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Soc Sci Med. Author manuscript; available in PMC Jun 1, 2010.
Published in final edited form as:
PMCID: PMC2730650
NIHMSID: NIHMS112066

Parent–child dietary intake resemblance in the United States: Evidence from a large representative survey

Abstract

We studied the association in dietary intakes and patterns between parents (aged 20–65 years) and their children (aged 2–18 years), using nationally representative data collected by the US Department of Agriculture (USDA) in the Continuing Survey of Food Intake by Individuals 1994–96. We analyzed two 24-h recall dietary data for 1061 fathers, 1230 mothers, 1370 sons and 1322 daughters. All analyses adjusted for sampling design complexity. We assessed multivariate-adjusted parent–child correlations in selected nutrients, food groups and overall dietary quality assessed using the new USDA 2005 Healthy Eating Index score (HEIn). The parent–child correlations were weak or moderate (0.20–0.33) for most intake measures. There were clear patterns of interaction with gender dyads in the intakes of calcium and dairy products (P < 0.05 for dyad × parental intake), whereby multivariate-adjusted correlations in mother–daughter or mother–child dyads were significantly stronger compared to their father–child counterparts. The reverse was true for multivariate-adjusted correlations in HEIn. Hispanics and other ethnic groups had significantly stronger resemblance than Non-Hispanic whites and blacks in soft drinks and HEIn. Resemblance in general was stronger among older children, though the reverse was true when considering agreement in HEIn's upper quintile. The influence of family income and parental education on the resemblance was small. In conclusion, parent–child dietary resemblance in the US is relatively weak, and varies by nutrients and food groups, and by the types of parent–child dyad and population groups. Factors other than parental eating behaviors seem to play an important role in affecting American young people's dietary intake.

Keywords: USA, Dietary, Diet, Dietary quality, Ethnicity, Food intake, Familial aggregation, Child, CSFII, Gender

Introduction

Children and adolescents adopt certain dietary behaviors that previous studies suggested might track into adulthood (Mikkila, Rasanen, Raitakari, Pietinen, & Viikari, 2005; Wang, Bentley, Zhai, & Popkin, 2002). It is therefore important to promote healthy eating among the youth to prevent occurrence of chronic conditions later on in life, particularly obesity, cardiovascular disease, type 2 diabetes and certain types of cancer. There are many ways by which promoting healthy eating among the youth can be achieved. However, one of the suggested means was to educate the parents and enhance their awareness about their own health as well as that of their children. This approach assumes that parents are gate keepers and role models for their children and that their nutrition beliefs and behaviors may actually influence that of their offspring.

However, there is a growing body of evidence suggesting that dietary intake among the youth moderately resembles that of their parents, particularly in the United States. To date, around fifteen such studies have been conducted (Adelekan & Adeodu, 1997; Cullen, Lara, & de Moor, 2002; Feunekes, de Graaf, Meyboom, & van Staveren, 1998; Feunekes, Stafleu, de Graaf, & van Staveren, 1997; Fisher, Mitchell, Smiciklas-Wright, & Birch, 2002; French, Story, & Jeffery, 2001; Galloway, Fiorito, Lee, & Birch, 2005; Laskarzewski et al., 1980; Oliveria et al., 1992; Park, Yim, & Cho, 2004; Patterson, Rupp, Sallis, Atkins, & Nader, 1988; Perusse et al., 1988; Rossow & Rise, 1994; Stafleu, Van Staveren, de Graaf, Burema, & Hautvast, 1994; Vauthier, Lluch, Lecomte, Artur, & Herbeth, 1996), seven of which were carried out in non-representative samples within the United States, limiting the ability to produce national population estimates. A number of published studies support familial resemblance in dietary patterns (Adelekan & Adeodu, 1997; Laskarzewski et al., 1980; Oliveria et al., 1992; Patterson et al., 1988; Perusse et al., 1988; Rossow & Rise, 1994; Stafleu et al., 1994; Vauthier et al., 1996). On the other hand, other studies show that the association is either very weak or non-existent (Cullen et al., 2002; Feunekes et al., 1997, 1998). The weak or non-existent association is likely related to young people’s eating patterns being affected by a myriad of complex factors, with the influence of parents and the family environment being only one of them (French et al., 2001; Popkin, 2006; Vereecken, Inchley, Subramanian, Hublet, & Maes, 2005).

To our knowledge, previous research has not examined familial resemblance in dietary intake using nationally representative data in the United States. Most previous studies are based on small and local samples, and the possible regional and between-group differences in the resemblance between child and parental dietary intakes could affect our understanding of the relationships at the national level. It is crucial to assess such an interrelationship to guide future dietary intervention programs which to date have been strictly targeted towards schools among other non-household settings (Abdel Gawwad, Fetohy, Fiala, Al Orf, & Al Saif, 2006; Agozzino, Esposito, Genovese, Manzi, & Russo Krauss, 2007; Fahlman, Dake, McCaughtry, & Martin, 2008; Janega et al., 2004; Nicklas et al., 1997; Podrabsky, Streichert, Levinger, & Johnson, 2007). Although previous studies assessed nutrient and food group associations between family members, none have evaluated associations in overall diet quality.

The present study examined parent–child dietary pattern interrelationships, using nationally representative data in the United States, and thus produced national population estimates. We estimated correlations in selected nutrients and food groups and assessed the intra-familial clustering in healthy behaviors through adherence to dietary guidelines (i.e., overall dietary quality) between parent and child dyads. We also tested for effect modification of these associations by selected individual and household-level characteristics.

Methods

Continuing Survey of Food Intakes by Individuals (CSFII) data

Data from the US Department of Agriculture (USDA) Continuing Survey of Food Intakes by Individuals (CSFII) 1994–96 were used (US Department of Agriculture ARS, 1994–96). A nationally representative multi-stage stratified sample of 16,103 non-institutionalized persons aged 0–90 years contained information about dietary intake (by one or two nonconsecutive 24-h recalls that were 3–10 days apart); socioeconomic, demographic and health parameters (Tippett & Cypel, 1997).

Study population

Parent sample

Among the 16,103 CSFII respondents, we included 9872 who were 20 years or older and had complete data on day 1 of recall. We excluded those over the age of 65 years (n = 2127) and those who completed only one 24-h dietary recall (n = 414), which resulted in a final sample of 7331 adults (aged 20–65 years, 3721 men and 3610 women) aged 20–65 years with both 24-h dietary recalls completed. Among them, 86% (n = 6303) were either the respondent or the spouse of the respondent and hence were eligible to be included in our sample. All other adults were other family members including, those having a “child of head of household” status. The rationale for excluding elderly subjects was that their dietary intake might differ from those of other family members due to illness or general poorer health.

Children sample

Children were matched to the adult sample by family relationship. Only those children aged 2–18 years with both completed 24-h recalls were considered. After matching parents with their children, the final sample sizes were: 2291 parents (1061 fathers and 1230 mothers) in 1473 households; 2692 children (1370 sons and 1322 daughters); a total of 4244 parent–child dyads (1156 mother–son, 1128 mother–daughter, 982 father–son and 978 father–daughter).

Measures

Dietary intakes and dietary quality indicators

Average dietary intakes from the two 24-h recalls were used. Among young children aged 2–9 years old in the total CSFII 1994–96, proxy response was the common pattern (only 7% and 11% self-reported dietary intake on days 1 and 2, respectively). Food groups and nutrients considered included energy (kcal/day), fat (g/day and % of energy), saturated fat (g/day and % of energy), cholesterol (milligrams; mg/day), sodium (mg/day), fiber (g/day), calcium (mg), sugars and candies (g/day), fruits and vegetables (g/day), dairy products (g/day), total and unsweetened soft drinks (g/day). The food groups and nutrients were selected because of their important influence on health outcomes, including obesity and the metabolic syndrome.

Diet quality index (HEIn)

To assess the overall quality of diet, we applied the newly revised USDA 2005 Healthy Eating Index (HEIn) (Britten, Marcoe, Yamini, & Davis, 2006; U.S. Department of Agriculture (USDA), 2005), which incorporated the new dietary recommendations and energy-adjusted all of its individual components (Britten et al., 2006). For many of the food group criteria, Mypyramid serving estimates rather than grams were used as made available by the USDA website http://www.ars.usda.gov/Services/. The HEIn score consisted of the sum of scores on twelve components covering dietary recommendations in terms of nutrient and food group intakes scored differently (0–5 to 0–20) according to their importance to overall dietary quality. These were developed based on a large body of evidence as outlined elsewhere (Britten et al., 2006). Appendix A table shows the criteria used for scoring each component. The HEIn could range between 0 and 100. Likely HEIn is a more robust measure of overall diet pattern (and dietary quality) than intakes of individual nutrient or food groups.

Other covariates

Covariates in our statistical models were included as either potential confounders or effect modifiers as they were previously shown to affect dietary intakes, particularly among adults (Beydoun, Powell, & Wang, 2008; Beydoun & Wang, 2008a, 2008b): age and gender of parents and children, parental ethnicity (Non-Hispanic whites, Non-Hispanic blacks, Hispanics, Others), education (years), employment status (yes vs. no), smoking status (current smoker vs. not), self-rated health (poor health vs. not), physical activity (currently sedentary, measured as never or rarely participates in vigorous activities vs. not), and number of chronic conditions (>2 conditions vs. not), household poverty income ratio category (PIR: 0–129 (poor); 130–299 (near poor) and 300+ (not poor)), and contextual factors including geographical region and degree of urbanization. Other covariates in children included reported body mass index (BMI) and reported physical activity (same categories as for adults).

Statistical analysis

All analyses were conducted using survey-related commands in STATA release 9.0 (STATA, 2005), which take complex sampling design into account (multi-stage stratified cluster as opposed to simple random sample) and produce nationally representative estimates of means, proportions and regression coefficients as well as correct estimates of standard errors (Lohr, 1999). In all tests, P < 0.05 was considered as statistically significant.

We first conducted descriptive analysis to describe the populations’ characteristics. Further, and to best examine commonality in dietary intakes between parents and children, we estimated adjusted Pearson correlation coefficients as approximated by the linear regression coefficient between parent and child intakes expressed as standardized z-scores. This approach allows us to account for complex survey design effects and also to control for potential confounders.

We also estimated overall observed agreement, expected agreement and the kappa estimate, which measures agreement beyond that by chance and is calculated based on observed agreement, expected agreement and the weight matrix. We presented both unweighted and weighted agreement percentages and their corresponding kappa values (Agresti, 1996; Wang & Wang, 2003). Note that the weight matrix and kappa estimation formula are presented in Appendix B. Next, we conducted multivariate logistic regression analysis to test whether “high diet quality” (top quintile or 80th percentile of HEIn) among parents predicted “high diet quality” among their children (i.e., agreement of having a healthy diet) controlling for other covariates.

Further, we assessed effect modifications, e.g., by gender of parents and children based on a statistically significant interaction term (DQIpj × dyad) introduced into the multivariate regression models for parent–child associations. The other potential effect modifiers we tested included ethnicity, children’s age, household income (PIR), and parental education. In all tests, a P-value of <0.05 was considered as statistically significant including interaction terms.

Results

Study population characteristics

The distribution of selected characteristics of parents and children stratified by gender is presented in Table 1. In general, fathers were older than mothers by an average of two years, and an appreciably higher percentage of them were employed (93% vs. 67%). In terms of health factors, while men had a higher BMI, women were more likely to assess their physical activity level as sedentary. Among children, mean age and BMI did not differ by gender, although physical activity did, with a higher proportion of girls reporting a sedentary lifestyle.

Table 1
Demographic and health characteristics of study sample: mean or % ± SEMa; CSFII 1994–96.

Dietary intake and patterns

The mean and standard errors of nutrients, food groups and the HEIn are presented for each selected family member according to status and gender (Table 2). In general, fathers had higher energy intake and absolute intakes of most foods and nutrients as well as relative intake of fat and saturated fat compared to mothers (P < 0.05), with the exceptions of sugars and candies and unsweetened soft drinks (P > 0.05). In addition, HEIn indicated a higher overall quality of dietary intake among mothers compared to fathers (50.3 vs. 48.4; P < 0.05). While gender differences between parents were significant for all nutrients, food groups and the HEIn, differences between sons and daughters in intake of some nutrients (fat and saturated fat as % energy), food groups (sugars and candies, fruits and vegetables and unsweetened soft drinks) and the HEIn were non-significant (P > 0.05).

Table 2
Nutrient, selected food group intakes and overall diet quality of parents and children: mean ± SEM; CSFII 1994–96.

Parent–child dietary correlations: overall and subgroup analysis

Parent–child correlations are presented as standardized regression coefficients adjusted for design complexity in Table 3. Analysis was further stratified first by gender dyads and second by selected characteristics including parents’ ethnicity, child’s age and the household’s PIR group. It is worth noting that in many cases, adjustment for potential confounders yielded markedly attenuated correlations (by >10%), when comparing crude to adjusted correlation coefficients (data not shown). Overall, parent–child correlations in nutrient and food group intakes ranged from <0.10 for fat and saturated fat as % energy to around 0.30 for fruits and vegetables and cholesterol intakes. Most of the multivariate-adjusted correlations were in the weak to moderate range (0.20–0.30). In addition, the adjusted correlation between parents and children in terms of overall diet quality was 0.26 (P < 0.01). Looking at the relationship by gender dyads, there were clear patterns of interaction in the case of calcium and intake of dairy products as well as the overall diet quality index (HEIn). In most of these cases, except the HEI, mother–child multivariate-adjusted correlations were significantly stronger compared to their father–child counterparts (P < 0.05 for dyad × dietary intake(parent) interaction term). For all other nutrients and food groups, interaction patterns by gender dyads were less evident and non-significant.

Table 3
Association between children and parents’ diet: multivariate-adjusted standardized regression coefficientsb,c stratified by gender dyads and selected characteristics; CSFII 1994–96.

When examining the strength of dietary pattern correlations between parents and their children across ethnic groups, we found that for overall dietary quality (HEI), Hispanics and “other” ethnicities had a significantly higher correlation compared to Non-Hispanic whites and blacks (0.36 and 0.38 vs. 0.30 and 0.27 respectively; P < 0.10 for ethnicity × dietary intake(parent) interaction term). Ethnic group differences in the strength of parent–child correlations were also noted for total soft drinks, in which the “other” minority groups had the strongest correlation.

The strength of parent–child correlations varied by child’s age as well for most nutrients, food groups and for the diet quality index, with few exceptions such as fruits and vegetables and sugars/candies. In most cases, children older than 10 years had a stronger association in dietary intake with their parents compared to younger children aged 2–10 years. The difference in correlation was particularly marked in the case of total and saturated fat intakes. However, when looking at overall diet quality, child–parent correlation in HEIn was similar for both younger and older children with no statistically significant interactions observed.

In terms of poverty income ratio, only one statistically significant interaction was noted for unsweetened soft drinks. In particular, the non-poor segment of the population exhibited a stronger parent–child correlation compared to the poor and near poor groups.

Agreement in diet quality between parents and children based on quintiles: overall and subgroup analysis

Table 4 shows the observed and expected agreement between parents’ and children’s HEI scores based on quintiles. In general, the agreement was weak as indicated by both the weighted and unweighted results (note both have accounted the complex sampling design effects). The observed unweighted agreement percentages ranged between 26.0% for father-son dyad and 33.1% for ‘other ethnic groups,’ while the weighted ones were 58.9% for father–son dyad and 64.6% for Hispanics. Weighted kappa estimates varied between groups and were the lowest among “other ethnic group” (kappa = 0.07) and highest among Hispanics (kappa = 0.28). All weighted kappas were significantly different from zero.

Table 4
Observed and expected percentage agreement,a kappa, and multivariate logistic regressionb for agreement in high diet quality between parents and their children (HEIn > 80th percentile), stratified by gender dyads and selected characteristics; ...

Further, we studied the child–parent association in high diet quality (top quintile, i.e., having a healthy diet) and adjusted for potential confounders using logistic regression models (see Table 4). In general, the odds for children having a healthy diet (HEIn score range: 61–81) increased by three-folds when their parents did so (HEIn score range: 62–86). There was a significant effect modification by age of the child, with the odds of upper quintile concordance between parents and children being increased to a greater extent among young children aged 2–10 years compared to older children (>10 years) (4.05 vs. 1.55, P < 0.05 for age(-child) × HEI(parent) interaction term). All other comparisons of odds ratios across strata did not indicate any interactions.

Testing potential influence of parental education on the child–parent dietary intake resemblance

We suspected that parental educational attainment might influence dietary choices and food habits, and thus, might affect the child–parent dietary intake resemblance. We tested parental education in years as an effect modifier, but overall, our analyses indicated that the influence of parental education on the resemblance was small. The interaction term with parental dietary intake in our models was significant only for cholesterol intake indicating that the correlation becomes significantly stronger but only by a value of 0.02 (P = 0.039) with each year of educational attainment by parents. We also conducted this analysis separately for each dyad. However, only for father–son pairs, father education significantly reduced the correlation by −0.03 per year (P = 0.025) for total saturated fat.

Discussion

To our knowledge, the present study is the first attempt to assess familial resemblance in dietary intakes and patterns, particularly between parents and their children using nationally representative data collected in the U.S. It is also the first to examine differences in the resemblance between groups (e.g., different parent–child dyads, ethnicity, family income, child age) in various dietary intake variables including nutrients, food groups and overall dietary quality.

Our study has several main interesting findings. First, the results provide insight into the extent of parental influence on children’s dietary behaviors and suggest that there is moderate resemblance, in terms of correlations, in intake and patterns within families. Similar to findings from previous studies of selected samples, the correlations are moderate (0.20–0.33) (Adelekan & Adeodu, 1997; Cullen et al., 2002; Feunekes et al., 1997, 1998; Fisher et al., 2002; French et al., 2001; Galloway et al., 2005; Laskarzewski et al., 1980; Oliveria et al., 1992; Park et al., 2004; Patterson et al., 1988; Perusse et al., 1988; Rossow & Rise, 1994; Stafleu et al., 1994; Vauthier et al., 1996), suggesting that young people’s dietary intakes are influenced by other factors in addition to household factors (Boutelle, Fulkerson, Neumark-Sztainer, Story, & French, 2007; Hanson, Neumark-Sztainer, Eisenberg, Story, & Wall, 2005), such as community and school (Fitzgibbon & Stolley, 2004; Jahns, Siega-Riz, & Popkin, 2001; Zizza, Siega-Riz, & Popkin, 2001) food environments, peer influence (Salvy, Romero, Paluch, & Epstein, 2007), television viewing and programs (Ayala et al., 2007; Boynton-Jarrett et al., 2003; Matheson et al., 2004), as well as individual factors such as self-image and self-esteem (Satia, Kristal, Curry, & Trudeau, 2001; Shariff & Yasin, 2005). For example, one of the earliest US studies estimated correlations between one parent and one of their children (aged 6–19 years) in terms of several nutrient intakes and found them to be 0.26, 0.30, 0.15 and 0.22 for energy, carbohydrates, saturated fat and polyunsaturated fat intake (P < 0.05), respectively (Laskarzewski et al., 1980). Another more recent US study (Oliveria et al., 1992) indicated that correlations were particularly high in fat intake as well as monounsaturated fatty acids (MUFAs) and polyunsaturated fatty acids (PUFAs) when mother–child relationships were considered (r ranged between 0.31 and 0.51, P < 0.05). These correlations were shown to be weaker in father–child associations (r = 0.10–0.22) with the exception of saturated fat (r = 0.40, P < 0.05).

In addition, our study suggested variations in the differences of HEI across gender dyads and in the correlations in terms of food groups and nutrients both across gender dyads and other selected characteristics. In particular, there were clear patterns of interaction in the case of intakes of calcium and dairy products, whereby in mother–daughter or mother–child in general, multivariate-adjusted correlations were significantly stronger compared to their father–child counterparts. Several prior studies came to similar conclusions (Feunekes et al., 1998; Laskarzewski et al., 1980; Oliveria et al., 1992; Park et al., 2004; Stanton, Fries, & Danish, 2003). At least two previous studies found that daughters had a higher resemblance to their parent’s diet compared to sons (Feunekes et al., 1997; Park et al., 2004). Other studies found no consistent patterns across macronutrients in terms of gender dyads (Vauthier et al., 1996). However, our findings indicated that the mother–daughter association in overall diet quality as measured by HEIn was the weakest among the four dyads, particularly after adjusting for child and parent characteristics.

We also found ethnic differences – Non-Hispanic blacks consistently exhibiting a weaker association than other ethnicities, specifically for soft drinks and the overall HEIn score. This may suggest that environmental factors may be more influential on certain ethnic groups especially in terms of overall diet quality, rendering the association between parents and their children weaker than expected in the total population. Another study came to a similar conclusion whereby white parent–child dyads had significantly higher correlations of fat and fiber intakes compared to African-American dyads (Stanton et al., 2003). However, an earlier study reported that parent–child nutrient correlations were particularly strong when parents were black mothers aged over 40 years, particularly in the case of energy and carbohydrate intakes (Laskarzewski et al., 1980). It is possible that the parent–child resemblance in dietary intakes might have changed over time, and the changes vary across population groups.

Our findings based on this national sample show that the influence of family SES on the parent–child resemblance in dietary intakes is small. We found no effect modification by family income and a very weak one by parental education independently of other demographic factors for HEIn, in which most of the food groups and nutrients were considered. To test effect modification of parental educational attainment independently of income, we further conducted a sensitivity analysis for all parent–children dyads as well as for each dyad separately. Overall, the results suggested that its influence on the resemblance was small and only significant for cholesterol intake.

Furthermore, we suspected that older children tend to have weaker resemblance with their parents’ intakes compared to their younger counterparts due the greater possible influence of factors outside the home based on some previous studies (Adelekan & Adeodu, 1997; French et al., 2001; Laskarzewski et al., 1980; Oliveria et al., 1992; Patterson et al., 1988; Perusse et al., 1988; Popkin, 2006; Rossow & Rise, 1994; Stafleu et al., 1994; Vauthier et al., 1996; Vereecken et al., 2005). In fact, stronger peer influence may affect children as they grow older and reach the adolescent stage who then experience more autonomy from parents when making eating choices (Worthington-Roberts & Rodwell Williams, 1999). In our study, we only found a stronger resemblance in older children when HEIn was assessed as a binary variable in terms of agreement between parents and children within the upper quintile. However, for some other individual nutrients and food groups, older children tended to have stronger multivariate-adjusted correlations, and there was no marked age interaction in terms of continuous HEIn. These mixed findings may be due to the fact that HEI includes a large number (n = 12) of components and the related cutpoints were energy-adjusted, while most individual food groups and nutrients considered were not. Day-to-day measurement errors might affect the individual dietary variables more seriously than on the HEI, and the influences varied by nutrients and food groups. Those findings also suggest that if one only focused on selected individual food groups or nutrient intakes (particularly those unadjusted for total energy intake) as opposed to overall dietary quality, the conclusion regarding familial dietary resemblance may be biased. This sheds important insight on the interpretation of previous findings and helps to guide future studies.

Finally, our analysis for parents and children separately also shows that their average intakes of several nutrients including fiber and calcium were inadequate while some were too high (e.g., sodium) compared to the 2005 daily recommended intakes (DRIs) (Institute of Medicine, 2005). The related DRI for fiber was 19–38 g/day for children depending on gender/age group and 21–38 g/day among adults depending on gender/age group; for calcium, 600–1000 mg/day for children and 800–1500 mg/day for adults; and for sodium, 700 mg per 1000 kcal diet, i.e. around 1540 mg for a 2200 kcal diet.

Our study has a number of strengths. First, we used nationally representative data and obtained population estimates of correlations that account for sampling design complexity. Second, we examined resemblance using the new 2005 USDA Healthy Eating Index, which helps assess overall dietary quality. Finally, we assessed effect modification by gender parent–child dyads and selected SES characteristics with sufficient sample heterogeneity to test effect modifiers.

Despite its strengths, our study had its limitations. First, the data were approximately 10 years old, but available data from other more recent nationally representative surveys such as the National Health and Nutrition Examination Surveys do not allow for such analyses based on the released data. We suspect it is unlikely that the association might have changed substantially over time over the past two decades, and our findings could assist in testing such time trends in parent–child correlations once more recent comparable data become available. Second, dietary intake was based solely on two 24-h recalls. Random intra-individual variations across the targeted time frame of intake may attenuate parent–child correlations (Oliveria et al., 1992). In addition, for very young children (2–6 years), reporting of dietary intakes was mostly by proxy which may bias the results away from the null (Tippett & Cypel, 1997). However, our results suggest that younger children, at least for most food groups and nutrients selected, are less likely to follow their parental dietary patterns than older one. Hence, the interaction with age may be even stronger. Despite these expectations, one has to be cautious about the impact of misreporting of dietary intake by parents for smaller children as well as the measurement error in reporting dietary intake by older children which may not be uncorrelated with their actual true intake. In addition, dietary intake cannot be assumed to be of equal validity across childhood and adulthood (Baxter, Hardin, Royer, Guinn, & Smith, 2008). Finally, clustering at the household level may have occurred given that each parent may be linked to more than one child per household. A sensitivity analysis was conducted whereby the cluster variable considered was the household. Our findings were similar, but we preferred to present the results with PSU as the cluster variable. We could not adjust for both.

Our findings have a number of important public health implications. First, the overall weak to moderate parent–child resemblance (r: 0.2–0.33) in food groups, nutrients and HEIn scores suggests that interventions targeting at parents could only have a moderate effect in improving their children’s diet. In particular, such interventions would be most effective when targeted at mothers, minority groups, and as early as possible in childhood (i.e. at ages 2–10 years). Economic well–being does not seem to affect the resemblance, indicating that intervention should be applied to all economic segments of the population (e.g., not only the low-SES families).

In conclusion, our findings based on nationally representative data show that the overall parent–child resemblance in dietary intakes in the United States is relatively weak, and is not as strong as many previously believed. And the resemblance varies by nutrients and food groups, and by types of parent–child dyad and population groups (e.g., age, ethnicity, and SES). Factors other than parental eating behaviors seem to play an important role in affecting American young people’s dietary intakes. Future studies should attempt to investigate how parental diet-related knowledge, perceptions and self-efficacy may impact children’s diet and whether parental dietary intake is acting as a mediator. Ethnic differences in parent–child dietary intakes’ associations should be further explored in terms of differential effects of environmental and non-household factors such as peer pressure and their impact on children’s dietary behavior in those ethnic groups in which correlations were found to be weak.

Acknowledgements

The study was supported in part by the Johns Hopkins Center for a Livable Future, the US Department of Agriculture (2044-05322), and the NIH (NIDDK R01 DK63383; NICHD, 1R03HD058077-01A1, R03HD056073).

Appendix A

New 2005 USDA Healthy Eating Index - components and standards for scoring.a

ComponentMaximum pointsStandard for maximum scoreStandard for minimum score of zero
Total fruit (includes 100% juice)5≥0.8 cup equiv. per 1000 kcalNo fruit
Whole fruit (not juice)5≥0.4 cup equiv. per 1000 kcalNo whole fruit
Total vegetables5≥1.1 cup equiv. per 1000 kcalNo vegetables
Dark green and orange vegetables and legumesb5≥0.4 cup equiv. per 1000 kcalNo dark green or orange vegetables or legumes
Total grains5≥3.0 oz equiv. per 1000 kcalNo grains
Whole grains5≥1.5 oz. equiv. per 1000 kcalNo whole grains
Milkc10≥1.3 cup equiv. per 1000 kcalNo milk
Meat and beans10≥2.5 oz equiv. per 1000 kcalNo meat or beans
Oilsd10≥12 g per 1000 kcalNo oil
Saturated fat10≤7% of energye≥15% energy
Sodium10≤0.7 g per 1000 kcale≥2.0 g per 1000 kcal
Calories from solid fat, alcohol, and added sugar (SoFAAS)20≤20% of energy≥50% energy
aIntakes between the minimum and maximum levels are scored proportionately, except for saturated fat and sodium (see note e).
bLegumes counted as vegetables only after meat and beans standard is met.
cIncludes all milk products, such as fluid milk, yogurt, and cheese.
dIncludes nonhydrogenated vegetable oils and oils in fish, nuts, and seeds.
eSaturated fat and Na get a score of 8 for the intake levels that reflect the 2005 Dietary Guidelines, 10% of energy from saturated fat and 1.1 g Na/1000 kcal, respectively.

Appendix B. Measuring agreement using weighted kappa

Kappa provides a measured of agreement between raters and the degree to which they concur in their respective sorting of N items into k mutually exclusive categories (Agresti, 1996). In our case, we are studying agreement across quintiles of the Healthy Eating Index (HEI) between parents and their children. When categories are ordinal (e.g. quintiles), it is possible to use weights depending on the distance between the misclassified quintiles of parents and those of their children. To obtain observed, expected probability of agreement and weighted kappa estimates for quintiles, the following steps are needed:

(i) Construct a linear weight matrix for kappa estimation: W

10.750.2500
0.7510.750.250
0.250.7510.750.25
00.250.7510.75
000.250.751
Linear weight =1|dist|distmax

Where distmax = k − 1 = 5 − 1 = 4 for quintiles; dist can range between 0 and 4.

Kappa(weighted)=(Agreement(observed prop.)Agreement(expected prop.))/(1Agreement(expected prop.))

(ii) Construct the agreement observed matrix: O

Parents
ChildrenP11P21P31P41P51
P12P22P32P42P52
P13P23P33P43P53
P14P24P34P44P54
P15P25P35P45P55

(iii) Construct the agreement “chance expected” matrix: E

P1=P2=P3=P4=P5=0.20
P1 × P1 = 0.040.040.040.040.04
0.04P2 × P2 = 0.040.040.040.04
0.040.04P3 × P3 = 0.040.040.04
0.040.040.04P4 × P4 = 0.040.04
0.040.040.040.04P5 × P5 = 0.04

(iv) Calculate Pobserved, Pexpected and weighted kappa:

Pobserved=O×W=P11×1+P12×0.75+P13×0.25+Pexpected=E×W=0.04×1+0.04×0.75+0.04×0.25+=0.50
Kappa(weighted)=(PobservedPexpected)/(1Pexpected)

Note: observed and expected P were also weighted by the sample weight.

References

  • Abdel Gawwad ES, Fetohy EM, Fiala L, Al Orf SM, Al Saif M. Impact evaluation of nutrition education intervention for an elementary school in riyadh city. Journal of the Egyptian Public Health Association. 2006;81(1–2):75–97. [PubMed]
  • Adelekan DA, Adeodu OO. Interrelationship in nutrient intake of Nigerian mothers and their children: nutritional and health implications. African Journal of Medicine and Medical Sciences. 1997;26(1–2):63–65. [PubMed]
  • Agozzino E, Esposito D, Genovese S, Manzi E, Russo Krauss P. Evaluation of the effectiveness of a nutrition education intervention performed by primary school teachers. Annali d’igiene. 2007;19(4):315–324. [PubMed]
  • Agresti A. An introduction to categorical data analysis. New York: Wiley; 1996.
  • Ayala GX, Baquero B, Arredondo EM, Campbell N, Larios S, Elder JP. Association between family variables and Mexican American children’s dietary behaviors. Journal of Nutrition Education and Behavior. 2007;39(2):62–69. [PubMed]
  • Baxter SD, Hardin JW, Royer JA, Guinn CH, Smith AF. Children’s recalls from five dietary-reporting validation studies. Intrusions in correctly reported and misreported options in school breakfast reports. Appetite. 2008;51(3):489–500. [PMC free article] [PubMed]
  • Beydoun MA, Powell LM, Wang Y. The association of fast food, fruit and vegetable prices with dietary intakes among US adults: is there modification by family income? Social Science & Medicine. 2008;66(11):2218–2229. [PubMed]
  • Beydoun MA, Wang Y. Do nutrition knowledge and beliefs modify the association of socio-economic factors and diet quality among US adults? Preventive Medicine. 2008a;46(2):145–153. [PubMed]
  • Beydoun MA, Wang Y. How do socio-economic status, perceived economic barriers and nutritional benefits affect quality of dietary intake among US adults? European Journal of Clinical Nutrition. 2008b;62(3):303–313. [PubMed]
  • Boutelle KN, Fulkerson JA, Neumark-Sztainer D, Story M, French SA. Fast food for family meals: relationships with parent and adolescent food intake, home food availability and weight status. Public Health Nutrition. 2007;10(1):16–23. [PubMed]
  • Boynton-Jarrett R, Thomas TN, Peterson KE, Wiecha J, Sobol AM, Gortmaker SL. Impact of television viewing patterns on fruit and vegetable consumption among adolescents. Pediatrics. 2003;112(6 Pt 1):1321–1326. [PubMed]
  • Britten P, Marcoe K, Yamini S, Davis C. Development of food intake patterns for the MyPyramid food guidance system. Journal of Nutrition Education and Behavior. 2006;38(Suppl 2):S78–S92. [PubMed]
  • Cullen KW, Lara KM, de Moor C. Familial concordance of dietary fat practices and intake. Family & Community Health. 2002;25(2):65–75. [PubMed]
  • Fahlman MM, Dake JA, McCaughtry N, Martin J. A pilot study to examine the effects of a nutrition intervention on nutrition knowledge, behaviors, and efficacy expectations in middle school children. Journal of School Health. 2008;78(4):216–222. [PubMed]
  • Feunekes GI, de Graaf C, Meyboom S, van Staveren WA. Food choice and fat intake of adolescents and adults: associations of intakes within social networks. Preventive Medicine. 1998;27(5 Pt 1):645–656. [PubMed]
  • Feunekes GI, Stafleu A, de Graaf C, van Staveren WA. Family resemblance in fat intake in The Netherlands. European Journal of Clinical Nutrition. 1997;51(12):793–799. [PubMed]
  • Fisher JO, Mitchell DC, Smiciklas-Wright H, Birch LL. Parental influences on young girls’ fruit and vegetable, micronutrient, and fat intakes. Journal of the American Dietetic Association. 2002;102(1):58–64. [PMC free article] [PubMed]
  • Fitzgibbon ML, Stolley MR. Environmental changes may be needed for prevention of overweight in minority children. Pediatric Annals. 2004;33(1):45–49. [PubMed]
  • French SA, Story M, Jeffery RW. Environmental influences on eating and physical activity. Annual Review of Public Health. 2001;22:309–335. [PubMed]
  • Galloway AT, Fiorito L, Lee Y, Birch LL. Parental pressure, dietary patterns, and weight status among girls who are “picky eaters” Journal of the American Dietetic Association. 2005;105(4):541–548. [PMC free article] [PubMed]
  • Hanson NI, Neumark-Sztainer D, Eisenberg ME, Story M, Wall M. Associations between parental report of the home food environment and adolescent intakes of fruits, vegetables and dairy foods. Public Health Nutrition. 2005;8(1):77–85. [PubMed]
  • Institute of Medicine. Dietary reference intakes for energy, carbohydrate, fiber, fat, fatty acids, cholesterol, protein, and amino acids. Food and Nutrition Board, Institute of Medicine, National Academies; 2005. [PubMed]
  • Jahns L, Siega-Riz AM, Popkin BM. The increasing prevalence of snacking among US children from 1977 to 1996. Journal of Pediatrics. 2001;138(4):493–498. [PubMed]
  • Janega JB, Murray DM, Varnell SP, Blitstein JL, Birnbaum AS, Lytle LA. Assessing intervention effects in a school-based nutrition intervention trial: which analytic model is most powerful? Health Education & Behavior. 2004;31(6):756–774. [PubMed]
  • Laskarzewski P, Morrison JA, Khoury P, Kelly K, Glatfelter L, Larsen R, et al. Parent–child nutrient intake interrelationships in school children ages 6 to 19: the Princeton School District Study. American Journal of Clinical Nutrition. 1980;33(11):2350–2355. [PubMed]
  • Lohr SL. Sampling: Design and analysis. Duxbury-Press; 1999.
  • Matheson DM, Wang Y, Klesges LM, Beech BM, Kraemer HC, Robinson TN. African-American girls’ dietary intake while watching television. Obesity Research. 2004;12(Suppl):32S–37S. [PubMed]
  • Mikkila V, Rasanen L, Raitakari OT, Pietinen P, Viikari J. Consistent dietary patterns identified from childhood to adulthood: the cardiovascular risk in Young Finns Study. British Journal of Nutrition. 2005;93(6):923–931. [PubMed]
  • Nicklas TA, Johnson CC, Farris R, Rice R, Lyon L, Shi R. Development of a school-based nutrition intervention for high school students: gimme 5. American Journal of Health Promotion. 1997;11(5):315–322. [PubMed]
  • Oliveria SA, Ellison RC, Moore LL, Gillman MW, Garrahie EJ, Singer MR. Parent–child relationships in nutrient intake: the Framingham Children’s Study. American Journal of Clinical Nutrition. 1992;56(3):593–598. [PubMed]
  • Park HS, Yim KS, Cho SI. Gender differences in familial aggregation of obesity-related phenotypes and dietary intake patterns in Korean families. Annals of Epidemiology. 2004;14(7):486–491. [PubMed]
  • Patterson TL, Rupp JW, Sallis JF, Atkins CJ, Nader PR. Aggregation of dietary calories, fats, and sodium in Mexican-American and Anglo families. American Journal of Preventive Medicine. 1988;4(2):75–82. [PubMed]
  • Perusse L, Tremblay A, Leblanc C, Cloninger CR, Reich T, Rice J, et al. Familial resemblance in energy intake: contribution of genetic and environmental factors. American Journal of Clinical Nutrition. 1988;47(4):629–635. [PubMed]
  • Podrabsky M, Streichert LC, Levinger D, Johnson DB. Campus-community-school partnerships to evaluate a multicomponent nutrition intervention. Public Health Reports. 2007;122(4):566–569. [PMC free article] [PubMed]
  • Popkin BM. Global nutrition dynamics: the world is shifting rapidly toward a diet linked with noncommunicable diseases. American Journal of Clinical Nutrition. 2006;84(2):289–298. [PubMed]
  • Rossow I, Rise J. Concordance of parental and adolescent health behaviors. Social Science & Medicine. 1994;38(9):1299–1305. [PubMed]
  • Salvy SJ, Romero N, Paluch R, Epstein LH. Peer influence on pre-adolescent girls’ snack intake: effects of weight status. Appetite. 2007 [PubMed]
  • Satia JA, Kristal AR, Curry S, Trudeau E. Motivations for healthful dietary change. Public Health Nutrition. 2001;4(5):953–959. [PubMed]
  • Shariff ZM, Yasin ZM. Correlates of children’s eating attitude test scores among primary school children. Perceptual & Motor Skills. 2005;100(2):463–472. [PubMed]
  • Stafleu A, Van Staveren WA, de Graaf C, Burema J, Hautvast JG. Family resemblance in energy, fat, and cholesterol intake: a study among three generations of women. Preventive Medicine. 1994;23(4):474–480. [PubMed]
  • Stanton CA, Fries EA, Danish SJ. Racial and gender differences in the diets of rural youth and their mothers. American Journal of Health Behavior. 2003;27(4):336–347. [PubMed]
  • STATA. Statistics/data analysis: Release 9. Texas: Stata Corporation; 2005.
  • Tippett K, Cypel Y. Design and operation: The continuing survey of food intakes by individuals and the diet and health knowledge survey, 1994–1996: Continuing survey of food intakes by individuals, 1994–1996 nationwide food surveys report. Washington, DC: Agriculture Research Service, US Department of Agriculture; 1997.
  • U.S. Department of Agriculture (USDA) Healthy Eating Index 2005. Center for Nutrition Policy and Promotion; 2005. http://www.cnpp.usda.gov/Publications/HEI/healthyeatingindex2005factsheet.pdf.
  • US Department of Agriculture ARS, F.S.R.G. Data (CD-ROM) and documentation for the 1994–96 continuing survey of food intakes by individuals (CSFII)-diet and health knowledge survey. National Technical Information Service Accession; 1994–96.
  • Vauthier JM, Lluch A, Lecomte E, Artur Y, Herbeth B. Family resemblance in energy and macronutrient intakes: the Stanislas Family Study. International Journal of Epidemiology. 1996;25(5):1030–1037. [PubMed]
  • Vereecken CA, Inchley J, Subramanian SV, Hublet A, Maes L. The relative influence of individual and contextual socio-economic status on consumption of fruit and soft drinks among adolescents in Europe. European Journal of Public Health. 2005;15(3):224–232. [PubMed]
  • Wang Y, Bentley ME, Zhai F, Popkin BM. Tracking of dietary intake patterns of Chinese from childhood to adolescence over a six-year follow-up period. Journal of Nutrition. 2002;132(3):430–438. [PubMed]
  • Wang Y, Wang X. How do statistical properties influence findings of tracking (maintenance) in epidemiologic studies? An example of research in tracking of obesity. European Journal of Epidemiology. 2003;18(11):1037–1045. [PubMed]
  • Worthington-Roberts BS, Rodwell Williams S. Nutrition throughout the life cycle. United States: McGraw-Hill; 1999.
  • Zizza C, Siega-Riz AM, Popkin BM. Significant increase in young adults’ snacking between 1977–1978 and 1994–1996 represents a cause for concern! Preventive Medicine. 2001;32(4):303–310. [PubMed]
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