Sociodemographic Associations With Blood Pressure in 10–14-Year-Old Adolescents
Bibliographic record
Abstract
PURPOSE: To determine the association between sociodemographic characteristics and blood pressure among a demographically diverse population-based sample of 10-14-year-old US adolescents. METHODS: We conducted cross-sectional analyses of data from the Adolescent Brain Cognitive Development Study (N = 4,466), year two (2018-2020). Logistic and linear regression models were used to determine the association between sociodemographic characteristics (sex, race/ethnicity, sexual orientation, household income, and parental education) with blood pressure among early adolescents. RESULTS: The sample was 49.3% female and 46.7% non-White. Overall, 4.1% had blood pressures in the hypertensive range. Male sex was associated with 48% higher odds of hypertensive-range blood pressures than female sex (95% confidence interval [CI], 1.02; 2.14), and Black race was associated with 85% higher odds of hypertensive-range blood pressures compared to White race (95% CI, 1.11; 3.08). Several annual household income categories less than $100,000 were associated with higher odds of hypertensive-range blood pressures compared to an annual household income greater than $200,000. We found effect modification by household income for Black adolescents; Black race (compared to White race) was more strongly associated with higher odds of hypertensive-range blood pressures in households with income greater than $75,000 (odds ratio 3.92; 95% CI, 1.95; 7.88) compared to those with income less than $75,000 (odds ratio 1.53; 95% CI, 0.80; 2.92). DISCUSSION: Sociodemographic characteristics are differentially associated with higher blood pressure in early adolescents. Future research could examine potential mediating factors (e.g., physical activity, nutrition, tobacco) linking sociodemographic characteristics and blood pressure to inform targeted interventions.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".