New model shows more realistic picture of intimate partner violence

ITHACA, N.Y. – Intimate partner violence is notoriously underreported and correctly diagnosed at hospitals only around a quarter of the time, but a new method provides a more realistic picture of who is most affected, even when cases go unrecorded.

PURPLE (Positive Unlabeled Relative PrevaLence Estimator), an algorithm developed by researchers at Cornell University, estimates how often underreported health conditions occur in different demographic groups. Using hospital data, the researchers showed that PURPLE can better quantify which groups of women are most likely to experience intimate partner violence compared with methods that do not correct for underreporting.

The new method was developed by Divya Shanmugam, postdoctoral researcher joining Cornell Tech this fall, and Emma Pierson, assistant professor of computer science.

PURPLE indicated that patients who are nonwhite, not legally married, on Medicaid or who live in lower-income or metropolitan areas are all more likely to experience intimate partner violence. These results match up with previous findings in the literature, demonstrating the plausibility of the algorithm’s results.

“Often we care about how commonly a disease occurs in one population versus another, because it can help us target resources to the groups who need it most,” Pierson said. “The challenge is, many diseases are underdiagnosed. Underreporting is intimately bound up with societal inequality, because often it tends to affect groups more if they have worse access to health services.”

The researchers applied PURPLE to two real-life datasets, one that included 293,297 emergency department visits to a hospital in the Boston area, and a second with 33.1 million emergency department visits to hospitals nationwide. PURPLE used demographic data along with actual diagnoses of intimate partner violence and associated symptoms, like a broken wrist or bruising, which could indicate the condition even when the patient was not actually diagnosed.

“These broad datasets, describing millions of emergency department visits, can produce relative prevalences that are misleading using only the observed diagnoses,” Shanmugam said. “PURPLE’s adjustments can bring us closer to the truth.”

The results show that correcting for underreporting is important to produce accurate estimates. Without this correction, the hospital datasets do not show a straightforward relationship between income level and rates of victimization. But PURPLE clearly shows that rates of violence are higher for women in lower income brackets, a finding that agrees with the literature.

Next, the researchers hope to see PURPLE applied to other often-underreported women’s health issues, such as endometriosis or polycystic ovarian syndrome.

For additional information, see this Cornell Chronicle story.

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