Using geographic methods to inform cancer screening interventions for South Asians in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Literature suggests that South Asians in Ontario, Canada are under-screened for breast, cervical and colorectal cancer. Accordingly, we are involved in a community-engaged multi-phase study aimed at increasing cancer screening for this vulnerable group. In the work described in this manuscript, we aimed to use visual displays of spatial analyses to identify the most appropriate small geographic areas in which to pilot targeted cancer screening interventions for Ontario's South Asian community. METHODS: We used Geographic Information Systems (GIS), including Local Indicators of Spatial Association (LISA) using GeoDa software, and population-level administrative data to create multi-layered maps of: i) rates of appropriate cancer screening, ii) the percentage of residents of South Asian ethnicity, and iii) the locations of primary care practices and community health centres by census tract in the Peel Region of Ontario (population: 1.2 million). The maps were shared with partner health service and community service organizations at an intervention development workgroup meeting to examine face validity. RESULTS: The lowest rates of appropriate cancer screening for census tracts across the region were 51.1% for cervical cancer, 48.5% for breast cancer, and 42.5% for colorectal cancer. We found marked variation both in screening rates and in the proportion of South Asians residents by census tract but lower screening rates in the region were consistently associated with larger South Asian populations. The LISA analysis identified a high-risk area consisting of multiple neighbouring census tracts with relatively low screening rates for all three cancer types and with a relatively large South Asian population. Partner organizations recognized and validated the geographic location highlighted by the LISA analysis. Many primary care practices are located in this high-risk area, with one community health centre located very nearby. CONCLUSIONS: In this populous region of Ontario, South Asians are more likely to reside in areas with lower rates of appropriate breast, cervical and colorectal cancer screening. We have identified a high-risk area appropriate for both patient- and provider-focused interventions. Geographic Information Systems, in particular LISA analyses, can be invaluable when working with health service and community organizations to define areas with the greatest need for interventions to reduce health inequities.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".