The potential for quality assurance systems to save costs and lives: the case of early infant diagnosis of HIV
Bibliographic record
Abstract
OBJECTIVES: Scaling up of point-of-care testing (POCT) for early infant diagnosis of HIV (EID) could reduce the large gap in infant testing. However, suboptimal POCT EID could have limited impact and potentially high avoidable costs. This study models the cost-effectiveness of a quality assurance system to address testing performance and screening interruptions, due to, for example, supply stockouts, in Kenya, Senegal, South Africa, Uganda and Zimbabwe, with varying HIV epidemics and different health systems. METHODS: We modelled a quality assurance system-raised EID quality from suboptimal levels: that is, from misdiagnosis rates of 5%, 10% and 20% and EID testing interruptions in months, to uninterrupted optimal performance (98.5% sensitivity, 99.9% specificity). For each country, we estimated the 1-year impact and cost-effectiveness (US$/DALY averted) of improved scenarios in averting missed HIV infections and unneeded HIV treatment costs for false-positive diagnoses. RESULTS: The modelled 1-year costs of a national POCT quality assurance system range from US$ 69 359 in South Africa to US$ 334 341 in Zimbabwe. At the country level, quality assurance systems could potentially avert between 36 and 711 missed infections (i.e. false negatives) per year and unneeded treatment costs between US$ 5808 and US$ 739 030. CONCLUSIONS: The model estimates adding effective quality assurance systems are cost-saving in four of the five countries within the first year. Starting EQA requires an initial investment but will provide a positive return on investment within five years by averting the costs of misdiagnoses and would be even more efficient if implemented across multiple applications of POCT.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".