COVID-19 policies and tuberculosis services in private health sectors of India, Indonesia, and Nigeria
Bibliographic record
Abstract
Introduction: The COVID-19 pandemic created unprecedented challenges in the field of global health. Nigeria, Indonesia and India are three high tuberculosis (TB) burden countries with large private health sectors. Both TB and the private health sector faced challenges in these countries because of COVID-19. This study aimed to compare the COVID-19 control measures and policies in the provision of TB care services and gain insights from policymakers on how the pandemic affected the provision of TB services in the private healthcare sector, how each country adapted, and identify lessons learned for health system preparedness. Methods: Qualitative, in-depth interviews were conducted among a purposive sample of 11 national and sub-national policymakers in each country. Thematic content analysis was conducted on the data collected using an adapted WHO Health Equity Policy Framework. Results: Results revealed three policy dimensions under costs, access, and quality. Under healthcare costs, policymakers highlighted resource allocation and diversion of TB resources to COVID response, and increased operational costs for private provider. Under healthcare access, key themes included reduced TB case detection due to fear of COVID-19, disrupted diagnostic services, and adaptations such as extended medicine supplies and tele-consultations. Under healthcare quality, themes included compromised TB diagnostic accuracy due to similar respiratory symptoms with COVID-19, and strain on laboratory infrastructure due to competing demands from both diseases. Policymakers across the three countries pointed to the need for strengthening private-public partnerships (PPP) for healthcare service delivery and continued private sector investment to facilitate the continuity of TB care within a pandemic context. Conclusion: The results of this study provide an overview of the impact of the pandemic from the perspective of private facilities and policymakers in Nigeria, Indonesia and India, which can inform future policy and ways forward in strengthening PPP for healthcare service delivery in high TB burden countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".