More and more in medicine, when considering treatment plans, clinicians are taking into account patients’ social determinants of health (SDOH) — nonmedical factors that influence health outcomes.

These are the conditions people are exposed to during birth, childhood and adulthood and include factors such as income, access to transportation and safe housing, and exposure to air and water pollution. 

SDOH resources related to Maryland residents had been limited to just a couple of tables from the U.S. Census Bureau and just five or so indicators, including income status. 

Precision Medicine at Work

As part of an SDOH working group to build resources for all of Johns Hopkins’ precision medicine centers of excellence, Kate Fitzgerald, Sc.D., an associate professor of neurology, expanded that infrastructure to build a much larger set of data. Housed in Johns Hopkins’ Precision Medicine Analytics Platform, a data analytics tool, the new table incorporates over 500 different indicators and neighborhood-level social factors that are linked to the specific latitude and longitude where a person lives. 

It includes features like the Area Deprivation Index (an evaluation of a region’s socioeconomic conditions that have been linked to health outcomes) and the Social Vulnerability Index (a tool that factors in socioeconomic status, household members, housing and transportation, and prevalence of those who are racial and ethnic minorities and English speakers, to determine what census tracts would need extra support during natural disasters). Fitzgerald also added information about the ruralness in which people live, access to green spaces and availability of nearby places to shop for healthy foods.

Researchers can link their own data to the tool to understand how these factors can affect disease outcomes, Fitzgerald says. For example, she and her colleagues found that people with multiple sclerosis who had fewer socioeconomic resources were more likely to experience faster retinal atrophy (death or degeneration of the cells in the light-sensing layer at the back of the eye).

“The program makes it very easy for you to understand where a patient is living and characterize the environment around them in multiple senses, from air pollution to wealth,” says Jack Iwashyna, M.D., Ph.D., Bloomberg Distinguished Professor of Social Science and Justice in Medicine, who is part of the project’s working group. “It provides a multidimensional sense of the geography right around the patient at a finely granular level.” 

The group is now working on another version of the tool that can drill down from neighborhood-level information to individuals and patients.