Mood/Anxiety disorders and their association with non-medical prescription opioid use and prescription opioid use disorder: longitudinal evidence from the National Epidemiologic Study on Alcohol and Related Conditions

Abstract

Objective

Nonmedical use of prescription opioids represents a national public health concern of growing importance. Mood and anxiety disorders are highly associated with nonmedical prescription opioid use. The authors examined longitudinal associations between nonmedical prescription opioid use and opioid disorder due to nonmedical opioid use with mood/anxiety disorders in a national sample, examining evidence for precipitation, self-medication and general shared vulnerability as pathways between disorders.

Method

Data were drawn from face-to-face surveys of 34,653 adult participants in Waves 1 and 2 of the National Epidemiologic Survey on Alcohol and Related Conditions. Logistic regression models explored the temporal sequence and evidence for the hypothesized pathways.

Results

Baseline lifetime nonmedical prescription opioid use was associated with incidence of any mood disorder, major depressive disorder (MDD), bipolar disorder, any anxiety disorder, and generalized anxiety disorder (GAD in Wave 2, adjusted for baseline demographics, other substance use, and comorbid mood/anxiety disorders). Lifetime opioid disorder was not associated with any incident mood/anxiety disorders. All baseline lifetime mood disorders and GAD were associated with incident nonmedical prescription opioid use at follow-up, adjusted for demographics, comorbid mood/anxiety disorders, and other substance use. Baseline lifetime mood disorders, MDD, dysthymia, and panic disorder, were associated with incident opioid disorder due to nonmedical prescription opioid use at follow-up, adjusted for the same covariates.

Conclusions

These results suggest that preciptiation, self-medication as well as shared vulnerability are all viable pathways between nonmedical prescription opioid use and opioid disorder due to nonmedical opioid use with mood/anxiety disorders.

Introduction

Prescription opioids are effective treatment for chronic and acute pain (Walwyn et al., 2010) and although most people use their medicines appropriately, recently, the nonmedical use of prescription opioids has increased dramatically in the United States and other countries around the world (Haydon et al., 2005; Kuehn, 2007; Huang et al., 2006; Blanco et al., 2007; Brands et al., 2010; Monheit, 2010). In 2008, past-year use of nonmedical prescription opioids were second only to marijuana, as the most frequently used illegal drugs in the US (Substance Abuse and Mental Health Services Administration [SAMHSA], 2009). Estimates from Wave 1 of the National Epidemiologic Study on Alcohol and Related Conditions (NESARC) indicate approximately 4.1% of the U.S. adult population met criteria for non-medical prescription opioid use in their lifetime and that nearly a third of the users met criteria for a prescription opioid use disorder in their lifetime.

Prescription opioids are highly reinforcing and prolonged use can produce neurological changes and physiological dependence. Nonmedical use of prescription opioids, which involves use without a prescription or in ways not recommended by a doctor (Huang et al., 2006; Blanco et al., 2007), is extremely dangerous and potentially fatal (Walwyn et al., 2010), representing a national public health concern of growing importance. Nonmedical users have an increased risk of developing a DSM-IV opioid use disorder (Huang et al., 2006; SAMHSA, 2010). To design effective prevention and treatment interventions to reduce non-medical use-related harm, research is needed to develop our knowledge of the determinants and consequences of nonmedical prescription opioid use. Cross-sectionally ascertained samples have shown that mood and anxiety disorders are strongly associated with nonmedical prescription opioid use and disorder (Sullivan et al., 2005; Huang et al., 2006; Becker et al., 2008; Tetrault et al., 2008; Grella et al., 2009), and may be particularly salient to our understanding of nonmedical use. However, the etiological relevance and clinical implications of this association depends on the temporal sequence of the onset of these disorders. If pre-existing psychiatric disorders lead to nonmedical use then prevention interventions focusing on individuals with mood and anxiety disorders may be necessary. Furthermore, careful screening and monitoring of nonmedical use may be required among individuals with these disorders who are prescribed opiate medication for pain. If mood and anxiety disorders are a consequence of nonmedical prescription opioid use, then interventions among nonmedical prescription opioid users may require an additional mood/anxiety disorder prevention or treatment component. Causal hypotheses remain largely unexplored as current knowledge of possible mechanisms of the linkage between mood and anxiety and opioid use is limited.

The association between mood/anxiety disorders and nonmedical prescription opioid use can arise in one or more non-mutually exclusive ways: nonmedical prescription opioid use leads to mood/anxiety disorders (“precipitation” hypothesis), mood/anxiety disorders lead to nonmedical prescription opioid use (“self-medication” hypothesis), and/or a third factor influences vulnerability to both (“shared vulnerability”). Additionally, these pathways may be operating in a bi-directional and synergistic way or only among certain subgroups. For example, one model (“precipitation”) suggests that nonmedical prescription opioid use could precipitate (i.e. lead to) mood and anxiety disorders. Specifically, behavioral and neural plasticity resulting from heavy drug use could trigger mood or anxiety disorders (Brady & Sinha, 2005). This may be particularly evident among individuals who develop DSM-IV opioid use disorder due to nonmedical use, since to develop a disorder their use of prescription opiates may be especially heavy. In support of this pathway, the DSM-IV includes diagnoses of substance (including prescription opioid)-induced mood anxiety disorders (American Psychiatric Association, 1994). Thus in the precipitational model, nonmedical prescription opioid use occurs before the onset of mood and anxiety disorders. In contrast, a second model (“self-medication”), postulates that individuals with mood and anxiety disorders may use prescription opioids nonmedically in order to temporarily relieve symptoms of anxiety and depression (Emrich et al., 1982; Saitoh et al., 2004). This pathway is grounded in a long history of basic science research demonstrating the anxiolytic and antidepressant properties of opioids (Emrich et al., 1982; Weber & Emrich, 1988). As an extension of this, individuals with substantial pain may develop mood and anxiety disorders, and then engage in nonmedical use of pain medication to relieve psychiatric symptoms, i.e. mood and anxiety disorders mediate the association between pain and nonmedical use. In self-medication models, nonmedical prescription opioid use occurs after mood and anxiety disorders.

A third model (“shared vulnerability”), which does not require sequencing, is an underlying shared vulnerability, in which a third factor (e.g., genetic liability, environmental stressors) influences risk for both drug use/dependence and psychiatric disorders. This is supported by behavioral genetic studies (Krueger et al., 2001; Young et al., 2002; Kendler et al., 2003; Lyons et al., 2008). This model may explain the association between nonmedical prescription opioid use and prescription opioid disorder with mood/anxiety disorders if underlying genetic factors influence both mood/anxiety disorders and nonmedical prescription opioid use/prescription opioid disorder.

Previously using diagnostic information obtained retrospectively among a large and nationally representative population based-sample we found evidence for the existence of both precipitational and self-medication models as well as for an underlying shared vulnerability (Martins et al., 2009). However, the sequence of nonmedical prescription opioid use and mood and anxiety disorders can be better understood with longitudinal population-based data on incident and preexisting use and diagnoses. Schepis and Hakes (2011) found evidence for the association between past-year nonmedical prescription opioid use and incident bipolar disorder among NESARC respondents with past psychopathology, as well as between lifetime nonmedical prescription opioid use and incident depressive, bipolar, and anxiety disorder among those with no history of psychopathology. However, Schepis and Hakes (2011) did not examine the influence of psychopathology on nonmedical opioid use and disorder, nor the influence of opioid disorder due to nonmedical use on psychopathology.. The present study was designed to provide novel information on longitudinal associations between nonmedical prescription opioid use and opioid disorder due to nonmedical opioid use with mood/anxiety disorders using data from the two waves of NESARC data collected approximately three years apart. The NESARC is a large nationally representative epidemiologic study which includes prospective, reliable and valid information on drug use and psychiatric diagnoses, and therefore represents a unique opportunity to examine the evidence for the precipitation, self medication, and general vulnerability models. A precipitational pathway is supported if nonmedical prescription opioid use and disorders due to this use at baseline predict incident mood/anxiety disorders at follow-up. A self-medication pathway is supported if mood/anxiety disorders at baseline predict incident nonmedical prescription opioid use and disorders due to this use at follow-up. A general vulnerability model is supported if evidence is present for both pathways. Our aim is not to tease apart the pathways; rather, we provide the first demonstration of longitudinal incidence data in a population-based sample and provide evidence on the strength of the possibility of each model separately.

Methods

Sample

The NESARC is a longitudinal survey with its first wave of interviews fielded in 2001–2002 and second wave in 2004–2005. The target population was the civilian non-institutionalized population residing in households and group quarters, 18 years and older. Blacks, Hispanics, and young adults (ages 18–24) were oversampled, with data adjusted for oversampling, household- and person-level non-response. The weighted data were then adjusted to represent the U.S. civilian population based on the 2000 Census. Interviews were conducted face-to-face by extensively trained interviewers of the U.S. Bureau of the Census. In 2001–2002 (Wave 1 of the study), 43,093 individuals were assessed for a lifetime history of psychiatric disorders as well as other information (Grant et al., 2004b). For Wave 2 (Hatzenbuehler et al., 2008), conducted in 2004–2005, interviewers re-interviewed all possible eligible respondents from Wave 1. Excluding respondents ineligible for the Wave 2 interview because they were deceased (n=1403), deported, mentally or physically impaired (n=781) or on active duty in the armed forces throughout the follow-up period (n=950), the Wave 2 response rate was 86.7%, and a cumulative response rate over the 2 surveys of 70.2%. Data were reweighted at Wave 2 to account for differential loss to follow-up and to be representative of the target population. This analysis includes the 34,653 respondents who completed interviews at Waves 1 and 2. The demographic characteristics of the eligible sample are provided in Table 1. All potential NESARC respondents were informed in writing about the nature of the survey, the statistical uses of the survey data, the voluntary aspect of their participation, and the federal laws that provide for the confidentiality of identifiable survey information. Respondents who gave consent were then interviewed. The research protocol, including informed consent procedures, was approved by the Census Bureau’s review board and the U.S. Office of Management and Budget.

Table 1.

Incident nonmedical prescription opioid use, incident opioid disorder due to nonmedical prescription opioid use, any incident mood disorder and any incident anxiety disorder in the overall sample (N=34653) by selected demographic characteristics, NESARC wave2 (incident data).

DEMOGRAPHIC CHARACTERISTIC (N)		INCIDENT NONMEDICAL PRESCRIPTION OPIOID USE (N=728)				INCIDENT ABUSE/DEPENDENCE DUE TO NONMEDICAL USE (N=191)				A NY INCIDENT MOOD DISORDER (N=2,032)				ANY INCIDENT ANXIETY DISORDER (N=2,003)
		Incidence by demographic characteristic		Demographic Differences		Incidence by demographic characteristic		Demographic Differences		Incidence by demographic characteristic		Demographic Differences		incidenceby demographic characteristic		Demographic Differences
		%	s.e.	Odds┼ Ratio	95% CI	%	s.e.	Odds┼ Ratio	95% CI	%	s.e.	Odds┼ Ratio	95% CI	%	s.e.	Odds ┼ Ratio	95% CI
Sex	Male (14564)	2.37	.16	1.0	REF	0.73	.10	1.0	REF	5.43	.25	1.0	REF	4.86	.22	1.0	REF
	Female (20089)	2.01	.12	0.8*	0.7–1.0	0.53	.01	0.7	0.5–1.0	8.61	.33	1.6**	1.5–1.8	8.67	.34	1.9**	1.6–2.1
Age at baseline	18–29 (6719)	3.89	.32	1.00	REF	1.33	.21	1.0	REF	10.16	.55	1.0	REF	8.69	.46	1.0	REF
	30–44 (11013)	2.21	.17	0.6**	0.5–0.7	0.55	.01	0.4**	0.3–0.6	7.72	.36	0.7**	0.6–0.9	7.50	.34	0.9*	0.7–1.0
	45–64 (10917)	1.64	.16	0.4**	0.3–0.5	0.43	.01	0.3**	0.2–0.6	6.02	.36	0.6**	0.5–0.7	6.50	.36	0.7**	0.6–0.9
	65+ (6004)	0.89	.14	0.2**	0.2–0.3	0.18	.01	0.1**	0.1–0.3	3.81	.31	0.4**	0.3–0.4	3.38	.31	0.4**	0.3–0.5
Race/Ethnicity	White (20174)	2.33	.13	1.0	REF	0.67	.01	1.0	REF	6.42	.24	1.0	REF	6.83	.24	1.0	REF
	African-American (6577)	1.94	.21	0.8	0.7–1.1	0.43	.12	0.6	0.4–1.1	8.34	.46	1.3**	1.1–1.5	7.10	.45	1.0	0.9–1.2
	Native- American (580)	1.76	.51	0.8	0.4–1.3	0.51	.30	0.8	0.2–2.6	10.87	1.72	1.8**	1.2–2.6	7.09	1.37	1.0	0.7–1.6
	Asian (966)	1.41	.54	0.6	0.3–1.3	0.46	.42	0.7	0.1–4.4	6.23	1.23	1.0	0.6–1.5	5.09	.99	0.7	0.5–1.1
	Hispanic (6356)	1.88	.24	0.8	0.6–1.1	0.58	.15	0.9	0.5–1.5	8.71	.55	1.4**	1.1–1.6	6.46	.54	0.9	0.8–1.1
Annual Family Income	0 to $19–999 (9366)	2.39	.24	1.0	REF	0.69	.14	1.0	REF	8.87	.43	1.0	REF	7.17	.42	1.0	REF
	$20-000 to $34–999 (7381)	1.97	.19	0.8	0.6–1.1	0.53	.01	0.8	0.4–1.3	7.11	.41	0.8*	0.7–0.9	7.29	.45	1.0	0.8–1.2
	$35-000 to $69–999 (10904)	2.27	.18	0.9	0.7–1.2	0.70	.11	1.0	0.6–1.7	6.55	.36	0.7**	0.6–0.8	6.13	.34	0.8*	0.7–1.0
	$70-000+ (7002)	2.06	.18	0.9	0.7–1.1	0.54	.12	0.8	0.4–1.5	5.94	.41	0.6**	0.5–0.8	6.75	.42	0.9	0.8–1.1
Employment Status	Unemployed (12246)	2.07	.19	1.0	REF	0.66	.12	1.0	REF	7.17	.32	1.0	REF	6.74	.31	1.0	REF
	Employed (22407)	2.24	.12	0.9	0.8–1.1	0.60	.01	1.1	0.7–1.7	6.92	.27	1.0	0.9–1.2	6.75	.27	1.0	0.9–1.1
Marital Status	Married (18413)	1.66	.01	1.0	REF	0.44	.01	1.0	REF	6.11	.25	1.0	REF	6.08	.25	1.0	REF
	Previously married (8564)	2.09	.20	1.3*	1.0–1.6	0.64	.12	1.4	0.9–2.3	6.75	.37	1.1	1.0–1.3	7.09	.44	1.2*	1.0–1.4
	Never married (7676)	3.87	.33	2.4**	2.0–2.9	1.17	.20	2.7**	1.7–4.1	10.11	.51	1.7**	1.5–2.0	8.50	.50	1.4**	1.2–1.7

^*p<0.05;

^**p<0.001

^┼ Crude (unadjusted) Odds Ratio

Any mood disorder includes: DSM-IV primary major depressive disorder (MDD), bipolar I, bipolar, and dysthymia

Any anxiety disorder includes: primary panic disorder, social anxiety disorder and specific phobia and generalized anxiety disorder (GAD)

Measures

The Alcohol Use Disorder and Associated Disabilities Interview Schedule (AUDADIS-IV; Grant et al., 2003), a structured diagnostic interview, was administered to NESARC participants using computer-assisted software with built-in skip, logic, and consistency checks. This instrument was specifically designed for experienced lay interviewers and was developed to advance measurement of substance use and mental disorders in large-scale surveys.

Nonmedical prescription opioid use and opioid disorder due to nonmedical use

Nonmedical use of prescription opioids was defined to respondents as using a prescription opioid: “without a prescription, in greater amounts, more often, or longer than prescribed, or for a reason other than a doctor said you should use them.” After the initial probe item, the respondent was given an extensive list of examples of prescription opioids and asked if s/he used any of the prescription opioids on the list or similar drugs ‘nonmedically’. If the response was positive, the respondent was asked to specify which prescription opioid s/he has used, when s/he had used it (lifetime, past-year, since last-interview) as well as asked about lifetime, past-year and since last interview frequency of use (for the purpose of this study those that had used prescription opioids nonmedically 12 or more times in their lifetime were classified as heavy users), and then the interviewer recorded the response. Over 30 symptom items are used by the AUDADIS to operationalize DSM-IV criteria to assess lifetime abuse and dependence according to DSM-IV criteria (Saitoh et al., 2004). Kappas for the AUDADIS-IV diagnosis of lifetime nonmedical prescription opioid use and disorder due to nonmedical prescription opioid use in general population and clinical settings range from 0.59 for lifetime dependence and 0.66 for lifetime use (Grant et al., 1995; Hasin et al., 1997), indicating the test-retest reliability to be good to fair (Fleiss, 1981; Byrt, 1996; Szklo & Javier-Nieto, 2004). It is important to note that Wave 2 of the NESARC combines prescription opioids with Cox-2 inhibitors (which have no abuse potential) in a single question about the nonmedical use of prescription pain medications, which could have partially inflated the incidence rate of prescription opioid use (but not disorder) in our study (Boyd & McCabe, 2009). Due to this problem in Wave 2 assessment we conducted sensitivity analyses that removed all incident nonmedical users who did not endorse at least one nonmedical opioid use disorder question in Wave 2 (the total n of incident nonmedical prescription opioid users decreased to 553). Odds ratios in the sensitivity analyses were very similar to those in which we included all incident nonmedical prescription opioid users (n=728), suggesting minimal/no bias due to the inclusion of Cox-2 inhibitor use in the list of NM prescription opioids in Wave 2 of the NESARC (data not shown, available upon request).

Mood and Anxiety Disorders

Mood disorders included DSM-IV primary major depressive disorder (MDD), bipolar, bipolar I, and dysthymia. Anxiety disorders included DSM-IV primary panic disorder, social anxiety disorder and specific phobia and generalized anxiety disorder (GAD). Diagnostic methods used in the AUDADIS-IV are described in detail elsewhere (Grant et al., 2004b, 2005; Hasin et al., 2005). In DSM-IV, “primary” excludes substance-induced disorders or those due to medical conditions; specific AUDADIS questions about the chronological relationship between intoxication or withdrawal and the full psychiatric syndrome implement DSM-IV criteria differentiating primary from substance-induced disorders. MDD diagnoses also ruled out bereavement. Test-retest reliability for AUDADIS-IV mood and anxiety diagnoses in general population and clinical settings was good to fair with kappa agreement statistics ranging from 0.42 for social anxiety disorder to 0.64 for MDD (Hasin et al., 1997; Canino et al., 1999; Grant et al., 2003). For the purpose of this study, besides the specific disorder variables, we created a variable that combined all DSM-IV mood disorders that a respondent endorsed as well as a variable that combined all DSM-IV anxiety disorders that a respondent endorsed.

Other Substance Use, Alcohol Disorder and Other Drug Use Disorder

The AUDADIS-IV assessed lifetime use of alcohol and other illegal drugs at Wave 1 of the NESARC (e.g., marijuana, cocaine, heroin, hallucinogens, inhalants, and nonmedical use of stimulants, sedatives, and tranquilizers) with similar sets of questions as those described for nonmedical prescription opioid use. For the purpose of this study we used data on alcohol and any other illegal drug use that occurred prior to baseline as two separate control variables (one for alcohol, one for all other illegal drugs) in the models in which nonmedical use was the predictor of interest. Similarly, the AUDADIS-IV included extensive questions to operationalize DSM-IV criteria to assess lifetime alcohol use disorders and other drug use disorders (Saitoh et al., 2004). For the purpose of this study we used baseline data on alcohol and other drug use disorders into two separate control variables for the models in which opioid disorder due to nonmedical use was the predictor of interest. AUDADIS-IV criteria and diagnoses for alcohol and drug use disorders have fair to excellent reliabilities (kappa values, 0.53–0.84(Grant et al., 1995; Hasin et al., 1997).

WaveWaveDemographic correlates

We examined the following potential correlates of nonmedical prescription opioid use and prescription opioid disorder due to nonmedical opioid use for inclusion as control variables: sex, age, race/ethnicity, family income, employment status, and marital status (see Table 1).

Incidence

Incidence was defined as new cases of nonmedical opioid use or disorder due to nonmedical opioid use among those with no history at Wave 1 or new cases of psychiatric disorder among those with no history at Wave 1 in the period comprised between NESARC Wave 1 and Wave 2 interviews (since last interview).

Statistical analysis

To examine the precipitational pathway, two sets of nested logistic regression analyses examined whether lifetime nonmedical prescription opioid use and disorders due to this use predict incident mood/anxiety disorders (any mood or anxiety disorder and specific mood/anxiety disorders) at follow-up. First, demographics were included in the models as covariates. Second, to test whether these associations persisted independently of other substance use and comorbid mood/anxiety disorders, we controlled for baseline substance use variables (with indicator variables representing other substance use), baseline comorbid mood/anxiety disorders (a binary variable representing baseline comorbid mood/anxiety disorders, n varies by model, available upon request). In the models in which nonmedical use was the predictor of interest, substance use covariates included baseline lifetime alcohol (n=28,482, 83.4% of the baseline respondents) and other drug use (marijuana, cocaine, hallucinogens, inhalants, heroin, nonmedical stimulant, nonmedical sedative, and nonmedical tranquilizer, n=7,497, 22.5% of the baseline respondents). In the models in which disorders due to nonmedical use was the predictor of interest we included baseline lifetime alcohol disorder (n=9,937, 30.4% of the baseline respondents) and other drug use disorder (n=3,332, 10.1% of the baseline respondents) as covariates.

Similarly two sets of nested logistic regression models were generated to address the self-medication pathway and determine whether mood/anxiety disorders at baseline predict incident nonmedical prescription opioid use and disorders due to this use at follow-up. First, we controlled for demographics. Second, to test whether these associations persisted independent of comorbid mood/anxiety disorders, a binary variable representing all other baseline lifetime mood/anxiety disorders (n varies by model, available upon request) was included. In addition, these models also controlled for baseline other substance use/substance use variables (described in detail in the former paragraph). Stata 10.0 survey commands were used in all analysis to account for sample weighting and the complex survey design (StataCorp, 2008).

Results

Incidence at Wave 2 (Table 1)

Among the sample of 34,653, there were 728 (2.3%) incident nonmedical prescription opioid users and 191(0.6%) subjects who met criteria for opioid disorder (abuse and/or dependence) due to nonmedical prescription opioid use in Wave 2 of the NESARC. Males, those in the younger age group, and those never married or no longer married were more likely to initiate nonmedical opioid use in the interval between Wave 1 and Wave 2. Respondents in the younger age group and those never married were more likely to meet criteria for an opioid disorder due to nonmedical use in Wave 2.

Additionally, there were 2,032 (7.0%) respondents with incident mood disorders and 2,003 (6.7%) respondents with incident anxiety disorders in Wave 2 of the NESARC. Incident mood disorders at Wave 2 were more likely to develop among females, those in the younger age group, African-Americans, Native Americans and Hispanics versus Whites, those with lower family income and those never married. Females, those in the younger age group, and those never married or no longer married were also more likely to have incident anxiety disorders at Wave 2.

The precipitational pathway: does nonmedical use precede mood/anxiety disorders? (Table 2)

Table 2.

Assessing the precipitational pathway: Baseline lifetime nonmedical prescription opioid use and abuse/dependence secondary nonmedical use in NESARC Wave 1 preceding incident mood/anxiety disorders in NESARC Wave 2.

INCIDENT (WAVE 2) MOOD AND ANXIETY DISORDERSa	BASELINE (WAVE 1) NONMEDICAL PRESCRIPTION OPIOID USE PREDICTORS
	Lifetime Nonmedical Prescription Opioid Use (N=1,499) b controlling for				Lifetime Abuse/Dependence due to Nonmedical Use (N=432) c controlling for
	Demographics		Demographics, other substance use, comorbid mood/anxiety disorders		Demographics		Demographics, other substance use disorders, comorbid mood/anxiety disorders
	Odds Ratiod	95% CI	Odds Ratioe	95% CI	Odds Ratiod	95% CI	Odds Ratiog	95% CI
Incident Mood Disorders
Any Mood Disorder (n=2,032)	2.1***	1.6–2.8	1.8***	1.4–2.3	2.0**	1.3–3.1	1.5	0.9–2.5
Major Depressive Disorder (n=1,668)	1.7***	1.3–2.2	1.4*	1.1–1.9	2.1**	1.3–3.3	1.6	1.0–2.6
Dysthymia (n=351)	1.4	0.9–2.4	1.0	0.6–1.7	2.8**	1.3–6.0	2.2	1.0–5.0
Bipolar I disorder (n=182)	1.7	0.8–3.6	1.7	0.8–3.6	1.7	0.5–5.9	1.9	0.5–6.9
Bipolar Disorder (n=261)	2.0*	1.1–3.6	2.0*	1.1–3.7	2.5	1.0–5.9	2.6	1.0–6.8
Incident Anxiety Disorders
Any Anxiety Disorder (n=2,003)	1.7***	1.3–2.1	1.4*	1.1–1.8	2.0*	1.4–3.0	1.6	1.0–2.4
Panic Disorder (n=647)	1.6*	1.1–2.4	1.3	0.9–2.0	2.3**	1.2–4.1	1.8	0.9–3.4
Social Anxiety Disorder (n=560)	1.7**	1.1–2.5	1.1	0.7–1.7	1.8	1.0–3.3	1.2	0.6–2.4
Specific Phobia (n=807)	1.5*	1.1–2.2	1.4	1.0–2.0	1.6	0.9–2.9	1.4	0.8–2.8
Generalized Anxiety Disorder (n=1,123)	2.1***	1.6–2.8	1.5**	1.1–2.1	2.5***	1.6–3.9	1.6	1.0–2.5

^aIn all analyses those with former mood/anxiety disorders were excluded (e.g., to investigate the association between baseline nonmedical opioid use and incident major depressive disorder all respondents with major depressive disorder at baseline were excluded).

^bReference is absence of lifetime nonmedical prescription opioid use at baseline (Wave 1)

^cReference is absence of lifetime abuse or dependence secondary to nonmedical prescription opioid use at baseline (Wave 1)

^dAdjusted for baseline demographics (sex, age, race, and baseline family income, marital status, and employment status).

^eAdjusted for baseline demographics, other substance use (alcohol, marijuana, cocaine, hallucinogens, inhalants, heroin, nonmedical stimulant, sedative and tranquilizer use), comorbid mood/anxiety disorders.

^gAdjusted for baseline demographics and other substance use disorders (alcohol, marijuana, cocaine, hallucinogens, inhalants, heroin, nonmedical stimulant, sedative and tranquilizer use), comorbid mood/anxiety disorders.

^*p<0.05;

^**p<0.01;

^***p<0.001

Nonmedical prescription opioid use

Baseline lifetime nonmedical prescription opioid use was associated with the incidence of any mood disorder, major depressive disorder (MDD), bipolar disorder, and all anxiety disorders (any and specific), at follow-up in the models adjusted for demographics as well as in the models further adjusted for baseline lifetime other substance use, and baseline comorbid lifetime mood/anxiety disorders (unadjusted models yielding similar results are available upon request). We ran sensitivity analyses to examine the precipitational pathway focusing on nonmedical prescription opioid users that can be considered as heavy prescription opioid users (used prescription opioids nonmedically at least 12 times in their lifetime,) and findings were very similar to the ones obtained when we included all lifetime nonmedical prescription opioid users.

Nonmedical opioid disorder due to nonmedical use

Baseline lifetime nonmedical opioid disorder due to nonmedical prescription opioid use was associated with any mood disorder, any anxiety disorder, as well as with several incident mood disorders and anxiety disorders at follow-up when adjusted for demographics only. In the models further adjusted for baseline lifetime alcohol disorder and drug disorder, and baseline comorbid lifetime mood/anxiety disorders, the associations were attenuated and none remained significantly associated with opioid disorder at follow-up.

The self-medication pathway: do mood/anxiety disorders precede nonmedical use? (Table 3)

Table 3.

Assessing the self-medication pathway: Baseline lifetime mood/anxiety disorders in NESARC Wave 1 preceding nonmedical prescription opioid use and abuse/dependence secondary nonmedical use in NESARC Wave 2.

BASELINE (WAVE 1) MOOD AND ANXIETY DISORDER PREDICTORSa	INCIDENT (WAVE 2) NONMEDICAL PRESCRIPTION OPIOID USE VARIABLES
	Incident Nonmedical Prescription Opioid Use (N=728) b controlling for				Incident Abuse/Dependence Secondary to Nonmedical Use (N=191) c controlling for
	Demographics		Demographics, comorbid mood/anxiety disorders, other substance use		Demographics		Demographics, comorbid mood/anxiety disorders, other substance use disorders
	Odds Ratiod	95% CI	Odds Ratioe	95% CI	Odds Ratiod	95% CI	Odds Ratioe	95% CI
Lifetime Mood Disorders
Any Mood Disorder (n=7,082)	1.9***	1.6–2.3	1.6***	1.3–2.0	2.8***	2.0–3.9	2.1***	1.5–3.0
Major Depressive Disorder (n=6,004)	1.9***	1.5–2.3	1.5**	1.2–2.8	2.6***	1.8–3.6	1.7**	1.2–2.5
Dysthymia (n=1,577)	2.2***	1.6–3.0	1.6*	1.1–2.3	3.6***	2.1–6.4	2.2*	1.1–4.2
Bipolar I disorder (n=791)	2.3***	1.5–3.4	1.7*	1.1–2.6	1.8	0.9–3.6	1.1	0.5–2.3
Bipolar Disorder (n=1,219)	2.5***	1.8–3.6	2.0***	1.4–2.8	2.2**	1.3–3.8	1.4	0.7–2.5
Lifetime Anxiety Disorders
Any Anxiety Disorder (n=6,132)	1.4**	1.1–1.8	1.1	0.9–1.4	1.9**	1.2–3.0	1.3	0.8–1.9
Panic Disorder (n=1,790)	1.7**	1.2–2.5	1.3	0.9–1.9	3.4***	1.9–6.1	2.3**	1.3–4.2
Social Anxiety Disorder (n=1,721)	1.5*	1.0–2.1	1.1	0.8–1.6	2.2*	1.2–4.2	1.4	0.8–2.7
Specific Phobia (n=3,407)	1.4*	1.0–1.8	1.1	0.8–1.5	1.4	0.8–2.5	0.9	0.5–1.7
Generalized Anxiety Disorder (n=1,493)	2.1***	1.5–2.9	1.6**	1.1–2.2	3.0**	1.6–5.6	1.9	1.0–3.6

^aReference is absence of specific mood/anxiety disorder

^bAnalyses conducted among those with no history of nonmedical prescription opioid use at Wave 1

^cAnalyses conducted among those with no history of abuse or dependence secondary to nonmedical prescription opioid use at Wave 1

^dAdjusted for baseline demographics (sex, age, race, and baseline family income, marital status, and employment status).

^eAdjusted for baseline demographics and other baseline lifetime mood/anxiety disorders, other substance use (prescription opioid use model)/substance use disorders (prescription opioid disorder model).

^*p<0.05;

^**p<0.01;

^***p<0.001

Nonmedical prescription opioid use

Almost all baseline lifetime mood/anxiety disorders (any and specific) were associated with incident nonmedical prescription opioid use at follow-up in models adjusted for demographics. In the models further adjusted for other comorbid baseline mood/anxiety disorders, and baseline substance use, all mood disorders (any and specific) and GAD (aOR:1.5[1.1, 2.1]) were associated with incident nonmedical prescription opioid use at follow-up.

Nonmedical opioid disorder due to nonmedical use

Adjusted for demographics, almost all (any and specific) baseline lifetime mood disorders and anxiety disorders were associated with incident opioid disorder due to nonmedical prescription opioid use at follow-up. In the models further adjusted for other baseline mood/anxiety disorders, and baseline substance use associations were attenuated but baseline lifetime any mood disorder (aOR:2.1[ 1.5,3.0]), MDD (aOR:1.7[1.2,2.5]), dysthymia (aOR:2.2[1.1,4.2]), and panic disorder (aOR:2.3[1.3,4.2]) remained associated with incident opioid disorder due to nonmedical prescription opioid use at follow-up.

Discussion

We find evidence that supports all three postulated causal models linking mood/anxiety disorders and nonmedical opioid use and disorder due to use and the use of incident data provides more assurance of the correct temporal sequencing. This study builds upon our prior cross-sectional study (Martins et al., 2009) as well as on Schepis and Hakes study (2011) and provides further support for a strong relationship between mood/anxiety disorders and nonmedical opioid use and disorder due to use. Previously, using survival analyses techniques with Wave 1 data only we also found support for all three models, however, in that paper we only explored the associations of mood/anxiety disorders with nonmedical use and dependence due to use (Martins et al., 2009). By focusing on incident cases, evidence for a precipitational model pathway was found, as nonmedical opioid use (but not disorder due to use) predicted mood/anxiety disorders, especially for respondents with nonmedical use preceding any anxiety disorder, since in the other direction the association was non-significant. Previously, when analyzing Wave 1 data only, we also found evidence for a strong association between dependence due to use and subsequent GAD and bipolar I disorder, findings that were not corroborated in these incident analyses with disorder [abuse/dependence] (Martins et al., 2009). By focusing on incident cases, evidence for a self-medication pathway was also found as mood/anxiety disorders predicted incident nonmedical opioid use and disorder, particularly for respondents with mood disorders such as dysthymia and bipolar I disorder preceding nonmedical use, and any mood disorders, MDD, dysthymia, and panic disorder predicting opioid disorder due to use, since in the other direction associations were non-significant. The previous study using only Wave 1 data found evidence for a strong self-medication pathway between preexisting bipolar I disorder and GAD and nonmedical opioid dependence due to use (Martins et al, 2009), Finally, the presence of a general shared vulnerability to both mood/anxiety disorders and nonmedical prescription opioid use cannot be ruled out, since in several cases the magnitude of the associations had similar strength in both directions-from nonmedical use/disorder due to use to mood/anxiety disorders and vice-versa, a finding also found previously in the Wave 1 data (Martins et al, 2009).

The risk of incident anxiety disorders was increased among respondents with baseline nonmedical opioid use after controlling for all covariates, providing support for a precipitational model in these cases. This is consistent with findings from Schepis and Hakes study (2011) that shows that the risk of incident anxiety disorders was increased among respondents withy baseline nonmedical opioid use without prior psychopathology. It should be noted that the increased risk for incident anxiety disorders was of similar magnitude for nonmedical users and heavy nonmedical users (sensitivity analyses, available upon request). Thus, using opioids (or even withdrawal from opioids) might precipitate anxiety disorders. This suggests that there is a subgroup of people particularly vulnerable to the future development of anxiety disorders and individuals using prescription opioids need to be closely monitored not only for the possibility of engaging in nonmedical use, but also for the development of comorbid psychiatric disorders. Increased risk of incident opioid disorder due to nonmedical use occurred among respondents with baseline mood disorders, MDD, dysthymia, and panic disorder, reinforcing the finding that respondents with mood disorders might use opioids nonmedically to alleviate their mood symptoms. This again is consistent with findings from cross-sectional studies (Sullivan et al., 2005; Huang et al., 2006; Becker et al., 2008; Tetrault et al., 2008; Grella et al., 2009) and builds upon our previous study (Martins et al., 2009) which provides support for a self-medication pathway between opioid disorder and mood/anxiety disorders. This is also consistent with findings from Robinson and colleagues (2011) who show that self-medication in anxiety disorders confers substantial risk of incident substance use disorders, however, in that study, the authors did not test for associations of baseline anxiety disorders specifically with incident prescription opioid disorder, nor adjusted for baseline other substance use. Thus, early identification and treatment of mood and anxiety disorders might reduce the risk for self-medication with prescription opioids and the risk of future development of an opioid disorder. Furthermore, an underlying shared vulnerability for nonmedical prescription opioid use and mood/anxiety disorders could exist. Thus, family and twin studies are needed to disentangle the relationship between nonmedical prescription opioid use/prescription opioid disorder with mood/anxiety disorders, since they could co-occur due to shared genetic or environmental risk factors, similar to what was found when examining the association between nicotine dependence and major depression (Martins et al., 2009). It is important to bear in mind that as other drug use disorders, opioid disorders due to nonmedical use is a genetically and phenotypically complex disorder (Compton et al., 2005b). Moreover, the social environment in which population groups grow up certainly plays a role in the underlying general vulnerability for nonmedical prescription opioid use/opioid disorder and mood/anxiety disorders for example, McCabe and colleagues (2007) have shown that, among college students, most of them use prescription opioids nonmedically to self-medicate, followed by a smaller proportion that use them to “get high” or to experiment with these drugs. In addition, studies have shown that the leading sources for obtaining prescription opioids for nonmedical use are family and friends (McCabe et al., 2007; Martins et al., 2008).

Several study limitations merit mention. First, loss to follow-up might have introduced bias in the sample and, consequently, in the generalization of results. Furthermore, all information is based on self-report, as in all large-scale epidemiologic surveys. As such, the validity of these results is predicated on the accuracy of the information provided by respondents. However, studies have shown that the AUDADIS-IV has good reliability and validity (Grant et al., 2003; Grant et al., 2005). The follow-up period between Waves 1 and 2 of the NESARC is only approximately three years, and does not include any new cases that could occur in future years. Thus, the results of our study may underestimate the true magnitude of the associations between nonmedical opioid use and incidence of psychiatric disorders. In addition as acknowledged earlier, Wave 2 of the NESARC combines prescription opioids with Cox-2 inhibitors (which have no abuse potential) in a single question about the nonmedical use of prescription pain medications, which could have somewhat inflated the incidence rate of prescription opioid use (but not disorder) in our study (Boyd & McCabe, 2009). However, the incidence of nonmedical prescription opioid use in Wave 2 of the NESARC is similar to rates obtained with data from the 2004 (2.9%) and 2005 (2.6%) National Survey of Drug Use and Health (using recent-onset use as a proxy of incidence, SAMHSA, 2005, 2006), that asks specifically about nonmedical prescription opioid use, and the sensitivity analyses we conducted suggest the incidence of nonmedical prescription opioid use in Wave 2 are not over-inflated. Further, we do not have information on whether nonmedical opioid users initiate opioid seeking euphoria (via medical prescription or not) or analgesia (via medical prescription or not) and where the first significant exposure occurred. Subtypes of opioid users may be unique in many aspects of comorbidity and demographics. Future research specifically focusing on prescription opioid use and disorders may be able to provide more information on subtypes of opioid users in the general population. Another major limitation of the study seems to be the use of lifetime use and lifetime diagnosis at baseline, however, when we attempted to run similar models with past-year use diagnosis at baseline, the sample sizes were largely reduced and we did not have power to run the models. Lastly, to reduce the overall complexity, our results are based on a set of statistical models that only included a narrow set of covariates. Our models were not adjusted for the ten personality disorders available in the NESARC dataset. However, estimates from models that also adjusted for pain, family history and antisocial personality disorder (ASPD) were similar to the ones reported (data not shown, available upon request).

Despite limitations, the present study adds substantial information to the literature on nonmedical prescription opioid use and prescription opioid disorder and psychopathology. Major strengths of the data include how the NESARC project was administered (national sampling frame and standardized questions) and the longitudinal character of the data (Grant et al., 1995; Grant et al., 2003). The large sample size of the NESARC allows for statistical power to detect evidence for the hypothesized pathways of not only nonmedical opioid use but also the less common condition of opioid disorder that had resulted from nonmedical prescription opioid use with psychiatric disorders. Further, the AUDADIS-IV has documented reliability and validity in assessing drug use disorders as well as psychiatric disorders.

In conclusion, this study provides support for a bi-directional pathway (self-medication and precipitational) between nonmedical prescription opioid use/opioid disorder due to nonmedical use and several mood/anxiety disorders. In addition, it does not rule out the existence of an underlying general vulnerability that could explain these associations. It is important for clinicians to investigate substance-induced mood/anxiety disorders when treating patients who use prescription opioids nonmedically or have a prescription opioid disorder as well as to ask patients with mood/anxiety disorders about their drug using behavior.