Analyzing Chlamydia and Gonorrhea Health Disparities from Health Information Systems: A Closer Examination Using Spatial Statistics and Geographical Information Systems

Abstract

The emergence and development of electronic health records have contributed to an abundance of patient data that can greatly be used and analyzed to promote health outcomes and even eliminate health disparities. However, challenges exist in the data received with factors such as data inconsistencies, accuracy issues, and unstructured formatting being evident. Furthermore, the current electronic health records and clinical information systems that are present do not contain the social determinants of health that may enhance our understanding of the characteristics and mechanisms of disease risk and transmission as well as health disparities research. Linkage to external population health databases to incorporate these social determinants of health is often necessary. This study provides an opportunity to identify and analyze health disparities using geographical information systems on two important sexually transmitted diseases in chlamydia and gonorrhea using Marion County, Indiana as the geographical location of interest. Population health data from the Social Assets and Vulnerabilities Indicators community information system and electronic health record data from the Indiana Network for Patient Care will be merged to measure the distribution and variability of greatest chlamydia and gonorrhea risk and to determine where the greatest areas of health disparities exist. A series of both statistical and spatial statistical methods such as a longitudinal measurement of health disparity through the Gini index, a hot-spot and cluster analysis, and a geographically weighted regression will be conducted in this study. The outcome and broader impact of this research will contribute to enhanced surveillance and increased effective strategies in identifying the level of health disparities for sexually transmitted diseases in vulnerable localities and high-risk communities. Additionally, the findings from this study will lead to improved standardization and accuracy in data collection to facilitate subsequent studies involving multiple disparate data sources. Finally, this study will likely introduce ideas for potential social determinants of health to be incorporated into electronic health records and clinical information systems.