Recommendations for Evaluating Compliance and Persistence With Hypertension Therapy Using Retrospective Data
Bibliographic record
Abstract
ypertension is a major risk factor for cardiovascular and cerebrovascular disease. The World Health Organization Global Burden of Disease Study estimates that nonoptimal blood pressure [(BP) ie, systolic BP of 115 mm Hg] is responsible annually for 7.1 million deaths and the loss of 64.3 disability-adjusted life years worldwide. he associated economic burden of hypertension is also substantial. The average annual medical care cost for individuals with hypertension has been estimated at $3900 (in year 2000 US dollars) in Canada, 2 with similar values ($3787) for the United States. The increase in medical care costs is greater for those with moderate-to-severe BP elevation (diastolic BP 104 mm Hg) than for those with mild disease. lthough a broad range of hypertension medications have been demonstrated to reduce BP, and BP control is an achievable goal, 5 reports suggest that up to two thirds of patients with hypertension are not successfully treated, that is, achieve BP control. 6 -8 According to the Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC-7), BP control rates are far below the "healthy people" goal of 50% set in 2000. 9 A major (and modifiable) reason for lack of BP control is failure by patients to use medications as prescribed. 10 Appropriate use of medications includes compliance, taking medications at the prescribed frequency/ interval and dose/dosing regimen, and persistence, continuing their use for the specified treatment time period, which, in the case of hypertension therapy, is usually lifelong. Poor compliance with hypertension medications is associated with adverse health outcomes. udies have demonstrated that poor BP control is associated with greater healthcare costs. For example, in the United States, inadequate control of hypertension has been estimated to result in 40 000 cardiovascular events, 8000 cardiovascular disease deaths, and approximately $964 million in direct medical expenditures. Similarly, poor compliance and lack of persistence with BP medications are associated with increased health care costs. Whereas patients who interrupted hypertension therapy had decreased medication costs, they had greater increases in costs for other health care, mainly reflecting increased hospital costs. In other disease states, such as diabetes and hypercholesterolemia, whereas increased compliance is associated with increased medication costs (because compliant patients use more medication), overall health care costs decrease because of better disease control and lower rates of adverse outcomes. ompliance and persistence can be measured in both retrospective and prospective studies, both of which can provide data on "real world" clinical practice. However, participants in prospective evaluations may not be comparable to broader patient populations. Furthermore, prospective evaluations can potentially introduce biases, particularly in patient behaviors related to compliance and persistence. Although the use of retrospective databases avoids these problems, the lack of consistent methods for evaluation of hypertension treatment compliance and persistence in such studies makes comparisons of results among studies difficult. As presented in a review by Lopatriello et al, In addition, the lack of broadly agreed-on methods creates substantial barriers for evaluating the potential impact of compliance and persistence on BP control. Given the importance of this issue, we have developed a set of recommendations for retrospective studies assessing compliance and persistence in hypertension.
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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 | 0.240 | 0.542 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".