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Published Online: 28 July 2022

Trends in Cannabis Use Disorder Diagnoses in the U.S. Veterans Health Administration, 2005–2019

Deborah S. Hasin, Ph.D. [email protected], Andrew J. Saxon, M.D., Carol Malte, M.S.W., Mark Olfson, M.D., M.P.H., Katherine M. Keyes, Ph.D., Jaimie L. Gradus, D.M.Sc., D.Sc., Magdalena Cerdá, Sc.D., … Show All … , Charles C. Maynard, Ph.D., Salomeh Keyhani, M.D., Silvia S. Martins, M.D., Ph.D., David S. Fink, Ph.D., Ofir Livne, M.D., Zachary Mannes, Ph.D., and Melanie M. Wall, Ph.D.Authors Info & Affiliations
Publication: American Journal of Psychiatry
Volume 179, Number 10
https://doi.org/10.1176/appi.ajp.22010034
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Abstract

Objective:

In the United States, adult cannabis use has increased over time, but less information is available on time trends in cannabis use disorder. The authors used Veterans Health Administration (VHA) data to examine change over time in cannabis use disorder diagnoses among veterans, an important population subgroup, and whether such trends differ by age group (<35 years, 35–64 years, ≥65 years), sex, or race/ethnicity.

Methods:

VHA electronic health records from 2005 to 2019 (range of Ns per year, 4,403,027–5,797,240) were used to identify the percentage of VHA patients seen each year with a cannabis use disorder diagnosis (ICD-9-CM, January 1, 2005–September 30, 2015; ICD-10-CM, October 1, 2015–December 31, 2019). Trends in cannabis use disorder diagnoses were examined by age and by race/ethnicity and sex within age groups. Given the transition in ICD coding, differences in trends were tested within two periods: 2005–2014 (ICD-9-CM) and 2016–2019 (ICD-10-CM).

Results:

In 2005, the percentages of VHA patients diagnosed with cannabis use disorder in the <35, 35–64, and ≥65 year age groups were 1.70%, 1.59%, and 0.03%, respectively; by 2019, the percentages had increased to 4.84%, 2.86%, and 0.74%, respectively. Although the prevalence of cannabis use disorder was consistently higher among males than females, between 2016 and 2019, the prevalence increased more among females than males in the <35 year group. Black patients had a consistently higher prevalence of cannabis use disorder than other racial/ethnic groups, and increases were greater among Black than White patients in the <35 year group in both periods.

Conclusions:

Since 2005, diagnoses of cannabis use disorder have increased substantially among VHA patients, as they have in the general population and other patient populations. Possible explanations warranting investigation include decreasing perception of risk, changing laws, increasing cannabis potency, stressors related to growing socioeconomic inequality, and use of cannabis to self-treat pain. Clinicians and the public should be educated about the increases in cannabis use disorder in general in the United States, including among patients treated at the VHA.
Cannabis use disorder is characterized by problematic patterns of cannabis use leading to clinically significant distress or impairment, accompanied by symptoms such as tolerance, withdrawal, continued use despite mental or physical problems, and neglect of other activities in order to use (1). Individuals with cannabis use disorder are likely to have considerable impairment in social and role/emotional functioning (2, 3). A recent meta-analysis showed that 22% of individuals with past-year cannabis use developed cannabis use disorder, and 33% of frequent users (weekly or more often) developed cannabis dependence (4). Despite these risks, Americans increasingly perceive cannabis as harmless (5–7), states are legalizing cannabis for medical and recreational use, cannabis products are becoming more potent (8), and among U.S. adults, rates of cannabis use and frequent use are increasing (5, 9–11).
Given these changes in perceptions, laws, potency, and rates of use, determining whether the prevalence of adult cannabis use disorder has increased over time is of considerable interest. Across three national household surveys, conducted in 1991–1992, 2001–2002, and 2012–2013, the prevalence of DSM-IV cannabis use disorder more than doubled, from 1.2% to 2.9% (9, 12, 13). However, in the yearly National Surveys on Drug Use and Health (NSDUH) between 2002 and 2014, despite increases in frequent cannabis users among adults, the prevalence of DSM-IV past-year cannabis use disorder remained stable at ∼1.5% (5). Further examination of NSDUH data up to 2017 using a proxy DSM-5 diagnosis showed that the prevalence of cannabis use disorder increased 24%, from 2.1% to 2.6%, largely as a result of increases in DSM-5-defined mild cases (10).
National electronic health record (EHR) patient databases provide additional sources of information on time trends, offering important advantages, including data based on clinical interviews, diagnoses that are likely to capture more severe cases that are especially in need of treatment and services, and very large sample sizes (14), potentially enabling study of change over time for conditions that are too rare to study in some population subgroups, such as individuals in minority racial/ethnic groups age 65 and older. EHR studies have indicated an increase in the prevalence of ICD-9-CM (15) cannabis use disorder among hospital inpatients between 2002 and 2011 (16) and between 1998 and 2014 (17), among inpatients in 27 states between 1997 and 2014 (18), and among Veterans Health Administration (VHA) patients between 2002 and 2009 (19). Limitations of these EHR studies include that the most recent year reported was 2014, that three of the studies covered inpatients only (16–18), and that ICD-9-CM was used, which was replaced by ICD-10-CM in 2015. Further, only one study tested for differences between demographic subgroups (13, 16), finding that in the general population, increases were greater among males, Blacks, and young adults ages 18–29 between 2001–2002 and 2012–2013 (13, 16). More information is needed to identify population subgroups with disparities in risk for cannabis use disorder (20).
The VHA is the nation’s largest integrated health care system, with over 5 million veterans receiving VHA health care each year. VHA patients are more likely than the general population to be characterized by low socioeconomic status and disability and painful medical conditions, often incurred or aggravated during military service (21–23). These characteristics are all associated with an increase in risk for cannabis use disorder (2, 16, 24), making VHA patients an important population group in which to understand trends in the prevalence of cannabis use disorder. The VHA offers extensive evidence-based treatment for substance use disorders (25–27). However, time trends in cannabis use disorder in the VHA have not been examined for over 10 years (19). To inform a potentially increasing need for services in the VHA, and to add an additional piece of information on U.S. trends in cannabis use disorders, we analyzed national VHA EHR data to study trends in cannabis use disorder prevalence by age, sex, and race/ethnicity for the years 2005–2019, a period spanning a major change in the U.S. cannabis landscape, and also the transition from ICD-9-CM to ICD-10-CM.

Methods

Sample

We analyzed U.S. Department of Veterans Affairs (VA) EHR data from January 1, 2005, to December 31, 2019. Data were obtained through the VA Corporate Data Warehouse, a data repository for all documented care provided at a VA facility or paid for by the VA, including patient demographic data and diagnostic codes. The study was approved by institutional review boards at the VA Puget Sound, the VA New York Harbor Healthcare System, and New York State Psychiatric Institute.
We identified all veterans who received outpatient VA care across the United States (at least one primary care, emergency department, or mental health clinic encounter) in a given year for each year from 2005 to 2019 (range of Ns per year, 4,403,027–5,797,240). From among these patients, we created 15 yearly data sets, one for each year from 2005 to 2019. Patients were excluded if, in a given year, they received hospice or palliative care (range of Ns, 7,774–80,440) or resided outside the 50 U.S. states or Washington, D.C. (range of Ns, 53,270–66,221).

Measures

The primary study outcome was an ICD diagnosis of cannabis use disorder. Aggregate counts of cannabis use disorder were calculated for each year. The classification system for patient records transitioned from ICD-9-CM to ICD-10-CM during the last quarter of 2015, so two sets of ICD diagnostic codes were used: ICD-9-CM from January 1, 2005, through September 30, 2015, and ICD-10-CM from January 10, 2015, through December 31, 2019 (no period existed when both sets of codes were in use). ICD-9-CM codes included 305.2X (cannabis abuse) and 304.3X (cannabis dependence). ICD-10-CM codes included F12.1 (abuse) and F12.2 (dependence). ICD-9-CM codes for abuse and dependence and ICD-10-CM codes for abuse and dependence were combined because the criteria for these disorders form a unidimensional construct (1). Codes indicating remission (ICD-9-CM codes 304.33 and 305.33; ICD-10-CM codes F12.11 and F12.21) were excluded (i.e., counted as no cannabis use disorder), as was the ICD-10-CM code F12.90, which indicates unspecified, uncomplicated cannabis use.
Demographic characteristics included sex (male, female), age group (<35 years, 35–64 years, ≥65 years), and race/ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, non-Hispanic Asian or Pacific Islander, non-Hispanic other [American Indian/Alaskan Native or multiple races], and unknown).

Statistical Analysis

The yearly proportions of patients with cannabis use disorder diagnoses was calculated as the number of patients diagnosed with a cannabis use disorder in a particular calendar year divided by the total number of patients in that year. Given highly unbalanced numbers of VHA patients across the age range, with most age 65 or older and <10% under age 35, all analyses were stratified by age group (<35 years, 35–64 years, ≥65 years). The degree of change (i.e., trends) in cannabis use disorder diagnoses over time was tested using logistic regression models that included study year, age category, year by age category, and sex and race/ethnicity as control variables. The predicted diagnostic prevalences of cannabis use disorder (i.e., adjusted proportions) in each year, the changes in the diagnostic prevalences, the differences in those trends between age groups, and the associated 95% confidence intervals were all obtained from the margins command in Stata, version 17 (28), based on the fitted logistic regression model. Given the midyear transition from ICD-9-CM to ICD-10-CM in 2015, we estimated changes in the predicted diagnostic prevalence of cannabis use disorder for two periods: the 2005–2014 period (complete years when ICD-9-CM was used) and the 2016–2019 period (complete years when ICD-10-CM was used). To further test for differential changes in cannabis use disorder diagnoses over time by sex and race/ethnicity groups, similar logistic regression models were run separately within each of the three age groups that included interaction terms with sex or race/ethnicity (respectively) and controlled for continuous age. Because the data represent a census (not a sample) of all VHA patients who received outpatient VA care each year, no additional statistical control was made for the fact that patients can appear in the data across multiple years. Relatedly, confidence intervals are provided but are based on standard statistical methods assuming sampling distributions; census data do not necessarily warrant uncertainty estimates of this type.

Results

Table S1 in the online supplement summarizes patient demographic characteristics in 2005 and 2019. Most were male (77.0%–98.2%), although between 2005 and 2019, the proportion of females ages 35–64 increased from 6.7% to 15.5%, and the proportion of females age 65 or older increased from 1.8% to 2.8%. While White patients were in the majority (59.0%–88.8%), by 2019, minority representation had increased in all age groups. For example, between 2015 and 2019, the proportion of patients under age 35 who were Hispanic increased from 8.8% to 13.3%, and among patients ages 35–64, the proportion of patients who were Black increased from 20.6% to 25.7%.

Overall Trends by Age Group

Figure 1 shows overall cannabis use disorder diagnoses for each year by age group, generated from the logistic regression model that controlled for sex and race/ethnicity. Table 1 lists model-predicted diagnostic prevalences in 2005, 2014, 2016, and 2019. In 2005, cannabis use disorder was diagnosed at similar rates among patients under age 35 (1.70%) and ages 35–64 (1.59%) and at a much lower rate among patients 65 or older (0.03%). By 2014, cannabis use disorder diagnoses increased in all age groups (to 4.55%, 2.94%, and 0.54% among those in the <35, 35–64, and ≥65 year groups, respectively), with significantly greater increases in those under age 35 than in the other age groups. In 2016, the first full year after transition to ICD-10-CM, cannabis use disorder diagnoses dropped slightly, and then resumed increases, reaching 4.84%, 2.86%, and 0.74%, respectively, among the <35, 35–64, and ≥65 year age groups by 2019. Significantly greater increases occurred between 2016 and 2019 among patients under age 35 compared to those age 65 or older.
FIGURE 1. VHA patients diagnosed with cannabis use disorder, 2005–2019, by age groupa
a For January 1, 2005, through September 30, 2015, ICD-9-CM diagnostic codes were used; for October 1, 2015, through December 31, 2019, ICD-10-CM codes were used; data for 2015 were omitted because of the transition from ICD-9-CM to ICD-10-CM on October 1, 2015. Error bars indicate 95% confidence intervals; confidence intervals are very small because of the large sample sizes. VHA=Veterans Health Administration.
TABLE 1. Change in the predicted diagnostic prevalence of cannabis use disorder among VHA patients, 2005–2019, by age groupa
 Predicted Prevalence of Cannabis Use Disorder, 2005–2014    Predicted Prevalence of Cannabis Use Disorder, 2016–2019    
 200520142005–2014 ChangeDifference in Age Group Changes, 2005–2014201620192016–2019 ChangeDifference in Age Group Changes, 2016–2019
Age Group%95% CI%95% CI%95% CI%95% CI%95% CI%95% CI%95% CI%95% CI
<35 years1.701.63, 1.764.554.49, 4.612.852.77, 2.94Ref. 4.354.29, 4.414.844.78, 4.900.490.40, 0.57Ref. 
35–64 years1.591.57, 1.612.942.92, 2.961.351.32, 1.38−1.50−1.59, −1.412.452.43, 2.472.862.84, 2.890.410.38, 0.44−0.07−0.16, 0.02
≥65 years0.030.03, 0.030.540.54, 0.550.520.51, 0.52−2.34−2.42, −2.250.510.50, 0.520.740.73, 0.750.240.22, 0.25−0.25−0.34, −0.16
a
Adjusted for sex and race/ethnicity. Data for 2015 were omitted because of the transition from ICD-9-CM to ICD-10-CM on October 1, 2015. Predicted prevalence among patients ages 35–64 and ≥65 increased significantly less than among patients under age 35 (the reference group), as shown by negative difference in differences and confidence intervals that did not include zero. VHA=Veterans Health Administration.

Trends by Sex

Across age groups, males were more likely than females to be diagnosed with cannabis use disorder. From 2005 to 2019 and within both ICD time periods, cannabis use disorder diagnoses increased among males and among females (Table 2; see also Figures S1A–C in the online supplement).
TABLE 2. Change in the predicted diagnostic prevalence of cannabis use disorder among VHA patients, 2005–2019, by sex and age groupa
 Predicted Prevalence of Cannabis Use Disorder, 2005–2014    Predicted Prevalence of Cannabis Use Disorder, 2016–2019    
 200520142005 to 2014 ChangeDifference in Gender Changes, 2005–2014201620192016 to 2019 ChangeDifference in Gender Changes, 2016–2019
Age Group and Sex%95% CI%95% CI%95% CI%95% CI%95% CI%95% CI%95% CI%95% CI
<35 years 
Women0.600.53, 0.672.112.02, 2.211.511.40, 1.63Ref. 2.212.12, 2.302.832.73, 2.930.620.49, 0.76Ref. 
Men1.851.78, 1.924.714.64, 4.772.862.76, 2.961.351.20, 1.504.464.40, 4.524.894.82, 4.960.430.34, 0.52−0.19−0.36, −0.03
35–64 years 
Women0.880.83, 0.931.551.51, 1.600.670.61, 0.74Ref. 1.321.28, 1.361.611.57, 1.650.290.24, 0.35Ref. 
Men1.751.73, 1.773.233.20, 3.251.471.44, 1.510.800.73, 0.872.652.62, 2.673.033.00, 3.050.380.35, 0.410.090.03, 0.15
≥65 years 
Women0.020.00, 0.030.170.14, 0.200.160.12, 0.19Ref. 0.200.18, 0.230.340.31, 0.370.130.09, 0.17Ref. 
Men0.040.03, 0.040.440.44, 0.450.400.40, 0.410.250.21, 0.280.450.44, 0.460.770.76, 0.780.320.31, 0.340.190.15, 0.23
a
Models include year, sex, year-by-sex interaction, race/ethnicity, and continuous age. Data for 2015 were omitted because of the transition from ICD-9-CM to ICD-10-CM on October 1, 2015. VHA=Veterans Health Administration.
Among patients ages 18–34 (see Figure S1A in the online supplement), ICD-9-CM cannabis use disorder diagnoses increased between 2005 and 2014 from 1.85% to 4.71% among males and from 0.60% to 2.11% among females. The increase was significantly greater among males than females. By 2019, ICD-10-CM cannabis use disorder was diagnosed in 4.89% of males and 2.83% of females. Between 2016 and 2019, the increase was significantly greater among females than males.
Among patients ages 35–64 (see Figure S1B in the online supplement), ICD-9-CM cannabis use disorder diagnoses increased between 2005 and 2014 from 1.75% to 3.23% among males and from 0.88% to 1.55% among females. Among patients age 65 or older (see Figure S1C in the online supplement), ICD-9-CM cannabis use disorder diagnoses increased from 0.04% to 0.44% among males and from 0.02% to 0.17% among females. By 2019, ICD-10-CM cannabis use disorder diagnoses had reached 3.03% among males and 1.61% among females ages 35–64, and 0.77% among males and 0.34% among females age 65 or older. During both ICD time periods, the increase in cannabis use disorder diagnoses was greater among males than females in the 35–64 and ≥65 year age groups.

Trends by Race/Ethnicity

Across time and within both ICD time periods, cannabis use disorder diagnoses increased in all racial/ethnic groups. Across racial/ethnic groups, Black patients were consistently more likely than others to be diagnosed with cannabis use disorder (Table 3; see also Figures S2A–C in the online supplement).
TABLE 3. Estimated change in the predicted diagnostic prevalence of cannabis use disorder among VHA patients, 2005–2019, by race/ethnicity and age groupa
 Predicted Prevalence of Cannabis Use Disorder, 2005–2014    Predicted Prevalence of Cannabis Use Disorder, 2016–2019    
 200520142005–2014 ChangeDifference in Racial/Ethnic Changes, 2005–2014201620192016–2019 ChangeDifference in Racial/Ethnic Changes, 2016–2019
Age Group and Race/Ethnicity%95% CI%95% CI%95% CI%95% CI%95% CI%95% CI%95% CI%95% CI
<35 years 
White1.701.62, 1.774.053.99, 4.122.362.26, 2.46Ref. 3.863.80, 3.934.164.09, 4.230.300.20, 0.40Ref. 
Black2.041.89, 2.196.356.17, 6.534.314.08, 4.551.961.70, 2.216.015.84, 6.187.146.95, 7.321.130.88, 1.380.830.56, 1.10
Hispanic/Latino1.060.90, 1.223.333.18, 3.482.272.05, 2.48−0.09−0.33, 0.153.353.21, 3.493.833.68, 3.980.480.27, 0.680.18−0.05, 0.41
Other/multiple1.541.25, 1.833.323.10, 3.551.781.41, 2.15−0.57−0.96, −0.193.082.87, 3.293.513.29, 3.730.430.13, 0.740.14−0.18, 0.45
Unknown0.550.44, 0.672.131.94, 2.331.581.35, 1.81−0.78−1.03, −0.532.212.02, 2.402.782.57, 2.980.570.29, 0.850.27−0.03, 0.56
35–64 years 
White1.261.24, 1.282.732.70, 2.761.471.44, 1.50Ref. 2.282.26, 2.312.682.65, 2.710.400.36, 0.43Ref. 
Black2.942.89, 2.994.324.26, 4.371.381.31, 1.45−0.09−0.17, −0.013.483.43, 3.533.903.85, 3.950.420.35, 0.490.02−0.06, 0.10
Hispanic/Latino1.431.35, 1.512.282.20, 2.360.860.74, 0.97−0.61−0.73, −0.501.881.81, 1.952.142.08, 2.210.260.17, 0.36−0.13−0.23, −0.03
Other/multiple1.621.51, 1.722.532.42, 2.650.920.76, 1.07−0.55−0.71, −0.392.091.99, 2.192.272.18, 2.370.180.04, 0.32−0.22−0.36, −0.07
Unknown0.970.89, 1.041.431.34, 1.510.460.35, 0.58−1.01−1.13, −0.891.141.06, 1.211.421.34, 1.490.280.18, 0.38−0.12−0.23, −0.01
≥65 years 
White0.020.02, 0.020.340.33, 0.350.320.31, 0.33Ref. 0.350.35, 0.360.670.66, 0.680.320.30, 0.33Ref. 
Black0.160.14, 0.181.041.01, 1.070.880.85, 0.920.560.53, 0.601.031.00, 1.061.521.49, 1.560.500.45, 0.540.180.13, 0.23
Hispanic/Latino0.050.03, 0.080.500.46, 0.540.440.40, 0.490.130.08, 0.170.490.45, 0.530.800.75, 0.850.310.25, 0.37−0.01−0.07, 0.06
Other/multiple0.050.03, 0.080.490.45, 0.540.440.39, 0.490.120.07, 0.180.490.45, 0.540.760.70, 0.820.270.19, 0.35−0.05−0.13, 0.03
Unknown0.03−0.02, 0.080.470.34, 0.600.440.30, 0.580.12−0.02, 0.260.460.33, 0.590.660.52, 0.790.200.01, 0.39−0.12−0.31, 0.07
a
Models include year, race/ethnicity, year-by-race/ethnicity interaction, sex, and continuous age. Data for 2015 were omitted because of the transition from ICD-9-CM to ICD-10-CM on October 1, 2015. VHA=Veterans Health Administration.
Among patients ages 18–34 (Table 3; see also Figure 2A in the online supplement), ICD-9-CM cannabis use disorder diagnoses increased between 2005 and 2014 from 2.04% to 6.35% among Black patients, from 1.06% to 3.33% among Hispanic patients, from 1.70% to 4.05% among White patients, and from 1.54% to 3.32% in the other/multiple group. By 2019, ICD-10-CM cannabis use disorder was diagnosed in 7.14% of Black patients, 3.83% of Hispanic patients, 4.16% of White patients, and 3.51% of the other/multiple group. With White patients as the reference group, cannabis use disorder diagnoses increased significantly more among Black patients during both the earlier and later ICD time periods.
Among patients ages 35–64 (Table 3; see also Figure S2B in the online supplement), ICD-9-CM cannabis use disorder diagnoses increased between 2005 and 2014 from 2.94% to 4.32% among Black patients, from 1.43% to 2.28% among Hispanic patients, from 1.26% to 2.73% among White patients, and from 1.62% to 2.53% in the other/multiple group. By 2019, ICD-10-CM cannabis use disorder was diagnosed in 3.90% of Black patients, 2.14% of Hispanic patients, 2.68% of White patients, and 2.27% of the other/multiple group. Between 2005 and 2014, compared to White patients, cannabis use disorder diagnoses in all other groups increased significantly less, and between 2016 and 2019, compared to White patients, increases among Black patients did not differ significantly, but diagnoses among Hispanic patients and in the other/multiple group increased significantly less than among White patients.
Among patients age 65 or older, between 2005 and 2014 (Table 3; see also Figure S2C in the online supplement), ICD-9-CM cannabis use disorder diagnoses increased from 0.16% to 1.04% among Black patients, from 0.05% to 0.50% among Hispanic patients, from 0.02% to 0.34% among White patients, and from 0.05% to 0.49% in the other/multiple group. By 2019, ICD-10-CM cannabis use disorder diagnoses had increased to 1.52% among Black patients, 0.80% among Hispanic patients, 0.67% among White patients, and 0.76% in the other/multiple group. Compared to White patients, the increase in cannabis use disorder diagnoses was significantly greater in all other racial/ethnic groups between 2005 and 2014, while only the increase among Black patients was significantly greater between 2016 and 2019.

Discussion

We examined time trends in diagnoses of cannabis use disorder from 2005 to 2019 among patients treated in the VHA. The years examined represent a period of substantial change in the cannabis landscape in the United States, and also the transition from ICD-9-CM to ICD-10-CM. Cannabis use disorder diagnoses more than doubled overall in the VHA, from 0.85% in 2005 to 1.92% in 2019. Increases were found across age, sex, and racial/ethnic subgroups of patients. The study findings are consistent with other U.S. studies showing increases in the prevalence of cannabis use and cannabis use disorder among adults during the past 20 years, including general population surveys (5, 9, 10, 24) and EHR studies (16–19). The present study, which extends findings from an earlier VHA study showing increasing cannabis use disorder diagnoses from 2002 to 2009 (19), indicates that cannabis use disorder continued to increase. Collectively, these studies indicate increases over time in cannabis use disorder among U.S. adults.
Both cannabis-specific factors and larger societal-level forces could explain the overall increases. One cannabis-specific factor is the increasing perception that cannabis is not a harmful substance (5–7). This perception could lead to more users and more cases of cannabis use disorder among these users, given that 20%–33% of cannabis users develop cannabis use disorder (4). Another cannabis-specific factor is increasing legalization, as suggested by national survey findings on adult cannabis use and cannabis use disorder (29–31). Determining whether state legalization of cannabis for medical or recreational use is selectively associated with increased rates of cannabis use disorder among VHA patients is a complex undertaking that is outside the scope of the present study, but this should be done in the future. Yet another cannabis-specific factor is the increasing delta-9-tetrahydrocannabinol potency of licit and illicit cannabis products (8, 32, 33), since higher potency predicts increased risk for progression from use to cannabis use disorder symptoms (34). Further studies of all these factors are needed, including in VHA patients.
Larger societal-level forces could also help explain the overall increases. Cannabis is used for varying reasons and motives, including to cope with stress, which is strongly associated with cannabis use disorder symptoms (35). The years 2005–2019 were stressful for many Americans, encompassing a major recession and an unequally distributed economic recovery. Fears of economic losses, and actual job loss, could have increased stress, for which cannabis may have been increasingly used to cope, leading to increased prevalences of cannabis use disorder, including among VHA patients, whose socioeconomic level is lower compared to other veterans and to U.S. adults as a whole (23, 36, 37). Studies are needed to investigate these speculative explanations.
Our findings indicate that, generally, male VHA patients had higher rates of cannabis use disorder diagnoses than females as well as greater increases in cannabis use disorder diagnoses over time (the only exception being patients under age 35 from 2016 through 2019). The general results are consistent with studies in the U.S. general population, one showing that the prevalence of cannabis use increased more among males than females between 2007 and 2014 (24), and the other showing a wider “gender gap” in adult cannabis use disorder prevalence in 2012–2013 than in 2001–2002 (13). Speculatively, economic factors may also help explain these trends. Although females consistently earn less on average than males (38), sex differences in earnings have decreased over time because male incomes have largely stagnated, while female incomes have increased (39, 40). Increased earnings are clearly advantageous for females, but they could have led to loss of perceived status and self-esteem and increased psychological distress in males, increasing male use of cannabis to cope, and thus greater increases in cannabis use disorder among males than females over time. These factors could have particularly impacted male VHA patients, given their lower socioeconomic level compared to other veterans and other U.S. adults, and the fact that service-related disabilities may have limited their employment opportunities.
The one exception to the greater male increases in cannabis use disorder among VHA patients occurred in the youngest age group (<35 years) in the period from 2016 to 2019, when females had the greater rate of increase. This is consistent with a meta-analysis of birth cohorts showing that the traditional “gender gap” in cannabis use narrowed considerably in recent birth cohorts as a result of greater increases among females than males (41). This may be due to societal-level changes in norms regarding gender-appropriate behaviors, of which cannabis use is one example. Another reason could be the growing numbers of younger female soldiers exposed to combat in recent wars (42), leading to increased postdischarge stress in younger female veterans, with accompanying cannabis use and cannabis use disorder.
The results also indicated that Black patients had higher rates of cannabis use disorder than White patients, and that among patients in the 18–34 and ≥65 year age groups, cannabis use disorder diagnoses increased more among Black than White patients (although this was not the case during the earlier period for patients ages 35–64, when rates among Whites increased considerably). The Black/White differences in change that were found are consistent with shifts observed in national surveys. For example, while rates of cannabis use were higher among White adolescents in 1991, this difference had reversed and rates had become higher among Black adolescents by 2018 (43, 44). In addition, the prevalence of cannabis use disorder was higher among Black than White adults in 2001–2002, a difference that had widened by 2012–2013 (13). Black individuals face multiple severe stressors, such as interpersonal and structural discrimination and socioeconomic disadvantage that increased between 2005 and 2019 (45, 46). Perceived racial discrimination predicts subsequent substance use (47–49), and structural racism may have similar associations (50, 51). This could have led to increasingly prevalent cannabis use to cope with stress and consequent cannabis use disorder among Black adults, including VHA patients.
In addition, racial disparities in pain management may have contributed to greater increases in cannabis use disorder among Black VHA patients. In adults in the general population, pain has been shown to be increasingly associated with cannabis use disorder over time (52). Service-related injuries are common among VHA patients, and over one-third have chronic pain (53). The VHA has developed a national strategy to deliver safe and effective pain management (54–56), and it also emphasizes racial equality in health care (57). Nevertheless, during the 2005–2019 period of changing cannabis norms and laws, racial disparities in VHA pain management could have led some Black patients to increasingly self-treat pain with cannabis, leading to higher rates of cannabis use disorder within this group. The only studies known to us with information by racial/ethnic group on authorized medical cannabis use or informal self-treatment did not show racial/ethnic differences (58, 59). However, both studies were based on data that are now nearly a decade old. Considerable attention has recently been devoted to the importance of identifying and addressing racial disparities in medical care and potential consequences of such disparities. Studies are needed to fill the gaps in knowledge about racial/ethnic differences in medical use of cannabis, especially given the increases in rates of cannabis use and cannabis use disorder among Black adults found in this study and others.
We know of no substantive explanation for the 2015–2016 drop in cannabis use disorder diagnoses in the VHA across age, sex, and racial/ethnic groups. This drop did not occur in the general population (60). However, it coincided with the VHA transition from ICD-9-CM to ICD-10-CM, which presented many challenges for EHR-based health research (17). Studies of the effects of the transition on rates of medical conditions have been inconsistent (61, 62). In studies specifically addressing alcohol (63), opioid (64), and cannabis use disorders (65), conducted in hospital (62–65) and emergency department patients, the results showed increases rather than decreases in prevalence after the ICD-10-CM transition, attributed to use of the greater number of relevant categories available in ICD-10-CM. However, these studies addressed patients in acute care, not those seen on an ongoing outpatient basis in a large integrated health care system, which is the situation for most VHA patients. In a subset of VHA patients, the ICD-10-CM transition had little effect on 2016 diagnoses of medical conditions, but was followed by decreases in alcohol, drug, and tobacco diagnoses (62), with no explanation offered.
Seeking to explain the decrease, we considered provider behaviors. VHA medical providers must add ICD codes to the EHR for new conditions and actively remove codes for resolved or remitted conditions. For cannabis use disorder, the definition of remission may be unclear and its occurrence difficult to assess. Thus, during the years that ICD-9-CM was used, active removal of cannabis use disorder diagnostic codes may not have seemed warranted, leaving a diagnosis entered at an earlier time automatically carried forward year-to-year. However, after the ICD-10-CM transition, all ICD-9-CM codes were removed, requiring providers to reenter codes for current conditions into the EHR. If current cannabis use disorder symptoms were not obvious, no code for ICD-10-CM cannabis use disorder would have been entered, resulting in an apparent drop in cannabis use disorder diagnoses. Subsequently, after the VHA completed the transition to ICD-10-CM in 2016, new or clearly continuing cannabis use disorder cases would be entered by providers. As our findings demonstrate, the 4-year ICD-10-CM period was characterized by steady increases in cannabis use disorder diagnoses across age, sex, and racial/ethnic groups, indicating that once the ICD-10-CM transition was completed, cannabis use disorder diagnoses in VHA patients continued to increase.
Several limitations of the study should be noted. VHA patients are not representative of all veterans or all adults. The ICD diagnoses we analyzed were not based on research interviews, but rather on provider diagnoses. Providers are most likely to be aware of and diagnose severe disorders (14), so mild cases of cannabis use disorder that might have been detected with research interviews could have been missed. While directly interviewing several million patients a year over the time period we covered is not feasible, the use of provider diagnoses in the EHR provides an opportunity to examine change over time in a large population. Our results should be interpreted as potentially representing increases over time in moderate to severe cases of cannabis use disorder (i.e., those most in need of care), but not necessarily increases in mild cases. Another source of potential surveillance bias is the possibility that patients were increasingly treated over time for another condition that was associated with cannabis use disorder, in which case cannabis use disorder diagnoses would have appeared to increase, although the increase would have been artifactual. Additionally, VHA providers may have become increasingly aware of the existence of cannabis use disorder and hence more likely to diagnose it, leading to artifactual but not actual increases in rates. Alternatively, as public attitudes toward cannabis have become more positive, providers may also have changed their views of cannabis, becoming less likely to perceive, diagnose, and note cannabis use disorder in the electronic medical record. This would have led to an artifactual attenuation of any observed increase in cannabis use disorder. The present VHA data sets do not enable us to investigate these possibilities, but in support of our findings, we note their consistency with other national studies, including those not relying on data from treated patients (9, 12, 24, 66). Another limitation is that the VHA database does not include a variable for cannabis use, so trends in cannabis use disorder diagnoses within users could not be examined.
The study also had notable strengths. These include a large source of data with very large sample sizes across many years, including 4 years of data after the introduction of ICD-10-CM, permitting examination of trends before and after the nomenclature transition. This permitted examination of trends by age, sex, and racial/ethnicity. To our knowledge, this is the first large EHR study to examine trends in cannabis use disorder diagnoses since 2016.
In summary, this study provides important information on increases in cannabis use disorder among VHA patients since 2005. While these trends were found across age, sex, and racial/ethnic subgroups, the increases were especially pronounced among male patients and among Black patients. The VHA offers an extensive and robust array of evidence-based substance use disorder treatments (25–27), but updated information on the extent of cannabis use disorders among VHA patients has not been available for many years. Many individuals can use cannabis without harms, and cannabis use does not have the same overdose or mortality risk profile as opioids or cocaine (67). However, cannabis use disorder is a diagnosable disorder with multiple risks of its own that is now more likely to occur among cannabis users than is commonly assumed (4). Therefore, information from the present study suggests a growing need for screening and treatment in the VHA for patients with cannabis use disorder. In addition, this study adds to the literature indicating increases in the prevalence of cannabis use disorder among U.S. adults over the past two decades. While an earlier household survey suggested that the increases were largely due to growing numbers of mild cases (10), the present study of VHA patients, with its reliance on the more severe cases likely to be known to providers and recorded in the EHR, suggests increases in severe cases as well. Evolving cannabis norms, laws, and products, promoted by the growing, multibillion-dollar cannabis industry (68), may be exerting an upward pressure on cannabis use disorder rates among VHA patients as well as in the general population, leading to a growing public health problem. Clinicians and the public should be educated about this problem, and policy makers should work toward policies that minimize it. Public and professional awareness of the risks (4), consequences (3), and increasing prevalence of cannabis use disorder would benefit adults in general, and veterans in particular, including those who receive treatment in the VHA.

Supplementary Material

File (appi.ajp.22010034.ds001.pdf)

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Information & Authors

Information

Published In

Go to American Journal of Psychiatry
Go to American Journal of Psychiatry
American Journal of Psychiatry
Volume 179 • Number 10 • October 2022
Pages: 748 - 757
PubMed: 35899381

History

Received: 12 January 2022
Revision received: 22 March 2022
Accepted: 2 May 2022
Published online: 28 July 2022
Published in print: October 2022

Keywords

  1. Substance-Related and Addictive Disorders
  2. Cannabis
  3. Cannabis Use Disorder
  4. Epidemiology
  5. Veterans

Authors

Details

Deborah S. Hasin, Ph.D. [email protected]
Department of Epidemiology (in Psychiatry) (Hasin), Department of Epidemiology (Olfson, Keyes, Martins, Livne, Mannes), and Department of Psychiatry (Olfson), Columbia University, New York; New York State Psychiatric Institute, New York (Hasin, Olfson, Fink, Wall); VA Puget Sound Health Care System, Seattle (Saxon, Malte, Maynard); Department of Psychiatry and Behavioral Sciences (Saxon) and Department of Health Systems and Population Health (Maynard), University of Washington, Seattle; Department of Epidemiology, Boston University, Boston (Gradus); Department of Population Health, New York University, New York (Cerdá); San Francisco VA Health System and Division of General Internal Medicine, University of California at San Francisco (Keyhani); Department of Biostatistics (in Psychiatry), Columbia University Medical Center, New York (Wall).
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Andrew J. Saxon, M.D.
Department of Epidemiology (in Psychiatry) (Hasin), Department of Epidemiology (Olfson, Keyes, Martins, Livne, Mannes), and Department of Psychiatry (Olfson), Columbia University, New York; New York State Psychiatric Institute, New York (Hasin, Olfson, Fink, Wall); VA Puget Sound Health Care System, Seattle (Saxon, Malte, Maynard); Department of Psychiatry and Behavioral Sciences (Saxon) and Department of Health Systems and Population Health (Maynard), University of Washington, Seattle; Department of Epidemiology, Boston University, Boston (Gradus); Department of Population Health, New York University, New York (Cerdá); San Francisco VA Health System and Division of General Internal Medicine, University of California at San Francisco (Keyhani); Department of Biostatistics (in Psychiatry), Columbia University Medical Center, New York (Wall).
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Carol Malte, M.S.W.
Department of Epidemiology (in Psychiatry) (Hasin), Department of Epidemiology (Olfson, Keyes, Martins, Livne, Mannes), and Department of Psychiatry (Olfson), Columbia University, New York; New York State Psychiatric Institute, New York (Hasin, Olfson, Fink, Wall); VA Puget Sound Health Care System, Seattle (Saxon, Malte, Maynard); Department of Psychiatry and Behavioral Sciences (Saxon) and Department of Health Systems and Population Health (Maynard), University of Washington, Seattle; Department of Epidemiology, Boston University, Boston (Gradus); Department of Population Health, New York University, New York (Cerdá); San Francisco VA Health System and Division of General Internal Medicine, University of California at San Francisco (Keyhani); Department of Biostatistics (in Psychiatry), Columbia University Medical Center, New York (Wall).
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Mark Olfson, M.D., M.P.H.
Department of Epidemiology (in Psychiatry) (Hasin), Department of Epidemiology (Olfson, Keyes, Martins, Livne, Mannes), and Department of Psychiatry (Olfson), Columbia University, New York; New York State Psychiatric Institute, New York (Hasin, Olfson, Fink, Wall); VA Puget Sound Health Care System, Seattle (Saxon, Malte, Maynard); Department of Psychiatry and Behavioral Sciences (Saxon) and Department of Health Systems and Population Health (Maynard), University of Washington, Seattle; Department of Epidemiology, Boston University, Boston (Gradus); Department of Population Health, New York University, New York (Cerdá); San Francisco VA Health System and Division of General Internal Medicine, University of California at San Francisco (Keyhani); Department of Biostatistics (in Psychiatry), Columbia University Medical Center, New York (Wall).
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Katherine M. Keyes, Ph.D.
Department of Epidemiology (in Psychiatry) (Hasin), Department of Epidemiology (Olfson, Keyes, Martins, Livne, Mannes), and Department of Psychiatry (Olfson), Columbia University, New York; New York State Psychiatric Institute, New York (Hasin, Olfson, Fink, Wall); VA Puget Sound Health Care System, Seattle (Saxon, Malte, Maynard); Department of Psychiatry and Behavioral Sciences (Saxon) and Department of Health Systems and Population Health (Maynard), University of Washington, Seattle; Department of Epidemiology, Boston University, Boston (Gradus); Department of Population Health, New York University, New York (Cerdá); San Francisco VA Health System and Division of General Internal Medicine, University of California at San Francisco (Keyhani); Department of Biostatistics (in Psychiatry), Columbia University Medical Center, New York (Wall).
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Jaimie L. Gradus, D.M.Sc., D.Sc.
Department of Epidemiology (in Psychiatry) (Hasin), Department of Epidemiology (Olfson, Keyes, Martins, Livne, Mannes), and Department of Psychiatry (Olfson), Columbia University, New York; New York State Psychiatric Institute, New York (Hasin, Olfson, Fink, Wall); VA Puget Sound Health Care System, Seattle (Saxon, Malte, Maynard); Department of Psychiatry and Behavioral Sciences (Saxon) and Department of Health Systems and Population Health (Maynard), University of Washington, Seattle; Department of Epidemiology, Boston University, Boston (Gradus); Department of Population Health, New York University, New York (Cerdá); San Francisco VA Health System and Division of General Internal Medicine, University of California at San Francisco (Keyhani); Department of Biostatistics (in Psychiatry), Columbia University Medical Center, New York (Wall).
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Magdalena Cerdá, Sc.D.
Department of Epidemiology (in Psychiatry) (Hasin), Department of Epidemiology (Olfson, Keyes, Martins, Livne, Mannes), and Department of Psychiatry (Olfson), Columbia University, New York; New York State Psychiatric Institute, New York (Hasin, Olfson, Fink, Wall); VA Puget Sound Health Care System, Seattle (Saxon, Malte, Maynard); Department of Psychiatry and Behavioral Sciences (Saxon) and Department of Health Systems and Population Health (Maynard), University of Washington, Seattle; Department of Epidemiology, Boston University, Boston (Gradus); Department of Population Health, New York University, New York (Cerdá); San Francisco VA Health System and Division of General Internal Medicine, University of California at San Francisco (Keyhani); Department of Biostatistics (in Psychiatry), Columbia University Medical Center, New York (Wall).
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Charles C. Maynard, Ph.D.
Department of Epidemiology (in Psychiatry) (Hasin), Department of Epidemiology (Olfson, Keyes, Martins, Livne, Mannes), and Department of Psychiatry (Olfson), Columbia University, New York; New York State Psychiatric Institute, New York (Hasin, Olfson, Fink, Wall); VA Puget Sound Health Care System, Seattle (Saxon, Malte, Maynard); Department of Psychiatry and Behavioral Sciences (Saxon) and Department of Health Systems and Population Health (Maynard), University of Washington, Seattle; Department of Epidemiology, Boston University, Boston (Gradus); Department of Population Health, New York University, New York (Cerdá); San Francisco VA Health System and Division of General Internal Medicine, University of California at San Francisco (Keyhani); Department of Biostatistics (in Psychiatry), Columbia University Medical Center, New York (Wall).
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Salomeh Keyhani, M.D.
Department of Epidemiology (in Psychiatry) (Hasin), Department of Epidemiology (Olfson, Keyes, Martins, Livne, Mannes), and Department of Psychiatry (Olfson), Columbia University, New York; New York State Psychiatric Institute, New York (Hasin, Olfson, Fink, Wall); VA Puget Sound Health Care System, Seattle (Saxon, Malte, Maynard); Department of Psychiatry and Behavioral Sciences (Saxon) and Department of Health Systems and Population Health (Maynard), University of Washington, Seattle; Department of Epidemiology, Boston University, Boston (Gradus); Department of Population Health, New York University, New York (Cerdá); San Francisco VA Health System and Division of General Internal Medicine, University of California at San Francisco (Keyhani); Department of Biostatistics (in Psychiatry), Columbia University Medical Center, New York (Wall).
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Silvia S. Martins, M.D., Ph.D.
Department of Epidemiology (in Psychiatry) (Hasin), Department of Epidemiology (Olfson, Keyes, Martins, Livne, Mannes), and Department of Psychiatry (Olfson), Columbia University, New York; New York State Psychiatric Institute, New York (Hasin, Olfson, Fink, Wall); VA Puget Sound Health Care System, Seattle (Saxon, Malte, Maynard); Department of Psychiatry and Behavioral Sciences (Saxon) and Department of Health Systems and Population Health (Maynard), University of Washington, Seattle; Department of Epidemiology, Boston University, Boston (Gradus); Department of Population Health, New York University, New York (Cerdá); San Francisco VA Health System and Division of General Internal Medicine, University of California at San Francisco (Keyhani); Department of Biostatistics (in Psychiatry), Columbia University Medical Center, New York (Wall).
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David S. Fink, Ph.D.
Department of Epidemiology (in Psychiatry) (Hasin), Department of Epidemiology (Olfson, Keyes, Martins, Livne, Mannes), and Department of Psychiatry (Olfson), Columbia University, New York; New York State Psychiatric Institute, New York (Hasin, Olfson, Fink, Wall); VA Puget Sound Health Care System, Seattle (Saxon, Malte, Maynard); Department of Psychiatry and Behavioral Sciences (Saxon) and Department of Health Systems and Population Health (Maynard), University of Washington, Seattle; Department of Epidemiology, Boston University, Boston (Gradus); Department of Population Health, New York University, New York (Cerdá); San Francisco VA Health System and Division of General Internal Medicine, University of California at San Francisco (Keyhani); Department of Biostatistics (in Psychiatry), Columbia University Medical Center, New York (Wall).
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Ofir Livne, M.D.
Department of Epidemiology (in Psychiatry) (Hasin), Department of Epidemiology (Olfson, Keyes, Martins, Livne, Mannes), and Department of Psychiatry (Olfson), Columbia University, New York; New York State Psychiatric Institute, New York (Hasin, Olfson, Fink, Wall); VA Puget Sound Health Care System, Seattle (Saxon, Malte, Maynard); Department of Psychiatry and Behavioral Sciences (Saxon) and Department of Health Systems and Population Health (Maynard), University of Washington, Seattle; Department of Epidemiology, Boston University, Boston (Gradus); Department of Population Health, New York University, New York (Cerdá); San Francisco VA Health System and Division of General Internal Medicine, University of California at San Francisco (Keyhani); Department of Biostatistics (in Psychiatry), Columbia University Medical Center, New York (Wall).
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Zachary Mannes, Ph.D.
Department of Epidemiology (in Psychiatry) (Hasin), Department of Epidemiology (Olfson, Keyes, Martins, Livne, Mannes), and Department of Psychiatry (Olfson), Columbia University, New York; New York State Psychiatric Institute, New York (Hasin, Olfson, Fink, Wall); VA Puget Sound Health Care System, Seattle (Saxon, Malte, Maynard); Department of Psychiatry and Behavioral Sciences (Saxon) and Department of Health Systems and Population Health (Maynard), University of Washington, Seattle; Department of Epidemiology, Boston University, Boston (Gradus); Department of Population Health, New York University, New York (Cerdá); San Francisco VA Health System and Division of General Internal Medicine, University of California at San Francisco (Keyhani); Department of Biostatistics (in Psychiatry), Columbia University Medical Center, New York (Wall).
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Melanie M. Wall, Ph.D.
Department of Epidemiology (in Psychiatry) (Hasin), Department of Epidemiology (Olfson, Keyes, Martins, Livne, Mannes), and Department of Psychiatry (Olfson), Columbia University, New York; New York State Psychiatric Institute, New York (Hasin, Olfson, Fink, Wall); VA Puget Sound Health Care System, Seattle (Saxon, Malte, Maynard); Department of Psychiatry and Behavioral Sciences (Saxon) and Department of Health Systems and Population Health (Maynard), University of Washington, Seattle; Department of Epidemiology, Boston University, Boston (Gradus); Department of Population Health, New York University, New York (Cerdá); San Francisco VA Health System and Division of General Internal Medicine, University of California at San Francisco (Keyhani); Department of Biostatistics (in Psychiatry), Columbia University Medical Center, New York (Wall).
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Notes

Send correspondence to Dr. Hasin ([email protected]).

Competing Interests

Dr. Hasin receives support from Syneos Health for an unrelated project. Dr. Saxon has received consulting fees from Indivior, travel support from Alkermes, research support from MedicaSafe, and royalties from UpToDate. Dr. Keyes has served as an expert witness in litigation. The other authors report no financial relationships with commercial interests.

Funding Information

Supported by NIDA grant R01DA048860, the New York State Psychiatric Institute, and the VA Centers of Excellence in Substance Addiction Treatment and Education.

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