Richey Piiparinen, Director of Urban Theory and Analytics at the Maxine Goodman Levin School of Urban Affairs, and Joshua Valdez, an Artificial Intelligence Scientist with Rust Belt Analytica, have authored a report, “Swimming Upstream: Getting to the Root Causes of Infant Mortality and Life Expectancy Outcomes in Cleveland, Ohio, and the US.” The analysis found that while Ohio and Cuyahoga County have world-class healthcare, they also have third-world health outcomes. Some findings include:

• Ohio’s life expectancy (76.8) ranks 12th worst nationally, well below Midwestern peers. Cuyahoga County’s life expectancy (77) ranks 41st out of 89 counties in the State.
• Black Cuyahoga County residents live shorter lives (73.6) than Whites (78.3) Hispanics (82.7) and Asians (89).
• With regard to infant mortality rates, Ohio’s infant mortality (6.97) ranks 9th worst in the nation.
• Rates vary dramatically by race, with Black Ohioans (14.3) having more than double the infant mortality rates of Whites (5.1) and Hispanics (5.8).
• Cuyahoga County’s infant mortality (9) is tied for second to last among those Ohio counties in which the figure was calculable. The infant mortality rate for Blacks in Cuyahoga County is quadruple that of Whites and nearly triple that of Hispanics.

The analysis, which studied social determinants of health (SDOH), intended to go beyond showing that health disparities occur, using a novel SDOH “big” dataset to shed insight on how they occur. In doing so, the analysis conceptualized SDOH as being upstream, or influenced by structural factors like class and race; midstream, or influenced by neighborhood factors like residential segregation, environmental toxins, and individual behavior; or downstream, influenced by the prevalence of chronic disease and psychosocial stress.

In all, the analysis calls for a methodological and conceptual approach in which SDOH researchers and practitioners are “swimming upstream” to address the root causes of health disparities from a policy standpoint, while continuing to tackle midstream and downstream factors through behavioral- and neighborhood-based intervention. To do this, the authors suggest implementing more data science practices into the social science field, an interdisciplinary movement termed “computational social science.” Read More »