Impact of Language Barriers on Complications and Mortality Among Immigrants With Diabetes: A Population-Based Cohort Study
Bibliographic record
Abstract
OBJECTIVE: Our objective was to examine the effect of language barriers on the risk of acute and chronic complications of diabetes and on mortality among immigrants. RESEARCH DESIGN AND METHODS: Linked health and immigration databases were used to identify 87,707 adults with diabetes who immigrated to Ontario, Canada, between 1985 and 2005. These individuals were included in our cohort and stratified by language ability at the time of their immigration application. Primary end points included: one or more emergency department visit or hospitalization for 1) hypo- or hyperglycemia, skin and soft tissue infection, or foot ulcer and 2) a cardiovascular event or death between April 1, 2005, and February 29, 2012. RESULTS: Our cohort was followed up for a median of 6.9 person-years. Immigrants with language barriers were older (mean age, 49 ± 15 vs. 42 ± 13 years; P < 0.001), more likely to have immigrated for family reunification (66% vs. 38%, P < 0.001), had less education (secondary school or less and no education, 82% vs. 53%; P < 0.001), and a higher use of health care (mean visits, 8.6 ± 12.1 vs. 7.8 ± 11.2; P < 0.001). Immigrants with language barriers were not found to have higher adjusted rates of diabetes complications (acute complications: hazard ratio [HR] 0.99, 95% CI 0.93-1.05; cardiovascular events or death: HR 0.95, 95% CI 0.91-0.99). Significant predictors included older age, being unmarried, living in a rural neighborhood, and having less education. Immigrants who were older (≥65 years) and who had arrived through family reunification had a lower risk of cardiovascular events or death (HR 0.88, 95% CI 0.81-0.96). CONCLUSIONS: In a heterogenous immigrant population with universal insurance, language barriers were not found to increase the risk of diabetes complications. However, their effect may vary based on age at time of landing, education level, marital status, and neighborhood of settlement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.000 | 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".