An Analysis of Adult Obesity and Hypertension in Appalachia
Bibliographic record
Abstract
Obesity is a major health problem in the United States, and the burden associated is high. Hypertension seems to be the most common obesity-related health problem. Studies show that hypertension is approximately twice as prevalent among the obese as in the non-obese population. This study has two main objectives. First, to examine the association between obesity and hypertension within the context of economic growth in Appalachia, and second to estimate the cost of hypertension linked to obesity in Appalachia. The study uses simultaneous equations and Logit analysis for estimations. Data are from Behavior Risk Factor Surveillance Systems (BRFSS) surveys of 2001 and 2009. Results for simultaneous analysis show that hypertension decreases with decreasing obesity, increasing employment growth, and increasing income. Logit analysis highlights the importance of controlling obesity, income growth, employment growth, education, and exercises in mitigating hypertension in Appalachia. Ageing indicates a high potential of getting hypertension. Total economic cost of hypertension linked to obesity is $9.35 billion, significantly a high cost to Appalachia. Overall, results reveal the impacts of obesity on hypertension and some possible ways of mitigation. Thus, results suggest a comprehensive set of policies to Appalachia which sufficiently improve employment opportunities, educational facilities, and healthcare facilities with adequately addressed to obesity and hypertension.
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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) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".