Main Outcomes and Measures Model-based predictions of weekly opioid overdose deaths in the United States were made for 20 and compared with actual observed opioid overdose deaths from the National Vital Statistics System.
Results were also compared with those from a baseline SARIMA (seasonal autoregressive integrated moving average) model, one of the most used approaches to forecasting injury mortality.Įxposures Time series data from 2014 to 2019 on emergency department visits for opioid overdose from the National Syndromic Surveillance Program, data on the volume of heroin and synthetic opioids circulating in illicit markets via the National Forensic Laboratory Information System, data on the search volume for heroin and synthetic opioids on Google, and data on post volume on heroin and synthetic opioids on Twitter and Reddit were used to train and validate prediction models of opioid overdose deaths. Objectives To build and validate a statistical model for estimating national opioid overdose deaths in near real time.ĭesign, Setting, and Participants In this cross-sectional study, signals from 5 overdose-related, proxy data sources encompassing health, law enforcement, and online data from 2014 to 2019 in the US were combined using a LASSO (least absolute shrinkage and selection operator) regression model, and weekly predictions of opioid overdose deaths were made for 20 to validate model performance. Importance Opioid overdose is a leading public health problem in the United States however, national data on overdose deaths are delayed by several months or more. Shared Decision Making and Communication.Scientific Discovery and the Future of Medicine.Health Care Economics, Insurance, Payment.