The COVID-19 pandemic has impacted patient’s visits to general practitioners (GPs). However, it is unclear what the impact of COVID-19 has been on the interaction among the local primary care clinics, the GP Department within the hospital and specialists.

The interaction among GPs referring to hospital-based specialists and specialists to local doctors was determined, comparing pre-pandemic 2019 and 2020 during the pandemic.

Reduced referrals from GPs to specialists were consistent with the reduction in specialist referrals back to the local doctors, which dropped by approximately 50% in 2020, particularly in the two most common chronic conditions (hypertension and diabetes mellitus).

Reduced referral of patients from local clinics to Tongren Hospital is probably due to the extensive online training provided to the local GPs to become more competent in handling local patients via telehealth. Our data provide some insight to assist in combatting the pandemic of COVID-19, offering objective evidence of the impact of COVID-19 on patient management by GPs.

Depressive symptoms, functional impairment, and decreased quality of life (QOL) are three important domains of major depressive disorder (MDD). However, the possible causal relationship between these factors has yet to be elucidated. Moreover, it is not known whether certain symptoms of MDD are more impairing than others. The network approach is a promising solution to these shortfalls.

The baseline data of a multicenter prospective project conducted in 11 governances of China were analyzed. In total, 1385 patients with MDD were included. Depressive symptoms, functioning disability, and QOL were evaluated by the 17-item Hamilton Depression Rating Scale (HAMD-17), the Sheehan Disability Scale (SDS), and the Quality of Life Enjoyment and Satisfaction Questionnaire-Short Form (Q-LES-Q-SF). The network was estimated through the graphical Least Absolute Shrinkage and Selection Operator (LASSO) technique in combination with the directed acyclic graph.

Three centrality metrics of the graphical LASSO showed that social life dysfunction, QOL, and late insomnia exhibited the highest strength centrality. The network accuracy and stability were estimated to be robust and stable. The Bayesian network indicated that some depressive symptoms were directly associated with QOL, while other depressive symptoms showed an indirect association with QOL mediated by impaired function. Depressed mood was positioned at the highest level in the model and predicted the activation of functional impairment and anxiety.

Functional disability mediated the relationship between depressive symptoms and QOL. Family functionality and suicidal symptoms were directly related to QOL. Depressed mood played the predominant role in activating both anxiety symptom and functional impairment.

The relationship of a diet low in fibre with mortality has not been evaluated. This study aims to assess the burden of non-communicable chronic diseases (NCD) attributable to a diet low in fibre globally from 1990 to 2019.

All data were from the Global Burden of Disease (GBD) Study 2019, in which the mortality, disability-adjusted life-years (DALY) and years lived with disability (YLD) were estimated with Bayesian geospatial regression using data at global, regional and country level acquired from an extensively systematic review.

All data sourced from the GBD Study 2019.

All age groups for both sexes.

The age-standardised mortality rates (ASMR) declined in most GBD regions; however, in Southern sub-Saharan Africa, the ASMR increased from 4·07 (95 % uncertainty interval (UI) (2·08, 6·34)) to 4·60 (95 % UI (2·59, 6·90)), and in Central sub-Saharan Africa, the ASMR increased from 7·46 (95 % UI (3·64, 11·90)) to 9·34 (95 % UI (4·69, 15·25)). Uptrends were observed in the age-standardised YLD rates attributable to a diet low in fibre in a number of GBD regions. The burden caused by diabetes mellitus increased in Central Asia, Southern sub-Saharan Africa and Eastern Europe.

The burdens of disease attributable to a diet low in fibre in Southern sub-Saharan Africa and Central sub-Saharan Africa and the age-standardised YLD rates in a number of GBD regions increased from 1990 to 2019. Therefore, greater efforts are needed to reduce the disease burden caused by a diet low in fibre.

The aim of this work was to develop a table-top exercise (TTX) program for mass-casualty incident (MCI) response based on a real incident to evaluate the program.

The TTX program was developed based on the 8 TTX design steps. Convenience sampling was adopted to recruit recently graduated physicians in China. After the TTX training, the participants completed a self-designed questionnaire, as well as the Simulation Design Scale (SDS) and Educational Practices in Simulation Scale (EPSS).

In total, 148 valid questionnaires were collected. The difficulty score of the TTX program was 3.69 ± 0.8. The participants evaluated the program highly, with a score of 4.72 ± 0.54 out of 5. Both the SDS and the EPSS had average scores higher than 4.5. Guided reflection/feedback (M = 4.68, SD = 0.41) and fidelity (M =4.66, SD = 0.57) were the 2 highest-rated SDS subscales. For the EPSS, diverse ways of learning and collaboration were the 2 highest-rated subscales. Multivariate stepwise regression analysis showed that the participants’ evaluations of the TTX training course were related to the EPSS score, the difficulty rating, the evaluation of the instructional props, and the degree of participant involvement (F = 24.385, P < 0.001).

A TTX program for MCIs was developed based on the 2014 Shanghai New Year Crush. The TTX kit is practical and sophisticated, and it provides an effective strategy for MCI training.

Researchers at the Centers for Disease Control and Prevention monitor unplanned school closure (USC) reports through online systematic searches (OSS) to assist public health emergency responses. We counted the additional reports identified through social media along with OSS to improve USC monitoring.

Facebook and Twitter data of public-school districts and private schools in counties affected by California wildfires in October and December of 2017 and January of 2018 were retrieved. We computed descriptive statistics and performed multivariable logistic regression for both OSS and social media data.

Among the 362 public-school districts in wildfire-affected counties, USCs were identified for 115 (32%) districts, of which OSS identified 104 (90%), Facebook, 59 (52%), and Twitter, 37 (32%). These data correspond to 4622 public schools, among which USCs were identified for 888 (19.2%) schools, of which OSS identified 722 (81.3%), Facebook, 496 (55.9%), and Twitter, 312 (35.1%). Among 1289 private schools, USCs were identified for 104 schools, of which OSS identified 47 (45.2%), Facebook, 67 (64.4%), and Twitter, 29 (27.9%). USC announcements identified via social media, in addition to those via OSS, were 11 public school districts, 166 public schools, and 57 private schools.

Social media complements OSS as additional resources for USC monitoring during disasters.

To aid emergency response, Centers for Disease Control and Prevention (CDC) researchers monitor unplanned school closures (USCs) by conducting online systematic searches (OSS) to identify relevant publicly available reports. We examined the added utility of analyzing Twitter data to improve USC monitoring.

Georgia public school data were obtained from the National Center for Education Statistics. We identified school and district Twitter accounts with 1 or more tweets ever posted (“active”), and their USC-related tweets in the 2015-16 and 2016-17 school years. CDC researchers provided OSS-identified USC reports. Descriptive statistics, univariate, and multivariable logistic regression were computed.

A majority (1,864/2,299) of Georgia public schools had, or were in a district with, active Twitter accounts in 2017. Among these schools, 638 were identified with USCs in 2015-16 (Twitter only, 222; OSS only, 2015; both, 201) and 981 in 2016-17 (Twitter only, 178; OSS only, 107; both, 696). The marginal benefit of adding Twitter as a data source was an increase in the number of schools identified with USCs by 53% (222/416) in 2015-16 and 22% (178/803) in 2016-17.

Policy-makers may wish to consider the potential value of incorporating Twitter into existing USC monitoring systems.

This study aimed to examine the efficacy of combining paroxetine and mirtazapine v. switching to mirtazapine, for patients with major depressive disorder (MDD) who have had an insufficient response to SSRI monotherapy (paroxetine) after the first 2 weeks of treatment.

This double-blind, randomized, placebo-controlled, three-arm study recruited participants from five hospitals in China. Eligible participants were aged 18–60 years with MDD of at least moderate severity. Participants received paroxetine during a 2-week open-label phase and patients who had not achieved early improvement were randomized to paroxetine, mirtazapine or paroxetine combined with mirtazapine for 6 weeks. The primary outcome was improvement on the Hamilton Rating Scale for Depression 17-item (HAMD-17) scores 6 weeks after randomization.

A total of 204 patients who showed early non-response to paroxetine monotherapy were randomly assigned to receive either mirtazapine and placebo (n = 68), paroxetine and placebo (n = 68) or mirtazapine and paroxetine (n = 68), with 164 patients completing the outcome assessment. At week 8, the least squares (LS) mean change of HAMD-17 scores did not significantly differ among the three groups, (12.98 points) in the mirtazapine group, (12.50 points) in the paroxetine group and (13.27 points) in the mirtazapine plus paroxetine combination group. Participants in the paroxetine monotherapy group were least likely to experience adverse effects.

After 8 weeks follow-up, paroxetine monotherapy, mirtazapine monotherapy and paroxetine/mirtazapine combination therapy were equally effective in non-improvers at 2 weeks. The results of this trial do not support a recommendation to routinely offer additional treatment or a switch in treatment strategies for MDD patients who do not show early improvement after 2 weeks of antidepressant treatment.

To investigate the association of regional fat depots with metabolic risk factors in Chinese women.

Total and regional fat depots including android fat and gynoid fat were measured by dual-energy X-ray absorptiometry. Central fat distribution was defined as android:gynoid fat ratio. Metabolic risk factors were defined as elevated TAG, reduced HDL-cholesterol, elevated blood pressure and elevated fasting plasma glucose. Logistic regression analyses were performed to examine the associations of regional fat depots with metabolic risk factors. The odds ratios of metabolic risks were further calculated according to tertiles of android fat and gynoid fat.

Participants were recruited from a community-based cross-sectional study. Face-to-face questionnaires, anthropometric and dual-energy X-ray absorptiometry measures were conducted.

Chinese women (n 609) aged 18–79 years.

Android fat and android:gynoid fat ratio were associated with significantly increased odds (OR = 1·4–3·7; P < 0·01) for almost all risk factors, whereas gynoid fat was independently associated with significantly decreased odds (OR = 0·3–0·6; P < 0·01). The inverse associations of gynoid fat with metabolic risk factors remained after adjusting for android fat. Even if their android fat level was in high, women in the highest tertile of gynoid fat had lower odds of having at least two metabolic risk factors compared with women in the lowest gynoid fat tertile (P for trend < 0·01).

There were opposite associations of android and gynoid fat with metabolic risks in Chinese women. Gynoid fat rather than android fat might be a more important inclusion in metabolic disease risk evaluation in female Asians.