Modelling prevalent cardiovascular disease in an urban Indigenous population
Bibliographic record
Abstract
OBJECTIVE: Studies have highlighted the inequities between the Indigenous and non-Indigenous populations with respect to the burden of cardiovascular disease and prevalence of predisposing risks resulting from historical and ongoing impacts of colonization. The objective of this study was to investigate factors associated with cardiovascular disease (CVD) within and specific to the Indigenous peoples living in Toronto, Ontario, and to evaluate the reliability and validity of the resulting model in a similar population. METHODS: The Our Health Counts Toronto study measured the baseline health of Indigenous community members living in Toronto, Canada, using respondent-driven sampling. An iterative approach, valuing information from the literature, clinical insight and Indigenous lived experiences, as well as statistical measures was used to evaluate candidate predictors of CVD (self-reported experience of discrimination, ethnic identity, health conditions, income, education, age, gender and body size) prior to multivariable modelling. The resulting model was then validated using a distinct, geographically similar sample of Indigenous people living in Hamilton, Ontario, Canada. RESULTS: The multivariable model of risk factors associated with prevalent CVD included age, diabetes, hypertension, body mass index and exposure to discrimination. The combined presence of diabetes and hypertension was associated with a greater risk of CVD relative to those with either condition and was the strongest predictor of CVD. Those who reported previous experiences of discrimination were also more likely to have CVD. Further study is needed to determine the effect of body size on risk of CVD in the urban Indigenous population. The final model had good discriminative ability and adequate calibration when applied to the Hamilton sample. CONCLUSION: Our modelling identified hypertension, diabetes and exposure to discrimination as factors associated with cardiovascular disease. Discrimination is a modifiable exposure that must be addressed to improve cardiovascular health among Indigenous populations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".