A Prediction Model for Unmet Needs of Elders with Dementia and Caregiving Experiences of Family Caregivers

PURPOSE: The purposes of this study were to develop and test a prediction model for caregiving experiences including caregiving satisfaction and burden in dementia family caregivers. METHODS: The stress process model and a two factor model were used as the conceptual frameworks. Secondary data analysis was done with 320 family caregivers who were selected from the Seoul Dementia Management Survey (2014) data set. In the hypothesis model, the exogenous variable was patient symptomatology which included cognitive impairment, behavioral problems, dependency in activity of daily living and in instrumental activity of daily living. Endogenous variables were caregiver's perception of dementia patient's unmet needs, caregiving satisfaction and caregiving burden. Data were analysed using SPSS/WINdows and AMOS program. RESULTS: Caregiving burden was explained by patient symptomatology and caregiving satisfaction indicating significant direct effects and significant indirect effect from unmet needs. The proposed model explained 37.8% of the variance. Caregiving satisfaction was explained by patient symptomatology and unmet needs. Mediating effect of unmet needs was significant in the relationship between patient symptomatology and caregiving satisfaction. CONCLUSION: Results indicate that interventions focusing on relieving caregiving burden and enhancing caregiver satisfaction should be provided to caregivers with high levels of dementia patients' unmet needs and low level of caregiving satisfaction.

Analysis of the Characteristics of the Older Adults with Depression Using Data Mining Decision Tree Analysis

PURPOSE: The purpose of this study was to develop a prediction model for the characteristics of older adults with depression using the decision tree method. METHODS: A large dataset from the 2008 Korean Elderly Survey was used and data of 14,970 elderly people were analyzed. Target variable was depression and 53 input variables were general characteristics, family & social relationship, economic status, health status, health behavior, functional status, leisure & social activity, quality of life, and living environment. Data were analyzed by decision tree analysis, a data mining technique using SPSS Window 19.0 and Clementine 12.0 programs. RESULTS: The decision trees were classified into five different rules to define the characteristics of older adults with depression. Classification & Regression Tree (C&RT) showed the best prediction with an accuracy of 80.81% among data mining models. Factors in the rules were life satisfaction, nutritional status, daily activity difficulty due to pain, functional limitation for basic or instrumental daily activities, number of chronic diseases and daily activity difficulty due to disease. CONCLUSION: The different rules classified by the decision tree model in this study should contribute as baseline data for discovering informative knowledge and developing interventions tailored to these individual characteristics.

Work-Relatedness of Occupational Contact Dermatitis in a Tire Factory / 대한산업의학회지

OBJECTIVES: This study investigated occupational contact dermatitis in a tire factory, prompted by a long history of complaints of skin ailments by the factory workers. METHODS: Participants (n=160) completed a questionnaire concerning job characteristics and skin symptoms, and received a medical examination. Fifty-four workers with suspected work-related contact dermatitis were chosen for a patch test of contact-related dermatitis. RESULTS: The most frequent positive reactions of patch test were produced by diphenylguanidine, formaldehyde and cobalt. Twenty-one of the 54 individuals (38.9%) were judged to have work-related skin disease. CONCLUSIONS: Exposure to natural and synthetic rubbers and additive materials pose a risk of contact dermatitis in a tire manufacturing environment. Improved sanitary work practices and public health awareness measures are suggested.