Background: Birth defects are a leading cause of neonatal mortality. Natural gas development (NGD) emits several potential teratogens, and U.S. production of natural gas is expanding.Objectives: We examined associations between maternal residential proximity to NGD and birth outcomes in a retrospective cohort study of 124,842 births between 1996 and 2009 in rural Colorado.Methods: We calculated inverse distance weighted natural gas well counts within a 10-mile radius of maternal residence to estimate maternal exposure to NGD. Logistic regression, adjusted for maternal and infant covariates, was used to estimate associations with exposure tertiles for congenital heart defects (CHDs), neural tube defects (NTDs), oral clefts, preterm birth, and term low birth weight. The association with term birth weight was investigated using multiple linear regression.Results: Prevalence of CHDs increased with exposure tertile, with an odds ratio (OR) of 1.3 for the highest tertile (95% CI: 1.2, 1.5); NTD prevalence was associated with the highest tertile of exposure (OR = 2.0; 95% CI: 1.0, 3.9, based on 59 cases), compared with the absence of any gas wells within a 10-mile radius. Exposure was negatively associated with preterm birth and positively associated with fetal growth, although the magnitude of association was small. No association was found between exposure and oral clefts.Conclusions: In this large cohort, we observed an association between density and proximity of natural gas wells within a 10-mile radius of maternal residence and prevalence of CHDs and possibly NTDs. Greater specificity in exposure estimates is needed to further explore these associations.Citation: McKenzie LM, Guo R, Witter RZ, Savitz DA, Newman LS, Adgate JL. 2014. Birth outcomes and maternal residential proximity to natural gas development in rural Colorado. Environ Health Perspect 122:412–417; http://dx.doi.org/10.1289/ehp.1306722

Air pollution exposures have been linked to neuroinflammation and neuropathology. Autopsy samples of the frontal cortex from control (n = 8) and pollution-exposed (n = 35) children and young adults were analyzed by RT-PCR (n = 43) and microarray analysis (n = 12) for gene expression changes in oxidative stress, DNA damage signaling, NFκB signaling, inflammation, and neurodegeneration pathways. The effect of apolipoprotein E (APOE) genotype on the presence of protein aggregates associated with Alzheimer's disease (AD) pathology was also explored. Exposed urbanites displayed differential (>2-fold) regulation of 134 genes. Forty percent exhibited tau hyperphosphorylation with pre-tangle material and 51% had amyloid-β (Aβ) diffuse plaques compared with 0% in controls. APOE4 carriers had greater hyperphosphorylated tau and diffuse Aβ plaques versus E3 carriers (Q = 7.82, p = 0.005). Upregulated gene network clusters included IL1, NFκB, TNF, IFN, and TLRs. A 15-fold frontal down-regulation of the prion-related protein (PrP(C)) was seen in highly exposed subjects. The down-regulation of the PrP(C) is critical given its important roles for neuroprotection, neurodegeneration, and mood disorder states. Elevation of indices of neuroinflammation and oxidative stress, down-regulation of the PrP(C) and AD-associated pathology are present in young megacity residents. The inducible regulation of gene expression suggests they are evolving different mechanisms in an attempt to cope with the constant state of inflammation and oxidative stress related to their environmental exposures. Together, these data support a role for air pollution in CNS damage and its impact upon the developing brain and the potential etiology of AD and mood disorders.

SUMMARY We consider selecting both fixed and random effects in a general class of mixed effects models using maximum penalized likelihood (MPL) estimation along with the smoothly clipped absolute deviation (SCAD) and adaptive LASSO (ALASSO) penalty functions. The maximum penalized likelihood estimates are shown to posses consistency and sparsity properties and asymptotic normality. A model selection criterion, called the ICQ statistic, is proposed for selecting the penalty parameters (Ibrahim, Zhu and Tang, 2008). The variable selection procedure based on ICQ is shown to consistently select important fixed and random effects. The methodology is very general and can be applied to numerous situations involving random effects, including generalized linear mixed models. Simulation studies and a real data set from an Yale infant growth study are used to illustrate the proposed methodology.

BackgroundSystematic attempts to identify best practices for reducing hospital readmissions have been limited without a comprehensive framework for categorizing prior interventions. Our research aim was to categorize prior interventions to reduce hospital readmissions using the ten domains of the Ideal Transition of Care (ITC) framework, to evaluate which domains have been targeted in prior interventions and then examine the effect intervening on these domains had on reducing readmissions.MethodsReview of literature and secondary analysis of outcomes based on categorization of English-language reports published between January 1975 and October 2013 into the ITC framework.Results66 articles were included. Prior interventions addressed an average of 3.5 of 10 domains; 41% demonstrated statistically significant reductions in readmissions. The most common domains addressed focused on monitoring patients after discharge, patient education, and care coordination. Domains targeting improved communication with outpatient providers, provision of advanced care planning, and ensuring medication safety were rarely included. Increasing the number of domains included in a given intervention significantly increased success in reducing readmissions, even when adjusting for quality, duration, and size (OR per domain, 1.5, 95% CI 1.1 - 2.0). The individual domains most associated with reducing readmissions were Monitoring and Managing Symptoms after Discharge (OR 8.5, 1.8 - 41.1), Enlisting Help of Social and Community Supports (OR 4.0, 1.3 - 12.6), and Educating Patients to Promote Self-Management (OR 3.3, 1.1 - 10.0).ConclusionsInterventions to reduce hospital readmissions are frequently unsuccessful; most target few domains within the ITC framework. The ITC may provide a useful framework to consider when developing readmission interventions.Electronic supplementary materialThe online version of this article (doi:10.1186/1472-6963-14-423) contains supplementary material, which is available to authorized users.

Summary There has been great interest in developing nonlinear structural equation models and associated statistical inference procedures, including estimation and model selection methods. In this paper a general semiparametric structural equation model (SSEM) is developed in which the structural equation is composed of nonparametric functions of exogenous latent variables and fixed covariates on a set of latent endogenous variables. A basis representation is used to approximate these nonparametric functions in the structural equation and the Bayesian Lasso method coupled with a Markov Chain Monte Carlo (MCMC) algorithm is used for simultaneous estimation and model selection. The proposed method is illustrated using a simulation study and data from the Affective Dynamics and Individual Differences (ADID) study. Results demonstrate that our method can accurately estimate the unknown parameters and correctly identify the true underlying model.