Towards measuring effective coverage: critical bottlenecks in quality- and user-adjusted coverage for major depressive disorder in São Paulo metropolitan area, Brazil
Bibliographic record
Abstract
Abstract Background Major depressive disorder (MDD) contributes to a significant proportion of disease burden, disability, economic losses, and impact on need of treatment and health care in Brazil, but systematic information about its treatment coverage is scarce. This paper aims to estimate the gap in treatment coverage for MDD and identify key bottlenecks in obtaining adequate treatment among adult residents in the São Paulo Metropolitan area, Brazil. Methods A representative face-to-face household survey was conducted among 2942 respondents aged 18+ years to assess 12-month MDD, characteristics of 12-month treatment received, and bottlenecks to deliver care through the World Mental Health Composite International Diagnostic Interview. Results Among those with MDD (n = 491), 164 (33.3% [SE, 1.9]) were seen in health services, with an overall 66.7% treatment gap, and only 25.2% [SE, 4.2] received effective treatment coverage, which represents 8.5% of those in need, with a 91.5% gap in adequate care (66.4% due to lack of utilization and 25.1% due to inadequate quality and adherence). Critical service bottlenecks identified were: use of psychotropic medication (12.2 percentage points drop), use of antidepressants (6.5), adequate medication control (6.8), receiving psychotherapy (19.8). Conclusions This is the first study demonstrating the huge treatment gaps for MDD in Brazil, considering not only overall coverage, but also identifying specific quality- and user-adjusted bottlenecks in delivering pharmacological and psychotherapeutic care. These results call for urgent combined actions focused in reducing effective treatment gaps within services utilization, as well as in reducing gaps in availability and accessibility of services, and acceptability of care for those in need.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".