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

BACKGROUND: There is no recognized gold standard method for estimating the number of individuals with substance use disorders (SUDs) seeking help within a given geographical area. This presents a challenge to policy makers in the effective deployment of resources for the treatment of SUDs. Internet search queries related to help seeking for SUDs using Google Trends may represent a low-cost, real-time, and data-driven infoveillance tool 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 needs, demand for treatment, and predictor of the health harms related to unmet treatment needs. We explore a continuum of hypotheses to account for different outcomes that might be expected to occur depending on the demand for treatment relative to the system capacity and the timing of help seeking in relation to trajectories of substance use and behavior change. METHODS: We used negative binomial regression models to examine temporal trends in the annual SUD help-seeking internet search queries from Google Trends by US 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 US 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<.05 for the corresponding Wald tests). We were able to identify outlier states for each drug over time (eg, West Virginia for both opioids and methamphetamine), indicating significantly higher help-seeking behaviors compared to national trends. Results from our validation analyses across different outcomes 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≤.05). CONCLUSIONS: This study demonstrates the clear potential for using internet search queries from Google Trends as an infoveillance tool to predict the demand for substance use treatment spatially and temporally, especially for opioid use disorders.

Medical Marijuana Users are More Likely to Use Prescription Drugs Medically and Nonmedically.

OBJECTIVES: Previous studies have found a negative population-level correlation between medical marijuana availability in US states, and trends in medical and nonmedical prescription drug use. These studies have been interpreted as evidence that use of medical marijuana reduces medical and nonmedical prescription drug use. This study evaluates whether medical marijuana use is a risk or protective factor for medical and nonmedical prescription drug use. METHODS: Simulations based upon logistic regression analyses of data from the 2015 National Survey on Drug Use and Health were used to compute associations between medical marijuana use, and medical and nonmedical prescription drug use. Adjusted risk ratios (RRs) were computed with controls added for age, sex, race, health status, family income, and living in a state with legalized medical marijuana. RESULTS: Medical marijuana users were significantly more likely (RR 1.62, 95% confidence interval [CI] 1.50-1.74) to report medical use of prescription drugs in the past 12 months. Individuals who used medical marijuana were also significantly more likely to report nonmedical use in the past 12 months of any prescription drug (RR 2.12, 95% CI 1.67-2.62), with elevated risks for pain relievers (RR 1.95, 95% CI 1.41-2.62), stimulants (RR 1.86, 95% CI 1.09-3.02), and tranquilizers (RR 2.18, 95% CI 1.45-3.16). CONCLUSIONS: Our findings disconfirm the hypothesis that a population-level negative correlation between medical marijuana use and prescription drug harms occurs because medical marijuana users are less likely to use prescription drugs, either medically or nonmedically. Medical marijuana users should be a target population in efforts to combat nonmedical prescription drug use.