# Impact of Medical and Recreational Marijuana Laws On Cannabis, Opioids And Psychiatric Medications: National Study of VA Patients, 2000 - 2024

> **NIH NIH R01** · NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC · 2021 · $903,071

## Abstract

Thirty states now have medical marijuana laws (MML), 9 have recreational marijuana laws (RML), and many
more states are considering such laws. The health effects of cannabis laws are controversial; understanding
them is a major public health and NIDA priority (PA-17-135). Thus far, only 3 studies of adults (2 of them ours;
Hasin et al., 2017, Martins et al., 2016) used multi-level modeling to examine MML effects on cannabis outcomes
with individual data. These studies suggested post-MML increases in cannabis use and Cannabis Use Disorder
(CUD). However, they left many questions unanswered, including whether MML have stronger effects in those
with key vulnerability factors (chronic pain, psychiatric disorders). In addition, soaring rates of opioid prescriptions
and overdoses have led to calls for MML as part of the solution to the US opioid crisis, but most MML-opioid
studies are ecological (a weak design to study individual behavior), and results from individual-level studies leave
the evidence unclear. Ecological studies also suggest that through cannabis substitution, MML reduce
medication prescriptions for common psychiatric disorders (e.g., PTSD, depression), but no individual-level
studies of this have been conducted. Importantly, RML effects are almost entirely unknown, a major gap in
knowledge. In Veterans Administration (VA) patients, CUD prevalence has doubled since 2002, and in those
age ≥35, is 2-6 times higher than in the general population. VA patients also have high rates of opioid
prescriptions, overdoses, and of chronic pain and psychiatric disorders that may increase their vulnerability to
adverse MML and RML effects. They thus are a large, vulnerable population in whom MML and RML effects are
unknown. We will investigate MML and RML effects utilizing a major resource, the individual data from the VA
Electronic Medical Record, available since 2000 from the ~5,000,000 patients served each year by the VA
healthcare system. We will create yearly EMR datasets, and merge this with National Death Index data, Medicare
data (for those age ≥65) and state-year MML and RML variables that we will create. Using multi-level models
and difference-in-difference tests, we will examine MML and RML effects on three main outcomes: cannabis
(use, CUD), opioids (prescriptions, fatal and non-fatal overdoses, opioid use disorders), and psychotropic
medication prescriptions (antidepressants, anxiolytics, sedatives/hypnotics). Importantly, we will determine if
pain, psychiatric disorders or demographics (sex, age, race/ethnicity) modify MML/RML effects. We will also
examine specific MML/RML provisions, time lags, and breakpoints in trends reflecting federal policy changes.
Analyses will incorporate individual- and state-level confounders, e.g., state norms, economic factors. We will
also explore alcohol and tobacco outcomes. The research team includes substance epidemiology/policy experts
and VA addiction and internal medicine experts. Findings will be di...

## Key facts

- **NIH application ID:** 10137905
- **Project number:** 5R01DA048860-03
- **Recipient organization:** NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC
- **Principal Investigator:** DEBORAH S HASIN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $903,071
- **Award type:** 5
- **Project period:** 2019-07-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10137905

## Citation

> US National Institutes of Health, RePORTER application 10137905, Impact of Medical and Recreational Marijuana Laws On Cannabis, Opioids And Psychiatric Medications: National Study of VA Patients, 2000 - 2024 (5R01DA048860-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10137905. Licensed CC0.

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