Adherence, Switches, and Drug Spending After Angiotensin Receptor Blocker Recalls and Shortages
Bibliographic record
Abstract
Importance: Angiotensin II receptor blockers (ARBs) are common treatments for hypertension, heart failure, and chronic kidney disease. From 2018 to 2019, hundreds of valsartan, losartan, and irbesartan products were recalled due to ingredient impurities. Objective: To estimate the impact of the 2018 to 2019 ARB shortages on medication adherence, switches to alternatives, and associated drug spending up to 18 months. Design, Setting, and Participants: This longitudinal cohort study with a difference-in-differences (DiD) analysis used pharmacy claims data from IQVIA's all-payer Formulary Impact Analyzer dataset from July 2017 to January 2020, comprising prerecall users of valsartan, irbesartan, and losartan vs similar nonrecalled medications (other ARBs, angiotensin-converting enzyme inhibitors [ACEIs]). Analyses were conducted from November 2023 to October 2025. Exposures: Use of the recalled drugs (valsartan, irbesartan, and losartan) at baseline vs comparison antihypertensives (nonrecalled ARBs, ACEIs). Main Outcomes and Measures: Mean proportion of days covered for ARBs and ACEIs, switches to alternatives, medication gaps of 30 or more days, and associated drug spending (insurer and patient out-of-pocket costs). Results: For 13.8 million ARB users (median [IQR] age in 2018, 66 [56-74] years; 54.8% female) vs 23.4 million comparison drug users (median [IQR] age in 2018, 62 [54-72] years; 46.0% female), mean proportion of days covered changed by 0.55 percentage points (pp; 95% CI, 0.34-0.76 pp) within 18 months. Relative changes in gaps of 30 or more days, insurer drug spending, and patient out-of-pocket drug spending changed by less than 5% (relative changes of -2.5%, 0.6%, and 3.7%, respectively). ARB users experienced an increase in medication switches in the 90 days after the valsartan recall (DiD estimate: 8.46 pp; 95% CI, 8.30-8.63 pp; 229.0% relative increase). Smaller increases in switching occurred after the first irbesartan and first losartan recalls (DiD estimate: 1.20 pp; 95% CI, 1.12-1.27 pp; 32.4% relative increase). The proportion of individuals switching was greater among those with Medicare (DiD estimate: 9.49 pp; 95% CI, 9.28-9.72 pp; 256.8% relative increase) or third-party insurance (DiD estimate: 7.81 pp; 95% CI, 7.57-8.04 pp; 210.8% relative increase) vs Medicaid fee-for-service insurance (DiD estimate: 2.54 pp; 95% CI, 2.31-2.77 pp; 43.1% relative increase) or among customers paying with cash (DiD estimate: 3.42 pp; 95% CI, 3.22-3.61 pp; 87.1% relative increase). Conclusions and Relevance: This cohort study shows that access to alternatives may have mitigated gaps in treatment during the 2018 to 2019 ARB recalls and drug shortages. Potential disparate impacts among certain subgroups highlight the need for policies to mitigate financial and other systematic access barriers to receiving health care during drug shortages.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".