Environmental and Health Disparities in Appalachian Ohio: Perceptions and Realities

Background. Appalachia is a region of the United States that faces significant environmental and health disparities. Understanding these disparities and the social determinants that contribute to them will help public health practitioners make better decisions. The purpose of this research is two-fold. First, through secondary data analysis, we document environmental and health disparities as well as demographic and economic conditions that may contribute to these disparities between Appalachian and non-Appalachian Ohio. Second, we examine perceptions of environmental health practitioners about the differences in environmental conditions between Appalachian and non-Appalachian Ohio. Methods. We gathered secondary data about economics, health, and the environment from the Ohio Department of Health, Healthy Ohio Community Profiles, the U.S. Environmental Protection Agency, and the U.S. Census. In addition, we conducted an online survey of 76 environmental health professionals across Ohio. Results. The secondary data indicates that there are significant differences between Appalachian and non-Appalachian Ohio in terms of socioeconomic, health, and environmental indicators. In addition, environmental health professionals perceive worse environmental conditions in the Appalachian region and indicate that there are environmental and health disparities found in this part of the state that do not exist elsewhere. Conclusions. The results contribute to understanding environmental and health conditions that contribute to health disparities in the Appalachian region as well as suggest approaches for public health practitioners to reduce these disparities

The Nuclear Legacy in Appalachia

Nestled in the rolling hills of Appalachia Ohio is a reminder of the role that the region played in winning the Cold War. For more than 40 years in rural Pike County, the 3,700-acre Portsmouth Gaseous Diffusion Plant (PORTS), or the “A-Plant” as the locals refer to it, enriched uranium for use in nuclear weapons. While the facility produced nuclear fuel for national security, it simultaneously exposed plant workers to chemicals and radiation and discharged pollution into the surrounding community. The A-Plant is now being demolished and the site repurposed. However, the site continues to affect the community as, for example, a middle school near it was closed in late spring of 2019 due to alarming levels of radiation detected in the building

Momentum distribution, vibrational dynamics and the potential of mean force in ice

By analyzing the momentum distribution obtained from path integral and phonon calculations we find that the protons in hexagonal ice experience an anisotropic quasi-harmonic effective potential with three distinct principal frequencies that reflect molecular orientation. Due to the importance of anisotropy, anharmonic features of the environment cannot be extracted from existing experimental distributions that involve the spherical average. The full directional distribution is required, and we give a theoretical prediction for this quantity that could be verified in future experiments. Within the quasi-harmonic context, anharmonicity in the ground state dynamics of the proton is substantial and has quantal origin, a finding that impacts the interpretation of several spectroscopies

Displaced path integral formulation for the momentum distribution of quantum particles

The proton momentum distribution, accessible by deep inelastic neutron scattering, is a very sensitive probe of the potential of mean force experienced by the protons in hydrogen-bonded systems. In this work we introduce a novel estimator for the end to end distribution of the Feynman paths, i.e. the Fourier transform of the momentum distribution. In this formulation, free particle and environmental contributions factorize. Moreover, the environmental contribution has a natural analogy to a free energy surface in statistical mechanics, facilitating the interpretation of experiments. The new formulation is not only conceptually but also computationally advantageous. We illustrate the method with applications to an empirical water model, ab-initio ice, and one dimensional model systems

Environmental Literacy of Ohio Adults

Author Institution: Food, Agricultural and Biological Engineering, The Ohio State University ; Strategic Research Group ; School of Health Sciences, Ohio UniversityEnvironmental literacy is defined as an understanding of natural systems combined with how they interact with human social systems. Past surveys have measured the pollution knowledge of adults. This study instead examined Ohio adult's knowledge of ecological principles as the basis of understanding. A telephone survey of 504 Ohio adults measured their knowledge of ecological principles. As a group, Ohio adults appear to understand four principles of ecology: biogeography, the earth as a biosphere, ecological energetics, and carrying capacity. Some additional attention should be paid to teaching Ohio adults about three principles of ecology: ecosystem succession, biotic interactions, and the importance of diversity. Most importantly, Ohio adults must learn more about the principle of materials cycling. Ohio adults showed poor understanding of the nitrogen, phosphorus, and hydrologic cycle and bioaccumulation

Profile of Ohio Adults with Low Environmental Literacy

Author Institution: Food, Agricultural and Biological Engineering, The Ohio State University ; Strategic Research Group ; School of Health Sciences, Ohio UniversityEnvironmental literacy is defined as an understanding of natural systems combined with how they interact with human social systems. An Ohio study measured adults' knowledge of ecological principles as the basis of understanding. A telephone survey of 504 Ohio adults measured their knowledge of ecological principles along with their demographics. Low literacy adults are significantly different from those who exhibit high literacy. The lowest literacy group was characterized as less educated, below the median household income, older, female, and minority. Low literacy adults are less likely to engage in outdoor activities, gain information from environmental groups, but are more likely to gain information from television. Low literacy adults are more likely than high literacy adults to use alternative transportation. In targeting environmental education programs to heads of households and Ohio voters, adults with low environmental literacy need to be approached differently than those with high literacy

Access to Health Care in Appalachia: Perception and Reality

Introduction: Health disparities such as cancer and diabetes are well documented in Appalachia. These disparities contribute to health status, and by many indicators, Appalachian people are less healthy than those who live in other parts of the country. Access to health care is one factor that contributes to health disparities. Access to care is complex and involves both intrinsic and extrinsic aspects, including satisfaction with quality of care. This research sought to compare Appalachian to non-Appalachian communities in terms of perceptions of access to care. Methods: We implemented a statewide survey to quantify perceptions of multiple components of access to care, including satisfaction with quality of care. We compared survey results to quantitative data from the County Health Rankings to document consistency with perceptions of access to care. We used chi-square analysis to compare Appalachian with non-Appalachian respondents. Results: More than 600 people completed the survey. Results of the survey identify significant differences between Appalachian and non-Appalachian residents’ perceptions of access to care and their satisfaction with health care. Specifically, Appalachian residents are less satisfied with convenience, information, quality, and courtesy of health care. They perceive providers relying on stereotypes when communicating with patients. Implications: Examining and documenting perceptions of health care is important because it could lead to improving access by focusing on cultural competency in addition to more resource intensive strategies. Health disparities in Appalachia might be minimized by being more compassionate and understanding of people who live here

Efficient multiple time scale molecular dynamics: using colored noise thermostats to stabilize resonances

Multiple time scale molecular dynamics enhances computational efficiency by updating slow motions less frequently than fast motions. However, in practice the largest outer time step possible is limited not by the physical forces but by resonances between the fast and slow modes. In this paper we show that this problem can be alleviated by using a simple colored noise thermostatting scheme which selectively targets the high frequency modes in the system. For two sample problems, flexible water and solvated alanine dipeptide, we demonstrate that this allows the use of large outer time steps while still obtaining accurate sampling and minimizing the perturbation of the dynamics. Furthermore, this approach is shown to be comparable to constraining fast motions, thus providing an alternative to molecular dynamics with constraints.Comment: accepted for publication by the Journal of Chemical Physic

The SNAP-tag technology revised: an effective chemo-enzymatic approach by using a universal azide-based substrate

SNAP-tag ® is a powerful technology for the labelling of protein/enzymes by using benzyl-guanine (BG) derivatives as substrates. Although commercially available or ad hoc produced, their synthesis and purification are necessary, increasing time and costs. To address this limitation, here we suggest a revision of this methodology, by performing a chemo-enzymatic approach, by using a BG-substrate containing an azide group appropriately distanced by a spacer from the benzyl ring. The SNAP-tag ® and its relative thermostable version (SsOGT-H5 ) proved to be very active on this substrate. The stability of these tags upon enzymatic reaction makes possible the exposition to the solvent of the azide-moiety linked to the catalytic cysteine, compatible for the subsequent conjugation with DBCO-derivatives by azide-alkyne Huisgen cycloaddition. Our studies propose a strengthening and an improvement in terms of biotechnological applications for this self-labelling protein-tag

Outcome Prediction for SARS-CoV-2 Patients Using Machine Learning Modeling of Clinical, Radiological, and Radiomic Features Derived from Chest CT Images

Featured Application The present study demonstrates that semi-automatic segmentation enables the identification of regions of interest affected by SARS-CoV-2 infection for the extraction of prognostic features from chest CT scans without suffering from the inter-operator variability typical of segmentation, hence offering a valuable and informative second opinion. Machine Learning methods allow identification of the prognostic features potentially reusable for the early detection and management of other similar diseases. (1) Background: Chest Computed Tomography (CT) has been proposed as a non-invasive method for confirming the diagnosis of SARS-CoV-2 patients using radiomic features (RFs) and baseline clinical data. The performance of Machine Learning (ML) methods using RFs derived from semi-automatically segmented lungs in chest CT images was investigated regarding the ability to predict the mortality of SARS-CoV-2 patients. (2) Methods: A total of 179 RFs extracted from 436 chest CT images of SARS-CoV-2 patients, and 8 clinical and 6 radiological variables, were used to train and evaluate three ML methods (Least Absolute Shrinkage and Selection Operator [LASSO] regularized regression, Random Forest Classifier [RFC], and the Fully connected Neural Network [FcNN]) for their ability to predict mortality using the Area Under the Curve (AUC) of Receiver Operator characteristic (ROC) Curves. These three groups of variables were used separately and together as input for constructing and comparing the final performance of ML models. (3) Results: All the ML models using only RFs achieved an informative level regarding predictive ability, outperforming radiological assessment, without however reaching the performance obtained with ML based on clinical variables. The LASSO regularized regression and the FcNN performed equally, both being superior to the RFC. (4) Conclusions: Radiomic features based on semi-automatically segmented CT images and ML approaches can aid in identifying patients with a high risk of mortality, allowing a fast, objective, and generalizable method for improving prognostic assessment by providing a second expert opinion that outperforms human evaluation