Cancer Screening in the Toronto Central LHIN by Sub-region and Neighbourhood: Evidence from an Applied Health Research Question (AHRQ)
Bibliographic record
Abstract
IntroductionThe Toronto Central (TC) LHIN has aligned its vision with the Ministry of Health and Long-Term Care and Cancer Care Ontario to prioritize participation in population-based cancer screening programs and to address screening inequities. This request was accepted by the ICES AHRQ review team to help impact policy and programs. Objectives and ApproachBy linking cancer screening data to geographic, provider and immigration databases, underlying contributors and barriers to cancer screening can be better understood and used to target interventions to specific groups and areas of the province. Thus, the purpose of this study was to: 1) determine the rates of cancer screening in the TC LHIN by sub-region and neighbourhood, and 2) determine how these differences vary by immigration status, primary care provider characteristics and neighbourhood income level. Using OHIP billing claims, screening for breast, cervical and colorectal cancer was identified between fiscal years 2013 to 2016. ResultsDuring the study period, 58.4% of eligible TC LHIN residents received a mammogram, 57.1% received a pap smear and 55.1% received colorectal cancer screening. Screening rates varied by Toronto neighbourhood: 48.4%–72.9%, 38.8%–70.1% and 42.7%–68.7% for mammograms, pap smears and colorectal cancer screening respectively. Residents who recently immigrated to Ontario received less cancer screening than non-immigrants; 51.8% of immigrant women eligible for cervical cancer screening received a pap smear compared to 60.4% of non-immigrant women in the TC LHIN East region. Not having a female physician (54.3% vs 68.6%), lacking comprehensive care (55.6% vs 62.5%), having a foreign trained physician (56.6% vs 64.7\%) and living in a lower income neighbourhood (51.6%-62.5%) were other factors associated with lower rates of cancer screening. Conclusion/ImplicationsCancer screening rates vary according to neighbourhood, and certain groups may be vulnerable to inadequate screening. These findings will help to address cancer screening disparities due to structural barriers, and will help in the delivery of culturally appropriate and relevant cancer screening educational packages and outreach programs in Toronto.
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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 | 0.015 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".