Sex and gender differences in co-occurring substance use and depressive disorders: a systematic review
Bibliographic record
Abstract
Background: Major depressive disorder (MDD) and substance use disorders (SUDs) frequently co-occur, influenced by sex (biological) and gender (sociocultural) factors. The extent and consistency of these differences across substance types in populations with co-occurring MDD remains unclear.Objectives: This systematic review synthesizes evidence on sex and gender differences in the prevalence, clinical characteristics, and treatment outcomes of individuals with co-occurring MDD and four SUDs: alcohol use disorder (AUD), cannabis use disorder (CUD), opioid use disorder (OUD) and cocaine use disorders (CoUD).Methods: Following PRISMA guidelines, we searched PsycINFO, MEDLINE, and Embase for peer-reviewed studies from inception to present. Eligible studies examined co-occurring MDD and at least one SUD, and disaggregated outcomes by sex or gender.Results: Forty-seven studies were included (N = 648,414), spanning diverse age groups and geographic regions. Women with SUDs were more likely to experience co-occurring MDD, particularly in AUD and OUD; findings were less consistent for CUD and CoUD. Men with MDD were more likely than women to report co-occurring AUD. Co-occurring MDD-SUD conferred increased suicide risk, particularly among women. Treatment-related findings were mixed: some evidence suggested MDD increased relapse risk in men but buffered relapse in women. Common methodological limitations included inconsistent definitions of sex and gender and reliance on cross-sectional designs.Conclusion: Sex and gender shape the risks and treatment trajectories of co-occurring MDD and SUDs, underscoring the need for personalized screening, suicide prevention, and relapse management strategies. Greater conceptual clarity and inclusion of gender-diverse individuals could inform equitable clinical practices and targeted interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".