Predictors of Adherence to Statins for Primary Prevention

Division of General Internal Medicine, Mount Sinai School of Medicine, 1470 Madison Ave, Box 1087, New York, NY 10029, USA.
Cardiovascular Drugs and Therapy (Impact Factor: 3.19). 08/2007; 21(4):311-6. DOI: 10.1007/s10557-007-6040-4
Source: PubMed


Statins are potent drugs for reducing cholesterol and cardiovascular disease; however, their effectiveness is significantly compromised by poor adherence. This prospective study was designed to identify potentially modifiable patient factors including medication, disease, and diet beliefs related to statin adherence.
Veterans (n = 71) given their first prescription of a statin for primary prevention were interviewed at baseline, 3 months, and 6 months regarding medication, disease, and diet beliefs along with self-reported statin adherence.
At 6-month follow-up, 55% of the cohort was non-adherent with 10% reporting never having started their statin, 50% reporting misconceptions about the duration of treatment and a median use of <2 months among those who discontinued their statin. Multivariate predictors of non-adherence were expected short treatment duration (OR = 3.6, 1.4-9.4), low perceived risk of myocardial infarction (OR = 3.1, 1.1-8.7), concern about potential harm from statins (OR = 2.5, 1.0-6.3), being Hispanic (OR = 3.9, 1.0-15.2), and younger age (OR = 4.2, 1.1-15.8).
Poor adherence to statins was common in this primary prevention population with frequent early discontinuation despite access to low-cost medicines. Patient factors regarding the perception of risk, toxic effects of medication, expected treatment duration, as well as socio-demographic factors, were significant predictors of poor adherence and warrant further exploration.

1 Follower
20 Reads
  • Source
    • "The MEMS data showed better medication adherence in the two experimental groups who received TM for antiplatelet medications compared to those who did not. The lack of difference in adherence among groups for statins may be consistent with the poor adherence that is generally seen with statin medications as well as the challenge of taking medications more than once daily, particularly statins that are generally prescribed for the evening [12]. Two-way messaging with the TM Reminders + TM Education group revealed a high response rate to the medication reminders with a significantly higher response to antiplatelet medications compared to statins. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Pharmacologic treatment for secondary prevention of coronary heart disease (CHD) is critical to prevent adverse clinical outcomes. In a randomized controlled trial, we compared antiplatelet and statin adherence among patients with CHD who received: (1) text messages (TM) for medication reminders and education, (2) educational TM only, or (3) No TM. A mobile health intervention delivered customized TM for 30 days. We assessed and analyzed medication adherence with electronic monitoring devices [Medication Event Monitoring System (MEMS)] by one-way ANOVA and Welch tests, two-way TM response rates by t-tests, and self-reported adherence (Morisky Medication Adherence Scale) by Repeated Measures ANOVA. Among 90 patients (76% male, mean age 59.2 years), MEMS revealed patients who received TM for antiplatelets had a higher percentage of correct doses taken (p=0.02), percentage number of doses taken (p=0.01), and percentage of prescribed doses taken on schedule (p=0.01). TM response rates were higher for antiplatelets than statins (p=0.005). Self-reported adherence revealed no significant differences among groups. TM increased adherence to antiplatelet therapy demonstrated by MEMS and TM responses. Feasibility and high satisfaction were established. Mobile health interventions show promise in promoting medication adherence.
    Patient Education and Counseling 11/2013; 94(2). DOI:10.1016/j.pec.2013.10.027 · 2.20 Impact Factor
  • Source
    • "However, little data is available regarding the decline in adherence over time and its associated risk factors. Recently, some studies have begun to explore more modifiable predictors of adherence, such as depression, medication knowledge, health literacy, and self-efficacy [5,6]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: The aim of this study was to establish a prediction model of medication adherence in elderly patients with chronic diseases and to identify variables showing the highest classification accuracy of medication adherence in elderly patients with chronic diseases using support vector machine (SVM) and conventional statistical methods, such as logistic regression (LR). We included 293 chronic disease patients older than 65 years treated at one tertiary hospital. For the medication adherence, Morisky's self-report was used. Data were collected through face-to-face interviews. The mean age of the patients was 73.8 years. The classification process was performed with LR (SPSS ver. 20.0) and SVM (MATLAB ver. 7.12) method. Taking into account 16 variables as predictors, the result of applying LR and SVM classification accuracy was 71.1% and 97.3%, respectively. We listed the top nine variables selected by SVM, and the accuracy using a single variable, self-efficacy, was 72.4%. The results suggest that self-efficacy is a key factor to medication adherence among a Korean elderly population both in LR and SVM. Medication non-adherence was strongly associated with self-efficacy. Also, modifiable factors such as depression, health literacy, and medication knowledge associated with medication non-adherence were identified. Since SVM builds an optimal classifier to minimize empirical classification errors in discriminating between patient samples, it could achieve a higher accuracy with the smaller number of variables than the number of variables used in LR. Further applications of our approach in areas of complex diseases, treatment will provide uncharted potentials to researchers in the domains.
    Healthcare Informatics Research 03/2013; 19(1):33-41. DOI:10.4258/hir.2013.19.1.33
  • Source
    • "Research has also implicated negative patient attitudes about lipid-lowering therapy [29,30] and limited understanding of the need for long-term therapy [29,31]. Increased patient education by the physician or an allied health personnel has been endorsed to address these attitudinal and knowledge barriers [32]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Reasons for race and gender differences in controlling elevated low density lipoprotein (LDL) cholesterol may be related to variations in prescribed lipid-lowering therapy. We examined the effect of lipid-lowering drug treatment and potency on time until LDL control for black and white women and men with a baseline elevated LDL. We studied 3,484 older hypertensive patients with dyslipidemia in 6 primary care practices over a 4-year timeframe. Potency of lipid-lowering drugs calculated for each treated day and summed to assess total potency for at least 6 and up to 24 months. Cox models of time to LDL control within two years and logistic regression models of control within 6 months by race-gender adjust for: demographics, clinical, health care delivery, primary/specialty care, LDL measurement, and drug potency. Time to LDL control decreased as lipid-lowering drug potency increased (P < 0.001). Black women (N = 1,440) received the highest potency therapy (P < 0.001) yet were less likely to achieve LDL control than white men (N = 717) (fully adjusted hazard ratio [HR] 0.66 [95% CI 0.56-0.78]). Black men (N = 666) and white women (N = 661) also had lower adjusted HRs of LDL control (0.82 [95% CI 0.69, 0.98] and 0.75 [95% CI 0.64-0.88], respectively) than white men. Logistic regression models of LDL control by 6 months and other sensitivity models affirmed these results. Black women and, to a lesser extent, black men and white women were less likely to achieve LDL control than white men after accounting for lipid-lowering drug potency as well as diverse patient and provider factors. Future work should focus on the contributions of medication adherence and response to treatment to these clinically important differences.
    BMC Cardiovascular Disorders 09/2011; 11(1):58. DOI:10.1186/1471-2261-11-58 · 1.88 Impact Factor
Show more