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BACKGROUND: Hydraulically fractured wells produce 2-14 million liters of wastewater, which may contain toxic and radioactive compounds. The wastewater is predominantly disposed of using Class II injection wells. OBJECTIVE: Our objective was to evaluate the relationship between sociodemographic characteristics and injection well locations in Ohio. METHODS: Using state and federal data sources, we classified Ohio census block groups by presence of injection wells, number of hydraulically fractured wells, sociodemographic factors (median household income, % white, population density, % [greater than or equal to]high school education, median age, voter turnout), and geographic information (land area, water area, situated over shale). We modeled the odds of having at least one injection well within a block group with respect to all covariates using three multivariable models incorporating different spatial components to account for similarities in neighboring block groups. RESULTS: In bivariate analyses, block groups with injection wells (n = 156) compared with those without (n = 9,049) had lower population density (71 vs. 2,210people/[mi.sup.2] or 27 vs. 854people/[km.sup.2]), larger median area (43.5 vs. 1.35[km.sup.2]), higher median age (42.8 vs. 40.2 y), and higher % white (98.1% vs. 92.1%). After adjustment using a spatial logistic regression model, the odds of a block group containing an injection well were 16% lower per $10,000 increase in median income [odds ratio (OR) = 0.837; 95% credible interval (CI): 0.719, 0.961] and 97% lower per 1,000 people/[mi.sup.2] (or per 386people/[km.sup.2]) increase (OR = 0.030; 95% CI = 0.008, 0.072). Block groups on shale and those containing fewer hydraulically fractured wells were more likely to include an injection well. Percentage white, median age, % [greater than or equal to] high school education, and % voter turnout were not significant predictors of injection well presence. CONCLUSION: In Ohio, injection wells were inversely associated with block groups' median incomes after adjusting for other sociodemographic and geographic variables. Research is needed to determine whether residents in census blocks with injection wells face increased risk of chemical exposures or adverse health outcomes. https://doi.org/10.1289/EHP2663, Introduction The production of natural gas has been increasing in the United States due to advances in drilling technologies, fluctuating oil prices, and a desire to replace coal with a [...]

Understanding the impact that environmental exposure during different stages of pregnancy has on the risk of adverse birth outcomes is vital for protection of the fetus and for the development of mechanistic explanations of exposure-disease relationships. As a result, statistical models to estimate critical windows of susceptibility have been developed for several different reproductive outcomes and pollutants. However, these current methods fail to adequately address the primary objective of this line of research; how to statistically identify a critical window of susceptibility. In this article, we introduce critical window variable selection (CWVS), a hierarchical Bayesian framework that directly addresses this question while simultaneously providing improved estimation of the risk parameters. Through simulation, we show that CWVS outperforms existing competing techniques in the setting of highly temporally correlated exposures in terms of (i) correctly identifying critical windows and (ii) accurately estimating risk parameters. We apply all competing methods to a case/control analysis of pregnant women in North Carolina, 2005-2008, with respect to the development of very preterm birth and exposure to ambient ozone and particulate matter $<$ 2.5 $\mu$m in aerodynamic diameter, and identify/estimate the critical windows of susceptibility. The newly developed method is implemented in the R package CWVS. [ABSTRACT FROM AUTHOR]

We measured severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentrations in primary sewage sludge in the New Haven, Connecticut, USA, metropolitan area during the Coronavirus Disease 2019 (COVID-19) outbreak in Spring 2020. SARS-CoV-2 RNA was detected throughout the more than 10-week study and, when adjusted for time lags, tracked the rise and fall of cases seen in SARS-CoV-2 clinical test results and local COVID-19 hospital admissions. Relative to these indicators, SARS-CoV-2 RNA concentrations in sludge were 0–2 d ahead of SARS-CoV-2 positive test results by date of specimen collection, 0–2 d ahead of the percentage of positive tests by date of specimen collection, 1–4 d ahead of local hospital admissions and 6–8 d ahead of SARS-CoV-2 positive test results by reporting date. Our data show the utility of viral RNA monitoring in municipal wastewater for SARS-CoV-2 infection surveillance at a population-wide level. In communities facing a delay between specimen collection and the reporting of test results, immediate wastewater results can provide considerable advance notice of infection dynamics. Testing sewage for the novel coronavirus reveals epidemiological trends. [ABSTRACT FROM AUTHOR]

We introduce a new nonstationary spatial covariance model for analyzing geostatistical point-referenced data that contain point sources (i.e., known locations that impact the outcome). Our model is based on viewing the spatial domain on the polar coordinate scale, with the point source representing the reference location. As a result, we incorporate distances from the point source and angles of the separation vector with respect to the point source into the covariance model definition in order to describe complex correlation patterns that may be induced by the point source. We apply the new model and several competing options to analyze the impact of a hog lot on house sales prices in Cedar Falls, Iowa. We find that the new model offers improved model fit and predictive ability through Watanabe–Akaike information criterion and cross-validation, respectively. Additionally, we design a simulation study to determine the impact that mean misspecification has on each model's ability to produce quality predictions. Overall, the new model is shown to consistently outperform the competitors and is useful even when the point source has no impact on the outcome. [ABSTRACT FROM AUTHOR]

Background: Fossil fuel extraction from deep shale rock formations using new drilling technologies such as hydraulic fracturing, has rapidly increased in the Unites States (U.S.) over the past decade. Increases in nonlocal, specialized workers to meet the demands of this complex industry have been suggested to influence rates of sexually transmitted infections (STI) in counties with shale drilling activity; these associations may vary geographically. In this multi-region analysis, we examine the associations between shale drilling activity and rates of three reportable STI in Colorado, North Dakota, and Texas, states with active shale drilling.Methods: We obtained annual reported rates of chlamydia, gonorrhea, and syphilis from the Centers for Disease Control and Prevention (CDC), number of active shale wells from Enverus (formerly known as DrillingInfo), and sociodemographic covariates from the U.S. Census Bureau. We used multivariable mixed-effects Poisson regression modeling to estimate rate ratios (RR) with 95% confidence intervals (CIs) adjusted for potential confounders and secular trends.Results: In Texas, county-years with high drilling activity had 10% increased rates of chlamydia (RR=1.10; 95% CI=1.04-1.17) and 15% increased rates of gonorrhea (RR=1.15; 95% CI = 1.04-1.28), compared to county-years with no drilling. No statistically significant associations were reported for syphilis or for any STIs in Colorado or North Dakota.Conclusions: Associations between shale drilling and chlamydia and gonorrhea in Texas may reflect increased risk in areas with higher drilling activity and a greater number of major metropolitan areas. Inter-state differences highlight the need for local epidemiology to prioritize community health policies. [ABSTRACT FROM AUTHOR]

Pneumococcal conjugate vaccines target 10 or 13 specific serotypes. To evaluate the overall efficacy of these products, the vaccine-targeted serotypes are typically aggregated into a single group. However, it is often desirable to evaluate variations in effects for different serotypes. These serotype-specific estimates are often based on small counts, resulting in a high degree of uncertainty (i.e., large standard errors and wide confidence intervals). An alternative is to use a hierarchical Bayesian statistical model, which estimates overall effectiveness while simultaneously providing estimates of serotype-specific vaccine effects. These shrunken serotype-specific estimators often have smaller mean squared errors (MSEs) than unbiased versions due to a large decrease in posterior uncertainty. We reanalyzed published data from a randomized controlled trial on the efficacy of 13-valent pneumococcal conjugate vaccine (PCV13) against community-acquired pneumonia caused by vaccine-targeted serotype using a hierarchical model. This model provides a potential framework for obtaining estimates of serotype-specific vaccine effects with reduced MSEs. [ABSTRACT FROM AUTHOR]

Introduction: We investigated the extent to which associations of ambient air pollutant concentrations and birth weight varied across birth weight quantiles.Methods: We analyzed singleton births ≥27 weeks of gestation from 20-county metropolitan Atlanta with conception dates between January 1, 2002 and February 28, 2006 (N = 273,711). Trimester-specific and total pregnancy average concentrations for 10 pollutants, obtained from ground observations that were interpolated using 12-km Community Multiscale Air Quality model outputs, were assigned using maternal residence at delivery. We estimated associations between interquartile range width (IQRw) increases in pollutant concentrations and changes in birth weight using quantile regression.Results: Gestational age-adjusted associations were of greater magnitude at higher percentiles of the birth weight distribution. Pollutants with large vehicle source contributions (carbon monoxide, nitrogen dioxide, PM2.5 elemental carbon, and total PM2.5 mass), as well as PM2.5 sulfate and PM2.5 ammonium, were associated with birth weight decreases for the higher birth weight percentiles. For example, whereas the decrease in mean birthweight per IQRw increase in PM2.5 averaged over pregnancy was -7.8 g (95% confidence interval = -13.6, -2.0 g), the quantile-specific associations were: 10th percentile -2.4 g (-11.5, 6.7 g); 50th percentile -8.9 g (-15.7, -2.0g); and 90th percentile -19.3 g (-30.6, -7.9 g). Associations for the intermediate and high birth weight quantiles were not sensitive to gestational age adjustment. For some pollutants, we saw associations at the lowest quantile (10th percentile) when not adjusting for gestational age.Conclusions: Associations between air pollution and reduced birth weight were of greater magnitude for newborns at relatively heavy birth weights. [ABSTRACT FROM AUTHOR]

The article presents a study which examined serotype-specific changes in the incidence of invasive pneumococcal disease (IPD) that occurred in different age groups after the introduction of 7-valent pneumococcal conjugate vaccine (PCV7) and 13-valent pneumococcal conjugate vaccine (PCV13) in Denmark. Topics include characteristics of data, importance of serotype replacement by age and comorbidity level, and differences in net effect of replacement on IPD due to variations in baseline incidence.

Glaucoma disease progression, as measured by visual field (VF) data, is often defined by periods of relative stability followed by an abrupt decrease in visual ability at some point in time. Determining the transition point of the disease trajectory to a more severe state is important clinically for disease management and for avoiding irreversible vision loss. Based on this, we present a unified statistical modeling framework that permits prediction of the timing and spatial location of future vision loss and informs clinical decisions regarding disease progression. The developed method incorporates anatomical information to create a biologically plausible data-generating model. We accomplish this by introducing a spatially varying coefficients model that includes spatially varying change points to detect structural shifts in both the mean and variance process of VF data across both space and time. The VF location-specific change point represents the underlying, and potentially censored, timing of true change in disease trajectory while a multivariate spatial boundary detection structure is introduced that accounts for the complex spatial connectivity of the VF and optic disc. We show that our method improves estimation and prediction of multiple aspects of disease management in comparison to existing methods through simulation and real data application. The R package spCP implements the new methodology. [ABSTRACT FROM AUTHOR]

Background: Previously we observed elevated odds ratios (ORs) for total pesticide exposure and 10 birth defects: three congenital heart defects and structural defects affecting the gastrointestinal, genitourinary and musculoskeletal systems. This analysis examines association of those defects with exposure to seven commonly applied pesticide active ingredients. Methods: Cases were live‐born singleton infants from the North Carolina Birth Defects Monitoring Program linked to birth records for 2003–2005; noncases served as controls (total n = 304,906). Pesticide active ingredient exposure was assigned using a previously constructed metric based on crops within 500 m of residence, dates of pregnancy, and likely chemical application dates for each pesticide‐crop combination. ORs (95% CI) were estimated with logistic regression for categories of exposure compared to unexposed. Models were adjusted for maternal race/ethnicity, age at delivery, education, marital status, and smoking status. Results: Associations varied by birth defect and pesticide combinations. For example, hypospadias was positively associated with exposures to 2,4‐D (OR50th to <90th percentile: 1.39 [1.18, 1.64]), mepiquat (OR50th to <90th percentile: 1.10 [0.90, 1.34]), paraquat (OR50th to <90th: 1.14 [0.93, 1.39]), and pendimethalin (OR50th to <90th: 1.21 [1.01, 1.44]), but not S‐metolachlor (OR50th to <90th: 1.00 [0.81, 1.22]). Whereas atrial septal defects were positively associated with higher levels of exposure to glyphosate, cyhalothrin, S‐metolachlor, mepiquat, and pendimethalin (ORs ranged from 1.22 to 1.35 for 50th to <90th exposures, and 1.72 to 2.09 for >90th exposures); associations with paraquat were null or inconsistent (OR 50th to <90th: 1.05 (0.87, 1.27). Conclusion: Our results suggest differing patterns of association for birth defects with residential exposure to seven pesticide active ingredients in North Carolina. [ABSTRACT FROM AUTHOR]

The association between a single interpregnancy interval (IPI) and birth outcomes has not yet been explored using matched methods. We modeled the odds of preterm birth, being small for gestational age, and having low birth weight in a second, live-born infant in a cohort of 192,041 sibling pairs born in Western Australia between 1980 and 2010. The association between IPI and birth outcomes was estimated from the interaction between birth order and IPI (with 18–23 months as the reference category), using conditional logistic regression. Matched analysis showed the odds of preterm birth were higher for siblings born following an IPI of <6 months (adjusted interaction odds ratio = 1.22, 95% confidence interval: 1.06, 1.38) compared with those born after an IPI of 18–23 months. There were no significant differences for IPIs of <6 months for other outcomes (small for gestational age or low birth weight). This is the first study to use matched analyses to investigate the association between a single IPI on birth outcomes. IPIs of <6 months were associated with increased odds of preterm birth in second-born infants, although the association is likely smaller than previously estimated by unmatched studies. [ABSTRACT FROM AUTHOR]

Environmental epidemiologic studies routinely utilize aggregate health outcomes to estimate effects of short-term (eg, daily) exposures that are available at increasingly fine spatial resolutions. However, areal averages are typically used to derive population-level exposure, which cannot capture the spatial variation and individual heterogeneity in exposures that may occur within the spatial and temporal unit of interest (eg, within a day or ZIP code). We propose a general modeling approach to incorporate within-unit exposure heterogeneity in health analyses via exposure quantile functions. Furthermore, by viewing the exposure quantile function as a functional covariate, our approach provides additional flexibility in characterizing associations at different quantile levels. We apply the proposed approach to an analysis of air pollution and emergency department (ED) visits in Atlanta over 4 years. The analysis utilizes daily ZIP code-level distributions of personal exposures to 4 traffic-related ambient air pollutants simulated from the Stochastic Human Exposure and Dose Simulator. Our analyses find that effects of carbon monoxide on respiratory and cardiovascular disease ED visits are more pronounced with changes in lower quantiles of the population’s exposure. Software for implement is provided in the R package nbRegQF [ABSTRACT FROM AUTHOR]

Summary: Although there is a literature on the distribution of food stores across geographic and social space, much of this research uses cross‐sectional data. Analyses attempting to understand whether the availability of stores across neighbourhoods is associated with diet and/or health outcomes are limited by a lack of understanding of factors that shape the emergence of new stores and the closure of others. We used quarterly data on supermarket and convenience store locations spanning seven years (2006–2012) and tract level census data in four US cities: Birmingham, Alabama; Chicago, Illinois; Minneapolis, Minnesota; San Francisco, California. A spatial discrete time survival model was used to identify factors that are associated with an earlier and/or later closure time of a store. Sales volume was typically the strongest indicator of store survival. We identified heterogeneity in the association between tract level poverty and racial composition with respect to store survival. Stores in high poverty, non‐white tracts were often at a disadvantage in terms of survival length. The observed patterns of store survival varied by some of the same neighbourhood sociodemographic factors as associated with lifestyle and health outcomes, which could lead to confusion in interpretation in studies of the estimated effects of introduction of food stores into neighbourhoods on health. [ABSTRACT FROM AUTHOR]

Identifying periods of increased vulnerability to air pollution during pregnancy with respect to the development of adverse birth outcomes can improve understanding of possible mechanisms of disease development and provide guidelines for protection of the child. Exposure to air pollution during pregnancy is typically based on the mother's residence at delivery, potentially resulting in exposure misclassification and biasing the estimation of critical windows of pregnancy. In this study, we determined the impact of maternal residential mobility during pregnancy on defining weekly exposure to particulate matter less than or equal to 10 pm in aerodynamic diameter (PM10) and estimating windows of susceptibility to term low birth weight. We utilized data sets from 4 Connecticut birth cohorts (1988-2008) that included information on all residential addresses between conception and delivery for each woman. We designed a simulation study to investigate the impact of increasing levels of mobility on identification of critical windows. Increased PM10 exposure during pregnancy weeks 16-18 was associated with an increased probability of term low birth weight. Ignoring residential mobility when defining weekly exposure had only a minor impact on the identification of critical windows for PM10 and term low birth weight in the data application and simulation study. Identification of critical pregnancy windows was robust to exposure misclassification caused by ignoring residential mobility in these Connecticut birth cohorts. [ABSTRACT FROM AUTHOR]