Association Between Social Vulnerability and Gastrointestinal Cancer Mortality in the United States Counties
Bibliographic record
Abstract
Background and Aims Social determinants of health contribute to disparities in gastrointestinal (GI) cancer mortality between individuals in the US. Their effects on count-level mortality rates remain uncertain. We aimed to assess the association between county social vulnerability and GI cancer mortality. Methods In this ecological study (2016–2020), we obtained US county Social Vulnerability Index (SVI) from the Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry and age-adjusted mortality rates (AAMRs) for GI cancers from Centers for Disease Control and Prevention WONDER (Wide-Ranging Online Data for Epidemiological Research). SVI ranges from 0 to 1, with higher indices indicating greater vulnerability. We presented AAMRs by quintiles of SVIs. We used Poisson regression through generalized estimating equation to calculate rate ratios (RRs) and 95% confidence intervals (CIs) for GI cancer mortality by quintiles of SVI. Results There were 799,968 deaths related to GI cancers from 2016 to 2020, resulting in an AAMR (95% CI) of 39.9 (41.4–51.2) deaths per 100,000 population. The largest concentration of counties with greater SVI and GI cancer mortality was clustered in the southern US. Counties with greater SVI had higher mortality related to all GI cancers (RR Q5 vs Q1 , 1.19 [95% CI, 1.14–1.24]), gastric cancer (1.58 [1.48–1.69]), liver cancer (1.54 [1.36–1.73]), and colorectal cancer (RR Q5 vs Q1 , 1.23 [95% CI, 1.15–1.31]). RRs for overall GI cancers were greater among individuals <45 years (1.24 [1.15–1.32]), men (1.22 [1.16–1.27]), Hispanic individuals (1.33 [1.18–1.50]), and rural counties (1.21 [1.14–1.27]) compared with their counterparts. Conclusion Socially disadvantaged counties face a disproportionately high burden of GI cancer mortality in the US. Targeted public health interventions should aim to address social inequities faced by underserved communities.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".