ObjectiveTo systematically review the antidepressant efficacy of selective serotonin re-uptake inhibitors (SSRIs) and their effect on inflammatory factors in adults with major depressive disorder (MDD). MethodsElectronic searches were conducted in PubMed, Embase, Web of Science Core Collection, ProQuest, JSTOR, PsycINFO, The Cochrane Library, Epistemonikos, China National Knowledge Infrastructure (CNKI), and Chinese Biomedical Literature Database (CBM) from database inception to December 31, 2024. Two reviewers independently screened literature, extracted data, and assessed the risk of bias of included studies. Meta-analysis was performed using R 4.4.2 software. ResultsA total of 62 controlled studies (including 63 reports) was included, consisting of 36 randomized controlled trials (RCTs), 4 non-randomized controlled trials (NRCTs), and 23 case-control studies. Meta-analysis showed that the overall antidepressant effect size of SSRIs was SMD ?3.18, 95%CI ?3.56 to ?2.80, with no statistically significant difference in efficacy between different SSRIs (Q=6.77, P=0.24). However, their antidepressant efficacy was influenced by the country of origin of the study participants and the duration of intervention. SSRIs exerted significant inhibitory effects on 17 pro-inflammatory factors, but with high heterogeneity. SSRIs had no significant overall effect on anti-inflammatory factors (SMD 0.81, 95%CI ?0.20 to 1.82). However, subgroup analysis revealed that escitalopram exerted significant promoting effects on IL-10 (SMD 1.11, 95%CI 0.61 to1.60) and IL-13 (SMD 2.40, 95%CI 1.84 to 2.95). ConclusionSSRIs are effective antidepressants but vary in their effects on inflammatory factors. Among them, escitalopram has a potential bidirectional regulatory effect on inflammatory factors, and more high-quality multicenter studies are needed in the future for verification..
ObjectiveTo systematically investigate the current status of reporting health economics evidence in clinical practice guidelines and expert consensuses published in China from 2021 to 2023, providing references for the formulation and revision of guidelines and consensuses in our country. MethodsComputer searches were conducted in the CNKI, CBM, WanFang Data, China Academic Journals Full-text Database, PubMed, and Web of Science to collect clinical practice guidelines and expert consensuses published in China from 2021 to 2023. Two researchers independently screened the literature, extracted information on the inclusion of economic evidence in guidelines and consensuses, and then used quantitative analysis methods for description. ResultsA total of 4 236 relevant articles were included, of which 1 066 (25.17%) reported health economics evidence; 120 (11.26%) reported health economics evidence in the formation of recommendation opinions; 109 (10.23%) reported health economics evidence in the grading of evidence quality; 832 (78.05%) reported health economics evidence in the interpretation and explanation of recommendation opinions. ConclusionThe reporting rate of health economics evidence in clinical practice guidelines and expert consensuses published in China is not high. The reporting rate of health economics evidence in consensuses is lower than that in guidelines. It is recommended that during the formulation process of guidelines and consensuses, the application of health economics evidence should be further strengthened in aspects such as the formation of recommendation opinions, the grading of evidence quality, and the interpretation and explanation of recommendation opinions, in order to improve the scientific, rigorous, and applicability of clinical practice guidelines and expert consensuses, and to play the role of guidelines and consensuses in optimizing the allocation of health resources, improving clinical diagnosis and treatment effects, and enhancing the quality of medical care.
In meta-analysis, heterogeneity in statistical measures across primary studies can significantly affect the efficiency of data synthesis and the accuracy of result interpretation. Such inconsistencies may introduce bias in effect size estimation and increase the complexity of pooled analyses. Therefore, establishing standardized approaches for data type transformation and harmonizing different statistical measures has become a critical step in ensuring the quality of meta-analyses. To achieve efficient and scientifically rigorous data integration, researchers need to master systematic data transformation techniques and develop standardized processing strategies. Based on this need, this study provides a comprehensive summary of effect size transformation methods in meta-analysis, focusing on standardizing binary and continuous variables. It offers practical guidance to support researchers in applying these methods consistently and accurately.