Estimating Weekly National Opioid Overdose Deaths in Near Real Time Using Multiple Proxy Data Sources

Abstract

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.

Objectives: To build and validate a statistical model for estimating national opioid overdose deaths in near real time.

Design, 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 2018 and 2019 to validate model performance. 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.

Exposures: 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.

Main outcomes and measures: Model-based predictions of weekly opioid overdose deaths in the United States were made for 2018 and 2019 and compared with actual observed opioid overdose deaths from the National Vital Statistics System.

Results: Statistical models using the 5 real-time proxy data sources estimated the national opioid overdose death rate for 2018 and 2019 with an error of 1.01% and -1.05%, respectively. When considering the accuracy of weekly predictions, the machine learning-based approach possessed a mean error in its weekly estimates (root mean squared error) of 60.3 overdose deaths for 2018 (compared with 310.2 overdose deaths for the SARIMA model) and 67.2 overdose deaths for 2019 (compared with 83.3 overdose deaths for the SARIMA model).

Conclusions and relevance: Results of this serial cross-sectional study suggest that proxy administrative data sources can be used to estimate national opioid overdose mortality trends to provide a more timely understanding of this public health problem.

Figure 1.. Weekly Number of Drug Overdose Deaths Involving Opioids—National Vital Statistics System, US, 2010-2019

Deaths were classified using International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes. Drug overdose deaths were identified using underlying cause of death codes X40 to X44 (unintentional), codes X60 to X64 (suicide), code X85 (homicide), or codes Y10 to Y14 (undetermined intent), as well as multiple cause of death codes specific to the opioid drug category: T40.0 (opium), T40.1 (heroin), T40.2 (natural and semisynthetic opioids), T40.3 (methadone), T40.4 (synthetic opioids other than methadone), or T40.6 (other and unspecified narcotics).

Figure 2.. Correlation of Health, Drug Supply, and Online Data Sources With Opioid Overdose Deaths

A, Data from the National Syndromic Surveillance Program on emergency department (ED) visits. B, Data from the National Forensic Laboratory Information System (NFLIS-Drug). C, Data from Google Trends. D, Data from Twitter posts. E, Data from Reddit posts.

Figure 3.. Weekly Opioid-Involved Overdose Deaths From Prediction Model Compared With Observed Deaths, US, 2018-2019

Data sources used in the prediction model include National Syndromic Surveillance Program emergency department data, National Forensic Laboratory Information System reports, Google search trends, Twitter posts, and Reddit posts.