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Published in final edited form as: Int J Eat Disord. 2011 Jan 7;44(6):561–566. doi: 10.1002/eat.20894

Behavioral Symptoms of Eating Disorders in Native Americans: Results from the Add Health Survey Wave III

Ruth H Striegel-Moore 1,2,*, Francine Rosselli 1, Niki Holtzman 1, Lisa Dierker 1, Anne E Becker 3,4, Gyda Swaney 5
PMCID: PMC11624507  NIHMSID: NIHMS2039436  PMID: 21823140

Abstract

Objective:

To examine prevalence and correlates (gender, Body Mass Index) of disordered eating in American Indian/Native American (AI/NA) and white young adults.

Method:

We examined data from the 10,334 participants (mean age 21.93 years, SD = 1.8) of the National Longitudinal Study of Adolescent Health (ADD Health) Wave III for gender differences among AI/NA participants (236 women, 253 men) and ethnic group differences on measures of eating pathology.

Results:

Among AI/NA groups, women were significantly more likely than men to report loss of control and embarrassment due to overeating. In gender-stratified analyses, a significantly higher prevalence of AI/NA women reported disordered eating behaviors compared with white women; there were no between group differences in prevalence for breakfast skipping or having been diagnosed with an eating disorder. Among men, disordered eating behaviors were uncommon and no comparison was statistically significant.

Discussion:

Our study offers a first glimpse into the problem of eating pathology among AI/NA individuals. Gender differences among AI/NA participants are similar to results reported in white samples. That AI/NA women were as likely as white women to have been diagnosed with an eating disorder is striking in light of well documented under-utilization of mental health care among AI/NA individuals.

Keywords: eating disorder, Native American, ethnicity, gender differences

Introduction

Epidemiological research of eating disorders has lagged behind research of other mental disorders leaving unanswered basic questions about prevalence in major demographic subgroups of a population.1 As noted in a recent review,2 whereas progress has been made in determining how common eating disorder syndromes or core symptoms are among the majority (white Americans) and several racial or ethnic minority populations in the United States (US),3–7 a gap in knowledge continues regarding eating disorders among the US Indigenous peoples (American Indian/Native American, Native Hawaiian, or Alaskan Natives, here abbreviated as AI/NA). Prevalence data for eating disorders in AI/NA populations are warranted both to complement epidemiological data concerning other major mental disorders in these populations and also to inform resource allocation for mental health interventions to reduce the substantial burden associated with eating disorders. Second, empirical data (from mostly white study populations) demonstrate that disordered eating behaviors, such as binge eating, are correlated with elevated body mass index (BMI) and predict weight gain.8 If a similar relation between eating pathology and obesity is established in AI/NA populations, it will have implications for interventional approaches to obesity, which is of major public health significance to Native Americans.

We could identify only one study reporting prevalence of formal eating disorder diagnoses in an adult AI/NA sample. This single study examined case records from a consecutive series of Indigenous Alaskan patients presenting at a community mental health center.9 However, given the well documented barriers to seeking care and biases in clinicians’ diagnostic assessments,10–12 a clinic-based sample is highly likely to underestimate true prevalence. Only two other studies report data concerning prevalence of symptoms of eating disorders in AI/NA adults, but have limited application to true prevalence estimates in AI/NA communities. For example, one study identified symptoms via the National Eating Disorders Screening Program conducted on US college campuses in 1996 (reported in two separate publications10,13). Data collected during this screening program did not estimate prevalence of specific eating disorders, although it did ask respondents about a previous diagnosis or care for an eating disorder. Given the voluntary nature of this program as well as its location on college campuses, comparative data relating AI/NA to other demographic groups were subject to selection biases relating to help-seeking and access to college sites. Bennett and Dodge14 explored racial/ethnic differences in the affective experience of binge eating using a subset of female participants (ages 19–27 years) of the third wave of the National Longitudinal Study of Adolescent Health (Add Health Wave III) that included a small number (N = 34) of AI/NA women. In analyses adjusted for socioeconomic differences, AI/NA women were found to be significantly more likely than white women (13.5% versus 7.1%, respectively) to report having eaten in the past seven days “so much in a short period of time that you would have been embarrassed if others had seen you do it.” Both groups were about equally likely to report having been afraid in the past seven days “to start eating because you thought you wouldn’t be able to stop or control your eating” (3.0% versus 2.6%, respectively). However, the authors did not utilize data regarding other eating disorder symptoms and data from male participants were excluded from the analyses.

All other eating disorder studies that have focused solely on or included a subset of AI/NA individuals in the US have recruited middle school or high school students (for review, see Ref. 2). In white samples, the mean onset age of eating disorders has been shown to be about 18 years15 and prevalence has been found to be greatest in early adulthood (18–25 years).4 Hence, studies of middle- or high school students are likely to underestimate the health and social burden attributable to eating disorders. Indeed, one study of African American women with Binge Eating Disorder (BED) found that age of onset in these women was significantly later than age of onset in a sample of white women with BED.16 Therefore, additional studies are needed to examine the prevalence of eating disorders in samples that have reached the developmental period of greatest risk for experiencing an eating disorder.

Notwithstanding the very limited empirical data relevant to eating disorders in AI/NA populations, findings from available studies warrant serious concern. In particular, studies of student samples suggest that behavioral symptoms of eating disorders are common among AI/NA children.17 In studies that assessed between group differences, results suggested either no significant difference18 or greater pathology among AI/NA students19 and in no case were AI/NA students less likely than white students to report such symptoms (for review, see Ref. 2). The primary aim of the present study is to contribute to this literature by utilizing extant Add Health Wave III data to examine the prevalence and correlates (gender, BMI) of disordered eating behaviors in young adult AI/NA men and women. We hypothesized that, as has been shown in studies of white samples,1 a significantly greater prevalence of AI/NA women than AI/NA men would report symptoms of disordered eating. Given the limited data and inconsistent findings concerning ethnic differences in previous studies, we did not pose a specific hypothesis regarding differences between AI/NA and white individuals. In light of the expected gender differences, comparisons of AI/NA, and white participants were stratified by gender.

Method

Participants

Data for this study were drawn from Wave III of Add Health (collected between August 2001 and April 2002). Wave III included 15,197 Wave I respondents who could be located and consented to be reinterviewed. At Wave III, respondents were between 18 and 27 years old.

Add Health is a nationally representative, probability-based survey that examines a broad range of healthrelated attitudes and behaviors of American adolescents. Wave I was conducted between September 1994 and April 1995 and included 20,745 students in grades 7–12 at that time. A systematic random sample of high schools and “feeder schools” was selected proportional to enrollment size and was subsequently representatively stratified by geographic region, urbanicity, school type, and ethnicity. Overall, there were 132 schools from 80 communities in the core study.

Procedure

Data collection in Add Health-III involved in-home interviews. An interviewer recorded respondents’ answers using a laptop computer-assisted personal interview system. Portions of the interview containing sensitive information were administered with an audio computer-assisted self-interview, allowing the participants to enter their responses directly into the computer.

Measures

Only items relevant to this study are described. Age, race, and ethnicity data were based on self-report consistent with major US census categories. Participants identifying with multiple racial/ethnic groups were categorized into one group based on self-reported preference. Body Mass Index (BMI) was calculated using self-reported height and weight.

Operationalization of Eating Disorder Symptoms for This Study.

Add Health did not measure binge eating as defined in the DSM-IV, requiring presence of overeating with loss of control.20 Rather, participants were asked the following two questions (each rated “yes” or “no”), the first referring to overeating and the second referring loss of control over the eating episode: “In the past seven days, have you eaten so much in a short period that you would have been embarrassed if others had seen you do it?” (“embarrassment”), and “In the past seven days, have you been afraid to start eating because you thought you wouldn’t be able to stop or control your eating?” (“loss of control”). Using these two items we created a proxy variable for binge eating, defining the behavior by the presence of both “embarrassment” and “loss of control” in relation to the eating episode.

Three items measured inappropriate behaviors used in the past seven days (yes/no) “in order to lose weight or stay the same weight.” Specifically, participants were asked if they had engaged in vomiting, taken laxatives, or used diuretics in the past seven days. For the present study, because of the low prevalence of these behaviors (e.g., only 27 of over 15,000 participants vomited as measure of weight control) we created a binary variable of “any inappropriate weight control behavior” (“yes” = yes to at least one of these three items; “no” = no to all three items). The Add Health interview also included a binary choice question asking participants whether they had “Ever been told by a doctor that you have an eating disorder, such as anorexia nervosa or bulimia,” which we refer to as “Ever diagnosed with an ED.”

The Add Health interview also included a question about breakfast eating (“On how many of the past seven days did you eat breakfast-that is, a meal within an hour of getting up?”) with responses ranging from zero to seven days. Although technically not an eating disorder symptom, we utilized this variable in the present study to examine breakfast skipping, a behavior common in individuals who are weight concerned or dieting.21,22 Consistent with several published studies23–26 we defined breakfast skipping as missing breakfast at least six times in the past seven days (yes/no).

Statistical Analysis

All statistical analyses were conducted using SPSS 17.0. Descriptive statistics were calculated to characterize key attributes of the sample relevant to study aims. Gender and ethnic group differences were examined using chi-square analyses for categorical variables and t-tests for continuous variables. (Because in white samples gender differences have been documented with a high degree of consistency, comparisons of white men and white women were not calculated for this report.)

Effect sizes were calculated for all statistically significant results at the p ≤ 0.05 level using Cohen’s d to explore significant t-test results and Number Needed to Treat (NNT) analyses to explore significant chi-square results. NNT was employed because it takes into account the base rates of each condition of interest.27 NNT was originally developed to help assess the efficacy of one treatment compared with another; however, in the current study NNT seeks to answer the question, “How many participants from one group do you have to see to find one more “failure” (e.g., one more participant who [binges]) than if you had sampled participants from the comparison group?”28 An NNT greater than nine is considered a weak effect, between 4 and 9 a moderate effect, and less than four a strong effect.

Results

Sample Description

The sample used in this report was restricted to those participants who self-identified as either a member of an AI/NA category or non-Hispanic White. It comprised 10,334 participants (mean age = 21.93, SD = 1.8), including 489 AI/NA (4.7%) (mean age = 22.17, SD = 1.8) and 9,845 white participants (mean age = 21.92, SD = 1.8). Among the men (N = 4,940), 253 self-identified as AI/NA; among the women (N = 5,394), 236 self-identified as AI/NA.

Gender Differences in Eating Pathology Among AI/NA Participants

Table 1 shows prevalence estimates of behavioral symptoms of eating disorders and mean BMI scores for all participants stratified by gender and ethnicity. Comparisons between AI/NA women and men (shown in the first three columns of Table 1) demonstrate that women were more likely than men to report disordered eating behaviors. These differences were statistically significant (p < 0.05) for overeating to the point of feeling embarrassed and for fear of losing control over eating. Endorsement of binge eating, inappropriate weight control behaviors, and ever having been diagnosed with an eating disorder were too rare among the AI/NA male respondents to permit statistical testing of gender differences. Skipping breakfast was equally common among AI/NA women and men. Mean BMI was lower in women than in men, although this result only approached statistical significance (p = 0.069).

TABLE 1.

Prevalence of behavioral symptoms of eating disorders in American Indian/Native American (AI/NA) and White Non-Hispanic women (W) and men (M)

Variable AI/NA W AI/NA M AI/NA: W versus M White W White M Women: AI/NA versus white Men: AI/NA versus white
Binge eating % (N) 3.8 (9) 0.8 (2) Not tested 1.3 (65) 0.4 (18) χ2(1) = 10.95, p = .001; NNT = 39 Not tested
Embarrassed % (N) 12.8 (30) 5.9 (15) χ2(1) = 6.80, p = .009; NNT = 15 6.6 (339) 4.7 (219) χ2(1) = 13.50, p < .001; NNT = 17 χ2(1) = .835, p = .361
Loss of control % (N) 7.2 (17) 2.4 (6) χ2(1) = 6.37, p = .012; NNT = 21 3.0 (152) 1.2 (55) χ2(1) = 13.57, p < .001; NNT = 24 χ2(1) = 2.85, p = .091
Inappropriate weight control behavior % (N) 1.3 (2) 0.0 (0) Not tested 1.7 (55) 0.8 (13) Not tested Not tested
Breakfast skipping % (N) 38.1 (90) 38.3 (97) χ2(1) = .002, p = .963 36.8 (1898) 38.5 (1803) χ2(1) = .174, p = .677 χ2(1) = .002, p = .967
Ever diagnosed with an Eating Disorder % (N) 4.3 (10) 0.4 (1) Not tested 4.6 (239) 0.4 (18) χ2(1) = .075, p = .784 Not tested
Mean BMI (kg/m2)(SD) 27.56 (7.6) 29.49 (14.0) t(462) = 1.83, p = .069 26.12 (8.1) 26.63 (8.9) t(5111) = −2.61, p = .009; d = .073 t(4734) = −4.66, p < .001; d = .135
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Note tested, No statistical comparisons were calculated when cell sizes were < 5; NNT, number needed to treat.

Because of the low prevalence of binge eating (n = 2) and inappropriate weight control behaviors (n = 0) we were unable to examine whether BMI was a correlate of these symptoms in AI/NA men. Likewise, too few AI/NA women reported inappropriate weight control behaviors (n = 2) for statistical analysis. Despite a relatively small number of AI/NA women reporting binge eating (n = 9), these women had a significantly higher mean BMI (32.85 kg/m2, SD = 6.7) in comparison with those who did not report binge eating (27.37 kg/m2, SD = 7.5; (t(221) = −22.02, p = 0.04, d = 0.27), albeit of small effect size.

Ethnic Group Differences in Eating Pathology

Women.

Test statistics for comparisons of the two ethnic groups of women (AI/NA versus white) are presented in the next to last column of Table 1. AI/NA women were significantly more likely than white women to report overeating to the point of feeling embarrassed and to have feared losing control over eating. Moreover, a significantly greater percentage of AI/NA female respondents met study criteria for binge eating when compared with white female respondents. Although prevalence of any of the inappropriate weight control behavior measured in Add Health (vomiting, laxative- or diuretic use) was similar in the AI/NA and white female groups, the low number of AI/NA respondents reporting these symptoms (n = 2) did not allow statistical comparison. The prevalence of reporting “ever having been diagnosed with an eating disorder” was comparable in AI/NA and white women. Mean BMI scores were significantly greater in AI/NA women than in white women. Breakfast skipping was equally common in both groups.

Men.

Test statistics for comparisons of AI/NA men versus white men are shown in the last column of Table 1. Compared to white men, AI/NA men had significantly greater mean BMI scores. None of the remaining comparisons reached statistical significance (p < 0.05) or the number of the individuals reporting the behaviors in question was so small that significance testing was not meaningful. Notably, a greater percent of AI/NA men reported loss of control than white men (2.4% and 1.2%, respectively), a difference that approached statistical significance (p = 0.091).

Discussion

In contrast to the availability of prevalence data for eating disorders in most of the major US ethnic and racial census categories, insufficient data exist to characterize the prevalence of eating disorders in AI/NA populations. In particular, few epidemiologic data exist on eating disorder prevalence or risk correlates for AI/NA adult populations and interpretation of available study prevalence data is limited by bias likely to have been introduced by the sampling strategy. Likewise, prevalence data for symptoms of disordered eating in high school and middle school samples likely underestimate the health burden attributable to eating disorders because of the young age of the study participants relative to the mean age of onset for eating disorders. This study presents the first comparative prevalence data for eating disorder symptoms in AI/NA and white young adults drawn from a large and geographically diverse community sample.

Our finding that eating disorder symptoms were more common in women than men is consistent with gender comparisons reported for white samples.1,28 Although we found few ethnic differences and very small effect sizes for between group differences, most of these were in the direction of greater pathology in the AI/NA sample compared with the white sample and warrant consideration of the possibility that eating pathology is more prevalent in AI/NA populations, similar to other mental health problems.29 Whereas most of the study data reflect point prevalence, we also assessed lifetime prevalence of having received an eating disorder diagnosis. A substantial number of AI/NA women had been told by a doctor that they had an eating disorder; this prevalence was comparable to that of white female study participants and is also consistent with epidemiological surveys of predominantly white samples.1 That eating disorders are diagnosed as frequently among AI/NA women as white women in the US is a striking finding given known ethnic disparities in access to mental health care in the US.10,30

Several limitations need to be considered. Notwithstanding the overall large sample size in Add Health Wave III, the number of AI/NA participants still is quite small, especially considering that eating disorders represent relatively lower frequency disorders (compared, for example, to mood disorders or substance use disorders, conditions with considerably higher prevalence than eating disorders). Furthermore, Add Health was not intended as a study of eating pathology; operationalization of binge eating differs from DSM IV criteria and we could thus examine only the prevalence of a limited number of ED symptoms rather than the prevalence of full ED syndromes using this data base. This study assessed point prevalence (within the past 7 days) of symptoms and likely underestimates lifetime prevalence of young adults. The validity of the assessment instrument in AI/NA populations is undetermined. Moreover, Add Health was designed to recruit a nationally representative sample and as such did not capture in large enough numbers the diversity of Indigenous peoples of the US Over 500 AI/NA tribes are recognized within the US; therefore, our conclusions about disordered eating in AI/NA reflect general observations that may not apply uniformly across various tribes.

These limitations are off-set by several strengths, including the rigorous sampling frame which ensured broad socioeconomic and geographic inclusion, the use of interview assessment and participant direct entry of sensitive data. Finally, although there has been extensive research about mental health problems among Indigenous peoples of the US, including a large scale epidemiological study focused exclusively on relatively homogenous cultural subgroups,29 such prior work did not include items measuring eating disorders. As such, the Add Health sample offers an important, albeit preliminary, glimpse into the problem of eating pathology among AI/NA young adults. Future studies should address specifically the cultural diversity across AI/NA populations regarding prevalence and presentation of eating disorder symptoms. Also, studies identifying risk factors, especially socio-cultural mediators, and moderators of course and treatment outcome would advance our understanding of social contributions to pathogenesis and inform future prevention and therapeutic interventions for AI/NA populations at risk or suffering from an eating disorder.

Acknowledgments

Supported by P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development.

Add Health is a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health website (http://www.cpc.unc.edu/addhealth). No direct support was received from grant P01-HD31921 for this analysis.

Ruth Striegel-Moore gratefully acknowledges Ms. Sara Young’s introduction into the cultural history and the current health and mental health issues experienced and resiliency exhibited by Native Americans living on tribal lands in the state of Montana. A member of the Crow Tribe, Ms. Young has worked in American Indian Education for over 30 years and was recognized in 2008 with the United States’ President’s Award for Excellence in Science, Mathematics, and Engineering Mentoring.

Footnotes

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