A longitudinal perspective to migrant health: Unpacking the immigrant health paradox in Germany
Bibliographic record
Abstract
Previous research finds that recent immigrants are healthier than the native-born, while more established immigrants exhibit worse health, suggesting a process of unhealthy assimilation. However, previous literature is mostly based on cross-sectional data or on longitudinal analyses similarly failing to disentangle individual-level variation from between-individual confounding. Moreover, previous longitudinal studies are often limited in their study of different health outcomes (few and mostly subjective health), populations (sometimes only elderly individuals), time periods (short panels) and geographical contexts (mostly Australia, Canada and USA). We address these limitations by comparing the health trajectories of adult immigrants and natives in Germany over extended periods, using data from years 2002–2021 of the German Socio-Economic Panel (SOEP), and investigating a wide range of health outcomes, including self-assessed physical and mental health measures, diagnosed illnesses, and health behaviors. We employ a longitudinal approach that stratifies immigrants by age at arrival, and compares them to natives of the same age. This allows us to estimate both Hierarchical Linear Models and more rigorous Fixed Effects models to further address confounding. Cross-sectionally, we confirm previous literature's findings: recent immigrants are healthier than natives and established immigrants. Longitudinally, we find support for the unhealthy assimilation hypothesis concerning subjective health and mental health, but not for the others health indicators or behaviors. We interpret these findings as possible evidence of immigrants' reduced access to timely health care and emphasize the need for greater longitudinal research investigating migrant gaps in various health outcomes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".