Global investments in pandemic preparedness and COVID-19
Bibliographic record
Abstract
Background The COVID-19 pandemic highlighted gaps in health surveillance systems, disease prevention, and \ntreatment globally. Among the many factors that might have led to these gaps is the issue of the financing of national \nhealth systems, especially in low-income and middle-income countries (LMICs), as well as a robust global system for \npandemic preparedness. We aimed to provide a comparative assessment of global health spending at the onset of the \npandemic; characterise the amount of development assistance for pandemic preparedness and response disbursed in \nthe first 2 years of the COVID-19 pandemic; and examine expectations for future health spending and put into context \nthe expected need for investment in pandemic preparedness. \nMethods In this analysis of global health spending between 1990 and 2021, and prediction from 2021 to 2026, we \nestimated four sources of health spending: development assistance for health (DAH), government spending, out-ofpocket spending, and prepaid private spending across 204 countries and territories. We used the Organisation for \nEconomic Co-operation and Development (OECD)’s Creditor Reporting System (CRS) and the WHO Global Health \nExpenditure Database (GHED) to estimate spending. We estimated development assistance for general health, \nCOVID-19 response, and pandemic preparedness and response using a keyword search. Health spending estimates \nwere combined with estimates of resources needed for pandemic prevention and preparedness to analyse future \nhealth spending patterns, relative to need. \nFindings In 2019, at the onset of the COVID-19 pandemic, US$9·2 trillion (95% uncertainty interval [UI] 9·1–9·3) was \nspent on health worldwide. We found great disparities in the amount of resources devoted to health, with high-income \ncountries spending $7·3 trillion (95% UI 7·2–7·4) in 2019; 293·7 times the $24·8 billion (95% UI 24·3–25·3) spent by \nlow-income countries in 2019. That same year, $43·1 billion in development assistance was provided to maintain or \nimprove health. The pandemic led to an unprecedented increase in development assistance targeted towards health; in \n2020 and 2021, $1·8 billion in DAH contributions was provided towards pandemic preparedness in LMICs, and \n$37·8 billion was provided for the health-related COVID-19 response. Although the support for pandemic preparedness \nis 12·2% of the recommended target by the High-Level Independent Panel (HLIP), the support provided for the healthrelated COVID-19 response is 252·2% of the recommended target. Additionally, projected spending estimates suggest \nthat between 2022 and 2026, governments in 17 (95% UI 11–21) of the 137 LMICs will observe an increase in national \ngovernment health spending equivalent to an addition of 1% of GDP, as recommended by the HLIP. \nInterpretation There was an unprecedented scale-up in DAH in 2020 and 2021. We have a unique opportunity at this \ntime to sustain funding for crucial global health functions, including pandemic preparedness. However, historical \npatterns of underfunding of pandemic preparedness suggest that deliberate effort must be made to ensure funding is \nmaintained.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".