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. Author manuscript; available in PMC: 2022 Feb 1.
Published in final edited form as: Int J Drug Policy. 2020 Nov 20;88:103017. doi: 10.1016/j.drugpo.2020.103017

Poly-prescription drug misuse across the life course: Prevalence and correlates across different adult age cohorts in the U.S.

Jason A Ford 1, Ty S Schepis 2, Sean Esteban McCabe 3,4,5,6
PMCID: PMC8005409  NIHMSID: NIHMS1647616  PMID: 33227640

Abstract

Background:

Most research on prescription drug misuse (PDM) focuses on the misuse of specific classes of psychoactive prescription drugs among adolescents or young adults. The current research addressed important gaps in the literature by assessing poly-prescription drug misuse (poly-PDM), the misuse of more than one class of psychoactive prescription drug, across different adult age cohorts.

Methods:

We used the 2015–2018 National Survey on Drug Use and Health to examine the prevalence of past-year poly-PDM and specific combinations of PDM. Multinomial logistic regression was used to identify demographic, health-related factors, and substance use behaviors that were significantly associated with poly-PDM.

Results:

The prevalence of poly-PDM decreases with age and is common among individuals who engage in PDM. Slightly more than one in four respondents in age cohorts 18–25 (31.66%, 95% CI = 30.35, 33.00) and 26–34 (29.92%, 95% CI = 25.82, 30.12) who engage in PDM, misused more than one class of prescription drug. Additionally, poly-PDM was identified as a high-risk type of PDM as roughly 60% of adults younger than 65 who endorse poly-PDM reported having a substance use disorder (SUD). While certain characteristics (i.e., race/ethnicity, marital status, depression, suicidal ideation, illegal drug use, and SUD) were consistently associated with poly-PDM across age cohorts, other characteristics (i.e., sexual identity, income, and justice involvement) varied across age cohorts. Finally, a comparison of poly-PDM to single PDM showed, in all age cohorts, that having an SUD was associated with an increased likelihood of poly-PDM, while Black adults were less likely than whites to report poly-PDM.

Conclusions:

By identifying prevalence and correlates of poly-PDM across adult age cohorts, the current research has significant implications. Understanding stability and heterogeneity in the characteristics associated with poly-PDM should inform interventions, identify at-risk groups, and shape public health approaches to dealing with high-risk substance use behavior.

Keywords: polysubstance use, prescription drug misuse, life course

Background

Polysubstance use has generally been defined as the use of more than one drug, including alcohol, simultaneously or during a defined period of time for either recreational or self-treatment motives (Connor et al., 2014). Research on polysubstance use is imperative as studies in the U.S. (Bailey et al., 2019; Evans et al., 2017; Hedegaard et al., 2020; Liu et al., 2018; McCabe et al., 2017a; Timko et al., 2018) and internationally (Carter et al., 2013; Chan et al., 2019; Chen et al., 2018; Gersing et al., 2017; Reyes et al., 2013) show that polysubstance use is common and increases the risk of adverse outcomes. As the prevalence of prescription drug misuse (PDM) surged and was followed by a dramatic increase in overdose deaths, research on PDM became a priority (Hedegaard et al., 2020; Miech et al., 2019; Sung et al., 2005). Importantly, a number of studies have shown that the majority of PDM involves the use of alcohol or other drugs, with this PDM-involved polysubstance use being associated with numerous negative outcomes (Fedorova et al., 2019; Grisby et al., 2019; Kelly et al., 2014; Morely et al., 2017; McCabe et al., 2015; Novak et al., 2016; Palamar et al., 2018; Schepis et al. 2016a; Votaw et al., 2020).

While research on polysubstance use is common, less attention has been given to poly-prescription drug misuse (poly-PDM), the use of more than one controlled medication across different classes. This includes research on the misuse of prescription benzodiazepines and opioids (Bouvier et al., 2018; Ford et al., 2018), prescription opioids and stimulants (Schepis et al., 2019a), or multiple classes of prescription drugs (McCabe et al. 2019). The lack of research is troubling as data from the 2018 National Survey on Drug Use and Health estimates that roughly 3.6 million U.S. adults misused more than one class of prescription drug in the past year, and among U.S. adults that reported any past-year PDM about 23% reported poly-PDM (Substance Abuse and Mental Health Services Administration, 2019). Poly-PDM is a high-risk type of PDM, as it increases the likelihood of experiencing negative consequences, including substance use disorders and poor physical and mental health (Bouvier et al., 2018; Ford et al., 2018; McCabe et al., 2019; Schepis et al., 2019a). Prior research has identified a number of factors that are associated with PDM among U.S. adults, including demographic characteristics (e.g., age, gender, race/ethnicity, income, marital status), health-related factors (e.g., depression, suicidal ideation, emergency room visits), and substance use behaviors (e.g., alcohol other drug use, and substance use disorders) (Blanco et al., 2018; Compton et al., 2018; Han et al., 2018; Schepis et al. 2020a). The high prevalence and risk associated with poly-PDM make it important to identify which of these factors are significantly associated with poly-PDM.

While focusing on the use of specific medication classes is important, it ignores the facts that poly-PDM is common and some combinations of prescription drugs are associated with an increased likelihood of adverse outcomes. In particular, the use/misuse of prescription opioids and benzodiazepines is associated with emergency department visits, poor treatment outcomes, and an increased risk for overdose (Curtin et al., 2017; Franklyn et al., 2017; Jones et al., 2015; Jones et al., 2012). Notably, 20.8% of adults who misused opioids and tranquilizers/sedative reported past-year suicidal ideation, compared to 11.8% for only opioid misuse and 12.6% for only tranquilizer/sedative misuse (Schepis et al., 2016b). Another prescription drug combination that comes with considerable risk of adverse outcomes, especially when misused, are opioids and stimulants (Glick et al., 2018; Schepis et al., 2019a; Trujillo et al., 2011). While not limited solely to prescription drugs, overdose deaths in the U.S. associated with the use of both opioids and psychostimulants increased nearly 900% between 2009 to 2018 (Hedegaard et al., 2020). Given key differences between PDM and other forms of substance use (Boyd et al., 2008; McHugh et al., 2015), the lack of research on poly-PDM is problematic and limits prevention, screening, and intervention efforts.

Due to high prevalence rates, most research on drug use focuses on adolescents or young adults. However, research has documented increased rates of drug use in older age cohorts, such as the Baby Boom generation (Degenhardt et al., 2007; Golub & Johnson, 2001; Johnson et al., 1998), with evidence suggesting that many adults are not maturing out of drug use (Boeri, 2018; Han et al., 2009). The lack of aging out may be attributable to access to prescription medications, as older adults use a relatively larger proportion of prescription drugs, particularly opioids and benzodiazepines (Campbell, et al., 2010; Olfson, King, & Schoenbaum, 2015). Access to prescription drugs is important as older adults are more likely to obtain the prescription drugs they misuse from medical sources (Schepis et al., 2018b).

Data from the National Survey on Drug Use and Health show that the prevalence of PDM trails only that of alcohol and marijuana among adults over 35 (Substance Abuse and Mental Health Services Administration, 2019) and there is evidence of increasing rates of prescription opioid and tranquilizer/sedative misuse among older adults (Schepis et al., 2018a; Schuler et al., 2019; West et al., 2015). While older age cohorts do report lower prevalence rates of PDM compared to younger age cohorts, there is some evidence that suggests older age cohorts may be more likely to experience adverse consequences (Chou et al., 2014). For example, compared to young adults, prescription opioids account for greater morbidity and mortality among older adults (McBain and Rose 2018; Rose et al., 2019). Additionally, research highlights a number of negative outcomes associated with PDM among older adults including a reduced quality of life, mental and physical health problems, suicidal ideation, substance use, substance use disorders, treatment, and mortality (Martins et al., 2010; Schepis et al., 2019b).

The current research

To address key gaps in the literature, the main objectives of the current study are to identify prevalence and correlates of poly-PDM across different adult age cohorts using data from a nationally representative sample of U.S. adults. While substance use behaviors and mental health problems are robust correlates of PDM across adult age cohorts, there appears to be more variation in the demographic characteristics associated with PDM across age cohorts (Becker et al., 2008; Schepis et al., 2014). The current research will determine if these patterns hold for poly-PDM as well. Identifying the prevalence and correlates, particularly adverse characteristics such as substance use disorder, of poly-PDM across different adult age cohorts should inform interventions and shape public health approaches to dealing with a high-risk type of substance use.

Methods

Data

The National Survey on Drug Use and Health (NSDUH) has a target population of civilians 12 years and older in the U.S. that are not institutionalized and is based on an independent, multistage area probability sample. Data was collected from respondents by a trained interviewer using a combination of computer-assisted face-to-face interviewing and computer-assisted self-interviewing. For the current study, we combined multiple years of NSDUH data, from 2015 to 2018. During this period, the weighted screening and interview response rates were consistently above 73% and 66% respectively. Further information regarding the methodology of the NSDUH are available elsewhere (Center for Behavioral Health Statistics and Quality, 2019). The current research focuses on adult respondents 18 and older.

Measures

Poly-prescription drug misuse

The NSDUH defined PDM as the “use of a prescription drug in any way a doctor did not direct”, including use without a prescription; use in greater amounts, more often, or longer than you were told to take it; use in any other way not directed by a doctor. The primary measure of poly-prescription drug misuse (poly-PDM) accounts for misuse across different classes of medication (i.e., opioid, tranquilizer, stimulant, and sedative) and was coded: no PDM, misuse of only one class of prescription drug (single PDM), and misuse of more than one class of prescription drug (poly-PDM). A second measure of poly-PDM was created to focus on specific combinations of prescription drug classes and was coded: no PDM, the misuse of only one class of prescription drug, opioid and tranquilizer/sedative misuse, opioid and stimulant misuse, tranquilizer/sedative and stimulant misuse, and opioid and stimulant and tranquilizer/sedative misuse.

Correlates

To identify characteristics associated with poly-PDM, the following demographic characteristics were included: sex (female/male), race/ethnicity (i.e., non-Hispanic white, non-Hispanic Black or African American, non-Hispanic Native American or Alaska Native, non-Hispanic Hawaiian or Pacific Islander, non-Hispanic Asian, non-Hispanic multiple racial identities, Hispanic), sexual identity (i.e., heterosexual, gay or lesbian, bisexual), family income (i.e., <$20,000, $20,000-$49,999, $50,000-$74,999, $75,000 or more), received government assistance (no/yes), marital status (i.e., married, widowed, divorced or separated, never married), educational attainment (i.e., less than high school, high school graduate, some college, college graduate), employment status (i.e., full-time, part-time, unemployed, other), military service (no/yes), county type (i.e., large metropolitan county, small metropolitan county, non-metropolitan county), and justice involvement (no/yes). A respondent was considered to be justice-involved if they reported being arrested, on probation, or on parole in the past 12 months.

The following measures assessed health-related factors during the past 12 months: insurance status (insured/uninsured), overall health (excellent, very good, good vs. fair, poor), disability status (no/yes), major depressive episode (no/yes), suicidal ideation (no/yes), emergency room visit (no/yes), and overnight hospital stay (no/yes). The following substance use behaviors were also included: past-month daily cigarette smoking, past-month heavy alcohol use, past-year marijuana use, past-year other illegal drug use, and past-year substance use disorder (all coded no/yes). The measure of substance use disorder (SUD) is based on DSM-IV substance use disorder assessment (abuse or dependence) from alcohol, marijuana, cocaine, heroin, hallucinogen, inhalant, methamphetamine, prescription opioid, tranquilizer, sedative, and stimulant use/misuse. Finally, given the use of four years of NSDUH data, a measure of survey year was also included in the analyses.

Analytic strategy

In order to account for the complex multistage sampling design of the NSDUH, analyses were conducted using the svyset and svy commands in STATA 15.0. Given the use of four-years of NSDUH data, an adjusted person-level weight was created (i.e., weight/4) per guidelines from the Substance Abuse and Mental Health Services Administration (Center for Behavioral Health Statistics and Quality, 2017). First, the prevalence of poly-PDM was estimated across different adult age cohorts (18–25, 26–34, 35–49, 50–64, 65+). Second, with a categorical measure of poly-PDM, multinomial logistic regression models were estimated to compare (1) poly-PDM to single PDM, (2) poly-PDM to no PDM, and (3) single-PDM to no PDM. Third, a final set of multinomial logistic regression models was estimated to compare various combinations of PDM to no PDM. Given the high prevalence and association with adverse outcomes we highlight the findings for combined opioid and tranquilizer/sedative misuse and show results for less common combinations (e.g., opioids and stimulants, and stimulants and tranquilizers/sedatives) in supplemental analysis. All models included demographic characteristics, health-related factors, substance use behaviors, and survey year. Given that the focus of the current research was to assess differences across the life course we estimated all multinomial logistic regression models separately for respondents based on age cohorts.

Results

Prevalence of poly-PDM

Table 1 shows that the prevalence of poly-PDM across age cohorts. In the first set of analyses shown, the prevalence of poly-PDM decreased from a high of 4.50% (95% CI = 4.26, 4.74) in the 18–25 age cohort to a low of 0.20% (95% CI = 0.12, 0.33) in the 65 or older age cohort. Analysis not shown in Table 1, highlights how common poly-PDM was among adults who reported any past-year PDM: 31.66% (95% CI = 30.35, 33.00) 18–25 age cohort, 29.92% (95% CI = 25.82, 30.12) 26–34 age cohort, 22.68% (95% CI = 20.71, 24.78) 35–49 age cohort, 17.11% (95% CI = 14.46, 20.11) 50–64 age cohort, and 9.70% (95% CI = 5.89, 15.56) 65 or older age cohort. In looking at specific combinations of PDM, it is clear that opioid and tranquilizer/sedative misuse was the most common. The prevalence of this combination of PDM decreased from a high of 1.45% (95% CI = 1.31, 1.60) in the 18–25 age cohort to a low of 0.13% (95% CI = 0.07, 0.23) in the 65 or older age cohort. It is also evident from Table 1 that poly-PDM was uncommon in the 65 or older age cohort, as only 27 total respondents report poly-PDM. Due to their low prevalence, respondents aged 65 and older were combined with respondents aged 50 and over for the remainder of the analyses.

Table 1:

Prevalence of past year poly-PDM and combined PDM across age cohorts

18–25 age cohort 26–34 age cohort 35–49 age cohort 50–64 age cohort 65+ age cohort
% (95% CI) % (95% CI) % (95% CI) % (95% CI) % (95% CI)
Poly-PDM
No PDM 85.80 (85.34, 86.25)
 89.23 (88.87, 89.58)
 93.61 (93.33, 93.89)
 95.32 (94.94, 95.67)
 97.96 (97.68, 98.21)
n = 47,947 n = 31,760 n = 42,463 n = 19,406 n = 14,599
Single PDM 9.70 (9.35, 10.07)
 7.76 (7.43, 8.10)
 4.94 (4.67, 5.21)
 3.88 (3.56, 4.22)
 1.84 (1.60, 2.11)
n = 5,342 n = 2,639 n = 2,285 n = 749 n = 270
Poly-PDM 4.50 (4.26, 4.74)
 3.01 (2.75, 3.27)
 1.45 (1.32, 1.58)
 0.80 (0.67, 0.96)
 0.20 (0.12, 0.33)
n = 2,401 n = 1,016 n = 684 n = 178 n = 27
Combined PDM
Opioid and Tranquilizer/Sedative 1.45 (1.31, 1.60)
 1.25 (1.12, 1.41)
 0.81 (0.72, 0.92)
 0.55 (0.45, 0.68)
 0.13 (0.07, 0.23)
n = 769 n = 423 n = 367 n = 126 n = 18
Opioid and Stimulant 0.89 (0.80, 0.98)
 0.60 (0.50, 0.71)
 0.25 (0.19, 0.31)
 0.04 (0.01, 0.07)
 0.0002 (0.00002, 0.001)
n = 496 n = 217 n = 119 n = 9 n = 1
Tranquilizer/Sedative and Stimulant 0.94 (0.81, 1.07)
 0.50 (0.41, 0.60)
 0.12 (0.08, 0.17)
 0.02 (0.00, 0.04)
n = 479 n = 149 n = 55 n = 7 n = 0
Opioid and Stimulant and Tranquilizer/Sedative 1.15 (1.04, 1.27)
 0.60 (0.51, 0.70)
 0.21 (0.16, 0.27)
 0.12 (0.07, 0.19)
 0.02 (0.00, 0.10)
n = 630 n = 208 n = 117 n = 23 n = 3
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The idea that poly-PDM is a high-risk type of PDM was evidenced by its association with past-year substance use disorder (SUD), see Figure 1. In the 18–25 age cohort, the cohort with the highest prevalence of poly-PDM, 64.60% (95% CI = 62.01, 67.11) of individuals who endorse poly-PDM reported an SUD. This compares to 38.27% (95% CI = 36.50, 40.07) of individuals who endorse single PDM and 9.79% (95% CI = 9.46, 10.13) of individuals with no PDM. Across most age cohorts the majority of individuals who endorse poly-PDM reported an SUD, including 59.48% (95% CI = 54.58, 64.20) in the 26–34 age cohort, 60.02% (95% CI = 55.29, 64.57) in the 35–49 age cohort, 64.07% (95% CI = 55.37, 71.92) in the 50–64 age cohort, and 44.92% (95% CI = 22.40, 69.74) in the 65 and older age cohort.

Figure 1:

Figure 1:

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Prevalence of any substance use disorder by PDM category

Poly-PDM vs. Single PDM

The use of a categorical measure of poly-PDM allowed for a comparison of poly-PDM to single PDM, findings shown in Table 2. Black adults in all age cohorts were less likely to report poly-PDM than whites. Additionally, Hispanic adults in age cohorts 18–25 and 26–34 were less likely to report poly-PDM compared to whites. Income was significant, but only for adults in age cohort 26–34, as individuals with an income between $20,000–$49,999 or $50,000–$74,999 were more likely to report poly-PDM compared to those with an income of less than $20,000. Marital status was significant in age cohorts 35–49 (RRR = 1.44, 95% CI = 1.07, 1.91) and 50 or older (RRR = 2.20, 95% CI = 1.22, 3.96), as individuals who were never married were more likely to report poly-PDM compared to those who were currently married. Regarding geographical residence, individuals in age cohorts 26–34 and 35–49 who lived in small metropolitan counties were more likely to report poly-PDM compared to those who lived in large metropolitan counties. Finally, justice involvement was significantly associated with poly-PDM only for adults in the 18–25 age cohort (RRR = 1.38, 95% CI = 1.12, 1.68).

Table 2:

Multinomial logistic regression results comparing poly-PDM to single PDM

18–25 age cohort 26–34 age cohort 35–49 age cohort 50+ age cohort
RRR 95% CI RRR 95% CI RRR 95% CI RRR 95% CI
Demographics
Female (reference)
Male 1.07 (0.92, 1.25) 0.98 (0.79, 1.21) 0.78 (0.59, 1.03) 0.66 (0.41, 1.07)
White (reference)
Black 0.70* (0.53, 0.93) 0.65* (0.44, 0.95) 0.48** (0.28, 0.80) 0.24*** (0.12, 0.46)
Native American/ Alaskan Native 0.48 (0.19, 1.21) 0.49 (0.11, 2.17) 1.05 (0.46, 2.38) 2.06 (0.46, 9.25)
Native Hawaiian/Pacific Islander 2.50 (0.79, 7.89) 0.63 (0.17, 2.31) 0.14* (0.02, 0.96) 0.00*** (0.00, 0.00)a
Asian 0.89 (0.63, 1.26) 0.33* (0.13, 0.80) 1.01 (0.41, 2.46) 0.00*** (0.00, 0.00)b
Multiple 0.85 (0.64, 1.12) 0.87 (0.51, 1.46) 1.82 (0.83, 3.98) 0.41* (0.18, 0.94)
Hispanic 0.78* (0.63, 0.96) 0.67* (0.46, 0.97) 0.69 (0.44, 1.06) 0.90 (0.34, 2.39)
Heterosexual (reference)
Lesbian/Gay 1.23 (0.91, 1.66) 0.98 (0.60, 1.59) 1.41 (0.81, 2.45) 2.18 (0.67, 7.10)
Bisexual 1.11 (0.86, 1.43) 1.23 (0.87, 1.72) 1.12 (0.62, 2.00) 1.82 (0.57, 5.81)
Less than $20,000 (reference)
$20,000 – $49,999 0.91 (0.75, 1.12) 1.31* (1.04, 1.63) 1.19 (0.74, 1.92) 0.70 (0.37, 1.28)
$50,000 – $74,999 0.99 (0.79, 1.23) 1.46* (1.02, 2.06) 1.29 (0.81, 2.05) 0.74 (0.34, 1.61)
$75,000 or more 1.01 (0.80, 1.25) 1.34 (0.98, 1.83) 1.28 (0.84, 2.24) 0.82 (0.36, 1.82)
Received Gov. Assistance 1.08 (0.90, 1.28) 1.24 (0.97, 1.57) 1.07 (0.82, 1.39) 1.22 (0.73, 2.03)
Married (reference)
Widowed 0.89 (0.45, 1.77) 1.48 (0.40, 5.40) 0.34 (0.10, 1.07) 1.27 (0.55, 2.80)
Divorced/Separated 0.65 (0.34, 1.26) 0.99 (0.68, 1.43) 1.27 (0.92, 1.72) 1.59 (0.93, 2.69)
Never Married 0.89 (0.66, 1.20) 1.11 (0.91, 1.35) 1.44* (1.07, 1.91) 2.20** (1.22, 3.96)
HS Dropout (reference)
HS Graduate 0.86 (0.66, 1.11) 0.73 (0.51, 1.02) 1.03 (0.65, 1.63) 1.28 (0.67, 2.45)
Some College 0.95 (0.75, 1.18) 0.94 (0.66, 1.33) 1.24 (0.82, 1.88) 1.26 (0.69, 2.26)
College Graduate 0.85 (0.62, 1.14) 0.96 (0.64, 1.44) 0.92 (0.56, 1.51) 1.09 (0.56, 2.13)
Employed Full-time (reference)
Part-time 0.86 (0.72, 1.00) 0.85 (0.62, 1.15) 1.23 (0.88, 1.72) 1.00 (0.49, 2.04)
Unemployed 1.08 (0.83, 1.41) 0.98 (0.68, 1.40) 1.01 (0.63, 1.60) 1.87 (0.58, 6.06)
Other 0.74** (0.60, 0.91) 1.01 (0.76, 1.32) 0.95 (0.69, 1.28) 1.04 (0.59, 1.81)
Military Service 1.27 (0.62, 2.58) 1.40 (0.87, 2.25) 0.94 (0.56, 1.56) 1.77 (0.97, 3.20)
Large Metro. County (reference)
Small Metro. County 1.07 (0.90, 1.26) 1.40** (1.11, 1.75) 1.30* (1.03, 1.64) 1.33 (0.80, 2.20)
Non Metro. County 1.05 (0.84, 1.28) 1.11 (0.79, 1.57) 1.08 (0.76, 1.52) 1.49 (0.80, 2.75)
Justice-Involved 1.38** (1.12, 1.68) 1.12 (0.80, 1.56) 1.00 (0.66, 1.51) 1.45 (0.66, 3.20)
Health-Related
Uninsured 0.94 (0.77, 1.15) 1.33** (1.08, 1.69) 0.98 (0.72, 1.34) 1.27 (0.55, 2.89)
Fair/Poor Health 0.98 (0.79, 1.21) 1.01 (0.72, 1.40) 0.99 (0.68, 1.41) 1.13 (0.67, 1.91)
Disability 0.84 (0.55, 1.27) 0.94 (0.57, 1.54) 0.83 (0.54, 1.25) 1.03 (0.49, 2.13)
Major Depression 1.05 (0.85, 1.29) 1.28 (0.95, 1.71) 1.30 (0.95, 1.77) 0.93 (0.48, 1.78)
Suicidal Ideation 1.42*** (1.17, 1.71) 1.02 (0.73, 1.42) 1.15 (0.76, 1.73) 1.43 (0.80, 2.56)
ER Visit 1.13 (0.96, 1.31) 1.19 (0.92, 1.51) 0.79* (0.62, 0.99) 1.08 (0.67, 1.71)
Hospitalization 1.04 (0.81, 1.34) 0.99 (0.67, 1.45) 1.26 (0.92, 1.71) 0.77 (0.43, 1.37)
Substance Use Behaviors
Daily Cigarette 1.28** (1.09, 1.50) 1.27 (0.99, 1.64) 1.34* (1.02, 1.74) 0.84 (0.48, 1.47)
Heavy Alcohol 1.04 (0.92, 1.16) 0.96 (0.75, 1.22) 1.15 (0.84, 1.58) 0.57* (0.32, 0.98)
Marijuana 1.37** (1.11, 1.67) 1.17 (0.87, 1.58) 1.49*** (1.21, 1.84) 1.02 (0.64, 1.61)
Other Illegal Drugs 2.34*** (2.00, 2.72) 2.43*** (1.86, 3.17) 1.58* (1.10, 2.26) 1.35 (0.71, 2.57)
Any Substance Use Disorder 1.88*** (1.63, 2.16) 1.73*** (1.31, 2.27) 2.31*** (1.69, 3.15) 5.90*** (3.72, 9.35)
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Relative-Risk Ratios and 95% Confidence Intervals are shown in the table (* p <.05, ** p <.01, *** p <.001).

A measure of survey year is included as a control in all models, but results are not shown in the table.

a

(RRR = 8.43e-11, 95%CI = 1.77e-11, 4.00e-10)

b

(RRR = 1.96e-11, 95%CI = 7.67e-11, 5.01e-10)

For health-related factors, adults in the 26–34 age cohort who were uninsured were more likely to report poly-PDM, while adults in the 18–25 age cohort who endorse suicidal ideation were more likely to report poly-PDM (RRR = 1.42, 95% CI = 1.17, 1.71). Substance use behaviors were consistently associated with poly-PDM. All substance use behaviors except heavy alcohol use were significant in the 18–25 age cohort. Substance use disorder was significantly associated with poly-PDM across all age cohorts and had a strong effect in the 50 or older age cohort (RRR = 5.90, 95% CI = 3.72, 9.35). Other illegal drug use was significantly associated with poly-PDM in all age cohorts with the exception of adults 50 or older, while both daily cigarette smoking and marijuana use were only significant in the 18–25 and 35–49 age cohorts. Interestingly, heavy alcohol use was only significantly associated with poly-PDM in the 50 or older age cohort, with individuals who endorse heavy alcohol use being less likely to report poly-PDM (RRR = 0.57, 95% CI = 0.32, 0.98).

Poly-PDM vs. No PDM

Next, we discuss the findings that assessed the association between poly-PDM and no PDM, see Table 3. Regarding race/ethnicity, both Black and Asian adults across all age cohorts were less likely to report poly-PDM compared to whites. Additionally, Hispanic adults in age cohorts 18–25, 26–34, and 35–49 were less likely to report poly-PDM compared to whites. A higher income was again significantly associated with poly-PDM in the 26–34 age cohort. Marital status was significantly associated with poly-PDM, as adults in age cohorts 26–34, 35–49, and 50+ who were never married were more likely to report poly-PDM compared to those who were currently married. The effect of marital status increased across age cohorts, from a low of (RRR = 1.27, 95% CI = 1.02, 1.57) in the 26–34 age cohort, to a high of (RRR = 2.39, 95% CI = 1.26, 4.52) in the 50 or older age cohort. Finally, justice involvement was significantly associated with poly-PDM in the 18–25 age cohort (RRR = 1.90, 95% CI = 1.55, 2.31).

Table 3:

Multinomial logistic regression results comparing poly-PDM to no PDM

18–25 age cohort 26–34 age cohort 35–49 age cohort 50+ age cohort
RRR 95% CI RRR 95% CI RRR 95% CI RRR 95% CI
Demographics
Female (reference)
Male 0.94 (0.83, 1.05) 0.88 (0.72, 1.09) 0.73** (0.59, 0.91) 0.67 (0.43, 1.02)
White (reference)
Black 0.50*** (0.39, 0.63) 0.37*** (0.25, 0.51) 0.23*** (0.13, 0.37) 0.20*** (0.09, 0.39)
Native American/ Alaskan Native 0.30** (0.13, 0.69) 0.50 (0.11, 2.15) 0.57 (0.28, 1.11) 1.02 (0.29, 3.55)
Native Hawaiian/Pacific Islander 1.62 (0.63, 4.10) 0.42 (0.12, 1.43) 0.13* (0.02, 0.87) 0.00*** (0.00, 0.00)a
Asian 0.53** (0.36, 0.77) 0.20** (0.08, 0.51) 0.35* (0.14, 0.81) 0.00*** (0.00, 0.00)b
Multiple 0.81 (0.62, 1.05) 0.71 (0.46, 1.10) 1.23 (0.63, 2.37) 0.51* (0.26, 0.98)
Hispanic 0.65*** (0.53, 0.78) 0.53** (0.37, 0.75) 0.48*** (0.32, 0.72) 0.97 (0.43, 2.16)
Heterosexual (reference)
Lesbian/Gay 1.16 (0.83, 1.61) 1.14 (0.72, 1.79) 1.41 (0.84, 2.35) 2.16 (0.87, 5.33)
Bisexual 1.16 (0.94, 1.42) 1.67** (1.23, 2.25) 1.12 (0.66, 1.87) 2.59 (0.90, 7.46)
Less than $20,000 (reference)
$20,000 – $49,999 0.83* (0.71, 0.97) 1.31** (1.07, 1.60) 1.33 (0.82, 2.15) 0.85 (0.46, 1.56)
$50,000 – $74,999 0.92 (0.73, 1.14) 1.48* (1.07, 2.04) 1.48 (0.97, 2.24) 0.99 (0.48, 2.02)
$75,000 or more 1.04 (0.87, 1.25) 1.61** (1.22, 2.13) 1.49 (0.93, 2.35) 1.04 (0.52, 2.06)
Received Gov. Assistance 0.99 (0.83, 1.18) 1.36* (1.05, 1.74) 1.20 (0.87, 1.63) 1.61* (0.99, 2.60)
Married (reference)
Widowed 1.69 (0.81, 3.51) 1.41 (0.55, 3.60) 0.60 (0.20, 1.78) 1.20 (0.53, 2.69)
Divorced/Separated 0.83 (0.41, 1.68) 1.20 (0.86, 1.67) 1.35 (0.99, 1.83) 1.65* (1.00, 2.73)
Never Married 1.23 (0.90, 1.68) 1.27* (1.02, 1.57) 1.53** (1.17, 1.98) 2.39** (1.26, 4.52)
HS Dropout (reference)
HS Graduate 0.88 (0.72, 1.07) 0.81 (0.57, 1.13) 0.97 (0.62, 1.52) 1.19 (0.66, 2.12)
Some College 1.20 (0.98, 1.46) 1.09 (0.78, 1.51) 1.27 (0.89, 1.81) 1.65 (0.97, 2.79)
College Graduate 1.16 (0.87, 1.53) 1.02 (0.70, 1.48) 1.06 (0.69, 1.64) 1.38 (0.77, 2.44)
Employed Full-time (reference)
Part-time 0.91 (0.78, 1.05) 0.80 (0.61, 1.05) 1.29 (0.96, 1.72) 1.01 (0.52, 1.83)
Unemployed 1.04 (0.82, 1.31) 1.04 (0.71, 1.53) 1.09 (0.71, 1.65) 1.63 (0.51, 5.17)
Other 0.77** (0.63, 0.94) 0.87 (0.67, 1.13) 0.83 (0.61, 1.12) 0.84 (0.50, 1.40)
Military Service 1.15 (0.560, 2.33) 1.28 (0.80, 2.02) 0.98 (0.59, 1.61) 1.33 (0.76, 2.32)
Large Metro. County (reference)
Small Metro. County 1.14 (0.96, 1.36) 1.19 (0.95, 1.48) 1.17 (0.94, 1.46) 1.06 (0.67, 1.65)
Non Metro. County 1.12 (0.95, 1.33) 0.94 (0.70, 1.27) 0.81 (0.58, 1.12) 1.10 (0.65, 1.85)
Justice-Involved 1.90*** (1.55, 2.31) 1.36 (0.99, 1.86) 1.17 (0.79, 1.73) 1.65 (0.85, 3.22)
Health-Related
Uninsured 0.92 (0.75, 1.10) 1.46** (1.13, 1.87) 1.12 (0.83, 1.50) 1.62 (0.78, 3.34)
Fair/Poor Health 0.87 (0.70, 1.08) 1.10 (0.79, 1.50) 1.14 (0.84, 1.52) 1.40 (0.85, 2.27)
Disability 0.98 (0.61, 1.54) 1.23 (0.81, 1.85) 1.10 (0.68, 1.76) 1.09 (0.54, 2.19)
Major Depression 1.31* (1.04, 1.63) 1.57*** (1.24, 1.98) 1.81*** (1.34, 2.44) 1.77 (0.95, 3.28)
Suicidal Ideation 1.73*** (1.43, 2.07) 1.73** (1.25, 2.38) 1.97** (1.31, 2.95) 3.07*** (1.68, 5.59)
ER Visit 1.30*** (1.1., 1.50) 1.39** (1.15, 1.68) 1.10 (0.88, 1.37) 1.15 (0.73, 1.80)
Hospitalization 1.19 (0.95, 1.48) 0.94 (0.66, 1.32) 1.24 (0.89, 1.72) 0.99 (0.53, 1.82)
Substance Use Behaviors
Daily Cigarette 2.00*** (1.71, 2.32) 1.69*** (1.34, 2.12) 1.42* (1.08, 1.84) 1.18 (0.69, 2.01)
Heavy Alcohol 1.66*** (1.47, 1.85) 1.23 (0.96, 1.55) 1.26 (0.93, 1.69) 0.54* (0.34, 0.88)
Marijuana 4.25*** (3.50, 5.16) 3.08*** (2.44, 3.89) 3.49*** (2.81, 4.33) 2.81*** (1.87, 4.21)
Other Illegal Drugs 6.64*** (5.78, 7.61) 6.74*** (5.25, 8.64) 4.91*** (3.50, 6.89) 2.67** (1.45, 4.90)
Any Substance Use Disorder 4.42*** (3.85, 5.06) 4.75*** (3.71, 6.07) 7.64*** (5.92, 9.85) 25.74*** (17.30, 38.28)
Open in a new tab

Relative-Risk Ratios and 95% Confidence Intervals are shown in the table (* p <.05, ** p <.01, *** p <.001).

A measure of survey year is included as a control in all models, but results are not shown in the table.

a

(RRR = 7.78e-11, 95%CI = 4.34e-11, 1.39e-10)

b

(RRR = 8.02e-11, 95%CI = 5.01e-11, 1.28e-10)

A number of health-related factors were significantly associated with poly-PDM. In particular, adults in all age cohorts who report suicidal ideation were more likely to report poly-PDM, with a strong effect in the 50 or older age cohort (RRR = 3.07, 95% CI = 1.68, 5.59). Major depression was significantly associated with poly-PDM in all age cohorts except adults 50 or older, with a strong effect in the 35–49 age cohort (RRR 1.81, 95% CI = 1.34, 2.44). Emergency room visits was significant in only the 18–25 and 26–34 age cohorts, with individuals who visited an emergency room being more likely to report poly-PDM.

Most of the substance use behaviors were significantly associated with poly-PDM. Individuals who report marijuana use, other illegal drug use, or an SUD were more likely to report poly-PDM across all age cohorts. While adults in all age cohorts except 50 or older who were daily cigarette smokers were more likely to report poly-PDM. Finally, heavy alcohol use was significantly associated with poly-PDM in the 18–25 age cohort (RRR = 1.66, 95% CI = 1.47, 1.85) and the 50 or older age cohort (RRR = 0.54, 95% CI = 0.34, 0.88).

Single PDM vs. No PDM

The results of the multinomial regression analyses comparing the misuse of only one class of PDM (single PDM) to no PDM are shown in Supplemental Table 1. In most age cohorts Black, Asian, and Hispanic adults were less likely to report single PDM compared to whites. Never married individuals in age cohorts 18–25 and 26–34 were more likely to report single PDM compared to those currently married. Individuals living in non-metropolitan counties in age cohorts 26–34, 35–49, and 50+ were all less likely to report single PDM compared to those living in large metropolitan counties. For adults in age cohorts 18–25 and 26–34 the likelihood of reporting single PDM was increased among those who reported justice involvement. In all age cohorts, adults who endorse suicidal ideation were more likely to report single PDM. Major depression was significant in every age cohort except 26–34 and ER visits was significant for individuals in age cohorts 26–34 and 35–49. Finally, adults in all age cohorts who reported marijuana use, other illegal drug use, or an SUD were more likely to report single PDM. The likelihood of single PDM was also increased for daily smokers, except those in age cohort 35–49, and heavy alcohol users in age cohorts 18–25 and 26–34.

For all of the multinomial regression analyses discussed thus far, illegal drug use was consistently associated with poly-PDM. However, in these analyses we combined several different types of illegal drugs (i.e., cocaine/crack, heroin, hallucinogens, inhalants, methamphetamine) into one category of any illegal drug use. In additional analyses, shown in Supplemental Table 2, we included each of these drugs separately in the multinomial logistic regression models. The majority of these drugs were significantly associated with single and poly-PDM in the 18–25, 26–34, and 35–49 age cohorts.

Specific PDM combinations

The final set of analyses focused on specific combinations of PDM. We highlight the findings for opioid and tranquilizer/sedative misuse compared to no PDM in Table 4. For race/ethnicity, Black adults in age cohorts 26–34, 35–49, and 50+ were less likely to report combined PDM than whites. Additionally, Hispanic adults in age cohorts 18–25, 26–34, 35–49 were less likely to report combined PDM compared to whites. Homosexual individuals in the 50 or older age cohort and bisexual individuals in age cohort 26–34 (RRR = 1.74, 95% CI = 1.19, 2.54) were more likely to report combined PDM than heterosexuals. Marital status was significant in the 50 or older age cohort, with divorced/separated (RRR = 1.89, 95% CI = 1.08, 3.27) and never married (RRR = 2.59, 95% CI = 1.12, 5.93) individuals being more likely to report this combined PDM. Finally, adults in the 18–25 age cohort with a history of justice involvement were more likely to report combined PDM (RRR = 2.08, 95% CI = 1.54, 2.82).

Table 4:

Multinomial logistic regression results comparing combined opioid and tranquilizer/sedative misuse to no PDM

18–25 age cohort 26–34 age cohort 35–49 age cohort 50+ age cohort
RRR 95% CI RRR 95% CI RRR 95% CI RRR 95% CI
Demographics
Female (reference)
Male 0.89 (0.72, 1.08) 0.82 (0.59, 1.12) 0.69** (0.53, 0.88) 0.66 (0.40, 1.08)
White (reference)
Black 0.78 (0.58, 1.06) 0.58* (0.37, 0.88) 0.30*** (0.18, 0.49) 0.19** (0.07, 0.48)
Native American/ Alaskan Native 0.65 (0.20, 2.07) 0.02** (0.00, 0.16) 0.61 (0.24, 1.52) 0.79 (0.15, 4.08)
Native Hawaiian/Pacific Islander 1.79 (0.36, 8.93) <0.00*** (0.00, 0.00)a 0.16 (0.01, 1.42) <0.00*** (0.00, 0.00)b
Asian 0.67 (0.33, 1.33) 0.29 (0.07, 1.12) 0.21* (0.06, 0.70) <0.00*** (0.00, 0.00)c
Multiple 0.87 (0.57, 1.31) 0.73 (0.35, 1.53) 1.05 (0.50, 2.17) 0.74 (0.37, 1.46)
Hispanic 0.70** (0.53, 0.91) 0.59* (0.39, 0.88) 0.43** (0.23, 0.78) 1.42 (0.61, 3.26)
Heterosexual (reference)
Lesbian/Gay 1.12 (0.66, 1.87) 1.02 (0.43, 2.42) 1.40 (0.64, 3.04) 2.81* (1.06, 7.39)
Bisexual 1.08 (0.77, 1.53) 1.74** (1.19, 2.54) 1.02 (0.49, 2.10) 2.27 (0.68, 7.51)
Less than $20,000 (reference)
$20,000 – $49,999 1.00 (0.78, 1.26) 1.24 (0.90, 1.70) 1.70 (0.92, 3.12) 0.82 (0.43, 1.59)
$50,000 – $74,999 1.24 (0.92, 1.66) 1.26 (0.77, 2.05) 1.54 (0.82, 2.90) 0.83 (0.38, 1.79)
$75,000 or more 1.36* (1.02, 1.82) 1.36 (0.84, 2.18) 1.34 (0.70, 2.54) 1.02 (0.48, 2.14)
Received Gov. Assistance 1.01 (0.77, 1.31) 1.55* (1.06, 2.26) 1.33 (0.93, 1.89) 1.38 (0.78, 2.41)
Married (reference)
Widowed 1.16 (0.44, 3.06) 1.31 (0.44, 3.84) 0.70 (0.18, 2.60) 1.38 (0.54, 3.51)
Divorced/Separated 0.38 (0.14, 1.02) 0.98 (0.59, 1.61) 1.27 (0.80, 2.03) 1.89* (1.08, 3.27)
Never Married 0.74 (0.48, 1.12) 1.26 (0.90, 1.76) 1.31 (0.97, 1.89) 2.59* (1.12, 5.93)
HS Dropout (reference)
HS Graduate 0.70* (0.53, 0.93) 0.69 (0.41, 1.15) 0.75 (0.45, 1.26) 1.29 (0.64, 2.58)
Some College 0.84 (0.62, 1.14) 0.92 (0.57, 1.46) 1.26 (0.79, 1.99) 1.83 (0.98, 3.40)
College Graduate 0.44** (0.26, 0.73) 0.79 (0.45, 1.37) 0.98 (0.55, 1.73) 1.25 (0.64, 2.39)
Employed Full-time (reference)
Part-time 0.82 (0.64, 1.04) 0.97 (0.65, 1.41) 1.54* (1.09, 2.16) 1.00 (0.52, 1.90)
Unemployed 1.20 (0.87, 1.65) 1.26 (0.72, 2.19) 1.17 (0.69, 1.97) 1.52 (0.39, 5.79)
Other 0.76 (0.57, 1.01) 0.97 (0.67, 1.37) 0.88 (0.56, 1.38) 0.59 (0.32, 1.07)
Military Service 1.90 (0.86, 4.19) 0.89 (0.47, 1.65) 0.91 (0.46, 1.80) 1.40 (0.74, 2.65)
Large Metro. County (reference)
Small Metro. County 1.40** (1.08, 1.81) 1.24 (0.89, 1.71) 1.27 (0.96, 1.68) 0.91 (0.57, 1.47)
Non Metro. County 1.28 (0.89, 1.82) 1.01 (0.65, 1.56) 0.79 (0.51, 1.22) 1.16 (0.64, 2.08)
Justice-Involved 2.08*** (1.54, 2.82) 1.15 (0.81, 1.62) 1.37 (0.85, 2.20) 1.86 (0.94, 3.67)
Health-Related
Uninsured 1.03 (0.77, 1.39) 1.72** (1.20, 2.47) 1.24 (0.78, 1.64) 2.07 (0.90, 4.75)
Fair/Poor Health 1.04 (0.81, 1.32) 1.17 (0.73, 1.89) 1.09 (0.73, 1.59) 1.19 (0.69, 2.02)
Disability 0.72 (0.40, 1.29) 1.43 (0.88, 2.32) 1.37 (0.81, 2.42) 1.28 (0.57, 2.84)
Major Depression 1.34 (0.97, 1.83) 1.31 (0.90, 1.88) 1.49* (1.33, 2.81) 1.77 (0.88, 3.55)
Suicidal Ideation 2.27*** (1.50, 2.56) 1.77** (1.19, 2.64) 1.89** (1.48, 3.71) 3.07** (1.52, 6.19)
ER Visit 1.48** (1.17, 1.84) 1.47** (1.10, 1.96) 1.12 (0.83, 1.62) 1.45 (0.86, 2.42)
Hospitalization 1.15 (0.84, 1.58) 1.19 (0.78, 1.80) 1.51* (1.02, 2.24) 0.90 (0.42, 1.93)
Substance Use Behaviors
Daily Cigarette 2.26*** (1.74, 2.94) 1.40* (1.00, 1.94) 1.44 (0.97, 2.14) 1.14 (0.62, 2.08)
Heavy Alcohol 1.29* (1.03, 1.61) 0.96 (0.69, 1.33) 1.12 (0.75, 1.66) 0.81 (0.44, 1.46)
Marijuana 3.52*** (2.57, 4.83) 2.16*** (1.50, 3.11) 2.83*** (2.15, 3.71) 3.03*** (1.90, 4.81)
Other Illegal Drugs 6.07*** (4.75, 7.75) 5.44*** (3.86, 7.67) 3.15*** (1.97, 5.01) 2.01 (0.94, 4.29)
Any Substance Use Disorder 4.71*** (3.72, 5.94) 6.94*** (4.72, 10.19) 9.00*** (6.20, 13.06) 19.84*** (11.67, 33.71)
Open in a new tab

Relative-Risk Ratios and 95% Confidence Intervals are shown in the table (* p <.05, ** p <.01, *** p <.001).

A measure of survey year is included as a control in all models, but results are not shown in the table.

a

(RRR = 1.14e-9, 95%CI = 5.74e-10, 2.28e-9)

b

(RRR = 3.63e-11, 95%CI = 1.89e-11, 6.94e-11)

c

(RRR = 4.00e-12, 95%CI = 2.47e-11, 6.49e-11)

Adults in all age cohorts who endorse suicidal ideation were more likely to report combined PDM, with a strong effect in the 50 or older age cohort (RRR = 3.07, 95% CI = 1.52, 6.19). Younger adults, age cohorts 18–25 and 26–34, who had visited an emergency room were more likely to report this combined PDM. Adults in all age cohorts who reported marijuana use, other illegal drug use, or an SUD were more likely to report this PDM combination. While daily cigarette smoking was significant in the 18–25 and 26–34 age cohorts and heavy alcohol use was only significant in the 18–25 age cohort.

In this final set of analyses, we also looked at other combinations of PDM, those findings are shown in Supplemental Tables 3 (opioid and stimulant misuse), 4 (tranquilizer/sedative and stimulant misuse), and 5 (opioid and tranquilizer/sedative and stimulant misuse).

Discussion

Given the association between polysubstance use and adverse outcomes found in both U.S. and international samples, the current research focused on poly-PDM and addressed important gaps in the literature. There are five main contributions of the current research. First, using data from a large nationally representative sample we found that the prevalence of poly-PDM decreased from younger to older age cohorts. Additionally, poly-PDM was common among adults who engage in any prescription drug, with roughly 30% in the 18–25 and 26–34 age cohorts and about 20% in the 35–49 and 50–64 age cohorts reporting poly-PDM. With regard to specific combinations of PDM, opioid and tranquilizer/sedative misuse was the most common, followed by opioid and stimulant misuse.

Second, the idea that poly-PDM is a high-risk type of PDM was clearly supported by the findings, across all age cohorts, that adults who endorse poly-PDM had the highest prevalence of past-year SUD. Notably, there was a substantial difference in the prevalence of SUD between adults who endorse single PDM and those who endorse poly-PDM. In most age cohorts the prevalence of reporting an SUD was twice as high for individuals who reported poly-PDM compared to single PDM. For example, in the 35–49 age cohort 29.57% who endorse single PDM reported an SUD, compared to 60.02% who endorse poly-PDM.

Third, a number of characteristics were significantly associated with poly-PDM across most age cohorts. For racial/ethnic identity, Black, Asian, and Hispanic adults were less likely to report poly-PDM compared to whites. These findings contradict other studies using the NSDUH that have identified Hispanics as being more likely to misuse prescription opioids (Han et al., 2018), benzodiazepines (Blanco et al., 2018), and stimulants (Compton et al., 2018) compared to whites. Furthermore, while some research suggests an age-race crossover effect for drug use, with whites at increased likelihood at younger ages and non-whites at increased likelihood at older ages (Banks et al., 2018; Keyes et al., 2015; Watt et al., 2008), this was not supported by the current research. Marital status was another important demographic characteristic. Adults who were never married, with the exception of the 18–25 age cohort, were more likely to report poly-PDM compared to respondents who were currently married. These findings are consistent with a strong protective effect of marriage that is evidenced in the literature (Dollar & Ray, 2013; Duncan et al., 2006; Han et al., 2017).

Two mental health-related factors, depression and suicidal ideation, were significantly associated with poly-PDM across most age cohorts. These findings extend prior research that has identified both depression and suicidal ideation as robust correlates of PDM, as well as substance use disorders associated with prescription drugs (Blanco et al., 2018; Compton et al., 2018; Han et al., 2018). More research on the association between suicidal ideation and poly-PDM is needed as only a few studies have assessed the relationship between suicidal ideation and PDM (Ford & Perna, 2015; Zullig & Divin, 2012). The association between suicidal ideation and poly-PDM is especially concerning given the overdose potential associated with the combined use/misuse of prescription opioids and benzodiazepines (Curtin et al., 2017; Franklyn et al., 2017; Jones et al., 2015; Jones et al., 2012).

Several substance use behaviors were also significantly associated with poly-PDM across most age cohorts. These findings are supportive of prior research that shows marijuana use and other illegal drug use are strongly associated with PDM (Blanco et al., 2018; Compton et al., 2018; Han et al., 2018) and that PDM-involved polysubstance use is common (Grisby et al., 2019; Morely et al., 2017; Palamar et al., 2018; Schepis et al. 2016a; Votaw et al., 2020). Notably, there was a strong association between having an SUD and reporting poly-PDM, as it was one of only two characteristics that was significantly associated with poly-PDM, in comparison to single PDM, across all age cohorts.

Fourth, we also found evidence of heterogeneity in correlates, particularly demographic characteristics, of poly-PDM across age cohorts. Sexual identity was only significant in age cohort 26–34, with bisexual individuals being more likely to report poly-PDM compared to heterosexuals. This is consistent with prior research that shows that sexual minority groups, especially bisexuals, are at increased likelihood for substance use and PDM (Duncan et al., 2019: Schuler et al., 2018). Income was only significant for respondents in age cohort 26–34, with the likelihood of reporting poly-PDM increasing with higher income. The positive association between poly-PDM and income may be attributable to increased access to healthcare, which is important given the link between medical use of prescription drugs and prescription drug misuse (McCabe et al., 2017b; McCabe et al., 2017c). Finally, justice involvement was only associated with poly-PDM in the 18–25 age cohort. This is supported by prior research that shows elevated prevalence of substance use and substance use disorders among justice-involved populations (Bronson et al., 2017; Fearn et al., 2016). The significance of justice involvement among younger adults makes sense as the prevalence of criminal offending decreases with age (Farrington, 1986; Greenberg, 1985).

One health-related factor, emergency room visits, appeared to be more important for younger adults, as it was only significant for individuals under 35. It is important to acknowledge that visits to emergency departments have been linked to drug seeking behavior (Grover et al., 2012; Zechnich et al., 1996). Additionally, emergency rooms may provide an opportunity for interventions, as frequent visits likely identify individuals with a number of high-risk factors for poly-PDM. Finally, heavy alcohol use was positively associated with poly-PDM in the 18–25 age cohort but negatively associated with poly-PDM in the 50 or older age cohort. This finding is consistent with research showing a higher prevalence of PDM-involved polysubstance use in young adults (McCabe et al. 2006). Research on motives for PDM may help us understand this finding, as younger age groups are more likely to endorse recreational motives while older age groups are more likely to endorse self-treatment motives (Schepis et al. 2020a; Schepis et al., 2020b). This suggests that younger people are more likely to be engaged in PDM as part of an overall pattern of drug use, while older people may be involved in PDM to treat more discrete medical problems.

Fifth, we identified a number of characteristics that distinguished poly-PDM from single PDM. Across all age cohorts, black adults were less likely to report poly-PDM compared to whites. Younger Hispanics were also less likely to report poly-PDM compared to whites. Older respondents who were never married were also more likely to report poly-PDM. Finally, adults who reported illegal drug use, all age cohorts except 65 or older, or an SUD, all age cohorts, were more likely to report poly-PDM. Given the increased risk associated with polysubstance use such as an SUD, identifying factors associated with poly-PDM, compared to single PDM, can provide critical information to inform intervention strategies.

Limitations

While the NSDUH is one of the most widely used epidemiological studies to assess substance use, a few limitations are worth noting. First, the NSDUH is a cross-sectional study, which makes it problematic to infer any causal relationships and identify factors associated with stability and change in poly-PDM over time. Second, the data were collected via self-report so self-report bias may also be an issue. Research indicates that self-reported substance use data are reliable and valid (Johnston et al., 1985; O’Malley et al., 1983). The NSDUH methodology takes several steps to address self-report bias, including but not limited to, collecting data via ACASI methods, including pictures and trade/generic names for prescription drugs (Center for Behavioral Health Statistics and Quality, 2019). Finally, it is important to acknowledge issues associated with selection bias among older respondents in the NSDUH. Importantly, the NSDUH likely under-samples older adults living in controlled access dwellings like nursing homes (Cunningham et al., 2015). We also know that drug use is associated with incarceration and increased morbidity, so sample attrition may be an issue for older respondents (Bronson et al., 2017; Naimi et al., 2019). This may account for the low prevalence of poly-PDM among older respondents, which limits statistical power to identify significant correlates.

Conclusions

Given a dearth of research on poly-PDM across different adult age cohorts, the current research has important implications for countries interested in reducing controlled medication misuse and related consequences. We found evidence that race/ethnicity, marital status, mental health problems, and substance use behaviors were robust correlates of poly-PDM across age cohorts and should be widely included in screening and interventions. However, we also found evidence of heterogeneity in risk factors across age cohorts. These findings highlight the importance of identifying which factors (e.g., sexual identity, income, justice involvement) are associated with poly-PDM at different stages of the life course and suggest that age-specific screenings and interventions may be necessary. Finally, these findings show that adults who endorse poly-PDM have high rates of substance use disorders, underscoring the risk associated with poly-PDM in comparison to single PDM.

Supplementary Material

1

Funding Statement:

This work was funded by R01 DA043691, R01 DA042146, R01 AA025684, R01 CA212517, and R01 DA031160 from the National Institute on Drug Abuse (NIDA), National Cancer Institute (NCI) and National Institute on Alcohol Abuse and Alcoholism (NIAAA). The NSDUH is funded by the Substance Abuse and Mental Health Services Administration (SAMHSA). The content is the authors’ responsibility and does not necessarily represent the views of NIDA, NCI, NIAAA, or SAMHSA who had no role in the design of the study, the analyses, interpretation of results or the decision to submit the manuscript for publication.

Footnotes

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