Understanding mental health trends during COVID-19 pandemic in the United States using network analysis.

The emergence of COVID-19 in the United States resulted in a series of federal and state-level lock-downs and COVID-19 related health mandates to manage the spread of the virus. These policies may negatively impact the mental health state of the population. This study focused on the trends in mental health indicators following the COVID-19 pandemic amongst four United States geographical regions, and political party preferences. Indicators of interest included feeling anxious, feeling depressed, and worried about finances. Survey data from the Delphi Group at Carnegie Mellon University were analyzed using clustering algorithms and dynamic connectome obtained from sliding window analysis. Connectome refers to the description of connectivity on a network. United States maps were generated to observe spatial trends and identify communities with similar mental health and COVID-19 trends. Between March 3rd, 2021, and January 10th, 2022, states in the southern geographic region showed similar trends for reported values of feeling anxious and worried about finances. There were no identifiable communities resembling geographical regions or political party preference for the feeling depressed indicator. We observed a high degree of correlation among southern states as well as within Republican states, where the highest correlation values from the dynamic connectome for feeling anxious and feeling depressed variables seemingly overlapped with an increase in COVID-19 related cases, deaths, hospitalizations, and rapid spread of the COVID-19 Delta variant.

Racial-ethnic disparities in psychological distress during the COVID-19 pandemic in the United States: the role of experienced discrimination and perceived racial bias.

BACKGROUND: Research on mental health disparities by race-ethnicity in the United States (US) during COVID-19 is limited and has generated mixed results. Few studies have included Asian Americans as a whole or by subgroups in the analysis. METHODS: Data came from the 2020 Health, Ethnicity, and Pandemic Study, based on a nationally representative sample of 2,709 community-dwelling adults in the US with minorities oversampled. The outcome was psychological distress. The exposure variable was race-ethnicity, including four major racial-ethnic groups and several Asian ethnic subgroups in the US. The mediators included experienced discrimination and perceived racial bias toward one's racial-ethnic group. Weighted linear regressions and mediation analyses were performed. RESULTS: Among the four major racial-ethnic groups, Hispanics (22%) had the highest prevalence of severe distress, followed by Asians (18%) and Blacks (16%), with Whites (14%) having the lowest prevalence. Hispanics' poorer mental health was largely due to their socioeconomic disadvantages. Within Asians, Southeast Asians (29%), Koreans (27%), and South Asians (22%) exhibited the highest prevalence of severe distress. Their worse mental health was mainly mediated by experienced discrimination and perceived racial bias. CONCLUSIONS: Purposefully tackling racial prejudice and discrimination is necessary to alleviate the disproportionate psychological distress burden in racial-ethnic minority groups.

Real-time risk ranking of emerging epidemics based on optimized moving average prediction limit-taking the COVID-19 pandemic as an example.

BACKGROUND: Mathematical models to forecast the risk trend of the COVID-19 pandemic timely are of great significance to control the pandemic, but the requirement of manual operation and many parameters hinders their efficiency and value for application. This study aimed to establish a convenient and prompt one for monitoring emerging infectious diseases online and achieving risk assessment in real time. METHODS: The Optimized Moving Average Prediction Limit (Op-MAPL) algorithm model analysed real-time COVID-19 data online and was validated using the data of the Delta variant in India and the Omicron in the United States. Then, the model was utilized to determine the infection risk level of the Omicron in Shanghai and Beijing. RESULTS: The Op-MAPL model can predict the epidemic peak accurately. The daily risk ranking was stable and predictive, with an average accuracy of 87.85% within next 7 days. Early warning signals were issued for Shanghai and Beijing on February 28 and April 23, 2022, respectively. The two cities were rated as medium-high risk or above from March 27 to April 20 and from April 24 to May 5, indicating that the pandemic had entered a period of rapid increase. After April 21 and May 26, the risk level was downgraded to medium and became stable by the algorithm, indicating that the pandemic had been controlled well and mitigated gradually. CONCLUSIONS: The Op-MAPL relies on nothing but an indicator to assess the risk level of the COVID-19 pandemic with different data sources and granularities. This forward-looking method realizes real-time monitoring and early warning effectively to provide a valuable reference to prevent and control infectious diseases.

Continuous Eligibility And Coverage Policies Expanded Children's Medicaid Enrollment.

We examined children's Medicaid participation during 2019-21 and found that as of March 2021, states newly adopting continuous Medicaid coverage for children during the COVID-19 pandemic experienced a 4.62 percent relative increase in children's Medicaid participation compared to states with previous continuous eligibility policies.

The environmental impact of surgical telemedicine: life cycle assessment of virtual vs. in-person preoperative evaluations for benign foregut disease.

BACKGROUND: Health care accounts for almost 10% of the United States' greenhouse gas emissions, accounting for a loss of 470,000 disability-adjusted life years based on the health effects of climate change. Telemedicine has the potential to decrease health care's carbon footprint by reducing patient travel and clinic-related emissions. At our institution, telemedicine visits for evaluation of benign foregut disease were implemented for patient care during the COVID-19 pandemic. We aimed to estimate the environmental impact of telemedicine usage for these clinic encounters. METHODS: We used life cycle assessment (LCA) to compare greenhouse gas (GHG) emissions for an in-person and a telemedicine visit. For in-person visits, travel distances to clinic were retrospectively assessed from 2020 visits as a representative sample, and prospective data were gathered on materials and processes related to in-person clinic visits. Prospective data on the length of telemedicine encounters were collected and environmental impact was calculated for equipment and internet usage. Upper and lower bounds scenarios for emissions were generated for each type of visit. RESULTS: For in-person visits, 145 patient travel distances were recorded with a median [IQR] distance travel distance of 29.5 [13.7, 85.1] miles resulting in 38.22-39.61 carbon dioxide equivalents (kgCO2-eq) emitted. For telemedicine visits, the mean (SD) visit time was 40.6 (17.1) min. Telemedicine GHG emissions ranged from 2.26 to 2.99 kgCO2-eq depending on the device used. An in-person visit resulted in 25 times more GHG emissions compared to a telemedicine visit (p < 0.001). CONCLUSION: Telemedicine has the potential to decrease health care's carbon footprint. Policy changes to facilitate telemedicine use are needed, as well as increased awareness of potential disparities of and barriers to telemedicine use. Moving toward telemedicine preoperative evaluations in appropriate surgical populations is a purposeful step toward actively addressing our role in health care's large carbon footprint.

Adverse events with risankizumab in the real world: postmarketing pharmacovigilance assessment of the FDA adverse event reporting system.

Background: Risankizumab, a humanized IgG1 monoclonal antibody that selectively inhibits IL-23, is currently approved for the treatment of moderate-to-severe plaque psoriasis and Crohn's disease. The real-world safety study of risankizumab in a large- sample population is currently lacking. The aim of this study was to evaluate risankizumab-associated adverse events (AEs) and characterize the clinical priority through the data mining of the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). Methods: Disproportionality analyses were performed by calculating the reporting odds ratios (RORs), deemed significant when the lower limit of the 95% confidence interval was greater than 1, to quantify the signals of risankizumab-related AEs from the second quarter (Q2) of 2019 to 2022 Q3. Serious and non-serious cases were compared, and signals were prioritized using a rating scale. Results: Risankizumab was recorded in 10,235 reports, with 161 AEs associated with significant disproportionality. Of note, 37 PTs in at least 30 cases were classified as unexpected AEs, which were uncovered in the drug label, such as myocardial infarction, cataract, pancreatitis, diabetes mellitus, stress, and nephrolithiasis. 74.68%, 25.32%, and 0% PTs were graded as weak, moderate, and strong clinical priorities, respectively. A total of 48 risankizumab-related AEs such as pneumonia, cerebrovascular accident, cataract, loss of consciousness, cardiac disorder, hepatic cirrhosis, and thrombosis, were more likely to be reported as serious AEs. The median TTO of moderate and weak signals related to risankizumab was 115 (IQR 16.75-305) and 124 (IQR 29-301) days, respectively. All of the disproportionality signals had early failure type features, indicating that risankizumab-associated AEs gradually decreased over time. Conclusion: Our study found potential new AE signals and provided valuable evidence for clinicians to mitigate the risk of risankizumab-associated AEs based on an extensive analysis of a large-scale postmarketing international safety database.

Self-Reported Cognitive Aging and Well-Being among Older Middle Eastern/Arab American Immigrants during the COVID-19 Pandemic.

BACKGROUND: The COVID-19 pandemic posed new challenges for cognitive aging since it brought interruptions in family relations for older adults in immigrant communities. This study examines the consequences of COVID-19 for the familial and social support systems of aging Middle Eastern/Arab immigrants in Michigan, the largest concentration in the United States. We conducted six focus groups with 45 participants aged 60 and older to explore participant descriptions of changes and difficulties faced during the pandemic relating to their cognitive health, familial and social support systems, and medical care. The findings indicate challenges around social distancing for older Middle Eastern/Arab American immigrants, which generated three overarching themes: fear, mental health, and social relationships. These themes provide unique insights into the lived experiences of older Middle Eastern/Arab American adults during the pandemic and bring to light culturally embedded risks to cognitive health and well-being. A focus on the well-being of older Middle Eastern/Arab American immigrants during COVID-19 advances understanding of how environmental contexts inform immigrant health disparities and the sociocultural factors that shape minority aging.

The Epidemiology of Long Coronavirus Disease in US Adults.

BACKGROUND: We estimated the prevalence of long COVID and impact on daily living among a representative sample of adults in the United States. METHODS: We conducted a population-representative survey, 30 June-2 July 2022, of a random sample of 3042 US adults aged 18 years or older and weighted to the 2020 US population. Using questions developed by the UK's Office of National Statistics, we estimated the prevalence of long COVID, by sociodemographics, adjusting for gender and age. RESULTS: An estimated 7.3% (95% confidence interval: 6.1-8.5%) of all respondents reported long COVID, corresponding to approximately 18 828 696 adults. One-quarter (25.3% [18.2-32.4%]) of respondents with long COVID reported their day-to-day activities were impacted "a lot" and 28.9% had severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection more than 12 months ago. The prevalence of long COVID was higher among respondents who were female (adjusted prevalence ratio [aPR]: 1.84 [1.40-2.42]), had comorbidities (aPR: 1.55 [1.19-2.00]), or were not (vs were) boosted (aPR: 1.67 [1.19-2.34]) or not vaccinated (vs boosted) (aPR: 1.41 [1.05-1.91]). CONCLUSIONS: We observed a high burden of long COVID, substantial variability in prevalence of SARS-CoV-2, and risk factors unique from SARS-CoV-2 risk, suggesting areas for future research. Population-based surveys are an important surveillance tool and supplement to ongoing efforts to monitor long COVID.

Misdiagnosis of Clostridioides difficile Infections by Standard-of-Care Specimen Collection and Testing among Hospitalized Adults, Louisville, Kentucky, USA, 2019-20201.

Although Clostridioides difficile infection (CDI) incidence is high in the United States, standard-of-care (SOC) stool collection and testing practices might result in incidence overestimation or underestimation. We conducted diarrhea surveillance among inpatients >50 years of age in Louisville, Kentucky, USA, during October 14, 2019-October 13, 2020; concurrent SOC stool collection and CDI testing occurred independently. A study CDI case was nucleic acid amplification test‒/cytotoxicity neutralization assay‒positive or nucleic acid amplification test‒positive stool in a patient with pseudomembranous colitis. Study incidence was adjusted for hospitalization share and specimen collection rate and, in a sensitivity analysis, for diarrhea cases without study testing. SOC hospitalized CDI incidence was 121/100,000 population/year; study incidence was 154/100,000 population/year and, in sensitivity analysis, 202/100,000 population/year. Of 75 SOC CDI cases, 12 (16.0%) were not study diagnosed; of 109 study CDI cases, 44 (40.4%) were not SOC diagnosed. CDI incidence estimates based on SOC CDI testing are probably underestimated.

Editorial: Infectious Disease Surveillance Using Artificial Intelligence (AI) and its Role in Epidemic and Pandemic Preparedness.

Artificial intelligence (AI), or machine learning, is an ancient concept based on the assumption that human thought and reasoning can be mechanized. AI techniques have been used in diagnostic medicine for several decades, particularly in image analysis and clinical diagnosis. During the COVID-19 pandemic, AI was critical in genome sequencing, drug and vaccine development, identifying disease outbreaks, monitoring disease spread, and tracking viral variants. AI-driven approaches complement human-curated ones, including traditional public health surveillance. Preparation for future pandemics will require the combined efforts of collaborative surveillance networks, which currently include the US Centers for Disease Control and Prevention (CDC) Center for Forecasting and Outbreak Analytics and the World Health Organization (WHO) Hub for Pandemic and Epidemic Intelligence, which will use AI combined with international cooperation to implement AI in surveillance programs. This Editorial aims to provide an update on the uses and limitations of AI in infectious disease surveillance and pandemic preparedness.

Characteristics Associated with Being a High Consumer of Sweet Foods and Sugar-Sweetened Beverages among US Adults during the COVID-19 Pandemic, 2021.

BACKGROUND: The COVID-19 pandemic impacted some dietary habits of Americans. OBJECTIVE: We examined characteristics associated with a high intake of sweet foods and sugar-sweetened beverages (SSB) during the COVID-19 pandemic among US adults. DESIGN: This was a cross-sectional study. PARTICIPANTS/SETTINGS: The SummerStyles survey data were collected in 2021 among 4034 US adults (≥18 years). MAIN OUTCOME MEASURES: The frequencies were measured of consuming various sweet foods (chocolate/candy, doughnuts/sweet rolls/Danish/muffins/Pop-Tarts, cookies/cake/pie/brownies, and ice cream/frozen desserts) and SSB (regular sodas, sweetened coffee/tea drinks fruit drinks, sports drinks, and energy drinks) during the COVID-19 pandemic. The responses were categorized into 0, >0 to <1, 1 to <2, and ≥2 times/day. The descriptive variables were sociodemographics, food insecurity, weight status, metropolitan status, census regions, and eating habit changes during the COVID-19 pandemic. STATISTICAL ANALYSES PERFORMED: Multinomial regressions were used to estimate adjusted odds ratios (AOR) for being a high consumer of sweet foods and SSB after controlling for characteristics. RESULTS: During 2021, 15% of adults reported consuming sweet foods ≥2 times/day, and 30% reported drinking SSB ≥2 times/day. The factors that were significantly associated with greater odds of high sweet food intake (≥2 times/day) were lower household income (AOR = 1.53 for <$35,000 vs. ≥$100,000), often/sometimes experiencing food insecurity (AOR = 1.41 vs. never), and eating more sweet foods than usual since start of the pandemic (AOR = 2.47 vs. same as usual). The factors that were significantly associated with greater odds of high SSB intake (≥2 times/day) were males (AOR = 1.51), lower education (AOR = 1.98 for ≤high school; AOR = 1.33 for some college vs. college graduate), currently having children (AOR = 1.65), living in nonmetropolitan areas (AOR = 1.34), and drinking more SSB than usual since the pandemic began (AOR = 2.23 vs. same as usual). Younger age, Black race, and reductions in consumption during COVID-19 were related to lower sweet food and SSB intakes. CONCLUSIONS: Our findings, which identified high consumers of sweet foods or SSB, can be used to inform efforts to reduce consumers' added sugars intake during pandemic recovery and support their health.

Productivity costs associated with reactive school closures related to influenza or influenza-like illness in the United States from 2011 to 2019.

INTRODUCTION: Schools close in reaction to seasonal influenza outbreaks and, on occasion, pandemic influenza. The unintended costs of reactive school closures associated with influenza or influenza-like illness (ILI) has not been studied previously. We estimated the costs of ILI-related reactive school closures in the United States over eight academic years. METHODS: We used prospectively collected data on ILI-related reactive school closures from August 1, 2011 to June 30, 2019 to estimate the costs of the closures, which included productivity costs for parents, teachers, and non-teaching school staff. Productivity cost estimates were evaluated by multiplying the number of days for each closure by the state- and year-specific average hourly or daily wage rates for parents, teachers, and school staff. We subdivided total cost and cost per student estimates by school year, state, and urbanicity of school location. RESULTS: The estimated productivity cost of the closures was $476 million in total during the eight years, with most (90%) of the costs occurring between 2016-2017 and 2018-2019, and in Tennessee (55%) and Kentucky (21%). Among all U.S. public schools, the annual cost per student was much higher in Tennessee ($33) and Kentucky ($19) than any other state ($2.4 in the third highest state) or the national average ($1.2). The cost per student was higher in rural areas ($2.9) or towns ($2.5) than cities ($0.6) or suburbs ($0.5). Locations with higher costs tended to have both more closures and closures with longer durations. CONCLUSIONS: In recent years, we found significant heterogeneity in year-to-year costs of ILI-associated reactive school closures. These costs have been greatest in Tennessee and Kentucky and been elevated in rural or town areas relative to cities or suburbs. Our findings might provide evidence to support efforts to reduce the burden of seasonal influenza in these disproportionately impacted states or communities.

Optimizing US Global Health Funding.

This Viewpoint recommends increasing US global health funding levels, outlines steps for ensuring optimal integration and coordination of activities, and discusses ways to elevate noncommunicable diseases.