Pre-treatment direct costs for people with tuberculosis during the COVID-19 pandemic in different healthcare settings in Bandung, Indonesia
Bibliographic record
Abstract
The tuberculosis (TB) program was massively disrupted due to the COVID-19 pandemic, which may have impacted on an increase in costs for people with TB (PWTB) and their households. We aimed to quantify the pre-treatment out-of-pocket costs and the factors associated with these costs from patients' perspective during the COVID-19 pandemic in Bandung, Indonesia. Adults with pulmonary TB were interviewed using a structured questionnaire for this cross-sectional study recruiting from 7 hospitals, 59 private practitioners, and 10 community health centers (CHCs) between July 2021 to February 2022. Costs in rupiah were converted into US dollars and presented as a median and interquartile range (IQR). Factors associated with costs were identified using quantile regression. A total of 252 participants were recruited. The median total pre-treatment cost was $35.45 (IQR 17.69-67.62). The highest median cost was experienced by participants from private hospitals ($54.51, IQR 29.48-98.47). The rapid antigen and PCR for SARS-CoV-2 emerged as additional medical costs among 26% of participants recruited in private hospitals. Visiting ≥ 6 providers before diagnosis ($38.40 versus $26.20, p < 0.001), presenting first at a private hospital ($50.68, p < 0.05) and private practitioners ($34.97, p < 0.05), and being diagnosed in the private health sector ($39.98 versus $20.30, p < 0.05) were significantly associated with higher pre-treatment costs. PWTB experienced substantial out-of-pocket costs in the process of diagnosis during the COVID-19 pandemic despite free TB diagnosis and treatment. Early detection and identification play an important role in reducing pre-diagnostic TB 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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".