ICD-11 and diabetes: opportunities for precision public health through enhanced classification

Diabetes mellitus poses a major global health challenge, yet its intrinsic heterogeneity has been poorly captured by traditional classification systems. The international classification of diseases, tenth revision (ICD-10), built on a phenotype-based framework, lacks the granularity needed to incorporate contemporary molecular insights into diabetes subtypes, hampering both clinical precision and public health surveillance.

This review examines how the recently implemented international classification of diseases, eleventh revision (ICD-11) addresses these limitations through its ontology-based architecture. We analyze three key areas. First, ICD-11 reclassifies prediabetes as a distinct reportable entity with formal subtyping of impaired fasting glucose and impaired glucose tolerance, which may support more precise epidemiological tracking and evidence-based prevention. Second, its Uniform Resource Identifier (URI)-based taxonomy provides granular classification across the diabetes spectrum, from type 1 and type 2 diabetes to monogenic forms such as maturity-onset diabetes of the young (MODY), with etiological stratification that can inform both clinical decision-making and population-level surveillance. Third, its postcoordination mechanism allows multidimensional documentation of complications, drug-induced diabetes, and unstable disease states, generating structured, machine-readable data with potential for real-world evidence generation and health system performance assessment. However, early implementation experiences across multiple countries reveal substantial challenges-including heterogeneous adoption timelines, coding inconsistencies, and workforce training gaps-that may constrain the realization of these benefits in practice. We also discuss the implications of these advances for health systems, including value-based reimbursement models and the production of actionable evidence for policy makers.

ICD-11 repositions diabetes classification toward a framework that offers substantially enhanced capacity for etiological and granular representation of the disease. By generating standardized, granular population-level data, it offers a potential infrastructure for precision public health. Yet realizing this potential will depend on health system capacity, workforce training, and sustained investment in implementation-particularly in resource-limited settings, where the risk of widening disparities is most acute. Future refinements should integrate genomic descriptors and dynamic update mechanisms to keep pace with rapid advances in diabetes research, with continued attention to health equity and global applicability.