Full-chain health impact assessment of traffic-related air pollution and childhood asthma
Introduction
Asthma is a chronic disease of the air passages leading to and from the lung, and is a condition that is often cited as the most common chronic disease of childhood (Gasana et al., 2012; Fabian et al., 2012; Gaffin and Phipatanakul, 2014). A recent meta-analysis showed statistically significant exposure-response relationships between traffic-related air pollution (TRAP) and development of asthma in children from birth to 18 years of age (Khreis et al., 2017c). The public health relevance of these relationships is largely unknown and the impact of TRAP exposures on the burden of childhood asthma is poorly documented. Due to the ubiquity of TRAP and the number of exposed children, the relatively small individual risks of TRAP-associated asthma could translate into significant public health impact.
Little work has been undertaken to estimate the burden of childhood asthma attributable to TRAP. Only four published studies, coming from the same research group, quantified the number of prevalent asthma cases attributable to TRAP (Perez et al., 2009; Perez et al., 2013; Künzli et al., 2008; Perez et al., 2012). Three of these studies were conducted in California, in Long Beach, Riverside and Los Angeles county (Künzli et al., 2008; Perez et al., 2009; Perez et al., 2012). The fourth study was conducted in 10 European cities (Perez et al., 2013). All four studies estimated the impacts of exposure to TRAP, characterized by proximity to major roadways, on asthma prevalence in children between birth and 18 years old. These studies suggested that 6% to 14% of prevalent childhood asthma cases were attributable to TRAP exposures; as characterized by traffic proximity (Table S1).
Despite pioneering in studying asthma as an outcome in the burden of disease assessment of TRAP, these studies relied on residential proximity to major roadways as the TRAP exposure metric. Proximity to major roadways is a crude exposure metric (Beevers et al., 2013; Jerrett et al., 2005) and alternative improved approaches are now more readily available (Khreis and Nieuwenhuijsen, 2017). Individual measurements are the preferred exposure assessment method, but since it is often not possible to measure air pollution exposures for the large populations included in health impact assessment and most epidemiological studies, many rely on less costly and more practical modeling approaches. Land-use regression (LUR) (Eeftens et al., 2012; Beelen et al., 2013; De Hoogh et al., 2014) and atmospheric dispersion (AD) modeling (Rancière et al., 2017; Yamazaki et al., 2014; De Hoogh et al., 2014) are two common modeling methods used to obtain air pollution exposure estimates for relatively large areas and number of people.
These two exposure modeling methods are fundamentally different and vary in their spatial and temporal resolution, specificity to traffic and advantages and disadvantages (Khreis and Nieuwenhuijsen (2017). AD models rely on mathematical formula and an understanding of underlying emission and dispersion processes to estimate air pollution exposures (Nieuwenhuijsen, 2015). On the other hand, LUR is an empirical method that uses least squares regression to combine air pollution measurements with geographic information system (GIS)-based predictor variables which reflect pollutant sources (for example, road, traffic and buildings density, green space etc.). The practical and policy advantage of AD modeling is that it allows for easier estimation of the contribution of different sources, such as traffic, to air pollution exposure estimates. On the other hand, the true contribution of traffic to the regression in LUR models is not always known or reported (Health Effects Institute, 2010).
In this study, we aimed to construct a full-chain health impact assessment model (Nieuwenhuijsen et al., 2017), to estimate the annual number of childhood asthma cases in Bradford, UK, attributable to air pollution, and specifically to TRAP. In the full-chain health impact assessment model, we combined four distinct models of traffic, emission, AD and health impact assessment (HIA), which covered the full-chain from the source of air pollution to the health impacts (Fig. 1). We then compared the burden of disease estimates obtained using the full-chain model with those obtained using exposure estimates from a LUR model, instead.
Section snippets
Setting
The study was set in Bradford, a city in the North of England, with an estimated 534,300 inhabitants (City of Bradford Metropolitan District Council, 2017). Bradford's population has a notably different structure from other cities in England and Wales (E&W) with more people under the age of 16 (Bradford has 22.6% whilst E&W have 18.7%) (Fielding, 2012). Based on the British government's residential area Index of Multiple Deprivation (IMD) (ESRI, 2017) and considering factors like income,
NO2 and NOx exposures
The annual average census tract levels of NO2 and NOx estimated with the AD model were 15.41 and 25.68 μg/m3, respectively. On average, 2.75 μg/m3 NO2 and 4.59 μg/m3 NOx were specifically contributed by traffic. The annual average census tract levels of NO2 and NOx estimated with the LUR models were higher and equaled 21.93 and 35.60 μg/m3, respectively. Table 3 shows the distribution of NO2 and NOx exposures across the census tracts, from the two exposure models.
Attributable number of cases
Using the full-chain HIA model,
Summary
This study provides the first full-chain HIA of TRAP and childhood asthma; using pollutant-specific exposure estimates (rather than exposure surrogates) and pollutant-specific meta-analytic exposure-response functions, whilst considering the full-chain of events from exposure source, through pathways to population health impacts.
The results indicate that between 18% to 38% of all childhood asthma cases in Bradford may be attributable to air pollution (Table 4), whilst 7% and 12% may be
Conclusions
This study provides the first full-chain HIA of TRAP and childhood asthma; using meta-analytic and pollutant-specific exposure-response functions and considering the full-chain from exposure source, through pathways to population health impacts. The burden of childhood asthma attributable to air pollution is poorly documented in the literature. We add to this evidence base demonstrating that between 18% to 38% of all childhood asthma cases in Bradford may be associated with air pollution. We
Acknowledgments
We thank Natalie Mueller and David Rojas-Rueda for their feedback and useful comments on the burden of disease assessment. We are also thankful for Marta Cirach for her prompt support with GIS.
Competing financial interests
The authors declare they have no competing interests.
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