Diabetes Screening Among Immigrants
Bibliographic record
Abstract
OBJECTIVE: To examine diabetes screening, predictors of screening, and the burden of undiagnosed diabetes in the immigrant population and whether these estimates differ by ethnicity. RESEARCH DESIGN AND METHODS: A population-based retrospective cohort linking administrative health data to immigration files was used to follow the entire diabetes-free population aged 40 years and up in Ontario, Canada (N = 3,484,222) for 3 years (2004-2007) to determine whether individuals were screened for diabetes. Multivariate regression was used to determine predictors of having a diabetes test. RESULTS: Screening rates were slightly higher in the immigrant versus the general population (76.0 and 74.4%, respectively; P < 0.001), with the highest rates in people born in South Asia, Mexico, Latin America, and the Caribbean. Immigrant seniors (age ≥65 years) were screened less than nonimmigrant seniors. Percent yield of new diabetes subjects among those screened was high for certain countries of birth (South Asia, 13.0%; Mexico and Latin America, 12.1%; Caribbean, 9.5%) and low among others (Europe, Central Asia, U.S., 5.1-5.2%). The number of physician visits was the single most important predictor of screening, and many high-risk ethnic groups required numerous visits before a test was administered. The proportion of diabetes that remained undiagnosed was estimated to be 9.7% in the general population and 9.0% in immigrants. CONCLUSIONS: Overall diabetes-screening rates are high in Canada's universal health care setting, including among high-risk ethnic groups. Despite this finding, disparities in screening rates between immigrant subgroups persist and multiple physician visits are often required to achieve recommended screening levels.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".