Socio-economic factors associated with the iron intake of preschoolers in the United States

Socio-economic factors associated with the iron intake of preschoolers in the United States

Nutrition Research, Vol. IS, No. 9, pp. 12Y7-1309, 1995 I YYS Elsevier Science Ltd Printed in the USA. All rights reserved 027 I-53 17/95 $9.50 + .I0 ...

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Nutrition Research, Vol. IS, No. 9, pp. 12Y7-1309, 1995 I YYS Elsevier Science Ltd Printed in the USA. All rights reserved 027 I-53 17/95 $9.50 + .I0

Pergamon

0271-5317(95)02005-G

SOCIO-ECONOMIC FACTORS ASSOCIATED WITH THE IRON INTAKE OF PRESCHOOLERS IN THE UNITED STATES

Donald Rose, Ph.D.‘, David Smallwood, Ph.D., James Blaylock, Ph.D. Economic Research Service, U.S. Department of Agriculture

ABSTRACT

This study investigated household income, food program participation, and diet awareness and attitudes of the household meal planner as predictors of dietary iron intake of preschoolers in the United States. Non-breastfeeding children, 1 to 5 years of age, with 3 days of dietary data from the 1989-91 Continuing Survey of Food Intake of Individuals were included in this sample (N=800). Two measures of intake were analyzed: a nutrient adequacy ratio (NAR), defined as iron intake divided by the RDA; and an index of nutritional quality (INQ), or diet density measure, which is the ratio of iron NAR to energy NAR. Data from the concurrent Diet and Health Knowledge Survey were used to create indicator variables for meal planners who were aware of anemia as a health problem related to iron intake and who felt it was important to choose a diet with plenty of grain products. After controlling for age, gender, ethnicity, household size, region, and urbanization, multi-variate regression analyses revealed that household income and participation in the WIC food program were positively associated with both iron Participation in the Food Stamp program was positively intake measures. associated with the iron NAR, but not the INQ. Although anemia awareness was not a significant predictor of intake, children from households whose main meal planners had a positive attitude about the importance of grains, consumed more iron as measured by either index. The findings suggest that dietary iron intakes of preschoolers continue to be affected by economic factors and that food transfer and/or educational interventions may be useful in improving these intakes. KEYWORDS:

Iron, Diets, Children, WIC, Food Stamps, CSFII

INTRODUCTION

Iron-deficiency is the single most prevalent nutritional deficiency in the United States (l), which in part explains why expert panels have identified iron as a priority category for nutrition

i Corresponding author: Donald Rose, USDA-ERS, 1112, Washington, DC 20005. 1297

1301 New York Avenue, NW, Room

D. ROSE et al.

monitoring (1.2). The concern over iron is well-placed; iron deficiency has been shown to cause functional impairments in work performance, behavior and intellectual development, and resistance to infections (3-5). Although trend data through the 1980s compiled from public health clinics by the Centers for Disease Control has shown a decrease in the anemia prevalence rates among low-income preschoolers (6), the rates are still high, ranging from 15 to 20 percent for those under 2 years of age (7). In addition to young children, adolescents and women of childbearing age are considered to be the groups at greatest risk of iron deficiency (1). It is generally accepted by those who have studied iron deficiency, that there is an association between the condition and socio-economic status (8). For example, data from NHANES II, which was conducted between 1976-1980, showed a prevalence of iron-deficiency anemia about 9 percent among infants l-2 years of age (1). The rate was over twice that for children living below the poverty line. The association between dietary iron intake and socioeconomic variables has been less well studied. Data from the 1986 Continuing Survey of Food Intake by Individuals (CSFII) reveal increasing intakes of iron by income group for 19-50 year old women, but the opposite for l-5 year old children (9). Of course many other factors besides income affect dietary intakes, such as schooling and nutrition awareness of the household meal preparer, participation in food programs, and individual-specific characteristics such as age, gender, and ethnicity. Few studies have considered these factors in a multivariate context. A recent National Academy of Sciences publication has called for increased research in this area in order to better understand the iron usage patterns of specific population groups (10). The broad goal of this paper is to determine the relevant social and economic factors associated with iron intake of preschoolers in the United States. One specific objective is to identify the relationship between household income and preschooler iron intake. A second objective is to explore the associations of household food program participation with iron intake of the child. The focus will be on two programs: the Food Stamp Program and the Supplemental Food Program for Women, Infants, and Children (WIG). The third objective of this research is to explore the associations between the diet awareness and attitude of the household’s main meal planner with the preschooler’s iron intake.

METHODS

The Study Sample The analyses presented here are based on data obtained from the Continuing Survey of Food Intake by Individuals (CSFII) and the companion Diet and Health Knowledge Survey (DHKS). These surveys were conducted by the U.S. Department of Agriculture’s Human Nutrition Information Service (HNIS) from 1989 to 1991. The surveys were based on a national multi-stage probability sample (11). HNIS attempted to collect information on all individuals within selected households. For this analysis, data were used from 800 preschool children aged one to five years. This represents all children who had three days of food intake data as well as the socioeconomic data described below needed for multivariate analyses. Children who breastfed on the survey days were excluded from this sample, since there was no way to assess their total dietary intake for those days. Dietary Data The CSFIl collected up to 3 consecutive days of dietary data on individuals - the first day

IRON INTAKE IN PRESCHOOLERS

was based on a 24-hour recall and the second two days were based on food records. The nutrient data base used to calculate nutrient intakes was developed by HNIS and contains approximately 6,250 items (12,13). Nutrient intake values were averaged for all three days for each individual. Two iron intake measures were used as dependent variables in the analyses presented here. For each individual, the Nutrient Adequacy Ratio (NAR) is the iron intake (in milligrams) divided by the iron RDA for that person and is expressed in percentage terms throughout this paper. The Index of Nutritional Quality (INQ) to be used here is the iron NAR divided by the calorie NAR, i.e.:

INQ

=

Iron&AR Kcal NAR

=

Iron Intake KCal Intake Iron RDA Kcal REI

This is essentially a measure of density of the diet in iron, such as that proposed by Hansen (14). We use NARs here as opposed to just intakes, since children of different ages have different average energy allowances (recommended energy intakes or REI). Note that after rearranging terms, the numerator represents the iron density of reported dietary intake and the denominator is an implicit recommendation regarding iron density of the diet, based on the RDA for iron and the REI for calories for a given age-sex group. Thus, the index measures the iron density of the diet, relative to a normative standard; values less than one represent diets less concentrated in iron than the implicit recommendation. It is important to note that these iron intake variables do not include intake from supplemental iron in pill form. Although some information is available on supplement intake habits and this is included as an independent variable in the analyses below, no information is available from the CSFII on the amounts ingested. The intake variables in these analyses also do not consider bioavailability of iron intake.

Socio-Economic

and Diet Knowledge Variables

Income, education, and food program participation were key variables of interest. A quadratic form of income was used in regression analyses; that is, both annual household income for the previous year and the square of this term were included. No imputed values for this variable were used. The number of years of schooling completed by the main meal planner of the household was used for the education variable. Separate indicator variables were used to represent current household participation in either of two U.S. Department of Agriculture food programs: the Food Stamp Program and the Special Supplemental Food Program for Women, Infants, and Children (WIG). Knowledge, attitudes and food behaviors of the person most responsible for meal planning and preparation in each household was collected in the Diet and Health Knowledge Survey (DHKS). This survey was conducted by telephone on the same households in the CSFII, within six weeks of the dietary interview. A number of questions about diet and health knowledge, attitudes, and behaviors were asked in this survey. For this analysis, three variables were created. Iron-anemia awareness is a variable which indicates whether the respondent identified anemia or “iron-poor blood” as a health problem related to how much iron a person eats. Importance of grains is a variable which indicates those people who felt that it was important to choose a diet with plenty of breads, cereals, and other grain products. This was a scaled question in which

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D. ROSE et al.

respondents were asked to rate how important it was to them personally from one (not important at all) to six (very important). Those ranking the statement from four to six were categorized as having a positive attitude about the importance of grains. Although the DHKS contains questions about attitudes towards other food groups, the grains group was selected for exploration since it contains fortified cereals and enriched flour products, which make this group the major source of iron in the U.S. diet (15). A variable was also created to indicate iron supplementation of the child’s diet. This would include those children who took a supplement every (or almost every) day, in which that supplement was either a multivitamin with minerals, a combination of vitamin C and iron, or an iron supplement by itself. Age, gender, and race-etbnicity of the child were used as control variables in multivariate regressions. Indicator variables were used for each year of age between 2 and 5, with those at one year of age being the base group. Indicator variables were created for non-Hispanic Blacks, Hispanics, and a non-Hispanic other group. Non-Hispanic Whites were the base group. Household control variables included household size, geographic region, urbanization, and whether the household was headed by a single female.

Statistical Methods Descriptive statistics presented in Table 1 were weighted using the three-year sample weights prepared by HNIS for individuals with completed 3-day diet records (11). Single equation multi-variate regression analyses were performed using the Statistical Analysis System (16). In these analyses, either iron NAR or iron INQ was used as the dependent variable and all socio-economic variables described above were used as independent variables. The results of unweighted regression analyses are reported in Table 3. The findings were similar with and without using the sample weights, probably since many of the factors used in creating the weights were included as independent variables. There continues to be controversy in the statistical community regarding design-based versus model-based inferences. DuMouchel and Duncan (17) have presented a statistical test for assessing when a weighted analysis should be used instead of an unweighted analysis. We did not find sufficient justification to use such an analysis; the null hypothesis (that the unweightcd version was correct) was not rejected in the case of the iron NAR model.

RESULTS

The weighted mean value of reported iron intake for all preschoolers with three days of diet information was 99.7 percent of the Recommended Dietary Allowance (RDA). A sizable portion of the population consumes below this amount, especially in certain high risk groups. As can be seen in Table 1, close to 57 percent of preschoolers consume iron at intakes below their RDA and nine percent consume less than half of their RDA. Younger children, children from households with less income or schooling, and those living in the South or West were more likely to consume less than half of their RDA than others. About 7 percent of White children consumed less than half of their RDA for iron, while 11 percent of Black children and 19 percent of Hispanic children consumed at that level. Descriptive statistics of the sample used in regression analyses are presented in Table 2. The average age of preschoolers in this sample is about 3 years. About 70 percent of the children are White, 16 percent Black, and 11 percent Hispanic. Household income averages

IRON INTAKE IN PRESCHOOLERS

$22,000 per year and 26 percent of the children in this sample come from households headed by a single female. About a quarter of the children live in households that participate in the WIC program, while 30 percent receive Food Stamps. Average years of schooling completed by the main meal planner is about 12 years. About 37 percent of the children come from households in which the main meal planner is aware that anemia or iron-poor blood is a health problem related to iron intake. Almost two-thirds of the sample felt that it was important to choose a diet with plenty of breads, cereals, and other grain products. Nearly 13 percent of the children received iron supplements on a regular basis.

Table 3 presents results of the multiple regression analyses. After controlling for all other variables listed in the table, income is positively and significantly (p < 0.05) related to iron intake of the child, whether measured using the iron nutrient adequacy ratio (NAR) or the index of nutritional quality (INQ). The square of income was negative and significant for both iron variables, indicating a decline in intakes of children from higher income households. Controlling for all other factors, children from households who participated in the WIC Program consumed significantly more iron, whether measured by iron NAR or iron INQ. The increase in iron NAR was about 10 percentage points for those from WIC households. Children from households participating in the Food Stamp Program also consumed iron at an NAR about ten percentage points higher than those not participating. Children from households whose main meal planners felt that it was important to choose a diet with plenty of grain products had higher intakes of iron, as measured by either the iron NAR or the iron INQ. Neither iron-anemia awareness nor iron supplementation were significant predictors of food iron intake, Older children consumed significantly higher amounts of iron as measured by either index. The iron NAR of Black children was significantly lower than that of White children. Children from the South and West consumed lower amounts of iron as measured by either index. Boys and girls consumed about the same amounts of iron.

DISCUSSION

A main finding of this research is that income is positively associated with iron intake in preschoolers. This finding is consistent with other studies in the United States which have shown higher rates of anemia in low-income populations (1,6,7,18). The results found here appear to be somewhat consistent with findings from the 1985 CSFII, but not the 1986 CSFII. In the 1985 survey, preschoolers from the low-income sample had a lower mean intake of iron, 9.4 mg, than did preschoolers in the all-income sample, 9.7 mg (19,20). The opposite relationship was true of the 1986 CSFII (9,21). However, there is a limit to comparisons between these surveys and results of the analyses presented here, since there was no statistical control in these earlier CSFII averages and there were notable differences in the sampling designs, weighting strategies, and dietary intake instruments between the two surveys (9,11,19-21). A quadratic relationship between income and iron NAR was observed in this analysis. The implication is that iron intake increases with increasing income up to a certain point and then begins to drop off. Figure 1 graphically depicts a simulation based on a regression model similar to the one used above, in which all covariates from Table 3 are held constant and normalized to a mean of zero. For convenience, income has been expressed as a percent of the poverty level.

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D. ROSE et al. TABLE 1 Percentage of Individuals

with Intakes at Selected Levels of the 1989 RDA for Iron.’ Individuals

Percent of Iron RDA < 50%

n All children

50-69%

70-99%

r 100%

---Percent of Individuals---

1060

8.7

17.9

30.2

43.2

Age l-3

665

12.3

22.5

31.5

33.7

4-5

395

3.0

10.8

28.1

58.1

5 130

638

11.5

17.6

30.2

40.7

131-350

318

9.1

15.5

27.9

47.5

> 350

104

4.9

22.2

33.8

39.1

c 12 years

344

12.2

14.4

26.5

46.9

12 years

389

8.1

19.3

33.8

38.9

> 12 years

327

7.6

18.5

29.2

44.8

Income’

Education

Region Northeast

197

3.6

16.7

34.9

44.8

Midwest

315

7.8

23.5

23.1

45.5

south

324

9.2

17.5

30.5

42.8

West

224

13.6

13.0

33.8

39.6

White

695

7.3

16.0

29.2

47.6

Black

191

10.9

21.5

37.0

30.6

Hispanic

132

19.0

17.9

32.2

30.9

Other

42

0.0

43.0

21.4

35.6

Ethnicity



Estimates an? weighted using the 1989-91 CSFII three-year weights, This sample includes all non-breastfeeding chiidren, aged 1 to 5, with 3 days of dietary intake data. Categories listed refer to income as a percent of the poverty level. Income eligibility for the Food Stamp program requires an annual income less than or equal to 130 percent of the poverty level.

IRON INTAKE IN PRESCHOOLERS

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TABLE 2 Descriptive Statistics of the Sample (N=800).’ Mean + SD or Percent

Variable Individual Characteristics

2.95 -e 1.41 52.1

Age

Percent Male Race-Ethnicity

69.4 15.9 11.2 3.5

Non-Hispanic White (%) Non-Hispanic Black (%) Hispanic (%) Other Race-Ethnicity (%) Household Characteristics

Income (in $lO,OOOs) Household size Living in single-female headed households (%) Food program participation Living in Food Stamp households (%) Living in WIC households (%) Region Living in Midwest (%)

2.20 + 2.02 4.58 + 1.64 26.4 30.1 25.1 31.2 18.4 29.9 20.5

Living in Northeast (%) Living in South (%) Living in West (%) Urbanization Living in suburban areas (%)

41.4 34.1 24.5

Living in central cities (%) Living in non metropolitan areas (%) Mealplanner

Characteristics

Schooling (years completed) Knowledge, attitude, behavior Aware of iron-anemia link (%) Feels grains are important (%) Supplements child’s diet with iron (%) Since this is a description of the analytic sample, estimates are unweighted.

12.06 f 2.48 37.4 65.2 12.8

D. ROSE et al.

1304

TABLE 3 Multiple Regression Results of Iron NAR and Iron INQ on Individual, Meal Planner, and Household Characteristics.’ Iron NAR Variable

B

SE

Iron INQ B

SE

Individual Characteristics

Intercept

90.14*

12.57

1.174*

0.134

Age 2 years old

10.85*

4.76

0.006

0.051

3 years old

15.78*

4.88

0.006

0.052

4 years old

20.43 *

5.08

0.405*

0.054

5 years old

34.67*

4.99

0.411*

0.053

2.60

3.10

-0.014

0.033

Black

-11.48*

5.38

-0.055

0.057

Hispanic

- 6.69

5.33

0.03 1

0.057

Other

33.28*

8.77

0.355*

0.094

Male Gender Race-ethnicity

Household Characteristics 7.48*

2.30

0.062*

0.024

Income squared

- 0.56*

0.19

-0.004*

0.002

Household size

- 2.09*

1.00

-0.019

0.011

0.12

4.68

-0.005

0.050

Food Stamps

10.01*

4.74

0.035

0.051

WIG

10.38*

4.18

0.094*

0.045

Income

Single female head Food program participation

Region Northeast

- 3.02

4.75

-0.090

0.051

South

- 9.31*

4.04

-0.125*

0.043

West

-13.59*

4.55

-0.105*

0.049

- 0.73

3.89

0.048

0.041

6.85

4.15

0.022

0.044

Urbanization Central city Non metropolitan

Mealplanner Characteristics - 1.21

0.74

-0.019*

0.008

Iron-anemia awareness

4.34

3.32

0.009

0.035

Importance of grains

9.67*

3.24

0.101*

0.035

- 5.59

4.69

Schooling

Iron supplementation

of child

-0.034

0.050

NAR refers to reportediron intake divided by the RDA. INQ is the iron NAR divided by the energy NAFL The table lists beta-coefficients (p) and their standard errors (SE). Coefficients with asterisks are significantly different than zero (p < 0.05).

IRON INTAKE IN PRESCHOOLERS

1305

118-

100

200 Income

300

400

500

(% of poverty)

Figure 1 - Simulation results of iron NAR vs. income as a percent of the poverty level. Outer lines represent a 95 percent confidence interval for the simulation line. Most government food assistance programs have income eligibility requirements at the lower end of this graph. For example, eligibility in the Food Stamp Program requires household income to be less than or equal to 130 percent of the poverty level; for the WIC Program it is 185 percent. The increasing slope of this line at low-income levels is reassuring from a policy point of view, since increases in additional income have their greatest impact on preschooler iron intake in precisely the segment of the population which is served by these programs. After controlling for household income, participation in the Food Stamp Program was significantly associated with an increased iron NAR. The increase was close to 10 percentage points. Most studies of the consumption effects of the Food Stamp program have documented increased spending on food and increased nutrient availability at a household level (22). However, few studies have investigated the effects of the program on the nutrient intake of individuals and even fewer on that of preschoolers. One analysis of 198586 CSFII data found a 9 percent difference in iron intake for preschoolers on Food Stamps vs. those in nonparticipating households, although the difference was not statistically significant (23). WIC Program participation was associated with a 10 percentage point increase in iron NAR and this finding has parallels in previous research. In a small study in Minnesota, less WIC children had low-iron intakes as compared with eligible non-participants (24). A national WIC evaluation found that WIC preschoolers consumed an average of 1.2 mg of iron per day more than the control group (25). This difference represents 12 percent of the iron RDA for preschoolers; a difference not unlike the result of 10 percent found here. An analysis of 1985-86 CSFII data found about a 5 percent increase in dietary intake of iron for preschoolers on WIC,

D. ROSE et al. but this was not statistically significant (23). Unlike Food Stamp participation, WIC was also associated with an improved iron INQ. Iron INQ is a measure of nutrient density; the finding indicates that the diets of preschoolers from WIC households were more concentrated in iron. Unlike, the Food Stamp Program, WIC was specifically designed as a nutrition program and iron is one of the target nutrients of the program. Participants are given coupons for specific food items, which include among other things, iron-fortified cereals. Whereas Food Stamps may be increasing the overall intake of foods through an income effect, WIC may be influencing the consumption of specific types of foods. This could explain the positive association of WIC participation with the diet density measure. Based on these analyses, it is tempting to argue that the WIC program caused improved iron intakes. However, it should be emphasized that prediction does not imply a cause and effect relationship. Clearly, in a free-living population study of this sort, individuals have not been randomly assigned to particular government food programs. Since respondents were self-selected onto the WIC Program, they may be qualitatively different in some underlying characteristic which also affects child nutrient intakes. It is not possible from these analyses to determine whether such a selection bias exists or if it does, how large it might be. Awareness by the household’s main meal planner of the link between iron intake and anemia was positively but not significantly related to iron intake of the child. Earlier analyses of the 1989-90 CSFII data did show a significant relationship of these two variables (26), but those analyses did not control for attitudes about the consumption of grains or iron supplementation. Preschoolers from households in which the meal planners felt that it was important to choose a diet with plenty of grain products had iron NARs about 10 percentage points higher than those from households whose meal planners did not feel this way. The difference is of the same magnitude as for WIC program participation. The iron INQ was also related to a positive attitude about the importance of grains. Iron supplementation of the child and years of schooling of the meal planner were not significantly related to iron NAR, although the latter variable was negatively associated with iron INQ. Taken together, the results from this study indicate that economic factors are significant predictors of iron intakes of preschoolers. The findings are also suggestive that nutrition education or food programs either by improving attitudes of the main meal planner or by increasing household resources can be useful in improving iron intakes of preschool children. Clearly, if socio-economic factors play a causal role in the iron intakes of preschoolers, their effects are mediated through the consumption of specific foods. While a detailed analysis of food group consumption is beyond the scope of this paper, a preliminary look at dietary sources of iron is presented in Table 4. This table shows children’s food group intakes and food group sources of iron intake by WIC program participation and excludes all children from households that are not income-eligible for the program. Those on WIC have higher total iron intakes, as expected from the multivariate analyses. Most of this difference occurs in the grains group, which is also the most important contributor to the diets of non-WIC children. It is interesting to note that WIC children have a greater iron intake from this food group, despite the fact that their overall intake of grains is less than non-WIC children. This would be consistent with the success of one objective of the WIC program, which is to promote the consumption of iron-rich cereals. The information in Table 4 must be considered preliminary, since there has been no attempt to disaggregate the nutrients from mixed dishes, nor has there been any statistical control for the socio-economic factors discussed previously. However, the table does point out the need for further research on the role of such factors on food choice as well as on nutrient intake.

IRON INTAKE IN PRESCHOOLERS

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TABLE 4 Mean Food Group Intake and Iron Intake from 9 Food Groups, by WIC Status.’

Food Groun

Mean Food Group Intake (g/day) Not on WIC WIG n=493 n=263

Dairy

405

Meats

106 15 13 210 129 90 5 221

Eggs Legumes Grains Fruits Vegetables Fats, oils Other

413 111 18 20 183 166 86 4 19.5

Total

Mean Iron Intake (mg/day) Not on WIC WIG n=493 n=263 0.34 1.63 0.20 0.28 5.90 0.31 0.63 0.01 0.22 9.51

0.39 1.68 0.23 0.43 6.99 0.43 0.63 0.01 0.16 10.93

Unadjusted estimates in this table are based on unweighted data. Sample includes all preschoolers in households that are income-eligible for the WIC program (i.e. income < 185 percent of poverty level).

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IRON INTAKE IN PRESCHOOLERS

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Accepted

for

publication

March 24,

1995.