Economic costs of accessing tuberculosis (TB) diagnostic services in Malawi: an analysis of patient costs from a randomised controlled trial of computer-aided chest x-ray interpretation
Bibliographic record
Abstract
Background: Patients with tuberculosis (TB) symptoms in low-resource settings face convoluted diagnostic and treatment linkage pathways, incurring substantial health-seeking costs. In the context of a randomised trial looking at the impact of novel diagnostics such as computer-aided chest x-ray diagnosis (CAD4TB), we aimed to investigate the costs incurred by patients seeking TB diagnosis and whether optimised diagnostic interventions could result in a reduction in the cost faced by households. Methods: PROSPECT was a three-arm randomised trial conducted in a public primary health clinic in Blantyre, Malawi during 2018-2019 (trial arms: standard of care [SOC]; HIV testing [HIV]; HIV testing and CAD4TB [HIV/TB]). The direct and indirect costs incurred by 219 PROSPECT participants over the 56-day follow-up period were collected. Costs were deemed catastrophic if they exceeded 20% of annual household income. We compared mean costs and used generalised linear regression models to examine whether the interventions could result in a reduction in total costs. Results: The mean total cost incurred by all 219 participants was US$12.11 (standard error (SE): 1.86). The indirect and direct cost was US$8.47 (SE: 1.66) and US$3.64 (SE: 0.38), respectively. The mean total cost composed of 5.6% of the average annual household income. In total, 5% (9/180) of the participants with complete income data incurred catastrophic costs. Compared to SOC, there was no statistically significant difference in the mean total cost faced by those in the HIV (ratio: 0.77, 95% CI: 0.51, 1.19) and HIV/TB arms (ratio: 0.85, 95% CI: 0.53, 1.37). Conclusions: Despite the absence of user fees, patients seeking healthcare with TB symptoms incurred catastrophic costs. The optimised TB diagnostic interventions that were investigated in the PROSPECT study did not significantly reduce costs. TB diagnosis interventions should be implemented alongside social protection policies whilst ensuring healthcare facilities are accessible by the poor.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".