Sui Zhu

Background In singleton pregnancies, gestational diabetes mellitus (GDM) causes maternal and neonatal complications. However, this association remains unclear in twin pregnancies. This study aimed to investigate the association between GDM and obstetric outcomes specifically in twin pregnancies. Methods A retrospective cohort study was conducted among women with twin pregnancies from January 2018 to July 2022. Maternal and neonatal outcomes were compared between women with and without GDM. Adjusted ORs, 95% CIs and SEs were estimated using logistic regression, adjusting for maternal age, prepregnancy body mass index and chorionicity. Results A total of 808 women (1616 neonates) were included in the study, of whom 178 had GDM, while 630 did not. GDM was found to be positively associated with (adjusted OR, (95% CI)) preterm birth (1.73 (1.17 to 2.54)), premature rupture of membranes (1.70 (1.03 to 2.79)) and hypothyroidism (2.58 (1.46 to 4.56)) in women with twin pregnancies. In regard to neonatal outcomes, GDM was positively associated with admission to neonatal ward (1.49 (1.13 to 1.96)), oxygen therapy (1.91 (1.45 to 2.52)), hyperbilirubinaemia (1.44 (1.08 to 1.90)), total use and prophylactic use of antibiotics (1.98 (1.17 to 3.33) and 1.88 (1.00 to 3.54), respectively). When stratified by gestational age, GDM remained positively associated with certain adverse outcomes exclusively in twin preterm infants. Conclusions Our study demonstrated that GDM is significantly associated with several adverse pregnancy outcomes in twin pregnancies, including hypothyroidism in pregnant women, preterm birth, admission to neonatal ward, oxygen therapy, hyperbilirubinaemia, the total and prophylactic use of antibiotics in twins. Specifically, in twin pregnancies, GDM may be more strongly associated with adverse outcomes in preterm infants than in term infants.

Robust forecasting of hepatitis B trends is important for long-term surveillance and public health planning in China. However, evidence remains limited regarding how different forecasting frameworks perform when applied to a long-term national hepatitis B surveillance series under a unified out-of-sample evaluation design. Monthly reported hepatitis B cases in China from January 2004 to December 2024 were obtained from national public surveillance databases. Data from January 2004 to December 2023 were used as the training set, whereas data from January to December 2024 were reserved as an independent validation set. Ten forecasting models were evaluated, including Seasonal autoregressive integrated moving average (SARIMA), Bayesian structural time series (BSTS), four standalone machine-learning models (random forest, support vector machine, XGBoost, and LightGBM), and four residual-based SARIMA-machine-learning hybrid models. Model performance was assessed using mean absolute error (MAE), range-normalized root mean square error (NRMSE), mean absolute percentage error (MAPE) and Nash-Sutcliffe Efficiency (NSE). In addition, the average annual percentage change (AAPC) was estimated to summarize the long-term annual trend in reported case counts.The best-performing models were further used to forecast monthly hepatitis B case counts in 2025. A total of 252 monthly observations were included. The mean monthly number of reported hepatitis B cases was 91,901. The annual reported case counts did not show a statistically significant long-term trend (AAPC = - 0.561%, P = 0.12), whereas the monthly series exhibited a stable seasonal pattern, with peaks typically occurring in March and troughs in December followed by January. Among the candidate SARIMA structures, SARIMA(2, 1, 0)(1, 0, 3)₁₂ was selected as the optimal model. In the overall model comparison, BSTS achieved the best validation performance, with the lowest MAE (10014.27), NRMSE (38.49%), MAPE (7.18%) and highest NSE(-0.92) among all ten models. Among the hybrid models, SARIMA-LightGBM performed best, with corresponding validation values of 10903.35, 40.14%, 8.07% and - 1.09, respectively. In 2025, BSTS achieved an MAE of 13,437.42, NRMSE of 9.53%, MAPE of 12.04%, and NSE of - 0.41, with 95% posterior predictive intervals used to quantify forecast uncertainty. Forecasting performance differed across model classes in long-term national hepatitis B surveillance. BSTS showed the best overall predictive performance. These findings support empirical model selection and suggest that BSTS-based forecasting may serve as a supportive tool for early warning, resource allocation, and long-term hepatitis B surveillance planning when interpreted together with reporting-system and policy context.

Background: Inhaled nitric oxide (iNO) has been shown to be effective in term and near-term infants with specific respiratory diseases. The effects and potential risks of iNO differ substantially in preterm infants with special pathophysiology. Specific study in this population is necessary. Purpose: To assess the short-term and long-term effects of iNO in preterm infants with respiratory diseases such as respiratory failure and respiratory distress syndrome. Methods: We conducted a meta-analysis of randomized controlled trials and cohort studies comparing iNO with placebo or blank control in preterm infants with respiratory diseases.Databases including PubMed, the Cochrane Library, Scopus, and Web of Science were searched from their inception until May 2025. The primary outcomes were death before discharge, death at 36 weeks’ postmenstrual age (PMA), bronchopulmonary dysplasia (BPD) and death or BPD. Results: Thirty-one trials met the inclusion criteria. iNO reduced the incidence of death or BPD (risk ratio [RR] 0.94, 95% confidence interval [CI] 0.88 to 0.99, 6 studies, 1954 infants) and BPD in the RCT subgroup (RR 0.91, 95% CI 0.84 to 0.99, 8 studies, 2196 infants).Increases of oxygenation index (Standardized mean difference (SMD) − 0.62, 95% CI − 0.81 to − 0.43, 2 studies, 441 infants) and Partial pressure of oxygen (PaO₂) (SMD 0.68, 95% CI 0.49 to 0.87, 2 studies, 441 infants) of preterm infants at 5 ppm initial concentration were observed in the iNO group. The iNO group demonstrated a higher risk of retinopathy of prematurity (RR 1.08, 95% CI 1.01 to 1.16, 17 studies, 7220 infants). No significant differences were observed in other neonatal morbidities or adverse events. Conclusions: iNO was associated with a reduced risk of death or BPD and a probably reduction of BPD. There was no effect on other morbidities or adverse events. Data on long-term respiratory and neurodevelopment outcomes are critically needed to further evaluate NO's efficacy in preterm infants, particularly Extremely low birth weight infants.

Background: Digital addiction, affecting a significant portion of the population, particularly young people, is linked to psychological issues and social problems, making its prevention and management a crucial public health issue. Objective: This umbrella review aimed to comprehensively analyze the factors influencing digital addiction by re-evaluating the methodologies and evidence quality of existing meta-analyses. Methods: Databases including PubMed, Web of Science, the Cochrane Library, and Embase were systematically searched for reviews and meta-analyses related to factors associated with digital addiction up to September 24, 2024. The methodological quality of the identified studies was assessed using the modified “A Measurement Tool to Assess Systematic Reviews 2” (AMSTAR 2) tool, while the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) tool was used to evaluate the quality of the evidence presented in the literature. Results: A total of 18 articles were included in the review, with AMSTAR 2 assessments revealing 6 moderate-quality, 4 low-quality, and 8 very low-quality studies. In total, 29 significant factors associated with digital addiction were identified. Notably, 4 studies achieved both moderate AMSTAR 2 and GRADE ratings, indicating that positive parent-child relationship (r=−0.15, 95% CI−0.18 to −0.11; P<.05) effectively mitigate the risks of digital addiction. Conversely, urban residence (odds ratio [OR] 2.32, 95% CI 1.19-4.53; P<.001), adverse childhood experiences (r=0.21, 95% CI 0.18-0.24; P<.001), and social anxiety (r=0.34, 95% CI 0.19-0.48; P<.001) were identified as factors that may increase the risk of developing digital addiction. Conclusions: This study found that social factors such as urban residence were associated with an increased risk of digital addiction, whereas social support served as a protective factor against this risk. Nonetheless, the methodologies used in analyzing the factors related to digital addiction require further refinement.

Background: Numerous studies have explored interventions to reduce digital addiction outcomes, but inconclusive evidence makes it difficult for decision-makers, managers, and clinicians to become familiar with all available literature and find appropriate interventions. Objective: This study aims to summarize and assess the certainty of evidence of interventions proposed to decrease digital addiction from published meta-analyses. Methods: An umbrella review of published meta-analyses was performed. We searched PubMed, Cochrane Library, Web of Science, and Embase for meta-analyses published up to February 2024. Eligible studies evaluated interventions using randomized controlled trials, nonrandomized controlled trials, or quasi-experimental studies and were assessed for methodological quality using Assessment of Multiple Systematic Reviews version 2. A random effects model was used to analyze data, considering heterogeneity and publication bias. Grading of Recommendations, Assessment, Development, and Evaluations was applied to assess evidence with certainty. Results: A total of 5 studies assessing 21 associations were included in the umbrella review, of which 4 (80%) were high-quality meta-analyses. Weak evidence was observed in 19 associations, whereas null associations appeared in the remaining 2 associations. These associations pertained to 8 interventions (group counseling, integrated internet addiction [IA] prevention programs, psychosocial interventions, reality therapy, self-control training programs, cognitive behavioral therapy, interventions to reduce screen time in children, and exercise) and 9 outcomes (self-control, self-esteem, internet gaming disorder symptoms, time spent gaming, IA scores, screen use time, interpersonal sensitivity longlines, anxiety, and depression). Cognitive behavioral therapy reduces anxiety (standardized mean difference [SMD] 0.939, 95% CI 0.311 to 1.586), internet gaming disorder symptoms (SMD 1.394, 95% CI 0.664 to 2.214), time spent gaming (SMD 1.259, 95% CI, 0.311 to 2.206), and IA scores (SMD –2.097, 95% CI –2.814 to –1.381). Group counseling had a large effect on improving self-control (SMD 1.296, 95% CI 0.269 to 2.322) and reducing IA levels (SMD –1.417, 95% CI –1.836 to –0.997). Exercise intervention reduced IA scores (SMD –2.322, 95% CI –3.212 to –1.431), depression scores (SMD –1.421, 95% CI –2.046 to –797), and interpersonal sensitivity scores (SMD –1.433, 95% CI –2.239 to –0.627). Conclusions: The evidence indicates that current interventions to reduce digital addiction are weak. Data from more and better-designed studies with larger sample sizes are needed to establish robust evidence. Trial Registration: PROSPERO CRD42024528173; crd.york.ac.uk/PROSPERO/display_record.php?RecordID=528173

BACKGROUND Numerous studies have explored interventions to reduce digital addiction outcomes, but inconclusive evidence makes it difficult for decision-makers, managers, and clinicians to get familiar with all available literature and find appropriate interventions. OBJECTIVE o summarize and assess the certainty of evidence of interventions proposed to decrease the digital addiction from published systematic reviews. METHODS An umbrella review of published systematic reviews was undertaken. Included studies were systematic reviews and meta-analyses of quantitative primary studies assessing an intervention that aimed to reduce digital addiction. RESULTS 5 studies assessing 21 associations were included in the umbrella review, of which 4 (80%) were high-quality systematic reviews. Weak evidence was observed in 19 associations, whereas null associations appeared in the rest 2 associations. These associations pertained to nine interventions (group counseling, intergrated internet addiction prevention program, psychosocial intervention, reality therapy, self-control training program, cognitive behavior therapy (CBT), interventions to reduce screen time in child and exercise) and ten outcomes (self-control, self-esteem, IGD symptoms, time spent gaming, IA scores, screen use time, interpersonal sensitivity longlines, anxiety and depression). CBT could reduce anxiety (0.939, 95% CI, 0.311 to 1.586), IGD symptoms (1.394, 95% CI, 0.664 to 2.214) and time spent gaming 1.259, 95% CI, 0.311 to 2.206) and IA scores (-2.097, 95% CI, -2.814 to -1.381). Group counseling had a large effect size on improving self-control (1.296, 95% CI, 0.269 to 2.322) and reduced internet addiction levels (-1.147, 95% CI, -1.836 to -0.997). Exercise intervention reduced IA scores (-2.322, 95% CI, -3.212 to -1.431) and depression (-1.421, 95% CI, -2.046 to -797) and interpersonal sensitivity (-1.433, 95% CI, -2.239 to -0.627). CONCLUSIONS The evidence indicates that current interventions to reduce digital addiction are weak. Data from more and better-designed studies with larger sample sizes are needed to establish robust evidence. CLINICALTRIAL The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement [14]. Our study protocol has been registered with PROSPERO.

Background: The COVID-19 pandemic has resulted in significant excess mortality globally. However, the differences in excess mortality between the Omicron and non-Omicron waves, as well as the contribution of local epidemiological characteristics, population immunity, and social factors to excess mortality, remain poorly understood. This study aims to solve the above problems. Methods: Weekly all-cause death data and covariates from 29 countries for the period 2015–2022 were collected and used. The Bayesian Structured Time Series Model predicted expected weekly deaths, stratified by gender and age groups for the period 2020–2022. The quantile-based g-computation approach accounted for the effects of factors on the excess all-cause mortality rate. Sensitivity analyses were conducted using alternative Omicron proportion thresholds. Results: From the first week of 2021 to the 30th week of 2022, the estimated cumulative number of excess deaths due to COVID-19 globally was nearly 1.39 million. The estimated weekly excess all-cause mortality rate in the 29 countries was approximately 2.17 per 100,000 (95% CI: 1.47 to 2.86). Weekly all-cause excess mortality rates were significantly higher in both male and female groups and all age groups during the non-Omicron wave, except for those younger than 15 years (P < 0.001). Sensitivity analysis confirmed the stability of the results. Positive associations with all-cause excess mortality were found for the constituent ratio of non-Omicron in all variants, new cases per million, positive rate, cardiovascular death rate, people fully vaccinated per hundred, extreme poverty, hospital patients per million humans, people vaccinated per hundred, and stringency index. Conversely, other factors demonstrated negative associations with all-cause excess mortality from the first week of 2021 to the 30th week of 2022. Conclusion: Our findings indicate that the COVID-19 Omicron wave was associated with lower excess mortality compared to the non-Omicron wave. This study’s analysis of the factors influencing excess deaths suggests that effective strategies to mitigate all-cause mortality include improving economic conditions, promoting widespread vaccination, and enhancing overall population health. Implementing these measures could significantly reduce the burden of COVID-19, facilitate coexistence with the virus, and potentially contribute to its elimination.

Background: Although studies have indicated that extreme temperature is strongly associated with respiratory diseases, there is a dearth of studies focused on children, especially in China. We aimed to explore the association between extreme temperature and children’s outpatient visits for respiratory diseases and seasonal modification effects in Harbin, China. Methods: A distributed lag nonlinear model (DLNM) was used to explore the effect of extreme temperature on daily outpatient visits for respiratory diseases among children, as well as lag effects and seasonal modification effects. Results: Extremely low temperatures were defined as the 1st percentile and 2.5th percentile of temperature. Extremely high temperatures were defined as the 97.5th percentile and 99th percentile of temperature. At extremely high temperatures, both 26 °C (97.5th) and 27 °C (99th) showed adverse effects at lag 0–6 days, with relative risks (RRs) of 1.34 [95% confidence interval (CI): 1.21–1.48] and 1.38 (95% CI: 1.24–1.53), respectively. However, at extremely low temperatures, both − 26 °C (1st) and − 23 °C (2.5th) showed protective effects on children’s outpatient visits for respiratory diseases at lag 0–10 days, with RRs of 0.86 (95% CI: 0.76–0.97) and 0.85 (95% CI: 0.75–0.95), respectively. We also found seasonal modification effects, with the association being stronger in the warm season than in the cold season at extremely high temperatures. Conclusions: Our study indicated that extremely hot temperatures increase the risk of children’s outpatient visits for respiratory diseases. Efforts to reduce the exposure of children to extremely high temperatures could potentially alleviate the burden of pediatric respiratory diseases, especially during the warm season.

Background The COVID-19 pandemic has resulted in significant excess mortality globally. However, the differences in excess mortality between the Omicron and non-Omicron waves, as well as the contribution of local epidemiological characteristics, population immunity, and social factors to excess mortality, remain poorly understood. This study aims to solve the above problems. Methods Weekly all-cause death data and covariates from 29 countries for the period 2015–2022 were collected and used. The Bayesian Structured Time Series Model predicted expected weekly deaths, stratified by gender and age groups for the period 2020–2022. The quantile-based g-computation approach accounted for the effects of factors on the excess all-cause mortality rate. Sensitivity analyses were conducted using alternative Omicron proportion thresholds. Results From the first week of 2021 to the 30th week of 2022, the estimated cumulative number of excess deaths due to COVID-19 globally was nearly 1.39 million. The estimated weekly excess all-cause mortality rate in the 29 countries was approximately 2.17 per 100000 (95% CI: 1.47 to 2.86). Weekly excess mortality rates were significantly higher in both male and female groups and all age groups during the non-Omicron wave, except for those younger than 15 years (P < 0.001).Sensitivity analysis confirmed the stability of the results. Positive associations with excess mortality were found for the constituent ratio of non-Omicron in all variants, new cases per million, positive rate, cardiovascular death rate, people fully vaccinated per hundred, extreme poverty, hospital patients per million humans, people vaccinated per hundred, and stringency index. Conversely, other factors demonstrated negative associations with excess mortality from the first week of 2021 to the 30th week of 2022. Conclusion The COVID-19 Omicron wave exhibited fewer excess deaths than the non-Omicron wave. Weekly excess mortality rates were significantly higher in both male and female groups and all age groups during the non-Omicron wave, except for those younger than 15 years. Factors such as the constituent ratio of non-Omicron in all variants, new cases per million, positive rate, cardiovascular death rate, people fully vaccinated per hundred, extreme poverty and some social factors were positively associated with excess mortality, while other factors showed negative associations.