Mapping Disease Burden of Major Depressive Disorder and Its Risk Factors in Low- and Middle-Income Countries
Bibliographic record
Abstract
Introduction: Major depressive disorder (MDD) is currently the second leading cause of life expectancy due to disability globally. This study aimed to examine the disease burden, risk factors, and temporal trends of MDD in low- and middle-income countries (LMICs) from 1990 to 2021. METHODS: Disability-adjusted life years (DALYs) data for 135 LMICs were obtained from the 2021 Global Burden of Disease (GBD) database. To assess trends in the burden of MDD over the past 3 decades, segmented regression analysis was applied to calculate the estimated annual percentage change. Spearman correlation analysis was conducted to examine the association between gross national income (GNI) and gender disparities in age-standardized DALY rates (ASDR) for MDD. We also explored how key risk factors - intimate partner violence, bullying victimization, and childhood sexual abuse - contributed to observed disparities. RESULTS: In 2021, LMICs accounted for 80.19% of the global burden of MDD. Age and gender disparities were significant, with DALY rates increasing markedly from adolescence and peaking around age 75. Gender disparities showed a higher burden among females, particularly in countries with higher GNI per capita. Analysis of risk factors revealed that intimate partner violence, bullying victimization, and childhood sexual assault were major contributors to the MDD burden, with notable variations across income levels and age groups. Temporal trends showed a marked increase in MDD burden across all income groups after 2019. CONCLUSION: MDD continues to impose a significant health burden in LMICs, disproportionately affecting females, youth, and elderly populations. The significant increase in MDD burden across all income groups after 2019 likely reflects the intensifying effects of global disruptions, with the COVID-19 pandemic being a key contributing factor. Populations with the highest burden also showed greater exposure to key risk factors. .
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".