Do Immigrants Experience Morbidity and Disability Disadvantages at Older Ages? A Research Note
Bibliographic record
Abstract
Prior studies show that Hispanic and Black immigrants are more susceptible to disabilities and chronic diseases in their later years than U.S.-born Whites, despite their health advantage at younger ages. Such studies often rely on data from the Health and Retirement Study (HRS), which disproportionately includes immigrants who arrived decades ago. The shortage of research on immigrants of other ethnoracial groups further makes it unclear whether the old-age declines in health advantages among Hispanic and Black immigrants are generalizable. Using the up-to-date HRS and National Health Interview Survey (NHIS) data, this study compares the prevalences of chronic diseases, functional limitations, and activity limitations between U.S.-born Whites and immigrants of various ethnoracial identities across datasets. We find that Hispanic and Black immigrants in the HRS exhibit significantly greater disability disadvantages at older ages in relation to native-born Whites than those in the NHIS. Older White and Asian immigrants encounter no health disadvantages regardless of data source. We demonstrate that the especially low socioeconomic status of Hispanic immigrants in the HRS, along with the two surveys' different measurements of activity limitations, partly contributes to the discrepancies between the surveys. We suggest that the HRS design is conducive to undersampling of immigrants arriving more recently, leading to its immigrants' unique socioeconomic profiles. This study underscores the need for scholars of immigration and health to be cautious about dataset-specific nuances.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".