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

When Are Statins Cost-Effective in Cardiovascular Prevention? A Systematic Review of Sponsorship Bias and Conclusions in Economic Evaluations of Statins

<div><p>Background</p><p>We examined sponsorship of published cost-effectiveness analyses of statin use for cardiovascular (CV) prevention, and determined whether the funding source is associated with study conclusions.</p> <p>Methods and Findings</p><p>We searched PubMed/MEDLINE (up to June 2011) to identify cost-effectiveness analyses of statin use for CV prevention reporting outcomes as incremental costs per quality-adjusted life years (QALY) and/or life years gained (LYG). We examined relationships between the funding source and the study conclusions by means of tests of differences between proportions. Seventy-five studies were included. Forty-eight studies (64.0%) were industry-sponsored. Fifty-two (69.3%) articles compared statins versus non-active alternatives. Secondary CV prevention represented 42.7% of articles, followed by primary CV prevention (38.7%) and both (18.7%). Overall, industry-sponsored studies were much less likely to report unfavourable or neutral conclusions (0% versus 37.1%; <i>p</i><0.001). For primary CV prevention, the proportion with unfavourable or neutral conclusions was 0% for industry-sponsored studies versus 57.9% for non-sponsored studies (<i>p</i><0.001). Conversely, no statistically significant differences were identified for studies evaluating secondary CV prevention (0% versus 12.5%; <i>p</i>=0.222). Incremental costs per QALY/LYG estimates reported in industry-sponsored studies were generally more likely to fall below a hypothetical willingness-to-pay threshold of US $50,000.</p> <p>Conclusions</p><p>Our systematic analysis suggests that pharmaceutical industry sponsored economic evaluations of statins have generally favored the cost-effectiveness profile of their products particularly in primary CV prevention.</p> </div

Flow diagram of study selection

<p><b>process</b>.</p

Variations in cost-effectiveness results by funding source and prevention category: a) Primary CV prevention and b) Secondary CV prevention.

<div><p>CV: Cardiovascular.</p> <p>Note: Each dot represents an incremental cost (in US$) per QALY/LYG in the reviewed articles. The horizontal line represents the willingness-to-pay threshold.</p></div

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

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