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

New York City High-Rises on Rock: Uncovering the Unknown Leads to Variable Foundation Solutions

Construction of high-rise towers in New York City continues to provide exciting challenges for design and construction teams. Sites are becoming increasingly more difficult to build on as “desirable” locations have long since been developed and developers are constructing on sites that were previously over looked. This paper describes two projects that provided unique challenges to the engineers and contractors. The first site is the New York Times Headquarters Tower. This site appeared to be a fairly straightforward foundation design, but became complicated as the subsurface conditions were uncovered. The second case history is the new Bank of America Tower which presented significant design challenges from the outset as it entailed a three basement excavation adjacent to subways and a historic theater façade that required protection. In both cases, close collaboration between the owner, design engineers, construction manager and eventual foundation contractors was required to complete the projects in a timely manner and without adversely affecting adjacent subways, pedestrian traffic, or adjacent historical structures

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

Smoking Habits Among Patients Diagnosed with Oral Lichen Planus

<p>Abstract</p> <p>Introduction</p> <p>Oral lichen planus (OLP) is one of the most common dermatologic diseases that manifests in the oral cavity. The purpose of this study was to evaluate the association between smoking habits and the clinical subtypes of OLP.</p> <p>Methods</p> <p>Oral findings and smoking data from 187 charts of OLP patients from an oral medicine clinic was reviewed and compared to data from 76 matched control patients.</p> <p>Results and Discussion</p> <p>Ninety-three patients were diagnosed with reticular OLP, 55 with atrophic and 39 with erosive forms of the disease. Symptomatic OLP occurred in 63.6% of patients. Fewer cases of reticular OLP were symptomatic than erosive OLP (p < 0.001). Significantly fewer OLP patients smoked than the control group (16% versus 25%) (p = 0.04). More patients with reticular OLP smoked than those with atrophic and erosive OLP (p = 0.002). It is hypothesized that the heat and irritation of smoking may aggravate symptomatic OLP lesions, and the risk of malignant transformation associated with tobacco use may play a role in patients stopping tobacco use. Because there were fewer smokers in patients with OLP, and because OLP carries an increased malignant risk, transformation of OLP may be due to a different etiology and of a different pathogenesis than squamous cell carcinoma not arising from lichen planus. Close follow-up of patients with OLP is indicated.</p

Issues Related to Combining Multiple Speciated PM2.5 Data Sources in Spatio-Temporal Exposure Models for Epidemiology: The NPACT Case Study

Background: Regulatory monitoring data have been the most common exposure data resource in studies of the association between long-term PM2.5 components and health. However, data collected for regulatory purposes may not be compatible with epidemiological study. Objectives: We aimed to explore three important features of the PM2.5 component monitoring data obtained from multiple sources to combine all available data for developing spatio-temporal prediction models in the National Particle Component and Toxicity (NPACT) study. Methods: The NPACT monitoring data were collected in an extensive monitoring campaign targeting cohort participants. The regulatory monitoring data were obtained from the Chemical Speciation Network (CSN) and the Interagency Monitoring of Protected Visual Environments (IMPROVE). We performed exploratory analyses to examine three features that could affect our approach to combining data: comprehensiveness of spatial coverage, comparability of analysis methods, and consistency in sampling protocols. In addition, we considered the viability of developing a spatio-temporal prediction model given: 1) all available data; 2) NPACT data only; and 3) NPACT data with temporal trends estimated from other pollutants. Results: The number of CSN/IMPROVE monitors was limited in all study areas. The different laboratory analysis methods and the protocol differences for sampling resulted in incompatible measurements between networks. Given these features, we determined that it was preferable to develop our spatio-temporal model using only the NPACT data and under simplifying assumptions. Conclusions: Investigators conducting epidemiological studies of long-term PM2.5 components need to be mindful of the features of the monitoring data and incorporate this understanding into exposure model development

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

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

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