The role of smoking status on postoperative complications and survival in lung cancer patients presenting to M.D. Anderson Cancer Center

It is estimated that 50% of all lung cancer patients continue to smoke after diagnosis. Many of these lung cancer patients who are current smokers often experience tremendous guilt and responsibility for their disease, and feel it might be too late for them to quit smoking. In addition, many oncologists may be heard to say that it is 'too late', 'it doesn't matter', 'it is too difficult', 'it is too stressful' for their patients to stop smoking, or they never identify the smoking status of the patient. Many oncologists feel unprepared to address smoking cessation as part of their clinical practice. In reality, physicians can have tremendous effects on motivating patients, particularly when patients are initially being diagnosed with cancer. More information is needed to convince patients to quit smoking and to encourage clinicians to assist patients with their smoking cessation. ^ In this current study, smoking status at time of lung cancer diagnosis was assessed to examine its impact on complications and survival, after exploring the reliability of smoking data that is self-reported. Logistic Regression was used to determine the risks of smoking prior to lung resection. In addition, survival analysis was performed to examine the impact of smoking on survival. ^ The reliability of how patients report their smoking status was high, but there was some discordance between current smokers and recent quitters. In addition, we found that cigarette pack-year history and duration of smoking cessation were directly related to the rate of a pulmonary complication. In regards to survival, we found that current smoking at time of lung cancer diagnosis was an independent predictor of early stage lung cancer. This evidence supports the idea that it is "never too late" for patients to quit smoking and health care providers should incorporate smoking status regularly into their clinical practice.^

Nurse likelihood to report a pediatric medication error: Examination of the "pre-reporting" period

Each year, hospitalized patients experience 1.5 million preventable injuries from medication errors and hospitals incur an additional $3.5 billion in cost (Aspden, Wolcott, Bootman, & Cronenwatt; (2007). It is believed that error reporting is one way to learn about factors contributing to medication errors. And yet, an estimated 50% of medication errors go unreported. This period of medication error pre-reporting, with few exceptions, is underexplored. The literature focuses on error prevention and management, but lacks a description of the period of introspection and inner struggle over whether to report an error and resulting likelihood to report. Reporting makes a nurse vulnerable to reprimand, legal liability, and even threat to licensure. For some nurses this state may invoke a disparity between a person‘s belief about him or herself as a healer and the undeniable fact of the error.^ This study explored the medication error reporting experience. Its purpose was to inform nurses, educators, organizational leaders, and policy-makers about the medication error pre-reporting period, and to contribute to a framework for further investigation. From a better understanding of factors that contribute to or detract from the likelihood of an individual to report an error, interventions can be identified to help the nurse come to a psychologically healthy resolution and help increase reporting of error in order to learn from error and reduce the possibility of future similar error.^ The research question was: "What factors contribute to a nurse's likelihood to report an error?" The specific aims of the study were to: (1) describe participant nurses' perceptions of medication error reporting; (2) describe participant explanations of the emotional, cognitive, and physical reactions to making a medication error; (3) identify pre-reporting conditions that make it less likely for a nurse to report a medication error; and (4) identify pre-reporting conditions that make it more likely for a nurse to report a medication error.^ A qualitative research study was conducted to explore the medication error experience and in particular the pre-reporting period from the perspective of the nurse. A total of 54 registered nurses from a large private free-standing not-for-profit children's hospital in the southwestern United States participated in group interviews. The results describe the experience of the nurse as well as the physical, emotional, and cognitive responses to the realization of the commission of a medication error. The results also reveal factors that make it more and less likely to report a medication error.^ It is clear from this study that upon realization that he or she has made a medication error, a nurse's foremost concern is for the safety of the patient. Fear was also described by each group of nurses. The nurses described a fear of several things including physician reaction, manager reaction, peer reaction, as well as family reaction and possible lack of trust as a result. Another universal response was the description of a struggle with guilt, shame, imperfection, blaming oneself, and questioning one's competence.^

Network-based analysis of genome-wide scans of natural selection

The genomic era brought by recent advances in the next-generation sequencing technology makes the genome-wide scans of natural selection a reality. Currently, almost all the statistical tests and analytical methods for identifying genes under selection was performed on the individual gene basis. Although these methods have the power of identifying gene subject to strong selection, they have limited power in discovering genes targeted by moderate or weak selection forces, which are crucial for understanding the molecular mechanisms of complex phenotypes and diseases. Recent availability and rapid completeness of many gene network and protein-protein interaction databases accompanying the genomic era open the avenues of exploring the possibility of enhancing the power of discovering genes under natural selection. The aim of the thesis is to explore and develop normal mixture model based methods for leveraging gene network information to enhance the power of natural selection target gene discovery. The results show that the developed statistical method, which combines the posterior log odds of the standard normal mixture model and the Guilt-By-Association score of the gene network in a naïve Bayes framework, has the power to discover moderate/weak selection gene which bridges the genes under strong selection and it helps our understanding the biology under complex diseases and related natural selection phenotypes.^