EDUCATION, RACE-ETHNICITY, AND MULTI-MORBIDITY AMONG ADULTS AGES 30-64 IN THE NATIONAL HEALTH INTERVIEW STUDY
Bibliographic record
Abstract
Abstract Demographic risk factors for multimorbidity (living with 2+ chronic conditions) have been identified in numerous population-based studies of older adults; however, there is less data on younger populations, despite the fact that approximately 24% of US adults age 18+ have multimorbidity. To examine the associations of education and race/ethnicity with mutimorbidity among adults aged 30-64 using cross-sectional data from the 2002-2014 National Health Interview Surveys. Compared to having a bachelor’s degree or higher, completing less than HS (OR=1.58, 95% CI = 1.50-1.66) or HS/some college (OR=1.32, 95% CI = 1.27-1.37) were both associated with increased odds of multimorbidity. Non-Hispanic Blacks had greater odds of multimorbidity (OR=1.07, 95% CI = 1.02-1.11) compared to Non-Hispanic Whites with comparable characteristics. Reducing multimorbidity through health promotion efforts across the socioeconomic spectrum and earlier in the life course will be a requirement to age successfully and support overall well-being in the aging US population.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".