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Abstract Medication adherence remains a significant unmet challenge for optimizing patient outcomes. Recent advances in the conceptualization, measurement, and support of medication adherence offer fresh opportunities to make a me...
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Abstract Medication adherence remains a significant unmet challenge for optimizing patient outcomes. Recent advances in the conceptualization, measurement, and support of medication adherence offer fresh opportunities to make a meaningful impact on adherence-related behavior and outcomes. These advances emphasize the multifaceted and dynamic nature of medication adherence, provide novel methods for monitoring medication adherence in clinical care, and articulate a set of multilevel strategies to more effectively improve and sustain medication adherence. Here, we offer recommendations for how clinicians can better engage with, and benefit from, these innovations to improve patient medication adherence and associated treatment outcomes.
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BACKGROUND: Information comparing characteristics of patients who do and do not pick up their prescriptions is sparse, in part because adherence measured using pharmacy claims databases does not include information on patients who...
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BACKGROUND: Information comparing characteristics of patients who do and do not pick up their prescriptions is sparse, in part because adherence measured using pharmacy claims databases does not include information on patients who never pick up their first prescription, that is, patients with primary non-adherence. Electronic health record medication order entry enhances the potential to identify patients with primary non-adherence, and in organizations with medication order entry and pharmacy information systems, orders can be linked to dispensings to identify primarily non-adherent patients. OBJECTIVE: This study aims to use database information from an integrated system to compare patient, prescriber, and payment characteristics of patients with primary non-adherence and patients with ongoing dispensings of newly initiated medications for hypertension, diabetes, and/or hyperlipidemia. DESIGN: This is a retrospective observational cohort study. PARTICIPANTS (OR PATIENTS OR SUBJECTS): Participants of this study include patients with a newly initiated order for an antihypertensive, antidiabetic, and/or antihyperlipidemic within an 18-month period. MAIN MEASURES: Proportion of patients with primary non-adherence overall and by therapeutic class subgroup. Multivariable logistic regression modeling was used to investigate characteristics associated with primary non-adherence relative to ongoing dispensings. KEY RESULTS: The proportion of primarily non-adherent patients varied by therapeutic class, including 7% of patients ordered an antihypertensive, 11% ordered an antidiabetic, 13% ordered an antihyperlipidemic, and 5% ordered medications from more than one of these therapeutic classes within the study period. Characteristics of patients with primary non-adherence varied across therapeutic classes, but these characteristics had poor ability to explain or predict primary non-adherence (models c-statistics∈=∈0.61-0.63). CONCLUSIONS: Primary non-adherence varies by therapeutic class. Healthcare delivery systems should pursue linking medication orders with dispensings to identify primarily non-adherent patients. We encourage conduct of research to determine interventions successful at decreasing primary non-adherence, as characteristics available from databases provide little assistance in predicting primary non-adherence.
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Information comparing characteristics of patients who do and do not pick up their prescriptions is sparse, in part because adherence measured using pharmacy claims databases does not include information on patients who never pick ...
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Information comparing characteristics of patients who do and do not pick up their prescriptions is sparse, in part because adherence measured using pharmacy claims databases does not include information on patients who never pick up their first prescription, that is, patients with primary non-adherence. Electronic health record medication order entry enhances the potential to identify patients with primary non-adherence, and in organizations with medication order entry and pharmacy information systems, orders can be linked to dispensings to identify primarily non-adherent patients.
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Background: Medication nonadherence is a worldwide issue that can lead to poor clinical outcomes and increased health-care costs. Objective: To determine the predictors of medication nonadherence. Methods: A retrospective chart re...
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Background: Medication nonadherence is a worldwide issue that can lead to poor clinical outcomes and increased health-care costs. Objective: To determine the predictors of medication nonadherence. Methods: A retrospective chart review was conducted for patients who received prescription medications from Cleveland Clinic outpatient pharmacies. Prediction variables consisted of demographics, socioeconomic status, number of medications, and number of daily administrations. These variables were analyzed using a logistic regression to determine independent predictors of medication adherence. Results: Between January and September 2015, over 300 000 eligible prescriptions were filled, corresponding with over 70 000 unique patients. Of these, 29 134 patients were included. After multivariable regression, increasing age (odds ratio [OR]: 1.01), household income (OR: 1.03), and medication count (OR: 1.05) were found to be associated with adherence. Male gender (OR: 0.88), African American (OR: 0.45), Hispanic (OR: 0.62), or other race (OR: 0.87), being single (OR: 0.92), and increasing frequency of administrations per day (OR: 0.76) were associated with nonadherence. Conclusion: Medication nonadherence was associated with nonwhite race, single status, male gender, low socioeconomic status, and increasing frequency of medication administration. Based on these results, a risk prediction tool could be created to determine which patients are at the highest risk of medication nonadherence.
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Aims Medication non-adherence is a significant health problem. There are numerous methods for measuring adherence, but no single method performs well on all criteria. The purpose of this systematic review is to (i) identify self-r...
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Aims Medication non-adherence is a significant health problem. There are numerous methods for measuring adherence, but no single method performs well on all criteria. The purpose of this systematic review is to (i) identify self-report medication adherence scales that have been correlated with comparison measures of medication-taking behaviour, (ii) assess how these scales measure adherence and (iii) explore how these adherence scales have been validated. Methods Cinahl and PubMed databases were used to search articles written in English on the development or validation of medication adherence scales dating to August 2012. The search terms used were medication adherence, medication non-adherence, medication compliance and names of each scale. Data such as barriers identified and validation comparison measures were extracted and compared. Results Sixty articles were included in the review, which consisted of 43 adherence scales. Adherence scales include items that either elicit information regarding the patient's medication-taking behaviour and/or attempts to identify barriers to good medication-taking behaviour or beliefs associated with adherence. The validation strategies employed depended on whether the focus of the scale was to measure medication-taking behaviour or identify barriers or beliefs. Conclusions Supporting patients to be adherent requires information on their medication-taking behaviour, barriers to adherence and beliefs about medicines. Adherence scales have the potential to explore these aspects of adherence, but currently there has been a greater focus on measuring medication-taking behaviour. Selecting the 'right' adherence scale(s) requires consideration of what needs to be measured and how (and in whom) the scale has been validated.
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This study examined the concordance between multiple measures of adherence, as well as sensitivity to detection of poor adherers, specificity, and predictive validity using a daily cholesterol-lowering regimen. Participants (N = 1...
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This study examined the concordance between multiple measures of adherence, as well as sensitivity to detection of poor adherers, specificity, and predictive validity using a daily cholesterol-lowering regimen. Participants (N = 180) aged 24 to 60 years participated in an adherence ancillary study in a clinical trial. Males constituted 53.9% of this well-educated, community sample. Data on adherence were collected over a 6-month period, using electronic monitoring, self-report, specific recall, and pill counts. Electronically monitored (odds ratio [OR] = 5.348) and Shea self-report (OR = 2.678) predicted cholesterol lowering. Days (78.9%) and intervals (84.2%) adherent and the Shea (73.7%) were sensitive to the detection of poor adherers. Moderate associations were found between measures of the same type. Low correlations were found otherwise. The electronic monitor was the most accurate and informative measure. The Shea self-report was the most accurate brief, global estimate of adherence. Other measures were not associated with clinical outcome or sensitive to poor adherence.
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Background: Early adherence is key to successful depression treatment, but nearly 60% of patients discontinue antidepressants within 3 months. Our study aimed to determine factors associated with poor early adherence to antidepres...
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Background: Early adherence is key to successful depression treatment, but nearly 60% of patients discontinue antidepressants within 3 months. Our study aimed to determine factors associated with poor early adherence to antidepressants in a large diverse sample of patients. Methods: Six Mental Health Research Network healthcare systems contributed data for adults with depression and a new antidepressant start, defined by a washout period of at least 270 days, between January 1, 2010 and December 31, 2012. Pharmacy fill and self-reported race/ethnicity data were obtained from the electronic medical record. Patients had early adherence if they had a second antidepressant fill within 180 days of the first. We used logistic regression to investigate the relationship between early adherence and patient characteristics. Results: A total of 177,469 adult patients had 184,967 new episodes of depression with a filled antidepressant prescription. Patients refilled their antidepressants within 180 days in 71% of episodes. Race/ethnicity was a strong predictor of early adherence, with patients from racial/ethnicminorities other than Native Americans/Alaskan Natives less likely (adjusted odd ratios 0.50-0.59) to refill their antidepressants than non-Hispanic whites. Age, neighborhood education, comorbidity burden, provider type and engagement in psychotherapy were also associated with adherence. Other apparent predictors of early adherence, including neighborhood income, gender, and prior mental health hospitalizations, were no longer significant in the fully adjusted model. Conclusions: Race/ethnicity was a robust predictor of early antidepressant adherence, with minority groups other than Native Americans/Alaskan Natives less likely to be adherent. Further research is needed to determine whether early nonadherence in specific minority populations is intentional, due to side effects or patient preference, or unintentional and appropriate for targeted interventions to improve adherence. (C) 2016 Wiley Periodicals, Inc.
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This study aimed to (1) determine adherence to diabetes medication in type 2 diabetes (T2D) patients; (2) describe respondents' attitudes and beliefs about medications, and barriers to adherence; and (3) to model predictors of non...
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This study aimed to (1) determine adherence to diabetes medication in type 2 diabetes (T2D) patients; (2) describe respondents' attitudes and beliefs about medications, and barriers to adherence; and (3) to model predictors of non-adherence. Data were collected using online and postal surveys. Diabetes patients aged >= 18 years who were members of the Australian Diabetes Council were invited to participate. Main outcome measures were adherence to diabetes medication using 8-item Morisky Medication Adherence Score (MMAS-8) and beliefs about medication using the Beliefs about Medicines Questionnaire. A total of 543 T2D patients responded to the survey. The median (interquartile range) MMAS-8 score was 6.8 (5.0-7.0). The prevalence of adherence (MMAS-8 score >= 6) was 64.6%. The proportion of respondents who expressed concern about taking medications was 53.6%. Potential predictors of adherence included age (OR, 1.83; 95% CI, 1.19-2.82), concern about medication (OR, 0.91; 95% CI, 0.87-0.96), knowledge of diabetes (OR, 0.85, 95% CI, 0.73-0.99), having difficulty in paying for medication (OR, 0.51; 95% CI,0.33-0.79), having more than one regular pharmacy (OR, 0.59; 95% CI, 0.36-0.95), and using insulin (OR, 0.49; 95% CI, 0.30-0.81). Adherence to taking diabetes medication in a sample of the Australian T2D patient population was suboptimal. An understanding of medication-taking behaviour will assist health-care professionals to deliver appropriate and effective interventions to enhance adherence and optimise diabetes control in T2D patients.
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Nonadherence to medical regimens has been described as an epidemic of the 21st century. The recent Surgeon General's Report on Bone Health and Osteoporosis points out that nonadherence is a problem in the bone field and addresses ...
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Nonadherence to medical regimens has been described as an epidemic of the 21st century. The recent Surgeon General's Report on Bone Health and Osteoporosis points out that nonadherence is a problem in the bone field and addresses the need to improve adherence with osteoporosis therapies. Studies show that less than 25% of patients who initiate drug therapy for osteoporosis achieve 1 yr of treatment without interrupting therapy. It is estimated that improving adherence could result in nearly 400,000 fewer fractures each year in the United States. This article addresses strategies that clinicians can use to increase adherence to osteoporosis medications.
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High adherence is key to microbicide effectiveness. Here we provide a description of adherence interventions and the adherence rates achieved in the CAPRISA 004 Tenofovir gel trial. Adherence support for the before-and-after dosin...
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High adherence is key to microbicide effectiveness. Here we provide a description of adherence interventions and the adherence rates achieved in the CAPRISA 004 Tenofovir gel trial. Adherence support for the before-and-after dosing strategy (BAT 24) was provided at enrolment and at each monthly study visit. This initially comprised individual counselling and was replaced midway by a structured theory-based adherence support program (ASP) based on motivational interviewing. The 889 women were followed for an average of 18 months and attended a total of 17,031 monthly visits. On average women reported five sex acts and returned 5.9 empty applicators per month. The adherence rate based on applicator count in relation to all reported sex acts was 72.2 % compared to the 82.0 % self-reported adherence during the last sex act. Adherence support activities, which achieve levels of adherence similar to or better than those achieved by the CAPRISA 004 ASP, will be critical to the success of future microbicide trials.
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