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At least 19 recordsLinked to original sources
PubMed2026

Methods for defining equity-stratifying variables: a systematic review of validation studies.

BACKGROUND AND OBJECTIVE: Disease burden is often disproportionally higher among those who are socially disadvantaged by factors defined in the PROGRESS-Plus framework (ie, Place of residence, Race/ethnicity/culture/language, Occupation, Gender/sex, Religion, Education, Socioeconomic status, and Social capital, with "Plus" covering features like age and disability). The accuracy and applicability of case definitions to identify these variables from administrative and clinical health data are unknown. We conducted a systematic review to explore how equity-stratifying variables, as categorized by the PROGRESS-Plus framework, have been defined and validated in epidemiologic studies using administrative health, population-level, or electronic health record (EHR) data. METHODS: Medline, EMBASE, CINAHL, Web of Science, and Google Scholar were searched from the inception of the databases to 2024 for validation studies of equity-stratifying variables in adults using administrative health datasets, health registries, or EHR data. Titles and abstracts, followed by relevant full-text articles, were screened in duplicate by two reviewers for eligibility. The data sources utilized, algorithms employed, and their associated performance measures were extracted and synthesized from included studies. Given substantial heterogeneity in study design, equity-stratifying variable definition, and performance metrics, meta-analysis was not possible. RESULTS: Of the 9099 unique citations screened, 188 full texts were reviewed and 116 were included in this review. Most studies were published between 2019 and 2024 (n = 64, 55%) and were validation studies of race/ethnicity definitions that used race/ethnicity codes or surname list algorithms (n = 66, 57%). No studies examined religion. Regarding the reported performance measure estimates, the race/ethnicity/culture/language equity-stratifying variables category had the largest variability across sensitivity, positive predictive value (PPV), and Cohen's Kappa. Occupation validation studies had the lowest variation in sensitivity and PPV. CONCLUSION: Despite an increasing number of publications reporting on the validation of equity-stratifying variables relevant to the PROGRESS-Plus framework, performance measures varied widely across studies. The significant heterogeneity in equity-stratifying variable definitions and methods used to validate them support the need for further rigorous validation of equity-stratifying variables in administrative and clinical health data. PLAIN LANGUAGE SUMMARY: Disease burden is often higher in people who experience financial hardships, lower level of education, discrimination due to race/ethnicity, and unstable housing. These social factors can be considered health equity factors and are important for understanding health inequalities. Health researchers often use large datasets, such as hospital or electronic health records (EHRs), to study these health equity factors. However, it is not clear how accurately these data sources capture information about people's social circumstances and how these factors are defined. In this study, we reviewed existing research to understand how health equity factors have been defined across health data sources and how accurate they are at measuring aspects of health equity and social disadvantage. Of the more than 9000 studies we identified, we included 116 that met our criteria for this systematic review. Most included studies focused on identifying race and ethnicity, often using codes or surname-based methods. We found that the accuracy of these methods varied widely across studies, meaning results may not always be reliable or comparable. Overall, our findings show that there are inconsistencies in how social factors are defined and measured in health data. This makes it difficult to fully understand and address health inequalities using routinely collected health data. More work is needed to develop and validate better quality and more consistent methods for capturing these important social factors.

Humans↗
PubMed2026

Global vaccine readiness: equity-by-design in pandemic preparedness and response.

INTRODUCTION: COVID-19 showed that rapid vaccine development and roll-out, while lifesaving, can still yield large, avoidable harms when equity is not considered from the outset. Disparities in vaccine timing and coverage, especially in low-resource settings, amplified health and economic burdens, highlighting the need for preparedness frameworks that combine speed with fairness. AREAS COVERED: We synthesize evidence from literature and policy reports regarding global vaccine roll-out, focusing on avertable mortality under alternative sharing scenarios, procurement design, pooled mechanisms such as COVAX, and the role of distributed manufacturing and delivery capacity. We also examine how transparent data-sharing, effective public communication, genomic surveillance, adaptive trial designs, and modeling hubs can support more responsive and equitable vaccine deployment. Across six reflection points, we translate these lessons into practical priorities for future pandemic readiness, including strengthening healthcare infrastructure, equitable procurement, data transparency, and safeguarding public health decision-making from political and commercial distortion. EXPERT OPINION: We argue that equity-by-design is essential if vaccine innovation is to deliver equitable public health impact. This requires geographically distributed manufacturing, transparency, equity-conditioned advance purchase agreements, and pre-agreed, epidemiology-triggered allocation of vaccines. We recommend institutionalizing disaggregated reporting, standardized data-sharing, greater pathogen genomic sequencing capacity, and communication strategies that support public health protection while countering misinformation.

Humans↗
PubMed2026

Sex-stratified mortality trends in preterm birth complications in Sierra Leone: progress, persistence, and equity implications.

BACKGROUND: Preterm birth complications remain a leading cause of neonatal mortality in Sierra Leone, despite recent health system gains. Evidence on long-term sex-specific disparities in mortality due to preterm birth complications is limited, constraining equitable neonatal care planning. OBJECTIVE: To examine two‑decade trends in sex‑stratified mortality from preterm birth complications using standardized equity indicators. METHODS: We conducted a retrospective longitudinal analysis of sex-disaggregated mortality estimates from the World Health Organization (WHO) Global Health Estimates (GHE), accessed through the WHO Health Equity Assessment Toolkit (HEAT), Built-in Database Edition (Version 6.0). Mortality rates per 100,000 population were extracted for 2001, 2006, 2011, 2016, and 2021. Inequality was assessed using absolute difference (D), relative ratio (R), population attributable risk (PAR), and population attributable fraction (PAF). RESULTS: Mortality declined substantially between 2001 and 2021 for both males (85.1-49.3 per 100,000) and females (71.2-39.9 per 100,000). Male mortality remained consistently higher across all years, with relative ratios indicating approximately 20-25% excess mortality among male neonates. Absolute inequalities narrowed modestly over time, whereas relative inequalities remained largely unchanged. PAR and PAF remained close to zero throughout the study period. Wider uncertainty intervals in earlier years reflected limited empirical data availability. CONCLUSION: Although preterm mortality declined over two decades, a persistent male disadvantage remained in Sierra Leone. These findings highlight the importance of integrating sex-disaggregated equity monitoring into neonatal policies and programmes. Future research should evaluate strategies to reduce the persistent excess mortality among male neonates while sustaining overall improvements in neonatal survival and progress toward Sustainable Development Goal 3.2.

Humans↗
PubMed2026

Ethical Governance of Open Data Across Biomedical Research, Healthcare, and Public Health: Privacy, Equity, Trust, and Controlled Access.

Open data has become central to biomedical research and public health, but health information is uniquely sensitive and difficult to share responsibly. In this narrative review, open data is considered as a spectrum of health-data sharing arrangements, ranging from public aggregate datasets to controlled-access repositories, federated analysis, and synthetic data. This narrative review synthesizes the scientific and societal rationale for greater openness with the ethical, legal, and governance constraints that shape what "open" can realistically mean in healthcare. We examine how data sharing supports reproducibility, machine learning, and more efficient research, while also enabling public health surveillance and learning health systems. Against these benefits, we analyze privacy and re-identification risks, consent challenges in large-scale secondary use, inequities including data colonialism, and tensions introduced by commercialization. We integrate lessons from prominent case examples spanning pandemic data sharing, genomic initiatives, population registries, patient-led rare disease infrastructures, and regional data spaces. Across these domains, experience suggests that durable progress depends less on unrestricted openness than on calibrated access, privacy-preserving architectures, clear accountability, and sustained public engagement. We conclude by proposing a pragmatic ethical orientation for healthcare open data: treat openness as a spectrum of controlled sharing arrangements, embed equity and reciprocity into governance, and institutionalize trust-building measures that can persist beyond emergencies and political cycles.

Data colonialism↗
PubMed2026

Equity in genome sequencing for rare disease diagnosis: a cross-sectional analysis of data from the UK 100,000 Genomes Project.

BACKGROUND: Genome sequencing has improved rare disease diagnosis and is now part of routine clinical care in the National Health Service in England. Automated prioritisation pipelines narrow millions of variants per patient to a small subset for clinical review, a process that relies on allele frequency resources that do not fully represent human genetic diversity. We assessed ancestry-related differences in variant prioritisation and diagnostic outcomes in patients from the UK 100,000 Genomes Project. METHODS: We analysed 29,405 rare disease probands with genome sequencing and linked clinical outcomes data. We used multivariable regression to assess ancestry-related differences in the number of variants prioritised for clinical review, the proportion of prioritised variants that were recorded as diagnostic, and diagnostic yield. We also evaluated the use of ancestry-stratified allele frequency filters derived from an independent, diverse UK cohort (n = 33,724). FINDINGS: Compared with the European ancestry group, the East African group had nearly three times more variants prioritised for clinical review (IRR 2.77, 95% CI 2.33-3.29). Other non-European groups also had significantly higher counts. Diagnostic yield was similar across ancestry groups after adjustment (LRT p = 0.1650). Prioritised variants were less likely to be recorded as diagnostic in East African (OR 0.32, 95% CI 0.22-0.46), West African (0.47, 0.39-0.57), South Asian (0.65, 0.58-0.73), and Middle Eastern (0.68, 0.54-0.86) groups. Applying ancestry-stratified allele-frequency filters removed 3.1% of prioritised variants overall-24.3% in the East African group-without loss of diagnostic sensitivity, including 29.5% of recorded VUS in this group. INTERPRETATION: Differences in the likelihood of prioritised variants being recorded as diagnostic partly reflect limitations of current allele frequency resources, which use broad population groupings that mask within-group diversity. Increased representation of diverse ancestries in reference databases and better estimation of ancestry-appropriate allele frequencies will help reduce inefficiencies and improve equity in variant prioritisation for rare disease diagnosis. FUNDING: The UK Department of Health and Social Care and the EU's Horizon 2020 Research and Innovation Programme.

Humans↗
PubMed2025

Covering medical care costs for participants in the eMERGE Network: Challenges for equity and implementation.

PURPOSE: To investigate the complexities of covering study-recommended medical care costs for individuals (in order to prevent lack of adherence due to financial reasons), which have received little attention. METHODS: We explored the deliberations, decisions, and challenges faced by the Electronic Medical Records and Genomics (eMERGE) Network during the implementation of a genomic research project recommending clinical care based on high-risk results defined largely by polygenic risk scores. Two surveys were disseminated to eMERGE sites: to identify preferences about payment for specific care recommendations (survey 1) and to understand the operational processes of covering medical care costs (survey 2). RESULTS: Paying for a subset of care recommendations for the funded study duration was identified as the most feasible approach for covering medical care costs for participants who received high-risk genomic results. Each eMERGE site, by necessity, used diverse approaches to pay for medical care costs. CONCLUSION: eMERGE researchers balanced competing concerns about bias, equity, study design, regulatory compliance, and cost in designing a unified approach to cover some of the recommended medical care costs in the study. Many implementation challenges were encountered. Findings can inform researchers and regulatory bodies about the implications and complications of covering medical care costs in translational research studies focused on prevention.

Humans↗
PubMed2026

Bundibugyo at the border: The 2026 Ebola outbreak and the case for pre-emptive countermeasure equity.

The 2026 Ebola outbreak caused by Bundibugyo ebolavirus in the Democratic Republic of the Congo and Uganda exposes a persistent structural flaw in global health security: preparedness remains overwhelmingly reactive and pathogen-specific. Despite the $518 million Africa CDC-WHO joint continental plan, no licensed BDBV vaccine or therapeutic is available; a 21-day (three-week) detection delay and cross-border transmission expose inadequate inter-epidemic investment in non-Zaire ebolavirus countermeasures. We argue for sustained, ring-fenced financing, institutionalised cross-border coordination, species-inclusive diagnostics, and real-time genomic data sharing to move African Ebola preparedness from reactive to pre-emptive.

Hemorrhagic Fever, Ebola↗
PubMed2026

The Network of National COVID-19 Data Portals: public health equity through collaboration.

The network of the national COVID-19 Data Portals was developed and linked to the COVID-19 Data Portal (https://www.covid19dataportal.org/)inresponsetothe need for rapid data sharing and analysis during the 2020-2022 SARS-CoV-2 pandemic. Built on open-source code developed by the Swedish COVID-19 Data Portal (now the Swedish Pathogens Portal, www.pathogens.se) the network included 12 national portals addressing demand for local open data sharing and access, across data types and resources. It provides a robust case study of national initiatives for FAIR (Findable, Accessible, Interoperable and Reusable) resources and a foundation for future pandemic preparedness across pathogens globally. In this paper we outline the structure of the origins of the network of National COVID-19 Datal Portals, the technical aspects and code originating from the Swedish Portal and provide an overview of the services and tools offered by each Portal. The paper showcases the process and operation of four Portals: Sweden, Poland, Spain, Norway and The Netherlands. In this study, we observe that pandemic response greatly benefits from an established infrastructure that can be quickly mobilised, developed and extended. Collaborations and preparation built on solid foundations over several years, supported by investment in the form of national and international research grants, is key for sustainability, continuation and readiness to deploy such efforts.

COVID-19↗
PubMed2026

Newborn Screening: Equity for Aboriginal and Torres Strait Islander Families in the Context of Emerging Genomics.

Australia's newborn bloodspot screening (NBS) program is offered to every newborn. It screens for 34 rare conditions with the potential for hundreds more to be added using genomics. Despite NBS being available in Australia since the 1960s, there is a lack of evidence regarding the participation and experiences of Aboriginal and Torres Strait Islander peoples in NBS. As Australia considers a future where genomics might be used in NBS, there is a critical window of opportunity to understand and prioritise the perspectives, hopes and fears of Aboriginal and Torres Strait Islander peoples regarding the utility of genomics in NBS.

Humans↗
PubMed2026

Strategies to improve recruitment to randomised trials.

BACKGROUND: Recruiting participants to randomised controlled trials (RCTs) is challenging. Identifying effective recruitment strategies would benefit health research: poor recruitment leads to underpowered trials, reducing the reliability of findings and increasing the risk of wasted resources, ethical concerns, and trial failure. Evidence to inform recruitment strategies is increasingly generated through Studies Within A Trial (SWATs), which are methodological studies embedded within host RCTs. This is an update of a review last published in 2018. OBJECTIVES: Primary: to quantify the effects of strategies to improve recruitment of participants to RCTs. Secondary: to evaluate recruitment strategies' cost-effectiveness and impact on retention, and the equity, diversity, and inclusion (EDI) characteristics of recruited participants. SEARCH METHODS: We used MEDLINE, Embase, and six other databases to identify the studies included in the review. We also sought unpublished recruitment SWATs through social media and targeted email dissemination to trial methodology networks. The latest search date was 16 February 2023. SELECTION CRITERIA: We included randomised SWATs evaluating trial recruitment strategies embedded in healthcare and non-healthcare trials. We excluded quasi-randomised, hypothetical, questionnaire-only, retention-only, or clinician incentive studies. DATA COLLECTION AND ANALYSIS: Primary outcome: proportion of eligible participants or centres recruited. SECONDARY OUTCOMES: cost-effectiveness, retention rates, and EDI characteristics of included participants. We conducted random-effects meta-analysis for strategies evaluated in at least two studies; otherwise, we synthesised results narratively. We reported effects as risk differences (RDs) with 95% confidence intervals (CIs), and assessed between-trial heterogeneity. We used GRADE to assess the certainty of evidence for the primary outcome. We expressed cost-effectiveness as the incremental cost per additional participant recruited in pounds sterling (GBP). MAIN RESULTS: We identified 91 eligible studies (53 new to this update), providing 94 comparisons and involving at least 176,747 participants. Eighty-one studies involved strategies aimed at trial participants, while 10 evaluated strategies aimed at recruiters. All were healthcare studies. We found 65 recruitment strategies; 49 were evaluated in a single study. Only five strategies were supported by high-certainty evidence according to GRADE criteria, and we focus on these strategies in the summary below. Open-label trials versus blinded, placebo trials. Open-label trials recruited more participants than blinded trials (RD 10%, 95% CI 8% to 12%; 3 studies, 9004 participants), corresponding to approximately 10 additional participants per 100 approached. The studies involved mostly women in the UK and Estonia. No cost or retention data were reported. Telephone reminder versus no telephone reminder. Telephone reminders to people who did not respond to an initial postal invitation boosted recruitment by 6% (95% CI 3% to 9%; 2 studies, 1450 participants), in trials with low underlying recruitment (we are less certain for trials with over 10% recruitment). The studies involved people with a mean age of 58 years in Canada and Norway. No cost or retention data were reported. Recruitment primer letter versus no letter. Pre-recruitment letters and leaflets designed to encourage participation made little or no difference to recruitment (absolute improvement 1%, 95% CI -1% to 2%; 2 studies, 5376 participants), and were associated with increased costs compared to not sending a primer (incremental cost: GBP 2.08). The studies involved mostly older white people in the UK and Ireland. Multimedia information via a digital link/QR code plus paper participant information leaflet (PIL) versus paper PIL alone. This made little or no difference to recruitment (absolute improvement 0%, 95% CI -1% to 1%; 7 studies, 11,612 participants) and retention (absolute improvement 0%, 95% CI -2% to 3%; 5 studies, 7403 participants), and increased costs compared to not including multimedia information (incremental cost: GBP 0.78). The studies involved people in the UK. Optimised, user-tested PIL versus standard PIL. Optimising participant information leaflets (e.g. through user-testing the leaflet with the target population to shape its content, format, and appearance) made little or no difference to recruitment: absolute improvement was 0% (95% CI 0% to 1%; 6 studies, 27,805 participants). The studies involved people in the UK. Only one study reported EDI data; participants were mostly older women. No cost or retention data were reported. We had moderate-certainty evidence for 13 other strategies; confidence was often reduced because the results came from single studies. Seven strategies involved changes to how potential participants received information; four involved changes to trial conduct; one targeted the recruiter or recruitment site; and one tested non-monetary incentives. We had much less confidence in the other 47 comparisons because the studies had design flaws, were single studies, or had very uncertain results. Costs were reported in only 17 of 91 studies. Strategy impact on retention was reported in 15 studies. All but one study (99%) were from high-income countries. The most reported demographics were age (49 studies), sex (32 studies), gender (27 studies), and education level (16 studies). AUTHORS' CONCLUSIONS: The evidence on strategies to improve trial recruitment remains broad but lacks depth. Of 65 strategies evaluated, only five were supported by high-certainty evidence. Open-label trial designs and telephone reminders to non-responders increased recruitment, while optimised participant information leaflets, recruitment primer letters, and multimedia information provided alongside a paper participant information leaflet had little or no effect. Reporting of participant characteristics was poor, limiting assessment of equity, diversity, and inclusion across most studies. Evidence is heavily skewed toward high-income countries. Future research must prioritise evaluations in low-to-middle-income settings and consistently report cost, retention, and EDI outcomes. We strongly urge the methodology research community to strengthen the evidence base by prioritising replications of existing strategies over the development and testing of new ones. FUNDING: National Institute for Health and Care Research (Advanced Fellowship, Adwoa Parker, reference:NIHR302256). Health Research Board, Republic of Ireland, Evidence Synthesis Ireland (grant ESI-2021-001) REGISTRATION: This review updates an earlier Cochrane review, which was first published in 2002 and subsequently updated in 2007, 2010, and 2018. Previous versions of the review and their protocols are available at: https://doi.org/10.1002/14651858.MR000013.pub2 https://doi.org/10.1002/14651858.MR000013.pub3 https://doi.org/10.1002/14651858.MR000013.pub4 https://doi.org/10.1002/14651858.MR000013.pub5 https://doi.org/10.1002/14651858.MR000013.pub6.

Randomized Controlled Trials as Topic↗
PubMed2026

(Re)imagining the Future of Genetic Counseling: A Reflexive Qualitative Analysis of Sociopolitical Power, Cultural Safety, Systemic Racism, and Comparative Practice in the United Kingdom, Aotearoa New Zealand and, Australia.

Genetic counseling is undergoing a rapid transformation as genomic medicine becomes embedded within mainstream healthcare systems. At the same time, the profession is being challenged to respond to systemic racism, colonial legacies, technological change, and evolving expectations regarding equity and justice. Historically, genetic counseling emerged within twentieth-century medical genetics and was influenced by political, social, scientific, and medical forces that included eugenic ideology, values, and practices. The profession has since evolved substantially toward psychosocial, patient-centered, and non-directive models of care. Contemporary debates regarding "newgenics" or "neugenics" further demonstrate how concerns regarding equity, reproductive ethics, disability, and genomic stratification continue to shape genomic healthcare discourse. This qualitative reflexive practice paper explores how systemic racism, colonial legacy, cultural safety and structural power shape genetic counseling practice in the United Kingdom (UK), Aotearoa New Zealand and Australia, and how these forces continue to reshape the profession's future identity. A reflexive, narrative, and comparative qualitative approach was employed, grounded in the authors' lived professional experiences across UK and Australasian contexts and informed by purposively selected policy, professional and scholarly literature relating to cultural safety, dignity, anti-racism, and Human Rights-Based Decision-Making. Through iterative reflexive dialogue, comparative analysis, and thematic synthesis, four interrelated themes were developed examining sociopolitical context, systemic racism, cultural safety and technologization within contemporary genetic counseling practice. Comparative analysis identified substantial differences in how culturally responsive practice is conceptualized and operationalized across settings. In Aotearoa, cultural safety is strongly shaped by Te Tiriti o Waitangi, bicultural accountability, and Māori sovereignty frameworks. In Australia, culturally safer genomic care has increasingly developed through Indigenous-led initiatives and workforce reform, including the Australian Alliance for Indigenous Genomics (ALIGN). In contrast, UK practice remains largely situated within equality, diversity, and inclusion (EDI) frameworks that may insufficiently address systemic racism and structural power within increasingly diverse populations. Reflexive clinical examples demonstrated how inequities may emerge through undocumented patient values, standardized pathways, assumptions regarding autonomy, and misinterpretation of culturally specific communication styles. Re-imagining the future of genetic counseling requires more than just technological advancement. It requires reflexive engagement with dignity, inequity, and the sociopolitical realities of the populations served. These insights re-imagine a culturally grounded, socially responsive future for genetic counseling in an era shaped by genomic mainstreaming, digital transformation, artificial intelligence and workforce reform and one in which the profession remains ethically anchored, relationally attuned, and committed to justice-oriented practice.

Humans↗
PubMed2026

Disparities in guideline-adherent cardiovascular preventive care for people with diabetes: A systematic review and meta-analysis.

BACKGROUND: Clinical practice guidelines offer guidance on delaying the progression of cardiovascular disease in people living with diabetes. We sought to determine whether guideline-recommended cardiovascular preventive care for people living with diabetes differs according to sociodemographic indicators, globally. METHODS: We conducted a systematic review of studies that compared the sociodemographic characteristics of people diagnosed with type 1 or 2 diabetes who received cardiovascular preventive care as recommended by guidelines to those who did not. Sociodemographic predictors were defined by PROGRESS+ (an equity framework). We searched MEDLINE, EMBASE, and APA PsychInfo from 2010 to January 21, 2026. Studies were screened independently by two people. One person assessed the risk of bias and extracted data, and another verified. We pooled results using a random-effects model and assessed the certainty of evidence using GRADE. RESULTS: Twenty-five studies were included. Meta-analyses showed female, Black, and Hispanic individuals had slightly lower odds of receiving guideline-recommended prescriptions for lipid-lowering medication compared to Male, and White individuals, respectively (OR:0.89, 95%CI:0.79,1.00, moderate certainty; OR:0.78, 95%CI:0.74,0.81, high certainty; OR:0.86, 95%CI:0.59,1.26, low certainty). Individuals aged 18-45 years had moderately lower odds (OR:0.33, 95%CI:0.19,0.57, moderate certainty), no observed association for Asian individuals. Asian individuals had moderately lower odds of antihypertensive medication prescription (OR:0.42, 95%CI:0.38,0.46, high certainty). Evidence suggests likely no association between HbA1c testing and sex/gender or between sex/gender and lipid panel testing. CONCLUSIONS: Some disparities in guideline-recommended cardiovascular preventive care among people living with diabetes were found. These results are consistent with previous reviews and highlight the need to ensure guidelines consider equity and with improved dissemination.

Humans↗
PubMed2026

Dataset Readiness Assessment With Large Language Model (DRAFT-LLM): A Multi-Axis Audit Guided by LLM.

This article details the Dataset Readiness Assessment for Training (DRAFT), a systematic method for determining whether a high-dimensional biological dataset is suitable for developing reliable, equitable (i.e., the extent to which model performance, error patterns, and potential benefits or harms are evaluated and found to be acceptably distributed across relevant demographic, biological, clinical, and contextual subgroups), and scientifically meaningful machine-learning models, and DRAFT Large Language Model (DRAFT-LLM), its optional human-in-the-loop extension for calibrating study-specific audits through structured, critically reviewed LLM guidance. Standard model validation often fails to detect when apparent performance is driven by spurious correlations, technical artifacts, or hidden stratification, leading to irreproducible and inequitable findings. DRAFT-LLM addresses this gap by shifting the focus from model tuning to structured dataset auditing, organized around Support Protocols 1 to 4 that capture the scientific intent, data structure, and governance constraints of a given study. These Support Protocols: (1) elicit and formalize investigator input into a study intake and dataset card; (2) compute standardized dataset statistics and structural summaries suitable for downstream analysis and LLM context; (3) configure the language model using form-based responses, safety guardrails, and governance rules; and (4) generate personalized instructions, prompts, and code templates for running DRAFT audits. Basic Protocols 1 to 3 are instantiated from this support layer for generalization, equity, and stability: they are reusable execution patterns whose concrete behavior is determined by the cards, statistics, and configurations defined in the Support Protocols. DRAFT-LLM and DRAFT are demonstrated in this article through an end-to-end case study on The Cancer Genome Atlas (TCGA). © 2026 Wiley Periodicals LLC. Support Protocol 1: Study intake and dataset card construction Support Protocol 2: Dataset structure and advanced summary statistics for LLM context Support Protocol 3: LLM configuration using structured form responses Support Protocol 4: Generation of personalized instructions for DRAFT audits Basic Protocol 1: Generalization audit Basic Protocol 2: Equity audit Basic Protocol 3: Stability audit.

Large Language Models↗
PubMed2026

Data-centric, robust, and explainable multimodal deep learning for clinical decision support: A systematic review.

PURPOSE: Multimodal deep learning is increasingly proposed for clinical decision support (CDS) under a "data-centric" framing that prioritizes label quality, missing-modality robustness, distribution shift, calibration, and explainability. Prior reviews have examined multimodal medical AI, CDS, and data-centric methods separately, but none address their intersection. We mapped the modalities, fusion strategies, and data-centric and explainability techniques used in this recent literature, quantified how often each is implemented rather than merely mentioned, assessed deployment-relevant evidence (external validation, clinical-outcome measurement, equity), and formally appraised study-level risk of bias. METHODS: Following the PRISMA 2020 statement (PROSPERO CRD420261427815; registered retrospectively), we screened 150 records and included primary, clinical, multimodal studies that applied machine or deep learning to a decision-support task and reported at least one quantitative result. Two reviewers screened and extracted data with consensus adjudication. Each study was coded against pre-specified operational definitions, separating implemented or empirically evaluated techniques from those only mentioned. Study-level risk of bias was assessed with PROBAST + AI. Synthesis was narrative. RESULTS: Thirty-one studies met inclusion; 30 (97%) were published between 2024 and 2026, with a median of three modalities (range 2-6), most commonly structured EHR (71%) and imaging (39%). Data-centric techniques were frequently reported (74-84% across label-noise, distribution-shift, calibration, missing-modality and class-imbalance handling; equity 61%). However, external validation was reported in only 4/31 studies (13%), a clinical or provider outcome in 3/31 (10%), and no study reported routine deployment. Overall risk of bias was high in 27/31 studies (87%), driven by the analysis domain. CONCLUSION: Within this recent, self-selected slice of the field, technical robustness and explainability techniques are widely reported but rarely validated out-of-distribution or against clinical outcomes, and the underlying evidence is at high risk of bias. Progress requires external multi-site validation, clinical-outcome measurement, formal bias appraisal, and adherence to AI reporting standards (e.g., TRIPOD + AI) before deployment can be justified.

Deep Learning↗
PubMed2026

Measuring economic efficiency in adult intensive care units: A systematic review of methods, metrics, and evidence.

OBJECTIVES: Intensive care units (ICUs) consume substantial hospital resources, yet "efficiency" is inconsistently defined and measured. This study systematically reviewed how economic efficiency has been conceptualised and quantified in adult ICUs and appraised the quality of evidence. METHODS: Following PRISMA 2020 and a PROSPERO-registered protocol (CRD420251107866), we searched MEDLINE, Embase, CINAHL, Cochrane Library and Web of Science (2000-August 2025), plus global grey sources. Eligible studies explicitly defined efficiency and reported an efficiency metric/model linking ICU inputs (e.g., staff, beds/capacity, time, consumables, or costs) to outputs/outcomes (e.g., throughput/discharges, length of stay/resource use, risk-adjusted mortality). Dual independent screening and extraction were performed. Study quality was appraised using MMAT, and findings were synthesised narratively (SWiM), given heterogeneity. RESULTS: 39 studies (2001-2025) from 17 countries were included, all from high-income or upper-middle-income settings. Four methodological families were identified: (1) frontier modelling (predominantly DEA; occasional SFA/RFDH), (2) benchmarking indicators (risk-adjusted mortality and LOS/resource-use ratios; "efficiency matrix" quadrant classification), (3) cost-outcome evaluations, and (4) operational/process metrics. Across families, variation in decision-making units, input/output selection, and risk adjustment limited comparability; long-term and patient-reported outcomes were absent, and equity considerations were uncommon. CONCLUSIONS: ICU efficiency research is feasible but fragmented and often methodologically limited. Standardised definitions, validated risk adjustment, uncertainty quantification, and inclusion of patient-centred and equity-relevant outcomes are needed before efficiency metrics can reliably inform value-based decision making.

Intensive Care Units↗
PubMed2026

From population to individual: advocating personalised digital tools for heat-health early warning in a changing climate.

Escalating heat extremes under climate change are imposing substantial health burdens, with 2023 and 2024 consecutively breaking global temperature records. Mounting evidence suggests that heatwaves elevate the risks of hospitalisation and mortality across multiple disease categories, including ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and acute kidney injury. Nonetheless, most existing heat-health warning systems remain primarily reliant on population-level predictions, and considering individual differences and disease-specific considerations when defining warning levels would benefit the effectiveness of early prevention for high-risk groups. In this Viewpoint, which is based on the framework of precision public health-delivering the right intervention to the right population at the right time-we propose a framework for personalised digital heat-health early warning tools comprising three dimensions: individualised, risk-stratified prediction models that generate tiered early warnings; personalised health prompts coupled with theory-informed behavioural interventions; and adaptive, equity-oriented alert delivery mechanisms tailored to diverse populations. Such tools have the potential to bridge precision disease prevention and climate adaptation, thereby helping to mitigate heat exposure risks and disease burdens, particularly among high-risk populations. Future implementation research will be essential to address substantial challenges related to feasibility, validation, and equity.

Journal Article↗
PubMed2025

"It just feels morally not right to Sell the data": Ethical and social perspectives on human genomic data sharing in Uganda-A phenomenological qualitative study.

While genomic data sharing enhances transparency and research efficiency, it also raises significant ethical and social challenges. This study explored stakeholders' perspectives on these issues, particularly around privacy, confidentiality, and equity in collaborative research. A phenomenological qualitative study was conducted between August and December 2023 at Makerere University College of Health Sciences, other research-intensive institutions, and national regulatory bodies. The study engaged 86 participants: 47 key informants (16 researchers, 14 ethics committee members, nine community advisory board members, and eight research regulators) and four deliberative focus group discussions with 39 participants. Interviews were transcribed verbatim, and thematic analysis was conducted using NVivo 14. Three major themes emerged: (1) stakeholders' experiences in genomic research, including their roles as participants, implementers, or overseers; (2) ethical concerns, such as informed consent, third-party data access, inequities between high-income and low- and middle-income country (LMIC) researchers and participants, and the lack of benefit-sharing frameworks; and (3) social implications, including stigma, discrimination, labeling, community perceptions of fairness, and the need for meaningful engagement. Participants emphasized the importance of protecting participant rights, promoting equity, and ensuring robust data governance and security. The theoretical frameworks of principlism and distributive justice provided a valuable lens for examining these concerns, particularly by highlighting the need to safeguard privacy and fairly distribute responsibilities and benefits in global collaborations. Participants also noted that perceptions of fairness are shaped by trust, local context, and past experiences with research factors that are critical for building equitable and respectful partnerships. This study underscores the urgent need to strengthen protections for research participants and promote fairness in genomic data sharing. Policies should, if adopted, emphasize culturally contextualized consent, active community engagement, restricted third-party data access, and strong data protection mechanisms to address existing inequities and prevent misuse.

LMICs↗
PubMed2025

Artificial intelligence in healthcare and medicine: clinical applications, therapeutic advances, and future perspectives.

Healthcare systems worldwide face growing challenges, including rising costs, workforce shortages, and disparities in access and quality, particularly in low- and middle-income countries. Artificial intelligence (AI) has emerged as a transformative tool capable of addressing these issues by enhancing diagnostics, treatment planning, patient monitoring, and healthcare efficiency. AI's role in modern medicine spans disease detection, personalized care, drug discovery, predictive analytics, telemedicine, and wearable health technologies. Leveraging machine learning and deep learning, AI can analyze complex data sets, including electronic health records, medical imaging, and genomic profiles, to identify patterns, predict disease progression, and recommend optimized treatment strategies. AI also has the potential to promote equity by enabling cost-effective, resource-efficient solutions in low-resource and remote settings, such as mobile diagnostics, wearable biosensors, and lightweight algorithms. Successful deployment requires addressing critical challenges, including data privacy, algorithmic bias, model interpretability, regulatory oversight, and maintaining human clinical oversight. Emphasizing scalable, ethical, and evidence-driven implementation, key strategies include clinician training in AI literacy, adoption of resource efficient tools, global collaboration, and robust regulatory frameworks to ensure transparency, safety, and accountability. By complementing rather than replacing healthcare professionals, AI can reduce errors, optimize resources, improve patient outcomes, and expand access to quality care. This review emphasizes the responsible integration of AI as a powerful catalyst for innovation, sustainability, and equity in healthcare delivery worldwide.

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