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Creating individual level air pollution exposures in an anonymised data safe haven: a platform for evaluating impact on educational attainment.

ABSTRACT: Introduction:There is a lack of evidence on the adverse effects of air pollution on cognition for people with air quality-related health conditions. We propose that educational attainment, as a proxy for cognition, may increase with improved air quality. This study will explore whether asthma and seasonal allergic rhinitis, when exacerbated by acute exposure to air pollution, is associated with educational attainment. Objective:To describe the preparation of individual and household-level linked environmental and health data for analysis within an anonymised safe haven. Also to introduce our statistical analysis plan for our study: COgnition, Respiratory Tract illness and Effects of eXposure (CORTEX). Methods:We imported daily air pollution and aeroallergen data, and individual level education data into the SAIL databank, an anonymised safe haven for person-based records. We linked individual-level education, socioeconomic and health data to air quality data for home and school locations, creating tailored exposures for individuals across a city. We developed daily exposure data for all pupils in repeated cross sectional exam cohorts (2009-2015). Conclusion:We have used the SAIL databank, an innovative, data safe haven to create individual-level exposures to air pollution and pollen for multiple daily home and school locations. The analysis platform will allow us to evaluate retrospectively the impact of air quality on attainment for multiple cross-sectional cohorts of pupils. Our methods will allow us to distinguish between the pollution impacts on educational attainment for pupils with and without respiratory health conditions. The results from this study will further our understanding of the effects of air quality and respiratory-related health conditions on cognition. Highlights:This city-wide study includes longitudinal routinely-recorded educational attainment data for all pupils taking exams over seven years;High spatial resolution air pollution data were linked within a privacy protected databank to obtain individual exposure at multiple daily locations;This study will use health data linked at the individual level to explore associations between air pollution, related morbidity, and educational attainment.

SUBMITTER: Mizen A

PROVIDER: S-EPMC7299475 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Creating individual level air pollution exposures in an anonymised data safe haven: a platform for evaluating impact on educational attainment.

Mizen Amy A Lyons Jane J Doherty Ruth R Berridge Damon D Wilkinson Paul P Milojevic Ai A Carruthers David D Akbari Ashley A Lake Iain I Davies Gwyneth A GA Sallakh Mohammad Al MA Mavrogianni Anna A Dearden Lorraine L Johnson Rhodri R Rodgers Sarah Elizabeth SE

International journal of population data science 20180821 1

<h4>Introduction</h4>There is a lack of evidence on the adverse effects of air pollution on cognition for people with air quality-related health conditions. We propose that educational attainment, as a proxy for cognition, may increase with improved air quality. This study will explore whether asthma and seasonal allergic rhinitis, when exacerbated by acute exposure to air pollution, is associated with educational attainment.<h4>Objective</h4>To describe the preparation of individual and househo ...[more]

PMID: 32934998

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Project description:Few epidemiologic studies have evaluated the effects of air pollution on the risk of Parkinson disease (PD).We investigated the associations of long-term residential concentrations of ambient particulate matter (PM) < 10 ?m in diameter (PM10) and < 2.5 ?m in diameter (PM2.5) and nitrogen dioxide (NO2) in relation to PD risk.Our nested case-control analysis included 1,556 self-reported physician-diagnosed PD cases identified between 1995 and 2006 and 3,313 controls frequency-matched on age, sex, and race. We geocoded home addresses reported in 1995-1996 and estimated the average ambient concentrations of PM10, PM2.5, and NO2 using a national fine-scale geostatistical model incorporating roadway information and other geographic covariates. Air pollutant exposures were analyzed as both quintiles and continuous variables, adjusting for matching variables and potential confounders.We observed no statistically significant overall association between PM or NO2 exposures and PD risk. However, in preplanned subgroup analyses, a higher risk of PD was associated with higher exposure to PM10 (ORQ5 vs. Q1 = 1.65; 95% CI: 1.11, 2.45; p-trend = 0.02) among women, and with higher exposure to PM2.5 (ORQ5 vs. Q1 = 1.29; 95% CI: 0.94, 1.76; p-trend = 0.04) among never smokers. In post hoc analyses among female never smokers, both PM2.5 (ORQ5 vs. Q1 = 1.79; 95% CI: 1.01, 3.17; p-trend = 0.05) and PM10 (ORQ5 vs. Q1 = 2.34; 95% CI: 1.29, 4.26; p-trend = 0.01) showed positive associations with PD risk. Analyses based on continuous exposure variables generally showed similar but nonsignificant associations.Overall, we found limited evidence for an association between exposures to ambient PM10, PM2.5, or NO2 and PD risk. The suggestive evidence that exposures to PM2.5 and PM10 may increase PD risk among female never smokers warrants further investigation. Citation: Liu R, Young MT, Chen JC, Kaufman JD, Chen H. 2016. Ambient air pollution exposures and risk of Parkinson disease. Environ Health Perspect 124:1759-1765;?http://dx.doi.org/10.1289/EHP135.

Project description:BackgroundPopulation exposure assessment methods that capture local-scale pollutant variability are needed for large-scale epidemiological studies and surveillance, policy, and regulatory purposes. Currently, such exposure methods are limited.MethodsWe created 2006 national pollutant models for fine particulate matter [PM with aerodynamic diameter ? 2.5 ?m (PM2.5)], nitrogen dioxide (NO2), benzene, ethylbenzene, and 1,3-butadiene from routinely collected fixed-site monitoring data in Canada. In multiple regression models, we incorporated satellite estimates and geographic predictor variables to capture background and regional pollutant variation and used deterministic gradients to capture local-scale variation. The national NO2 and benzene models are evaluated with independent measurements from previous land use regression models that were conducted in seven Canadian cities. National models are applied to census block-face points, each of which represents the location of approximately 89 individuals, to produce estimates of population exposure.ResultsThe national NO2 model explained 73% of the variability in fixed-site monitor concentrations, PM2.5 46%, benzene 62%, ethylbenzene 67%, and 1,3-butadiene 68%. The NO2 model predicted, on average, 43% of the within-city variability in the independent NO2 data compared with 18% when using inverse distance weighting of fixed-site monitoring data. Benzene models performed poorly in predicting within-city benzene variability. Based on our national models, we estimated Canadian ambient annual average population-weighted exposures (in micrograms per cubic meter) of 8.39 for PM2.5, 23.37 for NO2, 1.04 for benzene, 0.63 for ethylbenzene, and 0.09 for 1,3-butadiene.ConclusionsThe national pollutant models created here improve exposure assessment compared with traditional monitor-based approaches by capturing both regional and local-scale pollution variation. Applying national models to routinely collected population location data can extend land use modeling techniques to population exposure assessment and to informing surveillance, policy, and regulation.

Project description:BACKGROUND:Residential proximity to major roadways, and prenatal exposures to particulate matter <2.5??m (PM2.5) and ozone (O3) are linked to poor fetal outcomes but their relationship with childhood development is unclear. OBJECTIVES:We investigated whether proximity to major roadways, or prenatal and early-life exposures to PM2.5 and O3 increase the risk of early developmental delays. STUDY DESIGN:Prospective cohort. SETTINGS:New York State excluding New York City. PARTICIPANTS:4089 singletons and 1016 twins born between 2008 and 2010. EXPOSURES:Proximity to major roadway was calculated using road network data from the NY Department of Transportation. Concentrations of PM2.5 and O3 estimated by the Environmental Protection Agency Downscaler models were spatiotemporally linked to each child's prenatal and early-life addresses incorporating residential history, and locations of maternal work and day-care. OUTCOMES:Parents reported their children's development at ages 8, 12, 18, 24, 30 and 36 months in five domains using the Ages and Stages Questionnaire. Generalized mixed models estimated the relative risk (RR) and 95% CI for failing any developmental domain per 10 units increase in PM2.5 and O3, and for those living <1000?m away from a major roadway compared to those living further. Models adjusted for potential confounders. RESULTS:Compared to those >1000?m away from a major roadway, those resided 50-100?m [RR: 2.12 (1.00-4.52)] and 100-500?m [RR: 2.07 (1.02-4.22)] away had twice the risk of failing the communication domain. Prenatal exposures to both PM2.5 and ozone during various pregnancy windows had weak but significant associations with failing any developmental domain with effects ranging from 1.6% to 2.7% for a 10??g/m3 increase in PM2.5 and 0.7%-1.7% for a 10?ppb increase in ozone. Average daily postnatal ozone exposure was positively associated with failing the overall screening by 8 months [3.3% (1.1%-5.5%)], 12 months [17.7% (10.4%-25.5%)], and 30 months [7.6%, (1.3%-14.3%)]. Findings were mixed for postnatal PM2.5 exposures. CONCLUSIONS:In this prospective cohort study, proximity to major roadway and prenatal/early-life exposures to PM2.5 and O3 were associated with developmental delays. While awaiting larger studies with personal air pollution assessment, efforts to minimize air pollution exposures during critical developmental windows may be warranted.

Project description:BackgroundMendelian randomization has been previously used to estimate the effects of binary and ordinal categorical exposures-e.g. Type 2 diabetes or educational attainment defined by qualification-on outcomes. Binary and categorical phenotypes can be modelled in terms of liability-an underlying latent continuous variable with liability thresholds separating individuals into categories. Genetic variants influence an individual's categorical exposure via their effects on liability, thus Mendelian-randomization analyses with categorical exposures will capture effects of liability that act independently of exposure category.Methods and resultsWe discuss how groups in which the categorical exposure is invariant can be used to detect liability effects acting independently of exposure category. For example, associations between an adult educational-attainment polygenic score (PGS) and body mass index measured before the minimum school leaving age (e.g. age 10 years), cannot indicate the effects of years in full-time education on this outcome. Using UK Biobank data, we show that a higher educational-attainment PGS is strongly associated with lower smoking initiation and higher odds of glasses use at age 15 years. These associations were replicated in sibling models. An orthogonal approach using the raising of the school leaving age (ROSLA) policy change found that individuals who chose to remain in education to age 16 years before the reform likely had higher liability to educational attainment than those who were compelled to remain in education to age 16 years after the reform, and had higher income, lower pack-years of smoking, higher odds of glasses use and lower deprivation in adulthood. These results suggest that liability to educational attainment is associated with health and social outcomes independently of years in full-time education.ConclusionsMendelian-randomization studies with non-continuous exposures should be interpreted in terms of liability, which may affect the outcome via changes in exposure category and/or independently.

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Creating individual level air pollution exposures in an anonymised data safe haven: a platform for evaluating impact on educational attainment.

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Creating individual level air pollution exposures in an anonymised data safe haven: a platform for evaluating impact on educational attainment.

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