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.

Using PROC GLIMMIX and PROC SGPLOT to Demonstrate County-level Racial Disparities in Obesity in North Carolina

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 in reducing these disparities. 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, and a multilevel reweighted regression model using PROC GLIMMIX 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. These county-level estimates are then compared graphically using PROC SGPLOT. The distribution of prevalence estimates for Blacks is shifted to the right in comparison to the distribution for Whites; based on a two-sample test for differences in proportions the mean of the distribution of obesity prevalence estimates for Blacks is 35.7% higher than for Whites in North Carolina. This difference is statistically significant (p<.0001). 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 as well as providing a graphical representation of disparities, allowing for the implementation of interventions and dissemination of information to occur more effectively and efficiently.

Using PROC GLIMMIX and PROC SGPLOT to Demonstrate County-level Racial Disparities in Obesity in North Carolina

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 in reducing these disparities. 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, and a multilevel reweighted regression model using PROC GLIMMIX 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. These county-level estimates are then compared graphically using PROC SGPLOT. The distribution of prevalence estimates for Blacks is shifted to the right in comparison to the distribution for Whites; based on a two-sample test for differences in proportions the mean of the distribution of obesity prevalence estimates for Blacks is 35.7% higher than for Whites in North Carolina. This difference is statistically significant (p<.0001). 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 as well as providing a graphical representation of disparities, allowing for the implementation of interventions and dissemination of information to occur more effectively and efficiently.

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.

Multilevel Reweighted Regression Models to Estimate County-Level Racial Health Disparities Using PROC GLIMMIX

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, and this disconnect may be slowing the progress made in reducing these disparities. This paper focuses on establishing county-level prevalence estimates of diabetes among Non-Hispanic Whites and Non-Hispanic Blacks. These estimates are produced using multilevel reweighted regression models through the GLIMMIX procedure with 2006-2010 Behavioral Risk Factor Surveillance System data and 2010 census data. To examine whether racial disparities exist at the county level, the paper estimates the risk difference of prevalence estimates between races. It subsequently ranks counties and states by the magnitude of disparities.