Eating Disorder Treatment Access in the United States: Perceived Inequities Among Treatment Seekers
Abstract
Objective:
Although eating disorders are associated with high rates of psychological and physical impairments and mortality, only about 20% of individuals with eating disorders receive treatment. No study has comprehensively assessed treatment access for those with these disorders in the United States. The authors examined access to eating disorder treatments and how it might vary among some populations.
Methods:
Seekers of treatment for eating disorders (N=1,995) completed an online assessment of clinical demographic and anthropometric characteristics, barriers to eating disorder treatment access, and eating disorder symptomatology. Analyses were conducted to identify treatment access barriers, compare barriers to treatment access across demographic groups, and investigate relationships between barriers to treatment access and eating disorder symptoms.
Results:
Financial barriers (e.g., lack of insurance coverage) were the most frequently reported barrier to treatment access. Participants with historically underrepresented identities and with a diagnosis of other specified feeding or eating disorder (OSFED) reported more barriers related to financial challenges, geographic location, eating disorder identification, sociocultural factors, and treatment quality compared with those with historically represented identities (e.g., White and cisgender persons). Higher frequencies of reported barriers to treatment access were associated with more severe eating disorder symptoms and poorer illness trajectories.
Conclusions:
Financial barriers were the most significant impediment to accessing treatment among individuals seeking eating disorder treatment. Barriers to treatment access disproportionally affected underrepresented groups and those with an OSFED diagnosis.
HIGHLIGHTS
•
Financial barriers (e.g., lack of insurance coverage) were the most frequent treatment access barrier reported by individuals with eating disorders.
•
Treatment access barriers disproportionally affected underrepresented groups (e.g., gender, racial-ethnic, and sexual minority groups) and individuals with a diagnosis of other specified feeding or eating disorder.
•
Reports of greater barriers to treatment access were related to elevated eating disorder symptoms.
Eating disorders are psychiatric illnesses characterized by severe impairment, elevated mortality and relapse rates, and high rates of co-occurrence with other psychiatric disorders (e.g., anxiety, substance use, and trauma) (1–4). Eating disorders have a lifetime prevalence rate of 9%–13% in the U.S. population and an estimated lifetime economic and well-being cost of >$326.5 billion, making eating disorders a serious threat to public health (4, 5). Despite the high prevalence rates, substantial costs to society, and significant health problems associated with eating disorders, most individuals with these disorders (80%) never access treatment (6). Efforts to improve access to treatment for these serious illnesses are urgently needed.
Barriers to Treatment Access for Eating Disorders
Among the barriers to treatment access (BTAs) for eating disorders in the United States, treatment costs—averaging approximately $20,817 per eating disorder inpatient stay (7, 8)—represent a major hurdle to accessing treatment (9, 10). The cost of treatment may be unaffordable for most families given that the national median household income is $67,521, and even individuals with insurance coverage report difficulty accessing care (11). Other common BTAs include geographic, identification, sociocultural, and treatment quality barriers.
Geographic barriers include the inability to access care in many rural and underresourced locations; many patients in the United States must travel out of state for higher levels of care (12). Identification barriers include underdiagnosis of eating disorders because providers in nonspecialty care lack training in eating disorder assessment (13). Only for 20% of those with an eating disorder seen by outpatient providers (e.g., primary care physicians) the disorder is detected and accurately diagnosed (14). Individuals with marginalized identities are much less likely to receive an eating disorder diagnosis and subsequent treatment (9, 15–21).
Sociocultural BTAs include cultural (e.g., stigma) (6, 10, 20, 22) and personal (e.g., attitudes) (6, 10) factors. Because of harmful racial-ethnic, gender, and socioeconomic biases and misperceptions regarding eating disorders, individuals from racial-ethnic minority groups, men, and those with lower income and educational attainment are less likely to receive eating disorder treatment (23–26). In addition, many individuals with eating disorders do not seek treatment because they do not believe that they are “sick enough” or they hold other personal attitudes that preclude access to care (6). Finally, barriers related to treatment quality include lack of access to inclusive (e.g., gender-affirming), culturally sensitive, evidence-based, transdiagnostic, and integrative (i.e., coordinated) treatment. Each of these issues may prevent individuals from seeking and returning for eating disorder treatment.
The Present Study
To our knowledge, no comprehensive nationwide survey of BTAs among a heterogeneous sample of treatment seekers with self-reported eating disorders and both with and without lifetime access to eating disorder treatment has been conducted in the United States. The aim of this study was to examine eating disorder treatment access by comprehensively assessing BTAs, providing initial estimates of BTAs in the United States among those attempting to access treatment, comparing BTAs across different demographic characteristics, and examining the association between BTAs and eating disorder symptomatology.
We hypothesized that most participants would report multiple lifetime treatment barriers; that financial barriers would be the most frequently reported treatment barrier; that individuals with marginalized identities would report elevated BTAs; and that, across BTAs, higher frequencies of reported treatment barriers would be associated with higher levels of eating disorder symptomatology.
Methods
Participants
We recruited individuals to participate in an anonymous online survey through social media advertisements (e.g., Instagram, Twitter, Facebook, and laboratory and nonprofit websites) between January 2021 and June 2022. Study advertisement materials and an unsigned consent form (i.e., preamble) at the beginning of the survey invited individuals ages ≥18 years living in the United States with a self-reported eating disorder and who had attempted to access treatment to complete the survey on behalf of themselves or a minor in their care. All study procedures were approved by the University of Louisville Institutional Review Board. The study was preregistered on Open Science Framework (https://doi.org/10.17605/OSF.IO/9FN5T).
BTA Measures
To assess BTAs, three study authors (S.P.B, R.E., C.A.L.) designed a 109-item self-report instrument, informed by a comprehensive literature search and drawn from our experiences as the chief executive officer of an eating disorder nonprofit organization (R.E.), the director of an eating disorder clinic (C.A.L.), and eating disorder treatment providers (R.E., C.A.L.). The survey assessed respondents’ demographic information, clinical characteristics, treatment barriers, treatment history, and treatment experiences. We used five domains to assess BTAs: financial barriers (14 items), identification barriers (four items), treatment quality barriers (six items), sociocultural barriers (three items), and geographic barriers (two items). We analyzed barriers both at the item level and by using domain totals. (See the online supplement to this article for an explanation of how these categories map onto standard public health frameworks [27] and for a copy of the survey.)
Eating Disorder Symptoms
We assessed eating disorder symptoms and behaviors with nine items from the Eating Disorder Examination Questionnaire, version 6 (EDE-Q) (28), which has demonstrated excellent factor validity, internal consistency, test-retest reliability, convergent validity, and discriminant validity (29, 30). Items selected represent items from the original four EDE-Q subscales (i.e., eating concerns, shape concerns, weight concerns, and restraint) along with the four questions about the frequency of eating disorder behavior. Items were selected before publication of validated EDE-Q short forms.
For this study, we calculated an approximate EDE-Q global score by averaging responses to the following five items assessing symptoms over the past 28 days: “Have you been deliberately trying to limit the amount of food you eat to influence your shape or weight (whether or not you have succeeded)”? “Have you gone long periods of time (8 waking hours or more) without eating anything at all in order to influence your shape or weight”? “Has thinking about food, eating, or calories made it very difficult to concentrate on things you are interested in (for example, working, following a conversation, or reading)”? “Have you had a definite fear that you might gain weight”? and “Has your weight or shape influenced how you think about (judge) yourself as a person”? For descriptive purposes, we assessed behavior use in the past 28 days, including binge eating, loss-of-control eating, self-induced vomiting, and compulsive exercise. Internal consistency in this sample was good (ωt=0.86).
Data Analysis
For analyses, BTAs were conceptualized in two different ways: first, we calculated each treatment barrier (i.e., category) by adding the total number of items endorsed within each domain to yield a total score (i.e., frequency). Second, we dichotomized each treatment barrier into “not endorsed” (i.e., item total=0) and “endorsed” (i.e., item total ≥1).
We used independent-samples t tests and one-factor, between-subject analyses of variance (ANOVAs) to examine whether significant differences in overall reported barriers existed across demographic characteristics (i.e., sexual orientation, race and ethnicity, gender, education level, employment status, yearly household income, disability status, urbanicity, or primary self-reported eating disorder diagnosis).
We conducted Pearson chi-square tests to detect possible systematic differences in the likelihood to endorse individual treatment barriers on the basis of demographic characteristics. Comparisons were conducted between heterosexual and sexual minority participants and among White participants; American Indian, Native Alaska, Native Hawaiian, or Pacific Islander participants (which we combined into one category for analyses); Asian or South Asian participants; Black/African American participants; and participants who reported multiple races. We conducted comparisons between transgender or nonbinary participants and cisgender participants and between cisgender women and cisgender men. We conducted comparisons among those with a high school education (i.e., those with some high school or a high school diploma), postsecondary education (i.e., some college, an associate degree, or a bachelor’s degree), master’s degree, or doctoral degree and among those who reported that they were employed full-time, part-time, or unemployed. Finally, we conducted comparisons between those with a household income ≤$70,000 and those with a household income >$70,000. Because of the number of comparisons, we used Bonferroni corrections to adjust for potential familywise error, with alpha set at 0.05 (31).
We used independent-samples t tests to examine whether endorsement of overall and individual BTAs was associated with age at eating disorder onset, age at eating disorder diagnosis, eating disorder treatment delay, eating disorder symptoms, or body mass index (BMI). Finally, we used Pearson correlations to examine the relationship between total number of barrier items reported and eating disorder symptoms. Statistical analyses were performed with SPSS, version 28 (32).
Results
Sample Selection and Characteristics
The data set contained 2,238 respondents. Of those, 243 (11%) were excluded from the analyses because they met any of the following criteria: failing traffic light validation checks (N=53; see the online supplement ), submitting duplicate responses based on contact information supplied (i.e., e-mail; N=47), having an international zip code (N=35), being <18 years old (N=73), responding on behalf of someone else (N=19), or not responding to any BTA items (N=16) (see the online supplement ). The final sample consisted of 1,995 participants. Demographic and clinical characteristics of the study participants are shown in Tables 1 and 2. (See the online supplement for the results including participants who responded on behalf of someone else.)
| Characteristic | N | % |
|---|---|---|
| Race-ethnicity | | |
| Hispanic | 126 | 6 |
| American Indian or Alaska Native | 1 | <1 |
| Asian or South Asian | 0 | — |
| Black or African American | 5 | <1 |
| Caucasian or White | 38 | 2 |
| Native Hawaiian or Pacific Islander | 0 | — |
| Other | 0 | — |
| Multiracial | 10 | 1 |
| Not reported | 72 | 4 |
| Non-Hispanic | 1,869 | 94 |
| American Indian or Alaska Native | 7 | <1 |
| Asian or South Asian | 38 | 2 |
| Black or African American | 30 | 2 |
| Caucasian or White | 1,707 | 86 |
| Native Hawaiian or Pacific Islander | 3 | <1 |
| Other | 17 | 1 |
| Multiracial | 61 | 3 |
| Not reported | 6 | <1 |
| Gender | | |
| Cisgender man | 28 | 1 |
| Cisgender woman | 1,732 | 87 |
| Transgender man | 29 | 1 |
| Transgender woman | 6 | <1 |
| Gender nonbinary | 170 | 9 |
| Prefer not to disclose | 21 | 1 |
| Not reported | 9 | <1 |
| Sexual orientation | | |
| Heterosexual | 1,179 | 59 |
| Gay or lesbian | 166 | 8 |
| Bisexual | 391 | 20 |
| Pansexual | 119 | 6 |
| Asexual | 73 | 4 |
| Queer | 31 | 2 |
| Demisexual | 7 | <1 |
| Polysexual | 1 | <1 |
| Graysexual | 3 | <1 |
| Homoflexible | 1 | <1 |
| Heteroflexible | 1 | <1 |
| Omnisexual | 1 | <1 |
| Unsure/questioning | 5 | <1 |
| Unlabeled | 5 | <1 |
| Not reported | 12 | 1 |
| Disability | | |
| Yes | 395 | 20 |
| No | 1,600 | 80 |
| Employment status | | |
| Employed full-time | 910 | 46 |
| Employed part-time | 302 | 15 |
| Unemployed | 212 | 11 |
| Student | 430 | 22 |
| Retired | 16 | 1 |
| Other | 123 | 6 |
| Not reported | 2 | <1 |
| Highest level of education | | |
| Some high school | 28 | 1 |
| Completed high school | 120 | 6 |
| Some college | 437 | 22 |
| Associate degree | 154 | 8 |
| Bachelor’s degree | 730 | 37 |
| Master’s degree | 453 | 23 |
| Professional degree | 71 | 4 |
| Not reported | 2 | <1 |
| Annual household income ($) | | |
| 0–10,000 | 166 | 8 |
| 10,001–20,000 | 198 | 10 |
| 20,001–30,000 | 187 | 9 |
| 30,001–40,000 | 137 | 7 |
| 40,001–50,000 | 155 | 8 |
| 50,001–60,000 | 160 | 8 |
| 60,001–70,000 | 114 | 6 |
| 70,001–80,000 | 104 | 5 |
| 80,001–90,000 | 86 | 4 |
| 90,001–100,000 | 87 | 4 |
| 100,001–150,000 | 205 | 10 |
| 150,001–200,000 | 83 | 4 |
| >200,000 | 120 | 6 |
| Prefer not to disclose | 168 | 8 |
| Not reported | 25 | 1 |
| Location | | |
| Urban | 704 | 35 |
| Suburban | 1,051 | 53 |
| Rural | 222 | 11 |
| Not reported | 18 | 1 |
a
The mean±SD age was 30±9 years, and mean body mass index was 25±9.
| Characteristic | N | % |
|---|---|---|
| Any self-reported eating disorder diagnosisa | | |
| Anorexia nervosa | 1,113 | 56 |
| Bulimia nervosa | 514 | 26 |
| Binge eating disorder | 238 | 12 |
| Atypical anorexia nervosa | 412 | 21 |
| Atypical bulimia nervosa | 43 | 2 |
| Avoidant or restrictive food intake disorder | 165 | 8 |
| Other specified feeding or eating disorder | 260 | 13 |
| Eating disorder not otherwise specified | 474 | 24 |
| Never received a formal eating disorder diagnosis but suspicion of having an eating disorder | 229 | 11 |
| Never received a formal eating disorder diagnosis and no suspicion of having an eating disorderb | 10 | 1 |
| Primary self-reported eating disorder diagnosis | | |
| Anorexia nervosa | 594 | 30 |
| Bulimia nervosa | 173 | 9 |
| Binge eating disorder | 146 | 7 |
| Atypical anorexia nervosa | 233 | 12 |
| Atypical bulimia nervosa | 8 | <1 |
| Avoidant/restrictive food intake disorder | 96 | 5 |
| Other specified feeding or eating disorder | 162 | 8 |
| Not listed | 128 | 6 |
| Recovered from eating disorder | 425 | 21 |
| Not reported | 30 | 2 |
| Eating disorder behavior episode frequency in previous 28 days (M±SD) | | |
| Binge eating | 4±11 | |
| Loss-of-control eating | 4±10 | |
| Purging | 3±15 | |
| Compensatory exercise | 6±9 | |
| Self-reported anxiety disorder diagnosisa | | |
| Generalized anxiety disorder | 1,436 | 72 |
| Social anxiety disorder | 420 | 21 |
| Panic disorder | 330 | 17 |
| Separation anxiety disorder | 52 | 3 |
| Agoraphobia | 56 | 3 |
| Specific phobia | 60 | 3 |
| Illness anxiety disorder | 22 | 1 |
| Obsessive-compulsive disorder | 553 | 28 |
| PTSD | 854 | 43 |
| Not formally diagnosed as having an anxiety disorder but frequent experience of anxiety | 183 | 9 |
| Not formally diagnosed as having an anxiety disorder and no suspicion of having an anxiety disorder | 12 | 1 |
| Self-reported mood disorder | | |
| Major depressive disorder | 965 | 48 |
| Bipolar I | 88 | 4 |
| Bipolar II | 181 | 9 |
| Persistent depressive disorder (dysthymia) | 131 | 7 |
| Premenstrual dysphoric disorder | 111 | 6 |
| Cyclothymic disorder | 13 | 1 |
| Disruptive mood dysregulation disorder | 4 | <1 |
| No diagnosis of mood disorder but suspicion of having one | 43 | 2 |
| No diagnosis of a mood disorder and no suspicion of having one | 6 | <1 |
| Other co-occurring psychiatric diagnoses | | |
| Borderline personality disorder | 223 | 11 |
| Dissociative identity disorder | 17 | 1 |
| Substance use disorder | 185 | 9 |
| Autism spectrum disorder | 68 | 3 |
| Self-reported suicide and nonsuicidal self-injury historya | | |
| Attempted suicide | 657 | 33 |
| Suicidal thoughts or suicidal ideation | 1,456 | 73 |
| Nonsuicidal self-injury | 1,136 | 57 |
| Age at eating disorder onset (M±SD years) | 14±5 | |
| Age at eating disorder diagnosis (M±SD years) | 21±7 | |
| Treatment delay (M±SD years) | 7±7 | |
| N of treatment episodes (M±SD) | 5±9 | |
| Sought treatment for an eating disorder | | |
| Yes | 1,793 | 90 |
| No | 161 | 8 |
| Not reported | 41 | 2 |
| Received specialized treatment for an eating disorder | | |
| Yes | 1,454 | 73 |
| No | 496 | 25 |
| Not reported | 45 | 2 |
| Levels of care receiveda | | |
| Inpatient | 643 | 32 |
| Residential | 739 | 37 |
| Partial hospital program | 874 | 44 |
| Intensive outpatient | 973 | 49 |
| Outpatient therapy | 1,569 | 79 |
| Outpatient dietitian | 1,207 | 61 |
| In recovery or fully recovered from eating disorder | | |
| Yes | 1,170 | 59 |
| No | 780 | 39 |
| Not reported | 45 | 2 |
| Health insurance during care seeking | | |
| Yes | 1,769 | 89 |
| No | 194 | 10 |
| Not reported | 32 | 2 |
| Step taken to afford treatmenta | | |
| Worked overtime or a second job | 417 | 21 |
| Took out a second mortgage | 38 | 2 |
| Sold personal belongings | 266 | 13 |
| Took out personal loan | 151 | 8 |
| Stopped paying student loans | 258 | 13 |
| Accrued credit card debt | 504 | 25 |
| Arranged a payment plan or sliding scale rate with a provider | 757 | 38 |
| Other | 575 | 29 |
| Negative experience with treatment due to unethical or negligent treatment | | |
| Yes | 897 | 45 |
| No | 948 | 48 |
| Not reported | 150 | 8 |
| Recommended for a level of care that was inappropriate and potentially financially motivated by the person who recommended it | | |
| Yes | 162 | 8 |
| No | 1,686 | 85 |
| Not reported | 147 | 7 |
| Received unhelpful or damaging comments or care from providers not offering specific care for eating disorder (e.g., primary care or other providers) | | |
| Yes | 1,451 | 73 |
| No | 450 | 23 |
| Not reported | 94 | 5 |
a
Participants could have multiple diagnoses, histories, levels of care, or steps taken.
b
These participants did not suspect that they had an eating disorder at the time they took the survey but may have previously had suspicions, so they were included in the analyses.
BTA Characteristics
BTA endorsement rates and total scores are provided in Table 3. Participants endorsed a mean±SD of 3.0±1.3 barrier domains and 8.7±5.4 barrier items. Of the sample, 96% (N=1,923) endorsed at least one barrier. Financial barriers were the most frequently endorsed barrier, followed by disorder identification, sociocultural, treatment quality, and geographic barriers.
| Barrier | N | % |
|---|---|---|
| Lifetime financial | 1,610 | 81 |
| Insurance does not cover the right level of care | 867 | 43 |
| Could not figure out insurance | 436 | 22 |
| Could not figure out how to file an appeal | 233 | 12 |
| Appeal was denied | 336 | 17 |
| Insurance plan limits the number of visits | 459 | 23 |
| Recommended level of care was denied | 521 | 26 |
| Prematurely discharged from the right level of care | 598 | 30 |
| Could not afford out-of-pocket costs even though insurance covered the treatment | 612 | 31 |
| Insurance coverage ended before patient or treatment team was ready | 551 | 28 |
| Deemed not medically sick enough to receive the level of care needed | 588 | 29 |
| Not eligible for insurance | 482 | 24 |
| No eating disorder providers in network | 726 | 36 |
| The best eating disorder providers did not accept any insurance | 786 | 39 |
| Recommended for treatment that insurance did not pay for | 678 | 34 |
| Geographic | 785 | 39 |
| No nearby eating disorder providers | 673 | 34 |
| No treatment centers in state | 385 | 19 |
| Disorder identification | 1,599 | 80 |
| Not diagnosed as having an eating disorder until it was much more entrenched and harder to treat | 1,119 | 56 |
| Discouraged from seeking treatment because illness did not seem severe enough | 867 | 43 |
| Prescribed weight loss or diet changes instead of recognition of a mental health issue | 628 | 31 |
| Misdiagnosed as having a general medical illness instead of an eating disorder | 261 | 13 |
| Sociocultural | 1,455 | 73 |
| Bias in the medical community against people like me | 544 | 27 |
| Bias in the eating disorder community against people like me | 465 | 23 |
| Weight stigma as a barrier to accessing quality eating disorder treatment | 1,386 | 69 |
| Treatment quality | 1,323 | 66 |
| Discharged from higher level of care without step-down care | 630 | 32 |
| When moving from one provider to another, no notes were shared and had to start from scratch | 594 | 30 |
| Family or loved ones were available to be involved but not included in treatment | 185 | 9 |
| Treatment received did not consider race, gender, sexuality, religion, or culture | 217 | 11 |
| Treatment received was focused exclusively on weight and not any underlying issues | 667 | 33 |
| Treatment received focused only on eating disorder and did not address other relevant diagnoses | 736 | 37 |
a
Participants could endorse multiple barriers. The weight stigma question was dichotomized from a 5-point Likert scale in which any response of “slightly a barrier” to “completely a barrier” was coded as 1 and any response of “not a barrier” was coded as 0.
Associations of Disparities in BTAs With Demographic Characteristics
The mean number of BTAs reported varied by sexual orientation, gender, employment status, household income, disability status, and eating disorder diagnosis. Sexual minority participants reported significantly more BTAs than did heterosexual participants (p<0.001). Nonbinary participants reported significantly more BTAs than did cisgender women or cisgender men (p<0.05 for both). Unemployed participants reported significantly more BTAs compared with participants employed full-time (p<0.001) or part-time (p<0.05). Participants with a household income ≤$70,000 reported significantly more BTAs than did those with a household income >$70,000 (p<0.001). Participants who reported a disability reported significantly more BTAs compared with those who did not report a disability (p<0.001). Participants with anorexia nervosa reported significantly more barriers than those with binge eating disorder (BED; p<0.001), avoidant/restrictive food intake disorder (ARFID; p<0.05), or participants who had recovered (p<0.001). Participants with bulimia nervosa reported significantly more BTAs than those with BED (p<0.05), ARFID (p<0.05), or recovered participants (p<0.001). Participants with other specified feeding or eating disorder (OSFED) reported significantly more BTAs than did those with BED (p<0.001), ARFID (p<0.001), or recovered participants (p<0.001). No significant differences were detected for race, ethnicity, education level, or urbanicity. (The t test and ANOVA results are shown in the online supplement .)
Additional BTA Findings
Results from the chi-square analyses of differences in BTA endorsements across demographic characteristics are shown in the online supplement . Results from the independent-samples t test analyses of BTAs, eating disorder symptoms, illness trajectory, and BMI are shown in Table 4. The total number of BTAs was positively associated with the approximated EDE-Q global score (r=0.22, p<0.001).
| Barrier | Endorsed | Not endorsed | t | df | p | ||
|---|---|---|---|---|---|---|---|
| M | SD | M | SD | ||||
| Lifetime | | | | | | | |
| Age at eating disorder onset (years) | 14 | 5 | 15 | 5 | 1.09 | 1,948 | .28 |
| Age at eating disorder diagnosis (years) | 21 | 7 | 21 | 7 | −0.63 | 1,687 | .53 |
| Eating disorder treatment delay (years) | 7 | 7 | 6 | 7 | −1.18 | 1,669 | .24 |
| N of eating disorder symptoms | 3 | 2 | 3 | 1 | −6.12 | 1,954 | <.001 |
| BMI | 25 | 9 | 25 | 9 | 0.58 | 1,423 | .56 |
| Geographic | | | | | | | |
| Age at eating disorder onset (years) | 14 | 5 | 14 | 5 | −0.38 | 1,948 | .71 |
| Age at eating disorder diagnosis (years) | 21 | 7 | 21 | 7 | 0.56 | 1,687 | .58 |
| Eating disorder treatment delay (years) | 6 | 7 | 7 | 7 | 0.70 | 1,669 | .49 |
| N of eating disorder symptoms | 3 | 2 | 3 | 2 | −5.89 | 1,954 | <.001 |
| BMI | 24 | 8 | 25 | 9 | 3.43 | 1,423 | <.001 |
| Disorder identification | | | | | | | |
| Age at eating disorder onset (years) | 14 | 5 | 15 | 5 | 4.56 | 1,948 | <.001 |
| Age at eating disorder diagnosis (years) | 21 | 7 | 18 | 6 | −7.57 | 541.10 | <.001 |
| Eating disorder treatment delay (years) | 7 | 7 | 3 | 5 | −11.52 | 630.62 | <.001 |
| N of eating disorder symptoms | 3 | 1 | 3 | 2 | −4.76 | 1,954 | <.001 |
| BMI | 25 | 9 | 22 | 6 | −8.04 | 668.86 | <.001 |
| Sociocultural | | | | | | | |
| Age at eating disorder onset (years) | 14 | 5 | 15 | 5 | 4.42 | 1,941 | <.001 |
| Age at eating disorder diagnosis (years) | 21 | 7 | 20 | 7 | −1.94 | 1,685 | .05 |
| Eating disorder treatment delay (years) | 7 | 8 | 5 | 6 | −5.14 | 931.29 | <.001 |
| N of eating disorder symptoms | 3 | 2 | 2 | 1 | −9.61 | 1,947 | <.001 |
| BMI | 26 | 9 | 22 | 7 | −8.34 | 991.37 | <.001 |
| Treatment quality | | | | | | | |
| Age at eating disorder onset (years) | 14 | 5 | 15 | 5 | 1.83 | 1,948 | .07 |
| Age at eating disorder diagnosis (years) | 20 | 7 | 22 | 8 | 5.06 | 722.62 | <.001 |
| Eating disorder treatment delay (years) | 6 | 7 | 8 | 9 | 3.44 | 680.03 | <.001 |
| N of eating disorder symptoms | 3 | 2 | 3 | 2 | −3.57 | 1,954 | <.001 |
| BMI | 24 | 8 | 25 | 9 | 1.99 | 1,423 | .05 |
Discussion
We found that individuals who sought treatment for eating disorders endorsed at least three BTA domains and that participants with marginalized identities or an OSFED diagnosis reported the greatest inequities in accessing treatment for these disorders. Financial barriers were most frequently endorsed, compared with the other BTA categories. Given the increasing prevalence of these impairing and life-threatening disorders, it is important to increase access to eating disorder treatment. These data shed light on why only 20% of individuals with eating disorders in the United States access lifesaving care and pinpoint specific inequities that need improvement to increase access to eating disorder treatments.
Participants with marginalized identities were more likely to endorse barriers related to treatment quality and sociocultural factors than those with historically represented identities. This finding is consistent with those of earlier literature (23–26) highlighting that stigma and attitudes about treatment disproportionally affect individuals with marginalized identities. We also found that participants in rural areas, those who reported a household income ≤$70,000, and those with anorexia nervosa, bulimia nervosa, or OSFED were more likely to report geographic barriers than those in suburban or urban areas, those with a household income >$70,000, and those with BED or who reported having recovered, respectively. Most individuals must travel out of state to receive specialty eating disorder treatment (12), consistent with the fact that those in rural areas and those reporting a household income ≤$70,000 may experience geographic barriers. In addition, participants with anorexia nervosa, bulimia nervosa, or OSFED may be more likely to be referred to a higher level of care, which may require long-distance travel.
We found that participants who were more likely to report any of the BTAs reported more severe eating disorder symptoms. We also found that those who were more likely to report barriers due to disorder identification and sociocultural factors reported longer delays in treatment, lower age at eating disorder onset, higher BMI, and more eating disorder symptoms. Because stigma can affect the identification of eating disorders among those with marginalized identities (23–26), these findings support the observation that individuals with these identities experience longer treatment delays and report more eating disorder symptoms. Relatedly, participants who were more likely to report BTAs related to treatment quality reported less delay in treatment and lower age at receiving a diagnosis of eating disorder. Finally, we found that those who reported higher rates of lifetime treatment barriers had elevated eating disorder symptoms.
Evidence of treatment disparities may be explained by unique and compounded structural barriers faced by individuals with marginalized identities (33). For example, individuals with a sexual minority identity might experience both identification (e.g., delay in diagnosis) and sociocultural (e.g., stigmatizing medical experiences) barriers. Moreover, our results suggest that those experiencing multiple barriers may be not only less likely to access treatment but also more likely to experience elevated eating disorder pathology, which could lead to higher chronicity and vulnerability to relapse (34, 35).
Treatment inequities among individuals with eating disorder diagnoses could be related to real or perceived differences in severity among disorders. Individuals with some eating disorders may experience specific financial and geographic barriers. Evidence suggests that those who receive an eating disorder diagnosis on the basis of being underweight are likely to be referred for intensive eating disorder treatment (36), such as, for example, individuals with anorexia nervosa, who may need to travel out of state for acute medical stabilization. In contrast, individuals with bulimia nervosa, BED, or OSFED may be admitted to outpatient care, which is more accessible. However, individuals with bulimia nervosa, BED, or OSFED may be more likely to experience sociocultural barriers because of providers’ weight bias or because of the misconception that they are not “sick enough” compared with those with anorexia nervosa (6).
Clinicians and treatment centers should continue to advocate for and negotiate with insurance companies for affordable reimbursement rates to help alleviate financial barriers. Medical and mental health providers need to be trained on detecting eating disorders and conducting evidence-based, culturally competent screenings (e.g., with the SCOFF questionnaire) (37). Such training activities could help decrease potential provider biases and increase identification of eating disorders in outpatient settings, where most individuals with eating disorders are initially seen (14). In addition, state-specific referral resources should be created and maintained so that nonspecialty providers can easily refer their patients to specialists. Establishing legislative groups, such as the Kentucky or Missouri Eating Disorder Council, could be helpful for implementation of statewide advocacy, educational materials, and resources. To help alleviate treatment quality barriers, treatment centers should expand group materials to include diverse examples and activities (e.g., manuals should be inclusive of race, ethnicity, gender identity, sexuality, religious affiliation, and diverse cultures). Efforts to reduce weight stigma in health care may help alleviate BTAs related to disorder identification, sociocultural factors, and treatment quality, especially for individuals with larger bodies. Finally, to reduce geographic barriers, treatment centers could provide financial aid to those who need to travel out of state.
Some limitations of this study should be considered. We did not use epidemiological methods, so we could not estimate prevalence rates of BTAs. Participants self-reported their eating disorder and co-occurring diagnoses, precluding generalizability to populations with clinical eating disorders because of potential errors in self-reported diagnoses. Moreover, generalizability of the current findings may be limited to individuals who seek treatment in their lifetime, including those who suspect that they have an eating disorder. Indeed, findings from a comprehensive review of the literature (10) suggest several barriers to treatment before help seeking (e.g., failure to recognize one’s eating disorder or its seriousness and lack of awareness of treatment resources) that were not captured in the present study. In addition, we approximated the EDE-Q global score with an unvalidated subset of items from the EDE-Q, selected by using clinical judgment (by R.E. and C.A.L.) at the expense of psychometric strength. Therefore, interpretation of the findings regarding the relationships between BTAs and eating disorder symptoms should be interpreted with caution. Finally, participants who provided a response on behalf of someone else were removed from the analyses because of potential sample differences. Future research should examine potential differences in barriers between individuals who self-report BTAs and those who are collateral reporters to characterize access to eating disorder treatment from the perspective of caregivers and social support networks.
The strengths of the study included a low burden on participants due to the short duration of the survey, large sample size and diversity, data integrity, and the assessment of treatment experiences alongside systemic barriers to access. The study included responses across all 50 U.S. states and Puerto Rico.
Conclusions
To our knowledge, this study is the most comprehensive report to date on perceived barriers to eating disorder treatment access among individuals seeking treatment in the United States. The results highlight that barriers to eating disorder treatment access are pervasive and that treatment inequities affect individuals from various treatment-seeking stages and with different demographic characteristics and self-reported diagnoses. Of note, this study included individuals who suspected that they had an eating disorder (with or without receiving a diagnosis) and reported efforts to seek care. Although lack of recognition of an eating disorder or of motivation to seek treatment often delays or precludes seeking of eating disorder treatment, it is critical to attend to barriers that arise once an individual decides to pursue treatment. Our results reflect a severe system-level failure to meet the needs of individuals who report a desire and commitment to seek help. Given evidence suggesting that historically underrepresented groups are disproportionately excluded from eating disorder treatment, public health efforts to eliminate structural barriers to eating disorder treatment should prioritize equity and inclusion. Removing structural barriers to eating disorder treatment may lead to increased treatment seeking, decreased chronicity, and, ultimately, attenuation of the personal and societal burden accrued by these disorders.
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References
1.
Arcelus J, Mitchell AJ, Wales J, et al: Mortality rates in patients with anorexia nervosa and other eating disorders: a meta-analysis of 36 studies. Arch Gen Psychiatry 2011; 68:724–731
2.
Keel PK, Dorer DJ, Eddy KT, et al: Predictors of mortality in eating disorders. Arch Gen Psychiatry 2003; 60:179–183
3.
Smink FRE, van Hoeken D, Hoek HW: Epidemiology of eating disorders: incidence, prevalence and mortality rates. Curr Psychiatry Rep 2012; 14:406–414
4.
Udo T, Grilo CM: Psychiatric and medical correlates of DSM-5 eating disorders in a nationally representative sample of adults in the United States. Int J Eat Disord 2019; 52:42–50
5.
Galmiche M, Déchelotte P, Lambert G, et al: Prevalence of eating disorders over the 2000–2018 period: a systematic literature review. Am J Clin Nutr 2019; 109:1402–1413
6.
Hart LM, Granillo MT, Jorm AF, et al: Unmet need for treatment in the eating disorders: a systematic review of eating disorder specific treatment seeking among community cases. Clin Psychol Rev 2011; 31:727–735
7.
Streatfeild J, Hickson J, Austin SB, et al: Social and economic cost of eating disorders in the United States: evidence to inform policy action. Int J Eat Disord 2021; 54:851–868
8.
Personal Health Care (PHC) Indices—Overall 2019 [Data Set]. Baltimore, Centers for Medicare and Medicaid Services. http://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/Downloads/Tables.zip. Accessed Oct 4, 2022
9.
Cachelin FM, Rebeck R, Veisel C, et al: Barriers to treatment for eating disorders among ethnically diverse women. Int J Eat Disord 2001; 30:269–278
10.
Ali K, Farrer L, Fassnacht DB, et al: Perceived barriers and facilitators towards help-seeking for eating disorders: a systematic review. Int J Eat Disord 2017; 50:9–21
11.
Income and Poverty in the United States: 2020. Washington, DC, US Census Bureau, 2021. https://www.census.gov/library/publications/2021/demo/p60-273.html. Accessed April 12, 2024
12.
Cahill M, Pruitt A, Wagoner KO: Kentucky Eating Disorder Council Annual Report 2021. Frankfort, 2022
13.
Becker AE, Franko DL, Speck A, et al: Ethnicity and differential access to care for eating disorder symptoms. Int J Eat Disord 2003; 33:205–212
14.
Hach I, Ruhl UE, Rentsch A, et al: Recognition and therapy of eating disorders in young women in primary care. J Public Health 2005; 13:160–165
15.
Cachelin FM, Striegel-Moore RH: Help seeking and barriers to treatment in a community sample of Mexican American and European American women with eating disorders. Int J Eat Disord 2006; 39:154–161
16.
Kamody RC, Grilo CM, Udo T: Disparities in DSM-5 defined eating disorders by sexual orientation among US adults. Int J Eat Disord 2020; 53:278–287
17.
Mitchison D, Hay PJ: The epidemiology of eating disorders: genetic, environmental, and societal factors. Clin Epidemiol 2014; 6:89–97
18.
Presskreischer R, Steinglass JE, Anderson KE: Eating disorders in the US Medicare population. Int J Eat Disord 2022; 55:362–371
19.
Schaumberg K, Welch E, Breithaupt L, et al: The science behind the Academy for Eating Disorders’ nine truths about eating disorders. Eur Eat Disord Rev 2017; 25:432–450
20.
Sonneville KR, Lipson SK: Disparities in eating disorder diagnosis and treatment according to weight status, race/ethnicity, socioeconomic background, and sex among college students. Int J Eat Disord 2018; 51:518–526
21.
Strother E, Lemberg R, Stanford SC, et al: Eating disorders in men: underdiagnosed, undertreated, and misunderstood. Eat Disord 2012; 20:346–355
22.
Foran AM, O’Donnell AT, Muldoon OT: Stigma of eating disorders and recovery-related outcomes: a systematic review. Eur Eat Disord Rev 2020; 28:385–397
23.
Evans EJ, Hay PJ, Mond J, et al: Barriers to help-seeking in young women with eating disorders: a qualitative exploration in a longitudinal community survey. Eat Disord 2011; 19:270–285
24.
Progovac AM, Cortés DE, Chambers V, et al: Understanding the role of past health care discrimination in help-seeking and shared decision-making for depression treatment preferences. Qual Health Res 2020; 30:1833–1850
25.
Drury CA, Louis M: Exploring the association between body weight, stigma of obesity, and health care avoidance. J Am Acad Nurse Pract 2002; 14:554–561
26.
Waller G, Schmidt U, Treasure J, et al: Ethnic origins of patients attending specialist eating disorders services in a multiethnic urban catchment area in the United Kingdom. Int J Eat Disord 2009; 42:459–463
27.
Stefl ME, Prosperi DC: Barriers to mental health service utilization. Community Ment Health J 1985; 21:167–178
28.
Fairburn CG, Beglin SJ: Assessment of eating disorders: interview or self-report questionnaire? Int J Eat Disord 1994; 16:363–370
29.
Mond JM, Hay PJ, Rodgers B, et al: Validity of the Eating Disorder Examination Questionnaire (EDE-Q) in screening for eating disorders in community samples. Behav Res Ther 2004; 42:551–567
30.
Berg KC, Peterson CB, Frazier P, et al: Psychometric evaluation of the Eating Disorder Examination and Eating Disorder Examination–Questionnaire: a systematic review of the literature. Int J Eat Disord 2012; 45:428–438
31.
Bland JM, Altman DG: Multiple significance tests: the Bonferroni method. BMJ 1995; 310:170
32.
IBM SPSS Statistics for Windows, Version 28.0. Armonk, NY, IBM Corp: 2021
33.
Butkus R, Rapp K, Cooney TG, et al: Envisioning a better US health care system for all: reducing barriers to care and addressing social determinants of health. Ann Intern Med 2020; 172:S50–S59
34.
Carter JC, Mercer-Lynn KB, Norwood SJ, et al: A prospective study of predictors of relapse in anorexia nervosa: implications for relapse prevention. Psychiatry Res 2012; 200:518–523
35.
Vall E, Wade TD: Predictors and moderators of outcomes and readmission for adolescent inpatients with anorexia nervosa: a pilot study. Clin Psychol 2017; 21:143–152
36.
Yager J, Devlin MJ, Halmi KA, et al: Guideline watch (August 2012): practice guideline for the treatment of patients with eating disorders, 3rd edition. FOCUS 2014; 12:416–431
37.
Morgan JF, Reid F, Lacey JH: The SCOFF questionnaire: assessment of a new screening tool for eating disorders. BMJ 1999; 319:1467–1468
Information & Authors
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History
Received: 23 April 2023
Revision received: 27 February 2024
Accepted: 4 March 2024
Published online: 8 May 2024
Published in print: October 01, 2024
Keywords
Authors
Competing Interests
The authors report no financial relationships with commercial interests.
Funding Information
This research was funded by the National Eating Disorder Association (grant OGMB210612).
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