Sociodemographic characteristics as predictors of successful smoking cessation among a group of Hispanic Spanish speaking smokers

Few studies have examined predictors of smoking abstinence among Hispanic groups. The purpose of this dissertation was to examine the relations of sociodemographic characteristics and smoking related factors with smoking abstinence among a group of Hispanic Spanish speaking smokers. This study utilized previously collected data from Hispanic Spanish-speaking smokers (N = 246) who participated in a study entitled Smoking Cessation Services for Hispanic Smokers in Texas. ^ The first study examined sociodemographic characteristics and smoking related mechanisms that predicted smoking abstinence among this group. Two of the characteristics were related to smoking abstinence, marital status and acculturation level. Being unmarried increased the likelihood of being abstinent at the 12 week assessment (OR = 1.80). Those in the high acculturation group were twice as likely to be abstinent (OR = 2.24). Of the smoking related mechanisms, those with higher positive reinforcement expectancies were less likely to be abstinent (OR = .86), as were those with a higher level of affiliative attachment (OR = .86), a higher level of craving (OR = .78) and a higher tolerance to the effect of smoking (OR = .74). The second study was to examine the relationship of objective measures of socioeconomic status (SES) (income, education, or employment) with smoking abstinence among this group. This study also compared the relationship of a subjective measure of SES (Social Status Ladder) to smoking abstinence. None of the objective measures of SES were related to smoking abstinence at the 12 week assessment. The subjective measure of SES did predict smoking abstinence (OR = 1.9) indicting that those that rated themselves ≤4 on the SES scale were more likely to be abstinent. ^ Although this group was recruited using various methods across the state of Texas, the fact that they preferred to interact with the counselor in Spanish may limit the study findings. The results of this study highlight the need for research to examine specific subgroups of people and understand the special circumstances that influence their health behaviors. Furthering our knowledge of the relations between sociodemographic characteristics and smoking cessation could lead to interventions that reduce disparities in smoking cessation. ^

Taxonomy of nursing clinical credibility as described by nurses and physicians

Introduction: Nursing clinical credibility, a complex, abstract concept is rarely mentioned in the clinical setting, but is implicitly understood by nurses and physicians. The concept has neither been defined nor explored, despite its repeated use in literature. A review of the extant literature formed the basis for a concept analysis of nursing clinical credibility, which is currently under review for publication. ^ Methods: Using taxonomic analysis, findings of a descriptive qualitative research study in which registered nurses and physicians identified attributes of nursing clinical credibility as it applied to nurses in direct care roles in a hospital setting, formed the basis for development of taxonomies of nursing clinical credibility. A secondary review of literature was undertaken to verify congruence of the taxonomic domains with the work of previous researchers who studied credibility and source credibility. ^ Results: Three taxonomies of nursing clinical credibility emerged from the taxonomic analysis. Using an inductive approach, two separate taxonomies of nursing clinical credibility emerged; one was developed from the descriptions of nursing clinical credibility by registered nurses, and the other from physicians' descriptions of nursing clinical credibility. A third and final taxonomy reflects commonalities within both taxonomies. Three domains were consistent for both nurses and physicians: trustworthiness, expertise, and caring. The two disciplines differed in categories and emphases within the domains; however, both disciplines focused on the attributes of trustworthiness and caring, although physicians and nurses differed on components of expertise. ^ Discussion: Findings from this study of nursing clinical credibility concur with the work of previous researchers who identified trustworthiness and expertise as attributes of credibility and source credibility. Findings suggest however, that trustworthiness and expertise alone are not sufficient attributes of nursing clinical credibility. Caring emerged as an essential domain of nursing clinical credibility according to both nurses and physicians. ^ Products: Products of this research include a concept analysis, two discipline-specific taxonomies of nursing clinical credibility, a third final taxonomy, and a monograph that describes the development of the final taxonomy of nursing clinical credibility. ^

Tuberculosis in persons with concomittant diseases in Houston, Texas, 2004 to 2008

The purpose of this study is to evaluate characteristics of tuberculosis (TB) in diabetics and persons infected with HIV from 2004 to 2008 in Houston, Texas. This analysis will allow us to identify demographic trends. Previous studies have shown that in general, there is a higher risk for HIV+ persons to develop active TB, or to re-activate latent TB, as they progress in their HIV infection. In addition, similar to HIV, diabetes mellitus (DM) weakens the immune system so that persons with DM have also been shown to have a tendency to develop TB. This analysis will examine three areas of research: (a) to explore existing TB trends in Houston/Harris County and associated characteristics, (b) to ascertain the common risk factors of DM and HIV that are correlate with TB infections, and (c) from the analysis of the data, to determine if subsequent TB prevention programs are needed for specific subgroups.^

Data Accuracy in Medical Record Abstraction

Clinical Research Data Quality Literature Review and Pooled Analysis We present a literature review and secondary analysis of data accuracy in clinical research and related secondary data uses. A total of 93 papers meeting our inclusion criteria were categorized according to the data processing methods. Quantitative data accuracy information was abstracted from the articles and pooled. Our analysis demonstrates that the accuracy associated with data processing methods varies widely, with error rates ranging from 2 errors per 10,000 files to 5019 errors per 10,000 fields. Medical record abstraction was associated with the highest error rates (70–5019 errors per 10,000 fields). Data entered and processed at healthcare facilities had comparable error rates to data processed at central data processing centers. Error rates for data processed with single entry in the presence of on-screen checks were comparable to double entered data. While data processing and cleaning methods may explain a significant amount of the variability in data accuracy, additional factors not resolvable here likely exist. Defining Data Quality for Clinical Research: A Concept Analysis Despite notable previous attempts by experts to define data quality, the concept remains ambiguous and subject to the vagaries of natural language. This current lack of clarity continues to hamper research related to data quality issues. We present a formal concept analysis of data quality, which builds on and synthesizes previously published work. We further posit that discipline-level specificity may be required to achieve the desired definitional clarity. To this end, we combine work from the clinical research domain with findings from the general data quality literature to produce a discipline-specific definition and operationalization for data quality in clinical research. While the results are helpful to clinical research, the methodology of concept analysis may be useful in other fields to clarify data quality attributes and to achieve operational definitions. Medical Record Abstractor’s Perceptions of Factors Impacting the Accuracy of Abstracted Data Medical record abstraction (MRA) is known to be a significant source of data errors in secondary data uses. Factors impacting the accuracy of abstracted data are not reported consistently in the literature. Two Delphi processes were conducted with experienced medical record abstractors to assess abstractor’s perceptions about the factors. The Delphi process identified 9 factors that were not found in the literature, and differed with the literature by 5 factors in the top 25%. The Delphi results refuted seven factors reported in the literature as impacting the quality of abstracted data. The results provide insight into and indicate content validity of a significant number of the factors reported in the literature. Further, the results indicate general consistency between the perceptions of clinical research medical record abstractors and registry and quality improvement abstractors. Distributed Cognition Artifacts on Clinical Research Data Collection Forms Medical record abstraction, a primary mode of data collection in secondary data use, is associated with high error rates. Distributed cognition in medical record abstraction has not been studied as a possible explanation for abstraction errors. We employed the theory of distributed representation and representational analysis to systematically evaluate cognitive demands in medical record abstraction and the extent of external cognitive support employed in a sample of clinical research data collection forms. We show that the cognitive load required for abstraction in 61% of the sampled data elements was high, exceedingly so in 9%. Further, the data collection forms did not support external cognition for the most complex data elements. High working memory demands are a possible explanation for the association of data errors with data elements requiring abstractor interpretation, comparison, mapping or calculation. The representational analysis used here can be used to identify data elements with high cognitive demands.

Bayesian and survival model for tumor relapse status and disease-specific survival, with applications to breast cancer

Breast cancer is the most common non-skin cancer and the second leading cause of cancer-related death in women in the United States. Studies on ipsilateral breast tumor relapse (IBTR) status and disease-specific survival will help guide clinic treatment and predict patient prognosis.^ After breast conservation therapy, patients with breast cancer may experience breast tumor relapse. This relapse is classified into two distinct types: true local recurrence (TR) and new ipsilateral primary tumor (NP). However, the methods used to classify the relapse types are imperfect and are prone to misclassification. In addition, some observed survival data (e.g., time to relapse and time from relapse to death)are strongly correlated with relapse types. The first part of this dissertation presents a Bayesian approach to (1) modeling the potentially misclassified relapse status and the correlated survival information, (2) estimating the sensitivity and specificity of the diagnostic methods, and (3) quantify the covariate effects on event probabilities. A shared frailty was used to account for the within-subject correlation between survival times. The inference was conducted using a Bayesian framework via Markov Chain Monte Carlo simulation implemented in softwareWinBUGS. Simulation was used to validate the Bayesian method and assess its frequentist properties. The new model has two important innovations: (1) it utilizes the additional survival times correlated with the relapse status to improve the parameter estimation, and (2) it provides tools to address the correlation between the two diagnostic methods conditional to the true relapse types.^ Prediction of patients at highest risk for IBTR after local excision of ductal carcinoma in situ (DCIS) remains a clinical concern. The goals of the second part of this dissertation were to evaluate a published nomogram from Memorial Sloan-Kettering Cancer Center, to determine the risk of IBTR in patients with DCIS treated with local excision, and to determine whether there is a subset of patients at low risk of IBTR. Patients who had undergone local excision from 1990 through 2007 at MD Anderson Cancer Center with a final diagnosis of DCIS (n=794) were included in this part. Clinicopathologic factors and the performance of the Memorial Sloan-Kettering Cancer Center nomogram for prediction of IBTR were assessed for 734 patients with complete data. Nomogram for prediction of 5- and 10-year IBTR probabilities were found to demonstrate imperfect calibration and discrimination, with an area under the receiver operating characteristic curve of .63 and a concordance index of .63. In conclusion, predictive models for IBTR in DCIS patients treated with local excision are imperfect. Our current ability to accurately predict recurrence based on clinical parameters is limited.^ The American Joint Committee on Cancer (AJCC) staging of breast cancer is widely used to determine prognosis, yet survival within each AJCC stage shows wide variation and remains unpredictable. For the third part of this dissertation, biologic markers were hypothesized to be responsible for some of this variation, and the addition of biologic markers to current AJCC staging were examined for possibly provide improved prognostication. The initial cohort included patients treated with surgery as first intervention at MDACC from 1997 to 2006. Cox proportional hazards models were used to create prognostic scoring systems. AJCC pathologic staging parameters and biologic tumor markers were investigated to devise the scoring systems. Surveillance Epidemiology and End Results (SEER) data was used as the external cohort to validate the scoring systems. Binary indicators for pathologic stage (PS), estrogen receptor status (E), and tumor grade (G) were summed to create PS+EG scoring systems devised to predict 5-year patient outcomes. These scoring systems facilitated separation of the study population into more refined subgroups than the current AJCC staging system. The ability of the PS+EG score to stratify outcomes was confirmed in both internal and external validation cohorts. The current study proposes and validates a new staging system by incorporating tumor grade and ER status into current AJCC staging. We recommend that biologic markers be incorporating into revised versions of the AJCC staging system for patients receiving surgery as the first intervention.^ Chapter 1 focuses on developing a Bayesian method to solve misclassified relapse status and application to breast cancer data. Chapter 2 focuses on evaluation of a breast cancer nomogram for predicting risk of IBTR in patients with DCIS after local excision gives the statement of the problem in the clinical research. Chapter 3 focuses on validation of a novel staging system for disease-specific survival in patients with breast cancer treated with surgery as the first intervention. ^