Geospatial analysis of patients’ social determinants of health for health systems science and disparity research

King Cholera had wreaked havoc on the health of Londoners for generations, but the outbreak on August 31, 1854 was extreme even by a society accustomed to mass death. Within 3 days, 127 people had died, and within a week, a majority of the population had fled the area. John Snow, considered a pioneer of anesthesia for administering Chloroform to Queen Victoria during the birth of Prince Leopold in 1853, was skeptical of the dominant miasma (airborne) theory of disease spread for Cholera. With the help of what would later become known as a Voronoi Diagram, Snow was able to visualize geospatial proximity, namely that patients who had consumed water from the Broad Street pump eventually contracted Cholera, while those who consumed water from different sources did not contract the disease. Presented with this map to show clusters of disease, the local authorities disabled the pump (Fig. 1). We leave it to historians to dispute whether or not the famous map of the outbreak was created until after the outbreak. Regardless, the reasoning and approach to the epidemic employed by Snow became foundational work for epidemiology and. GA Snow linked employment with certain companies to worsening of disease transmission, and by identifying geographic clusters of mortality, linked the Cholera outbreak to a polluted water source, an approach similar to the focus of this manuscript. The cross-disciplinary expertise, required for GA, common in the days before medical specialization and enabled by the relative paucity of development in various fields compared with today, can be reborn with the advent of Electronic Health Records (EHR) systems that make the tools of medical geography accessible to a wide variety of health care researchers and clinical subspecialties. As the story of John Snow demonstrates, the combination of GA and specialty-based medical knowledge (eg, Anesthesiology and Surgery), can serve as a method to derive insights into socioeconomic and environmental drivers of perioperative care and may lead to the design of appropriate interventions to ameliorate disparities in outcomes.