Quantifying disparities in intimate partner violence: a machine learning method to correct for underreporting

The first step towards reducing the pervasive disparities in women's health is to quantify them. Accurate estimates of the relative prevalence across groups—capturing, for example, that a condition affects Black women more frequently than white women—facilitate effective and equitable health policy that prioritizes groups who are disproportionately affected by a condition. However, it is difficult to estimate relative prevalence when a health condition is underreported, as many women's health conditions are. In this work, we present PURPLE, a method for accurately estimating the relative prevalence of underreported health conditions which builds upon the literature in positive unlabeled learning. We show that under a commonly made assumption—that the probability of having a health condition given a set of symptoms remains constant across groups—we can recover the relative prevalence, even without restrictive assumptions commonly made in positive unlabeled learning and even if it is impossible to recover the absolute prevalence. We conduct experiments on synthetic and real health data which demonstrate PURPLE's ability to recover the relative prevalence more accurately than do previous methods. We then use PURPLE to quantify the relative prevalence of intimate partner violence (IPV) in two large emergency department datasets. We find higher prevalences of IPV among patients who are on Medicaid, not legally married, and non-white, and among patients who live in lower-income zip codes or in metropolitan counties. We show that correcting for underreporting is important to accurately quantify these disparities and that failing to do so yields less plausible estimates. Our method is broadly applicable to underreported conditions in women's health, as well as to gender biases beyond healthcare.

Divya Shanmugam, Kaihua Hou, Emma Pierson