The potential savings of using thiazides as the first choice antihypertensive drug: cost-minimisation analysis
Bibliographic record
Abstract
Abstract Background All clinical practice guidelines recommend thiazides as a first-choice drug for the management of uncomplicated hypertension. Thiazides are also the lowest priced antihypertensive drugs. Despite this, the use of thiazides is much lower than that of other drug-classes. We wanted to estimate the potential for savings if thiazides were used as the first choice drug for the management of uncomplicated hypertension. Methods For six countries (Canada, France, Germany, Norway, the UK and the US) we estimated the number of people that are being treated for hypertension, and the proportion of them that are suitable candidates for thiazide-therapy. By comparing this estimate with thiazide prescribing, we calculated the number of people that could switch from more expensive medication to thiazides. This enabled us to estimate the potential drug-cost savings. The analysis was based on findings from epidemiological studies and drug trials, and data on sales and prescribing provided by IMS for the year 2000. Results For Canada, France, Germany, Norway, the UK and the US the estimated potential annual savings were US$13.8 million, US$37.4 million, US$72.2 million, US$10.7 million, US$119.7 million and US$433.6 million, respectively. Conclusions Millions of dollars could be saved each year if thiazides were prescribed for hypertension in place of more expensive drugs. Our calculations are based on conservative assumptions. The potential for savings is likely considerably higher and may be more than US$1 billion per year in the US.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".