Cost-Effectiveness and Impact on Health Care Utilization of Interventions to Improve Medication Adherence and Outcomes in Asthma and Chronic Obstructive Pulmonary Disease: A Systematic Literature Review
Bibliographic record
Abstract
BACKGROUND: Poor adherence to asthma and chronic obstructive pulmonary disease maintenance therapies impairs health outcomes. Proven and cost-effective programs to promote adherence and persistence are not yet in regular widespread use. Implementation costs are a potential barrier to uptake of such programs. OBJECTIVE: We undertook a systematic literature review and narrative synthesis of studies investigating the cost-effectiveness of treatment adherence-promoting programs or that determined their impact on health care budget directly or via health care resource use (HCRU). METHODS: We identified relevant publications using Medline and PreMEDLINE (PubMed), Embase (Embase.com, Elsevier), and EconLit for publications between January 2000 and July 2021. We also searched clinical trial databases and selected conference proceedings. RESULTS: Of 1,910 potentially relevant articles, 26 met prespecified inclusion criteria and underwent data extraction. Eleven reported a direct assessment of adherence, 15 included economic evaluations, and 17 described HCRU. None included an analysis of biologic medication use. When they were studied, interventions were often found to be highly cost-effective, with dominant incremental cost-effectiveness ratios in some cases. Reductions in direct costs and HCRU (health care visits, hospital admissions, and/or the use of medications, including add-on/reliever treatment and antibiotics) were frequently reported. Reported use of maintenance treatments improved in some studies. Counseling and/or digitally informed programs were used in all cases in which favorable outcomes were observed. CONCLUSIONS: Adherence-promoting interventions are mostly cost-effective and often result in reduced HCRU and associated costs. Multidisciplinary care involving one-to-one advice and digitally enhanced communications appear to offer the greatest benefit.
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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 | 0.017 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.017 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".