ArticleBMJ global health2026
Clustering of countries based on national essential medicines lists: cross-sectional study.
Article in BMJ global health, 2026. 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
introductionNational essential medicines lists (NEMLs) guide medicine selection and procurement and are key tools for promoting equitable access. While countries adapt their lists to national priorities, underlying medical needs are broadly similar across settings; however, factors beyond epidemiological need influence which medicines are included on national lists. We assessed whether countries can be empirically grouped based on their NEML content and examined how these groupings relate to geography, economic status and selected health system characteristics.
methodsWe assessed NEMLs from 158 WHO Member States and selected non-Member States and territories. Countries were clustered using k-means analysis applied to principal components derived from binary medicine-inclusion data. Cluster profiles were characterised using ORs and compared across WHO region, gross domestic product (GDP) per capita, health expenditure, life expectancy and population size.
resultsFour medicine-based clusters were identified. Cluster 1 (66 countries, 41.8%), largely from the African Region and the Region of the Americas, emphasised medicines for infectious and neglected diseases, vaccines and antisera. Cluster 2 (65 countries, 41.1%) included countries from multiple regions listing a heterogeneous mix of older, off-patent medicines for symptomatic management and selected chronic conditions. Cluster 3 (18 countries, 11.4%), primarily from the European, Eastern Mediterranean and Region of the Americas, prioritised medicines for chronic noncommunicable diseases. Cluster 4 (9 countries, 5.7%), predominantly from the European Region, included newer, higher cost medicines. Cluster membership was significantly associated with WHO region (χ², p<0.001), but clusters did not correspond exclusively to any single region or income group, with substantial within-cluster heterogeneity in GDP and health expenditure.
conclusionCountries can be grouped according to the medicines prioritised on their NEMLs. These clusters reflect patterns in medicine selection and related health system policies that cut across traditional geographic and economic classifications and provide a complementary framework for comparative health system analysis and policy benchmarking.
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