Abstract
Background:
In the U.S. non-medical use of prescription opioids (NMOU) is prevalent and often accompanied by opioid withdrawal syndrome (OWS). OWS has not been studied using nationally representative data.
Objectives:
We examined the prevalence and clinical correlates of OWS among U.S. adults with NMOU.
Methods:
We used data from 36,309 U.S. adult participants in the 2012–2013 National Epidemiologic Survey on Alcohol and Related Conditions-III, 1,527 of whom reported past 12-month NMOU. Adjusted linear and logistic regression models examined associations between OWS and its clinical correlates, including psychiatric disorders, opioid use disorder (OUD; excluding the withdrawal criterion), medical conditions, and healthcare utilization among people with regular (i.e. ≥3 days/week) NMOU (n = 534).
Results:
Over half (50.4%) of the sample was male. Approximately 9% of people with NMOU met criteria for DSM-5 OWS, with greater prevalence of OWS (~20%) among people with regular NMOU. Individuals with bipolar disorder, dysthymia, panic disorder, and borderline personality disorder had greater odds of OWS (aOR range = 2.71–4.63). People with OWS had lower mental health-related quality of life (β=−8.32, p < .001). Individuals with OUD also had greater odds of OWS (aOR range = 26.02–27.77), an association that increased with more severe OUD. People using substance use-related healthcare services also had greater odds of OWS (aOR range = 6.93–7.69).
Conclusion:
OWS was prevalent among people with OUD and some psychiatric disorders. These findings support screening for OWS in people with NMOU and suggest that providing medication- assisted treatments and behavioral interventions could help to reduce the burden of withdrawal in this patient population.
Keywords: opioid withdrawal, opioid withdrawal syndrome, non-medical prescription opioids, opioids
Introduction
In the United States (U.S.), since 1999, over 500,000 people have died from opioid overdoses and overdose deaths involving prescription opioids have increased four-fold (1). Although access to prescription opioids has decreased considerably since 2006, nonmedical use of prescription opioids has remained prevalent (2, 3). In 2019, prescription opioids were among the most misused substances in the U.S., affecting more than 9 million U.S. adults (4).
Opioid withdrawal syndrome (OWS), as defined in the DSM-5, is marked by the development of medical and psychological sequalae that occur in people who abruptly stop or decrease their opioid use (5, 6). OWS has contributed to the public health burden of the ongoing opioid crisis as it is associated with stronger opioid craving (7), hospitalization (8, 9), relapse (10, 11), and overdose (12). Withdrawal symptoms following extended or regular use of short-acting analgesics (e.g., oxycodone and hydrocodone) typically occur within 8–12 hours after the last dose and include insomnia, anxiety, depressed mood, nausea/vomiting, fever, tachycardia, and gastrointestinal symptoms (13). OWS can also lead to vomiting, dehydration, and electrolyte imbalance that can be fatal (14).
OWS, is one of 11 criteria for opioid use disorder (OUD) and is therefore prevalent among individuals with the disorder (4, 14, 15). OUD is characterized by a problematic and persistent pattern of use that leads to clinically significant distress or impairment, though OWS is not required for the diagnosis (5, 16). While many people with OWS receive care at an emergency department or other acute care clinical setting (14, 17), research suggests that withdrawal symptoms are a prominent barrier to outpatient OUD treatment engagement (18) and the receipt of hospital-based medical interventions (19), often leading to discharge against medical advice (20) and mortality (21).
Because prescription opioid misuse carries substantial risks, and OWS plays an important role in the clinical manifestation and treatment of OUD, documenting the prevalence and clinical correlates of OWS among people who misuse prescription opioids in the U.S. population is an important public health issue. To date, epidemiologic studies of OWS have been limited geographically (12, 22, 23), based on small sample sizes (17), used outdated diagnostic criteria (23, 24), and been limited to select populations, including adults with chronic pain (22), people who inject drugs (12) or use heroin (23). Studies have yet to compare OWS among adults with and without OUD. Furthermore, no general population studies have examined OWS that is associated with the use of analgesic medications, which comprised over 90% of non-medical opioid use in 2019 (4). This information could help to reduce the short-term health risks of OWS and inform accurate diagnostic assessment and effective treatments for the condition.
Population-level data on DSM-5 OWS are needed to define its public health burden and identify at-risk groups. Drawing on data from the 2012–2013 National Epidemiologic Survey on Alcohol and Related Conditions–III (NESARC-III), the only national survey to assess OWS and its individual symptoms, we examined the prevalence of OWS and its sociodemographic, psychiatric, and medical correlates among U.S. adults who endorse use of non-medical prescription opioids. We also examined the associations between OWS and use of healthcare services, which have important implications for community, hospital, and outpatient substance use treatment resources and healthcare service planning.
Materials and Methods
NESARC-III Sample
The NESARC-III target population was civilians ≥18 years in households and selected group quarters. Respondents were selected through multistage probability sampling from primary (counties/groups of contiguous counties); secondary (groups of Census-defined blocks); and tertiary (households within secondary groups) sampling units. People who were Black, Asian, and Hispanic were oversampled. Data were adjusted for nonresponse and weighted to represent the U.S. population based on the 2012 American Community Survey, which compensated adequately for nonresponse. Face-to-face interviews in respondents’ homes were conducted with 36,309 participants, yielding a household response rate of 72%, a person-level response rate of 84%, and an overall response rate of 60.1%. Informed consent was recorded electronically, and respondents received $90.00 for their participation. Review boards at the National Institutes of Health and Westat (the NESARC-III contractor) approved the study protocol. The analytic sample included 1,527 adult participants with any past 12-month non-medical use of prescription opioids, but without any past 12-month heroin use. Non-medical use of prescription opioids is defined by use without a prescription or not as prescribed (“in greater amounts, more often, or longer than prescribed, or for any reason other than a doctor said you should use them.”)
Diagnostic Interview
The NIAAA Alcohol Use Disorder and Associated Disabilities Interview Schedule-5 (AUDADIS-5) is a structured, computer-assisted diagnostic interview designed for use by lay interviewers (25,26). The AUDADIS-5 covers the frequency and amount of drug and alcohol use and DSM-5 substance use and psychiatric disorders. Psychometrics for the AUDADIS-5 psychiatric disorders included in this study are provided below.
Measures
Opioid Withdrawal
We constructed the OWS variable used in all analytic models by applying its DSM-5 diagnostic criteria. The first part of OWS criterion A (heavy, prolonged use) was satisfied for all participants by including only respondents with regular non-medical use of prescription opioids. OWS criterion B was satisfied by constructing a variable coded positive if ≥3 of 9 withdrawal symptoms were endorsed. AUDADIS-5 asked about the following withdrawal symptoms: depressed mood, nausea and vomiting, muscle aches or cramps, lacrimation or rhinorrhea, pupillary dilatation, piloerection or sweating, yawning, fever, and insomnia. Instead of diarrhea, AUDADIS-5 queried participants about abdominal pain. Participants were asked whether these symptoms occurred within the past 12 months when opioid effects were wearing off (the morning after using or within a few days after stopping or cutting down). The occurrence of each withdrawal symptom was assessed as a dichotomous variable (yes/no). Participants were also coded as positive for OWS if they endorsed either Part A (presence of “the characteristic opioid withdrawal syndrome” or Part B (“opioids [or a closely related substance] are taken to relieve or avoid withdrawal symptoms”) of the withdrawal criterion of the DSM-5 OUD diagnosis.
Sociodemographic Characteristics
Sociodemographic characteristics assessed included gender (male, female), race/ethnicity (White, Black, American Indian/Alaska Native/Asian/Native Hawaiian/Pacific Islander, Hispanic) age (18–29, 30–44, 45–64, ≥65), marital status (married/cohabitating, widowed/divorced/separated, never married), education (≤some high school, high school graduate or GED, ≥some college), yearly household income (≤$19,999, $20,000–$34,999, $35,000-$69,999, ≥$70,000), urbanicity (rural, urban), and U.S. region (West, Northeast, Midwest, South).
Psychiatric Disorders
Psychiatric disorders assessed included 12-month mood disorders (major depressive disorder, dysthymia, bipolar 1, and bipolar 2); 12-month anxiety disorders (general anxiety disorder, social phobia, agoraphobia, specific phobias, and panic disorder); and personality disorders (borderline, schizotypal, and antisocial). Test-retest reliability was fair to moderate for mood (k=0.39–0.40) and anxiety disorders (k=0.43–0.51), with generally good to excellent reliability (intraclass correlation coefficients [ICCs]=0.59–0.79) for corresponding dimensional measures. The test-retest reliability of the personality disorders was good (κ=0.67–0.71), and higher for corresponding dimensional measures (ICCs=0.71–0.79; 25,26)
Opioid Use Disorder
We created a modified 12-month OUD variable that used its diagnostic criteria without the withdrawal criterion. Withdrawal is not a requisite criterion for OUD and therefore, OWS and OUD may occur independently. Participants with 12-month modified OUD were classified by the DSM-5 OUD severity gradient: mild (2–3 criteria), moderate (4–5 criteria), severe (≥6 criteria).
Health-related quality of life
The 12-item Short-Form Health Survey (SF-12) assessed current physical and mental health-related quality of life, using summary scores calculated from the following domains: general health, physical functioning, role physical, bodily pain, vitality, social functioning, role emotional, and mental health (27). The SF-12 is a valid and reliable, and is widely used in population surveys (28, 29). Consistent with scoring guidelines, each SF-12 norm-based score has a mean of 50, standard deviation of 10, and range of 0 to 100. Higher scores indicated better health-related quality of life.
Pain
Using an item from the SF-12, pain was assessed with a 5-point scale (not at all, a little bit, moderately, quite a bit, extremely). This item measures pain interference with “your normal work, including both work outside the home and housework” during the past 4 weeks. Consistent with scoring guidelines for other SF-12 items, scores were normed with a mean of 50, standard deviation of 10, and range of 0 to 100. Higher scores indicated less severe pain.
Medical conditions
We examined medical conditions that are associated with opioid use and OUD (30–32). These included past 12-month diagnoses of gastrointestinal disease, cardiovascular disease, metabolic disease, and cancer, confirmed by a physician. A dichotomous variable was used to indicate the presence of each condition or any past 12-month medical diagnosis.
Healthcare utilization
NESARC-III assessed past 12-month use of hospital-based healthcare services with the following three questions: 1) How many separate times did you stay in a hospital overnight or longer?, 2) How many days altogether did you spend in the hospital?, and 3) How many times did you receive medical care or treatment in a hospital or emergency room? Responses to each question were coded as a continuous variable. We also coded a dichotomous variable indicating any past 12-month substance use-related healthcare utilization, including detoxification clinics, inpatient treatment settings, outpatient treatment settings, rehabilitation programs, crisis center, Narcotics/Cocaine/Alcoholics Anonymous or any other 12-step meeting, methadone maintenance programs, private physician or any other professional, halfway house or therapeutic community, or any social service agency.
Statistical Analysis
We calculated the weighted prevalence for sociodemographic characteristics among respondents reporting any past 12-month non-medical opioid use. We also calculated prevalences of past 12-month DSM-5 OUD and OWS among participants reporting any past 12-month non-medical use of opioids (but not heroin), and stratified by frequency of use (<weekly, near daily, and daily). Considering that people with regular opioid use (≥3 times/week) have a higher likelihood of experiencing withdrawal symptoms (12), we created a group that included participants with 3–4 days, near daily, or daily non-medical use in the past week. In each of these groups, among those endorsing DSM-5 OWS, we calculated prevalances of each withdrawal symptom. We then performed logistic regression analyses to examine associations between OWS and several predictor variables among participants with regular non-medical opioid use. Sociodemographic characteristics, psychiatric disorders, OUD (without the withdrawal criterion), medical diagnoses, and treatment utilization were added in a stepwise manner without deletion, yielding three models: 1) unadjusted, 2) adjusted for sociodemographic characteristics, and 3) adjusted for sociodemographic characteristics and psychiatric disorders. We used similar adjusted models and linear regression to evaluate the relationship between OWS and the continuous measures of physical and mental health-related quality of life and pain and our three past 12-month hospital utilization variables. We present odds ratios (OR) and 95% confidence intervals.
To determine whether associations between OWS and predictor variables were confounded by symptoms of withdrawal from other substances (alcohol, tobacco, cannabis, sedatives/tranquilizers, stimulants, and cocaine), we included a fourth model in all analyses. For each substance, a withdrawal overlap variable was created that indicated whether any OWS symptoms overlapped with symptoms of withdrawal from other substances after a reduction or cessation of regular use of the other substance (e.g., regular cannabis users who reduced or stopped using cannabis and subsequently experienced withdrawal were coded as positive for “cannabis withdrawal overlap”). We used logistic regression to test the association of each substance overlap variable with OWS, adjusted for sociodemographic characteristics and all other substance withdrawal overlap variables. Because only alcohol withdrawal overlap was associated with OWS, we added an additional adjusted model (model 4) of associations between opioid withdrawal and predictor variables that controlled for sociodemographic characteristics, psychiatric disorders, and alcohol withdrawal. Analyses were conducted using SUDAAN 11.0, which accounts for the complex sample design.
Results
Sample characteristics and prevalence of OWS
The majority of past 12-month non-medical users of opioids (N=1,527) were male (50.4%) and White (68.2%), and resided in urban areas (79.1%). Many participants were age 45–64 years (33.7%), had at least some college education (53.7%), were married / living with someone as if married (47.2%), and had low household income (46.1%; Supplemental Table 1).
Nearly 9% (8.9%) of adults with any past 12-month non-medical use of prescription opioids met criteria for DSM-5 OWS, with increasing risk of OWS as frequency of use increased: <weekly (3.1%), near daily (18.2%), and daily (21.6%). Approximately 20% of people had OWS among those with regular non-medical opioid use. Among people with any past 12-month non-medical use of opioids, the most common symptom was pupillary dilatation/piloerection/sweating (71.3%), followed by muscle aches (70.3%), insomnia (68.9%), and depressed mood (63.3%). These symptoms were largely consistent across frequency groups. Nearly 20% of adults with any past 12-month non-medical use of prescription opioids met criteria for DSM-5 OUD (Table 1).
Table 1.
Prevalence of DSM-5 opioid withdrawal and withdrawal symptoms among those with past 12-month non-medical use of prescription opioids (N=1,527)a
| Any use (N=1,527)a | <Weekly use (n=875)b | Near daily use (n=91)c | Daily use (n=323)d | ≥3 times/week use (n=534)e,f | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| n | %* (SE) | n | %* (SE) | n | %* (SE) | n | %* (SE) | n | %* (SE) | |
| DSM-5 OUD | 295 | 19.90 (1.30) | 87 | 9.14 (1.12) | 37 | 42.24 (6.35) | 114 | 38.95 (3.65) | 185 | 37.76 (2.93) |
| DSM-5 OWS | 129 | 8.97 (0.93) | 29 | 3.06 (0.71) | 13 | 18.26 (4.70) | 63 | 21.64 (3.13) | 92 | 19.91 (2.30) |
| Opioid withdrawal symptomsg | ||||||||||
| Depressed mood | 78 | 63.31 (5.03) | 16 | 55.82 (9.38) | 9 | 66.96 (14.47) | 39 | 66.12 (6.95) | 58 | 67.92 (5.27) |
| Nausea/vomiting | 62 | 53.25 (5.55) | 14 | 50.58 (12.21) | 5 | 48.97 (16.60) | 29 | 52.32 (8.03) | 43 | 55.60 (6.26) |
| Muscle aches | 87 | 70.33 (4.71) | 20 | 66.22 (10.37) | 8 | 50.32 (13.82) | 45 | 78.09 (4.81) | 66 | 76.58 (4.38) |
| Lacrimation/rhinorrhea | 42 | 33.34 (4.87) | 8 | 25.10 (8.39) | 7 | 53.47 (16.75) | 26 | 42.28 (7.50) | 37 | 41.12 (5.77) |
| Pupillary dilatation/piloerection/sweating | 86 | 71.33 (4.76) | 21 | 78.95 (6.47) | 10 | 79.21 (12.13) | 43 | 72.58 (6.62) | 63 | 72.03 (5.82) |
| Abdominal painh | 69 | 51.95 (5.01) | 17 | 63.78 (9.69) | 8 | 62.39 (15.83) | 32 | 45.95 (7.62) | 49 | 52.09 (5.90) |
| Yawning | 69 | 57.39 (4.88) | 14 | 51.42 (11.18) | 5 | 28.86 (12.54) | 30 | 53.79 (7.89) | 48 | 55.32 (6.16) |
| Fever | 25 | 21.30 (4.10) | 6 | 28.12 (10.18) | 1 | 1.85 (1.96) | 15 | 23.86 (5.51) | 16 | 16.68 (4.10) |
| Insomnia | 86 | 68.99 (4.99) | 21 | 71.57 (10.69) | 7 | 51.08 (16.79) | 43 | 70.15 (7.97) | 61 | 69.20 (5.83) |
Note. Prevalences may vary due to sample weighting.
Respondents endorsing any past 12-month non-medical opioid use, but no 12-month heroin use.
Respondents endorsing past 12-month <weekly non-medical opioid use, but no past-12 month heroin use.
Respondents endorsing past 12-month near daily non-medical opioid use, but no past 12-month ≥3 times/week heroin use.
Respondents endorsing past 12-month daily non-medical opioid use, but no past 12-month ≥3 times/week heroin use.
Respondents endorsing past 12-month ≥3 times/week non-medical opioid use, but no past 12-month ≥3 times/week heroin use.
Includes participants with 3–4 days, near daily, and daily non-medical opioid use in the past week.
Among respondents endorsing DSM-5 opioid withdrawal.
AUDADIS-5 assessed abdominal pain instead of diarrhea
Weighted prevelances.
OUD=opioid use disorder. OWS=opioid withdrawal syndrome.
Approximately 20% of people with regular non-medical use of prescription opioids had OWS. Muscle aches (76.5%), pupillary dilatation, piloerection, or sweating (72.0%), insomnia (69.2%), depressed mood (67.9%), and nausea/vomiting (56.6%) were the most commonly reported withdrawal symptoms (Table 1). Over one-third (37.7%) of individuals with regular non-medical opioid use met criteria for DSM-5 OUD and of those, 51.3% had OWS, while less than 1% (n=2) of participants without OUD had OWS (data not shown).
Associations between sociodemographic and clinical characteristics, healthcare utilization, and OWS
In fully adjusted analyses, Black participants (vs. White; aOR=0.41, 95% CI: 0.18–0.93) had lower odds of OWS. Individuals who resided in the Northeast (vs. West; aOR=2.79, 95% CI: 1.24–6.29) were more likely to have OWS (Table 2). People with psychiatric conditions, including those with bipolar I disorder (aOR=3.99, 95% CI=1.45–10.99), dysthymia (aOR=4.63, 95% CI=2.25–9.51), panic disorder (aOR=2.71, 95% CI=1.36–5.39), specific phobia (aOR=3.29, 95% CI=1.57–6.89), and borderline personality disorder (aOR=3.79, 95% CI=2.05–7.04) had greater odds of OWS. OWS was also negatively associated with mental-health related quality of life (β=−8.32, p<0.001). OUD was highly associated with OWS (aOR=26.02, 95% CI=14.35–47.17) and the association increased as OUD severity increased (aOR range=5.19–222.08). Pain and medical conditions, except for cardiovascular disease (aOR=0.54, 95% CI=0.32–0.94), were not associated with OWS in adjusted analyses. Although OWS was not correlated with the utilization of any hospital services, there was greater odds of substance-related treatment with OWS in all models (aOR range=6.93–7.69; Table 3).
Table 2.
Associations between OWS and sociodemographic characteristics among adults with regular (≥3 times/week) non-medical use of prescription opioids (N=534)
| Model 1a | Model 2b | Model 3c | Model 4d | |
|---|---|---|---|---|
| OR (95% CI) | aOR (95% CI) | aOR (95% CI) | aOR (95% CI) | |
| Gender | ||||
| Female | Reference | Reference | Reference | Reference |
| Male | 1.08 (0.62–1.87) | 1.17 (0.65–2.09) | 1.18 (0.66–2.12) | 1.09 (0.60–1.99) |
| Race/Ethnicity | ||||
| White | Reference | Reference | Reference | Reference |
| Black | 0.50 (0.26–0.94) | 0.44 (0.20–0.98) | 0.49 (0.23–1.05) | 0.41 (0.18–0.93) |
| American Indian/Alaska Native | 2.00 (0.23–17.67) | 1.40 (0.19–10.07) | 1.76 (0.34–9.10) | 1.56 (0.22–11.33) |
| Asian/Native Hawaiian/Pacific Islander |
2.10 (0.22–20.46) | 3.65 (0.35–38.59) | 3.20 (0.12–84.24) | 4.88 (0.46–50.82) |
| Hispanic | 1.04 (0.51–2.10) | 0.99 (0.45–2.21) | 1.16 (0.48–2.79) | 1.08 (0.50–2.36) |
| Age (years) | ||||
| 18–29 | Reference | Reference | Reference | Reference |
| 30–44 | 0.75 (0.35–1.58) | 0.83 (0.38–1.83) | 1.08 (0.48–2.46) | 1.03 (0.47–2.26) |
| 45–64 | 0.40 (0.20–0.78) | 0.44 (0.21–0.93) | 0.51 (0.22–1.05) | 0.67 (0.30–1.48) |
| 65 and older | 0.16 (0.05–0.49) | 0.18 (0.04–0.72) | 0.30 (0.07–1.22) | 0.30 (0.07–1.22) |
| Educational | ||||
| Some high school or less | 2.13 (1.08–4.19) | 2.00 (0.93–4.29) | 2.23 (0.99–5.03) | 1.99 (0.91–4.35) |
| High school graduate (or GED) | 1.02 (0.52–2.02) | 1.05 (0.50–2.21) | 1.08 (0.48–2.43) | 1.06 (0.49–2.28) |
| Some college or higher | Reference | Reference | Reference | Reference |
| Household income | ||||
| $0 – $19,999 | 1.37 (0.48–3.86) | 0.98 (0.32–3.01) | 0.71 (0.22–2.26) | 0.92 (0.29–2.91) |
| $20,000 – $34,999 | 0.85 (0.29–2.48) | 0.82 (0.25–2.72) | 0.64 (0.17–2.31) | 0.73 (0.20–2.62) |
| $35,000 – $69,999 | 0.88 (0.31–2.53) | 0.90 (0.31–2.59) | 0.78 (0.24–2.54) | 0.85 (0.29–2.49) |
| $70,000 or greater | Reference | Reference | Reference | Reference |
| Marital status | ||||
| Married/cohabitating | Reference | Reference | Reference | Reference |
| Widowed/divorced/Separated | 1.45 (0.76–2.75) | 1.81 (0.94–3.49) | 1.70 (0.78–3.72) | 1.63 (0.81–3.26) |
| Never married | 2.05 (1.05–4.01) | 1.52 (0.78–2.93) | 2.06 (0.99–4.25) | 1.62 (0.82–3.20) |
| Urbanicity | ||||
| Rural | Reference | Reference | Reference | Reference |
| Urban | 0.67 (0.32–1.39) | 0.72 (0.35–1.49) | 0.72 (0.38–1.33) | 0.62 (0.29–1.28) |
| Region | ||||
| West | Reference | Reference | Reference | Reference |
| Northeast | 3.36 (1.57–7.21) | 2.79 (1.31–5.95) | 3.63 (1.80–7.35) | 2.79 (1.24–6.29) |
| Midwest | 1.63 (0.72–3.68) | 1.84 (0.80–4.25) | 2.10 (1.03–4.27) | 1.80 (0.80–4.03) |
| South | 1.40 (0.70–2.81) | 1.12 (0.55–2.26) | 1.28 (0.63–2.63) | 1.11 (0.53–2.29) |
Note. Significant ORs appear in bold.
Unadjusted.
Adjusted for sociodemographic characteristics (sex, race/ethnicity, age, marital status, 12-month personal income, educational level, urbanicity, region).
Adjusted for sociodemographic characteristics, any 12-month mood disorder (major depressive disorder, dysthymia, bipolar 1, bipolar 2), anxiety disorder (panic disorder, social phobia, agoraphobia, generalized anxiety disorder, specific phobia), and personality disorder (schizotypal, borderline, antisocial).
Adjusted for sociodemographic characteristics, any 12-month mood disorder, anxiety disorder, personality disorder, and withdrawal from alcohol.
Table 3.
Associations between OWS and psychiatric disorders, medical conditions, pain, quality of life, opioid use disorder, and healthcare utilization among adults with regular (≥3 times/week) non-medical use of prescription opioids (N=534)
| Model 1a | Model 2b | Model 3c | Model 4d | |
|---|---|---|---|---|
| OR (95% CI) | aOR (95% CI) | aOR (95% CI | aOR (95% CI) | |
| Mood disorders | ||||
| MDD | 1.41 (0.72–2.75) | 1.45 (0.77–2.74) | 1.02 (0.44–2.32) | 1.43 (0.73–2.79) |
| Bipolar 1 | 3.89 (1.46–10.36) | 4.53 (1.67–12.28) | 4.27 (1.26–16.20) | 3.99 (1.45–10.99) |
| Bipolar 2 | 4.81 (0.64–36.47) | 17.83 (0.41–778.49) | 12.10 (0.16–885.24) | 19.58 (0.46–832.88) |
| Dysthymia | 4.66 (2.25–9.64) | 4.89 (2.44–9.79) | 2.73 (1.21–6.15) | 4.63 (2.25–9.51) |
| Anxiety disorders | ||||
| Panic disorder | 4.00 (1.81–8.84) | 3.16 (1.50–6.67) | 2.68 (1.09–6.61) | 2.71 (1.36–5.39) |
| Agoraphobia | 1.87 (0.65–5.42) | 1.51 (0.59–3.90) | 0.46 (0.92–2.36) | 1.42 (0.57–3.52) |
| Social phobia | 1.59 (0.58–4.38) | 1.37 (0.47–3.99) | 0.86 (0.24–3.07) | 1.22 (0.42–3.54) |
| Specific phobia | 2.96 (1.33–6.55) | 3.09 (1.42–6.73) | 2.17 (0.96–4.88) | 3.29 (1.57–6.89) |
| GAD | 1.71 (0.93–3.13) | 1.85 (0.97–3.52) | 0.83 (0.37–1.88) | 1.74 (0.92–3.32) |
| Personality disorders | ||||
| Schizotypal | 2.14 (1.10–4.17) | 1.96 (1.01–3.82) | 0.47 (0.19–1.90) | 1.75 (0.88–3.51) |
| Borderline | 4.09 (2.30–7.32) | 3.97 (2.19–7.19) | 3.52 (1.76–7.04) | 3.79 (2.05–7.04) |
| Antisocial | 2.38 (1.15–4.92) | 1.50 (0.69–3.24) | 0.48 (0.16–1.46) | 1.25 (0.60–2.62) |
| Medical conditionse | ||||
| Medical diagnosis | 0.53 (0.30–0.93) | 0.78 (0.39–1.58) | 0.68 (0.36–2.97) | 0.73 (0.38–1.40) |
| Gastrointestinal disease | 0.64 (0.23–1.79) | 0.74 (0.27–2.00) | 0.78 (0.20–2.97) | 0.65 (0.23–1.79) |
| Cardiovascular disease | 0.54 (0.32–0.92) | 0.74 (0.41–1.33) | 0.54 (0.32–0.94) | 0.75 (0.42–1.32) |
| Metabolic disease | 0.44 (0.22–0.89) | 0.60 (0.26–1.33) | 0.78 (0.20–2.97) | 0.56 (0.27–1.15) |
| Cancer | 0.23 (0.06–0.95) | 0.27 (0.06–1.20) | 0.29 (0.05–1.87) | 0.29 (0.07–1.31) |
| Quality of lifef | ||||
| Mental | −8.32 (<0.001) | −8.08 (<0.001) | −4.22 (<0.001) | −3.70 (<0.01) |
| Physical | −4.77 (<0.05) | −4.99 (<0.01) | −2.62 (0.18) | −2.43 (0.20) |
| Painf | −2.46 (<0.001) | −2.51 (0.18) | −0.83 (0.66) | −2.16 (0.25) |
| Opioid Use Disorder | ||||
| No OUD | Reference | Reference | Reference | Reference |
| Any OUD | 27.77 (14.84–51.97) | 26.69 (14.66–48.61) | 27.72 (12.89–59.62) | 26.02 (14.35–47.17) |
| Mild OUD | 6.48 (2.80–15.01) | 5.40 (2.37–12.29) | 5.68 (2.19–14.73) | 5.19 (2.32–11.62) |
| Moderate OUD | 28.83 (11.59–71.73) | 35.06 (14.25–86.09) | 51.81 (16.09–108.62) | 34.88 (14.69–82.77) |
| Severe OUD | 149.52 (55.37–403.74) | 242.52 (82.84–709.96) | 235.86 (69.19–804.03) | 222.08 (77.34–637.65) |
| Healthcare Utilization | ||||
| Overnight hospitalizationsf | −0.18 (−0.52–0.17) | −0.12 (−0.44–0.20) | −0.23 (−0.57–0.11) | −0.15 (−0.48–0.82) |
| Hospital daysf | −1.57 (−3.60–0.47) | −0.84 (−2.77–1.07) | −1.18 (−3.52–1.15) | −1.37 (−3.45–0.72) |
| ED/hospital encountersf | 0.30 (−0.24–0.84) | 0.17 (−0.31–0.65) | −0.03 (−0.50–0.44) | 0.10 (−0.37–0.57) |
| SUD healthcareg | 7.53 (3.33–17.02) | 6.93 (3.25–14.75) | 7.69 (3.18–18.49) | 7.33 (3.47–15.51) |
Note. Significant odds ratios appear in bold.
Unadjusted.
Adjusted for sociodemographic characteristics (sex, race/ethnicity, age, marital status, 12-month personal income, educational level, urbanicity, region).
Adjusted for sociodemographic characteristics, any 12-month mood disorder (major depressive disorder, dysthymia, bipolar 1, bipolar 2), any 12-month anxiety disorder (panic disorder, social phobia, agoraphobia, generalized anxiety disorder, specific phobia), any personality disorder (schizotypal, borderline, antisocial).
Adjusted for sociodemographic characteristics, any 12-month mood disorder, any 12-month anxiety disorder, any personal disorder, and withdrawal from alcohol.
Past 12-month medical conditions.
Estimates reported in beta coefficients (p-values).
Past 12-month use of substance use-related healthcare services, including detoxification clinics, inpatient treatment settings, outpatient treatment settings, rehabilitation programs, crisis center, Narcotics/Cocaine/Alcoholics Anonymous or any other 12-step meeting, methadone maintenance programs, private physician or any other professional, halfway house or therapeutic community, or any other social service agencies.
MDD=major depressive disorder, GAD=generalized anxiety disorder, OUD=opioid use disorder, SUD=substance use disorder
Discussion
We examined the prevalence of DSM-5 OWS and its sociodemographic, clinical, and healthcare correlates among adults with non-medical use of prescription opioids in a nationally representative survey of the U.S. general population. We found that nearly 9% of adults with past 12-month non-medical prescription opioid use met criteria for OWS, with higher prevalence of OWS as frequency of non-medical opioids increased and among those with regular use (≥3 days/week). Adults with regular non-medical use of opioids were more likely to experience OWS if they had psychiatric disorders, lower mental health-related quality of life, and OUD. OWS was also strongly associated with greater use of substance use-related healthcare services. These findings underscore the importance of screening for OWS among people with non-medical prescription opioid use and suggest that the provision of psychiatric resources in substance use disorder healthcare settings may help reduce the burden of withdrawal in this group.
Rates of opioid withdrawal have varied significantly in previous studies, likely due to differences in the populations being studied (e.g., people who inject drugs, ICU patients, people with OUD), the type of opioids used by the populations being studied (e.g., heroin vs. prescription analgesics), and diagnostic criteria (DSM-IV, DSM-5). While measurement differences and sample characteristics make it difficult to compare the prevalence of opioid withdrawal across studies, prior studies and our own provide evidence that OWS is prevalent among people non-medical use of prescription opioids.
The odds of OWS among people with OUD increased as OUD severity worsened, even though we excluded the withdrawal criterion from our definition of OUD. This could be explained by heavier use and physical dependence, especially in people with moderate or severe OUD. In DSM-5, OWS is a distinct condition from OUD and does not necessitate its diagnosis (5). However, our results indicate that OWS occurred almost exclusively in participants with OUD.The criteria for OWS in the International Classification of Diseases, 10th Edition (ICD-10-CM) differs from that of DSM-5 as it requires the presence of a moderate or severe OUD for an OWS diagnosis. Contrary to ICD-10-CM specifications, participants with mild OUD were also susceptible to OWS in our sample Therefore, thorough monitoring of OWS is needed to ensure requisite withdrawal symptom management in people with less severe cases of OUD.
OWS symptoms were largely consistent across different frequencies of non-medical prescription opioid use. In line with prior research (33), depressed mood and insomnia were among the most prevalent symptoms of OWS, even as frequency of non-medical prescription opioid use increased. Depression and insomnia are associated with opioid craving, self-medication with opioids, relapse, and poor OUD treatment outcomes (33,34). In this study, the high burden of these symptoms underscores the importance of providing pharmacologic and behavioral treatments for opioid withdrawal. For example, buprenorphine and methadone are among the most effective medications for managing acute withdrawal symptoms and have a greater likelihood of treatment completion, as well as reduce long-term health risks for people with OUD. Moreover, behavioral interventions (e.g., cognitive behavioral therapy, mindfulness, sleep hygiene) are useful for long-term prevention of prescription opioid misuse and its underlying psychiatric symptoms (36). Over half of participants with regular non-medical opioid use and OWS experienced nausea, vomiting, and abdominal pain. Although OWS is rarely fatal, persistent vomiting and diarrhea are associated with dehydration and hypernatremia, which can lead to heart failure, particularly in patients with underlying cardiovascular disease, HIV, or liver disease (37,38).
The present study is among the first to assess associations between psychiatric conditions and OWS using DSM-5 criteria. In our sample, participants with bipolar disorder, dysthymia, panic disorder, and specific phobia had greater odds of OWS. Our findings are in line with those from another study showing greater antidepressant use in a sample of patients with prescription OUD who were experiencing moderate/severe withdrawal than those with none/mild withdrawal (22). Use of psychotropics, including some antidepressants and anxiolytics, is associated with a marked increase in withdrawal symptoms that may be attributable to withdrawal from the psychotropic or drug interactions with opioids (39,40). Moreover, self-medication with opioids to manage depression and avoid withdrawal symptoms is common (33). Unipolar and bipolar depression, panic disorder, and specific phobia are also strong risk factors of OUD (41) possibly because people with these underlying psychiatric conditions may experience more severe withdrawal due to neurobiological adaptations and physiological dependence caused by heavy opioid use (42).
OWS was not associated with hospitalization or emergency department visits. These findings contrast with those of previous studies that show that opioid poisoning and withdrawal are often treated in emergency departments and other acute care settings (14, 43). However, many opioid-related encounters in the hospital/emergency department are related to overdose or withdrawal from heroin and synthetic opioids, i.e., fentanyl and its analogs (44), particularly with the onset of the COVID-19 pandemic, which occurred well after the NESARC-III study was undertaken (45). In our study, opioid withdrawal was associated with the use of substance-related healthcare services in all models, including those adjusted for psychiatric disorders and alcohol withdrawal. These findings suggest that people using non-medical prescription opioids who experience opioid withdrawal are more likely to present to these settings for symptom management. Our relatively small sample prohibited an examination of OWS in relation to the use of specific substance use disorder healthcare services. Nonetheless, clinical monitoring of OWS related to non-medical prescription opioid use in non-hospital settings, especially outpatient treatment programs where detoxification resources are often unavailable and patients are at risk of relapse and treatment dropout, is warranted (13).
We note study limitations. First, as this was a cross-sectional study, we were unable to establish the temporality of the associations between opioid withdrawal and its correlates. Second, DSM-5 OWS Part 1 of Criterion A specifies that a reduction of heavy and prolonged opioid use is required for the onset of opioid withdrawal. While we included only adults with regular non-medical prescription opioid use, with regular use being a proxy for heavy use, we were unable to assess a reduction or previous duration of use. DSM-5 OWS Part 2 of Criterion A requires the administration of an opioid antagonist after a period of opioid use. This could not be accounted for as the AUDADIS-5 did not query it. Considering this, the prevalence of OWS may be underestimated. Third, Criterion D for OWS specifies that withdrawal symptoms cannot be attributable to withdrawal from another substance. Here, OWS symptoms overlapped significantly with alcohol withdrawal symptoms. However, associations between clinical characteristics, use of healthcare services, and OWS remained after controlling for alcohol withdrawal in a multivariate analysis. Fourth, due to few respondents in NESARC-III who regularly used heroin, we were unable to characterize the prevalence of opioid withdrawal, and its symptoms and correlates. Fifth, use of medications for opioid use disorder such as buprenorphine, methadone, and naltrexone and synthetic opioids, including fentanyl, were not assessed in NESARC-III and therefore no conclusions could be drawn regarding their association with OWS. These important questions require analysis of other nationally representative samples. Finally, while NESARC-III is the only national survey to assess OWS and withdrawal symptoms, data was published in 2012–2013, and findings from this study may not be generalizable to the current opioid crisis that has been marked by reductions in opioid prescribing and non-medical prescription opioid use (1,4). Nonetheless, prescription opioids remain the most misused class of opioids in the U.S (4) and therefore withdrawal from their use warrants national study.
Our findings demonstrate that people with regular use of non-medical prescription opioids are highly burdened by OWS and its symptoms, and those with psychiatric conditions and OUD are particularly affected.. Given our findings, thorough screening of opioid withdrawal in people with non-medical prescription opioid use, especially those who use opioids regularly, is recommended.. Furthermore, our findings support the provision of psychiatric treatments within substance use healthcare facilities, which may help to alleviate the high burden of OWS among people who misuse prescription opioids, which remains a prominent barrier to OUD treatment engagement and retention (13). Future general population studies should assess specific substance use disorder healthcare settings frequented by people using prescription opioids non-medically and the prevalence of and barriers to medication assisted treatment for opioid withdrawal in this group. National studies are also needed to assess prevalence and risks of OWS and its symptoms among people using heroin and synthetic opioids given their significant health risks and increasing use in the U.S (3, 4).
Supplementary Material
Funding:
This study was funded by the National Institute on Drug Abuse [grant numbers T32DA031099, R01DA048860, R21DA053156, R01DA054553, R01DA057351, K23DA057417], the National Institute on Alcohol Abuse and Alcoholism [grant number K01AA028199], the National Center for Complementary and Integrative Health [grant number 1K01AT012205-01A1] and support from the New York State Psychiatric Institute. The funders had no role in the design and conduct of the study, management, analysis and interpretation of the data, preparation, review or approval of the manuscript, or decision to submit the manuscript for publication.
Disclosures:
Dr. Hasin receives support from Syneos Health for an unrelated project on diagnosing opioid addiction in pain patients. Dr. Kranzler is a member of advisory boards for Dicerna Pharmaceuticals, Sophrosyne Pharmaceuticals, and Enthion Pharmaceuticals; a consultant to Sobrera Pharmaceuticals; the recipient of research funding and medication supplies for an investigator-initiated study from Alkermes; a member of the American Society of Clinical Psychopharmacology’s Alcohol Clinical Trials Initiative, which was supported in the last three years by Alkermes, Dicerna, Ethypharm, Lundbeck, Mitsubishi, Otsuka, and Pear Therapeutics; and a holder of U.S. patent 10,900,082 titled: “Genotype-guided dosing of opioid agonists,” issued 26 January 2021.
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