Medication Compliance and Disease Exacerbation in Patients with Asthma: a Retrospective Study of Managed Care Data
Overview
Pulmonary Medicine
Affiliations
Background: Compliance with asthma medications probably results in improved outcomes, but few studies have examined this relationship.
Objective: To examine the association between medication compliance and exacerbation in asthmatic patients.
Methods: Retrospective analysis of a managed care database. The 97,743 participants (aged 6-99 years; mean age, 32.8 years) had asthma and prescriptions for controller medications. Compliance with the index medication (the first controller medication prescribed) was measured using 2 methods: medication possession ratio (MPR), calculated for 365 days after the index date, and number of prescriptions for each index medication. Exacerbation was defined as 1 or more emergency department visits or hospitalizations within 1 year of the index date. Multivariate models were used to determine the odds of exacerbation based on relative compliance for each definition of compliance.
Results: Based on the median MPR, more-compliant patients were less likely to experience exacerbation than less-compliant patients (odds ratio, 0.94; 95% confidence interval, 0.91-0.97; P < .001). Using the 75th percentile MPR, risk of exacerbation was even smaller (odds ratio, 0.89; 95% confidence interval, 0.86-0.92; P < .001). All the cutoff points for compliance (> or = 2 through > or = 6 prescriptions) demonstrated significantly less exacerbations in more-compliant vs less-compliant patients after adjusting for covariates. As the criteria for compliance became more stringent, more-compliant patients became increasingly less likely to have an exacerbation vs less-compliant patients.
Conclusion: More-compliant asthmatic patients were significantly less likely to experience exacerbation than less-compliant asthmatic patients. These findings demonstrate the importance of improving medication compliance among asthmatic patients to impact outcomes.
Li S, Feng K, Lee J, Gong Y, Wu F, Newman B CPT Pharmacometrics Syst Pharmacol. 2024; 14(2):331-339.
PMID: 39575671 PMC: 11812940. DOI: 10.1002/psp4.13276.
Lizano-Barrantes C, Garin O, Mayoral K, Dima A, Pont A, Caballero-Rabasco M Front Pharmacol. 2024; 15:1340255.
PMID: 38549668 PMC: 10976946. DOI: 10.3389/fphar.2024.1340255.
Machine Learning Approaches to Predict Asthma Exacerbations: A Narrative Review.
Molfino N, Turcatel G, Riskin D Adv Ther. 2023; 41(2):534-552.
PMID: 38110652 PMC: 10838858. DOI: 10.1007/s12325-023-02743-3.
DeRosa N, Leung K, Vlahopoulos J, Lavino J Innov Pharm. 2022; 12(3).
PMID: 35601574 PMC: 9119992. DOI: 10.24926/iip.v12i3.4222.
Kim M, Jo E, Kim S, Kim M, Jung J, Kim J J Korean Med Sci. 2022; 37(7):e57.
PMID: 35191233 PMC: 8860771. DOI: 10.3346/jkms.2022.37.e57.