Healthcare Costs Attributable to Hypertension
Bibliographic record
Abstract
Accurately documenting the current and future costs of hypertension is required to fully understand the potential economic impact of currently available and future interventions to prevent and treat hypertension. The objective of this work was to calculate the healthcare costs attributable to hypertension in Canada and to project these costs to 2020. Using population-based administrative data for the province of Alberta, Canada (>3 million residents) from 2002 to 2010, we identified individuals with and without diagnosed hypertension. We calculated their total healthcare costs and estimated costs attributable to hypertension using a regression model adjusting for comorbidities and sociodemographic factors. We then extrapolated hypertension-attributable costs to the rest of Canada and projected costs to the year 2020. Twenty-one percent of adults in Alberta had diagnosed hypertension in 2010, with a projected increase to 27% by 2020. The average individual with hypertension had annual healthcare costs of $5768, of which $2341 (41%) were attributed to hypertension. In Alberta, the healthcare costs attributable to hypertension were $1.4 billion in 2010. In Canada, the hypertension-attributable costs were estimated to be $13.9 billion in 2010, rising to $20.5 billion by 2020. The increase was ascribed to demographic changes (52%), increasing prevalence (16%), and increasing per-patient costs (32%). Hypertension accounts for a significant proportion of healthcare spending (10.2% of the Canadian healthcare budget) and is projected to rise even further. Interventions to prevent and treat hypertension may play a role in limiting this cost growth.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".