Construction of a rapid depression screening model for blood donors in Guangzhou based on decision tree

Abstract

Objective To investigate the prevalence of depression in blood donors and analyze the related factors, so as to develop a rapid depression screening model for blood donors. Methods A total of 13 015 street whole blood donors in Guangzhou Blood Center during May to August, 2020 filled in an anonymous e-questionnaire, including social demography information and the Patient Health Questionnaire-9 before donation. The cut-off value for detecting depression was 10. Logistic regression by SPSS 26.0 was used to analyze depression related factors. 2-level decision tree with 30/10 as the minimum number of cases in parent/child node, 10-fold cross validation was used to cut items of PHQ-9 to form the depression screening model. Results 364 out of 13 015 (2.80%) street whole blood donors reported a score ≥ 10. Donors with 18-29 years old (P <0.05), unmarried (P<0.05), less than 50 000 RMB household income per year (P< 0.05) were more prone to depression. 81.96% donors in "<10 scores" group, while 3.85%donors in "≥ 10 scores" group were in two terminal nodes formed by Item-6, 2 and 4 of PHQ-9. After verification by the 10 fold crossover method, the estimated misclassification risk of the model was 1.7%. Conclusion The screening prevalence of depression based on PHQ-9 in Guangzhou blood donors was 2.8%(95% CI: 2.52%-3.09%) . Donation frequency was not related to depression. A rapid and efficient depression screening model for blood donors based on item-6, 2 and 4 of PHQ-9 was developed.

Keywords

• depression
• blood donor
• decision tree
• logistic regression
• rapid screening model
• guangzhou