Understanding the financial hardships faced by TB and HIV patients during the COVID-19 pandemic: a mixed-method study in Bandung and Yogyakarta, Indonesia
Bibliographic record
Abstract
The COVID-19 pandemic had significant widespread financial impacts, resulting in decreased household income, increased unemployment, and disrupted health services. Despite the higher prevalence of infections of tuberculosis (TB) and human immunodeficiency virus (HIV) in poorer populations, research on the financial challenges faced by these populations during the pandemic is still limited. Indonesia recorded the highest COVID-19 cases in Southeast Asia (6 815 156) while contending with the dual burden of HIV and TB. This study investigates the factors influencing out-of-pocket (OOP) payments and catastrophic health spending during the pandemic, alongside patients' challenges and coping mechanisms in Bandung and Yogyakarta, Indonesia. We employed a parallel convergent mixed-methods approach, combining quantitative analysis of OOP costs with qualitative interviews. The determinants of OOP payments were analysed using a two-part cluster-robust regression model. Catastrophic health spending was defined as OOP payments exceeding 10% of a household's annual income. Data on OOP spending were recorded via diaries, while qualitative data were gathered from in-depth interviews with TB and HIV patients and healthcare workers from January to October 2022. The findings indicated that 5.13% [95% confidence interval (CI): 2.99-7.28] of households incurred catastrophically. The median household spent USD 8.48 OOP, with nonmedical expenses comprising the largest share (median USD 5.93). Key predictors of higher costs included facility location in Yogyakarta (OOP costs difference USD 23.84, 95% CI: 9.90-37.77, P < .001), seeking care from public hospitals (USD 17.37, 95% CI: 8.83-25.90, P < .001), and the absence of health insurance (USD 10.49, 95% CI: 2.40-18.58, P = .011). Patients reported that job losses during lockdowns exacerbated financial strain, while coping strategies documented included borrowing, family contributions, and selling assets. This is the first study to focus on OOP spending and the financial hardships experienced by TB and HIV patients in Indonesia during the pandemic, providing insights for targeted policy and preparedness efforts to alleviate the financial burden during large-scale public health crises.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".