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. 2018 Oct 18;12:214–219. doi: 10.1016/j.pmedr.2018.10.007

Medical multimorbidity and drug use among adults in the United States

Benjamin H Han a,b,c,⁎, Domenic J Termine d, Alison A Moore e, Scott E Sherman a,b,c,f, Joseph J Palamar b,c
PMCID: PMC6202656  PMID: 30370208

Abstract

Adults with medical multimorbidity (≥2 chronic conditions) present challenges for health care systems to provide coordinated care. Substance use can complicate the management of chronic medical conditions, but little research has focused on the intersection of medical multimorbidity and substance use. This study uses cross-sectional analysis of 115,335 adult respondents of the 2012–2014 administrations of the National Survey on Drug Use and Health to estimate the prevalence and correlates of past-year drug use among adults with multimorbidity. The prevalence of past-year drug use was compared between individuals reporting 0, 1, and ≥2 chronic medical conditions. We used multivariable logistic regression to determine correlates of past-year drug use among adults with ≥2 chronic medical conditions. 53.1% reported no chronic conditions, 29.5% reported one chronic condition, and 17.4% reported ≥2 chronic conditions. Past-year drug use was reported by 18.3% of those with no chronic conditions, 14.8% with 1 chronic condition, and 11.6% with ≥2 chronic conditions. Cannabis and opioid analgesics (nonmedical use) were the most common drugs used across all three groups. In the adjusted model, among adults with medical multimorbidity, adults with past-year drug use were more likely to be younger, male, have lower income, and report current tobacco use, alcohol dependence, past-year depression, and having received mental health treatment in the past year. In this national cohort, fewer adults with multimorbidity reported substance use compared to adults with no or one chronic condition, however, this population with high multimorbidity may be particularly vulnerable to the negative effects of drug use.

Keywords: Multimorbidity, Chronic disease, Substance use

Highlights

  • •

    The prevalence of past-year drug use among adults with multimorbidity was 11.6%.

  • •

    The prevalence of substance use disorder was 2.1% among adults with multimorbidity.

  • •

    Screening for unhealthy substance use among adults with multimorbidity is an imperative.

1. Introduction

Adults with medical multimorbidity, usually defined as ≥2 concurrent chronic conditions (Fortin et al., 2012; Wallace et al., 2015), have high rates of healthcare utilization and associated costs (Lehnert et al., 2011; Lochner and Cox, 2013). In addition to often receiving poorly coordinated care (Boyd and Fortin, 2010), people with medical multimorbidity experience the consequences associated with having multiple chronic conditions including adverse effects from treatments or interventions, disability, and death (American Geriatrics Society, 2012). In addition, living with medical multimorbidity often includes taking multiple prescribed medications and careful monitoring of individual diseases. Substance use can have profound negative effects on chronic diseases, therefore drug use and substance use disorder (SUD) must be considered when developing effective care for the increasing number of adults with medical multimorbidity (Paez et al., 2009).

Use of various psychoactive drugs is associated with a wide range of physiologic effects that can affect the cardiovascular, gastrointestinal, hematological, pulmonary, and neurological systems, and therefore influence existing chronic disease and complicate management (Center for Substance Abuse Treatment, 2006). SUDs themselves are considered chronic diseases with known physiological changes in the brain and need ongoing treatment and relapse prevention (Han and Moore, 2018). Therefore, the interaction of substance use with chronic medical disease can lead to poor treatment outcomes for both SUDs and chronic medical conditions.

Many adults who are in treatment for SUD receive fragmented medical care for their other chronic conditions (Gourevitch et al., 2007), and unhealthy substance use often goes undiagnosed and unrecognized by primary care physicians (Saitz et al., 1997). The ability to manage chronic diseases depends on the awareness of substance use and its possible effects on chronic medical diseases. However, few studies have focused on the intersection of chronic medical disease and drug use, and there are no studies that estimate the prevalence of drug use among adults with multiple chronic conditions. Therefore, the objective for this study was to use cross-sectional data from a nationally representative sample of adults in the United States (US) to estimate the prevalence of drug use among adults with chronic medical diseases, and to determine demographic and behavioral correlates of drug use among adults with medical multimorbidity.

2. Methods

2.1. Data source and study sample

This study aggregated data from three cohorts (years 2012, 2013, and 2014) of the National Survey on Drug Use and Health (NSDUH), an annual cross-sectional survey of individuals in the US (Substance Abuse and Mental Health Services Administration [SAMHSA], 2018). A different cross-section of non-institutionalized respondents is sampled each year and therefore each cohort is independent of each other. NSDUH is a nationally representative sample of individuals living in the US (all fifty US states and the District of Columbia) and the sample was achieved by the following methods: census tracts were first chosen within each state, then sections in each tract were selected, then households, and then respondents were selected for the sample each year. Surveys were administered through computer-assisted interviewing—conducted by an interviewer and audio computer-assisted self-interviewing (ACASI). Sampling weights were provided by NSDUH to address individual and unit-level non-response. Weights were adjusted to ensure that approximations are consistent with estimates provided by the US Census Bureau. Detailed information regarding the NSDUH survey and its sampling methods can be found elsewhere (SAMHSA, 2018). The weighted interview response rates for NSDUH years 2012 through 2014 was 71.7–73.0%, and missing or incomplete data were revised by imputation.

2.2. Definitions

The NSDUH survey asks respondents if they had ever been informed by a doctor or other medical professional that they have ever had the following 12 medical diseases: asthma, bronchitis, cirrhosis of the liver, diabetes, heart disease, hepatitis, hypertension (high blood pressure), lung cancer, HIV/AIDS, sleep apnea, stroke, and ulcers. These medical diseases are frequently considered chronic medical conditions, and are included in either the validated Katz chronic disease comorbidity questionnaire (Katz et al., 1996)15 or the Hierarchical Condition Category system (Pope et al., 2004) and obstructive sleep apnea, which is recognized as one of the most prevalent chronic respiratory disorders (Lee and McNicholas, 2011) and therefore included in our analysis. Medical multimorbidity is defined in the literature as 2 or more chronic diseases (Fortin et al., 2012; Wallace et al., 2015). Using the above conditions to study medical multimorbidity (≥2 chronic conditions) with the NSDUH has been performed in other studies (Stanton et al., 2016).

Drug use was assessed by NSDUH by self-report of cannabis (marijuana and hashish), cocaine (including crack), heroin, inhalants, hallucinogens, and nonmedical use of prescription medications (including opioids, tranquilizers, stimulants, and sedatives). Nonmedical use of prescription medications was defined as use of a drug that was not prescribed or used for the experience or feeling it caused. Nicotine dependence was defined based on dependence criteria of the Nicotine Dependence Syndrome Scale (NDSS) (Shiffman et al., 2004) and alcohol dependence was defined by the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) (American Psychiatric Association, 1994). SUDs were determined by participant responses to a series of questions that determined if criteria would meet DSM-IV abuse or dependence (American Psychiatric Association, 1994) categories for each drug. While NSDUH is not a diagnostic interview, this method provided a proxy diagnosis.

2.3. Statistical analyses

Analyses include all respondents aged 18 and older. We aggregated data from the three separate cohort years into a single cross-section to be able to increase power for examining associations between variables with low prevalence (i.e., use of various drugs among those with multimorbidity). We first examined bivariable associations between demographic characteristics and number of self-reported conditions (i.e., 0, 1, ≥2). Demographic measures included age (i.e., 18–34, 35–49, 50–64, and ≥65), sex, race/ethnicity (i.e., non-Hispanic white, African American, Asian, Hispanic of any race, and other), education level (i.e., <high school, high school, some college, college or more), total family income (i.e., <$20,000, $20,000–$49,999, $50,000–$74,000, ≥$75,000), marital status (i.e., married, widowed, divorced or separated, never married). NSDUH only provides pre-coded categorical responses for the age and income variables, and therefore could not be analyzed as a continuous variable. Tobacco use (past month, year), nicotine dependence, alcohol use (past month, year), alcohol dependence, self-reported overall health (i.e., excellent, very good, good, fair), and self-reported mental health problems in the past-year (i.e., major depressive episode based on DSM IV criteria, self-reported anxiety, respondents who received mental health treatment) were also compared among adults reporting 0, 1, and ≥2 chronic conditions.

Bivariable analyses was also performed comparing past-year drug use and diagnosed substance use disorder for adults with 0, 1, and ≥2 chronic conditions. We evaluated correlates of past-year drug use among adults with medical multimorbidity using binary logistic regression. We first estimated odds of each covariate separately, generating unadjusted odds ratios (ORs). We then fit covariates simultaneously using multiple logistic regression. The adjusted ORs (AORs) represent the odds of each category with all else in the model being equal. All analyses were weighted as part of NSDUH to account for the complex survey design and to obtain accurate standard errors for estimates at the population level. More detailed information regarding the development of analysis weights are found elsewhere (Center for Behavioral Health Statistics and Quality, 2017). Since our analyses utilized data from 3 cohorts, we divided the weights by 3 to obtain nationally representative estimates. Stata SE 13 (Stata, 2013) was used for all analyses, and survey (“svy”) commands were utilized to provide accurate standard errors using Taylor series estimation methods (Heeringa et al., 2010). Secondary analysis of this publically available data was exempt for review by the New York University Langone Medical Center Institutional Review Board.

3. Results

The analytic sample included 115,335 respondents. Chronic medical conditions were common among respondents with weighted percentages of 29.5% of adults reporting 1 chronic condition and 17.4% reporting 2 or more conditions, and therefore 46.9% reported at least 1 chronic condition and 53.1% reported no chronic conditions (Table 1). Most adults with medical multimorbidity were middle-aged (50–64 years of age) and older adults (≥65 years old), while younger adults (18–34 years of age) were more likely to report no chronic conditions (p < 0.001). Compared with adults reporting no chronic conditions, adults with medical multimorbidity were also more likely to be non-Hispanic white, less educated, married, have nicotine dependence, drink alcohol less frequently, report having worse health status, and more likely to have depression, anxiety, and need mental health treatment in the past year (p < 0.001 for all comparisons).

Table 1.

Sample characteristics by number of chronic conditions, % (n) – United States 2012–2014a.

Number of chronic conditions
0 chronic conditions
 1 chronic condition
 ≥2 chronic conditions
 p-Value
n % (95% CI) n % (95% CI) n % (95% CI)
Characteristic 72,341 53.1% (52.6, 53.7) 29,419 29.5% (29.0, 30.0) 13,575 17.4% (17.0, 17.8)
Age group
(years of age)
 18–34 49,318 41.3 (40.6, 42.0) 14,634 21.9 (21.3, 22.5) 4083 10.7 (10.2, 11.2) <0.001
 35–49 15,365 28.9 (28.4, 29.5) 7051 23.6 (22.8, 24.4) 3406 18.3 (17.5, 19.1) <0.001
 50–64 5435 20.6 (19.9, 21.4) 4450 29.8 (28.8, 30.9) 3215 35.1 (33.7, 36.4) <0.001
 ≥65 2223 9.1 (8.7, 9.6) 3284 24.7 (23.8, 25.6) 2871 36.0 (34.6, 37.3) <0.001
Sex
 Male 33,975 48.0 (47.4, 48.5) 13,470 48.6 (47.8, 49.4) 5899 47.0 (45.7, 48.4) 0.12
 Female 38,366 52.0 (51.5, 52.6) 15,949 51.4 (50.6, 52.2) 7676 53.0 (51.6, 54.3) 0.12
Race/ethnicity
 Non-Hispanic White 43,301 62.5 (61.8, 63.2) 18,894 67.8 (66.6, 69.0) 9565 74.0 (72.7, 75.2) <0.001
 Non-Hispanic African American 8468 10.5 (10.0, 11.0) 4236 13.8 (13.0, 14.6) 1624 11.6 (10.6, 12.6) <0.001
 Hispanic 13,370 18.4 (17.9, 19.0) 3898 12.2 (11.5, 12.9) 1304 8.9 (8.2, 9.7) <0.001
 Non-Hispanic Asian 3710 6.4 (6.0, 6.8) 879 3.7 (3.2, 4.3) 313 3.0 (2.4, 3.6) <0.001
 Other 3492 2.2 (2.0, 2.3) 1512 2.6 (2.3, 2.8) 769 2.6 (2.3, 3.0) <0.001
Education
 <High school 10,602 13.4 (13.0, 13.9) 4251 14.0 (13.4, 14.7) 1920 13.2 (12.3, 14.0) <0.001
 High school 22,370 27.8 (27.2, 28.3) 9418 31.1 (30.3, 32.0) 4359 31.0 (30.0, 32.1) <0.001
 Some college 21,584 27.3 (26.7, 27.8) 8645 25.9 (25.1, 26.7) 3936 28.0 (26.9, 29.1) <0.001
 College or more 17,785 31.6 (30.9, 32.3) 7105 28.9 (28.0, 29.9) 3360 27.8 (26.7, 28.9) <0.001
Total family income
 <$20,000 42,615 44.5 (43.8, 45.2) 15,851 41.6 (40.7, 42.6) 6854 42.3 (41.2, 43.5) <0.001
 $20–$49,999 19,439 31.8 (31.2, 32.4) 8535 34.0 (33.0, 35.0) 4263 34.7 (33.5, 36.0) <0.001
 $50,000–$74,999 5345 11.5 (11.1, 12.0) 2596 11.4 (10.8, 12.1) 1260 11.1 (10.4, 11.9) <0.001
 ≥$75,000 4942 12.2 (11.6, 12.8) 2437 13.0 (12.3, 13.7) 1198 11.8 (11.0, 12.7) <0.001
Marital status
 Married 25,045 49.8 (49.1, 50.4) 11,871 54.5 (53.6, 55.5) 6363 56.2 (54.8, 57.6) <0.001
 Widowed 1007 3.4 (3.1, 3.6) 1218 8.0 (7.5, 8.6) 952 10.5 (9.7, 11.4) <0.001
 Divorced or separated 6019 12.5 (12.1, 12.9) 3447 15.1 (14.5, 15.8) 2294 18.4 (17.6, 19.4) <0.001
 Never married 40,270 34.4 (33.8, 35.0) 12,883 22.3 (21.7, 23.0) 3966 14.8 (14.0, 15.7) <0.001
Tobacco use
 Past month use 24,715 29.4 (28.8, 30.1) 9667 26.5 (25.8, 27.3) 4175 24.4 (23.4, 25.4) <0.001
 Past year use 30,524 35.5 (34.9, 36.1) 11,663 31.2 (30.4, 32.1) 4887 27.9 (26.8, 29.0) <0.001
 Past year nicotine dependenceb 6997 8.6 (8.3, 9.0) 3125 8.6 (8.2, 9.1) 1760 10.2 (9.6, 10.9) <0.001
Alcohol use
 Past month use 43,742 60.0 (59.3, 60.7) 17,180 55.3 (54.4, 56.3) 7026 48.4 (47.1, 49.6) <0.001
 Past year use 55,110 74.1 (73.5, 74.8) 21,806 69.6 (68.6, 70.5) 9327 63.6 (62.2, 65.0) <0.001
 Past year alcohol Use, but not dependence 51,826 70.7 (70.1, 71.4) 20,483 66.2 (65.2, 67.1) 8741 60.7 (59.3, 62.1) <0.001
 Past year alcohol dependencec 3284 3.4 (3.2, 3.6) 1323 3.4 (3.1, 3.7) 566 2.9 (2.6, 3.3) <0.001
Overall Health
 Excellent 21,590 30.1 (29.5, 30.7) 5141 15.6 (14.9, 16.3) 1102 7.0 (6.4, 7.6) <0.001
 Very good 29,479 39.2 (38.7, 39.7) 11,395 37.0 (36.1, 37.8) 3772 26.2 (25.0, 27.4) <0.001
 Good 16,793 23.3 (22.8, 23.9) 9055 32.1 (31.3, 33.0) 4875 36.8 (35.6, 38.0) <0.001
 Fair/poor 4467 7.3 (7.0, 7.6) 3820 15.3 (14.6, 16.1) 3821 30.0 (28.9, 31.2) <0.001
Mental health
 Past year major depressive episodec 4894 5.7 (5.5, 6.0) 2530 6.5 (6.1, 7.0) 1878 10.9 (10.2, 11.6) <0.001
 Past year anxiety 4511 5.7 (5.4, 5.9) 2272 6.0 (5.6, 6.4) 1807 10.4 (9.7, 11.1) <0.001
 Received mental health treatment in past year 8672 12.0 (11.6, 12.4) 4811 15.3 (14.7, 15.9) 3521 23.1 (22.0, 24.2) <0.001
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a

All percentages are weighted.

b

Based on the Nicotine Dependence Syndrome Scale (NDSS).

c

Based on the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition.

Table 2 presents frequencies and weighted percentages of specific drugs used by 0, 1 or ≥2 chronic conditions. Overall, 16.1% of the full study sample reported past-year use of an illegal drug or cannabis and 2.6% reported meeting criteria for a SUD. The most common drug used in the past-year was cannabis, and cannabis use disorder was the most common SUD. Nonmedical use of opioid analgesics was the next-most common and opioid-related SUD was the second most common SUD. Among all adults with chronic conditions, past-year drug use was reported by 14.8% with 1 chronic condition and 11.6% with ≥2 conditions, and drug use was considerably higher among adults with no chronic conditions (18.3%) (p < 0.001 for all comparisons). Among adults reporting no chronic conditions, 5.5% used ≥2 drugs, while 4.3% of adults with 1 chronic disease and 3.2% of adults with ≥2 chronic diseases reported use of >1 drug in the past year (p < 0.001). Criteria for past-year SUD was met by 2.4% with 1 chronic condition and 2.1% with ≥2 conditions, and 2.9% of adults with no chronic conditions (p < 0.001).

Table 2.

Past-year drug use by number of chronic conditions, % (n) – United States 2012–2014a.

Number of chronic conditions
0 chronic conditions (n = 72,341)
 1 chronic condition (n = 29,419)
 ≥2 chronic conditions (n = 13,575)
 p-Value
n % (95% CI) n % (95% CI) n % (95% CI)
Drug use category (past-year use)
 Any drug 18,419 18.3 (17.9, 18.8) 6663 14.8 (14.2, 15.4) 2448 11.6 (10.8, 12.4) <0.001
 Any drug, excluding cannabis 8360 8.5 (8.2, 8.9) 3216 7.3 (6.9, 7.7) 1240 5.8 (5.3, 6.4) <0.001
 Cannabis 15,722 14.9 (14.5, 15.2) 5446 11.2 (10.7, 11.8) 1909 8.4 (7.8, 9.0) <0.001
 Cocaine 2101 2.1 (2.0, 2.3) 758 1.7 (1.5, 1.9) 266 1.2 (1.0, 1.4) <0.001
 Heroin 371 0.3 (0.3, 0.4) 145 0.3 (0.2, 0.3) 76 0.3 (0.2, 0.4) <0.001
 Opioid analgesics 4359 4.6 (4.4, 4.8) 1758 4.1 (3.8, 4.4) 764 3.4 (3.1, 3.8) <0.001
 Hallucinogens 2607 2.1 (2.0, 2.3) 788 1.3 (1.1, 1.5) 263 0.7 (0.6, 0.9) <0.001
 Tranquilizers/sedatives 2243 2.3 (2.2, 2.5) 976 2.3 (2.1, 2.6) 467 2.1 (1.8, 2.4) 0.36
 Stimulants 1587 1.5 (1.3, 1.6) 558 1.0 (0.9, 1.1) 214 0.8 (0.7, 1.1) <0.001
 ≥2 illegal drug use 6006 5.5 (5.3, 5.8) 2196 4.3 (4.1, 4.6) 804 3.2 (2.8, 3.6) <0.001
Substance use disorder (past-year)
 Any drug 3208 2.9 (2.7, 3.1) 1279 2.4 (2.2, 2.7) 509 2.1 (1.8, 2.4) <0.001
 Any drug, excluding cannabis 1399 1.4 (1.3, 1.6) 612 1.4 (1.2, 1.6) 292 1.3 (1.1, 1.5) 0.58
 Cannabis 2124 1.7 (1.6, 1.8) 823 1.3 (1.1, 1.4) 266 1.0 (0.8, 1.2) <0.001
 Cocaine 328 0.4 (0.3, 0.5) 138 0.4 (0.3, 0.5) 81 0.4 (0.3, 0.6) 0.94
 Heroin 255 0.2 (0.2, 0.3) 107 0.2 (0.1, 0.2) 59 0.2 (0.2, 0.3) 0.58
 Opioid analgesics 762 0.8 (0.7, 0.9) 311 0.7 (0.6, 0.9) 175 0.8 (0.6, 1.0) 0.76
 Hallucinogens 145 0.1 (0.1, 0.1) 53 0.1 (0.1, 0.1) 19 0.0 (0.0, 0.1) 0.07
 Tranquilizers/sedatives 183 0.2 (0.1, 0.2) 110 0.2 (0.2, 0.3) 62 0.2 (0.2, 0.4) 0.25
 Stimulants 187 0.2 (0.1, 0.3) 96 0.2 (0.1, 0.2) 43 0.2 (0.1, 0.3) 0.84
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a

All percentages are weighted.

Table 3 presents results from the multivariable logistic regression model with past-year drug use as the outcome variable for adults with medical multimorbidity (n = 13,575). Results from the adjusted model suggest that among adults with multimorbidity, odds of past-year drug use decreased with increasing age (AOR 0.17 for adults ≥65, AOR 0.46 for adults 50–64, and AOR 0.57 for adults 35–49, all p < 0.001 with adults 18–34 as the comparison) and for females (AOR 0.52, p < 0.001). For race/ethnicity, compared to whites non-Hispanic African Americans (AOR 0.71, p < 0.05) and Asians (AOR 0.48, p < 0.05) also had lower odds for past-year drug use. For family income, compared to incomes <$20,000, higher incomes (AOR 0.53 for incomes ≥$75,000/year, p < 0.001; AOR 0.64 for incomes $50,000–$74,999, p < 0.01; AOR 0.62 for incomes $20,000–$49,999, p < 0.001) were associated with lower odds for past-year drug use. In contrast, respondents reporting past-month use of tobacco (AOR 2.45, p < 0.001), alcohol use (not meeting criteria for dependence) (AOR 3.33, p < 0.001), alcohol dependence (AOR 10.19, p < 0.001), past-year major depressive episode (AOR 1.28, p < 0.05), and received mental health treatment in the past year (AOR 1.79, p < 0.001) all had higher odds of reporting past-year drug use.

Table 3.

Correlates of past-year substance use among adults with medical multimorbiditya – United States 2012–2014.

Characteristic OR (95% CI) AOR (95% CI)
Age group
 18–34 1.00 1.00
 35–49 0.39‖ (0.34, 0.44) 0.57‖ (0.48, 0.69)
 50–64 0.25‖ (0.21, 0.29) 0.46‖ (0.37, 0.57)
 ≥65 0.06‖ (0.04, 0.08) 0.17‖ (0.11, 0.25)
Sex
 Male 1.00 1.00
 Female 0.68‖ (0.60, 0.77) 0.52‖ (0.45, 0.61)
Race/ethnicity
 Non-Hispanic White 1.00 1.00
 Non-Hispanic African American 0.85 (0.69, 1.06) 0.71‡ (0.55, 0.92)
 Hispanic 1.21 (0.94, 1.55) 0.94 (0.70, 1.26)
 Non-Hispanic Asian 0.38§ (0.22, 0.66) 0.48‡ (0.26, 0.88)
 Other 1.54‡ (1.08, 2.18) 1.12 (0.70, 1.81)
Education
 <High school 1.00 1.00
 High school 0.86 (0.68, 1.09) 0.81 (0.61, 1.09)
 Some college 1.13 (0.93, 1.38) 1.08 (0.83, 1.41)
 College or more 0.81 (0.65, 1.01) 1.08 (0.81, 1.44)
Total family income
 <$20,000 1.00 1.00
 $20,000–$49,999 0.57‖ (0.48, 0.67) 0.62‖ (0.50, 0.76)
 $50,000–$74,999 0.60§ (0.45, 0.80) 0.64§ (0.47, 0.88)
 ≥$75,000 0.47‖ (0.36, 0.61) 0.45‖ (0.32, 0.63)
Marital status
 Never married 1.00 1.00
 Widowed 0.13‖ (0.09, 0.19) 0.57‡ (0.33, 0.98)
 Divorced or separated 0.50‖ (0.41, 0.60) 0.80 (0.62, 1.04)
 Married 0.23‖ (0.20, 0.27) 0.49‖ (0.39, 0.62)
Tobacco use
 Past month use 4.57‖ (3.95, 5.29) 2.45‖ (2.05, 2.92)
Alcohol use
 Past year alcohol use, but not dependence 3.96‖ (3.21, 4.90) 3.33‖ (2.62, 4.23)
 Past year dependence 22.98‖ (17.26, 30.59) 10.19‖ (6.96, 14.94)
Overall health
 Fair/poor 1.00 1.00
 Good 0.86 (0.71, 1.04) 0.97 (0.65, 1.46)
 Very good 0.85 (0.71, 1.03) 0.97 (0.76, 1.23)
 Excellent 0.89 (0.65, 1.21) 0.97 (0.77, 1.22)
Mental health
 Past year major depressive episodeb 2.94‖ (2.49, 3.47) 1.28‡ (1.02, 1.61)
 Past year anxiety 2.78‖ (2.39, 3.24) 1.22 (0.95, 1.58)
 Received mental health treatment in past year 2.48‖ (2.13, 2.90) 1.79‖ (1.40, 2.30)
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a

Multimorbidity defined as 2 or more of the following: asthma, bronchitis, cirrhosis of the liver, diabetes, heart disease, hepatitis, hypertension (high blood pressure), lung cancer, HIV/AIDS, sleep apnea, stroke, and ulcers.

b

Based on the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition.

‡

p < 0.05.

§

p < 0.01.

‖

p < 0.001.

4. Discussion

In this large, nationally representative survey, we estimated the prevalence of past-year drug use among adults without chronic medical conditions and among those with one and two or more chronic medical conditions (i.e., medical multimorbidity). While the prevalence of past-year drug use was lower among adults with medical multimorbidity, compared to adults with no chronic conditions, nearly 12% of adults with multimorbidity reported engaging in past-year drug use. The lower prevalence of drug use among adults with multimorbidity may be due to both the fact that younger adults were more likely to engage in drug use and less likely to have chronic medical conditions, and some of those with multimorbidity may have stopped using drugs because of their morbidities (Naimi et al., 2016). In alcohol studies for example, the “sick quitter” hypothesis proposes that adults may stop drinking due to medical illness, hospitalizations, or declining health, and therefore this group is not included as individuals with alcohol-related problems even though alcohol may have contributed to their illnesses (Shaper et al., 1988). A similar phenomenon is likely occurring in observational studies with drug use including our study. However, we did find in multivariable models that among adults with medical multimorbidity, adults with alcohol dependence, current tobacco use, and adults with mental health problems were more likely to have engaged in past year drug use, and therefore be at particularly high risk for adverse effects of drug use. This emphasizes the importance of including SUDs and polysubstance use disorders to the multimorbidity framework as a distinct clinical profile that necessitates further research to better care for patients with complex multimorbid disease (Bhalla et al., 2017; North et al., 2016).

The intersection of substance use and medical multimorbidity is complex. Drug use has a wide array of physiologic effects on the body, that may negatively impact existing chronic medical disease and complicate its management. For example, cocaine use can impact both the cardiovascular and cerebrovascular systems that can lead to stroke, disability or sudden death, particularly among adults with pre-existing disease (Center for Substance Abuse Treatment, 2006). Second, substance use can also complicate the clinical management of existing chronic diseases. Studies have shown decreased adherence to antiretroviral therapy among adults with HIV who engage in active substance use (Pozner et al., 2005), and poor medication adherence for adults with psychosis who used cannabis (Nijhawan et al., 2008). This is particularly relevant for adults with medical multimorbidity, who often have complex medication schedules that demand careful monitoring and daily management. In addition, the complicated medical care that adults with medical multimorbidity face places them at risk for medication management mistakes as well as adverse drug effects and interactions (Schoeler et al., 2017). This emphasizes the importance of substance use screening for adults with chronic conditions. In addition, since many adults in SUD treatment often have fragmented primary care (Gourevitch et al., 2007), it is also important for SUD treatment providers to screen and ensure medical comorbidities are being managed.

In our study sample, cannabis was the most commonly used drug among adults with chronic disease. This is not surprising since cannabis is the most common drug used in the NSDUH study sample and given more positive attitudes and policies related to cannabis use (Flaherty et al., 2000; Gallup, 2018). Cannabis has been used and studied for medical treatment of chronic diseases including HIV, multiple sclerosis, chronic pain, seizure disorder, and other mental health disorders (National Institute on Drug Abuse [NIDA], 2018). Although this study cannot distinguish between medical use versus recreational use of cannabis, using cannabis for these types of conditions may partially explain its high prevalence use among adults with chronic disease (both self-medicated or prescribed in states where it is legal) (NIDA, 2018). The risks of cannabis have not been well-documented, particularly among older adults with medical multimorbidity who may be at higher risk for negative cardiovascular, pulmonary, and cognitive effects of cannabis use (Han and Moore, 2018). Further study is needed to better assess the benefits and risks of cannabis use for specific chronic diseases and overall use by adults with medical multimorbidity.

The statistically significant correlates of past-year drug use among adults with medical multimorbidity identified in this study include younger age, male sex, low family income (<$20,000), current tobacco use, alcohol use and alcohol dependence, having had a major depressive episode, and having received mental health treatment. The demographic findings are similar to the overall results of NSDUH among the general adult population for illegal drug use including cannabis, such as adults with younger age and male sex having higher rates of use (SAMHSA, 2018). However, our results are novel in that they identify a potentially very high-risk population of adults with the combination of medical multimorbidity with polysubstance use (drug, alcohol, and/or tobacco use) and/or mental health disorders. The strong association we found with alcohol dependence with concurrent drug use among adults with medical multimorbidity is alarming. Unhealthy alcohol use itself can cause, exacerbate, and complicate the management of several chronic medical diseases (Park and Wu, 2017), and therefore the combination of illegal drug use with alcohol use, especially if used concurrently, can be particularly dangerous for adults with existing chronic medical conditions. Further, the co-occurrence of substance use and SUD with mental health illnesses are well documented (National Institute of Alcohol Abuse and Alcoholism, 2000), and interventions for addressing patients with co-occurring conditions have been developed and studied (Chapman et al., 2005). The added mental health comorbidity is particularly important as one recent study of 843,584 veterans in the VA system who had at least three visits to a mental health clinic found 30.6% had co-occurring psychiatric and SUDs (Bhalla et al., 2017). The added burden of polysubstance use along with mental health problems highlights the need for a syndemic framework for caring for patients with compound multimorbidity, where the focus is how multiple health conditions are adversely affected by behavioral, psychiatric, biological, and social conditions (Bhugra and Ventriglio, 2017; Perry et al., 2015). The use of the syndemic framework can help identify intervention strategies to reduce harms and improve the management of chronic disease for adults with medical multimorbidity, polysubstance use, and mental health disorders. In practice, one approach using this framework could include the integration of primary care and chronic disease management into SUD and mental health treatment settings and vice-versa.

There are important limitations to this study. First, the NSDUH responses are based on self-report and thus are subject to both recall and social-desirability bias; although the survey attempts to limit the latter via ACASI (SAMHSA, 2018). Second, the NSDUH does not assess when a respondent first experienced or was diagnosed with a chronic medical disease, but asks only for lifetime prevalence. Therefore, participants may have had a chronic medical condition years before being surveyed and that condition may have been resolved and not overlap with past-year drug use. However, most of the conditions queried (i.e., hypertension, sleep apnea, heart disease, cirrhosis, stroke) tend to be lifelong. In addition, while the specific chronic diseases chosen in the NSDUH study design were based on expert opinion of medical diseases that are often related to substance use (SAMHSA, 2018), it does not include many chronic medical diseases that are often asked in clinical research and epidemiological studies to understand the burden of chronic disease in specific populations. It also does not specify “heart disease” further, thus this could include a wide range of cardiac diseases. Therefore, the generalizability of this study is limited to the specific chronic diseases asked of this study sample and how it was asked. Also, assessing DSM SUD criteria via surveys can also be limited as these are not full diagnostic interviews. Finally, our study classifies users of drugs if the individual used in the past-year and therefore does not distinguish between one-time use versus more frequent use, which may potentially have different risks and consequences. To help address this we did include substance use disorder in estimating substance use among adults with chronic diseases.

5. Conclusions

This study estimates the use of drugs by adults with medical multimorbidity and highlights areas that need to be better explored. Adults with multimorbidity should be screened for substance use to identify and treat unhealthy use, which can impact chronic disease. Further study is needed to better delineate the risks and harms of specific drug use not only on individual chronic diseases, but on individuals who live with and manage multiple chronic conditions.

Acknowledgments

Acknowledgments

This research was funded by several grants through the National Institutes of Health: K23DA043651 from the National Institute on Drug Abuse (Han), K24AA15957 from the National Institute on Alcohol Abuse and Alcoholism (Moore), K01 DA-038800 from the National Institute on Drug Abuse (Palamar), T35AG050998-01 from the National Institute on Aging (Termine), and K24DA038345 from the National Institute on Drug Abuse (Sherman). The National Institutes of Health provided financial support for the project and the preparation of the manuscript but did not have a role in the design of the study, the analysis of the data, the writing of the manuscript, nor the decision to submit the present research.

Conflict of interest

The authors declare no conflict of interest.

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