RACE/ETHNICITY, INCOME, AND NUTRITION RISK IN THE NATIONAL HEALTH AND NUTRITION EXAMINATION SURVEY (NHANES),

Transcription

1 RACE/ETHNICITY, INCOME, AND NUTRITION RISK IN THE NATIONAL HEALTH AND NUTRITION EXAMINATION SURVEY (NHANES), Jeanine T. Bentley University of Maryland, Baltimore County, Baltimore, MD Analytical Paper for Masters of Arts in Applied Sociology Abstract Previous literature that examines the competing effects of income and race/ethnicity on nutrition risk yields results that are mixed and inconclusive. In this paper, I investigate the effect of income and race/ethnicity on nutrition. The study uses the indicators of income, race/ethnicity, age, and gender from the National Health and Nutrition Examination Survey (NHANES) to analyze the effect of income and race/ethnicity on nutrient risk in the US population. Specifically, I determine whether income, race/ethnicity, or a combination of both is a more important determinant for risk of inadequate nutrition. The results suggest that 1) income is a stronger predictor of nutrition risk compared to race/ethnicity, 2) income has a moderate effect for Non-Whites concerning nutrition risk, and 3) income is the most important determinant for Non-Hispanic Whites. I discuss these findings in reference to previous literature on income, race/ethnicity, and nutrition and emphasize the need for future research that examines these combined factors in light of geographic location. 1

2 Introduction Dietary behaviors are linked to preventable diseases and premature deaths, contributing to heart disease, cancer, and stroke, the leading causes of death in the United States (Kant and Graubard, 2007). Seventy percent of all cancer is attributed to high-fat, low produce eating habits (Noia, Schinke, & Contento, 2005). Dietary fiber, which is obtained through the intake of fruits and vegetables, reduces blood cholesterol and prevents certain chronic diseases (Nayga, 1996). Consuming nutrient-dense foods and achieving the recommended dietary allowance of vitamins and minerals aids in the prevention of those chronic, avoidable diseases and lengthens and improves the quality of the lifespan of the population (Bowman, 2007). Unfortunately, research reveals that a large portion the population in the United States falls short of the dietary recommendations for the nutrient-dense food groups, except for meat and grains, and overconsume on empty calories such as solid fats, added sugars, and alcoholic beverages (Krebs- Smith et al., 2010). Roughly 27% of energy expended per day derives from energy-dense, nutrient-poor foods (EDNP) with desserts and sweeteners comprising 20% of that daily intake (Kant, 2000). Studies researching external determinants of nutrition explore a number of socioeconomic and sociocultural factors, such as income and race/ethnicity. Research into the impact of income and race and ethnicity on nutrition risk would determine which should have the emphasis when it comes to laws and policies. Therefore, this study asks if people are healthier because they have the income to acquire healthier food, or do certain races and ethnicities have a higher likelihood of consuming foods that lead to better health because of their culture and traditions? Finally, to what extent is nutrition risk the result of a combination of income and race/ethnicity? 2

3 Review of Literature Income and Nutrition Previous research suggests that higher income has a positive association with a larger intake of fruits and vegetables, vitamins A and C and calcium and potassium (Kant and Graubard, 2007). People from lower income levels have the highest rates of obesity from the consumption of fats, sugars, fast-food, and sugared beverages (Aggarwal, 2012). Income, in addition, has the heaviest impact on food insecurity and hunger (Rose, 1999). Specifically, income affects the probability of food insecurity, which decreases the probability of adults achieving the recommended dietary allowance (RDA). Research indicates that food-insufficient households have a higher chance of consuming <50% of the RDA of food (Rose, 1999). Lowincome adults ingest 526 kj and 1050 kj less compared to middle-income and high-income adults, respectively, including fruit, vegetables, milk, meat, poultry, and fish (Bowman, 2007). Developed from two Canadian studies of low-income females, Tarasuk, McIntyre, and Li (2007) observed that as household resources are depleted from increasing time since income receipt, women with moderate or severe food insecurity had significant declines in energy, carbohydrates, vitamin B-6, and fruit and vegetables. Nutrient-dense foods with higher diet costs compel low-income households to choose nutrient-poor foods with low diet costs, foods that are associated with chronic disease (Aggarwal et al., 2012). Sacrificing nutrient-dense foods causes depletions of vitamins and minerals vital to physical well-being. A study of infants from lowincome households that were born in Albany, New York, reveals that inadequate intakes of vitamin D persisted through infancy, <5% at 3 to 6 months, and increased to 61% for infants aged 24 months (Nolan et al., 2002). Zinc intakes, in addition, were low, less than two-thirds of the recommended daily allowance (RDA) for the 2- to 12-month infants. 3

4 While research establishes the effect of income on nutrition and eating behaviors for the population within the United States, the research often concentrates on one or two vitamins or concentrates only on nutrient-poor foods, leaving out a holistic view of income. Kant and Graubard (2007) concentrated on vitamins A and C, calcium, and potassium to conclude that the intake of fruits is directly associated with income, though neglect how nutrients from vegetables, dairy, bread and meat stimulate vitamin absorption. Aggarwal (2007) found that low-income is associated with the consumption of fast-food and sugared beverages, but their research does not examine how sugar and fat adversely affect the intake of nutrient-dense foods in different income brackets. In sum, research on income and nutrition neglects a holistic approach to daily recommended nutrient intake that showcases the complete list of nutrients required for a healthy individual and could provide crucial information on vitamin and mineral deficiencies. Since the income determines how much and, to an extent, the selection of foods households could purchase, research on income and nutrition tends to lean toward food security and less on household earnings. In these cases, studies typically assess income in terms of whether or not households have proper access to food and do not examine how households segregate their incomes toward food and make choices about nutrient intake. Income, for instance, is often associated with food deserts, areas where 33 percent of the population in low-income communities has low or no access to supermarkets or large grocery stores within a mile. The data, though, cannot tell us if the low-income people in these food deserts have nutrition risks and how income contributes to these access problems. Tarasuk, McIntyre, and Li (2007) research how length of time since last paycheck influenced nutrient deficiencies among Canadians, but it is unclear how these findings would apply to a US population. In addition, Rose (1999) observed that food insecurity decreases the prospect of adults meeting the 4

5 recommended daily requirements (RDA), but did not specify which requirements were lacking or explain how income directly affects this process. Therefore, more studies are needed that conceptualize nutrition risk holistically as a range of indicators and directly examine the effect of income. Race/Ethnicity and Nutrition Researchers, moreover, find that race/ethnicity is related to the variation in consumption of healthier options. Ethnicity may shape the preference of foods, food combinations, and meal preparations because of cultural differences (Kant and Graubard, 2007). However, the effects of race/ethnicity on nutrition risk are mixed, perhaps due to a limited number of studies that examine race/ethnicity in diverse samples. Newby et al. (2012) provide one of the most detailed examples of the relationship between diverse racial/ethnic background and holistic nutrition risk. These authors find that dietary disparities persist among non-hispanic Whites and blacks, consisting of higher intakes of dietary cholesterol and lower intakes of potassium, fiber, fruits, and vegetables for blacks. In addition, diet quality differs among non-hispanic Whites and blacks. The risk of stroke, resulting from a poor diet, varies, contingent on sex, geographic region, and race of the patient. Exploring a small of women in Louisiana, they found that black women had lower intakes of fiber, protein, calcium, magnesium, linoleic acid, arachidonic acid and similar intakes of total energy fat and carbohydrates. The authors also find that black women had higher intakes of carbohydrates and lower intakes of total fat, fiber, and alcohol. Black men, furthermore, consumed less riboflavin, niacin, vitamin B-12, vitamin D, calcium, potassium, magnesium, sodium, and iron compared to whites in each region. Cultural, racial nutrition 5

6 intakes played a crucial role on the health of blacks compared to whites in this study. Nutritional intake for blacks, however, falls behind whites and may be worse in the South. While disparities in health and nutrition exist among blacks and whites despite considerable gains in the United States, these disparities might imply genetic, environmental, behavioral, and societal issues that accompany race and ethnicity (Kant et al., 2007). Concerning the disparity among black and white in nutritional intake from 1971 to 2002, Kant et al. (2007) find that the percentage of whites and black women that consumed fruits declined while the percentage of vegetable consumption among black and white women increased. In addition, vitamin C intake decreased for white women while potassium and calcium intakes increased for all racial groups, not including black men. Despite the improvements in nutritional health, certain nutrient food intakes for blacks indicate a higher chronic disease risk for that racial group i.e. lower consumption of vegetables, potassium, and calcium. Ethnic differentials in dietary intakes, in their research, remain when controlling for income and education. Non-Hispanic Whites had higher serum concentrations of ß-cryptoxanthin and lutein + zeaxanthin while non-hispanic Blacks had lower concentrations of serum vitamin C, selenium, vitamin E. Overall, disparities among blacks and whites remained. Research on nutrition, despite establishing the effect of race and ethnicity on nutritional health, concentrates more on the dichotomous relationship between blacks and whites and less on different racial groups e.g. Asians and Native Americans which could limit the findings on how cultural influences impact nutrition. Newby et al. (2010, 2012) demonstrate how culture impacts the health and nutritional intake of the population through their research on blacks and whites in the 48 states, but the research could benefit from the nutritional intake of Hispanics to determine whether the increase in unhealthy habits stems from racial, cultural influences and not 6

7 from the culture within the South or from the access of nutrient-dense foods to areas in the South that have a higher density of blacks. In addition, research on the differentials in dietary intakes neglect to indicate whether these dietary improvements indicate deficiencies that should be addressed. Kant et al. (2007) examine race/ethnic differences concerning nutrition over the last thirty years, but fail to mention whether the vitamins, minerals, and food consumption mentioned cause these nutrition risks. Overall, studies of race and ethnicity and nutrition offer a perspective on the impact of racial differences on health and nutritional intakes and how cultural traditions affect food choices, preparation, and consumption. While current research explores nutrition intakes by race/ethnicity, fewer studies examine vitamins and mineral deficiencies by race/ethnicity. This multifaceted approach would allow researchers to also expose nutrition risk within a racial/ethnic group, in addition to comparing deficiencies across groups. Race/Ethnicity, Income, and Nutrition Research on income and race/ethnicity seldom examines both of these factors directly with a holistic approach to nutrition risk. Studies that incorporate income and race/ethnicity often incorporate socioeconomic variables i.e. race and gender, education, and occupation that could impact nutrition and analyze them together, which could obscure the variable that has the dominant impact income or race/ethnicity. Kirkpatrick et al. (2012) found that smaller proportions of non-hispanic Blacks met the minimum recommended intakes for whole fruits, total vegetables, other vegetables, total grains, and milk compared to non-hispanic Whites and Mexican Americans. Non-Hispanic black children, moreover, had lower intakes of whole fruit, oranges, and other vegetables, total grains, and milk in comparison to non-hispanic white 7

8 children. Research on nutrient intakes of adolescents by sociodemographic factors observed that, for males, annual household income was positively related to the nutrient adequacy ratios (NAR) for magnesium. Although studies have considered how income influences nutrition, research has been inconclusive on whether income or race/ethnicity has a larger impact on nutrient intake. Johnson et al. (1994) find an effect of race on nutrient intake and conclude that females were more likely to have inadequate intakes of essential vitamins and minerals. Income, though, has a minimal impact on nutrient intake concerning young adolescent males. On the other hand, Middaugh et al. (2012) find that fruit and vegetable intake has a significant, direct relation to income when income reaches levels of 400% the poverty threshold (PT) using the poverty income ratio (PIR). The increase in PIR levels, in addition, showed an increase in mean calorie intake, fat intake, and combined fruit and vegetable intake, an effect that remained even after age, sex, race/ethnicity, and calorie intake were added. Finally, Forshee and Storey (2006) also report that income has a significant, positive association with the Healthy Eating Index (HEI) for males and females after controlling for race/ethnicity. Yet, African-Americans had lower HEI scores than Non-Hispanic Whites, Hispanic, and other race/ethnicities when controlling for income. African- Americans consumed fewer fruits, vegetables, and less milk than non-hispanic Whites while non-hispanic Whites consumed more fats/oils than did other races. Research in these studies concentrates on specific vitamins, vitamin A and C in particular, and food groups -- sodium and iodine, for example (Caldwell et al., 2011; Kant and Graubard, 2007). Income and race/ethnicity have a significant impact on nutrient intake because income determines the quantity and quality of foods that people could be able to afford, and race/ethnicity determines the foods and food preparation that races could be more inclined of 8

9 consuming because of culture and tradition. Research shows that people with low incomes, who are more likely to be non-hispanic Blacks and Hispanics, have to settle with low-cost foods, which generally are high in calories, fat, sugar, and sodium (Izumi et al., 2011; Boeckner et al., 2007). Low consumption of nutrient-dense foods will cause them to fall short of the daily recommended vitamins, minerals, and calories, compared to people in the suburbs with access to conveniences that promote healthier behaviors. Since a large amount of research has determined a significant association between income and nutrient intake and race/ethnicity and nutrient intake, research should be conducted on these determinants simultaneously to establish how these variables promote and deter nutrition risk. Research Questions and Hypothesis Research Question This paper explores the following research questions. First, are Americans in danger of nutrition risk because they have low income? Inadequate income limits the quality and quantity of foods available to low-income people, which influences their consumption and, subsequently, their nutrition risk. Second, do certain races and ethnicities have a greater likelihood of consuming a wider, healthier variety of foods that reduce nutrition risk? Individuals may cultivate dietary habits through cultural practices that shape food choices, and certain racial/ethnic groups have poorer access to nutrient-dense foods, potentially yielding higher nutrition risk for certain groups. Third, do income and race/ethnicity affect nutrition risk jointly and if so, how? The intermingling of income and race/ethnicity might leave certain races with a higher likelihood of nutrition risk based on their income levels. 9

10 Hypothesis Results concerning the association of income and race/ethnicity on nutrition risk are mixed. Johnson et al. (1994) found that race had high correlation with nutrient intake while Bowman (2007) found that lower-income adults consumed less fruit, vegetables, milk, meat, poultry, and fish compared to middle- and high-income adults. Since no definitive conclusions exist within the literature, there are competing hypotheses about the effects of income and race/ethnicity on nutrition risk. First, one branch of literature suggests that income is the most important predictor of nutrition risk. Researchers posit that low-income families have inadequate resources for nutrient dense foods and consume less food in general which places them at risk for improper nutrition (Bowman, 2007; Tarasuk et al., 2007). I hypothesize that income will be the stronger predictor of nutrition risk. Second, another branch suggests that race/ethnicity is the most important predictor. Kant and Graubard (2007) found that the effect of race and ethnicity on dietary intakes remained after controlling for income and education. I, then, explore the competing hypothesis that race/ethnicity will be the stronger predictor of nutrition risk. However, the mixed outcomes within the literature on income and race/ethnicity may be because they are both important predictors of nutrient risk. In fact, the mixed results may be because the effect of income differs for different racial-ethnic groups. Therefore, my third hypothesis is that the combination of income and race/ethnicity are associated with nutrition risk. In other words, an interaction effect exists between income and race/ethnicity. 10

11 Methodology Data Data come from the National Health and Nutrition Examination Survey (NHANES) for (cross-sectional sample) conducted through the National Center for Health Statistics (NCHS) of the Centers for Disease Control and Prevention (CDC) (Centers for Disease Control and Prevention, 2011). Conducted regularly since 1960, the NHANES is dual-examination that combines household interviews from a trained interviewer and physical examinations designed to evaluate the health and nutrition of adults and children within the United States (Centers for Disease Control and Prevention, 2011). The NHANES interviews contain demographic, socioeconomic, dietary, and health-related questions. The physical examinations in the Mobile Examination Centers (MEC) consist of the medical, dental, physiological, and laboratory tests administered by medical employees, including detection of diseases, medical indicators, and health conditions such as obesity, anemia, hearing loss, and so on. The sample, using a stratified, multistage, national probability cluster design, represents the civilian, noninstitutionalized population of the United States aged two months and older, oversampling on participants aged 60 and older, African Americans, and Hispanics for reliable statistics (Centers for Disease Control and Prevention, 2011). Sampling consisted of four stages selection of Primary Sampling Units, segments within Primary Sampling Units, households within segments, and one or more participants per household (Centers for Disease Control and Prevention, 2011). Measures Nutrition Intake. Nutrition collection for the NHANES consisted of a 24 hour recall of the types and amounts of foods and beverages consumed, which estimate intakes of energy, 11

12 nutrients, and other food components (Centers for Disease Control and Prevention, 2011). Participants gave their dietary recall interview in-person in the MEC with a proxy adult respondent providing dietary information for children ages six to11. MEC employees use a set of measuring guides (various glasses, bowls, mugs, drink boxes and bottles, household spoons, measuring cups and spoons, a ruler, thickness sticks, bean bags, and circles) inside the MED dietary interview room to aid reporting amounts of foods. Vitamin A as retinol activity equivalents (mcg), vitamin C (mg), vitamin D (D2 + D3) (mcg), vitamin E (mg), niacin (mg), folate, total (mcg), calcium (mg), iron (mg), phosphorus (mg), zinc (mg), copper (mg), and selenium (mcg) were used in the research based on the DRI (Dietary Reference Intakes, 2011). The vitamins and minerals were recoded to include the DRI healthy ranges for males and females. The DRI ranges represent the Recommended Dietary Allowances (RDA), which is the average daily dietary intake level sufficient to meet the nutrient requirements of nearly all (97-98 percent) healthy individuals. Total risk signified the number of vitamins and minerals the participant has deficiencies in, ranging from one to thirteen potential deficiencies. In addition, participants were considered not at risk if they were inside those ranges. If the participant had nutrition levels outside of those healthy ranges, the participant was considered at risk for that vitamin or mineral (Dietary Reference Intakes, 2011). Income. Income (original variable INDHHIN2) is the total estimated household income (Centers for Disease Control and Prevention, 2011). NCHS used two methods to obtain household income family income data from a single CPS family and total family income from the reference person for multi-income households. For the research, the data were recoded to five categories (1 = < $20,000; 2 = $20,000-34,999; 3 = $35,000-64,999; 4 = $65,000-99,999; 12

13 and 5 = $ 100,000 and over). The classification for under $20,000 was placed in category one while the over $20,000 was placed in category two. Gender. Gender (original variable RIAGENDR) is the self-reported gender of the participant (Centers for Disease Control and Prevention, 2011). The gender variable was recoded to a dummy variable where male = 0 and female = 1. Race and Ethnicity. Race and ethnicity (original variable RIDRETH1) recorded from the self-identified responses to the survey questions (Centers for Disease Control and Prevention, 2011). The race and ethnicity variable was recoded into a dummy variable and compressed to three categories Non-Hispanic white, non-hispanic black, and other, which includes Mexican American, Hispanic, other race, and multiracial. Age. Age in years (original variable RIDAGEYR) recorded at the time of the household screening interview, from ages of 1 to 79 years of age with participants 80 years and older coded as 80 (Centers for Disease Control and Prevention, 2011). NCHS use the date of birth to determine the age of the participant. If the date of birth is missing or refused, NCHS input July for the month, 1 for the day of birth, and year of the interview minus the age the participant reported. The age variable was recoded into seven categories (1 = ages 1-2; 2 = ages 3-10; 3 = ages 11-18; 4 = ages 19-30; 5 = ages 31-50; 6 = ages 51-70; and 7 = ages >70). Analytic Strategy OLS Regression The analysis uses ordinary least squares (OLS) regression in predicting nutrition risk for the complete sample. To maximize the sample size and because of some missing data on income, the number of respondents varies depending on the variables in the models rather than using 13

14 listwise deletion. OLS regression analysis includes five models that determined nutrition risk within income and race/ethnicity and introduce gender and age as controls (Table 3). Model 1 contains an OLS regression analysis with income while model 1 adds gender and age (Table 3, Model 1 and 2). Model 3 exhibit the OLS regression on nutrition risk and race, including gender and age in model 4 (Table 3, Model 3 and 4). The last model, which involves the dependent, independent, and control variables, incorporates a two-way interaction effect between income and race/ethnicity (Table 3, Model 5). The interaction effect was significant for other and income, indicating that there are statistically significant differences in the effect of income between white respondents and the other race/ethnic category that consists of Mexican Americans, Hispanics, and multi-cultural participants. Consequently, for further understanding of the interaction effect, I performed an OLS regression of split samples for the subgroups of race/ethnicity to provide an in-depth observation into the confounding effects of income and race/ethnicity. The three race groups in model 2 were rerun to compare effect of income, but only differences between white and other are confirmed to be statistically significant. Results Sample Population Table 1 indicates that approximately 39% of the sample was ages 18 and under while 33% were in the 19 through 50 age range. Participants ages 51 and older accounted for 28% of the sample. The annual income for participants under $34,999 composed roughly 50% of the sample. Twenty-three percent of the sample had an annual income between $35,000 and $64,999 and 27% of the sample had an income over $65,000. The sample divided in roughly in half for gender with males comprising 49.6% and females 50.4%. Non-Hispanic Whites, 42% of the 14

15 sample, had 2.4% more participants than Other with 39%. Non-Hispanic Blacks category included 19% of the sample. Dependent Variable Table 2 signifies that 8 participants from the sample (N = 10537) had no nutrition risks from the vitamins and minerals researched in the study. Fifty-nine percent of participants were deficient and toxic in 3 to 7 of the vitamins and minerals while 35% of the sample had 8 more nutrition risks. Sixty-six participants had 1 nutritional risk while 520 had two. Among the vitamins and minerals in the research, 95% of participants were at risk for vitamin D and vitamin E deficiency. Seventy-seven percent were at risk for vitamin A. Phosphorous and selenium were the vitamins that participants were least likely to be at risk nutritionally. Bivariate Statistics Bivariate statistics were used to determine nutrition risk on the different annual household income levels and among race categories. Figure 1 demonstrates nutrition risk and income are inversely related. Nutrition risk decreases with an increase in income, though the differences are small. Among the income categories, a 0.99 difference exists between the mean risk score of incomes under $20,000 and incomes $100,000 and over. Figure 2 exhibit nutrition risk by race classifications. Non-Hispanic Blacks have the highest average risk score Mexican Americans, Hispanics, and multi-cultural participants that comprise the other category had an average risk score of 6.7 and Non-Hispanic Whites had the lowest risk score of There is a 0.73 difference between the Non-Hispanic Blacks and Whites risk scores. 15

16 OLS Regression Results Model 1 on Table 4 depicts the OLS regression of nutrition risk with income while Model 2 included the controls, gender and age. For each unit increase in income, there is a 0.26 decrease in risk, signifying a statistically significant inverse relationship between nutrition risk and income (p < 0.001). When the controls were included, participants have a 0.23 less chance of nutritional risk when their income increases (p < 0.001). Including gender and age to the regression strengthened the impact of income and explained 6% of the variation in the data compared to 1.3% in Model 1. Models 3 and 4 provide the results on the OLS regression for nutrition risk with race and then with the controls added. For Non-Hispanic Blacks, a statistically significant 0.73 unit of nutritional risk exists compared to white respondents while only a 0.46 exists for Mexican Americans, Hispanics, and multi-cultural participants that comprise the other category (p < 0.001). The likelihood of nutritional risk diminishes for Non-Hispanic Blacks following the inclusion of the controls, the 0.73 dropping to Including gender and age in Model 4 yields a model that explains 5.7% of the variation in the data compared to 0.9% in Model 3. In the final model, the impact of race on nutritional risk lessens to 0.51 for Non- Hispanic Blacks and 0.15 for Mexican Americans, Hispanics, and multi-cultural participants when incorporating income (p < 0.001). For each unit increase in income there is a 0.21 decrease in nutritional risk (p < 0.001). Adding income explained 6.6% of the variation. In table 3, Model 5 explains the most variation, but looking at Model 2 and Model 4, the income model with the controls explains the variation in nutrition risk more than Model 4 with race and the controls. Table 5 depicts the nutrition risk through the three groupings of race/ethnicity white, black, and other using OLS regression to explore the variation within and between groups. For Non-Hispanic Whites, each unit increase in income results in a 0.26 decrease in nutritional risk 16

17 (p < 0.001). Non-Hispanic Blacks and Mexican Americans, Hispanics, and multi-cultural participants experience lower reduction in nutrition risk with additional income, 0.19 and 0.15, respectively (p < 0.01). Income and controls explain 3.7% of the variation for Non-Hispanic Blacks while explaining 8.4% for Non-Hispanic Whites and 4.7% for Mexican Americans, Hispanics, and multi-cultural participants. Income has a greater, statistically significant impact on nutrition for Non-Hispanic Whites and Mexican Americans, Hispanics, and multi-cultural participants. The variation remains when r-square has been adjusted. Discussion Research in this paper delves into the impact of income and race/ethnicity on nutrition risk and asks whether income is the most important determinant for nutrition risk, if race/ethnicity is the most important determinant for nutrition risk, or nutrition risk is best explained by a combination of these two factors. In this discussions section, I review the major findings and reflect on them in light of my hypotheses and the literature on nutrition risk by income and race/ethnicity. The three major findings of this study are that income is a stronger predictor of nutrition risk, Non-Whites are at moderate nutrition risk, and income is the most important determinant for Non-Hispanic Whites. Income a Stronger Predictor of Nutrition Risk This study found that income is a stronger predictor of nutrition risk compared to race/ethnicity, confirming the first hypothesis on income and nutrition risk. The literature on income and nutrition found that income had a positive association with fruit and vegetable intake and vitamins and minerals. Kant and Graubard (2007) determined that an increase in income 17

18 resulted in larger intakes of vitamins A and C and calcium. Income, moreover, influences food insecurity and hunger more than race and age (Rose, 1999). Adults with low incomes have lower consumptions compared to middle and high-income groups (Rose, 1999). The results are consistent with the literature. When controlling for age and gender, income explains more of the variation in nutrition risk compared to the models with race and controls for age and gender. Income provides access to proper nutrition and variety of foods, which are usually more expensive. Inadequate income leads to poorer quality foods and unhealthy dietary habits and options. In addition, people with higher incomes live in areas that have greater access to grocery stores and stores that provide nutrient rich foods. These findings suggest that future research should concentrate more on income and necessitate additional research that delves deeper into the association between income and nutrition, looking at which vitamins and minerals income influences the most. Non-Whites at Moderate Nutrition Risk In addition, the study found that non-whites are at a moderate nutrition risk, which does not confirm the second hypothesis on race/ethnicity and nutrition risk. Literature on race/ethnicity determined that race and ethnicity impact nutrition and the intake of nutrient-dense foods. Newby (2012) found that Non-Hispanic Blacks consumed more cholesterol and less fruits and vegetables. Kant and Graubard (2007), in addition, posit that culinary preferences within cultures encourage poor dietary habits. The results were not consistent with the literature. Although a race/ethnicity effect on nutrition risk was present, it was weaker than the effect of income. Further, race/ethnicity explained less of the variation in nutrition risk compared to income. Therefore, non-hispanic Blacks and Mexican Americans, Hispanics, and multi-cultural 18

19 participants have a moderate nutrition risk. Despite cultural differences, people will incorporate healthier dishes into their lives if they have money for them. These findings suggest that future research on race/ethnicity and nutrition should include income. Previous research typically used income as a control or included the variable within socioeconomic status, obscuring the effects of income on nutrition. Income most Important Determinant for Whites Lastly, the results reveal that the effect of income varies by race/ethnicity, suggesting a joint effect of these two factors and confirming the third hypothesis. Specifically, my results suggest that income has the strongest effect for non-hispanic Whites. Articles researching nutrition intake with income and race/ethnicity remain mixed on which variable has the larger impact on the consumption of nutrient-dense foods. Research often includes education, which inhibits a definite view of how income and race/ethnicity play into nutrition. Johnson et al. (1994) found that race influenced nutritional intake for women and that income had a small impact on intakes of young males. Middaugh et al. (2012), in contrast, posit that income has a significant association with income. The results were consistent with the literature for income among whites. Income was the most important determinant for whites while income had a moderate impact on Non-Whites. Non-Hispanic Whites are more likely to have better dietary habits and more education on nutrition compared to Non-Whites so inadequate income would prevent the consumption of healthier foods more so than Non-Whites, who are more likely to have poor nutrition regardless of income. Finally, there was no evidence to suggest that the effect of income on nutrition risk differs for Non-Hispanic Whites compared to Non-Hispanic Blacks. Because both non-white groups have a higher likelihood of living in lower income households, 19

20 the effect of income may operate similarly for the non-hispanic Black group and the other race/ethnic group. Future studies should address monetary limitations in conjunction with cultural preferences to explore if these factors explain observed racial/ethnic variation in nutrition risk. The Importance of Considering Location in Future Research Finally, considering the combined influence of income and race/ethnicity on nutrition risk highlighted in this study, future studies should consider how geographic location factors into this process as well. Location and income, for instance, are highly correlated, as are location and race/ethnicity because of income and racial segregation in neighborhoods, neighborhood composition and additional factors. Although this study did not have access to location data, I offer some suggestion for future research in this direction. First, research regarding nutrition risk by income would benefit from data on location in that the importance of income as a predictor may be found in the details of residential location. Location would flesh out how income aids good nutrition. Studies that incorporate income and location employ the variables from a sociodemographic perspective, including variables from the social spectrum that could impact nutrition i.e. race, education, and gender. On the prevalence of overweight children in specified locations in Georgia, more overweight children were in rural growth and rural decline, compared to urban and suburban locations, and in poor counties compared to middle- and high-income counties (Lewis et al., 2006). Nayga (1996) found that the low dietary fiber in the North comes from the lower consumption of fruits and vegetables in that region. Income had statistical significance in the food purchased outside the home and food 20

21 purchased for consumption in the home. According to the research, increases in dietary fiber peak and then decline with higher incomes. Second, an examination of location and race/ethnicity could be beneficial because quality of food varies in urban areas and there is likely to be more poor quality foods in neighborhoods with a higher proportion of minorities. Boeckner et al. (2007) found that predominantly African American neighborhoods had a higher accessibility of full-service and fast-food restaurants compared to predominantly white neighborhoods. In addition, a location variable would reveal how race impacts nutrition and whether income remains the most important predictor. Reservation youths, in a study conducted on Native Americans and their nutrition had lower intakes of oranges and potatoes and high intakes of high-fat and salt-cured, nitrite-cured, smoked or pickled foods than the national sample of youths (Noia, Schinke, & Contento, 2005). Since these factors are closely correlated and difficult to determine which is the strongest predictor of nutrition, future research on the impact of income, race/ethnicity, and location on nutrition would assist in disentangling how these factors are associated with nutrition risk and provide a well-defined picture in regards to tackling nutrition deficiencies in the US population. Limitations Although informative, this study has three key limitations. First, the measurement of nutrition intake in the NHANES data is limited to 24 hour recall data of food consumed from participants. Misrepresentation of dietary intake could occur despite the instruments used to determine intake. Participants, in addition, concerned with their weight or proportions might understate their innutritious intake while exaggerating their consumption of healthy foods. Second, the data looks at the impact of nutrition risk cross-sectionally. Cross-sectional research, 21

22 while providing conclusions of occurrences within the population in that year, overlooks nutritional developments over time, which could give context to the nutritional fluctuations that occur per year. Lastly, information and data on location could not be obtained from NHANES. Geo coded data collected in the survey could determine urban, suburban, and rural subsections within the United States and add dimension to the nutrition results, though limited access from the Center for Disease Control and Prevention (CDC) prevents its use. Geo coded data could be acquired through submitting a proposal, but the CDC did not respond to our submitted proposal or to our s regarding the subject. Conclusion In summary, investigating whether income or race/ethnicity is a stronger predictor of nutrition risk was necessary because of the conflicting previous literature. Previous studies provided strong arguments for income and race/ethnicity, though failed to come to a consensus. The current cross-sectional research found a statistically significant association between income and nutrition risk compared to race/ethnicity, a moderate effect of income for Non-Whites concerning nutrition risk, and income as the most important determinant for Non-Hispanic Whites. These results are important in that it shows how income and race/ethnicity interact in relation to nutrition risk and that researchers should concentrate on income as a predictor of nutrition risk while controlling for race/ethnicity to glean a richer perspective on nutrition risk. Additional studies are needed to examine the impact of location and income on nutrition risk, a topic which is limited. Finally, these findings could contribute to future research on location, as income, race/ethnicity, and location often represent overlapping risk factors. 22

23 References Aggarwal, A., Monsivias, P., and Drewnowski, A. (2012). Nutrient Intakes Linked to Better Health Outcomes are Associated with Higher Diet Costs in the US. Degree of Nutrient Intakes and Diet Cost, 7(5), 1-9. Boeckner, L., Pullen, C., Walker, S., Oberdorfer, M., and Hageman, P. (2007). Eating Behaviors and Health History of Rural Middle to Older Women in the Midwestern United States. American Dietetic Association Journal, 107(2), Bowman, S. (2007). Low Economic Status is Associated with Suboptimal Intakes of Nutritious Foods by Adults in the National Health and Nutrition Examination Survey, Nutrition Research, 27, Caldwell, K., Makhnudov, A., Ely, E., Jones, R., and Wang, R. (2011). Iodine Status of the U.S. Population, National Health and Nutrition Examination Survey, and Thyroid, 21(4), Centers for Disease Control and Prevention. (2011). Documentation. National Health and Nutrition Examination Survey. Retrieved December 14, 2012 from Centers for Disease Control and Prevention. (2011). Dietary. National Health and Nutrition Examination Survey. Retrieved December 14, 2012 from Forshee, R. and Storey, M. (2006). Demographics, Not Beverage Consumption, is Associated with Diet Quality. International Journal of Food Sciences and Nutrition, 57(7/8),

24 Izumi, B. et al. (2011). Associations between Neighborhood Availability and Individual Consumption of Dark-Green and Orange Vegetables among Ethnically Diverse Adults in Detroit. Journal of the American Dietetic Association, 111(2), Johnson, R., Johnson, D., Wang, M., Smiciklas-Wright, H., and Guthrie, H. (1994). Characterizing Nutrient Intakes of Adolescents by Sociodemographic Factors. Journal of Adolescent Health, 15(2), Kant, A. (2000). Consumption of Energy-Dense, Nutrient-Poor Foods by Adult Americans: Nutritional and Health Implications. The Third National Health and Nutrition Examination Survey, American Journal of Clinical Nutrition, 72, Kant, A. and Graubard, B. (2007). Secular Trends in the Association of Socio-Economic Position with Self-Reported Dietary Attributes and Biomarkers in the U.S. Population: National Health and Nutrition Examination Survey (NHANES) to NHANES Public Health Nutrition, 10(2), Kant, A., Graubard, B., and Kumanyika, S. (2007). Trends in Black-White Differentials in Dietary Intakes of U.S. Adults, Journal of Preventive Medicine, 32(4). Kirkpatrick, S., Dodd, K., Reedy, J., and Krebs-Smith, S. (2012). Income and Race/Ethnicity are Associated with Adherence to Food-Based Dietary Guidance among US Adults and Children. Journal of the Academy of Nutrition and Dietetics, 112(5), Krebs-Smith, S., Guenther, P., Subar, A., and Kirkpatrick, S. (2010). Americans Do Not Meet Federal Dietary Recommendations. Journal of Nutrition, 140, Lewis, R., Meyer, M., Lehman, S., Trowbridge, F., Bason, J., Yurman, K., and Yin, Z. (2006). Prevalence and Degree of Childhood and Adolescent Overweight in Rural and Urban and Suburban Georgia. Journal of School Health, 76(4),

25 Middaugh, A., Fisk, P., Brunt, A., Rhee, Y. (2012). Few Associations between Income and Fruit and Vegetable Consumption. Journal of Nutrition Education and Behavior, 44(3), Nayga, R. (1996). Dietary Fiber Intake Away-from-Home and At-Home in the United States. Food Policy, 21(3), Newby, P., Noel, S., Grant, R., Judd, S., Shikany, J., and Ard, J. (2010). Race and Region are Associated with Nutrient Intakes among Black and White Men in the United States. The Journal of Nutrition, 141, Newby, P., Noel, S., Grant, R., Judd, S., Shikany, J., and Ard, J. (2012). Race and Region have Independent and Synergistic Effects on Dietary Intakes in Black and White Women. Nutrition Journal, 11(25), Noia, J., Schinke, S., and Contento, I. (2005). Dietary Patterns of Reservation and Non- Reservation Native American Youths. Ethnicity and Disease, 15(4), Nolan, K., Schell, M., Stark, A., and Gomez, M. (2002). Longitudinal Study of Energy and Nutrient Intakes for Infants from Low-Income, Urban Families. Public Health Nutrition, 5(3), Rose, D. (1999). Economic Determinants and Dietary Consequences of Food Insecurity in the United States. American Society for Nutritional Sciences, 129, 517S-20S. Tarasuk, V., McIntyre, L., and Li, J. (2007). Low-Income Women s Dietary Intakes are Sensitive to the Depletion of Household Resources in One Month. The Journal of Nutrition, 137(8), United States Department of Agriculture. (2011). Dietary Reference Intakes: Recommended Intakes for Individuals. National Agricultural Library. Retrieved December 14,

26 from Nutrition/DRIs/5_Summary%20Table%20Tables%201-4.pdf. 26

27 Figure 1. Nutrition Risk by Income 27

28 Figure 2. Nutrition Risk by Race 28

29 Table 1. Demographic Characteristics of Participants Frequency % Independent Variables Income (N = 10005) Under 20,000 20,000-34,999 35,000-64,999 65,000-99, ,000 and over Race (N = 10537) Non-Hispanic White Non-Hispanic Black Other Gender (N = 10537) Male Female Age (N = 10537) > Source: National Health and Nutrition Examination Survey 29

30 Table 2. The Frequency and Percentage of Participants with Occurrences of Nutrition Risk Frequency % Dependent Variable Total Risk Source: National Health and Nutrition Examination Survey 30

31 Table 3. The Frequency and Percentage of Participants with Occurrences of Nutritional Risk with Vitamins and Minerals Dependent Variable Components of Risk Frequency % At Risk At RiskNot At Risk Vitamin A (µg/d) Vitamin C (mg/d) Vitamin D (µg/d) Vitamin E (mg/d) Niacin (mg/d) Vitamin B6 (mg/d) Folate (µg/d) Calcium (mg/d) Copper (µg/d) Iron (mg/d) Phosphorus (mg/d) Selenium (µg/d) Zinc (mg/d) Source: National Health and Nutrition Examination Survey 31

32 Table 4. OLS Regression Results Predicting Nutrition Risk by Income and Race I II III IV V (N = 9172) (N = 9172) (N = 9623) (N = 9623) (N = 9172) Intercept *** *** *** *** *** (0.070) (0.101) (0.047) (0.092) (0.118) Income *** *** *** (0.024) (0.023) (0.023) Race/Ethnicity Black (Non-Hispanic) *** *** *** (0.086) (0.084) (0.086) Other *** *** * (0.068) (0.068) (0.070) Female *** *** *** (0.061) (0.060) (0.061) Age *** *** *** (0.017) (0.017) (0.017) R Sq R Sq. Adjusted Note: *p < 0.05; **p < 0.01; ***p < Source: National Health and Nutrition Examination Survey 32

33 Table 5. OLS Regression Results Predicting Nutrition Risk by Race White Black Other (N = 3993) (N = 1707) (N = 3472) Intercept *** *** *** (0.164) (0.235) (0.163) Income *** ** ** (0.032) (0.059) (0.042) Female *** *** *** (0.089) (0.144) (0.102) Age *** * *** (0.024) (0.040) (0.029) R Sq R Sq. Adjusted Note: *p < 0.05; **p < 0.01; ***p < Source: National Health and Nutrition Examination Survey 33