Reduced-rank spatio-temporal modeling of air pollution concentrations in the Multi-Ethnic Study of Atherosclerosis and Air Pollution

There is growing evidence in the epidemiologic literature of the relationship between air pollution and adverse health outcomes. Prediction of individual air pollution exposure in the Environmental Protection Agency (EPA) funded Multi-Ethnic Study of Atheroscelerosis and Air Pollution (MESA Air) study relies on a flexible spatio-temporal prediction model that integrates land-use regression with kriging to account for spatial dependence in pollutant concentrations. Temporal variability is captured using temporal trends estimated via modified singular value decomposition and temporally varying spatial residuals. This model utilizes monitoring data from existing regulatory networks and supplementary MESA Air monitoring data to predict concentrations for individual cohort members. In general, spatio-temporal models are limited in their efficacy for large data sets due to computational intractability. We develop reduced-rank versions of the MESA Air spatio-temporal model. To do so, we apply low-rank kriging to account for spatial variation in the mean process and discuss the limitations of this approach. As an alternative, we represent spatial variation using thin plate regression splines. We compare the performance of the outlined models using EPA and MESA Air monitoring data for predicting concentrations of oxides of nitrogen (NO$_x$)-a pollutant of primary interest in MESA Air-in the Los Angeles metropolitan area via cross-validated $R^2$. Our findings suggest that use of reduced-rank models can improve computational efficiency in certain cases. Low-rank kriging and thin plate regression splines were competitive across the formulations considered, although TPRS appeared to be more robust in some settings.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS786 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Risk Factors for Long-Term Coronary Artery Calcium Progression in the Multi-Ethnic Study of Atherosclerosis.

BackgroundCoronary artery calcium (CAC) detected by noncontrast cardiac computed tomography scanning is a measure of coronary atherosclerosis burden. Increasing CAC levels have been strongly associated with increased coronary events. Prior studies of cardiovascular disease risk factors and CAC progression have been limited by short follow-up or restricted to patients with advanced disease.Methods and resultsWe examined cardiovascular disease risk factors and CAC progression in a prospective multiethnic cohort study. CAC was measured 1 to 4 times (mean 2.5 scans) over 10 years in 6810 adults without preexisting cardiovascular disease. Mean CAC progression was 23.9 Agatston units/year. An innovative application of mixed-effects models investigated associations between cardiovascular disease risk factors and CAC progression. This approach adjusted for time-varying factors, was flexible with respect to follow-up time and number of observations per participant, and allowed simultaneous control of factors associated with both baseline CAC and CAC progression. Models included age, sex, study site, scanner type, and race/ethnicity. Associations were observed between CAC progression and age (14.2 Agatston units/year per 10 years [95% CI 13.0 to 15.5]), male sex (17.8 Agatston units/year [95% CI 15.3 to 20.3]), hypertension (13.8 Agatston units/year [95% CI 11.2 to 16.5]), diabetes (31.3 Agatston units/year [95% CI 27.4 to 35.3]), and other factors.ConclusionsCAC progression analyzed over 10 years of follow-up, with a novel analytical approach, demonstrated strong relationships with risk factors for incident cardiovascular events. Longitudinal CAC progression analyzed in this framework can be used to evaluate novel cardiovascular risk factors

Pragmatic Estimation of a Spatio-Temporal Air Quality Model With Irregular Monitoring Data

Statistical analyses of the health effects of air pollution have increasingly used GIS-based covariates for prediction of ambient air quality in “land-use” regression models. More recently these regression models have accounted for spatial correlation structure in combining monitoring data with land-use covariates. The current paper builds on these concepts to address spatio-temporal prediction of ambient concentrations of particulate matter with aerodynamic diameter less than 2.5 μm (PM2.5) on the basis of a model representing spatially varying seasonal trends and spatial correlation structures. Our hierarchical methodology provides a pragmatic approach that fully exploits regulatory and other supplemental monitoring data which jointly define a complex spatio-temporal monitoring design. We explain the elements of the computational approach, including estimation of smoothed empirical orthogonal functions (SEOFs) as basis functions for temporal trend, spatial (“land use”) regression by Partial Least Squares (PLS), modeling of spatio-temporal correlation structure, and generalized universal kriging prediction of ambient exposure for subjects in the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) project. Analyses are demonstrated in detail for the South California study area of the MESA Air project using AQS monitoring data from 2000 to 2006 and supplemental MESA Air monitoring data beginning in 2005. Results of application of the modeling and estimation methodology are presented also for five other MESA Air metropolitan study areas across the country with comments on current and future research developments

Predicting Intra-Urban Variation in Air Pollution Concentrations with Complex Spatio-Temporal Interactions

We describe a methodology for assigning individual estimates of long-term average air pollution concentrations that accounts for a complex spatio-temporal correlation structure and can accommodate unbalanced observations. This methodology has been developed as part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air), a prospective cohort study funded by the U.S. EPA to investigate the relationship between chronic exposure to air pollution and cardiovascular disease. Our hierarchical model decomposes the space-time field into a “mean” that includes dependence on covariates and spatially varying seasonal and long-term trends and a “residual” that accounts for spatially correlated deviations from the mean model. The model accommodates complex spatio-temporal patterns by characterizing the temporal trend at each location as a linear combination of empirically derived temporal basis functions, and embedding the spatial fields of coefficients for the basis functions in separate linear regression models with spatially correlated residuals (universal kriging). This approach allows us to implement a scalable single-stage estimation procedure that easily accommodates a significant number of missing observations at some monitoring locations. We apply the model to predict long-term average concentrations of oxides of nitrogen (NOx) from 2005-2007 in the Los Angeles area, based on data from 18 EPA Air Quality System regulatory monitors. The cross-validated R2 is 0.67. The MESA Air study is also collecting additional concentration data as part of a supplementary monitoring campaign. We describe the sampling plan and demonstrate in a simulation study that the additional data will contribute to improved predictions of long-term average concentrations

Biomass Fuel Use and Cardiac Function in Nepali Women.

BackgroundExposure to household air pollution (HAP) from cooking with biomass fuel affects billions of people. We hypothesized that HAP from woodsmoke, compared to other household fuels, was associated with adverse cardiovascular outcomes, of which there have been few studies.MethodsA cross-sectional study was completed in 299 females aged 40-70 years in Kaski District, Nepal, during 2017-18. All participants underwent a standard 12-lead ECG, ankle and brachial systolic blood pressure measurement, and 2D color and Doppler echocardiography. Current stove type was confirmed by inspection. Blood pressure, height, and weight were measured using a standardized protocol. Hypertension was defined as ≥140/90 mmHg or prior diagnosis. Hemoglobin A1c (HbA1c) was obtained, with diabetes mellitus defined as a prior diagnosis or HbA1C ≥ 6.5%. We used adjusted linear and logistic multivariable regressions to examine the relationship of stove type with cardiac structure and function.ResultsThe majority of women primarily used liquified petroleum gas (LPG) stoves (65%), while 12% used biogas, and 23% used wood-burning cook-stoves. Prevalence of major cardiovascular risk factors was 35% with hypertension, 19% with diabetes mellitus, and 15% current smokers. After adjustment, compared to LPG, wood stove use was associated with increased indexed left atrial volume (β = 3.15, 95% CI 1.22 to 5.09) and increased indexed left ventricular end diastolic volume (β = 7.97, 95% CI 3.11 to 12.83). There was no association between stove type and systemic hypertension, left ventricular mass, systolic dysfunction, diastolic dysfunction, pulmonary hypertension, abnormal ankle-brachial index, or clinically significant ECG abnormalities.ConclusionBiomass fuel use was associated with increased indexed left atrial volume and increased indexed left ventricular diastolic volume in Nepali women, suggesting subclinical adverse cardiac remodeling from HAP in this cross-sectional study. We did not find evidence of an association with hypertension or typical cardiac sequelae of hypertension. Future studies to confirm these results are needed

Historical Prediction Modeling Approach for Estimating Long-Term Concentrations of PM in Cohort Studies Before the 1999 Implementation of Widespread Monitoring

Introduction: Recent cohort studies use exposure prediction models to estimate the association between long-term residential concentrations of PM2.5 and health. Because these prediction models rely on PM2.5 monitoring data, predictions for times before extensive spatial monitoring present a challenge to understanding long-term exposure effects. The Environmental Protection Agency (EPA) Federal Reference Method (FRM) network for PM2.5 was established in 1999. We evaluated a novel statistical approach to produce high quality exposure predictions from 1980-2010 for epidemiological applications. Methods: We developed spatio-temporal prediction models using geographic predictors and annual average PM2.5 data from 1999 through 2010 from the FRM and the Interagency Monitoring of Protected Visual Environments (IMPROVE) networks. The model consists of a spatially-varying long-term mean, a spatially-varying temporal trend, and spatially-varying and temporally-independent spatio-temporal residuals structured using a universal kriging framework. Temporal trends in annual averages of PM2.5 before 1999 were estimated by using a) extrapolation based on PM2.5 data for 1999-2010 in FRM/IMPROVE, b) PM2.5 sulfate data for 1987-2010 in the Clean Air Status and Trends Network, and c) visibility data for 1980-2010 across the Weather-Bureau-Army-Navy network. We validated the resulting models using PM2.5 data collected before 1999 from IMPROVE, California Air Resources Board dichotomous sampler monitoring (CARB dichot), the Southern California Children’s Health Study (CHS), and the Inhalable Particulate Network (IPN). Results: The PM2.5 prediction model performed well across three trend estimation approaches when validated using IMPROVE and CHS data (R2= 0.84–0.91). Model performance using CARB dichot and IPN data was worse than those in IMPROVE most likely due to inconsistent sampling methods and smaller numbers of monitoring sites. Discussion: Our prediction modeling approach will allow health effects estimation associated with long-term exposures to PM2.5 over extended time periods of up to 30 years

Design of the Subpopulations and Intermediate Outcome Measures in COPD (SPIROMICS) AIR Study.

IntroductionPopulation-based epidemiological evidence suggests that exposure to ambient air pollutants increases hospitalisations and mortality from chronic obstructive pulmonary disease (COPD), but less is known about the impact of exposure to air pollutants on patient-reported outcomes, morbidity and progression of COPD.Methods and analysisThe Subpopulations and Intermediate Outcome Measures in COPD (SPIROMICS) Air Pollution Study (SPIROMICS AIR) was initiated in 2013 to investigate the relation between individual-level estimates of short-term and long-term air pollution exposures, day-to-day symptom variability and disease progression in individuals with COPD. SPIROMICS AIR builds on a multicentre study of smokers with COPD, supplementing it with state-of-the-art air pollution exposure assessments of fine particulate matter, oxides of nitrogen, ozone, sulfur dioxide and black carbon. In the parent study, approximately 3000 smokers with and without airflow obstruction are being followed for up to 3 years for the identification of intermediate biomarkers which predict disease progression. Subcohorts undergo daily symptom monitoring using comprehensive daily diaries. The air monitoring and modelling methods employed in SPIROMICS AIR will provide estimates of individual exposure that incorporate residence-specific infiltration characteristics and participant-specific time-activity patterns. The overarching study aim is to understand the health effects of short-term and long-term exposures to air pollution on COPD morbidity, including exacerbation risk, patient-reported outcomes and disease progression.Ethics and disseminationThe institutional review boards of all the participating institutions approved the study protocols. The results of the trial will be presented at national and international meetings and published in peer-reviewed journals

To what extent can headteachers be held to account in the practice of social justice leadership?

Internationally, leadership for social justice is gaining prominence as a global travelling theme. This article draws from the Scottish contribution to the International School Leadership Development Network (ISLDN) social justice strand and presents a case study of a relatively small education system similar in size to that of New Zealand, to explore one system's policy expectations and the practice realities of headteachers (principals) seeking to address issues around social justice. Scottish policy rhetoric places responsibility with headteachers to ensure socially just practices within their schools. However, those headteachers are working in schools located within unjust local, national and international contexts. The article explores briefly the emerging theoretical analyses of social justice and leadership. It then identifies the policy expectations, including those within the revised professional standards for headteachers in Scotland. The main focus is on the headteachers' perspectives of factors that help and hinder their practice of leadership for social justice. Macro systems-level data is used to contextualize equity and outcomes issues that headteachers are working to address. In the analysis of the dislocation between policy and reality, the article asks, 'to what extent can headteachers be held to account in the practice of social justice leadership?

The role of sand lances (Ammodytes sp.) in the Northwest Atlantic ecosystem: a synthesis of current knowledge with implications for conservation and management

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Staudinger, M. D., Goyert, H., Suca, J. J., Coleman, K., Welch, L., Llopiz, J. K., Wiley, D., Altman, I., Applegate, A., Auster, P., Baumann, H., Beaty, J., Boelke, D., Kaufman, L., Loring, P., Moxley, J., Paton, S., Powers, K., Richardson, D., Robbins, J., Runge, J., Smith, B., Spiegel, C., & Steinmetz, H. The role of sand lances (Ammodytes sp.) in the Northwest Atlantic ecosystem: a synthesis of current knowledge with implications for conservation and management. Fish and Fisheries, 00, (2020): 1-34, doi:10.1111/faf.12445.The American sand lance (Ammodytes americanus, Ammodytidae) and the Northern sand lance (A. dubius, Ammodytidae) are small forage fishes that play an important functional role in the Northwest Atlantic Ocean (NWA). The NWA is a highly dynamic ecosystem currently facing increased risks from climate change, fishing and energy development. We need a better understanding of the biology, population dynamics and ecosystem role of Ammodytes to inform relevant management, climate adaptation and conservation efforts. To meet this need, we synthesized available data on the (a) life history, behaviour and distribution; (b) trophic ecology; (c) threats and vulnerabilities; and (d) ecosystem services role of Ammodytes in the NWA. Overall, 72 regional predators including 45 species of fishes, two squids, 16 seabirds and nine marine mammals were found to consume Ammodytes. Priority research needs identified during this effort include basic information on the patterns and drivers in abundance and distribution of Ammodytes, improved assessments of reproductive biology schedules and investigations of regional sensitivity and resilience to climate change, fishing and habitat disturbance. Food web studies are also needed to evaluate trophic linkages and to assess the consequences of inconsistent zooplankton prey and predator fields on energy flow within the NWA ecosystem. Synthesis results represent the first comprehensive assessment of Ammodytes in the NWA and are intended to inform new research and support regional ecosystem‐based management approaches.This manuscript is the result of follow‐up work stemming from a working group formed at a two‐day multidisciplinary and international workshop held at the Parker River National Wildlife Refuge, Massachusetts in May 2017, which convened 55 experts scientists, natural resource managers and conservation practitioners from 15 state, federal, academic and non‐governmental organizations with interest and expertise in Ammodytes ecology. Support for this effort was provided by USFWS, NOAA Stellwagen Bank National Marine Sanctuary, U.S. Department of the Interior, U.S. Geological Survey, Northeast Climate Adaptation Science Center (Award # G16AC00237), an NSF Graduate Research Fellowship to J.J.S., a CINAR Fellow Award to J.K.L. under Cooperative Agreement NA14OAR4320158, NSF award OCE‐1325451 to J.K.L., NSF award OCE‐1459087 to J.A.R, a Regional Sea Grant award to H.B. (RNE16‐CTHCE‐l), a National Marine Sanctuary Foundation award to P.J.A. (18‐08‐B‐196) and grants from the Mudge Foundation. The contents of this paper are the responsibility of the authors and do not necessarily represent the views of the National Oceanographic and Atmospheric Administration, U.S. Fish and Wildlife Service, New England Fishery Management Council and Mid‐Atlantic Fishery Management Council. This manuscript is submitted for publication with the understanding that the United States Government is authorized to reproduce and distribute reprints for Governmental purposes. Any use of trade, firm or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government