Chideme Coster, Chikobvu Delson, Makoni Tendai
Department of Mathematical Statistics and Actuarial Sciences, University of the Free State, Bloemfontein, South Africa.
Risk Manag Healthc Policy. 2024 Feb 9;17:311-328. doi: 10.2147/RMHP.S439699. eCollection 2024.
To meet the blood requirements for transfusion therapy, blood banks need to ensure that blood inventories are maintained at desirable levels. There is a rising global need for optimal ways to manage blood supply and demand using statistical models in blood inventory planning and management. Thus, blood donation forecasting using donor-specific characteristics such as donor type and age is critical in managing the blood bank inventory.
The monthly blood donation data covering the period 2007 to 2018, collected from the National Blood Service Zimbabwe (NBSZ) was used in this study. The data is first disaggregated based on donor age, and further disaggregation is performed for each age group based on donor type. The hierarchical forecasting approaches, namely the bottom-up, top-down and the optimal combination methods were used in the data analysis. The Error-Trend-Seasonality (ETS) and Autoregressive Integrated Moving Average (ARIMA) methods are used in the hierarchical forecasting approaches to generate the forecasts.
New blood donors account for more than 55% of blood donations in Zimbabwe. The younger donors (16-29 years) dominate the blood donations, accounting for 89.2% of the donations. Young and new donors account for nearly 50% of the donations. The middle-aged and older donors have lower blood donations. The bottom-up approach under the ARIMA model outperformed all the other approaches. The future projections show that new and young donors will increase in blood donations, regular donations will decline slightly while the occasional donations are projected to remain constant.
Hierarchical forecasting is a unique approach in that the different aggregation levels reveal important features of the blood donation data. The lower percentage of regular donations is worrisome to blood authorities as it points to new blood donors not returning for further donations. Blood authorities need to develop policies that will encourage new and young donor categories to become regular donors.
为满足输血治疗的用血需求，血库需要确保血液库存维持在理想水平。全球对于在血液库存规划与管理中使用统计模型来优化血液供需管理方法的需求日益增长。因此，利用诸如献血者类型和年龄等特定献血者特征进行献血预测，对于血库库存管理至关重要。
本研究使用了从津巴布韦国家血液服务机构（NBSZ）收集的2007年至2018年的月度献血数据。数据首先按献血者年龄进行分解，然后针对每个年龄组再按献血者类型进一步分解。数据分析采用了分层预测方法，即自下而上、自上而下和最优组合方法。在分层预测方法中使用误差趋势季节性（ETS）和自回归积分移动平均（ARIMA）方法来生成预测。
在津巴布韦，新献血者占献血总量的比例超过55%。年轻献血者（16 - 29岁）在献血中占主导地位，占献血量的89.2%。年轻和新献血者占献血量的近50%。中年和老年献血者的献血量较低。ARIMA模型下的自下而上方法优于所有其他方法。未来预测表明，新献血者和年轻献血者的献血量将增加，定期献血量将略有下降，而不定期献血量预计将保持不变。
分层预测是一种独特的方法，因为不同的汇总级别揭示了献血数据的重要特征。定期献血比例较低令血液管理部门担忧，因为这表明新献血者没有再次献血。血液管理部门需要制定政策，鼓励新的和年轻的献血者类别成为定期献血者。
