To identify high-risk populations, health care systems have long used algorithms that rely on traditional sources of data, such as claims and utilization. Spurred by increased recognition of how social and environmental forces outside health care can impact risk — including socioeconomic status, housing, and access to transportation and nutritious food — many health care organizations are now seeking to also use information on health-related social needs to better identify potential high-risk populations. Gathering this information on an individual level, however, can be challenging. Publicly available data sources that provide sociodemographic snapshots of a specific community at the zip code or neighborhood level can offer a valuable resource for population-level analyses.
This resource, developed through CHCS’ Rising Risk Initiative, summarizes several publicly available surveys, indices, and other geographically based data sources that provide insight into some of the social determinants of health affecting communities. These data sources may serve as useful proxies for incorporating social determinants of health data into risk segmentation or stratification models, or provide additional data to help predict social risk at a population level. Incorporating social risk allows for algorithms to have more of a health equity focus — highlighting existing inequalities and some of the external conditions and forces that may be contributing to them. Health plans or health care organizations can then develop targeted and ideally culturally appropriate upstream interventions to better serve populations with fewer resources.