Testing and extending strategies for identifying genetic disease-related encounters in pediatric patients.

Purpose: To better understand health care utilization and develop decision support tools, methods for identifying patients with suspected genetic diseases (GDs) are needed. Previous studies had identified inpatient-relevant International Classification of Diseases (ICD) codes that were possibly, probably, or definitely indicative of GDs. We assessed whether these codes identified GD-related inpatient, outpatient, and emergency department encounters among pediatric patients with suspected GDs from a previous study (the North Carolina Clinical Genomic Evaluation by Next-Generation Exome Sequencing [NCGENES] study).

The influence of endogenous behaviors among social pairs: Social interaction effects of smoking.

We model the observed smoking outcomes of an individual and her social contact - a spouse, friend, sibling, parent or adult child - as a simultaneous move game with complete information. We allow an individual's smoking behavior to depend on her previous behavior and carefully account for observed and unobserved heterogeneity. Our econometric model addresses simultaneity, homophily, health endogeneity, non-random attrition, and multiplicity of equilibria together.

Impacts of local public smoking bans on smoking behaviors and tobacco smoke exposure.

This paper examines the immediate and long-term effects of public smoking bans on smoking prevalence, smoking regularity, smoking intensity, and secondhand tobacco smoke exposure. We supplement the extensive literature on the effects of various types of tobacco control legislation on smoking behavior in developed countries by studying the provincial smoking bans and more recent national ban of a middle-income country, Argentina. We focus on the difference between full and partial smoking bans, and take advantage of the time and province variation in ban implementation in order to determine the causal effects of each type of ban.

Smoking and Mortality: New Evidence from a Long Panel.

Many public health policies are rooted in findings from medical and epidemiological studies that fail to consider behavioral influences. Using nearly 50 years of data from Framingham Heart Study male participants, we evaluate the longevity consequences of different lifetime smoking patterns by jointly estimating smoking behavior and health outcomes over the life cycle, by richly including smoking and health histories, and by flexibly incorporating correlated unobserved heterogeneity. Unconditional difference-in-mean calculations that treat smoking behaviors as random indicate a 9.

Longitudinal Self-Management and/or Transition Readiness per the TRANSITION 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. TRANSITION 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.

Disentangling the Contemporaneous and Dynamic Effects of Human and Health Capital on Wages over the Life Cycle.

We quantify the life-cycle effects of human and health capital on the wage distribution of women, with a focus on health capital measured by body mass. We use NLSY79 data on women followed annually up to twenty years during the time of their lives when average annual weight gain is greatest. We measure the wage impact of current body mass (i.

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.

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.