Health and economic impact of oral PrEP provision across subgroups in western Kenya: a modelling analysis
Bibliographic record
Abstract
INTRODUCTION: Oral pre-exposure prophylaxis (PrEP) is a priority intervention for scale-up in countries with high HIV prevalence. Policymakers must decide how to optimise PrEP allocation to maximise health benefits within limited budgets. We assessed the health and economic impact of PrEP scale-up among different subgroups and regions in western Kenya. METHODS: We adapted an agent-based network model, EMOD-HIV, to simulate PrEP uptake in six counties of western Kenya across seven subgroups including serodiscordant couples (SDCs), adolescent girls and young women (AGYW), adolescent boys and young men, women with multiple partners and men with multiple partners. We modelled 5 years of PrEP provision assuming 90% PrEP uptake in the prioritised subgroups and evaluated outcomes over 20 years compared with a no PrEP scenario. All results are presented in 2021 USD$. RESULTS: Population PrEP coverage was highest in the broad AGYW scenario (8.3%, ~2 fold higher than the next highest coverage scenario) and lowest in the SDC scenario (0.37%). Across scenarios, PrEP averted 4.5%-21.3% of infections over the 5-year implementation. PrEP provision to SDCs was associated with the lowest incremental cost-effectiveness ratio (ICER), $245 per disability-adjusted life year (DALY) averted (CI $179 to $435), followed by women and men with multiple partners ($1898 (CI $1002 to $6771) and $2351 (CI $1 831 to $3494) per DALY averted, respectively). Targeted strategies were more efficient than broad provision even in high HIV prevalence counties; PrEP scale-up for AGYW with multiple partners had an ICER per DALY averted of $4745 (CI $2059 to $22 515) compared with $12 351 for broad AGYW (CI $7 050 to $33,955). In general, ICERs were lower in counties with higher HIV prevalence. CONCLUSIONS: PrEP scale-up can avert substantial HIV infections and increasing PrEP demand for subgroups at higher risk can increase efficiency of PrEP programmes. Our results on health and cost impact of PrEP across geographic regions in western Kenya can be used for budgetary planning and priority setting.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".