中国疾病预防控制中心周报最新文献

{"title":"The Epidemiological Characteristics and Spatiotemporal Clustering of Measles - China, 2005-2022.","authors":"Qianqian Liu, Jiuhong Li, Siyu Liu, Lin Tang, Xiaoqi Wang, Aodi Huang, Xia Xu, Yuexin Xiu, Hong Yang, Ning Wen, Lance Rodewald, Fuzhen Wang, Zundong Yin","doi":"10.46234/ccdcw2024.123","DOIUrl":"10.46234/ccdcw2024.123","url":null,"abstract":"<p><strong>Introduction: </strong>In recent years, the incidence of measles in China has consistently remained below 1 per 100,000 population, yet the disease has not been eliminated. This study aims to comprehensively analyze the epidemiological characteristics of measles from 2005 to 2022, identify high-risk populations and areas, and propose targeted interventions.</p><p><strong>Methods: </strong>We utilized data from the China Disease Prevention and Control Information System for our comprehensive analysis. Spatial autocorrelation was employed to examine the spatial clustering of measles, while spatiotemporal scanning analysis was used to detect spatiotemporal clustering to describe measles epidemiology during the study period.</p><p><strong>Results: </strong>Between 2005 and 2022, 732,218 measles cases were reported in China. Overall, the incidence of measles exhibited a downward trend, particularly during the periods of 2008-2011 and 2015-2022. In 2022, the incidence rate reached its historical low at 0.039 per 100,000 population. Measles predominantly affects young children. Since 2017, global spatial clustering has diminished, although hotspot areas persist in the western provinces. Spatial-temporal scanning identified a high-incidence cluster from 2005 to 2008, comprising 15 provinces in the western, central, and northern regions of China. Conversely, from 2016 to 2022, a low-incidence cluster was detected in the southern and central provinces.</p><p><strong>Conclusions: </strong>China has made significant progress in measles prevention and control. The recent low incidence and absence of substantial spatiotemporal clustering indicate that China is nearing measles elimination. However, there is a continuing need to enhance prevention and control efforts among very young children and in historic incidence hotspots in western provinces. Additionally, improving the diagnosis of vaccine-associated rash illnesses is essential.</p>","PeriodicalId":69039,"journal":{"name":"中国疾病预防控制中心周报","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11252050/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141725161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Epidemiological Characteristics of Mpox Cases - China, 2023.","authors":"Ruiqi Ren, Chao Li, Wenqing Bai, Yali Wang, Dan Li, Fan Ding, Niu Yan, Qi Wang, Nijuan Xiang, Lianmei Jin, Lei Zhou, Wenling Wang, Fan Lyu, Peilong Li, Dapeng Zhang, Yan Li, Quanyi Wang, Qun Li, Guoqing Shi","doi":"10.46234/ccdcw2024.118","DOIUrl":"10.46234/ccdcw2024.118","url":null,"abstract":"<p><strong>What is already known about this topic?: </strong>Since May 2022, a global outbreak of mpox has emerged in more than 100 non-endemic countries. As of December 2023, over 90,000 cases had been reported. The outbreak has predominantly affected men who have sex with men (MSM), with sexual contact identified as the principal mode of transmission.</p><p><strong>What is added by this report?: </strong>Since June 2023, China has faced an occurrence of mpox, predominantly affecting the MSM population. Approximately 90% of those affected reported engaging in homosexual behavior within 21 days prior to symptom onset, a trend that aligns with the global outbreak pattern. The prompt identification of cases, diligent tracing of close contacts, and the implementation of appropriate management strategies have successfully mitigated the spread of mpox virus in China.</p><p><strong>What are the implications for public health practice?: </strong>We propose that mpox is transmitted locally within China. Drawing from our experiences in controlling the virus spread, it is crucial to investigate and formulate effective surveillance and educational strategies. Importantly, we must encourage high-risk populations to promptly seek medical care upon the onset of symptoms.</p>","PeriodicalId":69039,"journal":{"name":"中国疾病预防控制中心周报","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11219300/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141536121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison Between Threshold Method and Artificial Intelligence Approaches for Early Warning of Respiratory Infectious Diseases - Weifang City, Shandong Province, China, 2020-2023.","authors":"Ting Zhang, Liuyang Yang, Ziliang Fan, Xuancheng Hu, Jiao Yang, Yan Luo, Dazhu Huo, Xuya Yu, Ling Xin, Xuan Han, Jie Shan, Zhongjie Li, Weizhong Yang","doi":"10.46234/ccdcw2024.119","DOIUrl":"10.46234/ccdcw2024.119","url":null,"abstract":"<p><strong>Introduction: </strong>Respiratory infectious diseases, such as influenza and coronavirus disease 2019 (COVID-19), present significant global public health challenges. The emergence of artificial intelligence (AI) and big data offers opportunities to improve traditional disease surveillance and early warning systems.</p><p><strong>Methods: </strong>The study analyzed data from January 2020 to May 2023, comprising influenza-like illness (ILI) statistics, Baidu index, and clinical data from Weifang. Three methodologies were evaluated: the adaptive dynamic threshold method (ADTM) for dynamic threshold adjustments, the machine learning supervised method (MLSM), and the machine learning unsupervised method (MLUM) utilizing anomaly detection. The comparison focused on sensitivity, specificity, timeliness, and warning consistency.</p><p><strong>Results: </strong>ADTM issued 37 warnings with a sensitivity of 71% and a specificity of 85%. MLSM generated 35 warnings, with a sensitivity of 82% and a specificity of 87%. MLUM produced 63 warnings with a sensitivity of 100% and specificity of 80%. The initial warnings from ADTM and MLUM preceded those from MLSM by five days. The Kappa coefficient indicated moderate agreement between the methods, with values ranging from 0.52 to 0.62 (<i>P</i><0.05).</p><p><strong>Discussion: </strong>The study explores the comparison between traditional methods and two machine learning approaches for early warning systems. It emphasizes the validation of machine learning's reliability and underscores the unique advantages of each method. Furthermore, it stresses the significance of integrating machine learning models with various data sources to enhance public health preparedness and response, alongside acknowledging limitations and the need for broader validation.</p>","PeriodicalId":69039,"journal":{"name":"中国疾病预防控制中心周报","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11219296/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141536115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The COVID-19 Vaccines Evaluation Program: Implementation, Management, and Experiences, 2021-2023.","authors":"Tingting Yan, Hui Zheng, Mingshuang Li, Chao Ma, Xuanyi Wang, Xiaoqi Wang, Zhenjun Li, Yuansheng Chen, Wenshang Hu, Lance Rodewald, Zhijie An, Zundong Yin, Zijian Feng","doi":"10.46234/ccdcw2024.120","DOIUrl":"10.46234/ccdcw2024.120","url":null,"abstract":"<p><p>In 2021, China's domestically produced coronavirus disease 2019 (COVID-19) vaccines received approval from regulatory bodies and were administered worldwide. Due to a low number of infections within China during that period, it became imperative to evaluate the vaccines' real-world effectiveness through international studies. To facilitate this, China CDC launched the COVID-19 Vaccines Evaluation Program (COVEP). This program formed research collaboration agreements with health institutes across five World Health Organization regions, addressing key questions about vaccine performance through ten cooperative agreements. The findings from COVEP projects reinforced confidence, both domestically and globally, in the effectiveness of the vaccines produced in China. Moreover, the outcomes observed internationally were frequently mirrored by later studies conducted within China. COVEP thus pioneered a novel approach for fostering cross-national research collaborations, addressing significant public health issues and exemplifying a framework for international cooperation. This approach is in line with the strategic objectives and other development efforts of China CDC's national disease control and prevention initiatives.</p>","PeriodicalId":69039,"journal":{"name":"中国疾病预防控制中心周报","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11219299/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141536120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Vol. 6 No. 1.","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.46234/ccdcw2024.003.].</p>","PeriodicalId":69039,"journal":{"name":"中国疾病预防控制中心周报","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11220451/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141536116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction:Vol. 6 No. 24.","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.46234/ccdcw2024.112.].</p>","PeriodicalId":69039,"journal":{"name":"中国疾病预防控制中心周报","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11220452/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141536117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the Lagged Correlation Between Baidu Index and Influenza-Like Illness - China, 2014-2019.","authors":"Xuan Han, Jiao Yang, Yan Luo, Dazhu Huo, Xuya Yu, Xuancheng Hu, Ling Xin, Liuyang Yang, Hualei Xin, Ting Zhang, Zhongjie Li, Weizhong Yang","doi":"10.46234/ccdcw2024.084","DOIUrl":"10.46234/ccdcw2024.084","url":null,"abstract":"<p><strong>Introduction: </strong>This study investigated the lagged correlation between Baidu Index for influenza-related keywords and influenza-like illness percentage (ILI%) across regions in China. The aim is to establish a scientific foundation for utilizing Baidu Index as an early warning tool for influenza-like illness epidemics.</p><p><strong>Methods: </strong>In this study, data on ILI% and Baidu Index were collected from 30 provincial-level administrative divisions (PLADs) spanning April 2014 to March 2019. The Baidu Index was categorized into Overall Index, Ordinary Index, Prevention Index, Symptom Index, and Treatment Index based on search query themes. The lagged correlation between the Baidu Index and ILI% was examined through the cross-correlation function (CCF) method.</p><p><strong>Results: </strong>Correlating the Baidu Overall Index of 30 PLADs with ILI% revealed CCF values ranging from 0.46 to 0.86, with a median lag of 0.5 days. Subcategory analysis indicated that the Prevention Index and Symptom Index exhibited quicker responses to ILI%, with median lags of -9 and -0.5 days, respectively, compared to 0 and 3 days for the Ordinary and Treatment Indexes. The median lag days between the Baidu Index and the ILI% were earlier in the northern PLADs compared to the southern PLADs.</p><p><strong>Discussion: </strong>The Prevention and Symptom Indexes show promising predictive capabilities for influenza-like illness epidemics.</p>","PeriodicalId":69039,"journal":{"name":"中国疾病预防控制中心周报","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11219297/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141536118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Protection of Omicron Sub-Lineage Infection Against Reinfection with Another Omicron Sub-Lineage: Systematic Review, Meta-Analysis, and Meta-Regression - Worldwide, 2022-2023.","authors":"Xu Guo, Zuyao Cheng, Junhong Li, Yudan Song, Hui Zheng, Yamin Wang, Chao Ma, Zijian Feng","doi":"10.46234/ccdcw2024.103","DOIUrl":"10.46234/ccdcw2024.103","url":null,"abstract":"<p><strong>What is already known about this topic?: </strong>Both the decline in immunity over time and the evolution of the virus play a role in the level of protection offered by a prior infection.</p><p><strong>What is added by this report?: </strong>Point estimates indicated variations in protection levels based on the initial infecting variant and the reinfecting variant. There was a consistent correlation between real-world protection, antigenic distance, and humoral immunity levels. Specifically, shorter antigenic distances and higher humoral immunity levels corresponded to enhanced real-world protection.</p><p><strong>What are the implications for public health practice?: </strong>Our findings suggest that virological and immunological studies could help identify and assess the epidemic risk posed by new variants before they become dominant. Prompt incorporation of the latest variants into the antigen components of the coronavirus disease 2019 (COVID-19) vaccines can significantly contribute to effective epidemic prevention and control measures.</p>","PeriodicalId":69039,"journal":{"name":"中国疾病预防控制中心周报","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11219298/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141536119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prevalence of Cannabis Use around the World: A Systematic Review and Meta-Analysis, 2000-2024.","authors":"Qianning Wang, Zhen Qin, Xing Xing, He Zhu, Zhongwei Jia","doi":"10.46234/ccdcw2024.116","DOIUrl":"10.46234/ccdcw2024.116","url":null,"abstract":"<p><p>This study aims to perform a systematic review and meta-analysis on the global prevalence of cannabis use to inform drug prevention strategies, policy-making, and resource allocation. This study initially screened 177,843 studies published between January 1, 2000, and January 15, 2024, using peer-reviewed databases including Web of Science, PubMed, Scopus, Embase, and Cochrane Library. Ultimately, 595 studies were identified for data extraction, and 39 of these were selected as country-representative studies. Heterogeneity among the selected studies was assessed using the chi-squared test and I² statistic, while sensitivity analysis was conducted to evaluate the robustness of the results. The prevalence of cannabis use varied between 0.42% and 43.90% across 33 European countries, 1.40% to 38.12% across 15 North and South American countries, 0.30% to 19.10% across 16 Asian countries, and 1.30% to 48.70% across 18 Oceania and African countries. The pooled prevalence of cannabis use was 12.0% [95% confidence interval (<i>CI</i>): 10.0, 14.3] in countries where cannabis is legalized, compared to 5.4% (95% <i>CI</i>: 4.3, 6.9) in non-legalized countries. Our findings indicate that the prevalence of cannabis use has disproportionately increased in most countries with the implementation of medical or recreational cannabis legalization policies and relevant geographic proximity. Increased efforts are needed to monitor newly cannabis-legalized countries and prevent initial use.</p>","PeriodicalId":69039,"journal":{"name":"中国疾病预防控制中心周报","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11196877/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141461150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Diagnosis and Treatment Adjustments for Travelers Infected with <i>Burkholderia pseudomallei</i>: A Case Report - Beijing Municipality, China, June 2023.","authors":"Shijie Li, Yushan He, Yiyang Mao, Jun Liu, Wenmiao Xu, Suyun Qian, Quan Wang","doi":"10.46234/ccdcw2024.090","DOIUrl":"10.46234/ccdcw2024.090","url":null,"abstract":"<p><strong>What is already known about this topic?: </strong><i>Burkholderia pseudomallei</i> (BP) infection leads to melioidosis, a tropical disease endemic to coastal provinces of southern China. Physicians in non-endemic areas, do not commonly consider this disease as a primary differential diagnosis for febrile patients.</p><p><strong>What is added by this report?: </strong>This article discusses a case of melioidosis in Northern China. The patient, who had recently visited a Southeast Asian region with high melioidosis prevalence, fell ill after exposure to contaminated water. The disease progressed quickly and with severity.</p><p><strong>What are the implications for public health practice?: </strong>Healthcare workers need to remain vigilant regarding travel-related diseases for accurate differential diagnosis and to provide timely and effective treatment, especially for patients with recent travel history or symptoms during travel.</p>","PeriodicalId":69039,"journal":{"name":"中国疾病预防控制中心周报","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11196878/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141461149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}