Rethinking Data Collection Methods During the Pandemic: Development and Implementation of CATI for the All of Us Research Program.

The All of Us Research Program is a longitudinal cohort study aiming to build a diverse database to advance precision medicine. The COVID-19 pandemic hindered the ability of participants to receive in-person assistance at enrollment sites to complete digital surveys. Therefore, the program implemented Computer-Assisted Telephone Interviewing (CATI) to facilitate survey completion remotely to combat the disrupted data collection procedures. In January 2021, All of Us implemented a 1-year CATI Pilot supporting 9399 participants and resulting in 16 337 submitted surveys. The pilot showed that CATI was successful in increasing survey completion and retention activities for the All of Us Research Program, given the additional remote support offered to participants. Given the success of the CATI Pilot, multimodal survey administration will continue.

Guidelines for Triage of COVID-19 Patients Presenting With Multisystemic Symptoms.

BACKGROUND AND OBJECTIVES: This article describes how multisystemic symptoms, both respiratory and nonrespiratory, can be used to differentiate coronavirus disease-2019 (COVID-19) from other diseases at the point of patient triage in the community. The article also shows how combinations of symptoms could be used to predict the probability of a patient having COVID-19. METHODS: We first used a scoping literature review to identify symptoms of COVID-19 reported during the first year of the global pandemic. We then surveyed individuals with reported symptoms and recent reverse transcription polymerase chain reaction (RT-PCR) test results to assess the accuracy of diagnosing COVID-19 from reported symptoms. The scoping literature review, which included 81 scientific articles published by February 2021, identified 7 respiratory, 9 neurological, 4 gastrointestinal, 4 inflammatory, and 5 general symptoms associated with COVID-19 diagnosis. The likelihood ratio associated with each symptom was estimated from sensitivity and specificity of symptoms reported in the literature. A total of 483 individuals were then surveyed to validate the accuracy of predicting COVID-19 diagnosis based on patient symptoms using the likelihood ratios calculated from the literature review. Survey results were weighted to reflect age, gender, and race of the US population. The accuracy of predicting COVID-19 diagnosis from patient-reported symptoms was assessed using area under the receiver operating curve (AROC). RESULTS: In the community, cough, sore throat, runny nose, dyspnea, and hypoxia, by themselves, were not good predictors of COVID-19 diagnosis. A combination of cough and fever was also a poor predictor of COVID-19 diagnosis (AROC = 0.56). The accuracy of diagnosing COVID-19 based on symptoms was highest when individuals presented with symptoms from different body systems (AROC of 0.74-0.81); the lowest accuracy was when individuals presented with only respiratory symptoms (AROC = 0.48). CONCLUSIONS: There are no simple rules that clinicians can use to diagnose COVID-19 in the community when diagnostic tests are unavailable or untimely. However, triage of patients to appropriate care and treatment can be improved by reviewing the combinations of certain types of symptoms across body systems.

Combined Symptom Screening and At-Home Tests for COVID-19.

BACKGROUND AND OBJECTIVE: At-home rapid antigen tests provide a convenient and expedited resource to learn about severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection status. However, low sensitivity of at-home antigen tests presents a challenge. This study examines the accuracy of at-home tests, when combined with computer-facilitated symptom screening. METHODS: The study used primary data sources with data collected during 2 phases at different periods (phase 1 and phase 2): one during the period in which the alpha variant of SARS-CoV-2 was predominant in the United States and another during the surge of the delta variant. Four hundred sixty-one study participants were included in the analyses from phase 1 and 374 subjects from phase 2. Phase 1 data were used to develop a computerized symptom screening tool, using ordinary logistic regression with interaction terms, which predicted coronavirus disease-2019 (COVID-19) reverse transcription polymerase chain reaction (RT-PCR) test results. Phase 2 data were used to validate the accuracy of predicting COVID-19 diagnosis with (1) computerized symptom screening; (2) at-home rapid antigen testing; (3) the combination of both screening methods; and (4) the combination of symptom screening and vaccination status. The McFadden pseudo-R2 was used as a measure of percentage of variation in RT-PCR test results explained by the various screening methods. RESULTS: The McFadden pseudo-R2 for the first at-home test, the second at-home test, and computerized symptom screening was 0.274, 0.140, and 0.158, respectively. Scores between 0.2 and 0.4 indicated moderate levels of accuracy. The first at-home test had low sensitivity (0.587) and high specificity (0.989). Adding a second at-home test did not improve the sensitivity of the first test. Computerized symptom screening improved the accuracy of the first at-home test (added 0.131 points to sensitivity and 6.9% to pseudo-R2 of the first at-home test). Computerized symptom screening and vaccination status was the most accurate method to screen patients for COVID-19 or an active infection with SARS-CoV-2 in the community (pseudo-R2 = 0.476). CONCLUSION: Computerized symptom screening could either improve, or in some situations, replace at-home antigen tests for those individuals experiencing COVID-19 symptoms.

Testing the Acceptability and Usability of an AI-Enabled COVID-19 Diagnostic Tool Among Diverse Adult Populations in the United States.

BACKGROUND AND OBJECTIVES: Although at-home coronavirus disease-2019 (COVID-19) testing offers several benefits in a relatively cost-effective and less risky manner, evidence suggests that at-home COVID-19 test kits have a high rate of false negatives. One way to improve the accuracy and acceptance of COVID-19 screening is to combine existing at-home physical test kits with an easily accessible, electronic, self-diagnostic tool. The objective of the current study was to test the acceptability and usability of an artificial intelligence (AI)-enabled COVID-19 testing tool that combines a web-based symptom diagnostic screening survey and a physical at-home test kit to test differences across adults from varying races, ages, genders, educational, and income levels in the United States. METHODS: A total of 822 people from Richmond, Virginia, were included in the study. Data were collected from employees and patients of Virginia Commonwealth University Health Center as well as the surrounding community in June through October 2021. Data were weighted to reflect the demographic distribution of patients in United States. Descriptive statistics and repeated independent t tests were run to evaluate the differences in the acceptability and usability of an AI-enabled COVID-19 testing tool. RESULTS: Across all participants, there was a reasonable degree of acceptability and usability of the AI-enabled COVID-19 testing tool that included a physical test kit and symptom screening website. The AI-enabled COVID-19 testing tool demonstrated overall good acceptability and usability across race, age, gender, and educational background. Notably, participants preferred both components of the AI-enabled COVID-19 testing tool to the in-clinic testing. CONCLUSION: Overall, these findings suggest that our AI-enabled COVID-19 testing approach has great potential to improve the quality of remote COVID testing at low cost and high accessibility for diverse demographic populations in the United States.

Differential diagnosis of COVID-19 and influenza.

This study uses two existing data sources to examine how patients' symptoms can be used to differentiate COVID-19 from other respiratory diseases. One dataset consisted of 839,288 laboratory-confirmed, symptomatic, COVID-19 positive cases reported to the Centers for Disease Control and Prevention (CDC) from March 1, 2019, to September 30, 2020. The second dataset provided the controls and included 1,814 laboratory-confirmed influenza positive, symptomatic cases, and 812 cases with symptomatic influenza-like-illnesses. The controls were reported to the Influenza Research Database of the National Institute of Allergy and Infectious Diseases (NIAID) between January 1, 2000, and December 30, 2018. Data were analyzed using case-control study design. The comparisons were done using 45 scenarios, with each scenario making different assumptions regarding prevalence of COVID-19 (2%, 4%, and 6%), influenza (0.01%, 3%, 6%, 9%, 12%) and influenza-like-illnesses (1%, 3.5% and 7%). For each scenario, a logistic regression model was used to predict COVID-19 from 2 demographic variables (age, gender) and 10 symptoms (cough, fever, chills, diarrhea, nausea and vomiting, shortness of breath, runny nose, sore throat, myalgia, and headache). The 5-fold cross-validated Area under the Receiver Operating Curves (AROC) was used to report the accuracy of these regression models. The value of various symptoms in differentiating COVID-19 from influenza depended on a variety of factors, including (1) prevalence of pathogens that cause COVID-19, influenza, and influenza-like-illness; (2) age of the patient, and (3) presence of other symptoms. The model that relied on 5-way combination of symptoms and demographic variables, age and gender, had a cross-validated AROC of 90%, suggesting that it could accurately differentiate influenza from COVID-19. This model, however, is too complex to be used in clinical practice without relying on computer-based decision aid. Study results encourage development of web-based, stand-alone, artificial Intelligence model that can interview patients and help clinicians make quarantine and triage decisions.

Approach to High Volume Enrollment in Clinical Research: Experiences from an All of Us Research Program Site.

Clinical trials and cohort studies are required to meet target recruitment of study participants within stipulated timelines, especially when the priority is to include populations traditionally unrepresented in biomedical research. By the third quarter of 2019, the University of Arizona-Banner Health Provider Organization (UA-Banner HPO) has enrolled > 30,000 core participants into the All of Us Research Program (AoURP), the research cohort of the Precision Medicine Initiative. The majority of enrolled participants meet the criteria for individuals under-represented in biomedical research. The enrollment goals were calculated based on a target of 20,000 as set by the National Institutes of Health and our health provider organization achieved enrollment numbers between 17% and 86% above the targeted daily enrollment. We evaluated enrollment methods and challenges to enrollments encountered by the UA-Banner Health Provider Organization into the AoURP. Challenges to enrollment centered around the need for high-touch engagement methods, time investment necessary for stakeholder inclusion, and the use of purely digital enrollment methods especially in populations under-represented in biomedical research. These challenges occurred at the level of the individual, provider, institutions, and community, and cumulatively impacted participant enrollment. Successful strategies for engagement and enrollment leveraged provider partners as advocates for the program. For high-volume enrollment in clinical research, it is important to engage leaders in the healthcare setting, patient providers, and tailor engagement and enrollment to potential participant needs. We emphasize the need for precision engagement and enrollment methods tailored to individual needs.

Multifamily group psychoeducation and cognitive remediation for first-episode psychosis: a randomized controlled trial.

BACKGROUND: Multifamily group psychoeducation (MFG) has been shown to reduce relapse rates among individuals with first-episode psychosis. However, given the cognitive demands associated with participating in this intervention (e.g., learning and applying a structured problem-solving activity), the cognitive deficits that accompany psychotic disorders may limit the ability of certain individuals to benefit from this intervention. Thus, the goal of this study is to examine whether individuals with first-episode psychosis who participate simultaneously in MFG and cognitive remediation--an intervention shown to improve cognitive functioning among individuals with psychotic disorders--will be less likely to experience a relapse than individuals who participate in MFG alone. METHODS/DESIGN: Forty individuals with first-episode psychosis and their caregiving relative will be recruited to participate in this study. Individuals with first-episode psychosis will be randomized to one of two conditions: (i) MFG with concurrent participation in cognitive remediation or (ii) MFG alone. The primary outcome for this study is relapse of psychotic symptoms. We will also examine secondary outcomes among both individuals with first-episode psychosis (i.e., social and vocational functioning, health-related quality of life, service utilization, independent living status, and cognitive functioning) and their caregiving relatives (i.e., caregiver burden, anxiety, and depression) DISCUSSION: Cognitive remediation offers the possibility of ameliorating a specific deficit (i.e., deficits in cognitive functioning) that often accompanies psychotic symptoms and may restrict the magnitude of the clinical benefits derived from MFG. TRIAL REGISTRATION: ClinicalTrials (NCT): NCT01196286.

Psychiatric morbidity following hysterectomy in Egypt.

OBJECTIVE: To evaluate psychiatric morbidity in Egyptian women before and after hysterectomy for benign indications. METHOD: A 2-year prospective observational study at Sohag University Hospital, Egypt, in which 96 women scheduled for hysterectomy were assessed for psychiatric comorbidity before and after the operation using the General Health Questionnaire (GHQ-28), Beck Depression Inventory, and Hamilton Anxiety Scale. RESULTS: Of these, 35 (36.5%) had scores of 4 or higher, signifying psychiatric comorbidity (group 1), and 61 (63.5%) had scores less than 4, suggesting no psychiatric comorbidity (group 2). Postoperatively, severe anxiety and depressive symptoms were common in group 1. In group 2, 48 (78.7%) of the 61 women had scores of 4 or higher. In that subgroup, depressive and anxiety symptoms were more common among nulliparas whereas women with a high parity experienced the least psychiatric comorbidity. CONCLUSIONS: As women undergoing hysterectomy risk psychiatric morbidity, gynecologists should consider less invasive interventions to treat benign conditions.

Prevalence of female genital cutting in Upper Egypt: 6 years after enforcement of prohibition law.

The objective of this study was to evaluate the prevalence of female genital cutting (FGC) in Upper Egypt, after 6 years of putting prohibition law into action. A total number of 3730 girls between the ages of 10-14 years were recruited to participate in this study. They were mainly preparatory school students (three urban and three rural areas). Social workers interviewed them as to whether they had undergone circumcision within the last 6 years or not. Subsequently, a questionnaire was sent to parents of girls who were positive for circumcision as to the circumstances surrounding the procedure. The prohibition law of FGC seems not to have altered the prevalence of this procedure. The majority of girls (84.9%) had had circumcision within the last 6 years with high prevalence in rural areas (92.5%). Circumcision was done for a combination of reasons, according to parents, with high rates of non-medical personnel participation (64.15%). This study's results indicate that the practice of FGC in Upper Egypt remains high despite enforcement of law. Extensive efforts are needed both to revise public awareness and to change attitudes regarding FGC.

Patient and partner perceptions about preventing genital herpes transmission.

Research over the past decade has provided a new understanding of genital herpes transmission and measures that can reduce transmission risk. It is unclear, however, how those affected by genital herpes access and interpret this information to make decisions about risk behaviours. This study measured how people with genital herpes and their partners perceived prevention methods, barriers and facilitating factors, and information sources. Formative evaluation was conducted, and survey data were collected from visitors to four websites (n=1849). Results suggest that the prevention messages of refraining from sex during disease outbreaks and condom use have had the greatest reach. Misconceptions about the potential role of suppressive antiviral therapy for genital herpes prevention persist among a substantial percentage of respondents. Accurate information concerning transmission between outbreaks, the effectiveness of condoms and the role of antiviral medication is critical in preventing the spread of genital herpes.