The impact of socioeconomic status (SES) on stage of cancer at time of diagnosis: A population-based study in Ontario, Canada
Bibliographic record
Abstract
6505 Background: Lower SES is known to be associated with worsened cancer survival. Here we evaluate the impact of SES on stage of cancer at diagnosis in Ontario which has universal health insurance. Methods: All incident cases of breast, colon, rectal, non-small cell lung, cervical and larynx cancer diagnosed in Ontario 2003–2005 were identified using the Ontario Cancer Registry. Stage information is only captured routinely for patients seen at Ontario's 8 Regional Cancer Centers (RCCs). This represents approximately 68% of the population and forms the basis for all analyses. Using a best stage grouping approach, cases were assigned stage based on pathologic TNM if available and clinical TNM otherwise. The population of Ontario was divided into quintiles based on community median household income reported in the 2001 Canadian census. Using postal code at time of diagnosis cases were assigned to quintiles (Q); Q1 represents the communities where the poorest 20% of the Ontario population resided. Comparisons between Q1 and Q2–5 were made using the chi-square test. A Cox model was used to evaluate overall survival, SES, stage, and age. Results: Stage at diagnosis was available for 19,239/23,254 (83%) of cases seen at RCCs. Among cases with breast cancer, those in Q1 were less likely to have stage I disease (43 vs 47%, p = 0.004) and more likely to have stage IV disease (5 vs 4%, 0.008) than Q2–5. With lung cancer, cases in Q1 were more likely to have stage I disease compared to Q2–5 (16 vs 13%, p = 0.015). Distribution of stage I and stage IV disease did not differ by SES across other individual diseases. However, for all 6 cancers combined, cases in Q1 were less likely than Q2–5 to have stage I disease (27 vs 30%, p = 0.001) and more likely to have stage IV disease (21 vs 18%, p < 0.0001). We found significant gradients in 3-year overall survival across Q1-Q5 for breast (5% absolute difference in survival, p < 0.001), colon (4%, p = 0.049), and cervical (18%, p = 0.031) cancers. Adjustment for stage and age only slightly diminished these survival gradients. Conclusions: Despite universal health care, SES remains associated with survival among patients with cancer in Ontario. These data suggest that the difference in outcome is only partially explained by differences in stage at diagnosis. No significant financial relationships to disclose.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| 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".