Education, race/ethnicity, and multimorbidity among adults aged 30–64 in the National Health Interview Survey
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Demographic risk factors for multimorbidity 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. Understanding multimorbidity earlier in the life course is critical because of the increased likelihood of long-term disability and loss of productivity associated with chronic disease progression. OBJECTIVE: 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. DESIGN: Multimorbidity was defined as having at least 2 of 9 self-reported health conditions. Educational attainment was categorized as less than high school (HS), completed HS or some college, and bachelor's degree or higher. Logistic regression models of multimorbidity controlled for time since last doctor's visit, demographic and socioeconomic measures. RESULTS: 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 net of all included covariates. 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. CONCLUSIONS: loss of quality of life, productivity, and well-being for non-elderly adults. 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 it