Health financing policies during the COVID-19 pandemic and implications for universal health care: a case study of 15 countries
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic was a health emergency requiring rapid fiscal resource mobilisation to support national responses. The use of effective health financing mechanisms and policies, or lack thereof, affected the impact of the pandemic on the population, particularly vulnerable groups and individuals. We provide an overview and illustrative examples of health financing policies adopted in 15 countries during the pandemic, develop a framework for resilient health financing, and use this pandemic to argue a case to move towards universal health coverage (UHC). METHODS: In this case study, we examined the national health financing policy responses of 15 countries, which were purposefully selected countries to represent all WHO regions and have a range of income levels, UHC index scores, and health system typologies. We did a systematic literature review of peer-reviewed articles, policy documents, technical reports, and publicly available data on policy measures undertaken in response to the pandemic and complemented the data obtained with 61 in-depth interviews with health systems and health financing experts. We did a thematic analysis of our data and organised key themes into a conceptual framework for resilient health financing. FINDINGS: Resilient health financing for health emergencies is characterised by two main phases: (1) absorb and recover, where health systems are required to absorb the initial and subsequent shocks brought about by the pandemic and restabilise from them; and (2) sustain, where health systems need to expand and maintain fiscal space for health to move towards UHC while building on resilient health financing structures that can better prepare health systems for future health emergencies. We observed that five key financing policies were implemented across the countries-namely, use of extra-budgetary funds for a swift initial response, repurposing of existing funds, efficient fund disbursement mechanisms to ensure rapid channelisation to the intended personnel and general population, mobilisation of the private sector to mitigate the gaps in public settings, and expansion of service coverage to enhance the protection of vulnerable groups. Accountability and monitoring are needed at every stage to ensure efficient and accountable movement and use of funds, which can be achieved through strong governance and coordination, information technology, and community engagement. INTERPRETATION: Our findings suggest that health systems need to leverage the COVID-19 pandemic as a window of opportunity to make health financing policies robust and need to politically commit to public financing mechanisms that work to prepare for future emergencies and as a lever for UHC. FUNDING: Bill & Melinda Gates Foundation.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| 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".