, Guo Y, Yang Y, Rolling CA, Liu E, et al. Long-term exposure to ambient fine particles associated with asthma: a cross-sectional study among older adults in six low- and middle-income countries. Environ Res 2019;168:141-145 [CrossRef] [Medline]
- Cai Y, Zijlema WL, Doiron D, Blangiardo M, Burton PR, Fortier I, et al. Ambient air pollution, traffic noise and adult asthma prevalence: a BioSHaRE approach. Eur Respir J 2017;49(1):1502127 [http://erj.ersjournals.com/cgi/pmidlookup?view=long&pmid=27824608] [CrossRef] [Medline]
- Hendryx M, Luo J, Chojenta C, Byles JE. Air pollution exposures from multiple point sources and risk of incident chronic obstructive pulmonary disease (COPD) and asthma. Environ Res 2019;179(Pt A):108783 [CrossRef] [Medline]
- Young MT, Sandler DP, DeRoo LA, Vedal S, Kaufman JD, London SJ. Ambient air pollution exposure and incident adult asthma in a nationwide cohort of U.S. women. Am J Respir Crit Care Med 2014;190(8):914-921 [https://europepmc.org/abstract/MED/25172226] [CrossRef] [Medline]
- Lazarevic N, Dobson AJ, Barnett AG, Knibbs LD. Long-term ambient air pollution exposure and self-reported morbidity in the Australian longitudinal study on women's health: a cross-sectional study. BMJ Open 2015;5(10):e008714 [https://bmjopen.bmj.com/lookup/pmidlookup?view=long&pmid=26503387] [CrossRef] [Medline]
- Solomon C, Poole J, Jarup L, Palmer K, Coggon D. Cardio-respiratory morbidity and long-term exposure to particulate air pollution. Int J Environ Health Res 2003;13(4):327-335 [CrossRef] [Medline]
- Anderson HR, Favarato G, Atkinson RW. Long-term exposure to air pollution and the incidence of asthma: meta-analysis of cohort studies. Air Qual Atmos Health 2011;6(1):47-56 [CrossRef]
- Wenzel SE. Asthma phenotypes: the evolution from clinical to molecular approaches. Nat Med 2012;18(5):716-725 [CrossRef] [Medline]
- Lipfert FW. Long-term associations of morbidity with air pollution: a catalog and synthesis. J Air Waste Manag Assoc 2018;68(1):12-28 [https://www.tandfonline.com/doi/full/10.1080/10962247.2017.1349010] [CrossRef] [Medline]
- Shin S, Bai L, Burnett RT, Kwong JC, Hystad P, van Donkelaar A, et al. Air pollution as a risk factor for incident chronic obstructive pulmonary disease and asthma. A 15-year population-based cohort study. Am J Respir Crit Care Med 2021;203(9):1138-1148 [https://www.atsjournals.org/doi/10.1164/rccm.201909-1744OC] [CrossRef] [Medline]
- Park J, Kim HJ, Lee CH, Lee CH, Lee HW. Impact of long-term exposure to ambient air pollution on the incidence of chronic obstructive pulmonary disease: a systematic review and meta-analysis. Environ Res 2021;194:110703 [https://linkinghub.elsevier.com/retrieve/pii/S0013-9351(20)31602-9] [CrossRef] [Medline]
- Wild CP. The exposome: from concept to utility. Int J Epidemiol 2012;41(1):24-32 [https://academic.oup.com/ije/article/41/1/24/650703?login=false] [CrossRef] [Medline]
- Dominici F, Peng RD, Barr CD, Bell ML. Protecting human health from air pollution: shifting from a single-pollutant to a multipollutant approach. Epidemiology 2010;21(2):187-194 [https://europepmc.org/abstract/MED/20160561] [CrossRef] [Medline]
- Ma Z, Hu X, Sayer AM, Levy R, Zhang Q, Xue Y, et al. Satellite-based spatiotemporal trends in PM2.5 concentrations: China, 2004-2013. Environ Health Perspect 2016;124(2):184-192 [https://ehp.niehs.nih.gov/doi/10.1289/ehp.1409481?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub 0pubmed] [CrossRef] [Medline]
- Xie Z. China’s historical evolution of environmental protection along with the forty years’ reform and opening-up. Environ Sci Ecotechnology 2020;1:100001 [https://www.sciencedirect.com/science/article/pii/S2666498419300018] [CrossRef]
- Tobías A, Carnerero C, Reche C, Massagué J, Via M, Minguillón MC, et al. Changes in air quality during the lockdown in Barcelona (Spain) one month into the SARS-CoV-2 epidemic. Sci Total Environ 2020;726:138540 [https://europepmc.org/abstract/MED/32302810] [CrossRef] [Medline]
- Meng X, Wang W, Shi S, Zhu S, Wang P, Chen R, et al. Evaluating the spatiotemporal ozone characteristics with high-resolution predictions in mainland China, 2013-2019. Environ Pollut 2022;299:118865 [https://linkinghub.elsevier.com/retrieve/pii/S0269-7491(22)00079-3] [CrossRef] [Medline]
- Kowal P, Chatterji S, Naidoo N, Biritwum R, Fan W, Lopez Ridaura R, SAGE Collaborators. Data resource profile: the World Health Organization study on global AGEing and adult health (SAGE). Int J Epidemiol 2012;41(6):1639-1649 [https://europepmc.org/abstract/MED/23283715] [CrossRef] [Medline]
- Wu F, Guo Y, Kowal P, Jiang Y, Yu M, Li X, et al. Prevalence of major chronic conditions among older Chinese adults: the Study on Global AGEing and adult health (SAGE) wave 1. PLoS One 2013;8(9):e74176 [https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0074176] [CrossRef] [Medline]
- Chen G, Chen J, Dong GH, Yang BY, Liu Y, Lu T, et al. Improving satellite-based estimation of surface ozone across China during 2008–2019 using iterative random forest model and high-resolution grid meteorological data. Sustain Cities Soc 2021;69:102807 [CrossRef]
- Hammer MS, van Donkelaar A, Li C, Lyapustin A, Sayer AM, Hsu NC, et al. Global estimates and long-term trends of fine particulate matter concentrations (1998-2018). Environ Sci Technol 2020;54(13):7879-7890 [https://pubs.acs.org/doi/10.1021/acs.est.0c01764] [CrossRef] [Medline]
- Hersbach H, Bell B, Berrisford P, Horányi A, Muñoz-Sabater J, Nicolas J, et al. Global reanalysis: goodbye ERA-Interim, hello ERA5. ECMWF Newsletter 2019;159:17-24 [CrossRef]
- Niu Y, Yang T, Gu X, Chen R, Meng X, Xu J, China Pulmonary Health Study Group. Long-term ozone exposure and small airway dysfunction: the China Pulmonary Health (CPH) study. Am J Respir Crit Care Med 2022;205(4):450-458 [CrossRef] [Medline]
- Hvidtfeldt UA, Severi G, Andersen ZJ, Atkinson R, Bauwelinck M, Bellander T, et al. Long-term low-level ambient air pollution exposure and risk of lung cancer - a pooled analysis of 7 European cohorts. Environ Int 2021;146:106249 [https://linkinghub.elsevier.com/retrieve/pii/S0160-4120(20)32204-2] [CrossRef] [Medline]
- Arokiasamy P, Uttamacharya U, Jain K, Biritwum RB, Yawson AE, Wu F, et al. The impact of multimorbidity on adult physical and mental health in low- and middle-income countries: what does the study on global ageing and adult health (SAGE) reveal? BMC Med 2015;13:178 [https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-015-0402-8] [CrossRef] [Medline]
- Zhang Z, Reinikainen J, Adeleke KA, Pieterse ME, Groothuis-Oudshoorn CGM. Time-varying covariates and coefficients in Cox regression models. Ann Transl Med 2018;6(7):121 [https://europepmc.org/abstract/MED/29955581] [CrossRef] [Medline]
- Thacher JD, Poulsen AH, Hvidtfeldt UA, Raaschou-Nielsen O, Brandt J, Geels C, et al. Long-term exposure to transportation noise and risk for type 2 diabetes in a nationwide cohort study from Denmark. Environ Health Perspect 2021;129(12):127003 [https://ehp.niehs.nih.gov/doi/10.1289/EHP9146?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub 0pubmed] [CrossRef] [Medline]
- Keil AP, Buckley JP, O'Brien KM, Ferguson KK, Zhao S, White AJ. A quantile-based g-computation approach to addressing the effects of exposure mixtures. Environ Health Perspect 2020;128(4):47004 [https://ehp.niehs.nih.gov/doi/10.1289/EHP5838?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub 0pubmed] [CrossRef] [Medline]
- Schenker N, Gentleman JF. On judging the significance of differences by examining the overlap between confidence intervals. Am Stat 2001;55(3):182-186 [CrossRef]
- McDonnell WF, Abbey DE, Nishino N, Lebowitz MD. Long-term ambient ozone concentration and the incidence of asthma in nonsmoking adults: the AHSMOG study. Environ Res 1999;80(2 Pt 1):110-121 [CrossRef] [Medline]
- Jacquemin B, Siroux V, Sanchez M, Carsin AE, Schikowski T, Adam M, et al. Ambient air pollution and adult asthma incidence in six European cohorts (ESCAPE). Environ Health Perspect 2015;123(6):613-621 [https://ehp.niehs.nih.gov/doi/10.1289/ehp.1408206?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub 0pubmed] [CrossRef] [Medline]
- Pang L, Yu P, Liu X, Fan Y, Shi Y, Zou S. Fine particulate matter induces airway inflammation by disturbing the balance between Th1/Th2 and regulation of GATA3 and Runx3 expression in BALB/c mice. Mol Med Rep 2021;23(5):378 [https://europepmc.org/abstract/MED/33760131] [CrossRef] [Medline]
- Liu K, Hua S, Song L. PM2.5 exposure and asthma development: the key role of oxidative stress. Oxid Med Cell Longev 2022;2022:3618806 [https://www.hindawi.com/journals/omcl/2022/3618806/] [CrossRef] [Medline]
- Sijan Z, Antkiewicz DS, Heo J, Kado NY, Schauer JJ, Sioutas C, et al. An in vitro alveolar macrophage assay for the assessment of inflammatory cytokine expression induced by atmospheric particulate matter. Environ Toxicol 2015;30(7):836-851 [CrossRef] [Medline]
- Xu X, Wang H, Liu S, Xing C, Liu Y, Aodengqimuge, et al. TP53-dependent autophagy links the ATR-CHEK1 axis activation to proinflammatory VEGFA production in human bronchial epithelial cells exposed to fine particulate matter (PM2.5). Autophagy 2016;12(10):1832-1848 [https://europepmc.org/abstract/MED/27463284] [CrossRef] [Medline]
- Corradi M, Alinovi R, Goldoni M, Vettori M, Folesani G, Mozzoni P, et al. Biomarkers of oxidative stress after controlled human exposure to ozone. Toxicol Lett 2002 05;134(1-3):219-225 [CrossRef] [Medline]
- Xue Y, Zhou Y, Bao W, Fu Q, Hao H, Han L, et al. STAT3 and IL-6 contribute to corticosteroid resistance in an OVA and ozone-induced asthma model with neutrophil infiltration. Front Mol Biosci 2021;8:717962 [https://europepmc.org/abstract/MED/34760922] [CrossRef] [Medline]
- Winquist A, Kirrane E, Klein M, Strickland M, Darrow LA, Sarnat SE, et al. Joint effects of ambient air pollutants on pediatric asthma emergency department visits in Atlanta, 1998-2004. Epidemiology 2014;25(5):666-673 [https://europepmc.org/abstract/MED/25045931] [CrossRef] [Medline]
- Schildcrout JS, Sheppard L, Lumley T, Slaughter JC, Koenig JQ, Shapiro GG. Ambient air pollution and asthma exacerbations in children: an eight-city analysis. Am J Epidemiol 2006;164(6):505-517 [https://academic.oup.com/aje/article/164/6/505/129785?login=false] [CrossRef] [Medline]
- Liu T, Jiang Y, Hu J, Li Z, Li X, Xiao J, et al. Joint associations of short-term exposure to ambient air pollutants with hospital admission of ischemic stroke. Epidemiology 2023;34(2):282-292 [CrossRef] [Medline]
- Jie Y, Ismail NH, Jie X, Isa ZM. Do indoor environments influence asthma and asthma-related symptoms among adults in homes?: a review of the literature. J Formos Med Assoc 2011;110(9):555-563 [https://linkinghub.elsevier.com/retrieve/pii/S0929-6646(11)00014-3] [CrossRef] [Medline]
- Sinha D, Ray MR. Health effects of indoor air pollution due to cooking with biomass fuel. In: Roberts SM, Kehrer JP, Klotz LO, editors. Studies on Experimental Toxicology and Pharmacology. AG, Switzerland: Humana Press; 2015:267-302
- Liu T, Meng H, Yu M, Xiao Y, Huang B, Lin L, et al. Urban-rural disparity of the short-term association of PM with mortality and its attributable burden. Innovation (Camb) 2021;2(4):100171 [https://linkinghub.elsevier.com/retrieve/pii/S2666-6758(21)00096-5] [CrossRef] [Medline]
- Dong GH, Qian Z, Liu MM, Wang D, Ren WH, Fu Q, et al. Obesity enhanced respiratory health effects of ambient air pollution in Chinese children: the Seven Northeastern Cities study. Int J Obes (Lond) 2013;37(1):94-100 [CrossRef] [Medline]
- Limaye S, Salvi S. Obesity and asthma: the role of environmental pollutants. Immunol Allergy Clin North Am 2014;34(4):839-855 [CrossRef] [Medline]
|COPD: chronic obstructive pulmonary disease|
|GBD: Global Burden of Disease|
|HR: hazard ratio|
|MDA8 O3: daily 8-hour maximum ozone concentrations|
|PAF: population-attributable fraction|
|PM2.5: particulate matter|
|WHO SAGE: World Health Organization Study on Global Ageing and Adult Health|
Edited by A Mavragani, T Sanchez; submitted 18.03.23; peer-reviewed by HW Lee, X Deng; comments to author 17.04.23; revised version received 08.05.23; accepted 21.06.23; published 03.08.23Copyright
©Jiahong Xu, Yan Shi, Gongbo Chen, Yanfei Guo, Weiling Tang, Cuiling Wu, Shuru Liang, Zhongguo Huang, Guanhao He, Xiaomei Dong, Ganxiang Cao, Pan Yang, Ziqiang Lin, Sui Zhu, Fan Wu, Tao Liu, Wenjun Ma. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 03.08.2023.
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