Building Health Insurance Literacy: Evidence from the Smart Choice Health Insurance™ Program

With the passage of the Patient Protection and Affordable Care Act (ACA) of 2010, the health insurance literacy of Americans became a critical issue. In response, a consumer education program was created and tested by university researchers and educators associated with Cooperative Extension. This article draws extensively on the emerging literature on health insurance literacy and on data from participants in the Smart Choice Health Insurance? program. The intent of the study was to understand socio-demographic and environmental variables that predict initial health insurance literacy and gains in health insurance literacy. A standardized instrument measuring health insurance literacy was used to collect the data. Multivariate analysis showed higher income consumers demonstrated greater initial health insurance literacy scores compared to middle income consumers, whereas younger, male and lower educated consumers reported lower initial health insurance literacy. After participating in the Smart Choice Health Insurance? program, consumers who made greater gains in their health insurance literacy tended to be female, higher income, and consumers residing in states that showed supportiveness of the ACA. The findings highlight the importance of considering sociodemographic characteristics in program design and delivery, as well as how contextual issues, such as the political environment, might impact the delivery of educational efforts. Findings from the analyses help inform ways to adapt and tailor educational opportunities that focus on health insurance literacy for a range of consumers.     

The Ethical Challenges of Socially Responsible Science

Social responsibility is an essential part of the responsible conduct of research that presents difficult ethical questions for scientists. Recognizing one’s social responsibilities as a scientist is an important first step toward exercising social responsibility, but it is only the beginning, since scientists may confront difficult value questions when deciding how to act responsibly. Ethical dilemmas related to socially responsible science fall into at least three basic categories: 1) dilemmas related to problem selection, 2) dilemmas related to publication and data sharing, and 3) dilemmas related to engaging society. In responding to these dilemmas, scientists must decide how to balance their social responsibilities against other professional commitments and how to avoid compromising their objectivity. In this article, we will examine the philosophical and ethical basis of social responsibility in science, discuss some of the ethical dilemmas related to exercising social responsibility, and make five recommendations to help scientists deal with these issues.     

A Diversity Index for Model Space Selection in the Estimation of Benchmark and Infectious Doses via Model Averaging

In chemical and microbial risk assessments, risk assessors fit dose‐response models to high‐dose data and extrapolate downward to risk levels in the range of 1–10%. Although multiple dose‐response models may be able to fit the data adequately in the experimental range, the estimated effective dose (ED) corresponding to an extremely small risk can be substantially different from model to model. In this respect, model averaging (MA) provides more robustness than a single dose‐response model in the point and interval estimation of an ED. In MA, accounting for both data uncertainty and model uncertainty is crucial, but addressing model uncertainty is not achieved simply by increasing the number of models in a model space. A plausible set of models for MA can be characterized by goodness of fit and diversity surrounding the truth. We propose a diversity index (DI) to balance between these two characteristics in model space selection. It addresses a collective property of a model space rather than individual performance of each model. Tuning parameters in the DI control the size of the model space for MA.     

The Box–Cox Transformation and Non‐Iterative Estimation Methods for Ordinal Log‐Linear Models

Recently Beh and Farver investigated and evaluated three non‐iterative procedures for estimating the linear‐by‐linear parameter of an ordinal log‐linear model. The study demonstrated that these non‐iterative techniques provide estimates that are, for most types of contingency tables, statistically indistinguishable from estimates from Newton's unidimensional algorithm. Here we show how two of these techniques are related using the Box–Cox transformation. We also show that by using this transformation, accurate non‐iterative estimates are achievable even when a contingency table contains sampling zeros.     

A note on bias reduction of maximum likelihood estimates for the scalar skew t distribution

The skew t distribution is a flexible parametric family to fit data, because it includes parameters that let us regulate skewness and kurtosis. A problem with this distribution is that, for moderate sample sizes, the maximum likelihood estimator of the shape parameter is infinite with positive probability. In order to try to solve this problem, Sartori (2006) has proposed using a modified score function as an estimating equation for the shape parameter. In this note we prove that the resulting modified maximum likelihood estimator is always finite, considering the degrees of freedom as known and greater than or equal to 2.     

SOS—Satisfied or Stuck,Why Older Rural Residents Stay Put: Aging in Place or Stuck in Place in Rural Utah*

As rural communities undergo substantial demographic and economic changes, understanding the migration intentions and their antecedents of rural elderly persons becomes increasingly important. Using data drawn from a survey of adults from 24 rural Utah communities conducted in 2008, we examine whether rural residents 60 years of age or older plan to remain in their present communities (N= 621). We use structural equation models (SEM) to estimate the relationships between a variety of individual and community-level background measures, including perceptions of local service quality, leaving one's community for health care, Internet use, attachment to and satisfaction with community, and plans to age in place. Results suggest that even as the rural context of economic decline, population loss, and distance to medical services may reduce the viability of staying in a community, a desire to remain in the community is primarily a function of perceptions of the quality of local services and community satisfaction. This research highlights the need to better understand the interplay between the availability of medical services and perceptions of distance as well as to understand the complex relationship between individual and community level characteristics for migration intentions.     

Time series models based on the unrestricted skew-normal process

The standard location and scale unrestricted (or unified) skew-normal (SUN) family studied by Arellano-Valle and Genton [On fundamental skew distributions. J Multivar Anal. 2005;96:93–116] and Arellano-Valle and Azzalini [On the unification of families of skew-normal distributions. Scand J Stat. 2006;33:561–574], allows the modelling of data which is symmetrically or asymmetrically distributed. The family has a number of advantages suitable for the analysis of stochastic processes such as Auto-Regressive Moving-Average (ARMA) models, including being closed under linear combinations, being able to satisfy the consistency condition of Kolmogorov’s theorem and providing the guarantee of the existence of such a SUN stochastic process. The family is able to be represented in a hierarchical form which can be used for the ease of simulation. In addition, it facilitates an EM-type algorithm to estimate the model parameters. The performances and suitability of the proposed model are demonstrated on simulations and using two real data sets in applications.