IMMIGRANT GENERATIONAL STATUS AND ETHNIC DIFFERENCES IN HEALTH
Bibliographic record
Abstract
We read with interest 2 recent reports using population-level epidemiological data to make inferences about ethnic differences in illicit drug use status1 or health status.2 Both reports were remiss in not attributing more importance to the influence of immigration history and immigrant generational status. Particularly for countries of origin distant from the United States and Canada and from which most immigrants came voluntarily (as opposed to immigrating as refugees), immigrants tend to have better health status and better health practices than is the norm either in their country of origin or among second-generation or later-generation persons sharing their national heritage.3 This phenomenon is known as the “healthy immigrant” effect.4,5 We applaud Delva and associates’ examination of the effects of acculturation, in which the authors used a question about first language spoken as a child (English or Spanish) as a proxy for acculturation status. First-generation immigrants are more likely to use a language other than English in the home than second or later generations.6 However, we take issue with the authors’ inclusion of respondents of Cuban and Puerto Rican origin in this examination. Most Cuban immigrants have come to the United States as refugees, and Puerto Rican immigrants have had many rights of US citizenship and much exposure to US culture before immigrating to the continental United States. Not surprisingly, Delva and associates found that only for Mexican Americans and “other Latin American” students was the language question a significant predictor of marijuana use or heavy drinking. Consistent with the healthy immigrant effect, use of Spanish in these populations was associated with a reduced likelihood of reporting marijuana use or heavy alcohol drinking. To their credit, Wu and Schimmele did incorporate a dummy variable called “immigrant,” reflecting whether a respondent had been born in Canada or not.7 Again, consistent with the healthy immigrant effect, they found functional health to be greater in first-generation Canadians than in Canadians who were born in Canada. This effect has elsewhere been observed to be particularly pronounced in Blacks, resulting in 7.8 to 9.4 years of extra longevity for first-generation immigrant Blacks compared with native-born Blacks.7 Wu and Schimmele should be more cautious in their speculative explanation for the superior functional health status of Canadian Blacks compared with the health of US Blacks. It would be more parsimonious to attribute the observed national differences in Black mortality rates to national differences in immigration history than to national differences in access to health care. Researchers examining racial/ethnic health disparities in countries characterized by substantial recent immigration should consider and report both immigration history and immigrant generational status because of these factors’ high potential for confounding with ethnicity as contributors to observed disparities in health status.
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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 | 0.012 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".