Evaluation of health system resilience in 60 countries based on their responses to COVID-19
Notice bibliographique
Résumé
ABSTRACT Introduction In 2020, the COVID-19 epidemic swept the world, and many national health systems faced serious challenges. To improve future public health responses, it’s necessary to evaluate the performance of each country’s health system. Methods We developed a resilience evaluation system for national health systems based on their responses to COVID-19 using four resilience dimensions: government governance and prevention, health financing, health service provision, and health workers. We determined the weight of each index by combining the three-scale and entropy-weight methods. Then, based on data from 2020, we used the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method to rank the health system resilience of 60 countries, then used hierarchical clustering to classify countries into groups based on their resilience level. Finally, we analyzed the causes of differences among countries in their resilience based on the four resilience dimensions. Results Switzerland, Japan, Germany, Australia, South Korea, Canada, New Zealand, Finland, the United States, and the United Kingdom had the highest health system resilience in 2020. Eritrea, Nigeria, Libya, Tanzania, Burundi, Mozambique, Republic of the Niger, Benin, Côte d’Ivoire, and Guinea had the lowest resilience. Government governance and prevention of COVID-19 will greatly affect a country’s success in fighting future epidemics, which will depend on a government’s emergency preparedness, stringency (a measure of the number and rigor of the measures taken), and testing capability. Given the lack of vaccines or specific drug treatments during the early stages of the 2020 epidemic, social distancing and wearing masks were the main defenses against COVID-19. Cuts in health financing had direct and difficult to reverse effects on health systems. In terms of health service provision, the number of hospitals and intensive care unit beds played a key role in COVID-19 clinical care. Conclusion Resilient health systems were able to cope more effectively with the impact of COVID-19, provide stronger protection for citizens, and mitigate the impacts of COVID-19. Our evaluation based on data from 60 countries around the world showed that increasing health system resilience will improve responses to future public health emergencies. Key Questions What is already known? According to a report by the World Health Organization, the COVID-19 epidemic placed the health systems of many countries at risk of collapse. At present, there is no evaluation index system to measure the resilience of each country’s health system against a pandemic, and there has been no quantitative assessment of the resilience of each country’s health system based on their responses to COVID-19. What are the new findings? We assessed, ranked, and quantified the health system resilience of 60 representative countries based on their responses to COVID-19 using data from 2020 on four dimensions of resilience: government governance and prevention, health financing, health service provision, and the health workforce. Western Europe, East Asia, North America, and Southern Oceania had better health system resilience, whereas Africa had low health system resilience, with very low health financing scores and weak health systems with structural and regional imbalances. Health system resilience was heavily influenced by government governance and prevention, as well as by government emergency preparedness, the stringency of their response (a measure of the number and rigor of the measures taken), and their testing capability. What do the new findings imply? Global health system resilience varied widely among countries, and many health systems remain weak and unprepared for another pandemic such as COVID-19. As a result, future pandemics will remain a major problem for humanity if improvements are not made by each government. In underdeveloped countries and regions, infectious diseases can be controlled more effectively through more efficient government governance and strict surveillance and detection measures, but achieving this depends heavily on the speed of government decision making and the level of policy formulation related to the most effective way to strengthen health systems and improve their resilience. Assistance from developed country will be essential in improving resilience.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,032 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».