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Jonathan D Chevinsky,1 Thomas A Wadden,2 Ariana M Chao2,3 1SUNY Downstate Health Sciences University, Brooklyn, NY, USA; 2Perelman School of Medicine at the University of Pennsylvania, Department of Psychiatry, Philadelphia, PA, USA; 3University of Pennsylvania School of Nursing, Department of Biobehavioral Health Sciences, Philadelphia, PA, USACorrespondence: Ariana M ChaoUniversity of Pennsylvania School of Nursing, 418 Curie Blvd, Philadelphia, PA 19104, USATel +1215-746-7183Fax +1215-898-2878Email arichao@upenn.eduAbstract: Type 2 diabetes mellitus (T2DM) is associated with an increased risk of disordered eating behaviors including binge eating disorder (BED). Comorbid BED in patients with T2DM has been associated with adverse clinical outcomes such as higher body mass index (BMI) and depressive symptoms. Identifying and addressing this disorder in patients with T2DM is a significant challenge for health-care providers. The purpose of this narrative review is to discuss current perspectives on BED in the context of T2DM with implications for screening and management of these highly comorbid conditions. BED continues to be underrecognized and underdiagnosed. However, there are established tools that providers can use to screen for BED such as the SCOFF Questionnaire and Questionnaire on Eating and Weight Patterns-5. There are several effective treatments for BED including cognitive behavioral therapy, interpersonal therapy, and lisdexamfetamine dimesylate. However, few studies have examined the effects of these treatments in patients with co-morbid T2DM and BED.Keywords: binge eating disorder, eating disorders, diabetes, obesity

The past decade witnessed substantial discoveries related to the psychosis spectrum. Many of these discoveries resulted from pursuits of objective and quantifiable biomarkers in tandem with the application of analytical tools such as machine learning. These approaches provided exciting new insights that significantly helped improve precision in diagnosis, prognosis, and treatment. This article provides an overview of how machine learning has been employed in recent biomarker discovery research in the psychosis spectrum, which includes schizophrenia, schizoaffective disorders, bipolar disorder with psychosis, first episode psychosis, and clinical high risk for psychosis. It highlights both human and animal model studies and explores a varying range of the most impactful biomarkers including cognition, neuroimaging, electrophysiology, and digital markers. We specifically highlight new applications and opportunities for machine learning to impact noninvasive symptom monitoring, prediction of future diagnosis and treatment outcomes, integration of new methods with traditional clinical research and practice, and personalized medicine approaches., Competing Interests: Declaration of Competing Interest The authors listed below declare no conflict of interest. Dr. John Torous is a scientific advisor for Precision Mental Wellness. Walid Yassin, Kendra M. Loedige, Cassandra M. J. Wannan, Kristina M. Holton, Jonathan Chevinsky, Mei-Hua Hall, Rochelle Ruby Ye, Poornima Kumar, Sidhant Chopra, Kshitij Kumar, Jibran Y. Khokhar, Eric Margolis, Alessandro De Nadai

Ongoing symptoms might follow acute COVID-19. Using electronic health information, we compared pre‒ and post‒COVID-19 diagnostic codes to identify symptoms that had higher encounter incidence in the post‒COVID-19 period as sequelae. This method can be used for hypothesis generation and ongoing monitoring of sequelae of COVID-19 and future emerging diseases.

Background: Up to 30% of American adults may have unhealthy drinking behavior, but only 17% get screened. There is promise in improving screening via technology, but there is a lack of published evidence supporting these efforts. We describe the development of Automated Alcohol Misuse Interventions (ATTAIN), an automated, web-based process to screen for and manage adults with unhealthy drinking behavior with minimal involvement of health-care personnel., Method: After creating a strategic business plan, ATTAIN was developed for the Southern California Permanente Medical Group using its integrated model of care, electronic medical records, and patient portal. ATTAIN is based on an automated branching questionnaire that screens for unhealthy drinking behavior and, when applicable, alcohol use disorders, and incorporates questions about readiness to change and interest in medications/counseling to assist with alcohol consumption reduction. Health plan members would be invited via email to fill out the screening questionnaire using the patient portal. Based on their responses, they would receive appropriate automated feedback and a link to a counseling video about the spectrum of alcohol use. Patients' responses would be captured in their medical record and sent to a designated provider for further help as needed. The process would be refined through successive quality improvement pilots. We project that ATTAIN will lead to reduced costs for the Southern California Permanente Medical Group ., Conclusion: This effort has paved the way for using ATTAIN to improve patient care and to reduce the costs associated with managing unhealthy drinking, and potentially leads to similar processes for other medical conditions and health-related behaviors., (Copyright © 2021 The Permanente Press. All rights reserved.)

Objectives: The objective of the study was to characterize the timing and trends of select mitigation policies, changes in community mobility, and coronavirus disease 2019 (COVID-19) epidemiology in Australia, Japan, Hong Kong, and Singapore., Study Design: Prospective abstraction of publicly available mitigation policies obtained from media reports and government websites., Methods: Data analyzed include seven kinds of mitigation policies (mass gathering restrictions, international travel restrictions, passenger screening, traveler isolation/quarantine, school closures, business closures, and domestic movement restrictions) implemented between January 1 and April 26, 2020, changes in selected measures of community mobility assessed by Google Community Mobility Reports data, and COVID-19 epidemiology in Australia, Japan, Hong Kong, and Singapore., Results: During the study period, community mobility decreased in Australia, Japan, and Singapore; there was little change in Hong Kong. The largest declines in mobility were seen in places that enforced mitigation policies. Across settings, transit-associated mobility declined the most and workplace-associated mobility the least. Singapore experienced an increase in cases despite the presence of stay-at-home orders, as migrant workers living in dormitories faced challenges to safely quarantine., Conclusions: Public policies may have different impacts on mobility and transmission of severe acute respiratory coronavirus-2 transmission. When enacting mitigation policies, decision makers should consider the possible impact of enforcement measures, the influence on transmission of factors other than movement restrictions, and the differential impact of mitigation policies on subpopulations., (Copyright © 2021. Published by Elsevier Ltd.)

Introduction: on January 30, 2020, the World Health Organization declared the novel coronavirus outbreak a Public Health Emergency of International Concern. As of October 5, 2020, there were over 34.8 million reported cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and more than 1 million reported deaths from coronavirus disease 2019 (COVID-19), globally. Non-pharmaceutical interventions, such as social distancing policies, hand hygiene, and mask use, are key public health measures to control COVID-19. In response to, or in some cases even before, the first wave of SARS-CoV-2 infections were reported in their countries, policy makers across Africa issued various social distancing policies., Methods: we describe social distancing policies issued from March 1 to April 24, 2020 in 22 Anglophone countries of sub-Saharan Africa. We reviewed policies identified online., Results: though all 22 countries closed schools and banned gatherings, they took a variety of approaches to sizes of gatherings banned and to stay-at-home orders, with 13 countries issuing national stay-at-home orders, four issuing subnational stay-at-home orders, and five not issuing stay-at-home orders. Enforcement provisions varied by country, as did funeral and health care exceptions., Conclusion: movement restrictions, business restrictions, and school closures can have substantial negative impacts on economies, education, nutrition, and routine health care. Yet easing or lifting of COVID-19 social distancing policies can lead to increased transmission. Our review documents a wide variety of policy alternatives used in Africa and can inform future adjustments as countries ease, lift, and reapply measures in response to their evolving epidemics., Competing Interests: The authors declare no competing interests., (Copyright: Andre Verani et al.)

West Nile virus (WNV) and St. Louis encephalitis virus (SLEV) are closely related mosquito-borne flaviviruses that can cause neuroinvasive disease. No concurrent WNV and SLEV disease outbreaks have previously been identified. When concurrent outbreaks occurred in 2015 in Maricopa County, Arizona, we collected data to describe the epidemiology, and to compare features of patients with WNV and SLEV neuroinvasive disease. We performed enhanced case finding, and gathered information from medical records and patient interviews. A case was defined as a clinically compatible illness and laboratory evidence of WNV, SLEV, or unspecified flavivirus infection in a person residing in Maricopa County in 2015. We compared demographic and clinical features of WNV and SLEV neuroinvasive cases; for this analysis, a case was defined as physician-documented encephalitis or meningitis and a white blood cell count >5 cells/mm 3 in cerebrospinal fluid. In total, we identified 82 cases, including 39 WNV, 21 SLEV, and 22 unspecified flavivirus cases. The comparative analysis included 21 WNV and 14 SLEV neuroinvasive cases. Among neuroinvasive cases, the median age of patients with SLEV (63 years) was higher than WNV (52 years). Patients had similar symptoms; rash was identified more frequently in WNV (33%) neuroinvasive cases than in SLEV (7%) cases, but this difference was not statistically significant ( p  = 0.11). In summary, during the first known concurrent WNV and SLEV disease outbreaks, no specific clinical features were identified that could differentiate between WNV and SLEV neuroinvasive cases. Health care providers should consider both infections in patients with aseptic meningitis or encephalitis.