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The objective was to identify individuals with undiagnosed prediabetes from administrative data using adaptive techniques. The data source was a national Medicare Advantage Prescription Drug (MAPD) plan administrative data set. A retrospective, cross-sectional study developed and evaluated data adaptive logistic regression, decision tree, neural network, and ensemble predictive models for metabolic syndrome and prediabetes using 3 mutually exclusive cohorts (N = 279,903). The misclassification rate (MCR), average squared error (ASE), c-statistics, sensitivity (SN), and false positive (FP) rates were compared to select the final predictive models. MAPD individuals with continuous enrollment from 2013 to 2014 were included. Metabolic syndrome and prediabetes were defined using clinical guidelines, diagnosis, and laboratory data. A total of 512 variables identified through subject matter expertise in addition to utilizing all data available were evaluated for the modeling. The ensemble model demonstrated better discrimination (c-statistics, MCR, and ASE of 0.83, 0.24, and 0.16, respectively), high SN, and low FP rate in predicting metabolic syndrome than the individual data adaptive modeling techniques. Logistic regression demonstrated better discrimination (c-statistics, MCR, and ASE of 0.67, 0.13, and 0.11 respectively), high SN, and low FP rate in predicting prediabetes than the other adaptive modeling techniques or ensemble methods. The scored data predicted prediabetes in 44% of the MAPD population, which is comparable to 2005-2006 National Health and Nutrition Examination Survey prediabetes rates of 41%. The logistic regression model demonstrated good performance in predicting undiagnosed prediabetes in MAPD individuals.

Objective: To examine the persistence of three newly initiated stimulant preparations among Medicaid children and adolescents with attention-deficit/hyperactivity disorder (ADHD) diagnosis., Methods: A retrospective longitudinal claims analysis was conducted by using Medicaid analytical eXtract data of four states. The study focused on patients between 6 and 19 years of age with ADHD diagnosis and a stimulant prescription from January 2003 to December 2005. Stimulants were grouped into short-acting stimulants (SAS), intermediate-acting stimulants (IAS), and long-acting stimulants (LAS). Persistence was measured by totaling the number of days the patient remained on the index stimulant therapy from the index prescription date provided the refill gap between two consecutive stimulant claims was no more than 30 days. All the stimulant recipients were uniformly followed for 1 year (365 days). Survival time ratios (STR) were calculated by using accelerated failure time models to examine variation in index stimulant persistence for each stimulant class., Results: Among the 46,135 patients with ADHD continuously followed for 1 year, 8,260 were SAS users, 4,314 were IAS users, and 33,561 were LAS users. Children who received IAS medications had 4% shorter persistence (STR, 0.96 [95% confidence interval [CI], 0.93-0.98]) when compared with those who received SAS medications, whereas those who received index LAS medications had 29% longer persistence (STR, 1.29 [95% CI, 1.27-1.32]). Multivariate accelerated failure time models revealed that Blacks and Hispanics had consistently lower persistence than their counterparts. Foster care was positively associated with index stimulant persistence in the three stimulant types. Further, addition of another stimulant and other psychotropic medications significantly improved persistence of index stimulant in all three stimulant classes., Conclusions: LAS had comparatively longer persistence than other stimulants. An understanding of demographic and clinical characteristics that influence treatment continuation can help improve stimulant persistence rates in ADHD.