Optimal timing of <scp>HIV</scp> home‐based counselling and testing rounds in Western Kenya
Bibliographic record
Abstract
INTRODUCTION: Weaknesses in care programmes providing anti-retroviral therapy (ART) persist and are often instigated by late HIV diagnosis and poor linkage to care. We investigated the potential for a home-based counselling and testing (HBCT) campaign to be improved through the optimal timing and enhancement of testing rounds to generate greater health outcomes at minimum cost. METHODS: Using a mathematical model of HIV care calibrated to longitudinal data from The Academic Model Providing Access To Healthcare (AMPATH) in Kenya, we simulated HBCT campaigns between 2016 and 2036, assessing the impact and total cost of care for each, for a further 20 years. RESULTS: We find that simulating five equally spaced rounds averts 1.53 million disability-adjusted life-years (DALYs) at a cost of $1617 million. By altering the timing of HBCT rounds, a four-round campaign can produce greater impact for lower cost. With "front-loaded" rounds, the cost per DALY averted is reduced by 12% as fewer rounds are required ($937 vs. $1060). Furthermore, improvements to HBCT coverage and linkage to care avert over two million DALYs at a cost per DALY averted of $621 (41% less than the reference scenario). CONCLUSIONS: Countries implementing HBCT can reduce costs by optimally timing rounds and generate greater health outcomes through improving linkage, coverage, and retention. Tailoring HBCT campaigns to individual settings can enhance patient outcomes for minimal cost.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".