The impact of overweight and obesity on health outcomes in the United States from 1990 to 2021
Bibliographic record
Abstract
AIM: Elevated body mass index (BMI) presents a significant public health challenge in the United States, contributing to considerable morbidity, mortality and economic burden. This study investigates the health burden of overweight and obesity in the United States from 1990 to 2021, leveraging the Global Burden of Disease data set to analyse trends, disparities and potential determinants of high BMI-related health outcomes. MATERIALS AND METHODS: or higher for adults. Statistical analyses included estimated annual percentage change (EAPC) in age-standardized DALY rates and age-standardized death rates. Pearson correlation was performed between EAPCs and the socio-demographic index (SDI), with significance set at p < 0.05. RESULTS: From 1990 to 2021, age-standardized DALY rates attributable to high BMI increased by 24.9%, whereas the age-standardized death rates increased by 5.2%. Age disparities showed DALYs peaking at 60-64 years for males and 65-69 years for females, with deaths peaking at 65-69 years for males and 90-94 years for females. A strong negative correlation was found between the EAPC in age-standardized DALY and death rates and the SDI. CONCLUSIONS: Overweight and obesity significantly impact public health in the United States, especially among older adults and lower socio-demographic regions. Comprehensive public health strategies integrating behavioural, technological and environmental interventions are crucial. Future research should focus on longitudinal studies, personalized interventions and policy-driven approaches to address the multifaceted influences on high BMI.
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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) | 0.001 | 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".