Allostatic Load Patterns by U.S. Citizenship Status and Length of U.S. Residency Among Adults, 2009–2018
Bibliographic record
Abstract
Introduction: Emerging research has documented that having a non-citizen status (e.g., temporary visa, undocumented) induces stress and, as a result, has adverse impacts on health. It is unclear whether chronic stress differs by citizenship status and length of U.S. residency using clinical biomarkers. The objective of this study was to examine allostatic load (or cumulative stress) by citizenship status and length of U.S. residency among U.S. adults. Methods: The study sample included 27,705 adult respondents (aged ≥20 years) from the 2009-2018 National Health and Nutrition Examination Surveys. Multivariable Poisson regression models were estimated with U.S. citizenship status (U.S.-born citizens, naturalized citizens, noncitizens) and U.S. residency (shorter: <15 years, longer: ≥15 years) on allostatic load. Allostatic load was defined with summative scores of 10 biomarkers (systolic blood pressure, diastolic blood pressure, high-density lipoprotein, total cholesterol, HbA1c, BMI, albumin, estimated glomerular filtration rate, white blood cell count, and asthma). Results: Naturalized citizens with shorter U.S. residency (females only) and noncitizens with shorter U.S. residency had lower allostatic load than U.S.-born citizens. Naturalized citizens with longer U.S. residency had greater allostatic load than naturalized citizens with shorter U.S. residency (females only). Noncitizens with longer U.S. residency had higher allostatic load than both noncitizens with shorter U.S. residency and naturalized citizens with shorter U.S. residency (females only). Conclusions: This study demonstrates nuanced impacts on allostatic load by citizenship status and length of U.S. residency, with differences by sex. The findings infer that citizenship status contributes to health inequities among immigrants, with greater attention needed to unpack citizenship-stress mechanisms.
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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 | 0.000 |
| 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) | 0.001 | 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 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".