Harmonizing national drug lists: an eight-pattern ATC/DDD analysis
Abstract
The Anatomical Therapeutic Chemical (ATC) classification and Defined Daily Dose (DDD) system of the World Health Organization (WHO) are recognized internationally as the gold standard in research methodology for drug use. Our focus is on drugs with multiple ATC codes and those with discrepancies in Route of Administration (Adm.R) and DDDs. A list of these medications was created to alert drug agencies to the critical need to audit and verify the accuracy of their national list harmonization. We analyzed the latest WHO ATC/DDD dataset, updated in July 2026, which includes 2737 Active Pharmaceutical Ingredients (API) with assigned DDD. A Python-based data-cleaning and harmonization workflow was developed to address variations in ATC codes, DDDs, and Adm.R. A decision-tree-based screening scheme with eight possible patterns was applied across the five ATC hierarchical levels to classify and harmonize ATC/DDD variants. Among 2737 WHO ATC/DDD records with assigned DDDs, 466 unique molecules met at least one of the eight classification conditions, yielding 913 distinct scenarios. Across the five ATC levels, 107 scenarios changed classification, whereas 806 remained stable. Changes occurred across four condition pairs (1/5, 2/6, 3/7, and 4/8), with the greatest variability observed when both DDD and Adm.R differed. Classification stabilized beyond ATC level 3. The 8-Pattern systematic screening scheme provides a systematic and reproducible approach for identifying multiclassified medicines and harmonizing ATC/DDD data. By preserving differences in ATC codes, DDDs, and Adm.R, it can reduce information loss and classification-related errors, supporting more accurate drug-utilization analyses, facilitating harmonization of National Drug Lists (NDLs), and improving Drug Utilization Research (DUR).
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BibTeX
@article{rajabi2026harmonizing,
title = {{Harmonizing national drug lists: an eight-pattern ATC/DDD analysis}},
author = {Mohammad Javad Rajabi and Mitra Mahmoudi Meymand and Mehrdad Vesali Sharif},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-75394-3},
url = {https://www.nature.com/articles/s41598-026-75394-3},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Harmonizing national drug lists: an eight-pattern ATC/DDD analysis
AU - Mohammad Javad Rajabi
AU - Mitra Mahmoudi Meymand
AU - Mehrdad Vesali Sharif
PY - 2026
DA - 2026/10/22
JO - Scientific Reports
DO - 10.1038/s41598-026-75394-3
UR - https://www.nature.com/articles/s41598-026-75394-3
AB - The Anatomical Therapeutic Chemical (ATC) classification and Defined Daily Dose (DDD) system of the World Health Organization (WHO) are recognized internationally as the gold standard in research methodology for drug use. Our focus is on drugs with multiple ATC codes and those with discrepancies in Route of Administration (Adm.R) and DDDs. A list of these medications was created to alert drug agencies to the critical need to audit and verify the accuracy of their national list harmonization. We analyzed the latest WHO ATC/DDD dataset, updated in July 2026, which includes 2737 Active Pharmaceutical Ingredients (API) with assigned DDD. A Python-based data-cleaning and harmonization workflow was developed to address variations in ATC codes, DDDs, and Adm.R. A decision-tree-based screening scheme with eight possible patterns was applied across the five ATC hierarchical levels to classify and harmonize ATC/DDD variants. Among 2737 WHO ATC/DDD records with assigned DDDs, 466 unique molecules met at least one of the eight classification conditions, yielding 913 distinct scenarios. Across the five ATC levels, 107 scenarios changed classification, whereas 806 remained stable. Changes occurred across four condition pairs (1/5, 2/6, 3/7, and 4/8), with the greatest variability observed when both DDD and Adm.R differed. Classification stabilized beyond ATC level 3. The 8-Pattern systematic screening scheme provides a systematic and reproducible approach for identifying multiclassified medicines and harmonizing ATC/DDD data. By preserving differences in ATC codes, DDDs, and Adm.R, it can reduce information loss and classification-related errors, supporting more accurate drug-utilization analyses, facilitating harmonization of National Drug Lists (NDLs), and improving Drug Utilization Research (DUR).
SN - 2045-2322
ER -