Physical Changes of Preschool Children during COVID-19 School Closures in Fujian, China.

The COVID-19 pandemic may constitute an "obesogenic lifestyle" that results in exacerbating childhood obesity. However, studies investigating regional sociodemographic factors including different age groups or sexes in children with obesity are lacking. We aimed to clarify the high obesity prevalence populations of preschool children to provide a regional basis for children's health policy during the COVID-19 school closures. From May to September 2019, a total of 29,518 preschool children were included in a large sample, multicenter cross-sectional study to explore physical status in Fujian Province by stratified cluster random sampling. In October 2019 and October 2020, we also conducted a cross-sectional study exploring physical development including changes in height, weight, and BMI of 1688 preschool children in Fuzhou before and after the COVID-19 school closures. Student' s t-test, Mann-Whitney U test, or chi-square test was used to assess differences in physical development and overweight and obesity rates among preschool children before and after school closures. For regional factors, the weight of urban preschool children of all ages became higher after the outbreak (p (age 3-4) = 0.009; p (age 4-5) < 0.001; p (age 5-6) = 0.002). For sex factors, overweight and obesity in boys had a greater prevalence than in girls before and after the outbreak. In four age groups, overweight and obesity rates in the 5-year-old group (15.5% and 9.9%) were higher than before (11.4% and 6.0%). The weight and BMI of 4- to 5-year-old children also increased faster than before (p < 0.001). The COVID-19 pandemic has promoted the epidemic of childhood obesity. Living in urban/coastal (economically developed) areas, boys, and aged 4-6 years old may be a susceptible population to obesity development after the outbreak.

Preventing, mitigating, and managing future pandemics for people with an intellectual and developmental disability ‐ Learnings from COVID‐19: A scoping review

Many people with an intellectual and developmental disability (IDD) are biologically, socially, and economically/politically vulnerable to developing SARS‐COV‐2 (COVID‐19) compared to the general population. Most governments have developed public‐health policies and strategies to address the challenges that COVID‐19 has presented. These policies and strategies have been based upon the general population and in fact could be detrimental to the health and well‐being of people with IDD. This paper provides a review of the key learning points emerging from the COVID‐19 literature, together with guidance for the provision of services and government interventions for people with an IDD for future pandemics. Using guidance from the Joanna Briggs Institute, a scoping review was used to explore the current literature (scientific and grey) on IDD and COVID‐19. Three core themes emerged from the review. Prevention/protection: User‐friendly accurate accessible information, handwashing and social distancing, Personal Protective Equipment, shielding, track and trace, testing, vaccine compliance/hesitancy, and training. Mitigation: Making reasonable adjustments both to where people live, and to community healthcare/clinical practice;and the use of technology as a pandemic‐response strategy. Treatment/Management: Access to acute hospitals and lifesaving equipment, using a suitable clinical fatality assessment instrument, stopping Do Not Resuscitate notices, individualised care plans and hospital passports, family/paid carers to support people in hospitals;and use of telehealth in clinical care. This is the first international scoping review that provides a narrative synthesis of emerging themes related to the COVID‐19 pandemic and people with an IDD. This paper highlights themes related to preventing, mitigating, and treating/managing the care of this population during the COVID‐19 pandemic, which can inform future public‐health policies. This paper also exposes the negative impacts of public‐health interventions in both High‐Income Countries and Low‐Middle Income Countries for this population including lapses in upholding human rights. These data provide a basis for learning from the COVID‐19 pandemic in planning for future pandemics. [ FROM AUTHOR] Copyright of Journal of Policy & Practice in Intellectual Disabilities is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Real-Time Mask Identification for COVID-19: An Edge-Computing-Based Deep Learning Framework.

During the outbreak of the Coronavirus disease 2019 (COVID-19), while bringing various serious threats to the world, it reminds us that we need to take precautions to control the transmission of the virus. The rise of the Internet of Medical Things (IoMT) has made related data collection and processing, including healthcare monitoring systems, more convenient on the one hand, and requirements of public health prevention are also changing and more challengeable on the other hand. One of the most effective nonpharmaceutical medical intervention measures is mask wearing. Therefore, there is an urgent need for an automatic real-time mask detection method to help prevent the public epidemic. In this article, we put forward an edge computing-based mask (ECMask) identification framework to help public health precautions, which can ensure real-time performance on the low-power camera devices of buses. Our ECMask consists of three main stages: 1) video restoration; 2) face detection; and 3) mask identification. The related models are trained and evaluated on our bus drive monitoring data set and public data set. We construct extensive experiments to validate the good performance based on real video data, in consideration of detection accuracy and execution time efficiency of the whole video analysis, which have valuable application in COVID-19 prevention.