What Puts Heart Failure Patients at Risk for Poor Medication Adherence?

Background: Medication 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 methods: Medication 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. Results: A 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\u3c0.001). Conclusion: Newly 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

Electronically Monitored Medication Adherence Predicts Hospitalization in Heart Failure Patients

Background: Hospitalization contributes enormously to health care costs associated with heart failure. Many investigators have attempted to predict hospitalization in these patients. None of these models has been highly effective in prediction, suggesting that important risk factors remain unidentified. Purpose: To assess prospectively collected medication adherence, objectively measured by the Medication Event Monitoring System, as a predictor of hospitalization in heart failure patients. Materials and methods: We used recently developed adaptive modeling methods to describe patterns of medication adherence in a sample of heart failure patients, and tested the hypothesis that poor medication adherence as determined by adaptive methods was a significant predictor of hospitalization within 6 months. Results: Medication adherence was the best predictor of hospitalization. Besides two dimensions of poor adherence (adherence pattern type and low percentage of prescribed doses taken), four other single factors predicted hospitalization: low hemoglobin, depressed ejection fraction, New York Heart Association class IV, and 12 or more medications taken daily. Seven interactions increased the predictive capability of the model: 1) pattern of poor adherence type and lower score on the Letter–Number Sequencing test, a measure of short-term memory; 2) higher number of comorbid conditions and higher number of daily medications; 3) higher blood urea nitrogen and lower percentage of prescribed doses taken; 4) lower hemoglobin and much worse perceived health compared to last year; 5) older age and lower score on the Telephone Interview of Cognitive Status; 6) higher body mass index and lower hemoglobin; and 7) lower ejection fraction and higher fatigue. Patients with none of these seven interactions had a hospitalization rate of 9.7%. For those with five of these interaction risk factors, 100% were hospitalized. The C-index (the area under the receiver-operating characteristics [ROC] curve) for the model based on the seven interactions was 0.83, indicating excellent discrimination. Conclusion: Medication adherence adds important new information to the list of variables previously shown to predict hospitalization in adults with heart failure

Predictors of Medication Nonadherence Differ among Black and White Patients with Heart Failure

Heart failure (HF) is a global public health problem, and outcomes remain poor, especially among ethnic minority populations. Medication adherence can improve heart failure outcomes but is notoriously low. The purpose of this secondary analysis of data from a prospective cohort comparison study of adults with heart failure was to explore differences in predictors of medication nonadherence by racial group (Black vs. White) in 212 adults with heart failure. Adaptive modeling analytic methods were used to model HF patient medication nonadherence separately for Black (31.7%) and White (68.3%) participants in order to investigate differences between these two racial groups. Of the 63 Black participants, 33.3% had low medication adherence, compared to 27.5% of the 149 White participants. Among Blacks, 16 risk factors were related to adherence in bivariate analyses; four of these (more comorbidities, lower serum sodium, higher systolic blood pressure, and use of fewer activities compensating for forgetfulness) jointly predicted nonadherence. In the multiple risk factor model, the number of risk factors in Black patients ranged from 0 to 4, and 76.2% had at least one risk factor. The estimated odds ratio for medication nonadherence was increased 9.34 times with each additional risk factor. Among White participants, five risk factors were related to adherence in bivariate analyses; one of these (older age) explained the individual effects of the other four. Because Blacks with HF have different and more risk factors than Whites for low medication adherence, interventions are needed that address unique risk factors among Black patients with HF

Racial Differences in Clinical Treatment and Self-Care Behaviors of Adults with Chronic Heart Failure

BackgroundIn the United States, the highest prevalence of heart failure (HF) is in blacks followed by whites. Compared with whites, blacks have a higher risk of HF‐related morbidity and mortality and HF‐related hospitalization. Little research has focused on explaining the reasons for these disparities. The purpose of this study was to examine racial differences in demographic and clinical characteristics in blacks and whites with HF and to determine if these characteristics influenced treatment, or together with treatment, influenced self‐care behaviors.Methods and ResultsThis was a secondary analysis of existing data collected from adults (n=272) with chronic HF enrolled from outpatient sites in the northeastern United States and followed for 6 months. After adjusting for sociodemographic and clinical characteristics within reduced (HFrEF) and preserved ejection fraction (HFpEF) groups, there were 2 significant racial differences in clinical treatment. Blacks with HFrEF were prescribed ACE inhibitors and hydralazine and isosorbide dinitrate (H‐ISDN) more often than whites. In the HFpEF group, blacks were taking more medications and were prescribed digoxin and a diuretic when symptomatic. Deficits in HF knowledge and decreased medication adherence, objectively measured, were more prominent in blacks. These racial differences were not explained by sociodemographic or clinical characteristics or clinical treatment variables. Premorbid intellect and the quality of support received contributed to clinical treatment and self‐care.ConclusionAlthough few differences in clinical treatment could be attributed solely to race, knowledge about HF and medication adherence is lower in blacks than whites. Further research is needed to explain these observations, which may be targets for future intervention research

On Quantitizing

Quantitizing, commonly understood to refer to the numerical translation, transformation, or conversion of qualitative data, has become a staple of mixed methods research. Typically glossed are the foundational assumptions, judgments, and compromises involved in converting disparate data sets into each other and whether such conversions advance inquiry. Among these assumptions are that qualitative and quantitative data constitute two kinds of data, that quantitizing constitutes a unidirectional process essentially different from qualitizing, and that counting is an unambiguous process. Among the judgments are deciding what and how to count. Among the compromises are balancing numerical precision with narrative complexity. The standpoints of “conditional complementarity,” “critical remediation,” and “analytic alternation” clarify the added value of converting qualitative data into quantitative form

Secondary analysis of electronically monitored medication adherence data for a cohort of hypertensive African-Americans

BackgroundElectronic monitoring devices (EMDs) are regarded as the “gold standard” for assessing medication adherence in research. Although EMD data provide rich longitudinal information, they are typically not used to their maximum potential. Instead, EMD data are usually combined into summary measures, which lack sufficient detail for describing complex medication-taking patterns. This paper uses recently developed methods for analyzing EMD data that capitalize more fully on their richness.MethodsRecently developed adaptive statistical modeling methods were used to analyze EMD data collected with medication event monitoring system (MEMS™) caps in a clinical trial testing the effects of motivational interviewing on adherence to antihypertensive medications in a cohort of hypertensive African-Americans followed for 12 months in primary care practices. This was a secondary analysis of EMD data for 141 of the 190 patients from this study for whom MEMS data were available.ResultsNonlinear adherence patterns for 141 patients were generated, clustered into seven adherence types, categorized into acceptable (for example, high or improving) versus unacceptable (for example, low or deteriorating) adherence, and related to adherence self-efficacy and blood pressure. Mean adherence self-efficacy was higher across all time points for patients with acceptable adherence in the intervention group than for other patients. By 12 months, there was a greater drop in mean post-baseline blood pressure for patients in the intervention group, with higher baseline blood pressure values than those in the usual care group.ConclusionAdaptive statistical modeling methods can provide novel insights into patients’ medication-taking behavior, which can inform development of innovative approaches for tailored interventions to improve medication adherence

Assessment of the Psychometric Properties of the Family Management Measure

Objective This paper reports development of the Family Management Measure (FaMM) of parental perceptions of family management of chronic conditions. Method By telephone interview, 579 parents of children age 3 to 19 with a chronic condition (349 partnered mothers, 165 partners, 65 single mothers) completed the FaMM and measures of child functional status and behavioral problems and family functioning. Analyses addressed reliability, factor structure, and construct validity. Results Exploratory factor analysis yielded six scales: Child's Daily Life, Condition Management Ability, Condition Management Effort, Family Life Difficulty, Parental Mutuality, and View of Condition Impact. Internal consistency reliability ranged from .72 to .91, and test-retest reliability from .71 to .94. Construct validity was supported by significant correlations in hypothesized directions between FaMM scales and established measures. Conclusion Results support FaMM's; reliability and validity, indicating it performs in a theoretically meaningful way and taps distinct aspects of family response to childhood chronic conditions

Supporting the Dynamic Careers of Licensed Practical Nurses: A Strategy to Bolster the Long-Term Care Nurse Workforce

As the U.S. population ages and the demand for long-term care increases, an insufficient number of licensed practical nurses (LPNs) is expected in the nursing workforce. Understanding the characteristics of LPN participation in the workforce is essential to address this challenge. Drawing on the theory of boundaryless careers, the authors examined longitudinal employment data from LPNs in North Carolina and described patterns in LPN licensure and career transitions. Two career patterns were identified: (a) the continuous career, in which LPNs were licensed in 75% or more of the years they were eligible to be licensed and (b) the intermittent career, in which lapses in licensure occurred. Findings indicated that LPNs who made job transitions were more likely to demonstrate continuous careers, as were Black LPNs. These findings suggest the importance of organizational support for LPN career transitions and support for diversity in the LPN workforce

Mothersʼ Psychological Distress and Feeding of Their Preterm Infants

To examine the change in psychological distress of mothers of preterm infants and its association with maternal feeding behaviors as the infant transitions to full oral feeding

Patterns of Family Management of Childhood Chronic Conditions and Their Relationship to Child and Family Functioning

Understanding patterns of family response to childhood chronic conditions based on a configuration of multiple variables or qualitative themes provides a comprehensive understanding of health-related challenges and their influence on family and child functioning. In this paper, we used the six scales comprising the Family Management Measure (FaMM) in a cluster analysis to describe a typology of family management and data from other measures of child and family functioning to validate and explain those clusters. The sample of 575 parents from 414 families of children who had diverse chronic conditions endorsed four patterns of response (Family Focused, Somewhat Family Focused, Somewhat Condition Focused, Condition Focused). We also considered the extent to which couples had shared or discrepant views of family management. Most (57%) families were in either the Family Focused or Somewhat Family Focused pattern. Single mothers were significantly less likely to be in the two patterns reflecting greater ease in family management and significantly more likely to be in the two patterns reflecting more difficulty. Patterns of family management were related significantly to family and child functioning, with families in the Family Focused and Somewhat Family Focused patterns demonstrating significantly better family and child functioning than families in the other two patterns