Analysis of risk factors affecting suicidal ideation in South Korea by life cycle stage

Objectives@#This study analyzed risk factors for suicidal ideation in South Koreans from a life cycle perspective. @*Methods@#A secondary analysis was conducted of data collected in 2015 as part of the 6th Korea National Health and Nutrition Examination Survey (KNHANES). The participants comprised 5,935 individuals aged 12 years or older. The statistical analysis reflected the complex sampling design of the KNHANES, and the Rao-Scott chi-square test and multiple logistic regression analysis were performed. @*Results@#The prevalence of suicidal ideation was 5.7% in adolescents, 3.7% in young adults, 5.4% in middle-aged adults, and 7.0% in older adults. Depression and stress were risk factors in every stage of the life cycle. In those aged 12 to 19 years, activity restrictions were significantly associated with suicidal ideation. Education and subjective health status were risk factors in adults aged 20 to 39 years, and education, activity restrictions, and quality of life were the major risk factors in those aged 40 to 64 years. For adults 65 years of age or older, the risk of suicidal ideation was higher among those with inappropriate sleep time. @*Conclusion@#The risk factors for suicidal ideation were found to be different across stages of the life cycle. This suggests a need for individualized suicide prevention plans and specific government policies that reflect the characteristics of each life cycle stage.

Developing the High-Risk Drinking Scorecard Model in Korea

OBJECTIVES: This study aimed to develop a high-risk drinking scorecard using cross-sectional data from the 2014 Korea Community Health Survey. METHODS: Data were collected from records for 149,592 subjects who had participated in the Korea Community Health Survey conducted from 2014. The scorecard model was developed using data mining, a scorecard and points to double the odds approach for weighted multiple logistic regression. RESULTS: This study found that there were many major influencing factors for high-risk drinkers which included gender, age, educational level, occupation, whether they received health check-ups, depressive symptoms, over-moderate physical activity, mental stress, smoking status, obese status, and regular breakfast. Men in their thirties to fifties had a high risk of being a drinker and the risks in office workers and sales workers were high. Those individuals who were current smokers had a higher risk of drinking. In the scorecard results, the highest score range was observed for gender, age, educational level, and smoking status, suggesting that these were the most important risk factors. CONCLUSION: A credit risk scorecard system can be applied to quantify the scoring method, not only to help the medical service provider to understand the meaning, but also to help the general public to understand the danger of high-risk drinking more easily.

Effect of Medicine Adherence on the Occurrence of Cerebrovascular Disorders in Diabetes Mellitus Patients / 한국역학회지

OBJECTIVES: To assess the association between the occurrence of cerebrovascular disorders and a medication adherence in diabetes mellitus patients. METHODS: Medical records from 1,114 new patients with diabetes mellitus were collected and the occurrence of cerebrovascular disorders was observed. Data was gathered from the health examination records of diabetes mellitus patients registered at the Korean Metabolic Syndrome Research from 1996 to 2005, medication records from the National Health Insurance Corporation and death data from the National Statistics Office from 1997 to 2007. Hazard ratios were analyzed using the Cox proportional hazard model to test the association between the occurrence of cerebrovascular disorders and the level of medication adherence. Medication adherence was calculated using Continuous measure of Medication Acquisition (CMA). RESULTS: Of 1,114 diabetes mellitus patients, cerebrovascular disorders occurred in 67 cases (6.1%). The mean duration for the development of a cerebrovascular disorder was 3.82 yr. Medication adherence (> or =0.8 vs. or =0.8 vs. 0.5-0.7 HR, 0.99; 95% CI, 0.33-2.95) was an independent factor associated with the occurrence of cerebrovascular disorders in diabetes mellitus. CONCLUSION: Increased medication adherence is necessary to prevent the occurrence of cerebrovascular disorders in diabetes mellitus patients. Furthermore we propose that CMA be considered as a method for monitoring medication adherence in clinics.

Population Pharmacokinetic and Pharmacodynamic Models of Propofol in Healthy Volunteers using NONMEM and Machine Learning Methods / 대한의료정보학회지

OBJECTIVES: The primary objective of this study is to compare model performance of machine learning methods with that of a previous study in which a nonlinear mixed effects model was created using NONMEM(R) for the pharmacokinetic and pharmacodynamic data for propofol. The secondary objective was to evaluate if a pharmacodynamic model describing the relationship between the dose of propofol and bispectral index (BIS) outperform that describing the relationship between a pharmacokinetic model derived-predicted concentrations of propofol and BIS. METHODS: Data were collected during a study involving the infusion of propofol into healthy volunteers. Pharmacokinetic and pharmacodynamic models were constructed using artificial neural networks (ANNs), support vector machines (SVMs), and multi-method ensembles and were compared with the nonlinear mixed effects method as implemented by NONMEM(R). Model performance was assessed by goodness-of-fit statistics, paired t-tests between predicted and observed values for each model and scatterplots. RESULTS: In pharmacokinetic analysis, ensemble I, the mean of ANN and NONMEM(R) predictions, achieved minimal error and the highest correlation coefficient. SVM produced the highest error and the lowest correlation coefficient. In pharmacodynamic analysis, ANN exhibited the best performance. An ANNModel describing the relationship between the dose of propofol and BIS was not inferior to an ANN model describing the relationship between predicted concentrations of propofol derived from an ANN pharmacokinetic model and BIS. CONCLUSIONS: In pharmacokinetic analysis, ensemble combined with ANN achieved slightly better performance than NONMEM(R). The relationship between the dose of propofol and BIS can be predicted without considering pharmacokinetics of propofol.

Decision Tree Approach Characterizing the Non-Examinees of Health Screening Services / 대한의료정보학회지

OBJECTIVE: The purpose of this study was to develop the decision tree models to classify the characteristics of those who had not undergone the health screening tests provided by the National Health Insurance Corporation. METHODS: Total of 5,102,761 subjects of health screening services in the year of 2002 was used. The data was divided into two data-sets (disease VS. non-disease group). The target variable was whether they took the health screening services. The number of input variables was 25 in total. RESULTS: The decision trees were classified into fourteen different types of non-examinees in the non-disease group and nineteen in the disease group. The ROC curve areas in the non-disease and disease groups were .761 and .714, respectively. CONCLUSION: The different types of non-examinees classified by the decision tree models would facilitate the foundation for the further analysis of individual characteristics and the effective health screening service management in future.