Text-messaging as a Tool for Medication Adherence and Behavior Change in Disease Management among Patients with Coronary Heart Disease

Background: Evidence-based medication and lifestyle modification are important for secondary prevention of cardiovascular disease but are underutilized. Mobile health strategies could address this gap but existing evidence is mixed. Therefore, we piloted a pre-post study to assess the impact of patient-directed text messages as a means of improving medication adherence and modifying major health risk behaviors among coronary heart disease (CHD) patients in Hainan, China.

Methods: 92 CVD patients were surveyed between June and August 2015 (before the intervention) and then between October and December 2015 (after 12 week intervention) about (a) medication use (b) smoking status,(c) fruit and vegetable consumption, and (d) physical activity uptake. Acceptability of text-messaging intervention was assessed at follow-up. Descriptive statistics, along with paired comparisons between the pre and post outcomes were conducted using both parametric (t-test) and non-parametric (Wilcoxon signed rank test) methods.

Results: The number of respondents at follow-up was 82 (89% retention rate). Significant improvements were observed for medication adherence (P<0.001) and for the number of cigarettes smoked per day (P=.022). However there was no change in the number of smokers who quitted smoking at follow-up. There were insignificant changes for physical activity (P=0.91) and fruit and vegetable consumption.

Systematic Molecular Phenotyping: A Path Toward Precision Emergency Medicine?

Precision medicine is an emerging approach to disease treatment and prevention that considers variability in patient genes, environment, and lifestyle. However, little has been written about how such research impacts emergency care. Recent advances in analytical techniques have made it possible to characterize patients in a more comprehensive and sophisticated fashion at the molecular level, promising highly individualized diagnosis and treatment. Among these techniques are various systematic molecular phenotyping analyses (e.g., genomics, transcriptomics, proteomics, and metabolomics). Although a number of emergency physicians use such techniques in their research, widespread discussion of these approaches has been lacking in the emergency care literature and many emergency physicians may be unfamiliar with them. In this article, we briefly review the underpinnings of such studies, note how they already impact acute care, discuss areas in which they might soon be applied, and identify challenges in translation to the emergency department (ED). While such techniques hold much promise, it is unclear whether the obstacles to translating their findings to the ED will be overcome in the near future. Such obstacles include validation, cost, turnaround time, user interface, decision support, standardization, and adoption by end-users.

Combining Information from Multiple Sources in Bayesian Modeling

Surveys can collect important data that inform policy decisions and drive social science research. Large government surveys collect information from the U.S. population on a wide range of topics, including demographics, education, employment, and lifestyle. Analysis of survey data presents unique challenges. In particular, one needs to account for missing data, for complex sampling designs, and for measurement error. Conceptually, a survey organization could spend lots of resources getting high-quality responses from a simple random sample, resulting in survey data that are easy to analyze. However, this scenario often is not realistic. To address these practical issues, survey organizations can leverage the information available from other sources of data. For example, in longitudinal studies that suffer from attrition, they can use the information from refreshment samples to correct for potential attrition bias. They can use information from known marginal distributions or survey design to improve inferences. They can use information from gold standard sources to correct for measurement error.

This thesis presents novel approaches to combining information from multiple sources that address the three problems described above.

The first method addresses nonignorable unit nonresponse and attrition in a panel survey with a refreshment sample. Panel surveys typically suffer from attrition, which can lead to biased inference when basing analysis only on cases that complete all waves of the panel. Unfortunately, the panel data alone cannot inform the extent of the bias due to attrition, so analysts must make strong and untestable assumptions about the missing data mechanism. Many panel studies also include refreshment samples, which are data collected from a random sample of new

individuals during some later wave of the panel. Refreshment samples offer information that can be utilized to correct for biases induced by nonignorable attrition while reducing reliance on strong assumptions about the attrition process. To date, these bias correction methods have not dealt with two key practical issues in panel studies: unit nonresponse in the initial wave of the panel and in the

refreshment sample itself. As we illustrate, nonignorable unit nonresponse

can significantly compromise the analyst's ability to use the refreshment samples for attrition bias correction. Thus, it is crucial for analysts to assess how sensitive their inferences---corrected for panel attrition---are to different assumptions about the nature of the unit nonresponse. We present an approach that facilitates such sensitivity analyses, both for suspected nonignorable unit nonresponse

in the initial wave and in the refreshment sample. We illustrate the approach using simulation studies and an analysis of data from the 2007-2008 Associated Press/Yahoo News election panel study.

The second method incorporates informative prior beliefs about

marginal probabilities into Bayesian latent class models for categorical data.

The basic idea is to append synthetic observations to the original data such that

(i) the empirical distributions of the desired margins match those of the prior beliefs, and (ii) the values of the remaining variables are left missing. The degree of prior uncertainty is controlled by the number of augmented records. Posterior inferences can be obtained via typical MCMC algorithms for latent class models, tailored to deal efficiently with the missing values in the concatenated data.

We illustrate the approach using a variety of simulations based on data from the American Community Survey, including an example of how augmented records can be used to fit latent class models to data from stratified samples.

The third method leverages the information from a gold standard survey to model reporting error. Survey data are subject to reporting error when respondents misunderstand the question or accidentally select the wrong response. Sometimes survey respondents knowingly select the wrong response, for example, by reporting a higher level of education than they actually have attained. We present an approach that allows an analyst to model reporting error by incorporating information from a gold standard survey. The analyst can specify various reporting error models and assess how sensitive their conclusions are to different assumptions about the reporting error process. We illustrate the approach using simulations based on data from the 1993 National Survey of College Graduates. We use the method to impute error-corrected educational attainments in the 2010 American Community Survey using the 2010 National Survey of College Graduates as the gold standard survey.