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. 2025 Aug 13;5(8):e0004587.
doi: 10.1371/journal.pgph.0004587. eCollection 2025.

Simulating the blood transfusion system in Kenya: Modelling methods and exploratory analyses

Yiqi Tian  1 , Bo Zeng  1 , Jana MacLeod  2   3 , Gatwiri Murithi  4 , Cindy M Makanga  3 , Hillary Barmasai  5 , Linda S Barnes  1   6 , Rahul S Bidanda  1 , Tecla Chelagat  3 , Tonny Ejilkon Epuu  3 , Abdirahaman Musa  3   7 , Robert Kamu Kaburu  3 , Jason Madan  8 , Jennifer Makin  9 , Alejandro Munoz-Valencia  10 , Carolyne Njoki  11   12 , Kevin Ochieng  4 , Bernard Oduor Olayo  4 , Jose Ricardo Paiz  13 , Kristina E Rudd  14 , Mark Yazer  15 , Juan Carlos Puyana  16   17 , Bopaya Bidanda  1 , Jayant Rajgopal  1 , Pratap Kumar  3   18
Affiliations

Simulating the blood transfusion system in Kenya: Modelling methods and exploratory analyses

Yiqi Tian et al. PLOS Glob Public Health. .

Abstract

The process of collecting blood from donors and making it available for transfusion requires a complex series of operations involving multiple actors and different resources at each step. Ensuring hospitals receive adequate and safe blood for transfusion is a common challenge across low- and middle-income countries, but is rarely addressed from a system level. This paper presents the first use of discrete event simulation to study the blood system in Kenya and to explore the effect of variations and perturbations at different steps of the system on meeting blood demand at patient level. A process map of the Kenyan blood system was developed to capture critical steps from blood donation to transfusion using interviews with blood bank, hospital and laboratory personnel at four public hospitals across three counties in Kenya. The blood system was simulated starting with blood collection, a blood bank where blood is tested and stored before it is issued, a major hospital attached to the blood bank, and several smaller hospitals served by the same blood bank. Values for supply-side parameters were based mainly on expert opinion; demand-side parameters were based on data from blood requisitions made in hospital wards, and dispatch of blood from the hospital laboratory. Illustrative examples demonstrate how the model can be used to explore impacts of changes in blood collection (e.g., prioritising different donor types), blood demand (e.g., differing clinical case mix), and blood distribution (e.g., restocking strategies) on meeting demand at patient level. The model can reveal potential process impediments in the blood system and aid in choosing between alternate strategies or policies for improving blood collection, testing, distribution or use. Such a systems approach allows for interventions at different steps in the blood continuum to be tested on blood availability for different patients presenting at diverse hospitals across the country.

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Conflict of interest statement

The authors have read the journal’s policy and have the following competing interests: PK is a director in Health-E-Net Limited, which has commercial interests in the PaperEMR® technology used to capture demand data on blood transfusion. LSB is a consultant to the blood and biotherapies sector including the Association for the Advancement of Blood and Biotherapies (AABB). None of the opinions or views expressed in this manuscript obligate, bind, or otherwise commit the AABB. No other commercial interests are reported for this manuscript. All other authors have no competing interests. There are no additional patents, products in development or marketed products associated with this research to declare. This does not alter our adherence to PLOS policies on sharing data and materials.

Figures

Fig 1
Fig 1. Blood collection and delivery subsystems.
Fig 2
Fig 2. Blood subsystem operations.
Fig 3
Fig 3. Increased supply: met demand for blood by hospital type (left) and patient type (right).
Abbreviations: BD, Blood Drive; FRD, Family Replacement Donor.
Fig 4
Fig 4. Change in demand mix: met demand for blood by hospital type.
Fig 5
Fig 5. Changes in restocking policies: overall met demand for blood (left) and met demand by hospital type (right).
Fig 6
Fig 6. Restocking policies: met demand by patient type.
Abbreviations: E-Only, blood prioritised for emergency patients only.
See this image and copyright information in PMC

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References

    1. Delaney M, Telke S, Zou S, Williams MJ, Aridi JO, Rudd KE, et al. The BLOODSAFE program: Building the future of access to safe blood in Sub-Saharan Africa. Transfusion. 2022;62(11):2282–90. doi: 10.1111/trf.17091 - DOI - PMC - PubMed
    1. Jacobs JW, Bates I, M’baya B, Eichbaum Q, Louw VJ, Al-Riyami AZ, et al. Ensuring a Safe and Sufficient Global Blood Supply. N Engl J Med. 2024;391(12):1079–81. doi: 10.1056/NEJMp2403596 - DOI - PubMed
    1. Roberts N, James S, Delaney M, Fitzmaurice C. The global need and availability of blood products: a modelling study. Lancet Haematol. 2019;6(12):e606–15. doi: 10.1016/S2352-3026(19)30200-5 - DOI - PubMed
    1. World Health Organization. Global status report on blood safety and availability 2021. Geneva: World Health Organization; 2022.
    1. Tapko JB, Mainuka P, Diarra-Nama AJ. Status of blood safety in the WHO African region. Geneva: World Health Organization; 2006.

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