Special Issue on Big Data Modelling for Diabetes Prevention, Treatment and Care Equity: Global and Cross-national Perspectives

Diabetes remains a largely silent yet rapidly escalating epidemic within the global non-communicable disease landscape. Type 2 diabetes prevalence continues to increase across high-, middle-, and low-income countries, with earlier onset and rising multimorbidity patterns reshaping disease trajectories. At the same time, marked socioeconomic and geographic inequalities persist in diagnosis, treatment access, and glycaemic control. Although advances in pharmacotherapy, risk prediction, and digital health tools have expanded management options, the high cost of newer glucose-lowering medications and technologies continues to limit equitable access. Significant gaps therefore remain in timely detection, continuity of care, and long-term prevention of complications.

The expansion of large-scale health databases, electronic health records, insurance claims, biobanks, registries, wearable devices, remote sensing, and population-based cohort studies offers transformative opportunities for diabetes research. Big data analytics, machine learning, artificial intelligence, and advanced statistical modelling can generate actionable insights for risk prediction, precision prevention, health system optimisation, and policy evaluation. However, methodological rigor, transparency, equity considerations, and cross-context comparability remain critical challenges.

This special issue of Global Health Research and Policy invites submissions that leverage large-scale, multi-source, or longitudinal data to advance diabetes epidemiology, prevention, clinical management, and health policy. We particularly welcome cross-national, comparative, and policy-relevant studies that generate transferable lessons across diverse settings.

Scope and Topics of Interest

We welcome empirical, methodological, and policy-oriented contributions addressing (but not limited to) the following themes:

1) Epidemiology, Diabetes,  Obesity and Related Complications risk Modelling

• Global and regional trends in diabetes incidence, prevalence, complications, and mortality
• Diabetes, obesity and related complications risk modelling and clustering
• Predictive modelling for type 2 diabetes onset, progression, and remission
• Geospatial and geotemporal modelling of diabetes risk, environmental exposures, and service access
• Early-life, life-course, and intergenerational determinants
• Multimorbidity, metabolic syndrome, and cardiometabolic trajectories
• Health inequality gradients, rural–urban disparities, and vulnerable populations

2) Big Data Methods, AI, and Spatial Analytics

• Machine learning and artificial intelligence applications in diabetes prediction, management, and complication risk
• Geotemporal and spatial modelling of diabetes burden and health service utilisation
• Validation, transportability, and calibration of risk scores across populations
• Federated learning and privacy-preserving cross-national analytics
• Integration of electronic health records, insurance claims, registries, biobank, and wearable data
• Real-world evidence generation and causal inference using large-scale observational data
• Algorithmic fairness, bias detection, and equity-aware modelling

3) Health Systems, Treatment Access, and Care Cascades

• Diagnosis, treatment, and glycaemic control cascades across health systems
• Access to and affordability of newer glucose-lowering agents and advanced technologies
• Inequities in access to expensive novel therapies and their impact on outcomes
• Adherence, persistence, and therapeutic intensification patterns
• Digital health interventions and remote monitoring
• Comparative performance of primary care systems
• Cost-effectiveness analysis, budget impact modelling, and sustainable financing strategies

4) Prevention, Diabesity Policy, and Population Health

• Evaluation of obesity and diabetes policies (e.g., sugar-sweetened beverage taxes)
• Food systems, nutrition transitions, and built environment modelling
• Urban design, walkability, and metabolic risk
• Community-based and digital prevention programmes
• School- and workplace-based interventions
• Policy simulation and microsimulation models for large-scale diabetes and obesity prevention

5) Complications, Ageing, and Long-term Outcomes

• Cardiovascular, renal, neurological, and ophthalmic complications
• Frailty, disability, and diabetes in ageing populations
• Health expectancy, quality of life, and compression/expansion of morbidity
• Long-term care needs and social protection implications
• Predictive modelling of complication trajectories and healthcare utilisation

6) Climate, Urbanisation, and Global Transitions

• Environmental exposures, air pollution, and diabetes risk
• Urbanisation, migration, and metabolic vulnerability
• Climate change, heat exposure, and glycaemic control
• Food insecurity, economic shocks, and chronic disease management
• System resilience and preparedness in the context of demographic and epidemiological transition

Data Requirements

We particularly encourage studies using:

• Nationally representative health surveys
• Electronic health record databases
• Health insurance claims data
• Diabetes registries
• Biobank-linked cohort studies
• Multi-country comparative databases
• Harmonized cross-national datasets

Cross-country comparative research and studies using harmonized measures are especially welcomed. High-quality single-dataset studies will be considered if they demonstrate strong global health relevance and clear policy implications.

Manuscript Types

We welcome all kinds of article types depicted in our submission guideline.

All submissions should demonstrate clear relevance to global health and articulate implications for policy, equity, and health system strengthening.

Guest Editors

Juliana NM Lui, assistant professor, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, China, E-mail: julianalui@cuhk.edu.hk

Junwen Zhou, Senior researcher, Economics of Population Health Research Centre, University of Oxford, United Kingdom, E-mail: junwen.zhou@ndph.ox.ac.uk

Jialong Tan, assistant professor, Dong Fureng Institute of Economic and Social Development, Wuhan University, China, E-mail: jialongtan@whu.edu.cn

Philip Clarke, Professor, Melbourne School of Population and Global Health, University of Melbourne, Australia, E-mail: philip.clarke@unimelb.edu.au

Manuscript Submission Information

Submission deadline: This is a long-term call.

Manuscripts should be submitted via the journal's online submission system. Authors should select the appropriate special issue title during submission or indicate in the cover letter that the manuscript is intended for the special issue on Big Data Analytics and Modelling for Diabetes Prevention and Policy. 

Global Health Research and Policy has transitioned to KeAi publishing after 31 December 2025. Authors are advised to consult the journal webpage for updated submission guidance or contact the editorial office if clarification is required.