Abstract
Background:
While alcohol use and prescription drug misuse (PDM) are common among adolescents, there is relatively little research on co-ingestion. This is disquieting as polysubstance use has become a major contributing factor in drug overdose deaths among young people in the U.S.
Methods:
The current research uses multiple years of data from the National Survey on Drug Use and Health (2015–2019) to assess characteristics associated with co-ingestion among adolescents aged 12–17 years old (N = 57,352). Multinomial logistic regression analysis is used to identify characteristics associated with past 30-day PDM with and without alcohol co-ingestion. The primary objective is to determine how youth experiences with parents, involvement in conventional activities, religiosity, social support, and school status are associated with co-ingestion.
Results:
Among adolescents who report past 30-day PDM, 18.6% co-ingest with alcohol and 77.5% of adolescents who co-ingest report at least one substance use disorder. Several youth experiences were significantly associated with opioid co-ingestion including increased conflict with parents (RRR 1.27, 95% CI 1.07–1.48), lower levels of religiosity (RRR 0.72, 95% CI 0.52–0.98), less social support (RRR 0.36, 95% CI 0.18–0.69), and not being in school (RRR 3.86, 95% CI 1.33–11.17). Additionally, emergency room visits, depression, and other substance use behaviors were also significantly associated with co-ingestion.
Conclusions:
Findings demonstrate a strong connection between co-ingestion and substance use disorder among U.S. adolescents. The findings from the current study can inform prevention and intervention efforts by identifying youth experiences and health-related factors that are associated with co-ingestion.
Keywords: alcohol, prescription drug misuse, co-ingestion, family, school, religiosity, social support
INTRODUCTION
Data from the U.S. Centers for Disease Control (CDC) indicate that drug overdose deaths continue to increase, with provisional counts totaling more than 100,000 deaths in the 12-month period between May 2020 and April 2021.1 Drug overdose has become a leading cause of death among young people, with data from the CDC showing a 133% increase in overdose deaths among adolescents aged 14 to 18 years old between 2019 and 2021.2,3 During this period overdose deaths among adolescents increased at a faster rate than the overall population. While opioids have played a prominent role, it is important to acknowledge that polysubstance use has become a major driver for the overdose crisis.4,5 This is especially true among adolescents and young adults as overdose deaths involving polysubstance use increased by 760%, between 1999 and 2018, compared to an increase of 384% among opioid-only overdose deaths.6
For these reasons it has become critically important to identify factors associated with polysubstance among adolescents, particularly the co-ingestion of commonly used substances such as alcohol and prescription drugs. Prescription drug misuse (PDM) and alcohol co-ingestion is common among adolescents and is associated with visits to the emergency room, mental health problems, and substance use disorders during adulthood.7–10 A relatively small number of studies have used data from a national sample of high school students to assess the co-ingestion of prescription opioids,11 tranquilizers,12 or stimulants13 with alcohol or other drugs. These studies show that among adolescents who misuse prescription drugs, co-ingestion is common, and the most commonly co-ingested substances are alcohol and marijuana. More problematic are findings that adolescents who co-ingest have higher rates of other substance use behaviors, are more likely to select non-oral routes of administration and endorse recreational motives, and report that co-ingestion produces a greater subjective high.11–13 In other words, co-ingestion is associated with characteristics of PDM that increase the likelihood of negative outcomes and could be a marker to identify at-risk adolescents. The current research extends these previous studies by focusing on the impact of social relationships (e.g., with parents, at school) on PDM/alcohol co-ingestion.
Given adolescence is a unique period in the life course some research focuses on the associations between youth experiences and PDM. While a few studies assess the association between youth experiences and polysubstance use in general, there is a noticeable lack of research that examines the association between youth experiences and PDM/alcohol co-ingestion.14,15 Within the domain of the family, a strong attachment to parents and parental disapproval of drug use are significantly associated with a lower likelihood of PDM.16,17 While there is a lack of research on involvement in conventional activities and PDM, time spent in unstructured activities socializing with friends is consistently associated with greater involvement in delinquency and substance use.18 Religiosity is also a key factor, as adolescents with higher levels of religiosity are less involved in PDM.19 Social support is also associated with lower levels of substance use during adolescence and may be a key factor in the association between stress/poor health and substance use.20 Finally, adolescents who have dropped out of school are more likely to report PDM.21
The current research has three primary aims. First. to determine the extent of PDM/alcohol co-ingestion among a nationally representative sample of U.S. adolescents. Second, to determine how the prevalence of substance use disorders varies by PDM/alcohol co-ingestion status. Third, we assess the association between youth experiences (i.e., relationship with parents, involvement in conventional activities, religiosity, social support, and school status) and PDM/alcohol co-ingestion. Additionally, analyses control for demographic characteristics, health-related factors, and other substance use behaviors. Findings from the current research should inform prevention, screening, and treatment efforts to identify at-risk adolescents prior to the transition to young adulthood when substance use is most prevalent.22
METHODS
Data
The National Survey on Drug Use and Health (NSDUH) has a target population of civilians 12 and older that are not institutionalized and is based on an independent, multistage area probability sample. Data were collected from respondents using a combination of computer-assisted face-to-face interviewing by a trained interviewer and computer-assisted self-interviewing. For the current study, multiple years (2015–2019) of NSDUH data were pooled and analysis was limited to adolescent respondents aged 12 to 17. Between 2015 and 2019 the weighted screening and interview response rates were consistently above 70% and 65% respectively. The Research Triangle International IRB provided oversight of the NSDUH, and the University of Central Florida IRB exempted this work from further oversight. Further information regarding the NSDUH methodology is available elsewhere.23
Measurement
Prescription drug misuse (PDM) is defined as use “…in any way a doctor did not direct, including without a prescription of your own; in greater amounts, more often, or longer than you were told to take it; in any other way a doctor did not direct” and is assessed separately by medication class (i.e., opioid, tranquilizer, stimulant, sedative). Respondents who report past 30-day PDM are asked about co-ingestion with alcohol: “During the past 30 days, did you use (medication class) in any way a doctor did not direct you to use (medication class) while you were drinking alcohol or within a couple of hours of drinking?” Participants with missing data on this item were logically imputed to have no co-ingestion if they did not engage in past-month alcohol use and/or past-month PDM. A mutually exclusive categorical outcome measures was created to identify respondents (1) with no lifetime PDM, (2) with lifetime or past-year PDM, (3) with past 30-day PDM without alcohol co-ingestion, and (4) with past 30-day PDM with alcohol co-ingestion. As respondents were asked about medication classes separately, we can assess alcohol co-ingestion across different medication classes as well as co-ingestion with any prescription drug.
The following independent variables of interest assess various youth experiences, with several items being used in previous research.16 First, a scale measuring parental support (alpha = .86) includes two items: parents let you know you did a decent job and parents tell you they are proud. Second, a single-item measure of parental conflict assesses the frequency of arguments and fights with parents. Third, a single-item measure of parental disapproval of alcohol use was also included. These measures are coded so that a higher score reflects greater support from parents, more conflict with parents, and greater disapproval of alcohol use. Fourth, a scale measuring involvement in conventional activities (alpha = .68) includes time spent involved with school-based, community-based, faith-based, or other conventional activities, with a higher score indicating greater involvement. Fifth, a scale measures religiosity (alpha = .84) with the following items: importance of religious beliefs, religious beliefs influence decisions, and important that friends share religious beliefs (higher score indicates higher levels of religiosity). Sixth, a single-item measure of social support identifies respondents who have someone (e.g., parent/guardian, boyfriend/girlfriend, other adult, some other person) to talk to about serious problems. Finally, we include a measure of school status to identify adolescents who have dropped out of school, those in school but at a high risk for dropout, and those in school with a low risk for dropout. Students are considered at-risk for dropout based on poor grades and stating that they hated going to school, a measure consistent with prior research.21
The following demographic characteristics (age, sex, race/ethnicity, total family income, and geographic residence), past-year health-related factors (emergency room visits, major depression, and self-rated overall health), past-year substance use behaviors (tobacco use, cannabis use, and other illegal drug use), and survey year are included in analytical models. We also included a measure of any past-year substance use disorder (SUD) as well as specific substance use disorders (i.e., alcohol, marijuana, cocaine, heroin, hallucinogens, inhalants, or methamphetamine). The SUD measures in the NSDUH are based on DSM-IV criteria that accounts for both abuse and dependence.24
Analytical Strategy
To begin, we estimated the prevalence of PDM with and without alcohol co-ingestion among adolescents. Next, we assessed the association between PDM/alcohol co-ingestion and substance use disorder. For the main analysis we used multinomial logistic regression and included youth experiences, demographic characteristics, health-related factors, substance use behaviors, and survey year in all models. For the multinomial logistic regression analyses we identified no lifetime PDM as the comparison group and focused on three different comparisons: (1) past 30-day PDM with alcohol co-ingestion, (2) past 30-day PDM without alcohol co-ingestion, and (3) lifetime or past-year PDM. To account for the complex multistage sampling design of the NSDUH, analyses were conducted using the syset and svy commands in STATA 16.0. Given the use of five-years of data an adjusted person-level weight was created (i.e., weight/5), per guidelines from the Substance Abuse and Mental Health Services Administration.23
RESULTS
Table 1 highlights sample characteristics (N = 57,352) for all measures included in the analyses. Past 30-day PDM with alcohol co-ingestion is uncommon among adolescents, ranging from a low of 0.003% for sedatives to a high of 0.16% for opioids. However, when we focus solely on respondents who reported any past 30-day PDM, roughly 19% reported co-ingestion with alcohol. Additionally, as the NSDUH is population-level data we can estimate that roughly 67,410 adolescents in the U.S. co-ingested alcohol with a prescription drug in the past 30-days. With alcohol and prescription sedative co-ingestion being quite rare it was not included in the multinomial logistic regression analysis.
Table 1:
Sample Characteristics (N = 57,352)1
| Prescription Opioid Misuse | Youth Experiences | ||||
| None | 93.72% | (93.43, 94.00) | Parental Support | 3.37 (mean)2 | (3.36, 3.38) |
| Lifetime or past year misuse | 5.41% | (5.14, 5.67) | Parental Conflict | 2.80 (mean)3 | (2.78, 2.82) |
| Past 30-day with no co-ingestion | 0.72% | (0.62, 0.80) | Parents Disapprove of Alcohol Use | 2.87 (mean)4 | (2.86, 2.88) |
| Past 30-day with co-ingestion | 0.16% | (0.12, 0.18) | Conventional Activities | 5.20 (mean)5 | (5.16, 5.24) |
| Prescription Tranquilizer Misuse | Religiosity | 2.53 (mean)2 | (2.52, 2.54) | ||
| None | 97.23% | (97.05, 97.42) | Social Support | 95.09% | (94.84, 95.33) |
| Lifetime or past year misuse | 2.25% | (2.08, 2.42) | In School (low dropout risk) | 87.79% | (87.42, 88.16) |
| Past 30-day with no co-ingestion | 0.36% | (0.29, 0.42) | In School (high dropout risk) | 8.54% | (8.24, 8.83) |
| Past 30-day with co-ingestion | 0.15% | (0.10, 0.19) | Not in School | 3.67% | (3.48, 3.84) |
| Prescription Stimulant Misuse | Demographic Characteristics | ||||
| None | 97.53% | (97.36, 97.70) | Age (12–17) | 14.66 (mean) | (14.64, 14.69) |
| Lifetime or past year misuse | 2.01% | (1.84, 2.17) | Male | 50.80% | (50.13, 51.46) |
| Past 30-day with no co-ingestion | 0.39% | (0.32, 0.45) | Race – White | 53.59% | (53.02, 54.15) |
| Past 30-day with co-ingestion | 0.07% | (0.03, 0.09) | Race – Black | 13.40% | (12.86, 13.92) |
| Prescription Sedative Misuse | Race – Hispanic | 23.39% | (22.80, 23.97) | ||
| None | 98.96% | (98.84, 99.06) | Race – Other | 9.62% | (9.21, 10.03) |
| Lifetime or past year misuse | 0.96% | (0.86, 1.06) | Total Family Income | 2.88 (mean)2 | (2.86, 2.90) |
| Past 30-day with no co-ingestion | 0.07% | (0.03, 0.01) | Lives in CBSA > 1 million people | 54.51% | (53.72, 55.29) |
| Past 30-day with co-ingestion | 0.003% | (0.0003, 0.006) | Lives in CBSA < 1 million people | 39.75% | (39.01, 40.47) |
| Any Prescription Drug Misuse | Does not live in a CBSA | 5.74% | (5.32, 6.15) | ||
| None | 90.98% | (90.63, 91.33) | Health-Related Factors | ||
| Lifetime or past year misuse | 7.47% | (7.16, 7.78) | 0 visits to emergency room | 72.38% | (71.85, 72.90) |
| Past 30-day with no co-ingestion | 1.25% | (1.13, 1.37) | 1 visit to emergency room | 14.97% | (14.55, 15.39) |
| Past 30-day with co-ingestion | 0.28% | (0.23, 0.33) | 2+ visits to emergency room | 12.65% | (12.30, 12.98) |
| Other Substance Use | Major Depression | 14.17% | (13.68, 14.66) | ||
| Tobacco Use | 9.98% | (9.62, 10.35) | Overall health is fair or poor | 4.13% | (3.88, 4.37) |
| Marijuana Use | 13.38% | (13.01, 13.75) | |||
| Other illegal Drug Use | 4.36% | (4.17, 4.55) |
Weighted proportions for dichotomous/categorical measures and weighted means for continuous measures are shown, with 95% confidence intervals.
Range for these measures are 1–4.
Range for this measure is 1–5.
Range for this measure is 1–3.
Range for this measure is 0–12.
To illustrate the importance of focusing on co-ingestion we estimated the prevalence of any past-year substance use disorder by PDM/alcohol co-ingestion status, see Figure 1. A large percentage of adolescents who report past 30-day PDM with alcohol co-ingestion indicated having a SUD in the past year. This included 78.42% (95% CI 65.31–87.52) who co-ingest with prescription opioids, 85.81% (95% CI 76.05–92.2) who co-ingest with prescription tranquilizers, and 66.81% (95% CI 52.32–78.69) who co-ingest with prescription stimulants. As highlighted in Figure 1, the presence of any SUD was substantially higher among adolescents with co-ingestion compared to those who report past 30-day PDM without co-ingestion. For example, 77% of adolescents who reported co-ingestion with any PDM indicated having an SUD compared to 44% of adolescents who indicated PDM without co-ingestion. We also conducted supplemental analysis that focused on specific types of SUD, see Supplemental Table 1. The findings were similar, with substantially higher rates of SUD among adolescents with co-ingestion compared to those without co-ingestion.
Figure 1:
Prevalence of any past-year substance use disorder by prescription drug misuse (PDM) and alcohol co-ingestion status
Prescription sedative misuse is not included due to low prevalence of alcohol co-ingestion.
Past 30-day PDM with alcohol co-ingestion
Several youth experiences were significantly associated with PDM/alcohol co-ingestion, see Table 2. Adolescents who report greater conflict with parents were significantly more likely to report alcohol co-ingestion with both prescription opioids (Relative Risk Ratio - RRR 1.27, 95% CI 1.07–1.48) and tranquilizers (RRR 1.27, 95% CI 1.01–1.59). Religiosity was also significantly associated with prescription opioid co-ingestion (RRR 0.72, 95% CI 0.52–0.98), as adolescents with higher levels of religiosity were less likely to report co-ingestion. Adolescents who had someone to talk to about their problems, a measure of social support, were also less likely to report co-ingestion with opioids (RRR 0.36, 95% CI 0.18–0.69). Finally, adolescents who had dropped out of school were more likely to report co-ingestion with both prescription opioids (RRR 3.86, 95% CI 1.33–11.17) and stimulants (RRR 8.36, 95% CI 2.47–28.33).
Table 2:
Characteristics associated with past 30-day prescription drug misuse (PDM) with alcohol co-ingestion1
| Any PDM co-ingestion | Opioid co-ingestion | Tranquilizer co-ingestion | Stimulant co-ingestion | |||||
|---|---|---|---|---|---|---|---|---|
| Youth Experiences | ||||||||
| Parental Support | 0.90 | (0.71, 1.13) | 0.81 | (0.61, 1.07) | 0.87 | (0.67, 1.12) | 1.01 | (0.64, 1.59) |
| Parental Conflict | 1.20* | (1.01, 1.41) | 1.27** | (1.07, 1.48) | 1.27* | (1.01, 1.59) | 1.12 | (0.71, 1.74) |
| Parents Disapprove Alcohol | 0.55* | (0.36, 0.83) | 0.47 | (0.26, 0.83) | 0.70 | (0.33, 1.46) | 0.68 | (0.29, 1.54) |
| Conventional Activities | 0.99 | (0.91, 1.09) | 1.01 | (0.90, 1.12) | 0.99 | (0.89, 1.08) | 1.07 | (0.89, 1.27) |
| Religiosity | 0.82* | (0.67, 0.99) | 0.72* | (0.52, 0.98) | 1.07 | (0.74, 1.54) | 0.82 | (0.45, 1.46) |
| Social Support | 0.46** | (0.29, 0.71) | 0.36** | (0.18, 0.69) | 0.65 | (0.33, 1.24) | 0.49 | (0.16, 1.48) |
| In School, low risk (ref.) | ||||||||
| In School, at risk | 1.60 | (0.85, 3.00) | 1.18 | (0.53, 2.58) | 1.83 | (0.84, 3.96) | 0.84 | (0.29, 2.41) |
| Not in School | 2.49 | (0.90, 6.86) | 3.86* | (1.33, 11.17) | 1.50 | (0.30, 7.44) | 8.36** | (2.47, 28.33) |
| Demographics | ||||||||
| Age | 1.30* | (1.02, 1.65) | 1.16 | (0.89, 1.49) | 1.13 | (0.83, 1.53) | 2.16** | (1.39, 3.34) |
| Sex (male) | 0.66 | (0.40, 1.07) | 0.64 | (0.28, 1.45) | 0.86 | (0.49, 1.52) | 1.04 | (0.40, 2.67) |
| White (ref.) | ||||||||
| Black | 1.16 | (0.49, 2.73) | 1.49 | (0.57, 3.87) | 1.21 | (0.33, 4.36) | 1.34 | (0.38, 4.64) |
| Hispanic | 1.27 | (0.78, 2.06) | 0.49 | (0.22, 1.09) | 1.77 | (0.92, 3.35) | 0.65 | (0.23, 1.79) |
| Other | 1.55 | (0.69, 3.47) | 1.45 | (0.53, 3.96) | 0.82 | (0.33, 1.96) | 0.79 | (0.21, 2.83) |
| Family Income | 1.11 | (0.91, 1.34) | 0.92 | (0.65, 1.28) | 1.14 | (0.90, 1.44) | 0.98 | (0.69, 1.39) |
| CBSA > 1 million (ref.) | ||||||||
| CBSA < 1 million | 0.88 | (0.56, 1.37) | 0.87 | (0.58, 1.31) | 0.77 | (0.37, 160) | 0.56 | (0.24, 1.26) |
| Not in a CBSA | 0.70 | (0.32, 1.48) | 0.54 | (0.20, 1.42) | 0.44 | (0.91, 4.28) | 0.73 | (0.23, 2.26) |
| Health | ||||||||
| No ER visits (ref.) | ||||||||
| 1 ER visit | 0.92 | (0.55, 1.51) | 0.92 | (0.45, 1.87) | 1.24 | (0.57, 2.65) | 0.59 | (0.20, 1.72) |
| 2+ ER Visits | 2.07* | (1.04, 4.11) | 2.81* | (1.10, 7.16) | 1.98 | (0.15, 1.26) | 0.73 | (0.14, 1.52) |
| Major Depression | 1.84* | (1.15, 2.92) | 2.05 | (0.89, 4.71) | 1.29 | (0.61, 2.70) | 5.02** | (2.02, 12.46) |
| Self-rated Health (fair/poor) | 0.74 | (0.35, 1.54) | 0.75 | (0.27, 2.06) | 0.64 | (0.21, 1.94) | 0.34 | (0.06, 1.65) |
| Substance Use Behaviors | ||||||||
| Tobacco Use | 5.87*** | (3.55, 1.54) | 3.87** | (1.80, 8.29) | 9.45*** | (3.75, 23.81) | 4.93** | (2.03, 11.98) |
| Marijuana Use | 8.24*** | (4.15, 16.36) | 8.55*** | (3.03, 24.09) | 24.43*** | (7.33, 81.35) | 4.12** | (1.74, 9.73) |
| Other Illegal Drug Use | 16.94*** | (11.12, 25.79) | 9.38*** | (5.33, 16.50) | 30.05*** | (16.42, 54.98) | 9.11*** | (4.59, 18.04) |
Multinomial logistic regression with no PDM as reference category with relative risk ratios and 95% confidence intervals. Due to low prevalence of co-ingestion, prescription sedative misuse is not included in the analysis. All models include a control for survey year
(* p < .05, ** p < .01, *** p < .001).
While the focus of the current study was youth experiences, the lack of research on co-ingestion warrants a discussion of the other measures shown in Table 2. All substance use behaviors (i.e., tobacco, marijuana, and other illegal drugs) were significantly associated with alcohol co-ingestion with prescription opioids, tranquilizers, and stimulants. Regarding the health-related factors, emergency room visits and depression were significant. Adolescents who had two or more visits to an emergency room were more likely to report alcohol co-ingestion with prescription opioids (RRR 2.81, 95% CI 1.10–7.16), while adolescents who indicated major depression were more likely to report alcohol co-ingestion with prescription stimulants (RRR 5.02, 95% CI 2.02–12.46). Finally, the likelihood of alcohol co-ingestion with prescription stimulants increased with age.
In supplemental analysis, we changed the comparison group in the multinomial regression analysis from no lifetime PDM to past 30-day PDM without alcohol co-ingestion, this allowed for a direct comparison between past 30-day PDM with and without alcohol co-ingestion, see Supplemental Table 2. In this analysis school status stood out, as adolescents who were not in school were more likely to report both opioid and stimulant co-ingestion with alcohol compared to adolescents who were in school with a low risk for dropout.
Past 30-day PDM without alcohol co-ingestion
The findings for PDM without alcohol co-ingestion in the past 30-days are shown in Table 3. For prescription opioids, adolescents with more support from their parents (RRR 0.85, 95% CI 0.72–0.99) and parents who disapprove of alcohol use (RRR 0.55, 95% CI 0.36–0.84) were less likely to report misuse, while adolescents who report more conflict with their parents (RRR 1.22, 95% CI 1.08–1.35) were more likely to report misuse. Parental disapproval of alcohol use was also significantly associated with prescription tranquilizer misuse (RRR 0.58, 95% CI 0.36–0.92). For prescription stimulants, greater support from parents was associated with decreased likelihood of misuse (RRR 0.78, 95% CI 0.63–0.95), while greater involvement in conventional activities was associated with increased likelihood of misuse (RRR 1.12, 95% CI 1.05–1.17).
Table 3:
Characteristics associated with past 30-day PDM without alcohol co-ingestion1
| Any PDM | Opioid Misuse | Tranquilizer Misuse | Stimulant Misuse | |||||
|---|---|---|---|---|---|---|---|---|
| Youth Experiences | ||||||||
| Parental Support | 0.79*** | (0.70, 0.88) | 0.85* | (0.72, 0.99) | 0.83 | (0.65, 1.05) | 0.78* | (0.63, 0.95) |
| Parental Conflict | 1.15** | (1.06, 1.25) | 1.22** | (1.08, 1.35) | 1.04 | (0.88, 1.22) | 0.99 | (0.86, 1.13) |
| Parents Disapprove Alcohol | 0.64** | (0.45, 0.88) | 0.55** | (0.36, 0.84) | 0.58* | (0.36, 0.92) | 1.30 | (0.82, 2.05) |
| Conventional Activities | 1.03 | (0.99, 1.07) | 1.03 | (0.98, 1.08) | 0.99 | (0.91, 1.06) | 1.12*** | (1.05, 1.17) |
| Religiosity | 0.95 | (0.80, 1.11) | 0.96 | (0.80, 1.15) | 0.86 | (0.66, 1.14) | 0.84 | (0.62, 1.14) |
| Social Support | 0.69* | (0.49, 0.96) | 0.65 | (0.42, 1.01) | 0.98 | (0.54, 1.76) | 0.62 | (0.34, 1.12) |
| In School, low risk (ref.) | ||||||||
| In School, at risk | 1.12 | (0.87, 1.43) | 1.30 | (0.94, 1.79) | 1.29 | (0.81, 2.04) | 1.17 | (0.77, 1.77) |
| Not in School | 0.90 | (0.74, 1.08) | 0.90 | (0.44, 1.81) | 0.83 | (0.31, 2.21) | 0.81 | (0.23, 2.73) |
| Demographics | ||||||||
| Age | 1.03 | (0.96, 1.11) | 0.96 | (0.84, 1.01) | 1.16* | (1.00, 1.32) | 1.11 | (0.97, 1.26) |
| Sex (male) | 0.90 | (0.74, 1.08) | 0.74* | (0.55, 0.98) | 1.08 | (0.68, 1.71) | 1.75** | (1.09, 2.81) |
| White (ref.) | ||||||||
| Black | 1.45* | (1.01, 2.06) | 2.09*** | (1.47, 2.97) | 1.26 | (0.60, 2.65) | 0.33** | (0.14, 0.74) |
| Hispanic | 1.43* | (1.03, 1.97) | 1.75** | (1.19, 2.56) | 1.33 | (0.75, 2.35) | 0.87 | (0.48, 1.56) |
| Other | 1.08 | (0.75, 1.53) | 1.13 | (0.75, 1.69) | 1.23 | (0.56, 2.71) | 1.05 | (0.59, 1.87) |
| Family Income | 1.10 | (0.98, 1.23) | 1.03 | (0.89, 1.19) | 1.15 | (0.93, 1.41) | 1.19 | (0.97, 1.46) |
| CBSA > 1 million (ref.) | ||||||||
| CBSA < 1 million | 1.26* | (1.05, 1.50) | 1.32 | (0.97, 1.80) | 1.08 | (0.73, 1.58) | 1.16 | (0.82, 1.64) |
| Not in a CBSA | 1.02 | (0.62, 1.69) | 1.43 | (0.78, 2.62) | 0.81 | (0.32, 2.04) | 0.60 | (0.22, 1.59) |
| Health | ||||||||
| No ER visits (ref.) | ||||||||
| 1 ER visit | 0.91 | (0.65, 1.24) | 0.96 | (0.65, 1.42) | 1.03 | (0.60, 1.72) | 0.76 | (0.48, 1.19) |
| 2+ ER Visits | 1.84*** | (1.45, 2.32) | 1.98*** | (1.44, 2.71) | 1.59 | (0.99, 2.52) | 1.39 | (0.82, 2.34) |
| Major Depression | 1.95*** | (1.58, 2.40) | 2.11*** | (1.57, 2.83) | 1.52 | (0.92, 2.73) | 2.13*** | (1.42, 3.17) |
| Self-rated Health (fair/poor) | 0.96 | (0.63, 1.43) | 1.07 | (0.64, 1.79) | 1.08 | (0.57, 2.03) | 0.65 | (0.29, 1.44) |
| Substance Use Behaviors | ||||||||
| Tobacco Use | 2.57*** | (1.97, 3.33) | 2.32*** | (1.53, 3.50) | 2.56*** | (1.65, 3.94) | 2.79*** | (1.64, 4.74) |
| Marijuana Use | 3.68*** | (2.65, 5.11) | 2.58*** | (1.73, 3.83) | 4.75*** | (2.92, 7.72) | 7.31*** | (3.79, 14.09) |
| Other Illegal Drug Use | 6.00*** | (4.61, 7.80) | 3.82*** | (2.75, 5.30) | 8.09*** | (5.29, 12.38) | 6.26*** | (3.91, 10.00) |
Multinomial logistic regression with no PDM as reference category with relative risk ratios and 95% confidence intervals. Due to low prevalence of co-ingestion, prescription sedative misuse is not included in the analysis. All models include a control for survey year
(* p < .05, ** p < .01, *** p < .001).
Similar to the previous model, all measures of substance use behaviors were significantly associated with past 30-day PDM without co-ingestion. For prescription opioids, both emergency room visits (RRR 1.98, 95% CI 1.44–2.71) and major depression (RRR 2.11, 95% CI 1.57–2.83) increased the likelihood of misuse, while only depression (RRR 2.13, 95% CI 1.42–3.17) was significantly associated with stimulant misuse. Regarding demographic characteristics prescription opioid misuse was more likely among females and both Black and Hispanic adolescents compared to white adolescents. For prescription tranquilizers, misuse was more likely among older adolescents. Finally, for prescription stimulants misuse was more likely among males and white adolescents compared to Black adolescents.
Lifetime or past-year PDM
The final set of results in the multinomial regression analyses, shown in Table 4, focused on adolescents who report lifetime PDM but no co-ingestion or PDM in the past 30-days. Several youth experiences were significantly associated with prescription opioid misuse in the expected direction, including parental support, parental conflict, and parental disapproval of alcohol use. Interestingly, adolescents involved in more conventional activities (RRR 1.02, 95% CI 1.00–1.03) were more likely to report opioid misuse. While there was no significant difference in opioid misuse among dropouts and adolescents in school at low risk for dropout, being in school but at-risk for dropout increased the likelihood of opioid misuse (RRR 1.14, 95% CI 1.00–1.29). For prescription tranquilizers, adolescents with more parental conflict were more likely to report misuse (RRR 1.18, 95% CI 1.12–1.25), while adolescents in school but at risk for dropout were more likely to report misuse (RRR 1.31, 95% CI 1.07–1.59). Finally, adolescents with greater parental conflict were more likely to report prescription stimulant misuse (RRR 1.25, 95% CI 1.17–1.25).
Table 4:
Characteristics associated with lifetime or past-year PDM1
| Any PDM | Opioid Misuse | Tranquilizer Misuse | Stimulant Misuse | |||||
|---|---|---|---|---|---|---|---|---|
| Youth Experiences | ||||||||
| Parental Support | 0.94* | (0.90, 0.98) | 0.91** | (0.85, 0.96) | 0.98 | (0.91, 1.06) | 0.98 | (0.88, 1.09) |
| Parental Conflict | 1.13*** | (1.08, 1.18) | 1.10*** | (1.05, 1.15) | 1.18*** | (1.12, 1.25) | 1.25*** | (1.17, 1.34) |
| Parents Disapprove Alcohol | 0.77*** | (0.68, 0.87) | 0.69*** | (0.61, 0.77) | 0.90 | (0.73, 1.10) | 1.02 | (0.79, 1.32) |
| Conventional Activities | 1.01 | (0.99, 1.02) | 1.02* | (1.00, 1.03) | 0.98 | (0.96, 1.00) | 1.01 | (0.97, 1.03) |
| Religiosity | 0.95* | (0.90, 0.99) | 0.98 | (0.92, 1.04) | 0.95 | (0.86, 1.05) | 0.95 | (0.86, 1.05) |
| Social Support | 0.92 | (0.77, 1.08) | 0.93 | (0.74, 1.15) | 1.13 | (0.88, 1.44) | 1.01 | (0.74, 1.37) |
| In School, low risk (ref.) | ||||||||
| In School, at risk | 1.17* | (1.02, 1.33) | 1.14* | (1.00, 1.29) | 1.31** | (1.07, 1.59) | 0.78 | (0.59, 1.02) |
| Not in School | 0.89 | (0.69, 1.12) | 0.86 | (0.66, 1.11) | 1.03 | 0.69, 1.51) | 0.86 | (0.46, 1.58) |
| Demographics | ||||||||
| Age | 1.03* | (1.00, 1.33) | 1.01 | (0.97, 1.03) | 0.94* | (0.89, 0.98) | 1.12*** | (1.04, 1.19) |
| Sex (male) | 0.97 | (0.88, 1.06) | 0.93 | (0.83, 1.03) | 0.94 | (0.80, 1.09) | 1.49*** | (1.25, 1.77) |
| White (ref.) | ||||||||
| Black | 1.06 | (0.92, 1.22) | 1.21* | (1.03, 1.41) | 0.63** | (0.46, 0.86) | 0.49*** | (0.35, 0.66) |
| Hispanic | 1.02 | (0.89, 1.16) | 1.12 | (0.97, 1.28) | 1.01 | (0.84, 1.21) | 0.82 | (0.63, 1.04) |
| Other | 0.92 | (0.77, 1.08) | 0.84 | (0.69, 1.02) | 0.98 | (0.73, 1.31) | 0.86 | (0.68, 1.08) |
| Family Income | 1.03 | (0.98, 1.07) | 1.02 | (0.96, 1.08) | 1.03 | (0.96, 1.10) | 1.07* | (1.01, 1.14) |
| CBSA > 1 million (ref.) | ||||||||
| CBSA < 1 million | 0.99 | (0.90, 1.08) | 1.02 | (0.92, 1.12) | 0.98 | (0.83, 1.17) | 0.93 | (0.83, 1.31) |
| Not in a CBSA | 0.90 | (0.78, 1.04) | 0.95 | (0.78, 1.16) | 0.83 | (0.58, 1.19) | 0.78 | (0.89, 1.43) |
| Health | ||||||||
| No ER visits (ref.) | ||||||||
| 1 ER visit | 1.11 | (0.98, 1.24) | 1.18* | (1.02, 1.35) | 1.14 | (0.91, 1.41) | 1.07 | (0.87, 1.31) |
| 2+ ER Visits | 1.33*** | (1.19, 1.47) | 1.41*** | (1.23, 1.59) | 1.32** | (1.10, 1.57) | 1.13 | (0.89, 1.43) |
| Major Depression | 1.38*** | (1.22, 1.55) | 1.27** | (1.11, 1.45) | 1.34*** | (1.15, 1.56) | 1.73*** | (1.44, 2.07) |
| Self-rated Health (fair/poor) | 1.20 | (0.97, 1.48) | 1.15 | (0.89, 1.48) | 1.10 | (0.80, 1.50) | 1.36 | (0.98, 1.85) |
| Substance Use Behaviors | ||||||||
| Tobacco Use | 1.74*** | (1.53, 1.98) | 1.54*** | (1.34, 1.75) | 2.18*** | (1.84, 2.58) | 2.18*** | (1.78, 2.66) |
| Marijuana Use | 2.29*** | (2.03, 2.57) | 1.96*** | (1.67, 2.29) | 2.99*** | (2.47, 3.61) | 3.57*** | (2.88, 4.42) |
| Other Illegal Drug Use | 3.08*** | (2.69, 3.53) | 2.97*** | (2.64, 3.35) | 4.18*** | (3.46, 5.06) | 4.54*** | (3.68, 5.58) |
Multinomial logistic regression with no PDM as reference category with relative risk ratios and 95% confidence intervals. Due to low prevalence of co-ingestion, prescription sedative misuse is not included in the analysis. All models include a control for survey year
(* p < .05, ** p < .01, *** p < .001).
As with the other models, substance use behaviors, emergency room visits, and major depression were significantly associated with multiple classes of PDM. Regarding age, tranquilizer misuse was more likely among young adolescents, while stimulant misuse was more common among older adolescents. Compared to white adolescents, Black adolescents were more likely to report opioid misuse, but less likely to report tranquilizer or stimulants misuse.
DISCUSSION
The current research assessed PDM/alcohol co-ingestion among a national sample of U.S. adolescents, which is critically important given the role polysubstance use plays in drug overdose deaths.4,5 Among adolescents who report past 30-day PDM, we found that co-ingestion with alcohol was common. This is an important at-risk population as 80% of adolescents who co-ingest reported at least one substance use disorder in the past year. Co-ingestion was more common among adolescents who misused prescription opioids and tranquilizers, and co-ingestion with opioids and tranquilizers was also linked with a higher likelihood of a substance use disorder. These findings are consistent with other studies on PDM co-ingestion among adolescents.11,12
The primary goal of the current study was to identify youth experiences that were significantly associated with PDM/alcohol co-ingestion while controlling for other relevant factors. Adolescents who reported higher levels of conflict with their parents were more likely to report co-ingestion involving opioids or tranquilizers. This finding is consistent with Hirschi’s social control theory in which he argues that close affectional ties to parents work to promote conforming behavior among adolescents, as deviance would jeopardize these important relationships.25 This finding is also consistent with research identifying a strong attachment to parents as a protective factor for PDM during adolescence.16,17
Another important youth experience was religiosity, as it was significantly associated with alcohol/opioid co-ingestion. Consistent with other studies, adolescents with higher levels of religiosity, which includes feelings, beliefs, and activities, have lower rates of substance use.19 Religiosity is associated with lower levels of substance use because it exposes adolescents to moral directives and conventional adults that discourage substance use.26 Furthermore, religious involvement also reduces interactions with deviant peers and provides access to social support that promotes greater overall well-being.26
The current research also identified a significant association between social support and alcohol/opioid co-ingestion, as adolescents who had someone to talk to about serious problems were less likely to report co-ingestion. Social support is a well-documented protective factor for substance use and has been shown to limit the negative impact that poor physical and mental health have on substance use.27,28 This finding makes it important to focus on the social context of drug use, as factors such as social support and social capital can also be relied on during the recovery process.29
Finally, school status stood out as an important youth experience. Adolescents who had dropped out of school were more likely to report alcohol co-ingestion with prescription opioids or stimulants. Other research shows that dropouts are at increased risk for prescription opioid misuse.1–21 The findings for prescription stimulants are particularly interesting as prior research generally shows higher rates of stimulant use among adolescents or young adults who are in school.21 This is important as prescription stimulant misuse has been identified as a risk factor for later methamphetamine use30 and there have been recent increases in overdose deaths associated with stimulants.2 Thus, future research should investigate the likelihood of prescription stimulants misusers, who are not in school, transitioning to methamphetamine use.
In addition to these youth experiences; a few other factors are worthy of discussion. All measures of substance use behaviors were significantly associated with co-ingestion with opioids, tranquilizers, or stimulants. This is consistent with studies that show a strong correlation between PDM and other substance use behaviors.31,32 This also highlights the importance of moving past a focus on PDM and placing a greater emphasis on polysubstance use, which is particularly important given its association with substance use disorders and overdose deaths.33Adolescents who report two or more visits to hospital emergency rooms (ER) were more likely to report alcohol/opioid co-ingestion. This finding is notable as ER visits are associated with prescription opioid misuse in older age groups and alcohol/opioid co-ingestion can lead to overdose.34 Also, adolescents who reported alcohol/stimulant co-ingestion were more likely to report major depression, a finding consistent with research on PDM.35 This association may be attributable to a “crash” that nonmedical prescription stimulant users experience that produce depressed mood and symptoms of depression;36,37 while the use/misuse of stimulants may also be associated with self-treatment as prior research shows that stimulant use/misuse may be a way to cope with daily stressors.38
Limitations
While the NSDUH is one of the most widely used epidemiological studies to assess substance use, a few limitations are worth noting. These results are from adolescents aged 12 to 17 in the civilian, non-institutionalized U.S. population, and should not be applied to other populations. The NSDUH is a cross-sectional study, which makes it problematic to infer any causal relationships. For example, it may be that parental conflict increases the likelihood of co-ingestion, or co-ingestion may increase the amount of parental conflict. Both self-report and self-selection bias are an issue. Accordingly, 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.23 Research does indicate that self-reported substance use data are reliable and valid.39,40 The NSDUH uses weighting to address non-response, ACASI interviewing, and visual cues (e.g., medication pictures) to promote accurate and honest reporting. Lastly, the NSDUH data only measures past 30-day alcohol/PDM co-ingestion, making it impossible to assess past-year or lifetime alcohol co-ingestion, co-ingestion with other substances, or frequency/dose of co-ingestion.
Conclusions
The current study finds compelling evidence of a link between alcohol/PDM co-ingestion and substance use disorder among U.S. adolescents and identifies youth experiences that may function as risk or protective factors. As youth experiences such as family environment, social support, and school status are modifiable it would be useful for prevention/intervention programs to target these youth experiences. Likewise, hospital emergency rooms may be an ideal place to screen for polysubstance use and identify this at-risk population. Additionally, the proper disposal of prescription medications should be promoted, as friends and family members are the most common sources for U.S. adolescents.41 Furthermore, prescribers should talk with adolescents, and their parents, about the risks associated with co-ingestion. The findings also highlight the importance of substance use screening with tools that assess co-ingestion, such as the DAST-10 or the AUDIT. Lastly, given the links between co-ingestion and depression and other substance use behaviors a multidisciplinary approach is needed to ensure all treatment needs are addressed.
Supplementary Material
Funding Statement:
The development of this article was supported by research grants R01DA031160 and R01DA043691 from the National Institute on Drug Abuse (NIDA), National Institutes of Health (NIH), Bethesda, Maryland. The National Survey on Drug Use and Health is funded by the Substance Abuse and Mental Health Services Administration (SAMHSA). The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. The content is solely the responsibility of the authors and does not necessarily represent the official views of NIDA, NIH, SAMHSA, or the U.S. Government.
Footnotes
Conflicts of interest: The authors have no conflict of interest to declare.
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