Advanced Test of Attention in Children with Attention-Deficit/Hyperactivity Disorder in Japan for Evaluation of Methylphenidate and Atomoxetine Effects

OBJECTIVE: This study was conducted to validate the Advanced Test of Attention (ATA) of the visual attention version of Japanese children with attention deficit/hyperactivity disorder (ADHD) and to evaluate the efficacy of methylphenidate (OROS-MPH) and atomoxetine medications. METHODS: To assess pharmacotherapy efficacy, the visual version of ATA was administered to 42 children with ADHD. Results were assessed using discriminant analysis, ANOVA for indices of ATA before and after medication treatment, and correlation analysis between the improvement of indices of ATA and clinical symptoms during medication treatment. RESULTS: Discriminant analysis showed that 69.0% of ADHD children were assigned correctly. The T score of commission errors increased as the trial progressed on the medication-off condition. T scores of commission errors and standard deviation of response times on medication-on condition were low compared to the medication-off condition. A few significant correlations were found between the improvements of indices of ATA and ADHD-Rating Scale (RS) during treatment. CONCLUSION: The performance of the visual version of ATA on medication-off condition reflected the features of ADHD. Furthermore, the medication treatment effects were confirmed sufficiently. In addition, results suggest that indices of ATA reflected aspects of ADHD symptoms that are difficult to elucidate for ADHD-RS. For assessing symptoms and effects of medical treatment in children with ADHD, ATA might be a useful assessment tool.

Stratified Sampling Design Based on Data Mining / 대한의료정보학회지

OBJECTIVES: To explore classification rules based on data mining methodologies which are to be used in defining strata in stratified sampling of healthcare providers with improved sampling efficiency. METHODS: We performed k-means clustering to group providers with similar characteristics, then, constructed decision trees on cluster labels to generate stratification rules. We assessed the variance explained by the stratification proposed in this study and by conventional stratification to evaluate the performance of the sampling design. We constructed a study database from health insurance claims data and providers' profile data made available to this study by the Health Insurance Review and Assessment Service of South Korea, and population data from Statistics Korea. From our database, we used the data for single specialty clinics or hospitals in two specialties, general surgery and ophthalmology, for the year 2011 in this study. RESULTS: Data mining resulted in five strata in general surgery with two stratification variables, the number of inpatients per specialist and population density of provider location, and five strata in ophthalmology with two stratification variables, the number of inpatients per specialist and number of beds. The percentages of variance in annual changes in the productivity of specialists explained by the stratification in general surgery and ophthalmology were 22% and 8%, respectively, whereas conventional stratification by the type of provider location and number of beds explained 2% and 0.2% of variance, respectively. CONCLUSIONS: This study demonstrated that data mining methods can be used in designing efficient stratified sampling with variables readily available to the insurer and government; it offers an alternative to the existing stratification method that is widely used in healthcare provider surveys in South Korea.