Declining HIV prevalence and incidence in perinatal women in Harare, Zimbabwe
Bibliographic record
Abstract
BACKGROUND: In several recent papers it has been suggested that HIV prevalence and incidence are declining in Zimbabwe as a result of changing sexual behavior. We provide further support for these suggestions, based on an analysis of more extensive, age-stratified, HIV prevalence data from 1990 to 2009 for perinatal women in Harare, as well as data on incidence and mortality. METHODOLOGY/PRINCIPAL FINDINGS: Pooled prevalence, incidence and mortality were fitted using a simple susceptible-infected (SI) model of HIV transmission; age-stratified prevalence data were fitted using double-logistic functions. We estimate that incidence peaked at 5.5% per year in 1991 declining to 1% per year in 2010. Prevalence peaked in 1998/9 [35.9% (CI95: 31.3-40.7)] and decreased by 67% to 11.9% (CI95: 10.1-13.8) in 2009. For women <20y, 20-24y, 25-29y, 30-34y and ≥35y, prevalence peaked at 25.4%, 34.2%, 47.1%, 44.0% and 33.5% in 1993, 1996, 1997, 1998 and 1999, respectively, declining thereafter in every age group. Among women <25y, prevalence peaked in 1994 at 28.8% declining thereafter by 69% to 8.9% (CI95: 6.8-11.5) in 2009. CONCLUSION/SIGNIFICANCE: HIV prevalence declined substantially among perinatal women in Harare after 1998 consequent upon a decline in incidence starting in the early 1990s. Our model suggests that this was primarily a result of changes in behavior which we attribute to a general increase in awareness of the dangers of AIDS and the ever more apparent increases in mortality.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".