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Childhood adverse events and methamphetamine use among men and women.

Social scientists, treatment practitioners, and policy makers have begun to recognize the devastating effects of the methamphetamine (MA) epidemic, which began to dramatically escalate in the 1990s. According to the World Health Organization and the United Nations Office on Drugs and Crime, over 42 million men and women regularly use MA, ranking it as the most widely used illicit drug in the world after cannabis (UNODC 2006). In comparison, cocaine and heroin are used by approximately 15 million people worldwide. MA use has further expanded from a predominantly Caucasian population into Hispanic and Asian populations (Rawson, Gonzales & Ling 2006; Rutkowski 2006; Rawson, Anglin & Ling 2002). Moreover, substantial increases in the number of female MA users have been reported--which has specific implications for individual treatment plans (Hohman, Oliver & Wright 2004; Rawson et al. 2004).

In fact, nearly half of the individuals currently entering state-funded treatment programs for MA dependence are women (SAMHSA 2008; CA ADP 2007; Johnson 2005). Within treatment programs for other drug categories, men typically outnumber women two to one (Straussner & Zelvin 1997; Anglin et al. 1987). A considerable amount of research has outlined complex differences regarding the treatment needs of women, as compared with men. As the number of men and women entering treatment for MA dependence increases across the country, it becomes vitally important to understand the pathways to, and consequences of, MA dependence and how they may differ by gender.

PREDOMINANT GENDER DIFFERENCES

Compared to their male counterparts, drug-dependent women in general are significantly more likely to have extensive histories of sexual and physical abuse in childhood and adulthood, more severe drug abuse histories, and coexisting psychiatric disorders (Messina et al. In press, 2007; Messina & Grella 2006; Langan & Pelissier 2001; Grella & Joshi 1999; Straussner & Zelvin 1997; Stevens & Glider 1994; Anglin, Hser & Booth 1987). A recent study directly examined gender differences between a cross-section of 350 MA users (Brecht et al. 2004). Identified differences between MA-using men and women were consistent with reported findings from larger, more general drug-using populations (e.g., women reported more extensive histories of sexual abuse and men reported more severe criminal histories).

CHILDHOOD ABUSE AND HOUSEHOLD DYSFUNCTION

A small number of studies have examined gender-specific pathways to addiction, particularly with regard to histories of childhood abuse. This small but growing body of research has shown that childhood trauma and abuse (predominantly assessing sexual and physical abuse) may affect the development of the brain and create greater sensitivity to subsequent life stressors, which could influence the development of alcohol and other drug dependence (Cicchetti & Toth 2005; Dube et al. 2003; De Bellis 2002). The relationship between childhood trauma and abuse and later addiction is also explained via stress-coping models. Within this model, growing up in an environment void of good models for adaptive coping strategies with multiple negative life events may place individuals at risk for developing addiction by elevating stress, reducing reinforcement from the social environment, and making the coping functions of alcohol and other drug use appear more attractive--including use as self-medication, or self-destructive impulses stemming from low self-esteem (Widom, Weiler & Cottler 1999; Wills & Hirky 1996).

Empirical evidence has shown that the trauma that results from childhood physical and sexual abuse specifically is a key contributor to alcohol and other drug dependence in adolescence and adulthood (Grella, Stein & Greenwell 2005; Brems et al. 2004; Wilson-Cohn, Strauss & Falkin 2002; Greene, Haney & Hurtado 2000). However, there is a greater need for investigation of the impact of all types of childhood trauma, including emotional abuse and neglect, sexual and physical abuse, and household dysfunction on substance use and the development of dependence. It is also unclear how childhood abuse and household dysfunction may (or may not) affect men and women differently.

Messina and colleagues (2007) recently reported the prevalence of childhood abuse and household dysfunction among predominantly MA-dependent men (n = 427) and women (n = 315) prisoners in California, and assessed the impact of the cumulative exposure of childhood abuse and household dysfunction on adult symptoms of traumatic distress (e.g., emotional, physical, and sexual abuse, parental separation/divorce, family violence, parental substance abuse or histories of imprisonment, and out of home placement). Women offenders reported significantly greater exposure to childhood abuse and household dysfunction than men and more often reported continued sexual abuse in adolescence and as an adult (28% vs. 0.7%). Linear regression results showed the impact of the indicators of childhood abuse on traumatic distress was strong and cumulative for both men and women, as greater exposure to child abuse and household dysfunction increased the likelihood of six out of seven indicators of psychological impairment (i.e., anxiety, depression, dissociation, sexual abuse trauma, sleep disturbance, and overall traumatic distress). However, women had significantly higher levels of psychological impairment overall. The above study did not report the impact of individual types of child abuse and how they may differ for men and women.

Hyman, Garcia, and Sinha (2006) recently conducted a retrospective analysis assessing the relationship between specific indicators of childhood abuse and adult substance abuse among cocaine-dependent treatment-seeking men (n = 55) and women (n = 32). Childhood abuse was associated with younger age of first alcohol use, which in turn was associated with younger age of first cocaine use for both men and women. Greater exposure to childhood abuse was also associated with overall substance abuse severity for both men and women. However, there were gender differences in the specific types of abuse that were related to outcomes and the strength of the associations. Childhood emotional abuse, sexual abuse, and overall maltreatment were all more strongly related to outcomes among the women. It should be noted that this study included a relatively small sample size.

The current study extends this body of literature, assessing the association between childhood abuse and household dysfunction and the onset of MA use and severity of dependence for men and women. It compares and contrasts in-depth baseline and three-year follow-up data from a diverse sample of MA-dependent men and women who participated in a variety of treatment programs in the western United States and Hawaii. We focus on identifying the prevalence of specific types of childhood abuse and household dysfunction and the collective impact of these experiences on the onset of MA use and severity of dependence among men and women. Data were analyzed for men and women separately to assess the predictive value of the selected individual indicators of childhood abuse and household dysfunction specific to gender (e.g., specific indicators of abuse and dysfunction are entered as independent variables to assess the association with earlier MA use or severity of dependence for women versus men). We then pool the data for men and women, and combine the total number of indicators of abuse and dysfunction to assess the overall impact of childhood abuse and household dysfunction.

Based on previous findings of the higher rates of self-reported child abuse among drug-dependent women and men, and the strong graded relationship between collective experiences of childhood abuse and household dysfunction and the development of alcohol and other drug dependence, we formulated three hypotheses:

1. MA-dependent women will report more types of childhood abuse and household dysfunction, compared with MA-dependent men.

2. Exposure to more types of childhood abuse and household dysfunction would be associated with earlier age of MA use.

3. Exposure to more types of childhood abuse and household dysfunction would be associated with greater severity of MA dependence.

We also formulated a research question regarding the predictive value of specific individual indicators of childhood abuse and household dysfunction and MA dependence for men and women, similar to the study by Hyman and colleagues (2006), to extend this limited body of research to MA-dependent men and women.

1. Are some indicators of childhood abuse and household dysfunction more strongly associated with onset and severity of MA dependence for women compared with men?

MATERIALS AND METHODS

Methamphetamine Abuse Treatment Special Studies (MAT-SS)

The data come from Methamphetamine Abuse Treatment Special Studies (MAT-SS), a Center for Substance Abuse Treatment (CSAT) study designed to examine the long-term effects and consequences of MA dependence. This follow-up study was designed to examine the status and functioning of 672 previous Methamphetamine Treatment Project (MTP) participants three years after treatment. The MTP consisted of 974 MA-dependent users who received eight to 16 weeks of outpatient treatment from eight different programs throughout seven geographical locations in Hawaii, Montana, and California between 1999 and 2001. Data included in the current analyses include MTP baseline and MAT-SS year-year follow-up interview information.

A total of 672 MTP participants provided written approval for future study contact, allowing the study team to attempt to recruit these individuals for participation in the current follow-up study. (1) Of these, 587 participated in the psychosocial interview process. As described, this is an 87% follow-up rate. The 85 individuals who provided contact consent but did not participate in the three-year interview were either lost at follow-up (41 were not found); were found, but could not be interviewed (three were deceased, eight had moved out of the country, one was incarcerated and not permitted to participate; 25 did not respond to contact attempts and/or were unable to schedule a convenient interview time); and seven chose not to participate. Analyses were completed to determine any differences between the original MTP sample of 974 and those 672 who subsequently consented and were included in the MATSS follow-up efforts. Results (not shown) indicate that there were no differences in background characteristics between those who consented to future follow-up contacts and those who we were prohibited from contacting. (2)

Participants

Participants in the current study are a subsample (N = 587) of the MTP participants who were contacted for the three-year follow-up interview (see Table 1). Approximately 60% of the follow-up participants were female, 68% were Caucasian, and the average age at baseline was 33 years (range: 18-56). The majority of the participants were high school graduates (76%) and were employed (62% full or part-time) at the time of the baseline interview. Eightyfour percent of the participants were single, divorced or separated, or widowed, and most lived with family and/or friends. A total of 71% of the participants experienced emotional/ physical/sexual abuse while growing up. A detailed description of the participants has been previously reported in an article discussing the larger MTP trial outcomes (Rawson et al. 2004).

Eligibility

Participants were eligible for the MTP study if they met the Diagnostic and Statistical Manual (DSM-IV) criteria for MA dependence, were MA users at baseline (used within the month prior to treatment entry), were English-language proficient, 18 years old or older, a resident of the same geographical location as the treatment facility, and could provide written consent to be contacted for future follow-up interviews. Potential participants were excluded from the study if they had a serious medical or psychiatric health condition that required imminent hospitalization or were pregnant. If participants were diagnosed with a medical/ psychiatric illness or were found to be pregnant, they were referred to other types of treatment services that could meet these more specialized needs.

Data Collection

In-depth interview data at baseline and the three-year follow-up consist of a range of topics on demographic and drug use characteristics, mental and psychological health, criminal justice involvement, family and children, abuse, treatment participation, and involvement in the production and sale of MA. Information was collected in face-to-face meetings and included both psychosocial and medical exam components. Participants could receive up to $200 in gift cards for their participation if all study components were completed. All recruitment and data collection procedures were approved by the respective location's Institutional Review Boards and the UCLA General Campus Institutional Review Board (IRB), and a federal Certificate of Confidentiality was obtained.

Measures

Background characteristics. The Addiction Severity Index (ASI; McLellan et al. 1992) was administered at baseline and the three-year follow-up to assess problem severity in seven areas affected by drug and alcohol misuse: medical, employment, illegal activity, family and social support, psychiatric, and drug and alcohol use. The ASI has been used with diverse populations and has been found to be reliable and valid (McLellan et al. 1992).

The Mini-International Neuropsychiatric Interview (M.I.N.I.; Sheehan et al. 1998) provides a rapid evaluation of psychiatric functioning in both clinical drug trials and routine clinical settings. It was designed as a brief structured interview for the major Axis I psychiatric disorders and one personality disorder in DSM-IV. The M.I.N.I. has high validation and reliability scores, compares favorably to the Structured Clinical Interview for the DSM, and can be administered in a shorter period of time (20 to 25 minutes). The M.I.N.I. was administered at the three-year follow-up to determine psychiatric conditions and symptoms in 16 domains (major depressive episode; dysthymia, suicidality, manic/hypomanic episode; panic disorder, agoraphobia, social phobia, obsessive-compulsive disorder, post-traumatic stress disorder, alcohol abuse and dependence, substance abuse/dependence, psychotic disorders, anorexia nervosa, bulimia nervosa, generalized anxiety disorder, and antisocial personality disorder). (3) The M.I.N.I diagnoses of current depression and/or antisocial personality disorder are included as covariates in the multivariate analyses.

The Life Experiences Timeline (LET) is a modified version of the Natural History Interview (Chou, Hser & Anglin 1996), formatted for electronic data entry (Hillhouse, Marinelli-Casey & Rawson 2005). The instrument uses an event-anchored timeline technique to collect retrospective and prospective data and was administered at the three-year follow-up. Information gathered from the LET includes detailed self-reported history of drug use and crime.

Childhood adverse events (CAEs). The Abuse and Violence Questionnaire was administered at baseline and at the three-year follow-up to assess the occurrence of childhood abuse before the age of 18 years and household dysfunction (while growing up). The Abuse and Violence Questionnaire includes sections from the Women's Interagency HIV Study (WIHS; Cohen et al. 2000), which assesses several types of abuse, physical violence, and domestic violence over the lifespan. Topics covered include forced sexual contact, type and extent of exposure to abuse and violence, and relationship to the abuser. The WIHS was funded by the National Institutes of Health (NIH) Institute on Allergy and Infectious Diseases as the first natural history of HIV infection in women (Cohen et al. 2000). We analyzed six indicators of CAEs from the Abuse and Violence Questionnaire results reported at the three-year follow-up. Cronbach's alpha of the six items of childhood abuse and household dysfunction indicated good reliability, or internal consistency, among the items (.73). (4) The indicators are listed and described below (see Table 2).

Childhood Abuse (Prior to the age of 18):

1. Emotional abuse was defined as being "emotionally abused or neglected (e.g., when the person who is responsible for you ignores you, yells and screams at you, calls you bad names or tells you that you are bad or stupid)."

2. Physical abuse was defined as being "abused or physically attacked (not sexually) by someone you knew who hit you hard enough to cause bleeding, bruising, broken bones, or other injuries, or physically hurt you intentionally."

3. Sexual abuse encompassed "having someone touch you in a private area or [who] makes you touch them in private areas, or has sexual relations with you against your will."

Household Dysfunction (While growing up):

4. Familial arguments was assessed by an item asking, "Did the adults in the household argue with each other often?"

5. Familial substance abuse was assessed by an item asking, "Did any adult in the household use drugs or drink too much?"

6. Witness abuse of other family members was assessed by an item asking, "Did anyone else in your family experience any kind of abuse?"

Data Analyses

Background characteristics, CAEs, and MA-related behaviors are summarized in terms of percentages, means, and standard deviations. Gender differences were analyzed using x2 for categorical variables or t tests for continuous variables. Because previous studies indicate that these events rarely occur in isolation and tend to be highly correlated (Messina & Grella 2006), we also provide the correlation coefficients between the various CAEs for men and women. Fisher's z transformations were then computed to determine if the correlation coefficients for women were significantly different from those for men. The gender differences discussed in the text are for P values [less than or equal to] .05. A collective abuse/dysfunction score was calculated by summing the number of abuse and household dysfunction items that each subject endorsed as happening at least once during childhood.

Linear regression analyses were then conducted separately for men and women for each of the dependent variables (i.e., age of onset of MA use and severity of MA use). Age of first MA use (i.e., onset) was a continuous variable ranging from age eight to 46 and the ASI Drug Composite score (i.e., severity) was also defined as a continuous variable. The initial regression models included the six individual indicators of CAEs to assess the predictive value relative to men and women. Models also included demographic variables and other correlates that were significantly related to the above outcomes (i.e., age, race, education, marital status, and the M.I.N.I. diagnosis of antisocial personality disorder or current depression). Additional regression models were then conducted assessing the overall impact of multiple indicators of CAEs (0-6) to the onset of MA use and severity of dependence (pooling the data for men and women).

Multiple imputation techniques were used to control for the occurrence of missing data among certain variables (Rubin 2004; Schafer 1997). Analyses were conducted separately among the datasets for which missing data for the independent variables were imputed using Monte Carlo Markov Chain methods. The estimates were then combined, taking into account the variance across imputations as well as within imputations.

RESULTS

Sample Characteristics

Table 1 displays the background characteristics at baseline and outlines the demographic differences between men (n = 236) and women (n = 351). MA-dependent women were significantly more likely than their male counterparts to describe themselves as Asian, Pacific Islander, Alaskan, or American Indian (20% vs. 9%, [chi square] = 13.1, p < .001), and were younger at age of admission (33 vs. 34, t = 2.3, p < .02). Compared with men, women were also more likely to have less than a high school education (22% vs. 15%, [chi square] = 4.4, p < .05), were more likely to be unemployed at treatment admission (30% vs. 11%, [chi square] = 30.5, p < .001), and were more likely to be a parent of a minor child (73% vs. 60%, [chi square] = 4.5, p < .05). No gender differences were found in marital status at baseline.

MA Use and Psychological Status at Treatment Admission

Women had a higher mean ASI Drug Composite score (.227 vs. .203, t test = 2.9, p < .01) and reported significantly more days of MA use in the 30 days prior to treatment admission (12.9 days vs. 10.6 days, t test = 2.87, p < .01) and reported smoking MA (vs. snorting or injecting) as the primary route of administration (66% vs. 58%, [chi square] = 4.6, p < .05). Compared with men, women were also more likely to report having previously attempted suicide (30% vs. 14%, [chi square] = 19.5, p < .001), and to have a current diagnosis of depression (18% vs. 12%, [chi square] =3.9, p < .05). No differences were found between men and women in diagnosis of antisocial personality disorder or age of first MA use.

Childhood Abuse and Household Dysfunction

Table 2 displays the prevalence of childhood abuse and household dysfunction by gender. In support of our first hypothesis, women were significantly more likely than men to report the occurrence of every indicator of childhood abuse and household dysfunction. Specifically, women more often reported emotional abuse prior to the age of 18 (55% vs. 37%, [chi square] =18.59, p < .001), were nearly twice as likely as men to report physical abuse prior to the age of 18 (42% vs. 27%, [chi square] =12.87, p < .001), and four times more likely than men to report sexual abuse prior to the age of 18 (42% vs. 11%, [chi square] =63.70, p < .001). Women were also more likely than men to report familial arguments (40% vs. 26%, [chi square] =11.88, p < .001), familial substance abuse while growing up (63% vs. 50%, [chi square] =9.81, p < .002), and witnessing familial violence to other family members (55% vs. 39%, [chi square] =13.74, p < .01).

Women were also twice as likely as men to report experiencing multiple types of abuse and household dysfunction (8.8% of the women reported experiencing six types of CAEs vs. 2.7 % of the men, [chi square] =8.5, p = .004). The collective abuse/dysfunction score was also related to psychological impairment for both men and women. Among men, a diagnosis of antisocial personality disorder was associated with a greater occurrence of indicators of childhood abuse and household dysfunction, compared to men with no antisocial personality disorder (mean CAEs = 2.6, SD 1.9 vs. mean CAEs = 1.6, SD 1.9, p < .001); however, a diagnosis of depression among the men was not significantly associated with CAEs (p < .06). Among women, a diagnosis of antisocial personality disorder was also associated with greater occurrence of indicators of childhood abuse and household dysfunction compared to women with no antisocial personality disorder (mean CAEs = 3.8, SD 1.5 vs. mean CAEs = 2.6, SD 1.9, p < .001). In addition, a diagnosis of depression among the women was also associated with a greater occurrence of indicators of childhood abuse and household dysfunction compared with those with no depression (mean CAEs = 3.6, SD 1.7 vs. mean CAEs = 2.8, SD 2.9, p < .003).

Table 3 shows the correlations between categories of CAEs for men and women (correlation coefficients for women are shown on the top right diagonal of the table and coefficients for men are shown on the bottom left diagonal of the table). Previous studies have shown that the separate categories of CAEs tend to be highly correlated. As expected, the relationship between the categories of CAEs was significant for nearly all of the comparisons among the women and the majority of comparisons among the men. However, z transformations revealed that women yielded significantly higher correlations among CAEs than men (p < .05). The correlation coefficients that were higher for men were not significantly different from those for the women.

Multivariate Analyses

Linear regression models were conducted separately for men and women assessing the predictive value of specific indicators of CAEs to onset of MA use (i.e., age of first MA use), while controlling for demographic characteristics (i.e., age, race, education, marital status) and diagnoses of current depression and antisocial personality disorder, as these factors were associated with the dependent variables. Due to the high degree of multi-colinearity between emotional abuse and sexual abuse, regression models differed slightly for men and women (i.e., emotional abuse was included in the regression model for men and sexual abuse was included in the regression model for women).

Age of Onset of MA Use

Men. Familial substance abuse ([beta] = -1.66, SE .84, p = .05) was significantly associated with younger age of first MA use for men. A diagnosis of antisocial personality disorder was also significantly related to younger age of first MA use ([beta] = -3.19, SE 1.02, p < .001). Age at treatment admission was positively related to onset of MA use ([beta] = -3.29, SE 1.19, p < .004). Education (high-school and beyond) was significantly related to later age of onset ([beta] = 2.34, SE 1.14, p < .04) A current diagnosis of depression as well as race, marital status, and the remaining indicators of childhood abuse and household dysfunction were not significantly associated with the age of onset of MA use for men.

Women. None of the individual types of childhood abuse and household dysfunction were significantly associated with the age of onset of MA use for women; however, familial substance abuse approached significance (p = .09). A diagnosis of antisocial personality disorder ([beta] =-3.28, SE .84, p < .001); being Caucasian ([beta] = -2.89, SE .72, p < .001); and age at treatment admission ([beta] = -5.53, SE .85, p < .001); were significantly related to earlier age of first MA use. A current diagnosis of depression, education, and marital status were also not related to the onset of MA use for women.

The findings indicate that individual indicators of abuse and household dysfunction are only mildly predictive of onset of MA use, with familial substance abuse being the only individual indicator predictive of onset--and only for men.

Pooled sample. A third regression was conducted to assess the collective effect of the indicators of childhood abuse and household dysfunction for pooled sample of men and women (see Table 4). There was a significant effect for the collective abuse/dysfunction score (p = .05). In addition, being female was significantly related to onset of MA use, with women being older than men at age of onset (p < .002). Age, being Caucasian, and antisocial personality disorder also remained significantly related to earlier onset of MA use (p < .001). The results provide support for our second hypothesis, as exposure to multiple indicators of childhood abuse and household dysfunction was significantly related to earlier onset of MA use.

Severity of Dependence

Linear regression models were conducted separately for men and women assessing the predictive value of specific indicators of childhood abuse and household dysfunction on the severity of MA dependence (i.e., ASI Drug Composite), while controlling for demographic characteristics, psychological status, and age of first use.

Men. Familial substance abuse was very nearly significantly associated with severity of dependence (p = .06). None of the other individual indicators of childhood abuse and household dysfunction were associated with severity of dependence among the men. Education was also significantly related to severity of dependence ([beta] =.05, SE .02, p < .02). A diagnosis of depression or antisocial personality disorder, age, race, and marital status were not related to the severity of dependence for men.

Women. Familial substance abuse was significantly associated with severity of dependence ([beta] =.03, SE .01, p = .05). None of the other individual indicators of childhood abuse and household dysfunction were associated with severity of MA use among the women. In addition, none of the demographic characteristics were related to severity of dependence.

The findings indicate that individual indicators of abuse and household dysfunction are only mildly predictive of severity of dependence, again with familial substance abuse being the only individual indicator predictive of severity of dependence--and only for women.

Pooled Sample. There was a significant main effect for overall collective exposure to childhood abuse and household dysfunction and severity of dependence (p < .03). The results provide support for the third hypothesis (see Table 5). In addition, depression, being Caucasian, being female, and being a high school graduate all approached significance (p < .07). Marital status, antisocial personality disorder, and age were not significantly related to severity of dependence.

DISCUSSION

The current study focused on identifying histories of childhood adverse events, organized under two categories--childhood abuse and household dysfunction--and the subsequent impact on the onset of MA use and severity of dependence among a diverse sample of MA-dependent men and women. Consistent with findings from previous studies among substance-dependent samples, women reported twice the amount of childhood abuse and household dysfunction compared to their male counterparts. Further, there were differential associations among the types of abuse and household dysfunction for men and women, suggesting different areas of vulnerability for childhood traumatic exposure. Additional analyses also revealed that higher prevalence of exposure to multiple types of CAEs was significantly associated with later Axis I and Axis II diagnoses for both men and women (specifically antisocial personality disorder among the men and both antisocial personality disorder and depression among the women).

We initially posed a research question regarding the predictive value of the six individual indicators of childhood abuse and household dysfunction. Separate analyses were conducted for men and women in order to examine whether specific types of CAEs (e.g., sexual abuse vs. familial substance abuse) affected the onset of MA use and severity of dependence differently relative to gender. One of the most important findings from this study was that familial substance abuse is the primary individual indicator of earlier MA use for men and severity of dependence for women. There is an extensive body of literature discussing the intergenerational cycle of abuse and dysfunction, including addiction; however, the finding that this factor provided the most predictive value, even after including other indicators of severe childhood abuse (sexual, physical, emotional abuse, etc.), is very informative with regard to implications for prevention and treatment (i.e., early interventions and familial participation in recovery).

The gender-specific regression results showed very little with regard to the predictive value of the other individual indicators of CAEs and the dependent variables. This is an unexpected finding, as it contrasts with our finding that women endorsed each separate indicator of childhood abuse and household dysfunction more frequently than the men. We would speculate that greater exposure to individual indicators of CAEs would result in significant associations with long-term behaviors including onset and severity of addiction, as found by Hyman and colleagues (2006). It is possible that the high degree of correlation between the indicators of CAEs created difficulty in finding independent associations with regard to onset of MA use and severity of dependence. Previous studies assessing similar indicators of abuse have also shown a high degree of correlation between the types of abuse and dysfunction. This finding suggests that specific types of child abuse rarely occur in isolation, and thus the impact of a collective childhood abuse/dysfunction score may be more predictive of adult problems.

In fact, our pooled regression results support this contention, as the overall impact of the collective forms of CAEs was significantly associated with the onset of MA use and severity of dependence. In addition, greater prevalence of CAEs was also associated with a diagnosis of antisocial personality disorder for both men and women, which in turn was associated with earlier age of onset of MA use. It is possible that other psychiatric diagnoses are a mediating factor in the onset of MA use and/or severity of dependence among those with histories of childhood trauma.

The different pathways and patterns of addiction for men and women, and the availability of gender-specific services, are all considered to be directly related to the likelihood of treatment entry and recovery. With increasing MA use across the nation, and increasing proportions of female MA users, the ecology of use may be changing. As experts have generally demonstrated positive treatment outcomes for MA users compared with nontreated groups of users (Hillhouse et al. 2007; Brecht et al. 2006; Rawson, Gonzales & Ling 2006; Rawson et al. 2004), it becomes increasingly important to understand the needs of MA-dependent women as compared to MA-dependent men.

Study Limitations

First, our measures of childhood abuse and household dysfunction and onset of MA use and severity of dependence were based on self-report. Thus, we were unable to validate these responses with objective measures. However, Cronbach's alpha of the six items of childhood abuse and household dysfunction indicated good reliability among the items.

Second, the Abuse and Violence Questionnaire relies on retrospective data collection, which can be confounded by many factors (e.g., underreporting based on concerns of stigmatization or overreporting due to recent experiences of violence; also, recall may be influenced by current mental health status). Responses to the questions reflected the respondent's interpretation of the questions, including questions regarding physical and sexual assault. However, the questions regarding such abuse and household dysfunction on the Abuse and Violence Questionnaire are very specific to avoid confusion. It is also important to consider that there may be gender differences in the willingness to self-disclose certain types of childhood abuse, with, for example, men possibly being less likely to disclose sexual abuse, which could have influenced the observed findings (Holmes & Slap 1998).

Third, the dichotomous nature of the questions (abuse happened or not) on the Abuse and Violence Questionnaire do not lend themselves to measuring the repeated incidence of CAEs--i.e., the number of times a particular form of abuse took place during childhood (e.g., years of physical and sexual abuse versus a single occurrence). The experience of ongoing and repeated trauma and abuse may be much different than the experience of a single act of abuse. Thus, the Abuse and Violence Questionnaire creates a "conservative" bias regarding the long-term circumstances of childhood abuse and household dysfunction. In addition, complex constructs of abuse and trauma are not easily reduced to single items, as measured here. Previous literature consistently reports that single items constructs underestimate prevalence of abuse and trauma. The weakness of the available measures may account in part for why these independent variables were not more strongly associated with the dependent variables.

Fourth, we had a relatively small sample size. Multivariate analyses sometimes resulted in small cell sizes, which could have created an inability to detect significant differences.

Conclusion

Histories of childhood abuse and household dysfunction are commonly reported by drug-dependent men and women in general, and to a much higher degree than those in the general population. However, the gender differences found among drug-dependent men and women are replicated here among MA-dependent men and women, as women more often report greater exposure to each type of childhood abuse and household dysfunction. With the rising number of MA-dependent men and women with co-occurring psychiatric diagnoses entering treatment, and the higher risk of relapse for those with co-occurring disorders, it is important to understand and address the potential effects of abuse throughout adulthood for both men and women. Most importantly, the finding that the familial substance abuse is a strong predictor of onset of MA use, and severity of dependence, indicates the need for early prevention and intervention strategies to break the repeatedly demonstrated intergenerational cycle of addiction. Since California MA users constitute the largest share of the state's treatment admissions and MA treatment admissions are increasing in many other states, the issues addressed by this study are salient.

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NOTES

(1.) During the middle of the initial MTP data collection period, changes in IRB requirements across sites resulted in the requirement to obtain participant approval to be contacted for future study opportunities. A new consent form was generated for new MTP participants, and a consent addendum was generated for those participants who had not yet completed all baseline study components. The current follow-up was the first to occur over a 25-year span of regularly scheduled follow-up assessments of this subgroup of 672 MA-dependent study participants who provided written consent for future contact.

(2.) Background characteristics included age, race/ethnicity, marital status, gender, education, employment, MA use history, and ASI composite scores.

(3.) The M.I.N.I. was included in the measurements as part of the MATSS extended funding. It was not originally administered as part of the MTP study.

(4.) Multigroup confirmatory factor analyses (CFA) shows measurement invariance for the six-item abuse and dysfunction questions. In the analysis, the factor loading parameters were simultaneously constrained to be equal across males and females. The overall fit was good with RMSEA of .004, CFI = .96 indicating good fit to the model.

Please address correspondence and reprint requests to Nena Messina, Ph.D., Integrated Substance Abuse Programs, 1640 S. Sepulveda Blvd., Suite 200, Los Angeles, CA 90025. Phone: 310-267-5509, fax: 310-312-0559, email: nmessina@ucla.edu

Nena Messina, Ph.D. *; Patricia Marinelli-Casey, Ph.D. **; Maureen Hillhouse, Ph.D. **; Richard Rawson, Ph.D. ***; Jeremy Hunter **** & Alfonso Ang, Ph.D. *****

([dagger]) This study was supported by grants TI 11440-01, TI 11427-01, TI 11425-01, TI 11443-01, TI 11484-01, TI 11441-01, TI 11410-01, and TI 11411-01, provided by the Center for Substance Abuse Treatment, Substance Abuse and Mental Health Services Administration, U.S. Department of Health and Human Services. The findings and conclusions of this study are those of the authors and do not necessarily represent the official policies of the funding agencies. The authors would like to give a special thanks to Florentina Marcu for her editing contributions and aid in creating the tables. We would also like to give thanks to the many treatment, research, and statistical staff who helped collect and analyze data, as well as to those who volunteered to be in the study.

* Research Criminologist, Integrated Substance Abuse Programs, University of California, Los Angeles.

** Associate Research Psychologist, Integrated Substance Abuse Programs, University of California, Los Angeles.

*** Adjunct Associate Professor, Integrated Substance Abuse Programs, University of California, Los Angeles.

**** Statistician, Integrated Substance Abuse Programs, University of California, Los Angeles.

***** Principal Statistician, UCLA School of Family Medicine, University of California, Los Angeles.
TABLE 1
Sample Characteristics at Treatment Admission by Gender

Characteristics Men Women
 (n = 236) (n = 351)
 % M (SD) % M (SD)

Race/Ethnicity
 Caucasian 71 65
 African American 2 2
 Hispanic 18 13
 Other ** 9 20
Marital Status
 Never Married 49 45
 Married 19 17
 Previously Married 33 38
Age at Admission * 34.3 (8.0) 32.7 (7.9)
Education at Admission
 Less than High School * 15 22
 High School Graduate 51 45
 College Graduate/Beyond 34 33
Employment
 Full time ** 64 37
 Part time/Irregular 20 24
 Student/Retired 6 5
 Unemployed ** 11 30
 Controlled Environment 3 3
Parent of Minor Child ** 60 73
Mean ASI Drug Composite ** .2 (1.0) .2 (1.0)
Mean Days MA Use 30 Days 10.6 (8.8) 12.9 (9.9)
 Prior to Admission **
Age of First MA Use 19.7 (5.8) 20.5 (6.8)
Route of Administration
 Oral/Nasal 11 9
 Smoking * 58 66
 IV 32 25
Antisocial Personality 27 25
 Disorder
Depression * 12 18
Suicide Attempted ** 14 30

Characteristics Total
 (N = 587)
 % M (SD)

Race/Ethnicity
 Caucasian 68
 African American 2
 Hispanic 15
 Other ** 15
Marital Status
 Never Married 47
 Married 16
 Previously Married 37
Age at Admission * 33.3 (7.9)
Education at Admission
 Less than High School * 19
 High School Graduate 76
 College Graduate/Beyond 4
Employment
 Full time ** 48
 Part time/Irregular 21
 Student/Retired 6
 Unemployed ** 22
 Controlled Environment 3
Parent of Minor Child ** 68
Mean ASI Drug Composite ** .2 (1.0)
Mean Days MA Use 30 Days 11.9 (9.6)
 Prior to Admission **
Age of First MA Use 20.2 (6.4)
Route of Administration
 Oral/Nasal 10
 Smoking * 63
 IV 28
Antisocial Personality 26
 Disorder
Depression * 15
Suicide Attempted ** 24

* p<.05.

** p<.01.

TABLE 2
Childhood Abuse and Household Dysfunction by Gender

Characteristics Men Women Total
 (n = 236) (n = 351) (N = 587)
 % M(SD) % M(SD) % M(SD)

Childhood Abuse
 1. Emotionally Abused
 Prior to Age 18 ** 37 55 48
 2. Physically Abused
 Prior to Age 18 ** 27 42 36
 3. Sexually Abused
 Prior to Age 18 ** 11 42 29
Household Dysfunction
 4. Familial Arguments ** 26 40 34
 5. Familial Substance
 Abuse Problems ** 50 63 58
 6. Witnessed Emotional/
 Physical/Sexual Abuse
 of Other Family
 Members ** 39 55 49
Total Items Endorsed from
Above
 0 ** 28 14 19
 1 ** 23 12 16
 2 16 14 15
 3 * 10 17 15
 4 * 12 19 16
 5 * 8 15 12
 6 ** 3 9 6
Mean Number of CAEs ** 2.1 3.1 2.7
 (1.9) (2.0) (2.0)

Note: data reported at three-year follow-up.

* p <.05,
** p <.01.

TABLE 3
Relationships Between Categories of Childhood Adverse Events by Gender

 1 2 3

Frequent Familial Arguments -- .275 ** .311 **
Familial Substance Abuse Problems .338 ** -- .331 **
Witness Family Abuse .274 ** .338 ** --
Emotional Abuse (Before Age 18) .311 ** .342 ** .562 **
Sexual Abuse (Before Age 18) .163 * 0.108 .133 *
Physical Abuse (Before Age 18) .402 ** .248 ** .410 **

 4 5 6

Frequent Familial Arguments .358 ** .120 * .262 **
Familial Substance Abuse Problems .258 ** .212 ** .220 **
Witness Family Abuse .201 ** .178 ** .309 **
Emotional Abuse (Before Age 18) -- .406 ** .519 **
Sexual Abuse (Before Age 18) 0.039 -- .238 **
Physical Abuse (Before Age 18) .545 ** 0.12 --

Note: Correlation coefficients for women (N = 351) are on the top
diagonal and correlation coefficients for the men (N = 236) are on the
bottom diagonal. Transformations revealed that women yielded
significantly higher correlations among adverse events than men
(p < .05). The correlation coefficients that were higher for men were
not significantly different from those for the women.

* p<.05

** p <.01.

TABLE 4
Effect of Childhood Abuse and Household Dysfunction on Age of First
Methamphetamine Use (Pooled Sample using Collective Abuse/Dysfunction
Score)

 Regression Std.
Pooled Sample (N = 587) Coefficients Error P-Value

Collective Abuse/Dysfunction Score -.28 0.14 0.05
Caucasian -2.40 0.54 0.001
Female 1.67 .54 0.002
Marital Status, Married (1 = Yes) -0.57 0.59 0.33
Education (1 = High School Graduate 0.48 0.64 0.46
 and Beyond)
Current Depression 0.60 0.73 0.41
Antisocial Personality Disorder -2.62 0.61 0.001
Age 25-34 2.76 0.73 0.001
Age 35-44 6.32 0.75 0.001
Age 45 up 10.96 1.12 0.001

Note: All analyses are conducted using missing data multiple
imputations.

TABLE 5
Effect of Childhood Abuse and Household Dysfunction on Severity of
Dependence (Pooled Sample Using Collective Abuse/Dysfunction Score)

 Regression Std.
Pooled Sample (N = 587) Coefficients Error P-Value

Collective Abuse/Dysfunction Score 0.01 0.01 0.03
Caucasian 0.02 0.01 0.07
Female 0.17 0.01 0.07
Marital Status, married (1 = yes) 0.16 0.01 0.13
Education (1 = High School Graduate 0.02 0.01 0.07
 and Beyond)
Current Depression 0.01 0.01 0.07
Antisocial Personality Disorder 0.01 0.01 0.41
Age 25-34 -0.01 0.01 0.66
Age 35-44 -0.01 0.01 0.39
Age 45 up -0.01 0.02 0.80

Note: All analyses are conducted using missing data multiple
imputations. The ASI Drug Composite ranges from 0-.52 among this
sample.
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Publication:Journal of Psychoactive Drugs
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Date:Nov 1, 2008
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