EMPIRICAL ARTICLE

Risk Factors for Binge Eating and Purging Eating Disorders: Differences Based on Age of Onset Karina L. Allen, PhD1,2* Susan M. Byrne, DPhil2 Wendy H. Oddy, PhD1 Ulrike Schmidt, PhD3 Ross D. Crosby, PhD4,5

ABSTRACT Objective: To (1) determine whether childhood risk factors for early onset binge eating and purging eating disorders also predict risk for later-onset binge eating and purging disorders, and (2) compare the utility of childhood and early adolescent variables in predicting lateronset disorders. Method: Participants (N 5 1,383) were drawn from the Western Australian Pregnancy Cohort (Raine) Study, which has followed children from pre-birth to age 20. Eating disorders were assessed when participants were aged 14, 17, and 20. Risk factors for early onset eating disorders have been reported previously (Allen et al., J Am Acad Child Psychiat, 48, 800– 809, 2009). This study used logistic regression to determine whether childhood risk factors for early onset disorders, as previously identified, would also predict risk for later-onset disorders (n 5 145). Early adolescent predictors of later-onset disorders were also examined.

child overweight at age 10 were significant multivariate predictors of binge eating and purging disorders with onset in later adolescence. Eating, weight, and shape concerns at age 14 were also significant in predicting later-onset disorders. In the final stepwise multivariate model, female sex and eating, weight, and shape concerns at age 14 were significant in predicting later-onset eating disorders, while parent-perceived child overweight at age 10 was not. Discussion: There is overlap between risk factors for binge eating and purging disorders with early and later onset. However, childhood exposures may be more important for early than later onset cases. C 2014 Wiley Periodicals, Inc. V Keywords: eating disorders; risk factors; binge eating; purging; Raine study; bulimia nervosa; purging disorder; binge eating disorder (Int J Eat Disord 2014; 47:802–812)

Results: Consistent with early onset cases, female sex and parent-perceived

Accepted 2 May 2014 Supported by National Health and Medical Research Council (NHMRC), Australia, NHMRC, Telethon Kids Institute (previously the Telethon Institute for Child Health Research), Raine Medical Research Foundation, University of Western Australia (UWA), Faculty of Medicine, Dentistry and Health Sciences at UWA, Women’s and Infant’s Research Foundation, Curtin University, and Canadian Institutes of Health Research, and Lions Eye Institute. Conflict of Interest: None of the authors report any conflicts of interest. *Correspondence to: Dr Karina Allen, School of Psychology, The University of Western Australia, M304, 35 Stirling Hwy, Crawley, WA 6009, Australia. E-mail: [email protected] 1 Telethon Kids Institute, The University of Western Australia, Crawley, Western Australia, Australia 2 School of Psychology, The University of Western Australia, Crawley, Western Australia, Australia 3 Section of Eating Disorders, Institute of Psychiatry, King’s College London, Strand, London, United Kingdom 4 Department of Clinical Neuroscience, University of North Dakota School of Medicine and Health Sciences, Grand Forks, North Dakota 5 Department of Biostatistics, Neuropsychiatric Research Institute, Grand Forks, North Dakota Published online 13 May 2014 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/eat.22299 C 2014 Wiley Periodicals, Inc. V

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Introduction Eating disorders affect up to 6% of young adult males and 15% of young adult females.1,2 They are one of the leading causes of burden of disease in young people, tend to persist over time, and are notoriously difficult to treat.3 Whilst risk factors for these conditions have been identified, their aetiology remains poorly understood overall.4 One of the limitations of extant risk factor research is that relatively few studies have employed a prospective design and made use of psychiatric as well as general control groups. Both are essential if risk factors for eating disorders are to be reliably determined.4,5 Further, risk factors for eating disorders may vary by age of onset,6 but very few studies have been able to consider this possibility. Studies using psychiatric and general control groups have tended to assess risk factors retrospectively, using the Oxford Risk Factor Interview or a International Journal of Eating Disorders 47:7 802–812 2014

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related adaptation.7–11 Thus, the possibility of recall biases cannot be discounted. In contrast, most studies employing a prospective design have conducted comparisons relative to a general control group but not a psychiatric control group.12–15 This prevents firm conclusions being made about the specificity of identified risk factors. Nonetheless, when reviewing findings to date, female sex, pregnancy and birth complications, early childhood eating problems, low self-esteem, low interoceptive awareness, ineffectiveness, negative affect, social problems, behavioral problems, dieting, and eating, weight, and shape concerns may be seen as risk factors for eating disorders.4,7–15 Obsessivecompulsive symptoms and perfectionism have also been linked to risk for anorexia nervosa (AN),7,10 whilst childhood and family obesity, alcohol/substance misuse, and family conflict have been linked to risk for bulimia nervosa (BN).4,9 To our knowledge, the only previous study to make use of a prospective design and psychiatric and control group comparisons is the 2009 report on risk factors for early onset eating disorders in the Western Australian Pregnancy Cohort (Raine) Study.16 The Raine Study recruited 2,900 women during pregnancy and has followed mothers and their offspring to young adulthood. At age 14, 6% of offspring met full or partial criteria for a DSM-IV eating disorder, with almost all cases being a binge eating or purging eating disorder [BN or subthreshold BN, binge eating disorder (BED) or subthreshold BED, or Purging Disorder]. Two variables were identified as specific, prospective, multivariate risk factors for a 14-year eating disorder: female sex and being perceived as overweight by one’s parent/s in middle childhood.16 Elevated maternal body mass index (BMI) during pregnancy and social problems, low social-related self-efficacy, and weak central coherence (WISC Block Design scores) in middle childhood were nonspecific risk factors, predicting eating disorder cases relative to the general control group only. This 2009 article focused on risk factors for eating disorders with onset by age 14.16 As the peak age of onset for binge eating and purging disorders is in late adolescence,1 extending results to eating disorders with a later onset is indicated. In addition, and as noted, very few studies have attempted to distinguish between risk factors for eating disorders by on age of onset. When research has been conducted, it has typically focused on BED, where results suggest that binge eating may develop before dieting for early onset BED, and after dieting for later-onset BED.17,18 The literature on BN is much smaller and no significant differences have International Journal of Eating Disorders 47:7 802–812 2014

been found between early and later-onset cases.6,19 However, studies to date have tended to compare groups on prior exposures (e.g., rates of childhood obesity) without examining prospective predictors of risk for each group (e.g., whether obesity predicts risk for early and later-onset cases). Results are also limited to treatment-seeking samples, which are unlikely to represent all BN cases. This study aimed to determine if childhood predictors of early onset binge eating and purging eating disorders, as identified in the Raine cohort previously,16 would also predict risk for binge eating and purging eating disorders with onset after age 14 and up until age 20 (later-onset disorders). Further, the study aimed to compare the utility of childhood and early adolescent variables in predicting risk for a later-onset eating disorder. Analyses made use of the same Raine Study sample. It was hypothesized that female sex and parentperceived child overweight in middle childhood would significantly predict later-onset binge eating and purging disorders. In addition, however, it was hypothesized that body weight and subclinical eating disorder symptoms in early adolescence would be stronger predictors of risk for later-onset disorders than childhood variables.

Method Participants Details of the Raine Study have been published previously.2,16,20 Briefly, 2900 women were recruited through the antenatal booking clinics at King Edward Memorial Hospital (KEMH) between May 1989 and November 1991. Women were enrolled between 16 and 20 weeks gestation and delivered 2,868 live birth children. Children and their parent/s were assessed at birth and ages 1, 2, 3, 5, 8, 10, 14, 17, and 20 years. Eating disorder measures were included in the 14, 17, and 20-year assessments. They were completed by 1,598 participants at age 14, 1,242 participants at age 17, and 1,243 participants at age 20. This study focuses on the 1,383 participants (49% male) who completed eating disorder measures at age 14 and at least one of the subsequent adolescent assessments. Mean ages were 14.01 (SD 0.19, range 5 13.0–15.1), 16.92 (SD 5 0.24, range 5 15.0–18.2) and 20.01 (SD 5 0.44, range 5 19.0–22.1) years at the 14-, 17-, and 20-year assessments, respectively. The 1,383 participants represent 76% of the sample who completed at least one of the 14 to 20-year assessments (n 5 1,878), 59% of the sample who were eligible to participate in the 14 through 20-year assessments (n 5 2,344), and 48% of the original cohort.

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Raine Study participants lost to follow-up were significantly more likely to be from single parent families, to have unemployed parent/s, to have lower family incomes, and to have externalizing behavior problems in the early years of the study, compared with those who remained in the study to adolescence. However, Raine Study adolescents who provided eating disorder data at age 14 and were subsequently lost to follow-up (n 5 215) did not differ significantly from those who remained in the study across adolescence on childhood or early adolescent variables, including eating disorder variables. Three groups of participants were created for analyses. The later-onset eating disorder group (n 5 145; 19% male) did not meet criteria for an eating disorder at age 14, but did meet criteria for DSM-5 BN, BED, or Purging Disorder at age 17 and/or 20. The general control group (n 5 954; 56% male) did not meet criteria for an eating disorder or report a diagnosed anxiety or depressive disorder at any assessment point. The psychiatric control group (n 5 115; 41% male) did not meet criteria for an eating disorder at any assessment point, and did not have a history of a diagnosed anxiety or depressive disorder by age 14, but received a diagnosed anxiety/depressive disorder by age 20. Diagnosed anxiety/depressive disorders were also common in the later-onset eating disorder group, with almost all (n 5 130; 89%) of this group receiving such a diagnosis by age 20. Participants with an early onset eating disorder at age 14 (n 5 95), and those who developed AN or atypical AN in later adolescence (n 5 12), were excluded from core analyses. Procedure Questionnaire and interview measures were administered to mothers at 16–20 weeks and 32 weeks gestation. Mothers received antenatal care at KEMH and were examined with their babies 2 days post-delivery. At subsequent follow-ups, questionnaire packages were completed by parent/s and a physical examination was conducted with children. Children completed self-report questionnaires at 14, 17, and 20 years. Data collection was approved by the ethics committees of KEMH, Princess Margaret Hospital for Children, and the University of Western Australia. Measures Eating Disorder Symptoms. The Raine Study eating disorder assessment items, and diagnostic algorithms for determining DSM-5 eating disorders, are outlined in detail elsewhere.2,21 Briefly, eating disorder symptoms were assessed using 24 self-report items adapted from the Eating Disorder Examination-Questionnaire (EDEQ).22 Items were rated on a simplified 4-point response scale ranging from 0 (“Not at all”) to 3 [“Most of the time

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(every day or nearly every day)] and were intended to be suitable for independent completion by adolescents of all ages.2,21 Diagnoses were based on responses to these items, combined with measured height and weight. There is good convergence between EDE-Q assessment of symptoms over one month and interview assessment over 3–6 months.23,24 Diagnoses of BN and Purging Disorder were made to DSM-5 criteria.2 Information was not sufficient to generate diagnoses of sub-threshold BN. For BED, the EDE-Q does not collect information on criterion B of the DSM-5 requirements (i.e., it does not determine whether three symptoms relating to dysregulated eating behavior or distress over eating are present). Over-evaluation of weight or shape was included in lieu of this criterion, to avoid inflation of BED diagnoses.2 Others have shown that over-evaluation of weight and shape reliably distinguishes between individuals with BED and individuals who binge eat without clinical impairment.25,26 Nonetheless, we acknowledge that our definition differs from strictly defined DSM-5 BED. In the 2009 report on risk factors for early onset eating disorders in the Raine Study, diagnoses were generated to DSM-IV criteria.16 However, that study considered full and partial DSM-IV eating disorders and combined full and partial cases for analyses. Binge eating and purging presentations given a “partial” diagnosis in DSM-IV are, in almost all instances, captured as “full” cases in DSM5. Accordingly, the change from DSM-IV to DSM-5 has not affected the classification of Raine participants, other than that DSM-IV “partial” cases are now captured as DSM-5 “full” cases. Diagnosed Depressive and Anxiety Disorders. At the 10-, 14-, 17-, and 20-year assessments, parents were asked whether their child had ever been diagnosed with an anxiety or depressive disorder by a health care practitioner. Adolescents were also asked to report this at the 14-, 17-, and 20-year assessments. Responses were used to categorize the psychiatric control group. Antenatal and Childhood Predictor Variables. We focused on the antenatal and childhood variables that entered the final models of risk for early onset eating disorders in the Raine Study.16 These variables spanned five domains (antenatal; parent and family functioning; child health; child personal and emotional functioning; eating, weight and shape related) and five assessment points (antenatal: age 1; age 5; age 8; age 10) and are summarized below. Full details of measures are provided in the 2009 report.16 Four antenatal variables were considered. Maternal BMI at 16 weeks gestation was calculated based on measured height and weight when the mother was enrolled in the study. Paternal BMI at 16 weeks gestation and paternal age at the child’s birth were calculated International Journal of Eating Disorders 47:7 802–812 2014

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based on mothers’ reports of their partner’s height, weight, and date of birth. Maternal drug use in the first 16 weeks of pregnancy was assessed via a confidential questionnaire completed by mothers at 16 weeks gestation, and was dichotomized to no illicit drug use versus any illicit drug use. Four variables were considered from the 1 and 5-year Raine Study follow-ups. Child BMI at age 1 was calculated from measured height and weight at the 1-year assessment. Child abdominal skin fold thickness at age 5 was measured by trained research assistants with callipers, at the 5-year assessment. Whether the child had a restricted diet at age 5 was assessed with a single questionnaire item completed by mothers: “Is your child’s diet restricted?” (yes/no). Family stress at age 5 was assessed using 10 items from the 67-item Life Stress Inventory.27 The 10 items were selected on the basis of their relevancy to parenting and general family life. Mothers were asked if any of the 10 events (e.g., “money problems”) were experienced by their family over the past 6 months. Summing responses gives a total score that can range from 0 to 10. Five items were considered from the 8-year Raine Study follow-up. Family stress at age 8 was assessed using the same 10 items from the Life Stress Inventory, described above. Maternal BMI was calculated from measured maternal height and weight at the 8-year assessment. Parent-perceived child overweight was assessed with a single item, which asked parents to indicate whether they thought their child was overweight. Response options were “no”, “yes, somewhat true”, and “yes, very true”. Responses dichotomized to not perceived as overweight vs. perceived as at least somewhat overweight. Child social problems were assessed using the parent-report Child Behavior Checklist (CBCL) for 4 to 18-year-old children.28 The CBCL assesses internalising (emotional) and externalizing (behavioral) problems. The Social Problems subscale contributes to the Internalising Problems scale and relates to difficulties with peer relationships, social anxiety, and bullying. Categorical scores were created using the established cut-points for normal versus clinically concerning scores. Scaled scores on the WISC Block Design were obtained following standard scoring procedures for the WISC-III,29 subsequent to individual administration of the Block Design task at the 8-year Raine Study follow-up. The task assesses ability to analyze and synthesize abstract visual stimuli, and is commonly used as a test of central coherence (the ability to integrate global “big picture” information without an excessive focus on details).29 Six items were considered from the 10-year Raine Study follow-up. Family stress at age 10 was assessed using the same 10 items from the Life Stress Inventory, and parent-perceived child overweight was assessed using the same item described for the 8-year follow-up. International Journal of Eating Disorders 47:7 802–812 2014

Child withdrawn problems were assessed using the Withdrawn subscale of the CBCL, which, like the Social Problems subscale, loads on the CBCL Internalizing scale. It relates to difficulties with low mood, withdrawal from social activities, and a preference for time alone. Child prosocial behavior was assessed using the Prosocial Behavior subscale of the Strengths and Difficulties Questionnaire (SDQ), parent-report.30 This assesses positive, helpful behavior to others, with higher scores reflecting more prosocial tendencies. People-related self-efficacy was assessed using the Problems with People subscale of the Cowen’s Self-Efficacy Scale for children,31 parentreport version. The subscale assesses how confident children are in managing difficulties relating to social interactions and working on problems with others. Higher scores reflect greater self-efficacy. Finally, attending an outpatient appointment in the last year was reported by parents as a dichotomous variable (not attended outpatient services vs. attended any), and reflects access to specialist services beyond primary care. Early Adolescent Predictor Variables. Early adolescent (14-year) predictors were chosen to complement the variables examined at earlier assessment points, and were drawn from the same domains described above and assessed using similar or identical measures. Parent and family functioning variables included socio-demographic information, family functioning (parent and adolescent report), parent physical and emotional well-being, and family exposure to stress. Child health variables included general ratings of adolescent health (parents and adolescent report), attendance at health services, and diagnosed health difficulties. Child personal and emotional functioning variables included parent responses to the CBCL,28 and adolescent responses to the Beck Depression Inventory-Youth (BDIY),32 Self-Perception Profile for Adolescents (SPPA),33 and questions on drug and alcohol use, bullying, and peer and school difficulties. Eating, weight, and shape-related variables included adolescent and parent BMI, adolescent and parent physical and sedentary activity, parental emphasis on weight/ shape (assessed with two parent-report questions asking whether weight/shape influenced judgments of the self and others), parent-perceived child overweight, parentperceived child overeating, and adolescent’s dietary restraint, eating, weight and shape concern, and engagement in binge eating and purging (vomiting or laxative misuse). Dietary restraint scores were calculated by taking the mean of the adapted EDE-Q items relating to restraint (4 items; a 5 0.78), and eating, weight, and shape concern scores were calculated by taking the mean of the adapted EDE-Q items relating to concerns (14 items; a 5 0.93). Eating, weight and shape concern items were combined on a single subscale due to high

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ALLEN ET AL. TABLE 1. Socio-demographic characteristics for participants with a later-onset binge eating or purging eating disorder, and general and psychiatric control participants (% [n] unless stated otherwise) Later-Onset Eating Disorder (n 5 145)

General Control (n 5 945)

Psychiatric Control (n 5 115)

44.0% (n 5 416)b

59.1% (n 5 58)b

92.1% (n 5 870)b 31.7% (n 5 300)b 16.58 (3.17) 29.35 (5.73) 22.02 (3.71) 31.75 (9.60) 24.33 (3.32)

87.8% (n 5 101)b 33.0% (n 5 38)b 16.27 (1.04) 29.78 (6.43) 21.74 (4.22) 31.11 (7.64) 24.81 (3.10)

72.5% (n 5 685)b 19.6% (n 5 185)c 14.01 (0.20) 20.72 (3.69)b 4.99 (5.29)b 44.28 (9.54)b 45.76 (10.09)b

63.6% (n 5 73) 23.4% (n 5 27)b 13.99 (0.20) 21.30 (3.93)b 8.88 (9.15)a 48.43 (10.06)a 48.67 (1.06)a

Female sex 81.4% (n 5 118)a Family characteristics at study enrolment (16 weeks gestation) Biological father living at home 84.8% (n 5 123)a Low family incomea 44.1% (n 5 64)a Mother’s education (yrs) (M[SD]) 16.28 (1.45) Mother’s age (M[SD]) 29.67 (6.04) Mother’s BMI (M[SD]) 22.71 (4.90) Father’s age (M[SD]) 32.98 (11.72) Father’s BMI (M[SD]) 24.57 (3.41) Family and adolescent characteristics in early adolescence (14-year follow-up) Biological father living at home 57.9% (n 5 84)a 31.7% (n 5 47)a Low family incomea Adolescent’s age (M[SD]) 14.03 (0.15) Adolescent’s BMI (M[SD]) 22.91 (4.12)a Adolescent’s BDI-Y score (M[SD]) 9.08 (7.45)a CBCL Internalizing score (M[SD]) 50.66 (11.81)a CBCL Externalizing score (M[SD]) 50.18 (11.20)a

Note. Columns with different subscripts are significantly different at p < .05, as determined using Chi square tests (with follow-up Fisher’s Exact tests) for categorical variables and one-way analysis of variance (with follow-up Tukey’s-b post hoc tests) for continuous variables. BMI: Body Mass Index, CBCL: Child Behavior Checklist. aEquivalent to the lowest two Australian income quintiles.

interitem correlations, and the high alpha coefficient obtained when using items jointly. Binge eating and purging were also identified from the adapted EDE-Q and were dichotomized to any binge eating/purging in the past month versus none. As adolescents with eating disorders at age 14 were excluded from analyses, binge eating and purging were only present at occasional levels (approximately monthly).

Statistical Analysis Analyses were conducted in four stages. Stages one through three addressed the study’s primary hypotheses. Stage four sought to complement previous studies6,19 by comparing early and later-onset eating disorder cases on rates of exposure to the identified childhood risk factors. First, the final multivariate logistic regression models identified for early onset cases16 were specified for this sample. Models were stratified by age but included predictors from all risk factor domains. Later-onset cases were compared with general and psychiatric control participants. Second, a series of logistic regression models were run to identify early adolescent (14-year) predictors of lateronset disorders. Univariate associations were examined between each predictor and outcome, and variables that predicted risk at p < .10, relative to general or psychiatric control participants, were retained for entry into domain-specific multivariate models. Subsequently, domain-specific multivariate models were created to consider the relative importance of predictors from the parent and family functioning, child health, child personal and emotional functioning, and eating, weight and shape domains. Variables that entered domain-specific

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models at p < .05 were entered into the final early adolescent model, along with child sex. This process is identical to that used in the early onset report.16 Third, a stepwise logistic regression model was created to examine the relative utility of childhood and early adolescent variables in predicting risk for later-onset eating disorders. Step 1 included child sex. Step 2 included the antenatal and childhood variables that predicted risk for a later-onset disorder relative to the general and psychiatric control groups. Step 3 included the early adolescent variables that predicted risk for a later-onset disorder relative to the general and psychiatric control groups. Nagelkerke R2 values34 were examined for each logistic regression model to give an approximate indication of effect size. Fourth, exposure to identified risk factors was compared across participants with an early onset binge eating or purging eating disorder,16 those with a later-onset disorder, and those with no eating disorder by age 20. These comparisons focused on the identified childhood risk factors for early onset (as per previous analyses16) or later-onset (as per analyses in this study) eating disorders. Consistent with early onset analyses,16 and given the large number of possible predictors, alpha was set to p < .01 for the final multivariate models of risk. Alpha was set to p < .05 for the direct comparisons between early onset and later-onset cases.

Results Preliminary Analyses

Missing eating disorder data were imputed for adolescents who missed one of the three adolescent assessments, using EM imputation with International Journal of Eating Disorders 47:7 802–812 2014

RISK FACTORS BY AGE OF ONSET TABLE 2. Early life predictors of later-onset binge eating and purging eating disorders, in age-specific multivariate modelsa

OR (95% CI for OR) Antenatal Child sex (female) Mother’s BMI (16 wk) Father’s BMI (16 wk) Maternal drug use (16 wk; y/n) Father’s age (birth) Nagelkerke R2 1–5 (yr) Child sex (female) 1 yr Child BMI 5 yr Family stress 5 yr Child’s diet restricted (y/n) 5 yr Child skin fold thickness Nagelkerke R2 8 yr Child sex (female) CBCL Social Problems (y/n) WISC Block Design scaled score Family stress Parent-perceived child overweight (y/n) Maternal BMI Nagelkerke R2 10 yr Child sex (female) Self-Efficacy–People CBCL Withdrawn Problems (y/n) SDQ Prosocial Behavior Outpatient appointment in last year (y/n) Family stress Parent-perceived child overweight (y/n) Nagelkerke R2

Later-Onset Eating Disorder versus General Control

Later-Onset Eating Disorder versus Psychiatric Control

5.82 (3.59–9.44)d 1.05 (1.01–1.10)b 1.01 (0.95–1.07) 2.08 (1.01–4.29)b

3.62 (1.90–6.92)d 1.09 (1.02–1.16)b 0.93 (0.85–1.02) 2.31 (0.70–7.66)

1.01 (0.98–1.04) 0.14

1.01 (0.97–1.05) 0.14

6.85 (3.59–13.10)d 0.92 (0.76–1.12) 1.26 (1.07–1.48)d 0.94 (0.57–1.54)

5.08 (2.24–11.51)d 1.08 (0.81–1.42) 1.00 (0.78–1.28) 0.81 (0.39–1.67)

1.07 (1.00–1.14)b

1.07 (0.97–1.18)

0.18

0.18

5.05 (2.85–8.93)d 3.19 (1.18–8.60)b 1.02 (0.94–1.10)

2.81 (1.34–5.91)c 4.62 (0.79–17.20) 1.02 (0.92–1.14)

1.18 (0.99–1.39) 1.08 (0.46–2.59)

0.91 (0.19–1.16) 0.69 (0.20–2.35)

1.05 (1.01–1.10)b 0.14

1.09 (1.02–1.17)c 0.15

7.18 (3.76–13.72)d 1.10 (0.75–1.61) 7.93 (2.37–16.52)d

4.74 (1.96–11.44)d 0.77 (0.43–1.40) 3.05 (0.47–19.79)

0.82 (0.70–0.97)b 1.99 (0.96–4.12)

0.92 (0.70–1.20) 2.10 (0.63–7.04)

1.06 (0.88–1.27) 3.41 (1.89–6.16)d

0.89 (0.68–1.18) 6.27 (2.05–19.23)d

0.21

0.25

a Predictors are variables that contributed significantly (p < .01) to agespecific multivariate models for early onset cases.16 BMI: body mass index; CBCL: child behavior checklist; CI: confidence intervals; OR: odds ratio; SDQ: strengths and difficulties questionnaire; WISC: Wechsler intelligence scale for children-III. b p < .05. c p < .01. d p < .001.

maximum likelihood (n 5 141 at age 17; n 5 140 at age 20). No evidence was found to suggest that data were not missing at random (Little’s MCAR X2 [1399] 5 1376, p 5 .664). Initially, analyses were run using the original, raw dataset. Subsequently, models were respecified using the imputed dataset. Results were comparable in all instances and are reported for imputed data. Socio-demographic characteristics for the eating disorder, general control, and psychiatric control groups are summarized in Table 1, at study enrolInternational Journal of Eating Disorders 47:7 802–812 2014

ment (16 weeks gestation) and at age 14. Chi square tests (with follow-up Fisher’s Exact tests) showed that eating disorder participants were significantly more likely to come from families with an absent biological father and a low family income, in pregnancy and at age 14, than participants in the two control groups. One-way analysis of variance (with follow-up Tukey’s-b post hoc tests) showed that eating disorder participants were significantly heavier than general and psychiatric control participants at age 14, and had significantly higher BDI-Y and CBCL internalizing and externalizing scores than general (but not psychiatric) control participants. Stage One: Early Life Predictors of Later-Onset Binge Eating and Purging Eating Disorders

Table 2 presents results from the age-stratified multivariate models examining associations between antenatal and childhood variables and risk for a later-onset eating disorder. All overall models were significant (ps < .004). Only female sex and parent-perceived child overweight at age 10 were significant in predicting eating disorder risk relative to general and psychiatric control participants. These results are similar to those reported for the early onset sample,16 but for early onset cases, parent-perceived child overweight at age 8 predicted risk relative to both control groups. The 10-year variable predicted risk relative to the general control group only. Maternal BMI at age 8 was also significant in predicting later-onset cases relative to psychiatric control participants. Family stress at age 5 and CBCL Withdrawn Problems at age 10 were significant in predicting later-onset cases relative to general control participants. Nagelkerke R2 values were small to moderate for each model, suggesting that a modest proportion of variance in eating disorder risk was accounted for by each set of childhood predictors. Stage Two: Early Adolescent Predictors of Later-Onset Binge Eating and Purging Eating Disorders

No child health variables were associated with lateronset eating disorders at p < .10 in univariate analyses. Parent and Family Functioning Predictors. Ten parent and family functioning variables were retained from univariate analyses (p < .10): receiving family support benefits; low family income; paternal unemployment; an absent biological father; family stress; family functioning; mother’s general health; and a maternal or paternal history of a mental/ emotional health problem. All were parent-report.

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ALLEN ET AL. TABLE 3. Early adolescent predictors of later-onset binge eating and purging eating disordersa

OR (95% CI for OR) Child sex (female) Beck Depression Inventory-Youth CBCL Externalizing Problems (y/n) SPPA Global Self-Esteem Consumed alcohol in the last 12 months (y/n) Maternal mental health problem–ever (y/n) Child BMI Eating, weight and shape concerns Objective binge eating (y/n) Nagelkerke R2

Later Onset Eating Disorder versus General Control

Later Onset Eating Disorder versus Psychiatric Control

5.06 (2.88–8.89)c 1.02 (0.98–1.06)

2.92 (1.43–5.94)c 0.95 (0.90–0.99)b

2.14 (1.23–3.72)c

1.48 (0.70–3.14)

0.61 (0.36–1.03) 1.38 (0.85–2.23)

0.90 (0.46–1.74) 0.95 (0.49–1.84)

2.39 (1.47–3.90)c

0.96 (0.51–1.81)

1.08 (1.02–1.12)c 2.74 (1.50–5.02)c

1.03 (0.97–1.10) 3.73 (1.62–8.62)c

1.70 (1.06–2.75)b 0.33

2.05 (1.07–3.92) 0.25

TABLE 4. Stepwise model of childhood and early adolescent predictors of later-onset binge eating and purging eating disordersa

OR (95% CI for OR) Step 1 Child sex (female) Step Nagelkerke R2 Step 2 Child sex (female) 10 year parent-perceived child overweight Step Nagelkerke R2 (DR2) Step 3 Child sex (female) 10 year parent-perceived child overweight 14 year eating, weight, and shape concerns Step Nagelkerke R2 (DR2)

Later Onset Eating Disorder Versus General Control

Later Onset Eating Disorder Versus Psychiatric Control

5.55 (3.49–8.83)d 0.12

2.93 (1.62–5.28)d 0.07

5.68 (3.55–9.07)d 2.74 (1.70–4.39)d

3.59 (1.91–6.75)d 3.96 (1.79–8.75)d

0.15 (0.03)

0.14 (0.07)

3.74 (2.82–6.12)d 1.62 (0.96 – 2.72)

2.47 (1.26–4.82)c 2.47 (1.08–5.66)b

5.71 (3.54–9.20)d

3.79 (1.85–7.77)d

0.24 (0.09)

0.22 (0.08)

a

Only variables that contributed to age- and domain-specific models at a p < .05 level are included. BMI: body mass index; CBCL: child behavior checklist; CI: confidence intervals; OR: odds ratio; SPPA: self-perception profile for adolescents. b p < .05. c p < .01.

In the multivariate model, a maternal history of a mental health problem was significant (p < .05) in predicting eating disorder risk relative to the general control group. This variable was retained for entry into the final early adolescent multivariate model (Table 3). Child Personal and Emotional Functioning Predictors.

Six child personal and emotional functioning variables were retained from univariate analyses: BDI-Y depression; all CBCL internalizing and externalizing Problem subscales; SPPA Global Self-Esteem; whether the adolescent had consumed alcohol in the past year; and whether the adolescent had a history of being bullied. Other than the CBCL, all measures were adolescent self-report. As all CBCL subscales were related to outcome, the broad internalizing and externalizing scales were used instead of individual subscales for subsequent analyses. In the multivariate model, BDI-Y depression, CBCL externalizing Problems, SPPC Global SelfEsteem, and consuming alcohol in the past year were significant in predicting eating disorder cases relative to the general control group. These were retained to the early adolescent multivariate model. Eating, Weight, and Shape-Related Predictors. Eleven Eating, Weight and Shape variables were retained from univariate analyses: dietary restraint; eating, weight and shape concerns; binge eating; purging; adolescent BMI; weight-related teasing; frequency of intense exercise; frequency of father’s exercise; frequency of the father helping the adolescent to

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a Only variables that predicted risk relative to general and psychiatric control participants at a p < .01 level, in previous age-specific multivariate models, are included. CI: confidence intervals; OR: odds ratio. b p < .05. c p < .01. d p < .001.

exercise; parent-perceived child overweight; and parent-perceived child overeating. Other than parent-perceived child overweight/child overeating, all variables were adolescent self-report. In the multivariate model, eating, weight and shape concerns were significant in predicting risk relative to the general and psychiatric control groups. Further, BMI was significant in predicting risk relative to the psychiatric control group only and binge eating was significant in predicting risk relative to the general control group only. These variables were added to the early adolescent multivariate model. Final Early Adolescent Multivariate Model. In the final early adolescent model, child sex and eating, weight and shape concerns were significant (p < .01) in predicting eating disorder risk relative to the general and psychiatric control groups. A maternal history of mental health difficulties, CBCL externalizing Problems, and adolescent BMI were significant in predicting risk relative to the general control group only (Table 3). Nagelkerke R2 values were moderate. Stage Three: Integration of Childhood and Early Adolescent Predictors

The stepwise logistic regression model is shown in Table 4. For comparisons with the general control group, each step was significant (p < .001) and female sex and eating, weight and shape concerns at age 14 contributed significantly to the model in International Journal of Eating Disorders 47:7 802–812 2014

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FIGURE 1 Percentages (with 95% CI) or mean scores (with SD) for childhood variables that predicted risk for an early or later-onset binge eating or purging eating disorder. Columns with different notations differ significantly at p < .05.

Step 3 (see Table 4). For comparisons with the psychiatric control group, each step was significant (p < .001) and female sex and eating, weight and shape concerns at age 14 again contributed significantly to the model in Step 3. Nagelkerke R2 statistics suggested that a greater proportion of variance in eating disorder risk was accounted for by eating, weight and shape concerns at age 14 than parent-perceived child overweight at age 10, for the general control group (step R2 change 5 0.03 vs. 0.09, for Steps 2 and 3, respectively). For the psychiatric control group, changes in Nagelkerke R2 were comparable for Steps 2 and 3. Stage Four: Differences Between Early and Later-Onset Cases

Figure 1 summarizes differences between Raine Study participants with a later-onset binge eating International Journal of Eating Disorders 47:7 802–812 2014

or purging disorder (n 5 145) and those with an early onset disorder (n 5 90)16 or no disorder (n 5 954), on childhood variables that predicted early or later-onset disorders. Five variables significantly distinguished between the early and later-onset groups. Compared with later-onset cases, early onset cases were more likely to be perceived as overweight by their parent/s at age 10, had higher mean maternal BMI scores at 16 weeks gestation and age 8, had lower mean WISC Block Design scores at age 8, and had lower mean people-related self-efficacy scores at age 10.

Discussion This study provides new data on differences between risk factors for early and later-onset binge eating and purging eating disorders. Consistent 809

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with hypothesis one, female sex and being perceived as overweight by one’s parent/s in middle childhood were significant multivariate predictors of risk for later-onset disorders in the Raine Study sample. This extends previous findings linking these predictors to early onset eating disorders in the cohort.16 Consistent with hypothesis two, the importance of parent-perceived child overweight was attenuated by adolescents’ eating, weight and shape concerns at age 14. Contrary to predictions, objective body weight was not predictive of risk. Later-onset cases were also found to have lower rates of exposure to weight-related childhood risks than early onset cases, and fewer difficulties with central coherence (WISC Block Design) and people-related self-efficacy in childhood. This is the first study to compare predictors of early and later-onset eating disorders in a single cohort followed over time. It is also one of very few risk factor studies to make use of prospective assessment and general as well as psychiatric control group comparisons. The finding that parentperceived child overweight is a robust predictor of binge eating and purging disorders, regardless of age of onset, underscores the importance of attending to this variable in eating disorder prevention efforts and in clinical practice. Parent’s level of concern about their child(ren)’s weight is a measurable and potentially modifiable risk factor. In this study and the report on early onset cases,16 it was also more important than objective child body weight in predicting eating disorder risk. Several recent studies converge with our findings to show that parental encouragement to lose weight may predict increases in weight and shape concerns in adolescent boys and girls,35 and influence body dissatisfaction in young adult women.36 Interestingly, however, many parents of overweight children are not concerned about their child’s weight.37 Consistent with this, analyses with early onset Raine Study cases showed that parents of healthy weight children who developed an eating disorder were only slightly more likely to rate their child as overweight than other parents.16 Instead, parents of overweight children who developed an eating disorder were generally accurate in classifying their child as overweight, while parents of other overweight children typically under-estimated their child’s size. Further research on the factors that may influence parental perceptions of child(ren)’s weight, and the mechanisms through which these perceptions may influence children’s own eating behavior and eating, weight, and shape concerns, is indicated. As others have noted, there is also a need for public health initiatives that promote 810

resilience to eating disorders and obesity.38 In the interim, health professionals may benefit from attending to eating patterns and general indices of health, as well as weight, when reviewing children’s growth and development. Whilst parent-perceived child overweight was an important childhood risk factor for later-onset eating disorders, it was not statistically significant when examined in the context of adolescents’ own eating, weight, and shape concerns at age 14. Being perceived as overweight in childhood may contribute to heightened eating, weight, and shape concern in early adolescence, so these risk factors may operate sequentially. Regardless, this study adds to an established evidence base4 supporting prospective associations between eating, weight, and shape concerns and eating disorder risk. In previous research, it has not always been possible to test the importance of eating, weight, and shape concerns relative to other related factors, such as dieting. In this cohort, it was concern about eating, weight, and shape, rather than behavioral responses to these concerns (or BMI), which predicted risk. Existing eating disorder prevention programs already seek to address factors that may reduce eating, weight, and shape concerns,39,40 and continuing to evaluate and disseminate these programs is important. Previous studies comparing participants with early and later-onset binge eating and purging disorders have found no significant between-group differences.6,19 In this study, the two groups differed significantly on four variables: maternal BMI during gestation, WISC block design scores at age 8, people-related self-efficacy at age 10, and parent-perceived child overweight at age 10. The early onset group showed higher rates of exposure in each instance. A key difference between this study and those conducted previously is the focus on population-based participants. It is possible that adolescents who receive treatment represent more severe cases, who are broadly similar regardless of when their disorder developed. In a population-based sample, there may be more scope to detect subtle group differences. Our results suggest that early onset cases may be more vulnerable to an eating disorder in early adolescence due to greater exposure to childhood risk factors, compared with children who do not develop an eating disorder until later (or at all). This study had a specific focus on childhood variables that had previously been linked to risk for an early onset eating disorder. It is possible that other childhood variables, not associated with early International Journal of Eating Disorders 47:7 802–812 2014

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onset cases, may also be relevant to the development of later-onset eating disordersa. It is also accepted that risk factors are likely to work together to increase risk for a disorder over time.4 This paper has emphasized predictor variables that could distinguish eating disorder cases from general and psychiatric control cases (i.e., specific risk factors). However, variables that distinguish eating disorder cases from general control cases only (nonspecific risk factors) are also likely to play a role in eating disorder development. For example, having a mother with a past history of mental health difficulties, one of the non-specific risk factors identified in this cohort, may provide a general vulnerability to psychiatric morbidity and work in conjunction with specific risk factors to predict onset of an eating disorder. Ongoing research is needed to determine how different risk factors work together, and to distinguish between effects that are additive (greater exposure leads to greater risk) and those that are interactive (exposure to multiple risk factors is necessary for an eating disorder to develop). Variables that predict eating disorder onset also need to be distinguished from those that predict disorder persistence. We have previously found that externalizing behavior problems in early adolescence, rather than weightrelated variables or eating, weight, and shape concerns, predict persistent eating pathology for Raine Study adolescents with an early onset eating disorder.41 Limitations of this research include the loss of more disadvantaged Raine Study participants to follow-up, which is common amongst longitudinal cohort studies; the small number of male eating disorder participants, which precluded a consideration of gender differences; and the use of selfreport eating disorder assessment. Self-report has been established as a valid means for detecting and classifying eating disorders,24 but does allow for the possibility of misreporting. As noted, we were also unable to classify BED to strict DSM-5 criteria. An additional limitation is that by assessing eating disorders at 14, 17, and 20 years, some participants may have developed an eating disorder between assessment points, which then remitted before the next assessment. Similarly, we do not have detailed data on the age at which disorders developed, and a When following the analytic approach used in the Allen et al. (2009) study (univariate followed by multivariate logistic regression models), female sex, paternal smoking during pregnancy, and parent-perceived childhood overweight at age 10 were significant childhood predictors of later-onset eating disorders in the Raine Study sample, in final multivariate models relative to general and psychiatric control participants.

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the later-onset group includes participants with a possible age of onset from 15 to 20 years. Finally, participants in the psychiatric control group were limited to adolescents with a diagnosed anxiety or depressive disorder. This may be seen as a conservative approach, as it does not capture adolescents who experienced difficulties with anxiety or depression but did not receive formal diagnosis or treatment. In summary, this study provides new data on risk factors for later-onset binge eating and purging eating disorders, and how these compare with previously reported risk factors for early onset cases. Being perceived as overweight by one’s parent/s in middle childhood may be a strong, specific risk factor for early and later-onset disorders. However, for lateronset eating disorders, eating, weight, and shape concerns in early adolescence may be more important than childhood exposures in predicting risk. The authors are extremely grateful to the Raine Study participants and their families and to the Raine Study team for cohort management and data collection.

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