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Volume 18, Issue 1 (2026)
Iran J War Public Health 2026, 18(1): 77-89 |
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Ethics code: 1403.283.IR.AJAUMS.REC
History
Received: 2026/02/22 | Accepted: 2026/04/1 | Published: 2026/01/29
Received: 2026/02/22 | Accepted: 2026/04/1 | Published: 2026/01/29
How to cite this article
Mohammadebrahimi H, Zareiyan A, Sharififar S, Teymouri F, Zargar Balaye Jame S. Dimensions and Components of Pharmaceutical and Medical Equipment Supply Chain Preparedness in Disasters. Iran J War Public Health 2026; 18 (1) :77-89
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1- Department of Health in Disasters and Emergencies, Faculty of Nursing, AJA University of Medical Sciences, Tehran, Iran
2- “Department of Health in Disasters and Emergencies, Faculty of Nursing” and “Research Center for Cancer Screening Epidemiology”, AJA University of Medical Sciences, Tehran, Iran
3- Department of Health Management and Economics, Faculty of Medicine, AJA University of Medical Sciences, Tehran, Iran
2- “Department of Health in Disasters and Emergencies, Faculty of Nursing” and “Research Center for Cancer Screening Epidemiology”, AJA University of Medical Sciences, Tehran, Iran
3- Department of Health Management and Economics, Faculty of Medicine, AJA University of Medical Sciences, Tehran, Iran
Abstract (371 Views)
Aims: This study aimed to identify and elucidate the dimensions of preparedness of the pharmaceutical and medical equipment supply chain in disaster settings within Iran's military health system.
Participants & Methods: This qualitative study employed conventional content analysis grounded in a constructivist paradigm. Purposive sampling with a maximum variation strategy was applied, and 18 senior managers involved in procurement, storage, and distribution processes participated in semi-structured interviews. Data were analyzed concurrently with collection using the five-step approach of Hsieh and Shannon, and findings were organized through framework synthesis.
Findings: A total of 739 initial codes were extracted and subsequently condensed into 6 main themes, 16 categories, and 45 subcategories. The identified dimensions included needs assessment, selection and stockpiling of pharmaceuticals and equipment, demand forecasting, logistics infrastructure and distribution, inter-sectoral and inter-organizational coordination, and human resource and technological support. Preparedness was found to be a multidimensional and dynamic construct influenced by structural alignment, intelligent forecasting, efficient logistics management, coordinated governance, and the integration of advanced technologies.
Conclusion: Strengthening supply chain preparedness for disasters requires a comprehensive approach that encompasses workforce capacity building, data-driven forecasting systems, structural reforms to logistics networks, and enhanced inter-organizational coordination.
Participants & Methods: This qualitative study employed conventional content analysis grounded in a constructivist paradigm. Purposive sampling with a maximum variation strategy was applied, and 18 senior managers involved in procurement, storage, and distribution processes participated in semi-structured interviews. Data were analyzed concurrently with collection using the five-step approach of Hsieh and Shannon, and findings were organized through framework synthesis.
Findings: A total of 739 initial codes were extracted and subsequently condensed into 6 main themes, 16 categories, and 45 subcategories. The identified dimensions included needs assessment, selection and stockpiling of pharmaceuticals and equipment, demand forecasting, logistics infrastructure and distribution, inter-sectoral and inter-organizational coordination, and human resource and technological support. Preparedness was found to be a multidimensional and dynamic construct influenced by structural alignment, intelligent forecasting, efficient logistics management, coordinated governance, and the integration of advanced technologies.
Conclusion: Strengthening supply chain preparedness for disasters requires a comprehensive approach that encompasses workforce capacity building, data-driven forecasting systems, structural reforms to logistics networks, and enhanced inter-organizational coordination.