Longitudinal Self-Management and/or Transition Readiness per the TRxANSITION Index among Patients with Chronic Conditions in Pediatric or Adult Care Settings.

OBJECTIVE: To evaluate the roles of key individual, family, and illness characteristics on the levels of and gains in longitudinal healthcare transition (HCT) readiness in the pediatric setting and/or self-management skills (SMS) in the adult-focused setting, we used a large dataset with longitudinal measurements from 2006 to 2015. STUDY DESIGN: This longitudinal observational study followed 566 adolescents and young adults with chronic conditions at University of North Carolina Hospitals. TRxANSITION Index measurements, which represent learning outcomes rather than health outcomes, were collected multiple times per patient and analyzed using a novel application of an education-based approach. RESULTS: Levels of and gains in HCT/SMS scores increased with age (P < .001) with smaller increases at older ages. Mastery of skills varied by age with self-management achieved after 20 years of age. Scores varied positively by father's education and negatively by mother's education and duration of diagnosis. Gains in scores further varied positively with private insurance and negatively with mother's education and duration of diagnosis. CONCLUSIONS: We found diminishing positive increases in HCT/SMS scores as patients become older and smaller levels of and gains in readiness among younger patients with more educated mothers. Risk factors for absolute level of HCT/SMS readiness and inadequate longitudinal gains are not always the same, which motivates a deeper understanding of this dynamic process through additional research. This information can guide providers to focus HCT/SMS preparation efforts on skills mastered at particular ages and to identify patients at risk for inadequate development of HCT/SMS skills.

A Generalized Assessment of the Impact of Regionalization and Provider Learning on Patient Outcomes.

We present a generalized model to assess the impact of regionalization on patient care outcomes in the presence of heterogeneity in provider learning. The model characterizes best regionalization policies as optimal allocations of patients across providers with heterogeneous learning abilities. We explore issues that arise when solving for best regionalization, which depends on statistically estimated provider learning curves. We explain how to maintain the problem's tractability and reformulate it into a binary integer program problem to improve solvability. Using our model, best regionalization solutions can be computed within reasonable time using current-day computers. We apply the model to minimally invasive radical prostatectomy and estimate that, in comparison to current care delivery, within-state regionalization can shorten length of stay by at least 40.8%.

A flexible approach for estimating the effects of covariates on health expenditures.

Our estimation strategy uses sequences of conditional probability functions, similar to those used in discrete time hazard rate analyses, to construct a discrete approximation to the density function of an outcome of interest conditional on exogenous explanatory variables. Once the conditional density function has been constructed, we can examine expectations of arbitrary functions of the outcome of interest and evaluate how these expectations vary with observed exogenous covariates. We demonstrate the features and precision of the conditional density estimation method (and compare it to other commonly used methods) through Monte Carlo experiments and an application to health expenditures using the RAND Health Insurance Experiment data. Overall, we find that the approximate conditional density estimator provides accurate and precise estimates of derivatives of expected outcomes for a wide range of types of explanatory variables.