Evidence-Based Practices : The Hidden Treasure to a More Inclusive Catholic Classroom

Response to Smith, Cheatham, and Mosher (this issue) Evidence-Based Practices to Promote Inclusion in Catholic School

Binge [Fantasy reality]

[My appreciation for mainstream pop culture is genuine, but I am not a passive consumer.] [Drawing from embodied experience and contemporary feminist theory, I design as a participant, cultural surveyor, and critic.] [From these vantage points, I binge-watch to discern the tropes of media such as reality TV romance and dead girl shows.] [My data bingeing leads to a process of archiving, de/recoding, and making visible the algorithm structuring pop culture.] [“Fantasy” is derived from the Greek phantazein, meaning “to make visible.”] [In this thesis, I demonstrate that the reality-fantasy relationship is not an either/or.] [Reality TV challenges this notion directly: it is more fantasy than reality.] [The line between reality and fantasy is further blurred when real women play fantasy dead girls;] [The plotline may be fictional but the violence against women is a reality.] [Like binge-watching, Binge fully immerses you in my pop culture world through both critique and celebration.

Influence of post-mortem aging time and method on flavor and tenderness of beef, and comparison of retail cutting yields, times, and value in thirteen beef subprimals from beef and Holstein cattle

2018 Fall.Includes bibliographical references.The objective of this study was to identify flavor and tenderness differences in beef aged for different lengths of time and using different methods. Strip loin sections from commodity, USDA Choice beef carcasses (n = 38) were randomly assigned to 1 of 8 aging treatments: 1) 3 d wet-aged; 2) 14 d wet-aged; 3) 28 d wet-aged; 4) 35 d wet-aged; 5) 49 d wet-aged; 6) 63 d wet-aged, 7) 21 d dry-aged; and 8) 14 d wet-aged followed by 21 d dry-aged (combination). Trained sensory panelists rated the cooked product for flavor and textural attributes, and samples were evaluated for Warner-Bratzler and slice shear force, fatty acid composition, amino acid composition, and volatile flavor compounds. Wet-aging of beef up to 35 d caused no changes (P > 0.05) in flavor notes. However, beef wet-aged for 49 d or longer was rated lowest (P 0.05) were identified between wet-aging, dry-aging, or the combination of both for any flavor attributes. Fatty acid profiles did not differ (P > 0.05) by aging length of time or method. Concentrations of amino acids and volatile flavor compounds increased (P 0.05) were noted. Results suggested that wet-aging to extreme lengths of time may have a dramatic effect on flavor profile of beef, without necessarily improving tenderness. Additionally, eating quality characteristics do not necessarily differ between wet- and dry-aged beef. Holsteins comprise approximately 20% of the U.S. fed beef slaughter, and the carcass characteristics of Holsteins tend to differ (on average) from those of traditional beef breeds. Retail cutting yields, cutting times, and resulting value were evaluated in thirteen subprimal cuts from carcasses of fed Holstein (n = 398) and beef-breed (n = 404) origin. Generally, subprimals from carcasses of beef-breeds were heavier (P 0.05) between cattle types. Only the amount of time taken to cut center-cut top sirloin butts derived from beef-breeds were faster (P < 0.01) than those for cuts from carcasses of Holsteins; in all other instances, times for cutting subprimals derived from Holstein carcasses were either faster (P < 0.05) or not different (P ≥ 0.05). Retail prices among cuts from differing breed types were minimal, but true differences (P < 0.05) in cutting yields for ribeye rolls and short loins from carcasses of Holsteins may generate greater values to a steak cutter or retailer. Such advantages could be attributed to smaller, more manageable, and leaner cuts produced from carcasses of Holsteins. Therefore, further research regarding retail cutting differences between cattle types may provide insight for operations seeking maximum retail yields and profit

A Socio-Spatial Analysis of Rural Poverty in East Tennessee

The incidence of poverty in rural areas is actually higher than that in urban places. This study fills a gap in geographic research by examining poverty in rural and small town communities in east Tennessee using the 1990 census. A cluster analysis of high poverty block groups identifies different categories of poor. Just as “who is poor?” varies across the United States, “who is poor?” in east Tennessee varies. The identity of the poor in rural east Tennessee is found to be contrary to popular images of povery in Appalachia. The massive reorganization of rural economies in recent decades is reshaping rural places and communities. Economic restructuring and social re-composition have directly affected employment and wage opportunities in rural areas, and have indirectly affected access to services such as affordable housing. The impact of the larger process of economic restructuring on urban environments and on urban neighborhood residents has been examined. There remains a need to examine the impact of these macro-level changes on rural communities and residents. Within the context of rural economic and social change, this study provides a detailed examination of the characteristics and composition of the poor in rural east Tennessee. Case-study analyses of a sample of high poverty rural block groups sheds light on the effects of restructuring on the residents of small communities and rural places. With increased knowledge of the characteristics of the poor in rural east Tennessee, comes the opportunity to improve poverty alleviation policy

Lipid-lowering pharmacotherapy and socioeconomic status: atherosclerosis risk in communities (ARIC) surveillance study

BACKGROUND: Lipid-reduction pharmacotherapy is often employed to reduce morbidity and mortality risk for patients with dyslipidemia or established cardiovascular disease. Associations between socioeconomic factors and the prescribing and use of lipid-lowering agents have been reported in several developed countries. METHODS: We evaluated the association of census tract-level neighborhood household income (nINC) and lipid-lowering medications received during hospitalization or at discharge among 3,546 (5,335 weighted) myocardial infarction (MI) events in the United States (US) Atherosclerosis Risk In Communities (ARIC) surveillance study (1999–2002). Models included neighborhood household income, race, gender, age, study community, year of MI, hospital type (teaching vs. nonteaching), current or past history of hypertension, diabetes or heart failure, and presence of cardiac pain. RESULTS: About fifty-nine percent of patients received lipid-lowering pharmacotherapy during hospitalization or at discharge. Low nINC was associated with a lower likelihood (prevalence ratio 0.89, 95% confidence interval: 0.79, 1.01) of receiving lipid-lowering pharmacotherapy compared to high neighborhood household income, and no significant change in this association resulted when adjusted for the above-mentioned covariates. CONCLUSION: Patient’s socioeconomic status appeared to influence whether they were prescribed a lipid-lowering pharmacotherapy after hospitalization for myocardial infarction in the US ARIC surveillance study (1999–2002)

Socioeconomic status and the progression of heart failure

This dissertation explores the relationship between socioeconomic status and the progression of heart failure following an incident heart failure hospitalization, defined in three domains: rehospitalization, mortality and self-rated health. Hospital admissions for heart failure are on the rise in the United States, and mortality remains high among heart failure patients. Meanwhile, self-rated health is a potent predictor of future health, and its trajectory among heart failure patients is unknown. The first aim was to estimate the effect of neighborhood socioeconomic and Medicaid status on the time to first rehospitalization and the rehospitalization rate. Participants who lived in low neighborhood socioeconomic areas at baseline who had multiple comorbidities during the incident heart failure hospitalization were rehospitalized faster and more often compared to participants living in high socioeconomic neighborhoods at baseline with multiple comorbidities. Meanwhile, Medicaid recipients with a low level of comorbidity were rehospitalized faster and more often compared to non-Medicaid recipients. The second aim was to estimate the effect of neighborhood socioeconomic and Medicaid status on the time to and risk of mortality. Participants who lived in low neighborhood socioeconomic areas at baseline who had multiple comorbidities during the index heartfailure hospitalization experienced a shorter time to death compared to participants living in high socioeconomic neighborhoods at baseline with multiple comorbidities. A comparison of the trajectory of self-rated health across time was examined among participants as part of the third aim. Predictors of a decline in self-rated health across time were assessed, and factors shown to contribute to poorer self-rated health regardless of incident disease status included advanced age, low educational attainment, current smoking and obesity. This dissertation brings to attention several areas for future research in cardiovascular disease epidemiology. The first is a need to better understand the relationship of socioeconomic status and the progression of heart failure in terms of its out-of-hospital management. The second is to explore the mechanisms underlying the relationship between poor socioeconomic status and increased mortality. Lastly, interventions can be tested to help understand how to improve self-rated health, and the resulting health outcomes, among aging adults

Coronary heart disease and mortality following a breast cancer diagnosis

BACKGROUND: Coronary heart disease (CHD) is a leading cause of morbidity and mortality for breast cancer survivors, yet the joint effect of adverse cardiovascular health (CVH) and cardiotoxic cancer treatments on post-treatment CHD and death has not been quantified. METHODS: We conducted statistical and machine learning approaches to evaluate 10-year risk of these outcomes among 1934 women diagnosed with breast cancer during 2006 and 2007. Overall CVH scores were classified as poor, intermediate, or ideal for 5 factors, smoking, body mass index, blood pressure, glucose/hemoglobin A1c, and cholesterol from clinical data within 5 years prior to the breast cancer diagnosis. The receipt of potentially cardiotoxic breast cancer treatments was indicated if the patient received anthracyclines or hormone therapies. We modeled the outcomes of post-cancer diagnosis CHD and death, respectively. RESULTS: Results of these approaches indicated that the joint effect of poor CVH and receipt of cardiotoxic treatments on CHD (75.9%) and death (39.5%) was significantly higher than their independent effects [poor CVH (55.9%) and cardiotoxic treatments (43.6%) for CHD, and poor CVH (29.4%) and cardiotoxic treatments (35.8%) for death]. CONCLUSIONS: Better CVH appears to be protective against the development of CHD even among women who had received potentially cardiotoxic treatments. This study determined the extent to which attainment of ideal CVH is important not only for CHD and mortality outcomes among women diagnosed with breast cancer

Discovering disease-disease associations using electronic health records in The Guideline Advantage (TGA) dataset

Certain diseases have strong comorbidity and co-occurrence with others. Understanding disease-disease associations can potentially increase awareness among healthcare providers of co-occurring conditions and facilitate earlier diagnosis, prevention and treatment of patients. In this study, we utilized the valuable and large The Guideline Advantage (TGA) longitudinal electronic health record dataset from 70 outpatient clinics across the United States to investigate potential disease-disease associations. Specifically, the most prevalent 50 disease diagnoses were manually identified from 165,732 unique patients. To investigate the co-occurrence or dependency associations among the 50 diseases, the categorical disease terms were first mapped into numerical vectors based on disease co-occurrence frequency in individual patients using the Word2Vec approach. Then the novel and interesting disease association clusters were identified using correlation and clustering analyses in the numerical space. Moreover, the distribution of time delay (Δt) between pair-wise strongly associated diseases (correlation coefficients ≥ 0.5) were calculated to show the dependency among the diseases. The results can indicate the risk of disease comorbidity and complications, and facilitate disease prevention and optimal treatment decision-making

Predicting mortality among patients with liver cirrhosis in electronic health records with machine learning

OBJECTIVE: Liver cirrhosis is a leading cause of death and effects millions of people in the United States. Early mortality prediction among patients with cirrhosis might give healthcare providers more opportunity to effectively treat the condition. We hypothesized that laboratory test results and other related diagnoses would be associated with mortality in this population. Our another assumption was that a deep learning model could outperform the current Model for End Stage Liver disease (MELD) score in predicting mortality. MATERIALS AND METHODS: We utilized electronic health record data from 34,575 patients with a diagnosis of cirrhosis from a large medical center to study associations with mortality. Three time-windows of mortality (365 days, 180 days and 90 days) and two cases with different number of variables (all 41 available variables and 4 variables in MELD-NA) were studied. Missing values were imputed using multiple imputation for continuous variables and mode for categorical variables. Deep learning and machine learning algorithms, i.e., deep neural networks (DNN), random forest (RF) and logistic regression (LR) were employed to study the associations between baseline features such as laboratory measurements and diagnoses for each time window by 5-fold cross validation method. Metrics such as area under the receiver operating curve (AUC), overall accuracy, sensitivity, and specificity were used to evaluate models. RESULTS: Performance of models comprising all variables outperformed those with 4 MELD-NA variables for all prediction cases and the DNN model outperformed the LR and RF models. For example, the DNN model achieved an AUC of 0.88, 0.86, and 0.85 for 90, 180, and 365-day mortality respectively as compared to the MELD score, which resulted in corresponding AUCs of 0.81, 0.79, and 0.76 for the same instances. The DNN and LR models had a significantly better f1 score compared to MELD at all time points examined. CONCLUSION: Other variables such as alkaline phosphatase, alanine aminotransferase, and hemoglobin were also top informative features besides the 4 MELD-Na variables. Machine learning and deep learning models outperformed the current standard of risk prediction among patients with cirrhosis. Advanced informatics techniques showed promise for risk prediction in patients with cirrhosis