PS-BPP04-2: GEOSTATISTICAL ANALYSIS OF HYPERTENSION IN CANADIAN POPULATION
Bibliographic record
Abstract
Objective: A quarter of the population is affected by hypertension in Canada and, around the world, it is the second most common conditions for consultation in primary care. Substantial improvement has been achieved in HTN control in Canada over the past twenty-five years. However, as noted in many studies, disparities associated with factors like sex, ethnicity, home location and health care access remain. Reliable information about trends in hypertension is needed at national and regional scale for the development of health policies and programs. Health geography and geographic information systems can provide a better understanding of the distribution 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 2015 and 2015 to 2019. It also seeks out to identify their populations characteristics using conventional statistical analysis. Canadian Community Health Survey (2005, 2015 & 2019) data were used for the analysis. Design and Method: Canadian Community Health Survey CCHS (2005, 2015, 2019) data were used for the analysis. The 130,000 respondents representing the whole population are located according to health regions and data can be analyzed using a geographic information system. Using age and gender as subdividing criteria for subgroups creation, we applied Moran I spatial autocorrelation analysis and Getis-Ord 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 and Conclusion: 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. In final conclusion, this approach could also be used in other countries seeking a better understanding of socio-demographic and geographic factors affecting hypertension rate in the country.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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".