Estimated cost-effectiveness of point-of-care testing in community pharmacies vs. self-testing and standard laboratory testing for HIV
Bibliographic record
Abstract
OBJECTIVE: Point-of-care-testing (POCT) for HIV at community pharmacies can enhance care linkage compared with self-tests and increase testing uptake relative to standard lab testing. While the higher test uptake may increase testing costs, timely diagnosis and treatment can reduce downstream HIV treatment costs and improve health outcomes. This study provides the first evidence on the cost-effectiveness of pharmacist-led POCT vs. HIV self-testing and standard lab testing. DESIGN: Dynamic transmission model. METHODS: We compared three HIV testing strategies: POCT at community pharmacies; self-testing using HIV self-test kits; and standard lab testing. Analyses were conducted from the Canadian health system perspective over a 30-year time horizon for all individuals aged 15-64 years in Canada. Costs were measured in 2021 Canadian dollars and effectiveness was captured using quality-adjusted life-years (QALYs). RESULTS: Compared with standard lab testing, POCT at community pharmacies would save $885 million in testing costs over 30 years. Though antiretroviral treatment costs would increase by $190 million with POCT as more persons living with HIV are identified and treated, these additional costs would be partly offset by their lower downstream healthcare utilization (savings of $150 million). POCT at community pharmacies would also yield over 5000 additional QALYs. Compared with HIV self-testing, POCT at community pharmacies would generate both higher costs and higher QALYs and would be cost-effective with an incremental cost-effectiveness ratio of $47 475 per QALY gained. CONCLUSIONS: Offering POCT at community pharmacies can generate substantial cost savings and improve health outcomes compared with standard lab testing. It would also be cost-effective vs. HIV self-testing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".