Complex Crisis

Assistant Professor of Public Health Jamie Chang is changing how we talk about risk of opioid overdose

More than 2 million Americans abuse opioids, while 130 die from overdose every day. In San Francisco alone, the number of people who inject drugs has more than doubled in the past decade. While the figures are grim, SCU Assistant Professor of Public Health Jamie Chang says something crucial is missing—individual stories. Specifically, what was different about the time that caused an overdose?

Examining a recent study from the San Francisco Department of Health that interviewed 40 long-term injection drug users who recently overdosed, Chang found a lot of false assumptions. Most overdose research, she says, focuses on quantitative factors like how much drug was used, whether it was injected or smoked; and it’s siloed by substance.

“But we’re finding most overdoses occurred with some kind of polysubstance use,” most commonly by combining methamphetamines or alcohol with opioids, Chang says.

[Illuminate Blog: Wide Gulf of Misconceptions Found Between Those Addicted to Opioids and Doctors Who Treat Them]

Generally, it’s thought the more combination of drugs used, the worse it is. But the story is more complicated: For example, an opioid user may switch to using alcohol in an effort to reduce their opioid intake and, as a result, their tolerance goes down significantly and increases their susceptibility to opioid overdose the next time they use. “So, it’s not just ‘alcohol plus opioid equals overdose,’” she says. “There has to be more nuance in the way we talk to people about risk, and teach precautions they need to take.”

Jamie Chang 1 600x480

Assistant Professor of Public Health Jamie Chang

Class of 2020

The Bronco community from far and wide come to celebrate a very special class.

Going Strong

Beloved SCU Economics Professor Mario Belotti Reflects On A “Very Lucky” Life

Unhealthy AI

New research led by Leavey School of Business Assistant Professor Michele Samorani exposed bias in medical appointment scheduling algorithms.