Breast cancer screening disparities among urban immigrants: a population-based study in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Breast cancer is one of the leading cause of mortality and morbidity in Canada. Screening is the most promising approach in identification and treatment of the disease at early stage of its development. Research shows higher rate of breast cancer mortality among ethno-racial immigrant women despite their lower incidence which suggests disparities in mammography screening. This study aimed to compare the prevalence of appropriate mammography screening among immigrant and native borne women and determine predicators of low mammography screening. METHODS: We conducted secondary data analyses on Ontario linked social and health databases to determine the proportion of women who were screened during the two-year period of 2010-2012 among 1.4 million screening-eligible women living in urban centres in Ontario. We used multivariate Poisson regression to adjust for various socio-demographic, health care-related and migration related variables. RESULTS: 64% of eligible women were appropriately screened. Screening rates were lowest among new and recent immigrants compared to referent group (Canadian-born women and immigrant who arrived before 1985) (Adjusted Rate Ratio (ARR) (0.87, 95% CI 0.85-0.88 for new immigrants and 0.90, 95% CI 0.89-0.91 for recent immigrants. Factors that were associated with lower rates of screening included living in low-income neighborhoods, having a male physician, having internationally-trained physician and not being enrolled in primary care patient enrolment models. Those not enrolled were 22% less likely to be screened compared to those who were (ARR 0.78, 95% CI 0.77-0.79). CONCLUSION: To enhance immigrant women screening rates efforts should made to increase their access to primary care patient enrolment models and preferably female health professionals. Support should be provided to interventions that address screening barriers like language, acculturation limitations and knowledge deficit. Health professionals need to be educated and take an active role in offering screening guidelines during health encounters.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 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".