Motivational interviewing and feedback for college drinkers: Handling missing values with complete case analysis and multiple imputation

Objective: In this secondary data analysis, three statistical methodologies were implemented to handle cases with missing data in a motivational interviewing and feedback study. The aim was to evaluate the impact that these methodologies have on the data analysis. ^ Methods: We first evaluated whether the assumption of missing completely at random held for this study. We then proceeded to conduct a secondary data analysis using a mixed linear model to handle missing data with three methodologies (a) complete case analysis, (b) multiple imputation with explicit model containing outcome variables, time, and the interaction of time and treatment, and (c) multiple imputation with explicit model containing outcome variables, time, the interaction of time and treatment, and additional covariates (e.g., age, gender, smoke, years in school, marital status, housing, race/ethnicity, and if participants play on athletic team). Several comparisons were conducted including the following ones: 1) the motivation interviewing with feedback group (MIF) vs. the assessment only group (AO), the motivation interviewing group (MIO) vs. AO, and the intervention of the feedback only group (FBO) vs. AO, 2) MIF vs. FBO, and 3) MIF vs. MIO.^ Results: We first evaluated the patterns of missingness in this study, which indicated that about 13% of participants showed monotone missing patterns, and about 3.5% showed non-monotone missing patterns. Then we evaluated the assumption of missing completely at random by Little's missing completely at random (MCAR) test, in which the Chi-Square test statistic was 167.8 with 125 degrees of freedom, and its associated p-value was p=0.006, which indicated that the data could not be assumed to be missing completely at random. After that, we compared if the three different strategies reached the same results. For the comparison between MIF and AO as well as the comparison between MIF and FBO, only the multiple imputation with additional covariates by uncongenial and congenial models reached different results. For the comparison between MIF and MIO, all the methodologies for handling missing values obtained different results. ^ Discussions: The study indicated that, first, missingness was crucial in this study. Second, to understand the assumptions of the model was important since we could not identify if the data were missing at random or missing not at random. Therefore, future researches should focus on exploring more sensitivity analyses under missing not at random assumption.^

The SanaViz: Human Centered Geovisualization to facilitate visual exploration of Public Health data

These three manuscripts are presented as a PhD dissertation for the study of using GeoVis application to evaluate telehealth programs. The primary reason of this research was to understand how the GeoVis applications can be designed and developed using combined approaches of HC approach and cognitive fit theory and in terms utilized to evaluate telehealth program in Brazil. First manuscript The first manuscript in this dissertation presented a background about the use of GeoVisualization to facilitate visual exploration of public health data. The manuscript covered the existing challenges that were associated with an adoption of existing GeoVis applications. The manuscript combines the principles of Human Centered approach and Cognitive Fit Theory and a framework using a combination of these approaches is developed that lays the foundation of this research. The framework is then utilized to propose the design, development and evaluation of “the SanaViz” to evaluate telehealth data in Brazil, as a proof of concept. Second manuscript The second manuscript is a methods paper that describes the approaches that can be employed to design and develop “the SanaViz” based on the proposed framework. By defining the various elements of the HC approach and CFT, a mixed methods approach is utilized for the card sorting and sketching techniques. A representative sample of 20 study participants currently involved in the telehealth program at the NUTES telehealth center at UFPE, Recife, Brazil was enrolled. The findings of this manuscript helped us understand the needs of the diverse group of telehealth users, the tasks that they perform and helped us determine the essential features that might be necessary to be included in the proposed GeoVis application “the SanaViz”. Third manuscript The third manuscript involved mix- methods approach to compare the effectiveness and usefulness of the HC GeoVis application “the SanaViz” against a conventional GeoVis application “Instant Atlas”. The same group of 20 study participants who had earlier participated during Aim 2 was enrolled and a combination of quantitative and qualitative assessments was done. Effectiveness was gauged by the time that the participants took to complete the tasks using both the GeoVis applications, the ease with which they completed the tasks and the number of attempts that were taken to complete each task. Usefulness was assessed by System Usability Scale (SUS), a validated questionnaire tested in prior studies. In-depth interviews were conducted to gather opinions about both the GeoVis applications. This manuscript helped us in the demonstration of the usefulness and effectiveness of HC GeoVis applications to facilitate visual exploration of telehealth data, as a proof of concept. Together, these three manuscripts represent challenges of combining principles of Human Centered approach, Cognitive Fit Theory to design and develop GeoVis applications as a method to evaluate Telehealth data. To our knowledge, this is the first study to explore the usefulness and effectiveness of GeoVis to facilitate visual exploration of telehealth data. The results of the research enabled us to develop a framework for the design and development of GeoVis applications related to the areas of public health and especially telehealth. The results of our study showed that the varied users were involved with the telehealth program and the tasks that they performed. Further it enabled us to identify the components that might be essential to be included in these GeoVis applications. The results of our research answered the following questions; (a) Telehealth users vary in their level of understanding about GeoVis (b) Interaction features such as zooming, sorting, and linking and multiple views and representation features such as bar chart and choropleth maps were considered the most essential features of the GeoVis applications. (c) Comparing and sorting were two important tasks that the telehealth users would perform for exploratory data analysis. (d) A HC GeoVis prototype application is more effective and useful for exploration of telehealth data than a conventional GeoVis application. Future studies should be done to incorporate the proposed HC GeoVis framework to enable comprehensive assessment of the users and the tasks they perform to identify the features that might be necessary to be a part of the GeoVis applications. The results of this study demonstrate a novel approach to comprehensively and systematically enhance the evaluation of telehealth programs using the proposed GeoVis Framework.

The cost of insuring the uninsured in the U.S.

Objectives: This study included two overarching objectives. Through a systematic review of the literature published between 1990 and 2012, the first objective aimed to assess whether insuring the uninsured would result in higher costs compared to insuring the currently insured. Studies that quantified the actual costs associated with insuring the uninsured in the U.S. were included. Based upon 2009 data from the Medical Expenditure Panel Survey (MEPS), the second objective aimed to assess and compare the self-reported health of populations with four different insurance statuses. The second part of this study involved a secondary data analysis of both currently insured and currently uninsured individuals who participated in the MEPS in 2009. The null hypothesis was that there were no differences across the four categories of health insurance status for self-reported health status and healthcare service use. The alternative hypothesis was that were differences across the four categories of health insurance status for self-reported health status and healthcare service use. Methods: For the systematic review, three databases were searched using search terms to identify studies that actually quantified the cost of insuring the uninsured. Thirteen studies were selected, discussed, and summarized in tables. For the secondary data analysis of MEPS data, this study compared four categories of health insurance status: (1) currently uninsured persons who will become eligible for Medicaid under the Patient Protection and Affordable Care Act (PPACA) healthcare reforms in 2014; (2) currently uninsured persons who will be required to buy private insurance through the PPACA health insurance exchanges in 2014; (3) persons currently insured under Medicaid or SCHIP; and (4) persons currently insured with private insurance. The four categories were compared on the basis of demographic information, health status information, and health conditions with relatively high prevalence. Chi-square tests were run to determine if there were differences between the four groups in regard to health insurance status and health status. With some exceptions, the two currently insured groups had worse self-reported health status compared to the two currently uninsured groups. Results: The thirteen studies that met the inclusion criteria for the systematic review included: (1) three cost studies from 1993, 1995, and 1997; (2) four cost studies from 2001, 2003, and 2004; (3) one study of disabilities and one study of immigrants; (4) two state specific studies of uninsured status; and (5) two current studies of healthcare reform. Of the thirteen studies reviewed, four directly addressed the study question about whether insuring the uninsured was more or less expensive than insuring the currently insured. All four of the studies provided support for the study finding that the cost of insuring the uninsured would generally not be higher than insuring those already insured. One study indicated that the cost of insuring the uninsured would be less expensive than insuring the population currently covered by Medicaid, but more expensive to insure than the populations of those covered by employer-sponsored insurance and non-group private insurance. While the nine other studies included in the systematic review discussed the costs associated with insuring the uninsured population, they did not directly compare the costs of insuring the uninsured population with the costs associated with insuring the currently insured population. For the MEPS secondary data analysis, the results of the chi-square tests indicated that there were differences in the distribution of disease status by health insurance status. As anticipated, with some exceptions, the uninsured reported lower rates of disease and healthcare service use. However, for the variable attention deficit disorder, the uninsured reported higher disease rates than the two insured groups. Additionally, for the variables high blood pressure, high cholesterol, and joint pain, the currently insured under Medicaid or SCHIP group reported a lower rate of disease than the two currently insured groups. This result may be due to the lower mean age of the currently insured under Medicaid or SCHIP group. Conclusion: Based on this study, with some exceptions, the costs for insuring the uninsured should not exceed healthcare-related costs for insuring the currently uninsured. The results of the systematic review indicated that the U.S. is already paying some of the costs associated with insuring the uninsured. PPACA will expand health insurance coverage to millions of Americans who are currently uninsured, as the individual mandate and insurance market reforms will require. Because many of the currently uninsured are relatively healthy young persons, the costs associated with expanding insurance coverage to the uninsured are anticipated to be relatively modest. However, for the purposes of construing these results, it is important to note that once individuals obtain insurance, it is anticipated that they will use more healthcare services, which will increase costs. (Abstract shortened by UMI.)^

UNDERSTANDING NURSE CREATED COGNITIVE ARTIFACTS: PERSONALLY-CREATED-COGNITIVE-ARTIFACTS AS EXTERNAL REPRESENTATIONS OF DISTRIBUTED COGNITION

Manuscript 1: “Conceptual Analysis: Externalizing Nursing Knowledge” We use concept analysis to establish that the report tool nurses prepare, carry, reference, amend, and use as a temporary data repository are examples of cognitive artifacts. This tool, integrally woven throughout the work and practice of nurses, is important to cognition and clinical decision-making. Establishing the tool as a cognitive artifact will support new dimensions of study. Such studies can characterize how this report tool supports cognition, internal representation of knowledge and skills, and external representation of knowledge of the nurse. Manuscript 2: “Research Methods: Exploring Cognitive Work” The purpose of this paper is to describe a complex, cross-sectional, multi-method approach to study of personal cognitive artifacts in the clinical environment. The complex data arrays present in these cognitive artifacts warrant the use of multiple methods of data collection. Use of a less robust research design may result in an incomplete understanding of the meaning, value, content, and relationships between personal cognitive artifacts in the clinical environment and the cognitive work of the user. Manuscript 3: “Making the Cognitive Work of Registered Nurses Visible” Purpose: Knowledge representations and structures are created and used by registered nurses to guide patient care. Understanding is limited regarding how these knowledge representations, or cognitive artifacts, contribute to working memory, prioritization, organization, cognition, and decision-making. The purpose of this study was to identify and characterize the role a specific cognitive artifact knowledge representation and structure as it contributed to the cognitive work of the registered nurse. Methods: Data collection was completed, using qualitative research methods, by shadowing and interviewing 25 registered nurses. Data analysis employed triangulation and iterative analytic processes. Results: Nurse cognitive artifacts support recall, data evaluation, decision-making, organization, and prioritization. These cognitive artifacts demonstrated spatial, longitudinal, chronologic, visual, and personal cues to support the cognitive work of nurses. Conclusions: Nurse cognitive artifacts are an important adjunct to the cognitive work of nurses, and directly support patient care. Nurses need to be able to configure their cognitive artifact in ways that are meaningful and support their internal knowledge representations.