Digital Health Innovations for Screening and Mitigating Mental Health Impacts of Adverse Childhood Experiences: Narrative Review.

Background: Exposures to both negative and positive experiences in childhood have proven to influence cardiovascular, immune, metabolic, and neurologic function throughout an individual's life. As such, adverse childhood experiences (ACEs) could have severe consequences on health and well-being into adulthood.

Objective: This study presents a narrative review of the use of digital health technologies (DHTs) and artificial intelligence to screen and mitigate risks and mental health consequences associated with ACEs among children and youth.

Drug providers' perspectives on antibiotic misuse practices in eastern Ethiopia: a qualitative study.

Objective: Antibiotic misuse includes using them to treat colds and influenza, obtaining them without a prescription, not finishing the prescribed course and sharing them with others. Although drug providers are well positioned to advise clients on proper stewardship practices, antibiotic misuse continues to rise in Ethiopia. It necessitates an understanding of why drug providers failed to limit such risky behaviours.

Inappropriate antibiotic access practices at the community level in Eastern Ethiopia.

Access to antibiotic medications is critical to achieving the Sustainable Development Goal for good health and well-being. However, non-prescribed and informal sources are implicated as the most common causes of inappropriate antibiotic access practices, resulting in untargeted therapy, which leads to antibiotic resistance. Hence, knowing antibiotic access practices at the community level is essential to target misuse sources.

Emergency department overcrowding and its associated factors at HARME medical emergency center in Eastern Ethiopia.

Introduction: Emergency department (ED) overcrowding has become a significant concern as it can lead to compromised patient care in emergency settings. Various tools have been used to evaluate overcrowding in ED. However, there is a lack of data regarding this issue in resource-limited countries, including Ethiopia.

A Digital Personal Health Library for the Management of Abortion-Related Care via Telemedicine.

Abortion remains a highly controversial topic in many countries, particularly in the United States. As the COVID-19 pandemic introduced new challenges and restrictions, society saw a marked increase in demand for self-managed care. Likewise, the utilization of abortion care via telemedicine sparked interest, especially in communities with high infection rates.

The Memphis Pandemic Health Informatics System (MEMPHI-SYS)-Creating a Metropolitan COVID-19 Data Registry Linked Directly to Community Testing to Enhance Population Health Surveillance.

The current coronavirus disease (COVID-19) pandemic has placed unprecedented strain on underfunded public health resources in the Southeastern United States. The Memphis, TN, metropolitan region has lacked infrastructure for health data exchange.This manuscript describes a multidisciplinary initiative to create a community-focused COVID-19 data registry, the .

Non-prescribed antibiotic use for children at community levels in low- and middle-income countries: a systematic review and meta-analysis.

Background: Non-prescribed antibiotic use is an emerging risky practice around the globe. An inappropriate use involving nonprescription access is one cause of the rapid increase in antibiotic resistance. Children commonly encounter many self-limiting illnesses for which they frequently use antibiotics without prescription.

Utilization of Digital Health Dashboards in Improving COVID-19 Vaccination Uptake, Accounting for Health Inequities.

The COVID-19 pandemic has introduced new challenges to the immunization sector, including highlighting already existent inequities related to vaccine access and delivery. Digital health solutions such as data dashboard systems to track and inform vaccine promotion and distribution can improve public health response. In this article, we review a few COVID-19 data dashboards and discuss how they played key roles in pandemic management.

Sentiment Analysis of the Covid-19 Vaccines on Social Media.

The COVID-19 pandemic fueled one of the quickest vaccine developments in history. Misinformation on online social media often leads to negative vaccine sentiment. We conducted a sentiment analysis and Latent Dirichlet Allocation topic modeling from Reddit communities focusing on the COVID-19 vaccine.

History Repeating-How Pandemics Collide with Health Disparities in the United States.

Across the United States, public health responses to the COVID-19 pandemic have fallen short. COVID-19 has exacerbated longstanding public health shortfalls in disadvantaged communities. Was this predestined? Understanding where we are today requires reflection on our longer journey.

Analyzing Relationships Between Economic and Neighborhood-Related Social Determinants of Health and Intensive Care Unit Length of Stay for Critically Ill Children With Medical Complexity Presenting With Severe Sepsis.

Objectives: Of the Social Determinants of Health (SDoH), we evaluated socioeconomic and neighborhood-related factors which may affect children with medical complexity (CMC) admitted to a Pediatric Intensive Care Unit (PICU) in Shelby County, Tennessee with severe sepsis and their association with PICU length of stay (LOS). We hypothesized that census tract-level socioeconomic and neighborhood factors were associated with prolonged PICU LOS in CMC admitted with severe sepsis in the underserved community.

Methods: This single-center retrospective observational study included CMC living in Shelby County, Tennessee admitted to the ICU with severe sepsis over an 18-month period.

Network Analysis of COVID-19 Vaccine Misinformation on Social Media.

Almost half of the world population has received at least one dose of vaccine against the COVID-19 virus. However, vaccine hesitancy amongst certain populations is driving new waves of infections at alarming rates. The popularity of online social media platforms attracts supporters of the anti-vaccination movement who spread misinformation about vaccine safety and effectiveness.

HemPHL: A Personal Health Library and mHealth Recommender to Promote Self-Management of Hemophilia.

Hemophilia is a rare inherited bleeding disorder characterized by the blood's inability to clot and could result in potentially life-threatening spontaneous bleeding into joints, organs, and tissues. Moreover, long-term management of this chronic disease is complex and costly. Current scientific evidence demonstrates that personalized digital health technologies could promote and facilitate the self-management of chronic diseases.

Proving the Correctness of Knowledge Graph Update: A Scenario From Surveillance of Adverse Childhood Experiences.

Knowledge graphs are a modern way to store information. However, the knowledge they contain is not static. Instances of various classes may be added or deleted and the semantic relationship between elements might evolve as well.

Implementing an Urban Public Health Observatory for (Near) Real-Time Surveillance for the COVID-19 Pandemic.

The COVID-19 pandemic is broadly undercutting global health and economies, while disproportionally impacting socially disadvantaged populations. An impactful pandemic surveillance solution must draw from multi-dimensional integration of social determinants of health (SDoH) to contextually inform traditional epidemiological factors. In this article, we describe an Urban Public Health Observatory (UPHO) model which we have put into action in a mid-sized U.

Explainable Artificial Intelligence Recommendation System by Leveraging the Semantics of Adverse Childhood Experiences: Proof-of-Concept Prototype Development.

Background: The study of adverse childhood experiences and their consequences has emerged over the past 20 years. Although the conclusions from these studies are available, the same is not true of the data. Accordingly, it is a complex problem to build a training set and develop machine-learning models from these studies.

Electrocardiographic changes predate Parkinson's disease onset.

Autonomic nervous system involvement precedes the motor features of Parkinson's disease (PD). Our goal was to develop a proof-of-concept model for identifying subjects at high risk of developing PD by analysis of cardiac electrical activity. We used standard 10-s electrocardiogram (ECG) recordings of 60 subjects from the Honolulu Asia Aging Study including 10 with prevalent PD, 25 with prodromal PD, and 25 controls who never developed PD.

Semantic Web of Things (SWoT) for Global Infectious Disease Control and Prevention.

This paper reports on the early-stage development of an analytics framework to support the semantic integration of dynamic surveillance data across multiple scales to inform decision making for malaria eradication. We propose using the Semantic Web of Things (SWoT), a combination of Internet of Things (IoT) and semantic web technologies, to support the evolution and integration of dynamic malaria data sources and improve interoperability between different datasets generated through relevant IoT assets (e.g.

The Personal Health Library: A Single Point of Secure Access to Patient Digital Health Information.

Traditionally, health data management has been EMR-based and mostly handled by health care providers. Mechanisms are needed to give patients more control over their health conditions. Personal Health Libraries (PHLs) provide a single point of secure access to patients' digital health information that can help empower patients to make better-informed decisions about their health care.

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.

Health intelligence: how artificial intelligence transforms population and personalized health.

Advances in computational and data sciences for data management, integration, mining, classification, filtering, visualization along with engineering innovations in medical devices have prompted demands for more comprehensive and coherent strategies to address the most fundamental questions in health care and medicine. Theory, methods, and models from artificial intelligence (AI) are changing the health care landscape in clinical and community settings and have already shown promising results in multiple applications in healthcare including, integrated health information systems, patient education, geocoding health data, social media analytics, epidemic and syndromic surveillance, predictive modeling and decision support, mobile health, and medical imaging (e.g.

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%.

The Promise of Machine Learning: When Will it be Delivered?

Background: The real-life applications of machine learning clinical decision making is currently lagging behind its promise. One of the critics on machine learning is that it doesn't outperform more traditional statistical approaches in every problem.

Methods And Results: Authors of "Predictive Abilities of Machine Learning Techniques May Be Limited by Dataset Characteristics: Insights From the UNOS Database" presented in the current issue of the Journal of Cardiac Failure that machine learning approaches do not provide significantly higher performance when compared to more traditional statistical approaches in predicting mortality following heart transplant.