Heterogeneity in Asian Americans’ mortality trends, 2000–2022
Bibliographic record
Abstract
OBJECTIVES: Although Asian Americans are the fastest-growing racial/ethnic group in the United States, their recent mortality trends have not been sufficiently studied. This study provides a comprehensive analysis of years of life lost (YLL) from age 25 to 84 among six largest Asian ethnic groups, comparing them to non-Hispanic Whites. METHODS: We analyzed data from the CDC Multiple Cause of Death database and the American Community Survey (2000-2022) using a cause of death decomposition method. RESULTS: Among college-educated individuals, all Asian ethnic groups experienced either a smaller decrease or an increase in YLL compared to Whites in 2000-2022. These disparities were not primarily driven by the COVID-19 pandemic, though Filipinos and Indians were disproportionately affected compared to Whites. Instead, the divergence in YLL trends predates 2020. Indians showed the most unfavorable trend, with YLL worsening even before the pandemic, followed by Koreans. At least 75% of the smaller YLL reductions among Asians were due to slower improvements in mortality from circulatory diseases, cancer, and diabetes. These findings indicate a less favorable mortality trend for Asian Americans compared to White Americans, particularly the college-educated, in the early 21st century. They also suggest that, over time, Asians may be deriving diminishing health returns from higher education compared to Whites. DISCUSSION: We discuss differential trends between Whites and Asians, as well as variation within Asian ethnic and educational groups, in the context of socioeconomic conditions, labor market dynamics, racialization in the United States, and stages of nutrition transition in countries of origin.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".