Socioeconomic Disparities and Other Factors in Dyslipidemia: Insights from NHANES 2017–2020 Data
Bibliographic record
Abstract
Introduction: Dyslipidemia, characterized by abnormal blood lipid levels, is a key risk factor for cardiovascular disease. Socioeconomic status can play a role in the development of chronic disease, including as an influence on risk factors for chronic diseases such as cardiovascular disease. Methods: This study analyzes the relationship between socioeconomic status and dyslipidemia using a population-based cross-sectional survey (NHANES 2017–2020 data). A cohort of 5862 adults was examined, focusing on socioeconomic factors (income, education, occupation) and their association with lipid profiles while controlling for sociodemographic, lifestyle, and medical variables, contributing to understanding how health disparities may affect chronic disease outcomes. Results: Low socioeconomic status was consistently associated with higher dyslipidemia risk, while high socioeconomic status demonstrated a modest protective effect. Age, BMI, hypertension, and diabetes were key predictors, highlighting the need for targeted interventions. Conclusions: This study underscores the critical role of socioeconomic status in dyslipidemia risk. Low socioeconomic status consistently increased the odds of dyslipidemia. While high socioeconomic status demonstrated some protective effects, these were diminished when accounting for lifestyle and clinical factors, highlighting the complex interplay of socioeconomic status and health behaviors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".