Use of Digital Health Technology Among Older Adults With Cancer in the United States: Findings From a National Longitudinal Cohort Study (2015-2021).

BACKGROUND: Despite the benefits of digital health technology use, older adults with cancer (ie, aged 65 years) have reported challenges to technology adoption. However, there has been a lack of a good understanding of their digital health technology use patterns and the associated influential factors in the past few years. OBJECTIVE: This study aimed to examine the trends in and factors associated with digital health technology use among older adults with cancer. METHODS: The National Health and Aging Trends Study (NHATS) data set is a national longitudinal cohort study with annual survey waves of Medicare beneficiaries 65 years and older. Participants were community-dwelling older adults who self-reported previous or current cancer diagnoses in each round. The study sample size of each round ranged from 1996 (2015) to 1131 (2021). Digital health technology use was defined as using the internet or online in the last month to order or refill prescriptions, contact medical providers, handle Medicare or other insurance matters, or get information about their health conditions. The association of sociodemographics, clinical factors (self-rated health, chronic conditions, difficulties in activities of daily living, dementia, anxiety, and depression), and physical function (Short Physical Performance Battery and grip strength) with digital health technology use was examined using design-based logistic regression. All statistical analyses accounted for the complex sample design. RESULTS: The prevalence of any digital health technology use increased from 36% in 2015 to 45% in 2019. In 2020-2021, which was amid the COVID-19 pandemic, it ranged from 51% to 52%. In terms of each digital health technology use behavior, in 2015, overall, 28% of older cancer survivors used digital health technology to obtain health information, followed by contacting clinicians (19%), filling prescriptions (14%), and handling insurance (11%). Greater use of digital health technology was associated with younger age, being White, having a college or higher education, having a higher income, having more comorbidities, nondementia, and having a higher gait speed. CONCLUSIONS: Digital health technology use in older adults with cancer has gradually increased, particularly during the COVID-19 pandemic. However, socioeconomic and racial disparities have remained in older cancer survivors. Additionally, older adults with cancer may have some unique features associated with digital health technology use; for example, their use of digital health may be increased by their comorbidities (ie, health care needs) and reduced by their frailty.

Post-authorization safety surveillance of Ad.26.COV2.S vaccine: Reports to the Vaccine Adverse Event Reporting System and v-safe, February 2021-February 2022.

BACKGROUND: On 2/27/2021, FDA authorized Janssen COVID-19 Vaccine (Ad.26.COV2.S) for use in individuals 18 years of age and older. Vaccine safety was monitored using the Vaccine Adverse Event Reporting System (VAERS), a national passive surveillance system, and v-safe, a smartphone-based surveillance system. METHODS: VAERS and v-safe data from 2/27/2021 to 2/28/2022 were analyzed. Descriptive analyses included sex, age, race/ethnicity, seriousness, AEs of special interest (AESIs), and cause of death. For prespecified AESIs, reporting rates were calculated using the total number of doses of Ad26.COV2.S administered. For myopericarditis, observed-to-expected (O/E) analysis was performed based on the number verified cases, vaccine administration data, and published background rates. Proportions of v-safe participants reporting local and systemic reactions, as well as health impacts, were calculated. RESULTS: During the analytic period, 17,018,042 doses of Ad26.COV2.S were administered in the United States, and VAERS received 67,995 reports of AEs after Ad26.COV2.S vaccination. Most AEs (59,750; 87.9 %) were non-serious and were similar to those observed during clinical trials. Serious AEs included COVID-19 disease, coagulopathy (including thrombosis with thrombocytopenia syndrome; TTS), myocardial infarction, Bell's Palsy, and Guillain-Barré syndrome (GBS). Among AESIs, reporting rates per million doses of Ad26.COV2.S administered ranged from 0.06 for multisystem inflammatory syndrome in children to 263.43 for COVID-19 disease. O/E analysis revealed elevated reporting rate ratios (RRs) for myopericarditis; among adults ages 18-64 years, the RR was 3.19 (95 % CI 2.00, 4.83) within 7 days and 1.79 (95 % CI 1.26, 2.46) within 21 days of vaccination. Of 416,384 Ad26.COV2.S recipients enrolled into v-safe, 60.9 % reported local symptoms (e.g. injection site pain) and 75.9 % reported systemic symptoms (e.g., fatigue, headache). One-third of participants (141,334; 33.9 %) reported a health impact, but only 1.4 % sought medical care. CONCLUSION: Our review confirmed previously established safety risks for TTS and GBS and identified a potential safety concern for myocarditis.

Advisory Committee on Immunization Practices Recommended Immunization Schedule for Children and Adolescents Aged 18 Years or Younger - United States, 2023.

At its October 2022 meeting, the Advisory Committee on Immunization Practices* (ACIP) approved the Recommended Child and Adolescent Immunization Schedule for Ages 18 Years or Younger, United States, 2023. The 2023 child and adolescent immunization schedule, available on the CDC immunization schedule website (https://www.cdc.gov/vaccines/schedules), summarizes ACIP recommendations, including several changes from the 2022 immunization schedule† on the cover page, tables, notes, and appendix. Health care providers are advised to use the tables, notes, and appendix together to determine recommended vaccinations for patient populations. This immunization schedule is recommended by ACIP (https://www.cdc.gov/vaccines/acip) and approved by CDC (https://www.cdc.gov), the American Academy of Pediatrics (https://www.aap.org), the American Academy of Family Physicians (https://www.aafp.org), the American College of Obstetricians and Gynecologists (http://www.acog.org), the American College of Nurse-Midwives (https://www.midwife.org), the American Academy of Physician Associates (https://www.aapa.org), and the National Association of Pediatric Nurse Practitioners (https://www.napnap.org).

Barriers to the Engagement of Complementary and Integrative Health Providers in Public Health Responses to COVID-19: Recommendations From a Multidisciplinary Expert Panel.

OBJECTIVE: The purpose of this project was to explore barriers to the involvement of complementary and integrative health (CIH) providers in the public health response to COVID-19 and potential solutions for future involvement in public health crises. METHODS: An expert panel of 10 people, which included doctors of chiropractic, naturopathic doctors, public health practitioners, and researchers from the United States, was convened for a day-long online panel discussion. Facilitators asked panelists how CIH practitioners could contribute and be mobilized. We summarized themes and recommendations from the discussion. RESULTS: Despite their skills and resources, few CIH providers participated in public health efforts like testing and contact tracing during the COVID-19 pandemic. Panelists described that CIH professionals may not have participated in those efforts due to the CIH providers possibly not having sufficient public health training and limited contact with public health professionals, as well as policy and financial challenges during the pandemic. Panelists proposed solutions to these barriers, including more public health training, stronger formal relationships between CIH and public health organizations, and improved financial support for both CIH care and public health efforts. CONCLUSION: Through an expert panel discussion, we identified barriers that hindered the involvement of CIH providers in the public health response to the COVID-19 pandemic. During future pandemics in the United States, public health planners should recognize CIH providers as part of the existing labor resource, with clinical expertise and community-level connections that can be called upon in a crisis. During future events, CIH professional leaders should be more proactive in seeking out a supportive role and sharing their knowledge, skills, and expertise.

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.

A deep learning predictive model for public health concerns and hesitancy toward the COVID-19 vaccines.

Throughout the pandemic era, COVID-19 was one of the remarkable unexpected situations over the past few years, but with the decentralization and globalization of efforts and knowledge, a successful vaccine-based control strategy was efficiently designed and applied worldwide. On the other hand, excused confusion and hesitation have widely impacted public health. This paper aims to reduce COVID-19 vaccine hesitancy taking into consideration the patient's medical history. The dataset used in this study is the Vaccine Adverse Event Reporting System (VAERS) dataset which was created as a corporation between the Food and Drug Administration (FDA) and Centers for Disease Control and Prevention (CDC) to gather reported side effects that may be caused by PFIEZER, JANSSEN, and MODERNA vaccines. In this paper, a Deep Learning (DL) model has been developed to identify the relationship between a certain type of COVID-19 vaccine (i.e. PFIEZER, JANSSEN, and MODERNA) and the adverse reactions that may occur in vaccinated patients. The adverse reactions under study are the recovery condition, possibility to be hospitalized, and death status. In the first phase of the proposed model, the dataset has been pre-proceesed, while in the second phase, the Pigeon swarm optimization algorithm is used to optimally select the most promising features that affect the performance of the proposed model. The patient's status after vaccination dataset is grouped into three target classes (Death, Hospitalized, and Recovered). In the third phase, Recurrent Neural Network (RNN) is implemented for both each vaccine type and each target class. The results show that the proposed model gives the highest accuracy scores which are 96.031% for the Death target class in the case of PFIEZER vaccination. While in JANSSEN vaccination, the Hospitalized target class has shown the highest performance with an accuracy of 94.7%. Finally, the model has the best performance for the Recovered target class in MODERNA vaccination with an accuracy of 97.794%. Based on the accuracy and the Wilcoxon Signed Rank test, we can conclude that the proposed model is promising for identifying the relationship between the side effects of COVID-19 vaccines and the patient's status after vaccination. The study displayed that certain side effects were increased in patients according to the type of COVID-19 vaccines. Side effects related to CNS and hemopoietic systems demonstrated high values in all studied COVID-19 vaccines. In the frame of precision medicine, these findings can support the medical staff to select the best COVID-19 vaccine based on the medical history of the patient.

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.