Summary

Connecting Datasets to Deepen Analysis and Reduce Indiana’s Infant Mortality Rate

In 2013, the state commissioned a data-driven analysis that unified information from previously unlinked sources across agencies, and KSM Consulting utilized sophisticated machine learning techniques to build predictive models that estimate risk for adverse birth outcomes.

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The Problem

Infant mortality is a complex social problem that cannot be isolated from education, economic factors, county of birth, and myriad other contributing elements—social determinants of health. Epidemiologists at work on the issue were getting only health data, which comprises a sliver of the full picture of infant mortality.

The Approach

Working with the State of Indiana, the KSM Consulting team developed a birth outcome risk quantification tool. This dynamic tool enables public health experts and policymakers to identify variables like age, ZIP code, and number of prenatal visits to calculate the risk of adverse outcomes. Evaluating the factors contributing to infant mortality at a granular level enables precise intervention where it can have the most profound effect.

The Outcome

Relying on a fuller picture of infant mortality, the State of Indiana developed focused, flexible programs to reach the citizens who most needed them. And they achieved incredible results: After years of stagnation in infant mortality, Indiana has seen progress for the first time since delving into the data in 2014.