Spatial analysis of ischemic heart disease in Manitoba
Bibliographic record
Abstract
Introduction: Chronic diseases rarely follow uniform distributions throughout geographical space and so identifying regions that have frequent occurrences or elevated prevalences is important. The disease of interest for this research project was ischemic heart disease (IHD). Objectives: The purpose of this project was to use statistical tools to detect spatial and temporal patterns of IHD in Manitoba. The objectives were to: (1) detect geographic clusters of acute myocardial infarctions (AMI) within Manitoba; (2) assess whether IHD is related to the geographic distribution of some its well-known risk factors; and (3) what relationship IHD has to the temporal dimension throughout geographic space. Methods: The first objective was assessed using the flexible spatial scanner to detect clusters of AMIs. The second objective was assessed using spatial Poisson regression models that modelled the spatial covariance with conditional autoregressive structures. The third objective was assessed by extending the spatial model to the temporal dimension by modeling the temporal covariance with random-walk covariance structures. Space-time interaction effects were assessed to complete the evaluation of the third objective. Results: One primary and eight secondary disease clusters of AMIs were identified, where the primary cluster occurred in the central Manitoba region. Hypertension prevalence and indigenous population proportion significantly predicted IHD prevalence. When controlling for temporal autocorrelation, indigenous population proportion was no longer a significant predictor of IHD. The results were within error the same for males and females when stratifying by sex. Modelled IHD prevalence was found to be decreasing over time, but the majority of this occurred in the female sub-group. Counter to this finding, IHD prevalence in some regions substantially increased over the study period. Conclusions: This research identified AMI clusters as well as modelled the spatial and temporal variation in IHD within 96 regions in Manitoba over 23 years. It was found that there were significant associations between IHD and the two covariates of hypertension and indigenous population proportion. The most significant effect was the space-time interaction, suggesting that the temporal patterns in IHD prevalence vary significantly throughout space, with some regions having significantly increasing trends over time counter to the provincial average.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".