Traffic related air pollution and incidence of childhood asthma: results of the Vesta case-control study

Zmirou, Denis; Gauvin, Stéphanie; Pin, Isabelle; Momas, Isabelle; Sahraoui, F.; Just, Jocelyne; Moullec, Yvon Le; Brémont, F.; Cassadou, Sylvie; Reungoat, P.; Albertini, M.; Lauvergne, Nathalie; Chiron, Mireille; Labbé, A.

doi:10.1136/jech.58.1.18

Cited by 166 publications

(134 citation statements)

References 35 publications

(26 reference statements)

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“…In France, a study of lifetime traffic exposure in young children found that exposure in the first 3 years of life was significantly associated with a diagnosis of asthma after controlling for known risk factors (Zmirou et al, 2004). In Belfast, Ireland, admissions to the emergency room for asthma were related to air pollutants in a time series analysis.…”

Section: Discussionmentioning

confidence: 99%

An investigation of the association between traffic exposure and the diagnosis of asthma in children

Gordian

Haneuse

Wakefield

2005

J Expo Sci Environ Epidemiol

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This study investigated whether proximity to traffic at residence location is associated with being diagnosed with asthma as a young child. A survey of parents of children (aged 5-7) in kindergarten and first-grade in 13 schools was completed in Anchorage, Alaska, and Geographical Information System (GIS) mapping was used to obtain an exposure measure based on traffic density within 100 m of the cross streets closest to the child's residence. Using the range of observed exposure values, a score of low, medium or high traffic exposure was assigned to each child. After controlling for individual level confounders, relative to the low referent group, relative risks (95% confidence intervals) of 1.40 (0.77, 2.55) and 2.83 (1.23,6.51) were obtained in the medium and high exposure groups, respectively. For the null hypothesis of no difference in risk, a significance level of 0.056 was obtained, which suggests that further investigation would be worthwhile. Children without a family history of asthma were more likely to have an asthma diagnosis if they resided in a high traffic area than children who had one or more parents with asthma. The relative risk for children without a family history of asthma is 2.43 (1.12, 5.28) for medium exposure and 5.43 (2.08, 13.74) for high exposure. For children with a family history of asthma, the relative risk is 0.66 (0.25, 1.74) for medium exposure and 0.67 (0.12, 3.69) for high exposure. The P-value for the overall ''exposure-effect'' (i.e. both main effects AND interaction terms) is 0.0097.

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Section: Discussionmentioning

confidence: 99%

An investigation of the association between traffic exposure and the diagnosis of asthma in children

Gordian

Haneuse

Wakefield

2005

J Expo Sci Environ Epidemiol

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“…Though many studies of the chronic health effects associated with long-term exposure to air pollution (often from large, well-known cohorts) examine the use of an alternative exposure metric in an epidemiologic study (Adar et al 2013;Brauer et al 2002;Jerrett et al 2008;Puett et al 2011;Young et al 2014;Zmirou et al 2004), only the seven studies found by the authors and described below examine multiple approaches to exposure assessment, and include comparison of the epidemiologic results obtained when using each exposure metric. However, many manuscripts lacked definitive conclusions about which exposure metric was most beneficial, and the wide range of health outcomes, exposure metrics, and exposure durations of interest used make it difficult to draw larger conclusions from these studies.…”

Section: Studies Of Chronic Health Effects Associated With Long-term mentioning

confidence: 99%

The importance of the exposure metric in air pollution epidemiology studies: When does it matter, and why?

Dionisio

Baxter

Burke

et al. 2015

Air Qual Atmos Health

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Exposure error in ambient air pollution epidemiologic studies may introduce bias and/or attenuation of the health risk estimate, reduce statistical significance, and lower statistical power. Alternative exposure metrics are increasingly being used in place of central-site measurements, with the intent of reducing exposure error. Dependent on the study design, health outcome, and pollutant of interest, these metrics may provide a means of reducing error (leading to less bias and uncertainty in health risk estimates) if they capture variability in exposure which is not represented when central-site measurements are used. We examine the current evidence for answering the question of when the choice of exposure metric matters and why, focusing on studies which examined multiple exposure metrics in the same epidemiologic study. We conclude that for timeseries and case-crossover studies, central-site measurements may be sufficient, especially for homogeneous pollutants, and in cohort and panel studies, approaches that increase spatial resolution of the exposure metrics do impact the health effect estimates. We note that while the current literature is widely dispersed across exposure metrics and health outcomes, the largest collective, common body of literature is focused on birth/pregnancy outcomes and traffic-related pollution. Also additional discussion and agreement is needed on how to classify metrics as Bdifferent^and Bbetter.^Primary recommendations are to provide context for the reasons behind selection of exposure metrics and to encourage collaboration between exposure scientists and epidemiologists when designing and implementing a study, as results can have important implications for the development of policies and regulations.

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“…(Gordian et al, 2006;Zmirou et al, 2004;Brauer et al, 2002), 천식으로 인한 입원율을 높이고 (Lin et al, 2002;Edwards et al, 1994) 참 고 문 헌…”

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Land Use Regression Model for Assessing Exposure and Impacts of Air Pollutants in School Children

Lee¹,

Leem²,

Kim³

et al. 2012

Journal of Korean Society for Atmospheric Environment

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Epidemiologic studies of air pollution need accurate exposure assessments at unmonitored locations. A land use regression (LUR) model has been used successfully for predicting traffic-related pollutants, although its application has been limited to Europe, North America, and a few Asian region. Therefore, we modeled traffic-related pollutants by LUR then examined whether LUR models could be constructed using a regulatory monitoring network in Metropolitan area in Korea. We used the annual-mean nitrogen dioxide (NO 2 ) in 2010 in the study area. Geographic variables that are considered to predict traffic-related pollutants were classified into four groups: road type, traffic intensity, land use, and elevation. Using geographical variables, we then constructed a model to predict the monitored levels of NO 2 . The mean concentration of NO 2 was 30.71 ppb (standard deviation of 5.95) respectively. The final regression model for the NO 2 concentration included five independent variables. The LUR models resulted in R 2 of 0.59. The mean concentration of NO 2 of elementary schools was 34.04 ppb (standard deviation of 5.22) respectively. The present study showed that even if we used regulatory monitoring air quality data, we could estimate NO 2 moderately well. These analyses confirm the validity of land use regression modeling to assign exposures in epi- demiological studies, and these models may be useful tools for assessing health effects of long-term exposure to traffic related pollution.

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Traffic related air pollution and incidence of childhood asthma: results of the Vesta case-control study

Cited by 166 publications

References 35 publications

An investigation of the association between traffic exposure and the diagnosis of asthma in children

An investigation of the association between traffic exposure and the diagnosis of asthma in children

The importance of the exposure metric in air pollution epidemiology studies: When does it matter, and why?

Land Use Regression Model for Assessing Exposure and Impacts of Air Pollutants in School Children

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