Utilization of Acid Suppression Medication in an Underserved Population.

OBJECTIVES: Acid suppression therapy (AST), composed of proton pump inhibitors (PPIs), histamine-2 receptor blockers, and antacids, is one of the most common medication groups used in the United States. Long-term AST is concerning, however, because it is linked with an increased risk of community-acquired pneumonia, Clostridium difficile infections, bone fractures, and nutritional deficiencies. The potentially harmful biological and economic consequences associated with the improper use of acid suppression medications presents a great deal of risk to those in underserved communities. We sought to determine the prevalence of AST in an underserved population and the common diagnoses and symptoms associated with therapy. In addition, we studied the frequency of suboptimal usage of PPIs in an indigent care population and the potential factors related to high-risk behaviors. METHODS: The study was a cross-sectional study using a survey that was distributed to participants during their regularly scheduled visits to a public sector provider of health care for low-income patients. RESULTS: Of the 176 participants surveyed, 70 (40%) were using AST. Esophagitis and gastroesophageal reflux disease were the most prevalent in our sample population. PPIs were the most common acid suppression medication used in our population. Of those using PPIs, 85% were never instructed to cease use. Of the 27 patients with PPI prescriptions, 26 used it in a suboptimal manner, and of those without prescriptions, 7 used it in a suboptimal manner. CONCLUSIONS: ASTs are prevalent in low-income populations, and patients are not being managed appropriately to minimize their risk for complications of AST.

Integration of Transcriptomic Data Identifies Global and Cell-Specific Asthma-Related Gene Expression Signatures.

Over 140,000 transcriptomic studies performed in healthy and diseased cell and tissue types, at baseline and after exposure to various agents, are available in public repositories. Integrating results of transcriptomic datasets has been an attractive approach to identify gene expression signatures that are more robust than those obtained for individual datasets, especially datasets with small sample size. We developed Reproducible Analysis and Validation of Expression Data (RAVED), a pipeline that facilitates the creation of R Markdown reports detailing reproducible analysis of publicly available transcriptomic data, and used it to analyze asthma and glucocorticoid response microarray and RNA-Seq datasets. Subsequently, we used three approaches to integrate summary statistics of these studies and identify cell/tissue-specific and global asthma and glucocorticoid-induced gene expression changes. Transcriptomic integration methods were incorporated into an online app called REALGAR, where end-users can specify datasets to integrate and quickly obtain results that may facilitate design of experimental studies.