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AbstractBACKGROUNDAdherence to antihypertensives is suboptimal, but previous methods of quantifying adherence fail to account for varying patterns of use over time. We sought to improve classification of antihypertensive adherence using group-based trajectory models, and to determine whether individual factors predict adherence trajectories.METHODSWe identified older adults initiating antihypertensive therapy during 2008–2011 using a 20% sample of Medicare (federal health insurance available to US residents over the age of 65) beneficiaries enrolled in parts A (inpatient services), B (outpatient services), and D (prescription medication). We developed monthly adherence indicators using prescription fill dates and days supply data in the 12 months following initiation. Adherence was defined as having at least 80% of days covered. Logistic models were used to identify trajectory groups. Bayesian information criterion and trajectory group size were used to select the optimal trajectory model. We compared the distribution of covariates across trajectory groups using multivariable logistic regression.RESULTSDuring 2008–2011, 282,520 Medicare beneficiaries initiated antihypertensive therapy (mean age 75 years, 60% women, 84% White). Six trajectories were identified ranging from perfect adherence (12-month adherence of 0.97, 40% of beneficiaries) to immediate stopping (12-month adherence of 0.10, 18% of beneficiaries). The strongest predictors of nonadherence were initiation with a single antihypertensive class (adjusted odds ratio = 2.08 (95% confidence interval: 2.00–2.13)), Hispanic (2.93 (2.75–3.11)) or Black race/ethnicity (2.04 (1.95–2.13)), and no prior history of hypertension (2.04 (2.00–2.08)) (Area under the receiving operating characteristic curve: 0.53).CONCLUSIONSThere is substantial variation in antihypertensive adherence among older adults. Certain patient characteristics are likely determinants of antihypertensive adherence trajectories.
American Journal of Hypertension – Oxford University Press
Published: Oct 1, 2017
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