SPATIAL VARIABILITY IN THE PREVALENCE OF HYPERTENSION: A CANADIAN PERSPECTIVE
Bibliographic record
Abstract
Objective: Despite significant improvements in hypertension treatment, high blood pressure (HBP) still affects 22.6% of Canadians (Hypertension Canada, 2016). However, disparities associated with risk factors like, age, sex, and lifestyle can impact global prevalence in population. It is also a well-known fact that prevalence varies geographically. Thus, spatial location can give us a different perspective of hypertension in population. Reliable information about trends in hypertension rate is needed at regional scale to support the development of health programs. Geographic information systems (GIS) can provide that information by detecting spatial clusters of high or low HTN rate at health regions scale. 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 statistical analysis. Canadian Community Health Survey (2005, 2015 & 2019) data were used for the analysis. Design and method: For the analysis, we used Canadian Community Health Survey from 2005, 2015 and 2019. The 130,000 respondents representing the whole population are located according to health regions and data can be analyzed using a GIS. Using age and gender as subgroups criteria creation, we applied spatial autocorrelation analysis and Getis-Ord hot analysis to identify geographically contiguous health regions with high or low HTN rates. We then extracted the records in CCHS database for all respondents living in these regions for supplemental statistical analysis. Results: HTN is still a problem in Canada. This is especially true for people aged 65 and older although 72.9% considered themselves in good health. Increase in HTN rate is spread all over Canada, although some regions showed a rate decrease. Getis-ord analysis located regions with low HTN rates while regions with high rates were also located. Cluster of health regions with high HTN rates shows more diabetes and more obese people even though 38.4% declared being in good health. Conclusions: Hypertension is still a major problem in Canada. Age and gender are still significant risk factors. Geographical variations in HTN rate are presents at health regions scale.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".