ArticleAdvances in therapy2018
Medication Adherence Patterns Among Patients with Multiple Serious Mental and Physical Illnesses.
Article in Advances in therapy, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Medication Adherence Trajectories: A Systematic Literature Review.Journal of managed care & specialty pharmacy · 2020Pooled it
- Primary adherence to medications for the treatment of major depressive disorder: A descriptive study using real-world data.Journal of managed care & specialty pharmacy · 2026Article
- Antidepressant adherence using group-based trajectory modeling among postpartum women with Texas Medicaid.Journal of managed care & specialty pharmacy · 2025Article
- Time Trend Analysis of Tuberculosis Treatment While Using Digital Adherence Technologies-An Individual Patient Data Meta-Analysis of Eleven Projects across Ten High Tuberculosis-Burden Countries.Tropical medicine and infectious disease · 2022Article
- Anxiety, Depression, and Pain: Considerations in the Treatment of Patients with Uncontrolled Hypertension.Current hypertension reports · 2020Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionPatients with mental and physical health conditions are complex to treat and often use multiple medications. It is unclear how adherence to one medication predicts adherence to others. A predictive relationship could permit less expensive adherence monitoring if overall adherence could be predicted through tracking a single medication.
methodsTo test this hypothesis, we examined whether patients with multiple mental and physical illnesses have similar adherence trajectories across medications. Specifically, we conducted a retrospective cohort analysis using health insurance claims data for enrollees who were diagnosed with a serious mental illness, initiated an atypical antipsychotic, as well as an SSRI (to treat serious mental illness), biguanides (to treat type 2 diabetes), or an ACE inhibitor (to treat hypertension). Using group-based trajectory modeling, we estimated adherence patterns based on monthly estimates of the proportion of days covered with each medication. We measured the predictive value of the atypical antipsychotic trajectories to adherence predictions based on patient characteristics and assessed their relative strength with the R-squared goodness of fit metric.
resultsWithin our sample of 431,591 patients, four trajectory groups were observed: non-adherent, gradual discontinuation, stop-start, and adherent. The accuracy of atypical antipsychotic adherence for predicting adherence to ACE inhibitors, biguanides, and SSRIs was 44.5, 44.5, and 49.6%, respectively (all p < 0.001 vs. random). We also found that information on patient adherence patterns to atypical antipsychotics was a better predictor of patient adherence to these three medications than would be the case using patient demographic and clinical characteristics alone.
conclusionAmong patients with multiple chronic mental and physical illnesses, patterns of atypical antipsychotic adherence were useful predictors of adherence patterns to a patient's adherence to ACE inhibitors, biguanides, and SSRIs.
fundingOtsuka Pharmaceutical Development & Commercialization, Inc.
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