Evaluation of health system resilience in 60 countries based on their responses to COVID-19
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".