Sensitivity and Specificity of the HIV Risk Assessment Tool Used by PEPFAR Partners in Edo, Bayelsa and Lagos States, Nigeria
Bibliographic record
Abstract
INTRODUCTION: Although HIV testing is a critical screening and entry point for accessing HIV treatment, HIV programs worldwide are strained by limited resources which require a practical and cost-effective strategy for screening and testing clients. Screening tools are becoming increasingly common given their presumed advantage of efficiency and cost-effectiveness in predicting and prioritizing clients who are most at risk of testing HIV positive. METHOD: This study assessed a Risk Assessment Tool (RAT) used by PEPFAR partners in Edo, Bayelsa, and Lagos states of Nigeria to determine the tool’s sensitivity and specificity for identifying HIV positivity. The assessment purposively selected the 20 most convenient health facilities. A penalized logistic regression model was also used to identify specific questions that predict True Positive. RESULT & CONCLUSION: The results indicate that the RAT used in the 3 states had poor accuracy, with a sensitivity of only 54%, meaning the RAT correctly identified 54% of the people who have HIV but failed to identify 46% of people who have HIV. The RAT’s specificity (77%) indicated that it correctly identified 77% of people who do not have HIV, but it also erroneously identified 23% of people as having HIV when they did not. The penalized logistic regression model demonstrated that clients who reported having unprotected sex in the previous 6 months accounted for 51% of those who tested positive to HIV. Likewise, those who reported having vaginal or urethral discharge accounted for 11%, while tuberculosis diagnosis or symptoms accounted for 8% of clients who tested positive to HIV. These three questions yielded the highest predictive values of clients who were likely to test positive.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".