Neurodevelopmental imprints of sociomarkers in adolescent brain connectomes.

Neural consequences of social disparities are not yet rigorously investigated. How socioeconomic conditions influence children's connectome development remains unknown. This paper endeavors to gauge how precisely the connectome structure of the brain can predict an individual's social environment, thereby inversely assessing how social influences are engraved in the neural development of the Adolescent brain.

Double-Edged Network Effects on Disclosing Traumatic Experiences Among Korean "Comfort Women".

This study examines the effects of social networks on the disclosure of stigmatizing and traumatic sexual assault experiences. We analyzed publicly archived oral histories of Korean "comfort women" from World War II, employing an innovative method combining word embedding analysis, word frequency comparison, and grounded theory. By extracting their significant social relationships from narrated survivor stories, we parsed two distinctive disclosure patterns according to timing of disclosure: early disclosers and late disclosers.

Stage distinctive communication networks of the online breast cancer community.

In this study, we reveal the distinctive communication network structures and contents of online breast cancer community posts in accordance with different cancer stages. Using data collected from community.breastcancer.

The effects of socioeconomic and geographic factors on chronic phase long-term survival after stroke in South Korea.

The stroke incidence has increased rapidly in South Korea, calling for a national-wide system for long-term stroke management. We investigated the effects of socioeconomic status (SES) and geographic factors on chronic phase survival after stroke. We retrospectively enrolled 6994 patients who experienced a stroke event in 2009 from the Korean National Health Insurance database.

Integrated Infodemic Surveillance System: The Case of COVID-19 in South Korea.

This study merges multiple COVID-19 data sources from news articles and social media to propose an integrated infodemic surveillance system (IISS) that implements infodemiology for a well-tailored epidemic management policy. IISS is an à-la-carte infodemic surveillance solution that enables users to gauge the epidemic related consensus, which compiles epidemic-related data from multiple sources and equipped with various methodological toolkits - topic modeling, Word2Vec, and social network analysis. IISS can provide reliable empirical evidence for proper policymaking.

The anatomy of COVID-19 comorbidity networks among hospitalized Korean patients.

Objectives: We aimed to examine how comorbidities were associated with outcomes (illness severity or death) among hospitalized patients with coronavirus disease 2019 (COVID-19).

Methods: Data were provided by the National Medical Center of the Korea Disease Control and Prevention Agency. These data included the clinical and epidemiological information of all patients hospitalized with COVID-19 who were discharged on or before April 30, 2020 in Korea.

A Data Science Approach to Analyze the Association of Socioeconomic and Environmental Conditions With Disparities in Pediatric Surgery.

Scientific evidence confirm that significant racial disparities exist in healthcare, including surgery outcomes. However, the causal pathway underlying disparities at preoperative physical condition of children is not well-understood. This research aims to uncover the role of socioeconomic and environmental factors in racial disparities at the preoperative physical condition of children through multidimensional integration of several data sources at the patient and population level.

Evidence-Based Health Intelligence with Globally Localized Epidemic Knowledge Base: Merging Pathological Data, Socio-Environmental Data and Intervention Knowledge Data.

The increased prevalence and frequency of infectious diseases are alarming with respect to the disproportionate fatalities across different regions, socio-economic conditions, and demographic groups. Combining pathological data, socio-environmental data, and extracted knowledge from white papers, we proposed a Globally Localized Epidemic Knowledge base (GLEK) that can be utilized for efficient and optimal epidemic surveillance. GLEK merges social, environmental, pathological, and governmental intervention data to provide efficient advice for epidemic control and intervention.

Geo-clustered chronic affinity: pathways from socio-economic disadvantages to health disparities.

Objective: Our objective was to develop and test a new concept (affinity) analogous to multimorbidity of chronic conditions for individuals at census tract level in Memphis, TN. The use of affinity will improve the surveillance of multiple chronic conditions and facilitate the design of effective interventions.

Methods: We used publicly available chronic condition data (Center for Disease Control and Prevention 500 Cities project), socio-demographic data (US Census Bureau), and demographics data (Environmental Systems Research Institute).

Applied Network Science for Relational Chronic Disease Surveillance.

Chronic diseases and conditions are the leading cause of death and disability in the United States. The number of people living with two or more chronic conditions has increased in the last decades and is expected to continue to rise over the upcoming years. Yet, traditional chronic disease surveillance practices have been specialized for a specific symptom or a single health condition.

A Hybrid Recommender System to Guide Assessment and Surveillance of Adverse Childhood Experiences.

Adverse Childhood Experiences (ACEs) are negative events or states that affect children, with lasting impacts throughout their adulthood. ACES are considered one of the major risk factors for several adverse health outcomes and are associated with low quality of life and many detrimental social and economic consequences. In order to enact better surveillance of ACEs and their associated conditions, it is instrumental to provide tools to detect, monitor and respond effectively.

Sociomarkers and biomarkers: predictive modeling in identifying pediatric asthma patients at risk of hospital revisits.

The importance of social components of health has been emphasized both in epidemiology and public health. This paper highlights the significant impact of social components on health outcomes in a novel way. Introducing the concept of sociomarkers, which are measurable indicators of social conditions in which a patient is embedded, we employed a machine learning approach that uses both biomarkers and sociomarkers to identify asthma patients at risk of a hospital revisit after an initial visit with an accuracy of 66%.

Adverse Childhood Experiences Ontology for Mental Health Surveillance, Research, and Evaluation: Advanced Knowledge Representation and Semantic Web Techniques.

Background: Adverse Childhood Experiences (ACEs), a set of negative events and processes that a person might encounter during childhood and adolescence, have been proven to be linked to increased risks of a multitude of negative health outcomes and conditions when children reach adulthood and beyond.

Objective: To better understand the relationship between ACEs and their relevant risk factors with associated health outcomes and to eventually design and implement preventive interventions, access to an integrated coherent dataset is needed. Therefore, we implemented a formal ontology as a resource to allow the mental health community to facilitate data integration and knowledge modeling and to improve ACEs' surveillance and research.

An Ontological Framework to Improve Surveillance of Adverse Childhood Experiences (ACEs).

Adverse Childhood Experiences (ACEs) have been proven to be linked to increased risks of a multitude of negative health outcomes and conditions when children reach adulthood and beyond. To better understand the relationship between ACEs and the associated health outcomes and eventually to pan and implement preventive interventions, access to an integrated coherent actionable data set is crucial. In this paper, we introduce a formal reusable ontological framework to capture the knowledge in the domain of Adverse Childhood Experiences to improve ACEs surveillance and response.

Association of Maternal Social Relationships With Cognitive Development in Early Childhood.

Importance: This study examines how different types of social network structures are associated with early cognitive development in children.

Objectives: To assess how social relationships and structures are associated with early cognitive development and to elucidate whether variations in the mother's social networks alter a child's early cognitive development patterns.

Design, Setting, And Participants: This cohort study used data from 1082 mother-child pairs in the University of Tennessee Health Science Center-Conditions Affecting Neurocognitive Development and Learning and Early Childhood project to examine the association between networks of different levels of complexity (triad, family, and neighborhood) and child cognitive performance after adjustment for the mother's IQ, birth weight, and age, and the father's educational level.

Disparities in Population-Level Socio-Economic Factors Are Associated with Disparities in Preoperative Clinical Risk Factors in Children.

African American children are more than twice as likely as white American children to die after surgery, and have increased risk for longer hospital stays, post-surgical complications, and higher hospital costs. Prior research into disparities in pediatric surgery outcomes has not considered interactions between patient-level Clinical Risk Factors (CRFs) and population-level Social, Economic, and Environmental Factors (SEEFs) primarily due to the lack of integrated data sets. In this study, we analyze correlations between SEEFs and CRFs and correlations between CRFs and surgery outcomes.

Geo-Distinctive Comorbidity Networks of Pediatric Asthma.

Most pediatric asthma cases occur in complex interdependencies, exhibiting complex manifestation of multiple symptoms. Studying asthma comorbidities can help to better understand the etiology pathway of the disease. Albeit such relations of co-expressed symptoms and their interactions have been highlighted recently, empirical investigation has not been rigorously applied to pediatric asthma cases.

POLE.VAULT: A Semantic Framework for Health Policy Evaluation and Logical Testing.

The major goal of our study is to provide an automatic evaluation framework that aligns the results generated through semantic reasoning with the best available evidence regarding effective interventions to support the logical evaluation of public health policies. To this end, we have designed the POLicy EVAlUation & Logical Testing (POLE.VAULT) Framework to assist different stakeholders and decision-makers in making informed decisions about different health-related interventions, programs and ultimately policies, based on the contextual knowledge and the best available evidence at both individual and aggregate levels.

A Semantic Framework for Logical Cross-Validation, Evaluation and Impact Analyses of Population Health Interventions.

Most chronic diseases are a result of a complex web of causative and correlated factors. As a result, effective public health or clinical interventions that intend to generate a sustainable change in these diseases most often use a combination of strategies or programs. To optimize comparative effectiveness evaluations and select the most efficient intervention(s), stakeholders (i.

Association of 5-hydroxytryptamine (serotonin) receptor 4 (5-HTR4) gene polymorphisms with asthma.

Background And Objective: The neurotransmitter, 5-hydroxytryptamine, acts as an immunomodulator by stimulating the release of inflammatory cytokines and regulating the function of dendritic cells and monocytes. The 5-hydroxytryptamine receptor 4 (HTR4) gene is located in a region previously linked to an increased risk of asthma and atopy. The aim of this study was to investigate the association between HTR4 and asthma.

Association between colony-stimulating factor 1 receptor gene polymorphisms and asthma risk.

Colony-stimulating factor 1 receptor (CSF1R) is expressed in monocytes/macrophages and dendritic cells. These cells play important roles in the innate immune response, which is regarded as an important aspect of asthma development. Genetic alterations in the CSF1R gene may contribute to the development of asthma.

Association of peroxisome proliferator-activated receptor-gamma gene polymorphisms with the development of asthma.

Background: The peroxisome proliferator-activated receptors (PPAR) are the nuclear hormone receptor superfamily of ligand-activated transcriptional factors. PPAR-gamma (PPARG) activation downregulates production of Th2 type cytokines and eosinophil function. Additionally, treatment with a synthetic PPARG ligand can reduce lung inflammation and IFN-gamma, IL-4, and IL-2 production in experimental allergic asthma.