Testing bias in calculating HIV incidence from the Serologic Testing Algorithm for Recent HIV Seroconversion
Bibliographic record
Abstract
OBJECTIVE: Incidence is critical in monitoring HIV infection in populations but often difficult to measure. The Serologic Testing Algorithm for Recent HIV Seroconversion (STARHS) can estimate HIV incidence from a single specimen at low cost. Nevertheless, HIV testing patterns may introduce bias, rendering interpretation of the STARHS result problematic. We found empirical evidence of such bias in Ontario using the STARHS formula with varied window periods METHODS: In a hypothetical population of homosexual men, we calculated HIV incidence from the STARHS assay on the basis of incidence density, study duration, STARHS window period and intertest interval. We also incorporated the increased likelihood of a newly infected person having an HIV test due to seroconversion illness or high-risk behaviours ('seroconversion effect' or SCE). We also varied the intertest interval inversely as a function of incidence density. To adjust incidence estimates for bias, we fit empirical STARHS data to an algebraic formula expressing measured HIV incidence as a function of SCE and incidence. RESULTS: Incidence density estimates were unbiased when SCE or incidence density-interval interactions were absent. However, estimated incidence density was higher than true incidence density in the presence of SCE, as much as seven-fold higher under certain conditions. The goodness-of-fit provided estimates with an excellent fit, yielding plausible results. CONCLUSION: HIV incidence from STARHS may be strongly biased because of early testing in recently infected persons, resulting in substantial overestimation, at least amongst men who have sex with men. Thus, incidence estimates from STARHS must be interpreted with considerable caution. Nevertheless, incidence estimates may be adjusted to yield unbiased results.
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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 | 0.066 | 0.246 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".