Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2020 Nov 1.
Published in final edited form as: J Psychoactive Drugs. 2019 Aug 14;51(5):441–452. doi: 10.1080/02791072.2019.1654151

Prevalence and correlates of depressive symptomology among young adults who use prescription opioids non-medically

Benjamin A Bouvier a, Elizabeth N Kinnard b, Jesse L Yedinak a, Yu Li a, Beth Elston a, Traci C Green a,c,d, Scott E Hadland e,f, Brandon DL Marshall a,*
PMCID: PMC6823154  NIHMSID: NIHMS1536955  PMID: 31411548

Abstract

Non-medical prescription opioid (NMPO) use and depression frequently co-occur and are mutually reinforcing in adults, yet NMPO use and depression in younger populations has been under-studied. We examined the prevalence and correlates of depressive symptomology among NMPO-using young adults. The Rhode Island Young Adult Prescription Drug Study (RAPiDS) recruited young adults in Rhode Island who reported past 30-day NMPO use. We administered the Center for Epidemiologic Studies Short Depression Scale (CES-D 10), and used modified Poisson regression to identify the independent correlates of depressive symptomology (CES-D 10 score ≥10). Over half (59.8%, n = 119) screened positive for depressive symptomology. In modified Poisson regression analysis, diagnostic history of depressive disorder and childhood verbal abuse were associated with depressive symptomology. Participants with depressive symptomology were more likely to report using prescription opioids non-medically to feel less depressed or anxious, to avoid withdrawal symptoms, and as a substitute when other drugs are not available. Among young adult NMPO users, depressive symptomology is prevalent and associated with distinct motivations for engaging in NMPO use and represents a potential subgroup for intervention. Improving guidelines with tools such as screening for depressive symptomology among young adult NMPO users may help prevent NMPO-related harms.

Keywords: prescription opioids, depressive symptomology, depression, young adults, adolescents, motivations

Introduction

Non-medical prescription opioid (NMPO) use is an ongoing public health problem in the United States (National Institute on Drug Abuse 2011). Increasing prevalence of NMPO use in the last two decades has resulted in significant public health, social, and economic consequences, including a 5.6-fold increase in the number of prescription opioid-attributable fatal overdose deaths from 1999 to 2015 (Kolodny et al. 2015, Chen, Hedegaard, and Warner 2014). The prevalence of NMPO use is highest among young adults aged 18–25, with 6 million (1 in 5) reporting lifetime NMPO use and 2.7 million (1 in 12) reporting NMPO use in the previous year (Center for Behavioral Health Statistics and Quality 2016, Martins et al. 2017). Young people who engage in NMPO use have an elevated risk of adverse health consequences, such as accidental overdose (Frank et al. 2015, Lankenau, Teti, Silva, Bloom, et al. 2012), infectious disease transmission (Hadland and Wood 2012, Surratt, Kurtz, and Cicero 2011), transition to heroin use (Jones et al. 2015, Cerda et al. 2015, McCabe et al. 2007), and initiation of injection drug use (Lankenau, Teti, Silva, Jackson Bloom, et al. 2012, Green et al. 2011).

NMPO use and depressive symptomology frequently co-occur (Goldner et al. 2014, Becker et al. 2008, Mackesy-Amiti, Donenberg, and Ouellet 2015, Ali et al. 2015, Amari et al. 2011, Fischer et al. 2012, Fink et al. 2015, Edlund et al. 2015). Moreover, the co-occurrence of NMPO use and depressive symptomology is associated with greater severity and persistence of both conditions (Kessler 2004, Rowe et al. 2004). This co-occurrence can result from one or more non-mutually exclusive and often reinforcing pathways. NMPO use may lead to depressive symptomology (“precipitation” hypothesis), depressive symptomology may lead to NMPO use (“self-medication” hypothesis), and/or a third factor might influence the development of both (“shared vulnerability” hypothesis) (Martins et al. 2012, Khantzian 1997).

In order to more fully elucidate the relationship between these two outcomes, understanding young adult motivations for substance use is crucial, particularly since psychiatric comorbidities may impact these motivations, as well as impact the opportunity for appropriate intervention (Dow and Kelly 2013, Goodman, Peterson-Badali, and Henderson 2011). Multiple studies have reported on motivations for engaging in NMPO use among young adults (Young, Glover, and Havens 2012, Boyd et al. 2006, Young et al. 2012, Drazdowski 2016); however, to date, no study has explored whether young adult NMPO users with depressive symptomology report distinct motivations for engaging in NMPO use. Exploring these motivations may also provide insight into the validity of the self-medication hypothesis of comorbid depressive symptomology and NMPO use (Khantzian 1997).

Co-occurring depressive symptomology has been studied in the context of heroin use, with many studies reporting a high prevalence among adults who use heroin. Sociodemographic factors such as female sex, homelessness, adverse childhood experiences, and other factors were associated with more severe depressive symptomology (Hadland et al. 2011, Wang et al. 2012, Wu et al. 2016, Tobin and Latkin 2003, Sordo et al. 2012, Chahua et al. 2014). However, no studies have compared motivations for engaging in heroin use between those who screen positive versus negative for depressive symptomology (Cornford, Umeh, and Manshani 2012). More research is needed to understand the co-occurrence of depressive symptomology and NMPO use among young people, especially in the context of existing research on the co-occurrence of depressive symptomology and heroin use.

Using data from the Rhode Island Young Adult Prescription Drug Study (RAPiDS), we explored the prevalence and correlates of depressive symptomology among NMPO-using young adults. We also report motivations for engaging in NMPO use among participants who screened positive for depressive symptomology compared to those who screened negative.

Methods

RAPiDS recruiting and enrollment procedures have been previously described (Marshall et al. 2018). In brief, young adult NMPO users were recruited through targeted canvassing (e.g., bus advertisements, flyers), internet-based recruitment (e.g., posting to online classifieds such as Craigslist), and word of mouth, and were invited to participate in RAPiDS between January 2015 and February 2016 if they: 1) lived in Rhode Island; 2) were between 18 and 29 years of age; 3) were able to provide informed consent; 4) were able to speak and feel comfortable completing a survey in English; and 5) reported NMPO use (defined as using prescription opioids without a prescription or not as a doctor directed) in the past 30 days. If eligible, participants completed a 45-minute, interviewer-administered survey at the Brown University School of Public Health or a public location of their choosing. The survey was administered as a computer-assisted interview by a trained interviewer. Sensitive portions of the survey (e.g., sexual behavior, injection drug use) were self-administered using a computer. Informed consent was obtained from each participant at the time of survey. Participants were compensated $25 USD. RAPiDS was approved by the Brown University Institutional Review Board. A total of 340 persons contacted the study, 230 screened eligible, and 199 completed the survey.

The primary outcome for this analysis was depressive symptomology, assessed using the 10-item Center for Epidemiologic Studies Short Depression Scale (CES-D 10) (Andresen et al. 1994, Carpenter et al. 1998, Kohout et al. 1993). RAPiDS was a pilot study that used brief assessments; thus, we used the CES-D 10 instead of full diagnostic criteria. The validity and reliability of the CES-D 10 are established, and the 10-item version shows good predictive accuracy compared to the 20-item version (Andresen et al. 1994, Carpenter et al. 1998, Kohout et al. 1993). Moreover, the CES-D 10 has been used to measure depressive symptomology among drug-using populations (Risser et al. 2010). The CES-D 10 includes ten statements about how participants have felt in the past week: eight assess negative mood (e.g. “I felt lonely”) and two assess positive mood (e.g. “I was happy”). Response options are: “Rarely or none of the time (0 days),” “Some or little of the time (1–2 days),” “Moderate amount of time (3–4 days),” and “Most or all of the time (5–7 days).” These response choices were given a score of 0, 1, 2, and 3, respectively, with the two positive mood items reverse-scored. Scores were considered invalid if more than one item was missing. If only one item was missing, its value was imputed as the mean of the participant’s other nine item scores. Total scores were the sum of each item score, with total possible scores ranging from 0 to 30. Consistent with prior studies, a score of 10 or greater was considered as screening positive for depressive symptomology (Andresen et al. 1994, Risser et al. 2010). While some studies have used higher CED-D 10 cut-offs, most studies, including those involving homeless men on parole and HIV-positive people, use a cut-off of 10 (Nyamathi et al. 2011, Kilburn et al. 2018, Zhang et al. 2012, Lima et al. 2008). Accordingly, we used a cut-off of 10 for our primary analyses. We also conducted a series of sensitivity analyses in which results were re-analyzed using a higher cut-off of 15, which was found to result in the most balanced combination of sensitivity (0.76) and specificity (0.75) in a prior study of patients enrolled in psychiatric partial hospitalization program (Bjorgvinsson et al. 2013).

Selection of factors associated with depressive symptomology was guided by previous research on depressive symptomology and NMPO use (Becker et al. 2008, Fink et al. 2015, Young, Glover, and Havens 2012). Overall, we included participant demographics, drug use behaviors, mental health history, and adverse childhood experiences. Specifically, we examined age; sex at birth (male vs. female); race (white, black, or mixed/other); Hispanic or Latino descent; sexual orientation (straight vs. lesbian, gay, bisexual, queer, or something else [LGBQ]); past six months homelessness (e.g. living in a shelter because of nowhere else to go, living in a place not ordinarily used for sleeping); ever incarcerated; ever overdosed by accident; ever injected or snorted drugs; using prescription opioids non-medically alone; and ever used heroin. We asked participants how often they used prescription opioids non-medically (never, once or a couple of times, about once per month, at least every week, every day). We used these answer choices to create a dichotomous variable: at least weekly NMPO use vs. less than weekly NMPO use. We also determined if and how often participants used molly/MDMA/ecstasy, mushrooms, GHB, ketamine, crystal methamphetamine, and cocaine in the last six months (never, once or a couple of times, about once per month, at least every week, every day). We created a poly-substance use variable: participants were considered to have engaged in at least monthly polysubstance use in the last six months if they reported using opioids and at least one of these substances at least once per month. We asked participants the first age they non-medically used the prescription opioid they use most regularly. We then created a variable representing years of non-medical use of this prescription opioid by subtracting this age from their current age. Next, mental health measures were ascertained. Participants were asked: “Have you been told that you have one or more of these diagnoses?” and were asked to check all the answers that applied: depressive disorder, bipolar disorder, anxiety disorder, ADHD or ADD, OCD, eating disorder, and psychosis. Then participants were asked if they had ever been hospitalized for a mental illness or depression. Finally, adverse childhood experience questions included ever being insulted or sworn at by a parent before the age of 18; ever being hit or injured by a parent before the age of 18; while growing up, ever living with someone who had a mental illness; while growing up, ever living with someone who was using street drugs; and while growing up, ever living with someone who went to jail or prison. We also included sexual assault or abuse before the age of 18.

Fisher’s exact test and the Wilcoxon rank sum test were used to determine the bivariate associations of these variables with depressive symptomology. Next, we developed multivariate stepwise regression models for a common outcome (screening positive for depressive symptomology) consistent with established protocols (Lima et al. 2008, Harrell 2015). First, a preliminary model was constructed using all variables significant in bivariate analyses with a standard cut-off of p < 0.05. In order to see if a more parsimonious model had a better model fit, we then subjected these variables to a sequential backwards selection procedure based on QIC value and Type III p-values. The variable with the highest p-value was removed sequentially until a final model with the lowest QIC was reached. We used modified robust Poisson regression to estimate standard error in the coefficients and calculate 95% confidence intervals and adjusted prevalence ratios (Zou 2004).

We also asked participants about motivations for NMPO use: “Thinking now about [prescription opioids], what are your most important reasons for using them without a doctor’s orders or not as a doctor directed?” The following response options were randomized, and the interviewer was instructed to read out the list, with participants encouraged to check any that applied: to feel good or get high, to feel less depressed or anxious, to relieve physical pain, to get a good sleep, to avoid withdrawal symptoms, to have a good time with friends, because I was pressured into it, or as a substitute when other drugs are not available. We conducted exploratory bivariate analyses using Fisher’s exact test with responses to this question and screening positive vs. negative for depressive symptomology as measured by the CES-D 10. All analyses were conducted in SAS version 9.3, and all p-values were two-sided.

Results

Almost all participants (99.5%, n = 199) had valid CES-D 10 scores. Valid scores ranged from 0 to 29, and the mean was 12.43 (SD = 7.07). Corrected item-total correlation was greater than 0.80 for each of the 10 items, and the overall Cronbach’s alpha was 0.86. Over half of participants (59.8%, n = 119) had a score greater than or equal to 10, and were thus considered as screening positive for depressive symptomology. A total of 77 participants (38.7%) screened positive at the higher cutoff of 15.

The median age of participants was 25 (IQR = 22–27), and 65.3%( n= 130) were assigned male sex at birth. The majority (61.3%) were white, while 16.6% were black, and 20.6% were mixed, bi-racial, multi-racial, or reported “other.” Twenty-seven participants (13.6%) reported Hispanic or Latino descent (see Table 1).. When asked how often in the last six months participants used these prescription opioids they used most, 39 (20.7%) answered once or a couple of times, 45 (23.9%) answered about once a month, 67 (35.6%) answered at least every week, and 26 (13.8%) answered every day. Fifty-six (28.1%) engaged in at least monthly illicit polysubstance use in the last six months.

Table 1:

Factors associated with depressive symptomology among young adult non-medical prescription opioid users in Rhode Island (N = 199)

Characteristic Total n (%) n=199 Screened Positive for Depressive Symptomology n (%) n=119 Screened Negative for Depressive Symptomology n (%) n=80 p - value
Age (median, IQR) a 25 (22–27) 25 (22–27) 24 (22–27) 0.79
Sex at birth
 Female 69 (34.7) 48 (40.3) 21 (26.3) 0.05
 Male 130 (65.3) 71 (59.7) 59 (73.8)
Race
 Black, African, Haitian, or Cape Verdean 33 (16.6) 22 (18.5) 11 (13.8) 0.51
 White 122 (61.3) 69 (58.0) 53 (66.3)
 Aggregated “Mixed/Other” 41 (20.6) 26 (21.8) 15 (18.8)
Hispanic or Latino
 Yes 27 (13.6) 20 (16.8) 7 (8.8) 0.14
 No 172 (86.4) 99 (83.2) 73 (91.3)
Sexual orientation
 Lesbian, Gay, Bisexual, Queer,
  or something else 27 (13.6) 21 (17.6) 6 (7.5) 0.05
 Straight 171 (85.9) 97 (81.5) 74 (92.5)
Been homeless in the last six months
 Yes 50 (25.1) 40 (33.6) 10 (12.5) <0.01
 No 149 (74.9) 79 (66.4) 70 (85.5)
Ever incarcerated
 Yes 93 (46.7) 59 (49.6) 34 (42.5) 0.39
 No 106 (53.3) 60 (50.4) 46 (57.5)
Ever overdosed by accident
 Yes 53 (26.6) 34 (28.6) 19 (23.8) 0.51
 No 146 (73.4) 85 (71.4) 61 (76.3)
Ever injected drugs
 Yes 59 (29.6) 41 (34.5) 18 (22.5) 0.08
 No 139 (69.8) 77 (64.7) 62 (77.5)
Ever sniffed or snorted an opioid
 Yes 116 (58.3) 73 (61.3) 43 (53.8) 0.24
 No  81 (40.7) 44 (37.0) 37 (46.3)
Typically use prescription opioids non-medically alone
 Yes 146 (73.4) 89 (74.8) 57 (71.3) 0.51
 No 51 (25.6) 28 (23.5) 23 (28.8)
Ever used heroin
 Yes 85 (42.7) 55 (46.2) 30 (37.5) 0.24
 No 114 (57.3) 64 (53.8) 50 (62.5)
At least weekly non-medical prescription opioid use
 Yes 93 (46.7) 59 (49.6) 34 (42.5) 0.24
 No 95 (47.7) 52 (43.7) 43 (53.8)
At least monthly illicit polysubstance use in the last six months
 Yes 56 (28.1) 40 (33.6) 16 (20.0) 0.04
 No 143 (71.9) 79 (66.4) 64 (80.0)
Age of first non-medical use of the prescription opioid used most regularly (median, IQR) a
19 (17–22) 19 (17–22) 19 (17–21) 0.22
Years of non-medical use of the prescription opioid used most regularly (median, IQR) a
5 (2–8) 4 (2–7) 5 (1–8) 0.46
Been diagnosed with a depressive disorder
 Yes 95 (47.8) 72 (60.5) 23 (28.8) <0.01
 No 101 (50.8) 45 (37.8) 56 (70.0)
Been diagnosed with a bipolar disorder
 Yes 49 (24.6) 39 (32.8) 10 (12.5) <0.01
 No 147 (73.9) 78 (65.5) 69 (86.3)
Been diagnosed with an anxiety disorder
 Yes 98 (49.2) 66 (55.5) 32 (40.0) 0.04
 No 98 (49.2) 51 (42.9) 47 (58.8)
Been diagnosed with ADHD or ADD
 Yes 78 (39.0) 48 (40.3) 30 (37.5) 0.77
 No 118 (59.0) 69 (58.0) 49 (61.3)
Been diagnosed with OCD
 Yes 27 (13.5) 20 (16.8) 7 (8.8) 0.14
 No 169 (84.5) 97 (81.5) 72 (90.0)
Been diagnosed with an eating disorder
 Yes 13 (6.5) 11 (9.2) 2 (2.5) 0.08
 No 183 (91.5) 106 (89.1) 77 (96.3)
Been diagnosed with psychosis
 Yes 11 (5.5) 9 (7.6) 2 (2.5) 0.20
 No 185 (92.5) 108 (90.8) 77 (96.3)
Ever been hospitalized for a mental illness or depression
 Yes 65 (32.7) 50 (42.0) 15 (18.8) <0.01
 No 134 (67.3) 69 (58.0) 65 (81.3)
Sexually assaulted or abused before the age of 18
 Yes 55 (27.6) 40 (33.6) 15 (18.8) 0.02
 No 138 (69.3) 75 (63.0) 63 (78.8)
Hit or injured by a parent before the age of 18
 Yes 73 (36.7) 55 (46.2) 18 (22.5) <0.01
 No 119 (59.8) 60 (50.4) 59 (73.8)
Insulted or sworn at by a parent before the age of 18
 Yes 137 (68.8) 94 (79.0) 43 (53.8) <0.01
 No 58 (29.1) 22 (18.5) 36 (45.0)
While growing up lived with someone who had a mental illness
 Yes 95 (47.7) 67 (56.3) 28 (35.0) <0.01
 No 97 (48.7) 46 (38.7) 51 (63.8)
While growing up lived with someone who was using street drugs
 Yes 101 (50.5) 64 (53.8) 37 (46.3) 0.18
 No 90 (45.0) 48 (40.3) 42 (52.5)
While growing up lived with someone who went to jail or prison
 Yes 86 (43.0) 52 (43.7) 34 (42.5) 0.77
 No 106 (53.0) 61 (51.3) 45 (56.3)
Open in a new tab

Not all columns add to 100% due to missing values

Significance ascertained using Fisher’s exact test unless otherwise noted

a

Significance ascertained using the Wilcoxon rank sum test

As shown in Table 1, compared to participants who screened negative for depressive symptomology, participants who screened positive were more likely to: be female; be LGBQ; have been homeless in the preceding six months; engage in at least monthly illicit polysubstance use in the preceding six months; have ever been told they had a depressive disorder, bipolar disorder, and/or anxiety disorder; have ever been hospitalized for a mental illness or depression; lived with someone who had a mental illness growing up; and have experienced adverse childhood experiences, including being sexually assaulted or abused, being hit or injured by a parent, and/or being insulted or sworn at by a parent before the age of 18.

In Table 2, we show our final exploratory multivariate model, which includes all the variables with significant associations (p < 0.05) in bivariate analyses. We explored whether removing variables with sequential backwards selection resulted in a more parsimonious model with better fit, but removing variables resulted in models with higher QIC values. We found that this first multivariable model with all the variables with significant bivariate associations had the lowest QIC, and we decided that this was our final multivariable model. In this model, ever being told about having a depressive disorder (adjusted prevalence ratio = 1.51, 95% CI: 1.14–1.99, p < 0.01) and being insulted or sworn at by a parent before the age of 18 (adjusted prevalence ratio = 1.50, 95% CI: 1.05–2.14, p = 0.02) remained significant when adjusting for the other covariates. The p-values for several variables shown in Table 2 were greater than the 0.05 cut-off for backward selection, but we decided to keep these variables in the final model because removing them increased the QIC.

Table 2:

Modified Poisson regression analysis of factors associated with depressive symptomology among young adult non-medical prescription opioid users in Rhode Island (N = 199)

Characteristic Adjusted prevalence ratio (95% CI) p – value
Female vs. male sex at birth 1.24 (0.97–1.58) 0.08
Lesbian, gay, bisexual, queer, or something else vs. straight 1.23 (0.90–1.69) 0.19
Been homeless in the last six months (yes vs. no) 1.22 (0.97–1.53) 0.09
Monthly illicit polysubstance use in the last six months (yes vs. no) 1.20 (0.95–1.52) 0.13
Been diagnosed with a depressive disorder (yes vs. no) 1.51 (1.14–1.99) <0.01
Been diagnosed with a bipolar disorder (yes vs. no) 1.08 (0.86–1.35) 0.50
Been diagnosed with an anxiety disorder (yes vs. no) 0.78 (0.60–1.02) 0.07
Been hospitalized for a mental illness or depression (yes vs. no) 1.10 (0.88–1.38) 0.40
Hit or injured by a parent before the age of 18 (yes vs. no) 1.20 (0.94–1.53) 0.14
Insulted or sworn at by a parent before the age of 18 (yes vs. no) 1.50 (1.05–2.14) 0.02
Sexually assaulted or abused before the age of 18 (yes vs. no) 0.92 (0.69–1.22) 0.55
While growing up lived with someone with a mental illness (yes vs. no) 1.03 (0.78–1.36) 0.83
Open in a new tab

As shown in Table 3, participants who screened positive for depressive symptomology were more likely to report using prescription opioids non-medically to feel less depressed or anxious, to avoid withdrawal symptoms, and as a substitute when other drugs are not available.

Table 3:

Motivations for using prescription opioids non-medically associated with depressive symptomology among young adult non-medical prescription opioid users in Rhode Island (N = 199)

Motivation for using prescription opioids non-medically Total n (%) n=199 Screened Positive for Depressive Symptomology n (%) n=119 Screened Negative for Depressive Symptomology n (%) n=80 p - value
To feel good or get high
 Yes 157 (78.9) 95 (79.8) 62 (77.5) 0.59
 No 40 (20.1) 22 (18.5) 18 (22.5)
To feel less depressed or anxious
 Yes 111 (55.8) 83 (69.7) 28 (35.0) <0.01
 No 86 (43.2) 34 (28.6) 52 (65.0)
To relieve physical pain
 Yes 150 (75.4) 91 (76.5) 59 (73.8) 0.61
 No 47 (23.6) 26 (21.8) 21 (26.3)
To get a good sleep
 Yes 107 (53.8) 66 (55.5) 41 (51.3) 0.56
 No 90 (45.2) 51 (42.9) 39 (48.8)
To avoid withdrawal symptoms
 Yes 82 (41.2) 56 (47.1) 26 (32.5) 0.04
 No 115 (57.8) 61 (51.3) 54 (67.5)
To have a good time with friends
 Yes 98 (49.2) 61 (51.3) 38 (47.5) 0.56
 No 99 (49.7) 56 (47.1) 42 (52.5)
Because I was pressured into it
 Yes 14 (7.0) 10 (8.4) 4 (5.0) 0.41
 No 183 (92.0) 107 (89.9) 76 (95.0)
As a substitute when other drugs are not available
 Yes 84 (42.2) 60 (50.4) 24 (30.0) <0.01
 No 113 (56.8) 57 (47.9) 56 (70.0)
Open in a new tab

Not all columns add to 100% due to missing values

Significance ascertained using Fisher’s exact test

Results were broadly similar in a series of sensitivity analyses which used a higher cutoff of 15. However, several drug-related behaviors were associated with depressive symptomatology in bivariate sensitivity analyses, including history of injection drug use (39.5% vs. 23.8% screening positive among those with and without a history of injection drug use, respectively, p < 0.02); history of sniffing or snorting an opioid (67.5% vs. 53.3%, p < 0.048); and history of heroin use (52.0% vs. 36.9%, p < 0.036). The multivariable results were similar, although recent homelessness attained statistical significance (adjusted prevalence ratio = 1.44; CI: 1.01 – 2.06; p = 0.049). Motivations for using prescription opioids non-medically were also similar, although persons who screened positive for depressive symptomatology at the higher cutoff were more likely to report NMPO use to get a good sleep (64.9% vs. 47.5%, p = 0.017).

Discussion

Among our sample of young NMPO users in Rhode Island, almost six in ten screened positive for depressive symptomology according to the CES-D 10, confirming the findings of two studies that have reported a high prevalence of co-occurring depressive symptomology among young adult NMPO users (Goldner et al. 2014, Fischer et al. 2012). Diagnostic history of depressive disorder and being insulted or sworn at by a parent before the age of 18 were independent correlates of current depressive symptomology in our modified Poisson regression analysis. While the prior diagnosis may be a reasonable predictor of current symptomology, the relationship between adverse childhood experiences and the severity or persistence of such symptomology among this population warrants further exploration. To our knowledge, our study is the first to report that among young adults who engage in NMPO use, depressive symptomology is associated with being LGBQ. This finding emphasizes the need to understand the co-occurring, syndemic factors and disparities affecting the population of young adult NMPO users and subpopulations such as young LGBQ NMPO users (Pachankis et al. 2018, Branstrom and Pachankis 2018, Wang, Burton, and Pachankis 2018, White Hughto et al. 2017). The finding that LGBQ status was not independently associated with depressive symptomology also requires further investigation. It is possible that other variables in the model (such as sex at birth and recent homelessness) confound the relationship between LGBQ status and depressive symptoms. Nonetheless, we believe that sexual orientation should be considered when conceptualizing the development of co-occurrence of NMPO use, depressive symptomology, and other outcomes.

Our finding that depressive symptomology was associated with using prescription opioids non-medically to feel less depressed or anxious may support the self-medication hypothesis (i.e. depressive symptomology may lead to NMPO use). Depressive symptomology was also associated with using prescription opioids non-medically to avoid withdrawal symptoms. This finding may be complicated by the fact that anxious or depressive symptoms can result from opioid withdrawal (World Health Organization 2009). Additional longitudinal research is needed to elucidate the onset and directionality of associations between mental health distress, NMPO use, and withdrawal symptoms. Furthermore, future research should differentiate self-medication of anxious or depressive symptoms with self-medication of withdrawal symptoms among young adults who use prescription opioids non-medically. Overall, these findings demonstrate that young adults with depressive symptomology have distinct motivations for NMPO use.

We did not find that age of NMPO use onset and years of NMPO use were associated with depressive symptomology. Given that our sensitivity analyses revealed such associations with history of injection drug use, history of sniffing or snorting an opioid, and history of heroin use, attention should be paid to the dynamics and trajectories of young adult NMPO use—specifically, route of administration of any opioid, and transitions to heroin use or injection. These measures have a bearing on substance use and mental health trajectories and may provide insight into the co-occurrence of NMPO use, mental health symptoms, and other outcomes (Twombly and Holtz 2008, Surratt, Kurtz, and Cicero 2011, McCabe et al. 2007, Marshall et al. 2016, Lankenau, Teti, Silva, Bloom, et al. 2012, Green et al. 2011, Goodman, Peterson-Badali, and Henderson 2011, Frank et al. 2015, Drazdowski 2016, Dow and Kelly 2013). As reasons for using prescription opioids non-medically often change over time, research should explore mental health symptomology and drug using patterns over time to capture key transition points in NMPO use trajectories (Boyd et al. 2006, Dow and Kelly 2013, Drazdowski 2016, Goodman, Peterson-Badali, and Henderson 2011). All of these dynamics have implications for addressing the rapidly increasing opioid use epidemic among young adults (Martins et al. 2017, Marshall et al. 2016, Young, Glover, and Havens 2012), and to target such efforts to sub-populations such as young LGBQ people who use drugs.

We recommend screening for both NMPO use and depressive symptomology among young adults because NMPO use is highest among this population (Center for Behavioral Health Statistics and Quality 2016), and screening provides an opportunity for early intervention against adverse outcomes (Marshall et al. 2016), as well as informing appropriate treatment options (Dow and Kelly 2013). Nationally, self-medication trends are leading to changes in prescribing patterns: the latest National Survey on Drug Use and Health study on NMPO suggests addressing prescribing guidelines due to the high prevalence of self-medicating for pain (Han et al. 2017). This study’s findings suggest the need for better screening in various settings to identify subgroups at risk for harms related to depressive symptomology and/or NMPO use. It is important to note that even when young people are screened for depressive symptomology, a low proportion actually receives services after screening (Young et al. 2012, Mackesy-Amiti, Donenberg, and Ouellet 2015, Allgaier et al. 2014). Furthermore, many people are more likely to seek mental health services than they are to seek substance use services (Khantzian 1997, Fischer et al. 2012, Allgaier et al. 2014). These points stress the need to not only increase screening, but to implement protocols for ensuring individuals are connected to the care they need, to integrate mental health and substance use treatment, and to reduce the stigma associated with receiving such services (Young et al. 2012, Wang, Burton, and Pachankis 2018, Marshall et al. 2016).

Our study is consistent with research on the co-occurrence of depressive symptomology and heroin use, in that we similarly found a high prevalence of depressive symptomology; moreover, variables including female sex, homelessness, polysubstance use, and adverse childhood experiences were associated with depressive symptomology. However, we are unable to contextualize our findings on the distinct motivations for NMPO use among young adults with depressive symptomology, due to the fact that similar research on motivations for heroin use among young adults has not yet been conducted. More research on motivations for using prescription opioids and other drugs is needed to fully contextualize this research and understand substance use trajectories.

If depressive symptomology does indeed lead to NMPO use (i.e., the self-medication pathway), screening young adults for both depressive symptomology and NMPO use in a variety of healthcare settings (e.g. medical office, emergency department) may help to identify those at increased risk and offer the opportunity to intervene before overdose or transition to heroin use occur (Twombly and Holtz 2008). This may be particularly relevant for young adults who are prescribed opioids for pain (Simoni-Wastila and Tompkins 2001). We were unable to test for evidence of the precipitation pathway (where NMPO use leads to depressive symptomology), given the cross-sectional nature of the study. Finally, our study suggests there may be an opportunity to explore the relationship between adverse childhood experiences and the presence or severity of depressive symptomology among young adults who use opioids nonmedically (Green et al. 2009, Salas et al. 2016, Garland, Pettus-Davis, and Howard 2013, Young, Glover, and Havens 2012).

Our study has several limitations. First, although the CES-D 10 is a validated screening instrument which has been used among substance-using populations, some degree of misclassification is possible (Andresen et al. 1994, Carpenter et al. 1998, Kohout et al. 1993). While we decided to use a cut-off of 10 for the CES-D 10 to be consistent with prior research, a higher cut-off could have been used, given that such a large proportion of our sample screened positive for depressive symptomology with the cut-off of 10. Future research should continue to explore what cut-off should be used for the CES-D 10 among young adults who use prescription opioids non-medically. Second, we had participants choose their primary motivations for using prescription opioids non-medically from a list, and it is possible that we did not accurately capture participants’ motivations for NMPO use (Drazdowski 2016). While motivations for prescription drug use change over time in young adulthood (Garnier-Dykstra et al. 2012, Hartung et al. 2013), we were unable to capture such evolving trajectories due to the cross-sectional nature of our study. Third, because our study was cross-sectional, we could not identify any causal relationships.. Longitudinal studies are needed to better understand causal relationships among depressive symptomology, NMPO use, and other variables. Fourth, while our selection of variables for our bivariate analyses was driven by prior literature, our multivariable analysis was based on backwards selection from these variables, rather than being exclusively theory-based. We decided that since our analysis was exploratory in nature, a data-driven variable selection procedure was appropriate. Future research investigating the etiology of depressive symptomology among young adult NMPO users should be more theory-based. Fifth, when we asked about participants’ psychiatric diagnoses, we asked if they had “been told” that they had one or more of the listed diagnoses without specifically asking if they had been told so by a medical professional. Future research should be more specific when asking about diagnostic history, and use standardized instruments to capture mental health diagnoses. Sixth, we did not use a standardized scale or comprehensive set of adverse childhood experiences measures, so we decided to analyze each adverse childhood experience individually. Future research should employ a standardized (and more comprehensive) adverse childhood experiences scale. Seventh, although all participants were considered NMPO users according to the study’s eligibility criteria, we did not obtain a uniform sample in terms of types of use. For example, a participant who misused their own prescription opioids that were previously prescribed for a legitimate injury, and a participant who bought prescription opioids from the illicit market would both be considered NMPO users. Thus, our findings cannot be extrapolated to a specific subpopulation of NMPO-using young adults. Due to the fact that we did not use verification strategies such as urine drug screens, it is possible that inaccurate reporting existed regarding participants NMPO users. Finally, although we used diverse recruitment strategies, our findings may not be generalizable to all young adults who engage in NMPO use.

Conclusion

Our findings emphasize the need to understand motivations for NMPO use among young adults with comorbid depressive symptomology, so we can better identify those at higher risk of health consequences by increasing screening, and provide opportunities for appropriate intervention. Our research demonstrates possible support for the self-medication hypothesis of co-occurring NMPO use and depressive symptomology, but emphasizes the need to further investigate all potential hypotheses of the development of this co-occurrence through longitudinal design (Kessler 2004). We hope that this research continues to inform the development of improved guidelines and other national strategies to effectively reduce the harms of co-occurring depressive symptomology and NMPO use among young adults.

Acknowledgments

We thank RAPiDS study participants and staff for their contribution to the research.

Funding details

This work was supported by the US National Institute on Drug Abuse under Grant R03-DA037770.

Footnotes

Disclosure of Interest Statement

The authors report no conflict of interest.

REFERENCES

  1. Ali MM, Dean D Jr., Lipari R, Dowd WN, Aldridge AP, and Novak SP. 2015. “The mental health consequences of nonmedical prescription drug use among adolescents.” J Ment Health Policy Econ 18 (1):3–15. [PubMed] [Google Scholar]
  2. Allgaier AK, Krick K, Opitz A, Saravo B, Romanos M, and Schulte-Korne G. 2014. “Improving early detection of childhood depression in mental health care: the Childrens Depression Screener (ChilD-S).” Psychiatry Res 217 (3):248–52. doi: 10.1016/j.psychres.2014.03.037. [DOI] [PubMed] [Google Scholar]
  3. Amari E, Rehm J, Goldner E, and Fischer B. 2011. “Nonmedical prescription opioid use and mental health and pain comorbidities: a narrative review.” Can J Psychiatry 56 (8):495–502. doi: 10.1177/070674371105600808. [DOI] [PubMed] [Google Scholar]
  4. Andresen EM, Malmgren JA, Carter WB, and Patrick DL. 1994. “Screening for depression in well older adults: evaluation of a short form of the CES-D (Center for Epidemiologic Studies Depression Scale).” Am J Prev Med 10 (2):77–84. [PubMed] [Google Scholar]
  5. Becker WC, Sullivan LE, Tetrault JM, Desai RA, and Fiellin DA. 2008. “Non-medical use, abuse and dependence on prescription opioids among U.S. adults: psychiatric, medical and substance use correlates.” Drug Alcohol Depend 94 (1–3):38–47. doi: 10.1016/j.drugalcdep.2007.09.018. [DOI] [PubMed] [Google Scholar]
  6. Bjorgvinsson T, Kertz SJ, Bigda-Peyton JS, McCoy KL, and Aderka IM. 2013. “Psychometric properties of the CES-D-10 in a psychiatric sample.” Assessment 20 (4):429–36. doi: 10.1177/1073191113481998. [DOI] [PubMed] [Google Scholar]
  7. Boyd CJ, McCabe SE, Cranford JA, and Young A. 2006. “Adolescents’ motivations to abuse prescription medications.” Pediatrics 118 (6):2472–80. doi: 10.1542/peds.2006-1644. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Branstrom R, and Pachankis JE. 2018. “Validating the Syndemic Threat Surrounding Sexual Minority Men’s Health in a Population-Based Study With National Registry Linkage and a Heterosexual Comparison.” J Acquir Immune Defic Syndr 78 (4):376–382. doi: 10.1097/QAI.0000000000001697. [DOI] [PubMed] [Google Scholar]
  9. Carpenter JS, Andrykowski MA, Wilson J, Hall LA, Rayens MK, Sachs B, and Cunningham LL. 1998. “Psychometrics for two short forms of the Center for Epidemiologic Studies-Depression Scale.” Issues Ment Health Nurs 19 (5):481–94. [DOI] [PubMed] [Google Scholar]
  10. Center for Behavioral Health Statistics and Quality. 2016. 2015 National Survey on Drug Use and Health: Detailed Tables. Rockville, MD: Substance Abuse and Mental Health Services Administration. [Google Scholar]
  11. Cerda M, Santaella J, Marshall BD, Kim JH, and Martins SS. 2015. “Nonmedical Prescription Opioid Use in Childhood and Early Adolescence Predicts Transitions to Heroin Use in Young Adulthood: A National Study.” J Pediatr 167 (3):605–12 e1–2. doi: 10.1016/j.jpeds.2015.04.071. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Chahua M, Sordo L, Barrio G, Domingo-Salvany A, Brugal MT, Molist G, de la Fuente L, Bravo MJ, and Itinere Project Group. 2014. “Non-fatal opioid overdose and major depression among street-recruited young heroin users.” Eur Addict Res 20 (1):1–7. doi: 10.1159/000346787. [DOI] [PubMed] [Google Scholar]
  13. Chen LH, Hedegaard H, and Warner M. 2014. “Drug-poisoning Deaths Involving Opioid Analgesics: United States, 1999–2011.” NCHS Data Brief (166):1–8. [PubMed] [Google Scholar]
  14. Cornford CS, Umeh K, and Manshani N. 2012. “Heroin users’ experiences of depression: a qualitative study.” Fam Pract 29 (5):586–92. doi: 10.1093/fampra/cms014. [DOI] [PubMed] [Google Scholar]
  15. Dow SJ, and Kelly JF. 2013. “Listening to youth: Adolescents’ reasons for substance use as a unique predictor of treatment response and outcome.” Psychol Addict Behav 27 (4):1122–31. doi: 10.1037/a0031065. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Drazdowski TK 2016. “A systematic review of the motivations for the non-medical use of prescription drugs in young adults.” Drug Alcohol Depend 162:3–25. doi: 10.1016/j.drugalcdep.2016.01.011. [DOI] [PubMed] [Google Scholar]
  17. Edlund MJ, Forman-Hoffman VL, Winder CR, Heller DC, Kroutil LA, Lipari RN, and Colpe LJ. 2015. “Opioid abuse and depression in adolescents: Results from the National Survey on Drug Use and Health.” Drug Alcohol Depend 152:131–8. doi: 10.1016/j.drugalcdep.2015.04.010. [DOI] [PubMed] [Google Scholar]
  18. Fink DS, Hu R, Cerda M, Keyes KM, Marshall BD, Galea S, and Martins SS. 2015. “Patterns of major depression and nonmedical use of prescription opioids in the United States.” Drug Alcohol Depend 153:258–64. doi: 10.1016/j.drugalcdep.2015.05.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Fischer B, Lusted A, Roerecke M, Taylor B, and Rehm J. 2012. “The prevalence of mental health and pain symptoms in general population samples reporting nonmedical use of prescription opioids: a systematic review and meta-analysis.” J Pain 13 (11):1029–44. doi: 10.1016/j.jpain.2012.07.013. [DOI] [PubMed] [Google Scholar]
  20. Frank D, Mateu-Gelabert P, Guarino H, Bennett A, Wendel T, Jessell L, and Teper A. 2015. “High risk and little knowledge: overdose experiences and knowledge among young adult nonmedical prescription opioid users.” Int J Drug Policy 26 (1):84–91. doi: 10.1016/j.drugpo.2014.07.013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Garland EL, Pettus-Davis C, and Howard MO. 2013. “Self-medication among traumatized youth: structural equation modeling of pathways between trauma history, substance misuse, and psychological distress.” J Behav Med 36 (2):175–85. doi: 10.1007/s10865-012-9413-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Garnier-Dykstra LM, Caldeira KM, Vincent KB, O’Grady KE, and Arria AM. 2012. “Nonmedical use of prescription stimulants during college: four-year trends in exposure opportunity, use, motives, and sources.” J Am Coll Health 60 (3):226–34. doi: 10.1080/07448481.2011.589876. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Goldner EM, Lusted A, Roerecke M, Rehm J, and Fischer B. 2014. “Prevalence of Axis-1 psychiatric (with focus on depression and anxiety) disorder and symptomatology among non-medical prescription opioid users in substance use treatment: systematic review and meta-analyses.” Addict Behav 39 (3):520–31. doi: 10.1016/j.addbeh.2013.11.022. [DOI] [PubMed] [Google Scholar]
  24. Goodman I, Peterson-Badali M, and Henderson J. 2011. “Understanding motivation for substance use treatment: the role of social pressure during the transition to adulthood.” Addict Behav 36 (6):660–8. doi: 10.1016/j.addbeh.2011.01.011. [DOI] [PubMed] [Google Scholar]
  25. Green TC, Black R, Grimes Serrano JM, Budman SH, and Butler SF. 2011. “Typologies of prescription opioid use in a large sample of adults assessed for substance abuse treatment.” PLoS ONE 6 (11):e27244. doi: 10.1371/journal.pone.0027244. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Green TC, Grimes Serrano JM, Licari A, Budman SH, and Butler SF. 2009. “Women who abuse prescription opioids: Findings from the Addiction Severity Index-Multimedia Version Connect prescription opioid database.” Drug Alcohol Depend 103 (1–2):65–73. doi: 10.1016/j.drugalcdep.2009.03.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Hadland SE, Marshall BD, Kerr T, Qi J, Montaner JS, and Wood E. 2011. “Depressive symptoms and patterns of drug use among street youth.” J Adolesc Health 48 (6):585–90. doi: 10.1016/j.jadohealth.2010.09.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Hadland SE, and Wood E. 2012. “Commentary on Bruneau et al. (2012): injection of prescription opioid pain relievers and infectious disease risk.” Addiction 107 (7):1328–9. doi: 10.1111/j.1360-0443.2012.03885.x. [DOI] [PubMed] [Google Scholar]
  29. Han Beth, Compton Wilson M, Blanco C, Crane Elizabeth, Lee Jinhee, and Jones CM. 2017. “Prescription Opioid Use, Misuse, and Use Disorders in U.S. Adults: 2015 National Survey on Drug Use and Health “ Annals of Internal Medicine 167 (5). [DOI] [PubMed] [Google Scholar]
  30. Harrell FJ. 2015. Regression modeling strategies: Springer International Publishing. [Google Scholar]
  31. Hartung CM, Canu WH, Cleveland CS, Lefler EK, Mignogna MJ, Fedele DA, Correia CJ, Leffingwell TR, and Clapp JD. 2013. “Stimulant medication use in college students: comparison of appropriate users, misusers, and nonusers.” Psychol Addict Behav 27 (3):832–40. doi: 10.1037/a0033822. [DOI] [PubMed] [Google Scholar]
  32. Jones CM, Logan J, Gladden RM, and Bohm MK. 2015. “Vital Signs: Demographic and Substance Use Trends Among Heroin Users - United States, 2002–2013.” MMWR Morb Mortal Wkly Rep 64 (26):719–25. [PMC free article] [PubMed] [Google Scholar]
  33. Kessler RC 2004. “The epidemiology of dual diagnosis.” Biol Psychiatry 56 (10):730–7. doi: 10.1016/j.biopsych.2004.06.034. [DOI] [PubMed] [Google Scholar]
  34. Khantzian EJ 1997. “The self-medication hypothesis of substance use disorders: a reconsideration and recent applications.” Harv Rev Psychiatry 4 (5):231–44. doi: 10.3109/10673229709030550. [DOI] [PubMed] [Google Scholar]
  35. Kilburn K, Prencipe L, Hjelm L, Peterman A, Handa S, and Palermo T. 2018. “Examination of performance of the Center for Epidemiologic Studies Depression Scale Short Form 10 among African youth in poor, rural households.” BMC Psychiatry 18 (1):201. doi: 10.1186/s12888-018-1774-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Kohout FJ, Berkman LF, Evans DA, and Cornoni-Huntley J. 1993. “Two shorter forms of the CES-D (Center for Epidemiological Studies Depression) depression symptoms index.” J Aging Health 5 (2):179–93. doi: 10.1177/089826439300500202. [DOI] [PubMed] [Google Scholar]
  37. Kolodny A, Courtwright DT, Hwang CS, Kreiner P, Eadie JL, Clark TW, and Alexander GC. 2015. “The prescription opioid and heroin crisis: a public health approach to an epidemic of addiction.” Annu Rev Public Health 36:559–74. doi: 10.1146/annurev-publhealth-031914-122957. [DOI] [PubMed] [Google Scholar]
  38. Lankenau SE, Teti M, Silva K, Bloom JJ, Harocopos A, and Treese M. 2012. “Patterns of prescription drug misuse among young injection drug users.” J Urban Health 89 (6):1004–16. doi: 10.1007/s11524-012-9691-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Lankenau SE, Teti M, Silva K, Bloom J. Jackson, Harocopos A, and Treese M. 2012. “Initiation into prescription opioid misuse amongst young injection drug users.” Int J Drug Policy 23 (1):37–44. doi: 10.1016/j.drugpo.2011.05.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Lima VD, Harrigan R, Murray M, Moore DM, Wood E, Hogg RS, and Montaner JS. 2008. “Differential impact of adherence on long-term treatment response among naive HIV-infected individuals.” AIDS 22 (17):2371–80. doi: 10.1097/QAD.0b013e328315cdd3 00002030-200811120-00017 [pii]. [DOI] [PubMed] [Google Scholar]
  41. Mackesy-Amiti ME, Donenberg GR, and Ouellet LJ. 2015. “Prescription opioid misuse and mental health among young injection drug users.” Am J Drug Alcohol Abuse 41 (1):100–6. doi: 10.3109/00952990.2014.940424. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Marshall BDL, Green TC, Elston B, Yedinak JL, Hadland SE, and Clark MA. 2018. “The Effectiveness of Internet- and Field-Based Methods to Recruit Young Adults Who Use Prescription Opioids Nonmedically.” Subst Use Misuse 53 (10):1688–1699. doi: 10.1080/10826084.2018.1425725. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Marshall BDL, Green TC, Yedinak JL, and Hadland SE. 2016. “Harm reduction for young people who use prescription opioids extra-medically: Obstacles and opportunities.” Int J Drug Policy 10.1016/j.drugpo.2016.01.022. doi: 10.1016/j.drugpo.2016.01.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Martins SS, Fenton MC, Keyes KM, Blanco C, Zhu H, and Storr CL. 2012. “Mood and anxiety disorders and their association with non-medical prescription opioid use and prescription opioid-use disorder: longitudinal evidence from the National Epidemiologic Study on Alcohol and Related Conditions.” Psychol Med 42 (6):1261–72. doi: 10.1017/s0033291711002145. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Martins SS, Segura Luis E., Santaella-Tenorio Julian, Perlmutter Alexander, Fenton Miriam C., Cerdá Magdalena, Keyes Katherine M., Ghandour Lilian A., Storr Carla L., and Hasin Deborah S.. 2017. “Prescription opioid use disorder and heroin use among 12–34 year-olds in the United States from 2002 to 2014.” Addictive Behaviors 65 (Supplement C):236–241. doi: 10.1016/j.addbeh.2016.08.033. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. McCabe SE, Cranford JA, Boyd CJ, and Teter CJ. 2007. “Motives, diversion and routes of administration associated with nonmedical use of prescription opioids.” Addict Behav 32 (3):562–75. doi: 10.1016/j.addbeh.2006.05.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. National Institute on Drug Abuse. 2011. NIDA Research Report Series: Prescription Drug Abuse. Bethesda, MD: National Institutes of Health. [Google Scholar]
  48. Nyamathi A, Leake B, Albarran C, Zhang S, Hall E, Farabee D, Marlow E, Marfisee M, Khalilifard F, and Faucette M. 2011. “Correlates of depressive symptoms among homeless men on parole.” Issues Ment Health Nurs 32 (8):501–11. doi: 10.3109/01612840.2011.569111. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Pachankis JE, Hatzenbuehler ML, Wang K, Burton CL, Crawford FW, Phelan JC, and Link BG. 2018. “The Burden of Stigma on Health and Well-Being: A Taxonomy of Concealment, Course, Disruptiveness, Aesthetics, Origin, and Peril Across 93 Stigmas.” Pers Soc Psychol Bull 44 (4):451–474. doi: 10.1177/0146167217741313. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Risser J, Cates A, Rehman H, and Risser W. 2010. “Gender differences in social support and depression among injection drug users in Houston, Texas.” Am J Drug Alcohol Abuse 36 (1):18–24. doi: 10.3109/00952990903544802. [DOI] [PubMed] [Google Scholar]
  51. Rowe CL, Liddle HA, Greenbaum PE, and Henderson CE. 2004. “Impact of psychiatric comorbidity on treatment of adolescent drug abusers.” J Subst Abuse Treat 26 (2):129–40. doi: 10.1016/S0740-5472(03)00166-1. [DOI] [PubMed] [Google Scholar]
  52. Salas J, Scherrer JF, Lustman PJ, and Schneider FD. 2016. “Racial differences in the association between nonmedical prescription opioid use, abuse/dependence, and major depression.” Subst Abus 37 (1):25–30. doi: 10.1080/08897077.2015.1129523. [DOI] [PubMed] [Google Scholar]
  53. Simoni-Wastila L, and Tompkins C. 2001. “Balancing diversion control and medical necessity: the case of prescription drugs with abuse potential.” Subst Use Misuse 36 (9–10):1275–96. [DOI] [PubMed] [Google Scholar]
  54. Sordo L, Chahua M, Bravo MJ, Barrio G, Brugal MT, Domingo-Salvany A, Molist G, De la Fuente L, and Itinere Project Group. 2012. “Depression among regular heroin users: the influence of gender.” Addict Behav 37 (1):148–52. doi: 10.1016/j.addbeh.2011.09.009. [DOI] [PubMed] [Google Scholar]
  55. Surratt H, Kurtz SP, and Cicero TJ. 2011. “Alternate routes of administration and risk for HIV among prescription opioid abusers.” J Addict Dis 30 (4):334–41. doi: 10.1080/10550887.2011.609805. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Tobin KE, and Latkin CA. 2003. “The relationship between depressive symptoms and nonfatal overdose among a sample of drug users in Baltimore, Maryland.” J Urban Health 80 (2):220–9. doi: 10.1093/jurban/jtg025. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Twombly EC, and Holtz KD. 2008. “Teens and the misuse of prescription drugs: evidence-based recommendations to curb a growing societal problem.” J Prim Prev 29 (6):503–16. doi: 10.1007/s10935-008-0157-5. [DOI] [PubMed] [Google Scholar]
  58. Wang K, Burton CL, and Pachankis JE. 2018. “Depression and Substance Use: Towards the Development of an Emotion Regulation Model of Stigma Coping.” Subst Use Misuse 53 (5):859–866. doi: 10.1080/10826084.2017.1391011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Wang P, Wu HC, Yen CN, Yeh YC, Chung KS, Chang HC, and Yen CF. 2012. “Predictors of the severity of depressive symptoms among intravenous heroin users receiving methadone maintenance treatment in Taiwan: an 18-month follow-up study.” Psychol Addict Behav 26 (1):145–50. doi: 10.1037/a0025175. [DOI] [PubMed] [Google Scholar]
  60. White Hughto JM, Pachankis JE, Willie TC, and Reisner SL. 2017. “Victimization and depressive symptomology in transgender adults: The mediating role of avoidant coping.” J Couns Psychol 64 (1):41–51. doi: 10.1037/cou0000184. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. World Health Organization. 2009. Clinical Guidelines for Withdrawal Management and Treatment of Drug Dependence in Closed Settings. Geneva. [PubMed] [Google Scholar]
  62. Wu Y, Yan S, Bao Y, Lian Z, Qu Z, and Liu Z. 2016. “Cross-sectional study of the severity of self-reported depressive symptoms in heroin users who participate in a methadone maintenance treatment program.” Shanghai Arch Psychiatry 28 (1):35–41. doi: 10.11919/j.issn.1002-0829.215127. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Young AM, Glover N, and Havens JR. 2012. “Nonmedical use of prescription medications among adolescents in the United States: a systematic review.” J Adolesc Health 51 (1):6–17. doi: 10.1016/j.jadohealth.2012.01.011. [DOI] [PubMed] [Google Scholar]
  64. Young AM, McCabe SE, Cranford JA, Ross-Durow P, and Boyd CJ. 2012. “Nonmedical use of prescription opioids among adolescents: subtypes based on motivation for use.” J Addict Dis 31 (4):332–41. doi: 10.1080/10550887.2012.735564. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Zhang W, O’Brien N, Forrest JI, Salters KA, Patterson TL, Montaner JS, Hogg RS, and Lima VD. 2012. “Validating a shortened depression scale (10 item CES-D) among HIV-positive people in British Columbia, Canada.” PLoS One 7 (7):e40793. doi: 10.1371/journal.pone.0040793. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Zou G 2004. “A modified poisson regression approach to prospective studies with binary data.” Am J Epidemiol 159 (7):702–6. [DOI] [PubMed] [Google Scholar]

RESOURCES