HIV-1 incidence in the era of rapid tests for recent infection in Livingstone District, Zambia
Bibliographic record
Abstract
Abstract Objective HIV incidence is not well documented where health services are delivered as a result failure of better understanding current transmission of HIV in a community. The aim was to determine the incidence of HIV-1 and factors associated with recent infection in Livingstone district using the HIV-1 recent infection testing algorithm, using the Rapid test for recent infection with HIV viral load testing to identify true HIV recent patients infected within 12 months. Results This was a laboratory based cross sectional study in which samples of newly diagnosed HIV positive adults sent to LUTH PCR laboratory for Recency testing and HIV VL testing were used. In our study participants, the younger age group were more likely to have been infected in the past 12 months, median age: recently infected 28 (23, 37.5) vs long term 33 (27, 40) p-value = 0.002. Out of the 768 clients subjected to RITA, 18.75% were true HIV recent, with the majority of them being female at 59.51%. 50.74% of the clients classified as recent were virally unsuppressed, p- value =0.000. Mahatima Gandhi clinic had a high number of recent infections relative to other facilities at 17.36%. Majority of the clients were captured under index modality with a percentage of HIV recent patients at 47.22%. Adjusted analysis indicated a significant association between age, HIV VL and recent infection (OR 0.97; 95%CI 0.95-0.99; p- value=0.002) and (OR 0.32; 95%CI 0.22-0.48; p- value =0.000). A high HIV incidence of recent infection with a 50.74% HIV VL unsuppressed clients was observed suggestive of high HIV transmission rate in the community. The majority of clients were captured under index testing indicating that most clients are less likely to seek medical care for HIV testing. Being virally unsuppressed and age were associated with recent infection. Facilities servicing low income areas may be hot spot zones where preventive and treatment interventions should be prioritized in the district.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".