Care pathways of individuals with tuberculosis before and during the COVID-19 pandemic in Bandung, Indonesia
Bibliographic record
Abstract
The COVID-19 pandemic is thought to have undone years' worth of progress in the fight against tuberculosis (TB). For instance, in Indonesia, a high TB burden country, TB case notifications decreased by 14% and treatment coverage decreased by 47% during COVID-19. We sought to better understand the impact of COVID-19 on TB case detection using two cross-sectional surveys conducted before (2018) and after the onset of the pandemic (2021). These surveys allowed us to quantify the delays that individuals with TB who eventually received treatment at private providers faced while trying to access care for their illness, their journey to obtain a diagnosis, the encounters individuals had with healthcare providers before a TB diagnosis, and the factors associated with patient delay and the total number of provider encounters. We found some worsening of care seeking pathways on multiple dimensions. Median patient delay increased from 28 days (IQR: 10, 31) to 32 days (IQR: 14, 90) and the median number of encounters increased from 5 (IQR: 4, 8) to 7 (IQR: 5, 10), but doctor and treatment delays remained relatively unchanged. Employed individuals experienced shorter delays compared to unemployed individuals (adjusted medians: -20.13, CI -39.14, -1.12) while individuals whose initial consult was in the private hospitals experienced less encounters compared to those visiting public providers, private primary care providers, and informal providers (-4.29 encounters, CI -6.76, -1.81). Patients who visited the healthcare providers >6 times experienced longer total delay compared to those with less than 6 visits (adjusted medians: 59.40, 95% CI: 35.04, 83.77). Our findings suggest the need to ramp up awareness programs to reduce patient delay and strengthen private provide engagement in the country, particularly in the primary care sector.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".