Health Effects of Metabolic Risks in the United States From 1990 to 2019
Bibliographic record
Abstract
INTRODUCTION: Metabolic risks including high body mass index, high fasting plasma glucose, high low-density lipoprotein cholesterol, high systolic blood pressure, kidney dysfunction and low bone mineral density, contribute heavy burden to the US health systems. We aimed to investigate the burden attributable to metabolic risks in the US from 1990 to 2019. METHODS: Using methodology of Global Burden of Disease Study, the deaths and DALYs attributable to metabolic risks were analyzed by age, gender, states, Socio-demographic Index (SDI) and diseases from 1990 to 2019 in the US. RESULTS: In 2019, the age-standardized death and DALY rates attributable to metabolic risks were 174.9 and 4738.7 per 100,000 people, accounting for 33.1% and 18.2% of death and DALY rates from all causes in the US, and there was a decrease by -32.5% and -21.2% in age-standardized death and DALY rates since 1990. The burden attributable to metabolic risks increased with age, and was higher in males than females. In addition, the burden varied widely across the states, generally in inverse proportion to the SDI levels, and the heaviest burden was observed in East and West South-Central of the US. Cardiovascular diseases carried heavy burden attributable to metabolic risks. CONCLUSION: The burden attributable to metabolic risks remained major public health concerns in the US. Prevention of metabolic risks should be a high priority in the US.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".