Sociodemographic correlates of cervical cancer screening rates in Calgary, AB: Matched Trend analysis of 2006, 2011 and 2016
Bibliographic record
Abstract
IntroductionCervical Cancer Screening (CCS) has reduced the incidence and mortality rates of cervical cancer (CC). However, the benefits are distributed unevenly since 30% of eligible women have not been screened within three years in Alberta. Women who have never been screened or are screened irregularly are most at risk for CC. Objectives and ApproachThe aim of this study was to understand who gets CCS and who does not, in Calgary, Alberta and analyze the CC policy implications since 2006-2016. CCS information of women aged 25-69 were obtained from Calgary Laboratory Services for the years 2006, 2011 and 2016 and matched with Canadian Census data. Negative binomial regression and Generalized Estimating Equations were used to test associations of CCS rates with socio-demographic variables for eligible women. CCS spatial trends over the years was studied using the GIS Hotspot analysis. ResultsMajor age and geographical variations were observed in CCS rates in Calgary. CCS rates in the recommended age groups varied from 40.6 % to 23.6 %. For age groups between 25 and 54, CCS rates were above 33\%, which implies that many women are having tests more than once every three years. Use was positively associated with median household income, education, Chinese ethnicity and negatively associated with ‘Black’ visible minority status. Women living in lower socio-economic areas of Calgary are screened at lower rates. Hotspot analysis maps revealed heterogeneous testing patterns in the city with relatively higher testing in the downtown, Southeast and Northwest quadrants of the city and relatively decreased CCS in the Northeast quadrant of Calgary Conclusion/ImplicationsScreening programs need to be strengthened with greater focus on including specific demographic groups and reducing overuse. Understanding current testing patterns are important in assessing the benefit to harm ratio of CCS and for monitoring and evaluation of CCS program.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| 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".