Incidence of Differentiated Thyroid Cancer by Socioeconomic Status and Urban Residence: Canada 1991–2006
Bibliographic record
Abstract
BACKGROUND: The incidence of thyroid cancer is increasing in Canada. The purpose of this study was to investigate the following questions. First, what was the magnitude of increased incidence of thyroid cancer in Canada from 1991-2006? Second, is there an association between socioeconomic status (SES) and thyroid cancer incidence in Canada? Third, does the relationship between SES and the incidence of thyroid cancer vary by rural/urban status? METHODS: Thyroid cancer cases were drawn from the Canadian Cancer Registry. Demographic and socioeconomic information were extracted from the Canadian Census of Population data. We linked cases to income quintiles (InQs) according to patients' postal codes, and categorized place of residence into city, town, or rural. We then performed a negative binomial regression analysis on the incidence of thyroid cancer to identify relationships between these variables. RESULTS: The overall incidence of thyroid cancer in Canada increased by 156% between 1991 and 2006. Incidence was significantly lower among individuals from lower InQs (incidence rate ratio 0.77 for lowest InQ compared to highest). The incidence of thyroid cancer was more than 25% lower in towns or rural areas compared to cities, after controlling for SES and demographic factors. Lastly, when we allowed the relationship between thyroid cancer incidence and geography of residence to vary by SES, we found that the difference in incidence between highest and lowest InQs was significantly larger in cities than in towns and was insignificant in rural areas. CONCLUSIONS: Our study confirmed a dramatic increase in thyroid cancer incidence in Canada. Thyroid cancer incidence was significantly higher in higher InQs and in cities. These data support the theory that increased access to imaging is largely responsible for this increased incidence.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".