GLOBAL CLINICIAN PERSPECTIVES ON NON-OPERATIVE HIP FRACTURE MANAGEMENT DURING COVID-19

There are currently no guidelines regarding clinician decision making in the type of hip fracture management among older adults. Cultural, social, structural and economic differences between global healthcare systems may result in differing approaches. This study's objectives were to identify possible factors influencing clinicians' decision to undertake a non-operative hip fracture management approach among older adults, and to determine whether there is global heterogeneity regarding these factors between high income countries (HIC), and low- and middle-income countries (LMIC) clinicians. A SurveyMonkey questionnaire was distributed to clinicians through the Fragility Fracture Network's Perioperative Special Interest Group and clinicians' personal networks between May 24 and July 25, 2021. 406 respondents from 51 countries returned the questionnaire, of which 225 respondents came from HIC and 180 from LMIC. Clinicians from HIC reported a greater median [IQR] estimated proportion of admitted patients with hip fracture undergoing surgery (96% [95–99]) than those from LMIC (85% [75–95]) of mean (SD) at 94% (8) compared to 81% (16) among LMIC clinicians (p=2.94e-23). Several factors seemed to influence the clinician hip fracture management decision making process. Global heterogeneity seems to exist between HIC and LMIC clinicians regarding factors such as anticipated life expectancy, ability to pay, treatment costs, insufficient resources, and perception of risk in hip fracture management decision-making. This is the first international sampling of clinician perspectives regarding hip fracture management. Further research is necessary for the development of best practice guidelines to improve hip fracture management decision-making and quality of hip fracture care among older adults.

The Dynamic Impact of COVID-19 Pandemic on Stock Returns: A TVP-VAR-SV Estimation for G7 Countries

The COVID-19 pandemic has profoundly and negatively impacted the global stock markets. Hence, we investigated the time-varying impact of the COVID-19 pandemic on stock returns during the period from January 27, 2020 to December 23, 2021 using the TVP-VAR-SV model and used G7 countries as our research sample. Our results imply that (i) the spread of the COVID-19 pandemic has a significant negative impact on stock returns, but the impact decreases as the time window increases;(ii) the timeliness, compulsoriness, and effectiveness of anti-epidemic policies implemented by governments are the important adjustment factors for stock returns;(iii) the impact of the early stage of the COVID-19 pandemic on the stock market trend gradually weakens as the intermediate time interval increases. In addition, over time, the duration of the negative impact of the COVID-19 pandemic on the stock returns became shorter, and the recovery rate of the impact became faster;(iv) under the managed floating exchange rate regime, the stock returns changed synchronously with the pressures of exchange rate appreciation and depreciation, and under the free-floating exchange rate regime, the effect of the exchange rate on stock returns was almost zero, while the impact of exchange rate channels in eurozone countries was related to the characteristics of national economies. Thus, governments should make greater efforts to improve the compulsion and effectiveness of epidemic prevention policies and strengthen their control over exchange rate fluctuations to alleviate the negative impact of the COVID-19 pandemic on the stock markets.

A Data Driven Study on the Variant of Covid-19 in Hong Kong

The new wave of COVID-19 in Hong Kong, China was overwhelming again by “dynamic zero” strategy and non-pharmaceutical interventions (DZ-NPIs), which makes a time challenge to control the variant of this epidemic. We describe the variant of Covid-19 in Kong Hong to the infected proportion of the population, cumulative confirmed cases, cumulative deaths and current hospitalizations by age group via statistical measure firstly, then establish time series model for fitting the accumulative confirmed cases, further to predict the trend for searching out possible turning time-points. Non-linear regression model is created to feature the deaths series, then we figure out the parameters and educe the controlling condition for this epidemic. We expect our data-driven modeling process providing some insights to the controlling strategy for the new wave of the Covid-19 variant in Hong Kong, even in the mainland of China © 2022 Ding and Xiang;Licensee Lifescience Global. This is an open access article licensed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution and reproduction in any medium, provided the work is properly cited

The value of CT signs combined with radiomics in the differentiation of COVID-19 from other viral pneumonias

Objective To explore the classification performance of combined model constructed from CT signs combined with radiomics for discriminating COVID-19 pneumonia and other viral pneumonia. Methods The clinical and CT imaging data of 181 patients with viral pneumonia confirmed by reverse transcription-polymerase chain reaction in 15 hospitals of Yunnan Province from March 2015 to March 2020 were analyzed retrospectively. The 181 patients were divided into COVID-19 group (89 cases) and non-COVID-19 group (92 cases), which were further divided into training cohort (126 cases) and test cohort (55 cases) at a ratio of 7∶3 using random stratified sampling. The CT signs of pneumonia were determined and the radiomics features were extracted from the initial unenhanced chest CT images to build independent and combined models for predicting COVID-19 pneumonia. The diagnostic performance of the models were evaluated using receiver operating characteristic (ROC) analysis, continuous net reclassification index (NRI) calibration curve and decision curve analysis. Results The combined models consisted of 3 significant CT signs and 14 selected radiomics features. For the radiomics model alone, the area under the ROC curve (AUC) were 0.904 (sensitivity was 85.5%, specificity was 84.4%, accuracy was 84.9%) in the training cohort and 0.866 (sensitivity was 77.8%, specificity was 78.6%, accuracy 78.2%) in the test cohort. After combining CT signs and radiomics features, AUC of the combined model for the training cohort was 0.956 (sensitivity was 91.9%, specificity was 85.9%, accuracy was 88.9%), while that for the test cohort was 0.943 (sensitivity was 88.9%, specificity was 85.7%, accuracy was 87.3%). The AUC values of the combined model and the radiomics model in the differentiation of COVID-19 group and the non-COVID-19 group were significantly different in the training cohort (Z=-2.43, P=0.015), but difference had no statistical significance in the test cohort (Z= -1.73, P=0.083), and further analysis using the NRI showed that the combined model in both the training cohort and the test cohort had a positive improvement ability compared with radiomics model alone (training cohort: continuous NRI 1.077, 95%CI 0.783-1.370;test cohort: continuous NRI 1.421, 95%CI 1.051-1.790). The calibration curve showed that the prediction probability of COVID-19 predicted by the combined model was in good agreement with the observed value in the training and test cohorts;the decision curve showed that a net benefit greater than 0.6 could be obtained when the threshold probability of the combined model was 0-0.75. Conclusion The combination of CT signs and radiomics might be a potential method for distinguishing COVID-19 and other viral pneumonia with good performance.

Analysis on the infection of 109 close contacts with COVID-19

Objective: To investigate the infection of novel coronavirus in close contacts with COVID-19 patients, and to analyze the risk factors and the effectiveness of intervention measures. Methods: The close contacts were investigated by questionnaire, and infection of novel coronavirus was counted after 14 days of observation. Chi-square inspection and multivariate Logistic regression analysis was used to study the correlation between some factors and infection, such as age, weight, basic diseases, smoking, drinking, et al. The influence of wearing masks, washing hands, and preventive drugs on the prognosis of close contacts was analyzed. Results: A total of 109 close contacts were investigated, and 20 cases were diagnosed as COVID-19. The age of the infected group was significantly higher than that of the non-infected group (66.0±14.8 vs 44.3±21.3, P< 0.01). Old age, hypertension, and insomnia were high risk factors for infection of novel coronavirus. Frequent hand- washing could significantly reduce the risk of novel coronavirus infection. Preventive use of antiviral drugs and traditional Chinese medicine could not reduce the risk of novel coronavirus infection. The use of antibiotics couldn't reduce the risk of new coronavirus infection in close contacts. Conclusion: Elderly people, especially those with hypertension, have a high risk of new coronavirus infection. Frequent hand washing and good sleep could effectively reduce the risk of new coronavirus infection. © 2021, Editorial Board of Medical Journal of Wuhan University. All right reserved.