Stricter containment policies in the second quarter of 2021 are associated with lower excess mortality
Bibliographic record
Abstract
In the wake of the COVID-19 pandemic, societies and health systems in Latin America and the Caribbean (LAC) continue to face large-scale threats with far-reaching consequences for the health and well-being of its population.More than ever, health systems in the region need to be strengthened to not only deliver high performance in typical times, but also to be resilient against major shocks, such as pandemics, the effects of climate change, or financial crises.While the pandemic laid bare the vulnerabilities of even the most highly funded and well-prepared health systems in high-income OECD countries, the tragic health outcomes of COVID-19 in the LAC region were in large part associated with structural limitations and chronic underinvestment in health.LAC countries had to tackle COVID-19 with far fewer doctors, nurses, and hospital beds than the average of OECD countries.In such context, while policy responses mitigated the loss of many lives in the region, in 2020 and 2021 there were still 2.3 million more deaths in LAC than was expected for those years in absence of the pandemic.Furthermore, the weaknesses of the health systems in LAC were further compounded by a range of social challenges, including high levels of poverty, income inequality, and labour informality; large swaths of the population living in informal settlements without access to essential services; and the growing threats to the region's rich ecosystems that also affect the populations that are most directly integrated with them.The consequences of climate change add a layer of complexity to health systems in a geographically diverse region, which includes high-altitude mountains and glaciers, the world's largest tropical rainforest, several small island nations, and megalopolises with tens of millions of people each.Health systems in the region must prepare for changing patterns of infectious diseases, exposure to extreme temperatures and catastrophic weather events, and rising sea levels, or risk dire consequences for societies in the region.In the face of these multiple challenges, it is imperative to develop effective health strategies that consider the complex realities of the LAC region.The limited budget available for health in the region makes the task of providing high-quality health services more challenging, requiring innovative solutions that are based on data, evidence, and the best ideas.However, the investments that were needed to strengthen health systems are a fraction of what the pandemic cost the economies of LAC countries.Similarly, making health systems in the region greener and resilient to face the challenges of climate change is urgent.As we move forward, more and better investment in health will be necessary in order to ensure that the health requirements of the population are met with greater efficiency and focused on people's needs.This volume, jointly prepared by the OECD and the World Bank, aims to offer an important contribution to these efforts by combining a retrospective analysis of lessons to be learned from the response to the pandemic in the region with a prospective look at how health systems can prepare for the future challenge of climate change.It further brings together the most complete and up-to-date set of data and indicators on all aspects of health systems in the LAC region.The OECD and the World Bank will continue to work together and reach out to key partners, such as the Pan-American Health Organization, to support governments and societies in the region to improve the performance of their health systems.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".