PREVALENCE AND INCIDENCE OF HORMONAL-RELATED CANCERS (HRCs) IN RURAL SASKATCHEWAN: THE SASKATCHEWAN RURAL HEALTH STUDY
Bibliographic record
Abstract
A significant number of rural Canadians dwellers are affected by hormone-related cancers (HRCs) like breast and prostate cancer. Little is known about the rates of occurrence and contextual factors that are associated with the development of breast and prostate cancers in rural residents. This study aimed to determine the incidence and prevalence of breast and prostate cancer among Saskatchewan rural dwellers and to explore individual and contextual factors that are associated with the prevalence of these cancers. To accomplish our goal, we utilized and analyzed data from the Saskatchewan Rural Health Study (SRHS). The SRHS involved a prospective cohort conducted in two phases: the baseline survey (in 2010) and a 4-year follow-up survey (in 2014). In the baseline, the SRHS research team obtained completed questionnaires from 4624 households, including information about 8261 individuals, 18 years and older. Questionnaires were returned from 2797 households comprised of 4867 individuals, in the follow-up survey. Crude prevalence and incidence of HRCs were calculated using appropriate formulae. Adjusted prevalence was calculated using logistic regression based on generalized estimating equations approach to account for hierarchy in the data (individuals within a household). Our study reported the crude prevalence of HRCs (breast and prostate cancers combined) as 3.0%, and 3.4% respectively, at the baseline and follow-up. The adjusted prevalence analysis showed that following variables were significantly associated with HRCs (breast and prostate cancers combined): exposure to radiation (odds ratio [OR] 3.39; 95% confidence interval [CI]: 2.23, 4.84), previous history of cancer in a sibling (i.e. brother or sister) (OR = 1.51, 95% CI: 1.11, 2.07) and a positive history of cancer in father (OR=1.37; 95% CI= 1.01, 1.86). The current study showed cumulative incidence for breast and prostate cancers in rural Saskatchewan as 0.86% and 1.08%, respectively. When combined (i.e. breast and prostate cancers together), the cumulative incidence for HRCs was 0.97%. In summarizing our study findings, HRCs were slightly more prevalent amongst non-farm residents 3.2% when compared with farm residents 2.8% and within the eastern part of the province (6.5%, 6.6%, respectively) as compared to the western part (4.6%, 6.3%, respectively) among both farm and non-farm residents, at the baseline. It appears that the prevalence of HRCs among farm and non-farm rural residents depend on the complex interplay among a variety of factors such as individual and contextual factors.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".