Identifying Factors Associated With Hypertension Using Structural Equation Modeling: Evidence from a Large Kurdish Cohort Study in Iran
Bibliographic record
Abstract
Abstract BackgroundIdentifying the risk factors leading to hypertension can help explain why some populations are at a greater risk for developing hypertension than others. The present study seeks to identify the causal paths among the risk factors of hypertension in 35- to 65-year-old participants in western Iran.MethodsThe secondary analysis was conducted using the data obtained in the recruitment stage of Ravansar Non-Communicable Disease (RaNCD) cohort. Each of the latent variables were confirmed by confirmatory factor analysis. Using Structural Equation Modeling (SEM), we assessed the direct and indirect effects of the risk factors associated with hypertension.ResultsSocioeconomic status (SES), physical activity, lipid profile, obesity, Diabetes and family history of hypertension had a diverse impact on the blood pressure, directly and (or) indirectly. When investigating the latent and observers risk factors and the interrelations between different risk factors, The standardized total effect (the sum of direct and indirect effects, βt) of SES, physical activity, lipid profile, obesity, on the blood pressure was( -0.09 vs -0.14), (-0.04 vs -0.04), (0.13 vs 0.13), (0.24 vs 0.15) in men and women respectively. Diabetes had a direct relationship with the blood pressure in women (0.03).ConclusionWith regard to control of high blood pressure, public health interventions must target obesity,SES, lifestyle and nutritional factors such as hyperlipidemia and hyperglycemia in Iranian 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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".