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 [27,28]. 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 Multimedia Appendix 1).

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).

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 [29]. The annual average MDA8 O₃ was calculated using the daily data in each grid. Annual mean PM_2.5 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 [30]. The annual average temperature was obtained from ERA5 [31].

We first calculated the annual mean exposure to MDA8 O₃, PM_2.5, 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 [32,33].

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 Multimedia Appendix 1 [34]. 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 Multimedia Appendix 1).

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.

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 (Table 1).

The mean PM_2.5 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. PM_2.5 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 Multimedia Appendix 1).

We observed positive nonlinear exposure-response curves of MDA8 O₃ and PM_2.5 in the presence of asthmatic symptoms (Figures 1 and 2). Linear analyses showed that per quantile increment of MDA8 O₃ (HR 1.12, 95% CI 1.01-1.24) and PM_2.5 (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 PM_2.5 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; Table 2).

Joint effect analyses showed that per quantile increment of MDA8 O₃ and PM_2.5 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 PM_2.5 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; Table 2).

Figure 3 shows the PAFs of asthmatic symptoms attributable to MDA8 O₃ and PM_2.5, estimated by the individual and joint associations. In the total participants, the individual PAFs caused by MDA8 O₃ and PM_2.5 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 PM_2.5 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 PM_2.5 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 PM_2.5 was 18.63% (95% CI 7.73%-28.83%). More detailed information is shown in Figure 3.

Results indicated that HRs did not substantially change after adjustment for different confounders (Table S4 in Multimedia Appendix 1).

In this national cohort study, we found a positive correlation between individual and joint associations on long-term exposure to ambient PM_2.5 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 PM_2.5 and MDA8 O₃ exposures, and ambient PM_2.5 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 PM_2.5 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 PM_2.5 and MDA8 O₃ were separately associated with an increased risk of asthma incidence [6-8]. For example, the Health Effects Institute reported that long-term exposure to higher PM_2.5 was associated with an increased risk of asthma, which was from the European Cohorts in the ELAPSE Project [6]. The Epidemiological Study of the Genetic and Environmental Factors of Asthma reported that the risk of asthma increased with O₃ exposure [7]. McDonnell et al [39] 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 PM_2.5 and MDA8 O₃ with the incidence of asthma [12,13,40]. For example, a prospective cohort from Australia reported a null association between long-term exposure to PM_2.5 with adult-onset asthma [12]. 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 [6]. 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 PM_2.5 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 PM_2.5 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 PM_2.5 induces the release of inflammatory mediators from alveolar macrophages, which is an important pathogenesis of pulmonary inflammation in the development of asthma. PM_2.5 induces inflammatory responses, associated with Th1/Th2 pathway imbalance and leads to Th2-oriented inflammation [41]. Apart from that, PM_2.5 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 [42,43]. In this study, PM_2.5 also induced a significant upregulation of vascular endothelial growth factor A production, a signaling event that controls vascular remodeling [44]. Furthermore, exposure to O₃ is associated with an increase in free radicals and biomarkers associated with oxidative stress [45]. Ozone exposure also induced airway inflammation with increased numbers of neutrophils [46].

More importantly, we quantified the joint associations of long-term exposure to PM_2.5 and MDA8 O₃ in the presence of asthmatic symptoms in a Chinese population. Our findings had 2 major implications. First, PM_2.5 (68%) contributed more to the joint associations than O₃ (32%), suggesting the more important role of PM_2.5 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 [47]. Several studies reported similar findings to this study [47-49]. For example, Winquist et al [47] 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 PM_2.5 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 [2]. As a result, there were about 37,000 cases with asthmatic symptoms, which may have been caused by exposure to PM_2.5 and O₃.

The results of stratified analyses showed greater effects of PM_2.5 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 [50,51], 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 [52]. 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 [53,54]. For example, a cross-sectional study in northeastern Chinese cities reported that obesity enhanced the respiratory health effects caused by air pollution in children [53]. 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 [53,54]. 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 Multimedia Appendix 1). 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 PM_2.5, 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 PM_2.5 and O₃ were individually and jointly associated with a higher presence of asthmatic symptoms, and ambient PM_2.5 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.