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At least 19 recordsLinked to original sources
PubMed2026

Making waves: toward systems-level interpretation of hormonal and endogenous biomarkers in wastewater-based epidemiology.

Wastewater-based epidemiology (WBE) has proven invaluable for population health monitoring, most notably during the COVID-19 pandemic. Yet current WBE largely relies on exogenous markers such as drugs, pathogens, and their metabolites, limiting surveillance to what communities are exposed to. We argue for expanding WBE towards endogenous biomarkers, particularly hormones, which provide insights into physiological stress, metabolic function, and endocrine activity. Hormone-based WBE offers new opportunities to capture population-level biological responses to societal and environmental stressors, disasters, and chronic disease burdens at the community scale. This perspective outlines a systems-level framework for integrating hormonal signals in wastewater with clinical data, behavioral indicators, environmental factors, and digital markers to support more robust and context-aware public health surveillance. We highlight key technical considerations, interpretive challenges, and opportunities for translational pilot studies. By moving beyond exposure tracking toward more integrated interpretation of biological responses, hormone-informed WBE may contribute to more resilient, inclusive, and actionable public health infrastructure.

Humans↗
PubMed2025

ClarID: A Human-Readable and Compact Identifier Specification for Biomedical Metadata Integration.

BACKGROUND: In biomedical research, subjects and biospecimens are commonly tracked using simple IDs or UUIDs, which guarantee uniqueness but convey no embedded semantic information. Contextual metadata (such as tissue type, diagnosis, or assay) is often stored separately, making integration, cohort selection, and downstream analysis cumbersome. While structured barcoding systems exist in large consortia (e.g., TCGA, GTEx) or domain-specific contexts (e.g., SPREC, GOLD), no unified, extensible framework currently spans both subjects and biosamples in a human- and machine-readable way. METHODS: We developed ClarID, a domain-agnostic specification that supports two identifier formats: (i) a human-readable form (e.g., 'CNAG_Test-HomSap-00001-LIV-TUM-RNA-C22.0-TRT-P1W' that encodes key metadata such as project, species, subject_id, tissue, assay, disease, timepoint and duration (from that event); and (ii) a compact version named 'stub' (e.g., 'CT01001LTR0N401T1W') optimized for filenames, pipelines, and labeling.ClarID is implemented through an open-source command-line tool, ClarID-Tools, which processes tabular metadata files (CSV/TSV) and uses a YAML-based codebook to generate, decode, and validate identifiers, as well as to create and read QR codes. The tool supports bulk and single-sample processing and allows easy integration with institutional workflows. RESULTS: To demonstrate ClarID's utility, we applied it to datasets from the Genomic Data Commons (GDC), generating interpretable identifiers for more than 113,000 clinical records (subjects) and 4,255 biospecimen records. All materials, including pre-processing scripts, input and encoded data, are publicly available and fully reproducible via the accompanying GitHub repository and Google Colab. CONCLUSIONS: ClarID fills a critical gap between opaque accession numbers and rich metadata schemas by embedding key context directly into structured identifiers. It enhances traceability, facilitates downstream analysis, and remains adaptable to project-specific needs through a configurable codebook. The accompanying ClarID-Tools software is freely available, together with full documentation and reproducible pipelines, at https://github.com/CNAG-Biomedical-Informatics/clarid-tools.

Biosample identifiers↗
PubMed2026

CAUSAL artificial intelligence and data-driven decision intelligence in personalized medicine: a review of healthcare informatics systems.

This review examines the integration of causal artificial intelligence (AI) and data-driven decision intelligence within healthcare informatics systems to advance personalized medicine and clinical decision-making. A narrative review methodology was employed, synthesizing interdisciplinary literature from major databases, including PubMed, Scopus, Web of Science, IEEE Xplore, and ScienceDirect. Studies focusing on causal inference, decision intelligence, and healthcare informatics applications in personalized medicine were included. Data were extracted on methodological approaches, healthcare settings, analytical techniques, and clinical applications, followed by thematic synthesis. Findings indicate that causal AI enhances clinical decision support by enabling estimation of treatment effects and simulation of intervention outcomes at the individual patient level. Integration of multimodal health data such as electronic health records, genomic data, and real-time monitoring improves prediction accuracy and supports tailored treatment strategies. Additionally, causal models improve interpretability, fostering clinician trust and facilitating transparent decision-making. Robust healthcare informatics infrastructures, including interoperable systems and data warehouses, were identified as critical enablers of causal analytics. Overall, causal AI represents a transformative advancement in healthcare analytics, supporting more informed, individualized, and evidence-based clinical decisions. Its integration within healthcare informatics systems has significant potential to improve patient outcomes and guide the future of intelligent, personalized healthcare delivery.

Precision Medicine↗
PubMed2025

Integrating explainable artificial intelligence with multiomics systems biology and electronic health record data mining for personalized drug repurposing in Alzheimer's disease.

Alzheimer's disease (AD) is characterized by region- and patient-specific molecular heterogeneity, which hinders therapeutic design. In this study, we introduce PRISM-ML (PRecision-medicine using Interpretable Systems and Multiomics with Machine Learning), an open-source integrated analysis pipeline that combines interpretable machine learning with systems biology and electronic health records data mining to elucidate the molecular diversity of AD and predict promising drug repurposing opportunities. First, we integrated and harmonized transcriptomic (bulk RNA-seq) and genomic (genome-wide association study) data from 2105 brain samples, each with matched data from the same individual (1363 AD patients, 742 controls; 9 tissues), sourced from three independent studies. Random forest classifiers with SHapley Additive exPlanations identified patient-specific biomarkers; unsupervised clustering resolved 36 molecularly distinct subtissues (defined as clusters of samples within a brain tissue that share a specific expression pattern); and gene-gene coexpression networks prioritized 262 high-centrality bottleneck genes as putative regulators of dysregulated pathways. Next, knowledge graph-based drug repurposing predicted six Food and Drug Administration (FDA)-approved drugs that simultaneously target multiple bottleneck genes and multiple AD-relevant pathways. Notably, in a large US de-identified insurance-claims database (n&#x2009;=&#x2009;364&#xa0;733), exposure to promethazine, one of the candidate drugs, was associated with a 57%-62% lower incidence of AD versus an active antihistamine comparator (adjusted hazard ratio 0.38; inverse-probability weighted 0.43; both P&#x2009;<&#x2009;.001), providing real-world support for its repurposing potential. In summary, PRISM-ML, as an explainable multiomics analysis pipeline, is readily transferable to other complex diseases, advancing precision medicine.

Alzheimer Disease↗
PubMed2026

Toward a unified approach: Considerations for bioinformatic and sequencing activities & data in wastewater surveillance of biologic public health threats.

Genomic technologies such as PCR and next-generation sequencing (NGS) have greatly advanced public health surveillance, especially during COVID-19, by enabling detailed tracking of pathogen spread, origins, and variants. While PCR is vital for targeted detection, falling NGS costs have made large-scale, high-throughput sequencing more feasible, supporting broader pathogen monitoring-including the detection of vaccine escape variants and new strains. Applying NGS to wastewater offers valuable population-level insights but faces challenges such as variable sample complexity, the need for skilled staff, suitable platforms, and robust IT infrastructure. Although there are currently a lot of efforts towards defining guidelines for sampling, analysis, and integrating wastewater data into public health policy, such as the recently published International Cookbook for Wastewater Practitioners, they often lack universal applicability, emphasizing the analytical approaches in favour of the NGS-based approaches. However, standardising protocols for sampling, sequencing, and analysis is crucial to ensure reliable, comparable data across surveillance systems worldwide. Pilot studies and continuous refinement are recommended to overcome implementation hurdles and fully realise the benefits of NGS in wastewater surveillance. This work attempts to outline these challenges and opportunities across the entire wastewater surveillance workflow, from data generation to reporting, and provide some concrete suggestions and considerations across the spectrum of activities. We further highlight that the infrastructure, funding and government-policy context in which surveillance operates acts as an enabling condition for these activities, and that technical standardisation alone is unlikely to deliver durable, comparable surveillance in its absence.

considerations↗
PubMed2025

A One Health perspective: Genomic insights into temporal trends of antimicrobial resistance and zoonotic transmission risks in Escherichia coli from human and swine.

Antimicrobial resistance (AMR) poses a significant challenge within the One Health framework. By integrating genomic data from 824 E. coli isolates obtained from 22 swine farms in southwestern China with 8432 publicly available genomes from human and swine sources, this study provides comprehensive insights into the temporal trends and divergence of AMR in human and swine E. coli populations, the risk of AMR transmission from swine to human, and the evolutionary mechanisms underlying the human adaptation of ST2 strains. The results revealed an overall increase in AMR until approximately 2016, followed by a subsequent decline. However, resistance to tetracyclines, quinolones, and phenicols continues to exhibit an upward trend, highlighting the urgency of enhancing regulatory measures targeting these drugs. Horizontal gene transfer play pivotal roles in shaping distinct AMR profiles in human and swine strains. ST2 E. coli was identified as a major carrier of AMR in both human and swine, and also served as the primary reservoir of blaNDM-5 within the human-associated lineage. During evolution, ST2 E. coli underwent significant genetic changes, including the enrichment of blaNDM-5 and remodeling of virulence factors, facilitating its transition from a generalist lineage colonizing both human and swine to a human-adapted lineage.

Humans↗
PubMed2026

From fragmentation to coordination: strengthening One Health research to support H5N1 preparedness in Cambodia.

OBJECTIVES: Highly pathogenic avian influenza A (H5N1) remains a major zoonotic threat, characterized by persistent transmission in Cambodia since its re-emergence in 2023. Despite strengthened surveillance and the establishment of the Inter-Ministerial Coordination Committee on One Health, limited integration of research across sectors constrains preparedness and response. This viewpoint examines how research supports the One Health system in Cambodia. METHODS: This viewpoint draws on insights obtained from the first national multistakeholder workshop on H5N1, held in March 2026. RESULTS: Fragmentation across epidemiological, clinical, behavioral, environmental, and genomic domains limits the generation of actionable evidence and delays its translation into policy. CONCLUSION: We propose the establishment of a multisectoral technical working group on H5N1 research embedded within the Inter-Ministerial Coordination Committee on One Health to align research priorities, strengthen data integration, and improve evidence-to-policy translation. This approach could enhance national preparedness while simultaneously positioning Cambodia as a model for coordinated One Health research in the Western Pacific region and beyond.

Avian influenza A (H5N1)↗
PubMed2026

Multimodal artificial intelligence and machine learning in oncology: from data integration to precision cancer care.

Cancer remains a major global health burden, with approximately 20 million new cases and 9.7 million cancer-related deaths reported globally in 2022. While advances in radiological imaging, molecular profiling, and clinical data have enhanced the interpretation of disease progression, the availability of multiple such modalities still does not meet the needs of a large patient population. This narrative review focuses on the role of multimodal artificial intelligence and machine learning in bridging the gap in interpreting heterogeneous modalities to improve risk prediction, prognostic assessment, and treatment decision-making in precision oncology. Multimodal frameworks such as Pathomic Fusion illustrate how complementary histopathological and genomic information can be integrated for cancer diagnosis and prognostic modeling. Multimodal models have demonstrated potential in virtual biopsy, cancer screening, prognostic prediction, radiotherapy planning, intraoperative guidance, and clinical-trial design using digital twins and synthetic control arms. The major limitations of incorporating multimodal artificial intelligence and machine learning in oncology include data heterogeneity, demographic or institutional biases, and reproducibility challenges that hinder translation. Accordingly, appropriate data-governance strategies, fairness audits, and privacy-preserving approaches such as federated learning should be considered where appropriate. Future progress will depend on the development of standardized benchmarking datasets, robust external validation, seamless integration with electronic health records and picture archiving and communication systems, and the implementation of explainable, secure, and clinically validated multimodal artificial intelligence frameworks that support precision oncology in routine clinical practice.

deep learning↗
PubMed2024

Evaluating the impact of modeling choices on the performance of integrated genetic and clinical models.

PURPOSE: The value of genetic information for improving the performance of clinical risk prediction models has yielded variable conclusions. Many methodological decisions have the potential to contribute to differential results. We performed multiple modeling experiments integrating clinical and demographic data from electronic health records with genetic data to understand which decisions may affect performance. METHODS: Clinical data in the form of structured diagnostic codes, medications, procedural codes, and demographics were extracted from 2 large independent health systems, and polygenic risk scores (PRS) were generated across all patients of European ancestry with genetic data in the corresponding biobanks. Crohn's disease was studied based on its substantial genetic component, established electronic health records-based definition, and sufficient prevalence for training and testing. We investigated the impact of choices regarding the PRS integration method, training sample, model complexity, and performance metrics. RESULTS: Overall, our results showed that including PRS resulted in higher performance, but this gain was only robust in situations with limited clinical information. We found consistent performance increases from more compute-intensive models, such as random forest, but the impact of other decisions varied by site. CONCLUSION: This work highlights the importance of considering methodological decision points in interpreting the impact of PRS on prediction performance in clinical models.

Humans↗
PubMed2023

Evaluating the impact of modeling choices on the performance of integrated genetic and clinical models.

The value of genetic information for improving the performance of clinical risk prediction models has yielded variable conclusions. Many methodological decisions have the potential to contribute to differential results across studies. Here, we performed multiple modeling experiments integrating clinical and demographic data from electronic health records (EHR) and genetic data to understand which decision points may affect performance. Clinical data in the form of structured diagnostic codes, medications, procedural codes, and demographics were extracted from two large independent health systems and polygenic risk scores (PRS) were generated across all patients with genetic data in the corresponding biobanks. Crohn's disease was used as the model phenotype based on its substantial genetic component, established EHR-based definition, and sufficient prevalence for model training and testing. We investigated the impact of PRS integration method, as well as choices regarding training sample, model complexity, and performance metrics. Overall, our results show that including PRS resulted in higher performance by some metrics but the gain in performance was only robust when combined with demographic data alone. Improvements were inconsistent or negligible after including additional clinical information. The impact of genetic information on performance also varied by PRS integration method, with a small improvement in some cases from combining PRS with the output of a clinical model (late-fusion) compared to its inclusion an additional feature (early-fusion). The effects of other modeling decisions varied between institutions though performance increased with more compute-intensive models such as random forest. This work highlights the importance of considering methodological decision points in interpreting the impact on prediction performance when including PRS information in clinical models.

Preprint↗
PubMed2026

The Biobank Rare Variant consortium powers the discovery of rare genetic associations through global collaboration.

Rare coding variants can have large effects on disease risk and provide direct routes from human genetics to disease mechanisms and therapeutic targets, but their discovery is constrained by sample size, particularly for low-prevalence diseases. Here we establish the Biobank Rare Variant Analysis (BRaVa) consortium, a global rare variant association resource that integrates sequencing and linked health-record data from ten biobanks and cohorts comprising over 1.2 million individuals across diverse ancestries. We performed gene-based meta-analyses of rare coding variation across 33 clinical endpoints and 11 quantitative traits. Aggregating evidence across biobanks and ancestries identified 514 gene-trait associations, including 31 not previously reported in prior studies or curated association resources following systematic literature review. Notably, 36.1% of gene-level associations were undetectable in any individual biobank, and 91 emerged only through cross-ancestry meta-analysis, demonstrating that federated integration enables discovery beyond the reach of single cohorts. Similar gains were observed at the variant level, where 25.0% of phenotype-locus associations were detectable only through meta-analysis. Effect size estimates were correlated across ancestries with concordant directions of effect, supporting the generalizability of rare variant associations. The identified signals implicate pathways involved in transcriptional and epigenetic regulation, metabolism, vascular and epithelial biology, and immune function, highlighting rare coding variation as an engine for biological discovery across medical record phenotypes. For example, damaging variation in ANKRD12 implicates inflammatory transcriptional dysregulation in asthma and chronic obstructive pulmonary disease, and ultra-rare predicted loss-of-function variants in NAA15 link protein acetylation processes to type 2 diabetes risk. BRaVa establishes a scalable framework and freely available community resource for rare variant meta-analysis across global biobanks. Public release of gene- and variant-level association summary statistics provides a reference map of rare coding variant associations to support disease gene discovery, biological interpretation, and therapeutic target prioritization as sequencing-linked health-record resources continue to expand.

Journal Article↗
PubMed2026

Pneumococcal population structure influences the effects of air pollution on invasive disease risk in South Africa.

Streptococcus pneumoniae is highly diverse, comprising over 100 serotypes and hundreds of genomic lineages amid widespread vaccination. While it can cause invasive pneumococcal disease (IPD) which exhibits pronounced seasonal spikes, the interplay between pneumococcal diversity and environmental drivers remains unexplored. Here we analysed 59,017 IPD cases over 19&#x2009;years from South Africa, incorporating 4,350 genome-sequenced isolates, using Bayesian spatiotemporal models to link environmental exposure and pneumococcal diversity. Cumulatively, across an 8-week period, moderate relative humidity (33-49%) and cold minimum temperatures (4-10&#x2009;&#xb0;C) increased IPD risk by 5% and 4%, respectively. Conversely, warm maximum temperatures (27-38&#x2009;&#xb0;C) were associated with up to a 10% increased risk within a week of exposure. There was a positive association between air pollution (PM2.5) and IPD, although it varied by age, disease presentation, and most notably serotype and lineage. Specifically, the lag time between PM2.5 exposure and disease onset varied by serotype, with only serotypes 4, 8 and 23F conferring an immediate IPD risk. High prevalence of GPSC21 lineage (serotype 19F) also modified the pollution response, shifting the lag structure to produce immediate risk of disease following high PM2.5 exposure. Our results demonstrate that pneumococcal population structure shapes air quality risk which in turn can shape the fitness landscape of microbial populations. Integration of these data may inform public health policy.

Journal Article↗
PubMed2026

Beyond the clinic: a community-embedded, multidomain framework for early detection of glaucoma.

Glaucoma remains one of the leading causes of acquired irreversible blindness worldwide, with normal-tension glaucoma representing the dominant subtype in Japan and several East Asian populations. The insidious, asymptomatic progression of this condition, combined with the demonstrated inadequacy of intraocular pressure alone as a screening criterion, creates a critical gap between disease burden and case detection. Population-based epidemiological studies consistently reveal that the majority of individuals with glaucoma are undiagnosed. Two responses have been suggested: incorporation of retinal imaging into annual health checkups, which warrants formal prospective evaluation, and characterization of individuals at higher risk - integrating genomic risk, oxidative stress biomarkers, systemic lifestyle factors, and ocular blood flow dynamics - which may help identify those in whom damage is most likely to occur. The principal contribution of this Perspective is therefore the implementation model rather than the individual screening components. We introduce the Living Lab ('neighborhood health lab'), a community co-creation platform established under the Japan Science and Technology Agency COI-NEXT 'Vision to Connect' hub at Tohoku University, as a scalable model for operationalizing this framework. Embedded within commercial retail environments, the Living Lab integrates non-invasive screening, longitudinal health data collection, and evidence-based health product development-exemplified by the Ronbun Recipe&#xae; concept-within a stakeholder-aligned ecosystem encompassing citizens, researchers, industry, and municipal authorities. Conceived as a platform for well-being rather than as a disease-specific screening service, it engages individuals who are asymptomatic, undiagnosed, and outside existing screening pathways, and who would not otherwise be assessed at all.

Humans↗
PubMed2026

The Role of Artificial Intelligence for Intimate Partner Violence Prevention: A Systematic Review.

INTRODUCTION: Intimate partner violence (IPV), encompassing physical, sexual, emotional and economic abuse, remains a pervasive global health concern. Traditional prevention efforts face obstacles such as underreporting, delayed detection and limited personalised support. Emerging artificial intelligence (AI) approaches offer new opportunities to enhance IPV prevention. AIM: This systematic review maps and synthesises evidence on AI-driven tools in IPV prevention based on studies published between 2004 and 2024. METHODS: Following PRISMA 2020 guidelines and PROSPERO registration, we searched PubMed, Embase, CINAHL, PsycINFO, IEEE Xplore and Web of Science. Eligible studies explicitly evaluated AI technologies targeting IPV prediction, screening, intervention or support delivery. Study quality was appraised using the Mixed Methods Appraisal Tool (MMAT). RESULTS: Of 1304 records initially identified, 41 studies met eligibility criteria. AI applications ranged from machine learning (ML) for risk prediction and natural language processing (NLP) for IPV detection in clinical and social media data, to image analysis for forensic evaluation and chatbot-based support. Predictive modelling demonstrated strong discriminative performance, while NLP-based screening detected IPV with notable sensitivity. Chatbots showed feasibility and user acceptability, but evidence of their direct impact on reducing IPV incidence was limited, with one randomised controlled trial showing a modest reduction. Key challenges identified included algorithmic bias, data privacy risks and barriers to integration across health and social care systems. DISCUSSION: AI-informed interventions show promise for improving IPV detection, risk assessment, and scalable support, but questions remain about long-term effectiveness, ethical fairness, transparency and equitable implementation. Future interdisciplinary research should address these concerns to responsibly deploy AI in IPV prevention. RELEVANCE TO CLINICAL PRACTICE: The findings highlight the importance of trauma-informed, culturally responsive care and provider training in AI applications. Nurse-led innovation and policy advocacy will be crucial for safe, equitable integration of AI in IPV prevention.

Artificial Intelligence↗
PubMed2025

Integrating explainable AI with multiomics systems biology and EHR data mining for personalized drug repurposing in Alzheimer's disease.

Alzheimer's disease (AD) is characterized by region- and patient-specific molecular heterogeneity, which hinders therapeutic design. In this study, we introduce PRISM-ML (PRecision-medicine using Interpretable Systems and Multiomics with Machine Learning), an open-source integrated analysis pipeline that combines interpretable machine learning with systems biology and electronic health record (EHR) data mining to elucidate the molecular diversity of AD and predict promising drug repurposing opportunities. First, we integrated and harmonized transcriptomic (bulk RNA-seq) and genomic (genome-wide association study) data from 2105 brain samples, each with matched data from the same individual (1363 AD patients, 742 controls; nine tissues), sourced from three independent studies. Random forest classifiers with SHapley Additive exPlanations (SHAP) identified patient-specific biomarkers; unsupervised clustering resolved 36 molecularly distinct "subtissues" (clusters of samples); and gene-gene co-expression networks prioritized 262 high-centrality bottleneck genes as putative regulators of dysregulated pathways. Next, knowledge graph-based drug repurposing predicted six FDA-approved drugs that simultaneously target multiple bottleneck genes and multiple AD-relevant pathways. Notably, in a large U.S. de-identified insurance-claims database (n = 364733), exposure to promethazine, one of the candidate drugs, was associated with a 57-62 % lower incidence of AD versus an active antihistamine comparator (adjusted hazard ratio 0.38; inverse-probability weighted 0.43; both p < 0.001), providing real-world support for its repurposing potential. In summary, PRISM-ML, as an explainable multi-omics analysis pipeline, is readily transferable to other complex diseases, advancing precision medicine.

Computational Biology↗
PubMed2026

Leveraging traveller genomics for LMIC diarrhoeal disease management.

Diarrhoeal pathogens impose a substantial global health burden, disproportionately affecting low- and middle-income countries (LMICs). However, in these settings, health-seeking behaviours, suboptimal microbiological capacity, and challenges in establishing genomics capacity constrain effective surveillance, including surveillance of antimicrobial resistance (AMR). In contrast, high-income countries routinely generate and share large volumes of diarrhoeal pathogen genomes through established systems, with a significant proportion originating from travellers returning from LMICs. These data reveal strong geographical structuring of lineages and clinically relevant AMR patterns, demonstrating untapped potential to support improvements in geographically granulated surveillance to support antimicrobial treatment recommendations. In this opinion article, we outline the potential to integrate traveller-derived microbial genomic data into LMIC public health decision-making and highlight the scientific, ethical, practical, and governance considerations for implementation.

antimicrobial resistance↗
PubMed2026

Co-location of services: an umbrella review to consider how primary care estates could be better used to support disadvantaged groups.

AIM: To examine how co-located community and health services in primary care could support disadvantaged groups. BACKGROUND: Co-locating services is thought to improve access, collaboration, and patient outcomes. There are thousands of primary care premises across the UK. At a time of stagnating or widening health inequalities, they present an ideal opportunity to support communities, especially in disadvantaged areas. METHOD: We conducted a systematic umbrella review. Articles were retrieved from Ovid MEDLINE and Ovid Embase with supplementary snowball and grey literature searches. Reviews of co-located services supporting disadvantaged groups in primary care between 2010 and February 2024 were included. Quality and risk of bias were assessed using the Joanna Briggs Institute checklist. Two reviewers assessed eligibility, extracted data and assessed quality. Outcomes relating to health, welfare, healthcare utilization, and activity and processes were assessed. Data were narratively synthesized using a convergent integrated approach. FINDINGS: 2626 studies were screened, supplemented by snowball and grey literatures searches. Thirteen reviews were included for synthesis. One review included meta-analysis. Three models of care were identified; legal advice, welfare advice, and complementary health care. Data were synthesized according to themes: access and engagement, quality of care, efficiency, improved health, and improved social factors. We found co-located services can improve access to care, engagement in treatment, and quality of care for disadvantaged groups. Improvements to social determinants of health and mental health and well-being outcomes were reported. Findings were inconsistent when considering the impact of co-location on efficiency. We conclude that co-located services in primary care have the potential to improve identification of people most in need and improve their access to high quality health care and social support. Policy makers and practitioners should maximize the use of primary care estates to support disadvantaged groups and communities.

Humans↗