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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. [ABSTRACT FROM AUTHOR]

The article discusses research on the link between obesity and healthcare resource utilization (HRU) as well as medical costs. Topics include comorbidities associated with increasing body mass index (BMI) among U.S. adults, the use of drugs in relation to increasing BMI, and the connection between obesity and cardiometabolic conditions. The economic impacts of obesity are noted.

Objective:To evaluate the positive predictive value of claims-based V85 codes for identifying individuals with varying degrees of BMI relative to their measured BMI obtained from medical record abstraction. Methods:This was a retrospective validation study utilizing administrative claims and medical chart data from 1 January 2009 to 31 August 2015. Randomly selected samples of patients enrolled in a Medicare Advantage Prescription Drug (MAPD) or commercial health plan and with a V85 claim were identified. The claims-based BMI category (underweight, normal weight, overweight, obese class I–III) was determined via corresponding V85 codes and compared to the BMI category derived from chart abstracted height, weight and/or BMI. The positive predictive values (PPVs) of the claims-based BMI categories were calculated with the corresponding 95% confidence intervals (CIs). Results:The overall PPVs (95% CIs) in the MAPD and commercial samples were 90.3% (86.3%–94.4%) and 91.1% (87.3%–94.9%), respectively. In each BMI category, the PPVs (95% CIs) for the MAPD and commercial samples, respectively, were: underweight, 71.0% (55.0%–87.0%) and 75.9% (60.3%–91.4%); normal, 93.8% (85.4%–100%) and 87.8% (77.8%–97.8%); overweight, 97.4% (92.5%–100%) and 93.5% (84.9%–100%); obese class I, 96.9 (90.9%–100%) and 97.2% (91.9%–100%); obese class II, 97.0% (91.1%–100%) and 93.0% (85.4%–100%); and obese class III, 85.0% (73.3%–96.1%) and 97.1% (91.4%–100%). Conclusions:BMI categories derived from administrative claims, when available, can be used successfully particularly in the context of obesity research. [ABSTRACT FROM PUBLISHER]

Purpose We compared methods to control bias and confounding in observational studies including inverse probability weighting (IPW) and stabilized IPW (sIPW). These methods often require iteration and post-calibration to achieve covariate balance. In comparison, entropy balance (EB) optimizes covariate balance a priori by calibrating weights using the target's moments as constraints. Methods We measured covariate balance empirically and by simulation by using absolute standardized mean difference (ASMD), absolute bias (AB), and root mean square error (RMSE), investigating two scenarios: the size of the observed (exposed) cohort exceeds the target (unexposed) cohort and vice versa. The empirical application weighted a commercial health plan cohort to a nationally representative National Health and Nutrition Examination Survey target on the same covariates and compared average total health care cost estimates across methods. Results Entropy balance alone achieved balance (ASMD ≤ 0.10) on all covariates in simulation and empirically. In simulation scenario I, EB achieved the lowest AB and RMSE (13.64, 31.19) compared with IPW (263.05, 263.99) and sIPW (319.91, 320.71). In scenario II, EB outperformed IPW and sIPW with smaller AB and RMSE. In scenarios I and II, EB achieved the lowest mean estimate difference from the simulated population outcome ($490.05, $487.62) compared with IPW and sIPW, respectively. Empirically, only EB differed from the unweighted mean cost indicating IPW, and sIPW weighting was ineffective. Conclusion Entropy balance demonstrated the bias-variance tradeoff achieving higher estimate accuracy, yet lower estimate precision, compared with IPW methods. EB weighting required no post-processing and effectively mitigated observed bias and confounding. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]