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. Author manuscript; available in PMC: 2021 May 25.
Published in final edited form as: J Child Adolesc Subst Abuse. 2014 Jan 28;23(2):101–108. doi: 10.1080/1067828x.2012.748601

Predicting an Alcohol Use Disorder in Urban American Indian Youth

Linda R Stanley 1, Kimberly Miller 1, Fred Beauvais 1, Patricia Silk Walker 2, R Dale Walker 3
PMCID: PMC8147299  NIHMSID: NIHMS420863  PMID: 34040334

Abstract

This study examines predictors of alcohol use disorders (AUDs) among an urban American Indian cohort who were followed from approximately age 11 to age 20. Approximately 27% of the sample had a lifetime diagnosis of alcohol abuse or dependence. The results indicated that externalizing, but not internalizing, behaviors, family conflict, and school liking served as significant predictors of an AUD. Neither having an alcoholic mother nor an alcoholic father was found to be significantly predictive of an alcohol use disorder at ages 19–20. Finally, early alcohol initiation is a substantial predictor of an AUD and acts as a partial mediator.

Alcohol use continues to be a major health problem among most American Indian groups (Beauvais, Jumper-Thurman, & Burnside, 2004; Welty, 2002). Prevalence rates indicate that American Indians have higher rates of alcohol dependence, especially among males, than the rest of the population, though the differences are not as dramatic as often portrayed (May, 1994; Mitchell, Beals, Novins, & Spicer, 2003). For example, Mitchell et al. (2003) found that lifetime rates of DSM-III-R alcohol dependence for men in two American Indian community samples, Southwest and Northern Plains, were approximately 50% higher than those found for the National Comorbidity Survey. According to 2002–2005 data from the National Survey on Drug Use and Health (NSDUH), American Indian and Alaska Native males aged 12 or older, though less likely to have used alcohol in the past year than males in other racial groups, were more likely to have a past year alcohol use disorder (Office of Applied Studies, 2007).

We examine the etiology of alcohol abuse and dependence among an urban American Indian cohort who were followed from approximately age 11 to age 20. Emergence of an alcohol use disorder (AUD) is the culmination of a development process that can begin in early childhood (Zucker, 2006). Here, we focus on late childhood factors that have been hypothesized as predictors of AUDs since identifying these risk factors for this population may give direction for identifying those at risk of later abuse or dependence and may point to specific early prevention efforts.

Although several research studies have examined early precursors of abuse/dependence, Fothergill and Ensminger (2006) note that much existing research has focused on substance use, distinct from abuse and dependence, and that existing research on substance use disorders often uses samples of individuals from clinical and treatments settings, and often focuses on males. In addition, when studies have focused specifically on predicting abuse/dependence, many have had to rely on DSM-III-R diagnoses which can be significantly different than DSM-IV diagnoses for the adolescent population (Mikulich, Hall, Whitmore, & Crowley, 2001). This study contributes in that we are able to address, at least in part, these concerns by using data that contain DSM-IV diagnoses and a variety of variables measured in late childhood that are hypothesized to predict these diagnoses for a non-treatment sample of urban American Indian male and female youth.

Preadolescence predictors of abuse and dependence

Specific to AUDs, Zucker (2006) has argued for a developmental psychopathological framework that takes into account social, individual, and biological factors that have been found to be precursors to the presence of AUDs. He makes a strong case for heterogeneity of the phenotype, with subtypes differing by heritability, psychiatric comorbidity, family influences, and other environmental factors.

Children of alcoholics (COAs) report higher rates of alcohol abuse and dependence in young adulthood (Slutske et al., 2008), earlier ages of alcohol initiation, and higher rates and faster acceleration of alcohol use starting in adolescence and continuing into adulthood (Chassin, Pitts, DeLucia & Todd, 1999). Evidence that these may be due, at least in part, to a genetic component to alcoholism has accumulated over the past three to four decades (McGue, 1997; Slutske et al., 2008). Studies using sibling, twin, and/or adopted pair data have found that problem use of alcohol by adolescents is likely highly heritable while initiation is not (Pagan et al., 2006; Rhee et al., 2003).

Significant evidence has also shown a robust relationship between early childhood behavior and adult AUDs (Zucker, 2006), including a link between delinquent and aggressive activity in childhood and earlier onset of alcohol use and problems. Several studies found that children with externalizing behavior problems (i.e., conduct disorder, aggressiveness, impulsivity) have higher rates of alcohol use and that these behavior problems predict both adolescent and adult alcohol use and AUDs (Englund, Egeland, Oliva, & Collins, 2008; Garcia-Reid, Reid, & Peterson, 2005; Kuperman, et al., 2005; Siebenbrumer, Englund, Egeland & Hudson, 2006). Several general population studies, including those cited for a significant relationship between aggressive behavior and AUDs, and COA studies have also found a significant relationship between internalizing problems and later AUDs, as well as early drinking onset. For example, Caspi, Moffitt, Newman, and Silva. (1996) found that behavior inhibition at age 3 predicted more alcohol problems at age 21 while Hawkins and colleagues (1992, 1999) found a link between internalizing behavior alcohol abuse and dependence.

Numerous environmental influences have also been linked to the development of substance abuse problems in adolescents. A secure bond to the family has been shown to protect against drug use while the absence of a strong bond has been linked to both early initiation of use and later misuse (for reviews, see Denton and Kampfe, 1994; Vakalahi, 2001; Kuntsche and Kuendig, 2006). Similarly, when youth have positive bonding to school, they are less likely to participate in illegal behaviors such as drug and alcohol use while those who are less engaged and committed to school tend to report significantly higher rates of drug use (Blum, McNeely, & Nonnemaker, 2002; Bryant, Schulenberg, O’Malley, Bachman, & Johnston, 2003; Guo, Hawkins, Hill, & Abbott, 2001).

A final variable of interest is age of initiation. Young people who begin drinking before age 11 had a rate of alcohol dependence 1.62 times greater than those who delayed drinking until they were 15–17 years of age and 3.67 times greater than those who did not start consuming alcohol until they were 18–20 years of age (Guttmannova, et al., 2011). Further, several studies demonstrate adolescents who use alcohol regularly before age 14 or 15 have more chronic alcohol dependence problems as adults (Dawson, Goldstein, Chou, Ruan, & Grant, 2008; DeWit, Adlaf, Offord, & Ogbourne, 2000; Grant, Stinson & Hartford, 2001; Guttmannova, et al., 2011). In addition, Hawkins et al. (1997) found earlier alcohol initiation fully mediated other baseline risk factors for alcohol misuse, including parental drinking, proactive parenting, school bonding, peer alcohol initiation, and ethnicity. Likewise, childhood factors linked to AUDs have been found to be linked to early alcohol use (Brook, Whiteman, & Finch, 1992), leading to the suggestion that early alcohol use is a mediating factor between childhood factors and AUDs.

Method

Data

This study involves a secondary analysis of American Indian Research (AIR) data collected from a large sample of urban Indian youth and their parents/caretaker residing in the Seattle area. Detailed information on the collection of the data from this community sample can be found at Walker et al. (1996). Data were collected annually, one-on-one, during the time period 1988–89 through 1996–97, with 9 points of assessment. Adolescent subjects were followed from a mean age of 11.7 years through a mean age of 19.7 years, whether or not they were enrolled in school. Separate interviews were held with the youth and their caretaker(s).

For this analysis, the first year and the last year of assessments are used, and two cohorts are combined to increase sample size. One cohort (n=224) was recruited from schools in the Seattle area with high Indian enrollments. The second cohort (n=66) was recruited from the roster of age appropriate (11–12 years old) American Indian patients of the urban Indian clinic. It was initially recruited to increase the size of the base year sample and to improve power in estimating statistical models, especially since some dropout from the study was expected from year 1 to year 9. The final sample size for this analysis was 281, as nine clinic sample adolescents who did not meet criteria as urban citizens were excluded from the analysis. (Urban was defined as a home address at recruitment in a U.S. Census Bureau defined postal zip code of greater than 25,000 population size.) The only significant difference in the two cohorts occurs in annual household income, with the health clinic cohort reporting lower income than the school cohort. This is to be expected because the health clinic reaches out to lower income populations. The subjects were from fifty tribes representing nine cultural areas.

Measures

A large number of instruments were included in the AIR project. Nearly all of the measures used for this study were standardized instruments with demonstrated reliability and validity. All predictors were measured at time 1, except where noted below, while diagnosed alcohol disorders were measured at time 9. Where appropriate, items were combined into scales by calculating the means of the item responses. Table 1 gives the means and standard deviations for all variables.

Table 1.

Variable Means and Standard Deviations by Presence of AUD

Diagnosed AUD (n=66) No AUD (n=176)
Mean SD Mean SD
Gender (female=1) .364* .485 .574 .496
Income (000s) 20.110 12.679 21.602 13.805
Alcoholic mother .448 .502 .339 .475
Alcohol father .500 .505 .344 .476
Internalizing behaviors 9.288 8.694 8.454 6.899
Externalizing behaviors 12.197* 9.952 8.722 6.887
Family cohesion 6.924* 2.100 7.132 1.713
Family conflict 3.292* 2.123 2.523 1.811
Likes school 1.091* .547 1.313 .613
Early alcohol initiation .773* .422 .488 .501
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*:

p < .05.

Measures whether variable mean for respondents with an AUD is different from mean for respondents without an AUD

Alcohol dependence and abuse.

DSM-IV lifetime alcohol dependence and abuse were assessed using the Semi-Structured Assessment for the Genetics of Alcoholism-II (SSAGA-II) (Bucholz, 1994) instrument at time 9. The SSAGA-II has been specifically designed to study lifetime and current status of alcoholism and associated co-morbid psychiatric diagnoses. Because only seven respondents (2.9%) met the criteria for dependence, abuse and dependence are grouped into one category of alcohol use disorders.

Lifetime prevalence of alcohol abuse/dependence disorders was measured at time 5 for the mother and father. Diagnostic data were obtained by administering selected Family History Assessment Module(s) (FHAM: Janca, Bucholz, Janca, & Jabos-Laster, 1991) for surrogate reports on family history or in the case of self-report, the SSAGA-II (Bucholz et al., 1994). Prior to time 5, no reliable and valid instruments were available to measure alcohol diagnosis and co-morbid disorders in families for whom alcohol use disorders are a significant problem. At time 5, the SSAGA was in development with good preliminary evidence of reliability and validity. Because research has found differences in influence of an alcoholic father versus an alcoholic mother, dummy variables are computed separately for each.

Externalizing and internalizing behaviors.

The Child Behavior Checklist (CBCL; Achenbach, 1991) was used to measure behavior problems of social withdrawal, somatic complaints, anxiety/depression, social problems, thought problems, attention problems, delinquent behavior, and aggressive behavior at time 1. As standard for the CBCL, a measure for internalizing behavior problems combined the social withdrawal, somatic complaints, and anxiety/depression scales (α = .91) while externalizing behavior problems combined the aggressive behavior and the delinquent behavior scales (α = .93).

Environmental Influences.

Family functioning was measured with two variables, family cohesion and family conflict, using the Moos Family Relationships Index (Moos & Moos, 1986). Family cohesion (e.g., family members really help and support one another) and family conflict (e.g., we fight a lot in our home) each consist of 9 items with alphas of .77 and .70, respectively. School liking was measured with one item (“do you like school”) on a 3-point scale. A measure of peer use at age 11 was initially used but was found to be insignificant and was dropped from the analysis.

Alcohol initiation.

Early alcohol initiation measures whether a respondent reported having “had more than a sip or taste” of an alcoholic drink like wine, beer or liquor in the last 12 months at age 14 or before (Grant & Dawson, 1997). Respondents who at the time of first assessment reported an earlier age when they first drank more than a sip or two were also included in the early initiator group. Finally, gender (female=1) and household income (in thousands measured in time 1 were included as control variables.

Analysis

Logistic regression was used to estimate several models, where predictors were entered into models according to their proximity to the outcome variable as suggested by Kosterman et al. (2000) with less proximal predictors (e.g., gender, income, parental alcoholism) being entered first and early alcohol use being entered last. Mediation tests, following MacKinnon & Dwyer (1993), were performed where the initial variables - parental alcoholism, family conflict and cohesion, school liking, and internalizing and externalizing behaviors - are hypothesized to be mediated by early alcohol use. A Sobel test was conducted for each initial variable; calculations explicitly accounted for the dichotomous nature of the mediator (early alcohol initiation) and outcome (dependence or abuse) variables (Herr, 2009).

Missing data procedures.

Since the data were collected via face to face interviews, the amount of missing data are quite small, ranging between 4% and 6% for most variables. For several variables, notably alcoholism of the mother and father and income, missing data are approximately 10%. Approximately 76% of observations had no missing data, due to the low attrition (less than 10%) and the face-to-face survey method.

To account for missing data, multiple imputation (Shafer & Graham, 2002) was completed using ICE in Stata software, Version 10.0 (Royston, 2004, 2005, 2007). ICE imputes by chained equations, and its major strength is that there is no multivariate joint distribution assumption, thus allowing different types of variables to be imputed together. Simulation studies have shown that in practice it performs well (Royston, 2005).

The use of multiple imputation is based on the assumption that missing data are missing at random (MAR). For data to be missing at random, the probability that a response variable is observed depends on the values of other variables that have been observed. If MAR is at least a reasonable approximation, using multiple imputation to account for missing data results in approximately unbiased estimates of all parameters and good estimates of the standard errors.

In this estimation, the MAR assumption should be reasonably accurate. For example, the likelihood that income is missing in time 1 is likely related to who completed the caretaker survey in time 1, an observed variable. Many observed variables that were hypothesized to be related to the observability of the model variables were included in the imputation, such as the child’s caretaker in time 1.

In total, ten imputed data sets were created and analyzed, and the parameter estimates were then combined using the procedures outlined by (Rubin, 1987).

Results

Descriptive statistics for all variables are presented in Table 1. Approximately 27% of the sample had a lifetime diagnosis of either alcohol abuse or alcohol dependence. Those diagnosed with abuse or dependence were significantly more likely to be male, score higher on externalizing behaviors and family conflict, like school less and have less family cohesion, and be early alcohol initiators. Although nearly 45% of those diagnosed with an AUD had an alcoholic mother and 50% had an alcoholic father, compared to 34% of those without an AUD having either an alcoholic mother or father, these differences were not significant.

Odds ratios for each model estimated are presented in Table 2, along with their confidence intervals. Column 1 presents the results of the model that includes gender, income, alcoholic mother, alcoholic father, and internalizing and externalizing behaviors as predictor variables. Gender is significant in predicting a future alcohol use disorder, with the expected odds of an AUD for a female to be .51 times the odds of an AUD for a male. The only other significant variable is externalizing behavior; a unit increase in the externalizing behavior scale score increases the odds of an AUD by 1.06, all else equal. For a youth who has an externalizing behavior score one standard deviation above the mean, this translates into a 78% increase in the odds of an AUD compared to a youth with a mean level of externalizing behavior, all else equal. Note that having an alcoholic mother or an alcoholic father did not significantly increase the odds of an AUD by time period 9. Interestingly, the odds ratio associated with an alcoholic father is relatively large (1.67), but its confidence interval does include one.

Table 2.

Odds Ratios and Their Confidence Intervals for Probability of an AUD

Model 1 Model 2 Model 3
Variable OR 95% CI OR 95% CI OR 95% CI
Gender .51* (0.27, 0.97) .59 (.30, 1.14) .54a (.27, 1.06)
Income (000s) 1.00 (0.98, 1.03) 1.01 (.98, 1.03) 1.01 (.99, 1.04)
Alcoholic mom 1.27 (0.66, 2.44) 1.24 (.63, 2.42) 1.17 (.60, 2.29)
Alcoholic dad 1.67 (0.84, 3.34) 1.70 (.82, 3.54) 1.68 (.78, 3.58)
Internalizing problems .96 (0.91, 1.02) .95 (.91, .99) .96 (.91, 1.02)
Externalizing problems 1.06* (1.01, 1.12) 1.06* (1.02, 1.11) 1.05a (.99, 1.12)
Family cohesion 1.13 (.94, 1.35) 1.12 (.93, 1.35)
Family conflict 1.26* (1.04, 1.49) 1.20* (1.00, 1.45)
Likes school .50* (.30, .85) .56* (.33, .94)
Early alcohol initiation 2.48* (1.22, 5.05)
Average pseudo-R2 across imputations .06 .10 .13
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*:

p < .05;

a:

p < .10

In the next equation, the environmental variables associated with family and school were added into the model. As in the previous model, externalizing behavior at time 1 was significant in predicting an AUD by time 9, with an odds ratio equal to that in the first equation. Family conflict and liking school were also significant in predicting an AUD. A unit increase in family conflict increases the odds of an AUD by 1.26; a youth who has a family conflict score 1 standard deviation above the mean will have odds of an AUD about 55% higher than that of a youth with the mean level of family conflict. A unit increase in liking school decreases the odds of an AUD by .50. A youth who scores 1 standard deviation higher on liking school will have odds of an AUD about 34% less by time 9.

In the final equation, early alcohol initiation was added into the model. As expected, it has a significant positive impact on predicting an AUD by time 9. The odds of developing an AUD by about age 20 for a respondent who initiates alcohol use by age 14 are about 2.48 times the odds for a respondent who is not an early initiator. The significant predictors from the first two models are, for the most part, still significant in predicting AUDs by time 9; however, the odds ratios are somewhat closer to 1 than in previous models, suggesting mediation by early alcohol initiation. However, a Sobel test of mediation for externalizing behavior, family conflict, and likes school showed that only family conflict is significantly mediated by early alcohol initiation.

Discussion

Approximately 27% of this urban Indian young adult sample had a lifetime AUD diagnosis (2.9% alcohol dependence; 24% abuse diagnosis). It is difficult to tell how these numbers compare with the general population. There are several studies in the literature that attempt to determine the rates of abuse and dependence for adolescents; however many of them use clinical populations and others use differing time frames for the diagnosis (e.g., past year). The closest assessment comes from a 1995 self-report, paper and pencil school survey by Harrison, Fulkerson and Beebe (1998). They drew from a sample of 74,000 9th and 12th graders in the state of Minnesota. For the 12th graders, who are closest in age to the sample in the present study, they found that 10.7% of the 12th graders met the criteria for dependence and 22.7% met the criteria for abuse. Several cautions are inherent in attempts to compare Harrison et al.’s findings with those from the current study. Their survey was administered to youth who were enrolled in and attended school on the day of administration; they restricted their sample to those youth who had used any drug within the past year; the survey used fourteen questions and did not include the probes necessary to clarify responses; and the survey was conducted in a classroom with teacher proctors.

The primary goal of this study was to determine how pre-adolescence environmental and other measures predict the probability of developing an AUD in early adult urban Indian youth. In addition, we also tested whether early alcohol initiation mediated the relationship between these variables and the development of an AUD. The results indicated that of the behavioral indicators, only externalizing behaviors served as a significant predictor of an AUD. This finding is consistent with other research that has found a link between delinquent and aggressive activity and later development of AUDs (Caspi et al., 1996; Hawkins et al. 1992, 1999; Zucker, Chermack, & Curran, 2000; Tarter et al., 1999, 2003).

However, our findings are not consistent with other studies that have found a significant relationship between internalizing behaviors and development of an AUD (Garnier & Stein, 2002; Tarter et al., 1999, 2003). One contributing factor for not finding a relationship may lie in the means of measuring the level of internalizing behaviors. Internalizing behaviors are harder to effectively assess and quantify as an outside observer because they are not as disruptive to family functioning as external behaviors (Kolko & Kazdin, 1993; Loeber & Schmaling, 1985), and the caregiver is not the person experiencing the symptoms. In addition, depression, anxiety and other internalizing disorders in children are often manifested through externalizing behaviors (i.e., aggression, defiance). Youngstrom, Loeber, & Stouthamer-Loeber (2000) found that male youth reported significantly higher levels of internalizing behavior than did either their teachers or primary caregivers. These findings suggest that caregivers may underreport internalizing symptoms of anxious or depressed youth; this could then lead to an inability to discern a relationship between the internalizing behaviors and later development of an AUD disorder. On the other hand, past research has typically found a stronger and more direct relationship between externalizing behaviors and later AUD development than that for internalizing behaviors. Our findings may simply reflect this stronger, more direct association.

Neither having an alcoholic mother nor an alcoholic father was found to be significantly predictive of an alcohol use disorder at ages 19–20, conflicting with the significant evidence showing that COAs have higher rates of alcohol abuse and dependence in young adulthood. However, despite the lack of significance of parental AUDs on adolescents’ development of AUDs, the odds ratio associated with having an alcoholic father was relatively large, ranging from 1.67 to 1.70 in all three models. This is consistent with research that has found a direct relationship between paternal alcoholism and drug abuse (Cadoret, Yates, Troughton, Woodworth, & Stewart, 1995). The lack of significance for this odds ratio may reflect a higher level of measurement error for this particular measure due to the protocol used to gather this data. In particular, the Family History Assessment Module used to gather surrogate data was completed, in the majority of cases, by the mother or a caregiver other than the father. The surrogate reporter may not have had direct contact information to complete the survey, as evidenced by over 50% of respondents not living with their biological father. Interaction terms between variables measuring who the child lived with in time 1 and alcoholic status of the parents were constructed and added to the final model. These interaction terms were not significant, indicating the relationship between having a parent with a lifetime diagnosis of alcoholism and the presence of an AUD at time 9 did not depend on whether the child lived with that parent at time 1.

Family conflict was found to be significantly related to the later presence of an AUD while family cohesion was not. Family conflict at this early age may indicate a stressful environment for the child that is not specifically related to externalizing or internalizing behaviors of the child. Whether or not this family conflict can be, at least in part, attributed to a drinking parent or family member is unknown. The relationship of family conflict to the presence of an AUD was found to be partially mediated by early alcohol initiation, i.e. family conflict predicts early alcohol initiation which predicts an AUD.

Interestingly, school liking at time 1 was a significant predictor of an AUD at time 9. School liking or bonding has been found to be predictive of alcohol use and alcohol-related problems in a number of longitudinal and cross-sectional research studies (Shears, Edwards, & Stanley, 2006; Bryant et al., 2003). However, less evidence exists that it is predictive of a future AUD. This finding is especially significant in terms of the size of the odds ratio and the relatively long period of time between the measurement of school liking and that of an AUD. Whether there is an underlying variable that is common to these two variables (other than those already included in the model) is an issue for future research. However, the results do point to the possibility that not liking or bonding to school early on could be an indicator of possible alcohol use problems later.

As expected and reflected in a large body of literature, early alcohol initiation is a substantial predictor of an AUD at age 20. The odds of an AUD increase by 2.5 times if a youth is an early initiator. Interestingly, early initiation does not significantly mediate the relationship between externalizing behaviors and school liking and the presence of an AUD. This is not to say that the likelihood of early initiation does not increase as school liking decreases or as externalizing behaviors increase. It does, however, suggest that there is a direct relationship between both externalizing behaviors and school liking and the presence of an AUD, in addition to the relationship between early alcohol initiation and an AUD.

Finally, a variable measuring peer use of alcohol at time 1 was ultimately deleted from the models presented due to its insignificance and the lack of variation in the variable. This is not surprising given that at time 1, peer use of alcohol was low or nonexistent for the vast majority of the respondents. This also points to the fact that targeting attention to relationships with the family and school, in addition to early behavior by the respondent, can be key to preventing later AUDs.

Limitations and Conclusions

Although this study is an important contribution to the study of urban Indian youth, it does not include a representative sample of urban Indian youth. Rather, all youth resided in the metropolitan Seattle area. Thus, results cannot be generalized to all urban Indian youth. In addition, because this study made use of existing data, it is limited in the measures available; although the results are generally consistent with theory and other research, some relationships may have been stronger if more complete measures of the relevant constructs had been available. For example, school liking was measured with a single item (“I like school”); a more comprehensive scale measuring school bonding or liking would be preferable. We did substitute our measure of school liking with Harter’s (1982) school competence scale; however, this measure was insignificant in predicting an AUD. This may point to the fact that it is not competence in school that is important at age 11 in terms of future AUD development, but rather bonding or liking of school. Another potential issue with the data occurs for the lifetime measures of mother and father alcohol disorders. A past year measure in time 1, in addition to a lifetime measure in time 9, would have aided in separating genetic and environmental influences on youth development of an AUD. However, the data included only the measure of a lifetime alcohol disorder at time 5.

Finally, using measures that are nine years apart does not assure that the relationships are causal, although there is substantial improvement over cross-sectional data and shorter term longitudinal data because the number of alternative explanations is considerably reduced.

In large part, this study has replicated findings from other investigations that have found early externalizing behaviors, school liking, family conflict, and early initiation are robust predictors of alcohol-related problems and alcohol use disorders. This is important in that the findings were found to apply to a population about which very little is known, urban American Indian youth. One third of the youth in this study are enrolled (a measure of tribal census that does not address identity, values or adherence to customs) in their tribes and many of them travel back to their tribal homes on a regular basis. They do have contact with their cultural roots and are familiar with their tribal customs and values. Interventions proven effective in the general population need to be adapted to both local context and culture, including the values and customs of the tribe.

Acknowledgments

Support for this research has been provided by grants #RAA015404A, Fred Beauvais, PI and #R01AA07103, R. Dale Walker, PI, both from the National Institute on Alcohol Abuse and Alcoholism of the National Institutes of Health.

Contributor Information

Patricia Silk Walker, Department of Public Health & Preventive Medicine, Oregon Health & Sciences University, Portland, OR.

R. Dale Walker, Departments of Psychiatry and Public Health & Preventive Medicine, Oregon Health & Sciences University, Portland, OR.

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