Medication Adherence Patterns after Hospitalization for Coronary Heart Disease. A Population-Based Study Using Electronic Records and Group-Based Trajectory Models

<div><p>Objective</p><p>To identify adherence patterns over time and their predictors for evidence-based medications used after hospitalization for coronary heart disease (CHD).</p><p>Patients and Methods</p><p>We built a population-based retrospective cohort of all patients discharged after hospitalization for CHD from public hospitals in the Valencia region (Spain) during 2008 (n = 7462). From this initial cohort, we created 4 subcohorts with at least one prescription (filled or not) from each therapeutic group (antiplatelet, beta-blockers, ACEI/ARB, statins) within the first 3 months after discharge. Monthly adherence was defined as having ≥24 days covered out of 30, leading to a repeated binary outcome measure. We assessed the membership to trajectory groups of adherence using group-based trajectory models. We also analyzed predictors of the different adherence patterns using multinomial logistic regression.</p><p>Results</p><p>We identified a maximum of 5 different adherence patterns: 1) Nearly-always adherent patients; 2) An early gap in adherence with a later recovery; 3) Brief gaps in medication use or occasional users; 4) A slow decline in adherence; and 5) A fast decline. These patterns represented variable proportions of patients, the descending trajectories being more frequent for the beta-blocker and ACEI/ARB cohorts (16% and 17%, respectively) than the antiplatelet and statin cohorts (10% and 8%, respectively). Predictors of poor or intermediate adherence patterns were having a main diagnosis of unstable angina or other forms of CHD vs. AMI in the index hospitalization, being born outside Spain, requiring copayment or being older.</p><p>Conclusion</p><p>Distinct adherence patterns over time and their predictors were identified. This may be a useful approach for targeting improvement interventions in patients with poor adherence patterns.</p></div

Predictors of poor or intermediate adherence trajectory groups. Multinomial logistic regression analysis.

<p>ACEI: angiotensin-converting enzyme inhibitors; ARB: angiotensin receptor blockers; CHD: coronary heart disease; AMI: acute myocardial infarction; COPD: chronic obstructive pulmonary disease. The reference category is the nearly-always adherent trajectory group. Estimates for peripheral vascular disease, cancer and dementia were not included due to their high random error.</p

Adherence trajectory patterns for the four cohorts.

<p>AD: adherent; EG: early gap; OU: occasional users; SD: slow decline; FD: fast decline, ACEI: angiotensin-converting enzyme inhibitors; ARB: angiotensin receptor blockers.</p

Patient characteristics of the four medication cohorts^a.

<p>Patient characteristics of the four medication cohorts<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0161381#t001fn002" target="_blank">^a</a>.</p

Adherence for the 9-month follow-up for each trajectory and therapeutic group.

<p>Adherence for the 9-month follow-up for each trajectory and therapeutic group.</p

Hospitalization risk maps of the spatial region effect -upper row, exp(<i>v</i>_<i>j</i>)- and of the global spatial component -middle row, exp(<i>v</i>_<i>j</i> + <i>u</i>_{<i>i</i>(<i>j</i>)})- for Percutaneous Coronary Intervention (PCI; left), Colectomy in Colorectal Cancer (CCC; middle) and Chronic Obstructive Pulmonary Disease (COPD, right).

<p>At the bottom row, the average temporal trend exp (<i>γ</i>_<i>t</i>) (2002–2013).</p

Variance decomposition according to the model fitted per each condition (left hand side) and sensibility analysis excluding regions with singular behavior.

<p>Variance decomposition according to the model fitted per each condition (left hand side) and sensibility analysis excluding regions with singular behavior.</p

Space-time relative risk estimates (exp(<i>β</i> + <i>u</i>_{<i>i</i>(<i>j</i>)} + <i>v</i>_<i>j</i> + <i>γ</i>_<i>t</i> + <i>δ</i>_<i>jt</i>)) for Percutaneous Coronary Intervention (PCI).

<p>Space-time relative risk estimates (exp(<i>β</i> + <i>u</i>_{<i>i</i>(<i>j</i>)} + <i>v</i>_<i>j</i> + <i>γ</i>_<i>t</i> + <i>δ</i>_<i>jt</i>)) for Percutaneous Coronary Intervention (PCI).</p

Chronic Obstructive Pulmonary Disease (COPD) admissions along 2002–2013.

<p>Chronic Obstructive Pulmonary Disease (COPD) admissions along 2002–2013.</p

Space-time relative risk estimates (exp(<i>β</i> + <i>u</i>_{<i>i</i>(<i>j</i>)} + <i>v</i>_<i>j</i> + <i>γ</i>_<i>t</i> + <i>δ</i>_<i>jt</i>)) for Colectomy in Colorectal Cancer (CCC).

<p>Space-time relative risk estimates (exp(<i>β</i> + <i>u</i>_{<i>i</i>(<i>j</i>)} + <i>v</i>_<i>j</i> + <i>γ</i>_<i>t</i> + <i>δ</i>_<i>jt</i>)) for Colectomy in Colorectal Cancer (CCC).</p