Introduction The outbreak of the coronavirus disease 2019 (COVID-19) poses an unprecedented challenge to healthcare workers (HCWs) globally. This study investigated potential factors related to depression, anxiety, and stress in a sample of Chinese HCWs during the peak of the COVID-19 epidemic. Methods An online survey was distributed to Chinese HCWs using respondent-driven sampling. Data were collected between February 13th and February 20th, 2020, immediately following the COVID-19 contagion peak in Hubei. A total of 1208 respondents were eligible for analysis. Mental health problems and social support were measured by the Depression Anxiety Stress Scales-21 (DASS-21) and the Perceived Social Support Scale (PSS). Results The prevalence rates of depression, (DASS-depression > 9) anxiety (DASS-anxiety > 7) and stress (DASS-stress > 14) were 37.8%, 43.0% and 38.5%, respectively. Multivariate logistic regressions revealed that stress, anxiety, and depression were positively related to lower levels of social support, longer working hours, discrimination experience and workplace violence. The scarcity of medical equipment was correlated with increased stress and depression. Chinese HCWs working at COVID 19 designated hospitals were more likely to report anxiety. Additionally, volunteering to work in the frontline health facilities was inversely associated with depression. Conclusion Mental health problems among Chinese HCWs were alarming during the peak of the COVID-19 epidemic. Health facilities require appropriate and standing services that address the mental health of healthcare workers, particularly during epidemic outbreaks.

ObjectiveThe purpose of this study was to present the design, model, and data analysis of an interrupted time series (ITS) model applied to evaluate the impact of health policy, systems, or environmental interventions using count outcomes. Simulation methods were used to conduct power and sample size calculations for these studies.MethodsWe proposed the models and analyses of ITS designs for count outcomes using the Strengthening Translational Research in Diverse Enrollment (STRIDE) study as an example. The models we used were observation-driven models, which bundle a lagged term on the conditional mean of the outcome for a time series of count outcomes.ResultsA simulation-based approach with ready-to-use computer programs was developed to calculate the sample size and power of two types of ITS models, Poisson and negative binomial, for count outcomes. Simulations were conducted to estimate the power of segmented autoregressive (AR) error models when autocorrelation ranged from −0.9 to 0.9, with various effect sizes. The power to detect the same magnitude of parameters varied largely, depending on the testing level change, the trend change, or both. The relationships between power and sample size and the values of the parameters were different between the two models.ConclusionThis article provides a convenient tool to allow investigators to generate sample sizes that will ensure sufficient statistical power when the ITS study design of count outcomes is implemented.

ObjectiveTo study the association between glucose metabolism disorders and hepatotropic virus infection.MethodsA cross-sectional analysis was performed using data from the REACTION study (Risk Evaluation of Cancers in Chinese Diabetic Individuals: A Longitudinal Study). Outcomes of the analysis were test results of kidney function, liver function, lipid metabolism, and the prevalence of hepatitis B virus (HBV) infection and potential hepatitis C virus (HCV) infection (positive hepatitis C virus antibody) among individuals with and without diabetes mellitus (DM) or pre-diabetes mellitus (pre-DM).ResultsOf the 10,080 patients who participated in the study, 7665 eligible subjects were included in the analysis. There was no significant difference in the prevalence of HBV infection between DM and normal subjects, pre-DM and normal subjects, and DM or pre-DM and normal subjects (p-values of 0.9180, 0.8154, and 0.6448, respectively). There was also no significant difference in the prevalence of potential HCV infection between DM and normal subjects, pre-DM and normal subjects, and DM or pre-DM and normal subjects (p-values of 0.1190, 0.0591, and 0.5591, respectively). Lipid metabolism showed a significant difference between DM or pre-DM subjects and normal subjects (p-values were less than 0.0221 in all cases). Multiple logistic regression analysis revealed hypertension as the leading significant variable associated with DM, pre-DM, and both. Other significant factors included gender, body mass index, age, and alanine aminotransferase.ConclusionNo significant association was detected between DM or pre-DM and HBV or potential HCV infection. Significant association was detected between lipid metabolism disorders and DM, but this association was absent in pre-DM patients when adjusting for other factors.

Background: The objective of this study was to investigate the use of ensemble methods to improve the prediction of fetal macrosomia and large for gestational age from prenatal ultrasound imaging measurements. Methods: We evaluated and compared the prediction accuracies of nonlinear and quadratic mixed-effects models coupled with 26 different empirical formulas for estimating fetal weights in predicting large fetuses at birth. The data for the investigation were taken from the Successive Small-for-Gestational-Age-Births study. Ensemble methods, a class of machine learning techniques, were used to improve the prediction accuracies by combining the individual models and empirical formulas. Results: The prediction accuracy of individual statistical models and empirical formulas varied considerably in predicting macrosomia but varied less in predicting large for gestational age. Two ensemble methods, voting and stacking, with model selection, can combine the strengths of individual models and formulas and can improve the prediction accuracy. Conclusions: Ensemble learning can improve the prediction of fetal macrosomia and large for gestational age and have the potential to assist obstetricians in clinical decisions.

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We compared healthcare worker SARS-CoV-2 infection rates between March-August 2020 in two similar hospitals with high versus low airborne infection isolation room utilization rates but otherwise identical infection control policies. We found no difference in healthcare worker infection rates between the two hospitals nor between patient-facing vs non-patient-facing providers.

Objective To assess the rural–urban disparity in caesarean section rates using electronic health records from hospitals located in the Province of Inner Mongolia, which is a minority area in Northeastern China. Methods The study examined the electronic health records of women that gave birth in three major public hospitals between January 2012 and December 2016. Multinomial regression analyses were used to estimate rural–urban disparities in caesarean section rates. Results Data from 61 903 women were examined. Caesarean section rates increased slightly over the study period and the rate was significantly higher in rural compared with urban hospitals (48% versus 38%). This disparity consistently increased over time. Multinomial regression analyses showed that maternal age, ethnicity, health insurance type, employment status, reproductive history and the newborn’s sex were significant risk factors associated with caesarean section rate. Furthermore, stratified analysis of first-time pregnancies, minorities and different age groups showed that the odds of undergoing an emergency caesarean section was lower in the rural hospital, but the odds of undergoing a planned caesarean section was higher in the rural hospital. Conclusions Caesarean section rates have increased in rural areas and the disparity in rural–urban areas has increased substantially over the years.

BackgroundThis study proposes the use of machine learning algorithms to improve the accuracy of type 2 diabetes predictions using non-invasive risk score systems.MethodsWe evaluated and compared the prediction accuracies of existing non-invasive risk score systems using the data from the REACTION study (Risk Evaluation of Cancers in Chinese Diabetic Individuals: A Longitudinal Study). Two simple risk scores were established on the bases of logistic regression. Machine learning techniques (ensemble methods) were used to improve prediction accuracies by combining the individual score systems.ResultsExisting score systems from Western populations performed worse than the scores from Eastern populations in general. The two newly established score systems performed better than most existing scores systems but a little worse than the Chinese score system. Using ensemble methods with model selection algorithms yielded better prediction accuracy than all the simple score systems.ConclusionOur proposed machine learning methods can be used to improve the accuracy of screening the undiagnosed type 2 diabetes and identifying the high-risk patients.