Overdose data, often scattered and siloed, pose challenges for researchers racing to predict risk
As fatal overdoses once again rise — accounting for 92,183 deaths in 2020, a 30% increase from the year before — public health researchers are racing to develop better tools to prevent them.
Some see promise in models that pull in data and spit out predictions about who is at highest risk of developing opioid use disorder or overdosing, giving health officials and physicians an idea of where to target strained prevention resources. But experts say that a scattered and siloed system to collect data on overdoses and outcomes is hamstringing efforts to further develop and deploy those models.
