Rural–urban disparities in knowledge,behaviors, and mental health during COVID-19 pandemic: A community-based cross-sectional survey

To examine the knowledge level, behaviors, and psychological status of the Chinese population during the COVID-19 pandemic, and to explore the differences between urban and rural areas.We carried out a cross-sectional survey of the knowledge, behaviors related to COVID-19, and mental health in a probability sample of 3001 community residents in 30 provinces or districts across China from February 16–23, 2020. Convenience sampling and a snowball sampling were adopted. We used General Anxiety Disorder (GAD), the 9-item Patient Health Questionnaire (PHQ-9), and knowledge and behaviors questionnaire of community residents regarding COVID-19 designed by us to investigate the psychological status, disease-related knowledge, and the behavior of Chinese urban and rural residents during the pandemic.The average score of anxiety and depression among urban residents was 9.15 and 11.25, respectively, while the figures in rural areas were 8.69 and 10.57, respectively. There was a statistically significant difference in the levels of anxiety (P < .01) and depression (P < .01). Urban participants reported significantly higher levels of knowledge regarding COVID-19 in all aspects (transmission, prevention measures, symptoms of infection, treatment, and prognosis) (P < .01), compared to their rural counterparts. While a majority of respondents in urban areas obtained knowledge through WeChat, other apps, and the Internet (P < .01), residents in rural areas accessed information through interactions with the community (P < .01). Urban residents fared well in exchanging knowledge about COVID-19 and advising others to take preventive measures (P < .01), but fared poorly in advising people to visit a hospital if they displayed symptoms of the disease, compared to rural residents (P < .01). Regression analysis with behavior showed that being female (OR = 2.106, 95%CI = 1.259–3.522), aged 18 ≤ age < 65 (OR = 4.059, 95%CI = 2.166–7.607), being satisfied with the precautions taken by the community (OR = 2.594, 95%CI = 1.485–4.530), disinfecting public facilities in the community (OR = 2.342, 95%CI = 1.206–4.547), having knowledge of transmission modes (OR = 3.987, 95%CI: 2.039, 7.798), symptoms (OR = 2.045, 95%CI = 1.054–4.003), and outcomes (OR = 2.740, 95%CI = 1.513–4.962) of COVID-19, and not having anxiety symptoms (OR = 2.578, 95%CI = 1.127–5.901) were positively associated with affirmative behavior in urban areas. Being married (OR = 4.960, 95%CI = 2.608–9.434), being satisfied with the precautions taken by the community (OR = 2.484, 95%CI = 1.315–4.691), screening to ensure face mask wearing before entering the community (OR = 8.809, 95%CI = 2.649–19.294), and having knowledge about precautions (OR = 4.886, 95%CI = 2.604–9.167) and outcomes (OR = 2.657, 95%CI = 1.309–5.391) were positively associated with acceptable conduct in rural areas.The status of anxiety and depression among urban residents was more severe compared to those living in rural areas. There was a difference in being positively associated with constructive behaviors between rural and urban areas.     

Hypertension is a risk factor for adverse outcomes in patients with coronavirus disease 2019: a cohort study

Abstract

Background

Comorbidities are commonly seen in patients with coronavirus disease 2019 (COVID-19), but the clinical implication is not yet well-delineated. We aim to characterize the prevalence and clinical implications of comorbidities in patients with COVID-19.     

从武汉抗疫看中医药文化认同与新的医学模式

从武汉抗疫中医药的投入使用情况,反思现代临床和国人所呈现出的中医药文化认同和就医习惯,探索其历史文化根源及社会因素与医学发展的内在关联。回顾历史,中医屡次临危受命,不负重托,如今大疫当前,中医药再次发挥了重要作用。故新时代探讨如何遵循中医药发展规律,传承精华、守正创新,坚持中医药原创优势,形成中医药学科优势整体化呈现的新模式医学,对提升全社会的中医药认知度具有重要意义。     

Rethinking PICO in the Machine Learning Era: ML-PICO

Background  Machine learning (ML) has captured the attention of many clinicians who may not have formal training in this area but are otherwise increasingly exposed to ML literature that may be relevant to their clinical specialties. ML papers that follow an outcomes-based research format can be assessed using clinical research appraisal frameworks such as PICO (Population, Intervention, Comparison, Outcome). However, the PICO frameworks strain when applied to ML papers that create new ML models, which are akin to diagnostic tests. There is a need for a new framework to help assess such papers. Objective  We propose a new framework to help clinicians systematically read and evaluate medical ML papers whose aim is to create a new ML model: ML-PICO (Machine Learning, Population, Identification, Crosscheck, Outcomes). We describe how the ML-PICO framework can be applied toward appraising literature describing ML models for health care. Conclusion  The relevance of ML to practitioners of clinical medicine is steadily increasing with a growing body of literature. Therefore, it is increasingly important for clinicians to be familiar with how to assess and best utilize these tools. In this paper we have described a practical framework on how to read ML papers that create a new ML model (or diagnostic test): ML-PICO. We hope that this can be used by clinicians to better evaluate the quality and utility of ML papers.     

The relationship between childhood maltreatment and psychopathology in adults undergoing bariatric surgery

Background

A history of childhood maltreatment and psychopathology are common in adults with obesity.