Evidence map›Paper›PMID 35273839›Full record

ArticleCureus2022

Blood Donation in Lebanon: A Six-Year Retrospective Study of a Decentralized Fragmented Blood Management System.

Jules-Joel Bakhos, Myra Khalife, Yorgui Teyrouz, Youakim Saliba

Abstract read
In one paragraph

Article in Cureus, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Jules-Joel BakhosDepartment of Medical Research, Donner Sang Compter (DSC), Beirut, LBN.
Myra KhalifeDepartment of Medical Research, Donner Sang Compter (DSC), Beirut, LBN.
Yorgui TeyrouzDepartment of Medical Research, Donner Sang Compter (DSC), Beirut, LBN.
Youakim SalibaFaculty of Medicine, Saint Joseph University, Beirut, LBN.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction In developing countries, the lack of a sufficient and safe blood supply is a significant impediment to providing health care. Lebanon is notable for its absence of a Donor Management System to ensure continuous donor recruitment and scheduling. Herein, we report the findings of Lebanon's first large retrospective population-based study to investigate blood types and donation that is critical for managing community blood supply. Methods The non-remunerated voluntary blood donors were recruited by the non-profit organization "Donner Sang Compter". The study spanned six years, from August 2015 to May 2021, and included 36,002 people from 18 districts throughout Lebanon's nine governorates. Results The most prevalent blood type was A (42%), followed by O (37.48%), B (13.86%), and the AB group (6.84%). RhD+ groups were predominant (88.45%), with A+ being the most (37.84%) and AB- being the least prevalent (1.05%). Furthermore, blood type and donation profiling revealed a substantial geographical variation in the frequency of blood groups, despite the relatively small country's area. As for blood donation, when gender and age were considered, young male donors dominated the pool across the country. Conclusion This study on blood type prevalence and blood donor demographics may pave the way for the development of a more coherent and integrated blood management system in Lebanon, as opposed to the fragmented and decentralized system now in existence. These findings also provide crucial clinical information for the country's future transfusion medicine policies and practices, which is vital in such a precarious part of the world.

Indexed as

blood donationblood type prevalencedeveloping worldfragmented blood systemlebanon

Identifiers

PMID35273839
PMCPMC8901104

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.