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

Abstract

IMPORTANCEOpioid 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 overdoserelated, 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. RESULTSStatistical 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 RELEVANCEResults 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.

Development of a Machine Learning Model to Estimate US Firearm Homicides in Near Real Time

“…18 Although machine learning models are increasingly being used to forecast and study infectious diseases (eg, influenza-like illness 28 and COVID-19 29 ), applying these methods to noninfectious diseases is recent. 10,14 Reporting of infectious diseases is much faster than for noninfectious diseases (eg, 1-2 weeks for influenza 30 Researching the utility of other data sources, such as news reports, social media, and information collated by the Gun Violence Archive, 26 would also be beneficial.…”

“…Although machine learning models are increasingly being used to forecast and study infectious diseases (eg, influenza-like illness and COVID-19), applying these methods to noninfectious diseases is recent . Reporting of infectious diseases is much faster than for noninfectious diseases (eg, 1-2 weeks for influenza vs 1 year for topics such as suicide and firearm injury); as a result, infectious disease models use the previous week’s standard data (eg, infection rates, hospitalizations, and deaths) to build timely forecasts.…”

“…Secondary data sources, such as social media and search query data, have been used to track and predict various health outcomes, including mental health and suicide. [10][11][12][13][14] Although a previous study 15 observed a strong correlation between firearm-related online search data and firearm death rates across US states, limited work has been done to combine information from multiple data sources.…”

“…Drawing from previous successes estimating suicide and opioid overdose fatalities using machine learning approaches, 10,14 this study uses anonymous time series count data from online, publicly available health service and domestic violence hotline data sources as inputs into machine learning models to estimate the weekly and annual burden of firearm homicides in near real time and for forecasted periods and discusses the utility of this approach for public health prevention.…”

Characterizing Help-Seeking Searches for Substance Use Treatment From Google Trends and Assessing Their Use for Infoveillance: Longitudinal Descriptive and Validation Statistical Analysis

“…Two studies explored the relationship between drug-related internet search queries and opioid-related emergency department (ED) visits in the United States, and both demonstrated the predictive potential of internet search data [12,13]. Three further studies found strong associations between drug-related internet search queries and opioid-related overdose deaths at the national, state, and county levels [14][15][16]. Elsewhere, studies have demonstrated the potential use of opioid-related data from social media platforms, including Twitter and Reddit, to inform surveillance efforts [17][18][19].…”