State cannabis legalization and opioid use disorder in the US Veterans Health Administration, 2005 to 2019

Abstract: Aim: Cannabis use is legal in many U.S. states, and opioids may be substituted for cannabis. Fewer opioids may reduce its consequences, including opioid use disorder (OUD). We examined associations of medical and recreational cannabis law (MCL/RCL) enactment and changes in OUD prevalence among Veterans Health Administration (VHA) patients, and whether these trends differed by age (18-34, 35-64, 65-75). Methods: Using VHA electronic health records from 2005-2019 (∼4.3-5.6 million patients/year), we created yearly cross-sectional datasets and extracted ICD-9/10-CM OUD diagnoses. We calculated the adjusted yearly prevalence of OUD, controlling for continuous age, sex, race, ethnicity, and time-varying state covariates. We then used staggered-adoption difference-in-difference analyses to estimate the effect with 95% confidence interval (CI) of MCL and RCL enactment on changes in OUD prevalence, accounting for the year that state laws were enacted and covariates. Results: From 2005-2019, adjusted OUD prevalence decreased from 1.09% to 1.05% in states without cannabis laws, and increased from 1.13% to 1.31% and 1.20% to 1.22% in MCL and RCL states, respectively. MCL-only enactment was associated with a 0.10% (CI:0.09-0.12, p <0.001) absolute increase in OUD prevalence, while RCL enactment was associated with a 0.08% (CI:0.70-0.10, p <0.001) absolute increase in OUD prevalence. The effect of MCL and RCL on OUD prevalence was greatest in patients aged 65-75 years, with an absolute increase of 0.09% (CI:0.08-0.10, p <0.001) and 0.22% (CI:0.20-0.23, p <0.001), respectively. In veterans age 18-34 and 35-64, MCL enactment resulted in a more modest increase in OUD prevalence (age 18-34: absolute increase=0.08%, CI: 0.04-0.12, p <0.001; age 35-64: absolute increase=0.07%, CI: 0.06-0.09, p <0.001), while RCL was not associated with OUD. Conclusions: Over time, OUD was disproportionately prevalent in patients residing in states with MCL, with further increases in OUD among older adults who also resided in states with RCL. These findings contradict the notion that access to legal cannabis reduces opioid-related harms.

Read the full article
Report a problem with this article

Related articles

  • More for Researchers

    Identifying opioid relapse during COVID-19 using natural language processing of nationwide Veterans Health Administration electronic medical record data

    Abstract: Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress notes, can be mined for clinically relevant information, including the presence of substance use and relapse-critical markers of risk and recovery from opioid use disorder (OUD). In this study, we used natural language processing (NLP) to automate the extraction of opioid relapses, and the timing of these occurrences, from veteran patients' electronic medical record. We then demonstrated the utility of our NLP tool via analysis of pre-/post-COVID-19 opioid relapse trends among veterans with OUD. For this demonstration, we analyzed data from 107,606 veterans OUD enrolled in Veterans Health Administration, comparing a pandemic-exposed cohort (n = 53,803; January 2019-March 2021) to a matched prepandemic cohort (n = 53,803; October 2017-December 2019). The recall of our NLP tool was 75% and our precision was 94%, demonstrating moderate sensitivity and excellent specificity. Using the NLP tool, we found that the odds of opioid relapse postpandemic onset were proportionally higher compared to prepandemic trends, despite patients having fewer mental health encounters from which to derive instances of relapse postpandemic onset. In this research application of the tool, and as hypothesized, we found that opioid relapse risk was elevated postpandemic. The application of NLP Methods: to identify and monitor relapse risk holds promise for future surveillance, risk prevention, and clinical outcome research.