Comparing quality of diabetes care between immigrants and non-immigrants within dimensions of marginalization: A population-based cohort study
Bibliographic record
Abstract
AIMS: Immigrants in western countries face an increased risk of developing diabetes and have been shown to receive lower quality of diabetes care. However, it is uncertain whether this disparity in care persists when comparing immigrants and non-immigrants with similar levels of marginalization with respect to the social determinants of health. METHODS: Using population-based healthcare administrative data linked to immigration and neighbourhood census data, we conducted a retrospective cohort study of all people aged ≥ 40 years with diabetes in Ontario, Canada on 1 April 2019. Process measures (testing for HbA1c, LDL-cholesterol and urine albumin-creatinine ratio; eye examinations; and appropriate prescriptions) and outcome measures (achieving guideline-recommended targets for laboratory tests) over the following year were ascertained. They were compared between immigrants and non-immigrants overall and within the highest and lowest quintiles of three measures of marginalization: material deprivation, residential instability and dependency. RESULTS: There were 1,449,589 people with diabetes included in the study (22.6 % immigrants). Immigrants were less likely than non-immigrants to achieve many of the process quality indicators and were less likely to achieve both HbA1c and LDL-cholesterol targets. These findings were similar when stratified within the highest and lowest quintiles of material deprivation, residential instability and dependency. CONCLUSIONS: Even within similar levels of marginalization, immigrants were less likely to achieve many quality indicators for diabetes care than non-immigrants. This finding suggests that the gap in quality of care between immigrants and non-immigrants is not simply due to differences in these social determinants of health, and highlights the intersecting impact of immigration and marginalization. However, the disparities were relatively small, so the greater issue is the overall low achievement of these quality indicators among all people with diabetes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".