Health Equity in Cancer Screening in Calgary – A Geographic Approach to Account for Population Socioeconomic Status
Bibliographic record
Abstract
IntroductionThere is substantial evidence that cancer screening rates are lower among Canadians with low socioeconomic status (SES) than they are among those with higher SES. In order to optimize cancer screening, there is a need to reduce inequities in cancer screening. Objectives and ApproachThe purpose of this study is to understand how breast, colorectal and cervical cancer screening participation varies by socioeconomic status within local geographic areas (LGAs) in the city of Calgary. A Bayesian multilevel regression method with a spatial component was used to estimate Standardized Incidence Rates (SIR) at the LGA level. Bivariate spatial clustering analyses between screening rates at the Dissemination Area (DA) level and Pampalon material and social deprivation index was performed to better understand spatial structures of low and high screening rates compared to high and low material and social deprivation scores within LGAs. ResultsThe effect of material (income, education and employment) and social (living alone, separated, and divorced or windowed) deprivation on lower screening rates was stronger for breast cancer screening, compared to cervical and colorectal screening. Estimated likelihood of screening significantly decreased from the least deprived to the most deprived (9% for the material component and 18% for the social component for Breast cancer; 8% for the material component and 10% for the social component for cervical cancer screening). Clusters of lower screening rates and higher social and material deprivation were identified in the northeastern and central areas of the city. Conclusion/ImplicationsThe study allowed identifying LGAs and neighborhoods within those LGAs that have lower screening rates likely to be explained by the material and social deprivation of the population. The approach provides additional evidence for planning targeted interventions and reducing inequities for screening.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".