A comparison of methods for estimating the incidence of human immunodeficiency virus infection in repeat blood donors
Bibliographic record
Abstract
BACKGROUND: The incidence of human immunodeficiency virus (HIV) in repeat blood donors has been estimated using seven methods. Although incidence is always calculated as cases per person-time, approaches to selecting cases and calculating person-time vary. Incidence estimates have not been compared among methods. STUDY DESIGN AND METHODS: The seven methods were compared in a simulation study. Because three methods used information from donations made before an estimation interval, 8 years of donation and infection history were simulated, and Years 7 and 8 were treated as the estimation interval for all methods. An exponential random variate was assigned to each donor to simulate the time to infection. Infection risk was constant over 8 years in one scenario but increased at various rates in seven other scenarios. The infection risk scenarios were combined with four mixes of donation frequency to generate 32 test conditions. RESULTS: Three methods produced biased estimates under all conditions. Three other methods were biased under most conditions. Bias from most methods increased as donation frequency declined. The single method that consistently produced unbiased estimates was the only method that involved the standard epidemiological approach of tabulating all interdonation intervals (IDIs) within the estimation interval. Bias was eliminated from one of the consistently biased methods by a simple modification that involved the average IDI in a sample of donors. CONCLUSION: The standard epidemiological approach is recommended if required data are available. Otherwise, the modified method involving the estimated average IDI should be considered. Investigators should use caution when comparing incidence estimates among studies that use different estimation methods or donation frequencies.
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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 | 0.115 | 0.279 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".