Hospitalization for heart disease, stroke, and diabetes mellitus among Indian-born persons: a small area analysis
Bibliographic record
Abstract
BACKGROUND: We set out to describe the risk of hospitalization from heart disease, stroke, and diabetes among persons born in India, all foreign-born persons, and U.S.-born persons residing in New York City. METHODS: We examined billing records of 1,083,817 persons hospitalized in New York City during the year 2000. The zip code of each patient's residence was linked to corresponding data from the 2000 U.S. Census to obtain covariates not present in the billing records. Using logistic models, we evaluated the risk of hospitalization for heart disease, stroke and diabetes by country of origin. RESULTS: After controlling for covariates, Indian-born persons are at similar risk of hospitalization for heart disease (RR = 1.02, 95% confidence interval 1.02, 1.03), stroke (RR = 1.00, 95% confidence interval, 0.99, 1.01), and diabetes mellitus (RR = 0.96 95% confidence interval 0.94, 0.97) as native-born persons. However, Indian-born persons are more likely to be hospitalized for these diseases than other foreign-born persons. For instance, the risk of hospitalization for heart disease among foreign-born persons is 0.70 (95% confidence interval 0.67, 0.72) and the risk of hospitalization for diabetes is 0.39 (95% confidence interval 0.37, 0.42) relative to native-born persons. CONCLUSIONS: South Asians have considerably lower rates of hospitalization in New York than reported in countries with national health systems. Access may play a role. Clinicians working in immigrant settings should nonetheless maintain a higher vigilance for these conditions among Indian-born persons than among other foreign-born populations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".