Developing County-Level Estimates of Racial Disparities in Obesity Using Multilevel Reweighted Regression

Background: The agenda to reduce racial health disparities has been set primarily at the national and state levels. These levels may be too far removed from the individual level where health outcomes are realized. This disconnect may be slowing the progress made in reducing these disparities. We use a small area analysis technique to fill the void for county-level disparities data. Methods:Behavioral Risk Factor Surveillance System data is used to estimate the prevalence of obesity by county among Non-Hispanic Whites and Non-Hispanic Blacks. A modified weighting system was developed based on demographics at the county level. A multilevel reweighted regression model is fit to obtain county-level prevalence estimates by race. To examine whether racial disparities exist at the county level, these rates are compared using risk difference and rate ratio. Results: Gulf County, Florida was ranked as having the largest disparity in absolute terms (risk difference). New York County, New York was ranked as having the largest disparity in relative terms (risk ratio). Based on the average risk difference, the top five states with the largest average disparity were: Oklahoma, Kentucky, Ohio, Washington D.C., and Kansas. The top five states with the largest average relative disparity were: Washington D.C., Massachusetts, Colorado, Kentucky, and New York. Conclusions: Addressing disparities based on factors such as race/ethnicity, geographic location, and socioeconomic status is a current public health priority. This study takes a first step in developing the statistical infrastructure needed to target disparities interventions and resources to the local areas with greatest need.

Mining Through Resumes: Utilizing SAS to Increase Efficiency and Objectivity in the Hiring Process

In the current job market, it is common to be inundated with resumes and applications. It has become increasingly important to streamline the evaluation process in order to sift through these candidates. Anecdotally, we recently received 50 resumes for 2 positions, many of which did not meet the minimum qualifications for employment. In order to minimize the time spent evaluating these resumes, and maximize the objectivity and efficiency of the process, we developed a SAS macro to determine which candidates should progress to a first round interview.

SAS ® for Budgeting an Ideal Wedding

When considering beverages at a wedding reception, there are often two possible payment options: (1) a set price per person per hour; (2) a fixed price per drink. We developed a SAS macro to help choose the most cost effective option.