Small-area variation in screening for cancer, glucose and cholesterol in Ontario: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Screening for cervical, breast and colon cancers, and elevations of cholesterol and glucose, reduces premature cause-specific mortality from these cancers and circulatory diseases. Despite primary care reforms and incentives, and promotion of cancer-screening programs among individuals, participation is suboptimal. We aimed to examine participation as of Dec. 31, 2011, by factors of deprivation, demographics and primary care at the small-area level. METHODS: From health care administrative databases, we identified people eligible for each screening test, and their participation, in each dissemination area (referred to as small areas, n = 18 950) in Ontario. We calculated rates for each test among small areas (overall and stratified by demographic, socioeconomic and primary care descriptors) and stratified by sex for all tests combined. We loaded all data into a geographic information system. Funnel plots were generated showing the percentage of eligible people who completed screening for all tests by small area, stratified by sex. Overall and stratified screening prevalence ratios were calculated among small areas. RESULTS: Among small areas, the mean and SD for participation in all tests combined was 31.6% (SD 11.0%) for women and 41.2% (SD 12.0%) for men. Screening prevalence among small areas, for each test and for all tests combined, overall and stratified by sex, declined with decreasing percentage with high school completion, decreasing socioeconomic quintile, and decreasing percentage with an identifiable primary care physician. INTERPRETATION: Our results show that the rate of participation in all eligible screening tests among small areas is much lower than the rate of participation in any one particular test. This finding has implications for the design and implementation of strategies to improve rates of screening.
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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 | 0.001 | 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) | 0.000 | 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".