An overview and comparison of haemovigilance reporting forms across six countries relative to the WHO template
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Manmeet Kaur
and
Jayant Kumar Kairi
Abstract
Objectives
Haemovigilance monitors, identifies, reports, investigates, and analyses adverse event near-misses and reactions related to transfusion and blood product manufacture.
Methods
This was an observational study in which we analyzed and compared the WHO haemovigilance template form for hospital and blood establishment haemovigilance reporting forms from Australia, Canada, India, New Zealand, South Africa, and the United States. All data from these reporting systems/forms was tabulated. The study analyzed data elements from each form, scoring them based on their presence in the WHO template and country forms. Higher scores indicated greater comparability and more comprehensive data collection.
Results
We identified 57 data fields in haemovigilance reporting forms from six countries and the WHO template, essential for collecting information on suspected transfusion products and reactions. The US FDA form has the most fields at 40 (70 %), followed by Canada with 33 (58 %) and India with 27 (47 %). New Zealand’s form has the fewest at 16 (28 %), followed by South Africa with 17 (30 %).
Conclusions
Effective haemovigilance systems require time and commitment to develop, often starting small and growing with stakeholder involvement. A straightforward reporting form, accessible to all, is crucial for success.
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Research ethics: The local Institutional Review Board deemed the study exempt from review.
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Informed consent: Not applicable.
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Author contributions: Dr. Manmeet Kaur: study conception, methodology, data collection, data analysis and interpretation of results, draft manuscript, review and editing. Dr. Saurav Misra: study conception and design, review and editing, supervision, draft manuscript, review and editing. Dr. Jayant Kumar Kairi: review and editing, supervision, draft manuscript, review and editing and graphical abstract.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors declare no conflicts of interest.
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Research funding: None declared.
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Data availability: The raw data can be obtained on request from the corresponding author.
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