Help-Seeking for Substance Use Treatment on Internet Search Engines (Preprint)

BACKGROUND There is no recognized “gold standard” method for estimating the number of individuals with substance use disorders (SUD) seeking help within a given geographical area. This presents a challenge to policymakers in the effective deployment of resources for the treatment of SUD. Internet search queries related to help-seeking for SUDs may represent a low-cost, real-time, and data-driven strategy to address this shortfall in information. OBJECTIVE This paper assesses the feasibility of using search query data related to help-seeking for SUDs as an indicator of unmet treatment need, demand for treatment, and a predictor of the health harms related to unmet treatment need. METHODS We used negative binomial regression models to examine temporal trends in the annual SUD help-seeking internet search queries by U.S. state for cocaine, methamphetamine, opioids, cannabis, and alcohol from 2010 to 2020. To validate the value of these data for surveillance purposes, we then used negative binomial regression models to investigate the relationship between SUD help-seeking searches and state-level outcomes across the continuum of care (including lack of care). We started by looking at associations with self-reported treatment need using data from the National Survey on Drug Use and Health, a national survey of the U.S. general population. Next, we explored associations with treatment admission rates from the Treatment Episode Data Set, a national data system on SUD treatment facilities. Finally, we studied associations with state-level rates of people experiencing and dying from an opioid overdose, using data from the Agency for Healthcare Research and Quality and the CDC WONDER database. RESULTS Statistically significant differences in help-seeking searches were observed over time between 2010 and 2020 (based on p-values less than .05 for the corresponding Wald tests). Results showed positive, statistically significant associations for the models relating to treatment need for alcohol use, treatment admissions for opioid and methamphetamine use, emergency department visits related to opioid use, and opioid overdose mortality data (based on regression coefficients having p-values less than or equal to .05). CONCLUSIONS This study demonstrates clear potential for using internet search queries to predict the demand for substance use treatment spatially and temporally, especially that for opioid use disorders. CLINICALTRIAL No clinical trials were used in this study.