Concentrated HIV subepidemics in generalized epidemic settings
Bibliographic record
Abstract
PURPOSE OF REVIEW: A relatively neglected topic to date has been the occurrence of concentrated epidemics within generalized epidemic settings and the potential role of targeted interventions in such settings. We review recent studies in high-risk groups as well as findings relating to geographical heterogeneity and the potential for targeting 'high-transmission zones' in the 10 countries with highest HIV prevalence. RECENT FINDINGS: Our review of recent studies confirmed earlier findings that, even in the context of generalized epidemics, MSM have a substantially higher prevalence than the general population. Estimates of prevalence of HIV among people who inject drugs (PWID) in sub-Saharan African countries are rarely available and, when they are, often outdated. We identified recent studies of sex workers in Kenya and Uganda. In all three cases - MSM, PWID, and sex workers - HIV prevalence estimates are mostly based on convenience. Moreover, good estimates of the total size of these populations are not available. Our review of recent studies of high-risk populations defined on the basis of geography showed high levels of both new and existing infections in Kenya (slums), South Africa (peri-urban communities), and Uganda (fishing villages). SUMMARY: Recent empirical findings combined with evidence from phylogenetic studies and supported by mathematical models provide a clear rationale for testing the feasibility, acceptability, and effectiveness of targeted HIV prevention approaches in hyperendemic populations to supplement measures aimed at the general population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".