Disparities in air quality downscaler model uncertainty across socioeconomic and demographic indicators in North Carolina.

Studies increasingly use output from the Environmental Protection Agency's Fused Air Quality Surface Downscaler ("downscaler") model, which provides spatial predictions of daily concentrations of fine particulate matter (PM2.5) and ozone (O3) at the census tract level, to study the health and societal impacts of exposure to air pollution. Downscaler outputs have been used to show that lower income and higher minority neighborhoods are exposed to higher levels of PM2.5 and lower levels of O3. However, the uncertainty of the downscaler estimates remains poorly characterized, and it is not known if all subpopulations are benefiting equally from reliable predictions. We examined how the percent errors (PEs) of daily concentrations of PM2.5 and O3 between 2002 and 2016 at the 2010 census tract centroids across North Carolina were associated with measures of racial and educational isolation, neighborhood disadvantage, and urbanicity. Results suggest that there were socioeconomic and demographic disparities in surface concentrations of PM2.5 and O3, as well as their prediction uncertainties. Neighborhoods characterized by less reliable downscaler predictions (i.e., higher PEPM2.5 and PEO3) exhibited greater levels of aerial deprivation as well as educational isolation, and were often non-urban areas (i.e., suburban, or rural). Between 2002 and 2016, predicted PM2.5 and O3 levels decreased and O3 predictions became more reliable. However, the predictive uncertainty for PM2.5 has increased since 2010. Substantial spatial variability was observed in the temporal changes in the predictive uncertainties; educational isolation and neighborhood deprivation levels were associated with smaller increases in predictive uncertainty of PM2.5. In contrast, racial isolation was associated with a greater decline in the reliability of PM2.5 predictions between 2002 and 2016; it was associated with a greater improvement in the predictive reliability of O3 within the same time frame.

Risk Factors for Sudden Infant Death in North Carolina.

Background: Sudden infant death syndrome (SIDS) is the sudden, unexplained death of infants <1 year old. SIDS remains a leading cause of death in US infants. We aim to identify associations between SIDS and race/ethnicity, birth weight/gestational age, and socioeconomic/environmental factors in North Carolina (NC) to help identify infants at risk for SIDS. Methods and Results: In this IRB-approved study, infant mortality 2007-2016 and death certificate-linked natality 2007-2014 were obtained from the NC Department of Health and Human Services. General, NC natality statistics 2007-2016 were obtained from CDC Wonder. Association between SIDS/total infant death and covariates (below) were calculated. Total infant mortality decreased 2007-2016 by an average of 14 deaths/100,000 live births per year, while SIDS incidence remained constant. Risk ratios of SIDS/total infant deaths, standardized to Non-Hispanic White, were 1.76/2.41 for Non-Hispanic Black and 0.49/0.97 for Hispanic infants. Increased SIDS risk was significantly and independently associated with male infant sex, Non-Hispanic Black maternal race/ethnicity, young maternal age, low prenatal care, gestational age <39 weeks, birthweight <2500 g, low maternal education, and maternal tobacco use (p < 0.01). Maternal previous children now deceased also trended toward association with increased SIDS risk. Conclusions: A thorough SIDS risk assessment should include maternal, socioeconomic, and environmental risk factors as these are associated with SIDS in our population.

Co-Expression Network Analysis of MicroRNAs and Proteins in Severe Traumatic Brain Injury: A Systematic Review.

Traumatic brain injury (TBI) represents one of the leading causes of mortality and morbidity worldwide, placing an enormous socioeconomic burden on healthcare services and communities around the world. Survivors of TBI can experience complications ranging from temporary neurological and psychosocial problems to long-term, severe disability and neurodegenerative disease. The current lack of therapeutic agents able to mitigate the effects of secondary brain injury highlights the urgent need for novel target discovery. This study comprises two independent systematic reviews, investigating both microRNA (miRNA) and proteomic expression in rat models of severe TBI (sTBI). The results were combined to perform integrated miRNA-protein co-expression analyses with the aim of uncovering the potential roles of miRNAs in sTBI and to ultimately identify new targets for therapy. Thirty-four studies were included in total. Bioinformatic analysis was performed to identify any miRNA-protein associations. Endocytosis and TNF signalling pathways were highlighted as common pathways involving both miRNAs and proteins found to be differentially expressed in rat brain tissue following sTBI, suggesting efforts to find novel therapeutic targets that should be focused here. Further high-quality investigations are required to ascertain the involvement of these pathways and their miRNAs in the pathogenesis of TBI and other CNS diseases and to therefore uncover those targets with the greatest therapeutic potential.

SPATIAL DISTRIBUTED LAG DATA FUSION FOR ESTIMATING AMBIENT AIR POLLUTION.

We introduce spatial (DLfuse) and spatiotemporal (DLfuseST) distributed lag data fusion methods for predicting point-level ambient air pollution concentrations, using, as input, gridded average pollution estimates from a deterministic numerical air quality model. The methods incorporate predictive information from grid cells surrounding the prediction location of interest and are shown to collapse to existing downscaling approaches when this information adds no benefit. The spatial lagged parameters are allowed to vary spatially/spatiotemporally to accommodate the setting where surrounding geographic information is useful in one area/time but not in another. We apply the new methods to predict ambient concentrations of eight-hour maximum ozone and 24-hour average PM2.5 at unobserved spatial locations and times, and compare the predictions with those from several state-of-the-art data fusion approaches. Results show that DLfuse and DLfuseST often provide improved model fit and predictive accuracy when the lagged information is shown to be beneficial. Code to apply the methods is available in the R package DLfuse.

Leveraging Data Analytics and Scalable Information Technology Tools to Deliver a Culture of Care during an Emergent Natural Disaster.

Rice University's Culture of Care represents a commitment to ensuring that all are treated with respect, compassion, and deep care. Rice leveraged information technology (IT) to deliver its Culture of Care, in responding to Hurricane Harvey. IT tools were used to gather key information on Rice's over 12000 community members. These data were fused with structured university data, enabling data-driven disaster response, with actionable information pushed to local managers. Our successful communication and response programs were all driven by the data analyses.

Association of autism with induced or augmented childbirth in North Carolina Birth Record (1990-1998) and Education Research (1997-2007) databases.

IMPORTANCE: One in 88 children in the United States is diagnosed as having autism spectrum disorder. Significant interest centers on understanding the environmental factors that may contribute to autism risk. OBJECTIVE: To examine whether induced (stimulating uterine contractions prior to the onset of spontaneous labor) and/or augmented (increasing the strength, duration, or frequency of uterine contractions with spontaneous onset of labor) births are associated with increased odds of autism. DESIGN, SETTING, AND PARTICIPANTS: We performed an epidemiological analysis using multivariable logistic regression modeling involving the North Carolina Detailed Birth Record and Education Research databases. The study featured 625,042 live births linked with school records, including more than 5500 children with a documented exceptionality designation for autism. EXPOSURES: Induced or augmented births. MAIN OUTCOMES AND MEASURES: Autism as assessed by exceptionality designations in child educational records. RESULTS: Compared with children born to mothers who received neither labor induction nor augmentation, children born to mothers who were induced and augmented, induced only, or augmented only experienced increased odds of autism after controlling for potential confounders related to socioeconomic status, maternal health, pregnancy-related events and conditions, and birth year. The observed associations between labor induction/augmentation were particularly pronounced in male children. CONCLUSIONS AND RELEVANCE: Our work suggests that induction/augmentation during childbirth is associated with increased odds of autism diagnosis in childhood. While these results are interesting, further investigation is needed to differentiate among potential explanations of the association including underlying pregnancy conditions requiring the eventual need to induce/augment, the events of labor and delivery associated with induction/augmentation, and the specific treatments and dosing used to induce/augment labor (e.g., exogenous oxytocin and prostaglandins).