How do psychobehavioural variables shed light on heterogeneity in COVID-19 vaccine acceptance? Evidence from United States general population surveys on a probability panel and social media.

OBJECTIVES: To (1) understand what behaviours, beliefs, demographics and structural factors predict US adults' intention to get a COVID-19 vaccination, (2) identify segments of the population ('personas') who share similar factors predicting vaccination intention, (3) create a 'typing tool' to predict which persona people belong to and (4) track changes in the distribution of personas over time and across the USA. DESIGN: Three surveys: two on a probability-based household panel (NORC's AmeriSpeak) and one on Facebook. SETTING: The first two surveys were conducted in January 2021 and March 2021 when the COVID-19 vaccine had just been made available in the USA. The Facebook survey ran from May 2021 to February 2022. PARTICIPANTS: All participants were aged 18+ and living in the USA. OUTCOME MEASURES: In our predictive model, the outcome variable was self-reported vaccination intention (0-10 scale). In our typing tool model, the outcome variable was the five personas identified by our clustering algorithm. RESULTS: Only 1% of variation in vaccination intention was explained by demographics, with about 70% explained by psychobehavioural factors. We identified five personas with distinct psychobehavioural profiles: COVID Sceptics (believe at least two COVID-19 conspiracy theories), System Distrusters (believe people of their race/ethnicity do not receive fair healthcare treatment), Cost Anxious (concerns about time and finances), Watchful (prefer to wait and see) and Enthusiasts (want to get vaccinated as soon as possible). The distribution of personas varies at the state level. Over time, we saw an increase in the proportion of personas who are less willing to get vaccinated. CONCLUSIONS: Psychobehavioural segmentation allows us to identify why people are unvaccinated, not just who is unvaccinated. It can help practitioners tailor the right intervention to the right person at the right time to optimally influence behaviour.

Discovery of interconnected causal drivers of COVID-19 vaccination intentions in the US using a causal Bayesian network.

Holistic interventions to overcome COVID-19 vaccine hesitancy require a system-level understanding of the interconnected causes and mechanisms that give rise to it. However, conventional correlative analyses do not easily provide such nuanced insights. We used an unsupervised, hypothesis-free causal discovery algorithm to learn the interconnected causal pathways to vaccine intention as a causal Bayesian network (BN), using data from a COVID-19 vaccine hesitancy survey in the US in early 2021. We identified social responsibility, vaccine safety and anticipated regret as prime candidates for interventions and revealed a complex network of variables that mediate their influences. Social responsibility's causal effect greatly exceeded that of other variables. The BN revealed that the causal impact of political affiliations was weak compared with more direct causal factors. This approach provides clearer targets for intervention than regression, suggesting it can be an effective way to explore multiple causal pathways of complex behavioural problems to inform interventions.

Researching COVID to enhance recovery (RECOVER) pediatric study protocol: Rationale, objectives and design (preprint)

Importance: The prevalence, pathophysiology, and long-term outcomes of COVID-19 (post-acute sequelae of SARS-CoV-2 [PASC] or "Long COVID") in children and young adults remain unknown. Studies must address the urgent need to define PASC, its mechanisms, and potential treatment targets in children and young adults. Observations: We describe the protocol for the Pediatric Observational Cohort Study of the NIHs REsearching COVID to Enhance Recovery (RECOVER) Initiative. RECOVER-Pediatrics is an observational meta-cohort study of caregiver-child pairs (birth through 17 years) and young adults (18 through 25 years), recruited from more than 100 sites across the US. This report focuses on two of five cohorts that comprise RECOVER-Pediatrics: 1) a de novo RECOVER prospective cohort of children and young adults with and without previous or current infection; and 2) an extant cohort derived from the Adolescent Brain Cognitive Development (ABCD) study (n=10,000). The de novo cohort incorporates three tiers of data collection: 1) remote baseline assessments (Tier 1, n=6000); 2) longitudinal follow-up for up to 4 years (Tier 2, n=6000); and 3) a subset of participants, primarily the most severely affected by PASC, who will undergo deep phenotyping to explore PASC pathophysiology (Tier 3, n=600). Youth enrolled in the ABCD study participate in Tier 1. The pediatric protocol was developed as a collaborative partnership of investigators, patients, researchers, clinicians, community partners, and federal partners, intentionally promoting inclusivity and diversity. The protocol is adaptive to facilitate responses to emerging science. Conclusions and Relevance: RECOVER-Pediatrics seeks to characterize the clinical course, underlying mechanisms, and long-term effects of PASC from birth through 25 years old. RECOVER-Pediatrics is designed to elucidate the epidemiology, four-year clinical course, and sociodemographic correlates of pediatric PASC. The data and biosamples will allow examination of mechanistic hypotheses and biomarkers, thus providing insights into potential therapeutic interventions.

Combating Misinformation/ Disinformation in Online Social Media: A Multidisciplinary View

Recently, the viral propagation of mis/disinformation has raised significant concerns from both academia and industry. This problem is particularly difficult because on the one hand, rapidly evolving technology makes it much cheaper and easier to manipulate and propagate social media information. On the other hand, the complexity of human psychology and sociology makes the understanding, prediction and prevention of users' involvement in mis/disinformation propagation very difficult. This themed series on "Multi-Disciplinary Dis/Misinformation Analysis and Countermeasures" aims to bring the attention and efforts from researchers in relevant disciplines together to tackle this challenging problem. In addition, on October 20th, 2021, and March 7th 2022, some of the guest editorial team members organized two panel discussions on "Social Media Disinformation and its Impact on Public Health During the COVID-19 Pandemic," and on "Dis/Misinformation Analysis and Countermeasures - A Computational Viewpoint." This article summarizes the key discussion items at these two panels and hopes to shed light on the future directions.

Statistical consulting in the healthcare professions: A model of student-led consulting services

Statistical consulting is a common and vital activity undertaken by those with advanced statistical training but may not be widely available to health professionals and trainees without access to dedicated statistical consulting cores. Here, we present a novel model of statistical consulting used by a student chapter of the American Statistical Association housed in a graduate health professions university. This student-led organization provides statistical consulting services for faculty, staff and students university-wide. Information on the methods of advertising consulting services, the role of a professional statistician faculty advisor, resources available to consultants and university community members and the ways in which consulting services were adapted to address the barriers introduced by the COVID-19 pandemic are addressed. Data from 108 consults performed over the past two and a half years with 88 different consultees are analysed and discussed. This article outlines an innovative model of student-led statistical consultation for healthcare professionals and trainees and aims to provide a template for future student-led organizations who similarly aim to perform university statistical consultations.

Impact of diabetes status and related factors on COVID-19-associated hospitalization: A nationwide retrospective cohort study of 116,370 adults with SARS-CoV-2 infection.

AIMS: We examined diabetes status (no diabetes; type 1 diabetes [T1D]; type 2 diabetes [T2D]) and other demographic and clinical factors as correlates of coronavirus disease 2019 (COVID-19)-related hospitalization. Further, we evaluated predictors of COVID-19-related hospitalization in T1D and T2D. METHODS: We analyzed electronic health record data from the de-identified COVID-19 database (December 2019 through mid-September 2020; 87 US health systems). Logistic mixed models were used to examine predictors of hospitalization at index encounters associated with confirmed SARS-CoV-2 infection. RESULTS: In 116,370 adults (>=18 years old) with COVID-19 (93,098 no diabetes; 802 T1D; 22,470 T2D), factors that independently increased risk for hospitalization included diabetes, male sex, public health insurance, decreased body mass index (BMI; <25.0-29.9 kg/m2), increased BMI (>25.0-29.9 kg/m2), vitamin D deficiency/insufficiency, and Elixhauser comorbidity score. After further adjustment for concurrent hyperglycemia and acidosis in those with diabetes, hospitalization risk was substantially higher in T1D than T2D and in those with low vitamin D and elevated hemoglobin A1c (HbA1c). CONCLUSIONS: The higher hospitalization risk in T1D versus T2D warrants further investigation. Modifiable risk factors such as vitamin D deficiency/insufficiency, BMI, and elevated HbA1c may serve as prognostic indicators for COVID-19-related hospitalization in adults with diabetes.

The mechanism of RNA capping by SARS-CoV-2.

The RNA genome of SARS-CoV-2 contains a 5' cap that facilitates the translation of viral proteins, protection from exonucleases and evasion of the host immune response1-4. How this cap is made in SARS-CoV-2 is not completely understood. Here we reconstitute the N7- and 2'-O-methylated SARS-CoV-2 RNA cap (7MeGpppA2'-O-Me) using virally encoded non-structural proteins (nsps). We show that the kinase-like nidovirus RdRp-associated nucleotidyltransferase (NiRAN) domain5 of nsp12 transfers the RNA to the amino terminus of nsp9, forming a covalent RNA-protein intermediate (a process termed RNAylation). Subsequently, the NiRAN domain transfers the RNA to GDP, forming the core cap structure GpppA-RNA. The nsp146 and nsp167 methyltransferases then add methyl groups to form functional cap structures. Structural analyses of the replication-transcription complex bound to nsp9 identified key interactions that mediate the capping reaction. Furthermore, we demonstrate in a reverse genetics system8 that the N terminus of nsp9 and the kinase-like active-site residues in the NiRAN domain are required for successful SARS-CoV-2 replication. Collectively, our results reveal an unconventional mechanism by which SARS-CoV-2 caps its RNA genome, thus exposing a new target in the development of antivirals to treat COVID-19.

A Tale of Two Crises in Peru Livelihoods and Social Reproduction During the 1980s and the COVID-19 Pandemic

The COVID-19 pandemic has wreaked havoc on the livelihoods of people around the world. Structural economic constraints are highlighted at such moments of crisis, while those most affected have recourse to their repertoire of managing strategies. This case study of people from Allpachico, a Peruvian peasant community, compares their responses to the current crisis with their responses to one in the 1980s, showcasing similarities in strategies (especially reciprocity and the sale or exchange of necessary reproductive tasks and products) and differences in the form they take. In the 1980s, women's work and kin reciprocity helped people access use-values. By 2020, neoliberalism had transformed the national economy and Allpachiqueño migrants overwhelmingly had precarious informal and contract work. Reciprocity and reproductive tasks are still central to livelihood, but now tend to be monetized rather than involving use-values. As that earlier crisis shattered both secure employment and peasant farming to lay the basis for neoliberalism, so now it appears that the COVID-19 pandemic, through the monetization of government support and reciprocity alike, is accelerating financialization in the form of financial services and debt. © 2022 University of Toronto. All rights reserved.

Similar Accuracy of Surgical Plans After Initial In-Person and Telemedicine Evaluation of Spine Patients.

OBJECTIVE: The aim of this study was to compare accuracy of surgical plans generated from in-person and telemedicine evaluations and assess the reasons for surgical plan changes between initial evaluation and surgery. The secondary objective was to assess the effect of changes in surgical planning on postoperative outcomes. METHODS: In this retrospective cohort study, consecutive patients who were evaluated as new patients by orthopaedic spine faculty between 2019 and 2021 were divided by appointment type: telemedicine (n = 39) and in-person (n = 92). Patients were included if the surgeon documented a definitive surgical plan at the initial visit. The primary outcome was change in surgical plan from initial assessment to actual procedure performed. RESULTS: There was no significant difference in the accuracy of initial surgical plans between the telemedicine and in-person cohorts (79.5% vs. 82.6%, P = 0.673). The most common modification in the surgical plan (79%) was change in the number of operated levels, of which 18 of 19 patients had 1 added operated level. Less common reasons were change in approach (13%) and change in procedure (8%). Patients with changes to their surgical plan experienced longer length of stay (3.1 vs. 2.0 days, P = 0.027) than patients with consistent surgical plans. CONCLUSIONS: Telemedicine and in-person evaluations generated similarly accurate surgical plans. Changes to the initial surgical plans most often involved adding operative levels. Our findings show that telemedicine visits are an acceptable option for preoperative assessment to generate surgical plans; however, further research is needed.

Effect of the Nintendo Ring Fit Adventure Exergame on Running Completion Time and Psychological Factors Among University Students Engaging in Distance Learning During the COVID-19 Pandemic: Randomized Controlled Trial.

BACKGROUND: The COVID-19 outbreak has not only changed the lifestyles of people globally but has also resulted in other challenges, such as the requirement of self-isolation and distance learning. Moreover, people are unable to venture out to exercise, leading to reduced movement, and therefore, the demand for exercise at home has increased. OBJECTIVE: We intended to investigate the relationships between a Nintendo Ring Fit Adventure (RFA) intervention and improvements in running time, cardiac force index (CFI), sleep quality (Chinese version of the Pittsburgh Sleep Quality Index score), and mood disorders (5-item Brief Symptom Rating Scale score). METHODS: This was a randomized prospective study and included 80 students who were required to complete a 1600-meter outdoor run before and after the intervention, the completion times of which were recorded in seconds. They were also required to fill out a lifestyle questionnaire. During the study, 40 participants (16 males and 24 females, with an average age of 23.75 years) were assigned to the RFA group and were required to exercise for 30 minutes 3 times per week (in the adventure mode) over 4 weeks. The exercise intensity was set according to the instructions given by the virtual coach during the first game. The remaining 40 participants (30 males and 10 females, with an average age of 22.65 years) were assigned to the control group and maintained their regular habits during the study period. RESULTS: The study was completed by 80 participants aged 20 to 36 years (mean 23.20, SD 2.96 years). The results showed that the running time in the RFA group was significantly reduced. After 4 weeks of physical training, it took females in the RFA group 19.79 seconds (P=.03) and males 22.56 seconds (P=.03) less than the baseline to complete the 1600-meter run. In contrast, there were no significant differences in the performance of the control group in the run before and after the fourth week of intervention. In terms of mood disorders, the average score of the RFA group increased from 1.81 to 3.31 for males (difference=1.50, P=.04) and from 3.17 to 4.54 for females (difference=1.38, P=.06). In addition, no significant differences between the RFA and control groups were observed for the CFI peak acceleration (CFIPA)_walk, CFIPA_run, or sleep quality. CONCLUSIONS: RFA could either maintain or improve an individual's physical fitness, thereby providing a good solution for people involved in distance learning or those who have not exercised for an extended period. TRIAL REGISTRATION: ClinicalTrials.gov NCT05227040; https://clinicaltrials.gov/ct2/show/NCT05227040.

Information-seeking patterns during the COVID-19 pandemic across the United States: Longitudinal analysis of Google trends data

Background: The COVID-19 pandemic has impacted people's lives at unprecedented speed and scale, including how they eat and work, what they are concerned about, how much they move, and how much they can earn. Traditional surveys in the area of public health can be expensive and time-consuming, and they can rapidly become outdated. The analysis of big data sets (such as electronic patient records and surveillance systems) is very complex. Google Trends is an alternative approach that has been used in the past to analyze health behaviors;however, most existing studies on COVID-19 using these data examine a single issue or a limited geographic area. This paper explores Google Trends as a proxy for what people are thinking, needing, and planning in real time across the United States. Objective: We aimed to use Google Trends to provide both insights into and potential indicators of important changes in information-seeking patterns during pandemics such as COVID-19. We asked four questions: (1) How has information seeking changed over time? (2) How does information seeking vary between regions and states? (3) Do states have particular and distinct patterns in information seeking? (4) Do search data correlate with-or precede-real-life events? Methods: We analyzed searches on 38 terms related to COVID-19, falling into six themes: social and travel;care seeking;government programs;health programs;news and influence;and outlook and concerns. We generated data sets at the national level (covering January 1, 2016, to April 15, 2020) and state level (covering January 1 to April 15, 2020). Methods used include trend analysis of US search data;geographic analyses of the differences in search popularity across US states from March 1 to April 15, 2020;and principal component analysis to extract search patterns across states. Results: The data showed high demand for information, corresponding with increasing searches for coronavirus linked to news sources regardless of the ideological leaning of the news source. Changes in information seeking often occurred well in advance of action by the federal government. The popularity of searches for unemployment claims predicted the actual spike in weekly claims. The increase in searches for information on COVID-19 care was paralleled by a decrease in searches related to other health behaviors, such as urgent care, doctor's appointments, health insurance, Medicare, and Medicaid. Finally, concerns varied across the country;some search terms were more popular in some regions than in others. Conclusions: COVID-19 is unlikely to be the last pandemic faced by the United States. Our research holds important lessons for both state and federal governments in a fast-evolving situation that requires a finger on the pulse of public sentiment. We suggest strategic shifts for policy makers to improve the precision and effectiveness of non-pharmaceutical interventions and recommend the development of a real-time dashboard as a decision-making tool. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Trends Over Time in the Risk of Adverse Outcomes Among Patients With Severe Acute Respiratory Syndrome Coronavirus 2 Infection.

BACKGROUND: We aimed to describe trends in adverse outcomes among patients who tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) between February and September 2020 within a national healthcare system. METHODS: We identified enrollees in the national United States Veterans Affairs healthcare system who tested positive for SARS-CoV-2 between 28 February 2020 and 30 September 2020 (n = 55 952), with follow-up extending to 19 November 2020. We determined trends over time in incidence of the following outcomes that occurred within 30 days of testing positive: hospitalization, intensive care unit (ICU) admission, mechanical ventilation, and death. RESULTS: Between February and July 2020, there were marked downward trends in the 30-day incidence of hospitalization (44.2% to 15.8%), ICU admission (20.3% to 5.3%), mechanical ventilation (12.7% to 2.2%), and death (12.5% to 4.4%), which subsequently plateaued between July and September 2020. These trends persisted after adjustment for sociodemographic characteristics, comorbid conditions, documented symptoms, and laboratory tests, including among subgroups of patients hospitalized, admitted to the ICU, or treated with mechanical ventilation. From February to September, there were decreases in the use of hydroxychloroquine (56.5% to 0%), azithromycin (48.3% to 16.6%), vasopressors (20.6% to 8.7%), and dialysis (11.6% to 3.8%) and increases in the use of dexamethasone (3.4% to 53.1%), other corticosteroids (4.9% to 29.0%), and remdesivir (1.7% to 45.4%) among hospitalized patients. CONCLUSIONS: The risk of adverse outcomes in SARS-CoV-2-positive patients decreased markedly between February and July, with subsequent stabilization from July to September. These trends were not explained by changes in measured baseline patient characteristics and may reflect changing treatment practices or viral pathogenicity.

Risk Factors for Testing Positive for Severe Acute Respiratory Syndrome Coronavirus 2 in a National United States Healthcare System.

BACKGROUND: Identifying risk factors for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection could help health systems improve testing and screening strategies. The aim of this study was to identify demographic factors, comorbid conditions, and symptoms independently associated with testing positive for SARS-CoV-2. METHODS: This was an observational cross-sectional study at the Veterans Health Administration, including persons tested for SARS-CoV-2 nucleic acid by polymerase chain reaction (PCR) between 28 February and 14 May 2020. Associations between demographic characteristics, diagnosed comorbid conditions, and documented symptoms with testing positive for SARS-CoV-2 were measured. RESULTS: Of 88 747 persons tested, 10 131 (11.4%) were SARS-CoV-2 PCR positive. Positivity was associated with older age (≥80 vs <50 years: adjusted odds ratio [aOR], 2.16 [95% confidence interval {CI}, 1.97-2.37]), male sex (aOR, 1.45 [95% CI, 1.34-1.57]), regional SARS-CoV-2 burden (≥2000 vs <400 cases/million: aOR, 5.43 [95% CI, 4.97-5.93]), urban residence (aOR, 1.78 [95% CI, 1.70-1.87]), black (aOR, 2.15 [95% CI, 2.05-2.26]) or American Indian/Alaska Native Hawaiian/Pacific Islander (aOR, 1.26 [95% CI, 1.05-1.52]) vs white race, and Hispanic ethnicity (aOR, 1.52 [95% CI, 1.40-1.65]). Obesity and diabetes were the only 2 medical conditions associated with testing positive. Documented fevers, chills, cough, and diarrhea were also associated with testing positive. The population attributable fraction of positive tests was highest for geographic location (35.3%), followed by demographic variables (27.1%), symptoms (12.0%), obesity (10.5%), and diabetes (0.4%). CONCLUSIONS: The majority of positive SARS-CoV-2 tests were attributed to geographic location, demographic characteristics, and obesity, with a minor contribution of chronic comorbid conditions.

Cirrhosis and Severe Acute Respiratory Syndrome Coronavirus 2 Infection in US Veterans: Risk of Infection, Hospitalization, Ventilation, and Mortality.

BACKGROUND AND AIMS: Whether patients with cirrhosis have increased risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and the extent to which infection and cirrhosis increase the risk of adverse patient outcomes remain unclear. APPROACH AND RESULTS: We identified 88,747 patients tested for SARS-CoV-2 between March 1, 2020, and May 14, 2020, in the Veterans Affairs (VA) national health care system, including 75,315 with no cirrhosis-SARS-CoV-2-negative (C0-S0), 9,826 with no cirrhosis-SARS-CoV-2-positive (C0-S1), 3,301 with cirrhosis-SARS-CoV-2-negative (C1-S0), and 305 with cirrhosis-SARS-CoV-2-positive (C1-S1). Patients were followed through June 22, 2020. Hospitalization, mechanical ventilation, and death were modeled in time-to-event analyses using Cox proportional hazards regression. Patients with cirrhosis were less likely to test positive than patients without cirrhosis (8.5% vs. 11.5%; adjusted odds ratio, 0.83; 95% CI, 0.69-0.99). Thirty-day mortality and ventilation rates increased progressively from C0-S0 (2.3% and 1.6%) to C1-S0 (5.2% and 3.6%) to C0-S1 (10.6% and 6.5%) and to C1-S1 (17.1% and 13.0%). Among patients with cirrhosis, those who tested positive for SARS-CoV-2 were 4.1 times more likely to undergo mechanical ventilation (adjusted hazard ratio [aHR], 4.12; 95% CI, 2.79-6.10) and 3.5 times more likely to die (aHR, 3.54; 95% CI, 2.55-4.90) than those who tested negative. Among patients with SARS-CoV-2 infection, those with cirrhosis were more likely to be hospitalized (aHR, 1.37; 95% CI, 1.12-1.66), undergo ventilation (aHR, 1.61; 95% CI, 1.05-2.46) or die (aHR, 1.65; 95% CI, 1.18-2.30) than patients without cirrhosis. Among patients with cirrhosis and SARS-CoV-2 infection, the most important predictors of mortality were advanced age, cirrhosis decompensation, and high Model for End-Stage Liver Disease score. CONCLUSIONS: SARS-CoV-2 infection was associated with a 3.5-fold increase in mortality in patients with cirrhosis. Cirrhosis was associated with a 1.7-fold increase in mortality in patients with SARS-CoV-2 infection.

How we approach thrombosis risk in children with COVID-19 infection and MIS-C.

Thrombosis within the microvasculature and medium to large vessels is a serious and common complication among critically ill individuals with coronavirus disease 2019 (COVID-19). While children are markedly less likely to develop severe disease than adults, they remain at risk for thrombosis during acute infection and with the post-acute inflammatory illness termed multisystem inflammatory syndrome in children. Significant knowledge deficits in understanding COVID-19-associated coagulopathy and thrombotic risk pose clinical challenges for pediatric providers who must incorporate expert opinion and personal experience to manage individual patients. We discuss clinical scenarios to provide framework for characterizing thrombosis risk and thromboprophylaxis in children with COVID-19.

Information-Seeking Patterns During the COVID-19 Pandemic Across the United States: Longitudinal Analysis of Google Trends Data.

BACKGROUND: The COVID-19 pandemic has impacted people's lives at unprecedented speed and scale, including how they eat and work, what they are concerned about, how much they move, and how much they can earn. Traditional surveys in the area of public health can be expensive and time-consuming, and they can rapidly become outdated. The analysis of big data sets (such as electronic patient records and surveillance systems) is very complex. Google Trends is an alternative approach that has been used in the past to analyze health behaviors; however, most existing studies on COVID-19 using these data examine a single issue or a limited geographic area. This paper explores Google Trends as a proxy for what people are thinking, needing, and planning in real time across the United States. OBJECTIVE: We aimed to use Google Trends to provide both insights into and potential indicators of important changes in information-seeking patterns during pandemics such as COVID-19. We asked four questions: (1) How has information seeking changed over time? (2) How does information seeking vary between regions and states? (3) Do states have particular and distinct patterns in information seeking? (4) Do search data correlate with-or precede-real-life events? METHODS: We analyzed searches on 38 terms related to COVID-19, falling into six themes: social and travel; care seeking; government programs; health programs; news and influence; and outlook and concerns. We generated data sets at the national level (covering January 1, 2016, to April 15, 2020) and state level (covering January 1 to April 15, 2020). Methods used include trend analysis of US search data; geographic analyses of the differences in search popularity across US states from March 1 to April 15, 2020; and principal component analysis to extract search patterns across states. RESULTS: The data showed high demand for information, corresponding with increasing searches for coronavirus linked to news sources regardless of the ideological leaning of the news source. Changes in information seeking often occurred well in advance of action by the federal government. The popularity of searches for unemployment claims predicted the actual spike in weekly claims. The increase in searches for information on COVID-19 care was paralleled by a decrease in searches related to other health behaviors, such as urgent care, doctor's appointments, health insurance, Medicare, and Medicaid. Finally, concerns varied across the country; some search terms were more popular in some regions than in others. CONCLUSIONS: COVID-19 is unlikely to be the last pandemic faced by the United States. Our research holds important lessons for both state and federal governments in a fast-evolving situation that requires a finger on the pulse of public sentiment. We suggest strategic shifts for policy makers to improve the precision and effectiveness of non-pharmaceutical interventions and recommend the development of a real-time dashboard as a decision-making tool.

Development of COVIDVax Model to Estimate the Risk of SARS-CoV-2-Related Death Among 7.6 Million US Veterans for Use in Vaccination Prioritization.

Importance: A strategy that prioritizes individuals for SARS-CoV-2 vaccination according to their risk of SARS-CoV-2-related mortality would help minimize deaths during vaccine rollout. Objective: To develop a model that estimates the risk of SARS-CoV-2-related mortality among all enrollees of the US Department of Veterans Affairs (VA) health care system. Design, Setting, and Participants: This prognostic study used data from 7 635 064 individuals enrolled in the VA health care system as of May 21, 2020, to develop and internally validate a logistic regression model (COVIDVax) that predicted SARS-CoV-2-related death (n = 2422) during the observation period (May 21 to November 2, 2020) using baseline characteristics known to be associated with SARS-CoV-2-related mortality, extracted from the VA electronic health records (EHRs). The cohort was split into a training period (May 21 to September 30) and testing period (October 1 to November 2). Main Outcomes and Measures: SARS-CoV-2-related death, defined as death within 30 days of testing positive for SARS-CoV-2. VA EHR data streams were imported on a data integration platform to demonstrate that the model could be executed in real-time to produce dashboards with risk scores for all current VA enrollees. Results: Of 7 635 064 individuals, the mean (SD) age was 66.2 (13.8) years, and most were men (7 051 912 [92.4%]) and White individuals (4 887 338 [64.0%]), with 1 116 435 (14.6%) Black individuals and 399 634 (5.2%) Hispanic individuals. From a starting pool of 16 potential predictors, 10 were included in the final COVIDVax model, as follows: sex, age, race, ethnicity, body mass index, Charlson Comorbidity Index, diabetes, chronic kidney disease, congestive heart failure, and Care Assessment Need score. The model exhibited excellent discrimination with area under the receiver operating characteristic curve (AUROC) of 85.3% (95% CI, 84.6%-86.1%), superior to the AUROC of using age alone to stratify risk (72.6%; 95% CI, 71.6%-73.6%). Assuming vaccination is 90% effective at preventing SARS-CoV-2-related death, using this model to prioritize vaccination was estimated to prevent 63.5% of deaths that would occur by the time 50% of VA enrollees are vaccinated, significantly higher than the estimate for prioritizing vaccination based on age (45.6%) or the US Centers for Disease Control and Prevention phases of vaccine allocation (41.1%). Conclusions and Relevance: In this prognostic study of all VA enrollees, prioritizing vaccination based on the COVIDVax model was estimated to prevent a large proportion of deaths expected to occur during vaccine rollout before sufficient herd immunity is achieved.

BMI and Outcomes of SARS-CoV-2 Among US Veterans.

OBJECTIVE: The purpose of this study is to examine the associations of BMI with testing positive for severe acute respiratory coronavirus 2 (SARS-CoV-2) and risk of adverse outcomes in a cohort of Veterans Affairs enrollees. METHOD: Adjusted relative risks/hazard ratios (HRs) were calculated for the associations between BMI category (underweight, normal weight, overweight, class 1 obesity, class 2 obesity, and class 3 obesity) and testing positive for SARS-CoV-2 or experiencing hospitalization, intensive care unit admission, mechanical ventilation, and death among those testing positive. RESULTS: Higher BMI categories were associated with higher risk of a positive SARS-CoV-2 test compared with the normal weight category (class 3 obesity adjusted relative risk: 1.34, 95% CI: 1.28-1.42). Among 25,952 patients who tested positive for SARS-CoV-2, class 3 obesity was associated with higher risk of mechanical ventilation (adjusted HR [aHR]: 1.77, 95% CI: 1.35-2.32) and mortality (aHR: 1.42, 95% CI: 1.12-1.78) compared with normal weight individuals. These associations were present primarily in patients younger than 65 and were attenuated or absent in older age groups (interaction P < 0.05). CONCLUSION: Veterans Affairs enrollees with higher BMI were more likely to test positive for SARS-CoV-2 and were more likely to be mechanically ventilated or die if infected with SARS-CoV-2. Higher BMI contributed relatively more to the risk of death in those younger than 65 years of age as compared with other age categories.

PRO115 A Patient-Centric Approach to Designing a MIXED-Methods Observational Study Involving Adolescents with X-Linked Hypophosphatemia

Objectives: A European, prospective, multi-centre, mixed-methods observational study is in development to investigate treatment transition and disease experience in adolescents with X-Linked Hypophosphatemia (XLH), a rare, genetic life-long phosphate-wasting disorder. To ensure robustness, feasibility and patient-centricity of study design, a UK patient-public involvement (PPI) project was undertaken. Methods: During June 2020, four adolescents (14-18 years), recruited via patient group XLH UK, underwent 1:1 semi-structured 60-minute telephone interviews (following informed consent). Participants described their experience of known symptoms and impacts of XLH identified from the literature and burosumab trials (pain, stiffness, mobility and physical function, tiredness/fatigue, sleep, emotional wellbeing) and then prioritised their importance for measurement. The acceptability of proposed methods for data capture (e.g. use of wearables, smartphone app), recruitment, and retention of participants was also discussed. Responses were summarised narratively in MS Excel to aid future study design. Results: The interference of pain, stiffness and tiredness/fatigue on daily physical activities were identified as key endpoints, alongside modifications to daily activities and socialising for symptom management. Mild symptoms were reported by most participants, either due to effective treatment or reduced activity during COVID-19. One participant was more concerned with emotional wellbeing, relating to self-confidence, and favoured its measurement in studies. No-one reported problematic sleep. All were willing to participate in a 12-month study, answering one daily question using a phone app and wearing a wrist-device daily to capture activity, although night-time use was less acceptable. Recruitment via social media and doctors was perceived as optimal and compensatory monetary vouchers were favoured. Conclusions: This PPI project identified patient-relevant endpoints of pain, stiffness and fatigue/ tiredness to be examined according to their interference of daily physical activities or socialising. Measurement using wearables and smartphones was viewed as acceptable and feasible for adolescents with XLH, which may therefore relieve burden on the healthcare system.

Computational Intelligence Techniques for Combating COVID-19: A Survey

Computational intelligence has been used in many applications in the fields of health sciences and epidemiology. In particular, owing to the sudden and massive spread of COVID-19, many researchers around the globe have devoted intensive efforts into the development of computational intelligence methods and systems for combating the pandemic. Although there have been more than 200,000 scholarly articles on COVID-19, SARS-CoV-2, and other related coronaviruses, these articles did not specifically address in-depth the key issues for applying computational intelligence to combat COVID-19. Hence, it would be exhausting to filter and summarize those studies conducted in the field of computational intelligence from such a large number of articles. Such inconvenience has hindered the development of effective computational intelligence technologies for fighting COVID-19. To fill this gap, this survey focuses on categorizing and reviewing the current progress of computational intelligence for fighting this serious disease. In this survey, we aim to assemble and summarize the latest developments and insights in transforming computational intelligence approaches, such as machine learning, evolutionary computation, soft computing, and big data analytics, into practical applications for fighting COVID-19. We also explore some potential research issues on computational intelligence for defeating the pandemic.