Journal of Clinical Epidemiology最新文献

{"title":"ESIC series, Paper 7: Capacity sharing in Evidence Synthesis: Recommendations from the Evidence Synthesis Infrastructure Collaborative (ESIC) Working group 5.","authors":"Ana Beatriz Pizarro, Claudia Alarcón-López, Zewdie Birhanu, Getahun Fetensa, Yemisi Takwoingi, Tamara Kredo, Verónica Osorio-Calderón, Firmaye Bogale, Andrea Yearwood, Simon Lewin, Eva Madrid, Fadi El-Jardali","doi":"10.1016/j.jclinepi.2026.112521","DOIUrl":"10.1016/j.jclinepi.2026.112521","url":null,"abstract":"<p><strong>Aim: </strong>To develop actionable recommendations for capacity sharing as a core enabler of a sustainable global evidence synthesis ecosystem.</p><p><strong>Background: </strong>The Evidence Synthesis Infrastructure Collaborative (ESIC), launched in 2024 with Wellcome Trust support, seeks to strengthen evidence synthesis methods, capacity, and infrastructure worldwide. Working Group 5 (WG5) focused on capacity sharing-defined as the equitable, reciprocal exchange of skills, knowledge, and resources-moving beyond traditional unidirectional capacity-building models to foster inclusive, collaborative approaches.</p><p><strong>Methods: </strong>WG5 employed a two-phase, four-stage process informed by the Double Diamond design framework. Phase 1 mapped existing capabilities and gaps through a Capability Maturity Assessment across regions. Phase 2 identified and prioritized solutions via iterative consultations, literature reviews, expert feedback, and alignment with other ESIC working groups. Twenty participants from 22 countries contributed, ensuring diverse representation.</p><p><strong>Results: </strong>The Capability Maturity Assessment revealed stark disparities: high-income regions exhibited mature systems, while many LMICs lacked mechanisms for sustained collaboration. Barriers included fragmented digital infrastructure, skills gaps, weak mentorship, limited knowledge translation, and insufficient end-user engagement, with language as a cross-cutting challenge. WG5 proposed a portfolio of ten interlinked solutions, including an ESIC Knowledge Hub, knowledge translation tools powered by artificial intelligence, regional collaborating centers, mentorship programs, continuous professional development modules, and competency frameworks. These solutions aim to rebalance inequities, enhance efficiency, and foster south-led innovation.</p><p><strong>Conclusion: </strong>WG5's recommendations provide a scalable, costed roadmap to embed equity and cocreation in global evidence synthesis. By integrating digital tools, human infrastructure, and participatory governance, the proposed portfolio offers funders and stakeholders a menu of options to transform a fragmented system into a connected, inclusive, and resilient evidence ecosystem.</p>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"112521"},"PeriodicalIF":5.8,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149018790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ESIC series, Paper 4: Data sharing and reuse in evidence synthesis: Recommendations from the Evidence Synthesis Infrastructure Collaborative (ESIC) Working Group 2.","authors":"Krishna Kishore Pandalaneni","doi":"10.1016/j.jclinepi.2026.112526","DOIUrl":"10.1016/j.jclinepi.2026.112526","url":null,"abstract":"<p><strong>Objectives: </strong>Evidence synthesis data processes remain fragmented, with frequent duplication of data extraction, study-quality assessment, and certainty appraisal. These inefficiencies occur within a broader evidence ecosystem shaped by inequities in access, publication practices, language, infrastructure, technology, and representation, which can affect the visibility and reuse of evidence from non-English and low- and middle-income country contexts. The objective of this commentary was to identify practical solutions for strengthening data sharing and reuse so that evidence produced for one synthesis can be reused across multiple contexts.</p><p><strong>Study design and setting: </strong>Working Group 2 (WG2) followed the ESIC planning process mapped to the Double Diamond framework. Stage 1 mapped existing capabilities through consultations, interviews, and document review. Stage 2 assessed maturity across 16 core capabilities and identified critical gaps. Stage 3 generated potential strategies through workshops, surveys, and feedback from the Global SDG Synthesis Coalition and other ESIC working groups. Stage 4 prioritized solutions via impact-effort-feasibility analysis, supplemented with strategy bundling, coalition consultation, and indicative costing.</p><p><strong>Results: </strong>WG2 identified five high-priority solutions, designed to build on existing repositories, standards, platforms, and quality assurance practices where successful models already exist: a connected system of synthesis data repositories, interoperable data standards, metadata standards for discoverability, open access standards for equitable reuse, and scalable quality assurance mechanisms. These solutions aim to reduce duplication, improve discoverability, and strengthen transparency, with a cross-cutting strategy on sustainable funding and incentive alignment to ensure long-term viability.</p><p><strong>Conclusion: </strong>WG2 provides a structured framework for transforming data sharing and reuse into a reusable global public good. By fostering interoperability, inclusivity, and sustainability, these solutions will reduce duplication, strengthen equity, and ensure that decision makers worldwide benefit from timely, trustworthy, and relevant evidence.</p><p><strong>Plain language summary: </strong>Evidence reviews often repeat work that has already been done, such as extracting data from the same studies or assessing study quality again. This wastes time and resources and can make it harder to include evidence from non-English and lower-resource settings. This paper identifies five practical ways to make evidence synthesis data easier to find, share, check, and reuse: connected data repositories, common data standards, better metadata for discoverability, open access standards, and quality assurance processes. These changes could make evidence synthesis faster, fairer, and more reliable for decision makers.</p>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"112526"},"PeriodicalIF":5.8,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149018733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Methods for ascertainment and categorization of race and ethnicity for clinical research: an overview of reviews.","authors":"Teresa Semalulu, Melanie Anderson, Zayd Schafer, Fadi Kharouf, Emma Neary, Azza Eissa, Shahad Al-Matar, Sadeem AlFraih, Virginia Carrizo Abarza, Abimbola K Saka, Justin Smith, Pankti Mehta, Richard Cook, Zahi Touma, Sindhu R Johnson","doi":"10.1016/j.jclinepi.2026.112503","DOIUrl":"10.1016/j.jclinepi.2026.112503","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Objectives: &lt;/strong&gt;The methods used to ascertain and categorize race and ethnicity in clinical research influence study design and may influence the interpretation and comparability of findings between studies. Use of inconsistent approaches across the literature may contribute to heterogeneity of findings, with some methods suggesting patterns not representative of underlying processes. This study aimed to systematically review methods used to ascertain race and ethnicity, evaluate systems of racial and ethnic categorization, and comparatively evaluate methods of ascertainment and categorization of race and ethnicity used in clinical research.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Study design and setting: &lt;/strong&gt;An umbrella review was conducted in accordance with the Joanna Briggs Institute methodology for umbrella reviews and reported following Preferred Reporting Items for Overviews of Reviews (PRIOR) statement. We searched five databases (Ovid MEDLINE, Ovid Embase, CINAHL, Scopus, and Sociological Abstracts) from January 2002 to April 2025 using terms related to race and ethnicity, conceptualization, genetics, and clinical research. English-language review articles published in peer-reviewed journals were reviewed by at least two reviewers. Titles, abstracts, and full-text articles were screened, and references of selected articles and gray literature. Data were abstracted using a standardized form, and further analyzed and summarized. A nonsystemic, purposive, environmental scan of the gray literature was included to capture representative examples of documents related to the study objectives.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The search generated 12,783 articles, with 139 review articles included. We identified six methods for the ascertainment of race and ethnicity: self-report, social assignment, name-based ethnic classification, geographic assignment, ancestry, and use of multimethod strategies. Twenty-one systems of categorization were identified, with racial categories ranging from 3 to 15. Terminology and systems of categorization varied both within and across geographic regions, reflecting population-specific contexts.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;Self-report is the most common method of ascertainment; however alternative methods can be considered when self-reported data are not available. These methods capture different dimensions of race and ethnicity and should be selected and interpreted based on the research question and study context, rather than viewed as interchangeable. The variability in the categorization and terminology used for race and ethnicities suggests the need for informed use of these constructs, to support appropriate contextualization and interpretation of results. Improved methodologic consistency in the use of race and ethnicity is essential to strengthen the rigor of clinical research.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Plain language summary: &lt;/strong&gt;Race and ethnicity are commonly used in research studies to measure risk of health","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"112503"},"PeriodicalIF":5.8,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149018760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ESIC series: paper 9 estimating initial costs of evidence synthesis infrastructure collaborative solutions: assumptions and overall results.","authors":"Subhash Pokhrel, Stephen Hanney","doi":"10.1016/j.jclinepi.2026.112524","DOIUrl":"10.1016/j.jclinepi.2026.112524","url":null,"abstract":"<p><strong>Background and objective: </strong>To estimate the initial costs of implementing the Evidence Synthesis Infrastructure Collaborative (ESIC) solutions and therefore provide funders with a costed menu of options for strengthening global evidence synthesis systems. ESIC was established to enhance the timeliness, relevance, and affordability of evidence synthesis worldwide. Five working groups and a governance planning group were tasked with identifying priority solutions to address systemic gaps in infrastructure, capacity, and governance. We were tasked with costing these solutions from a funder's perspective to inform strategic investment decisions.</p><p><strong>Methods: </strong>We followed a structured approach with planning of the costing occurring concurrently with the solutions being identified by the groups. We identified resource requirements, measured quantities, and applied unit costs. Inputs included personnel, tools, and system requirements, with indirect costs set at 20% of direct costs and an additional 5% for monitoring, evaluation, and learning. Data were gathered through resource-use questionnaires, literature reviews, and consultations with ESIC groups. Salary costs were estimated using a 3 × 3 matrix of seniority and geographic bands (Global North; Global South-high/upper-middle income; Global South-low/lower-middle income). Sensitivity analyses addressed uncertainties, particularly in salary assumptions. A separate costing exercise was conducted for a Vanguard Suite, a prioritized collection of 100 living evidence syntheses.</p><p><strong>Results: </strong>The total estimated cost for all 40 solutions identified by the groups was US$278.3 million (range: US$257.2-299 million). Employment costs accounted for the largest share, with 80% of staff time and 78% of senior roles allocated to the Global South, reflecting ESIC's equity goals. Individual group costs ranged from US$9.1 million (governance) to US$70.5 million (data solutions). The suite of living evidence syntheses was costed at US$19.2 million.</p><p><strong>Conclusion: </strong>This costing exercise provides transparent, equity-focused information to assist funders when selecting scalable solutions aligned with ESIC's mission. Although estimates involve assumptions, they offer a pragmatic basis for investment planning and demonstrate ESIC's commitment to affordability, inclusivity, and global impact.</p>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"112524"},"PeriodicalIF":5.8,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149018736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ESIC series, Paper 3: Strengthening demand-side engagement: Recommendations from the Evidence Synthesis Infrastructure Collaborative (ESIC) Working Group 1.","authors":"Paul Flowerdew, Eleanor Williams, Shelly-Ann Hunte, Peter Bragge","doi":"10.1016/j.jclinepi.2026.112525","DOIUrl":"10.1016/j.jclinepi.2026.112525","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background and objectives: &lt;/strong&gt;A persistent challenge to evidence use is the disconnect between producers of evidence syntheses and users. This gap is driven by well-documented barriers faced by decision-makers, including limited access to high-quality or relevant evidence emanating from evaluation and research processes, findings not being delivered in a timely way, insufficient capacity for knowledge translation and evidence uptake, and resource constraints. The Evidence Synthesis Infrastructure Collaborative (ESIC) was established to guide the Wellcome Trust's planned investment in optimizing the impact of research on policy and practice. ESIC Working Group 1 (WG1) aimed to explore demand-side engagement, specifically optimizing engagement between evidence synthesis producers and evidence users and identifying solutions addressing high-priority challenges in the connection between evidence synthesis and policy/practice.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;WG1 undertook a 6-month, multistage planning process to systematically identify challenges, assess existing capabilities, and develop scalable solutions to support demand-side engagement. The group, comprising professionals from diverse sectors and regions, was guided by the 'double diamond' design framework, a Capability Maturity Model and an Impact-Effort Matrix. The activities comprised the following 4 stages: discovery (capability profile assessment), definition (capability maturity assessment), development (identify solutions), and delivery (prioritize solutions). Data collection included surveys, workshops, and consultations within and beyond WG1, with external stakeholders purposefully recruited by WG1 members.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Findings revealed significant gaps in data, technology, platforms, resources, processes, and systems, with most demand-side capabilities rated as 'initial' or 'developing'. The assessment highlighted sectoral and regional disparities, particularly affecting marginalized groups. Five potential solutions were developed. Three were considered high priority - establishment of regional demand-side secretariats, provision of support to evidence intermediaries, and coproduction labs. Two were recognized as valuable complements - thematic repositories such as question banks and evidence-gap maps; and grants to end-users to build evidence synthesis awareness and capacity. These actions are designed to enhance capacity, foster collaboration, and promote equity in evidence-informed decision-making.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;Challenges related to demand-side engagement vary significantly across sectors and geographic contexts. Effective solutions therefore encompass a broad array of strategies addressing factors including low engagement of marginalized communities; structural imbalances in research focus; restrictive paywalls; and lack of transparency in decision-making. Because most identified capabilities were heavily skewed to evidence","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"112525"},"PeriodicalIF":5.8,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149018777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Authors' reply to Brett Dyer: investigating associations between an outcome and some variables is not only relevant for prognostic factor research.","authors":"Theresa Ullmann, Georg Heinze, Michael Kammer, Susanne Strohmaier, Mariana Nold, Daniela Dunkler","doi":"10.1016/j.jclinepi.2026.112515","DOIUrl":"10.1016/j.jclinepi.2026.112515","url":null,"abstract":"","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"112515"},"PeriodicalIF":5.8,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149002905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A taxonomy of research tasks: looking within descriptive, predictive, and causal research.","authors":"Brett P Dyer","doi":"10.1016/j.jclinepi.2026.112516","DOIUrl":"10.1016/j.jclinepi.2026.112516","url":null,"abstract":"","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"112516"},"PeriodicalIF":5.8,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148984202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Methodological innovation to support incorporation of environmental impacts in health research","authors":"Beatriz Goulao,&nbsp;Dwayne Boyers,&nbsp;Kristin Konnyu","doi":"10.1016/j.jclinepi.2026.112358","DOIUrl":"10.1016/j.jclinepi.2026.112358","url":null,"abstract":"","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"197 ","pages":"Article 112358"},"PeriodicalIF":5.8,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148201111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editor's Choice: September 2026","authors":"Andrea C. Tricco,&nbsp;David Tunnicliffe,&nbsp;Samantha Chakraborty,&nbsp;David Tovey,&nbsp;The Living Evidence Ecosystem Continued","doi":"10.1016/j.jclinepi.2026.112470","DOIUrl":"10.1016/j.jclinepi.2026.112470","url":null,"abstract":"","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"197 ","pages":"Article 112470"},"PeriodicalIF":5.8,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148867827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the importance of multiple aspects of cluster validation in multimorbidity research","authors":"Nikolaj Olerud Holm,&nbsp;Anne Frølich,&nbsp;Helle Gybel Juul-Larsen,&nbsp;Juliette Tavenier,&nbsp;Line Jee Hartmann Rasmussen,&nbsp;Morten Baltzer Houlind,&nbsp;Anders Stockmarr","doi":"10.1016/j.jclinepi.2026.112377","DOIUrl":"10.1016/j.jclinepi.2026.112377","url":null,"abstract":"","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"197 ","pages":"Article 112377"},"PeriodicalIF":5.8,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148260460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}