Understanding COVID-19 Vaccine Uptake and Hesitancy Among People With HIV in Freetown, Sierra Leone: A Cross-sectional Study (preprint)

Objectives: People living with HIV (PWH) are at increased risk of COVID-19-related morbidity and mortality, yet less is known about COVID-19 vaccination uptake and hesitancy, especially in sub-Saharan Africa. We aimed to evaluate COVID-19 vaccine uptake and hesitancy among PWH in Sierra Leone. Methods: We conducted a cross-sectional study in a convenience sample of PWH in routine care at Connaught Hospital in Freetown, Sierra Leone from April through June 2022. We collected sociodemographic and health-related data. We used the VAX Scale, a validated instrument to assess attitudes towards COVID-19 vaccination. From the responses, we constructed hesitancy (VAX) scores, with higher scores implying negative attitudes toward vaccination. We used generalized linear models to identify factors associated with vaccine hesitancy. Results: A total of 490 PWH were enrolled (71.4% female, median age 38 years, median CD4 count 412 cells/mm3, 83.9% virologically suppressed). About 17.3% had received at least one dose of a COVID-19 vaccine. The mean VAX score was 43.14 {+/-} 7.05, corresponding to 59.9% of participants classified as vaccine-hesitant. Preference for natural immunity (65.8%) and concerns about commercial profiteering (64.4%) were the commonest reasons for hesitancy, followed by mistrust of vaccine benefits (61.4%) and worries about future side effects (48.0%). In adjusted regression analysis, being Muslim ({beta} = 2.563, p < 0.001) and residence in urban areas ({beta} = 1.709, p = 0.010) were associated with greater vaccine hesitancy, while having tested ever for COVID-19 was associated with lesser vaccine hesitancy ({beta} = -3.417, p = 0.027). Conclusion: We observed a low COVID-19 vaccine uptake and high hesitancy among PWH in Sierra Leone. Our findings underscore the need to address vaccine hesitancy as a critical element of efforts to boost COVID-19 vaccine uptake among this population in Sierra Leone.

Socioeconomic disparities in mobility behavior during the COVID-19 pandemic in developing countries (preprint)

Mobile phone data have played a key role in quantifying human mobility during the COVID-19 pandemic. Existing studies on mobility patterns have primarily focused on regional aggregates in high-income countries, obfuscating the accentuated impact of the pandemic on the most vulnerable populations. By combining geolocation data from mobile phones and population census for 6 middle-income countries across 3 continents between March and December 2020, we uncovered common disparities in the behavioral response to the pandemic across socioeconomic groups. When the pandemic hit, urban users living in low-wealth neighborhoods were less likely to respond by self-isolating at home, relocating to rural areas, or refraining from commuting to work. The gap in the behavioral responses between socioeconomic groups persisted during the entire observation period. Among low-wealth users, those who used to commute to work in high-wealth neighborhoods pre-pandemic were particularly at risk, facing both the reduction in activity in high-wealth neighborhood and being more likely to be affected by public transport closures due to their longer commute. While confinement policies were predominantly country-wide, these results suggest a role for place-based policies informed by mobility data to target aid to the most vulnerable.

The scope and vulnerabilities of the internet of medical things (IoMT)

The year 2020 witnessed a major shift in our society and the global economy due to the onset of COVID-19. Many newer trends are expected to surface as people grow more digitally savvy and embrace technology while working from home. This has also impacted the medical industry worldwide and has made healthcare preventive, predictive, and personalized. In healthcare, the Internet of Things (IoT) refers to a network of connected medical devices that can generate, collect, and store data as well as connect to a network, analyze data, and transmit data of various types such as medical images, physiological and vital body signatures, and genomics data. Real-time monitoring, improved diagnostics, robotic surgical interventions, and other medical IoT applications can all help improve outcomes in healthcare. Medical IoT refers to IoT devices and applications tailored to healthcare demands and environments. It includes sensors and apps for monitoring healthcare remotely, telemedicine consultation, and delivery. Medical IoT also uses AI and machine learning to assist life-transforming advancements in existent medical devices, such as the smart inhaler for asthma sufferers. IoT devices offer a lot of new opportunities for patient monitoring, both by the doctors and by the patients themselves. This is made possible by a variety of wearable IoT devices that promise an array of benefits but also pose challenges for all stakeholders in the healthcare industry. Medical IoT devices enable the collection of patient data in real-time, which is processed and evaluated thereafter. The information gathered is centralized for computing, processing, and storage. Centralization can be hazardous as it is vulnerable to multiple threats: failure at one point, mistrust, manipulation, tampering of data, and privacy evasion. Blockchain can address such critical issues by offering decentralized computation and storage for IoT data. COVID-19 brought out the benefits of technology and has reinforced the need to develop and secure more advanced applications including Medical IoT. We have advanced much, but there is a huge scope to explore, expand, and establish. © 2022 Nova Science Publishers, Inc. All rights reserved.

A 10-Year Follow-up Survey of Psychiatry Resident Education in Consultation-Liaison Psychiatry.

BACKGROUND: In 2010, the Academy of Consultation-Liaison (then Academy of Psychosomatic Medicine) surveyed US residency programs to understand training in consultation-liaison (CL) psychiatry, leading to recommendations in 2014. Since then, residency training in CL has evolved in the context of competing training demands, increased prioritization of electives, and reactions to coronavirus 2019. OBJECTIVE: To determine the current state of residency training in CL across the United States, including the structure of core and elective resident rotations in CL, attending physician staffing, presence of fellows and other trainees, didactic curriculum, and impact of coronavirus 2019. METHODS: Members of the Academy of Consultation-Liaison Residency Education Subcommittee designed and piloted an 81-question survey tool that was sent to program directors of 269 US general psychiatry training programs for voluntary completion. RESULTS: One hundred three of 269 programs responded to the survey, yielding a response rate of 38.3%. Responding programs were larger and more likely to have a CL fellowship than nonresponding programs. Of the 103 responding programs, 82.5% have more than the minimally required time on CL, with 46.6% reporting an increase in total CL time in the past decade. Since 2010, 18.4% of responding programs changed the placement of the CL rotation, with 43.7% now adherent to the 2014 Academy of Psychosomatic Medicine recommendation to include core CL training in the second half of residency. Thirty-five percent of responding programs require residents to rotate on more than 1 CL service, and 19.4% have a required outpatient CL component. Faculty full-time equivalent varies widely. Of all services included, 33.8% report that all CL faculty are board-certified in CL psychiatry, whereas 18.7% have no board-certified faculty. Of the 103 responding programs, 36.9% offer a CL fellowship, but 31.1% report no residency graduates pursuing CL fellowships in the past 5 years. Of the included programs, 77.7% have a formal CL curriculum for residents, with 34.0% reporting a separate didactic series during the CL rotation. CONCLUSIONS: Among the responding programs, the amount of time spent on core CL rotations has increased in the past decade, but programs have also shifted CL training earlier in the course of residency. Residency programs are increasingly challenged to provide an optimal CL experience, and updated guidance from Academy of Consultation-Liaison may be appreciated.

Implementation of Extracorporeal Membrane Oxygenation Without On-Site Cardiac Surgery or Perfusion Support: A Tale of Two County Hospitals.

Patients with refractory respiratory and cardiac failure may present to noncardiac surgery centers. Prior studies have demonstrated that acute care surgeons, intensivists, and emergency medicine physicians can safely cannulate and manage patients receiving extracorporeal membrane oxygenation (ECMO). Harborview Medical Center (Harborview) and Hennepin County Medical Center (Hennepin) are both urban, county-owned, level 1 trauma centers that implemented ECMO without direct, on-site cardiac surgery or perfusion support. Both centers 1) use an ECMO specialist model staffed by specially trained nurses and respiratory therapists and 2) developed comparable training curricula for ECMO specialists, intensivists, surgeons, and trainees. Each program began with venovenous ECMO to provide support for refractory hypoxemic respiratory failure and subsequently expanded to venoarterial ECMO support. The coronavirus disease 2019 (COVID-19) pandemic created an impetus for restructuring, with each program creating a consulting service to facilitate ECMO delivery across multiple intensive care units (ICUs) and to promote fellow and resident training and experience. Both Harborview and Hennepin, urban county hospitals 1,700 miles apart in the United States, independently implemented and operate adult ECMO programs without involvement from cardiovascular surgery or perfusion services. This experience further supports the role of ECMO specialists in the delivery of extracorporeal life support.

Computational Simulation Model to Predict Behavior Changes in Inflammatory Bowel Disease Patients during the COVID-19 Pandemic: Analysis of Two Regional Japanese Populations.

Managing inflammatory bowel disease (IBD) is a major challenge for physicians and patients during the COVID-19 pandemic. To understand the impact of the pandemic on patient behaviors and disruptions in medical care, we used a combination of population-based modeling, system dynamics simulation, and linear optimization. Synthetic IBD populations in Tokyo and Hokkaido were created by localizing an existing US-based synthetic IBD population using data from the Ministry of Health, Labor, and Welfare in Japan. A clinical pathway of IBD-specific disease progression was constructed and calibrated using longitudinal claims data from JMDC Inc for patients with IBD before and during the COVID-19 pandemic. Key points considered for disruptions in patient behavior (demand) and medical care (supply) were diagnosis of new patients, clinic visits for new patients seeking care and diagnosed patients receiving continuous care, number of procedures, and the interval between procedures or biologic prescriptions. COVID-19 had a large initial impact and subsequent smaller impacts on demand and supply despite higher infection rates. Our population model (Behavior Predictor) and patient treatment simulation model (Demand Simulator) represent the dynamics of clinical care demand among patients with IBD in Japan, both in recapitulating historical demand curves and simulating future demand during disruption scenarios, such as pandemic, earthquake, and economic crisis.

Revealing the extent of the first wave of the COVID-19 pandemic in Kenya based on serological and PCR-test data [version 2;peer review: 2 approved, 1 approved with reservations]

Policymakers in Africa need robust estimates of the current and future spread of SARS-CoV-2. We used national surveillance PCR test, serological survey and mobility data to develop and fit a county-specific transmission model for Kenya up to the end of September 2020, which encompasses the first wave of SARS-CoV-2 transmission in the country. We estimate that the first wave of the SARS-CoV-2 pandemic peaked before the end of July 2020 in the major urban counties, with 30-50% of residents infected. Our analysis suggests, first, that the reported low COVID-19 disease burden in Kenya cannot be explained solely by limited spread of the virus, and second, that a 30-50% attack rate was not sufficient to avoid a further wave of transmission.

A computational framework for modelling infectious disease policy based on age and household structure with applications to the COVID-19 pandemic.

The widespread, and in many countries unprecedented, use of non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic has highlighted the need for mathematical models which can estimate the impact of these measures while accounting for the highly heterogeneous risk profile of COVID-19. Models accounting either for age structure or the household structure necessary to explicitly model many NPIs are commonly used in infectious disease modelling, but models incorporating both levels of structure present substantial computational and mathematical challenges due to their high dimensionality. Here we present a modelling framework for the spread of an epidemic that includes explicit representation of age structure and household structure. Our model is formulated in terms of tractable systems of ordinary differential equations for which we provide an open-source Python implementation. Such tractability leads to significant benefits for model calibration, exhaustive evaluation of possible parameter values, and interpretability of results. We demonstrate the flexibility of our model through four policy case studies, where we quantify the likely benefits of the following measures which were either considered or implemented in the UK during the current COVID-19 pandemic: control of within- and between-household mixing through NPIs; formation of support bubbles during lockdown periods; out-of-household isolation (OOHI); and temporary relaxation of NPIs during holiday periods. Our ordinary differential equation formulation and associated analysis demonstrate that multiple dimensions of risk stratification and social structure can be incorporated into infectious disease models without sacrificing mathematical tractability. This model and its software implementation expand the range of tools available to infectious disease policy analysts.

Impact of Confinement on Coping Strategies and Psychosocial Factors among University Students.

The pandemic has had psychological effects on the university population. Factors such as emotional intelligence, coping strategies and levels of anxiety, depression and stress have been affected by the situation generated by COVID-19. This study aims to analyze how EI, coping strategies and levels of anxiety, depression and stress have been affected by the situation generated by the pandemic in a population of 567 students from the University of Jaén (Spain). For this purpose, we administered three instruments: the Wong and Law emotional intelligence scale (WLEIS), the Spanish version of the coping strategies inventory (CSI) and the depression anxiety stress scales (DASS-21). At the same time, we asked students to describe their personal circumstances during confinement and their tendency to follow the measures and recommendations promoted by the Ministry of Health. The results obtained showed a positive relationship between EI and coping strategies and a negative relationship with levels of depression, anxiety and stress. A positive relationship was also found between coping and levels of anxiety, depression and stress. It was also found that the circumstances in which students experienced the period of confinement also modulated their levels of EI, coping strategies and their levels of depression, anxiety and stress.

COVID-19 Vaccine Hesitancy among Healthcare Workers and Trainees in Freetown, Sierra Leone: A Cross-Sectional Study.

Despite having safe and efficacious vaccines against COVID-19, vaccine hesitancy is widespread. Although a trusted source of information, vaccine hesitancy has been reported among healthcare professionals, yet few studies have explored this phenomenon in sub-Saharan Africa. We conducted a cross-sectional survey of healthcare professionals in Sierra Leone from January to March 2022. Measures included sociodemographic/health-related information and COVID-19-related concerns. From the responses, we constructed a hesitancy (VAX) score, with higher scores implying negative attitudes or unwillingness to vaccinate. Multivariate linear regression was used to access factors associated with vaccine hesitancy. Overall, 592 participants submitted responses (67.2% female, mean age 29 years, 5.6% physicians/pharmacists, 44.3% medical students, 29.2% nurses, 20.9% nursing students). The mean VAX score was 43.27 ± 8.77, with 60.1% of respondents classified as vaccine hesitant (>50th percentile) and 13.8% as highly hesitant (>75th percentile). Worries about unforeseen future effects (76.3%), a preference for natural immunity (59.5%), and profiteering/mistrust of health authorities (53.1%) were the most common concerns. Being a medical student (ß = 0.105, p = 0.011) and previously refusing a recommended vaccine (ß = 0.177, p < 0.001) were predictors of COVID-19 vaccine hesitancy. Our findings call for addressing vaccine hesitancy among healthcare professionals as an essential component of strategies aimed at increasing COVID-19 vaccine uptake in this setting.

Does Remdesivir Impact the Clinical Outcome of Patients with COVID 19 Infection?

Background. Remdesivir (RDV), was included for the treatment of mild to moderate COVID-19 since July 2020 in our institution, following the initial results from ACTT-1 interim analysis report. With the adoption of RDV, there seems to be anecdotal evidence of efficacy as evidenced by early fever defervescence, quick recovery when on oxygen with decreased need for ventilation and ICU care. We aimed to study the impact of RDV on clinical outcomes among patients with moderate to severe COVID -19. Methods. Nested case control study in the cohort of consecutive patients with moderate to severe COVID - 19. Cases were patients initiated on RDV and age and sex- matched controls who did not receive RDV were included. The primary outcome was in-hospital mortality. Secondary outcomes were, duration of hospital stay, need for ICU, duration of oxygen therapy and need for ventilation. Results. A total of 926 consecutive patients with COVID - 19 were included, among which 411 patients were cases and 515 controls. The mean age of the cohort was 57.05±13.5 years, with male preponderance (75.92%). The overall in-hospital mortality was 22.46%(n=208). On comparison between cases and controls there was no statistically significant difference with respect to primary outcome [22.54% vs. 20.78%, (p value: 0.17)]. Progression to non-invasive ventilation (NIV) was higher among the controls [24.09% vs. 40.78% (p value: < 0.001∗)]. Progression to invasive ventilation was also higher among the controls [5.35% vs. 9.71% (p value: 0.014∗)]. In subgroup analysis among critically ill patients, the use of RDV showed decrease in mortality (OR 0.32 95% CI;0.13 - 0.75 p value - 0.009∗). Conclusion. RDV did not decrease the in-hospital mortality among moderate to severe COVID - 19. However, there seems to be a significant reduction in mortality in critically ill patients.

A computational framework for modelling infectious disease policy based on age and household structure with applications to the COVID-19 pandemic (preprint)

The widespread, and in many countries unprecedented, use of non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic has highlighted the need for mathematical models which can estimate the impact of these measures while accounting for the highly heterogeneous risk profile of COVID-19. Models accounting either for age structure or the household structure necessary to explicitly model many NPIs are commonly used in infectious disease modelling, but models incorporating both levels of structure present substantial computational and mathematical challenges due to their high dimensionality. Here we present a modelling framework for the spread of an epidemic that includes explicit representation of age structure and household structure. Our model is formulated in terms of tractable systems of ordinary differential equations for which we provide an open-source Python implementation. Such tractability leads to significant benefits for model calibration, exhaustive evaluation of possible parameter values, and interpretability of results. We demonstrate the flexibility of our model through four policy case studies, where we quantify the likely benefits of the following measures which were either considered or implemented in the UK during the current COVID-19 pandemic: control of within- and between-household mixing through NPIs; formation of support bubbles during lockdown periods; out-of-household isolation (OOHI); and temporary relaxation of NPIs during holiday periods. Our ordinary differential equation formulation and associated analysis demonstrate that multiple dimensions of risk stratification and social structure can be incorporated into infectious disease models without sacrificing mathematical tractability. This model and its software implementation expand the range of tools available to infectious disease policy analysts.

ISCOM-like Nanoparticles Formulated with Quillaja brasiliensis Saponins Are Promising Adjuvants for Seasonal Influenza Vaccines.

Vaccination is the most effective public health intervention to prevent influenza infections, which are responsible for an important burden of respiratory illnesses and deaths each year. Currently, licensed influenza vaccines are mostly split inactivated, although in order to achieve higher efficacy rates, some influenza vaccines contain adjuvants. Although split-inactivated vaccines induce mostly humoral responses, tailoring mucosal and cellular immune responses is crucial for preventing influenza infections. Quillaja brasiliensis saponin-based adjuvants, including ISCOM-like nanoparticles formulated with the QB-90 saponin fraction (IQB90), have been studied in preclinical models for more than a decade and have been demonstrated to induce strong humoral and cellular immune responses towards several viral antigens. Herein, we demonstrate that a split-inactivated IQB90 adjuvanted influenza vaccine triggered a protective immune response, stronger than that induced by a commercial unadjuvanted vaccine, when applied either by the subcutaneous or the intranasal route. Moreover, we reveal that this novel adjuvant confers up to a ten-fold dose-sparing effect, which could be crucial for pandemic preparedness. Last but not least, we assessed the role of caspase-1/11 in the generation of the immune response triggered by the IQB90 adjuvanted influenza vaccine in a mouse model and found that the cellular-mediated immune response triggered by the IQB90-Flu relies, at least in part, on a mechanism involving the casp-1/11 pathway but not the humoral response elicited by this formulation.

COVID-19 transmission dynamics underlying epidemic waves in Kenya.

Policy decisions on COVID-19 interventions should be informed by a local, regional and national understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission. Epidemic waves may result when restrictions are lifted or poorly adhered to, variants with new phenotypic properties successfully invade, or infection spreads to susceptible subpopulations. Three COVID-19 epidemic waves have been observed in Kenya. Using a mechanistic mathematical model, we explain the first two distinct waves by differences in contact rates in high and low social-economic groups, and the third wave by the introduction of higher-transmissibility variants. Reopening schools led to a minor increase in transmission between the second and third waves. Socioeconomic and urban­rural population structure are critical determinants of viral transmission in Kenya.

Estimating SARS-CoV-2 Seroprevalence (preprint)

Governments and public health authorities use seroprevalence studies to guide responses to the COVID-19 pandemic. Seroprevalence surveys estimate the proportion of individuals who have detectable SARS-CoV-2 antibodies. However, serologic assays are prone to misclassification error, and non-probability sampling may induce selection bias. In this paper, nonparametric and parametric seroprevalence estimators are considered that address both challenges by leveraging validation data and assuming equal probabilities of sample inclusion within covariate-defined strata. Both estimators are shown to be consistent and asymptotically normal, and consistent variance estimators are derived. Simulation studies are presented comparing the estimators over a range of scenarios. The methods are used to estimate SARS-CoV-2 seroprevalence in New York City, Belgium, and North Carolina.

Impact of Timing of Tocilizumab Use in Hospitalized Patients With SARS-CoV-2 Infection.

BACKGROUND: SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) continues to be a global challenge due to the lack of definitive treatment strategies. We sought to determine the efficacy of early administration of anti-interleukin 6 therapy in reducing hospital mortality and progression to mechanical ventilation. METHODS: This was a retrospective chart review of 11,512 patients infected with SARS-CoV-2 who were admitted to a New York health system from March to May 2020. Tocilizumab was administered to subjects at the nasal cannula level of oxygen support to maintain an oxygen saturation of >88%. The Charlson comorbidity index was used as an objective assessment of the burden of comorbidities to predict 10-year mortality. The primary outcome of interest was hospital mortality. Secondary outcomes were progression to mechanical ventilation; the prevalence of venous thromboembolism and renal failure; and the change in C-reactive protein, D-dimer, and ferritin levels after tocilizumab administration. Propensity score matching by using a 1:2 protocol was used to match the tocilizumab and non-tocilizumab groups to minimize selection bias. The groups were matched on baseline demographic characteristics, including age, sex, and body mass index; Charlson comorbidity index score; laboratory markers, including ferritin, D-dimer, lactate dehydrogenase, and C-reactive protein values; and the maximum oxygen requirement at the time of tocilizumab administration. Mortality outcomes were evaluated based on the level of oxygen requirement and the day of hospitalization at the time of tocilizumab administration. RESULTS: The overall hospital mortality was significantly reduced in the tocilizumab group when tocilizumab was administered at the nasal cannula level (10.4% vs 22.0%; P = .002). In subjects who received tocilizumab at the nasal cannula level, the progression to mechanical ventilation was reduced versus subjects who were initially on higher levels of oxygen support (6.3% vs 18.7%; P < .001). There was no improvement in mortality when tocilizumab was given at the time of requiring non-rebreather, high-flow nasal cannula, noninvasive ventilator, or invasive ventilator. CONCLUSIONS: Early use of anti-interleukin 6 therapy may be associated with improved hospital mortality and reduction in progression to more severe coronavirus disease 2019.

Moderating Factors in University Students’ Self-Evaluation for Sustainability

Background: Self-evaluation is a multidimensional construct that has raised increasing interest within educational research at different educational stages. Different studies have pointed out the important role that Student Self-Assessment plays in improving student learning and ensuring the sustainability in instructional and evaluation processes. Method: The aim of this study with 630 university students is to analyze how engagement profiles and study strategies (measured by questionnaire) can predict the accuracy of students’ self-assessment of their achievements. For this purpose, the UWE-9 questionnaire was used to evaluate engagement, the Study Techniques Questionnaire scale to measure study strategies and a content-based test to evaluate performance, along with a self-assessment test in which the student had to estimate the level of achievement obtained in the content-based test, once the test had been completed. Results: The results show that both the academic engagement and the study strategies undertaken by students can be important factors that may influence different aspects of learning in the educational context. Students with higher performance and more engagement tend to show greater skills with student self-assessment (SSA) and students with better study habits tend to have better scores, greater confidence in the SSAs delivered and better skills for self-assessment. Conclusions: Findings suggests that providing opportunities for students to have a greater involvement in the construction of their learning and in its evaluation raises positive attitudes, which results in increased performance in order to achieve greater sustainability in the learning process assessment.