Dataset Information

Cardioprotective medication adherence in Western Australians in the first year after myocardial infarction: restricted cubic spline analysis of adherence-outcome relationships.

ABSTRACT: Adherence to cardioprotective medications following myocardial infarction (MI) is commonly assessed using a binary threshold of 80%. We investigated the relationship between medication adherence as a continuous measure and outcomes in MI survivors using restricted cubic splines (RCS). We identified all patients aged ?65 years hospitalised for MI from 2003-2008 who survived one-year post-discharge (n = 5938). Adherence to statins, beta-blockers, renin angiotensin system inhibitors (RASI) and clopidogrel was calculated using proportion of days covered to one-year post-discharge (landmark date). Outcomes were 1-year all-cause death and major adverse cardiac events (MACE) after the landmark date. Adherence-outcome associations were estimated from RCS Cox regression models. RCS analyses indicated decreasing risk for both outcomes above 60% adherence for statins, RASI and clopidogrel, with each 10% increase in adherence associated with a 13.9%, 12.1% and 18.0% decrease respectively in adjusted risk of all-cause death (all p?

SUBMITTER: Greenland M

PROVIDER: S-EPMC7062740 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Cardioprotective medication adherence in Western Australians in the first year after myocardial infarction: restricted cubic spline analysis of adherence-outcome relationships.

Greenland Melanie M Knuiman Matthew W MW Hung Joseph J Nedkoff Lee L Arnet Isabelle I Rankin Jamie M JM Kilkenny Monique F MF Sanfilippo Frank M FM

Scientific reports 20200309 1

Adherence to cardioprotective medications following myocardial infarction (MI) is commonly assessed using a binary threshold of 80%. We investigated the relationship between medication adherence as a continuous measure and outcomes in MI survivors using restricted cubic splines (RCS). We identified all patients aged ≥65 years hospitalised for MI from 2003-2008 who survived one-year post-discharge (n = 5938). Adherence to statins, beta-blockers, renin angiotensin system inhibitors (RASI) and clop ...[more]

PMID: 32152400

Similar Datasets

Project description:Medication non-adherence is a major impediment to the management of cardiovascular disease risk factors. A better understanding of the modifying factors underlying medication non-adherence among individuals with known cardiovascular disease may inform approaches for addressing non-adherence.The purpose of this study was to identify demographic and patient characteristics, medical comorbidities, psychosocial factors, and health belief-related factors associated with medication non-adherence among patients with known cardiovascular disease.We performed secondary analysis of baseline data from a randomized trial.The study included 405 patients with a diagnosis of hypertension and history of acute myocardial infarction that was diagnosed within a three-year period prior to enrollment.Baseline demographics and patient characteristics, medical comorbidities, psychosocial factors, health belief-related factors, and patient-reported medication non-adherence were analyzed.Of 405 patients, 173 (42.7 %) reported medication non-adherence. Factors associated with non-adherence in bivariate analysis included younger age, non-white race, having less than 12 years of education, smoking, financial insecurity, identifying as nervous or tense, higher life chaos score, greater worry about having a myocardial infarction, and greater worry about having a stroke. Using multivariable modeling, we determined that age (OR 0.97 per additional year, 95 % CI, 0.95-0.99), life chaos (OR 1.06 per additional point, 95 % CI, 1.00-1.11), and worry about stroke (OR 1.12 per additional point, 95 % CI, 1.01-1.25) remained significantly associated with self-reported medication non-adherence.We found that worry about having a stroke, higher life chaos, and younger age were all significantly associated with self-reported medication non-adherence in patients with cardiovascular disease and a history of myocardial infarction. Further research exploring these factors as targets for intervention is needed, as is additional research examining modifiable causes of medication non-adherence among patients with cardiovascular disease.

Project description:The healthy adherer effect is a phenomenon in which patients who adhere to medical therapies tend to pursue health-seeking behaviors. Although the healthy adherer effect is supposed to affect health outcomes in patients with coronary artery disease, evaluation of its presence and extent is not easy. This study aimed to assess the relationship between medication adherence and lifestyle modifications and health-related quality of life among post-acute myocardial infarction (AMI) patients.A cross-sectional study was conducted in 417 post-AMI patients who underwent percutaneous coronary intervention (PCI). Patients were recruited from 11 university hospitals from December 2015 to March 2016 in South Korea. Details regarding socio-demographic factors, six health behaviors (low-salt intake, low-fat diet and/or weight-loss diet, regular exercise, stress reduction in daily life, drinking in moderation, and smoking cessation), medication adherence using the Modified Morisky Scale (MMS), and HRQoL using the Coronary Revascularization Outcome Questionnaire (CROQ) were surveyed in a one-on-one interview.In the univariate logistic analysis, sex (female), age (?70 years), MMS score (?5), and CROQ score were associated with adherence to lifestyle modification. In the multiple logistic analysis, a high MMS score (?5) was associated with adherence to lifestyle modification after adjusting for sex, age, marital status, education, and family income (adjusted odds ratio [OR]?=?11.7, 95% confidence interval [CI]?=?1.5-91.3). After further adjusting for the CROQ score, the association between high MMS score and adherence to lifestyle modification was significant (adjusted OR?=?11.5, 95% CI?=?1.4-93.3).Adherence to medication was associated with adherence to lifestyle modification, suggesting the possible presence of the healthy adherer effect in post-AMI patients. After further adjusting for HRQoL, the association remained. To improve health outcome in post-AMI patients, early detection of patients with poor adherence to medication and lifestyle modification and motivational education programs to improve adherence are important. In addition, the healthy adherer effect should be considered in clinical research, in particular, in studies evaluating the effects of therapies on health outcomes.

Project description:BackgroundDropout is a common problem in longitudinal clinical trials and cohort studies, and is of particular concern when dropout occurs for reasons that may be related to the outcome of interest. This paper reviews common parametric models to account for dropout and introduces a Bayesian semi-parametric varying coefficient model for exponential family longitudinal data with non-ignorable dropout.MethodsTo demonstrate these methods, we present results from a simulation study and estimate the impact of drug use on longitudinal CD4 + T cell count and viral load suppression in the Women's Interagency HIV Study. Sensitivity analyses are performed to consider the impact of model assumptions on inference. We compare results between our semi-parametric method and parametric models to account for dropout, including the conditional linear model and a parametric frailty model. We also compare results to analyses that fail to account for dropout.ResultsIn simulation studies, we show that semi-parametric methods reduce bias and mean squared error when parametric model assumptions are violated. In analyses of the Women's Interagency HIV Study data, we find important differences in estimates of changes in CD4 + T cell count over time in untreated subjects that report drug use between different models used to account for dropout. We find steeper declines over time using our semi-parametric model, which makes fewer assumptions, compared to parametric models. Failing to account for dropout or to meet parametric assumptions of models to account for dropout could lead to underestimation of the impact of hard drug use on CD4 + cell count decline in untreated subjects. In analyses of subjects that initiated highly active anti-retroviral treatment, we find that the estimated probability of viral load suppression is lower in models that account for dropout.ConclusionsNon-ignorable dropout is an important consideration when analyzing data from longitudinal clinical trials and cohort studies. While methods that account for non-ignorable dropout must make some unavoidable assumptions that cannot be verified from the observed data, many methods make additional parametric assumptions. If these assumptions are not met, inferences can be biased, making more flexible methods with minimal assumptions important.

Project description:ObjectivesPoor medication adherence in low-income and middle-income countries is a major cause of suboptimal hypertension and diabetes control. We aimed to identify key factors associated with medication adherence in western Kenya, with a focus on cost-related and economic wealth factors.SettingWe conducted a cross-sectional analysis of baseline data of participants enrolled in the Bridging Income Generation with Group Integrated Care study in western Kenya.ParticipantsAll participants were ≥35 years old with either diabetes or hypertension who had been prescribed medications in the past 3 months.Primary and secondary outcome measuresBaseline data included sociodemographic characteristics, wealth and economic status and medication adherence information. Predictors of medication adherence were separated into the five WHO dimensions of medication adherence: condition-related factors (comorbidities), patient-related factors (psychological factors, alcohol use), therapy-related factors (number of prescription medications), economic-related factors (monthly income, cost of transportation, monthly cost of medications) and health system-related factors (health insurance, time to travel to the health facility). A multivariable analysis, controlling for age and sex, was conducted to determine drivers of suboptimal medication adherence in each overarching category.ResultsThe analysis included 1496 participants (73.7% women) with a mean age of 60 years (range 35-97). The majority of participants had hypertension (69.2%), 8.8% had diabetes and 22.1% had both hypertension and diabetes. Suboptimal medication adherence was reported by 71.2% of participants. Economic factors were associated with medication adherence. In multivariable analysis that investigated specific subtypes of costs, transportation costs were found to be associated with worse medication adherence. In contrast, we found no evidence of association between monthly medication costs and medication adherence.ConclusionSuboptimal medication adherence is highly prevalent in Kenya, and primary-associated factors include costs, particularly indirect costs of transportation. Addressing all economic factors associated with medication adherence will be important to improve outcomes for non-communicable diseases.Trial registration numberNCT02501746.

Dataset Information

Cardioprotective medication adherence in Western Australians in the first year after myocardial infarction: restricted cubic spline analysis of adherence-outcome relationships.

Publications

Cardioprotective medication adherence in Western Australians in the first year after myocardial infarction: restricted cubic spline analysis of adherence-outcome relationships.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets