National Profiles of Coronavirus Disease 2019 Mortality Risks by Age Structure and Preexisting Health Conditions
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Although individual age and preexisting health conditions are well-documented risk factors for coronavirus disease 2019 (COVID-19) mortality, it is unclear whether these 2 factors capture unique dimensions of risk for epidemic severity at the national level. In addition, no studies have examined whether national distributions of these factors are associated with epidemic experiences to date. RESEARCH DESIGN AND METHODS: Drawing on surveys of older adults from 42 countries and estimated case fatality ratios by age and preexisting health conditions, we document and compare national profiles of COVID-19 mortality risks among older adults. We develop 2 measures of national risk profiles: one based on age structures and another based on distributions of preexisting health conditions. Our analysis compares these constructs and documents their associations with national COVID-19 mortality rates. RESULTS: National profiles of COVID-19 mortality risk based on age structure and preexisting health conditions are moderately uncorrelated, capturing different aspects of risk. Both types of national risk profiles correlate meaningfully with countries' COVID-19 mortality experiences to date. DISCUSSION AND IMPLICATIONS: Measures of population age structure are readily available for every country in the world, while cross-national measures of older adult population health are more limited. In the COVID-19 crisis, these factors give different pictures of the countries with high and low risks of COVID-19 mortality. Moreover, our results suggest that both types of national risk profiles based on population health reflect current COVID-19 mortality severity in several countries, highlighting the need for more cross-national comparative data on older adult population health.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".