Evaluating the cost-effectiveness of TB diagnostic strategies in HIV-positive patients in Lusaka, Zambia
Bibliographic record
Abstract
Background: Zambia is a high burden country for both human immunodeficiency virus (HIV) and tuberculosis (TB) infection and HIV clinic enrollees are a high-risk group for active TB. The advent of low-cost rapid-diagnostic technologies for TB, such as the Gene Xpert MTB/RIF assay and microscopic-observation drug-susceptibility (MODS) culture, suggest an affordable and promising diagnostic method to quickly identify and treat cases, but the cost-effectiveness of these tests are not well-established in high burden areas with a high prevalence of HIV. Methods: The objective of this study was to determine the cost-effectiveness of different TB diagnostic strategies in a cohort of HIV-infected patients in Lusaka. A cost-effectiveness analysis (CEA) was carried out using decision analysis modeling. Using the cohort proportions of HIV clinic enrollees at the Centre for Infectious Disease Research in Zambia (CIDRZ), the analysis compared the following strategies: standard of care TB diagnosis (a combination of symptom screening, sputum smear microscopy, and chest x-ray in a diagnostic algorithm), enhanced TB screening (symptom screening, sputum smear microscopy, chest x-ray and TB culture), Gene Xpert MTB/RIF Assay, MODS, and empiric treatment. Outcomes analyzed were number of cases of active pulmonary TB diagnosed and incremental cost-effectiveness ratios (ICERS). Results: Preliminary results from the base case analysis found that the standard of care was US$75.74 per case of TB detected, US$327.80 per case detected using enhanced screening, US$108.90 per case detected using Xpert, and US$41.74 per case detected using MODS. The ICERS ranged from US$34.00 per case averted using MODS or Xpert to US$252.00 per case averted using enhanced screening, where the standard of care was the comparison group. Conclusion: The decision analysis model suggests that using rapid diagnostics, particularly MODS, is very cost-effective for diagnosing TB in HIV clinic enrollees in Lusaka, Zambia.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".