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. Author manuscript; available in PMC: 2016 Dec 1.
Published in final edited form as: Psychol Addict Behav. 2015 Dec;29(4):924–932. doi: 10.1037/adb0000080

Sociodemographic and Psychiatric Diagnostic Predictors of 3-Year Incidence of DSM-IV Substance Use Disorders among Men and Women in the National Epidemiologic Survey on Alcohol and Related Conditions

Risë B Goldstein 1, Sharon M Smith 1, Deborah A Dawson 1, Bridget F Grant 1
PMCID: PMC5084620  NIHMSID: NIHMS818131  PMID: 26727008

Abstract

Incidence rates of alcohol and drug use disorders (AUDs and DUDs) are consistently higher in men than women, but information on whether sociodemographic and psychiatric diagnostic predictors of AUD and DUD incidence differ by sex is limited. Using data from Waves 1 and 2 of the National Epidemiologic Survey on Alcohol and Related Conditions, sex-specific 3-year incidence rates of AUDs and DUDs among United States adults were compared by sociodemographic variables and baseline psychiatric disorders. Sex-specific logistic regression models estimated odds ratios for prediction of incident AUDs and DUDs, adjusting for potentially confounding baseline sociodemographic and diagnostic variables. Few statistically significant sex differences in predictive relationships were identified and those observed were generally modest. Prospective research is needed to identify predictors of incident DSM-5 AUDs and DUDs and their underlying mechanisms, including whether there is sex specificity by developmental phase, in the role of additional comorbidity in etiology and course, and in outcomes of prevention and treatment.

Keywords: Substance use disorders, incidence, predictors, epidemiology, gender differences

Incidence (first onset) rates of alcohol and drug use disorders (AUDs and DUDs) are consistently higher in men than women across diagnostic systems, regardless of whether abuse and dependence are combined or considered separately (Table 1). However, few studies have investigated whether predictors of incident AUDs and DUDs differ by sex. Bijl, de Graaf, Ravelli, Smit, and Vollebergh (2002) found no sex difference in prediction by age of incident AUDs in a nationally representative sample of adults in the Netherlands; Wittchen et al. (2008) reported similar findings from epidemiologically ascertained adolescents and young adults in the Munich Early Developmental Stages of Psychopathology (EDSP) cohort. Conversely, Mattisson, Bogren, Horstmann, and Öjesjö (2010) found a tendency toward later onsets of AUDs among women than men in the Lundby cohort.

Table 1. Previous Incidence Studies Reporting Sex-Specific Rates of Alcohol and Drug Use Disorders.

Study Diagnostic
Classification
System		 Alcohol Use Disorder
incidence/100 person-years		 Drug Use Disorder incidence/100
person-years
Men Women Men Women
W. W. Eaton et al. (1989, Epidemiologic
 Catchment Area Survey)		 DSM-III Abuse and
 dependence
 combined:
 3.67		 Abuse and
 dependence
 combined:
 0.61		 Abuse and
 dependence
 combined:
 1.66		 Abuse and
 dependence
 combined:
 0.66
Newman & Bland (1998, Edmonton
 Survey Follow-Up)		 DSM-III Abuse and
 dependence
 combined:
 4.48		 Abuse and
 dependence
 combined:
 1.27		 Abuse and
 dependence
 combined:
 1.36		 Abuse and
 dependence
 combined:
 0.82
Crum, Chan, Chen, Storr, & Anthony
 (2005, Baltimore Epidemiologic
 Catchment Area Follow-Up Study)		 DSM-III-R Dependence:
 0.76		 Dependence:
 0.30		 Not reported Not reported
Bijl, de Graaf, Ravelli, Smit, & Vollebergh
 (2002, Netherlands Mental Health
 Survey and Incidence Study)		 DSM-III-R Abuse: 4.09
 Dependence:
 0.82		 Abuse: 0.91
 Dependence:
 0.18		 Abuse: 0.48
 Dependence:
 0.21		 Abuse: 0.07
 Dependence:
 0.32
Grant et al. (2009, National Epidemiologic
 Survey on Alcohol and Related
 Conditions)		 DSM-IV 1 Abuse: 1.6
 Dependence:
 2.5		 Abuse: 0.6
 Dependence:
 1.1		 Abuse: 0.3
 Dependence:
 0.5		 Abuse: 0.3
 Dependence:
 0.2
C.-S. Lee, Liao, Liu, W.-C. Lee, & Cheng
 (2013, Taiwan Aboriginal Study
 Project)2 		DSM-IV Abuse and
 dependence
 combined:
 2.71		 Abuse and
 dependence
 combined:
 1.40		 Not reported Not reported
Mattison, Bogren, Horstmann, & Öjesjö
 (2010, Lundby Study)2 		DSM-IV Abuse and
 dependence
 combined:
 0.38		 Abuse and
 dependence
 combined:
 0.06		 Not reported Not reported
Von Sydow, Lieb, Pfister, Höfler,
 Sontag, & Wittchen (2001, Munich
 Early Developmenal Stages of
 Psychopathology Study)3,4 		DSM-IV Not reported Not reported Abuse: 3.5
 Dependence:
 0.6		 Abuse: 0.3
 Dependence:
 0.1
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1

The hierarchical preemption of incident abuse by previous dependence was suspended in this study.

2

Age standardized

3

Incidence calculated over mean follow-up of 42 months.

4

Only cannabis use disorders were reported.

Zimmermann et al. (2003) did not find sex differences in prediction of incident AUD by anxiety disorders over a mean follow-up of 42 months among the Munich EDSP cohort. To our knowledge, however, the sex specificity of other predictors of AUD and DUD incidence, including unmarried status and existing psychiatric disorders (Crum, Chan, Chen, Storr, & Anthony, 2005; de Graaf, ten Have, Tuithof, & van Dorsselaer, 2013; W. W. Eaton et al., 1989; Grant et al., 2009; Newman & Bland, 1998; Zimmermann et al., 2003), has not been investigated. Both sociodemographic characteristics and existing psychiatric disorders may contribute differentially in men and women to the etiology and course of chronologically secondary AUDs and DUDs. Sex specificity could reflect differences in risk and protective factors, including gendered patterns of exposure to substances and social acceptability of their use. In addition to informing further etiologic investigations, identification of sex differences in predictors of AUD and DUD incidence may guide appropriate tailoring of preventive and therapeutic interventions for these and chronologically primary psychiatric disorders.

Accordingly, this study’s goals were to: (a) estimate sex-specific incidence rates of DSM-IV AUDs and DUDs in a large, nationally representative U.S. sample; (b) provide sex-specific data on sociodemographic risk factors; and (c) estimate sex-specific prediction of incident AUDs and DUDs by baseline Axis I and Axis II disorders.

Method

Sample

The entire research protocol, including informed consent procedures, was approved by the institutional review board of the U.S. Census Bureau and the Office of Management and Budget. Wave 2 (W2) is the 3-year prospective follow-up of the Wave 1 (W1) National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) sample (Grant, Moore, Shepard, & Kaplan, 2003; Grant, Kaplan, Moore, & Kimball, 2007). The W1 NESARC (overall response rate=81.0%, n=43,093) represented U.S. residents ≥ 18 years old of households and selected group quarters. Individuals 18 to 24 years old, non-Hispanic Blacks, and Hispanics were oversampled. In-person reinterviews of all W1 respondents were attempted in W2. Among those alive, resident in the U.S., and not incapacitated or on active military duty throughout the follow-up period, the W2 response rate was 86.7% (n=34,653); the cumulative response rate was 70.2% across the 2 waves. W2 respondents did not differ from W2 respondents plus eligible nonrespondents sociodemographically or on any W1 lifetime psychiatric disorder (Grant et al., 2009).

Assessments

Substance use disorders

Diagnostic assessments utilized the Alcohol Use Disorder and Associated Disabilities Interview Schedule—DSM-IV Versions (AUDADIS-IV) for Waves 1 (Grant, Dawson, & Hasin, 2001) and 2 (Grant, Dawson, & Hasin, 2004). DSM-IV criteria for alcohol and drug-specific abuse and dependence for 10 drug categories were queried at both waves (Compton, Thomas, Stinson, & Grant, 2007; Grant et al., 2009; Hasin, Stinson, Ogburn, & Grant, 2007). Abuse diagnoses required that ≥ 1 abuse criterion; and dependence diagnoses, ≥ 3 dependence criteria, be met in the same year for the same substance. Drug-specific disorders are aggregated to yield any drug abuse and any drug dependence. Nicotine dependence was diagnosed similarly (Grant, Hasin, Chou, Stinson, & Dawson, 2004). Reliability of AUDADIS-IV AUDs (kappa=.70-.84), DUDs (kappa=.53-.79), and nicotine dependence (kappa=.60-.63), and their validity, are extensively documented in clinical and general population samples (Compton et al., 2007; Grant, Dawson, et al., 2003; Hasin et al., 2007).

Other psychiatric disorders

DSM-IV primary mood (MDD, dysthymia, and bipolar I and II) and anxiety (panic, social and specific phobias, and generalized anxiety) disorder diagnoses were assessed at W1 (Grant, Hasin, et al., 2005; Grant, Stinson, et al., 2005). DSM-IV primary diagnoses excluded substance- and illness-induced cases; MDD diagnoses ruled out bereavement. Lifetime posttraumatic stress disorder (PTSD) and attention-deficit/hyperactivity disorder (ADHD) were assessed at W2 (Ruan et al., 2008) but considered as predictors herein only if prevalent up to W1.

All DSM-IV personality disorders (PDs) were assessed on a lifetime basis: avoidant, dependent, obsessive-compulsive, paranoid, schizoid, histrionic, and antisocial PDs at W1 (Grant, Hasin, Stinson, et al., 2004); borderline, schizotypal, and narcissistic PDs, at W2 (Grant et al., 2008). Test-retest reliabilities of AUDADIS-IV mood and anxiety (kappa=.42-.65), PD (kappa=.40-.71), and ADHD (kappa=.71) diagnoses were fair to good (Grant, Dawson, et al., 2003; Ruan et al., 2008). Convergent validity of mood, anxiety, and PD diagnoses was good to excellent (Grant, Hasin, Stinson, et al., 2004; Grant, Hasin, et al., 2005; Grant, Stinson, et al., 2005).

Statistical Analyses

Incidence of each AUD and DUD was estimated as a percentage, the numerator comprising individuals with no lifetime history of the target disorder (e.g., alcohol dependence) at W1 who developed it during follow-up and the denominator comprising all respondents with no lifetime history of the disorder at W1 (population at risk). Individuals with alcohol dependence can later develop abuse, though this has not been observed for DUDs (Grant et al., 2009). Nevertheless, the hierarchical preemption under DSM-IV by dependence of subsequent abuse was suspended for both AUDs and DUDs.

Because of the low incidence of AUDs and DUDs, particularly among women, 3-year rates were considered so as to have sufficient cases for meaningful analyses. Incidence rates were compared by sociodemographic and psychiatric predictors, stratified on sex, using standard contingency table approaches. All sociodemographic predictors were entered simultaneously into sex-specific logistic regressions for each incident disorder.

Sex-specific logistic regressions estimated prediction of each incident AUD and DUD by specific psychiatric disorders, adjusted for sociodemographic variables and all other psychiatric disorders. Adjustment for diagnostic covariates tests the hypothesis that incidence is predicted by the pure (noncomorbid) form of a specific baseline disorder (Compton et al., 2007; Hasin et al., 2007). ORs were considered significant when their 95% confidence intervals excluded 1.00. When incidence is < 10%, as with AUDs and DUDs reported herein, the OR closely approximates the relative risk (Zhang & Yu, 1998).

Sex differences in ORs were assessed in models including sex × predictor interaction terms among the total sample, with alpha-to-stay = .05. No adjustments were made for multiple comparisons. All analyses utilized SUDAAN (Research Triangle Institute, 2008) to adjust for the NESARC’s complex sample design.

Results

Alcohol Use Disorders

Three-year incidence ±SE of alcohol abuse among men and women was 8.39% ± 0.40 and 3.12% ± 0.19, respectively, chi-square(1)= 83.71, p < .0001; of alcohol dependence, 4.62% ± 0.23 and 2.18%±0.14, respectively, chi-square(1)= 56.03, p < .0001). Higher rates among men were observed in all sociodemographic subgroups examined (data available upon request). ORs for sociodemographic predictors of AUDs did not differ by sex (Table 2), except for reduced incidence of dependence among Hispanic women, but no association in Hispanic men, versus non-Hispanic Whites.

Table 2. Adjusted1 Odds Ratios (95% Confidence Intervals) for Three-Year Incidence of DSM-IV Alcohol and Drug Use Disorders2 by Wave 1 Sociodemographic Characteristics among Male and Female NESARC Respondents.

Baseline
Characteristic		 Alcohol Abuse Alcohol Dependence Any drug abuse Any drug dependence
Men (n at
risk=8653)		 Women (n at
risk=16,626)		 Men (n at
risk=12,155)		 Women (n at
risk=18,541)		 Men (n at
risk=12,575)		 Women (n at
risk=18,702)		 Men (n at
risk=14,091)		 Women (n at
risk=19,711)
Age, years
 18-29 7.6 (4.76-
12.23)		 23.0 (10.68-
49.70)		 14.5 (7.85-
26.66)		 22.5 (9.92-
50.82)		 29.8 (8.64-
102.87)		 34.1 (11.20-
104.03)		 14.6 (3.98-
53.21)		 44.2 (7.78-
251.09)
 30-44 4.2 (2.62-6.85) 13.0 (6.12-
27.67)		 7.9 (4.23-14.75) 11.6 (5.18-
26.13)		 16.9 (5.01-57.03) 14.0 (4.74-
41.57)		 11.4 (2.98-
43.83)		 18.2 (3.37-
98.44)
 45-64 3.2 (1.97-5.06) 4.9 (2.23-10.83) 6.3 (3.50-11.42) 6.1 (2.65-13.94) 10.8 (3.14-37.44) 4.9 (1.59-15.23) 6.6 (1.65-26.15) 7.4 (1.38-39.00)
 65+ 1.0 (referent) 1.0 (referent) 1.0 (referent) 1.0 (referent) 1.0 (referent) 1.0 (referent) 1.0 (referent) 1.0 (referent)
Race or ethnicity
 Non-Hispanic White 1.0 (referent) 1.0 (referent) 1.0 (referent) 1.0 (referent) 1.0 (referent) 1.0 (referent) 1.0 (referent) 1.0 (referent)
 Non-Hispanic Black 0.6 (0.43-0.82) 0.6 (0.46-0.83) 1.2 (0.87-1.60) 0.9 (0.64-1.18) 1.0 (0.61-1.72) 0.7 (0.42-1.09) 0.9 (0.56-1.56) 0.8 (0.42-1.61)
 Native American 0.9 (0.43-1.73) 0.7 (0.25-1.73) 1.4 (0.64-2.99) 1.4 (0.41-5.05) 0.5 (0.12-2.01) 0.7 (0.19-2.25) 2.3 (0.82-6.53) 0.6 (0.17-2.28)
 Asian/
  Pacific Islander		 0.3 (0.12-0.56) 0.4 (0.12-1.38) 0.6 (0.32-1.20) 0.6 (0.21-1.64) 0.7 (0.28-1.94) 1.0 (0.42-2.51) 1.2 (0.35-3.76) 0.9 (0.14-6.22)
 Hispanic 0.7 (0.49-0.87) 0.4 (0.30-0.65) 1.2 (0.88-1.58) 0.5 (0.30-0.82) 0.6 (0.37-0.94) 0.5 (0.27-1.01) 0.6 (0.32-1.11) 0.6 (0.26-1.15)
Marital status
 Married/cohabiting 1.0 (referent) 1.0 (referent) 1.0 (referent) 1.0 (referent) 1.0 (referent) 1.0 (referent) 1.00 (referent) 1.0 (referent)
 Separated/divorced/
  widowed		 1.6 (1.17-2.08) 1.5 (1.06-2.14) 1.5 (1.06-2.08) 1.4 (0.85-2.15) 1.9 (1.11-3.41) 1.8 (1.07-3.13) 2.6 (1.31-5.01) 3.2 (1.74-5.71)
 Never married 1.8 (1.42-2.27) 2.0 (1.45-2.65) 1.6 (1.23-2.20) 2.2 (1.59-2.99) 2.1 (1.37-3.27) 1.6 (1.08-2.42) 2.3 (1.23-4.20) 1.6 (0.84-2.99)
Education
 < High school 1.1 (0.83-1.51) 0.6 (0.42-0.92) 1.2 (0.87-1.68) 1.0 (0.65-1.55) 1.2 (0.72-1.88) 1.0 (0.52-1.84) 1.5 (0.85-2.50) 0.9 (0.41-2.04)
 High school 1.2 (0.93-1.43) 1.2 (0.88-1.54) 1.2 (0.92-1.55) 1.0 (0.74-1.39) 0.9 (0.59-1.22) 1.3 (0.86-1.84) 1.0 (0.58-1.55) 2.0 (1.28-3.13)
 Postsecondary 1.0 (referent) 1.0 (referent) 1.0 (referent) 1.0 (referent) 1.0 (referent) 1.0 (referent) 1.0 (referent) 1.0 (referent)
Past-year personal
income
 < $20,000 0.9 (0.64-1.15) 0.9 (0.64-1.19) 1.7 (1.32-2.30) 1.3 (0.88-1.85) 2.4 (1.52-3.89) 1.2 (0.73-1.91) 3.1 (1.71-5.50) 1.2 (0.58-2.28)
 $20,000-34,999 0.7 (0.54-0.92) 1.2 (0.88-1.65) 1.2 (0.93-1.64) 1.5 (1.02-2.15) 1.5 (0.97-2.39) 0.8 (0.40-1.53) 1.4 (0.74-2.62) 0.7 (0.23-1.94)
 $35,000+ 1.0 (referent) 1.0 (referent) 1.0 (referent) 1.0 (referent) 1.0 (referent) 1.0 (referent) 1.0 (referent) 1.0 (referent)
Census region of
residence
 Northeast 0.9 (0.63-1.29) 0.8 (0.50-1.16) 0.8 (0.53-1.12) 0.7 (0.50-1.10) 0.8 (0.51-1.26) 0.8 (0.51-1.36) 0.5 (0.22-0.95) 1.2 (0.52-2.64)
 Midwest 1.1 (0.80-1.45) 1.3 (0.91-1.81) 0.8 (0.54-1.04) 1.0 (0.70-1.45) 0.7 (0.36-1.29) 0.8 (0.45-1.25) 0.4 (0.21-0.78) 0.6 (0.26-1.33)
 South 1.0 (0.73-1.33) 0.9 (0.67-1.24) 0.9 (0.67-1.24) 0.9 (0.62-1.33) 0.8 (0.52-1.21) 1.1 (0.64-1.75) 0.5 (0.27-0.86) 1.2 (0.59-2.61)
 West 1.0 (referent) 1.0 (referent) 1.00 (referent) 1.00 (referent) 1.0 (referent) 1.0 (referent) 1.0 (referent) 1.0 (referent)
Urbanicity of
residence
 Urban 0.9 (0.71-1.22) 1.2 (0.93-1.65) 1.2 (0.87-1.60) 1.4 (0.94-2.14) 1.3 (0.84-2.10) 1.3 (0.78-2.12) 1.3 (0.76-2.21) 1.0 (0.48-1.88)
 Rural 1.0 (referent) 1.0 (referent) 1.0 (referent) 1.0 (referent) 1.0 (referent) 1.0 (referent) 1.0 (referent) 1.0 (referent)
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1

Odds ratios associated with statistically significant (p < 0.05) sex × predictor interaction terms in the total sample are italicized and those that are also themselves statistically significant are additionally presented in bold font.

2

The hierarchical preemption of incident abuse by previous dependence was suspended in this study.

Adjusted ORs for prediction of AUDs by most psychiatric disorders did not differ by sex (Table 3). A significant sex × bipolar I interaction identified reduced odds of abuse for men and no association for women. Though significant in both sexes, significantly greater ORs were observed among women than men for prediction of both AUDs by nicotine dependence. A significant sex × generalized anxiety disorder (GAD) interaction identified reduced odds of dependence for men and no association for women.

Table 3. Odds Ratios (95% Confidence Intervals) for 3-Year Incidence of DSM-IV Alcohol and Drug Use Disorders1 by Wave 1 Lifetime Psychiatric Diagnoses among Male and Female NESARC Respondents Adjusted for Sociodemographic Characteristics and Additional Baseline Psychiatric Comorbidity.

Baseline Disorder Alcohol Abuse Alcohol Dependence Any Drug Abuse Any Drug Dependence
Men (n at
risk=8653)		 Women (n at
risk=16,626)		 Men (n at
risk=12,155)		 Women (n at
risk=18,541)		 Men (n at
risk=12,575)		 Women (n at
risk=18,702)		 Men (n at
risk=14,091)		 Women (n
at risk=19,711)
Any mood disorder 0.5 (0.34-0.73) 1.0 (0.76-1.35) 0.8 (0.57-1.07) 1.1 (0.78-1.61) 0.9 (0.61-1.31) 1.7 (1.23-2.33) 0.9 (0.51-1.50) 1.5 (0.87-2.58)
 Major depressive
  disorder		 0.7 (0.46-1.06) 1.2 (0.85-1.60) 0.9 (0.63-1.33) 1.2 (0.84-1.75) 1.1 (0.68-1.85) 1.7 (1.19-2.45) 1.1 (0.61-1.92) 1.5 (0.87-2.74)
 Dysthymia 0.5 (0.23-1.01) 1.3 (0.69-2.28) 0.4 (0.18-1.05) 0.9 (0.43-1.74) 2.2 (0.96-5.18) 1.5 (0.74-2.95) 1.3 (0.24-7.36) 0.9 (0.29-2.51)
 Bipolar I 0.5 (0.21-0.94) 1.0 (0.60-1.69) 0.7 (0.36-1.27) 1.1 (0.52-2.15) 0.6 (0.28-1.42) 1.0 (0.57-1.91) 0.5 (0.23-0.87) 0.7 (0.33-1.54)
 Bipolar II 0.9 (0.36-2.01) 0.3 (0.09-1.04) 1.3 (0.58-2.95) 0.7 (0.24-1.80) 0.2 (0.03-0.75) 0.9 (0.28-2.84) 1.3 (0.49-3.40) 0.7 (0.21-2.41)
Any anxiety disorder 0.9 (0.64-1.19) 0.9 (0.70-1.22) 0.9 (0.62-1.22) 1.5 (1.09-1.99) 1.0 (0.65-1.62) 1.0 (0.64-1.47) 1.5 (0.86-2.43) 1.1 (0.64-1.80)
 Panic disorder with or
  without agoraphobia		 1.2 (0.60-2.51) 0.7 (0.39-1.19) 1.6 (0.91-2.96) 0.7 (0.33-1.27) 1.5 (0.71-3.25) 1.0 (0.58-1.56) 2.2 (0.94-4.97) 1.2 (0.70-1.92)
 Social phobia 0.5 (0.27-0.93) 0.6 (0.31-1.06) 0.6 (0.35-1.09) 0.8 (0.46-1.40) 0.3 (0.08-1.20) 1.0 (0.56-1.63) 1.0 (0.44-2.10) 1.2 (0.72-2.07)
 Specific phobia 0.7 (0.44-1.08) 0.9 (0.59-1.30) 0.7 (0.43-1.21) 1.1 (0.70-1.66) 0.4 (0.18-0.83) 0.8 (0.44-1.53) 0.6 (0.27-1.25) 0.7 (0.29-1.87)
 Generalized anxiety
  Disorder		 1.6 (0.93-2.91) 1.0 (0.62-1.71) 0.5 (0.22-0.93) 1.1 (0.62-1.88) 1.4 (0.58-3.20) 1.0 (0.54-1.68) 1.3 (0.50-3.28) 0.8 (0.34-1.95)
 Posttraumatic stress
  disorder2 		1.1 (0.64-1.91) 1.4 (1.00-2.09) 1.2 (0.77-1.97) 2.0 (1.37-3.02) 3.3 (1.76-6.09) 2.4 (1.46-3.90) 3.0 (1.74-5.31) 2.2 (1.10-4.47)
Alcohol abuse1 2.0 (1.50-2.61) 2.1 (1.50-3.02) 1.7 (1.55-2.58) 1.2 (0.70-2.02 1.1 (0.68-1.88) 0.8 (0.34-1.65)
Alcohol dependence1 2.7 (1.80-4.01) 3.5 (2.17-5.74) 1.5 (1.00-2.24) 1.6 (0.91-2.76) 1.5 (0.85-2.66) 1.2 (0.57-2.43)
Any drug abuse1 1.6 (1.05-2.37) 1.5 (0.87-2.44) 1.2 (0.84-1.63) 1.2 (0.74-1.84) 3.7 (2.04-6.74) 2.1 (1.05-4.31)
Any drug dependence1 1.5 (0.59-3.65) 1.7 (0.64-4.71) 0.9 (0.45-1.58) 1.3 (0.56-2.86) — 3 — 3
Nicotine dependence 1.4 (1.01-1.91) 2.1 (1.53-2.79) 1.5 (1.07-2.02) 2.3 (1.60-3.19) 1.5 (1.01-2.33) 1.7 (1.14-2.59) 1.1 (0.67-1.92) 1.9 (1.00-3.63)
Attention-deficit/
 hyperactivity disorder2 		1.2 (0.69-1.99) 1.1 (0.54-2.31) 2.0 (1.24-3.12) 1.0 (0.42-2.23) 2.2 (1.35-3.51) 1.5 (0.68-3.13) 2.3 (1.29-3.93) 1.3 (0.51-3.43)
Any personality
 disorder		 1.7 (1.32-2.18) 1.7 (1.25-2.29) 2.4 (1.84-3.17) 1.9 (1.34-2.70) 2.5 (1.80-3.52) 3.2 (2.22-4.67) 3.6 (2.23-5.69) 8.1 (4.38-14.80)
 Paranoid 1.1 (0.68-1.82) 0.6 (0.28-1.08) 1.0 (0.60-1.77) 0.7 (0.40-1.15) 0.5 (0.21-1.36) 0.7 (0.36-1.34) 0.5 (0.24-0.93) 0.9 (0.42-2.05)
 Schizoid 0.8 (0.40-1.46) 0.6 (0.28-1.29) 1.3 (0.71-2.21) 0.6 (0.28-1.36) 0.5 (0.16-1.61) 1.8 (1.00-3.22) 1.4 (0.59-3.22) 2.2 (1.18-3.96)
 Schizotypal2 1.2 (0.71-1.93) 1.4 (0.78-2.41) 1.5 (0.97-2.27) 2.3 (1.35-3.86) 1.6 (0.92-2.66) 4.4 (2.56-7.59) 3.1 (1.81-5.22) 7.1 (3.20-15.54)
 Histrionic 0.7 (0.33-1.64) 1.5 (0.75-3.07) 0.7 (0.36-1.29) 0.9 (0.38-2.12) 0.6 (0.25-1.34) 0.6 (0.22-1.54) 0.6 (0.26-1.42) 0.9 (0.22-3.25)
 Narcissistic2 1.6 (1.06-2.28) 1.7 (1.03-2.95) 1.7 (1.17-2.34) 1.7 (0.99-2.93) 2.0 (1.22-3.14) 2.2 (1.29-3.72) 3.6 (2.27-5.84) 1.9 (1.01-3.67)
 Antisocial 1.5 (0.84-2.72) 0.9 (0.34-2.20) 1.0 (0.65-1.64) 0.4 (0.15-0.87) 1.4 (0.79-2.42) 1.1 (0.44-2.57) 1.3 (0.72-2.37) 1.4 (0.57-3.33)
 Borderline2 2.5 (1.73-3.69) 1.9 (1.23-2.91) 4.0 (2.77-5.79) 3.9 (2.55-5.92) 3.8 (2.41-6.05) 7.0 (4.61-10.72) 3.5 (1.95-6.10) 7.9 (4.15-15.17)
 Avoidant 0.7 (0.30-1.74) 0.8 (0.41-1.44) 0.7 (0.24-2.21) 1.0 (0.50-1.90) 0.3 (0.27-0.99) 1.2 (0.54-2.47) 1.0 (0.35-2.59) 1.8 (0.73-4.17)
 Dependent — 4 — 4 — 4 — 4 — 4 — 4 — 4 — 4
 Obsessive-compulsive 0.8 (0.48-1.18) 0.8 (0.48-1.24) 0.9 (0.58-1.28) 0.7 (0.42-1.19) 0.4 (0.18-0.76) 0.9 (0.56-1.54) 0.4 (0.17-0.83) 1.2 (0.64-2.24)
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Odds ratios associated with statistically significant (p < 0.05) sex × predictor interaction terms in the total sample are italicized and those that are also themselves statistically significant are additionally presented in bold font.

1

The hierarchical preemption of incident abuse by previous dependence was suspended in this study.

2

These disorders were queried on a lifetime basis at Wave 2.

3

Odds ratios not computed because no men with Wave 1 lifetime drug dependence developed incident drug abuse over follow-up.

4

Odds ratios not computed because low prevalence of this disorder, particularly in men, contributed to too many zero cells among covariates.

Drug Use Disorders

Three-year incidence ± SE of any drug abuse was 2.24% ± 0.19 among men and 1.22% ± 0.11 among women, chi-square(1)= 19.57, p < .0001; of any drug dependence, 1.16% ± 0.13 and 0.58%±0.09, respectively, chi-square(1)= 12.02, p=.0005. Similar to findings for AUDs, higher rates were observed among men in all sociodemographic subgroups (data available upon request); however, ORs for sociodemographic predictors of DUDs did not differ by sex (Table 2).

Again similar to findings for AUDs, there were few sex differences in adjusted ORs for psychiatric predictors of DUDs (Table 3). Interactions with sex were noted for prediction of abuse by schizotypal, borderline, avoidant, and obsessive-compulsive PDs. Schizotypal PD positively predicted abuse in women but not men. Borderline PD positively predicted abuse in both sexes, but more strongly in women; avoidant and obsessive-compulsive PDs negatively predicted abuse in men but not women.

The only sex difference in prediction of dependence was observed with obsessive-compulsive PD: reduced odds in men; no association in women.

Discussion

Consistent with our previous findings on lifetime prevalences and psychiatric comorbidity of AUDs and DUDs (Goldstein, Dawson, Chou, & Grant, 2012), incidence rates were higher in men, but there were few significant sex differences in predictors. Predictors could operate similarly despite differential prevalences, whether singly or in joint distributions, between men and women (cf. Huang et al., 2006). The only significant sex difference in sociodemographic predictors, reduced odds among Hispanic women but not Hispanic men, versus non-Hispanic whites, for alcohol dependence, may reflect gendered norms regarding acceptability of alcohol use and associated behaviors (e.g., Zemore, 2007). Such norms may either protect against dependence, or reduce affected women’s willingness to report symptoms.

Significantly larger ORs were observed in women than men for prediction of AUDs but not DUDs by nicotine dependence. Although part of the externalizing spectrum, nicotine dependence is less informative about broader externalizing liability and less strongly related to the underlying externalizing dimension than antisocial PD and dependence on other substances (Markon & Krueger, 2005). Predictive relationships may also have been subject to “ceiling effects” reflecting comparatively high baseline prevalence of nicotine dependence (men: 20.0%; women: 15.6%).

Borderline PD predicted drug abuse significantly more strongly among women than men. This PD loads on both the internalizing subfactor of distress and the externalizing factor of psychopathology (N. R. Eaton et al., 2011). Previous studies have located DUDs at a more severe point than AUDs along the externalizing spectrum (Carragher et al., 2014; Kendler, Prescott, Myers, & Neale, 2003; Krueger, Markon, Patrick, & Iacono, 2005), and prevalences of most externalizing disorders are lower among women than men (Grant & Weissman, 2007). The stronger predictive relationships of borderline PD to incident DUDs among women, despite lack of sex differences in borderline PD prevalence (Grant et al., 2008), may thus reflect higher concentrations of externalizing liability among women than men with borderline PD, and women’s greater vulnerability to more severe externalizing pathology.

ORs for schizotypal PD and drug abuse were also higher in women. Previous studies identified relationships between use and disorders associated with cannabis, the most commonly used drug in the NESARC sample, and schizophrenia spectrum disorders, including schizotypal PD (Davis, M. T. Compton, Wang, Levin, & Blanco, 2013; Di Forti, Morrison, Butt, & Murray, 2007; Schiffman, Nakamura, Earleywine, & LaBrie, 2005; Stefanis et al., 2014). However, the directionality of those relationships could not be determined because the studies were cross-sectional. Respondents with schizotypal PD may have been more likely to use drugs before W1, becoming diagnosable with a DUD only during follow-up. To our knowledge, no studies have identified plausible explanations for the sex differences we observed in predictive relationships.

Finally, avoidant PD negatively predicted drug abuse and obsessive-compulsive PD negatively predicted both DUDs in men but not women. With lower prevalence in men (Grant, Hasin, Stinson, et al., 2004), essential features including hypersensitivity to negative evaluation, and correlates including high harm avoidance (Joyce et al., 2003), avoidant PD may protect men from socially disvalued behaviors like problematic drug use, for which they are otherwise at greater risk than women. Similarly, while prevalence of obsessive-compulsive PD does not differ by sex (Grant, Hasin, Stinson, et al., 2004), its essential features including scrupulosity, overconscientiousness, and inflexibility about morality and values may deter DUD development more strongly among men.

Study limitations include its reliance on self-reports. Collateral data sources are particularly important for PD assessment to mitigate potential distortions in respondents’ self-appraisals, including lack of insight into the effects of symptomatic behaviors on role functioning (Clark, 2007; Pedersen, Karterud, Hummelen, & Wilberg, 2013; Zimmerman, 1994). Additionally, the 3-year follow-up yielded relatively few incident cases of AUDs and DUDs, particularly among women.

The NESARC sample was limited by design to general population U.S. adults. Therefore, the applicability of these findings to other populations, and to individuals < 18 years old at baseline in the general U.S. population, is unclear. Moreover, respondents youngest at W2 were 20 years old, beyond the ages of peak hazards for AUDs and DUDs (Compton et al., 2007; Hasin et al., 2007). That relationships between specific baseline disorders and incident AUDs and DUDs may vary across developmental phases (Grant et al., 2009), including by sex, could explain our unexpected findings of reduced risks of incident alcohol abuse among men with bipolar I; of alcohol dependence among men with GAD; of drug abuse among men with avoidant and obsessive-compulsive PDs; and of drug dependence among men with obsessive-compulsive PD. Future longitudinal studies should involve longer follow-up periods, consider including institutional subsamples, and capture earlier developmental phases.

Ideally, all diagnostic predictors would have been assessed at W1, but respondent and interviewer burden made it infeasible. Mitigating this concern for PDs, respondents were explicitly queried about symptoms occurring most of the time, throughout their lives, regardless of the situation or whom they were with (Grant, Hasin, Stinson, et al., 2004). Respondents with each PD were also more impaired than those with no PD on the Mental Component Summary score of the Short Form 12-Item Health Survey, version 2 (Gandek et al., 1998), and more often reported stressful life events such as relationship breakups and financial, interpersonal, or employment problems, at each wave, regardless of when specific PDs were assessed (Skodol et al., 2011).

The tendency of respondents to recall and report onsets as more recent than they actually were for Axis I disorders (Prusoff, Merikangas, & Weissman, 1988), substance use (Johnson & Schultz, 2005), and medical conditions (Raphael & Marbach, 1997) may likewise mitigate time-of-assessment concerns for PTSD. While we are unaware of findings documenting forward telescoping specifically in PTSD, its occurrence is plausible given findings on other disorders.

The few sex differences we identified in sociodemographic and diagnostic predictors of AUD and DUD incidence were modest, yielding limited implications for sex-specific targeting of prevention and early identification efforts. Nevertheless, AUDs and DUDs and their predictors confer substantial burdens on affected individuals, their social networks, and health, social service, and correctional systems (Brown, 2010; Sirotich, 2009; Whiteford et al., 2013). Treatment utilization for AUDs and DUDs is low, despite the availability of a growing range of empirically supported therapies (Compton et al., 2007; Hasin et al., 2007). Despite the lack of strong sex-specific signals, this study’s findings, together with those of previous prospective studies (e.g., Grant et al., 2009; de Graaf et al., 2013; Fergusson, Horwood, & Ridder, 2007) reinforce the need for comprehensive assessment and evidence-based treatment of mental health and substance use disorders in both sexes, regardless of clients’ chief complaints and the clinical settings to which they present.

Prevalences are similar (Dawson, Goldstein, & Grant, 2013; Goldstein et al., 2015) and concordances excellent between DSM-IV dependence and DSM-5 moderate to severe AUDs and DUDs (Compton, Dawson, Goldstein, & Grant, 2013; Goldstein et al., 2015). These findings plus similarity in clinical profiles between alcohol dependence and DSM-5 moderate to severe AUD (Dawson et al., 2013) suggest that predictors may be similar across these disorders, despite increases in sociodemographic and substance use diversity of the population since the W1 and W2 NESARC data were collected. Conversely, divergence of clinical profiles (Dawson et al., 2013) suggests caution in extrapolating from abuse to mild DSM-5 disorders. Prospective research is needed to identify sociodemographic and diagnostic predictors of and mechanisms underlying incident DSM-5 AUDs and DUDs. Whether there are sex differences in predictive relationships across developmental phases, the role of additional comorbidity in etiology and course, perceived need and barriers to treatment for AUDs and DUDs, and outcomes of prevention and treatment also warrants examination.

Acknowledgments

The National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) is funded by the National Institute on Alcohol Abuse and Alcoholism (NIAAA) with supplemental support from the National Institute on Drug Abuse. This research was supported in part by the Intramural Program of the National Institutes of Health, NIAAA. A preliminary version of parts of this paper was presented at the 167th Annual Meeting of the American Psychiatric Association, May, 2014, New York, NY. The authors extend their thanks to S. Patricia Chou, Ph.D., and Tulshi D. Saha, Ph.D., for invaluable assistance with the revision of this manuscript.

Footnotes

The views and opinions expressed in this report are those of the authors and should not be construed to represent the views of sponsoring organizations, agencies, or the U.S. government.

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