Data Sources

We used a random 10% sample of patients from the IMS Lifelink+ database from 2006 to 2014. IMS Lifelink+ is a nationally representative database of commercially insured patients and includes their enrollment information and inpatient, outpatient, and insurance-funded pharmacy claims. The demographic variables in the person-level enrollment file include age, sex, state of residence, and type of payer (Medicare, Medicaid, commercial insurance, etc.). The inpatient and outpatient files contain diagnosis information, procedure codes, and the date of service for all insurance claims filed for an enrollee from 2006 to 2014. The pharmacy file includes retail as well as mail order prescription records from 2006 to 2014.

The Area Health Resource File was used to gather state-level data on unemployment rates and the number of physicians per 1000 residents for each study year (2006–2014). It is a publically available dataset which gathers data from multiple sources.25 It has socioeconomic data and data on availability and type of healthcare professionals and health facilities.

The National Alliance for Model State Drug Laws (NAMSDL), which comprises information on state policies concerning alcohol and drug regulation, was used to gather information on states’ implementation of Prescription Drug Monitoring Programs and Pain Clinic Laws.26

Study Subjects

For each year, the study population comprised of all persons aged ≥ 18 on January 1st of that particular year. The subjects were also required to have pharmacy and medical benefits for the entire year. Persons with missing or invalid age, gender, state of residence, and payer information in the respective year were excluded for that year. We restricted the sample to adults because medical marijuana is exclusively legalized for adult consumption. Because a subject could meet these criteria in multiple years, each observation represented a person year and not a unique patient.

Study Outcomes

Opioid Use: At least one opioid prescription in the year was defined as opioid use for that year.

Chronic Opioid Use: At least 90 days of opioid use, with a gap of no greater than 30 days between consecutive opioid prescriptions, within a 180-day period, in a calendar year, was defined as chronic opioid use for that year. This definition of chronic opioid use was derived from previously published studies.27,28,29

High-Risk Opioid Use: Opioid use along with at least one of the following: (a) At least 1 day of overlap between opioid and benzodiazepine prescription (b) A maximum daily dose for opioids being ≥ 120 morphine milligram equivalents (MME) (c) A substance use disorder diagnosis in the same year as an opioid prescription was defined as high-risk opioid use.

Proprietary Generic Product Identifier codes were used to identify claims for opioids and benzodiazepines, and diagnosis codes to identify substance use disorder are provided in Supplementary Table 1.

Independent Variable

The main independent variable was an indicator of whether medical marijuana law was in effect for each state in each particular year between 2006 and 2014. In the years prior to being effective, it was coded as zero and years after becoming effective it was coded as 1, and the year when it became effective it was coded as the fraction of the year in which the law became effective. The start dates for MML in each state, defined as the date when the statute legalizing medical marijuana became effective, were obtained from previous literature and publically available data (Supplementary Table 2.).21, 30

Covariates

Individual- and state-level factors that could influence the likelihood of opioid use were included as covariates. Individual-level demographics factors (gender, payer-type, date of birth, and state of residence) were obtained from the enrollment file. These demographics remained constant for each observation of the same patient. Conditions that might influence the likelihood of opioid use in that particular year were obtained from the medical claims. The conditions determined were based on a previously published study28 and included chronic non-cancer pain (joint pain, back or neck pain, neuropathic pain, headache fibromyalgia), non-chronic pain (abdominal pain, chest pain, other pain), cancer, childbirth, dental visit, trauma, surgery, inpatient admissions for conditions other than those mentioned earlier, and emergency room visit for conditions other than those mentioned earlier. If a patient was included in the sample for more than 1 year, they might have separate conditions in each year depending on their medical claims for that year. The diagnosis and procedure codes to identify each of these conditions are provided in the Supplementary Table 1. State-level factors included the presence of a prescription monitoring program, whether pain clinic laws were in effect, the rate of physicians per 1000 residents, and the proportion of unemployed residents.

Analysis

Frequency counts and proportions of the patient-level characteristics were calculated for the following three mutually exclusive groups: states that never legalized medical marijuana on or before 2014, pre-MML for states which passed the legislation from 2006 to 2014, and post MML for states which legalized medical marijuana any time in or before 2014. The frequency counts represent patient years, not unique patients. The distributions for each of the characteristics were compared between the three groups using chi-square tests. Means and standard deviations were calculated for the two state-level characteristics, rate of physicians, and proportion unemployed and comparisons between the three groups were made using one-way ANOVA.

To determine the effect of MML on opioid utilization measures, two-level, multivariate hierarchical logistic regression models were estimated. Hierarchical models allowed us to account for clustering of individual patients within states, but due to large number of enrollees in our final sample and computational limitations of software, we were unable to account for clustering of multiple observations within the same person. The state of residence was incorporated as a random effect. Study year, patient-year-level characteristics and state-level characteristics were all incorporated in the models as fixed effects. Mean predicted probability of opioid use, chronic opioid use, and high-risk opioid use was calculated for MML vs no MML states.

Sub-group Analysis

To determine the impact of MML on opioid utilization among non-cancer pain patients, we conducted a sub-group analysis by excluding all observations for persons with a cancer diagnosis (except non-melanoma skin cancer) in any year from 2006 to 2014.

In another sub-group analysis, we estimated the effect of MML on opioid utilization among chronic non-cancer pain patients. We restricted the sample to cancer-free adults which had at least one diagnosis for a chronic non-cancer pain condition in the particular year.

Sensitivitiy Analysis

To account for the delay between effective date and the actual availability of medical marijuana, a sensitivity analysis was conducted where the start date of MML was considered to be the date when the first dispensary commenced operations for states that prohibit cultivation by patients or designated caregivers and use dispensaries to distribute marijuana (Supplementary Table 2). Next, models where the state of residence and year were specified as fixed effects were used to model each outcome. Third, because of our inability to account for nesting of multiple observations within an individual, we conducted a person-level analysis by including only the first observation for each patient that met the inclusion/exclusion criteria. Lastly, a falsification test was conducted that explored the impact of MML on the likelihood of antihyperlipidemic and antihypertensive drug use. The falsification test was conducted to determine whether our findings might be explained by residual confounding and a null finding for these outcomes would refute, though not disprove, that residual confounding is responsible for any observed differences in our opioid outcomes.