Spatially Continuous Maps of Disease Risk: An Analysis of Mortality Disparities in the United States
Bibliographic record
Abstract
Introduction: In the U.S., race and education have proven to be critical determinants of health outcomes inequalities, influenced by both location and time. Lung cancer (primarily linked to smoking), along with drug overdoses, alcohol poisoning, and suicide, emerge as important contributors to mortality risk. The purpose of this study is to enhance the understanding of geographic disparities in mortality related to lung cancer and external causes of death in the U.S. This study focuses on a finer geographic scale while examining the components of variation in mortality rates, stratifying by both education and race. Methods: This study used an aggregated spatial model and Bayesian inference to create continuous maps of risk. The study analyzes mortality counts from 1999 to 2015. This approach not only allows one to analyze and combine data sources at different spatial resolutions in a shorter time, but also facilitates standardized comparisons across age, sex, education, and spatial location. Results: Education emerged as the primary source of variation in both types of mortality, with growing disparities since 1999. Spatially, mortality risk varies more among the less educated and less among the more educated. Lung cancer rates have declined for the most educated but stayed steady for the less educated, while external causes of death rates rose for the less educated but remain unchanged for the more educated ones. Racial differences are significant for external causes of death but minimal for lung cancer. Conclusions: These findings highlight the need to go beyond education as a simple predictor variable in mortality studies, while also accounting for the differences in the nature of spatial variation. While the impact of education on mortality rates is unsurprising, the fact that Black Americans on average encounter inferior socioeconomic conditions compared to White Americans, may explain why this group is usually recognized at higher risk. This investigation suggests that addressing racial disparities in education levels could help to reduce tobacco-related mortality discrepancies effectively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".