The World Health Organization has endorsed COVID-19 is no longer a global public health emergency: How they took this step and what we should do right now?

The mediating effect of resilience and COVID-19 anxiety on the relationship between social support and insomnia among healthcare workers: a cross-sectional study

Background

Insomnia in healthcare workers has become a topic of concern in the health system. The high infectivity and longevity of the COVID-19 pandemic have resulted in great pressure and a high incidence of insomnia among healthcare workers. Insomnia among healthcare workers has a negative impact on high-quality healthcare services in addition to their health. Thus, it's necessary to explore insomnia's underlying mechanisms.

Object

The present research's aims were threefold: explored the association between social support, resilience, COVID-19 anxiety, and insomnia among healthcare workers during the pandemic, elucidated the underlying mechanism of insomnia, and offered recommendations for improving the health of these workers.

Materials and methods

A cross-sectional design was adopted. From May 20 to 30, 2022, 1038 healthcare workers were selected to fill out the Oslo 3-item Social Support Scale, the eight-item Athens Insomnia Scale, the Coronavirus Anxiety Scale, and the Brief Resilience Scale. Descriptive statistics and correlations were analyzed by SPSS 25.0. Mediation analysis was conducted by Mplus 8.3 using 5000 bootstrap samples.

Results

Of the participating 1038 healthcare workers, the prevalence of insomnia was 41.62% (432/1038). Significant associations were found involving insomnia, resilience, COVID-19 anxiety, and social support. Insomnia was directly affected by social support. Moreover, three indirect pathways explain how social support affected insomnia: resilience's mediating role, COVID-19 anxiety's mediating role, and the chain-mediation role of resilience and COVID-19 anxiety.

Conclusion

The results validated our hypotheses and supported the opinion of Spielman et al. 's three-factor model of insomnia. Social support of healthcare workers has an indirect impact on insomnia in addition to its direct one via independent and chain-mediation effects of resilience and COVID-19 anxiety.

Developing an interpretable machine learning model for predicting COVID-19 patients deteriorating prior to intensive care unit admission using laboratory markers

Coronavirus disease (COVID-19) remains a significant global health challenge, prompting a transition from emergency response to comprehensive management strategies. Furthermore, the emergence of new variants of concern, such as BA.2.286, underscores the need for early detection and response to new variants, which continues to be a crucial strategy for mitigating the impact of COVID-19, especially among the vulnerable population. This study aims to anticipate patients requiring intensive care or facing elevated mortality risk throughout their COVID-19 infection while also identifying laboratory predictive markers for early diagnosis of patients. Therefore, haematological, biochemical, and demographic variables were retrospectively evaluated in 8,844 blood samples obtained from 2,935 patients before intensive care unit admission using an interpretable machine learning model. Feature selection techniques were applied using precision-recall measures to address data imbalance and evaluate the suitability of the different variables. The model was trained using stratified cross-validation with k=5 and internally validated, achieving an accuracy of 77.27%, sensitivity of 78.55%, and area under the receiver operating characteristic (AUC) of 0.85; successfully identifying patients at increased risk of severe progression. From a medical perspective, the most important features of the progression or severity of patients with COVID-19 were lactate dehydrogenase, age, red blood cell distribution standard deviation, neutrophils, and platelets, which align with findings from several prior investigations. In light of these insights, diagnostic processes can be significantly expedited through the use of laboratory tests, with a greater focus on key indicators. This strategic approach not only improves diagnostic efficiency but also extends its reach to a broader spectrum of patients. In addition, it allows healthcare professionals to take early preventive measures for those most at risk of adverse outcomes, thereby optimising patient care and prognosis.

The Dengue Prevalence and Mortality Rate Surpass COVID-19 in Bangladesh: Possible Strategies to Fight Against a Double-Punch Attack

Dengue is a vector-borne viral disease caused by multiple serotypes (DENV-1, DENV-2, DENV-3, and DENV-4) of the dengue virus. It has been a public health concern since 2000 in Bangladesh. However, Bangladesh experienced a higher prevalence and death rate in the year 2022 than the previous year surpassing the COVID-19 situation. While climatic factors had always been a prominent reason for dengue incidence, reports stated that DEN 4 serotype was identified for the first time in the country, which made the dengue cases worse. In this article, we presented the 5 years prevalence of hospitalization and death cases owing to dengue fever and also provided a comparison of death cases caused by dengue and COVID-19 in Bangladesh. We described the possible reasons for the sudden surges of dengue infection and mentioned the actions led by the government to deal with this dengue occurrence. Lastly, we recommend a few strategies to counter the future outbreak of dengue infection in the country.