Emily Shupe

Top Clin Nutr Vol. 33, No. 3, pp. 247–258 c 2018 Wolters Kluwer Health, Inc. All rights reserved. Copyright ORIGINAL RESEARCH Literacy’s Role in Health Disparities A Mediator of Race and Income With Anemia in African American and White Adults Emily S. Shupe, MS; Ryan T. Pohlig, PhD; Marie Fanelli Kuczmarski, PhD; Alan B. Zonderman, PhD; Michele K. Evans, PhD Literacy impacts diet quality and may play a role in preventing anemia. This study investigated whether literacy mediates the relationships between race or poverty status and diet quality and anemia. Diet quality was evaluated using mean adequacy ratios for 1895 white and African American adults from Healthy Aging in Neighborhoods of Diversity across the Life Span study. Anemia was diagnosed by World Health Organization standards. Path analysis explored the influence of race and poverty on anemia. Anemia was diagnosed in 223 participants. The synergistic effects of poverty, race, and diet quality influence anemia. Literacy mediated the effects of race and poverty on mean adequacy ratios and anemia, highlighting the role of literacy in physical well-being. Key words: African American, anemia, diet quality, health disparities, inflammation, iron deficiency, literacy A CCORDING to the National Health and Nutrition Examination Survey data, the prevalence of anemia in the United States rose Author Affiliations: Departments of Behavioral Health and Nutrition (Ms Shupe and Dr Kuczmarski) and Health Sciences (Dr Pohlig), University of Delaware, Newark; and Laboratory of Epidemiology & Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, Maryland (Drs Zonderman and Evans). This work is supported by the Intramural Research Program, National Institute on Aging, National Institutes of Health, grant Z01-AG000194. The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article. Correspondence: Emily S. Shupe, MS, Department of Behavioral Health and Nutrition, University of Delaware, 206B McDowell Hall Newark, DE 19716-. DOI: 10.1097/TIN- from 4% to 7.1% from 2003 to 2012, with an average of 5.6% of adults meeting the criteria for anemia.1 While 5.6% would indicate that anemia is only of mild public health concern (World Health Organization citation), anemia can be a serious health concern for specific groups such as African Americans (AAs), adults older than 60 years, nonpregnant women of reproductive age, and pregnant women. National Health and Nutrition Examination Survey data indicate that anemia is more prevalent in AAs than in whites (Ws) (14.9% and 4%, respectively) and in females than in males (7.6% and 3.5%, respectively), with prevalence peaking at ages 40 to 49 years of age and again at 80 to 85 years of age. Among US adults, the prevalence of anemia appears evenly distributed among nutritional anemia (NA), anemia of inflammation (AI) and unexplained anemia (UA).2 Nutritional anemia is typically the result of deficiencies 247 Copyright © 2018 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited. 248 TOPIC IN CLINICAL NUTRITION/JULY–SEPTEMBER 2018 of iron, folic acid, and/or vitamin B12 .2 Anemia of inflammation is most commonly associated with underlying conditions such as atherosclerosis, diabetes mellitus, malignancy, and rheumatologic disorders.3–5 While research on the demographic statistics of individuals classified as either NA or AI is limited, some research suggests that AAs tend to have AI while Ws tend to have NA.2,6,7 Given the possible etiologies associated with UA,8,9 UA was not included in this analysis. To reduce the prevalence of anemia, several factors may contribute to its development and should be explored. Race, poverty status, and literacy are among the factors that contribute to health disparities.10,11 The higher prevalence of anemia in AAs than in Ws could be due to several factors. A 2014 report by the US Census Bureau found more AAs than Ws are uninsured (22% and 13.7%, respectively), more AAs live in poverty than Ws (26.2% and 10.1%, respectively), and AAs have a lower median income than Ws.11 African Americans have higher rates of many of the leading causes of death including, hypertension, diabetes, heart disease, stroke, and cancer.10 Those in the lowest income groups, compared with those with higher incomes, are at a greater risk for developing anemia.12,13 Iron deficiency anemia is the most common anemia among low-income groups.14 Individuals in the lowest income group (<100% of the federal poverty level) are almost 5 times as likely to have poor health as those in the highest income group (≥400% federal poverty level) and are almost twice as likely to have diabetes and heart disease,15 both of which may be associated with developing anemia.5,16 Literacy and the discordance in estimates of literacy levels of individuals by physicians and other health professionals have been suggested as important contributors to health disparities.17,18 Low literacy is significantly associated with poor overall health status, more hospitalization, and greater rates of mortality.19–21 African Americans and other minority races are more likely to have marginal or low literacy skills.22 Individuals with low incomes also tend to indicate lower health literacy compared with those with higher incomes.22 Understanding the role of literacy among racially and economically diverse populations may be critical to reducing the disparities of health conditions such as anemia. Literacy appears to contribute to the dietary differences observed between AAs and Ws, with AAs having lower diet quality, resulting in a greater need to improve diet quality to optimize health.23,24 In addition, low-income adults generally have poorer diet quality than higher-income adults.25,26 Dietary intake may contribute to the development of anemia, either via energy insufficiency and inadequate micronutrient intake (NA)27 or energy imbalance of macronutrients leading to inflammatory conditions that contribute to the development of AI.28,29 The exploration of literacy and diet quality on the presence of anemia in a racially and socioeconomically diverse population might be beneficial for the prevention and intervention efforts. The Health Aging in Neighborhoods of Diversity across the Life Span (HANDLS) study was designed to investigate the effects of race and socioeconomic status (SES) on health disparities.30 Although the primary health outcomes were cardiovascular disease and cognitive function, comprehensive medical, dietary, and demographic data were collected. Using data from this study, the aims of this project were to investigate (1) the effects of race and poverty status on the presence of NA and AI and (2) the roles that literacy and diet quality play in the complex relationship between race and poverty status and the presence of NA or AI. METHODS HANDLS study The HANDLS study is a prospective population-based cohort study. The baseline cohort -) comprised 3720 AA and W adults 30 to 64 years of age. Participants were drawn from 13 predetermined neighborhoods (defined as groups of contiguous census tracts) in the city of Baltimore, Copyright © 2018 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited. Literacy’s Role in Health Disparities Maryland. Four factors were selected for sampling: age (7, 5-year age bands from 30 to 64 years), self-reported race (AA and W), sex, and SES (operationalized by household incomes below or above 125% of the 2004 federal poverty guidelines). Those participants with household income above 125% of the poverty line were identified as above poverty and those below as below poverty (BP). The HANDLS baseline population had a mean age of 47.7 years, 59% were AA, 45% were male, and 41% of participants were BP.30 All participants spoke English. Individuals were excluded if they were pregnant or within 6 months of active treatment for cancer. Further detailed information on the study design, eligibility, and recruitment of participants can be found elsewhere.30 The HANDLS baseline data were collected in 2 phases conducted 4 to 10 days apart.31 Phase 1 was a household survey conducted in the participant’s home by a recruitment and sampling contractor. Participants completed questionnaires detailing their background, demographic information, education experience, occupational status, household income, physical activity, and health status information. A trained interviewer administered the first 24-hour dietary recall. The second phase took place in mobile research vehicles, where a medical history survey, a physical examination, fasting blood draw, cognitive testing, and body composition measurements, and physical performance measures were administered by a physician or member of medical staff. A trained interviewer administered a second 24-hour dietary recall. Study population Of the 3720 eligible participants enrolled at baseline, 2177 participants completed both 24-hour dietary recalls. Of the 2177 with complete dietary data, 89 participants were excluded for having chronic kidney disease or sickle cell disease and 111 were excluded for missing blood markers of anemia (serum ferritin, transferrin saturation percentage [FeSat], serum folate, and serum vitamin B12 ). Anemia criteria were applied to the remaining 249 1977 eligible participants. Participants with UA were excluded (n = 82). No other exclusions were made. Institutional review boards at National Institute of Environmental Health Sciences and at the University of Delaware approved the study protocol. All HANDLS participants provided written informed consents and were compensated monetarily. A flow diagram of the household sampling to eligible participants for this study is presented in Figure 1. Dietary methods The United States Department of Agriculture (USDA) Automated Multiple Pass Method was used to conduct both 24-hour dietary recalls. Recalls were conducted on all days of the week to account for variations in diet between weekdays and weekends. The Automated Multiple Pass Method involves 5 steps designed to provide cues and prompt thorough recall for all foods and drinks consumed throughout the previous day.32 These steps include (1) quick list of all foods consumed the previous day; (2) a forgotten foods list that includes probes for commonly forgotten foods; (3) probes to determine the time a food was consumed and at which meal; (4) detailed questions including amounts of foods consumed, additions to foods, and where food was obtained; and (5) a final review to probe any foods not previously remembered. An illustrated food model booklet as well as other visual measurement aids was used to increase the accuracy of estimating food and drink quantities. A trained coder using the USDA Survey Net data-processing system to match the foods with codes in the Food and Nutrient Database for Dietary Studies version 3.0 coded each recall.33 The nutritional supplement questionnaire was not administered during baseline data collection to minimize subject burden since the participants were on the mobile research vehicles for at least 6 hours. Clinical measures Participants were weighed (kg) without shoes and coats using a calibrated Health O Meter digital scale (Pelstar, LLC, Alsip, Copyright © 2018 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited. 250 TOPIC IN CLINICAL NUTRITION/JULY–SEPTEMBER 2018 Figure 1. A flow diagram of the household sampling to eligible participants for this Healthy Aging in Neighborhoods of Diversity across the Life Span study. CKD indicates chronic kidney disease; HANDLS, Healthy Aging in Neighborhoods of Diversity across the Life Span; HIV, human immunodeficiency virus. Illinois). Height (cm) was obtained with the participant’s heels and back against a height meter supplied by Novel Products, Inc (Rockton, Illinois), and body mass index (BMI) was calculated from measured height and weight. Body mass index was calculated using the equation: BMI = weight (kg)/[height (m)]2 .31 For this project, participants were categorized as having a BMI of less than 25 (normal) or a BMI of 25 or more (overweight and obese). Fasting venous blood specimens were collected from participants in the morning when they arrived at the mobile research vehicles visit and analyzed by Quest Diagnostics, Inc (Chantilly, Virginia). Fasting blood results included 2 indicators for iron (Fe) status: FeSat (transferrin saturation) (serum iron/total iron binding capacity) and serum ferritin (ng/mL), plus measures of serum folate (ng/mL), serum vitamin B12 (pg/mL), and C-reactive protein (CRP) (mg/L). Serum Fe and total iron binding capacity were assessed using the standard clinical laboratory spectrophotometric assay. Serum ferritin was measured using a standard chemiluminescence assay. Serum folate and vitamin B12 were measured using enzyme immunoassay. High-sensitivity CRP levels were assessed by the nephelometric method utilizing latex particles coated with CRP monoclonal antibodies. All assays were considered reliable but with any method there are limitations that are described on the Quest diagnostics Web site.34 Assessment of anemia Participants with anemia were identified by the World Health Organization standard that Copyright © 2018 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited. Literacy’s Role in Health Disparities defines anemia as a Hb level less than 13 g/dL in men and less than 12 g/dL in women.35 After identifying individuals with anemia, the type of anemia was determined.36,37 Anemic individuals were classified with NA if they had low serum ferritin (≤30 ng/mL) or normal serum ferritin (31-99 ng/mL) and as low FeSat (<16%) if they had folate less than 4 ng/mL or if they had vitamin B12 less than 200 pg/mL.37 Anemia of inflammation was defined as having elevated serum ferritin (≥100 ng/mL) or normal serum ferritin (31-99 ng/mL) with elevated FeSat (>45%). Measures Variables included in the analyses were race, poverty status, literacy, and diet quality. Race was self-reported as AA or W. Poverty status was defined as self-reported household income dichotomized into less or greater than 125% of the 2004 federal poverty guidelines. Literacy was assessed using the reading subtest of the Wide Range Achievement Test (WRAT)—3rd Edition.38 The WRAT measures the ability to recognize and name letters and words. The total WRAT Reading score equaled the sum of total correctly pronounced letters and total correctly pronounced words and served as the literacy measurement. A WRAT score of 37 to 40 represents a sixth- to eighthgrade reading level, and a score of 41 to 46 represents a high school reading level. Nutrient adequacy ratio (NAR) and mean adequacy ratio (MAR) scores were used to assess nutrient-based diet quality.39,40 The NAR score was determined by taking each participant’s daily intake of a nutrient divided by the recommended dietary allowance for that nutrient. Nutrient adequacy ratio scores were determined for 17 micronutrients: vitamins A, C, D, E, B6 , B12 , folate, iron, thiamin, riboflavin, niacin, copper, zinc, calcium, magnesium, phosphorus, and selenium. The recommended dietary allowance was adjusted for the age and sex of participants and vitamin C was adjusted for smokers.41 The NAR score was then converted into a percent with values exceeding 100 truncated to 100. Mean adequacy ratio scores were calcu- 251 lated by averaging the NAR scores: MAR = ( NAR scores)/17.40 Nutrient adequacy ratio and MAR were calculated separately for each daily intake and then averaged together. Mean adequacy ratio scores, based on food intakes only, were used as the nutrient-based diet quality variable. Covariates Demographic characteristics included age (years) and sex (male or female). Cigarette smoking was coded as current smoker or nonsmoker. Handgrip strength, which is used as an indicator of total-body muscle strength and physical performance, was assessed using the Jamar Hydraulic Hand Dynamometer.42 The hand dynamometer registers the maximum kilograms of force per trial. Two trials were conducted with both the right and left hands. The mean of the 2 trials by the dominant hand was used in the analysis. Statistical analyses Demographic and descriptive statistics means and standard errors for continuous variables and frequencies and percentages for categorical variables are reported in the Table. One-way analyses of variance for continuous variables and χ 2 tests for categorical variables were used to test for differences among participants with NA, participants with AI, and participants not diagnosed with anemia. Two statistical programs were used for analysis: SPSS software, release 23 and Mplus software, version 7.31. For the path analysis, standardized estimates are reported. Alpha level was set to .05. Path analysis is a special case of Structural Equation Modeling in which all variables in the model are directly observed. It is a multivariate statistical technique that allows researchers to test complicated models, while allowing variables to be both outcomes and predictors at the same time. Conceptually, it can be considered a set of simultaneously estimated multiple regression equations. Approaching this analysis through multiple regressions rather than path analysis would be burdensome as it would require a separate Copyright © 2018 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited. 252 TOPIC IN CLINICAL NUTRITION/JULY–SEPTEMBER 2018 Table. Characteristics of Healthy Aging in Neighborhoods of Diversity Across the Life Span Study Participants Categorized by Anemia Diagnosis by World Health Organization Criteriaa Nutritional Anemia (N = 160) Sex; % female Race; % AA Income, %; <125% poverty Current smoker, % BMI ≥25 kg/m2 , % Age, X̄ ± SE, y WRAT score, X̄ ± SE MAR, X̄ ± SE Handgrip strength, X̄ ± SE, kg CRP mg/L, X̄ ± SE Energy, X̄ ± SE, kcal Hb, X̄ ± SE, g/dL Serum iron, X̄ ± SE, μg/dL Serum ferritin, X̄ ± SE, ng/mL FeSat, X̄ ± SE, % Folate, X̄ ± SE, ng/mL Vitamin B12 , X̄ ± SE, pg/mL a 85.6 80.6a 51.9a 38.5a 75.6 43.8 ± 0.7a 41.3 ± 0.6a,b 67.3 ± 1.2a 30.9 ± 0.7a 8.4 ± 1.0a 1847 ± 80 11.0 ± 0.1a 45.8 ± 3.0a 21.0 ± 1.8a 11.8 ± 0.6a 12.8 ± 0.4a 480.3 ± 17.1a Anemia of Inflammation (N = 63) b 42.9 90.5a 47.6a,b 55.6b 67.7 52.8 ± 1.1b 39.4 ± 1.1a 72.3 ± 1.5a,b 35.5 ± 1.5a,b 15.0 ± 4.3b 2061 ± 134 11.6 ± 0.1b 73.5 ± 5.5b 226.4 ± 19.1b 23.1 ± 1.7b 13.7 ± 0.9a,b 585.5 ± 38.9b No Anemia (N = 1672) b 53.8 52.0b 40.8b 49.1b 70.8 47.8 ± 0.2c 42.5 ± 0.2b 71.8 ± 0.4b 35.6 ± 0.5b 4.3 ± 0.2c 2032 ± 24 14.1 ± 0.03c 88.9 ± 0.9c 129.6 ± 4.0c 26.0 ± 0.3b 14.5 ± 0.2b 512.7 ± 5.7a P