Back

Association of social media-sourced blood donors with transfusion delay and donor-related irregularities: A multicentre study in Bangladesh

Hoque, A.; Rahman, M.; Basak, S. K.; Mamun, A. A.

2026-04-17 health systems and quality improvement
10.64898/2026.04.08.26350439 medRxiv
Show abstract

BackgroundIn the absence of structured donor registries, social media platforms have become a dominant mechanism for blood donor recruitment in many low-resource settings. However, the implications of this shift for transfusion timeliness and system reliability remain unclear. ObjectiveTo evaluate the impact of social media-sourced donors on transfusion delay, donor reliability, and hemovigilance-related outcomes compared with conventional donor pathways. MethodsThis prospective analytical study included 400 transfusion episodes across tertiary hospitals in Bangladesh. Donor sources were categorized as social media (SM) or conventional (CON). The primary outcome was delay-to-transfusion. Secondary outcomes included donor-related irregularities, documentation completeness, near-miss events, and acute transfusion reactions. Multivariable logistic regression identified predictors of delay [&ge;]4 hours. ResultsSocial media-sourced donors were associated with significantly longer transfusion delays (5.98 vs 2.97 hours; p<0.001). Delay [&ge;]4 hours occurred in 83.6% of SM cases versus 17.6% of CON cases (OR 23.78). Donor-related irregularities were observed in 85% of SM episodes and absent in CON donors. Safety outcomes did not differ significantly between groups. Social media donor sourcing remained the strongest independent predictor of delay (adjusted OR 18.09). ConclusionUnregulated social media-based donor recruitment introduces substantial delays and undermines system reliability without improving access. Integration of digital tools into regulated donor systems is essential to strengthen transfusion timeliness and hemovigilance in resource-limited settings.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

1
BMJ Open
554 papers in training set
Top 0.7%
20.9%
2
PLOS ONE
4510 papers in training set
Top 7%
20.1%
3
Transfusion
18 papers in training set
Top 0.1%
15.8%
50% of probability mass above
4
BMC Medical Informatics and Decision Making
39 papers in training set
Top 0.7%
3.9%
5
The American Journal of Tropical Medicine and Hygiene
60 papers in training set
Top 1%
2.9%
6
Frontiers in Bioengineering and Biotechnology
88 papers in training set
Top 0.9%
2.2%
7
Scientific Reports
3102 papers in training set
Top 61%
1.6%
8
JAMA Network Open
127 papers in training set
Top 3%
1.4%
9
F1000Research
79 papers in training set
Top 2%
1.4%
10
Journal of Thrombosis and Haemostasis
28 papers in training set
Top 0.4%
1.4%
11
Cancers
200 papers in training set
Top 3%
1.3%
12
Journal of Clinical Medicine
91 papers in training set
Top 5%
1.2%
13
Journal of Infection
71 papers in training set
Top 2%
1.0%
14
Frontiers in Digital Health
20 papers in training set
Top 1%
1.0%
15
PLOS Global Public Health
293 papers in training set
Top 5%
1.0%
16
Journal of the American Heart Association
119 papers in training set
Top 3%
1.0%
17
BMJ Open Quality
15 papers in training set
Top 0.7%
0.8%
18
Journal of Clinical Pathology
12 papers in training set
Top 0.4%
0.8%
19
Transactions of The Royal Society of Tropical Medicine and Hygiene
16 papers in training set
Top 0.5%
0.8%
20
The Lancet
16 papers in training set
Top 0.6%
0.8%
21
The American Journal of Pathology
31 papers in training set
Top 0.4%
0.8%
22
Clinical Infectious Diseases
231 papers in training set
Top 4%
0.8%
23
BMC Infectious Diseases
118 papers in training set
Top 5%
0.8%
24
Nature Medicine
117 papers in training set
Top 5%
0.8%
25
Cytotherapy
14 papers in training set
Top 0.4%
0.7%
26
JMIRx Med
31 papers in training set
Top 2%
0.5%
27
Journal of Medical Internet Research
85 papers in training set
Top 5%
0.5%
28
BMC Public Health
147 papers in training set
Top 7%
0.5%