Community-based HPV self-collection versus visual inspection with acetic acid in Uganda: a cost-effectiveness analysis of the ASPIRE trial
Bibliographic record
Abstract
BACKGROUND: Cervical cancer is the leading cause of cancer death for women in Uganda, despite the potential for prevention through organised screening. Community-based self-collected human papillomavirus (HPV) testing has been proposed to reduce barriers to screening. OBJECTIVE: Our objective was to evaluate the cost-effectiveness of the Advances in Screening and Prevention of Reproductive Cancers (ASPIRE) trial, conducted in Kisenyi, Uganda in April 2014 (n=500). The trial compared screening uptake and compliance with follow-up in two arms: (1) community-based (ie, home or workplace) self-collected HPV testing (facilitated by community health workers) with clinic-based visual inspection with acetic acid (VIA) triage of HPV-positive women ('HPV-VIA') and (2) clinic-based VIA ('VIA'). In both arms, VIA was performed at the local health unit by midwives with VIA-positive women receiving immediate treatment with cryotherapy. DESIGN: We informed a Monte Carlo simulation model of HPV infection and cervical cancer with screening uptake, compliance and retrospective cost data from the ASPIRE trial; additional cost, test performance and treatment effectiveness data were drawn from observational studies. The model was used to assess the cost-effectiveness of each arm of ASPIRE, as well as an HPV screen-and-treat strategy ('HPV-ST') involving community-based self-collected HPV testing followed by treatment for all HPV-positive women at the clinic. OUTCOME MEASURES: The primary outcomes were reductions in cervical cancer risk and incremental cost-effectiveness ratios (ICERs), expressed in dollars per year of life saved (YLS). RESULTS: HPV-ST was the most effective and cost-effective screening strategy, reducing the lifetime absolute risk of cervical cancer from 4.2% (range: 3.8%-4.7%) to 3.5% (range: 3.2%-4%), 2.8% (range: 2.4%-3.1%) and 2.4% (range: 2.1%-2.7%) with ICERs of US$130 (US$110-US$150) per YLS, US$240 (US$210-US$280) per YLS, and US$470 (US$410-US$550) per YLS when performed one, three and five times per lifetime, respectively. Findings were robust across sensitivity analyses, unless HPV costs were more than quadrupled. CONCLUSIONS: Community-based self-collected HPV testing followed by treatment for HPV-positive women has the potential to be an effective and cost-effective screening strategy.
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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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".