Search

ABSTRACTObjectives:As a degenerative disease, the progression of dementia needs continued care provision and poses both psychological and financial burden for family caregivers of persons with dementia (PWD). This study seeks to compare predictors of care costs and caregiver burden, and to identify modifiable factors that could alleviate the burden faced by dementia caregivers.Methods: This study interviewed 231 PWD-caregiver dyads in a dementia clinic at a teaching hospital in southern Taiwan in 2013. A follow-up study was conducted a year later, and 167 dyads completed the second interview. Data collected included PWD characteristics, caregiver characteristics, relationship to PWD, and social support to caregivers. Caregiver burden was measured with the Zarit Burden Interview instrument. The association between each predictor variable and cost of care and caregiver burden scores was examined using linear mixed models.Results: Predictors of care costs were found to be different from predictors of caregiver burden: functional declines measured by Katz's activities of daily living (ADL) scale were associated with total cost as compared to behavioral disturbance measured by Neuropsychiatric Inventory (NPI), which showed no impact on care costs. However, NPI was a significant predictor of caregiver burden. Caregivers who were better-off financially also reported significantly lower caregiver burden.Conclusions: Since predictors of care costs were different from the predictors of caregiver burden, providing training to caregivers in addressing PWD's behavioral disturbance and proving financial assistance to low income caregivers could be effective in reducing caregiver burden. [ABSTRACT FROM AUTHOR]

Sample size determination is essential during the planning phases of clinical trials. To calculate the required sample size for paired right-censored data, the structure of the within-paired correlations needs to be pre-specified. In this article, we consider using popular parametric copula models, including the Clayton, Gumbel, or Frank families, to model the distribution of joint survival times. Under each copula model, we derive a sample size formula based on the testing framework for rank-based tests and non-rank-based tests (i.e., logrank test and Kaplan- Meier statistic, respectively). We also investigate how the power or the sample size was affected by the choice of testing methods and copula model under different alternative hypotheses. In addition to this, we examine the impacts of paired-correlations, accrual times, follow-up times, and the loss to follow-up rates on sample size estimation. Finally, two real-world studies are used to illustrate our method and R code is available to the user. [ABSTRACT FROM AUTHOR]

Aim To investigate the relationship between nursing hours per patient day and the inpatient mortality rate in Taiwan. Background Nursing hours per patient day has been associated with better patient outcomes. The literature is inconclusive on the relationship between nursing hours per patient day and the inpatient mortality rate, and no studies have yet examined this issue in Taiwan. Methods A retrospective longitudinal study analysed data from the 'Nursing Utilization of Resources, Staffing and Environment on Outcome Study: NURSE-outcome study'. Hierarchical regression estimated the relationship between nursing hours per patient day and in-hospital mortality rate after controlling for confounding variables. Results The mean nursing hours per patient day in Taiwan was 2.3, while the mean inpatient mortality rate was 0.73% higher nursing hours per patient day was associated with a lower inpatient mortality rate after controlling for confounding variables. The total explained variance of this study in inpatient mortality rate was 19.9%. Significant relationships to inpatient mortality were found in levels of hospitals, seasonal variation and nurses' work experience. Conclusion Nursing hours per patient day affects the mortality rate among hospitalised patients in Taiwan. Implications for nursing management According to the results, we suggested the government and managers in Taiwan double the nursing hours per patient day so that the inpatient mortality rate will decline by 1.1%. This might be the optimal nurse configuration that could provide a balance between cost-effectiveness and patient safety. [ABSTRACT FROM AUTHOR]

This article considers Bayesian variable selection problems for binary responses via stochastic search variable selection and Bayesian Lasso. To avoid matrix inversion in the corresponding Markov chain Monte Carlo implementations, the componentwise Gibbs sampler (CGS) idea is adopted. Moreover, we also propose automatic hyperparameter tuning rules for the proposed approaches. Simulation studies and a real example are used to demonstrate the performances of the proposed approaches. These results show that CGS approaches do not only have good performances in variable selection but also have the lower batch mean standard error values than those of original methods, especially for large number of covariates. [ABSTRACT FROM PUBLISHER]

Mild cognitive impairment (MCI) is a prodromal stage of Alzheimer's disease (AD) that causes a significant burden in caregiving and medical costs. Clinically, the diagnosis of MCI is determined by the impairment statuses of five cognitive domains. If one of these cognitive domains is impaired, the patient is diagnosed with MCI, and if two out of the five domains are impaired, the patient is diagnosed with AD. In medical records, most of the time, the diagnosis of MCI/AD is given, but not the statuses of the five domains. We may treat the domain statuses as missing variables. This diagnostic procedure relates MCI/AD status modeling to multiple‐instance learning, where each domain resembles an instance. However, traditional multiple‐instance learning assumes common predictors among instances, but in our case, each domain is associated with different predictors. In this article, we generalized the multiple‐instance logistic regression to accommodate the heterogeneity in predictors among different instances. The proposed model is dubbed heterogeneous‐instance logistic regression and is estimated via the expectation‐maximization algorithm because of the presence of the missing variables. We also derived two variants of the proposed model for the MCI and AD diagnoses. The proposed model is validated in terms of its estimation accuracy, latent status prediction, and robustness via extensive simulation studies. Finally, we analyzed the National Alzheimer's Coordinating Center‐Uniform Data Set using the proposed model and demonstrated its potential. [ABSTRACT FROM AUTHOR]