High temperatures are associated with adverse respiratory health outcomes and increases in ambient air pollution. Limited research has quantified air pollution's mediating role in the relationship between temperature and respiratory morbidity, such as emergency department (ED) visits. In this study, we conducted a causal mediation analysis to decompose the total effect of daily temperature on respiratory ED visits in Los Angeles from 2005 to 2016. We focused on ambient ozone as a mediator because its precursors and formation are directly driven by sunlight and temperature. We estimated natural direct, indirect, and total effects on the relative risk scale across deciles of temperature exposure compared to the median. We utilized Bayesian additive regression trees (BART) to flexibly characterize the nonlinear relationship between temperature and ozone and quantified uncertainty via posterior prediction and the Bayesian bootstrap. Our results showed that ozone partially mediated the association between high temperatures and respiratory ED visits, particularly at moderately high temperatures. We also validated our modeling approach through simulation studies. This study extends the existing literature by considering acute respiratory morbidity and employing a flexible modeling approach, offering new insights into the mechanisms underlying temperature-related health risks.
High ambient temperature poses significant health risk globally. Studies have reported associations between short-term high temperature exposure and various adverse health outcomes, including total and cause-specific mortality (Moghadamnia et al., 2017;Lin et al., 2023;Hajat and Kosatky, 2010), hospital encounters (Lay et al., 2018;Winquist et al., 2016;Knowlton et al., 2009;Lin et al., 2009;Lee and Yoon, 2024;Ma et al., 2019), and poor birth outcomes (Strand et al., 2011). In particular, heat exposure can exacerbate respiratory diseases, such as asthma, chronic obstructive pulmonary disease and respiratory tract infections (Anderson et al., 2013;Zhu et al., 2025). Potential mechanisms include heat-induced changes in lung function (Kenny et al., 2010), pulmonary injury due to inhaling hot air (Hayes Jr et al., 2012), and the use of medications that impact thermo-regulation (Meade et al., 2020). The number of hospitalizations and disease burden for respiratory disease attributable to extreme heat is projected to increase over the next 50 to 70 years due to climate change (Lin et al., 2012).
Ambient ground-level ozone is another important environmental risk factor for respiratory health. Studies have consistently shown that short-term exposure to ozone is associated with respiratory mortality and morbidity (Magzamen et al., 2017;Ahn et al., 2025), and can further harm lung tissue, increase inflammation in the airways, and heighten the lungs’ sensitivity to other irritants (Filippidou and Koukouliata, 2011). Ozone is a secondary pollutant generated by sunlight-driven chemical reactions between NO x and volatile organic compounds (VOC), including methane (CH 4 ), CO and other more complex organic compounds (Fowler et al., 2008). Ambient ozone concentrations are often highly correlated with temperature (Bloomer et al., 2009), primarily due to temperature-dependent increases in chemical reaction rates and enhanced emissions of ozone precursor compounds (Doherty et al., 2017). As a result, ozone may act as a potential mediator on the causal pathway linking temperature to respiratory outcomes.
Multiple studies have explored the potential mediating role of ozone under a causal mediation framework and have reported positive relative risk of indirect effects through ozone with increasing temperature (Alari et al., 2023;Gao et al., 2025). Other studies have identified positive indirect effects for ozone in the pathway between temperature and other health outcomes, including non-accidental deaths (Bae et al., 2023), cardiovascular diseases (Gong et al., 2024), and glomerular filtration rate decrease (Huang et al., 2025). Despite the growing body of evidence supporting the mediating role of ozone in the relationship between temperature and health, these studies all assumed a linear exposure-mediator association between temperature and ambient ozone. In addition, the outcome regression between temperature and the health outcome was also assumed to be linear and interactions between temperature and ozone were not considered. It remains unclear how well these restrictive parametric assumptions hold in practice, motivating interest in flexible nonparametric models that can achieve strong performance under minimal assumptions.
In this study, we performed a causal mediation analysis to estimate the total, natural indirect, and natural direct effects of short-term temperature exposure on respiratory emergency department (ED) visits through ambient ozone concentration motivated by a dataset of daily respiratory ED visits in the Los Angeles metropolitan area from 2005 to 2016. We relax the strong parametric assumptions of previous analyses by using Bayesian additive regression trees (BART) to model the exposure-mediator regression. We then compute 95% confidence intervals for our mediation effect estimates through a computationally efficient Bayesian bootstrap method. We validate our method through a comprehensive simulation experiment.
Daily counts of respiratory ED visits were obtained from the California Office of Statewide Health Planning and Development for the Los Angeles metropolitan area from 2005 to 2016. These records included both patients who were admitted to the hospital following an ED visit and those who were discharged directly from the ED. Respiratory ED visits were identified using primary and secondary International Classification of Diseases (ICD) diagnosis codes (ICD-9 460-519 before October 1st, 2015; ICD-10 J00-J99 afterwards). We restricted the analysis to the warm season from May to October, resulting in a total of 2,208 days.
Daily maximum temperature in degree Celsius and daily average absolute humidity were acquired from the High-resolution Urban Meteorology for Impacts Datasets (HUMID), a gridded dataset that provides near-surface temperature data for the contiguous U.S. at a 1km spatial resolution (Newman et al., 2024). This dataset explicitly accounts for urban heat islands by employing an urba
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