The UK's foreign aid cuts: implications for financing health systems globally
Bibliographic record
Abstract
Background The UK is a major provider of official development assistance (ODA) to low-income and middle-income countries (LMICs). The UK recently announced that 102 countries and territories would not receive an ODA budget for the 2021–22 year. Given that the UK is the second largest health ODA donor, these cuts could have serious implications for health systems in LMICs. We aimed to understand how these cuts might affect financing for health systems in countries receiving UK aid. Methods We analysed domestic and external funding for 134 countries that received UK ODA in 2019–20 that had data available. Our goal was to understand the differences between countries that will continue to receive aid in 2020–21 (termed budget safe, n=34) and those that will not (termed budget cut, n=100) and quantify the role the UK plays in financing health systems among both cohorts. Findings 53 (53%) of 100 budget-cut countries are LMICs, and sub-Saharan Africa is the region with the largest share (27 countries [27%]). The UK makes up less than 10% of health ODA for almost all budget-cut countries (95 [95%]). The health systems of two budget-cut countries in particular might be faced with financing challenges given their high ratios of UK health aid to domestic government health expenditures: The Gambia (1·24:1) and Eritrea (0·33:1). Although most budget-safe countries are LMICs (26 [76%]), a quarter (eight [24%]) are upper-middle-income or high-income, signalling the geostrategic importance of some wealthier countries to the new UK aid agency. The UK is a large health donor among budget-safe countries: it contributes more than 10% of the health ODA budget in 11 (32%) of 34 countries. Many low-income budget-safe countries in sub-Saharan Africa also exhibit high ratios of UK health ODA to domestic government health expenditures (eg, South Sudan [3·15:1], Sierra Leone [0·48:1], and the Democratic Republic of the Congo [0·34:1]). Interpretation The 2021–22 UK budget cuts might not be as catastrophic for as many health systems as expected, although some countries might be affected more than others. The UK is a major player in most budget-safe countries, and several countries, particularly low-income countries in sub-Saharan Africa, show high ratios of reliance on UK health aid. Our analysis focused on financing for health systems, but we did not consider the potential effect on funding cuts to health outcomes, analysed potential country responses to these cuts, or propose mechanisms for closing funding gaps. Funding None.
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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 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.001 |
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".