Risk For Poor Medication Adherence Research Articles

Are anxiety and depression risk factors for poor medication adherence in patients with an implantable cardioverter defibrillator?

Abstract Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): TrygFonden The Lundbeck Foundation Patient@Home Background The implantable cardioverter defibrillator (ICD) is the preferred treatment for prevention of sudden cardiac death. Most patients with an ICD receive adjunctive medication to manage their heart disease. Poor medication adherence is a prognostic risk factor for cardiac patients and is linked to psychological factors, such as anxiety and depression. To our knowledge, no studies have examined if anxiety and/or depression at the time of ICD implantation is associated with medication adherence during the first year post-implantation. This information is important as it could help health care professionals identify and prevent poor medication adherence in ICD patients. Purpose To examine if anxiety and/or depression at ICD implantation are associated with medication adherence one-year post-implantation in ICD patients. Methods Secondary outcome analysis of the ACQUIRE-ICD, multisite, randomised controlled trial. Out of 478 patients with a first-time ICD or cardiac resynchronization therapy defibrillator (CRT-D) included in the trial, 433 patients (90.6%) took heart medication. Of these 433 patients, 322 patients (74.4%) were included in the analyses as they completed assessments at both baseline (implantation) and at 12 months post-implantation. Medication adherence was measured with the 8-item Morisky Medication Adherence Scale (MMAS), anxiety with Generalized Anxiety Disorder scale (GAD-7), and depression with Patient Health Questionnaire-9 (PHQ-9). Results The mean age of the participants was 60.2 ± 11.2, mainly male (83.9%). Medication adherence was generally medium to high at baseline [6.8% low adherence, 40.1% medium adherence, 53.1% high adherence, mean MMAS score: 7.31 ± 0.94), and at 12-months follow-up (8.1% low adherence, 37.3% medium adherence, 54.6% high adherence, mean MMAS score: 7.33 ± 0.93). In univariate analyses, depression scores at baseline were significantly negatively associated with medication adherence at 12-months post-implantation while adjusting for baseline MMAS scores (B = -0.02, 95% CI [- 0.04, -0.00], p = .04). This association remained statistically significant in the multivariate analysis also adjusting for gender, intervention group, heart failure severity (NYHA class I-II versus III-IV), and implantation center (B = -0.03, 95% CI [- 0.05, -0.00], p = .02). Anxiety scores at baseline were not significantly associated with medication adherence at 12-months post-implantation in neither the univariate (B = -0.02, 95% CI [- 0.04, 0.01], p = .14) nor adjusted analyses (B = -0.02, 95% CI [- 0.04, 0.00], p = .09). Conclusion Depression symptoms at the time of ICD implantation were associated with medication adherence at 12 months post-implantation with higher depression symptoms being associated with lower medication adherence. Anxiety symptoms were not predictive of medication adherence. Patients presenting with depressive symptoms at ICD implantation might be at risk for poor medication adherence.

Speech recognition can help evaluate shared decision making and predict medication adherence in primary care setting.

ObjectiveAsthma is a common chronic illness affecting 19 million US adults. Inhaled corticosteroids are a safe and effective treatment for asthma, yet, medication adherence among patients remains poor. Shared decision-making, a patient activation strategy, can improve patient adherence to inhaled corticosteroids. This study aimed to explore whether audio-recorded patient-primary care provider encounters can be used to: 1. Evaluate the level of patient-perceived shared decision-making during the encounter, and 2. Predict levels of patient’s inhaled corticosteroid adherence.Materials and methodsShared decision-making and inhaled corticosteroid adherence were assessed using the SDM Questionnaire-9 and the Medication Adherence Report Scale for Asthma (MARS-A). Speech-to-text algorithms were used to automatically transcribe 80 audio-recorded encounters between primary care providers and asthmatic patients. Machine learning algorithms (Naive Bayes, Support Vector Machines, Decision Tree) were applied to achieve the study’s predictive goals.ResultsThe accuracy of automated speech-to-text transcription was relatively high (ROUGE F-score = .9). Machine learning algorithms achieved good predictive performance for shared decision-making (the highest F-score = .88 for the Naive Bayes) and inhaled corticosteroid adherence (the highest F-score = .87 for the Support Vector Machines).DiscussionThis was the first study that trained machine learning algorithms on a dataset of audio-recorded patient-primary care provider encounters to successfully evaluate the quality of SDM and predict patient inhaled corticosteroid adherence.ConclusionMachine learning approaches can help primary care providers identify patients at risk for poor medication adherence and evaluate the quality of care by measuring levels of shared decision-making. Further work should explore the replicability of our results in larger samples and additional health domains.

Identification of Cardiovascular Patient Groups at Risk for Poor Medication Adherence: A Cluster Analysis.

BackgroundPoor medication adherence limits the secondary prevention of cardiovascular diseases (CVDs) and leads to increased morbidity, mortality, and costs. Identifying groups of patients at risk of poor adherence behavior could enable an intervention to be developed and target patients appropriately.ObjectiveThe first aim of this study was to identify homogeneous subgroups of cardiovascular outpatients based on their cardiovascular risk factors. Subsequently, differences in medication adherence between these groups were examined.MethodsIn this retrospective, observational study, patients with an established CVD were included. Well-known cardiovascular risk factors such as smoking, diet, exercise, blood lipid levels, blood pressure, and body mass index were collected. To identify patient subgroups, a 2-step cluster analytic procedure was performed. Differences between the groups on medication adherence were determined on the outcome of the Modified Morisky Scale. Data collection took place between October 2011 and January 2013.ResultsCardiovascular risk factors of 530 patients were included in the cluster analysis. Three groups were identified. Compared with other clusters (clusters 1 and 2), cluster 3 contained significantly fewer patients who could be classified as highly adherent and more patients classified as medium adherent (23% and 57%, respectively; P = .024). This group was characterized by a younger age (53% were <55 years old) and using a relatively low number of different medications (41% used <4 different medications). Besides, in this subgroup the most smokers (37%), unhealthy alcohol users (27%), and patients with unhealthy eating habits (14%) were present.ConclusionThis study showed that cardiovascular patients who are relatively young and have an unhealthy lifestyle are at risk for nonadherent behavior.

Differential Medication Attitudes to Antihypertensive and Mood Stabilizing Agents in response to an Automated Text-Messaging Adherence Enhancement Intervention.

Individuals with serious mental illnesses such as bipolar disorder (BD) are at an increased risk for poor medication adherence compared to the general population. Individuals with BD also have high rates of chronic comorbid medical conditions like hypertension (HTN), diabetes, and cardiovascular disease. Cognitive-behavioral therapies often integrate strategies to improve medication adherence by targeting medication attitudes and self-efficacy, but the pathway toward behavior change needs further investigation. This 3-month prospective, single-arm cohort study tested an automated SMS intervention entitled Individualized Texting for Adherence Building- Cardiovascular (iTAB-CV) in 38 participants with BD and HTN. The Tablets Routine Questionnaire (TRQ) measures the percentage of BD and HTN non-adherence over the past week and the past month. Attitudinal and habit measures including the Brief Illness Perception Questionnaire (Brief IPQ), the Medication Adherence Self-Efficacy Scale-Revised (MASES-R), the Self-Report Habit Index (SRHI), the Beliefs about Medicines Questionnaire (BMQ), and the Attitudes toward Mood Stabilizers Questionnaire (AMSQ) were given for BD and HTN medications. Correlational analyses were run to determine the associations between BD and HTN attitudinal and habit indices. Additionally, longitudinal analyses were conducted to determine if attitudes changed over time as a function of a 2-month mobile-health intervention. Illness attitudes towards BD were worse than towards HTN at the start of the study. Attitudes toward BD and towards mood-stabilizing drugs as well as antihypertensives improved following a mHealth intervention aimed at improving adherence. Furthermore, self-efficacy and habit strength for both BD and HTN drugs were correlated and were responsive to the intervention, with most of the change occurring after the first month of the intervention and not requiring the addition of the explicit reminders. Participants who received iTAB-CV showed improved attitudes towards BD and mood-stabilizing medication, and had an improvement in self-efficacy and habit strength towards taking both BD and HTN medications. Increased attention to mechanisms of change in mHealth interventions for adherence may facilitate impact. It should be noted that the methodology of the study limits drawing causal conclusions and suggests the need for a randomized control trial.

[Poor medication adherence and worsening of heart failure--a vicious circle].

Despite of markedly improved options for treatment, chronic heart failure is associated with recurrent worsening of symptoms. Poor medication adherence has adverse effects on frequency and progression of congestive heart failure. There are three relevant areas of problems that could be aggravated by each other:There is the problem of changes in pharmacokinetics in worsening heart failure. Proportional to the severity of heart failure, there is an existing intestinal edema and changes of intestinal bacterial colonization that may affect a drug's absorption and, hence, its efficacy.Depression and impaired cognitive function is quite common in patients with chronic heart failure. Depression both predicts hospitalization and mortality rate as well as poor medication adherence in CHF. Compared to stable CHF patients, cognitive function deteriorates significantly while decompensation leading to impaired medication adherence.Shown by recent studies, there is a higher risk for poor medication adherence after a cardiovascular event.Poor medication adherence is associated with an increased rate of cardiovascular events not only in heart failure, but also in all cardiovascular diseases. Hence, there is a need for specific and long term interventions to improve medication adherence at an early stage.

What puts heart failure patients at risk for poor medication adherence?

BackgroundMedication nonadherence is a major cause of hospitalization in patients with heart failure (HF), which contributes enormously to health care costs. We previously found, using the World Health Organization adherence dimensions, that condition and patient level factors predicted nonadherence in HF. In this study, we assessed a wider variety of condition and patient factors and interactions to improve our ability to identify those at risk for hospitalization.Materials and methodsMedication adherence was measured electronically over the course of 6 months, using the Medication Event Monitoring System (MEMS). A total of 242 HF patients completed the study, and usable MEMS data were available for 218 (90.1%). Participants were primarily white (68.3%), male (64.2%), and retired (44.5%). Education ranged from 8–29 years (mean, 14.0 years; standard deviation, 2.9 years). Ages ranged from 30–89 years (mean, 62.8 years; standard deviation, 11.6 years). Analyses used adaptive methods based on heuristic searches controlled by cross-validation scores. First, individual patient adherence patterns over time were used to categorize patients in poor versus better adherence types. Then, risk factors for poor adherence were identified. Finally, an effective model for predicting poor adherence was identified based on identified risk factors and possible pairwise interactions between them.ResultsA total of 63 (28.9%) patients had poor adherence. Three interaction risk factors for poor adherence were identified: a higher number of comorbid conditions with a higher total number of daily medicines, older age with poorer global sleep quality, and fewer months since diagnosis of HF with poorer global sleep quality. Patients had between zero and three risk factors. The odds for poor adherence increased by 2.6 times with a unit increase in the number of risk factors (odds ratio, 2.62; 95% confidence interval, 1.78–3.86; P<0.001).ConclusionNewly diagnosed, older HF patients with comorbid conditions, polypharmacy, and poor sleep are at risk for poor medication adherence. Interventions addressing these specific barriers are needed.

What puts heart failure patients at risk for poor medication adherence?

What puts heart failure patients at risk for poor medication adherence?

Type D Personality Predicts Poor Medication Adherence in Patients with Heart Failure in the USA

Type D (distressed) personality and medication nonadherence have been associated with poor health outcomes. Type D personality is associated with poor medication adherence in patients with coronary artery disease. However, the relationship between type D personality and medication adherence in patients with heart failure (HF) remains unknown. Therefore, the goal of this study was to examine the association between type D personality and medication adherence in patients with HF. This was a sub-analysis of baseline data from a randomized controlled trial with 84 patients with HF in the USA. Demographic, clinical, and psychological data were collected at baseline by interview, questionnaires, and medical record review. Type D personality was assessed using the Type D Personality Scale (DS14). Medication adherence was measured using both objective (Medication Event Monitoring System, MEMS) and self-reported (Morisky Medication Adherence Scale, MMAS-4) measures. Patients started medication adherence monitoring with the MEMS bottle at baseline and is used continuously for a month. Multiple regressions were used to explore the relationships between type D personality and medication adherence while adjusting for demographic, clinical, and psychological factors. Patients with type D personality were more likely to have poor medication adherence. Type D personality was associated with medication adherence before and after adjusting for covariates when it was analyzed as a categorical variable. However, type D personality was not associated with medication adherence when analyzed as a dimensional construct. Negative affectivity, a component of type D personality, was associated with medication adherence. As a dimensional construct, type D personality may not reflect the components of the personality associated with poor outcomes. Negative affectivity was associated with medication adherence in patients with HF. Interventions aiming to improving/enhancing medication adherence need to take into account patients with the negative affectivity component of type D personality who are at higher risk for poor medication adherence, which may lead to adverse health outcomes.

Impact of patient cost sharing on medication adherence among asthmatic patients on dual-controller therapy

Introduction Asthmatic patients are at risk for poor medication adherence when cost sharing keeps patients from accessing required controller medications. The risk for poor medication adherence would be even higher when asthmatic patients are in need of multiple controller drugs. This study aimed to quantify the effect of cost-sharing levels on medication adherence among asthmatic patients in the USA who were initiated on dual-controller therapies. Methods This study was a retrospective cohort study. Using a large administrative commercial claims and encounters dataset (MarketScan), patients who were initiated on dual-controller therapy (inhaled corticosteroids and long-acting beta-2-agonist or inhaled corticosteroids and leukotriene receptor antagonist) during 1 July 2000 and 30 June 2001 were identified from pharmacy claims. For asthmatic patients aged 6–65 years, medication adherence (i.e., medication possession ratio) was measured in a 1-year follow-up period. Patients' average out-of-pocket expenses were determined for a month's supply and then stratified into the following four categories: US$0–15, US$16–30, US$31–45 and >US$45. Logistic regression analysis was used to estimate odds ratio (OR) for medication adherence. Results Altogether, 1447 patients met the study criteria. Of these patients, 898 (62.1%) were initiated on inhaled corticosteroids and long-acting beta-2-agonist, and 549 (37.9%) were initiated on inhaled corticosteroids and leukotriene receptor antagonist. The study showed significant association of cost-sharing levels and poor medication adherence. Compared with patients on US$0–15 cost-sharing level, patients on US$16–30 (OR = 0.449, 95% confidence interval (CI) = 0.312–0.616), US$31–45 (OR = 0.246, 95% CI = 0.168–0.358) and US$46 or higher (OR = 0.131, 95% CI = 0.084–0.206) levels all had lower odds of acceptable medication adherence. Conclusions Cost sharing was associated with poor medication adherence among asthmatic patients who needed dual controllers. Healthcare providers and payers should take caution when asthmatic patients face increased levels of cost sharing to fill prescription for dual controllers.

Patient Factors, But Not Provider and Health Care System Factors, Predict Medication Adherence in Hypertensive Black Men

The problem of medication adherence is pronounced in hypertensive black men. However, factors influencing their adherence are not well understood. This secondary analysis of the ongoing Counseling African Americans to Control Hypertension (CAATCH) randomized clinical trial investigated the patient, provider, and health care system factors associated with medication adherence among hypertensive black men. Participants (N=253) were aged 56.6±11.6 years, earned <$20,000 yearly (72.7%), and almost one half were on Medicaid (44%). Mean systolic blood pressure was 148.7±15.8 mm Hg and mean diastolic blood pressure was 92.7±9.8 mm Hg. Over one half of participants (54.9%) were nonadherent. In a hierarchical regression analysis, the patient factors that predicted medication adherence were age, self-efficacy, and depression. The final model accounted for 32.1% of the variance (F=7.80, df 10, 165, P<.001). In conclusion, age, self-efficacy, and depression were associated with antihypertensive medication adherence in black men followed in Community/Migrant Health Centers. Age is a characteristic that may allow clinicians to predict who may be at risk for poor medication adherence. Depression can be screened for and treated. Self-efficacy is modifiable and its implications for practice would be the development of interventions to increase self-efficacy in black men with hypertension.

Treatment Adherence Among Low-Income, African American Children with Persistent Asthma

Objective. The study aims to assess medication adherence and asthma management behaviors and their modifiable predictors in low-income children with persistent asthma. Methods. The authors conducted a cohort study of 143 children ages 6 to 11 prescribed a daily inhaled controller medicine that could be electronically monitored. Children were recruited from clinics or the emergency department of an urban children's hospital. Data were collected at baseline (T1) and 1 year later (T2). Outcome measures were adherence to controller medications as measured by electronic monitoring devices, observed metered-dose inhaler and spacer technique, exposure to environmental tobacco smoke, and attendance at appointments with primary health care provider. Results. Medication adherence rates varied across medications, with higher rates for montelukast than for fluticasone. Eleven percent to 15% of children demonstrated metered dose inhaler and spacer technique suggesting no drug delivery, and few (5% to 6%) evidenced significant exposure to environmental tobacco smoke. Less than half of recommended health care visits were attended over the study interval. Few psychosocial variables were associated with adherence at T1 or in the longitudinal analyses. Fluticasone adherence at T2 was predicted by caregiver asthma knowledge. Conclusions. A substantial number of low-income children with persistent asthma receive less than half of their prescribed inhaled controller agent. Patients without Medicaid, with low levels of caregiver asthma knowledge, or with caregivers who began childrearing at a young age may be at highest risk for poor medication adherence.

Predictors of Medication Adherence Using a Multidimensional Adherence Model in Patients with Heart Failure

Objective: The purpose of this study is to explore important factors contributing to medication adherence in patients with heart failure (HF) based on the World Health Organization's multidimensional adherence model (MAM). Background: Medication adherence in HF is a complex and poorly understood phenomenon. It is imperative that medication adherence be studied using an appropriate model and multivariate approach to get comprehensive information about predictors of medication adherence. Methods and Results: Data from 134 patients with HF were analyzed to determine the best multivariate medication adherence model given variables derived from the MAM. Medication adherence was measured objectively using the Medication Event Monitoring System (MEMS) for three months. Four indicators of adherence were assessed by the MEMS: 1) dose-count, the percentage of prescribed doses taken; 2) dose-days, the percentage of days the correct number of doses taken; 3) dose-time, the percentage of doses taken on schedule; and 4) therapeutic coverage, the percentage of time therapeutic drug action maintained. Barriers to medication adherence, ethnicity, and perceived social support composed the best group of variables associated with dose-count (P < .001). New York Heart Association (NYHA) functional class, barriers to medication adherence, financial status, and perceived social support were the best model associated with dose-day (P < .001). Barriers to medication adherence and financial status were the best group of predictors associated with dose-time (P = .005). The best group of variables of therapeutic coverage was NYHA, barriers to medication adherence, and ethnicity (P < .001). The patient-related and healthcare system-related variables proposed by the MAM were not significant predictors. Conclusion: The most influential variable associated with medication adherence was barriers to medication adherence. Assessing patients’ barriers to taking prescribed medications should be the first step in implementing any intervention to improve medication adherence. Minorities and those who had more severe symptoms as reflected by NYHA functional class, less income, and less social support should be considered at high risk for poor medication adherence. Objective: The purpose of this study is to explore important factors contributing to medication adherence in patients with heart failure (HF) based on the World Health Organization's multidimensional adherence model (MAM). Background: Medication adherence in HF is a complex and poorly understood phenomenon. It is imperative that medication adherence be studied using an appropriate model and multivariate approach to get comprehensive information about predictors of medication adherence. Methods and Results: Data from 134 patients with HF were analyzed to determine the best multivariate medication adherence model given variables derived from the MAM. Medication adherence was measured objectively using the Medication Event Monitoring System (MEMS) for three months. Four indicators of adherence were assessed by the MEMS: 1) dose-count, the percentage of prescribed doses taken; 2) dose-days, the percentage of days the correct number of doses taken; 3) dose-time, the percentage of doses taken on schedule; and 4) therapeutic coverage, the percentage of time therapeutic drug action maintained. Barriers to medication adherence, ethnicity, and perceived social support composed the best group of variables associated with dose-count (P < .001). New York Heart Association (NYHA) functional class, barriers to medication adherence, financial status, and perceived social support were the best model associated with dose-day (P < .001). Barriers to medication adherence and financial status were the best group of predictors associated with dose-time (P = .005). The best group of variables of therapeutic coverage was NYHA, barriers to medication adherence, and ethnicity (P < .001). The patient-related and healthcare system-related variables proposed by the MAM were not significant predictors. Conclusion: The most influential variable associated with medication adherence was barriers to medication adherence. Assessing patients’ barriers to taking prescribed medications should be the first step in implementing any intervention to improve medication adherence. Minorities and those who had more severe symptoms as reflected by NYHA functional class, less income, and less social support should be considered at high risk for poor medication adherence.

Validation of the Hopkins Medication Schedule To Identify Difficulties in Taking Medications

Medications often represent the first line of defense in preventing or delaying the progression of chronic diseases. The health implications of improper medication compliance, and failure to identify it, are considerable. The authors thus developed and validated the Hopkins Medication Schedule (HMS), a new objective test of one's ability to understand and implement a routine prescription medication. The authors gave a hypothetical physician's prescription for two common medications (antibiotics and aspirin) to 360 high-functioning, community-dwelling, older participants in the Women's Health and Aging Study II and asked them to fill in a daily schedule for taking these medications and to fill in the compartments of a daily pillbox. These scored and timed performances were evaluated for their ability to predict concurrent and 3-year participant-reported difficulty in performing instrumental activities of daily living (IADLs) and for their associations with memory and attention. Although fewer than 2% of participants reported difficulty in taking medications, nearly 22% were completely unable to complete the schedule, fill the pillbox, or both. The 7% of participants who reported difficulty in any IADL also performed poorly on the HMS. In addition, performance on the schedule and the pillbox predicted concurrent difficulty. In adjusted regression analyses, the schedule was most highly associated with memory and the pillbox with learning and executive function. The HMS had concurrent validity for participant-reported IADL difficulty. Furthermore, in community-dwelling older women who largely reported no difficulty in taking medications, the HMS identified nearly 22% who could not write or implement a routine medication regimen. This standardized measure may identify those at increased risk for poor medication adherence and, more broadly, IADL difficulty.

Medication adherence among HIV+ adults: effects of cognitive dysfunction and regimen complexity.

Although the use of highly active antiretroviral therapy in the treatment of HIV infection has led to considerable improvement in morbidity and mortality, unless patients are adherent to their drug regimen (i.e., at least 90 to 95% of doses taken), viral replication may ensue and drug-resistant strains of the virus may emerge. The authors studied the extent to which neuropsychological compromise and medication regimen complexity are predictive of poor adherence in a convenience sample of 137 HIV-infected adults. Medication adherence was tracked through the use of electronic monitoring technology (MEMS caps). Two-way analysis of variance revealed that neurocognitive compromise as well as complex medication regimens were associated with significantly lower adherence rates. Cognitively compromised participants on more complex regimens had the greatest difficulty with adherence. Deficits in executive function, memory, and attention were associated with poor adherence. Logistic regression analysis demonstrated that neuropsychological compromise was associated with a 2.3 times greater risk of adherence failure. Older age (>50 years) was also found to be associated with significantly better adherence. HIV-infected adults with significant neurocognitive compromise are at risk for poor medication adherence, particularly if they have been prescribed a complex dosing regimen. As such, simpler dosing schedules for more cognitively impaired patients might improve adherence.