Supporting Parkinson's disease medication safety for nurses in the acute care setting through an educational intervention study.

INTRODUCTION: Patient medication safety in the acute care setting is a foundational action provided by nurses and healthcare providers for safe patient care. Hospitalization of patients with Parkinson's disease (PD) can be dangerous due to the unique and variable medication regimen required. Patients with PD often have their medication administered inappropriately in the acute care setting (e.g., holding a PD medication in preparation for surgery, not administering the medication on the patient's home schedule, and delaying administration). The research question posed in this study was the following: does a PD medication educational intervention in the clinical setting enhance knowledge, comfort, and competence of practicing nurses in the care of patients with PD regarding their medication safety? DESIGN: A mixed methods study design was used for this 5-month, two-part study with a sample of practicing RNs at three different hospitals. Part one of the study assessed nurses' initial knowledge of PD and PD medication safety and included an educational intervention. Part two of the study occurred 3 months later and evaluated if knowledge from the educational intervention was retained. METHODS: The study was conducted in two parts and included a pre-test, educational intervention, post-test, and follow-up test 3 months later. The educational intervention consisted of a 15-minute video of two PD advanced practice nurses being interviewed regarding the general care of a patient with PD. The pre-test, post-test, and follow-up test were identical and consisted of six questions regarding knowledge, comfort, and self-perceived competency. Participants were additionally asked three open-ended questions at follow-up to gain insight on the effectiveness of the educational intervention. RESULTS: A total sample of 252 RNs participated in this study. Statistically significant improvements in knowledge, comfort, and self-perceived competency were observed in the post-test scores compared to pre-test scores. These statistically significant improvements were retained after 3 months, despite a 42.9% decrease in the number of responders (n = 252 vs. n = 144). Additionally, compared to the post-test, there were no statistically significant declines in knowledge, comfort, or competency in the follow-up test. Qualitative findings indicated that the training regarding PD medications was retained and found to be valuable, even if it was seldom applied in practice. CONCLUSION: A review of the literature and this study both support the need for increased education for practicing nurses as it relates to PD and PD medication safety. Healthcare systems, organizations, and associations that support continuing education for nurses create a stronger workforce. Education has been found to keep nurses up to date on the latest advances in care and treatment while also providing exposure to other areas of nursing beyond their clinical settings. CLINICAL RELEVANCE: Promoting better patient outcomes through safe medication administration is a hallmark of nursing care excellence. This study found that supporting the use of an educational intervention of PD medication safety for nurses improved RN levels of knowledge, comfort, and competency up to 3 months later. As the population of those with PD increases, healthcare systems, and nurses must now, more than ever, be poised to care for these individuals. This is a critical point in PD patient care since persons with PD are hospitalized 1.5 times more than their peers without PD.

A Year in the Life of U.S. Frontline Health Care Workers: Impact of COVID-19 on Weight Change, Physical Activity, Lifestyle Habits, and Psychological Factors.

Healthcare workers (HCWs) experienced significantly higher burdens and life demands due to the COVID-19 pandemic. This study sought to assess the longitudinal effects among HCWs throughout the pandemic. Qualtrics surveys collected self-reported data on weight changes, eating patterns, physical activity (PA), and psychological factors with data organized by timepoints prior to the pandemic (PP0­prior to March 2020), baseline (M0­January 2021), month 6 (M6­July 2021), and month 12 (M12­January 2022). Eating patterns were negatively impacted at the M0, with reported increases in snacking/grazing (69.7%), fast food/take-out consumption (57.8%), and alcohol (48.8%). However, by M6 and M12 there were no statistically significant differences in eating patterns, suggesting that eating patterns normalized over time. Mean weight increased from PP0 to M0 by 2.99 pounds (p < 0.001, n = 226) and from PP0 to M6 by 2.12 pounds (p < 0.027, n = 146), though the difference in mean weight from PP0 to M12 was not statistically significant (n = 122). PA counts decreased from 8.00 sessions per week PP0 to 6.80 by M0 (p = 0.005) before jumping to 12.00 at M6 (p < 0.001) and 10.67 at M12 (p < 0.001). Psychological factors comparing M0 to M12 found statistically significant differences for depression (p-value = 0.018) and anxiety (p-value = 0.001), meaning depression and anxiety were initially increased but improved by M12. Additionally, higher scores on depression and insomnia scales were associated with lower PA levels. These overall results imply that the COVID-19 pandemic had immediate effects on the eating patterns, weight changes, PA, and psychological factors of HCWs; however, routines and lifestyle habits appeared to have normalized one year later.

Parkinson's Disease Medication Administration During a Care Transition: The Impact of Interprofessional Team Simulation on Student Competency, Comfort, and Knowledge.

AIM: This study investigated the impact of an interprofessional mock code on students' comfort and competency related to Parkinson's disease (PD) medication administration during care transitions. BACKGROUD: Patients with PD are at increased risk for medication errors during hospitalization. Individualization of PD medication creates vulnerability during care transitions. METHOD: Four interprofessional groups took part in this study: baccalaureate degree senior nursing students (n = 113), master's level nurse anesthesia students (n = 35), doctor of osteopathic medicine fourth-year students (n = 32), and doctor of clinical psychology fourth-year students (n = 22). Groups participated in an unfolding case study simulation involving a mock code with a focus on the omission of time-sensitive PD medication. Pre- and postsimulation test results were compared. RESULTS: Findings indicated an increased understanding among three of the four groups relating to medication timing during care transitions. CONCLUSION: All groups improved with respect to perceived comfort and competency.

Maximum Likelihood Estimation in a Semicontinuous Survival Model with Covariates Subject to Detection Limits.

Semicontinuous data are common in biological studies, occurring when a variable is continuous over a region but has a point mass at one or more points. In the motivating Genetic and Inflammatory Markers of Sepsis (GenIMS) study, it was of interest to determine how several biomarkers subject to detection limits were related to survival for patients entering the hospital with community acquired pneumonia. While survival times were recorded for all individuals in the study, the primary endpoint of interest was the binary event of 90-day survival, and no patients were lost to follow-up prior to 90 days. In order to use all of the available survival information, we propose a two-part regression model where the probability of surviving to 90 days is modeled using logistic regression and the survival distribution for those experiencing the event prior to this time is modeled with a truncated accelerated failure time model. We assume a series of mixture of normal regression models to model the joint distribution of the censored biomarkers. To estimate the parameters in this model, we suggest a Monte Carlo EM algorithm where multiple imputations are generated for the censored covariates in order to estimate the expectation in the E-step and then weighted maximization is applied to the observed and imputed data in the M-step. We conduct simulations to assess the proposed model and maximization method, and we analyze the GenIMS data set.

Model validation and influence diagnostics for regression models with missing covariates.

Missing covariate values are prevalent in regression applications. While an array of methods have been developed for estimating parameters in regression models with missing covariate data for a variety of response types, minimal focus has been given to validation of the response model and influence diagnostics. Previous research has mainly focused on estimating residuals for observations with missing covariates using expected values, after which specialized techniques are needed to conduct proper inference. We suggest a multiple imputation strategy that allows for the use of standard methods for residual analyses on the imputed data sets or a stacked data set. We demonstrate the suggested multiple imputation method by analyzing the Sleep in Mammals data in the context of a linear regression model and the New York Social Indicators Status data with a logistic regression model.

A flexible cure rate model with dependent censoring and a known cure threshold.

We propose a flexible cure rate model that accommodates different censoring distributions for the cured and uncured groups and also allows for some individuals to be observed as cured when their survival time exceeds a known threshold. We model the survival times for the uncured group using an accelerated failure time model with errors distributed according to the seminonparametric distribution, potentially truncated at a known threshold. We suggest a straightforward extension of the usual expectation-maximization algorithm approach for obtaining estimates in cure rate models to accommodate the cure threshold and dependent censoring. We additionally suggest a likelihood ratio test for testing for the presence of dependent censoring in the proposed cure rate model. We show through numerical studies that our model has desirable properties and leads to approximately unbiased parameter estimates in a variety of scenarios. To demonstrate how our method performs in practice, we analyze data from a bone marrow transplantation study and a liver transplant study. Copyright © 2016 John Wiley & Sons, Ltd.

Statistical Methods for Generalized Linear Models with Covariates Subject to Detection Limits.

Censored observations are a common occurrence in biomedical data sets. Although a large amount of research has been devoted to estimation and inference for data with censored responses, very little research has focused on proper statistical procedures when predictors are censored. In this paper, we consider statistical methods for dealing with multiple predictors subject to detection limits within the context of generalized linear models. We investigate and adapt several conventional methods and develop a new multiple imputation approach for analyzing data sets with predictors censored due to detection limits. We establish the consistency and asymptotic normality of the proposed multiple imputation estimator and suggest a computationally simple and consistent variance estimator. We also demonstrate that the conditional mean imputation method often leads to inconsistent estimates in generalized linear models, while several other methods are either computationally intensive or lead to parameter estimates that are biased or more variable compared to the proposed multiple imputation estimator. In an extensive simulation study, we assess the bias and variability of different approaches within the context of a logistic regression model and compare variance estimation methods for the proposed multiple imputation estimator. Lastly, we apply several methods to analyze the data set from a recently-conducted GenIMS study.

A Fast EM Algorithm for Fitting Joint Models of a Binary Response and Multiple Longitudinal Covariates Subject to Detection Limits.

Joint modeling techniques have become a popular strategy for studying the association between a response and one or more longitudinal covariates. Motivated by the GenIMS study, where it is of interest to model the event of survival using censored longitudinal biomarkers, a joint model is proposed for describing the relationship between a binary outcome and multiple longitudinal covariates subject to detection limits. A fast, approximate EM algorithm is developed that reduces the dimension of integration in the E-step of the algorithm to one, regardless of the number of random effects in the joint model. Numerical studies demonstrate that the proposed approximate EM algorithm leads to satisfactory parameter and variance estimates in situations with and without censoring on the longitudinal covariates. The approximate EM algorithm is applied to analyze the GenIMS data set.

Flexible Modeling of Survival Data with Covariates Subject to Detection Limits via Multiple Imputation.

Models for survival data generally assume that covariates are fully observed. However, in medical studies it is not uncommon for biomarkers to be censored at known detection limits. A computationally-efficient multiple imputation procedure for modeling survival data with covariates subject to detection limits is proposed. This procedure is developed in the context of an accelerated failure time model with a flexible seminonparametric error distribution. The consistency and asymptotic normality of the multiple imputation estimator are established and a consistent variance estimator is provided. An iterative version of the proposed multiple imputation algorithm that approximates the EM algorithm for maximum likelihood is also suggested. Simulation studies demonstrate that the proposed multiple imputation methods work well while alternative methods lead to estimates that are either biased or more variable. The proposed methods are applied to analyze the dataset from a recently-conducted GenIMS study.