Global Epidemiology最新文献

{"title":"Commentary on the commentary “On measurement error, PSA doubling time, and prostate cancer”","authors":"Lawrence L. Kupper , Sandra L. Martin","doi":"10.1016/j.gloepi.2025.100187","DOIUrl":"10.1016/j.gloepi.2025.100187","url":null,"abstract":"","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":"9 ","pages":"Article 100187"},"PeriodicalIF":0.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420222","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":"All are not created equal: Method descriptions in an epidemiology publication differ among media summaries – A case study comparison","authors":"Lilianne Samad,&nbsp;J.E. Reed","doi":"10.1016/j.gloepi.2025.100188","DOIUrl":"10.1016/j.gloepi.2025.100188","url":null,"abstract":"<div><div>It is common to see mass media headlines about health-related topics in traditional and online news outlets, as well as on social media platforms. What a consumer might not realize is that often these headlines are a distillation of results reported in epidemiologic publications. Journalists make decisions about what information to include and exclude, hopefully without compromising the main conclusions. In this exercise, sixty-three media articles that summarized one peer-reviewed journal publication (Zhang et al., 2021) describing results from a cohort study on coffee and tea consumption and risk of stroke and dementia were compared to determine the consistency of details among them. The most heterogeneity was observed in whether articles compared results with other literature. There was some variation in inclusion of a measure of frequency within the study population, and in details describing measurement of exposure. However, most of the articles were consistent in either including or excluding other methodological details in the main text. The results of the present comparison have implications for readers, researchers, and journalists. Readers must know that media summaries of peer reviewed studies are just that – summaries. It is likely that some information from the original source is not represented by the article, and that additional information might be necessary to craft an informed opinion on a given topic.</div></div>","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":"9 ","pages":"Article 100188"},"PeriodicalIF":0.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420221","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":"On the current and future potential of simulations based on directed acyclic graphs","authors":"Lutz P. Breitling ,&nbsp;Anca D. Dragomir ,&nbsp;Chongyang Duan ,&nbsp;George Luta","doi":"10.1016/j.gloepi.2025.100186","DOIUrl":"10.1016/j.gloepi.2025.100186","url":null,"abstract":"<div><div>Real-world data are playing an increasingly important role in regulatory decision making. Adequately addressing bias is of paramount importance in this context. Structural representations of bias using directed acyclic graphs (DAGs) provide a unified approach to conceptualize bias, distinguish between different types of bias, and identify ways to address bias. DAG-based data simulation further enhances the scope of this approach. Recently, DAGs have been used to demonstrate how missing eligibility information can compromise emulated target trial analysis, a cutting edge approach to estimate treatment effects using real-world data. The importance of simulation for methodological research has received substantial recognition in the past few years, and others have argued that simulating data based on DAGs can be especially helpful for understanding various epidemiological concepts. In the present work, we present two concrete examples of how simulations based on DAGs can be used to gain insights into issues commonly encountered in real-world analytics, i.e., regression modelling to address confounding bias, and the potential extent of selection bias. Increasing accessibility and extending the simulation algorithms of existing software to include longitudinal and time-to-event data are identified as priorities for further development. With such extensions, simulations based on DAGs would be an even more powerful tool to advance our understanding of the rapidly growing toolbox of real-world analytics.</div></div>","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":"9 ","pages":"Article 100186"},"PeriodicalIF":0.0,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143100420","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":"Multisystem inflammatory syndrome in children (MIS-C) associated with COVID-19, clinical characteristics: A multi-center observational study from Jordan","authors":"Marwan Shalabi ,&nbsp;Salam Ghanem ,&nbsp;Iyad Al-Ammouri ,&nbsp;Amirah Daher ,&nbsp;Enas Al-zayadneh ,&nbsp;Alaa Alsmadi ,&nbsp;Mais Ayyoub ,&nbsp;Samah Abughanam ,&nbsp;Mariam Jabr ,&nbsp;Montaha Al-Iede","doi":"10.1016/j.gloepi.2025.100185","DOIUrl":"10.1016/j.gloepi.2025.100185","url":null,"abstract":"<div><h3>Objective</h3><div>Multisystem inflammatory syndrome of childhood (MIS-C) is a newly recognized entity associated with COVID-19 in children. The objective was to describe the clinical course for 74 patients diagnosed with this disease.</div></div><div><h3>Methods</h3><div>A multicenter retrospective study including 5 major hospitals in Jordan was conducted. Data from children admitted with confirmed SARS-CoV-2 infection or were in close contact with confirmed cases were collected. Total of 74 patients were diagnosed with MIS-C. Clinical, laboratory, radiological and therapeutic data were collected by retrospective chart review.</div></div><div><h3>Results</h3><div>Fever, abdominal pain, hypoxia and other manifestation occurred. Cardiac findings were less common and did not include coronary findings. Treatments were mainly Corticosteroids and IVIG. No mortality was found in this series but serious disease occurred and some patients were admitted to Pediatric Intensive Care Unit.</div></div><div><h3>Conclusions</h3><div>This study described the epidemiology, clinical course, management, and outcome of MIS-C cases in Jordan. The findings were consistent with what has been described from other regions globally. There was a wide spectrum in the severity of presentation. Abdominal pain was more prevalent and some children were misdiagnosed as surgical acute abdomen.</div></div>","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":"9 ","pages":"Article 100185"},"PeriodicalIF":0.0,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143157041","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":"Nurse-led medication self-management intervention in the improvement of medication adherence in adult patients with multi-morbidity: A Protocol for a Feasibility Randomized controlled trial","authors":"Kalpana Singh ,&nbsp;George V. Joy ,&nbsp;Asma Al Bulushi ,&nbsp;Albara Mohammad Ali Alomari ,&nbsp;Kamaruddeen Mannethodi ,&nbsp;Jibin Kunjavara ,&nbsp;Nesiya Hassan ,&nbsp;Zeinab Idris ,&nbsp;Mohd Abdel Daem Mohd Yassin ,&nbsp;Badriya Al Lenjawi","doi":"10.1016/j.gloepi.2025.100184","DOIUrl":"10.1016/j.gloepi.2025.100184","url":null,"abstract":"<div><h3>Background</h3><div>Multimorbidity in adult patients puts them at a considerable risk of not taking their medications as prescribed. It is well known that patients with chronic conditions with self-management help is an excellent way to improve medication compliance. The impact of the medication self-management intervention in adult patients with multimorbidity is not well known, yet. This paper presents the protocol to assess the efficacy of a nurse-led medication self-management intervention in enhancing medication adherence and health outcomes for adult patients with multimorbidity.</div></div><div><h3>Methods</h3><div>The Standard Protocol Items: Guidelines for Interventional Trials 2013 statement is followed by the study protocol. This study is a two-arm, single centre, open label, randomized controlled trial. Adult patients with multimorbidity will be recruited from National Cancer Center Research, QATAR. A total of 100 participants will be randomly assigned to either standard care alone or standard care along with the medication self-management intervention. Clinical nursing specialists will deliver the intervention. Three in-person education sessions and two weekly phone conversations for follow-up are part of the 6-week intervention. Participants in the control group continue to receive all aspects of the standard care provided by healthcare professionals, including consultations regarding patients' diseases and treatments, management of chronic conditions, prescription of medications, referrals to hospital specialists, health education, and management of chronic conditions.</div><div>The 8-item mo-risky-8 Medication Adherence Scale was used to measure medication adherence as the primary outcome. Secondary outcomes include medication self-management capacity (medication knowledge, medication beliefs, and medication self-efficacy), treatment experiences (medication treatment satisfaction and treatment burden), and depressive symptoms. All outcomes will be assessed at baseline, immediately post-intervention, and at 3-month post-intervention.</div></div><div><h3>Discussion</h3><div>This study will offer proof of the merits of a nurse-delivered medication self-management intervention for adult patients with multimorbidity and adherence issues. If the study findings are helpful in enhancing patient adherence and health outcomes, it is anticipated that they will offer healthcare professionals evidence-based self-management support tools for routine chronic condition management.</div><div><strong>Trial registration:</strong> The trial is registered at <span><span>clinicaltrial.org</span><svg><path></path></svg></span> (<span><span>NCT05645653</span><svg><path></path></svg></span>;9Dec2022).</div></div>","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":"9 ","pages":"Article 100184"},"PeriodicalIF":0.0,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11786918/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143081411","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":"Modeling the determinants of attrition in a two-stage epilepsy prevalence survey in Nairobi using machine learning","authors":"Daniel M. Mwanga ,&nbsp;Isaac C. Kipchirchir ,&nbsp;George O. Muhua ,&nbsp;Charles R. Newton ,&nbsp;Damazo T. Kadengye","doi":"10.1016/j.gloepi.2025.100183","DOIUrl":"10.1016/j.gloepi.2025.100183","url":null,"abstract":"<div><h3>Background</h3><div>Attrition is a challenge in parameter estimation in both longitudinal and multi-stage cross-sectional studies. Here, we examine utility of machine learning to predict attrition and identify associated factors in a two-stage population-based epilepsy prevalence study in Nairobi.</div></div><div><h3>Methods</h3><div>All individuals in the Nairobi Urban Health and Demographic Surveillance System (NUHDSS) (Korogocho and Viwandani) were screened for epilepsy in two stages. Attrition was defined as probable epilepsy cases identified at stage-I but who did not attend stage-II (neurologist assessment). Categorical variables were one-hot encoded, class imbalance was addressed using synthetic minority over-sampling technique (SMOTE) and numeric variables were scaled and centered. The dataset was split into training and testing sets (7:3 ratio), and seven machine learning models, including the ensemble Super Learner, were trained. Hyperparameters were tuned using 10-fold cross-validation, and model performance evaluated using metrics like Area under the curve (AUC), accuracy, Brier score and F1 score over 500 bootstrap samples of the test data.</div></div><div><h3>Results</h3><div>Random forest (AUC = 0.98, accuracy = 0.95, Brier score = 0.06, and F1 = 0.94), extreme gradient boost (XGB) (AUC = 0.96, accuracy = 0.91, Brier score = 0.08, F1 = 0.90) and support vector machine (SVM) (AUC = 0.93, accuracy = 0.93, Brier score = 0.07, F1 = 0.92) were the best performing models (base learners). Ensemble Super Learner had similarly high performance. Important predictors of attrition included proximity to industrial areas, male gender, employment, education, smaller households, and a history of complex partial seizures.</div></div><div><h3>Conclusion</h3><div>These findings can aid researchers plan targeted mobilization for scheduled clinical appointments to improve follow-up rates. These findings will inform development of a web-based algorithm to predict attrition risk and aid in targeted follow-up efforts in similar studies.</div></div>","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":"9 ","pages":"Article 100183"},"PeriodicalIF":0.0,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143100421","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":"10 ","pages":"Article 100232"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146702741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":"10 ","pages":"Article 100222"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146702744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":"9 ","pages":"Article 100191"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146809290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":"9 ","pages":"Article 100187"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146809266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}