GEOGRAPHICAL ANALYSIS OF HYPERTENSION IN CANADIAN POPULATION
Bibliographic record
Abstract
Objective: A quarter of population is affected by hypertension in Canada. Substantial improvement has been achieved in HTN control in Canada over the past twenty-five years. However, disparities associated with factors like sex, ethnicity, location and health care access remain. Reliable information about trends in hypertension is needed at regional scale for the development of health policies and programs. Health geography and geographic information systems can provide a better understanding of HTN and its risk factors by detecting clusters using spatial analysis and mapping rates of HTN. The objectives of the study were to identify health regions with significantly high or low HTN rate using geostatistics and compare evolution from 2005 to 2019. It also seeks out to identify their populations’ characteristics using conventional statistical analysis. Design and method: Canadian Community Health Survey (2005, 2015, 2019) data were used for the analysis. The 130,000 respondents are located according to health regions and data can be analyzed using a geographic information system. Using age and gender as criteria for subgroups creation, we applied spatial autocorrelation analysis and hot spot/cold analysis to identify geographically contiguous health regions with high or low HBP rates. We then extracted the records in CCHS database for all respondents living in these regions for further statistical analysis. Results: HTN is still a major problem in Canadian population, especially for people aged 65 years and older although 72.9% considered themselves in good, very good or excellent health in 2015. Increase in HTN rate in health regions is spread all over Canada, although some regions showed a decrease in HTN rate. Getis-ord analysis located a cold spot region in western Canada while a hot spot was located in Ontario, central Canada. Cluster of health regions with high HTN rates shows more diabetes and more obese people even though 38.4% declared being in good, very good or excellent health. Conclusions: Increase in hypertension rate in health regions is spread all over Canada, even tough some regions showed a decrease in HBP rate. Canadians 65 years and older with HBP where more prone to obesity & diabetes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".