Pooled Prevalence of Depressive Symptoms Among Medical Students: an Individual Participant Data Meta-analysis

Overview

Journal BMC Psychiatry

Publisher Biomed Central

Specialty Psychiatry

Date 2023 Apr 14

PMID 37059978

Authors

Wilson Baldin Zatt

Kenneth Lo

Wilson Tam

Affiliations

Soon will be listed here.

Abstract

Background: The methodological choice of aggregated estimates for meta-analysis may be notable for some common drawbacks, including variations in the cut-off values of depression, and lower statistical power for analyzing the associated factors. The study aimed to refine the precision of previous findings on the prevalence of depressive symptoms among medical students, through gathering individual participant data (IPD) as identified from our previous reviews.

Material And Methods: In the present study, we searched MEDLINE, EMBASE, PsycINFO, WanFang, Scielo and LILACS to identify published systematic reviews and meta-analyses up to March 2018, then individual data was requested for further analysis (PROSPERO registration: CRD42018091917). The participants' age, sex, year of study, scores for depressive symptoms, and other predictor variables were requested. To pool the prevalence from the included studies, random-effects model (two-step method) was used. Multiple linear regression was used to examine the associated factors on the depression z-scores (one-step method).

Results: Of the 249 studies, the datasets of 34 studies were included. The crude prevalence was 19.4% (95% CI: 18.8%, 19.9%) by one-step method and the pooled prevalence was 18.1% (95% CI: 14.1%, 22.1%) by two-step method. Multiple linear regression revealed that being a female, older age, and senior year of study were significantly associated with the z-score.

Conclusion: The pooled prevalence of depressive symptoms from the Individual Participant Data (IPD) meta-analysis was lower than the previous meta-analyses using aggregated data. Age, sex, and year of study were significantly associated with the depression z-score. IPD meta-analysis may provide a more accurate estimation of disease burden, and allow verification of associated factors.

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