obesity reviews

doi: 10.1111/obr.12139

Etiology/Pediatric Obesity

Associations between predictors of children’s dietary intake and socioeconomic position: a systematic review of the literature D. M. Zarnowiecki1, J. Dollman1 and N. Parletta2

1

Exercise for Health and Human Performance

Group, School of Health Sciences, University of South Australia, Adelaide, South Australia, Australia; 2School of Population Health, University of South Australia, Adelaide, South Australia, Australia

Received 14 September 2013; revised 5 November 2013; accepted 2 December 2013

Address for correspondence: Dr DM Zarnowiecki, School of Health Sciences, University of South Australia, GPO Box 2471, Adelaide SA 5001, Australia. E-mail: [email protected]

Summary Socioeconomically disadvantaged children are at higher risk of consuming poor diets, in particular less fruits and vegetables and more non-core foods and sweetened beverages. Currently the drivers of socioeconomically related differences in children’s dietary intake are not well understood. This systematic review explored whether dietary predictors vary for children of different socioeconomic circumstances. Seven databases and reference lists of included material were searched for studies investigating predictors of 9–13-year-old children’s diet in relation to socioeconomic position. Individual- and population-based cross-sectional, cohort and epidemiological studies published in English and conducted in developed countries were included. Twenty-eight studies were included in this review; most were conducted in Europe (n = 12) or North America (n = 10). The most frequently used indicators of socioeconomic position were parent education and occupation. Predictors of children’s dietary intake varied among children of different socioeconomic circumstances. Socioeconomic position was consistently associated with children’s nutrition knowledge, parent modelling, home food availability and accessibility. Indeterminate associations with socioeconomic position were observed for parent feeding practices and food environment near school. Differences in the determinants of eating between socioeconomic groups provide a better understanding of the drivers of socioeconomic disparities in dietary intake, and how to develop targeted intervention strategies. Keywords: Children, dietary intake, predictors, socioeconomic position. obesity reviews (2014) 15, 375–391

Introduction The link between social disadvantage and poor health is a critical public health challenge. Lower socioeconomic position (SEP) is associated with poorer dietary intake, lower physical activity levels, and higher risks of obesity and cardiovascular diseases (1–5). Childhood conditions may influence adult health outcomes through health behaviours engaged in during the years of growth and development (2), and therefore children of low SEP form an important target group for improving health behaviours. To understand why differences in dietary intake occur between children of dif© 2014 The Authors obesity reviews © 2014 International Association for the Study of Obesity

fering SEP, more consideration is needed of how SEP may influence the drivers of children’s diet. A considerable body of literature has identified a combination of personal and environmental determinants of children’s dietary intake (6–8). Healthy eating, particularly intake of fruits and vegetables, may be determined by better nutrition knowledge, positive attitudes to healthy eating, higher preferences for fruits and vegetables, and higher self-efficacy for healthy eating (7,9,10). Conversely, positive attitudes towards consuming unhealthy foods such as fat- and energy-dense snack foods and soft drinks, may contribute to higher consumption of these items (10–12). 375 15, 375–391, May 2014

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The home environment is the most widely studied setting for influences on children’s eating (7). Parents’ health attitudes may positively impact children’s fruit and vegetable intake and concern for healthful eating (13). Children of parents with higher nutrition knowledge may have healthier diets (9), and parents’ nutrition knowledge and health attitudes may inform the types of foods parents make available in their homes (14,15). Home availability can enable higher consumption of fruits and vegetables (7,16), whereas availability of ‘junk foods’ and soft drinks can enable higher consumption of these items (8,10,17). Children’s food consumption may also be influenced by the types of foods they see their parents consume, particularly for fruits, vegetables, sweetened drinks, snack foods and takeaway meals (7,17,18). Children’s food intake may also be influenced by the feeding practices employed by parents. Strict control and restriction of snack foods may enhance children’s preference for those foods and may inadvertently lead to higher intake (17,19); conversely having fewer rules around eating may also contribute to less fruits, and higher fat and sugar intake (10,20,21). Environmental influences external to the home environment may also influence children’s eating. Children may be more likely to consume fruits and vegetables, but also soft drinks and snack foods, if their friends consume these foods (8,22,23). School food policies may encourage healthy eating and discourage consumption of unhealthy foods and sweetened drinks (24,25). Conversely, neighbourhood environment settings with fast food, takeaway and convenience stores in close proximity to schools and homes may increase consumption of unhealthy foods and drinks, possibly displacing the intake of fruits and vegetables (26,27). Socioeconomic factors in childhood may provide children with different environmental exposures that influence eating, and differences in exposures may influence development of personal attributes affecting food choices, leading to SEP-related differences in the determinants of eating (1,2). There is currently poor evidence from which to develop interventions to address socioeconomic disparities in dietary intake because the development of intervention strategies has been largely based on observations in the general population, which have not taken into account determinants that may be unique to different socioeconomic groups. A better understanding of the differences in determinants of eating between socioeconomic groups will enable the development of targeted intervention strategies. Therefore, the aim of this systematic review was to explore whether dietary predictors are different for children of differing socioeconomic circumstances.

Methods A systematic search guided by the PRISMA Statement was conducted to identify relevant studies investigating differ-

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obesity reviews

ences in predictors of 9–13-year-old children’s diet in relation to SEP. This search was conducted using the databases of Medline and EMBASE (via Ovid); and Academic Search Premier, CINAHL, ERIC, Health Source and PSYCHinfo/ PSYCHarticles (via EBSCO). The search strategy (full search strategy available from authors) combined key search terms for correlates of dietary outcomes (i.e. correlate, predictor, environment, food intake) with key terms for socioeconomic factors (i.e. socioeconomic, social determinant, education, occupation, income) using both key word and Medical Subject Headings (MeSH). Within each search category, all key words and MeSH terms were combined using the Boolean operator ‘OR’, and the results were subsequently combined with the ‘AND’ Boolean operator. Where featured within databases, final searches were limited to the English language, humans, publication year (January 1990–May 2012) and child age categories. Additionally, reference lists of included publications were screened to identify titles of papers containing key terms. The full inclusion and exclusion criteria are listed in Table 1. All individual- and population-based crosssectional, cohort or epidemiological studies were included where differences in correlates of dietary intake were compared between socioeconomic groups. The focus of this review was children aged 9 to 13 years as children of this age are at a critical period when they begin to gain greater autonomy in making their own food choices, while also still being heavily influenced by their parents and home environment (28). During this time, and as children enter into adolescence, they undergo many social and biological changes which may influence their health behaviours, and the patterns of behaviour established at this time may have consequences for the rest of their lives (29). Therefore factors influencing children’s dietary intake during this transitional age may be unique compared to younger children and adolescents. The original intention was to review papers including only children aged 9–13 years; however, the number of papers that met this criterion was too few to make meaningful comparisons. Therefore, the age criteria was expanded to include any studies involving children aged 9–13 years as part of a broader age range of participants. Intervention studies were only included if they reported comparisons of baseline or control group data with SEP. Publications reporting multiple dependent variables or age groups were utilized and only results fitting the inclusion criteria of this review were extracted. For papers reporting the same data from the same study, only the earliest publication was included to avoid duplication. For cross-sectional studies reporting results from both univariate and multivariate analyses, only results of multivariate analyses were reported in this review as they control for other variables. Studies were excluded if SEP was included only as a covariate or adjusted for in analysis, with no statistics on SEP reported. © 2014 The Authors obesity reviews © 2014 International Association for the Study of Obesity

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Table 1 Study inclusion and exclusion criteria Inclusion criteria Involve children aged 9–13 years, including within a broader age range of up to 18 years. Investigate differences in predictors of children’s diet (as primary focus or as one of more outcomes) in relation to at least one indicator of socioeconomic position (SEP; including education, occupation, income, employment status, social class, area-level SEP, other definitions of SEP identified by authors). Intervention studies only if reporting comparison of baseline or control group data Full-text papers published in peer-reviewed journals in the English language between January 1990 and May 2012. Studies conducted in developed countries classified as ‘high income’ using current (2011) data by the World Bank, currently having a gross national income of more than US$12,276 per capita (66). Studies conducted on humans. Exclusion criteria No statistical analysis approaches reported. Intervention studies reporting only changes in dietary correlates as a result of an intervention. Qualitative papers. Studies not conducted on humans. Studies in languages other than English, with no English translation provided. Multifactorial studies where the effect of diet could not be separated from other factors. Studies investigating dietary intake correlates in relation to eating disorders. Studies involving ‘special’ groups of participants, for instance, overweight/obese children, homeless youth, pregnant women, acutely ill or institutionalized individuals. Studies conducted in countries classified as ‘lower middle income’ and ‘low income’ using current (2011) data by the World Bank, currently having a gross national income of less than US$12,276 per capita (66). Papers in low-SEP groups only, with no comparison among groups of different SEP

Screening titles and abstracts initially identified potential papers. If the abstract was not available or did not provide sufficient data, the entire paper was retrieved and screened to determine whether it met the inclusion criteria. Relevant data from included studies were extracted onto standardized forms developed for this review, and tabulated to highlight the state of the literature for identified socioeconomic predictors of children’s diet. Discrepancies over extracted data were resolved through discussion among the authors. For each SEP variable, study findings were categorized according to intrapersonal, home environment and social and neighbourhood variables. Most papers provided more than one sample as multiple variables, age groups, or different genders were examined. A sample was defined as the smallest subsample used in analysis of relevant data. Results were extracted for each relevant sample, therefore the number of samples exceeds the number of included papers. Studies were summarized according to the number of samples finding positive, inverse or no associations for each SEP indicator and dietary outcome. Associations were coded according to the direction of the association between predictor and SEP variable, with ‘+’ for a positive association ‘−’ for an inverse association and ‘0’ for no association. Associations were regarded as significant according to the P value used in the study, which was P ≤ 0.05 or P ≤ 0.01, as some studies accounted for multiple comparisons by modifying the P value. Using coding rules defined by Sallis et al. (30), socioeconomic variables were classified as having no association (0–33%), indeterminate association (34–59%) © 2014 The Authors obesity reviews © 2014 International Association for the Study of Obesity

and consistent positive or negative association (60–100%) with correlates of dietary intake, based on the percentage of findings supporting the expected association divided by the total number of associations for the variable. Summaries of association were calculated only for variables where more than one sample was measured. To enable comparison of studies reporting mediation effects of dietary correlates on associations between SEP and dietary intake, the proportion of the mediated effect was calculated for studies that did not report this, using data reported within the papers. In the absence of an established quality appraisal tool for cross-sectional studies, five criteria were developed using the Strobe Statement (31) to assess study quality. A quality score (out of 5) was determined for each study using a 5-item checklist, where a value of 0 (absent or inadequately described) or 1 (explicitly described and present) was assigned for each of the following criteria: (i) employed a theoretical framework to justify variables under investigation; (ii) study participants randomly selected or likely to be representative of the target population; (iii) measures evaluated for reliability; (iv) measures evaluated for validity; and (v) power analysis conducted and reported to determine whether the study was adequately powered to detect the hypothesized relationship. Criteria three and four were scored 0.5 if some but not all measures were evaluated for reliability and validity. Two researchers independently reviewed each study, and scoring discrepancies were discussed to reach a consensus.

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Records identified through database searching (n = 1058) Duplicate papers removed (n = 249) Records screened (n = 809) Records excluded upon review of title and abstract (n = 640) Full-text papers assessed for eligibility (n = 169)

Additional records identified through screening reference lists (n = 4) Papers included in review (n = 28)

Full-text papers excluded (n = 145) - Wrong study aim, i.e. no SEP x predictor analysis; did not measure dietary predictors (n = 69) - Wrong age range (n = 41) - Review paper (n = 12) - Reported intervention results (n = 6) - Study protocol or validation paper (n = 6) - Conducted in low or middle SEP country (n = 5) - SEP variable was ethnicity (n = 3) - Special population (n = 2) - Qualitative paper (n = 1)

Figure 1 Flow diagram study selection process for review. SEP, socioeconomic position.

Results The initial searches identified 1058 papers, of which 640 papers were excluded upon review of abstracts (Fig. 1). Full-text papers were retrieved and reviewed for 169 studies, of which 145 papers were excluded. A further four papers were identified from reviewing reference lists of included papers, providing a total of 28 papers for inclusion in the review. The characteristics of included papers are shown in Table 2. All but one paper included both boys and girls (32). Sample size of studies ranged from 90 to over 31 000 participants. Most studies were conducted in Europe (n = 12) and North America (n = 10). Nineteen studies reported information for validity and/or reliability

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of measures, or provided a reference to data reported elsewhere. There was little consistency in the way SEP was defined across studies. Most studies reported one indicator of SEP (n = 22), and the most frequently used indicators were parent education (n = 11), school SEP (n = 5) or district/neighbourhood SEP (n = 5).

Overall effects The measured predictors of children’s dietary intake differed considerably across studies, and few studies measured the same combination of predictors and SEP indicators. As shown in Table 3, five intrapersonal variables were measured in at least two different studies, of which only children’s © 2014 The Authors obesity reviews © 2014 International Association for the Study of Obesity

C

C/L

C

C, P C/I CG/I

C

C

C C

C

C

CG/I

n = 2529; age: 12–15 years; boys and girls combined; Australia

n = 896; mean age12.5 years; boys and girls combined; Norway

n = 267; age 7–12 years; boys and girls combined; USA

n = 90; age 11–14 years; boys only; USA

n = 187; age 8.1–12.5 years; boys and girls combined; USA

n = 712; mean age 12.4 years; boys and girls combined; USA

n = 9107; age 12–17 years; boys andgirls combined; New Zealand

n = 1488 2001 and n = 1339 2008; age 10–12 years; boys and girls combined; Norway

n = 167; mean age 11.4 years; boys and girls combined; Greece

n = 3264; age 12–15 years; boys and girls combined; Australia

n = 4211 (111 primary schools); age 9–11 years; boys and girls combined; Wales

n = 962; age 11–12 years; boys and girls combined; Norway

n = 1406; Grades 5–6; boys and girls combined; USA

n = 3528; age 12.5–17.5 years; Boys and girls combined; 10 European countries

Ball et al. (35)

Bere et al. (33)

Cardel et al. (52)

Edmonds et al. (32)

Geller et al. (48)

Geller and Dzewaltowski (47)

Utter et al. (41)

© 2014 The Authors obesity reviews © 2014 International Association for the Study of Obesity

Hilsen et al. (39)

Koui and Jago (67)

MacFarlane et al. 2007 (40)

Moore et al. 2007 (49)

Sandvik et al. 2010 (38)

Lien et al. 2002 (53)

Hallstrom et al. 2011 (45)

Adolescent questionnaire

Adolescent questionnaire

Parent questionnaire (SEP); child questionnaire

School data (SEP); child questionnaire

Parent questionnaire (SEP); adolescent questionnaire

Parent questionnaire (SEP); child questionnaire

Child questionnaire

Census data (SEP); adolescent questionnaire

Adolescent questionnaire

School records; child questionnaire

Census data; parent telephone interview; store observation

Parent questionnaire

Adolescent questionnaire

Parent questionnaire (SEP); adolescent questionnaire

Instruments

Maternal and Paternal education; Maternal and Paternal occupation; Employment; Family wealth; Family affluence scale

Composite SEP score

Parent occupation

School SEP (%students receiving free meals)

Maternal education

Maternal education; paternal education

Parent education

Neighbourhood deprivation

Lunch status (receive free school lunch)

Lunch status (receive free school lunch)

Median census tract income

Social class (Hollingshead Index of education and occupation)

Parent education Income

Maternal education

SEP measures

Personal factors for breakfast consumption (hunger, taste, health concern, routine, ease of preparation, medical reasons); socioenvironmental factors (parents/guardian, food availability, others, price, school environment, friends)

Intentions; attitudes; subjective norms; barriers

Attitudes; social influence; intention (F); preferences (F); home availability (F); self-efficacy

Attitudes (breakfast intake)

Home meal environment; eating rules; home food availability (FV; non-core foods; sweet drinks)

Home F and V availability

Home FV accessibility; FV preferences

Eating attitudes; home food availability (FV, chocolate, sweets, soft drinks); school support for healthy eating

Proxy efficacy

Self-efficacy FV; proxy efficacy FV parents and school staff

Home FJV availability; store FJV availability; restaurant FJV availability

Child feeding practices – restriction and pressure to eat

FV accessibility; FV modelling; FV intention; FV preferences; FV self-efficacy; FV knowledge

Self-efficacy; importance of health behaviours; social observation mother/friend; social support family/friend; home availability snack foods

Variables

2.0

3.5

2.5

3.0

1.0

2.5

2.5

1.0

3.0

3.0

1.0

4.0

2.5

3.0

Quality Score

Child dietary predictors and SEP

C

Study design

Sample characteristics

Study

Table 2 Characteristics of reviewed studies

obesity reviews D. M. Zarnowiecki et al. 379

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C C C B/L

n = 849; age 4–14 years; boys and girls combined; Netherlands

613 fast food restaurants and 1292 schools; School age; USA

n = 1560; age 5–6 and 10–12 years; boys and girls combined; Australia

n = 1106; age 8–14 years; boys and girls separate analyses; Canada

Hupkens et al. (60)

Austin et al. (42)

Timperio et al. 2009 (44)

Sylvestre et al. 2007 (68)

Parent questionnaire

Census data (SEIFA)

USA Census data; business information database

Parent questionnaire

Adolescent questionnaire

Adolescent questionnaire

USA National schools database; business information database

Parent questionnaire

Parent questionnaire

Parent questionnaire; adolescent questionnaire

School data (SEP); adolescent questionnaire

Adolescent questionnaire

Census data (SEP); Store observation; adolescent questionnaire

Child questionnaire

Instruments

Maternal education

Neighbourhood SEP

Census tract median household income

Maternal education

Adolescent education

Parent education; Family affluence

School SEP (%students receiving free meals); School SEP (% low income students)

Parent education

Income

Income; Parent education (household average)

School SEP (parent income at school)

Composite SEP score

Neighbourhood unemployment rate

Parent occupation

SEP measures

Mother’s fruit and vegetable intake

Proximity of fast food outlets to home and along route to school

Proximity of fast food restaurants to schools

Food rules (rescription and restriction); health consideration; taste consideration

Friendship group snack and soft drink intake; school availability snack foods and soft drinks

Food establishments near home (fast food shops, restaurants, convenience stores)

Food establishments near school (convenience stores, limited service restaurants, etc.)

Parent modelling FV intake

Food advertising exposure

Use of health information; health knowledge; self-efficacy; diet behaviours (i.e. reading food labels); child + adult report of all outcomes

Nutrition knowledge; self-efficacy; dietary locus of control

Taste preferences; attitudes; self-efficacy; social support; family meal patterns; Food security; home FV availability

Neighbourhood energy-dense food supply

Exposure to TV viewing and advertisements

Variables

2.0

1.5

1.0

2.0

1.5

2.0

1.0

2.5

1.5

2.5

4.0

4.0

1.5

2.5

Quality Score

D. M. Zarnowiecki et al.

B/L, baseline data from longitudinal study; C, cross-sectional; CG, control group; C/L, cross-sectional data from longitudinal study; F, fruit; I, intervention; J, juice; L, longitudinal; N, not reported; P, pilot study; SEIFA, Socioeconomic Index for Areas; SEP, socioeconomic position; V, vegetables; Y, yes reported in paper or elsewhere.

C

n = 749; age 12.4–17.6 years; Boys and girls combined; Netherlands

C

n = 1762; age 7–10 years; boys and girls combined; Netherlands

Rodenburg et al. 2012 (37)

Wouters et al. (23)

C

n = 234; age 4–12 years; boys and girls combined; Netherlands

Buijzen et al. 2008 (51)

C

C/L

n = 824 children, n = 973 adults, 525 households; Age 12–19 years; boys and girls combined; USA

Rimal 2003 (36)

n = 24,796; mean age 14.5 years (grades 7–13); boys and girls combined; Hong Kong

C

n = 4441; age 12–18 years; boys and girls separate; Australia

O’Dea and Wilson 2006 (34)

Ho et al. (26)

C

n = 3597; Mean age 14.9y (Grades 7–9); boys and girls combined; USA

Neumark-Sztainer et al. 2003 (16)

C

C

n = 3440 Age 13–15 years; boys and girls combined; Germany

Lange et al. 2011 (50)

31,622 middle and secondary schools; boys and girls combined; USA

C

n = 13,035; age 8.8–11.4 years; Boys and girls combined; 9 European countries

Klepp et al. 2007 (46)

Sturm (43)

Study design

Sample characteristics

Study

Table 2 Continued

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Child dietary predictors and SEP

D. M. Zarnowiecki et al. 381

Table 3 Overall comparison of predictors with SEP Predictor variable

Intrapersonal variables Child self-efficacy Attitudes Preferences Nutrition knowledge Intentions Home environment Parent modelling Parent/family support Permissive food rules Pressure, demand and control Restriction Fruit and vegetable home availability Fruit and vegetable home accessibility Non-core food and drink home availability Social, school and neighbourhood environment Social support (friends/peers) Supportive school environment Neighbourhood energy-dense food supply Food environment surrounding school

Related to SEP

Unrelated to SEP

Number of samples

(34,35) (35,45,49) (33,39) (33,34) (33,38)

(16,33,34,38,48) (16,33,38,41,45,53) (16,38,45) (33) (16,33)

10 13 11 4 5

(33,35–38) (35,45) (60) (38,52) (52,60) (16,32,35,38,40,41) (33,39,40) (35,40,41)

(33,68) (45) (40) (40,60) (40,67) (38) (40)

10 8 5 4 2 14 5 9

(45) (41,45) (26,44,50) (42–44)

(16,35,45) (45) (26) (43)

9 8 28 17

nutrition knowledge was consistently positively associated with SEP in three of four samples (33,34). Of eight home environment variables measured, three were consistently associated with SEP and four had indeterminate associations. Positive associations with SEP were identified for parent modelling in seven of 10 samples (33,35–38) and fruit and vegetable home accessibility in four of five samples (33,39,40). Non-core food and drink availability was negatively associated with SEP in seven of 10 samples (35,40,41). Indeterminate associations with SEP were observed for fruit and vegetable availability, permissive food rules, controlling feeding practices and restriction. Of four social, school and neighbourhood environment variables measured, only the food environment surrounding the school was indeterminately associated with SEP (42–44).

Education level Education was associated with intrapersonal predictors in 10 of 21 samples, and home environment in 28 of 51 samples (Table 4). A consistent association was observed for children’s self-efficacy with education, whereby selfefficacy was positively related with maternal education in two of three samples, both from the same study (35). Indeterminate associations were observed for education with preferences and intentions, which were positively associated with education in 50% of studies (33,39). A number of elements of the home environment were studied in relation to education, but most were measured in only © 2014 The Authors obesity reviews © 2014 International Association for the Study of Obesity

Associations (n)

Summary

−

+

% Studies

Assoc.

7 8 9 1 3

3 4 2 3 2

30 31 18 75 40

0 0 0 + ?

3 6 3 2

7 2

70 25 40 50 50 50 80 78

+ 0 ? ? ? ? + −

22 13 18 41

0 0 0 ?

1

2 2 1 2

0

7

5 1 2

2 5 5

7 5 21 5

1 7 4

2 1 2 7

one study. Education was consistently positively associated with family support for healthy eating in two of three samples (35,45), and home accessibility of fruits and vegetables in three of three samples (33,39,40). Education was negatively associated with home availability of non-core foods in three of four samples (35,40), and sweetened drinks in two of three samples (40). The association of parent modelling with education was indeterminate overall, but over 50% of samples showed a positive association of modelling with education (33,35–37). No consistent associations were identified for education with social, school or neighbourhood predictors.

Occupation Occupation was associated with dietary predictors in 14 of 46 samples, and was studied mostly in relation to intrapersonal and home factors (Table 4). Occupation was positively associated with intrapersonal factors in four of 22 samples (38,45), showing an indeterminate association with children’s health attitudes, positive in two of four samples (45); and no consistent associations with any other intrapersonal variable. Occupation was significantly associated with home environment in eight of 15 samples (38,46), and was positively associated with home fruit availability in three of three samples originating from the same study (38). Occupation was investigated in relation to three social, school and neighbourhood factors, but was not associated with any of these factors.

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Child self-efficacy for FV intake Preferences for fruit and vegetables Knowledge of FV recommendations Intention of FV intake

Observation of friends’ intake Friend/peer support friends Supportive school environment Cost of food Presence of food businesses near home

Social, school and neighbourhood environments (45a)

(23cNC)

(60a) (40a) (40a) (40a) (40a) (35aFV,40aF) (35a,40aNC1,2) (40aSD1,2) (33,39,40a)

(60a) (60aii,iv) (60a iii)

(33iiFV,35a,36,37F) (35a,45b) (36) (36) (36)

(35aF NC) (35a) (45a) (33FV) (39FV) (33) (33ii) (45abi)

Related to SEP

(33) (33) (33) (33)

(23cSD,35a) (35a,45ab) (45ab) (45b) (26i–ii F,FV,NC,HF)

(45ab) (40aV,67abFV) (40aNC3) (40a J)

(40a i–iii) (40a iv,60ai) (40a v–vii) (60a)

(36)

(33iFV,68aFV) (45a)

(33i) (45abii–iii) (45ab)

(33FV) (45b) (45ab)

Unrelated to SEP

1 1 1 1

3 3 2 2 12

7 3 1 1 1 1 1 5 3 3 1 1 1 1 1 3 7 4 3 3

3 3 4 1 2 6 2

Number of samples

1

1

3 2

1

1

2

1

1

–

1 1 1 1

2 3 2 1 12

2 5 1 1

3 2 3 1

1

3 1

1 4 2

1 1 2

0

Associations (n)

3

1 2

1

1

1

1

4 2 1 1 1

2 1 2 1 1 1

+

0

33 0 0

33 29 75 66 100

40 33 0

57 67

50 4 0

67 33 50

% studies

Summary∧

0

0 0 0

0 0 − − +

? 0 0

? +

? 0 0

+ 0 ?

Assoc

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Income Intrapersonal attributes

Parent modelling Parent/family support Parents’ use of health information Parents health knowledge Household discussion about health Parents self-efficacy Parent health considerations Permissive food rules Controlling food rules Meal time rules Foods prescription Restriction of foods Evening meal unpleasant time Evening meal pleasant time TV on during meal times Sufficient food available at home Home availability fruit and vegetables Home availability ‘non-core’ snack foods Home availability sweetened drinks Home accessibility fruits and vegetables

Child self-efficacy Importance of health/behaviours Preferences/taste Knowledge (FV recommendations) Intention (FV intake) Importance of facilitators Importance of medical reasons

Home environment

Education level Intrapersonal attributes

Predictor variable

Table 4 Summary of studies reporting associations of SEP with predictors of children’s dietary intake

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Parent modelling Parents’ use of health information Parents health knowledge Household discussion about health Family communication style Parents self-efficacy Home FV accessibility Exposure to food advertisements

© 2014 The Authors obesity reviews © 2014 International Association for the Study of Obesity

Friend/peer support Supportive school environment Cost of food

Social, school and neighbourhood environments

Parent/family support Food available at home

Friend/peer support Supportive school environment Cost of food Food businesses near home

Social, school and neighbourhood environments

(26i F,FV, ii NC)

(45h) (45h)

(45d) (45e)

(38) (38i–iii)

(46) (46) (38) (38)

(38) (45di)

(45de)

(33) (51)

(36) (36) 51i

(33,36)

Related to SEP

2 2 2 12

2 2

(45gh) (45fg) (45g) (45g) (45gh) (26i HF,NC,26ii F,FV, HF,26iii F, FV,HF,NC)

2 2 6 2

3 3 3

1 1 1 1 3 1 3 3 1

4 4 1 1 9 3

2 1 1 1 2 1 1 1

Number of samples

(45gh) (45gh) (45ghi–iii) (45gh)

(45ef) (45df) (45def)

(45def) (38)

(45def)

(45defii,iii,efi) (45def)

(38F,45f) (38F,45def) (38)

(51ii) (36)

(36)

Unrelated to SEP

3

1

1

1 1

1

1

–

1 1 2 9

2 2

2 2 6 2

2 2 3

3 1

3

8 3

2 4 1

1 1

1

0

Associations (n)

1 1

1

3

1 1

1 1

2

1

1 1

2

+

50 50 0 25

0 0

0 0 0 0

33 0 0

100 0

0

11 0

50 0 0

100

% studies

Summary∧

? ? 0 0

0 0

0 0 0 0

0 0 0

+ 0

0

0 0

? 0 0

+

Assoc

Child dietary predictors and SEP

Home environment

Asset-based – family affluence/perceived wealth Intrapersonal attributes Importance of health Importance of taste Importance of facilitators Importance of medical reasons

TV on during dinner Hours of TV viewing per day Parent modelling (fruit intake) Parent encouragement (fruit intake) Parent/family support Parent demand (FV intake) Home fruit availability Breakfast food available at home Parent facilitation (fruit intake)

Home environment

Parent occupation/employment Intrapersonal attributes Attitudes/ importance of health Taste/food preferences Self-efficacy Intention (Eat fruit daily) Importance of facilitators Importance of medical reasons

Home environment

Predictor variable

Table 4 Continued

obesity reviews D. M. Zarnowiecki et al. 383

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Self-efficacy Health/nutrition attitudes Taste preferences (FV) Intentions Subjective norms Barriers Pressure to eat Restriction Home FV availability Food security Family meals Social support

(32FV,41) (41NC,SD) (41) (44,50) (44,42) (43ji) (43ji–ii) (43ki,43ji–ii) (43ji–ii,43k ii)

(34ii) (47i–iii,48i) (49) (34i–ii)

(52) (52) (16)

Related to SEP

(43ki) (43ki–ii) (43k ii) (43ki)

(34i–ii)

(34i,48FV) (48ii) (41)

(16) (16) (16)

(16) (16,53) (16) (53) (53) (53)

Unrelated to SEP

4 5 2 2 2 3 2 1 4 3 2 4 4 4

1 2 1 1 1 1 1 1 1 1 1 1

Number of samples

1 2 2

2 2 1 2

1 1

–

1 2 1 1

2

3 1 1

1 1 1

1 2 1 1 1 1

0

Associations (n)

50 100 50 50 50 0

2 3

1 3

1

25 80 50 100 0 33 100

0

% studies

1 4 1 2

1

+

Summary∧

? + ? ? ? 0

0 + ? + 0 0 −

0

Assoc

D. M. Zarnowiecki et al.

F, fruits; FF, fast food/take-away foods; J, fruit juice; NC, non-core foods including all types of energy-dense foods (sweet and salty snack foods, cakes, etc.); SD, sweetened drinks, including soft drinks, energy drinks etc.; V, vegetables. Numerical scripts (i)/(ii) designate different samples from the same study, as indicated: (2) Two cohorts (i) 2002, (ii) 2005 (where year not indicated, cohorts combined for analyses); (5) Proxy efficacy representing children’s ability to influence (i) parents, (ii) school staff; (6) Separate samples for grades (i) six, (ii) seven and (iii) eight; (10) (i–vii) represents seven statements analysed separately. Refer reference for detail; (12) (i–iii) Separate analyses of three statements describing home availability, refer reference for detail; (14) Measured importance of three facilitators of eating breakfast (i) habits/routine, (ii) hunger, (iii) easy to prepare; (18) Separate analyses for (i) boys and (ii) girls; (20) Two domains of ‘family communication style’ (i) concept-oriented, (ii) socio-oriented; (22) Repeated analyses for (i) at least one business within 400 m of schools, (ii) density of food outlets within 400 m of schools; (23) Types of food businesses, (i) restaurants, (ii) fast food shops, (iii) convenience stores; (25) (i–iv) Represents four statements analysed separately. Refer to reference for details. Superscripts indicate SEP variables for: amother’s education; bfather’s education; cadolescents’ education; dmother’s occupation; efather’s education; femployment status; gfamily affluence scale; hhow ‘well off’ is family; jschool lunch status; ktitle one school eligibility. ∧Summary: associations were classified as no association if 0–33% studies support association; indeterminate if 34–59% studies support association; positive or negative association if 60–100% of studies support association.

Neighbourhood and school SEP Intrapersonal attributes Self-efficacy Proxy efficacy Attitudes Nutrition knowledge Dietary locus of control Home environment Home fruit and vegetable availability Home non-core food availability School and Supportive school environment neighbourhood Neighbourhood energy-dense food supply environment Fast food restaurants near school Food businesses near school Convenience stores near school Restaurants near school Snack stores near school

Home environment

Composite SEP score Intrapersonal attributes

Predictor variable

Table 4 Continued

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Area-level SEP Area and school SEP were measured in 41 samples, 15 in relation to intrapersonal predictors, five in relation to home environment, and 22 in relation to social, school and neighbourhood environments (Table 4). In eight of 15 samples, area-level SEP was positively related to intrapersonal factors (34,47–49). Consistent positive associations were identified for SEP with proxy efficacy (children’s self-efficacy for influencing parents and teachers to provide healthy foods) in four of five samples (47,48), and children’s nutrition knowledge in two of two samples (34). An indeterminate association was identified for SEP with health attitudes, with a positive association identified in one of two studies (49). Area-level SEP was significantly associated with school and neighbourhood environments in 17 of 22 samples (41–44,50); however, the direction of these associations varied considerably, and therefore, only the presence of fast food restaurants near schools was consistently positively associated with SEP in three of three samples (42,44). Indeterminate associations with area-level SEP were observed for the presence of food businesses, convenience stores and restaurants near schools (43).

Other SEP indices Income was studied in 14 samples overall (Table 4). No associations were identified between income and intrapersonal variables. Income was associated with home environment factors in seven of 10 samples (33,36,51), and was not studied in relation to social, school or neighbourhood environment factors. Parent modelling was positively associated with income in two of two studies (33,36); and family communication style was associated with income in one of two samples (51). In all other instances, income was measured once in relation to the predictor variable, and therefore, it was not possible to identify consistency of associations. Asset-based SEP indicators were not associated with any intrapersonal or home environment variables. Indeterminate associations were identified for asset-based variables with peer support and school environment, which were positive in one of two samples (45). Composite SEP was associated with three of six home environment variables each measured in one sample; pressure to eat (52), restriction (52) and home fruit and vegetable availability (16).

Mediator effects As shown in Table 5, five studies tested associations of SEP with children’s dietary intake for mediation by personal and environmental predictors (33,35,37,39,49). Self-efficacy, attitudes, preferences, knowledge and inten© 2014 The Authors obesity reviews © 2014 International Association for the Study of Obesity

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D. M. Zarnowiecki et al. 385

tions were tested as mediators of associations of education with intake of fruits, vegetables, snack foods and fast food, and in all instances were shown to act as mediators (33,35,39). Associations of school SEP with breakfast intake were mediated by attitudes to breakfast in two of three samples (49). Observation of mother’s food intake and modelling mediated associations of SEP with fruit, vegetable, snack food and fast food intake (33,35,37). Family support for healthy eating mediated associations of education with fruit, snack and fast food intake, but the mediation effect was small, ranging from 7% to 13% (35). Home fruit and vegetable availability mediated associations of education with fruit and fast food intake, but not snack food intake (35). Non-core food availability mediated associations of education with fruit, snack and fast food intake (35). In two studies, accessibility mediated associations of education and income with fruit and vegetable intake (33,39). Mediation by social environment variables was less consistent. Observation of best friend-mediated associations of fruit and snack food intake with education, but the mediation effect was smaller than 5% (35); and support from friends did not mediate SEP–diet associations (35). One study evaluated multiple-mediator models, finding accessibility to be the most prominent mediator of fruit and vegetable intake associations with SEP (33).

Moderator effects As shown in Table 6, six studies tested SEP variables as moderators of predictor-diet relationships (23,26,37,38,51, 53). Two studies tested moderation effects of SEP on home environment variables. Income moderated the association of advertising exposure with consumption of advertised brands, whereby a stronger relationship was observed in the high-SEP group (51). Associations of parent and child fruit intake were not moderated by education (37). Two studies tested for moderation effects of SEP on social and neighbourhood environment. The association of snack intake with friends’ snack intake was moderated by SEP, with positive associations observed in low-SEP teens only (23). Perceived restaurant availability was positively associated with fruit and vegetable intake in low and middle SEP adolescents but not high-SEP adolescents (26). Likewise, perceived fast food store availability was associated with non-core food and drink intake in low SEP, but not middle and high-SEP adolescents (26). No study tested associations of intrapersonal predictors and dietary intake on its own for moderation by SEP, but two studies tested path models which included intrapersonal variables (38,53). One study showed that a composite SEP score did not moderate a path model including intentions, attitudes, norms and barriers as predictors of fruit and vegetable intake (53). The second study showed that prediction of fruit intake by a path model

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Table 5 Studies investigating mediation effects of dietary correlates on SEP disparities in food intake Study

SEP variable

Mediator

Intrapersonal variables (35) MumEd

Self-efficacy ↑F

(35)

MumEd

Self-efficacy ↓NC

(35)

MumEd

Importance of health behaviours

(49)

School SES

Attitudes to breakfast

(33)

Ed

FV preferences

(39) (33)

Ed Ed

Preferences Knowledge

(33)

Ed

FV intention

Home environment variables (35) MumEd Observation – mother

(33) (37)

Ed Inc Ed

FV modelling FV modelling F modelling

(35)

MumEd

Support – family

(35)

MumEd

FV home availability

(33)

Ed

FV accessibility

(33) (39) (35)

Inc Ed MumEd

FV accessibility FV accessibility NC home availability

Social environment variables (35) MumEd Observation – best friend

(35)

MumEd

Multiple mediator models (33) Ed (33)

Ed

(33)

Inc

Support – friends

Accessibility, preferences, knowledge Accessibility, modelling, intention, preferences, knowledge Accessibility, modelling

Pathway mediated

Mediation Y/N

%

MumEd → F intake MumEd →snack food MumEd → fast food MumEd → F intake MumEd → snack food MumEd → fast food MumEd → F intake MumEd → snack food MumEd → fast food School SES → ‘healthy’ breakfast School SES → ‘unhealthy’ breakfast School SES → skipping breakfast Ed → FV intake (2002) Ed → FV intake (2005) Ed → change FV intake Ed → FV intake (2002) Ed → FV intake (2005) Ed → FV intake (2005)

Y Y Y Y Y Y Y Y Y Y N Y Y Y Y Y Y Y

12.4 30.4 11.9 11.8 69.6 26.2 11.8 39.1 14.3 Full − Full 40.0 33.0 30.0 20.0 9.0 24.0

MumEd → F intake MumEd → snack food MumEd → fast food Ed → FV intake 2005 Ed → FV intake (2002/2005) Ed (hi vs. lo) → F intake Ed (mid vs. lo) → F intake MumEd → F intake MumEd → snack food MumEd → fast food MumEd → fruit intake MumEd → snack food MumEd → fast food Ed → FV intake 2002 Ed → FV intake 2005 Ed → FV intake (2002/2005) Ed → change FV intake MumEd → fruit intake MumEd → snack food MumEd → fast food

Y Y Y Y Y Y Y Y Y Y Y N Y Y Y Y Y Y Y Y

10.7 13.0 16.7 29.0 39.0 43.0 45.0 10.7 13 7.1 10.7 − 14.3 90.0 52.0 80.0 34.0 7.1 Full 35.7

Y Y N N N* N

2.4 4.3 − − −

Ed → FV intake 2002

Y

Ed → FV intake 2005

Y

92% (45% accessibility; −4% preferences; 3% knowledge) 60% (14% accessibility; 3% modelling; 1% intention; −3% preferences; 1% knowledge

Ed → FV intake (2002/2005)

Y

MumEd MumEd MumEd MumEd MumEd MumEd

→ → → → → →

F intake snack food fast food fruit intake snack food fast food

89% (50% accessibility; 9% modelling)

Ed, parent education; F, fruits; Inc, income; MumEd, maternal education; NC, non-core foods (i.e. ‘junk foods’/snack foods); V, vegetables. *Not a mediator as increased the magnitude of the association between MumEd and snack food intake.

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Table 6 Studies testing moderation of food intake and predictor associations by SEP Study

SEP Moderator

Home environment (51) Income

(37)

Education

Pathway moderated

Moderation

Advertising exposure → consumption of advertised brands Advertising exposure → overall food consumption Advertising exposure → energy-dense food intake Parent F intake → child F intake

Social and neighbourhood environment (23) Teen Education Friends’ snack intake →teen snack intake

(26)

Family affluence

Multivariable path models (38) Occupation

(53)

Composite SEP score

Y/N

Outcomes

Y

Stronger relationship in high SEP

N N N

– – –

Y

↑ Friends’ snack intake associated with ↑ intake in low-SEP teen, but not high SEP – ↓ F intake when ↑ perceived restaurant availability in low and mid SEP adolescents, but not high SEP ↓ FV intake when ↑ perceived restaurant availability in low and mid SEP adolescents, but not high SEP −

Friends’ soft drink intake →teen soft drink intake Perceived restaurant availability → F intake

N Y

Perceived restaurant availability → FV intake

Y

Perceived restaurant availability → non-core and high-fat food intake Perceived fast food store availability → non-core food and drink intake Perceived fast food store availability →F intake; → F and vegetable intake; and → high-fat food intake Perceived conveniences store availability →F intake; FV intake; → high-fat food intake; and → non-core food/drink intake

N

N

↑ Non-core food and drink intake if ↑ perceived fast food stores in low SEP, but not mid and high SEP −

N

−

Attitudes, social influences, preferences, home F availability, self-efficacy → intention to eat F → F intake

Y

Intentions, attitudes, subjective norms, barriers→ FV intake

N

Self-efficacy most strongly related to intention in low SEP; home availability associated with F intake in high SEP, but not low and mid SEP −

Y

F, fruit; V, vegetable.

including attitudes, self-efficacy, preferences, social influences and home availability was moderated by occupation (38). In this model, self-efficacy was most strongly related to intentions to consume fruit in low-SEP children, and home availability was associated with fruit intake in high SEP, but not low and mid SEP (38).

Discussion The purpose of this review was to evaluate the published literature investigating associations among SEP and predictors of children’s dietary intake. One intrapersonal predictor, children’s nutrition knowledge, and several home environment factors, including parent modelling and home food availability and accessibility, were consistently associated with SEP, but no social, school or neighbourhood environment factors were consistently associated with SEP. Some predictors were indeterminately associated with SEP and warrant further investigation because of the small number of studies available. In general, few studies meas© 2014 The Authors obesity reviews © 2014 International Association for the Study of Obesity

ured the same combination of predictors and SEP indicators, limiting comparability of studies and interpretation of results. A vast array of SEP indicators was measured in reviewed studies, and indicators were differentially associated with dietary intake correlates, suggesting that SEP may influence children’s dietary intake through distinct pathways. The most consistent findings were observed with education as the socioeconomic indicator, consistent with findings of other authors (7). Education may reflect parents’ capacity to access, interpret and put into practice health information (1). Associations with occupation were quite variable, although a number of home environment factors appeared to be influenced by occupation. Occupation-related factors not directly related to SEP may impact on children’s eating, contributing to variable results. Time spent in employment (full vs. part time), the number of parents in the family who work, or the type of occupation may all impact on daily activities and time use patterns, and influence parents’ food provision (1).

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The only intrapersonal variable consistently associated with SEP was children’s nutrition knowledge, which was higher in children of high SEP. Children’s nutrition knowledge may be positively related to parents’ nutrition knowledge (54,55), and may influence their food intake (9). SEP may be positively associated with parents’ health knowledge and household discussion about health, which may influence children’s nutrition knowledge (36,56). Other intrapersonal variables were not consistently associated with SEP, but results from mediation studies suggested that attitudes, self-efficacy, preferences and intentions might partially explain socioeconomic differences in children’s dietary intake (33,35,39,49). In particular, attitudes to health may be a key limiting factor for fruit and vegetable consumption, as low-SEP youth who perceived their health to be important were likely to consume more fruits and vegetables (57). This review found that a number of parental influences may differ by SEP. Parents of low SEP were less likely to model healthy eating behaviours (33,35–37), which is consistent with socioeconomic gradients in adults’ dietary intake showing that adults of low SEP have poorer dietary intake (3,58). Parent modelling of healthy eating behaviours has been positively associated with children’s eating behaviours (7,59). Therefore, targeting improvements in parents’ food intake may help to improve diets of children from low SEP. Associations of child feeding practices with SEP were indeterminate, and the nature of differences in feeding practices was unclear. One study showed that lowSEP parents used more restrictive feeding practices, while another study showed that high-SEP parents restricted more foods (52,60). Differences between these studies may be attributed to the way restrictive feeding was measured. Cardel et al. (52) measured restriction of high-fat foods, sweets and junk foods, and identified negative associations with SEP. In contrast, Hupkens et al. (60) measured restriction of commonly consumed healthy and unhealthy foods, for instance bread, butter, vegetables, and dessert, finding that high-SEP parents reported restricting intake of a larger number of unhealthy foods, and prescribing intake of healthier foods. Two studies in this review found that lowSEP parents engaged in more controlling feeding practices (38,52). Controlling food intake is often done with good intentions to improve children’s food intake, but may inadvertently lead to stronger preferences for restricted foods (61). Therefore ineffective restriction and food controlling practices may suggest one avenue for explaining poorer dietary intake in low-SEP children. Conversely, one study found that parents of low-SEP children employed more permissive food rules (60). Permissive parenting may be associated with poorer dietary behaviours in children, characterized by higher intake of fat, sweet foods, snacks and soft drinks, and lower vegetable intake (20,21,62). Socioeconomic factors such as education or occupation may

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differentially influence feeding practices, contributing to the inconsistent results identified. Occupation may determine if parents are present during children’s meals, and therefore the opportunities they have to restrict food intake, whereas education may be associated with parents’ knowledge about nutrition and this may be reflected in the feeding practices employed (63). Children of low SEP may have more snack foods and sweetened drinks and less fruits and vegetables available at home (16,35,40,41). When parents adopt a permissive parenting style, the types of foods available to children in their environment may determine their food choices. Therefore, more permissive food rules within an environment that has higher availability of non-core foods and less fruits and vegetables, may be one explanation for poorer dietary intake among low-SEP children. The association of SEP and fruit and vegetable availability was indeterminate in this review; however, consistent associations were determined for SEP and fruit and vegetable accessibility (33,39,40), suggesting that in low-SEP households, accessibility may be more important than availability. Availability relates to whether the foods are present in the environment, whereas accessibility pertains to factors that facilitate easy consumption, such as the form and location of the foods. Fruits and vegetables often require some preparation, for instance cutting, before they can be consumed. Therefore, even if they are available, children may not consume fruits and vegetables if they are not easy to access, especially in an environment where there is higher availability of non-core foods and drinks that are often packaged in a ready-to-eat form. Furthermore, in an environment where availability is high (i.e. high SEP), accessibility may be less important than in environments where availability is low (i.e. low SEP). This is supported by mediation studies that found that availability and accessibility partially explained socioeconomic differences in dietary outcomes (33,35,39). Low-SEP children may be more susceptible to environmental differences in food availability, with lower availability of fruits and vegetables, and higher availability of non-core and fast foods detrimentally related to food intake in low- but not high-SEP youth (26,38). Peer influences and social support were not associated with SEP in this review; however, findings were mixed overall, and too few studies have currently evaluated associations of SEP with social influences to draw any strong conclusions. One study showed that observation of friendmediated associations of education with fruit and snack food intake (35), and another study found that snack food intake was associated with friends’ snack food intake in low-SEP teens but not in high-SEP teens (23). Likewise, neighbourhood food environment was not associated with SEP overall, but findings were inconsistent among studies. Over 40 samples were compared in this review, but these © 2014 The Authors obesity reviews © 2014 International Association for the Study of Obesity

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samples were derived from only five papers, as many different definitions of neighbourhood environment were used within included studies (26,43). Individuals may purchase foods in areas other than their neighbourhood, which clouds our understanding of the relationship between neighbourhood environment and food intake (64). There is little consensus as to the best way to define neighbourhood buffers, leading to considerable variation among studies (27,42,43). The reviewed studies also varied considerably in the direction of association between SEP and neighbourhood food supply, with studies finding higher availability of fast food/convenience stores in low-SEP areas (50), and conversely higher availability in high-SEP areas (44). It may be that associations of food environments and SEP are contextually specific, and may differ between regions and countries. This was the first review to consider how predictors of children’s dietary intake differ with SEP. Strengths of this review were the systematic approach adopted, the use of coding associations, and the differentiation between various SEP indicators to consider which, if any, are most consistently associated with correlates of children’s dietary intake. The review was limited to studies from developed countries, as trajectories of SEP and obesity associations in developing countries are opposite to those observed in developed countries (65). Interpretation of findings was limited by the relatively small number of studies published, which used a diverse range of SEP indicators and measured a large number of different dietary correlates. A number of identified predictors showed no association with SEP; however a considerable limitation for interpreting findings is that so few studies measured common predictors, therefore limiting the ability to compare findings across studies. Many of the predictors that showed no association with SEP were measured in only one or two studies, and hence it was not possible to conclude that these predictors do not vary by SEP. More studies testing associations of dietary correlates with SEP are needed to generate more convincing evidence for understanding socioeconomic disparities in children’s diet. Further still, although the aim of the review was to identify associations of SEP and dietary predictors specifically in children aged 9–13 years, the broad and diverse age range categories employed among studies meant that it was not possible to only include studies of children aged 9–13 years. Therefore the inclusion of studies that included participants outside this age range may limit interpretation of results. Overall evaluated studies were of low quality, with all but eight studies scoring 2.5 or less out of five according to the quality rating scale developed for this study. In particular, no studies reported whether they had conducted a power analysis to determine whether the study was adequately powered to detect the hypothesized relationships. Approximately, half of studies did not employ a theoretical framework to justify the selection of © 2014 The Authors obesity reviews © 2014 International Association for the Study of Obesity

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variables under investigation. Promisingly, only eight of 28 studies did not evaluate the reliability or validity of measures, although it must be noted that in some instances reliability and validity were poor. The majority of studies reviewed were cross-sectional and therefore it is not possible to make conclusions about causality of the associations between dietary predictors and SEP.

Conclusions This review found that predictors of children’s dietary intake may vary for children of different socioeconomic circumstances. In particular, children’s nutrition knowledge, parent modelling and home food availability and accessibility were consistently associated with SEP. However this review also demonstrated that too little research has been conducted in this area to draw strong conclusions about which predictors of dietary intake vary by SEP, and therefore, how best to target health interventions to lowSEP populations based on these differences. In the future, there is a need for studies to measure interactions of SEP with dietary correlates, and for more consideration of the type of socioeconomic indicator used and the mechanisms by which SEP indicators may influence dietary intake.

Conflict of interest statement The authors declare no conflicts of interest.

Acknowledgments NP is supported by National Health and Medical Research Council Program Grant funding (# 320860 and 631947). The authors would like to acknowledge the assistance of Zoe Richards, research assistant in the School of Health Sciences at University of South Australia, in acting as a second reviewer for critical appraisal of study quality.

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