The epidemiology of delayed HIV diagnosis in Ibadan, Nigeria
Bibliographic record
Abstract
BACKGROUND: Human immunodeficiency virus infection (HIV) is one of the major health burdens in Nigeria. Delayed HIV diagnosis remains a significant driver of HIV transmission. The risk factors of delayed HIV diagnosis have not been widely studied in Nigeria. This observational study examined demographic risk factors for delayed HIV diagnosis and the trends in the annual total cases of delayed HIV diagnosis in Ibadan, Nigeria. METHODS: We examined the data on HIV patients enrolled in care at the University College Hospital's Antiretroviral Therapy (ART) clinic in Ibadan, Nigeria. Delayed HIV diagnosis was defined as a Cluster of Differentiation 4 (CD4) count of less than 350 cells/mm³ at the time of diagnosis. The association between delayed HIV diagnosis and risk factors was analyzed using logistic regression. The trends in the annual total cases of delayed HIV diagnosis over time were examined. RESULTS: This study included 3458 HIV patients. There were 1993/3458 prevalent cases of delayed HIV diagnosis (57.6%). The risk factors for delayed HIV diagnosis were older age, retirement, marriage separation, never married, and widowed female. The factors that were significantly associated with a low risk of delayed HIV diagnosis were student and tertiary education. There was a progressive decline in the annual cases of delayed HIV diagnosis. CONCLUSIONS: Although the cases of delayed HIV diagnosis are still high, they are declining. Human immunodeficiency virus testing should be targeted at populations at risk of delayed diagnosis. Considerable public awareness and education programs about HIV testing may significantly reduce delayed HIV diagnosis in Nigeria.
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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 | 0.000 | 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 | 0.001 |
| 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".