Associations among socioeconomic status, patterns of care, and outcomes in breast cancer patients in a universal health care system: Ontario's experience
Bibliographic record
Abstract
BACKGROUND: The Canadian health care system provides equitable access to equivalent standards of care. The authors investigated to determine whether patients with breast cancer who had different socioeconomic status (SES) received different care and had different overall survival (OS) in Ontario, Canada. METHODS: Women who were diagnosed with breast cancer between 2004 and 2009 were identified from the Ontario Cancer Registry and linked to provincial databases to ascertain patient demographics, screening, diagnosis, treatment patterns, and survival. SES was defined as neighborhood income by postal code and was divided into income quintiles (Q1-Q5; with Q5 the highest SES quintile). Univariable and multivariable analyses were used to examine the associations between: 1) SES and mammogram screening and breast cancer treatments, and 2) SES and OS. RESULTS: In total, 34,776 patients with breast cancer who had information on disease stage available at diagnosis were identified. Seventy-six percent of women were aged >50 years. Patients with higher SES were more likely to be diagnosed at an earlier stage (Q5 [44.3%] vs Q1 [37.7%]; odds ratio [OR], 1.31; 95% confidence interval [CI], 1.23-1.41; P < .0001) and also were more likely to receive adjuvant chemotherapy (Q5 vs Q1: OR, 1.18; 95% CI, 1.10-1.26; P < .0001) and radiotherapy (Q5 vs Q1: OR, 1.24; 95% CI, 1.15-1.33; P < .0001). The 5-year OS rates for Q1 through Q5 were 80%, 81%, 82.2%, 83.9%, and 85.7%, respectively (P < .0001). After adjusting for patient demographics, cancer stage at diagnosis, adjuvant chemotherapy, trastuzumab, radiotherapy and surgery types, higher SES remained associated with better OS (P = .0017). CONCLUSIONS: In a universal health care system, higher SES is associated with greater screening and treatments and with better OS after adjusting for screening, cancer stage at diagnosis, and treatments.
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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 | 0.000 |
| 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) | 0.000 | 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".