Standard profiling analysis aims to evaluate medical providers, such as hospitals, nursing homes, or dialysis facilities, with respect to a patient outcome. The outcome, for instance, may be mortality, medical complications, or 30-day (unplanned) hospital readmission. Profiling analysis involves regression modeling of a patient outcome, adjusting for patient health status at baseline, and comparing each provider's outcome rate (e.g., 30-day readmission rate) to a normative standard (e.g., national "average"). Profiling methods exist mostly for non time-varying patient outcomes. However, for patients on dialysis, a unique population which requires continuous medical care, methodologies to monitor patient outcomes continuously over time are particularly relevant. Thus, we introduce a novel time-dynamic profiling (TDP) approach to assess the time-varying 30-day readmission rate. TDP is used to estimate, for the first time, the risk-standardized time-dynamic 30-day hospital readmission rate, throughout the time period that patients are on dialysis. We develop the framework for TDP by introducing the standardized dynamic readmission ratio as a function of time and a multilevel varying coefficient model with facility-specific time-varying effects. We propose estimation and inference procedures tailored to the problem of TDP and to overcome the challenge of high-dimensional parameters when examining thousands of dialysis facilities.

Background. Recently, test developers have created rapid point-of-care tests that can simultaneously detect multiple infections within the same specimen using a single device. The SD BIOLINE Duo HIV/Syphilis rapid point-of-care test uses a solid-phase immunochromatographic assay to detect immunoglobulin (Ig)G, IgM, and IgA antibodies to human immunodeficiency virus (HIV)-specific antigens (HIV-1 gp41, sub O, HIV-2 gp36) and recombinant Treponema pallidum antigen (17 kDa) in human serum. This study was a multisite laboratory-based evaluation of the performance of SD BIOLINE HIV/Syphilis Duo test using previously characterized sera in 6 countries.Methods. Laboratories in Ghana, Mexico, Laos, Togo, Kenya, and Myanmar participated in the evaluation during 2012–2013. Each site characterized sera using T pallidum particle agglutination assay or T pallidum hemagglutination assay and HIV enzyme immunoassay, Western blot, and/or HIV antibody rapid tests. Those gold standard test results were compared with SD BIOLINE Duo test results. We calculated the sensitivity and specificity of test performance and used the exact binomial method to calculate 95% confidence intervals (CIs).Results. The sensitivity and specificity for the HIV antibody test component (n = 2336) were estimated at 99.91% (95% CI, 99.51% and 100%) and 99.67% (95% CI, 99.16% and 99.91%), respectively. For the T pallidum test component (n = 2059), the sensitivity and specificity were estimated at 99.67% (95% CI, 98.82% and 99.96%) and 99.72% (95% CI, 99.29% and 99.92%), respectively.Conclusions. The sensitivity and specificity of the SD BIOLINE HIV/Syphilis Duo test were consistently high across sera specimens from 6 countries around the world. Dual rapid tests should be considered for improved HIV and syphilis screening coverage.

Objective The overall rate of obesity is rising in the USA; this is also reflected in the military population. It is important that providers appropriately diagnose obesity and discuss treatment options with their patients. The purpose of this study was to investigate diagnosis of obesity compared to documented body mass index (BMI) in the military health system. Methods Institutional review board approval was obtained by the 59th Medical Wing (Lackland Air Force Base, Texas) as an exempt study. This study included active duty military service members aged 18-65 years who sought outpatient care at a military treatment facility from September 2013 to August 2018 with a weight within the range of 31.8-226.8 kg and height between 121.9 and 215.9 cm. Data were collected from the Clinical Data Repository vitals and M2 encounter data to determine the percentage of each sub-population with a diagnosis of obesity according to BMI (≥30 kg/m2) and International Classification of Diseases diagnosis codes. Results Using BMI, 19.2% of female and 26.8% of male service members can be diagnosed with obesity; however, only 42.2% and 35.1%, respectively, with a BMI ≥30 was diagnosed as such. This discrepancy was consistent among all service branches and BMI ranges. Conclusion This study demonstrates that obesity is underdiagnosed compared to BMI. This may result in insufficient resources being provided to patients to reduce weight. Further investigation is warranted to identify causes of underdiagnosis and potential barriers to diagnosis.

Among patients on dialysis, cardiovascular disease and infection are leading causes of hospitalization and death. Although recent studies have found that the risk of cardiovascular events is higher after an infection-related hospitalization, studies have not fully elucidated how the risk of cardiovascular events changes over time for patients on dialysis. In this work, we characterize the dynamics of cardiovascular event risk trajectories for patients on dialysis while conditioning on survival status via multiple time indices: (1) time since the start of dialysis, (2) time since the pivotal initial infection-related hospitalization and (3) the patient’s age at the start of dialysis. This is achieved by using a new class of generalized multiple-index varying coefficient (GM-IVC) models. The proposed GM-IVC models utilize a multiplicative structure and one-dimensional varying coefficient functions along each time and age index to capture the cardiovascular risk dynamics before and after the initial infection-related hospitalization among the dynamic cohort of survivors. We develop a two-step estimation procedure for the GM-IVC models based on local maximum likelihood. We report new insights on the dynamics of cardiovascular events risk using the United States Renal Data System database, which collects data on nearly all patients with end-stage renal disease in the U.S. Finally, simulation studies assess the performance of the proposed estimation procedures.