Dataset Information

Modeling individual exposures to ambient PM2.5 in the diabetes and the environment panel study (DEPS).

ABSTRACT: Air pollution epidemiology studies of ambient fine particulate matter (PM2.5) often use outdoor concentrations as exposure surrogates, which can induce exposure error. The goal of this study was to improve ambient PM2.5 exposure assessments for a repeated measurements study with 22 diabetic individuals in central North Carolina called the Diabetes and Environment Panel Study (DEPS) by applying the Exposure Model for Individuals (EMI), which predicts five tiers of individual-level exposure metrics for ambient PM2.5 using outdoor concentrations, questionnaires, weather, and time-location information. Using EMI, we linked a mechanistic air exchange rate (AER) model to a mass-balance PM2.5 infiltration model to predict residential AER (Tier 1), infiltration factors (Finf_home, Tier 2), indoor concentrations (Cin, Tier 3), personal exposure factors (Fpex, Tier 4), and personal exposures (E, Tier 5) for ambient PM2.5. We applied EMI to predict daily PM2.5 exposure metrics (Tiers 1-5) for 174 participant-days across the 13?months of DEPS. Individual model predictions were compared to a subset of daily measurements of Fpex and E (Tiers 4-5) from the DEPS participants. Model-predicted Fpex and E corresponded well to daily measurements with a median difference of 14% and 23%; respectively. Daily model predictions for all 174?days showed considerable temporal and house-to-house variability of AER, Finf_home, and Cin (Tiers 1-3), and person-to-person variability of Fpex and E (Tiers 4-5). Our study demonstrates the capability of predicting individual-level ambient PM2.5 exposure metrics for an epidemiological study, in support of improving risk estimation.

SUBMITTER: Breen M

PROVIDER: S-EPMC6147059 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Modeling individual exposures to ambient PM2.5 in the diabetes and the environment panel study (DEPS).

Breen Michael M Xu Yadong Y Schneider Alexandra A Williams Ronald R Devlin Robert R

The Science of the total environment 20180219

Air pollution epidemiology studies of ambient fine particulate matter (PM2.5) often use outdoor concentrations as exposure surrogates, which can induce exposure error. The goal of this study was to improve ambient PM2.5 exposure assessments for a repeated measurements study with 22 diabetic individuals in central North Carolina called the Diabetes and Environment Panel Study (DEPS) by applying the Exposure Model for Individuals (EMI), which predicts five tiers of individual ...[more]

PMID: 29396342

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Project description:BackgroundFew studies examining the associations between long-term exposure to ambient air pollution and mortality have considered multiple pollutants when assessing changes in exposure due to residential mobility during follow-up.ObjectiveWe investigated associations between cause-specific mortality and ambient concentrations of fine particulate matter (? 2.5 ?m; PM2.5), ozone (O3), and nitrogen dioxide (NO2) in a national cohort of about 2.5 million Canadians.MethodsWe assigned estimates of annual concentrations of these pollutants to the residential postal codes of subjects for each year during 16 years of follow-up. Historical tax data allowed us to track subjects' residential postal code annually. We estimated hazard ratios (HRs) for each pollutant separately and adjusted for the other pollutants. We also estimated the product of the three HRs as a measure of the cumulative association with mortality for several causes of death for an increment of the mean minus the 5th percentile of each pollutant: 5.0 ?g/m3 for PM2.5, 9.5 ppb for O3, and 8.1 ppb for NO2.ResultsPM2.5, O3, and NO2 were associated with nonaccidental and cause-specific mortality in single-pollutant models. Exposure to PM2.5 alone was not sufficient to fully characterize the toxicity of the atmospheric mix or to fully explain the risk of mortality associated with exposure to ambient pollution. Assuming additive associations, the estimated HR for nonaccidental mortality corresponding to a change in exposure from the mean to the 5th percentile for all three pollutants together was 1.075 (95% CI: 1.067, 1.084). Accounting for residential mobility had only a limited impact on the association between mortality and PM2.5 and O3, but increased associations with NO2.ConclusionsIn this large, national-level cohort, we found positive associations between several common causes of death and exposure to PM2.5, O3, and NO2.CitationCrouse DL, Peters PA, Hystad P, Brook JR, van Donkelaar A, Martin RV, Villeneuve PJ, Jerrett M, Goldberg MS, Pope CA III, Brauer M, Brook RD, Robichaud A, Menard R, Burnett RT. 2015. Ambient PM2.5, O3, and NO2 exposures and associations with mortality over 16 years of follow-up in the Canadian Census Health and Environment Cohort (CanCHEC). Environ Health Perspect 123:1180-1186; http://dx.doi.org/10.1289/ehp.1409276.

Project description:BackgroundAccumulating evidence suggests that individuals with glucose metabolism disorders are susceptible to mortality associated with fine particles. However, the mechanisms remain largely unknown.ObjectivesWe examined whether particle-associated respiratory inflammation differed between individuals with prediabetes and healthy control participants.MethodsBased on a panel study [A prospective Study COmparing the cardiometabolic and respiratory effects of air Pollution Exposure on healthy and prediabetic individuals (SCOPE)] conducted in Beijing between August 2013 and February 2015, fractional exhaled nitric oxide (FeNO) was measured from 112 participants at two to seven visits to indicate respiratory inflammation. Particulate pollutants-including particulate matter with an aerodynamic diameter of ≤2.5μm (PM2.5), black carbon (BC), ultrafine particles (UFPs), and accumulated-mode particles-were monitored continuously at a single central monitoring site. Linear mixed-effects models were used to estimate associations between ln-FeNO with pollutant concentrations at individual 1-h lags (up to 24 h) and with average concentrations at 8 and 24 h before the clinical visit. We evaluated glucose metabolism disorders as a potential modifier by comparing associations between participants with high vs. low average fasting blood glucose (FBG) and homeostasis model assessment insulin resistance (HOMA-IR) levels.ResultsFeNO was positively associated with all pollutants, with the strongest associations for an interquartile range increase in 1-h lagged exposures (ranging from 21.3% for PM2.5 to 74.7% for BC). Associations differed significantly according to average HOMA-IR values when lagged 6-18 h for PM2.5, 15-19 h for BC, and 6-15 h for UFPs, with positive associations among those with HOMA-IR≥1.6 while associations were closer to the null or inverse among those with HOMA-IR<1.6. Associations between PM2.5 and FeNO were consistently higher among individuals with average FBG≥6.1 mmol/L vs. low FBG, with significant differences for multiple hourly lags.DiscussionGlucose metabolism disorders may aggravate respiratory inflammation following exposure to ambient particulate matter. https://doi.org/10.1289/EHP4906.

Project description:BackgroundMany approaches to quantifying air pollution exposures have been developed. However, the impact of choice of approach on air pollution estimates and health-effects associations remains unclear.ObjectivesOur objective is to compare particulate matter with aerodynamic diameter ≤2.5μm (PM2.5) concentrations and resulting health effects associations using multiple estimation approaches previously used in epidemiologic analyses.MethodsWe assigned annual PM2.5 exposure estimates from 1999 to 2004 derived from 11 different approaches to Women's Health Initiative Memory Study (WHIMS) participant addresses within the contiguous US. Approaches included geostatistical interpolation approaches, land-use regression or spatiotemporal models, satellite-derived approaches, air dispersion and chemical transport models, and hybrid models. We used descriptive statistics and plots to assess relative and absolute agreement among exposure estimates and examined the impact of approach on associations between PM2.5 and death due to natural causes, cardiovascular disease (CVD) mortality, and incident CVD events, adjusting for individual-level covariates and climate-based region.ResultsWith a few exceptions, relative agreement of approach-specific PM2.5 exposure estimates was high for PM2.5 concentrations across the contiguous US. Agreement among approach-specific exposure estimates was stronger near PM2.5 monitors, in certain regions of the country, and in 2004 vs. 1999. Collectively, our results suggest but do not quantify lower agreement at local spatial scales for PM2.5. There was no evidence of large differences in health effects associations with PM2.5 among estimation approaches in analyses adjusted for climate region.ConclusionsDifferent estimation approaches produced similar spatial patterns of PM2.5 concentrations across the contiguous US and in areas with dense monitoring data, and PM2.5-health effects associations were similar among estimation approaches. PM2.5 estimates and PM2.5-health effects associations may differ more in samples drawn from smaller areas or areas without substantial monitoring data, or in analyses with finer adjustment for participant location. Our results can inform decisions about PM2.5 estimation approach in epidemiologic studies, as investigators balance concerns about bias, efficiency, and resource allocation. Future work is needed to understand whether these conclusions also apply in the context of other air pollutants of interest. https://doi.org/10.1289/EHP12995.

Project description:BackgroundStudies on the extent to which long-term exposure to ambient particulate matter (PM) with aerodynamic diameter ≤2.5μm (PM2.5) contributes to adult mortality in India are few, despite over 99% of Indians being exposed to levels that the World Health Organization (WHO) considers unsafe.ObjectiveWe conducted a retrospective cohort study within the Million Death Study (MDS) to provide the first-ever quantification of national mortality from exposure to PM2.5 in India from 1999 to 2014.MethodsWe calculated relative risks (RRs) by linking a total of ten 3-y intervals of satellite-based estimated PM2.5 exposure to deaths 3 to 5 y later in over 7,400 small villages or urban blocks covering a total population of 6.8 million. We applied using a model-based geostatistical model, adjusted for individual age, sex, and year of death; smoking prevalence, rural/urban residency, area-level female illiteracy, languages, and spatial clustering and unit-level variation.ResultsPM2.5 exposure levels increased from 1999 to 2014, particularly in central and eastern India. Among 212,573 deaths at ages 15-69 y, after spatial adjustment, we found a significant RR of 1.09 [95% credible interval (CI): 1.04, 1.14] for stroke deaths per 10-μg/m3 increase in PM2.5 exposure, but no significant excess for deaths from chronic respiratory disease and ischemic heart disease (IHD), all nonaccidental causes, and total mortality (after excluding stroke). Spatial adjustment attenuated the RRs for chronic respiratory disease and IHD but raised those for stroke. The RRs were consistent in various sensitivity analyses with spatial adjustment, including stratifying by levels of solid fuel exposure, by sex, and by age group, addition of climatic variables, and in supplementary case-control analyses using injury deaths as controls.DiscussionDirect epidemiological measurements, despite inherent limitations, yielded associations between mortality and long-term PM2.5 inconsistent with those reported in earlier models used by the WHO to derive estimates of PM2.5 mortality in India. The modest RRs in our study are consistent with near or null mortality effects. They suggest suitable caution in estimating deaths from PM2.5 exposure based on MDS results and even more caution in extrapolating model-based associations of risk derived mostly from high-income countries to India. https://doi.org/10.1289/EHP9538.

Dataset Information

Modeling individual exposures to ambient PM2.5 in the diabetes and the environment panel study (DEPS).

Publications

Modeling individual exposures to ambient PM<sub>2.5</sub> in the diabetes and the environment panel study (DEPS).

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