Risk factors and long-term health consequences of macrosomia:a prospective study in Jiangsu Province,China

We sought to determine risk factors associated with fetal macrosomia and to explore the long-term consequence of infant macrosomia at the age of 7 years.A prospective population based cohort study was designed to examine the associations between maternal and perinatal characteristics and the risk of macrosomia.A nested case-control study was conducted to explore the long-term health consequence of infant macrosomia.The mean maternal age of the macrosomia group was 24.74±3.32 years,which is slightly older than that in the control group（24.35±3.14 years,P = 0.000）.The mean maternal body mass index（BMI） at early pregnancy was 22.75±2.81 kg/m 2,which was also higher than that in the control group（21.76±2.59 kg/m 2,P = 0.000）.About 64.6% of macrosomic neonates were males,compared with 51.0% in the control group（P = 0.000）.Compared with women with normal weight（BMI：18.5-23.9 kg/m 2）,women who were overweight（BMI：24-27.9 kg/m 2） or obese（BMI ≥ 28 kg/m 2）,respectively,had a 1.69-fold（P = 0.000） and a 1.49-fold（P = 0.000） increased risks of having a neonate with macrosomia,while light weight（BMI〈18.5 kg/m 2） women had an approximately 50% reduction of the risk.Furthermore,macrosomia infant had a 1.52-fold and 1.50-fold risk,respectively,of developing overweight or obesity at the age of 7 years（P = 0.001 and P = 0.000）.Older maternal age,higher maternal BMI at early pregnancy and male gender were independent risk factors of macrosomia.Macrosomic infant was associated with an increased predisposition to develop overweight or obesity at the beginning of their childhood.

Implementation of Clinical Diagnostic Criteria and Universal Symptom Survey Contributed to Lower Magnitude and Faster Resolution of the COVID-19 Epidemic in Wuhan

自新型冠状病毒肺炎(coronavirus disease 2019, COVID-19)疫情发生以来,中国大多数的病例集中在武汉市。虽然早期病例数和死亡人数迅速增加,但通过采取多种防控措施,疫情得以快速遏制。纵观全球,疫情已蔓延至全球六大洲的187个国家,确诊病例数已超过300万,这一数字仍在快速增长。在此特殊背景下,有必要对我国疫情防控措施开展科学的、定量的评估,为全球疫情防控提供决策依据。为此,本研究评估了临床诊断标准实施和全城症状排查对武汉市疫情控制的贡献。考虑COVID-19的传播机理、隔离措施等,建立SEIR+Q传播动力学模型。基于武汉市截至2020年2月14日官方公布的每日确诊病例数和未确诊的临床诊断病例数进行建模,并预测2月14日以后的疫情态势。基于实际疫情数据,与模型预测趋势相比较,评价防控措施效果。结果显示,若维持2月14日以前防控措施不变,那么预测将于3月25日和4月29日,每日新增病例数分别降至100例和10例以下,将于5月31日首次现零。而事实上,截至3月6日,武汉市每日新增病例数降至100例以下,截至3月11日降至10例以下,3月18日首次实现零增长,较之模型预测结果分别提前了19 d、49 d和74 d。截至3月30日,实际累计病例数为50 006例,比模型预测值减少19 951例。有效再生数[effective reproductive number, R(t)]分析显示,2月6-10日的第一次全城症状排查后,R(t)显现出下降趋势,2月12-14日的临床诊断标准实施和2月17-19日的第二次全城症状排查后,R(t)显现出较大的降幅,与实际情况较为一致。综上所述,武汉市临床诊断标准的实施和全城症状排查等综合防控措施成效显著,可为世界各国的疫情防控决策提供科学依据。

Modeling the transmission dynamics of COVID-19 epidemic: a systematic review

The outbreak and rapid spread of COVID-19 has become a public health emergency of international concern.A number of studies have used modeling techniques and developed dynamic models to estimate the epidemiological parameters,explore and project the trends of the COVID-19,and assess the effects of intervention or control measures.We identified 63 studies and summarized the three aspects of these studies:epidemiological parameters estimation,trend prediction,and control measure evaluation.Despite the discrepancy between the predictions and the actuals,the dynamic model has made great contributions in the above three aspects.The most important role of dynamic models is exploring possibilities rather than making strong predictions about longer-term disease dynamics.

Association of three single nucleotide polymorphisms of ESR1 with breast cancer susceptibility:a meta-analysis

Expression of estrogen receptors is correlated with breast cancer risk,but inconsistent results have been reported.To clarify potential estrogen receptor（ESR）-related breast cancer risk,we analyzed genetic variants of ESR1 in association with breast cancer susceptibility.We performed a meta-analysis to investigate the association between rs2234693,rs1801132,and rs2046210（single nucleotide polymorphisms of ESR1）,and breast cancer risk.Our analysis included 44 case-control studies.For rs2234693,the CC genotype had a higher risk of breast cancer compared to the TT or CT genotype.For rs2046210,the AA,GA,or GA ＋ GG genotype had a much higher risk compared to the GG genotype.No significant association was found for the rs 1801132 polymorphism with breast cancer risk.This meta-analysis demonstrates association between the rs2234693 and rs2046210 polymorphisms of ESR1 and breast cancer risk.The correlation strength between rs2234693 and breast cancer susceptibility differs in subgroup assessment by ethnicity.

Global COVID-19 Pandemic Waves: Limited Lessons Learned Worldwide over the Past Year

The occurrence of coronavirus disease 2019(COVID-19)was followed by a small burst of cases around the world;afterward,due to a series of emergency non-pharmaceutical interventions(NPIs),the increas-ing number of confirmed cases slowed down in many countries.However,the lifting of control measures by the government and the public’s loosening of precautionary behaviors led to a sudden increase in cases,arousing deep concern across the globe.arousing deep concern across the globe.This study evaluates the situation of the COVID-19 pandemic in countries and territories worldwide from January 2020 to February 2021.According to the time-varying reproduction number(R(t))of each country or territory,the results show that almost half of the countries and territories in the world have never controlled the epidemic.Among the countries and territories that had once contained the occurrence,nearly half failed to maintain their prevention and control,causing the COVID-19 pandemic to rebound across the world—resulting in even higher waves in half of the rebounding countries or territories.This work also proposes and uses a time-varying country-level transmission risk score(CTRS),which takes into account both R(t)and daily new cases,to demonstrate country-level or territory-level transmission potential and trends.Time-varying hierarchical clustering of time-varying CTRS values was used to successfully reveal the countries and territories that contributed to the recent aggravation of the global pandemic in the last quarter of 2020 and the beginning of 2021,and to identify countries and territories with an increasing risk of COVID-19 transmission in the near future.Furthermore,a regression analysis indicated that the introduction and relaxation of NPIs,including workplace closure policies and stay-at-home requirements,appear to be associated with recent global transmission changes.In conclu-sion,a systematic evaluation of the global COVID-19 pandemic over the past year indicates that the world is now in an unexpected situation,with limited lessons learned.Summarizing the lessons learned could help in designing effective public responses for constraining future waves of COVID-19 worldwide.

Drawing on the Development Experiences of Infectious Disease Surveillance Systems Around the World

High-quality infectious disease surveillance systems are foundational to infectious disease prevention and control.Current major infectious disease surveillance systems globally can be categorized as either indicatorbased,which are more specific,or event-based,which are more timely.Modern surveillance systems commonly utilize multi-source data,strengthened information sharing,advanced technology,and improved early warning accuracy and sensitivity.International experience may provide valuable insights for China.China’s existing infectious disease surveillance systems require urgent enhancements to monitor emerging infectious diseases and improve the integration and learning capabilities of early warning models.Methods such as establishing multi-stage surveillance systems,promoting cross-sectoral and cross-provincial data sharing,applying advanced technologies like artificial intelligence,and cultivating professional talent should be adopted to enhance the development of intelligent and multipoint-triggered infectious disease surveillance systems in China.

Transmission dynamics model and the coronavirus disease 2019 epidemic:applications and challenges

Since late 2019,the beginning of coronavirus disease 2019(COVID-19)pandemic,transmission dynamics models have achieved great development and were widely used in predicting and policymaking.Here,we provided an introduction to the history of disease transmission,summarized transmission dynamics models into three main types:compartment extension,parameter extension and population-stratified extension models,highlight the key contribution of transmission dynamics models in COVID-19 pandemic:estimating epidemiological parameters,predicting the future trend,evaluating the effectiveness of control measures and exploring different possibilities/scenarios.Finally,we pointed out the limitations and challenges lie ahead of transmission dynamics models.