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. 2023 May 19:1–17. Online ahead of print. doi: 10.1007/s11469-023-01084-0

Racial and Ethnic Disparities and Prevalence in Prescription Drug Misuse, Illicit Drug Use, and Combination of Both Behaviors in the United States

Yen-Han Lee 1,✉, Chase Woods 2, Mack Shelley 3, Stephan Arndt 4, Ching-Ti Liu 5, Yen-Chang Chang 6,✉
PMCID: PMC10198020  PMID: 37363760

Abstract

This study examines racial and ethnic disparities and prevalence in prescription drug misuse, illicit drug use, and the combination of both behaviors in the United States. Using five waves of the National Survey on Drug Use and Health (NSDUH, 2015–2019; n = 276,884), a multinomial logistic regression model estimated the outcomes of prescription drug misuse, illicit drug use, and the combination of both behaviors. Participants’ age was considered as an interaction effect. Approximately 5.4%, 2.9%, and 2.5% misused prescription drug, used illicit drug, or had both behaviors, respectively. Compared with White participants, Black (AOR = 0.69, 99.9 CI: 0.61, 0.79) and Asian (AOR = 0.60, 99.9% CI: 0.42, 0.87) participants had significantly lower odds of reporting prescription drug misuse. Individuals who were classified as others had higher odds of reporting illicit drug use (AOR = 1.31; 99.9% CI: 1.05, 1.64), compared with White participants. Black (AOR = 0.40, 99.9% CI: 0.29, 0.56) and Hispanic (AOR = 0.71, 99.9% CI: 0.55, 0.91) participants were significantly less likely to have both prescription drug misuse and illicit drug use behaviors. Interaction analysis showed that Black participants between 18 and 49 years old were less likely to participate in prescription drug misuse. However, Black participants who were 50 years of age or above were more likely to engage in illicit drug use and the combination of both prescription drug misuse and illicit drug use (all p < 0.001). Hispanic adult participants between 18 and 49 years old were more likely to engage in illicit drug use. Successful intervention and cessation programs may consider the cultural and age disparities among different racial and ethnic groups.

Keywords: Prescription drug misuse, Illicit drug use, Multiple drug use, Racial and ethnic disparities

Introduction

Racial and ethnic disparities continue to be a factor associated with different health outcomes or health behaviors in the United States among various age groups (Chen et al., 2016; Churchwell et al., 2020; Fiscella & Sanders, 2016; Gandhi et al., 2020; Gollust et al., 2018; Hammonds & Reverby, 2019; Lebrun & LaVeist, 2011; Manuel, 2018; Norton et al., 2016; Suntai et al., 2020). Racial and ethnic disparities also exist in other public health challenges like illicit drug use and prescription drug misuse. For example, although opioid overdose death rates in the United States have leveled off, the rates have been found 40% higher in Black individuals as compared to their White counterparts (Larochelle et al., 2021). Nevertheless, no change was observed among other races and ethnicities related to opioid overdose death rates in the same study (Larochelle et al., 2021).

In the United States, the most commonly used drugs are marijuana, opioids, heroin, methamphetamine (meth), MDMA, and cocaine (National Institute on Drug Abuse [NIDA], 2018). Prescription drug misuse, which is the use of physician-prescribed drugs, usually for pain treatment, not as intended, is a common form of drug use. In 2015, it was estimated that about 52,204 deaths were attributed to prescription drug misuse, and about 63.1% of those involved opioids (Shupp et al., 2020). Additionally, according to a previous study, 7 million Americans had reported non-medical use of prescription drugs at some point in their lives, making it the second most used drug in the country (Blanco et al., 2013).

Illicit stimulants are a major concern. The most used stimulants are meth and cocaine. In 2020, NIDA reported that about 2.6 million Americans aged 12 and up reported having used meth in the previous 12 months (NIDA, 2022). In recent years, the death rate for psychostimulants, which includes meth, had risen higher than the death rate for prescription opioids, given that drugs like fentanyl is being frequently mixed with methamphetamine (Center for Disease Control and Prevention [CDC], 2022; Shearer et al., 2020).

The combination of illicit drug use and prescription drug misuse is also a critical concern. Multiple drug use, defined as the use of more than one drug at a time, often involves the use of an illicit drug in combination with the misuse of prescription drugs (Lee et al., 2020). In Lee et al. (2020), although the number of adult individuals who misused prescription drugs declined over time, the number of illicit drug uses increased in states with and without medical marijuana legalization. In another study by Kelly and colleagues (2014), the authors observed that approximately 16.4% of the study sample reported the combination of illicit drug use and prescription drug misuse among young adults (Kelly et al., 2014). Furthermore, using the large study sample from the National Survey on Drug Use and Health (NSDUH), Lee et al. (2023) observed that about 29.2% and 28.9% of the study participants without cancer reported prescription pain reliever misuse for getting high and relaxation, respectively. Often, when individuals misuse prescription drugs, they are more likely to engage in sniffing and substitution (Lankenau et al., 2012). Therefore, it is plausible to speculate that illicit drug use and prescription drug misuse may go together.

Our study examines racial and ethnic disparities in prescription drug misuse, illicit drug use, and the combination of both behaviors in one unified research effort. In addition to the racial and ethnic disparities in prescription drug misuse, illicit drug use, and the combination of both behaviors, this research also included both adolescent and adult populations to increase the generalizability of this work. This holistic approach is important because knowledge regarding both prescription drug misuse and illicit drug use is somewhat limited and because multiple drug use may bring further detrimental effects to human health. Five waves from the NSDUH dataset are used to examine this topic of interest and examine trends for each racial and ethnic group in illicit drug use, prescription drug misuse, and the combination of both behaviors. This study may provide a more comprehensive picture for further policy implications and educational strategies geared towards addressing different communities and varying drug use behaviors.

Materials and Methods

Study Sample

We extracted data from five waves of NSDUH (2015–2019) to study the topic of interest. NSDUH is a nationally representative and cross-sectional dataset, which includes data collected from the non-institutionalized populations in the United States, including adolescents and adults. The Substance Abuse and Mental Health Services Administration (SAMHSA) collected data annually to include comprehensive topics of substance use such as misuse of prescription medication, illicit drug use, drug policy, alcohol and cigarette consumption, and other health-related measurements. Further information regarding the NSDUH dataset can be found on their official website (https://www.samhsa.gov/data/release/2019-national-survey-drug-use-and-health-nsduh-releases). A total of 276,884 participants who answered all questions of interests without missing values were obtained in the final study sample. Because we only relied on a publicly available and secondary dataset without identifiable information, this study did not fall into the category of human subject research.

Major Predictor

In the NSDUH questionnaire, there is a variable surveying participants’ race and ethnicity. We coded this major predictor as a categorical variable: White, Black, Asian, Hispanic, and others (including those who were Native Americans, Alaska Natives, Native Hawaiians, and others).

Outcome Variable

The outcome variable in this research included four categories regarding prescription drug misuse and/or illicit drug use in the past 12 months: none (those who did not use illicit drugs nor misuse prescription drugs), prescription drug misuse only, illicit drug use only, and the combination of both prescription drug misuse and illicit drug use. The category of prescription drug misuse included pain relievers, tranquilizers and/or sedatives, and stimulants. For illicit drug use, the category included cocaine and/or crack, inhalants, heroin, hallucinogens, and methamphetamine. The combination of both prescription drug misuse and illicit drug use included participants who misused prescription drug and illicit drugs when they participated in the survey. All categories were mutually exclusive. This type of classification method has been used in another research (Lee et al., 2020).

Covariates

We selected a set of covariates to describe participants’ demographic and socioeconomic characteristics: age (less than 18 years of age, 18–49 years of age, 50 years of age and above), sex (female, male), income (< 20,000, 20,000–49,999, 50,000–74,999, 75,000 or above; measured in US dollars), government assistance programs (no, yes), and county (non-metro, small metro, and large metro). Additional measurements also were included to represent basic information of other substance use behaviors: cigarette use (no, yes), alcohol use (no, yes), and marijuana/hashish use (no, yes). Number of types of health insurance (None, 1 type of insurance, and 2 types or above) was also added in this research as a covariate, given that health insurance possession may be a confounder affecting people’s ability to obtain prescription drugs (Goldman et al., 2007). The measurement of number of types of health insurance included Medicaid, Tricare, private insurance, and other types. We included the survey wave to perform year-fixed-effect regression and investigate trends over time (2015, 2016, 2017, 2018, 2019).

Statistical Analysis

Because the outcome variable was a non-ordinal categorical variable, we estimated a year-fixed-effect multinomial logistic regression model to examine the associations of racial and ethnic disparities with prescription drug misuse, illicit drug use, and the combination of both behaviors. Participants who never used any illicit drugs or misused any prescription drugs were considered as the reference level. Given the large sample size of the NSDUH dataset, we used p < 0.001 to determine the statistical significance. We reported adjusted odds ratios (AORs) and 99.9% confidence intervals (99.9% CIs) as the main statistical results. Furthermore, we added participants’ age and sex as interaction terms. However, only participants’ age achieved statistical significance based on preliminary analysis (p < 0.001). Therefore, we did not include participants’ sex as an interaction effect. Our regression models also included the NSDUH survey weights with Taylor series linearization to adjust standard errors for the complex sampling design. We used STATA 13 for statistical analyses.

Results

Sample Characteristics

Table 1 shows the characteristics of the final study sample, categorized by different consumption status (unweighted n = 276,884). In the overall study sample, 58.54% were white, 52.48% were female, and 60.76% were between 18 and 49 years of age. More than 50% of the study participants had an annual household income over 50,000 USD and around 78.48% of the participants did not have government assistance programs. Only 36.04% of the participants resided in non-metropolitan areas. Most of the study participants did not smoke (51.35%) and did not use marijuana/hashish (56.63%); however, about 72.5% of the study participants used alcohol.

Table 1.

Descriptive statistics of the final study sample (n = 276,884): National Survey on Drug Use and Health (NSDUH), 2015–2019

Prescription drug and illicit drug used1
Overall None Prescription drug misuse only Illicit drug use only Prescription drug misuse and illicit drug use
Unweighted n (%) n = 276,884 (100%) n = 246,837 (89.15%) n = 15,000 (5.42%) n = 8,012 (2.89%) n = 7,035 (2.54%)
n(%) n(%) n(%) n(%) n(%)
Race and ethnicity
White 162,080 (58.54) 143,141 (57.99) 9495 (63.3) 4639 (57.9) 4805 (68.3)
Black 35,223 (12.72) 32,293 (13.08) 1680 (11.2) 856 (10.68) 394 (5.6)
Asian 12,509 (4.52) 11,767 (4.77) 329 (2.19) 264 (3.3) 149 (2.12)
Hispanic 51,106 (18.46) 45,945 (18.61) 2512 (16.75) 1550 (19.35) 1099 (15.62)
Others 15,966 (5.77) 13,691 (5.55) 984 (6.56) 703 (8.77) 588 (8.36)
Age (in years)
< 18 65,113 (23.52) 60,017 (24.31) 2311 (15.41) 1789 (22.33) 996 (14.16)
18–49 168,247 (60.76) 145,183 (58.82) 11,354 (75.69) 5824 (72.69) 5886 (83.67)
>=50 43,524 (15.72) 41,637 (16.87) 1335 (8.9) 399 (4.98) 153 (2.17)
Sex
Female 145,298 (52.48) 131,026 (53.08) 8035 (53.57) 3247 (40.53) 2990 (42.5)
Male 131,586 (47.52) 115,811 (46.92) 6965 (46.43) 4765 (59.47) 4045 (57.5)
Income
< 20,000 52,436 (18.94) 45,025 (18.24) 3384 (22.56) 2081 (25.97) 1946 (27.66)
20,000–49,999 84,403 (30.48) 74,928 (30.36) 4702 (31.35) 2571 (32.09) 2202 (31.3)
50,000–74,999 43,030 (15.54) 38,723 (15.69) 2183 (14.55) 1171 (14.62) 953 (13.55)
75,000 or above 97,015 (35.04) 88,161 (35.72) 4731 (31.54) 2189 (27.32) 1934 (27.49)
Government assistance programs
No 217,301 (78.48) 194,762 (78.9) 11,291 (75.27) 5973 (74.55) 5275 (74.98)
Yes 59,583 (21.52) 52,075 (21.1) 3709 (24.73) 2039 (25.45) 1760 (25.02)
County
Non-metro 99,787 (36.04) 89,196 (36.14) 5491 (36.61) 2635 (32.89) 2465 (35.04)
Small metro 77,768 (28.09) 69,225 (28.04) 4197 (27.98) 2360 (29.46) 1986 (28.23)
Large metro 99,329 (35.87) 88,416 (35.82) 5312 (35.41) 3017 (37.66) 2584 (36.73)
Cigarette use
No 142,177 (51.35) 135,195 (54.77) 4229 (28.19) 2067 (25.8) 686 (9.75)
Yes 134,707 (48.65) 111,642 (45.23) 10,771 (71.81) 5945 (74.2) 6349 (90.25)
Alcohol use
No 76,132 (27.5) 73,821 (29.91) 1289 (8.59) 904 (11.28) 118 (1.68)
Yes 200,752 (72.5) 173,016 (70.09) 13,711 (91.41) 7108 (88.72) 6917 (98.32)
Marijuana/hashish use
No 156,809 (56.63) 151,619 (61.42) 3574 (23.83) 1386 (17.3) 230 (3.27)
Yes 120,075 (43.37) 95,218 (38.58) 11,426 (76.17) 6626 (82.7) 6805 (96.73)
Health insurance
None 34,416 (12.43) 29,580 (11.98) 2181 (14.54) 1395 (17.41) 1260 (17.91)
1 type 77,235 (27.89) 68,412 (27.72) 4277 (28.51) 2470 (30.83) 2076 (29.51)
2 types or more 165,233 (59.68) 148,845 (60.3) 8542 (56.95) 4147 (51.76) 3699 (52.58)
Wave
2015 55,850 (20.17) 49,444 (20.03) 3460 (23.07) 1501 (18.73) 1445 (20.54)
2016 55,720 (20.12) 49,603 (20.1) 3175 (21.17) 1475 (18.41) 1467 (20.85)
2017 55,121 (19.91) 49,073 (19.88) 3037 (20.25) 1532 (19.12) 1479 (21.02)
2018 55,207 (19.94) 49,495 (20.05) 2741 (18.27) 1630 (20.34) 1341 (19.06)
2019 54,986 (19.86) 49,222 (19.94) 2587 (17.25) 1874 (23.39) 1303 (18.52)
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1Prescription drugs: pain relievers, tranquilizers and/or sedatives, and stimulants

Illicit drugs: cocaine and/or crack, inhalants, heroin, hallucinogens, and methamphetamine

Overall Trends of Prescription Drug Misuse, Illicit Drug Use, and the Combination of Both Behaviors Among Different Racial and Ethnic Groups

Figure 1 shows the trends in prescription drug misuse, illicit drug use, and the combination of both behaviors, categorized by different racial and ethnic groups. For prescription drug misuse, the general trends showed declining consumption across different racial and ethnic groups from 2015 to 2019. There was a slight increase among Black participants between 2018 and 2019 and among Asian participants between 2015 and 2016. However, illicit drug use was more varied. Among White participants, the percentage of illicit drug use increased from 2015 to 2019. The usage among Black participants stayed relatively consistent from 2015 to 2019. For the combination of both behaviors, Hispanic use increased slightly between 2015 and 2017, dropped rapidly in 2018, and showed some increase again in 2019. For Asian participants, the percentage of both behaviors increased between 2017 and 2018.

Fig. 1.

Fig. 1

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Trends and prevalence of prescription drug misuse, illicit drug use, and the combination of both behaviors by different racial and ethnic groups

Associations of Racial and Ethnic Disparities with Prescription Drug Misuse, Illicit Drug Use, and the Combination of Both Behaviors

Table 2 demonstrates the main results from the multinomial regression model (weighted N = 1,337,712,428). Compared with White participants, Black (AOR = 0.69, 99.9% CI: 0.61, 0.79) and Asian (AOR = 0.60, 99.9% CI: 0.42, 0.87) participants had lower odds of prescription drug misuse. Others had higher odds of reporting illicit drug use (AOR = 1.31, 99.9% CI: 1.05, 1.64), compared with White participants. Black, Asian, and Hispanic participants were not significantly different from White participants in terms of illicit drug use (p > 0.05). Black (AOR = 0.40, 99.9% CI: 0.29, 0.56) and Hispanic (AOR = 0.71, 99.9% CI: 0.55, 0.91) participants were less likely to have both prescription drug misuse and illicit drug use behaviors than White participants.

Table 2.

Associations of racial and ethnic disparities with prescription drug misuse, illicit drug use, and the combination of both behaviors estimated by the multinomial logistic regression model. (n = 276,884, weighted N = 1,337,712,428)

Prescription drug misuse only Illicit drug use only Prescription drug misuse and illicit drug use
AOR 99.9% CI AOR 99.9% CI AOR 99.9% CI
Race and ethnicity
White
Black 0.69* (0.61,0.79) 0.88 (0.70,1.09) 0.40* (0.29,0.56)
Asian 0.60* (0.42,0.87) 1.23 (0.92,1.65) 0.86 (0.57,1.31)
Hispanic 0.90 (0.78,1.04) 1.05 (0.92,1.20) 0.71* (0.55,0.91)
Others 0.92 (0.72,1.19) 1.31* (1.05,1.64) 1.01 (0.80,1.29)
Age (in years)
Less than 18
18–49 (age1) 0.70* (0.61,0.82) 0.38* (0.33,0.44) 0.36* (0.30,0.44)
>=50 (age2) 0.34* (0.29,0.41) 0.11* (0.09,0.15) 0.04* (0.02,0.07)
Sex
Female
Male 0.93 (0.84,1.03) 1.75* (1.56,1.96) 1.59* (1.39,1.81)

Income

(in USD)

< 20,000
20,000–49,999 0.85* (0.75,0.97) 0.66* (0.54,0.80) 0.57* (0.48,0.67)
50,000–74,999 0.78* (0.67,0.91) 0.54* (0.45,0.65) 0.44* (0.35,0.56)
>=75,000 0.72* (0.63,0.83) 0.42* (0.35,0.51) 0.38* (0.30,0.49)
Government assistance programs
No
Yes 1.16* (1.01,1.35) 1.06 (0.90,1.25) 1.08 (0.91,1.29)
County
Non-metro
Small metro 1.24* (1.06,1.45) 1.30 (1.00,1.70) 1.31* (1.05,1.64)
Large metro 1.33* (1.15,1.55) 1.55* (1.19,2.02) 1.68* (1.39,2.02)
Cigarette use
No
Yes 1.41* (1.26,1.58) 1.64* (1.34,2.00) 2.93* (2.37,3.62)
Alcohol use
No
Yes 1.85* (1.47,2.33) 1.30* (1.01,1.66) 3.83* (2.48,5.91)
Marijuana /Hashish use
No
Yes 3.35* (3.03,3.72) 9.39* (7.75,11.38) 26.10* (18.68,36.47)
Health insurance
No
1 type 0.93 (0.80,1.09) 0.87 (0.73,1.06) 0.89 (0.73,1.08)
2 types or more 0.83* (0.73,0.94) 0.71* (0.57,0.87) 0.67* (0.55,0.82)
Wave
2015
2016 0.78* (0.66,0.92) 0.71* (0.53,0.96) 0.72* (0.54,0.96)
2017 0.72* (0.60,0.87) 0.81 (0.60,1.09) 0.71* (0.53,0.97)
2018 0.67* (0.56,0.80) 0.87 (0.64,1.16) 0.73* (0.55,0.97)
2019 0.66* (0.55,0.80) 0.95 (0.71,1.26) 0.69* (0.55,0.88)
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*p < 0.001

Reference level for outcome: Individuals who did not report illicit drug use and prescription drug misuse

Interaction Analysis

Table 3 shows the results of interaction analysis by adding participants’ age as an interaction term. In the interaction analysis, Black participants between 18 and 49 years old were less likely to participate in prescription drug misuse (AOR = 0.66, 99.9% CI: 0.47, 0.93). However, Black participants who were 50 years of age or older were more likely to engage in illicit drug use and the combination of both prescription drug misuse and illicit drug use (all p < 0.001). Hispanic participants ages 18–49 had significantly higher levels of illicit drug use (p < 0.001).

Table 3.

Associations of racial disparities with prescription drug misuse, illicit drug use, and the combination of both behaviors estimated by the multinomial logistic regression model, with the interaction effects of participants’ age and gender. (n = 276,884, weighted N = 1,337,712,428)

Prescription drug misuse only Illicit drug use only Prescription drug misuse and illicit drug use
AOR 99.9% CI AOR 99.9% CI AOR 99.9% CI
Race
White
Black 0.97 (0.72,1.30) 0.58* (0.40,0.84) 0.27* (0.14,0.54)
Asian 0.69 (0.37,1.29) 0.83 (0.40,1.71) 1.56 (0.57,4.29)
Hispanic 0.93 (0.71,1.22) 0.67* (0.49,0.92) 0.67 (0.43,1.04)
Others 1.02 (0.70,1.48) 1.01 (0.70,1.45) 1.02 (0.53,1.99)
Age
< 18
18–49 (age1) 0.77* (0.64,0.92) 0.33* (0.27,0.40) 0.37* (0.29,0.46)
>=50 (age2) 0.35* (0.28,0.43) 0.08* (0.06,0.11) 0.03* (0.02,0.05)
Sex
Female
Male 0.93 (0.84,1.03) 1.75* (1.56,1.96) 1.59* (1.39,1.81)

Income

(in USD)

< 20,000
20,000–49,999 0.85* (0.75,0.97) 0.66* (0.54,0.81) 0.57* (0.48,0.68)
50,000–74,999 0.78* (0.67,0.92) 0.54* (0.45,0.66) 0.44* (0.35,0.56)
>=75,000 0.73* (0.63,0.84) 0.43* (0.35,0.52) 0.39* (0.31,0.49)
Government assistance programs
No
Yes 1.16* (1.01,1.35) 1.07 (0.91,1.26) 1.09 (0.91,1.29)
County
Non-metro
Small metro 1.24* (1.06,1.45) 1.31* (1.00,1.71) 1.32* (1.05,1.65)
Large metro 1.33* (1.14,1.53) 1.56* (1.19,2.03) 1.68* (1.39,2.02)
Cigarette use
No
Yes 1.41* (1.25,1.58) 1.61* (1.32,1.95) 2.90* (2.34,3.58)
Alcohol use
No
Yes 1.87* (1.48,2.35) 1.29* (1.01,1.65) 3.86* (2.50,5.96)
Marijuana /Hashish use
No
Yes 3.36* (3.03,3.73) 9.47* (7.82,11.48) 26.25* (18.68,36.90)
Health insurance
No
1 type 0.93 (0.79,1.08) 0.88 (0.73,1.06) 0.89 (0.73,1.08)
2 types or more 0.82* (0.73,0.94) 0.70* (0.57,0.86) 0.67* (0.55,0.82)
Wave
2015
2016 0.78* (0.66,0.92) 0.71* (0.53,0.95) 0.72* (0.54,0.95)
2017 0.72* (0.60,0.87) 0.81 (0.60,1.09) 0.71* (0.53,0.96)
2018 0.67* (0.56,0.80) 0.86 (0.64,1.16) 0.73* (0.55,0.97)
2019 0.66* (0.55,0.80) 0.94 (0.70,1.26) 0.69* (0.55,0.87)
Interaction
Race X Age
Black X age11 0.66* (0.47,0.93) 1.07 (0.70,1.62) 1.17 (0.58,2.38)
Black X age22 0.75 (0.45,1.26) 4.61* (2.68,7.95) 6.72* (1.74,25.91)
Asian X age1 0.84 (0.44,1.62) 1.55 (0.69,3.49) 0.51 (0.16,1.57)
Asian X age2 0.93 (0.18,4.72) 0.84 (0.04,18.27) 0.86 (0.02,33.56)
Hispanic X age1 0.87 (0.66,1.15) 1.55* (1.08,2.24) 1.00 (0.62,1.60)
Hispanic X age2 1.38 (0.85,2.24) 2.08 (0.89,4.86) 2.37 (0.43,13.20)
Others X age1 0.89 (0.55,1.42) 1.39 (0.89,2.19) 0.98 (0.45,2.10)
Others X age2 0.94 (0.46,1.91) 0.82 (0.21,3.17) 1.13 (0.25,5.19)
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*p < 0.001

1age1: 18 to 49 years of age

2age2: 50 years of age or above

Reference level for outcome: Individuals who did not report illicit drug use and prescription drug misuse

Discussion

General Discussion and the Trends

This study examined racial and ethnic disparities, trends over time, and prevalence of prescription drug misuse, illicit drug use, and the combination of both behaviors. In general, racial and ethnic groups showed different characteristics in prescription drug misuse, illicit drug use, and both behaviors. Compared with White participants, Black and Asian participants were significantly less likely to misuse prescription drugs. Other races had significantly higher odds of using illicit drugs, compared with White participants. Black and Hispanic participants were significantly less likely to have both prescription drug misuse and illicit drug use behaviors than White participants. For interaction analysis, we observed that Black participants between 18 and 49 years old were less likely to participate in prescription drug misuse. However, Black participants who were 50 years of age or older were more likely to engage in illicit drug use and in the combination of prescription drug misuse and illicit drug use. Hispanic adult participants ages 18–49 were more likely to engage in illicit drug use.

The prevalence of prescription drug misuse generally declined among different racial and ethnic groups in the United States. In a previous study, Conn and Marks (2014) observed that White adolescents reported higher rates of prescription drug misuse than Black and Hispanic adolescents. Our study observations paralleled their findings. In the 2019 wave of NSDUH, the figure shows that the percentage of prescription drug misuse among White participants was still the highest among different racial and ethnic groups, except individuals who were classified as others. The increasing trend of White participants using illicit drugs should deserve further intervention and policy attention. Vaughn and colleagues (2018) argued that White individuals are more likely to become sensation seekers and explore psychoactive experiences as compared to other minority groups, especially among young adults and adolescents. In addition, we noticed a slight increase in illicit drug use among Hispanics between 2018 and 2019. However, this temporal increase in such a short time may be a sampling issue related to the original NSDUH data collection strategy for different time points. Further research efforts should continue to examine the trends of illicit drug use among Hispanics.

It is noteworthy to mention that an increase was observed for both prescription drug misuse and illicit drug use among Asian individuals between 2015 and 2019, except in 2017. The increasing prevalence among Asians may reflect their long history of facing racism in the United States prior to the COVID-19 outbreak (De Leon, 2020). Asian individuals who used substances are less likely to disclose their experiences unless the situation has been exacerbated (Fong & Tsuang, 2007). Some minority groups may use substances to cope with racism (Gerrard et al., 2012), possibly including Asians (Yoo et al., 2010). However, we need to keep in mind that trends did not represent statistically significant findings for racial and ethnic disparities. Therefore, these observations regarding trends should be interpreted separately from the regression analysis.

Discussions Based on Multinomial Logistic Regression

The negative associations of Black and Asian participants with prescription drug misuse and the negative associations of Black and Hispanic participants with the combination of both prescription drug misuse and illicit drug use should be specifically noted. First, Adams and colleagues (2018) pointed out that minorities were more skeptical of prescription drugs than White participants. These findings may indicate that members of minority groups may not initiate the use of prescription drugs, even when the drugs are prescribed for treatments. This overarching distrust can also be observed elsewhere. In a report examining the confidence in scientists for acting in the public interest (Funk et al., 2020), 41% of White adults vs. 27% of Black adults had a great deal of confidence in scientists. However, we should point out that this rationale is based on previous literature and statistics (Funk et al., 2020) that we did not have sufficient empirical evidence in this research to support such claim. Further research should examine long-term distrust of science and medicine and of prescription drug use and misuse among minority groups.

Other issues in the medical setting, like racial discrimination, may also help to explain our findings. Swift and colleagues (2019) pointed out the fact that Black patients were less likely to report opioid misuse than White patients might be related to racial discrimination that Black participants were more likely to report in the medical setting. Therefore, the chance of Black patients receiving prescribed drugs, such as opioids, is lower than for White patients (Swift et al., 2019). In addition, the authors argued that, if such racial differences did not exist in the medical setting, the incidence of opioid misuse among Black patients might be lower (Swift et al., 2019). Public health practitioners should be aware of these racial disparities to help prevent excessive prescription drug use and misuse, but they also need to educate their White patients on the potential dangers of excessive use. Receiving prescribed drugs for medical treatments does not mean excessive use that may lead to prescribed drugs misuse afterwards.

Nevertheless, despite the observations that minorities were less likely to report prescription drug misuse (Black and Asian) and the combination of both prescription drug misuse and illicit drug (Black and Hispanic), individuals who were classified as others had higher odds of reporting illicit drug use as compared to their White counterparts. Because the category of others included a substantial number of Native Americans, Alaska Natives, and Native Hawaiians, it is highly possible that these groups may suffer from barriers to substance use treatments and do not have sufficient education to help drug overdoses (Rural Health Information Hub [RHIH], 2020). Other issues, like historical trauma these people may have encountered across generations (e.g., oppression and colonialization), may become factors with higher incidence of drug use and substance use treatment (RHIH, 2020). We should point out that many indigenous populations across the world face similar challenges like lack of healthcare services in different parts of the world (Lee et al., 2019; Smye et al., 2023; Spillane et al., 2022), substance use interventions and treatments may specifically focus on providing culturally based treatments and services within the local communities for supporting substance use cessation (Burlew et al., 2021; Maina et al., 2020; Urbanoski, 2017). However, we should state that illicit drug use was the only statistically significant association observed. Further research efforts should continue examining prescription drug misuse and the combination of both prescription drug misuse and illicit drug use among individuals who were classified as other racial and ethnic groups in this research.

Discussions Based on Important Covariates

Some empirical results regarding the covariates in the statistical models should be mentioned, especially marijuana use. We observed that individuals who used marijuana reported higher odds of illicit drug use, prescription drug misuse, or the combination of both behaviors. Previous studies have discussed that marijuana use goes hand-in-hand with other drugs and that more frequent marijuana use may lead to higher chances of utilizing other substances, such as cocaine, heroin, and crack (Golub & Johnson, 1994; Hall & Lynskey, 2005).

In terms of participants’ age, adult participants had lower odds of reporting prescription drug misuse, illicit drug use, and the combination of both behaviors. It is noteworthy to mention that adolescents are more prone to report prescription drug misuse such as prescribed opioids (Appiah et al., 2023; Sung et al., 2005). Older adults may receive more prescription drugs (Charlesworth et al., 2015), but they may receive prescription drugs for medical treatments, not for recreational purposes. Receiving prescription drugs for medical treatments may not directly translate to misuse. Even with the non-prescribed pain relievers, study has shown that such use occurs early in adolescence (Wu et al., 2008). Individual risk factors related to drug use among adolescents were traits of high impulsivity, emotional regulation impairment, and rebelliousness (Nawi et al., 2021). To address prescription drug misuse, illicit drug use, and the combination of both behaviors, successful intervention programs should include participants’ age as one primary factor.

Discussions Based on Interaction Analysis

The results regarding older Black participants paralleled with some findings from a previous research study comparing illicit drug use between younger and older Black individuals (Whitehead et al., 2014). The authors (Whitehead et al., 2014) observed that older Black participants were two times more likely to use crack in the past 6 months as compared to their younger counterparts. In another study by Suntai et al. (2020), older Black adults were less likely to complete a substance use treatment program than their White counterparts. Lack of substance use treatment may be a factor associated with higher illicit drug use and the combination of both prescription drug misuse and illicit drug use, as observed in this research.

The same positive associations between Hispanic participants who were between 18 and 49 years old and illicit drug use were also observed in our interaction analysis. However, in the same study by Suntai et al. (2020), the authors found that Hispanic individuals were 26% more likely to complete the substance use treatment program than their White counterparts. Nevertheless, another study observed that both Blacks and Hispanics are less likely to complete substance use treatments than Whites (Saloner & Lê Cook, 2013). These inconsistencies and the reasons associated with illicit drug use among racial and ethnic minority groups may warrant further research efforts to narrow the knowledge gaps. This finding may also indicate, when public health practitioners and healthcare providers design interventions, that the discrepancies in racial and ethnic minority groups should be considered.

Next, the negative associations observed between younger Black adults and prescription drug misuse deserve further discussion and research efforts. For example, in previous research examining prescription opioid misuse among adolescents and young adults (Hudgins et al., 2019), younger Black populations had lower rates of opioid medication misuse as compared to their White counterparts. One potential rationale for our finding is that racial and ethnic minorities had lower adherence and access to prescribed medications than White patients in the first place (Ding et al., 2022), and Black individuals were less likely to use healthcare services than White individuals (Dickman et al., 2022). Therefore, this research may help interpret the negative findings between younger Black participants and prescription drug misuse.

Study Limitations

We should point out some study limitations as caveats. First, because the NSDUH dataset is cross-sectional in nature, we were able to examine only the associations of racial and ethnic disparities with prescription drug misuse, illicit drug use, and the combination of both behaviors. Terminology such as “impact” was avoided for interpretation. Second, self-reported bias might occur, given that we relied on a large and nationally representative survey. Specifically, the empirical results from this research are also subject to recall and social desirability biases that may have contributed to underestimates of illicit drug use and prescription drug misuse. Finally, assessments of illicit drug use and prescription drug misuse were limited to the past 12 months. These assessments may exclude individuals who used illicit drugs or misused prescription drugs prior to the past year.

Conclusion

In spite of these limitations, our study adds to the body of literature to confirm that racial and ethnic disparities persist on issues like prescription drug misuse, illicit drug use, and the combination of both behaviors in the United States. The present research also added participants’ age as an interaction term to examine racial and ethnic disparities by age. Taken together, the empirical evidence from this research may indicate that successful interventions should provide culturally specific programs and address needs across different age groups.

Funding

The authors did not receive any funding sources and/or financial donation in terms of authorship and publication of this research.

Declarations

Ethics approval and consent to participate

We carried out a secondary analysis and relied on the publicly available National Survey on Drug Use and Health (NSDUH) without identified information. Therefore, this study does not fall into the category of human subjects research.

Disclosure of interest form

None declared.

Competing interests

The authors declared no competing interests for this research.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Yen-Han Lee, Email: yil5050@bu.edu.

Yen-Chang Chang, Email: yenchang@mx.nthu.edu.tw.

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