The coronavirus disease 2019 (COVID-19) pandemic has posed a profound psychological impact on healthcare workers. However, the role of positive affect in moderating the effect of perceived stress on the psychological states of healthcare workers remains unknown. This study aimed to analyze the moderating effect of positive affect on the association between stress and the mental health of healthcare workers during the COVID-19 pandemic. This cross-sectional study evaluated the relationships between perceived stress (the Perceived Stress Scale), positive affect (the Positive and Negative Affect Schedule), depression (the Patient Health Questionnaire-9), and anxiety (the Generalized Anxiety Disorder 7-item Scale) during the COVID-19 pandemic in 644 Chinese healthcare workers who completed online self-reports. The results revealed a significant negative association between positive affect and psychological problems, including stress, depression, and anxiety. At the total group level, multiple regression analysis showed that positive affect alleviated the influence of perceived stress on depression, but no significant moderating effect was found for anxiety. In the subgroups divided by perceived stress, the moderating effect of positive affect on depression was only significant in healthcare workers with a high level of perceived stress. These results suggested that positive affect played a moderative role in alleviating the effect of stress on depression among healthcare workers, particularly those with a high level of stress, thus emphasizing the importance of positive affect as an intervention strategy for promoting the mental health of healthcare workers in the context of the ongoing COVID-19 pandemic.

Background: The escalating complexity of the COVID-19 epidemic underscores the need for heightened attention to booster vaccinations. This study aims to examine the changing trend in the public’s intention to receive the second COVID-19 booster vaccination over time and the associated factors following the COVID-19 policy optimization in China. Method: Eight cross-sectional surveys utilizing SMS questionnaire links were conducted in Guangzhou, China, from December 2022 to April 2023. The Mann–Kendall test was employed to analyze the trend in intentions to receive the second booster vaccination across the survey time. Adjusted and multivariate logistic analyses were used to analyze the factors associated with vaccination intention. Parallel analyses were performed for two subgroups with different COVID-19 infection statuses. Results: A total of 9860 respondents were surveyed in the eight rounds, of which 8048 completed the first booster vaccination and were included in the analysis. The overall COVID-19 infection rate was 60.0% (4832/8048), while the overall vaccination intention was 72.2% (5810/8048) among respondents. The vaccination intention exhibited a significant declining trend over time, decreasing from 81.5% in December 2022 to 52.2% in April 2023. An adjusted logistic regression analysis revealed that anxiety and depression were negatively associated with an intention to receive the second booster vaccination, while COVID-19-related preventive behaviors and a high engagement in COVID-19-related information were positively associated with an intention to receive the second booster vaccination. A subgroup analysis revealed that the association between psychological and behavioral characteristics and vaccination intention remained relatively stable among individuals with different histories of COVID-19 infections. Conclusion: There was a significant decline in the intention to receive the second booster vaccination following the optimization of the COVID policy in China. Our findings emphasize the urgency of the second booster vaccination and provide a foundation for the development of tailored interventions to enhance and sustain vaccination intention among the public.

Objective: Since the lifting of the COVID-19 restrictions in China in November 2022, there has been a notable surge in the COVID-19 infection rate. Little is known about the prevalence of long COVID among the general adult population and its impact on COVID-19-related worries and behaviors after the policy change. Methods: This cross-sectional study recruited 1530 adults with prior COVID-19 infection in Guangzhou from February to March 2023. Logistic regression analyses and trend analyses were performed to investigate the associations between long COVID- and COVID-19-related worries and preventive behaviors. Results: The estimated prevalence of long COVID among adults in China was 18.0% (95% confidence interval: 16.1% to 20.0%). Common long COVID symptoms included cough (60.7%), fatigue (47.6%), dyspnea (34.5%), palpitation (26.2%), and insomnia (25.1%). Adjusted for background variables, individuals with long COVID exhibited higher level of COVID-19-related worries compared to those who had fully recovered from the infection (reference: without long COVID; adjusted odds ratios ranged from 1.87 to 2.55, all p values < 0.001). Participants primarily expressed worries regarding the potential for COVID-19 reinfection, the impact of the pandemic on daily life, the increasing number of COVID-19 cases and deaths, and the capacity of the healthcare system. While long COVID did not statistically significantly affect their preventive behaviors. Conclusions: Long COVID was prevalent among the general adult population in China after lifting the COVID-19 restrictions, and it had a significant impact on COVID-19-related worries. This study highlights the importance of monitoring the mental health of individuals with long COVID and developing targeted intervention strategies to improve their adherence to preventive measures.

Nonprobability sampling has been increasingly used in epidemiologic research, yet direct inference based on such samples is subject to selection bias. Current adjustment methods commonly rely on a reference probability-based survey sample that shares a set of covariates with the nonprobability sample. However, these common covariates are often limited and may bias estimates in the presence of population heterogeneity. Existing methods generally assume population homogeneity in models and fail to address such heterogeneity adequately. To overcome this limitation, we propose the Nonprobability Heterogeneity-adjusted Doubly Robust (NHDR) method, a novel inference framework that explicitly accounts for population heterogeneity during selection bias adjustment. NHDR proceeds in three stages: (1) identifying latent subpopulations via finite mixture modeling; (2) incorporating the resulting latent-class structure as a grouping variable into mixed-effects models for both the propensity score and outcome projection; and (3) constructing a doubly robust estimator that integrates these adjusted models. The key methodological contribution of NHDR is its formal integration of latent-class-based population structure into a doubly robust estimation framework, which enables more reliable inference under heterogeneous population settings. Simulation studies demonstrate that the proposed method control the coverage probabilities well in most scenarios. Under heterogeneous conditions, NHDR consistently outperforms existing methods achieving an average reduction in relative bias of approximately 1.8–4.5% and a corresponding decrease in mean squared error of about 5.1–15.5 compared to the benchmark method. We illustrate the practical utility of NHDR by applying it to estimate nine health indicators using data from the Health Monitoring SMS Survey in Guangzhou, China, with the seventh Guangdong Health Service Survey serving as the reference sample.