Prevalence and incidence of diagnosed hypertension in Alberta, Canada
Bibliographic record
Abstract
IntroductionThe prevalence of diagnosed hypertension in Canada is projected to increase despite the incidence rate decreasing. Previous work around the world has utilized survey data to provide estimates of prevalence and incidence. Administrative data is population-level, and may provide more reliable estimates of provincial prevalence and incidence than could be achieved using survey data. 
 Objectives and Approach
 
 To produce age and sex-specific prevalence and incidence estimates of diagnosed hypertension in Alberta from 2007 to 2015,
 To project estimates to the fiscal year of 2019/2020.
 
 Data from the Discharge Abstract Database, physician claims database, National Ambulatory Care Reporting System, and provincial health insurance registry will be linked using unique anonymous personal identifier and gender. A validated case definition of diagnosed hypertension for use in administrative datasets will be used to identify annual prevalent and incident cases from claims data. Obstetric cases will be excluded. The provincial health insurance registry will be used to estimate denominator values.
 ResultsResults of this analysis are not available for the time of abstract submission as the timeline for this analysis projects completion in April 2018.
 Conclusion/ImplicationsMaintained surveillance of diagnosed hypertension is important to inform health policy and spending decisions, to monitor efficacy of public health interventions, and to inform patient care. Furthermore, diagnosis guidelines have been updated since 2017. Providing estimates for the prevalence of diagnosed hypertension in Alberta five years into the future to compare to actual prevalence estimates may indicate whether changes in prevalence are due to actual changes in health status or to changes in diagnosis guidelines.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".