The COVID-19 syndemic: a perfect storm for the life expectancy of the most disadvantaged Americans
Bibliographic record
Abstract
BACKGROUND: To explore the syndemic nature of the COVID-19 pandemic by identifying which subpopulations in the United States (US) suffered the greatest losses in life expectancy (LE) in 2020 and 2021, and to which extent these losses can be attributed to COVID-19 and 'other' causes of death. METHODS: We analysed individual death records for 2018-2021 from the National Vital Statistics System and population counts from the American Community Survey. Life table and continuous change decomposition analyses were used to quantify cause-/.specific contributions to changes in LE over time in population subgroups defined by sex, educational attainment, and race/ethnicity. RESULTS: From 2019 to 2020, educational differences in LE (high minus low education) increased substantially by 5.0 and 2.6 years in Hispanic men and women, respectively, with increases of one to two years among Black and White adults. Nearly all losses in LE among high-education Hispanic and White groups were due to COVID-19, while among low-education White and Black groups, COVID-19 accounted for 40%-47% of the total losses in LE. Changes in LE were much smaller during 2020-2021. CONCLUSIONS: COVID-19 widened preexisting inequalities in LE in the US, both via direct mortality and through syndemic interactions with other diseases and health conditions. The underlying social, political, economic, and environmental factors driving the clustering and interaction of diseases among the most disadvantaged Americans need to be addressed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".