Comparing the factorial structure, invariance, and predictive validity of transtheoretical model constructs for alcohol use across restricted and unrestricted settings

With substance abuse treatment expanding in prisons and jails, understanding how behavior change interacts with a restricted setting becomes more essential. The Transtheoretical Model (TTM) has been used to understand intentional behavior change in unrestricted settings, however, evidence indicates restrictive settings can affect the measurement and structure of the TTM constructs. The present study examined data from problem drinkers at baseline and end-of-treatment from three studies: (1) Project CARE (n = 187) recruited inmates from a large county jail; (2) Project Check-In (n = 116) recruited inmates from a state prison; (3) Project MATCH, a large multi-site alcohol study had two recruitment arms, aftercare (n = 724 pre-treatment and 650 post-treatment) and outpatient (n = 912 pre-treatment and 844 post-treatment). The analyses were conducted using cross-sectional data to test for non-invariance of measures of the TTM constructs: readiness, confidence, temptation, and processes of change (Structural Equation Modeling, SEM) across restricted and unrestricted settings. Two restricted (jail and aftercare) and one unrestricted group (outpatient) entering treatment and one restricted (prison) and two unrestricted groups (aftercare and outpatient) at end-of-treatment were contrasted. In addition TTM end-of-treatment profiles were tested as predictors of 12 month drinking outcomes (Profile Analysis). Although SEM did not indicate structural differences in the overall TTM construct model across setting types, there were factor structure differences on the confidence and temptation constructs at pre-treatment and in the factor structure of the behavioral processes at the end-of-treatment. For pre-treatment temptation and confidence, differences were found in the social situations factor loadings and in the variance for the confidence and temptation latent factors. For the end-of-treatment behavioral processes, differences across the restricted and unrestricted settings were identified in the counter-conditioning and stimulus control factor loadings. The TTM end-of-treatment profiles were not predictive of drinking outcomes in the prison sample. Both pre and post-treatment differences in structure across setting types involved constructs operationalized with behaviors that are limited for those in restricted settings. These studies suggest the TTM is a viable model for explicating addictive behavior change in restricted settings but calls for modification of subscale items that refer to specific behaviors and caution in interpreting the mean differences across setting types for problem drinkers. ^

The movement toward patient safety: State action related to reporting and disclosure of healthcare-associated infections

Increasing attention has been given to the problem of medical errors over the past decade. Included within that focused attention has been a strong interest in reducing the occurrence of healthcare-associated infections (HAIs). Acting concurrently with federal initiatives, the majority of U.S. states have statutorily required reporting and public disclosure of HAI data. Although the occurrence of these state statutory enactments and other state initiatives represent a recognition of the strong concern pertaining to HAIs, vast differences in each state’s HAI reporting and public disclosure requirements creates a varied and unequal response to what has become a national problem.^ The purpose of this research was to explore the variations in state HAI legal requirements and other state mandates. State actions, including statutory enactments, regulations, and other initiatives related to state reporting and public disclosure mechanisms were compared, discussed, and analyzed in an effort to illustrate the impact of the lack of uniformity as a public health concern.^ The HAI statutes, administrative requirements, and other mandates of each state and two U.S. territories were reviewed to answer the following seven research questions: How far has the state progressed in its HAI initiative? If the state has a HAI reporting requirement, is it mandatory or voluntary? What healthcare entities are subject to the reporting requirements? What data collection system is utilized? What measures are required to be reported? What is the public disclosure mechanism? How is the underlying reported information protected from public disclosure or other legal release?^ Secondary publicly available data, including state statutes, administrative rules, and other initiatives, were utilized to examine the current HAI-related legislative and administrative activity of the study subjects. The information was reviewed and analyzed to determine variations in HAI reporting and public disclosure laws. Particular attention was given to the seven key research questions.^ The research revealed that considerable progress has been achieved in state HAI initiatives since 2004. Despite this progress, however, when reviewing the state laws and HAI programs comparatively, considerable variations were found to exist with regards to the type of reporting requirements, healthcare facilities subject to the reporting laws, data collection systems utilized, reportable measures, public disclosure requirements, and confidentiality and privilege provisions. The wide variations in state statutes, administrative rules, and other agency directives create a fragmented and inconsistent approach to addressing the nationwide occurrence of HAIs in the U.S. healthcare system. ^

Rigorously assessing whether the data backs the back school.

A rigorous between-subjects methodology employing independent random samples and having broad clinical applicability was designed and implemented to evaluate the effectiveness of back safety and patient transfer training interventions for both hospital nurses and nursing assistants. Effects upon self-efficacy, cognitive, and affective measures are assessed for each of three back safety procedures. The design solves the problem of obtaining randomly assigned independent controls where all experimental subjects must participate in the training interventions.

Using incomplete citation data for MEDLINE results ranking.

Information overload is a significant problem for modern medicine. Searching MEDLINE for common topics often retrieves more relevant documents than users can review. Therefore, we must identify documents that are not only relevant, but also important. Our system ranks articles using citation counts and the PageRank algorithm, incorporating data from the Science Citation Index. However, citation data is usually incomplete. Therefore, we explore the relationship between the quantity of citation information available to the system and the quality of the result ranking. Specifically, we test the ability of citation count and PageRank to identify "important articles" as defined by experts from large result sets with decreasing citation information. We found that PageRank performs better than simple citation counts, but both algorithms are surprisingly robust to information loss. We conclude that even an incomplete citation database is likely to be effective for importance ranking.

Using incomplete citation data for MEDLINE results ranking.

Information overload is a significant problem for modern medicine. Searching MEDLINE for common topics often retrieves more relevant documents than users can review. Therefore, we must identify documents that are not only relevant, but also important. Our system ranks articles using citation counts and the PageRank algorithm, incorporating data from the Science Citation Index. However, citation data is usually incomplete. Therefore, we explore the relationship between the quantity of citation information available to the system and the quality of the result ranking. Specifically, we test the ability of citation count and PageRank to identify "important articles" as defined by experts from large result sets with decreasing citation information. We found that PageRank performs better than simple citation counts, but both algorithms are surprisingly robust to information loss. We conclude that even an incomplete citation database is likely to be effective for importance ranking.

Integrating sequence information in microarray data analysis by free energy modeling

Microarray technology is a high-throughput method for genotyping and gene expression profiling. Limited sensitivity and specificity are one of the essential problems for this technology. Most of existing methods of microarray data analysis have an apparent limitation for they merely deal with the numerical part of microarray data and have made little use of gene sequence information. Because it's the gene sequences that precisely define the physical objects being measured by a microarray, it is natural to make the gene sequences an essential part of the data analysis. This dissertation focused on the development of free energy models to integrate sequence information in microarray data analysis. The models were used to characterize the mechanism of hybridization on microarrays and enhance sensitivity and specificity of microarray measurements. ^ Cross-hybridization is a major obstacle factor for the sensitivity and specificity of microarray measurements. In this dissertation, we evaluated the scope of cross-hybridization problem on short-oligo microarrays. The results showed that cross hybridization on arrays is mostly caused by oligo fragments with a run of 10 to 16 nucleotides complementary to the probes. Furthermore, a free-energy based model was proposed to quantify the amount of cross-hybridization signal on each probe. This model treats cross-hybridization as an integral effect of the interactions between a probe and various off-target oligo fragments. Using public spike-in datasets, the model showed high accuracy in predicting the cross-hybridization signals on those probes whose intended targets are absent in the sample. ^ Several prospective models were proposed to improve Positional Dependent Nearest-Neighbor (PDNN) model for better quantification of gene expression and cross-hybridization. ^ The problem addressed in this dissertation is fundamental to the microarray technology. We expect that this study will help us to understand the detailed mechanism that determines sensitivity and specificity on the microarrays. Consequently, this research will have a wide impact on how microarrays are designed and how the data are interpreted. ^

The social construction of infant mortality: From grassroots to medicalization

Infant mortality as a problematic situation has been recognized for some 130 years in one form or another. It has undergone various changes in its empirical dimensions relative to whom we study within the population, what we study--low birth-weight vs. pre-term births--and how we study it--whether demographically or medically. An analysis of the process by which the condition was raised by claims makers as an intolerable situation among America's urban residents reveals that demographic and medical data were sparse. Nonetheless, a judgement about the meaning and significance of the condition was made, and that interpretation led to the promulgation of systems to both document and address the condition as it has come to be defined.^ This investigation depicts the historical context and natural history of infant mortality as one of a number of social problems that came to be defined through the interplay among groups and individuals making claims and how their claims came to the public policy agenda as worthy of collective resources--who won, who lost and why. The process of social definition focuses attention on the claims makers and the ways they contrast the meaning, origins and remedies for this troubling condition. The historical context becomes the frame of reference for understanding the actions of the claims makers and the meaning and significance they attached to the problem.^ We purport that "context" provides a closer reality than disjoined "value free" accounts. Context provides the evidence for the definition, who participated in the process, why and by what means.^ The role of women in the definitional process reveals the differences in approaches utilized by the women of the settlement house reform movement and African-American women working at the grass-roots. Much of the work done by these two groups provided options to the problem's remedy; however, their differences paved the way to our current (principally medically-oriented) definition and its inherent limitations. ^

Application of the general linear model to assess the effect of missing data on bone marrow mononuclear cell therapy with infusion timing and follow-up MRI

With most clinical trials, missing data presents a statistical problem in evaluating a treatment's efficacy. There are many methods commonly used to assess missing data; however, these methods leave room for bias to enter the study. This thesis was a secondary analysis on data taken from TIME, a phase 2 randomized clinical trial conducted to evaluate the safety and effect of the administration timing of bone marrow mononuclear cells (BMMNC) for subjects with acute myocardial infarction (AMI).^ We evaluated the effect of missing data by comparing the variance inflation factor (VIF) of the effect of therapy between all subjects and only subjects with complete data. Through the general linear model, an unbiased solution was made for the VIF of the treatment's efficacy using the weighted least squares method to incorporate missing data. Two groups were identified from the TIME data: 1) all subjects and 2) subjects with complete data (baseline and follow-up measurements). After the general solution was found for the VIF, it was migrated Excel 2010 to evaluate data from TIME. The resulting numerical value from the two groups was compared to assess the effect of missing data.^ The VIF values from the TIME study were considerably less in the group with missing data. By design, we varied the correlation factor in order to evaluate the VIFs of both groups. As the correlation factor increased, the VIF values increased at a faster rate in the group with only complete data. Furthermore, while varying the correlation factor, the number of subjects with missing data was also varied to see how missing data affects the VIF. When subjects with only baseline data was increased, we saw a significant rate increase in VIF values in the group with only complete data while the group with missing data saw a steady and consistent increase in the VIF. The same was seen when we varied the group with follow-up only data. This essentially showed that the VIFs steadily increased when missing data is not ignored. When missing data is ignored as with our comparison group, the VIF values sharply increase as correlation increases.^