Cost of tuberculosis diagnosis and treatment from the patient perspective in Lusaka, Zambia.
Bibliographic record
Abstract
SETTING: Urban primary health centres in Lusaka, Zambia. OBJECTIVES: 1) To estimate patient costs for tuberculosis (TB) diagnosis and treatment and 2) to identify determinants of patient costs. METHODS: A cross-sectional survey of 103 adult TB patients who had been on treatment for 1-3 months was conducted using a standardised questionnaire. Direct and indirect costs were estimated, converted into US$ and categorised into two time periods: 'pre-diagnosis/care-seeking' and 'post-diagnosis/treatment'. Determinants of patient costs were analysed using multiple linear regression. RESULTS: The median total patient costs for diagnosis and 2 months of treatment was $24.78 (interquartile range 13.56-40.30) per patient--equivalent to 47.8% of patients' median monthly income. Sex, patient delays in seeking care and method of treatment supervision were significant predictors of total patient costs. The total direct costs as a proportion of income were higher for women than men (P < 0.001). Treatment costs incurred by patients on the clinic-based directly observed treatment strategy were more than three times greater than those incurred by patients on the self-administered treatment strategy (P < 0.001). CONCLUSION: Clinic-based treatment supervision posed a significant economic burden on patients. The creation or strengthening of community-based treatment supervision programmes would have the greatest potential impact on reducing patients' TB-related costs.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".