Predicting potential drop-out and future commitment for first-time donors based on first 1.5-year donation patterns: the case in Hong Kong Chinese donors.
OBJECTIVE
Adequate blood supply is crucial to the health-care system. To maintain a stable donor pool, donation-promotion strategies should not only be targeted in recruitment but also focus on retaining donors to give blood regularly. A study using statistical modelling is conducted to understand the first 4-year donation patterns for drop-out and committed first-time blood donors and to build model for the donor-type identification based on their first 1.5-year donation patterns.


METHODS
First-time whole blood (n= 20 631) adult donors recruited in year 2000 and 2001 in Hong Kong were observed for more than 4 years. Cluster analysis was first applied to group donor type by their similarities in donation behaviour under the surveillance period. A decision tree model based on a shorter surveillance period (1.5 years) is then built to predict the donor type.


RESULTS
Three donation patterns - one-time, drop-out, and committed donor behaviour - were identified in cluster analysis. Three variables - donation frequencies in the first-year and in the half-year period after first year, and the number of donation centre visits in the following half year after first year, were able to predict drop-out donors with potential to become committed and committed donors with relatively lower donation frequency.


CONCLUSIONS
The present statistical modelling is able to identify those donors with potential to become committed donors and those committed donors who can donate more frequently. This information is useful for development of targeted donor retention strategies.
Yu
P L H
PL
Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong. plhyu@hku.hk
Chung
K H
KH
Lin
C K
CK
Chan
J S K
JS
Lee
C K
CK
D000328:Adult; D044466:Asian People; D001782:Blood Donors / Q000600:supply & distribution*; D005260:Female; D005544:Forecasting / Q000379:methods; D006723:Hong Kong; D006801:Humans; D008297:Male; D015233:Models, Statistical; D011237:Predictive Value of Tests