Diabetes Care of Non-obese Korean Americans: Considerable Room for Improvement
Bibliographic record
Abstract
BACKGROUND: Family doctors are increasingly managing the diabetes care of Korean-Americans. Little is known about the prevalence of diabetes among non-obese Korean-Americans, or the extent to which they receive timely and appropriate diabetes care. The purpose of this investigation is to: (1) identify the prevalence of diabetes and to determine the adjusted odds of diabetes among non-obese Korean-Americans compared to non-Hispanic White (NHW) Americans, (2) examine the factors associated with having diabetes in a large sample of non-obese KoreanAmericans, and (3) determine the prevalence and adjusted odds of optimal frequency of eye care, foot care and A1C blood glucose level monitoring among non-obese Korean-Americans with diabetes in comparison to NHWs with diabetes. METHODS: Secondary analysis of population-based data from the combined 2007, 2009, and 2011 adult California Health Interview Survey. The sample included 74,361 respondents with body mass index (BMI) <30 kg/m2 (referred to as 'non-obese BMI'), of whom 2,289 were Korean-Americans and 72,072 were NHWs, and 4,576 had diabetes. RESULTS: The prevalence and adjusted odds of diabetes among non-obese Korean-Americans are significantly higher than among their NHW peers. More than 90% of Korean-Americans with diabetes were non-obese. NHWs had substantially higher odds of having optimal frequency of eye care, foot care and A1C glucose level monitoring, even after adjusting for insulin dependence, sex, age, education, income, and BMI. CONCLUSION: Non-obese Korean-Americans are at higher risk for diabetes and are much less likely to receive optimal diabetes care in comparison to NHWs. Targeted outreach is necessary.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".