A Systematic Review of Medication Adherence Thresholds Dependent of Clinical Outcomes
Bibliographic record
Abstract
Background: In pharmacotherapy, the achievement of a target clinical outcome requires a certain level of medication intake or adherence. Based on Haynes’s early empirical definition of sufficient adherence to antihypertensive medications as taking ≥80% of medication, many researchers used this threshold to distinguish adherent from non-adherent patients. However, we propose that different diseases, medications and patient’s characteristics influence the threshold of the adherence rate above which the clinical outcome is satisfactory. Moreover, the assessment of adherence and clinical outcomes may differ greatly and should be taken into consideration. We aimed at investigating medication adherence thresholds in relation to clinical outcomes. Method: We searched for studies that determined the relationship between adherence rates and clinical outcomes in the databases PubMed, Embase® and Web of Science™ until December 2017, limited to English-language. Our outcome measure was any threshold value of adherence. Inclusion criteria were 1) any measurement of medication adherence; 2) any assessment of clinical outcomes 3) any method to define medication adherence thresholds in relation to clinical outcomes. Two authors independently screened titles and abstracts for relevance, reviewed full-texts, and extracted items. The results of the included studies are presented qualitatively. Result: We analyzed six articles that assessed clinical outcomes linked to adherence rates in seven chronic disease states. Medication adherence was measured with Medication Possession Ratio (MPR, n = 3), Proportion of Days Covered (PDC, n = 1), both (n = 1), or Medication Event Monitoring System (MEMS). Clinical outcomes were event free episodes, hospitalization, cortisone use, reported symptoms and reduction of lipid levels. To find the relationship between the targeted clinical outcome and adherence rates, three studies applied logistic regression and three used survival analysis. Five studies defined adherence thresholds between 46% and 92%. One study confirmed the 80% threshold as valid to distinguish adherent from non-adherent patients. Conclusion: The analyzed studies were highly heterogeneous, predominantly concerning methods of calculating adherence. We could not compare studies quantitatively, mostly because adherence rates could not be standardized. Therefore, we cannot reject or confirm the validity of the historical 80% threshold. Nevertheless, the 80% threshold was clearly questioned as a general standard.
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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 | 0.021 | 0.114 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.013 |
| Bibliometrics | 0.024 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".