Binge Eating among Women Veterans in Primary Care: Comorbidities and Treatment Priorities

Women's Health Issues xxx-xx (2016) 1–9

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Original article

Binge Eating among Women Veterans in Primary Care: Comorbidities and Treatment Priorities Diane L. Rosenbaum, PhD a,b,*, Rachel Kimerling, PhD a,c, Alyssa Pomernacki, MPH a, Karen M. Goldstein, MD d,e, Elizabeth M. Yano, PhD, MSPH f,g, Anne G. Sadler, PhD, RN h,i, Diane Carney, MA a, Lori A. Bastian, MD, MPH j,k, Bevanne A. Bean-Mayberry, MD f,l, Susan M. Frayne, MD, MPH a,m a

VA Palo Alto Health Care System, Center for Innovation to Implementation, Palo Alto, California Department of Psychology, Drexel University, Philadelphia, Pennsylvania c VA Palo Alto Health Care System, National Center for PTSD, Palo Alto, California d Durham VA Medical Center, Health Services Research and Development (HSR&D) Center for Health Services Research in Primary Care, Durham, North Carolina e Department of Medicine, Duke University, Durham, North Carolina f VA Greater Los Angeles Healthcare System, Health Services Research and Development (HSR&D) Center for the Study of Healthcare Innovation, Implementation and Policy, Los Angeles, California g Department of Health Policy and Management, UCLA Fielding School of Public Health, Los Angeles, California h Iowa City VA Health Care System, Health Services Research and Development (HSR&D) Center for Comprehensive Access and Delivery Research and Evaluation, Iowa City, Iowa i Department of Psychiatry, University of Iowa Hospitals and Clinics, Iowa City, Iowa j VA Connecticut Healthcare System, Health Services Research and Development (HSR&D) Pain Research, Informatics, Multi-Morbidities, and Education Center, West Haven, Connecticut k Division of General Internal Medicine, University of Connecticut Health Center, Farmington, Connecticut l Department of Medicine, University of California Los Angeles (UCLA) David Geffen School of Medicine, Los Angeles, California m Division of General Medical Disciplines, Stanford University, Stanford, California b

Article history: Received 6 August 2015; Received in revised form 8 February 2016; Accepted 9 February 2016

a b s t r a c t Background: Little is known about the clinical profile and treatment priorities of women with binge eating disorder (BED), a diagnosis new to the fifth edition of Diagnostic and Statistical Manual of Mental Disorders. We identified comorbidities and patients’ treatment priorities, because these may inform implementation of clinical services. Methods: Data were collected from women veteran primary care patients. Analyses compared those who screened positive for BED (BEDþ), and those without any binge eating symptoms (BED). Results: Frequencies of comorbid medical and psychological disorders were high in the BEDþ group. The BEDþ group’s self-identified most common treatment priorities were mood concerns (72.2%), weight loss (66.7%), and body image/ food issues (50%). Among those with obesity, a greater proportion of the BEDþ group indicated body image/food issues was their top treatment priority (12.9% vs. 2.8%; p < .01), suggesting that these patients may be more apt to seek treatment beyond weight management for their problematic eating patterns. Conclusions: Women primary care patients with BED demonstrate high medical and psychological complexity; their subjective treatment priorities often match objective needs. These findings may inform the development of targeted BED screening practices for women with obesity in primary care settings, and the eventual adoption of patient-centered BED treatment resources. Published by Elsevier Inc. on behalf of the Jacobs Institute of Women's Health.

* Correspondence to: Diane L. Rosenbaum, PhD, Department of Psychology, Drexel University. Phone: ---; fax: ---. E-mail address: [email protected] (D.L. Rosenbaum).

Binge eating disorder (BED) is a new diagnosis in the fifth edition of Diagnostic and Statistical Manual of Mental Disorders (DSM-5). Diagnostic criteria include binge eating episodes

1049-3867/$ - see front matter Published by Elsevier Inc. on behalf of the Jacobs Institute of Women's Health. http://dx.doi.org/10.1016/j.whi.2016.02.004

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(eating an objectively large amount of food while feeling a loss of control) in the absence of compensatory behaviors, accompanied by associated features and marked distress or impairment (American Psychiatric Association, 2013). Previously listed as a “disorder for future research” (American Psychiatric Association, 2000), inclusion of BED in the DSM-5 distinguishes this condition from other eating disorders, and from less distressing obesity-related overeating behaviors. With lifetime prevalence rates of 2.0% to 3.5% in the general population (Hudson, Hiripi, Pope, & Kessler, 2007), BED is the most common eating disorder in the United States. As a frequent comorbidity of obesity (Hudson et al., 2007), BED recently has been identified as a public health problem in need of greater clinical attention (Kessler et al., 2013). BED is particularly important as a women’s health issue, because it is more common among women than men (Hudson et al., 2007) and may have distinct patterns of negative affect in women compared with men (Rosenbaum & White, 2015). Further, although there have been efforts to enhance implementation of behavioral treatments for obesity within VA (Damschoder & Lowery, 2013), there have been few published studies that may inform eating disorder interventions in VA settings. In particular, it is important to examine BED in the context of women veterans, given that they are a rapidly growing group (Women Veterans Task Force, 2012) with risk factors for the development of BED, such as high rates of trauma exposure (Brewerton, Rance, Dansky, O’Neil, & Kilpatrick, 2014; Zinzow, Grubaugh, Monnier, Suffoletta-Maierle, & Frueh, 2007). Because there is a need to mitigate the problem of BED among this group, steps to inform effective management of binge eating among women veterans are needed, including a better understanding of the clinical profile of women veterans with BED. Mental health comorbiditiesdespecially depression and anxietydoften are associated with BED (Hudson et al., 2007; Whiteside et al., 2007). The high rate of mental health disorders can be understood through theoretical models of binge eating, which point to negative affect as critical to the development and maintenance of BED (Fairburn, 2008; Fairburn, Cooper, & Shafran, 2003; Ivezaj, Kalebjian, Grilo, & Barnes, 2014); binge eating may reflect a patient’s attempt to limit the salience of emotional distress and “escape from awareness” (Heatherton & Baumeister, 1991), for example, in the context of prior trauma (Harrington, Crowther, Henrickson, & Mickelson, 2006). Comorbid conditions that arise through shared vulnerability factors (e.g., prior trauma) or current distress may potentially complicate BED treatment. In addition to better understanding the clinical profile of women veterans with BED, enhancing clinical care necessitates an assessment of their treatment priorities. Clinically significant BED is more common among women than men (Hudson et al., 2007; Rosenbaum & White, 2015), and evidence suggests that sensitivity to women veteran’s priorities and preferences is critical to delivery of patient-centered care (Kimerling et al., 2015; Yano, Haskell, & Hayes, 2014). Therefore, understanding the extent to which women veterans with indicators of a potential to utilize mental health and/or behavioral medicine services rank BED management as one of their top mental health treatment concerns may help providers to anticipate which patients are most likely to invest their time and energy engaging in treatment for BED. This knowledge may aid clinicians in selecting specific resources and referrals to pursue with clients who may have multiple significant comorbidities, perhaps improving the efficiency of clinical care. Yet, research examining women’s mental health and behavioral medicine treatment priorities in

the context of BED is absent from the current literature. This paper adds to the current literature by identifying objective care needs (i.e., comorbidities) and examining them alongside patients’ self-identified priorities for care. In addition to the limited literature on treatment priorities in the context of BED, the limited examination of BED in primary care settings, where mental health conditions of any variety are most likely to present (Regier, Goldberg, & Taube, 1978; Wang et al., 2005), is a notable gap in the literature. As noted by Grilo, White, Barnes, and Masheb (2013), much of the existing BED literature draws from specialty clinical research populations (e.g., eating disorder research clinics), which is potentially problematic. Specialty research clinics may not be representative of many patients with BED owing to the greater severity of eating pathology that occur within those settings, and demographic differences (Wilfley, Pike, Dohm, Striegel-Moore, & Fairburn, 2001). Although limited data are available, primary care settings seem to be a valuable location for identifying and evaluating BED (Grilo et al., 2014; Ivezaj et al., 2014; Westerberg & Waitz, 2013). Moreover, primary care clinics are likely to serve as the gateway €l et al., 2010) and mental for treatment of obesity (Hitchcock Noe health issues (Post & Van Stone, 2008). However, to our knowledge, no published studies have examined patient-identified treatment priorities among women with BED in primary care. The main goals of the current study were to characterize comorbidity in patients with BED in primary care settings and to investigate whether binge eating influences women’s prioritization of eating and weight-related treatment services or services for other health issues. Among patients across four geographically dispersed Veterans Health Administration (VA) primary care clinics, we compared the 1) profile of potential treatment indicators (i.e., trauma exposure, psychological symptoms, comorbidities) and 2) patient-driven priorities for mental health care, between women with BED to those without binge eating symptoms. Because BED is associated closely with obesity (Hudson et al., 2007; Hudson et al., 2006; Tanofsky-Kraff et al., 2013), we performed additional analyses among a subset of patients with this comorbidity. Method Study Design and Setting Women veterans completed interviewer-administered surveys in primary care clinics as part of a larger cross-sectional study (Kimerling et al., 2015) conducted at the four founding sites of the VA Women’s Health Practice-Based Research Network (Frayne et al., 2013). The VA Women’s Health PracticeBased Research Network, a partnership of clinicians and researchers across 60 sites nationally, provides infrastructure for evaluating health services-related research questions among a broad base of women veterans through multisite research endeavors. The VA Central Institutional Review Board approved this study. Sample To achieve a representative sample of established primary care patients (at least two visits in the prior year), we identified potentially eligible women from administrative data in the National Patient Care Database (VHA Medical SAS Outpatient Datasets and Inpatient Encounters Dataset FY2009, 2011). Potentially eligible women were notified about the study via a

D.L. Rosenbaum et al. / Women's Health Issues xxx-xx (2016) 1–9

mailed flyer and informed that study staff might approach them at their primary care visit. Participants were recruited sequentially over a span of 8 months. Trained staff obtained informed consent and assessed inclusion criteria (i.e., no active psychotic disorder diagnosis, serious cognitive impairment, acute medical illness, or current emotional crisis). A total of 687 eligible primary care patients were contacted for recruitment, and 515 agreed to participate. Of these, most (484 women or 94% of primary care patients) were determined to be mental health stakeholders, defined as those with subjective or objective needs for mental health services. Mental health service need was broadly defined to include both traditional psychological services (e.g., psychotherapy for mood and anxiety concerns), and behavioral medicine care (e.g., smoking cessation, weight management, pain management) that could occur in the absence of a psychiatric diagnosis or prior mental health care. For example, a woman with a positive screen for pain on our objective measure (i.e., 36-Item Short Form Health Survey– Bodily pain subscale items; McHorney, Ware, & Raczek, 1999) and/or an expressed interest in receiving help from a mental health professional such as a psychologist, psychiatrist, counselor, social worker, or therapist would be identified as being a mental health stakeholder, even though in this scenario she may not have a mental health diagnosis. We chose to evaluate a stakeholder sample to learn more about priorities for care for several reasons: 1) it would not be fruitful to ask women without mental health/behavioral medicine service needs to rank their priorities in this domain of care, 2) this group, by its nature, represents those with a meaningful interest in the services offered through VA mental health care and therefore yields the most informative data pertaining to service delivery, and 3) the concerns and priorities identified among stakeholders are more likely to be representative of the larger population of women veterans who may utilize enhanced service offerings. Statistically speaking, choosing a stakeholder sample (i.e., those with the potential to utilize mental health services) confers protection against obtaining a zero inflated data distribution by omitting those for whom priorities of care would not be applicable. Therefore, our sample totaled 484 women veteran primary care patients with the potential to also use mental health services. The potential to use mental health services and/or behavioral medicine care was determined via assessment observed need and subjective report. Observed need was defined as at least one positive screen for psychological distress as assessed by screening questionnaires and survey items. The full description of psychological and behavioral medicine screening instruments appears in the Appendix. Subjective mental health need, including behavioral medicine care delivered by mental health professionals, was assessed via a survey item asking participants whether, in the past year, they “wanted (or needed) help with personal or family problems from a mental health professional, such as a psychologist, psychiatrist, counselor, social worker, or therapist.” This method of identifying perceived mental health care needs has been used in previous research (Kimerling & Baumrind, 2005).

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Demographics and military service (i.e., recent deployment) were also self-reported. After completion of the questionnaires, interviewers administered a card sorting activity in which participants rank ordered up to five mental health services as their priorities for care. Measures Questionnaires Binge eating was assessed using the Eating Disorder module of the Patient Health Questionnaire (Spitzer, Kroenke, & Williams, 1999; Striegel-Moore et al., 2010). This brief screening measure has strong sensitivity (100%) and specificity (92%) for detecting BED. Of note, the Eating Disorder module was developed before the introduction of BED in DSM-5, and therefore differs slightly from the current diagnostic criteria in its assessment of binge eating frequency. Specifically, the Eating Disorder module asks if binge eating occurs as often as twice per week on average for the last 3 months, whereas the DSM-5 criteria specify that episodes need only occur on average once per week for 3 months (American Psychiatric Association, 2013; Striegel-Moore et al., 2010). Therefore, the Eating Disorder module may potentially underestimate true cases of BED given DSM-5 criteria. The Patient Health Questionnaire, which has demonstrated diagnostic validity similar to a clinicianadministered diagnostic evaluation (Spitzer et al., 1999), was used to screen for anxiety and depression symptoms. The depression (Kroenke, Spitzer, & Williams, 2003) and anxiety € we, 2007) screening (Kroenke, Spitzer, Williams, Monahan, & Lo items have good specificity and sensitivity (Kroenke, Spitzer, €we, 2009). Military sexual harassment and asWilliams, & Lo sault were assessed using the sexual harassment subscale of the Deployment Risk and Resilience Inventory (King, King, & Vogt, 2003). This measure has good psychometrics, including internal consistency (a ¼ 0.86–0.88; King, King, Vogt, Knight, & Samper, 2006). Card sorting exercise The interviewer presented a stack of 15 cards, each listing a mental health service with a brief, general, description of the symptoms that may be served within the specialty. For example, the “Body Image or Food Issues” card contained the description: “You may have trouble controlling or changing your eating or how you view your body.” The cards also contained brief descriptions of interventions associated with each service (e.g., individual psychotherapy). As detailed previously (Kimerling et al., 2015), the following 15 categories of mental health services were included among the cards: body image or food issues, weight loss/weight management, mood (including anxiety, depression, and general distress), posttraumatic stress disorder (PTSD), military sexual harassment and/or assault, smoking cessation, intimate partner violence, parenting support, family therapy, couple’s counseling, sexual functioning/intimacy, substance use, coping with medical problems/chronic illness, pain management, and sleep difficulties. The services listed on the cards corresponded with the mental health service needs that were assessed via screening measures.

Procedures Other Data Sources In conjunction with the primary care visit, interviewers administered questionnaires, including validated screens for binge eating, anxiety symptoms, depression symptoms, and exposure to military sexual harassment and assault.

Primary care clinic use, height, and weight data, were gathered through medical records and the VA’s National Patient Care Database (VA Information Resource Center, 2011). Diagnostic

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D.L. Rosenbaum et al. / Women's Health Issues xxx-xx (2016) 1–9

information was gathered from the active problem list in participants’ electronic medical records. Medical and psychological conditions were classified based on ICD-9-CM codes (National Center for Health Statistics, 2010). Data Analysis Descriptive analyses of body mass index (BMI), sociodemographic, and use characteristics examined all participants (n ¼ 484). Subsequent analyses testing between-group differences were conducted in the Analytic Sample (n ¼ 454) that excluded women with distinct clinical presentations: a) women with subclinical BED (n ¼ 19), b) women with bulimia nervosa (n ¼ 9), c) women missing information on the bulimia nervosa screener (n ¼ 2), and d) women with anorexia nervosa (n ¼ 0). This made it possible to perform main analyses comparing two well-defined groups: those who screened positive for BED (BEDþ) and those without any binge eating symptoms (BED). Using c2 and t tests, we compared the BEDþ versus BED groups on BMI, sociodemographic and health care use characteristics, exposure to military sexual harassment and/or assault, psychological symptoms (anxiety, depression), psychological disorders (adjustment disorders, PTSD, acute stress disorder, anxiety disorders, mood disorders, alcohol use disorder/substance use disorder, and sleep disorders), obesity, and counts of medical disorders (categorized as 4, 6, or 8 chronic medical conditions). Then, we estimated unadjusted and age-adjusted odds ratios (OR, AOR) and 95% confidence intervals (CI) from a series of logistic regression analyses sequentially examining each characteristic (exposure to military sexual harassment and/or assault, psychological symptoms, psychological disorders, obesity, and counts of medical disorders) as a function of BED status (BEDþ vs. BED). Because there were no differences between those with and without positive BED screenings with regard to age, the primary reason that we elected to adjust for age was that it was likely associated with variables of interest (i.e., number of comorbid medical conditions, frequent primary

care visits). Based on the literature, we suspected that obesity would be the primary confounding factor in our analyses. Therefore, we also elected to conduct all of our analyses in the overall sample, as well as in a subsample of those with obesity based on a BMI of 30 kg/m2 (obesity subsample; n ¼ 208) to address the potential that some of the associated conditions and treatment priorities may be driven by the excess weight that is often associated with BED. Finally, we used c2 analyses to compare the BEDþ versus BED groups on priorities for mental health care. Patients were able to rank order up to 5 priorities for care; we examined both overall treatment priorities (issues that women included, at any rank, among their five possible selections) and first-ranked treatment priorities (i.e., ranked first out of the five selected priorities). All data analyses used SPSS Statistics Version 21 (IBM Corp., 2012). Results Descriptive Analyses The rates of positive BED screens (7.5%) and obesity (47.0%) were high. Overall, 22.0% had class 1 obesity (BMI ¼ 30.0– 34.9 kg/m2), 14.8% had class 2 obesity (BMI ¼ 35.0–39.9 kg/m2), and 10.2% had class 3 obesity (BMI  40 kg/m2). As Table 1 shows, women in the BEDþ group were more likely than the BED group to be unmarried/unpartnered, to have four or more primary care visits in the past year, and to have lower annual household income. In the obesity subsample, the direction of group differences in demographic characteristics was similar, although most did not attain statistical significance. Trauma Exposure, Psychological Symptoms, and Psychological Conditions Among women in the BEDþ group, trauma exposure was common as were psychological symptoms and psychological

Table 1 Sociodemographics and Use Characteristics for Women Primary Care Patients with (BEDþ) Positive Binge Eating Disorder Screenings and without Symptoms of BED (BED)

Race/ethnicity Asian or NH/PI/AI/AN Black/African American Hispanic/Latina White Other/Unknown Relationship status Married/partnered Divorced/separated/widowed Never married Household income <$20,000/y $20,000 to <$50,000/y $50,000/y LGB/Q Children in the home 4 primary care visits Mean age (SD)

Full Sample

Analytic Sample*

All (N ¼ 484), %

BEDþ (n ¼ 36), %

BED (n ¼ 418), %

Obesity Subsample*

2.5 24.8 7.4 61.2 4.1

0.0 19.4 11.1 69.4 0.0

2.9 24.9 7.2 60.8 4.3

38.5 37.7 17.8

19.4 47.2 27.8

41.0 41.7 17.3

36.2 38.6 25.2 10.6 21.3 52.4 51.7 (13.4)

55.6 27.8 16.7 11.1 16.7 72.2 50.8 (13.6)

34.4 39.2 26.3 10.4 22.7 50.7 51.9 (13.4)

p

BEDþ (n ¼ 31), %

BED (n ¼ 177), %

0.0 19.4 9.7 70.9 0.0

2.3 28.8 7.3 57.1 4.5

19.4 48.4 25.8

41.8 39.0 19.2

54.8 29.0 16.1 9.7 19.4 70.9 52.7 (12.7)

33.9 46.9 19.2 10.8 22.6 49.7 52.8 (11.3)

.37

.40

.03

.06

.04

.94 .36 .01 .83

p

.08

.85 .69 .03 .98

Abbreviations: AI, American Indian; AN, Alaska Native; BED, binge eating disorder; LGB/Q, lesbian, gay, bisexual, questioning; NH, Native Hawaiian; PI, Pacific Islander. * The analytic sample included those with BEDþ screens and those without and without symptoms of BED (BED); women with subclinical BED (n ¼ 19), BN (n ¼ 9), or missing data (n ¼ 2) were excluded from BED groups. The obesity subsample was the subset of the analytic sample with a body mass index of 30 kg/m2.

D.L. Rosenbaum et al. / Women's Health Issues xxx-xx (2016) 1–9

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priorities. The most common first-ranked treatment priority among the BEDþ group was physical pain, followed by mood concerns, body image/food issues, and coping with medical problems. More women in the BEDþ group than in the BED group rated body image/food issues as their top treatment priority. Table 3 also presents priorities of the Obesity Subsample. For the BEDþ group, mood and weight loss/weight management were tied as the most common overall treatment priorities, followed by coping with chronic medical problems. The proportions including body image/food issues and military sexual harassment and/or assault in their overall treatment priorities were greater among the BEDþ group than the BED group. Treatment of physical pain was the most common first-ranked treatment priority among the BEDþ group, followed by support for coping with medical problems. The percentage of women in the BEDþ group with body image/food issues as their first-ranked treatment priority was higher than the BED group. Interestingly, the proportion of obese women with weight loss/ weight management as their first-ranked treatment priority was lower in the BEDþ group than in the BED group.

conditions (Table 2). Before and after age adjustment, there were significantly greater odds of depression symptoms, diagnosed PTSD, mood disorders, and sleep disorders in the BEDþ group compared with the BED group. Within the obesity subsample, the BEDþ group had greater odds of a mood disorder diagnosis and lower odds of diagnosed acute stress disorder (95% CI, 0.138– 0.961, presented as rounded to the nearest tenth in Table 2). Medical Conditions The most common medical conditions other than obesity across BED status groups were chronic pain (86.1% and 77.3% for BEDþ and BED, respectively), lipid disorders (55.6% and 43.5% for BEDþ and BED, respectively), and hypertension (50.0% and 42.6% for BEDþ and BED, respectively). Women in the BEDþ group had significantly greater odds of obesity (86.1% and 42.4% for BEDþ and BED, respectively). Women in the BEDþ group had higher odds of having eight or more chronic medical condition diagnoses than those in the BED group; however, this difference was not significant in the obesity subsample (Table 2). Priorities for Care

Discussion

Table 3 details women’s priorities for care. Among the BEDþ group, the most commonly selected overall treatment priority was mood concerns with weight loss and body image/ food issues following. Nonetheless, comparing the BEDþ versus BED groups, weight loss and body image/food issues were more commonly included in overall treatment priorities for women in the BEDþ group. Conversely, significantly fewer women in the BEDþ group than in the BED group included couples/romantic relationships among their overall treatment

We evaluated women veteran primary care patients in the VA with the potential to seek mental health and/or behavioral medicine services, and found that women who screened positive for BED had a more complex clinical profile and distinct treatment priorities. Broadly, to highlight our main findings, women in the BEDþ group were more likely than other women to have a low household income, and were less likely to be married or partnered. Depression symptoms and several psychological conditions (PTSD, mood disorders, and sleep disorders) were also

Table 2 Rates and Odds of Trauma Exposure, Psychological Symptoms and Comorbid Conditions for Women Primary Care BEDþ and Patients Analytic Sample (n ¼ 454)

Military sexual harassmentx Anxiety symptoms Depression symptoms Psychological diagnoses Adjustment disorders PTSD Acute stress disorder Anxiety disorders Mood disorders Alcohol and substance disorders Sleep Disorders Medical conditions Obesityk 4 chronic medical conditions 6 chronic medical conditions 8 chronic medical conditions

Obesity Subsample (n ¼ 208) y

z

BEDþ (%)

BED (%)

OR

CI

AOR

95% CI

BEDþ (%)

BED (%)

OR

CI

AOR

CI

75.00

62.67

1.79

0.82–3.90

1.77

0.80–3.89

77.42

59.89

2.30

0.94–5.62

2.33

0.95–5.74

61.11 44.44

52.52 27.51

1.42 2.12*

0.71–2.85 1.06–4.21

1.40 2.11*

0.70–2.83 1.06–4.22

54.84 41.94

48.86 25.42

1.27 2.12

0.59–2.74 0.96–4.67

1.27 2.12

0.59–2.74 0.96–4.67

5.55 50.00 25.00 19.44 75.00 8.33

6.46 33.01 30.38 17.46 51.20 10.29

0.85 2.03* 0.76 1.14 2.86** 0.79

0.19–3.74 1.02–4.02 0.35–1.67 0.48–2.70 1.31–6.23 0.23–2.69

0.86 2.01* 0.78 1.14 2.84** 0.79

0.19–3.79 1.01–4.02 0.35–1.75 0.48–2.71 1.30–6.19 0.23–2.68

6.45 48.39 19.35 16.13 74.19 9.68

5.08 31.07 37.85 13.56 52.54 9.60

1.29 2.08 0.39 1.23 2.60* 1.01

0.27–6.26 0.96–4.51 0.15–1.01 0.13–3.50 1.10–6.12 0.28–3.67

1.28 2.08 0.36* 1.22 2.61* 1.01

0.26–6.26 0.96–4.51 0.14–0.96 0.43–3.51 1.11–6.15 0.28–3.67

41.66

22.01

2.53**

1.26–5.10

2.53**

1.25–5.09

41.94

28.25

1.83

0.84–4.02

1.84

0.84–4.06

3.28–22.72 0.59–2.59

d 61.29

d 67.23

d 0.77

d 0.35–1.70

d 0.76

d 0.33–1.76

***

***

86.11 58.33

42.44 54.78

8.44 1.16

3.21–22.14 0.58–2.30

8.64 1.24

25.00

21.05

1.25

0.57–2.76

1.34

0.59–3.04

29.03

28.81

1.01

0.44–2.34

0.98

0.40–2.37

13.88

5.50

2.77*

1.00–7.79

2.97*

1.04–8.47

16.13

8.47

2.08

0.70–6.20

2.09

0.68–6.38

Abbreviations: þ, has symptoms of BED; , does not have symptoms of BED; BED, binge eating disorder; PTSD, posttraumatic stress disorder. * p < .05; **p < .01; ***p < .001. y Odds ratios (OR)/age-adjusted odds ratios (AOR) > 1 reflect higher odds in BEDþ group. z CI ¼ 95% confidence interval, rounded to the nearest hundredth. x Includes military sexual harassment and/or assault. k Obesity is determined by height and weight, not ICD-9-CM code.

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Table 3 Frequency of Common Treatment Priorities among Women Primary Care BEDþ and BED Patientsy Treatment Priority

Analytic Sample (n ¼ 454) Overall Priorities BEDþ (%)

Mood Weight loss/management Body image/food issues Stressful/traumatic experiences Physical pain Coping with medical problems Sleep problems Military sexual harassment and/or assault Couples/romantic relationships

72.2 66.7* 50.0*** 50.0 50.0 50.0 47.2 33.3 2.8*

z

Obesity Subsample (n ¼ 208) First-Ranked Priority

Overall Priorities

First-Ranked Priority

BED (%)

BEDþ (%)

BED (%)

BEDþ (%)

BED (%)

BEDþ (%)

BED (%)

60.5 49.0 23.2 49.3 62.0 57.7 51.2 23.0 15.6

16.7 2.8 13.9*** 2.8 22.2 13.9 2.8 11.1 0.0

8.9 10.0 2.2 7.9 17.7 21.8 9.3 7.9 2.9

67.7 67.7 51.6* 45.2 48.4 58.1 48.4 35.5* 3.2

53.7 70.6 29.4 44.6 63.3 53.7 49.2 18.1 14.1

12.9 3.2* 12.9** 3.2 19.4 16.1 3.2 12.9 0.0

5.1 19.8 2.8 6.8 18.6 19.8 7.3 6.2 1.1

Abbreviations: þ, has symptoms of BED; , does not have symptoms of BED; BED, binge eating disorder. * p < .05; ***p < .001. y Table 3 shows the 9 most common patient-ranked treatment priorities across subsamples. There were no group differences for less common priorities (i.e., family issues, sexual activity/intimacy, parenting issues, smoking, relationship violence, alcohol/drugs). z Percentages of women who included each treatment priority may add to >100, as up to five treatment priorities were ranked.

more common among the BEDþ group, as were obesity and number of medical comorbidities. With regard to treatment priorities, women in the BEDþ group often selected support for mood issues, weight loss/weight management, and body image/ food issues among their priorities for mental health treatment. Generally, and among the subgroup with obesity, a greater proportion of women in the BEDþ group than in the BED group prioritized body image/food issues. A more detailed discussion follows regarding our findings about 1) the clinical profile of women veterans who screened positive for BED and 2) their priorities for care. Supporting our first aim, we found rates of psychological conditions were significantly higher among women who screened positive for BED compared with those without symptoms of BED. This finding is consistent with earlier research (Grilo, White, & Masheb, 2009; Grucza, Przybeck, & Cloninger, 2007; Hudson et al., 2007; Rieger, Touyz, & Beumont, 2002). Women in the BEDþ group had twice the odds for PTSD and mood disorders. This is consistent with theories that binge eating may result from maladaptive efforts to cope with distressing emotional experiences (Ivezaj et al., 2014; Whiteside et al., 2007). Our study also found exposure to military sexual harassment and/or assault was high among women veterans with BED, and although not significantly different across groups, may be a relevant factor in clinical complexity. Among some who have experienced sexual trauma, binge eating and obesity may be perceived as protective, to reduce future sexualization by others (Wiederman, Sansone, & Sansone, 1999). The psychological complexity of women with BED suggests a need for treatment approaches designed to manage comorbid conditions. A transdiagnostic approach could be one promising modality for effective management of mental health comorbidities (Bullis, Fortune, Farchione, & Barlow, 2014). In addition to evaluating differences in psychological complexity between women with and without positive BED screens, we also evaluated their differences in medical complexity. Our results suggest that women with BED (versus those without any symptoms of BED) exhibit greater medical complexity, evidenced by more frequent primary care visits and higher rates of medical comorbidities. This study also found that women who screened positive for BED are more than eight-fold as likely to be obese, underscoring the importance of the identification and treatment of BED in primary care settings, where obesity is a common focus of care. The rate of obesity found

within the current study is somewhat higher than what has been found in previous studies of BED, but supports previous findings of a strong association between binge eating and greater weight (Grucza et al., 2007; Hudson et al., 2007; Reichborn-Kjennerud, Bulik, Sullivan, Tambs, & Harris, 2004). Coupled with the increased odds of obesity, the high rates of medical comorbidities may suggest that women with BED could have increased stress related to the burden of managing multiple chronic conditions. Additional study is needed to better understand the longitudinal risks and adverse outcomes associated with BED. This study is the first to our knowledge to examine binge eating in primary care settings across several regions of the United States, and to examine binge eating in women veterans in primary care. Based on these data, it seems that in our stakeholder sample, women veterans with BED may be especially heavy users of primary care services, even after taking obesity into account. This could be because primary care offers a wide array of treatment services, spanning physical and mental health concerns, and women screening positive for BED in this sample exhibited more psychological and medical complexity. Assessment of BED in primary care might be a promising approach to improving case identification. Brief screening instruments (Fairburn & Beglin, 1994; Striegel-Moore et al., 2010; Yanovski, 1993), and diagnostic assessment tools (Fairburn & Cooper, 1993; First, Spitzer, Gibbon, & Williams, 2002) are readily available. These data also support the calls from other researchers to promote greater attention to eating disorders at VA (Maguen et al., 2012), as existing eating disorder prevalence estimates in VA settings may underestimate the true occurrence of these problems (Striegel-Moore, Garvin, Dohm, & Rosenheck, 1999). Given the clinical complexity among BEDþ women, the second aim of this study, to identify treatment priorities of BEDþ women, is important so as to elucidate patient priorities for BED treatment in the context of potentially competing treatment priorities related to comorbid conditions. Selfidentified priorities for care generally matched with areas of greatest psychological difficulty. Thus, treatment of eating concerns is both an objective and subjective priority of care for many women with BED; this alignment of objective need with patient priorities bodes well for treatment outcomes, because patients’ symptoms are more likely to improve when they perceive their needs are met (Roth & Crane-Ross, 2002). Moreover, we found that women with BED coupled with obesity place a higher priority on addressing psychological aspects of eating concerns

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(“food issues”) than on broad-based weight goals (“weight management”). This has important implications for providers given that standard behavioral weight loss approaches have demonstrated mixed results for treating BED in patients with obesity (Wilson, 2011). Some have suggested that the limited ability of standard behavioral weight loss to address negative affect and the connection between emotions and loss of control eating render it insufficient for BED (Wilson, Grilo, & Vitousek, 2007). Similarly, previous research has found that within the VA’s weight management program (i.e., MOVE!; available: www. move.va.gov/), some individuals with mental health conditions (e.g., depression, PTSD) experience worse outcomes (Hoerster et al., 2014; Littman et al., 2015), underscoring the importance of addressing emotional factors that may underlie difficulties with weight management among VA health care users. Therefore, weight reduction may not be an optimal initial treatment target as this is unlikely to yield meaningful improvements among those with serious binge eating difficulties (Masheb et al., 2015), and was a lower priority in our sample. Although it may initially seem that BED-specific care is an optimal initial treatment target recommendation for those with BED, given that a greater proportion of women in the BEDþ group selected “food issues” as a priority, it is important to consider individual differences in priorities among patients. Specifically, it is noteworthy that approximately one-half of women in the BEDþ group did not include this topic among their treatment priorities. It may be that those patients did not feel sufficiently ready to make changes to their eating behaviors yet, which suggests that approaches to increase openness to engagement in treatment, such as motivational interviewing (Miller & Rollnick, 2012), may be an appropriate precursor to BED-focused treatment for some. Alternatively, lower prioritization of BED treatment may reflect women’s higher prioritization of other foci. Dysfunctional eating may feel less distressing to patients than conditions that affect mobility (e.g., physical pain) or that otherwise present a daily medical care burden. There is also a possibility that some may be less apt to seek mental health treatment than medical treatment, and/or express psychological distress through physical symptoms, particularly in the context of prior trauma and negative affect (Elklit & Christiansen, 2009), which are known correlates of BED (Fairburn, 2008; Harrington et al., 2006). This makes primary care a particularly promising setting in which to approach patients about BED and its comorbidities. Overall, the expressed priorities of the patients in this sample, as well as the existing literature, support directing patients with obesity and BED to resources designed to address the psychological components underlying their comorbid physical and mental health disorders. Cognitive–behavioral therapies and interpersonal therapy have the greatest benefit in binge eating symptom reduction, and associated negative emotionality, with newer third-wave behavioral therapies, such as dialectical behavioral therapy, also demonstrating promising results (Klein, Skinner, & Hawley, 2012; Telch, Agras, & Linehan, 2001; Wilson, 2011). Matching treatment to patients’ priorities may be especially relevant to psychotherapeutic approaches, because patients’ engagement, and investment of time and energy into prioritizing therapy, is critical to its success (Beck, 2011). Instituting screening for BED among patients with obesity, who would otherwise be offered standard behavioral weight management, may help to improve efficiency in care and improve outcomes. Further, there is a need for additional development and implementation of BED-focused, patient-centered

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treatments sensitive to the competing priorities women experience in their daily lives. Caveats and Future Research The sample for this study included women veterans receiving primary care services in VA, and was focused on those with a subjective or objective need for mental health services, because these were the primary stakeholders in learning more about patient priorities for such services. Also, the sampling frame was stratified on use frequency (Kimerling et al., 2015). Therefore, when interpreting these findings it is important to remember that the rate of BED in our sample may not represent the rate of BED in women primary care patients more generally. Additionally, although we used a screening questionnaire that has been supported as an effective tool for detecting BED (Striegel-Moore et al., 2010), screening measures are generally less accurate than gold standard clinician-administered diagnostic interviews (Fairburn & Cooper, 1993; First et al., 2002), which protect against self-report bias. However, this was a conservative measure of BED as compared with DSM-5 criteria. Indeed, although our study was not designed to assess prevalence of BED, the proportion of primary care patients in our sample who screened positive for BED (7.5%) was in line with rates of BED reported in other primary care-based studies of women (i.e., 3.3%–8.5%; Johnson, Spitzer, & Williams, 2001), and to those seen in evaluations of BED among Iraq/Afghanistan Veterans (i.e., 8.4%; Hoerster et al., 2015). Further, the number of BEDþ individuals in our sample was still relatively small (n ¼ 36). Future research to understand the prevalence of BED among women veterans is needed. Although this study yields important information regarding treatment priorities for women with BED, future studies can examine additional details of preferences for treatment, including co-located BED and weight management resources, women-specific clinical environments for BED-related treatment needs, and whether tele-mental health or smartphone applications may be acceptable options for care, especially for the third of women veterans living in rural areas (Frayne et al., 2014). Implementation studies examining BED interventions could help to increase effective use of treatment resources; recent data suggest that veterans with high-frequency binge eating are unlikely to be successful in existing behavioral weight loss programs (Masheb et al., 2015). Finally, future research is warranted to inform best methods for disseminating knowledge of effective screening tools to VA clinicians and clinicians in other practice settings, along with research examining the implementation of empirically supported BED treatment, such as protocols based on cognitive–behavioral therapy. Implications for Policy and/or Practice These data have implications for potential expansion and improvement of care for women who screen positive for BED. Now that BED is a stand-alone diagnosable mental health condition, treatment for BED is likely to increase in availability and use. Positive BED screens in women veterans are associated with a particularly heavy burden of psychological symptoms, mental health diagnoses, and medical comorbidities, all important considerations in resource allocation and treatment planning. Bringing the patient’s voice into the dialogue, our findings provide patient perspectives on the treatment priorities of women with BED, suggesting that BED-focused treatment and related weight-management care are likely to be priorities for many

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women with BED, the preponderance of whom also have comorbid obesity. To optimally address patient preferences, further evaluation may be needed to determine whether BED-focused treatment may be best delivered through adaptation and expansion of the existing weight management program within VA, or through mental health services. These findings may inform the development of targeted BED screening practices for women with obesity in primary care settings, and the eventual adoption of patient-centered BED treatment resources. Acknowledgments This study was funded by the VA HSR&D (Project SDR 10– 012). Dr. Yano’s effort was funded by a VA HSR&D Service Senior Research Career Scientist Award (RCS 05–195). The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the United States Government. Supplementary Data Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.whi.2016.02.004. References American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed., text rev.) Washington, DC: Author. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.) Arlington, VA: American Psychiatric Publishing. Beck, J. S. (2011). Cognitive behavior therapy: Basics and beyond (2nd ed.) New York: Guilford Press. Bradley, K., Bush, K., Epler, A., Dobie, D., Davis, T., Sporleder, J., . Kivlahan, D. R. (2003). Two brief alcohol-screening tests from the Alcohol Use Disorders Identification Test (AUDIT): Validation in a female Veterans Affairs patient population. Archives of Internal Medicine, 163(7), 821–829. Brewerton, T. D., Rance, S. J., Dansky, B. S., O’Neil, P. M., & Kilpatrick, D. G. (2014). A comparison of women with child-adolescent versus adult onset binge eating: Results from the National Women’s Study. International Journal of Eating Disorders, 47(7), 836–843. Bullis, J. R., Fortune, M. R., Farchione, T. J., & Barlow, D. H. (2014). A preliminary investigation of the long-term outcome of the Unified Protocol for Transdiagnostic Treatment of Emotional Disorders. Comprehensive Psychiatry, 55(8), 1920–1927. Buysse, D. J., Reynolds, C. F., 3rd, Monk, T. H., Berman, S. R., & Kupfer, D. J. (1989). The Pittsburgh Sleep Quality Index: A new instrument for psychiatric practice and research. Psychiatry Research, 28(2), 193–213. Damschoder, L. J., & Lowery, J. C. (2013). Evaluation of a large-scale weight management program using the consolidated framework for implementation research (CFIR). Implementation Science, 8, 51. Elklit, A., & Christiansen, D. M. (2009). Predictive factors for somatization in a trauma sample. Clinical Practice and Epidemiology in Mental Health, 5. Fairburn, C. G. (2008). Eating disorders: the transdiagnostic view and the cognitive behavioral theory. In C. G. Fairburn (Ed.), Cognitive behavior therapy and eating disorders. (pp. 7–22). New York: Guilford Press. Fairburn, C. G., & Beglin, S. J. (1994). Assessment of eating disorders: Interview or self-report questionnaire? International Journal of Eating Disorders, 16(4), 363–370. Fairburn, C. G., & Cooper, Z. (1993). The Eating Disorder Examination. In C. G. Fairburn, & G. T. Wilson (Eds.), Binge eating: Nature, assessment, and treatment. (pp. 317–360). New York: Guilford Press. Fairburn, C. G., Cooper, Z., & Shafran, R. (2003). Cognitive behavior therapy for eating disorders: A “transdiagnostic” theory and treatment. Behavior Research and Therapy, 41, 509–528. First, M. B., Spitzer, R. L., Gibbon, M., & Williams, J. B. W. (2002). Structured Clinical Interview for DSM-IV-TR axis I disorders, research version, patient edition. (SCID-I/P). New York: Biometrics Research, New York State Psychiatric Institute. Frayne, S. M., Carney, D. V., Bastian, L., Bean-Mayberry, B., Sadler, A., Klap, R., . (2013). The VA Women’s Health Practice-Based Research Network: Amplifying women veterans’ voices in VA research. Journal of General Internal Medicine, 28(Suppl 2), 504–509. Frayne, S. M., Phibbs, C. S., Saechao, F., Maisel, N. C., Friedman, S. A., Finlay, A., . (2014). Sourcebook: Women Veterans in the Veterans Health Administration.

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Author Descriptions Diane L. Rosenbaum, PhD, is a postdoctoral fellow at Drexel University in the Department of Psychology. Her research interests include binge eating disorder and maladaptive eating, obesity and weight control, and weight and eating interventions for women. Rachel Kimerling, PhD, is a clinical psychologist and investigator with the National Center for PTSD and the Center for Innovation to Implementation at VA Palo Alto Health Care System. Her research focuses on women’s health services, veterans, trauma, and PTSD. Alyssa Pomernacki, MPH, is a coordinator for the VA Women’s Health PracticeBased Research Network at the Center for Innovation to Implementation, VA Palo Alto Health Care System. Her research interests include women’s health services, veterans, public health programming. Karen M. Goldstein, MD, MSPH, is a researcher at the Durham VAMC Center for Health Services Research in Primary Care, and an Assistant Professor at Duke University. Her research focuses on cardiovascular disease in women, and health outcomes in vulnerable populations. Elizabeth M. Yano, PhD, MSPH, is Director, VA HSR&D Center for the Study of Innovation, Implementation and Policy, and Adjunct Professor, Health Policy and Management at the UCLA School of Public Health. Her research focuses on primary care delivery models. Anne G. Sadler, PhD, RN, is a researcher, Center for Comprehensive Access & Delivery Research and Evaluation, Iowa City VA Health Care System, and an associate professor, Psychiatry, The University of Iowa. She studies military violence risk factors and postdeployment care engagement. Diane Carney, MA, is the Program Manager of the VA Women’s Health PracticeBased Research Network at the Center for Innovation to Implementation, VA Palo Alto Health Care System. Her research interests include women’s health research, veterans, and patient-centered care. Lori A. Bastian, MD, MPH, is Senior Research Associate, VA Connecticut Healthcare System, and Professor, Division Chief of General Internal Medicine, University of Connecticut Health Center. Her research focuses on improving care for women veterans, women’s health behaviors, and smoking cessation. Bevanne A. Bean-Mayberry, MD, MHS, is an investigator at VA Greater Los Angeles Healthcare System, Center for the Study of Healthcare Innovation, Implementation and Policy and UCLA David Geffen School of Medicine. Her research focuses on improving women’s health care services. Susan M. Frayne, MD, MPH, is a researcher, VA Palo Alto Health Care System, Center for Innovation to Implementation, and Professor of Medicine, Stanford University. Her research includes health care for women veterans, improving primary care, and health sequelae of trauma.