Adjusting ante-natal clinic data for improved estimates of HIV prevalence among women in sub-Saharan Africa
Bibliographic record
Abstract
OBJECTIVES: To find a simple and robust method for adjusting ante-natal clinic data on HIV prevalence to represent prevalence in the general female population in the same age range, allowing for fertility differences by HIV status. BACKGROUND: HIV prevalence comparisons for pregnant women and women in the general community show that prevalence in the latter is significantly higher than in the former. An adjustment procedure is needed that is specific for the demographic and epidemiological circumstances of a particular population, making maximum use of data that can easily be collected in ante-natal clinics or are widely available from secondary sources. METHODS: Birth interval length data are used to allow for subfertility among HIV-positive women. To allow for infertility, relative HIV prevalence ratios for fertile and infertile women obtained in community surveys in populations with similar levels of contraception use are applied to demographic survey data that describe the structure of the population not at risk of child-bearing. RESULTS: For populations with low contraception use, the procedure yields estimates of general female HIV prevalence of 35-65% higher than the observed ante-natal prevalence, depending on population structure. Results were verified using general population prevalence data collected in Kisesa (Tanzania) and Masaka (Uganda). For high contraception use populations, adjusted values range from 15% higher to 5% lower, but only limited verification has been possible so far. CONCLUSIONS: The procedure is suitable for estimating general female HIV prevalence in low contraception use populations, but the high contraception variant needs further testing before it can be applied widely.
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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 | 0.009 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".