Cost‐effectiveness of nurse‐led versus doctor‐led antiretroviral treatment in South Africa: pragmatic cluster randomised trial
Bibliographic record
Abstract
OBJECTIVE: To estimate the cost-effectiveness of nurse-led versus doctor-led antiretroviral treatment (ART) for HIV-infected people. DESIGN: Cost-effectiveness analysis alongside a pragmatic cluster randomised controlled trial in 31 primary care clinics (16 intervention, 15 controls) in Free State Province, South Africa. Participants were HIV-infected patients, aged ≥16 years. Cohort 1 (CD4 count ≤350 cells/μl, not yet receiving ART at enrolment): consisted of 5 390 intervention patients and 3 862 controls; Cohort 2 (already received ART for ≥6 months at enrolment) of 3 029 intervention patients and 3 202 controls. Nurses were authorised and trained to initiate and represcribe ART. Management and ART provision were decentralised to primary care clinics. In control clinics, doctors initiated and re-prescribed ART, nurses monitored ART. Main outcome measure(s) were health service costs, death (cohort 1) and undetectable viral load (<400 copies/ml) (cohort 2) during the 12 months after enrolment. RESULTS: For Cohort 1, the intervention had an estimated incremental cost of US$102.52, an incremental effect of 0.42% fewer deaths and an incremental cost-effectiveness ratio (ICER) of US$24 500 per death averted. For Cohort 2, the intervention had an estimated incremental cost of US$59.48, an incremental effect of 0.47% more undetectable viral loads and an ICER of US$12 584 per undetectable viral load. CONCLUSIONS: Nurse-led ART was associated with higher mean health service costs than doctor-led care, with small effects on primary outcomes, and a high associated level of uncertainty. Given this, and the shortage of doctors, further implementation of nurse-led ART should be considered, although this may increase health service costs.
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 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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".