Global investments in pandemic preparedness and COVID-19
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,006 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,000 | 0,003 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».