Analysis of risk factors for co-morbid anxiety and depression in pregnant women

Co-morbid anxiety and depression (CAD) is defined as the co-existence of anxiety and depression. During pregnancy, women are more prone to negative emotions, such as anxiety and depression, than the general female population. The incidence of CAD during pregnancy is 1-26 %. The aims of this study were to investigate the incidence and influencing factors of CAD during pregnancy. The study cohort included 3053 pregnant women who underwent maternity check-ups at a tertiary hospital in China. Demographic characteristics, level of social support, and psychological characteristics were collected from participants via a self-reported questionnaire, which included the General Demographic Information Questionnaire, Pregnancy Stress Scale, the Generalized Anxiety Scale, Patient Health Questionnaire-9, and Perceived Social Support Scale. A binary logistic regression model and a categorical decision tree based on the Chi-square Automatic Interaction Detector algorithm were used to identify factors influencing CAD during pregnancy, and the differences between the two models were analysed and compared. The results of the logistic regression and decision tree models identified pregnancy stress, social support, pregnancy knowledge, couple relationship satisfaction, advanced maternal age, and occupation as factors influencing CAD during pregnancy. Pregnancy stress was the most influential factor. The areas under the curve of the classification decision tree and logistic regression models were 0.801 (95 % CI: 0.778-0.823) and 0.827 (95 % CI: 0.807-0.847), respectively, with specificities of 63 and 77 %, and sensitivities of 83.9 and 76.3 %. Logistic regression excels when dealing with linear relationships, while decision trees are particularly useful when dealing with nonlinear relationships. Therefore, combining logistic regression and decision trees can achieve some degree of model diversity, thus improving predictive power and confidence in the results.