ArticleBMC research notes2025
Leveraging nationwide epidemiological big data for diabetes modeling and disease surveillance: an innovative approach for advancing public health policy in developing countries.
Article in BMC research notes, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectiveDiabetes is escalating into a global crisis, with low- and middle-income countries bearing the heaviest burden. Yet few nations openly harness nationwide health data to guide precise, evidence-based interventions. This study pioneers the large-scale integration of Iran's decade-long electronic prescription records with advanced modelling tools to illuminate national and regional dynamics in antidiabetic drug use. By fusing big data analytics with policy-driven insight, our aim was not only to expose inequities and inefficiencies but to create a transferable blueprint for equitable, guideline-aligned diabetes care worldwide.
resultsAcross 84 million prescriptions (2013-2023), metformin dominated treatment, but novel agents such as DPP-4 and SGLT2 inhibitors surged after 2017, reshaping the therapeutic landscape. Stark contrasts emerged: first-line metformin initiation ranged from 58.3% in underserved Sistan & Baluchestan to 91.7% in resource-advantaged Tehran; inappropriate early adoption of new agents and premature insulinisation persisted in several regions. These patterns mirror global fault lines-where innovation coexists with inequity-and underscore the urgency for adaptive, data-led policy. Our proposed multi-tier model-combining real-time digital surveillance, prescriber upskilling, and patient empowerment-offers a novel, globally relevant pathway for transforming big data into equitable, future-ready diabetes strategies in resource-limited health systems.
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