Cryptococcal Meningitis Screening and Community-based Early Adherence Support in People With Advanced Human Immunodeficiency Virus Infection Starting Antiretroviral Therapy in Tanzania and Zambia: A Cost-effectiveness Analysis
Bibliographic record
Abstract
BACKGROUND: A randomized trial demonstrated that among people living with late-stage human immunodeficiency virus (HIV) infection initiating antiretroviral therapy, screening serum for cryptococcal antigen (CrAg) combined with adherence support reduced all-cause mortality by 28%, compared with standard clinic-based care. Here, we present the cost-effectiveness. METHODS: HIV-infected adults with CD4 count <200 cells/μL were randomized to either CrAg screening plus 4 weekly home visits to provide adherence support or to standard clinic-based care in Dar es Salaam and Lusaka. The primary economic outcome was health service care cost per life-year saved as the incremental cost-effectiveness ratio (ICER), based on 2017 US dollars. We used nonparametric bootstrapping to assess uncertainties and univariate deterministic sensitivity analysis to examine the impact of individual parameters on the ICER. RESULTS: Among the intervention and standard arms, 1001 and 998 participants, respectively, were enrolled. The annual mean cost per participant in the intervention arm was US$339 (95% confidence interval [CI], $331-$347), resulting in an incremental cost of the intervention of US$77 (95% CI, $66-$88). The incremental cost was similar when analysis was restricted to persons with CD4 count <100 cells/μL. The ICER for the intervention vs standard care, per life-year saved, was US$70 (95% CI, $43-$211) for all participants with CD4 count up to 200 cells/μL and US$91 (95% CI, $49-$443) among those with CD4 counts <100 cells /μL. Cost-effectveness was most sensitive to mortality estimates. CONCLUSIONS: Screening for cryptococcal antigen combined with a short period of adherence support, is cost-effective in resource-limited settings.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".