Epidemiology of depression in primary care: Findings from the Mental Health in Primary Care (MeHPriC) project, Lagos, Nigeria
Bibliographic record
Abstract
OBJECTIVE: To estimate the rate and correlates of depression in primary care using data from the Mental Health in Primary Care (MeHPriC) project, Lagos, Nigeria. METHODS: Adult attendees (n=44,238) of 57 primary care facilities were evaluated for depression using the Patient Health Questionnaire (PHQ-9). Apart from the socio-demographic details, information was also collected regarding the use of alcohol and other psychoactive substances, presence of chronic medical problems, level of functionality, and perceived social support. Anthropometrics measures (weight and height) and blood pressure were also recorded. RESULTS: A total of 27,212 (61.5%) of the participants were females. There were 32,037 (72.4%) participants in the age group 25-60 years. The rate of major depression (PHQ-9 score 10 and above) was 15.0% (95% CI 14.6-15.3). The variables independently associated with depression include age 18-24 years (OR 1.69), female sex (OR 2.39), poor social support (OR 1.14), having at least one metabolic syndrome component (OR 1.57), significant alcohol use (OR 1.13) and functional disability (OR 1.38). CONCLUSION: Our study showed that the rate of depression in primary care in Nigeria is high. Screening for all primary care attendees for depression will be an important step towards scaling up mental health services in Nigeria and other developing countries.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".