Predictive validity of the personal background and preparation survey and effectiveness of targeted interventions among health science community college students

Existing data, collected from 1st-year students enrolled in a major Health Science Community College in the south central United States, for Fall 2010, Spring 2011, Fall 2011 and Spring 2012 semesters as part of the "Online Navigational Assessment Vehicle, Intervention Guidance, and Targeting of Risks (NAVIGATOR) for Undergraduate Minority Student Success" with CPHS approval number HSC-GEN-07-0158, was used for this thesis. The Personal Background and Preparation Survey (PBPS) and a two-question risk self-assessment subscale were administered to students during their 1st-year orientation. The PBPS total risk score, risk self-assessment total and overall scores, and Under Representative Minority Student (URMS) status were recorded. The purpose of this study is to evaluate and report the predictive validity of the indicators identified above for Adverse Academic Status Events (AASE) and Nonadvancement Adverse Academic Status Events (NAASE) as well as the effectiveness of interventions targeted using the PBPS among a diverse population of health science community college students. The predictive validity of the PBPS for AASE has previously been demonstrated among health science professions and graduate students (Johnson, Johnson, Kim, & McKee, 2009a; Johnson, Johnson, McKee, & Kim, 2009b). Data will be analyzed using binary logistic regression and correlation using SPSS 19 statistical package. Independent variables will include baseline- versus intervention-year treatments, PBPS, risk self-assessment, and URMS status. The dependent variables will be binary AASE and NAASE status. ^ The PBPS was the first reliable diagnostic and prescriptive instrument to establish documented predictive validity for student Adverse Academic Status Events (AASE) among students attending health science professional schools. These results extend the documented validity for the PBPS in predicting AASE to a health science community college student population. Results further demonstrated that interventions introduced using the PBPS were followed by approximately one-third reduction in the odds of Nonadvancement Adverse Academic Status Events (NAASE), controlling for URMS status and risk self-assessment scores. These results indicate interventions introduced using the PBPS may have potential to reduce AASE or attrition among URMS and nonURMS attending health science community colleges on a broader scale; positively impacting costs, shortages, and diversity of health science professionals.^

Psychosocial job characteristics as predictors of overall health: Results from a community-based cross-sectional study conducted in southeast Texas in 2001 and 2002

Stress at the workplace exposes people to increased risk for poor physical and/or mental health. Recently psychological and social disadvantages have been proven to place the worker at risk for mental or physical health outcomes. The overall purpose of this study was to study full time employed study subjects and (1) describe the various psychosocial job characteristics in a population of low income individuals stratified by race/ethnicity residing in Houston and Brownsville, Texas and (2) examine the associations between psychosocial job characteristics and physical, mental, and self rated health. It was observed that having a low level of education is associated with having very little or no control, security, and social support at the workplace. Being Mexican American was associated with having good job control, job security, job social support and having a less demanding job. Furthermore, the psychosocial job characteristics were associated with mental health outcomes but not with physical and self rated health. ^

The use of invasive myocardial revascularization procedures following myocardial infarction and the effectiveness of these procedures in the biethnic community of Nueces County, Texas, 1988--1991

High levels of poverty and unemployment, and low levels of health insurance coverage may pose barriers to obtaining cardiac care by Mexican Americans. We undertook this study to investigate differences in the use of invasive myocardial revascularization procedures received within the 4-month period following hospitalization for a myocardial infarction (MI) between Mexican Americans and non-Hispanic whites in the Corpus Christi Heart Project (CCHP). The CCHP is a population-based surveillance program for hospitalized MI, percutaneous transluminal coronary angioplasty (PTCA), and aortocoronary bypass surgery (ACBS). Medical record data were available for 1706 patients identified over a three-year period. Mexican Americans had significantly lower rates of receiving a PTCA following MI than non-Hispanic Whites (RR: 0.56, 95% CI: 0.44-0.70). No meaningful ethnic difference was seen in the rates of ACBS use. History of PTCA use appeared to interact with ethnicity. Among patients without a history of PTCA use, Mexican Americans were less likely to receive a PTCA than non-Hispanic whites (RR: 0.59; 95% CI: 0.46-0.76). Among patients with a history of PTCA use, however, Mexican Americans were more likely to receive a PTCA than non-Hispanic whites (RR: 1.47; 95% CI: 0.75-2.87).^ Differences in the effectiveness of a first-time PTCA and first-time ACBS between Mexican Americans and non-Hispanic whites in the CCHP were also investigated. Mexican Americans were more likely to receive a 2nd PTCA (RR: 1.56, 95% CI: 1.11-2.17) and suffer a subsequent MI (RR: 1.42, 95% CI: 1.03-1.96) following a first-time PTCA than non-Hispanic whites. No meaningful ethnic differences were found in the rates of death and rates of ACBS following a first-time PTCA. Also, no significant ethnic differences were found in the rates of any of the events following a first-time ACBS. After adjusting for potential demographic, socioeconomic, clinical and angiographic confounders using Cox regression analysis, Mexican Americans were still more likely to receive a 2nd PTCA (HR: 1.38; 95% CI: 0.99-1.93) following a first-time PTCA than non-Hispanic whites. A significant difference in the rates of a subsequent MI following a first-time PTCA persisted (HR: 1.39, 95% CI: 1.01-1.93). (Abstract shortened by UMI.) ^

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.^