Development and Validation of the Hypertension Population Risk Tool: A Population-Based Diagnostic Algorithm for Canadians
Bibliographic record
Abstract
Abstract Objectives Effective, equitable hypertension prevention requires an understanding of which populations are at risk. We aimed to develop and validate the Hypertension Population Risk Tool (HTNPoRT) - a diagnostic model derived with only readily available data, suitable for individual screening and population health planning. Methods We analyzed data from the Canadian Health Measures Survey (cycles 1–6, 2007–2019). The study included community-dwelling respondents aged 20–79 years. The primary outcome was hypertension, defined as measured systolic/diastolic blood pressure of 140/90 mm Hg or current antihypertensive medication use. Sex-specific logistic regression models were developed using 16 predictors, including 4 sociodemographic, 3 psychosocial, 2 health status, 5 health behavioural, and 2 chronic condition variables. The model was fully prespecified, including the stepdown procedure to derive parsimonious models. Results Of 19,643 participants, 5,152 (26.2%) had hypertension. The final models included age, body mass index, diabetes, and family history of hypertension. Optimism-corrected c-statistics were 0.86 (95% CI: 0.85–0.87) for men and 0.88 (95% CI: 0.87–0.88) for women. Calibration showed relative differences between observed and predicted risk of 1.02% (men) and 1.41% (women), and consistent performance across 179 of 181 policy-relevant subgroups. Predicted hypertension risk in Canada varied but rose markedly with older age, diabetes, and obesity. Conclusions HTNPoRT is a well-performing predictive algorithm that relies only on minimal non-invasive, self-reported data. It is suitable for both individual risk screening and population-level surveillance to inform hypertension prevention strategies targeting both the general population and high-risk groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".