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). METHODS: Using the electronic medical records of 140 pediatric patients from the NCGENES study, we characterized the presence of ICD codes representing possible, probable, or definite GD-related diagnoses across encounter types. In addition, we examined codes from encounters for which initially no GD-related codes had been found and determined whether these codes were indicative of a GD. RESULTS: Among NCGENES patients with visits between 2014 and 2017, 92% of inpatient, 75% of emergency department, and 63% of outpatient encounters included ≥1 GD-related code. Encounters with highly specific (ie, definite) GD codes had fewer low-specificity GD codes than encounters with only low-specificity GD codes. We identified an additional 32 ICD-9 and 56 ICD-10 codes possibly indicative of a GD. CONCLUSION: Code-based strategies can be refined to assess health care utilization among pediatric patients and may contribute to a systematic approach to identify patients with suspected GDs.

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. Using network data from the Framingham Heart Study, we find smaller social interaction effects among spouses and friends than Christakis and Fowler (2008) who pioneered such exploration with these data. We also find that social interaction impacts of the endogenous behaviors of siblings or a parent and an adult child are not statistically significant after disentangling them from homophily. In addition, we find that the effects of social contacts' cardiovascular disease shocks on individual smoking behavior are not statistically significant.

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. We find that full bans reduce national smoking prevalence over time, especially among younger demographic groups, but have no significant impact on intensity of smoking among smokers. Full bans also benefit nonsmokers, as they are associated with a significant reduction in environmental tobacco smoke exposure. Partial bans do not significantly impact smoking prevalence, and are found to increase smoking intensity among individuals who smoke every day. These findings provide support for ratification of full bans by all provinces according to the National Tobacco Control Law of 2011.

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.

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 9.3 year difference in age of death between lifelong smokers and nonsmokers; our findings suggest the bias-corrected difference is 4.3 years.

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.e., the contemporaneous or direct effect) while controlling for observed measures of human capital (namely, educational attainment, employment experience, marital status tenure, and family size) and the impacts of an evolving body mass (i.e., the dynamic or indirect effects) on the endogenous histories of behaviors that produce these human capital stocks. We find significant differences in the contemporaneous and dynamic effects of body mass on wages by age, by race, and by wage level.

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.