Global Epidemiology最新文献

{"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":"Interaction between opium use and cigarette smoking on bladder cancer: An inverse probability weighting approach based on a multicenter case-control study in Iran","authors":"Rahim Akrami ,&nbsp;Maryam Hadji ,&nbsp;Hamideh Rashidian ,&nbsp;Maryam Nazemipour ,&nbsp;Ahmad Naghibzadeh-Tahami ,&nbsp;Alireza Ansari-Moghaddam ,&nbsp;Kazem Zendehdel ,&nbsp;Mohammad Ali Mansournia","doi":"10.1016/j.gloepi.2024.100182","DOIUrl":"10.1016/j.gloepi.2024.100182","url":null,"abstract":"<div><h3>Introduction</h3><div>Opium and cigarette smoking have been identified as significant cancer risk factors. Recently, the International Agency for Research on Cancer (IARC) classified opium as a Group 1 carcinogen in 2020.</div></div><div><h3>Method</h3><div>Using data from a multicenter case-control study in Iran called IROPICAN, involving 717 cases of bladder cancer and 3477 controls, we assessed the interactions on the causal additive scale between opium use and cigarette smoking and their attributing effects to evaluate public health relevance and test for different mechanistic interaction forms to provide new insights for developing of bladder cancer. A minimally sufficient set of confounders was identified using a causal directed acyclic graph, and the data were analysed employing multiple logistic regression and the inverse probability-of-treatment weighting estimator of the marginal structural linear odds model.</div></div><div><h3>Results</h3><div>Our findings indicated a significant increase in the risk of bladder cancer associated with concurrent opium use and cigarette smoking (adjusted OR = 6.34, 95 % CI 5.02–7.99; <em>p</em> &lt; 0.001), demonstrating a super-additive interaction between these exposures (Weighted RERI_OR = 2.02, 95 % CI 0.47–3.58; <em>p</em> = 0.005). The presence of a super-additive interaction suggests that interventions targeting opium users who smoke cigarettes would yield greater benefits compared to non-opium users. Furthermore, there was a mechanistic interaction between two exposures (<em>P</em>-value = 0.005) if we assumed two of the exposures have positive monotonic effects, i.e., there must be a sufficient-component cause for developing bladder cancer, which has both opium use and cigarette smoking as components.</div></div><div><h3>Conclusion</h3><div>There is a causal additive interaction between opium use and cigarette smoking. We observed a super-additive interaction, suggesting the need to focus interventions on specific subgroups. Furthermore, the presence of mechanistic interactions offers profound insights into the mechanisms of cancer induction.</div></div>","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":"9 ","pages":"Article 100182"},"PeriodicalIF":0.0,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11751544/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143025082","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":"ACCREDIT: Validation of clinical score for progression of COVID-19 while hospitalized","authors":"Vinicius Lins Costa Ok Melo,&nbsp;Pedro Emmanuel Alvarenga Americano do Brasil PhD","doi":"10.1016/j.gloepi.2024.100181","DOIUrl":"10.1016/j.gloepi.2024.100181","url":null,"abstract":"<div><div>COVID-19 is no longer a global health emergency, but it remains challenging to predict its prognosis.</div></div><div><h3>Objective</h3><div>To develop and validate an instrument to predict COVID-19 progression for critically ill hospitalized patients in a Brazilian population.</div></div><div><h3>Methodology</h3><div>Observational study with retrospective follow-up. Participants were consecutively enrolled for treatment in non-critical units between January 1, 2021, to February 28, 2022. They were included if they were adults, with a positive RT-PCR result, history of exposure, or clinical or radiological image findings compatible with COVID-19. The outcome was characterized as either transfer to critical care or death. Predictors such as demographic, clinical, comorbidities, laboratory, and imaging data were collected at hospitalization. A logistic model with lasso or elastic net regularization, a random forest classification model, and a random forest regression model were developed and validated to estimate the risk of disease progression.</div></div><div><h3>Results</h3><div>Out of 301 individuals, the outcome was 41.8 %. The majority of the patients in the study lacked a COVID-19 vaccination. Diabetes mellitus and systemic arterial hypertension were the most common comorbidities. After model development and cross-validation, the Random Forest regression was considered the best approach, and the following eight predictors were retained: D-dimer, Urea, Charlson comorbidity index, pulse oximetry, respiratory frequency, Lactic Dehydrogenase, RDW, and Radiologic RALE score. The model's bias-corrected intercept and slope were − 0.0004 and 1.079 respectively, the average prediction error was 0.028. The ROC AUC curve was 0.795, and the variance explained was 0.289.</div></div><div><h3>Conclusion</h3><div>The prognostic model was considered good enough to be recommended for clinical use in patients during hospitalization (<span><span>https://pedrobrasil.shinyapps.io/INDWELL/</span><svg><path></path></svg></span>). The clinical benefit and the performance in different scenarios are yet to be known.</div></div>","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":"9 ","pages":"Article 100181"},"PeriodicalIF":0.0,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11754157/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143030008","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":"AI-assisted exposure-response data analysis: Quantifying heterogeneous causal effects of exposures on survival times","authors":"Louis Anthony Cox Jr. ,&nbsp;R. Jeffrey Lewis ,&nbsp;Saumitra V. Rege ,&nbsp;Shubham Singh","doi":"10.1016/j.gloepi.2024.100179","DOIUrl":"10.1016/j.gloepi.2024.100179","url":null,"abstract":"<div><div>AI-assisted data analysis can help risk analysts better understand exposure-response relationships by making it relatively easy to apply advanced statistical and machine learning methods, check their assumptions, and interpret their results. This paper demonstrates the potential of large language models (LLMs), such as ChatGPT, to facilitate statistical analyses, including survival data analyses, for health risk assessments. Through AI-guided analyses using relatively recent and advanced methods such as Individual Conditional Expectation (ICE) plots using Random Survival Forests and Heterogeneous Treatment Effects (HTEs) estimated using Causal Survival Forests, population-level exposure-response functions can be disaggregated into individual-level exposure-response functions. These reveal the extent of heterogeneity in risks across individuals for different levels of exposure, holding other variables fixed. By applying these methods to an illustrative dataset on blood lead levels (BLL) and mortality risk among never-smoker men from the NHANES III survey, we show how AI can clarify inter-individual variations in exposure-associated risks. The results add insights not easily obtained from traditional parametric or semi-parametric models such as logistic regression and Cox proportional hazards models, illustrating the advantages of non-parametric approaches for quantifying heterogeneous causal effects on survival times. This paper also suggests some practical implications of using AI in regulatory health risk assessments and public policy decisions.</div></div>","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":"9 ","pages":"Article 100179"},"PeriodicalIF":0.0,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11757793/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143047962","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":"Lower limb lymphoedema-related mental depression: A systematic review and meta-analysis of non-cancer-related studies","authors":"Tegene Atamenta Kitaw ,&nbsp;Addisu Getie ,&nbsp;Solomon Gebremichael Asgedom ,&nbsp;Molalign Aligaz Adisu ,&nbsp;Befkad Derese Tilahun ,&nbsp;Alemu Birara Zemariam ,&nbsp;Addis Wondmagegn Alamaw ,&nbsp;Abebe Merchaw Faris ,&nbsp;Tesfaye Engdaw Habtie ,&nbsp;Melesse Abiye Munie ,&nbsp;Eyob Shitie Lake ,&nbsp;Gizachew Yilak ,&nbsp;Mulat Ayele ,&nbsp;Molla Azmeraw ,&nbsp;Biruk Beletew Abate ,&nbsp;Ribka Nigatu Haile","doi":"10.1016/j.gloepi.2024.100180","DOIUrl":"10.1016/j.gloepi.2024.100180","url":null,"abstract":"<div><h3>Background</h3><div>Lower limb lymphoedema, characterized by persistent swelling in the legs due to lymphatic dysfunction, not only imposes a physical burden but is also associated with significant mental depression. While emerging research suggests a strong link between lower limb lymphoedema and depression, the extent of the problem remains underexplored. This study aims to investigate the relationship between lower limb lymphoedema and mental depression through a meta-analysis of existing studies.</div></div><div><h3>Methods</h3><div>A comprehensive search was conducted across databases including PubMed, MEDLINE, EMBASE, International Scientific Indexing, Web of Science, and Google Scholar. Study quality was assessed using the Joanna Briggs Institute (JBI) critical appraisal tool. A weighted inverse variance random-effects model was used for pooled estimates, along with subgroup analysis, heterogeneity assessment, publication bias testing, and sensitivity analysis. The prediction interval was computed to estimate where future observations may fall. The review protocol was registered in PROSPERO (CRD42024541596).</div></div><div><h3>Results</h3><div>Thirteen studies involving 3503 patients with lower limb lymphoedema due to lymphatic filariasis, podoconiosis, or leprosy were included. The pooled estimate of depression related to lower limb lymphoedema was 38.4 % (95 % CI: 26.3 %, 50.5 %). High heterogeneity (I² = 81.48 %) highlighted significant variability among the studies. Depression was more prevalent among leprosy patients (38.1 %) and podoconiosis patients (36.4 %), showing little difference between the two. However, the prevalence was notably lower among those with lymphatic filariasis (22.4 %). A higher prevalence of depression was found in Africa (39.4 %) compared to other regions (36.1 %).</div></div><div><h3>Conclusion</h3><div>Patients with lower limb lymphoedema experience disproportionately high rates of mental depression compared to the general population. Integrating mental health assessment and treatment into care packages for lymphoedema management is essential, with special attention needed for leprosy patients.</div></div>","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":"9 ","pages":"Article 100180"},"PeriodicalIF":0.0,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11743871/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143012958","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 need for methodological pluralism in epidemiological modelling","authors":"Pieter Streicher ,&nbsp;Alex Broadbent ,&nbsp;Joel Hellewell","doi":"10.1016/j.gloepi.2024.100177","DOIUrl":"10.1016/j.gloepi.2024.100177","url":null,"abstract":"<div><div>During the Covid-19 pandemic, the best-performing modelling groups were not always the best-resourced. This paper seeks to understand and learn from notable predictions in two reports by the UK's Scientific Advisory Group for Emergencies (SAGE). In July 2021, SAGE reported that, after the upcoming lifting of restrictions (“Freedom Day”) cases would “almost certainly remain extremely high for the rest of the summer” and that hospitalisations per day would peak between 100 and 10,000. Cases were not “extremely high” and began to decline, while hospitalisations initially lay outside (above) SAGE's confidence bounds, and only came within the expected range when the upper and lower bound moved so far apart as no longer to be useful for policy or planning purposes. The second episode occurred in December 2021, when SAGE projected 600–6000 deaths per day at peak in the scenario where restrictions remained as they were (referred to as “Plan B\"). In the event, restrictions did not change, and deaths peaked at 202, well below the lower bound, even though this spanned one order of magnitude. We argue that the fundamental problem was over-reliance on mechanistic approaches to disease modelling, and that a methodologically pluralist approach would have helped. We consider various ways this could have been done, including evaluating past performance and considering data from elsewhere. We show how the South African Covid-19 Modelling Consortium performed better by learning from experience and using multiple methods. We conclude in favour of methodological pluralism in infectious disease modelling, echoing calls for methodological pluralism in recent literature on causal inference.</div></div>","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":"9 ","pages":"Article 100177"},"PeriodicalIF":0.0,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11731489/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143013008","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}