Blood product collection and supply: a matter of money?
BACKGROUND
Previous studies have shown that countries with a low or medium Human Development Index (HDI) transfuse far fewer blood products than countries with a high HDI. HDI comprises both economical and non-economical elements. We considered the hypothesis that non-economical, cultural differences may be additional factors in understanding blood donation and blood supply differences.


METHODS
We quantified the explained variance, r(2), in: the number of donors, the number of whole blood collections and the number of red blood cell units supplied to hospitals for 25 European countries. Candidate predictors were Hofstede's cultural dimensions, the demographic factor Old Age Dependency Ratio and the three components of HDI: Gross National Income, Life Expectancy and the Educational Development Index.


RESULTS
The cultural dimension Power Distance was the best sole predictor of whole blood collection (r(2) = 56.8%) and the number of donors (r(2) = 25.1%). The Educational Development Index best predicted the number of red blood cell units (r(2) = 45.0%). Multivariable models including the cultural dimension Power Distance and the Educational Development Index gave the best results in predicting the number of whole blood collections and red blood cell units supplied and, to a lesser extent, the number of donors, with adjusted r(2) values of 63.6%, 51.9% and 28.6%, respectively. In contrast, Gross National Income made no significant predictive contribution to any of the multivariable models. Neither did the other cultural dimensions, Life Expectancy or Old Age Dependency Ratio.


CONCLUSIONS
The effects of education level and cultural aspects should be taken into account as influencers on donation behaviour. The concept of power distance, in particular, presents a challenge to blood donor managers in cross-cultural and multi-cultural donor management contexts.
de Kort
W
W
Sanquin Blood Bank Southeast Region, Nijmegen, the Netherlands. w.dekort@sanquin.nl
Wagenmans
E
E
van Dongen
A
A
Slotboom
Y
Y
Hofstede
G
G
Veldhuizen
I
I
D000293:Adolescent; D000328:Adult; D000368:Aged; D001771:Blood Banks / Q000191:economics*; D001782:Blood Donors / Q000706:statistics & numerical data*; D001803:Blood Transfusion / Q000191:economics* / Q000706:statistics & numerical data; D003469:Culture; D003710:Demography; D019049:Developed Countries / Q000191:economics; D003906:Developing Countries / Q000191:economics; D004522:Educational Status; D017707:Erythrocyte Transfusion / Q000191:economics / Q000706:statistics & numerical data; D005060:Europe; D005260:Female; D006803:Human Development; D006801:Humans; D007182:Income; D008017:Life Expectancy; D008297:Male; D008875:Middle Aged; D018962:Phlebotomy / Q000191:economics*; D055815:Young Adult