Patient and family costs associated with tuberculosis, including multidrug-resistant tuberculosis, in Ecuador.
Bibliographic record
Abstract
BACKGROUND: There is little published information on the costs of multidrug-resistant tuberculosis (MDR-TB) for patients and their families in low- or middle-income countries. METHODS: Between February and July 2007, patients with microbiologically confirmed active TB who had received 2 months of treatment completed an interviewer-administered questionnaire on direct out-of-pocket expenditures and indirect costs from lost wages. Clinical data were abstracted from their medical records. RESULTS: Among 104 non-MDR-TB patients, total TB-related patient costs averaged US$960 per patient, compared to an average total cost of US$6880 for 14 participating MDR-TB patients. This represents respectively 31% and 223% of the average Ecuadorian annual income. The high costs associated with MDR-TB were mainly due to the long duration of illness, which averaged 22 months up to the time of the interview. This resulted in very long periods of unemployment. Most patients experienced a significant drop in income, particularly the MDR-TB patients, all of whom were earning less than US$100/month at the time of the interview. CONCLUSION: Direct and indirect costs borne by patients with active TB and their families are very high in Ecuador, and are highest for patients with MDR-TB. These costs are important barriers to treatment completion.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".