S-15-3: COST-EFFECTIVENESS OF A FULL SCOPE OF PHARMACIST CARE FOR HYPERTENSION IN AUSTRALIA
Bibliographic record
Abstract
Hypertension is the single most important cause of premature morbidity and mortality in the world. Over half of all heart disease and stroke are attributable to hypertension, which is associated with approximately 10% of direct medical costs globally. Clinical trial evidence has conclusively demonstrated that pharmacist care, including patient education, consultation, and/or prescribing, significantly reduces blood pressure, including a Canadian randomised trial, RxACTION, which demonstrated an 18.3 mmHg reduction in systolic blood pressure (SBP) with pharmacist prescribing and care. Objective: To evaluate the economic impact of pharmacist management of hypertension in an Australian setting. Methods: A Markov cost effectiveness model was developed to extrapolate potential differences in long-term cardiovascular and renal disease outcomes, using Framingham risk equations and other published risk equations. We assumed an absolute reduction of systolic blood pressure of 18.3 mmHg (from the RxACTION trial). The model incorporated health outcomes, costs, and quality of life to estimate an overall incremental cost effectiveness ratio. Costs considered included direct medical costs as well as the costs associated with implementing the pharmacist intervention strategy. Results: For a SBP reduction of 18.3 mmHg, the estimated impact is 0.22 fewer cardiovascular events per person, and, discounted at 5% per year: 0.2 additional life years, 0.3 additional quality-adjusted life years, and $AU 5,348 cost savings over a lifetime per person. As such, pharmacist care in hypertension is economically dominant, i.e., both more effective and cost saving compared to usual care. At the population level, it has been reported that 34% of Australian adults have hypertension, of whom 68% are not optimally controlled. If 50% of poorly controlled hypertensives could access a full scope of pharmacist care, it is estimated that this would result in 2.1 million life years saved, and a cost saving of $AU 31.1 billion over a 30-year time horizon. Across a range of one way and probabilistic sensitivity analyses of key parameters and assumptions, pharmacist intervention remained both effective and cost saving, indicating robustness of the model. Conclusion: Pharmacist care, including prescribing, when added to usual care by general practitioners, would lead to better BP outcomes and a net cost saving of over $AU 30 billion. If implemented, this would have important public health implications.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".