Temporal Trends in Thyroid Cancer Incidence in California—Letter
Bibliographic record
Abstract
Horn-Ross and colleagues (1) recently reported on increasing thyroid cancer (TC) incidence in California neighborhoods, which was not explained by immigration in various racial groups. TC incidence is also increasing in Ontario, with significant regional variability (2). National Canadian data have suggested increased TC incidence in regions with the highest prevalence of foreign-born individuals (3). We explored whether the prevalence of immigrants and racial minorities could explain Ontario regional TC incidence, using publicly available data in women.The female, age-adjusted TC incidence rate and number of cases were obtained for each of the 14 Ontario Local Health Integration Network (LHIN) regions from 2007 Cancer Care Ontario data (4). Immigrant and racial data from 2006 were retrieved from a government-sponsored report (5). The relationship between explanatory variables and TC incidence was assessed through separate Poisson regression models, with female TC case count being the response variable and the expected number of cases for each LHIN used as an “offset” term. An overdispersed (or quasi-Poisson) model was used, allowing for the possibility that variation in the case counts is more substantial than predicted by sampling error. To account for non-normally distributed data, risk estimates were described as relative rates for interquartile ranges (IQR, the difference between the 25th and 75th percentiles), and their respective 95% confidence intervals (CI) (based on SEs, with an implicit assumption of spatial independence of intervals). R software (R version 3.0.2, R Foundation for Statistical Computing) was used.The total number of TC cases reported in Ontario women in 2007 was 1,639 (mean incidence 23.5/100,000; ref. 4). The highest TC incidence was in and around Toronto (i.e., Central, Toronto Central, Mississauga Halton, and Central East LHINs), with many of these regions reporting some of the highest prevalence rates for new immigrants or visible minorities. The relative rate for IQR of age-adjusted TC risk was 1.90 (95% CI, 1.73–2.10), according to the regional percentage of immigrants. The relative rate for IQR for TC risk, according to the percentage prevalence of visible minorities, was 1.75 (95% CI, 1.59–1.92).Our preliminary observations on the relationship between Ontario female TC incidence and immigrant/visible minority status need to be confirmed. Differences in study design (including lack of individual-level data on trends over time in our analysis), immigration patterns, and healthcare system funding, may, explain why our findings contrast with that of Horn-Ross and colleagues (1).No potential conflicts of interest were disclosed.A.M. Sawka holds a Chair in Health Services Research from Cancer Care Ontario (funded by the Ontario Ministry of Health and Long-term Care). P.E. Brown is funded by the Natural Sciences and Engineering Research Council of Canada.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".