The prevalence and management of diabetes among Vietnamese Americans: A population-based survey of an understudied ethnic group
Bibliographic record
Abstract
OBJECTIVES: Although obesity remains relatively rare among Vietnamese Americans, the prevalence of diabetes has increased in this population. This study aims to: 1. Estimate the prevalence of diabetes among non-obese Vietnamese American adults compared to non-obese non-Hispanic whites (NHW). 2. Identify factors associated with diabetes among non-obese Vietnamese Americans. 3. Examine whether Vietnamese Americans and NHW with diabetes are equally as likely to receive optimal frequency of diabetes care (i.e., hemoglobin A1C monitoring, foot care, eye care). METHODS: We conducted a secondary analysis of non-obese adult Vietnamese Americans using pooled data from the 2007, 2009, 2011 and 2013-2016 waves of the California Health Interview Survey (CHIS). RESULTS: Only 9% of Vietnamese Americans with diabetes are obese. Non-obese Vietnamese Americans have 60% higher adjusted odds of diabetes compared to non-obese NHW. Among non-obese Vietnamese Americans, those who were older, ever smokers and born outside US had a higher prevalence of diabetes. We found both Vietnamese Americans and NHW with diabetes received similar levels of care. DISCUSSION: Non-obese Vietnamese Americans have much higher odds of diabetes than NHW. Health professionals can effectively minimize disparities between Vietnamese Americans and NHW with diabetes through appropriate monitoring of foot care, eye care and A1C 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) | 0.001 | 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 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".