Health and tuberculosis systems resilience, the role of the private sector and pandemic preparedness: insights from a cross-country qualitative study with policy-makers in India, Indonesia and Nigeria
Bibliographic record
Abstract
Introduction The COVID-19 pandemic was an unprecedented challenge to health systems worldwide and had a severe impact on tuberculosis (TB) case notifications and service delivery. India, Indonesia and Nigeria are high TB-burden countries where the majority of initial care-seeking happens in the private health sector. The objectives of this study were to (1) explore policy-makers’ perspectives on the impact of the COVID-19 pandemic on private sector TB service delivery in India, Indonesia and Nigeria and (2) identify cross-cutting insights for pandemic preparedness with respect to TB service delivery. Methods From May to November 2021, 33 interviews were conducted with key policy-makers involved in health service administration, TB service delivery and/or the COVID-19 response in India, Indonesia and Nigeria (n=11 in each country). Interviews focused on the impact of COVID-19 on TB services and lessons learnt for pandemic preparedness with respect to TB in each study context. Data were analysed thematically using a hybrid inductive-deductive approach, informed by Haldane et al’s Determinants of Health Systems Resilience Framework. Results Policy-makers highlighted the crucial role of intersectoral collaboration, effective governance, innovative financing strategies, health workforce reallocation and technological advancements such as virtual consultations and mHealth in strengthening TB service delivery amid the COVID-19 pandemic. India relied on patient–provider support agencies to implement a joint strategy for TB care across sectors and states. Indonesia engaged networks of private provider professional associations to facilitate coordination of the COVID-19 response. Nigeria implemented a pandemic policy for public–private referral for the continuity of TB care. Conclusions Countries implemented varied measures to support TB service delivery during the COVID-19 pandemic. This study presents insights from three countries (India, Indonesia and Nigeria) that together offer a ‘menu’ of possibilities for supporting pandemic preparedness with respect to TB care vis-à-vis strengthening health systems resilience.
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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 | 0.019 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".