HIV Prevalence Comparison Between Antenatal Sentinel Surveillance and Demographic and Health Survey in Rwanda§
Bibliographic record
Abstract
OBJECTIVE: To compare HIV prevalence from antenatal surveillance to that of the demographic and health survey (DHS), and to identify factors determining the difference of HIV prevalence between women recruited in these two surveys in Rwanda in 2005. METHODS: Comparative cross-sectional study of HIV prevalence and socio-demographic factors collected by the antenatal survey in 13,745 pregnant women, seen in 30 health centres located throughout the country and those collected by the DHS among 5641 women, aged 15-49 years living in households located throughout the country. Log-binomial regression and direct standardization were used to estimate and compare HIV prevalence between the two surveys. RESULTS: HIV prevalence in the antenatal survey was slightly higher than that in DHS women (4.1% versus 3.6% p=0.103). Socio-demographic characteristics were differently distributed between the two populations. Whereas, 59%, 93%, 53% of pregnant women were aged 20-29 years, married or cohabiting and living in rural areas respectively, the corresponding proportions among DHS women were 35%, 48% and 83% (p<0.001). Simultaneous standardization of antenatal prevalence according to the distribution of socio-demographic characteristics in the DHS gave an overall HIV prevalence estimate of 3.6%, similar to the prevalence measured among DHS women. CONCLUSIONS: HIV prevalence in the antenatal survey overestimated that among women of the general population in Rwanda in 2005. This overestimation could be corrected by standardization of antenatal prevalence according to the distribution of age, geographical area, marital status, parity, and education, in 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 | 0.003 | 0.006 |
| 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 | 0.000 | 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".