Occupational Hearing Loss for Platinum Miners in South Africa: A Case Study of Data Sharing Practices and Ethical Challenges in the Mining Industry

Depression and Anxiety Symptoms and Their Associated Factors Among Chinese Residents After the Lifting of the Dynamic Zero-COVID Policy: A Cross-Sectional Study

Purpose

China lifted its strict zero-Covid approach on December 7, 2022. This study aimed to investigate depression and anxiety symptoms and their associations among Chinese residents after the change in public policy.

Methods

A cross-sectional sample of 925 Chinese residents (726 females and 199 males) was recruited using convenience and snowball sampling approach between 16 and 25 December 2022. Participants completed online questionnaires on basic information, depression, anxiety, COVID-19 related perceptions, and protective behaviors change.

Results

Mild and moderate-to-severe depression symptoms were reported by 35.6% and 19.1% of participants, respectively. Nearly 40% of participants reported mild anxiety and 18.7% reported moderate-to-severe anxiety. Results from multinomial logistic regression analysis indicated that male gender, younger age, the presence of chronic disease, poorer self-rated mental health status, perceived impact, and worry were risk factors for both depression and anxiety, while higher education and protective behaviors change were protective factors. Besides, living with or caring for children (4-6 years), family members or other housemates currently with influenza-like symptoms, and perceived severity were also risk factors for depression.

Conclusion

Our findings provided initial evidence that Chinese residents may face heightened depression and anxiety during the early stage after the policy was released. Furthermore, we identified some vulnerable populations in need of prioritizing mental health assistance and some potentially modifiable factors associated with depression and anxiety, which provides an important guide for developing timely and effective psychological interventions and preparing for future pandemics.

Explicit modeling of antibody levels for infectious disease simulations in the context of SARS-CoV-2

Measurable levels of immunoglobulin G antibodies develop after infections with and vaccinations against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). These antibody levels are dynamic: due to waning, antibody levels will drop over time. During the COVID-19 pandemic, multiple models predicting infection dynamics were used by policymakers to support the planning of public health policies. Explicitly integrating antibody and waning effects into the models is crucial for reliable calculations of individual infection risk. However, only few approaches have been suggested that explicitly treat these effects. This paper presents a methodology that explicitly models antibody levels and the resulting protection against infection for individuals within an agent-based model. The model was developed in response to the complexity of different immunization sequences and types and is based on neutralization titer studies. This approach allows complex population studies with explicit antibody and waning effects. We demonstrate the usefulness of our model in two use cases.