ObjectiveTo systematically evaluate the detection rate of postpartum depression in Chinese maternal population and to provide a scientific basis for the prevention and treatment of postpartum depression.MethodsWe searched CNKI, WanFang Data, VIP, PubMed, EMbase and The Cochrane Library databases to collect studies on the detection rate of postpartum depression in Chinese maternal population from January, 2001 to August, 2019. Two reviewers independently screened literature, extracted data and evaluated the risk of bias of included studies. Meta-analysis was performed using Stata15.0 software.ResultsA total of 24 studies involving 38 357 cases were included. Meta-analysis results showed that the total detection of postpartum depression in Chinese females was 15% (95% CI 12% to 17%). Subgroup analysis showed that the detection of postpartum depression was 12.3% (95% CI 9.3% to 15.2%) in the south and 17.3% (95%CI 12.1% to 22.5%) in the north. According to the Edinburgh postpartum depression scale, the rate was 14.5% (95%CI 11.4% to 17.5%); and for other scales, the rate was 15.0% (95% CI 8.9% to 21.2%); simple random sampling method was 14.8% (95%CI 11.0% to 18.7%), and cluster sampling was 16.3% (95%CI 12.0% to 20.5%). The rate was 15.8% (95%CI 9.3% to 22.3%) from 2001 to 2010, 13.5% (95%CI 7.7% to 19.2%) from 2011 to 2014, and 14.8% (95%CI 10.9% to 18.6%) from 2015 to 2019. Sensitivity analysis showed that the combined results were stable.ConclusionsThe detection rate of postpartum depression in Chinese maternal population is high, and early screening and related intervention should be paid more attention to these population.
ObjectiveTo systematically evaluate postpartum depression risk prediction models in order to provide references for the construction, application and optimization of related prediction models. MethodsThe CNKI, VIP, WanFang Data, PubMed, Web of Science and EMbase were electronically searched to collect studies on predictive model for the risk of postpartum from January 2013 to April 2023. Two reviewers independently screened the literature, extracted data, and assessed the quality of the included studies based on PROBAST tool. ResultsA total of 10 studies, each study with 1 optimal model were evaluated. Common predictors included prenatal depression, age, smoking history, thyroid hormones and other factors. The area under the curve of the model was greater than 0.7, and the overall applicability was general. Overall high risk of bias and average applicability, mainly due to insufficient number of events in the analysis domain for the response variable, improper handling of missing data, screening of predictors based on univariate analysis, lack of model performance assessment, and consideration of model overfitting. ConclusionThe model is still in the development stage. The included model has good predictive performance and can help early identify people with high incidence of postpartum depression. However, the overall applicability of the model needs to be strengthened, a large sample, multi-center prospective clinical study should be carried out to construct the optimal risk prediction model of PPD, in order to identify and prevent PPD as soon as possible.