Spatial-temporal analysis of bladder cancer risk in the New England Bladder Cancer Study
Bibliographic record
Abstract
Background: Exploring spatial-temporal patterns of disease incidence can identify areas of significantly elevated or decreased risk, providing potential clues about disease risk factors and timing of exposure. Aims: We sought to explore the spatial-temporal risk of bladder cancer in three New England states in the United States. Methods: We examined bladder cancer risk in relation to residential location based on interview data from a large, population-based case-control study conducted in Maine, New Hampshire, and Vermont from 2001 to 2004 (N = 500 urothelial carcinoma case patients and 602 control subjects). Subjects in the analysis data set resided within the study area for the 25-year period before study enrollment. We used crude and adjusted generalized additive models to spatially model the probability of being a case. We adjusted for several important risk factors, including smoking history, occupational history, and exposure to drinking water contaminants (arsenic, disinfection by-product exposure). We evaluated models at several different time periods independently to explore the presence of significant risk areas in a time frame of etiologic relevance. We also modeled cumulative spatial risk over 25 years before diagnosis of disease. Results: Risk of bladder cancer varied over space and the pattern of unexplained risk was consistent in time windows of 5, 10, 15, 20 years before diagnosis and at time of diagnosis. Analyses stratified by French Canadian status revealed a distinct spatial pattern of unexplained risk among French Canadians. Conclusions: We found a significant association between spatial location and bladder cancer risk after adjusting for several important risk factors. Additional analyses of etiologic factors to determine the reason for this association will be presented.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".