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Impact of geocoding methods on associations between long-term exposure to urban air pollution and lung function.

ABSTRACT: BACKGROUND: Errors in address geocodes may affect estimates of the effects of air pollution on health. OBJECTIVE: We investigated the impact of four geocoding techniques on the association between urban air pollution estimated with a fine-scale (10 m × 10 m) dispersion model and lung function in adults. METHODS: We measured forced expiratory volume in 1 sec (FEV1) and forced vital capacity (FVC) in 354 adult residents of Grenoble, France, who were participants in two well-characterized studies, the Epidemiological Study on the Genetics and Environment on Asthma (EGEA) and the European Community Respiratory Health Survey (ECRHS). Home addresses were geocoded using individual building matching as the reference approach and three spatial interpolation approaches. We used a dispersion model to estimate mean PM10 and nitrogen dioxide concentrations at each participant's address during the 12 months preceding their lung function measurements. Associations between exposures and lung function parameters were adjusted for individual confounders and same-day exposure to air pollutants. The geocoding techniques were compared with regard to geographical distances between coordinates, exposure estimates, and associations between the estimated exposures and health effects. RESULTS: Median distances between coordinates estimated using the building matching and the three interpolation techniques were 26.4, 27.9, and 35.6 m. Compared with exposure estimates based on building matching, PM10 concentrations based on the three interpolation techniques tended to be overestimated. When building matching was used to estimate exposures, a one-interquartile range increase in PM10 (3.0 ?g/m3) was associated with a 3.72-point decrease in FVC% predicted (95% CI: -0.56, -6.88) and a 3.86-point decrease in FEV1% predicted (95% CI: -0.14, -3.24). The magnitude of associations decreased when other geocoding approaches were used [e.g., for FVC% predicted -2.81 (95% CI: -0.26, -5.35) using NavTEQ, or 2.08 (95% CI -4.63, 0.47, p = 0.11) using Google Maps]. CONCLUSIONS: Our findings suggest that the choice of geocoding technique may influence estimated health effects when air pollution exposures are estimated using a fine-scale exposure model.

SUBMITTER: Jacquemin B

PROVIDER: S-EPMC3764075 | biostudies-literature | 2013 Sep

REPOSITORIES: biostudies-literature

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Impact of geocoding methods on associations between long-term exposure to urban air pollution and lung function.

Jacquemin Bénédicte B Lepeule Johanna J Boudier Anne A Arnould Caroline C Benmerad Meriem M Chappaz Claire C Ferran Joane J Kauffmann Francine F Morelli Xavier X Pin Isabelle I Pison Christophe C Rios Isabelle I Temam Sofia S Künzli Nino N Slama Rémy R Siroux Valérie V

Environmental health perspectives 20130703 9

<h4>Background</h4>Errors in address geocodes may affect estimates of the effects of air pollution on health.<h4>Objective</h4>We investigated the impact of four geocoding techniques on the association between urban air pollution estimated with a fine-scale (10 m × 10 m) dispersion model and lung function in adults.<h4>Methods</h4>We measured forced expiratory volume in 1 sec (FEV1) and forced vital capacity (FVC) in 354 adult residents of Grenoble, France, who were participants in two well-char ...[more]

PMID: 23823697

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Project description:BackgroundAir pollution exposures have been shown to adversely impact health through a number of biological pathways associated with glucose metabolism. However, few studies have evaluated the associations between air pollution and glycosylated hemoglobin (HbA1c) levels. Further, no studies have evaluated these associations in US populations or investigated whether associations differ in diabetic as compared to non-diabetic populations. To address this knowledge gap, we investigated the associations between airborne fine particulate matter (PM2.5) and nitrogen dioxide (NO2) and HbA1c levels in both diabetic and non-diabetic older Americans. We also examined the impact of PM2.5 and NO2 on prevalent diabetes mellitus (DM) in this cohort.MethodsWe used multilevel logistic and linear regression models to evaluate the association between long-term average air pollutant levels and prevalence of DM and HbA1c levels, respectively, among 4121 older (57+ years) Americans enrolled in the National Social Life, Health, and Aging Project between 2005 and 2011. All models adjusted for age, sex, body mass index, smoking status, race, household income, education level, neighborhood socioeconomic status, geographic region, urbanicity and diabetic medication use. We estimated participant-specific exposures to PM2.5 on a six-kilometer grid covering the conterminous U.S. using spatio-temporal models, and to NO2 using nearest measurements from the Environmental Protection Agency's Air Quality System. HbA1c levels were measured for participants in each of two data collection waves from dried blood spots and log-transformed prior to analysis. Participants were considered diabetic if they had HbA1c values≥6.5% or reported taking diabetic medication.ResultsThe prevalence of diabetes at study entry was 22.2% (n=916) and the mean HbA1c was 6.0±1.1%. Mean one-year moving average PM2.5 and NO2 exposures were 10.4±3.0μg/m3 and 13.1±7.0 ppb, respectively. An inter-quartile range (IQR, 3.9μg/m3) increase in one-year moving average PM2.5 was positively associated with increased diabetes prevalence (prevalence odds ratio, POR 1.35, 95% CI: 1.19, 1.53). Similarly, an IQR (8.6 ppb) increase in NO2 was also significantly associated with diabetes prevalence (POR 1.27, 95% CI: 1.10, 1.48). PM2.5 (1.8%±0.6%, p<0.01) and NO2 (2.0%±0.7%, p<0.01) exposures were associated with higher HbA1c levels in diabetic participants, while only NO2 was significantly associated with HbA1c in non-diabetic participants (0.8%±0.2%, p<0.01). Significant dose response relationships were identified for both pollutants in diabetic participants and for NO2 in non-diabetic participants.Conclusions/interpretationsIn a cohort of older men and women in the United States, PM2.5 and NO2 exposures were significantly associated with prevalence of DM and increased HbA1c levels among both non-diabetic and diabetic participants. These associations suggest that air pollution could be a key risk factor for abnormal glucose metabolism and diabetes in the elderly.

Project description:BackgroundLong-term exposures to air pollution has been reported to be associated with inflammation and oxidative stress. However, the underlying metabolic mechanisms remain poorly understood.ObjectivesWe aimed to determine the changes in the blood metabolome and thus the metabolic pathways associated with long-term exposure to outdoor air pollution and ambient temperature.MethodsWe quantified metabolites using mass-spectrometry based global untargeted metabolomic profiling of plasma samples among men from the Normative Aging Study (NAS). We estimated the association between long-term exposure to PM2.5, NO2, O3, and temperature (annual average of central site monitors) with metabolites and their associated metabolic pathways. We used multivariable linear mixed-effect regression models (LMEM) while simultaneously adjusting for the four exposures and potential confounding and correcting for multiple testing. As a reduction method for the intercorrelated metabolites (outcome), we further used an independent component analysis (ICA) and conducted LMEM with the same exposures.ResultsMen (N?=?456) provided 648 blood samples between 2000 and 2016 in which 1158 metabolites were quantified. On average, men were 75.0?years and had an average body mass index of 27.7?kg/m2. Almost all men (97%) were not current smokers. The adjusted analysis showed statistically significant associations with several metabolites (58 metabolites with PM2.5, 15 metabolites with NO2, and 6 metabolites with temperature) while no metabolites were associated with O3. One out of five ICA factors (factor 2) was significantly associated with PM2.5. We identified eight perturbed metabolic pathways with long-term exposure to PM2.5 and temperature: glycerophospholipid, sphingolipid, glutathione, beta-alanine, propanoate, and purine metabolism, biosynthesis of unsaturated fatty acids, and taurine and hypotaurine metabolism. These pathways are related to inflammation, oxidative stress, immunity, and nucleic acid damage and repair.ConclusionsUsing a global untargeted metabolomic approach, we identified several significant metabolites and metabolic pathways associated with long-term exposure to PM2.5, NO2 and temperature. This study is the largest metabolomics study of long-term air pollution, to date, the first study to report a metabolomic signature of long-term temperature exposure, and the first to use ICA in the analysis of both.

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Impact of geocoding methods on associations between long-term exposure to urban air pollution and lung function.

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Impact of geocoding methods on associations between long-term exposure to urban air pollution and lung function.

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