Childhood asthma and household exposures to nitrogen dioxide and fine particles: a triple-crossover randomized intervention trial.

OBJECTIVE: Triple-crossover randomized controlled intervention trial to test whether reduced exposure to household NO2 or fine particles results in reduced symptoms among children with persistent asthma. METHODS: Children (n = 126) aged 5-11 years with persistent asthma living in homes with gas stoves and levels of NO2 15 ppb or greater recruited in Connecticut and Massachusetts (2015-2019) participated in an intervention involving three air cleaners configured for: (1) NO2 reduction: sham particle filtration and real NO2 scrubbing; (2) particle filtration: HEPA filter and sham NO2 scrubbing; (3) control: sham particle filtration and sham NO2 scrubbing. Air cleaners were randomly assigned for 5-week treatment periods using a three-arm crossover design. Outcome was number of asthma symptom-days during final 14 days of treatment. Treatment effects were assessed using repeated measures, linear mixed models. RESULTS: Measured NO2 was lower (by 4 ppb, p < .0001) for NO2-reducing compared to control or particle-reducing treatments. NO2-reducing treatment did not reduce asthma morbidity compared to control. In analysis controlling for measured NO2, there were 1.8 (95% CI -0.3 to 3.9, p = .10) fewer symptom days out of 14 in the particle-reducing treatment compared to control. CONCLUSIONS: It remains unknown if using an air cleaner alone can achieve levels of NO2 reduction large enough to observe reductions in asthma symptoms. We observed that in small, urban homes with gas stoves, modest reductions in asthma symptoms occurred using air cleaners that remove fine particles. An intervention targeting exposures to both NO2 and fine particles is complicated and further research is warranted. REGISTRATION NUMBER: NCT02258893.

A community-based evaluation of proximity to unconventional oil and gas wells, drinking water contaminants, and health symptoms in Ohio.

Over 4 million Americans live within 1.6 km of an unconventional oil and gas (UO&G) well, potentially placing them in the path of toxic releases. We evaluated relationships between residential proximity to UO&G wells and (1) water contamination and (2) health symptoms in an exploratory study. We analyzed drinking water samples from 66 Ohio households for 13 UO&G-related volatile organic compounds (VOCs) (e.g., benzene, disinfection byproducts [DBPs]), gasoline-range organics (GRO), and diesel-range organics. We interviewed participants about health symptoms and calculated metrics capturing proximity to UO&G wells. Based on multivariable logistic regression, odds of detection of bromoform and dibromochloromethane in surface water decreased significantly as distance to nearest UO&G well increased (odds ratios [OR]: 0.28-0.29 per km). Similarly, distance to nearest well was significantly negatively correlated with concentrations of GRO and toluene in ground water (rSpearman: -0.40 to -0.44) and with concentrations of bromoform and dibromochloromethane in surface water (rSpearman: -0.48 to -0.50). In our study population, those with higher inverse-distance-squared-weighted UO&G well counts within 5 km around the home were more likely to report experiencing general health symptoms (e.g. stress, fatigue) (OR: 1.52, 95%CI: 1.02-2.26). This exploratory study, though limited by small sample size and self-reported health symptoms, suggests that those in closer proximity to multiple UO&G wells may be more likely to experience environmental health impacts. Further, presence of brominated DBPs (linked to UO&G wastewater) raises the question of whether UO&G activities are impacting drinking water sources in the region. The findings from this study support expanded studies to advance knowledge of the potential for water quality and human health impacts; such studies could include a greater number of sampling sites, more detailed chemical analyses to examine source attribution, and objective health assessments.

Spatiotemporal calibration and resolution refinement of output from deterministic models.

Spatiotemporal calibration of output from deterministic models is an increasingly popular tool to more accurately and efficiently estimate the true distribution of spatial and temporal processes. Current calibration techniques have focused on a single source of data on observed measurements of the process of interest that are both temporally and spatially dense. Additionally, these methods often calibrate deterministic models available in grid-cell format with pixel sizes small enough that the centroid of the pixel closely approximates the measurement for other points within the pixel. We develop a modeling strategy that allows us to simultaneously incorporate information from two sources of data on observed measurements of the process (that differ in their spatial and temporal resolutions) to calibrate estimates from a deterministic model available on a regular grid. This method not only improves estimates of the pollutant at the grid centroids but also refines the spatial resolution of the grid data. The modeling strategy is illustrated by calibrating and spatially refining daily estimates of ambient nitrogen dioxide concentration over Connecticut for 1994 from the Community Multiscale Air Quality model (temporally dense grid-cell estimates on a large pixel size) using observations from an epidemiologic study (spatially dense and temporally sparse) and Environmental Protection Agency monitoring stations (temporally dense and spatially sparse). Copyright © 2016 John Wiley & Sons, Ltd.

Patterns of birth cohort-specific smoking histories, 1965-2009.

BACKGROUND: Characterizing the smoking patterns for different birth cohorts is essential for evaluating the impact of tobacco control interventions and predicting smoking-related mortality, but the process of estimating birth cohort smoking histories has received limited attention. PURPOSE: Smoking history summaries were estimated beginning with the 1890 birth cohort in order to provide fundamental parameters that can be used in studies of cigarette smoking intervention strategies. METHODS: U.S. National Health Interview Surveys conducted from 1965 to 2009 were used to obtain cross-sectional information on current smoking behavior. Surveys that provided additional detail on history for smokers including age at initiation and cessation and smoking intensity were used to construct smoking histories for participants up to the date of survey. After incorporating survival differences by smoking status, age-period-cohort models with constrained natural splines were used to estimate the prevalence of current, former, and never smokers in cohorts beginning in 1890. This approach was then used to obtain yearly estimates of initiation, cessation, and smoking intensity for the age-specific distribution for each birth cohort. These rates were projected forward through 2050 based on recent trends. RESULTS: This summary of smoking history shows clear trends by gender, cohort, and age over time. If current patterns persist, a slow decline in smoking prevalence is projected from 2010 through 2040. CONCLUSIONS: A novel method of generating smoking histories has been applied to develop smoking histories that can be used in micro-simulation models, and has been incorporated in the National Cancer Institute's Smoking History Generator. These aggregate estimates developed by age, gender, and cohort will provide a complete source of smoking data over time.

Modeling effects of traffic and landscape characteristics on ambient nitrogen dioxide levels in Connecticut.

An integrated exposure model was developed that estimates nitrogen dioxide (NO(2)) concentration at residences using geographic information systems (GIS) and variables derived within residential buffers representing traffic volume and landscape characteristics including land use, population density and elevation. Multiple measurements of NO(2) taken outside of 985 residences in Connecticut were used to develop the model. A second set of 120 outdoor NO(2) measurements as well as cross-validation were used to validate the model. The model suggests that approximately 67% of the variation in NO(2) levels can be explained by: traffic and land use primarily within 2 km of a residence; population density; elevation; and time of year. Potential benefits of this model for health effects research include improved spatial estimations of traffic-related pollutant exposure and reduced need for extensive pollutant measurements. The model, which could be calibrated and applied in areas other than Connecticut, has importance as a tool for exposure estimation in epidemiological studies of traffic-related air pollution.

Integrated exposure modeling: a model using GIS and GLM.

Traffic exhaust is a source of air contaminants that have adverse health effects. Quantification of traffic as an exposure variable is complicated by aerosol dispersion related to variation in layout of roads, traffic density, meteorology, and topography. A statistical model is presented that uses Geographic Information Systems (GIS) technology to incorporate variables into a generalized linear model that estimates distribution of traffic-related pollution. Exposure from a source is expressed as an integral of a function proportional to average daily traffic and a nonparametric dispersion function, which takes the form of a step, polynomial, or spline model. The method may be applied using standard regression techniques for fitting generalized linear models. Modifiers of pollutant dispersion such as wind direction, meteorology, and landscape features can also be included. Two examples are given to illustrate the method. The first employs data from a study in which NO(2) (a known pollutant from automobile exhaust) was monitored outside of 138 Connecticut homes, providing a model for estimating NO(2) exposure. In the second example, estimated levels of nitrogen dioxide (NO(2)) from the model, as well as a separate spatial model, were used to analyze traffic-related health effects in a study of 761 infants.