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The goal of this project is to analyze data from NCAA Division One Men's basketball teams during the regular season to predict how they will perform during the National Championships, colloquially known as March Madness. I use a data set that ranks teams according to their Pomeroy College Basketball Ratings[1]. These ratings give in depth basketball statistics for each year from 2002 until present and use several different measures to help quantify how good or bad a team is. My analysis will take three parts: single linear analysis, multiple linear analysis, and polynomial regression. I start by attempting to do a single linear analysis on the data from the year 2016, first using Adjusted Offensive Efficiency as the predictor and then using Adjusted Defensive Efficiency as the predictor. Next, I attempt a multiple linear analysis and find that by using both the Adjusted Offensive Efficiency and Adjusted Defensive Efficiency, the predictions greatly improve, but still are not perfect. Finally, I attempt polynomial regression using Adjusted Offensive Efficiency as the predictor. After running each of these methods, I found that none of these can predict the perfect bracket, however the multiple linear regression is able to perform surprisingly well, making the correct final ranking predictions approximately 62.33% of the time.[1] https://kenpom.com/

Value-based payment models may improve patient health by targeting quality of care over quantity of health services. Social workers in primary care settings are well-positioned to improve the quality of health services for vulnerable patients by identifying and addressing patients' social determinants of health. This case study describes a Plan Do Study Act (PDSA) quality improvement approach implemented and refined by social workers to proactively address clinical quality gaps in one family medicine practice. The studied program – entitled Gap Closure Day – was led by a team of social workers to improve quality outcomes of patients. Findings highlight the important roles of social workers as members of health care teams to improve the quality of health services and address health equity. [ABSTRACT FROM AUTHOR]

Hypertensive disorders are a common cause of maternal mortality. Whether interpregnancy BMI (body mass index kg/m2) gain is associated with hypertensive disorders in a subsequent pregnancy is not unclear. To examine the association between interpregnancy BMI and hypertensive disorders in women without a history of hypertensive disorders in pregnancy. This was a retrospective cohort study of all women who had more than one singleton pregnancy at 23 weeks' gestation or greater at a single academic institution. Only the second pregnancy in the dataset was analyzed. We excluded women who had any hypertensive disorder in the index pregnancy. Interpregnancy BMI change was calculated by the change of early pregnancy BMI (within 14 weeks' gestation) measured in the office between the index pregnancy compared to that of the subsequent pregnancy. Women were categorized according to interpregnancy BMI change (BMI loss greater than 2 kg/m2, BMI change ±2 kg/m2, and BMI gain greater than 2 kg/m2). The primary outcome was any hypertensive disorder (chronic hypertension and pregnancy-associated hypertension). Multivariable logistic regression was performed to calculate adjusted odds ratios (aOR) with 95% confidence interval (95%CI) after adjusting for predefined covariates. Of 3068 women who were analyzed, 342 (11%), 1698 (55%), and 1028 (34%) had interpregnancy BMI loss greater than 2 kg/m2, interpregnancy BMI change ±2 kg/m2, and interpregnancy BMI gain greater than 2 kg/m2, respectively. Interpregnancy BMI gain greater than 2 kg/m2 compared to interpregnancy BMI loss more than 2 kg/m2 was associated with increased odds of hypertensive disorders (8.3% vs. 4.0%; adjusted odds ratio 2.20 [95% confidence interval 1.55–3.13]) and pregnancy-associated hypertension (adjusted odds ratio 2.25 [95% confidence interval 1.54–3.27]). Interpregnancy BMI loss greater than 2 kg/m2 compared to interpregnancy BMI change ±2 kg/m2 was not associated with increased odds of any hypertensive disorders (5.3% vs. 4.0%; adjusted odds ratio 0.58 [95% confidence interval 0.32–1.05]). Compared to interpregnancy BMI change ±2 kg/m2, interpregnancy BMI gain greater than 2 kg/m2 was associated with increased odds of any hypertensive disorder. Weight control after pregnancy could be a potentially modifiable factor that may reduce the risk of hypertensive disorders. [ABSTRACT FROM AUTHOR]