IMPORTANCE Congenital cytomegalovirus infection (cCMVi) is one of the most common infections associated with childhood hearing loss. Prevention and mitigation of cCMVi-related hearing loss will require an increase in newborn screening, which is not yet available in China. OBJECTIVETo estimate the cost-effectiveness of newborn screening strategies for cCMVi from the perspective of the Chinese health care system. DESIGN, SETTING, AND PARTICIPANTSA decision tree for a simulated cohort population of 15 000 000 live births was developed to compare the costs and health effects of 3 mutually exclusive interventions: (1) no screening, (2) targeted screening using CMV polymerase chain reaction assay for newborns who fail a universal hearing screening, and (3) universal screening for CMV among all newborns. Markov diagrams were used to evaluate the lifetime horizon (76 years). MAIN OUTCOMES AND MEASURESCost, hearing-related health outcomes, and incremental costeffectiveness ratios (ICERs) were estimated based on a direct medical costs perspective. Costs and ICERs were reported in 2018 US dollars. RESULTSIncidence of cCMVi among newborns was reported to be approximately 0.7% in China.Targeted screening was less costly but also less effective than universal screening, identifying 41% of cases needing antiviral treatment and preventing nearly half of less severe or profound hearing loss.To avoid 1 CMV-related severe or profound hearing loss, 13 and 16 newborns need to be treated by targeted and universal screening, respectively. The ICERs of targeted and universal screening vs no screening were $79 and $2087 per quality-adjusted life-year gained, respectively, at the discounted rate of 3.5%. Both screening options were cost-effective for the Chinese health care system based on the willingness-to-pay threshold of 3 × gross domestic product per capita. The sensitivity analysis showed that the prevalence of cCMVi, as well as diagnosis and treatment costs, were key factors that may be associated with decision-making. CONCLUSIONS AND RELEVANCETo achieve cost-effectiveness and best health outcomes, universal screening could be considered for the Chinese population. While the results are specific to China, the model may easily be adapted for other countries.

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BackgroundDuring the COVID-19 pandemic, the frontline medical staff faced more workload and heavier physical and mental stress, which increased their job burnout and negative emotions. However, little is known about the potential factors mediating and moderating these relations. This study investigates the association between long working hours and depressive symptoms among frontline medical staff in China, and explores the potential mediating effect of job burnout, and moderating effect of family and organizational support on these associations.MethodsData of 992 frontline medical staff who participated in the prevention and control of COVID-19 was obtained from the online survey conducted in November to December 2021 in China. Depressive symptoms were evaluated using the Patient Health Questionaire-9 (PHQ-9). Moderated mediating model was employed to understand the relationship between long working hours (X), depressive symptoms (Y) mediated through job burnout (M), moderated by family support (W1) and organizational support (W2), while controlling all possible covariates.Results56.96% of participants worked more than 8 h per day. 49.8% of them had depressive symptoms (PHQ-9 ≥ 5) and 65.8% experienced job-related burnout. Long working hours was positively associated with depressive symptoms score (β = 0.26, 95% CI:0.13 ~ 0.40). Mediation analyses revealed that job burnout significantly mediated this relationship (indirect effect = 0.17, 95% CI: 0.08 ~ 0.26). Moderated mediation further indicated that both two interactions of social support (family support W1, organizational support W2) and job burnout were negatively related to depressive symptoms among frontline medical staff, indicating that higher social support being less job burnout with lower depressive symptoms.ConclusionLonger working hours and higher job burnout may contribute to worse mental health among frontline medical staff. Social support could buffer the detrimental effects by reducing their job burnout.ContributionThe main contribution of this study was to estimate the negative effect of long working hours on depressive symptoms among frontline medical staff and explore the potential mediating role of job burnout and moderating role of social support on these associations.

BackgroundIt is challenging to deal with mixture models when missing values occur in clustering datasets.Methods and ResultsWe propose a dynamic clustering algorithm based on a multivariate Gaussian mixture model that efficiently imputes missing values to generate a “pseudo-complete” dataset. Parameters from different clusters and missing values are estimated according to the maximum likelihood implemented with an expectation-maximization algorithm, and multivariate individuals are clustered with Bayesian posterior probability. A simulation showed that our proposed method has a fast convergence speed and it accurately estimates missing values. Our proposed algorithm was further validated with Fisher’s Iris dataset, the Yeast Cell-cycle Gene-expression dataset, and the CIFAR-10 images dataset. The results indicate that our algorithm offers highly accurate clustering, comparable to that using a complete dataset without missing values. Furthermore, our algorithm resulted in a lower misjudgment rate than both clustering algorithms with missing data deleted and with missing-value imputation by mean replacement.ConclusionWe demonstrate that our missing-value imputation clustering algorithm is feasible and superior to both of these other clustering algorithms in certain situations.

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