Abstract
Objective:
Given continued increases in “deaths of despair”, there is a need to examine associations of factors across multiple domains of despair (i.e., cognitive, emotional, behavioral, biological) with opioid-related behaviors. An understanding of current and early life correlates of prescription opioid behaviors can help inform clinical care, public health interventions, and future life course research.
Methods:
Using data from Waves I (1994-1995; participants ages 12-18 years) and V (2016-2018; participants ages 34-42 years) of the National Longitudinal Study of Adolescent to Adult Health (N=10,685), we examined adolescent and adult demographic, mental and physical health, substance use, and behavioral characteristics associated with past 30-day prescription opioid use only, misuse only, and both use and misuse to no recent use or misuse in adulthood.
Results:
Overall, 2.3% of adult participants reported past 30-day prescription opioid use only, 6.3% reported past 30-day misuse only, and 1.3% reported both prescribed use and misuse in the past 30 days. Physical health conditions in adolescence and adulthood were most common among those reporting use only and both use and misuse. Mental health conditions, other substance use, and delinquent behaviors in adolescence and adulthood were most common among those reporting misuse only and both use and misuse.
Conclusions:
Results from this nationally representative sample highlight the prevalence of specific prescription opioid behaviors and underscore the importance of targeting underlying drivers of prescription opioid use and misuse early in the life course. Continued implementation individual- and population-level approaches will be critical to addressing continued demand for opioids.
Keywords: prescription opioid use, prescription opioid misuse
Introduction
The United States is in the midst of an opioid crisis, with nearly 450,000 individuals dying from an opioid overdose between 1999 and 2018 (Centers for Disease Control and Prevention, 2020). Over the past two decades, the opioid crisis has rapidly evolved from an initial surge in overdose deaths due to prescription opioids to a dramatic increase in deaths due to heroin and other synthetic (e.g., fentanyl) opioids (Centers for Disease Control and Prevention, 2020). Though opioid prescribing and prescription opioid misuse have decreased over the last few years, the overall burden and contribution to overdose deaths remain high (Centers for Disease Control and Prevention, 2020; Jones, 2017; Olfson, Wang, Wall, & Blanco, 2020).
Increases in opioid overdose deaths in the U.S. are part of a larger trend of increases in “deaths of despair,” or deaths due to drug overdose, alcohol-related diseases, and suicide (Case & Deaton, 2015). To date, research on deaths of despair has largely focused on economic drivers underlying these outcomes. However, recent calls in the literature emphasize the need to expand upon this work to consider a broader range of factors potentially contributing to and co-occurring with these outcomes (Dasgupta, Beletsky, & Ciccarone, 2018; Ranade, Wunder, Terzian, & Ungureanu, 2020; Shanahan et al., 2019). Specifically, recent research highlights the need to examine multiple, interrelated domains of despair, including the cognitive (e.g., hopelessness, pessimism), emotional (e.g., sadness, loneliness), behavioral (e.g., self-harm, substance use), and biological (e.g., chronic conditions) domains, and their association with opioid-related behaviors (Shanahan, et al., 2019). A comprehensive understanding of correlates of appropriate medical use (hereafter referred to as ‘use’) and misuse of prescription opioids across these domains, and potential social and structural factors underpinning these correlates, can contribute to the development of more effective prevention and intervention strategies to improve quality of life among those using prescription opioids and reduce long-term opioid-related harms.
Prior U.S. studies have examined correlates of prescription opioid use and misuse (Cragg et al., 2019). However, many of these studies have been conducted in small clinical samples or single healthcare systems, which may not reflect the larger U.S. context (Cragg, et al., 2019). A few recent studies examined correlates of prescription opioid use and misuse in nationally representative samples using data from the National Survey on Drug Use and Health and the National Health and Nutrition Examination Survey (Blanco, Wall, Liu, & Olfson, 2019; B. Han et al., 2017; Han, Compton, Jones, & Cai, 2015; Hu, Griesler, Wall, & Kandel, 2017; Jones, 2017; Marsh, Park, Lin, & Bersamira, 2018; Martins et al., 2017; Mojtabai, 2018). Results from these studies demonstrate associations of prescription opioid use, prescription opioid misuse, and opioid use disorders (OUD) with indicators of economic disadvantage as well as physical and mental health conditions and high-risk substance use behaviors (Blanco, et al., 2019; Han, et al., 2017; Han, et al., 2015; Hu, et al., 2017; Jones, 2017; Marsh, et al., 2018; Martins, et al., 2017; Mojtabai, 2018). However, prior nationally representative studies have not specifically compared characteristics of those engaging in prescription opioids use only, misuse only, and both use and misuse to those with no recent prescription opioid use or misuse.
Prior nationally representative studies among adults have largely focused on examining correlates of prescription opioid misuse and OUD (Blanco, et al., 2019; Han, et al., 2015; Hu, et al., 2017; Jones, 2017; Marsh, et al., 2018; Martins, et al., 2017). One study examined correlates of prescription opioid use only, both use and misuse, and use with OUD, but only focused on demographic correlates (Han, et al., 2017). A comprehensive examination of a range of characteristics associated with use only, misuse only, and both use and misuse can help to improve our understanding of the distinction, if any, between these populations, and guide intervention and research priorities targeted to these nuanced behaviors. Specifically, knowledge of correlates of use only can help to identify the unique needs of this patient population and inform clinical and programmatic interventions to support improved wellbeing. Information regarding correlates of misuse only can help to identify populations most likely to engage in misuse and further the development of appropriate harm reduction strategies to prevent potential adverse consequences associated with misuse, such as progression to OUD and overdose. Understanding correlates of both use and misuse can help to inform clinical and public health intervention strategies to improve overall care and prevent misuse among those prescribed opioids.
To add to the evidence base regarding correlates of prescription opioid-related behaviors, we examined adolescent and adult characteristics, spanning multiple domains of despair, associated with prescription opioid use only, misuse only, and both use and misuse, compared to no recent use or misuse, in a prospective, nationally-representative sample of U.S. adults from the National Longitudinal Study of Adolescent to Adult Health (Add Health). We examined both adolescent and adult correlates as knowledge of early life correlates can help illuminate potential points for prevention in childhood and adolescence while knowledge of more proximal correlates can help to inform care and intervention for those currently engaging in prescription opioid use and misuse.
Methods
Data source
Add Health is a nationally representative sample of U.S. adolescents (N=20,745) in grades 7-12 in 1994-1995 (Wave I). Follow-up interviews with participants were conducted in 1996 (Wave II; N=14,738 participants in grades 8-12), 2001-2002 (Wave III; N=15,197 participants 18-26 years), 2008 (Wave IV; N=15,701 participants 24-32 years), and 2016-2018 (Wave V; N=12,300 participants 34-42 years) (Harris et al., 2019). Response rates ranged from 77%-80% at each wave. We used data from Waves I and V and restricted the sample to participants with valid sampling weights and strata (N=10,685).
Measures
At Wave V, participants reporting past 30-day prescription medication use were asked to present medication containers so the interviewer could record medication names. If medication containers were unavailable, participants were asked to self-report medication names. We used resources from the Centers for Disease Control and Prevention (https://www.cdc.gov/drugoverdose/resources/data.html) and the Substance Abuse and Mental Health Services Administration (https://www.samhsa.gov/data/report/drug-reference-vocabulary) to conduct text searches of medication names to identify prescription opioids. We did not include medications prescribed in outpatient settings to treat OUD (e.g., Suboxone).
Prescription opioid misuse was assessed at Wave V by asking participants if they had taken pain killers or opioids, such as Vicodin, OxyContin, Percocet, Demerol, Percodan, or Tylenol with codeine, in the past 30 days that were not prescribed for them, in larger amounts, more often, or for longer periods than prescribed, or only for the feeling or experience they caused (Kennett, Painter, Hunter, Granger, & Bowman, 2010).
Participants self-reported demographics, mental and physical health outcomes, and substance use and delinquent behaviors at Waves I and V. A description of each measure is available in Supplemental Table 1.
Statistical analysis
We compared participant characteristics at Wave I and participant characteristics at Wave V among those reporting recent prescription opioid use only, those reporting recent prescription opioid misuse only, and those reporting both recent prescription opioid use and misuse to those reporting no recent prescription opioid use or misuse. For characteristics that were measured similarly at Waves I and V, we examined whether these characteristics were present in adolescence only, adulthood only, both adolescence and adulthood, or neither by patterns of prescription opioid use and misuse. We provide Chi-square p-values for comparisons made, but avoid presenting and interpreting results as “statistically significant” or “not statistically significant” as recommended by the American Statistical Association (Wasserstein & Lazar, 2016). Analyses were conducted in SAS 9.4 and accounted for the complex sampling design of Add Health. This study was reviewed and approved by the Institutional Review Board at the University of North Carolina at Chapel Hill.
Results
At Wave V, 2.3% (95% confidence interval (CI) 1.9, 2.8) of adult participants reported past 30-day prescription opioid use only, 6.3% (95% CI 5.5, 7.0) reported past 30-day prescription opioid misuse only, and 1.3% (95% CI 1.0,1.6) reported both prescribed use and misuse in the past 30 days.
Demographics
Compared to those reporting no recent prescription opioid use or misuse, among those reporting prescription opioid use only, there was a higher percent of individuals who identified as White, non-Hispanic (73.0% vs. 66.2%) and individuals who were separated, divorced, or widowed (26.7% vs. 15.4%) (Table 1). Among those reporting both prescription opioid use and misuse, there was a higher percent of individuals who identified as White, non-Hispanic (79.3% vs. 66.2%) and individuals who were separated, divorced, or widowed (25.5% vs 15.4%) compared to those reporting no use or misuse.
Table 1.
Participant characteristics in adolescence and adulthood by patterns of recent opioid use and misuse in adulthood, National Longitudinal Study of Adolescent to Adult Health (N=10,685)
| Demographics in adulthood (Wave V, age 32-42 years) | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| No recent prescription opioid use or misuse in adulthood (Wave V) (N=9,653) | Prescription opioid use only in adulthood (Wave V) (N=224) | Prescription opioid misuse only in adulthood (Wave V) (N=620) | Prescription opioid use and misuse in adulthood (Wave V) (N=134) | Chi-square p-value | |||||
|
| |||||||||
| N | % (95% CI) | N | % (95% CI) | N | % (95% CI) | N | % (95% CI) | ||
| Age, mean (95% CI) | 37.4 (37.2, 37.7) | 37.4 (36.9, 37.8) | 37.5 (37.1, 37.8) | 37.7 (37.2, 38.2) | |||||
| Sex | 0.4356 | ||||||||
| Male | 4,117 | 50.3 (49.0, 51.7) | 80 | 43.1 (34.7, 51.4) | 272 | 51.8 (45.9, 57.8) | 52 | 49.0 (36.6, 61.3) | |
| Female | 5,536 | 49.7 (48.3, 51.0) | 144 | 56.9 (48.6, 65.3) | 348 | 48.2 (42.2, 54.1) | 82 | 51.0 (38.7, 63.4) | |
| Race/ethnicity | 0.1790 | ||||||||
| White, non-Hispanic | 5,563 | 66.2 (60.4, 72.1) | 146 | 73.0 (64.8, 81.2) | 341 | 65.3 (57.8, 72.8) | 92 | 79.3 (69.1, 89.4) | |
| Black, non-Hispanic | 1,774 | 14.5 (10.8, 18.3) | 38 | 10.1 (4.9, 15.3) | 126 | 15.1 (10.5, 19.7) | 22 | 10.1 (2.4, 17.8) | |
| Other race or Hispanic | 2,291 | 19.2 (15.1, 23.4) | 38 | 16.9 (9.8, 24.0) | 153 | 19.5 (13.6, 25.5) | 19 | 10.6 (3.4, 17.8) | |
| Education | <0.0001 | ||||||||
| High school, GED, or less | 1,681 | 20.6 (18.3, 23.0) | 46 | 24.0 (14.9, 33.0) | 175 | 30.8 (25.6, 36.1) | 38 | 34.7 (22.1, 47.3) | |
| Junior college or trade school | 2,696 | 29.0 (27.3, 30.7) | 79 | 35.3 (26.8, 43.8) | 191 | 31.0 (26.3, 35.7) | 50 | 34.7 (22.9, 46.5) | |
| College or more | 5,260 | 50.3 (47.1, 53.5) | 99 | 40.7 (31.3, 50.1) | 253 | 38.1 (32.6, 43.6) | 46 | 30.6 (20.2, 41.1) | |
| Marital status | <0.0001 | ||||||||
| Never married | 2,465 | 26.0 (24.0, 28.0) | 58 | 24.5 (17.0, 32.0) | 233 | 35.6 (29.7, 41.5) | 56 | 35.6 (24.9, 46.2) | |
| Married | 5,778 | 58.6 (56.5, 60.7) | 124 | 48.8 (40.0, 57.5) | 282 | 43.4 (38.1, 48.8) | 53 | 39.0 (26.3, 51.7) | |
| Separated, divorced, or Widowed | 1,392 | 15.4 (14.2, 16.6) | 42 | 26.7 (18.1, 35.4) | 104 | 21.0 (16.1, 25.8) | 25 | 25.5 (13.9, 37.0) | |
| Employment status | <0.0001 | ||||||||
| Not currently working | 1,411 | 16.1 (14.7, 17.5) | 76 | 39.1 (30.1, 48.0) | 151 | 27.5 (22.6, 32.3) | 43 | 35.2 (23.9, 46.5) | |
| Currently working for pay | 8,210 | 83.9 (82.5, 85.3) | 148 | 61.9 (52.0, 69.9) | 469 | 72.5 (52.0, 69.9) | 91 | 64.8 (53.5, 76.1) | |
| Military service | 0.2831 | ||||||||
| No | 8,985 | 92.2 (91.2, 93.1) | 200 | 88.9 (83.6, 94.1) | 582 | 94.0 (91.3, 96.6) | 122 | 92.2 (86.7, 97.6) | |
| Yes | 650 | 7.8 (6.9, 8.8) | 24 | 11.1 (5.9, 16.4) | 36 | 6.0 (3.4, 8.7) | 12 | 7.8 (2.4, 13.3) | |
| Income | <0.0001 | ||||||||
| <$30,000 | 1,061 | 14.5 (12.5, 16.5) | 48 | 30.8 (22.4, 39.3) | 125 | 25.9 (20.5, 31.2) | 30 | 33.4 (19.9, 47.0) | |
| $30,000 – $74,999 | 2,379 | 31.2 (29.6, 32.8) | 69 | 35.6 (25.5, 45.8) | 169 | 34.9 (28.8, 41.1) | 44 | 39.1 (25.7, 52.5) | |
| ≥$75,000 | 4,567 | 54.2 (51.4, 57.0) | 69 | 33.5 (24.3, 42.8) | 203 | 39.2 (33.1, 45.3) | 36 | 27.4 (16.2, 38.7) | |
| Health insurance | <0.0001 | ||||||||
| Private | 7,562 | 76.3 (74.3, 78.3) | 134 | 51.8 (42.6, 61.0) | 389 | 61.7 (56.0, 67.5) | 72 | 45.1 (33.0, 57.3) | |
| Medicaid, Medicare, or other government | 1,244 | 14.7 (13.0, 16.4) | 76 | 40.0 (30.5, 49.5) | 144 | 24.9 (19.3, 30.5) | 46 | 37.4 (25.4, 49.4) | |
| No insurance | 743 | 9.0 (8.0, 10.0) | 11 | 8.2 (1.4, 14.9) | 79 | 13.3 (9.4, 17.2) | 15 | 17.5 (7.1, 27.8) | |
| Foreclosure, eviction, or repossession in past 10 years | <0.0001 | ||||||||
| No | 8,058 | 83.4 (82.1, 84.6) | 171 | 78.6 (72.0, 85.2) | 462 | 72.8 (67.3, 78.3) | 97 | 70.9 (60.0, 81.7) | |
| Yes | 1,537 | 16.6 (15.4, 17.9) | 52 | 21.4 (14.8, 28.0) | 153 | 27.2 (21.7, 32.7) | 37 | 29.1 (18.3, 40.0) | |
| Difficulty paying bills in past 10 years | <0.0001 | ||||||||
| No | 4,988 | 50.1 (48.2, 51.9) | 76 | 29.1 (21.5, 36.8) | 216 | 34.3 (29.1, 39.6) | 34 | 19.0 (10.1, 27.9) | |
| Yes | 4,602 | 49.9 (48.1, 51.8) | 147 | 70.9 (63.2, 78.5) | 400 | 65.7 (60.4, 70.9) | 100 | 81.0 (72.1, 89.9) | |
|
| |||||||||
| Demographics in adolescence (Wave I, age 12-18 years) | |||||||||
|
| |||||||||
| Resident parent receipt of public assistance | 0.0043 | ||||||||
| No | 8,544 | 90.3 (88.7, 91.9) | 198 | 90.4 (84.9, 95.9) | 519 | 86.4 (81.4, 91.3) | 106 | 84.1 (74.9, 93.4) | |
| Yes | 888 | 9.7 (8.1, 11.3) | 20 | 9.6 (4.1, 15.1) | 78 | 13.6 (8.7, 18.6) | 21 | 15.9 (6.6, 25.1) | |
| Resident parent highest level of education | <0.0001 | ||||||||
| High school, GED, or less | 3,592 | 41.9 (38.4, 45.4) | 95 | 42.5 (33.1, 51.9) | 298 | 54.5 (47.8, 61.3) | 67 | 52.2 (40.5, 63.9) | |
| Junior college or trade school | 2,021 | 22.1 (20.5, 23.7) | 46 | 27.6 (19.5, 35.6) | 104 | 18.5 (14.6, 22.5) | 35 | 34.4 (23.0, 45.9) | |
| College or more | 3,590 | 36.0 (32.3, 39.7) | 73 | 29.9 (21.4, 38.4) | 177 | 26.9 (20.9, 33.0) | 22 | 13.4 (3.4, 23.3) | |
| At least one resident parent currenting working for pay | 0.0016 | ||||||||
| No | 897 | 9.6 (8.1, 11.0) | 29 | 13.5 (7.4, 19.5) | 89 | 15.0 (10.9, 19.2) | 17 | 17.3 (6.6, 28.0) | |
| Yes | 8,755 | 90.4 (89.0, 91.9) | 195 | 86.5 (80.5, 92.6) | 531 | 85.0 (80.8, 89.1) | 117 | 82.7 (72.0, 93.3) | |
There were several notable economic correlates of prescription opioid use and misuse. The prevalence of annual income <$30,000, a foreclosure, eviction, or repossession in the past 10 years, and difficulties paying bills in the past 10 years was higher among those reporting recent use, misuse, or both compared to those reporting neither. For example, 81.0% of those reporting both use and misuse, 65.7% of those reporting misuse only, and 70.9% of those reporting use only had difficulties paying bills in the past 10 years compared to 49.9% of those reporting no use or misuse. Similarly, there was a high prevalence of Medicaid, Medicare, or other government health insurance (40.0%) among those reporting use only, a relatively high prevalence of no health insurance among those reporting both use and misuse (17.5%), and a high prevalence of a high school education or less among those reporting use only (30.8%) and both use and misuse (34.7%).
Indicators of economic disadvantage were also prevalent in adolescence among those with prescription opioid use or misuse in adulthood. Among those reporting both use and misuse, a higher percent had parents who received public assistance (15.9% vs. 9.7%), parents with a high school education or less (52.2% vs. 41.9%), and neither parent working for pay (17.3% vs. 9.6%) in adolescence compared to those reporting no recent use or misuse.
Cognitive and emotional domain of despair: Mental health
The prevalence of lifetime depression, post-traumatic stress disorder (PTSD), and anxiety diagnoses, past 12-month suicidal ideation or attempts and psychological counseling, and difficulty sleeping in the past month was higher among those reporting recent prescription opioid use, misuse, or both compared to those reporting neither, with prevalence typically highest among those reporting use only or those reporting both use and misuse (Table 2). For example, 51.0% of those reporting both use and misuse, 40.9% of those reporting misuse only, and 51.1% of those reporting use only had a lifetime depression diagnosis compared to 24.4% of those reporting neither. An exception to this was that the prevalence of suicidal ideation or attempts in the past 12 months was slightly higher among those reporting misuse only (17.0%) and those reporting both use and misuse (18.1%) compared to those reporting use only (15.1%). Early life correlates indicated that depressive symptoms and suicidal ideation or attempts in adolescence were higher among those reporting prescription opioid use, misuse, or both in adulthood compared to those with no recent use or misuse. For example, 20.7% of those reporting both use and misuse, 20.9% of those reporting misuse only, and 22.0% of those reporting use only had suicidal ideation or attempts in adolescence compared to 12.9% of those reporting no recent prescription opioid use or misuse.
Table 2.
Cognitive and emotional domains of despair: Mental health outcomes in adolescence and adulthood by patterns of recent opioid use and misuse in adulthood, National Longitudinal Study of Adolescent to Adult Health (N=10,685)
| Mental health in adulthood (Wave V, age 32-42 years) | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| No recent prescription opioid use or misuse in adulthood (Wave V) (N=9,653) | Prescription opioid use only in adulthood (Wave V) (N=224) | Prescription opioid misuse only in adulthood (Wave V) (N=620) | Prescription opioid use and misuse in adulthood (Wave V) (N=134) | Chi-square p-value | |||||
|
| |||||||||
| N | % (95% CI) | N | % (95% CI) | N | % (95% CI) | N | % (95% CI) | ||
| Lifetime depression diagnosis | <0.0001 | ||||||||
| No | 7,356 | 75.6 (73.9, 77.3) | 117 | 48.9 (40.5, 57.3) | 388 | 59.1 (53.9, 64.2) | 65 | 49.0 (35.7, 62.4) | |
| Yes | 2,261 | 24.4 (22.7, 26.1) | 105 | 51.1 (42.7, 59.5) | 230 | 40.9 (35.8, 46.1) | 69 | 51.0 (37.6, 64.3) | |
| Lifetime anxiety diagnosis | <0.0001 | ||||||||
| No | 7,609 | 78.4 (76.9, 79.8) | 125 | 52.7 (43.5, 61.9) | 420 | 63.9 (58.4, 69.4) | 74 | 52.2 (40.0, 64.4) | |
| Yes | 2,006 | 21.6 (20.2, 23.1) | 98 | 47.3 (38.1, 56.5) | 198 | 36.1 (30.6, 41.6) | 60 | 47.8 (35.6, 60.0) | |
| Lifetime PTSD diagnosis | <0.0001 | ||||||||
| No | 9,067 | 93.4 (92.5, 94.3) | 177 | 83.6 (77.4, 89.7) | 540 | 83.6 (79.6, 87.6) | 112 | 81.9 (72.5, 91.2) | |
| Yes | 546 | 6.6 (5.7, 7.4) | 45 | 16.5 (10.3, 22.6) | 76 | 16.4 (12.4, 20.5) | 22 | 18.1 (8.8, 27.5) | |
| Suicidal ideation or attempt in past 12 months | <0.0001 | ||||||||
| No | 8,880 | 93.6 (92.9, 94.3) | 196 | 84.9 (78.2, 91.7) | 512 | 83.0 (78.3, 87.7) | 113 | 81.9 (72.1, 91.6) | |
| Yes | 591 | 6.4 (5.7, 7.1) | 28 | 15.1 (8.3, 21.8) | 95 | 17.0 (12.3, 21.7) | 21 | 18.1 (8.4, 27.9) | |
| Difficulty falling or staying asleep in past month | <0.0001 | ||||||||
| No | 4,653 | 48.5 (46.9, 50.1) | 71 | 28.4 (20.8, 36.0) | 184 | 29.8 (24.7, 35.0) | 33 | 19.3 (11.1, 27.5) | |
| Yes | 4,989 | 51.5 (49.9, 53.1) | 153 | 71.6 (64.0, 79.2) | 435 | 70.2 (65.0, 75.3) | 101 | 80.7 (72.5, 88.9) | |
| Psychological counseling in past 12 months | <0.0001 | ||||||||
| No | 8,251 | 86.3 (85.2, 87.4) | 170 | 77.3 (70.9, 83.6) | 499 | 79.6 (75.0, 84.2) | 100 | 75.1 (64.7, 85.5) | |
| Yes | 1,351 | 13.7 (12.6, 14.8) | 52 | 22.7 (16.4, 29.1) | 115 | 20.4 (15.8, 25.0) | 34 | 24.9 (14.5, 35.3) | |
|
| |||||||||
| Mental health in adolescence (Wave I, age 12-18 years) | |||||||||
|
| |||||||||
| Elevated depressive symptoms a | <0.0001 | ||||||||
| No | 7,790 | 81.8 (80.5, 83.1) | 167 | 74.4 (67.2, 81.6) | 448 | 74.9 (70.9, 78.9) | 98 | 70.2 (60.0, 80.4) | |
| Yes | 1,863 | 18.2 (16.9, 19.5) | 57 | 25.6 (18.4, 32.8) | 172 | 25.1 (21.1, 29.1) | 36 | 29.8 (19.6, 40.0) | |
| Difficulty falling or staying asleep in past 12 months | 0.1886 | ||||||||
| No | 7,324 | 76.0 (74.5, 77.5) | 168 | 75.9 (68.4, 83.3) | 452 | 71.8 (66.9, 76.7) | 88 | 69.0 (58.8, 79.1) | |
| Yes | 2,324 | 24.0 (22.5, 25.5) | 56 | 24.1 (16.7, 31.6) | 168 | 28.2 (23.3, 33.1) | 46 | 31.0 (20.9, 41.2) | |
| Suicidal ideation or attempt in past 12 months | <0.0001 | ||||||||
| No | 8,308 | 87.1 (86.2, 88.1) | 171 | 78.0 (70.8, 85.3) | 492 | 79.1 (69.9, 88.8) | 103 | 79.3 (69.9, 88.8) | |
| Yes | 1,278 | 12.9 (11.9, 13.8) | 52 | 22.0 (14.7, 29.2) | 118 | 20.9 (16.8, 25.0) | 30 | 20.7 (11.2, 30.1) | |
Suppressed due to N<10.
Based on responses to the Center for Epidemiologic Studies Depression Scale (CES-D), with a score of ≥16 indicating elevated depression symptoms.
Biological domain of despair: Physical health
Among those reporting prescription opioid use only and those reporting both prescription opioid use and misuse, there was a higher prevalence of fair or poor self-rated health, activity limitations, serious injuries, and falls in the past 12 months, and migraines compared to those with no recent use or misuse (Table 3). For example, 55.8% of those reporting use only and 54.7% of those reporting both use and misuse indicated activity limitations due to health in the past 12 months compared to 22.5% of those with no use or misuse. In contrast, the prevalence of lifetime hepatitis B or C diagnoses (2.8% vs. 0.9%) and failure to get medical care when needed in the past 12 months (47.3% vs. 21.3%) was higher among those with both prescription opioid use and misuse compared to those with no recent use or misuse.
Table 3.
Biological domain of despair: Physical health outcomes in adolescence and adulthood by patterns of recent opioid use and misuse in adulthood, National Longitudinal Study of Adolescent to Adult Health (N=10,685)
| Physical health in adulthood (Wave V, age 32-42 years) | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| No recent prescription opioid use or misuse in adulthood (Wave V) (N=9,653) | Prescription opioid use only in adulthood (Wave V) (N=224) | Prescription opioid misuse only in adulthood (Wave V) (N=620) | Prescription opioid use and misuse in adulthood (Wave V) (N=134) | Chi-square p-value | |||||
|
| |||||||||
| N | % (95% CI) | N | % (95% CI) | N | % (95% CI) | N | % (95% CI) | ||
| Self-rated general health | <0.0001 | ||||||||
| Fair or poor | 1,168 | 13.0 (11.7, 14.2) | 80 | 40.8 (32.3, 49.4) | 157 | 26.9 (22.0, 31.9) | 51 | 36.7 (24.6, 48.8) | |
| Good, very good, or excellent | 8,469 | 87.0 (85.8, 88.3) | 143 | 59.2 (50.6, 67.7) | 463 | 73.1 (68.1, 78.0) | 83 | 63.3 (51.2 (75.4) | |
| Activity limitations due to health in past 12 months | <0.0001 | ||||||||
| No | 7,512 | 77.5 (76.0, 78.9) | 97 | 44.2 (36.1, 52.2) | 354 | 56.4 (50.4, 62.4) | 59 | 45.3 (33.1, 57.5) | |
| Yes | 2,121 | 22.5 (21.1, 24.0) | 126 | 55.8 (47.8, 63.9) | 264 | 43.6 (37.6, 49.6) | 75 | 54.7 (42.5, 66.9) | |
| Ever had migraines | <0.0001 | ||||||||
| No | 7,699 | 79.9 (78.5, 81.2) | 121 | 49.9 (40.8, 59.0) | 443 | 73.2 (68.4, 78.1) | 85 | 64.7 (52.8, 76.6) | |
| Yes | 1,909 | 20.1 (18.8, 21.5) | 103 | 50.1 (41.0, 59.2) | 176 | 26.8 (21.9, 31.6) | 49 | 35.3 (23.4, 47.2) | |
| Lifetime cancer diagnosis | 0.0122 | ||||||||
| No | 9,405 | 97.6 (97.1, 98.2) | 211 | 94.8 (90.9, 98.8) | 596 | 97.5 (96.1, 98.9) | * | * | |
| Yes | 208 | 2.4 (1.8, 2.9) | 12 | 5.2 (1.2, 9.1) | 20 | 2.5 (1.1, 3.9) | * | * | |
| Lifetime hepatitis B or C diagnosis | - | ||||||||
| No | 9,556 | 99.1 (98.8, 99.4) | * | * | 601 | 97.2 (95.3, 99.1) | 134 | 100.0 | |
| Yes | 68 | 0.9 (0.6, 1.2) | * | * | 15 | 2.8 (0.9, 4.7) | 0 | 0.0 | |
| Serious injury in past 12 months | <0.0001 | ||||||||
| No | 8,269 | 87.8 (88.9, 88.7) | 144 | 65.4 (58.0, 72.9) | 466 | 77.4 (72.9, 81.8) | 82 | 61.9 (50.0, 73.8) | |
| Yes | 1,129 | 12.2 (11.3, 13.1) | 80 | 34.6 (27.1, 42.0) | 137 | 22.6 (18.2, 27.1) | 52 | 38.1 (26.2, 50.0) | |
| Fall in past 12 months | <0.0001 | ||||||||
| No | 7,269 | 76.8 (75.4, 78.1) | 125 | 52.0 (42.6, 61.4) | 411 | 68.5 (63.3, 73.8) | 66 | 51.5 (39.0, 64.0) | |
| Yes | 2,143 | 23.2 (21.9, 24.6) | 99 | 48.0 (38.6, 57.4) | 193 | 31.5 (26.2, 36.7) | 67 | 48.5 (36.0, 61.0) | |
| Did not get medical care when should have in past 12 months | <0.0001 | ||||||||
| No | 7,636 | 78.7 (77.3, 80.0) | 154 | 68.1 (59.7, 76.6) | 382 | 60.6 (54.1, 67.1) | 83 | 52.7 (40.4, 65.0) | |
| Yes | 1,989 | 21.3 (20.0, 22.7) | 68 | 31.9 (23.4, 40.3) | 236 | 39.4 (32.9, 45.9) | 51 | 47.3 (35.0, 59.6) | |
|
| |||||||||
| Physical health in adolescence (Wave I, age 12-18 years) | |||||||||
|
| |||||||||
| Self-rated general health | 0.0080 | ||||||||
| Fair or poor | 597 | 5.9 (5.2, 6.6) | 17 | 9.6 (4.7, 14.5) | 67 | 9.7 (6.3, 13.1) | 13 | 6.0 (1.2, 10.8) | |
| Good, very good, or excellent | 9,053 | 94.1 (93.4, 94.8) | 207 | 90.4 (85.5, 95.3) | 553 | 90.3 (86.9, 93.7) | 121 | 94.0 (89.2, 98.8) | |
| Regular headaches in past 12 months | 0.1540 | ||||||||
| No | 6,756 | 70.6 (69.2, 72.1) | 145 | 60.6 (50.8, 70.4) | 423 | 70.0 (65.3, 74.7) | 90 | 70.4 (58.5, 82.4) | |
| Yes | 2.894 | 29.4 (27.9, 30.8) | 79 | 39.4 (29.6, 49.2) | 197 | 30.0 (25.3, 34.7) | 44 | 29.6 (17.6, 41.5) | |
| Regular muscle or joint pain in past 12 months | 0.2258 | ||||||||
| No | 6,998 | 72.8 (71.3, 74.4) | 152 | 65.4 (56.9, 73.9) | 440 | 71.2 (65.4, 76.9) | 96 | 76.4 (67.7, 85.1) | |
| Yes | 2,650 | 27.2 (25.6, 28.7) | 72 | 34.6 (26.1, 43.1) | 179 | 28.8 (23.1, 34.6) | 38 | 23.6 (14.9, 32.3) | |
| Serious injury in past 12 months | 0.0229 | ||||||||
| No | 8,273 | 86.0 (84.8, 87.2) | 181 | 80.6 (73.1, 88.1) | 517 | 83.3 (79.5, 87.1) | 103 | 75.2 (63.7, 86.8) | |
| Yes | 1,360 | 14.0 (12.8, 26.9) | 43 | 19.4 (11.9, 26.9) | 100 | 16.7 (12.9, 20.5) | 31 | 24.8 (13.2, 36.3) | |
| Did not get medical care when should have in past 12 months | 0.0004 | ||||||||
| No | 7,775 | 81.9 (80.7, 83.1) | 169 | 75.7 (68.3, 83.0) | 475 | 79.3 (74.8, 83.7) | 92 | 62.5 (47.8, 77.1) | |
| Yes | 1,871 | 18.1 (16.9, 19.3) | 55 | 24.3 (17.0, 31.7) | 145 | 20.7 (16.3, 25.2) | 42 | 37.5 (22.9, 52.2) | |
Suppressed due to N<10.
Evidence of poor physical health among those with prescription opioid use or misuse was also present in adolescence. Regular headaches (39.4% vs. 29.4%) and muscle or joint pains (34.6% vs. 27.2%) in adolescence were more prevalent among those reporting use only compared to those reporting no recent use or misuse. Serious injuries (24.8% vs. 14.0%) and failure to get medical care when needed (37.5% vs. 18.1%) in adolescence were highest among those reporting both prescription opioid use and misuse compared to those with neither.
Behavioral domain of despair: Substance use
The prevalence of lifetime and past 30-day cigarette use was higher among those reporting recent prescription opioid use, misuse, or both compared to those reporting no recent use or misuse (Table 4). Among those reporting misuse only and those reporting both use and misuse, the prevalence of past 30-day marijuana use, illicit drug use, and misuse of prescription sedatives, tranquilizers, and stimulants was higher compared to those reporting no recent use or misuse. For example, 24.1% of those reporting misuse only and 18.0% of those reporting both use and misuse indicated past 30-day prescription tranquilizer misuse compared to 1.9% of those reporting no recent use or misuse. In contrast, the prevalence of past 30-day alcohol use and binge drinking in the past 12 months was highest among those reporting misuse only and those with no recent use or misuse.
Table 4.
Behavioral domain of despair: Substance use, delinquent behaviors, and victimization in adolescence and adulthood by patterns of recent opioid use and misuse in adulthood, National Longitudinal Study of Adolescent to Adult Health (N=10,685)
| Substance use, delinquency, and victimization in adulthood (Wave V, age 32-42 years) | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| No recent prescription opioid use or misuse in adulthood (Wave V) (N=9,653) | Prescription opioid use only in adulthood (Wave V) (N=224) | Prescription opioid misuse only in adulthood (Wave V) (N=620) | Prescription opioid use and misuse in adulthood (Wave V) (N=134) | Chi-square p-value | |||||
|
| |||||||||
| N | % (95% CI) | N | % (95% CI) | N | % (95% CI) | N | % (95% CI) | ||
| Ever regular cigarette use | <0.0001 | ||||||||
| No | 5,739 | 54.1 (51.7, 56.5) | 96 | 37.2 (28.4, 46.1) | 266 | 35.8 (29.9, 41.8) | 60 | 37.8 (26.7, 48.8) | |
| Yes | 3,890 | 45.9 (43.5, 48.3) | 128 | 62.8 (53.9, 71.6) | 353 | 64.2 (58.2, 70.1) | 74 | 62.2 (51.2, 73.3) | |
| Cigarette use in past 30 days | <0.0001 | ||||||||
| No | 7,493 | 74.4 (72.7, 76.2) | 143 | 58.9 (50.3, 67.5) | 361 | 52.4 (46.0, 58.7) | 85 | 53.3 (41.9, 64.6) | |
| Yes | 2,153 | 25.6 (23.8, 27.3) | 81 | 41.1 (32.5, 49.7) | 258 | 47.6 (41.3, 54.0) | 49 | 46.7 (35.4, 58.1) | |
| Alcohol use in past 30 days | 0.0648 | ||||||||
| No | 2,489 | 26.9 (25.1, 28.7) | 75 | 33.7 (25.9, 41.6) | 168 | 29.6 (24.2, 35.0) | 46 | 37.1 (26.1, 48.0) | |
| Yes | 7,135 | 73.1 (71.3, 74.9) | 148 | 66.3 (58.4, 74.1) | 448 | 70.4 (65.0, 75.8) | 88 | 62.9 (52.0, 73.9) | |
| Binge drinking in past 12 months | 0.0516 | ||||||||
| No | 5,337 | 53.8 (51.8, 55.8) | 135 | 60.6 (52.6, 68.5) | 313 | 47.6 (41.6, 53.6) | 75 | 58.1 (46.4, 69.7) | |
| Yes | 4,305 | 46.2 (44.2, 48.2) | 89 | 39.4 (31.5, 47.4) | 306 | 52.4 (46.4, 58.4) | 59 | 41.9 (30.2, 53.6) | |
| Marijuana use in past 30 days | <0.0001 | ||||||||
| No | 7,987 | 80.3 (78.9, 81.8) | 174 | 72.9 (64.7, 81.2) | 395 | 56.0 (51.3, 60.7) | 92 | 62.2 (50.2, 74.1) | |
| Yes | 1,655 | 19.7 (18.2, 21.1) | 50 | 27.1 (18.8, 35.3) | 224 | 44.0 (39.3, 48.7) | 42 | 37.8 (25.9, 49.8) | |
| Illicit drug use in past 30 days | <0.0001 | ||||||||
| No | 9,383 | 97.0 (96.4, 97.5) | * | * | 517 | 80.4 (75.9, 84.9) | 124 | 92.6 (86.1, 99.1) | |
| Yes | 270 | 3.0 (2.5, 3.6) | * | * | 103 | 19.6 (16.1, 24.1) | 10 | 7.4 (0.9, 13.9) | |
| Misuse of prescription sedatives in past 30 days | <0.0001 | ||||||||
| No | 9,481 | 98.3 (98.0, 98.6) | * | * | 490 | 77.9 (73.3, 82.4) | 107 | 86.1 (77.9, 94.3) | |
| Yes | 160 | 1.7 (1.4, 2.0) | * | * | 112 | 22.1 (17.6, 26.7) | 23 | 13.9 (5.7, 22.1) | |
| Misuse of prescription tranquilizers in past 30 days | <0.0001 | ||||||||
| No | 9,469 | 98.1 (97.7, 98.5) | * | * | 481 | 75.9 (71.4, 80.4) | 108 | 82.0 (72.7, 91.4) | |
| Yes | 160 | 1.9 (1.5, 2.3) | * | * | 122 | 24.1 (19.6, 28.6) | 22 | 18.0 (8.6, 27.3) | |
| Misuse of prescription stimulants in past 30 days | <0.0001 | ||||||||
| No | 9,463 | 98.1 (97.6, 98.5) | * | * | 499 | 81.4 (76.6, 86.3) | 113 | 89.0 (93.4, 94.5) | |
| Yes | 174 | 1.9 (1.5, 2.4) | * | * | 104 | 18.6 (13.7, 23.4) | 18 | 11.0 (5.5, 16.6) | |
| Non-violent delinquent behaviors in past 12 months a | <0.0001 | ||||||||
| No | 9,229 | 96.8 (96.3, 97.4) | 214 | 93.3 (88.6, 98.0) | 534 | 86.8 (83.2, 90.3) | 115 | 89.6 (82.5,96.7) | |
| Yes | 239 | 3.2 (2.6, 3.7) | 10 | 6.7 (2.0, 11.4) | 72 | 13.2 (9.7, 16.8) | 18 | 10.4 (3.3, 17.5) | |
| Violent delinquent behaviors in past 12 months b | <0.0001 | ||||||||
| No | 9,273 | 97.5 (97.0, 98.0) | 212 | 94.9 (91.4, 98.5) | 565 | 93.9 (91.6, 96.2) | 123 | 92.4 (85.1, 99.8) | |
| Yes | 194 | 2.5 (2.0, 3.0) | 12 | 5.1 (1.5, 8.6) | 41 | 6.1 (3.8, 8.4) | 11 | 7.6 (0.2, 14.9) | |
| Ever arrested | <0.0001 | ||||||||
| No | 6,803 | 67.4 (65.7, 69.2) | 150 | 64.3 (54.4, 74.3) | 337 | 51.7 (45.8, 57.6) | 74 | 47.1 (33.4, 60.8) | |
| Yes | 2,639 | 32.6 (30.8, 34.3) | 73 | 35.7 (25.7, 45.6) | 269 | 48.3 (42.4, 54.2) | 60 | 52.9 (39.2, 66.6) | |
| Ever driving under the influence (DUI)/driving while impaired (DWI) charge | <0.0001 | ||||||||
| No | 8,768 | 89.0 (87.8, 90.2) | 207 | 89.1 (82.8, 95.4) | 521 | 82.1 (78.0, 86.3) | 114 | 72.9 (58.3, 87.6) | |
| Yes | 873 | 11.0 (9.8, 12.2) | 17 | 10.9 (4.6, 17.2) | 96 | 17.9 (13.7, 22.0) | 20 | 27.1 (12.4, 41.7) | |
| Ever other alcohol or drug related charge | <0.0001 | ||||||||
| No | 8,700 | 87.7 (86.5, 88.9) | 199 | 85.4 (78.7, 92.0) | 488 | 77.3 (73.3, 81.3) | 110 | 76.8 (65.2, 88.4) | |
| Yes | 953 | 12.3 (11.1, 13.5) | 25 | 14.6 (8.0, 21.3) | 132 | 22.7 (18.7, 26.7) | 24 | 23.2 (11.6, 34.8) | |
| Ever incarcerated | <0.0001 | ||||||||
| No | 8,598 | 86.3 (84.8, 87.7) | 185 | 80.3 (72.6, 87.9) | 478 | 74.7 (69.5, 79.9) | 97 | 65.4 (52.6, 78.2) | |
| Yes | 1,046 | 13.7 (12.3, 15.2) | 39 | 19.7 (12.1, 27.4) | 138 | 25.3 (20.1, 30.5) | 37 | 34.6 (21.7, 47.4) | |
|
| |||||||||
| Substance use, delinquency, and victimization in adolescence (Wave I, age 12-18 years) | |||||||||
|
| |||||||||
| Any cigarette use | <0.0001 | ||||||||
| No | 4,315 | 42.6 (40.2, 44.9) | 87 | 37.4 (29.4, 45.4) | 211 | 32.0 (26.2, 37.8) | 34 | 22.1 (13.2, 31.0) | |
| Yes | 5,293 | 57.4 (55.1, 59.8) | 136 | 62.6 (54.6, 70.6) | 406 | 68.0 (62.2, 73.8) | 100 | 77.9 (69.0, 86.8) | |
| Any alcohol use | 0.0004 | ||||||||
| No | 4,298 | 43.9 (41.1, 46.7) | 99 | 44.9 (35.6, 54.3) | 230 | 33.4 (28.0, 38.8) | 46 | 29.7 (18.6, 40.7) | |
| Yes | 5,308 | 56.1 (53.3, 58.9) | 124 | 55.1 (45.7, 64.4) | 384 | 66.6 (61.2, 72.0) | 85 | 70.3 (59.3, 81.4) | |
| Any marijuana use | <0.0001 | ||||||||
| No | 7,124 | 73.7 (71.1, 76.3) | 149 | 65.3 (56.5, 74.1) | 384 | 59.7 (53.8, 65.6) | 84 | 60.0 (47.5, 72.6) | |
| Yes | 2,444 | 26.3 (23.7, 28.9) | 74 | 34.7 (25.9, 43.5) | 229 | 40.3 (34.4, 46.2) | 47 | 40.0 (27.4, 52.5) | |
| Any illicit drug use | 0.0024 | ||||||||
| No | 8,827 | 91.6 (90.4, 92.8) | 203 | 89.6 (84.1, 95.0) | 528 | 86.0 (82.4, 89.5) | 115 | 85.4 (75.8, 95.1) | |
| Yes | 759 | 8.4 (7.2, 9.6) | 20 | 10.4 (5.0, 15.9) | 83 | 14.0 (10.5, 17.6) | 16 | 14.6 (4.9, 24.2) | |
| Non-violent delinquent behaviors in past 12 months c | 0.0463 | ||||||||
| No | 5,762 | 59.4 (57.4, 61.3) | 141 | 60.8 (52.4, 69.1) | 318 | 53.0 (48.2, 57.9) | 66 | 48.8 (35.1, 62.6) | |
| Yes | 3,853 | 40.6 (38.7, 42.6) | 82 | 39.2 (30.9, 47.6) | 298 | 47.0 (42.1, 51.8) | 67 | 51.2 (37.4, 64.9) | |
| Violent delinquent behaviors in past 12 months d | 0.0002 | ||||||||
| No | 5,913 | 58.8 (56.8, 60.9) | 115 | 52.7 (43.0, 62.5) | 315 | 48.9 (43.1, 54.7) | 66 | 43.2 (32.9, 53.5) | |
| Yes | 3,708 | 41.2 (39.1, 43.2) | 108 | 47.3 (37.5, 57.0) | 302 | 51.1 (45.3, 56.9) | 68 | 56.8 (46.5, 67.1) | |
| Ever suspended from school | <0.0001 | ||||||||
| No | 7,453 | 75.6 (72.9, 78.3) | 150 | 67.0 (58.3, 75.8) | 397 | 63.7 (57.7, 69.8) | 93 | 67.2 (54.6, 79.8) | |
| Yes | 2,193 | 24.4 (21.7, 27.1) | 74 | 33.0 (24.2, 41.7) | 222 | 36.3 (30.2, 42.3) | 41 | 32.8 (20.2, 45.4) | |
Suppressed due to N<10.
Non-violent delinquent behaviors at Wave V include having sold drugs; having stolen something worth >$50; or having deliberately damaged someone else’s property in the past 12 months.
Violent delinquent behaviors at Wave V include having pulled a knife or gun on someone, having shot or stabbed someone, or having been in a serious physical fight in the past 12 months.
Non-violent delinquent behaviors at Wave I include having painted graffiti on someone else’s property; having deliberately damaged someone else’s property; having taken something from a store without paying; having driven a car without the owner’s permission; having stolen something worth >$50; having stolen something worth <$50; having gone into a house or building to steal something; or having sold drugs in the past 12 months.
Violent delinquent behaviors at Wave I include having been in a serious physical fight; having hurt someone badly enough to need bandages or care from a medical professional; having used or threatened someone with a weapon to get something; having been in a group fight; having pulled a knife or gun on someone; having shot or stabbed someone; having carried a weapon to school; or having used a weapon in a fight in the past 12 months.
Substance use in adolescence was common among those with prescription opioid use or misuse in adulthood. The prevalence of any cigarette, alcohol, marijuana, and illicit drug use in adolescence was higher among those reporting misuse only and those reporting both use and misuse in adulthood compared to those reporting no recent use or misuse. For example, 14.0% of those reporting misuse and 14.6% of those reporting both use and misuse had used illicit drugs in adolescence compared to 8.4% of those reporting no prescription opioid use or misuse.
Behavioral domain of despair: Delinquent behaviors
Among those reporting prescription opioid misuse only and those reporting both use and misuse, the prevalence of non-violent and violent delinquent behaviors in the past 12 months and lifetime arrest, incarceration, driving under the influence (DUI) charges, and other alcohol or drug related charges was higher compared to those reporting no recent use or misuse (Table 4). For example, 34.6% of those reporting both use and misuse and 25.3% of those reporting misuse only had ever been incarcerated compared to 13.7% with no recent use or misuse.
Delinquent behaviors in adolescence were also common among those with prescription opioid use or misuse in adulthood. The prevalence of violent delinquent behaviors and school suspensions in adolescence was higher among adults reporting use, misuse, or both compared to those reporting no recent use or misuse. For example, 32.8% of those reporting both use and misuse, 36.3% of those reporting misuse only, and 33.0% of those reporting use only were suspended from school in adolescence compared to 24.4% of those with no recent use or misuse. Non-violent delinquent behaviors in adolescence were more prevalent among those reporting prescription opioid misuse only (47.0%) and those reporting both use and misuse (51.2%) compared to those with neither (40.6%).
Persistence of characteristics from adolescence to adulthood
The prevalence of persistent depression (i.e., depression in both adolescence and adulthood), suicidal ideation or attempts, and difficulty falling or staying asleep was higher among those reporting recent prescription opioid use only, misuse only, and both use and misuse compared to those reporting neither, with prevalence highest among those reporting both use and misuse (Table 5). For example, 15.4% of those reporting both use and misuse had persistent depression compared to 6.5% of those reporting neither, and 26.2% of those reporting both use and misuse had persistent difficulties falling or staying asleep compared to 14.7% of those reporting neither.
Table 5.
Persistence of mental health, physical health, substance use and delinquent behaviors from adolescence to adulthood by patterns of recent opioid use and misuse in adulthood, National Longitudinal Study of Adolescent to Adult Health (N=10,685)
| No recent prescription opioid use or misuse in adulthood (Wave V) (N=9,653) | Prescription opioid use only in adulthood (Wave V) (N=224) | Prescription opioid misuse only in adulthood (Wave V) (N=620) | Prescription opioid use and misuse in adulthood (Wave V) (N=134) | Chi-square p-value | |||||
|---|---|---|---|---|---|---|---|---|---|
|
| |||||||||
| N | % (95% CI) | N | % (95% CI) | N | % (95% CI) | N | % (95% CI) | ||
| Depression | <0.0001 | ||||||||
| Neither | 6,143 | 63.9 (62.3, 65.5) | 89 | 37.7 (29.2, 46.2) | 288 | 45.9 (40.6, 51.2) | 46 | 34.6 (22.8, 46.4) | |
| Adolescence and adulthood | 635 | 6.5 (5.8, 7.2) | 28 | 14.2 (7.7, 20.6) | 72 | 12.0 (8.6, 15.5) | 17 | 15.4 (5.7, 25.1) | |
| Adulthood only | 1,626 | 17.8 (16.4, 19.3) | 77 | 36.6 (29.3, 44.0) | 158 | 28.9 (24.5, 33.3) | 52 | 35.6 (23.2, 47.9) | |
| Adolescence only | 1,228 | 11.7 (10.6, 12.9) | 29 | 11.5 (6.2, 16.8) | 100 | 13.1 (9.7, 16.5) | 19 | 14.4 (6.6, 22.2) | |
| Suicidal ideation or attempts | <0.0001 | ||||||||
| Neither | 7,741 | 82.4 (81.4, 83.3) | 155 | 68.5 (60.3, 76.7) | 415 | 66.5 (61.4, 71.7) | 89 | 66.5 (55.7, 77.3) | |
| Adolescence and adulthood | 170 | 1.9 (1.5, 2.3) | 12 | 5.6 (1.4, 9.8) | 28 | 5.1 (2.5, 7.8) | * | * | |
| Adulthood only | 421 | 4.6 (4.0, 5.1) | 16 | 9.5 (3.7, 15.3) | 67 | 12.1 (7.7, 16.6) | * | * | |
| Adolescence only | 1,108 | 11.2 (10.3, 12.1) | 40 | 16.4 (10.0, 22.7) | 90 | 16.2 (12.4, 19.9) | 24 | 15.3 (7.0, 23.6) | |
| Difficulty falling or staying asleep | <0.0001 | ||||||||
| Neither | 3,767 | 39.2 (37.8, 40.7) | 60 | 23.3 (16.1, 30.5) | 146 | 23.4 (18.8, 28.0) | * | * | |
| Adolescence and adulthood | 1,439 | 14.7 (13.4, 16.0) | 45 | 19.1 (12.6, 25.5) | 130 | 21.9 (17.3, 26.4) | 37 | 26.2 (16.4, 36.0) | |
| Adulthood only | 3,550 | 36.7 (35.3, 38.2) | 108 | 52.6 (44.1, 61.0) | 305 | 48.3 (43.1, 53.5) | 64 | 54.5 (43.8, 65.2) | |
| Adolescence only | 885 | 9.3 (8.5, 10.1) | 11 | 5.1 (1.1, 9.0) | 38 | 6.4 (3.9, 8.9) | * | * | |
| Did not get medical care when needed | <0.0001 | ||||||||
| Neither | 6,323 | 65.8 (64.2, 67.4) | 118 | 52.1 (43.1, 61.2) | 310 | 50.2 (44.3, 56.2) | 62 | 35.7 (23.4, 48.0) | |
| Adolescence and adulthood | 557 | 5.3 (4.7, 5.9) | 19 | 8.4 (3.5, 13.3) | 73 | 10.4 (7.1, 13.6) | 21 | 20.5 (9.4, 31.7) | |
| Adulthood only | 1,432 | 16.0 (14.7, 17.3) | 49 | 23.5 (16.6, 30.3) | 163 | 29.0 (23.1, 35.0) | 30 | 26.8 (15.1, 38.5) | |
| Adolescence only | 1,314 | 12.8 (11.8, 13.9) | 36 | 16.0 (9.8, 22.2) | 72 | 10.4 (6.6, 14.1) | 21 | 17.0 (6.6, 27.4) | |
| Migraines or regular headaches | <0.0001 | ||||||||
| Neither | 5,721 | 59.7 (57.9, 61.5) | 88 | 33.5 (24.7, 42.3) | 327 | 54.7 (49.6, 59.8) | 66 | 52.1 (39.8, 64.4) | |
| Adolescence and adulthood | 915 | 9.3 (8.4, 10.3) | 46 | 23.0 (14.3, 31.7) | 81 | 11.5 (8.3, 14.7) | 25 | 16.9 (6.4, 27.5) | |
| Adulthood only | 994 | 10.8 (9.9, 11.8) | 57 | 27.1 (18.1, 36.1) | 95 | 15.2 (11.2, 19.2) | 24 | 18.3 (8.2, 28.5) | |
| Adolescence only | 1,979 | 20.2 (18.9, 21.4) | 33 | 16.4 (9.3, 23.5) | 116 | 18.6 (14.1, 23.0) | 19 | 12.7 (5.0, 20.3) | |
| Serious injury | <0.0001 | ||||||||
| Neither | 7,134 | 75.9 (74.5, 77.2) | 123 | 57.9 (50.1, 65.8) | 400 | 63.7 (58.2, 69.1) | 68 | 50.4 (37.8, 62.9) | |
| Adolescence and adulthood | 207 | 2.4 (1.9, 2.9) | 22 | 11.9 (5.7, 18.1) | 30 | 3.0 (1.5, 4.4) | 17 | 13.2 (5.9, 20.5) | |
| Adulthood only | 922 | 9.8 (9.0, 10.6) | 58 | 22.6 (16.3, 29.0) | 107 | 19.4 (14.9, 23.8) | 35 | 24.9 (13.7, 36.1) | |
| Adolescence only | 1,153 | 11.9 (10.9, 12.9) | 21 | 7.5 (3.8, 11.2) | 70 | 14.0 (10.1, 17.9) | 14 | 11.6 (3.2, 19.9) | |
| Cigarette use | <0.0001 | ||||||||
| Neither | 3,839 | 36.8 (34.8, 38.9) | 68 | 28.1 (20.3, 35.8) | 169 | 23.7 (18.4, 28.9) | * | * | |
| Adolescence and adulthood | 1,667 | 19.8 (18.3, 21.4) | 61 | 31.4 (23.4, 39.3) | 214 | 39.0 (32.9, 45.1) | 43 | 41.2 (29.4, 53.1) | |
| Adulthood only | 486 | 5.8 (5.0, 6.7) | 20 | 9.7 (5.0, 14.4) | 44 | 8.6 (5.1, 12.1) | * | * | |
| Adolescence only | 3,626 | 37.5 (35.6, 39.5) | 75 | 30.9 (23.3, 38.5) | 192 | 28.7 (23.6, 33.9) | 57 | 36.7 (24.5, 48.8) | |
| Binge drinking | <0.0001 | ||||||||
| Neither | 1,109 | 15.5 (13.9, 17.1) | 28 | 16.1 (8.5, 23.6) | 56 | 12.1 (7.1, 17.2) | 11 | 9.8 (3.3, 16.3) | |
| Adolescence and adulthood | 1.360 | 22.4 (20.0, 24.8) | 26 | 17.8 (10.3, 25.3) | 131 | 31.9 (25.2, 38.6) | 25 | 28.5 (15.7, 41.4) | |
| Adulthood only | 2,945 | 46.3 (42.9, 49.8) | 63 | 41.1 (31.3, 50.9) | 175 | 39.1 (31.1, 47.0) | 34 | 27.9 (15.8, 40.0) | |
| Adolescence only | 978 | 15.8 (14.2, 17.3) | 31 | 25.1 (15.9, 34.2) | 81 | 16.9 (11.9, 22.0) | 24 | 33.7 (20.9, 46.5) | |
| Marijuana use | <0.0001 | ||||||||
| Neither | 6,177 | 62.1 (59.8, 64.5) | 119 | 47.5 (38.5, 56.4) | 273 | 38.9 (34.2, 43.6) | 64 | 45.1 (33.9, 56.3) | |
| Adolescence and adulthood | 700 | 8.2 (7.2, 9.3) | 20 | 9.3 (4.3, 14.4) | 110 | 23.5 (18.6, 28.5) | 21 | 23.0 (11.1, 34.9) | |
| Adulthood only | 955 | 11.6 (10.4, 12.8) | 30 | 17.9 (10.7, 25.1) | 114 | 21.0 (16.0, 26.0) | 21 | 15.2 (5.9, 24.4) | |
| Adolescence only | 1,744 | 18.0 (16.0, 20.1) | 54 | 25.3 (16.8, 33.8) | 119 | 16.5 (12.8, 20.3) | 26 | 16.8 98.1, 25.5) | |
| Illicit drug use | <0.0001 | ||||||||
| Neither | 8,608 | 89.1 (87.8, 90.3) | 202 | 88.2 (82.6, 93.9) | 450 | 69.0 (63.9, 74.1) | 107 | 78.6 (68.5, 88.8) | |
| Adolescence and adulthood | 47 | 0.6 (0.4, 0.8) | * | * | 22 | 3.0 (1.0, 4.9) | * | * | |
| Adulthood only | 223 | 2.5 (2.0, 3.0) | * | * | 81 | 17.0 (12.6, 21.4) | * | * | |
| Adolescence only | 712 | 7.6 (6.7, 9.0) | 18 | 9.9 (4.5, 15.3) | 61 | 11.0 (7.8, 14.2) | 15 | 13.9 (4.4, 23.4) | |
| Non-violent delinquent behaviors | <0.0001 | ||||||||
| Neither | 5,561 | 57.6 (55.5, 59.6) | 136 | 58.0 (49.5, 66.4) | 283 | 47.8 (42.8, 52.8) | 60 | 46.5 (32.9, 60.1) | |
| Adolescence and adulthood | 149 | 1.8 (1.4, 2.2) | * | * | 46 | 8.7 (5.9, 11.5) | * | * | |
| Adulthood only | 90 | 1.4 (1.0, 1.7) | * | * | 26 | 4.5 (2.1, 6.8) | * | * | |
| Adolescence only | 3,704 | 39.3 (37.4, 41.1) | 77 | 35.3 (27.0, 43.6) | 252 | 39.1 (34.1, 44.0) | 55 | 43.0 (29.3, 56.7) | |
| Violent delinquent behaviors | <0.0002 | ||||||||
| Neither | 5,744 | 57.7 (55.6, 59.9) | 111 | 51.2 (41.4, 61.1) | 296 | 46.1 (40.5, 51.6) | 62 | 40.7 (29.7, 51.7) | |
| Adolescence and adulthood | 132 | 1.8 (1.3, 2.2) | * | * | 27 | 3.5 (1.8, 5.3) | * | * | |
| Adulthood only | 62 | 0.7 (0.5, 0.9) | * | * | 14 | 2.5 (0.9, 4.2) | * | * | |
| Adolescence only | 3,576 | 39.8 (37.9, 41.8) | 100 | 43.7 (34.0, 53.3) | 275 | 47.9 (42.0, 53.7) | 61 | 51.7 (40.6, 62.8) | |
Note: Adolescence = Wave I (12-18 years); Adulthood = Wave V (32-42 years).
Suppressed due to N<10.
Compared to those reporting no recent use or misuse, not getting medical care when needed, both in adolescence and adulthood, was higher among those reporting recent prescription opioid use and misuse (20.5%) compared to those reporting neither (5.3%). The prevalence of persistent migraines or headaches and serious injuries was higher among those reporting use only and both use and misuse compared to those reporting neither. For example, 23.0% of those reporting use only compared to 9.3% of those reporting no recent use or misuse had persistent migraines or headaches.
Persistent cigarette use, binge drinking, and marijuana use were more prevalent among those reporting prescription opioid misuse only and both use and misuse compared to those reporting no recent use or misuse. For example, 39.0% of those reporting misuse only and 41.2% of those reporting both use and misuse reported cigarette use in both adolescence and adulthood, as compared to 19.8% reporting no recent use or misuse. Persistent illicit drug use (3.0%), non-violent delinquent behaviors (8.7%), and violent delinquent behaviors (3.5%) were most common among those reporting both use and misuse.
Discussion
Using rich longitudinal data, spanning more than 20 years, we examined a comprehensive set of adolescent and adult correlates of prescription opioid use and misuse in a nationally representative sample of U.S. adults. Results highlight demographic, mental and physical health, substance use, and behavioral characteristics across multiple domains of despair that are associated with specific patterns of prescription opioid use and misuse, offering insight on the current status of the opioid crisis in the U.S. and providing data that can help inform clinical care, target public health interventions, and guide future research.
Of the specific patterns of prescription opioid use and misuse, misuse alone was most common, with more than 6% of adult participants reporting use of prescription opioids not prescribed to them, in larger amounts, more often, or for longer periods than prescribed, or only for the feeling or experience they caused in the past 30 days. A smaller percent reported appropriate use (approximately 2%) and both use and misuse (approximately 1%) in the past 30 days. Efforts to reduce the supply of prescription opioids in the U.S., including prescribing guidelines, prescription drug monitoring programs, and medication take-back events, have been implemented over the last several years (Dasgupta, et al., 2018; Haegerich, Jones, Cote, Robinson, & Ross, 2019). Fewer initiatives have focused on addressing the demand for opioids (Dasgupta, et al., 2018). Among individuals engaging in prescription opioid misuse, frequently cited motivations for misuse include relieving physical pain and coping with anxiety and sadness (Cicero & Ellis, 2017; Garland, Hanley, Thomas, Knoll, & Ferraro, 2015; Han, et al., 2017). Our results indicate that despite efforts to reduce the supply of prescription opioids, prescription opioid misuse remains common among U.S. adults. This suggests that enhanced efforts to address the demand for opioids through treatment and prevention of underlying conditions are needed.
Demographic characteristics differed by patterns of prescription opioid use and misuse. In particular, indicators of economic disadvantage were more common among those reporting recent prescription opioid use, misuse, or both compared to those with neither, with prevalence especially high among those reporting both recent use and misuse. Specifically, one-third of those reporting both prescription opioid use and misuse had <$30,000 annual income, and more than 80% had difficulties paying bills in the past 10 years. Indicators of economic disadvantage were also evident in adolescence, more than two decades prior to our measures of prescription opioid use and misuse. For example, during adolescence, nearly 20% of those reporting both prescription opioid use and misuse had parents who received public assistance. These results align with prior research (Han, et al., 2017; Han, et al., 2015; Marsh, et al., 2018; Mojtabai, 2018) and the hypothesis that diseases of despair (i.e., drug overdose, alcohol-related diseases, and suicide) are intricately linked to poverty and economic wellbeing (Dasgupta, et al., 2018). Importantly, our results support the hypothesis that early economic disadvantage, as measured in adolescence, may increase vulnerability and contribute to prescription opioid outcomes much later in the life course. Population-level policies to help improve economic stability, such as increases in minimum wage and enhancements to programs that assist with basic needs (e.g., the Supplemental Nutrition Assistance Program), may help to address prescription opioid use and misuse across the life course.
Our results demonstrated differences in the cognitive, emotional, biological, and behavioral domains of despair (Shanahan, et al., 2019) in both adolescence and adulthood by patterns of prescription opioid use and misuse. Consistent with prior research (Cragg, et al., 2019; Han, et al., 2017), across the cognitive and emotional domains, indicators of poor mental health, both in adolescence and adulthood, were more common among those reporting recent prescription opioid use, misuse, or both compared to those reporting neither. Among those reporting both use and misuse, a sizeable percent had persistent poor mental health, with indicators of poor mental health present in both adolescence and adulthood. Notably, approximately half of those reporting use only and half of those reporting both use and misuse had lifetime depression and anxiety diagnoses, but only 20-25% had received psychological counseling in the past 12 months. Prior research indicates that overall treatment for mood and anxiety disorders is low in the U.S. (Mojtabai, Olfson, & Han, 2016). Enhanced access to and availability of evidence-based mental health services, particularly early in the life course, may help reduce later prescription opioid use and misuse as a mechanism for coping with emotional distress (Cicero & Ellis, 2017; Garland, et al., 2015; Han, et al., 2017). In addition, integration of substance use disorder and mental health treatment in primary and pediatric care, where early signs of risk, distress, and potential harm may be first detected, could help improve treatment access, continuity of care, and mental health outcomes for those using opioids (Davis, Moore, Meyers, Mathews, & Zerth, 2016; Wakeman et al., 2019).
In the biological domain, indicators of poor physical health, including activity limitations, injuries, and pain in adolescence and adulthood, were most common among those reporting prescription opioid use only and those reporting both use and misuse. Several physical health issues, including migraines or headaches and serious injuries, persisted from adolescence to adulthood among those reporting use only and both use and misuse. Prescription opioid use may reflect appropriate pharmacological treatment to address ongoing physical health conditions. Combined use and misuse may reflect potential inadequacy of this treatment. Alternative and integrative treatment modalities, including cognitive behavioral therapy, exercise, physical therapy, and biofeedback, may supplement pharmacological treatment options and ultimately improve care and outcomes (Thomas et al., 2016). However, these treatment modalities are often not readily accessible to economically disadvantaged populations, those at highest risk for poor opioid-related outcomes (Becker et al., 2017). Additional research is also needed to assess the effectiveness of alternative treatment modalities in adolescent populations (Fisher et al., 2014), where early treatment may reduce the need for later prescription opioid use and potential opioid-related harms.
In the behavioral domain, substance use - including recent marijuana and illicit drug use and misuse of other types of prescription medications in adulthood, as well as cigarette, alcohol, marijuana, and illicit drug use in adolescence - was most common among those reporting recent prescription opioid misuse only, though prevalence was also elevated among those reporting use only and those reporting both use and misuse. Persistent substance use behaviors from adolescence to adulthood were most common among those reporting misuse only and both use and misuse. Importantly, polysubstance use is a key risk factor for numerous adverse outcomes, including fatal and non-fatal overdose (Betts et al., 2015; Martin et al., 2014). Among those using opioids, common motivations for concurrent use of additional substances include altering or enhancing the pain relieving and euphoric effects of opioids and self-medicating psychiatric symptoms and feelings of loneliness (Chatterjee et al., 2019). Understanding underlying motivations for substance use, including continued substance use from adolescence to adulthood, and implementing individual treatment plans in tandem with larger scale public health interventions to address these motivations may help to reduce risks and harms associated with polysubstance use.
Also in the behavioral domain, delinquent behaviors, including criminal justice system involvement and non-violent and violent delinquent behaviors in adulthood and adolescence, were most common among those reporting recent prescription opioid misuse only and those reporting both recent use and misuse. Persistence of delinquent behaviors from adolescence to adulthood was most common among those reporting misuse only. Delinquent behaviors typically peak in adolescence and decrease rapidly across adulthood (Moffitt, 2018). Continued delinquent behaviors among those engaging in prescription opioid misuse in adulthood may indicate that these individuals did not make normative gains in inhibitory control (i.e., impulsivity), an important mechanism underlying both substance use and delinquency, across adolescence and adulthood (Heitzeg, Cope, Martz, & Hardee, 2015). The interplay of such biological mechanisms with social factors, particularly economic disadvantage, in contributing to opioid-related outcomes is an area worth further investigation (Ulirsch et al., 2014).
Public health implications
Results identify adolescent and adult correlates of prescription opioid use and misuse across multiple demographics and domains of despair. Most notably, results demonstrate associations of factors in adolescence with prescription opioid use and misuse more than 20 years later, underscoring the need to acknowledge and address underlying correlates of prescription opioid use and misuse early in the life course. The results also highlight the domains of despair most closely associated with specific patterns of prescription opioid use and misuse. Factors in the cognitive, emotional, and biological domains were most common among those engaging in prescription opioid use, while factors in the behavioral domain were most common among those engaging in prescription opioid misuse. Across all domains, prevalence of specific factors was particularly elevated among those engaging in both use and misuse. In addition, indicators of economic disadvantage were most common among those engaging in both use and misuse. Collectively, these results indicate that improvements in individual treatment for pain and mental health across the life course are needed to improve wellbeing for those prescribed opioids and prevent progression from use to misuse. Importantly, such efforts need to be carefully balanced against the risk of stigmatizing appropriate medical use of prescription opioids. The results also indicate that improvements in individual treatment for substance use disorders, combined with programmatic and policy intervention to address social and structural factors, such as economic disadvantage, associated with prescription opioid misuse are needed to reduce potential harms and improve quality of life.
Limitations
Several limitations are worth note. First, Add Health data are based on self-report and are subject to social desirability and recall bias. Specifically, participants may have underreported recent prescription opioid misuse due to potential stigma. However, prior research has established sufficient reliability of the measure of prescription opioid misuse included in Add Health (Kennett, et al., 2010). Second, past 30-day prescription opioid use and misuse may represent unique events or may be part of a longer pattern of use or misuse. We were unable to examine correlates of chronic opioid use and misuse separately from correlates of intermittent use and misuse. Third, there is potential for selection bias due to systematic differences between Wave I Add Health participants who did and did not participate at Wave V. However, prior research has examined the impact of selection bias due to non-response in Add Health and found that such bias is small (Brownstein, 2020).
Conclusions
Overall, our results provide recent estimates of patterns of prescription opioid use and misuse in a nationally representative sample, indicating that prescription opioid misuse continues to be common among U.S. adults. Results highlighting adolescent and adult correlates of these patterns across demographics and multiple domains of despair suggest the need for targeted public health intervention at multiple stages of the life course to address factors contributing to the ongoing demand for opioids in the U.S. Additional research is needed to explore the ways in which indicators of economic disadvantage and the domains of despair are interrelated across the life course to further tease apart the complex longitudinal pathways leading to prescription opioid use and misuse.
Supplementary Material
Acknowledgements:
This research uses data from Add Health, a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill, and funded by grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health Web site (http://www.cpc.unc.edu/addhealth). No direct support was received from grant P01-HD31921 for this analysis.
Footnotes
Disclosure Statement: The authors have no conflicts of interest relevant to this study to disclose.
References
- Becker WC, Dorflinger L, Edmond SN, Islam L, Heapy AA, & Fraenkel L (2017). Barriers and facilitators to use of non-pharmacological treatments in chronic pain. BMC Family Practice, 18(1), 41. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Betts KS, McIlwraith F, Dietze P, Whittaker E, Burns L, Cogger S, & Alati R (2015). Can differences in the type, nature or amount of polysubstance use explain the increased risk of non-fatal overdose among psychologically distressed people who inject drugs? Drug and Alcohol Dependence, 154, 76–84. [DOI] [PubMed] [Google Scholar]
- Blanco C, Wall MM, Liu S-M, & Olfson M (2019). Toward a Comprehensive Developmental Model of Prescription Opioid Use Disorder. The Journal of Clinical Psychiatry, 81(1). [DOI] [PubMed] [Google Scholar]
- Brownstein NB, W D Tabor J Entzel P Daza E Mullan Harris K (2020). Non-response in Wave IV of the National Longitudinal Study of Adolescent to Adult Health, from https://www.cpc.unc.edu/projects/addhealth/documentation/guides/W4_nonresponse.pdf
- Case A, & Deaton A (2015). Rising morbidity and mortality in midlife among white non-Hispanic Americans in the 21st century. Proceedings of the National Academy of Sciences, 112(49), 15078–15083. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Centers for Disease Control and Prevention. (2020). Understanding the Epidemic, from https://www.cdc.gov/drugoverdose/epidemic/index.html
- Chatterjee A, Lopez D, Ramkellawan S, Brown R, Smith K, Gaeta JM, & Baggett TP (2019). “That’s what we call the cocktail”: Non-opioid medication and supplement misuse among opioid users. Substance Abuse, 1–8. [DOI] [PubMed] [Google Scholar]
- Cicero TJ, & Ellis MS (2017). Understanding the demand side of the prescription opioid epidemic: does the initial source of opioids matter? Drug and Alcohol Dependence, 173, S4–S10. [DOI] [PubMed] [Google Scholar]
- Cragg A, Hau JP, Woo SA, Kitchen SA, Liu C, Doyle-Waters MM, & Hohl CM (2019). Risk factors for misuse of prescribed opioids: A systematic review and meta-analysis. Annals of Emergency Medicine. [DOI] [PubMed] [Google Scholar]
- Dasgupta N, Beletsky L, & Ciccarone D (2018). Opioid crisis: no easy fix to its social and economic determinants. American Journal of Public Health, 108(2), 182–186. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Davis MJ, Moore KM, Meyers K, Mathews J, & Zerth EO (2016). Engagement in mental health treatment following primary care mental health integration contact. Psychological Services, 13(4), 333. [DOI] [PubMed] [Google Scholar]
- Fisher E, Heathcote L, Palermo TM, de C Williams AC, Lau J, & Eccleston C (2014). Systematic review and meta-analysis of psychological therapies for children with chronic pain. Journal of Pediatric Psychology, 39(8), 763–782. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Garland EL, Hanley AW, Thomas EA, Knoll P, & Ferraro J (2015). Low dispositional mindfulness predicts self-medication of negative emotion with prescription opioids. Journal of Addiction Medicine, 9(1), 61. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Haegerich TM, Jones CM, Cote P-O, Robinson A, & Ross L (2019). Evidence for state, community and systems-level prevention strategies to address the opioid crisis. Drug and alcohol dependence, 204, 107563. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Han B, Compton WM, Blanco C, Crane E, Lee J, & Jones CM (2017). Prescription opioid use, misuse, and use disorders in U.S. adults: 2015 National Survey on Drug Use and Health. Annal of Internal Medicine, 167(5), 293–301. [DOI] [PubMed] [Google Scholar]
- Han B, Compton WM, Jones CM, & Cai R (2015). Nonmedical prescription opioid use and use disorders among adults aged 18 through 64 years in the United States, 2003-2013 JAMA, 314(14), 1468–1478. [DOI] [PubMed] [Google Scholar]
- Harris KM, Halpern CT, Whitsel EA, Hussey JM, Killeya-Jones LA, Tabor J, & Dean SC (2019). Cohort Profile: The National Longitudinal Study of Adolescent to Adult Health (Add Health). International Journal of Epidemiology. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Heitzeg MM, Cope LM, Martz ME, & Hardee JE (2015). Neuroimaging Risk Markers for Substance Abuse: Recent Findings on Inhibitory Control and Reward System Functioning. [ReviewPaper], Current Addiction Reports, 2(2), 91–103. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hu M-C, Griesler P, Wall M, & Kandel DB (2017). Age-related patterns in nonmedical prescription opioid use and disorder in the US population at ages 12-34 from 2002 to 2014. Drug and Alcohol Dependence, 177, 237–243. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Jones CM (2017). The paradox of decreasing nonmedical opioid analgesic use and increasing abuse or dependence: An assessment of demographic and substance use trends, United States, 2003–2014. Addictive Behaviors, 65, 229–235. [DOI] [PubMed] [Google Scholar]
- Kennett J, Painter D, Hunter SR, Granger RA, & Bowman KR (2010). Assessing the reliability of key measures in the National Survey on Drug Use and Health using a test-retest methodology, from https://www.samhsa.gov/data/sites/default/files/2k6ReliabilityP/2k6ReliabilityP.pdf
- Marsh JC, Park K, Lin Y-A, & Bersamira C (2018). Gender differences in trends for heroin use and nonmedical prescription opioid use, 2007–2014. Journal of Substance Abuse Treatment, 87, 79–85. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Martin G, Vallance K, Macdonald S, Stockwell T, Ivsins A, Chow C, … Duff C (2014). Nonfatal overdose from alcohol and/or drugs among a sample of recreational drug users. Journal of Substance Use, 19(3), 239–244. [Google Scholar]
- Martins SS, Segura LE, Santaella-Tenorio J, Perlmutter A, Fenton MC, Cerdá M, … Hasin DS (2017). Prescription opioid use disorder and heroin use among youth nonmedical prescription opioid users from 2002 to 2014. Addictive Behaviors, 65, 236. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Moffitt TE (2018). Male antisocial behaviour in adolescence and beyond. Nature Human Behaviour, 2(3), 177–186. [PMC free article] [PubMed] [Google Scholar]
- Mojtabai R (2018). National trends in long-term use of prescription opioids. Pharmacoepidemiology and Drug Safety, 27(5), 526–534. [DOI] [PubMed] [Google Scholar]
- Mojtabai R, Olfson M, & Han B (2016). National trends in the prevalence and treatment of depression in adolescents and young adults. Pediatrics, 138(6), e20161878. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Olfson M, Wang S, Wall MM, & Blanco C (2020). Trends in opioid prescribing and self-reported pain among US adults: As efforts to curb inappropriate opioid prescribing continue, an examination of trends in short-term and longer-term opioid prescriptions for US adults who self-report various pain levels. Health Affairs, 39(1), 146–154. [DOI] [PubMed] [Google Scholar]
- Ranade R, Wunder J, Terzian M, & Ungureanu I (2020). Opioids and suicide: Opportunities for comprehensive prevention. Journal of Public Health Management and Practice, 26(3), 291–293. [DOI] [PubMed] [Google Scholar]
- Shanahan L, Hill SN, Gaydosh LM, Steinhoff A, Costello EJ, Dodge KA, … Copeland WE (2019). Does despair really kill? A roadmap for an evidence-based answer. American Journal of Public Health, 109(6), 854–858. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Thomas DA, Maslin B, Legler A, Springer E, Asgerally A, & Vadivelu N (2016). Role of alternative therapies for chronic pain syndromes. Current Pain and Headache Reports, 20(5), 29. [DOI] [PubMed] [Google Scholar]
- Ulirsch JC, Weaver MA, Bortsov AV, Soward AC, Swor RA, Peak DA, … Domeier RM (2014). No man is an island: living in a disadvantaged neighborhood influences chronic pain development after motor vehicle collision. Pain, 155(10), 2116–2123. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Wakeman SE, Rigotti NA, Chang Y, Herman GE, Erwin A, Regan S, & Metlay JP (2019). Effect of integrating substance use disorder treatment into primary care on inpatient and emergency department utilization. Journal of General Internal Medicine, 34(6), 871–877. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Wasserstein RL, & Lazar NA (2016). The ASA statement on p-values: context, process, and purpose. The American Statistical Association, 70(2), 129–133. [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.