Small-area spatio-temporal analyses of bladder and kidney cancer risk in Nova Scotia, Canada
Bibliographic record
Abstract
Background: Bladder and kidney cancers are the ninth and twelfth most common type of cancer worldwide, respectively. Internationally, rates vary ten-fold, with several countries showing rising incidence. This study describes the spatial and spatio-temporal variations in the incidence risk of these diseases for Nova Scotia, a province located in Atlantic Canada, where rates for bladder and kidney cancer exceed those of the national average by about 25 % and 35 %, respectively. Methods Cancer incidence in the 311 Communities of Nova-Scotia was analyzed with a spatial autoregressive model for the case counts of bladder and kidney cancers (3,232 and 2,143 total cases, respectively), accounting for each Community's population and including variables known to influence risk. A spatially-continuous analysis, using a geostatistical Local Expectation-Maximization smoothing algorithm, modeled finer-scale spatial variation in risk for south-western Nova Scotia (1,810 bladder and 957 kidney cases) and Cape Breton (1,101 bladder, 703 kidney). Results Evidence of spatial variations in the risk of bladder and kidney cancer was demonstrated using both aggregated Community-level mapping and continuous-grid based localized mapping; and these were generally stable over time. The Community-level analysis suggested that much of this heterogeneity was not accounted for by known explanatory variables. There appears to be a north-east to south-west increasing gradient with a number of south-western Communities have risk of bladder or kidney cancer more than 10 % above the provincial average. Kidney cancer risk was also elevated in various northeastern communities. Over a 12 year period this exceedance translated in an excess of 200 cases. Patterns of variations in risk obtained from the spatially continuous smoothing analysis generally mirrored those from the Community-level autoregressive model, although these more localized risk estimates resulted in a larger spatial extent for which risk is likely to be elevated. Conclusions Modelling the spatio-temporal distribution of disease risk enabled the quantification of risk relative to expected background levels and the identification of high risk areas. It also permitted the determination of the relative stability of the observed patterns over time and in this study, pointed to excess risk potentially driven by exposure to risk factors that act in a sustained manner over time.
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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 | 0.001 |
| 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) | 0.029 | 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".