Arkansas Foundation for Medical Care

The Gail and CARE models estimate breast cancer risk for white and African-American (AA) women, respectively. The aims of this study were to compare metropolitan and nonmetropolitan women with respect to predicted breast cancer risks based on known risk factors, and to determine if population density was an independent risk factor for breast cancer risk. A cross-sectional survey was completed by 15,582 women between 35 and 85years of age with no history of breast cancer. Metropolitan and nonmetropolitan women were compared with respect to risk factors, and breast cancer risk estimates, using general linear models adjusted for age. For both white and AA women, tisk factors used to estimate breast cancer risk included age at menarche, history of breast biopsies, and family history. For white women, age at first childbirth was an additional risk factor. In comparison to their nonmetropolitan counterparts, metropolitan white women were more likely to report having a breast biopsy, have family history of breast cancer, and delay childbirth. Among white metropolitan and nonmetropolitan women, mean estimated 5-year risks were 1.44% and 1.32% (p<0.001), and lifetime risks of breast cancer were 10.81% and 10.01% (p<0.001), respectively. AA metropolitan residents were more likely than those from nonmetropolitan areas to have had a breast biopsy. Among AA metropolitan and nonmetropolitan women, mean estimated 5-year risks were 1.16% and 1.12% (p=0.039) and lifetime risks were 8.94%, and 8.85% (p=0.344). Metropolitan residence was associated with higher predicted breast cancer risks for white women. Among AA women, metropolitan residence was associated with a higher predicted breast cancer risk at 5years, but not over a lifetime. Population density was not an independent risk factor for breast cancer.

The methods for healthcare reform are strikingly underdeveloped, with much reliance on political power. A methodology that combined methods from sources such as clinical trials, experience-based wisdom, and improvement science could be among the aims of the upcoming work in the USA on comparative effectiveness and on the agenda of the Center for Medicare and Medicaid Innovation in the Centers for Medicare and Medicaid Services. Those working in quality improvement have an unusual opportunity to generate substantial input into these processes through professional organisations such as the Academy for Healthcare Improvement and dominant leadership organisations such as the Institute for Healthcare Improvement.

I would like to demonstrate how you can create colorful PDF files using ODS (Output Delivery System) with different predefined style templates for the layout and color scheme of both tables and graphs. There are a few different ways you can customize your reports in the Report Procedure. Traffic lighting is a very popular technique and can be achieved by changing the font and/or the background colors. I prefer to customize my reports by adding background color to highlight a row, column, and/or cell in a table. I will share my favorite styles and show you how to look at your tables in the different styles available. I will briefly explain how you can create your own style by changing the color scheme of an existing style so that it can be used for all of your reports. I will explain how I used the pieces of code to get the table with a row, a column and individual cells highlighted with several different colors. I will focus on creating PDF files using ODS, but these methods can be used with other output destinations. This paper is for beginner to intermediate level SAS® programmers with experience creating tables with Report procedure.