Depression, Anxiety, and Other Mental Disorders in Patients With Cancer in Low- and Lower-Middle–Income Countries: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
PURPOSE: Cancer is a growing public health issue in low- and lower-middle-income countries (LLMICs), but the mental health consequences in this setting have not been well-characterized. We aimed to systematically evaluate the available literature on the prevalence, associates, and treatment of mental disorders in patients with cancer in LLMICs. METHODS: We systematically searched Medline, PsycINFO, EMBASE, and CINAHL. We performed a random effects meta-analysis to determine the pooled prevalence of major depression or anxiety disorders in this population, defined by Diagnostic and Statistical Manual of Mental Disorders or International Classification of Diseases criteria. We qualitatively reviewed studies that examined the prevalence of depressive or anxiety disorders defined by self-report tools, the prevalence of other mental disorders, associated factors of depressive and anxiety symptoms, and the treatment of mental disorders in this population. RESULTS: Forty studies spanning a 15-year period were included in the review. The pooled prevalence defined by Diagnostic and Statistical Manual of Mental Disorders or International Classification of Diseases criteria was 21% for major depression (95% CI, 15 to 28) and 18% for anxiety disorders (95% CI, 8 to 30). Depressive and anxiety symptoms were most frequently associated with advanced disease and low levels of education. Among the four studies evaluating treatment, three evaluated the effectiveness of psychotherapy and one evaluated a yoga program. CONCLUSION: The prevalence of depression and anxiety in patients with cancer generally appears higher in LLMICs than in upper-income countries. Our findings demonstrate the existence of a significant and underappreciated disease burden. We suggest that clinicians remain vigilant to psychiatric symptoms. Improved screening and treatment are likely to improve quality of life and reduce both morbidity and mortality.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.030 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".