Obesity-Associated Cancers: A United States Cancer Statistics (USCS) Database Analysis
Bibliographic record
Abstract
BACKGROUND: Obesity is a well-established risk factor for various cancers, contributing to significant public health burdens. Disparities in obesity-associated cancer incidence exist across racial, age, and geographic groups, necessitating targeted prevention and intervention strategies. OBJECTIVE: The aim of this study is to analyze the incidence rates of obesity-associated cancers across different racial, age, and geographic groups in the United States from 2017 to 2021, identifying key disparities to inform public health interventions. METHODS: A retrospective analysis of cancer incidence data from national registries was conducted. Age-adjusted incidence rates (per 100,000 population) were calculated across racial/ethnic groups, age cohorts, and US states. Descriptive statistics and confidence intervals were used to assess disparities. RESULTS: Black, non-Hispanic individuals had the highest obesity-associated cancer incidence (184.8 per 100,000), followed by American Indian/Alaska Native populations (179.3 per 100,000). Incidence rates increased with age, peaking at 75-79 years (788.7 per 100,000 overall). Geographically, Midwestern and Southern states exhibited higher incidence rates, with West Virginia reporting the highest (188.3 per 100,000) and Nevada the lowest (149.5 per 100,000). These findings highlight significant racial, age, and regional disparities. CONCLUSION: The study underscores the need for targeted public health strategies, including enhanced screening, culturally tailored interventions, and policy-driven approaches to address obesity and its related cancer risks. Future research should explore individual-level risk factors and effective interventions to promote equitable healthcare access and improved cancer outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".