Open Access Data Research Articles

EPCO-26. LONGITUDINAL SINGLE-CELL ANALYSES IDENTIFY DRIVERS OF GENETIC, EPIGENOMIC, AND CELLULAR EVOLUTION IN IDH-MUTANT GLIOMA

Abstract Despite standard treatment, IDH-mutant gliomas inevitably recur. Previous studies suggest that therapeutic resistance may result from a combination of intratumoral cellular heterogeneity, epigenetic evolution, and acquired genetic alterations. However, how these multilayered molecular features interact to influence the evolutionary paths of IDH-mutant gliomas remains incompletely understood. To chart this evolution, 75 glioma samples were longitudinally collected from 35 patients (n = 13 oligodendroglioma, n = 22 astrocytoma) and profiled using single nucleus RNA sequencing, ATAC sequencing, and bulk DNA sequencing. Analysis of 331,016 nuclei (snRNA) revealed eight tumor microenvironment cell types and five pan-IDH-mutant malignant cellular states. The malignant states were distributed along a cellular hierarchy of stem-like populations (neural progenitor cell-like, oligodendrocyte progenitor cell-like, and undifferentiated) and more differentiated populations (mesenchymal-like and astrocyte-like) with cycling cells enriched in the stem-like populations. Joint open chromatin accessibility data (snATAC) was available for a subset of malignant cells (71,088 nuclei) and state-specific differentially accessible peaks supported that these states are epigenetically encoded. Across both subtypes, higher tumor grade was associated with reduced malignant cell differentiation and increased cycling populations. Between the two time points, we identified that there was a longitudinal increase in the cycling, undifferentiated, and mesenchymal-like populations with a corresponding decrease in the astrocyte-like population. Longitudinal genetic analysis revealed that 19 of 35 tumors acquired at least one of the following genetic alterations: treatment-associated hypermutation, cell cycle alteration, or large changes in copy number alterations. Importantly, tumors that acquired these key genetic alterations demonstrated significant shifts towards reduced differentiation and increased cycling populations while those tumors that did not had more stable malignant profiles. Collectively, our results suggest a common cellular hierarchy across IDH-mutant gliomas with a shift towards reduced differentiation, increased cycling populations during disease progression that is driven by acquired genetic alterations.

DRAMP 4.0: an open-access data repository dedicated to the clinical translation of antimicrobial peptides.

Antimicrobial peptides (AMPs) are potential candidates for treating multidrug-resistant bacterial infections, yet only a small number of them have progressed into clinical trials. The main challenges include the poor stability and hemolytic/cytotoxic properties of AMPs. Considering this, in the update of the Data Repository of Antimicrobial Peptides (DRAMP), a new annotation on serum and protease stability is added, and special efforts were made to update the hemolytic/cytotoxic information of AMPs. The DRAMP 4.0 currently holds 30 260 entries (8 001 newly added), consisting of 11 612 general entries, 17 886 patent entries, 96 clinical entries, 377 specific entries, 110 entries with stability data, and 179 expanded entries. A total of 2891 entries possess experimentally determined hemolytic activity information, while 2674 entries contain cytotoxicity data by experimental validation. The update also covers new annotations, statistics, categories, functions, and download links. DRAMP is available online at http://dramp.cpu-bioinfor.org/.

Demystifying the Use of Open-Access Data in Smart Heritage Implementations

Smart Heritage, a concept closely linked to Smart Cities and Smart Tourism, is an emerging field focused on enhancing heritage identity, visitor experience, and cultural sustainability. While initial frameworks have been developed, there is a gap in applying Smart Heritage at the precinct level, especially in large-scale heritage sites. This study addresses this gap by examining how open-access data can be utilised in a real-world case study of Chinatown Melbourne, a key urban heritage precinct. Data sources include archival maps, open-access databases, and 3D models provided by the local city council, covering resources such as on-street parking, pedestrian activity, microclimate, and dwelling functionalities. This study employed a structured methodology that transitions from global best practices to local applications, linking these data resources to Smart Heritage applications and identifying opportunities for improving urban management, heritage curation, and the tourism experience within the case study precinct. The findings offer practical insights for researchers and policymakers, demonstrating how data can support the development of culturally sustainable and technologically integrated heritage precincts. Future research should explore additional data types and case studies to further advance the field of Smart Heritage.

Fully automated artificial intelligence-based cine cardiac magnetic resonance image quantification

Abstract Background Analyzing cine cardiac magnetic resonance (CMR) images is a task that demands substantial experience and time, and it is subject to both inter- and intra-observer variability. Existing automated tools in the clinics for image analysis often necessitate substantial editing to enable accurate quantification. Objective To tackle this issue, in this study, we developed and validated a fully automated artificial intelligence (AI)-based system for the analysis of cine CMR images, aiming to address these challenges. Method In this study, 1,038 patients with CMR images were enrolled. Out of these, 350 CMR images, along with corresponding segmentations for the left ventricle myocardium (LVM) and the left- and right ventricles (LV and RV, respectively), sourced from open access data from 5 centers and databases (with different cardiac diseases), were utilized for model development. The remaining images from two local registries were used to evaluate the model. A 2D U-Net network with deep supervision, enhanced with various image augmentation techniques, was developed for automatic segmentation. The developed model was subsequently utilized to segment the test dataset of 4D (3D+time) short-axis cine CMR images. End-diastolic volumes (EDV) and end-systolic volumes (ESV) were automatically detected from these segmentations and evaluated against the ground truth calculations performed by two experienced physicians using commercially validated software. Results In the test set, Dice scores of 0.84, 0.90, and 0.93 were achieved for the RV, LV, and LVM, respectively. The mean percentage error for EDV was 0.6% for LV and -6.32% for RV. Regarding ESV, the mean percentage error was 2.95% for LV and -6.32% for RV. A -6.95% error was achieved for LV mass calculation. The stroke volumes for LV and RV were calculated with a mean percent error of -2.52% and -8.43%, respectively. We achieved a mean percent error of -3.22% for LV and -1.64% for RV ejection fraction. Conclusion The fully automated AI-based quantitative analysis of cine cardiac MR images developed in this study demonstrated high agreement with the ground truth across large datasets, including those from external centers. This method can significantly reduce the time required for image analysis and enhance the accuracy and consistency of cardiac quantitative assessments in clinical settings.

Connectome-based Predictive Models of General and Specific Cognitive Control.

Cognitive control, the ability to adapt thoughts and actions to shifting contexts and goals, is composed primarily of three distinct yet interrelated components: Inhibition, Shifting, and Updating. While prior research has examined the nature of different cognitive components as well as their inter-relationships, fewer studies examined whole-brain connectivity to predict individual differences for the three cognitive components and associated tasks. Here, using the Connectome-based Predictive Modelling (CPM) approach and open-access data from the Human Connectome Project, we built brain network models to successfully predict individual performance differences on the Flanker task, the Dimensional Change Card Sort task, and the 2-Back task, each putatively corresponding to Inhibition, Shifting, and Updating. We focused on grayordinate fMRI data collected during the 2-Back tasks after confirming superior predictive performance over resting-state and volumetric data. High cross-task prediction accuracy as well as joint recruitment of canonical networks, such as the frontoparietal and default-mode networks, suggest the existence of a common cognitive control factor. To directly investigate the relationships among the three cognitive control components, we developed new measures to disentangle their shared and unique aspects. Our analysis confirmed that a shared control component can be well predicted from functional connectivity patterns densely located around the frontoparietal, default-mode and dorsal attention networks. In contrast, the Shifting-specific and Inhibition-specific components exhibited lower cross-prediction performance, indicating their distinct and specialized roles. Notably, the Updating-specific component showed significant cross-prediction with the general control factor, suggesting its central role in cognitive control. Given the limitation that individual behavioral measures do not purely reflect the intended cognitive constructs, our study demonstrates the need to distinguish between common and specific components of cognitive control.

Extension and Further Replication of the Reliability, Criterion Validity, and Treatment Sensitivity of the PANSS10 and PANSS20 for Pediatric Trials.

Introduction: The Positive and Negative Syndrome Scale (PANSS) is a widely accepted outcome measure for pediatric schizophrenia trials; however, it has notable limitations. Psychometric investigations have shown a multifactorial structure and some items have limited utility assessing symptom severity in children. To address these issues, we developed and evaluated optimized 10- and 20-item PANSS short-forms (PANSS10 and PANSS20) using patient-level clinical trial data. This study further assesses these optimized forms using independent clinical trial data. Methods: We examined patient-level data from a randomized pediatric schizophrenia trial comparing paliperidone ER to aripiprazole. Data were accessed through the Yale Open Data Access (YODA) secure platform. Analyses included confirmatory factor analyses, graded response models, ω score reliability, internal consistency, sensitivity to change, and criterion validity versus the Clinical Global Impressions of Severity (CGI-S). Bland-Altman analyses examined score calibration versus the 30-item PANSS and inclusion cut scores. Results: Participants (N = 288) were ages 12 to 17 years (M = 15.3, SD = 1.46; 66% male). Total scores for the PANSS10 and PANSS20 showed strong correlations with the 30-item PANSS (0.90 and 0.97, respectively). Average inter-item correlations were 0.10 and 0.14 and ωTotal reliabilities were 0.74 and 0.85. Both PANSS10 and PANSS20 scores showed reliability >0.80-2.3 to 4.5 SD and -3.0 to 6.0 SD about mean symptom severity, respectively. Sensitivity to treatment was also similar (partial eta squared 0.23 and 0.22), as was correlation with CGI-S at baseline (0.45 and 0.48; not significantly different). The mean item-average discrepancy with the 30-item PANSS was 0.095 for PANSS10 and 0.033 for PANSS20. Conclusions: The optimized PANSS forms continue to show impressive reliability, validity, and calibration compared with the 30-item PANSS. Researchers should consider replacing the 30-item PANSS with the PANSS10 as a clinical outcome and screening measure due to its length and psychometric performance.

What is the real value of omics data? Enhancing research outcomes and securing long-term data excellence.

A wealth of high-throughput biological data, of which omics constitute a significant fraction, has been made publicly available in repositories over the past decades. These data come in various formats and cover a range of species and research areas providing insights into the complexities of biological systems; the public repositories hosting these data serve as multifaceted resources. The potentially greater value of these data lies in their secondary utilization as the deployment of data science and artificial intelligence in biology advances. Here, we critically evaluate challenges in secondary data use, focusing on omics data of human embryonic kidney cell lines available in public repositories. The emerging issues are obstacles faced by secondary data users across diverse domains as they concern platforms and repositories, which accept deposition of data irrespective of their species type. The evolving landscape of data-driven research in biology prompts re-evaluation of open access data curation and submission procedures to ensure that these challenges do not impede novel research opportunities through data exploitation. This paper aims to draw attention to widespread issues with data reporting and encourages data owners to meticulously curate submissions to maximize not only their immediate research impact but also the long-term legacy of datasets.

Andean Landscape Legacies: Comprehensive Remote Sensing Mapping and GIS Analysis of Long-Term Settlement and Land Use for Sustainable Futures (NW Argentina)

The Andes region has an exceptional record of high-altitude settlements integrated within widespread regional chains of mobility and exchange. The Sierra de Aconquija (NW Argentina, south-central Andes) is an effective climatic barrier that has afforded an enduring indigenous approach to land use, mobility, and exchange over millennia. Despite this rich history, the Sierra has been largely considered marginal in pre-Columbian regional cultural developments. Today, the expansion of extractive industries threatens the region’s heritage and the sustainable futures of local communities. Innovative, integrative methodologies are needed for landscape characterisation, heritage assessment, and sustainable policy development. Building on earlier work, we undertook the first comprehensive mapping of archaeological features over 3800 sq. km of the Sierra using interpreter-led assessment of commercial and open-access satellite imagery and DSM data, to verify earlier assumptions and to identify previously unnoticed trends in the aggregation, distribution, and connectivity of archaeological features. The mapping identified 6794 features distributed unevenly but with clear tendencies towards maximising topographic, ecologic, and connectivity advantages expressed consistently across the study area. The outcomes confirm the important role the Sierra had in pre-Hispanic times, highlighting the significance of ancient indigenous practices for the sustainability of vulnerable upland landscapes both in the Andes and worldwide.

Genome-wide diversity, linkage disequilibrium, and admixture in the main Colombian Creole pig breeds

Colombian Creole pigs have adapted to tropical conditions for over 500 years. They have been modified by natural and artificial selection in different regions. At present, the diversity and current introgression status are unknown. The objective was to estimate the genomic diversity, linkage disequilibrium, population structure, and admixture of four Colombian pig breeds and their relationship with other breeds worldwide. Three Colombian pig breeds (SPE-San Pedreño, 11 samples; ZUN-Zungo, 11 samples; CM-Casco de Mula, ten samples) from the conservation nucleus and one biotype not recognized as a breed (CCH-Criollo Chocoano, seven samples) were genotyped using the Illumina GGP-Porcine80K chip. Open-access data from seven international breeds were also included. Colombian Creole pigs showed moderate genetic differentiation (FST 0.14) globally, but several groups of animals separated, suggesting local clustering due to geographical isolation or different founding effects. Colombian Creole pigs showed breed imprinting and specific grouping in all analyses except for CCH, which, like the Ecuadorian Creole, was a cluster of admixtures. The Colombian Creole pigs revealed a significant relationship with the Iberian pig and some other breeds to varying degrees. However, good maintenance of the conservation nucleus was evidenced. Potential adaptive genes, mainly related to immunological functions, were found, according to FST and pcadapt analyses. This study provides a foundation and scientific data for policy decisions on zoogenetic resources.

Seafood traceability program design: Examination of the United States' Seafood Import Monitoring Program.

The United States' current Seafood Import Monitoring Program (SIMP) and a potential extension are undergoing review, yet quantitative evaluation of the current program is lacking. The SIMP is a traceability program aimed at reducing imports of seafood products that are of illegal, unreported, and unregulated (IUU) origin or associated with seafood fraud. We conducted a quantitative examination of the SIMP's current scope and design by synthesizing publicly available trade data along with measures of IUU fishing and seafood mislabeling. We found prioritized shipments amounted to 33% of 2016 imported tonnage. The SIMP species groups had higher IUU scores and mislabeling rates relative to non-SIMP groups, but the difference was consistent with random prioritization suggesting potential benefits from program expansion. Furthermore, two-thirds of imported volume lacked a mislabeling rate and 5% lacked species information, underlining the urgent need for improved open-access data on globalized seafood supply chains.

Placebo rates in Crohn’s disease randomized clinical trials: An individual patient data meta-analysis

Understanding placebo rates is critical for efficient clinical trial design. We assessed placebo rates and associated factors using individual patient data (IPD) from Crohn's disease (CD) trials. We conducted a meta-analysis of phase 2/3 placebo-controlled trials evaluating advanced therapies in moderate-to-severe CD (2010-2021). Deidentified IPD were obtained through Vivli Inc. and Yale University Open Data Access Project. Primary outcomes were clinical response and remission. Pooled placebo rates and 95% confidence intervals (CIs) were estimated using one- and two-stage meta-analytical approaches. Regression analyses identified patient-level factors associated with placebo rates. Using IPD from eight induction (n=1147) and four maintenance (n=524) trials, overall placebo clinical response and remission rates for induction were 27% (95%CI=23-32%) and 10% (95%CI=8-14%) respectively, and 32% (95%CI=23-42%) and 22% (95%CI=14-33%) for maintenance, respectively. Among bio-naïve patients, placebo response and remission rates during induction were 29% (95%CI=24-35%) and 11% (95%CI=8-15%) respectively, and 26% (95% CI=20-33%) and 10% (95% CI=8-14%) for bio-exposed, respectively. During maintenance, bio-naïve response and remission rates were 41% (95%CI=34-48%) and 32% (95%CI=24-40%), respectively, and 29% (95%CI=24-34%) and 16% (95%CI=13-21%) for bio-exposed, respectively. Higher baseline C-reactive protein concentration predicted lower placebo rates, while higher baseline albumin levels and body mass index increased the odds of placebo outcomes. Increased baseline Crohn's Disease Activity Index and 2-item patient-reported outcome scores predicted higher response rates in induction, lower response rates in maintenance, and lower remission rates in induction and maintenance. Patient- and trial-level characteristics influence placebo rates in CD trials. Careful implementation of eligibility criteria, outcome definitions, and patient stratification may reduce placebo rates.

Validating alternative oxidase (AOX) gene family as efficient marker consortium for multiple-resilience in Xylella fastidiosa-infected Vitis holobionts.

AOX gene family in motion marks in-born efficiency of respiration adjustment; can serve for primer screening, genotype ranking, in vitro-plant discrimination and a SMART perspective for multiple-resilient plant holobiont selection. The bacteria Xylella fastidiosa (Xf) is a climate-dependent, global threat to many crops of high socio-economic value, including grapevine. Currently designed breeding strategies for Xf-tolerant or -resistant genotypes insufficiently address the danger of biodiversity loss by focusing on selected threats, neglecting future environmental conditions. Thus, breeding strategies should be validated across diverse populations and acknowledge temperature changes and drought by minimizing the metabolic-physiologic effects of multiple stress-induced oxygen shortages. This research hypothesizes that multiple-resilient plant holobionts achieve lifelong adaptive robustness through early molecular and metabolic responses in primary stress target cells, which facilitate efficient respiration adjustment and cell cycle down-regulation. To validate this concept open-access transcriptome data were analyzed of xylem tissues of Xf-tolerant and -resistant Vitis holobionts from diverse trials and genetic origins from early hours to longer periods after Xf-inoculation. The results indicated repetitive involvement of alternative oxidase (AOX) transcription in episodes of down-regulated transcripts of cytochrome c oxidase (COX) at various critical time points before disease symptoms emerged. The relation between transcript levels of COX and AOX ('relCOX/AOX') was found promising for plant discrimination and primer screening. Furthermore, transcript levels of xylem-harbored bacterial consortia indicated common regulation with Xf and revealed stress-induced early down-regulation and later enhancement. LPS priming promoted the earlier increase in bacterial transcripts after Xf-inoculation. This proof-of-principle study highlights a SMART perspective for AOX-assisted plant selection towards multiple-resilience that includes Xf-tolerance. It aims to support timely future plant diagnostics and in-field substitution, sustainable agro-management, which protects population diversity and strengthens both conventional breeding and high-tech, molecular breeding research. Furthermore, the results suggested early up-regulation of bacterial microbiota consortia in vascular-enriched tissues as a novel additional trait for future studies on Xf-tolerance.

Novel multi-omics deconfounding variational autoencoders can obtain meaningful disease subtyping.

Unsupervised learning, particularly clustering, plays a pivotal role in disease subtyping and patient stratification, especially with the abundance of large-scale multi-omics data. Deep learning models, such as variational autoencoders (VAEs), can enhance clustering algorithms by leveraging inter-individual heterogeneity. However, the impact of confounders-external factors unrelated to the condition, e.g. batch effect or age-on clustering is often overlooked, introducing bias and spurious biological conclusions. In this work, we introduce four novel VAE-based deconfounding frameworks tailored for clustering multi-omics data. These frameworks effectively mitigate confounding effects while preserving genuine biological patterns. The deconfounding strategies employed include (i) removal of latent features correlated with confounders, (ii) a conditional VAE, (iii) adversarial training, and (iv) adding a regularization term to the loss function. Using real-life multi-omics data from The Cancer Genome Atlas, we simulated various confounding effects (linear, nonlinear, categorical, mixed) and assessed model performance across 50 repetitions based on reconstruction error, clustering stability, and deconfounding efficacy. Our results demonstrate that our novel models, particularly the conditional multi-omics VAE (cXVAE), successfully handle simulated confounding effects and recover biologically driven clustering structures. cXVAE accurately identifies patient labels and unveils meaningful pathological associations among cancer types, validating deconfounded representations. Furthermore, our study suggests that some of the proposed strategies, such as adversarial training, prove insufficient in confounder removal. In summary, our study contributes by proposing innovative frameworks for simultaneous multi-omics data integration, dimensionality reduction, and deconfounding in clustering. Benchmarking on open-access data offers guidance to end-users, facilitating meaningful patient stratification for optimized precision medicine.

Implementing national performance indicator ontology: A case study of Botswana's development agendas

This paper presents the National Performance Indicator Ontology (NPIOnto), a knowledge model designed for the semantic representation of national performance indicators (NPIs) and related performance variables to enhance the semantic capabilities of open data. The research addresses the lack of semantic representation for NPIs, particularly within Botswana's development agendas, which impedes the effective use, accessibility, and integration of performance data essential for monitoring and achieving sustainable development goals. NPIOnto was developed through five steps: discovering the ontology purpose, studying the development agenda knowledge domain, developing the conceptual knowledge model, formalizing the conceptual model, and evaluating the formal model's effectiveness in enhancing the semantic capabilities of open data resources. The evaluation, based on practical application in Botswana, demonstrated that NPIOnto significantly improves the accessibility, understandability, interoperability, integrability, and intelligibility of open data. Insights from the Botswana case study indicate that while NPIOnto is tailored to meet Botswana's development agenda needs, it can be adapted for use in other developing countries.

Aceh Government Accountability: Implementation of Open Access Data Aceh to Realize Public Transparency a Review of Disclosure Theory

Aceh Government Accountability: Implementation of Open Access Data Aceh to Realize Public Transparency a Review of Disclosure Theory

Space Traffic Management: A Shared Space Object Catalog

This article provides an overview of, and terminology associated with, the topic of a “shared space object catalog,” especially as it relates to and for the purposes of promoting space sustainability and enabling Space Traffic Coordination (STC) and Space Traffic Management (STM). First, the terminology and organizations associated with a shared space object catalog are discussed. Then the requirements for a shared catalog are discussed along with the current state of play. Essential elements for a successful shared catalog are laid out, including but not limited to the configuration, data rules, data integrity and legal and policy issues. Finally, some recommendations and conclusions are drawn. The primary recommendation of this paper is that for a shared catalog to be successful it will be important to develop and utilize open-access space data exchange standards, methodologies, best practices, and potentially even open-source tools at the international level. To that end the Appendix contains information on open access data exchange standards.

TOOL CONDITION MONITORING IN MACHINING USING ROBUST RESIDUAL NEURAL NETWORKS

Tool condition monitoring (TCM) aims to improve process efficiency, quality and tool maintenance costs by monitoring critical variables such as tool wear. This study proposes a deep learning (DL) architecture based on process-informed robust residual networks (Robust-ResNet) to predict tool wear in milling processes using time series of internal computer numerical control (CNC) signals. The Robust-ResNet architecture uses skip connections to move through multiple convolutional layers, avoiding the vanishing gradient problem of other neural network algorithms. The study includes an evaluation of the binding of process information as input to the architecture and an attention mechanism between skips to make more robust predictions. The proposed architecture has been trained and optimised using an open access data set of face milling time series. In this particular case, AC and DC signals have been used together with the corresponding tool wear values. The results of this study demonstrate the benefits of using deep learning techniques in the prediction of tool wear using internal signals provided by the CNC itself. The implementation of the proposed architecture is expected to help reduce maintenance costs, improve product quality and increase production efficiency in milling manufacturing processes.

The role of Open Access Data in democratizing healthcare AI: A pathway to research enhancement, patient well-being and treatment equity in Andalusia, Spain.

The role of Open Access Data in democratizing healthcare AI: A pathway to research enhancement, patient well-being and treatment equity in Andalusia, Spain.

A Follow up on the Continuum Theory of Eco-Anxiety: Analysis of the Climate Change Anxiety Scale Using Item Response Theory among French Speaking Population.

The mental health impact of the environmental crisis, particularly eco-anxiety, is a growing research topic whose measurement still lacks consensus. This study aims to use item response theory (IRT) to gain a deeper understanding of the constructs measured by existing questionnaires. To conduct this review, we applied the graded response model with the help of the MIRT package in R on open-access data from the short French version of the Climate Change Anxiety Questionnaire, which measures cognitive-emotional impairment and functional impairment. The models tested in this study are the one, two, and three-factor models, and the bifactor model. After model selection, the psychometric properties of the selected model were tested. Our results suggest that the unidimensional model seems to be the most appropriate for measuring eco-anxiety. The item difficulty parameter extracted from the IRT enabled us to discuss the severity levels of the items comprising this tool. The Climate Change Anxiety Questionnaire appears to be more appropriate for measuring moderate to severe eco-anxiety. Avenues for improving this questionnaire and the measurement of eco-anxiety in general are then discussed.

Area-level factors associated with variation in involuntary psychiatric hospitalisation across England: a cross-sectional, ecological study.

Involuntary hospitalisations for mental health care are rising in many high income countries, including England. Looking at variation between areas can help us understand why rates are rising and how this might be reversed. This cross-sectional, ecological study aimed to better understand variation in involuntary hospitalisations across England. The unit of analysis was Clinical Commissioning Groups (CCGs), NHS bodies responsible for delivering healthcare to local areas in England. 205 CCGs were included in the analysis. Demographic, clinical, and socioeconomic variables at CCG-level were extracted from national, open access data bases. The outcome variable was the rate of involuntary hospitalisation for psychiatric care under the 1983 Mental Health Act in 2021/22. There was a four-fold difference between the CCGs with the highest and lowest involuntary hospitalisations. In an adjusted analysis, CCGs with a higher percentage of severe mental illness in the population, higher percentage of male population, and higher community and outpatient mental health care use showed a higher rate of involuntary hospitalisation. Depression, urbanicity, deprivation, ethnicity, and age were not strongly associated with involuntary hospitalisation after adjustment. These variables explained 10.68% of the variation in involuntary hospitalisations across CCGs. Some demographic and clinical variables explained variation in involuntary hospitalisation between areas in England, however, most of the variance was unexplained. Complex relationships between urbanicity, deprivation, age, and ethnicity need to be further explored. The role of other influences, such as variation in service organisation or clinical practice, also need to be better understood.