Estimation of the sexual transmission of HIV in Kenya and Uganda on the trans-Africa highway: the continuing role for prevention in high risk groups
Bibliographic record
Abstract
OBJECTIVE: To explore the effect of transactional sex on the trans-Africa highway from Mombasa-Kampala in contributing to the HIV epidemic and the impact that an effective prevention intervention could have. METHODS: Variables for input into a simple model of HIV prevention, AVERT, were derived from a study of hot spots of transactional sex on the trans-Africa highway. Diaries were completed by a sample of sex workers at selected sites of transactional sex for a period of 28 consecutive days. Key information elicited included numbers, types and occupations of clients, numbers of liaisons, sexual acts in each liaison, and condom use. 857 diaries were distributed and 578 received and usable in 30 sites. A sexual patterning matrix was completed by 202 truckers at the Malaba border point as part of a health seeking behaviour survey. Two methods were employed to estimate female sex worker (FSW) numbers on the highway. FSW focus group discussions (FGDs) at 15 sites were carried out and included questioning on the number of sex workers at the site. As most transactional sex on the highway is centred on bars and lodgings, a patron census and survey of 1007 bars and lodgings was carried out which included questions on the presence and proportions of FSWs among the clientele. RESULTS: There are an estimated 8000 FSWs on the trans-Africa highway from Mombasa to Kampala. Annual numbers of different sexual partners per FSW were 129, annual numbers of sexual acts per FSW were 634, percentage of sexual acts protected by condom use was 77.7%. Using these input data an estimated 3200-4148 new HIV infections occur on this portion of the trans-Africa highway in 1 year. Having a 90% condom use programme in place could prevent almost two thirds of these infections and cumulative incidence would decline from 1.29% to 0.42%. CONCLUSIONS: In generalised epidemics there has been a debate as to the place of targeted interventions. In the current east African epidemic we show that a targeted intervention could have significant impact in averting HIV infections related to the trans-Africa highway.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".