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 Memphis Pandemic Health Informatics System (MEMPHI-SYS). MEMPHI-SYS leverages test result data updated directly from community-based testing sites, as well as a full complement of public health data sets and knowledge-based informatics. It has been guided by relationships with community stakeholders and is managed alongside the largest publicly funded community-based COVID-19 testing response in the Mid-South. MEMPHI-SYS has supported interactive Web-based analytic resources and informs federally funded COVID-19 outreach directed toward neighborhoods most in need of pandemic support.MEMPHI-SYS provides an instructive case study of how to collaboratively establish the technical scaffolding and human relationships necessary for data-driven, health equity-focused pandemic surveillance, and policy interventions.

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. Disparities cataloged during COVID-19 reflect the same unequal host exposure and susceptibility risks that shaped previous pandemics. In this review, we provide historical context to better understand current events and to showcase forgotten lessons which may motivate future action to protect our most vulnerable citizens.

Defining Radiation Treatment Interruption Rates During the COVID-19 Pandemic: Findings From an Academic Center in an Underserved Urban Setting.

PURPOSE: Our purpose was to characterize radiation treatment interruption (RTI) rates and their potential association with sociodemographic variables in an urban population before and during the COVID-19 pandemic. METHODS AND MATERIALS: Electronic health records were retrospectively reviewed for patients treated between January 1, 2015, and February 28, 2021. Major and minor RTI were defined as ≥5 and 2 to 4 unplanned cancellations, respectively. RTI was compared across demographic and clinical factors and whether treatment started before or after COVID-19 onset (March 15, 2020) using multivariate logistic regression analysis. RESULTS: Of 2240 study cohort patients, 1938 started treatment before COVID-19 and 302 started after. Patient census fell 36% over the year after COVID-19 onset. RTI rates remained stable or trended downward, although subtle shifts in association with social and treatment factors were observed on univariate and multivariate analysis. Interaction of treatment timing with risk factors was modest and limited to treatment length and minor RTI. Despite the stability of cohort-level findings showing limited associations with race, geospatial mapping demonstrated a discrete geographic shift in elevated RTI toward Black, underinsured patients living in inner urban communities. Affected neighborhoods could not be predicted quantitatively by local COVID-19 transmission activity or social vulnerability indices. CONCLUSIONS: This is the first United States institutional report to describe radiation therapy referral volume and interruption patterns during the year after pandemic onset. Patient referral volumes did not fully recover from an initial steep decline, but local RTI rates and associated risk factors remained mostly stable. Geospatial mapping suggested migration of RTI risk toward marginalized, minority-majority urban ZIP codes, which could not otherwise be predicted by neighborhood-level social vulnerability or pandemic activity. These findings signal that detailed localization of highest-risk communities could help focus radiation therapy access improvement strategies during and after public health emergencies. However, this will require replication to validate and broaden relevance to other settings.

Exploring celebrity influence on public attitude towards the COVID-19 pandemic: social media shared sentiment analysis.

OBJECTIVES: The COVID-19 pandemic has introduced new opportunities for health communication, including an increase in the public's use of online outlets for health-related emotions. People have turned to social media networks to share sentiments related to the impacts of the COVID-19 pandemic. In this paper, we examine the role of social messaging shared by Persons in the Public Eye (ie, athletes, politicians, news personnel, etc) in determining overall public discourse direction. METHODS: We harvested approximately 13 million tweets ranging from 1 January 2020 to 1 March 2022. The sentiment was calculated for each tweet using a fine-tuned DistilRoBERTa model, which was used to compare COVID-19 vaccine-related Twitter posts (tweets) that co-occurred with mentions of People in the Public Eye. RESULTS: Our findings suggest the presence of consistent patterns of emotional content co-occurring with messaging shared by Persons in the Public Eye for the first 2 years of the COVID-19 pandemic influenced public opinion and largely stimulated online public discourse. DISCUSSION: We demonstrate that as the pandemic progressed, public sentiment shared on social networks was shaped by risk perceptions, political ideologies and health-protective behaviours shared by Persons in the Public Eye, often in a negative light. CONCLUSION: We argue that further analysis of public response to various emotions shared by Persons in the Public Eye could provide insight into the role of social media shared sentiment in disease prevention, control and containment for COVID-19 and in response to future disease outbreaks.

An Urban Population Health Observatory System to Support COVID-19 Pandemic Preparedness, Response, and Management: Design and Development Study.

BACKGROUND: COVID-19 is impacting people worldwide and is currently a leading cause of death in many countries. Underlying factors, including Social Determinants of Health (SDoH), could contribute to these statistics. Our prior work has explored associations between SDoH and several adverse health outcomes (eg, asthma and obesity). Our findings reinforce the emerging consensus that SDoH factors should be considered when implementing intelligent public health surveillance solutions to inform public health policies and interventions. OBJECTIVE: This study sought to redefine the Healthy People 2030's SDoH taxonomy to accommodate the COVID-19 pandemic. Furthermore, we aim to provide a blueprint and implement a prototype for the Urban Population Health Observatory (UPHO), a web-based platform that integrates classified group-level SDoH indicators to individual- and aggregate-level population health data. METHODS: The process of building the UPHO involves collecting and integrating data from several sources, classifying the collected data into drivers and outcomes, incorporating data science techniques for calculating measurable indicators from the raw variables, and studying the extent to which interventions are identified or developed to mitigate drivers that lead to the undesired outcomes. RESULTS: We generated and classified the indicators of social determinants of health, which are linked to COVID-19. To display the functionalities of the UPHO platform, we presented a prototype design to demonstrate its features. We provided a use case scenario for 4 different users. CONCLUSIONS: UPHO serves as an apparatus for implementing effective interventions and can be adopted as a global platform for chronic and infectious diseases. The UPHO surveillance platform provides a novel approach and novel insights into immediate and long-term health policy responses to the COVID-19 pandemic and other future public health crises. The UPHO assists public health organizations and policymakers in their efforts in reducing health disparities, achieving health equity, and improving urban population health.

Healthcare professionals' perceptions and recommendations regarding adolescent vaccinations in Georgia and Tennessee during the COVID-19 pandemic: A qualitative research.

INTRODUCTION: Despite its benefits, HPV vaccine uptake has been historically lower than other recommended adolescent vaccines in the United States (US). While hesitancy and misinformation have threatened vaccinations for many years, the adverse impacts from COVID-19 pandemic on preventive services have been far-reaching. OBJECTIVES: To explore the perceptions and experiences of adolescent healthcare providers regarding routine vaccination services during the COVID-19 pandemic. METHODOLOGY: Between December 2020 and May 2021, in-depth qualitative interviews were conducted via Zoom video conferencing among a purposively selected, diverse group of adolescent healthcare providers (n = 16) within 5 healthcare practices in the US southeastern states of Georgia and Tennessee. Audio recordings were transcribed verbatim and analyzed using a rapid qualitative analysis framework. Our analysis was guided by the grounded theory and inductive approach. RESULTS: Participants reported that patient-provider communications; effective use of presumptive languaging; provider's continuing education/training; periodic reminders/recall messages; provider's personal conviction on vaccine safety/efficacy; early initiation of HPV vaccination series at 9 years; community partnerships with community health navigators/vaccine champions/vaccine advocates; use of standardized forms/prewritten scripts/standard operating protocols for patient-provider interactions; and vaccine promotion through social media, brochures/posters/pamphlets as well as outreaches to schools and churches served as facilitators to adolescent HPV vaccine uptake. Preventive adolescent services were adversely impacted by the COVID-19 pandemic at all practices. Participants highlighted an initial decrease in patients due to the pandemic, while some practices avoided the distribution of vaccine informational materials due to sanitary concerns. CONCLUSION: As part of a larger study, we provided contextual information to refine an intervention package currently being developed to improve adolescent preventive care provision in healthcare practices. Our results could inform the implementation of comprehensive intervention strategies that improve HPV vaccination rates. Additionally, lessons learned (e.g. optimizing patient- provider interactions) could be adopted to expand COVID-19 vaccine acceptance on a sizable scale.

Applied artificial intelligence in healthcare: Listening to the winds of change in a post-COVID-19 world.

This editorial article aims to highlight advances in artificial intelligence (AI) technologies in five areas: Collaborative AI, Multimodal AI, Human-Centered AI, Equitable AI, and Ethical and Value-based AI in order to cope with future complex socioeconomic and public health issues.

Fine-tuned Sentiment Analysis of COVID-19 Vaccine-Related Social Media Data: Comparative Study.

BACKGROUND: The emergence of the novel coronavirus (COVID-19) and the necessary separation of populations have led to an unprecedented number of new social media users seeking information related to the pandemic. Currently, with an estimated 4.5 billion users worldwide, social media data offer an opportunity for near real-time analysis of large bodies of text related to disease outbreaks and vaccination. These analyses can be used by officials to develop appropriate public health messaging, digital interventions, educational materials, and policies. OBJECTIVE: Our study investigated and compared public sentiment related to COVID-19 vaccines expressed on 2 popular social media platforms-Reddit and Twitter-harvested from January 1, 2020, to March 1, 2022. METHODS: To accomplish this task, we created a fine-tuned DistilRoBERTa model to predict the sentiments of approximately 9.5 million tweets and 70 thousand Reddit comments. To fine-tune our model, our team manually labeled the sentiment of 3600 tweets and then augmented our data set through back-translation. Text sentiment for each social media platform was then classified with our fine-tuned model using Python programming language and the Hugging Face sentiment analysis pipeline. RESULTS: Our results determined that the average sentiment expressed on Twitter was more negative (5,215,830/9,518,270, 54.8%) than positive, and the sentiment expressed on Reddit was more positive (42,316/67,962, 62.3%) than negative. Although the average sentiment was found to vary between these social media platforms, both platforms displayed similar behavior related to the sentiment shared at key vaccine-related developments during the pandemic. CONCLUSIONS: Considering this similar trend in shared sentiment demonstrated across social media platforms, Twitter and Reddit continue to be valuable data sources that public health officials can use to strengthen vaccine confidence and combat misinformation. As the spread of misinformation poses a range of psychological and psychosocial risks (anxiety and fear, etc), there is an urgency in understanding the public perspective and attitude toward shared falsities. Comprehensive educational delivery systems tailored to a population's expressed sentiments that facilitate digital literacy, health information-seeking behavior, and precision health promotion could aid in clarifying such misinformation.

Association Between Neighborhood Factors and Adult Obesity in Shelby County, Tennessee: Geospatial Machine Learning Approach.

BACKGROUND: Obesity is a global epidemic causing at least 2.8 million deaths per year. This complex disease is associated with significant socioeconomic burden, reduced work productivity, unemployment, and other social determinants of health (SDOH) disparities. OBJECTIVE: The objective of this study was to investigate the effects of SDOH on obesity prevalence among adults in Shelby County, Tennessee, the United States, using a geospatial machine learning approach. METHODS: Obesity prevalence was obtained from the publicly available 500 Cities database of Centers for Disease Control and Prevention, and SDOH indicators were extracted from the US census and the US Department of Agriculture. We examined the geographic distributions of obesity prevalence patterns, using Getis-Ord Gi* statistics and calibrated multiple models to study the association between SDOH and adult obesity. Unsupervised machine learning was used to conduct grouping analysis to investigate the distribution of obesity prevalence and associated SDOH indicators. RESULTS: Results depicted a high percentage of neighborhoods experiencing high adult obesity prevalence within Shelby County. In the census tract, the median household income, as well as the percentage of individuals who were Black, home renters, living below the poverty level, 55 years or older, unmarried, and uninsured, had a significant association with adult obesity prevalence. The grouping analysis revealed disparities in obesity prevalence among disadvantaged neighborhoods. CONCLUSIONS: More research is needed to examine links between geographical location, SDOH, and chronic diseases. The findings of this study, which depict a significantly higher prevalence of obesity within disadvantaged neighborhoods, and other geospatial information can be leveraged to offer valuable insights, informing health decision-making and interventions that mitigate risk factors of increasing obesity prevalence.

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. Polarity analysis suggested these communities expressed positive sentiment regarding the vaccine. However, topic modeling revealed community members mainly focused on the side effects and vaccination experience.

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. Innovative translation of their displays could strengthen the response to numerous public health concerns, including equitable COVID-19 vaccination distribution/uptake and preparedness for future disease outbreaks.

Examining the Implementation of Digital Health to Strengthen the COVID-19 Pandemic Response and Recovery and Scale up Equitable Vaccine Access in African Countries.

The COVID-19 pandemic has profoundly impacted the world, having taken the lives of over 6 million individuals. Accordingly, this pandemic has caused a shift in conversations surrounding the burden of diseases worldwide, welcoming insights from multidisciplinary fields including digital health and artificial intelligence. Africa faces a heavy disease burden that exacerbates the current COVID-19 pandemic and limits the scope of public health preparedness, response, containment, and case management. Herein, we examined the potential impact of transformative digital health technologies in mitigating the global health crisis with reference to African countries. Furthermore, we proposed recommendations for scaling up digital health technologies and artificial intelligence-based platforms to tackle the transmission of the SARS-CoV-2 and enable equitable vaccine access. Challenges related to the pandemic are numerous. Rapid response and management strategies-that is, contract tracing, case surveillance, diagnostic testing intensity, and most recently vaccine distribution mapping-can overwhelm the health care delivery system that is fragile. Although challenges are vast, digital health technologies can play an essential role in achieving sustainable resilient recovery and building back better. It is plausible that African nations are better equipped to rapidly identify, diagnose, and manage infected individuals for COVID-19, other diseases, future outbreaks, and pandemics.

Social Determinants and Indicators of COVID-19 Among Marginalized Communities: A Scientific Review and Call to Action for Pandemic Response and Recovery.

Coronavirus disease 2019 (COVID-19) has placed massive socio-psychological, health, and economic burdens including deaths on countless lives; however, it has disproportionally impacted certain populations. Co-occurring Social Determinants of Health (SDoH) disparities and other underlying determinants have exacerbated the COVID-19 pandemic. This literature review sought to (1) examine literature focused on SDoH and COVID-19 outcomes ie, infectivity, hospitalization, and death rates among marginalized communities; and (2) identify SDoH disparities associated with COVID-19 outcomes. We searched electronic databases for studies published from October 2019 to October 2021. Studies that were selected were those intersecting SDoH indicators and COVID-19 outcomes and were conducted in the United States. Our review underscored the disproportionate vulnerabilities and adverse outcomes from COVID-19 that have impacted racial/ethnic minority communities and other disadvantaged groups (ie, senior citizens, and displaced/homeless individuals). COVID-19 outcomes were associated with SDoH indicators, ie, race/ethnicity, poverty, median income level, housing density, housing insecurity, health-care access, occupation, transportation/commuting patterns, education, air quality, food insecurity, old age, etc. Our review concluded with recommendations and a call to action to integrate SDoH indicators along with relevant health data when implementing intelligent solutions and intervention strategies to pandemic response/recovery among vulnerable populations.

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. We conducted a semantic network analysis to explore and analyze COVID-19 vaccine misinformation on the Reddit social media platform.

Public sentiment analysis and topic modeling regarding COVID-19 vaccines on the Reddit social media platform: A call to action for strengthening vaccine confidence.

BACKGROUND: The COVID-19 pandemic fueled one of the most rapid vaccine developments in history. However, misinformation spread through online social media often leads to negative vaccine sentiment and hesitancy. METHODS: To investigate COVID-19 vaccine-related discussion in social media, we conducted a sentiment analysis and Latent Dirichlet Allocation topic modeling on textual data collected from 13 Reddit communities focusing on the COVID-19 vaccine from Dec 1, 2020, to May 15, 2021. Data were aggregated and analyzed by month to detect changes in any sentiment and latent topics. RESULTS: Polarity analysis suggested these communities expressed more positive sentiment than negative regarding the vaccine-related discussions and has remained static over time. Topic modeling revealed community members mainly focused on side effects rather than outlandish conspiracy theories. CONCLUSION: Covid-19 vaccine-related content from 13 subreddits show that the sentiments expressed in these communities are overall more positive than negative and have not meaningfully changed since December 2020. Keywords indicating vaccine hesitancy were detected throughout the LDA topic modeling. Public sentiment and topic modeling analysis regarding vaccines could facilitate the implementation of appropriate messaging, digital interventions, and new policies to promote vaccine confidence.

Addressing Parental Vaccine Hesitancy and Other Barriers to Childhood/Adolescent Vaccination Uptake During the Coronavirus (COVID-19) Pandemic.

Routine childhood immunizations are proven to be one of the most effective public health interventions at controlling numerous deadly diseases. Therefore, the CDC recommends routine immunizations for children and adolescent populations against vaccine-preventable diseases e.g., tetanus, pertussis, diphtheria, etc. This current review sought to examine barriers to pediatric vaccine uptake behaviors during the COVID-19 pandemic. We also explored the implications for parental vaccine hesitancy/delay during an ongoing health crisis and proposed recommendations for increasing vaccine confidence and compliance. Our review determined that the receipt for vaccinations steadily improved in the last decade for both the United States and Tennessee. However, this incremental progress has been forestalled by the COVID-19 pandemic and other barriers i.e. parental vaccine hesitancy, social determinants of health (SDoH) inequalities, etc. which further exacerbate vaccination disparities. Moreover, non-compliance to routine vaccinations could cause an outbreak of diseases, thereby, worsening the ongoing health crisis and already strained health care system. Healthcare providers are uniquely positioned to offer effective recommendations with presumptive languaging to increase vaccination rates, as well as, address parental vaccine hesitancy. Best practices that incorporate healthcare providers' quality improvement coaching, vaccination reminder recall systems, adherence to standardized safety protocols (physical distancing, hand hygiene practices, etc.), as well as, offer telehealth and outdoor/drive-through/curbside vaccination services, etc. are warranted. Additionally, a concerted effort should be made to utilize public health surveillance systems to collect, analyze, and interpret data, thereby, ensuring the dissemination of timely, accurate health information for effective health policy decision-making e.g., vaccine distribution, etc.

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.S. metropolitan region to provide near real-time analysis and dashboarding of ongoing COVID-19 conditions. Our goal is to illuminate associations between SDoH factors and downstream pandemic health outcomes to inform specific policy decisions and public health planning.