Accuracy of self-reported HIV-testing history and awareness of HIV-positive status in four sub-Saharan African countries
Bibliographic record
Abstract
BACKGROUND: In many countries in sub-Saharan Africa, self-reported HIV testing history and awareness of HIV-positive status from household surveys are used to estimate the percentage of people living with HIV (PLHIV) who know their HIV status. Despite widespread use, there is limited empirical information on the sensitivity of those self-reports, which can be affected by nondisclosure. METHODS: Bayesian latent class models were used to estimate the sensitivity of self-reported HIV-testing history and awareness of HIV-positive status in four Population-based HIV Impact Assessment surveys in Eswatini, Malawi, Tanzania, and Zambia. Antiretroviral (ARV) metabolite biomarkers were used to identify persons on treatment who did not accurately report their status. For those without ARV biomarkers, we used a pooled estimate of nondisclosure among untreated persons that was 1.48 higher than those on treatment. RESULTS: Among PLHIV, the model-estimated sensitivity of self-reported HIV-testing history ranged from 96% to 99% across surveys. The model-estimated sensitivity of self-reported awareness of HIV status varied from 91% to 97%. Nondisclosure was generally higher among men and those aged 15-24 years. Adjustments for imperfect sensitivity did not substantially influence estimates of PLHIV ever tested (difference <4%) but the proportion of PLHIV aware of their HIV-positive status was higher than the unadjusted proportion (difference <8%). CONCLUSION: Self-reported HIV-testing histories in four Eastern and Southern African countries are generally robust although adjustment for nondisclosure increases estimated awareness of status. These findings can contribute to further refinements in methods for monitoring progress along the HIV testing and treatment cascade.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".