Incidence of breast and colorectal cancer among immigrants in Ontario, Canada: a retrospective cohort study from 2004-2014
Bibliographic record
Abstract
BACKGROUND: Studies have shown that morbidity and mortality rates due to cancer among recent immigrants are lower than those among the native-born population. The objectives of this study were to describe the incidence of colorectal and breast cancer among immigrants from major regions of the world compared to Canadian-born residents of the province of Ontario and to examine the role of length of stay and neighborhood income. METHODS: Retrospective cohort study including all individuals 18 years and over residing in Ontario from 2004 to 2014. Age-standardized incidence rates (ASIR) were calculated for immigrants from each world region versus Canadian-born residents and stratified by neighborhood income quintile and length of stay. Binomial regression analysis was used to determine the association of neighbourhood income, length of stay, and location of birth with colorectal and breast cancer incidence. RESULTS: Canadian immigrants born in South Asia had the lowest colorectal and breast cancer incidence (colorectal: women: ASIR = 0.14; men: ASIR = 0.18; breast: ASIR = 1.00) compared to long-term residents during the study period (colorectal: women: ASIR = .57; men: ASIR = .72; breast cancer ASIR = 1.61). In multivariate analyses, neighboorhood income did not consistently play a significant role in colorectal cancer incidence; however higher neighbourhood income was a risk factor for breast cancer among immigrant women (adjusted relative risk for highest neighboorhood income quintile versus lowest income quintile =1.21, 95% CI = 1.18-1.24). Increased length of stay was associated with higher risk of cancer. After adjusting for age, neighborhood income, and length of stay, those born in Europe and Central Asia had the highest risk of colorectal cancer compared to those born in East Asia and Pacific and those born in the Middle East had the greatest additional risk of breast cancer. CONCLUSIONS: After correcting for age, breast and colorectal cancer incidence rates among immigrants differ according to their region of birth and recent immigrants to Ontario have lower colorectal and breast cancer incidence rates than their native-born peers. However, those advantages diminish over time. These findings call for Ontario to generate tools and interventions to maintain the health of the immigrant population, particularly for those groups with a higher incidence of cancer.
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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) | 0.005 | 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".