3
Diverse Populations

The obesity epidemic is occurring among boys and girls throughout the United States, among younger children and adolescents, across all socioeconomic groups and among all racial/ethnic subpopulations. However, in several racial/ethnic groups, in low-income populations, and among recent immigrants to the United States, the rates of obesity among children and youth are alarmingly high or are increasing faster than average. The children and youth who are at the highest risk for obesity often experience other social, economic, and health disparities concurrently and do not live in environments that inherently support health-promoting behaviors. In addition, although some of the risk factors for obesity are relatively ubiquitous in settings where American children and youth spend their time (e.g., communities, schools, shopping malls, retail stores, and home), epidemiologic evidence shows that African-American, Hispanic/ Latino, American Indian/Alaska Native, and Pacific Islander populations and children experiencing poverty are more likely to live in environments with inadequate support for health-promoting behaviors. Assessing the impact of these different environments presents an enormous challenge for tracking progress against obesity in diverse populations. On the other hand, the diversity of the U.S. population and the variation in patterns of obesity-related risks also provide opportunities to expand our understanding of how trends, patterns, and risk factors manifest in certain environments and to identify the types of interventions that are likely to be effective when they are tailored specifically to build on a population’s characteristics, perspectives, and social and cultural assets.



The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement



Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 74
Progress in Preventing Childhood Obesity: How Do We Measure Up? 3 Diverse Populations The obesity epidemic is occurring among boys and girls throughout the United States, among younger children and adolescents, across all socioeconomic groups and among all racial/ethnic subpopulations. However, in several racial/ethnic groups, in low-income populations, and among recent immigrants to the United States, the rates of obesity among children and youth are alarmingly high or are increasing faster than average. The children and youth who are at the highest risk for obesity often experience other social, economic, and health disparities concurrently and do not live in environments that inherently support health-promoting behaviors. In addition, although some of the risk factors for obesity are relatively ubiquitous in settings where American children and youth spend their time (e.g., communities, schools, shopping malls, retail stores, and home), epidemiologic evidence shows that African-American, Hispanic/ Latino, American Indian/Alaska Native, and Pacific Islander populations and children experiencing poverty are more likely to live in environments with inadequate support for health-promoting behaviors. Assessing the impact of these different environments presents an enormous challenge for tracking progress against obesity in diverse populations. On the other hand, the diversity of the U.S. population and the variation in patterns of obesity-related risks also provide opportunities to expand our understanding of how trends, patterns, and risk factors manifest in certain environments and to identify the types of interventions that are likely to be effective when they are tailored specifically to build on a population’s characteristics, perspectives, and social and cultural assets.

OCR for page 74
Progress in Preventing Childhood Obesity: How Do We Measure Up? Much remains to be learned about the role of race/ethnicity, socioeconomic status (SES), and regional disparities in childhood obesity to build an evidence base that will support the most effective strategies and promising practices. Progress in preventing childhood obesity should include an examination of efforts to define and address the contexts and mechanisms that lead to and perpetuate childhood obesity in environments with excessive risks. Several recommendations in the Health in the Balance report (IOM, 2005) emphasized the need for prominent government leadership and community collaboration to develop and promote programs and policies that will collectively encourage healthful eating patterns and physical activity behaviors, particularly for young populations at the highest risk for obesity and related chronic diseases. Helping at-risk children and youth balance their energy intakes and their energy expenditures requires an understanding of the complex and interacting influences of the social, economic, and built environments and the adverse environmental conditions that low-income and racially/ethnically diverse populations encounter as they regularly attempt to obtain affordable foods, beverages, and meals that contribute to a healthful diet and find opportunities to engage in recreational play and physical activity (Day, 2006; Glanz et al., 2005; Goodman, 2003; Gordon-Larsen et al., 2006; IOM, 2005; Jetter and Cassady, 2005; Powell et al., 2004). A multifaceted approach to address obesity and related health considerations is relevant to children and youth overall but leads to different perspectives on the appropriate solutions for specific populations, depending on their historical and sociopolitical contexts and the timing and rate of relevant economic and lifestyle transitions (Kumanyika, 1994; Kumanyika and Golden, 1991). Because the focus of this report is on evaluation, this chapter provides only a brief overview of the context for these issues. The reader is referred to the extensive body of research regarding the interactions among societal, cultural, genetic, and biobehavioral risk and protective factors and their implications for promoting population health (Dabelea et al., 2000; Gillman et al., 2003; Halfon and Hochstein, 2002; Halfon and Inkelas, 2003; IOM, 2001, 2003; Krieger, 1994; Krieger and Davey Smith, 2004; McEwen, 2001; NRC and IOM, 2000, 2004; Reilly et al., 2005; Rosenbloom, 2002). The chapter focuses on the key issues relevant to improving the implementation and evaluation of obesity prevention efforts involving high-risk and culturally diverse populations. UNDERSTANDING SPECIFIC CONTEXTS Many aspects of society have changed concomitantly with the rise in childhood obesity. A broader understanding of the potential interplay

OCR for page 74
Progress in Preventing Childhood Obesity: How Do We Measure Up? among these societal changes and children’s biological and behavioral responses is needed. For the population subgroups most affected by obesity, the relevant adverse social and environmental factors may be concentrated or magnified. A variety of causal pathways are associated with many aspects of systemic disadvantage (e.g., poverty, substandard housing, limited educational opportunities, and low levels of social integration), which can lead to adverse health, social, and economic circumstances (Gostin and Powers, 2006). These factors have greater impacts on those with the fewest resources to buffer these influences. The subpopulations that have the highest prevalence of obesity are those considered to be socially, economically, and politically disadvantaged in other respects. Thus, although the determinants of obesity are generally of the same nature among all population groups, special considerations are needed to assess whether the pathways to progress in preventing childhood obesity in the entire population will also reach the subgroups who are the most affected. Long-term investments that support the development or adaptation and evaluation of childhood obesity prevention initiatives are needed. These initiatives also need to be adapted to various settings, contexts, and subpopulations for their dissemination if they are proven to be effective. Obesity Prevalence Obesity and its risk-behavior determinants (e.g., high levels of consumption of energy-dense and low-nutrient diets, physical inactivity, and sedentary behaviors) are major drivers of health disparities by race/ethnicity as they contribute to three of the disease categories responsible for the majority of excess mortality: type 2 diabetes, cardiovascular diseases (CVDs), and cancer (Wong et al., 2002). Obesity and at-risk obesity prevalence rates in American children and youth reveal significant differences by age and sex and between racial/ethnic groups (Freedman et al., 2006; Hedley et al., 2004; Ogden et al., 2006; Sherry et al., 2004). There does not appear to be a general excess risk in all age and gender groups, although the increases in obesity over the past 30 years have been especially pronounced at the upper part of the body mass index (BMI) distribution for children and youth. From 1971–1974 to 1999–2002, the prevalence of individuals in certain subgroups with BMI levels above the 99th percentile increased substantially. Additionally, African-American girls ages 6 to 17 years have experienced greater increases in obesity prevalence than white children and adolescents. These increases began in the 1970s, whereas the increases among individuals in other racial/ethnic groups were not observed until the 1980s (Freedman et al., 2006). By tracking obesity prevalence rates in subpopulations and evaluating the progress achieved by obesity prevention interventions, it may be pos-

OCR for page 74
Progress in Preventing Childhood Obesity: How Do We Measure Up? sible to reduce the risk of obesity in these groups and ensure that primary obesity prevention strategies are reaching the entire target population. It is also important to track increases in BMI levels and obesity prevalence because childhood obesity is associated with an increased risk of CVD and mortality in adulthood (Li et al., 2004; Srinivasan et al., 2002). In 2003–2004, National Health and Nutrition Examination Survey (NHANES) data showed that 33.6 percent of U.S. children and adolescents were either obese (17.1 percent) or at risk of becoming obese (16.5 percent) (Ogden et al., 2006). The analysis of trends from NHANES data reveal that, when compared with non-Hispanic white cohorts, non-Hispanic black and Mexican-American children and adolescents, ages 2 to 19 years, have a greater prevalence of obesity.1 Non-Hispanic African-Americans and Mexican-American children and adolescents are also at greater risk of becoming obese (Hedley et al., 2004; Ogden et al., 2006). The prevalence rates of obesity and at risk for obesity in children and adolescents by age, sex, and racial/ethnic group for 2003 and 2004 are shown in Figure 3-1. State-specific prevalence and trends among 2- through 4-year-old children from low-income U.S. families were examined from 1989 to 2000. The results showed significant increases in obesity among low-income children in 30 states and significant decreases in childhood underweight in 26 states (Sherry et al., 2004). Although no geographic predominance was apparent, the number of states reporting obesity prevalence rates of more than 10 percent increased from 11 in 1989 to 28 in 2000. The number of states reporting decreased rates of underweight also rose during the same time frame, as reflected by 9 states in 1989 and 23 states in 2000 that had underweight prevalence rates equal to or less than 5 percent (Sherry et al., 2004). National obesity prevalence data are limited for American Indian/ Alaska Native and Asian/Pacific Islander children and youth. Nevertheless, an analysis of data for 9,464 American Indian children and youth, ages 5 to 18 years, compared with NHANES II data (1976 to 1980) found that 39 percent had BMIs above the 85th percentile (Jackson, 1993). Another analysis estimated the prevalence of obesity in a sample of 1,704 7-year-old children from 7 American-Indian communities across the United States to be between 27 and 30 percent for boys and girls, and the at-risk obesity prevalence was estimated at 20 to 21 percent for boys and girls (Caballero et al., 2003). In 2002 and 2003, an analysis of 11,538 American Indians, ages 5 to 17 years, attending 55 schools on 12 reservations located in the 1 Obesity is defined in this report as a BMI for age at or above the sex-specific 95th percentile of the Centers for Disease Control and Prevention (CDC) BMI charts developed in 2000. At risk for obesity is defined as a BMI for age at or above the sex-specific 85th percentile but less than the 95th percentile of the CDC BMI charts.

OCR for page 74
Progress in Preventing Childhood Obesity: How Do We Measure Up? FIGURE 3-1 Percentage of U.S. children and adolescents ages 2 to 19 years who are obese or at risk for obesity by sex and race/ethnicity in 2003 to 2004. SOURCE: Ogden et al. (2006). Aberdeen Area of the Indian Health Service (North Dakota, South Dakota, Iowa, and Nebraska), found that both the obesity prevalence and the at-risk obesity prevalence rates exceeded the levels for all U.S. children at almost every age group. The obesity prevalence rate was 24 percent for 5-year-old American-Indian children. Nearly one half (47 percent) of 5-year-old boys and 41 percent of 5-year-old girls were at risk for obesity (Zephier et al., 2006). There are limited data on the prevalence rates of obesity among Alaska-Native children and youth. One analysis examined the BMI levels of 1,632 students, ages 3 to 18 years, in 20 rural Alaskan communities. Results showed that nearly 50 percent were either obese (25 percent; n = 407) or at risk for obesity (24 percent; n = 392) (Dirks et al., 2006). National prevalence data for Asian/Pacific-Islander populations are not available. However, the obesity prevalence rate among a sample of low-income preschool children in Hawaii was 8.7 percent in 1997, whereas the national mean that year was 10.3 percent. However, the rate caught up to the national mean in 2002, with prevalence rates of 13.1 percent in Hawaii and 13.5 percent nationally (Baruffi et al., 2004). A cross-sectional study of 21,911 Samoan, Filipino, Hawaiian, Asian, Hispanic/Latino, African-American, and white toddlers ages 12 to 59 months participating in the Hawaii Special Supple-

OCR for page 74
Progress in Preventing Childhood Obesity: How Do We Measure Up? mental Nutrition Program for Women, Infants, and Children (WIC) in 1997 and 1998 found a higher than expected prevalence of obesity among preschoolers ages 2 to 4 years. Samoan toddlers had the highest prevalence of obesity at 27 percent, followed by Filipinos (12.4 percent), Hawaiians (11.3 percent), Hispanics (10.1 percent), Asians (9.0 percent), whites (8.5 percent), and African Americans (7.3 percent) (Baruffi et al., 2004). Health Effects of Childhood Obesity Type 2 diabetes, which accounted for less than 3 percent of all cases of new-onset diabetes among children and adolescents two decades ago, today accounts for between 30 and 45 percent of new-onset cases among adolescences and young adults (ADA, 2000; Rosenbloom et al., 1999). An analysis of data from NHANES (1999–2000), representing 27 million U.S. adolescents, showed a higher prevalence of impaired fasting glucose levels (an indicator for type 2 diabetes risk) among obese adolescents (17.8 percent) that among adolescents of normal weight (7.0 percent). In addition, the data showed that the rate of impaired fasting glucose levels was most pronounced among Mexican-American adolescents (13.0 percent) compared with the rates among African-American (4.2 percent) and white adolescents (7.0 percent) (Williams et al., 2005). On the basis of an analysis of data from NHANES (1999 to 2002) with a sample of 4,370 adolescents ages 12 to 19 years, the prevalence of type 2 diabetes was substantial and was projected to affect more than 39,000 U.S. adolescents and 2.77 million adolescents with impaired fasting glucose levels. These estimates present important implications for public health because of the high level of conversion from an impaired fasting glucose level to type 2 diabetes in adults and the increased risk of CVD in individuals with type 2 diabetes (Duncan, 2006). American-Indian/Alaska-Native, African-American, and Hispanic/Latino adolescents have a higher prevalence of type 2 diabetes than white adolescents; and this rise has occurred in parallel with the obesity epidemic among both children and adults (Gahagan et al., 2003; Oeltmann et al., 2003; Pinhas-Hamiel and Zeitler, 2005). American Indians/Alaska Natives have the highest rates of type 2 diabetes of any racial/ethnic group in the United States (National Diabetes Information Clearinghouse, 2005) and significant rates of heritability of CVD (North et al., 2003). Type 2 diabetes is a major public health crisis among American Indian/Alaska Native adolescents. From 1990 to 1998, the total number of young American Indians/Alaska Natives diagnosed with diabetes increased by 71 percent, and the prevalence increased by 46 percent (6.4 per 1,000 population to 9.3 per 1,000 population). An increase in prevalence was greater among adolescents ages 15 to 19 years and young adults (Acton et al., 2002). Between

OCR for page 74
Progress in Preventing Childhood Obesity: How Do We Measure Up? 1990 and 2001, the prevalence of type 2 diabetes increased by 106 percent among 15- to 19-year-old American-Indian youth and increased by 50 percent among American-Indian children and youth younger than 15 years of age (IHS National Diabetes Program, 2004). Sociodemographic Profiles Racial/Ethnic Diversity of the U.S. Population According to the 2000 U.S. Census, approximately 30 percent of the U.S. population identified themselves as members of a racial or an ethnic minority group. The 2000 Census counted more than 36 million African Americans (12.9 percent of the population); more than 35 million Hispanics/Latinos (12.5 percent) who live in the United States and another 3.8 million who live in the Commonwealth of Puerto Rico; nearly 12 million Asians (4.2 percent); 874,000 Native Hawaiians and other Pacific Islanders; and 4.3 million people (1.5 percent of the total U.S. population) reported that they were American Indians/Alaska Natives (2.4 million or 1 percent reported only American Indian/Alaska Native as their race), of which 35.9 percent live in American Indian areas (which include reservations) or Alaska Native village areas (Hobbs and Stoops, 2002; Ogunwole, 2006). By 2050, it is projected that these groups will account for almost 50 percent of the U.S. population (MBDA, 1999). The ethnic minority population is expected to account for nearly 90 percent of the total growth in the U.S. population, from an ethnic/racial minority population of 69 million in 1995 to one of 186 million in 2050 (MBDA, 1999). Thus, the term “minority” is increasingly misleading as a descriptor for diverse racial/ ethnic groups in the aggregate, at least with respect to the proportion of the multicultural U.S. population. Geographic Variation in Population Diversity Each of the 50 states throughout the nation has growing childhood, youth, and adult obesity burdens that are leading to overwhelming economic, health, and social challenges that will reverberate at the federal, state, and local levels. Certain issues for high-risk populations dominate in certain states and localities where racial and ethnic minority populations are a numerical majority. Currently, this is the case in California, given the higher proportion of Hispanics/Latinos and African Americans compared with the proportion of non-Hispanic whites. Hispanics/Latinos, who represent 20 different nationalities, are the nation’s largest and fastest-growing ethnic group as the result of ongoing immigration and natural increases in the birth rates of Hispanic/Latino

OCR for page 74
Progress in Preventing Childhood Obesity: How Do We Measure Up? citizens (NRC, 2006a). Given current demographic trends, nearly one in five U.S. residents will be of Hispanic/Latino origin by 2025 (NRC, 2006b).2 In addition to California, it is projected that by 2025, Texas, New Mexico, Hawaii, and the District of Columbia will also have population distributions in which non-Hispanic whites will be the minority (MBDA, 1999). Collectively, these four states and the District of Columbia represent 25 percent of the entire U.S. population. From this perspective on U.S. population demographics, finding ways to meet the challenges of addressing childhood obesity in racially, ethnically, and culturally diverse contexts becomes an urgent priority. Socioeconomic Status, Poverty, Health Disparities, and Health Outcomes Socioeconomic Status and Poverty Patterns of disease are best understood within the context of social determinants of health, which represent the societal conditions that affect health and that can be changed by social and health policies and programs. Three categories of social determinants that potentially affect health include social institutions (e.g., cultural and faith-based organizations, political structures, and economic systems including availability and distribution of fresh fruits and vegetables), physical surroundings (e.g., neighborhoods, worksites, towns, and cities), and social relationships (e.g., social networks and the differential treatment of subpopulations) (Anderson et al., 2003b). The implications of social determinants of health for assessing and evaluating progress in obesity prevention are discussed at the end of this chapter and in Chapter 6. Research has shown that the SES characteristics of the neighborhood in which individuals live (e.g., average income and percent unemployment) are better predictors than individual characteristics of morbidity and mortality (Diez-Roux et al., 1997; Finkelstein et al., 2004; Jargowsky, 1997), and that poverty is the most powerful single determinant of health (Lynch et al., 1997). Poverty causes poor health through its connection with reduced access to and use of health care services (with the quality of preventive, primary, and specialty health care services for this population often 2 The terms Hispanic and Latino are used interchangeably in this report. Both terms are used in the U.S. census. The United States population includes individuals from 20 Spanish-speaking nationalities, of which nearly one-half are foreign born and 40 percent are undocumented immigrants. Immigrants from Mexico, Puerto Rico, and Cuba represent more than 75 percent of the Hispanic/Latino population in the United States (NRC, 2006a,b).

OCR for page 74
Progress in Preventing Childhood Obesity: How Do We Measure Up? being lower), food insecurity3 and low-quality diets, and behaviors that do not support healthy lifestyles (DHHS and AHRQ, 2005; NCHS, 2005). African-American, Hispanic/Latino, and American Indian/Alaska Native households are substantially overrepresented among all U.S. households with incomes below the poverty level (DeNavas-Walt et al., 2005; NRC, 2006b; Robert and House, 2000). Moreover, children and adults in families with incomes below or near the federal poverty level4 have poorer health outcomes than those in families with higher incomes (DHHS and AHRQ, 2005; IOM, 2003). In 2003, 11 percent of U.S. children had no health insurance (Annie E. Casey Foundation, 2006). Children in low-income families are substantially more likely than children in higher-income families to lack health care coverage (NCHS, 2005). In 2002 and 2003, uninsured children were three times more likely than their counterparts with insurance (32 percent versus 11 percent, respectively) to have not had a visit to a physician or health clinic for health care within the previous year (NCHS, 2005). Racial/ethnic minority children and youth face a number of barriers to receiving timely, appropriate, and high-quality health care services (NCHS, 2005; NRC, 2006a). Children covered by Medicaid are nearly six times more likely than children covered by private insurance to be treated for obesity. In addition, the treatment of obesity in children covered by Medicaid is more expensive (approximately $6,700/year) than the treatment of obesity for children covered by private insurance (approximately $3,700/year) (Thomson Medstat, 2006). Children with obesity experience higher rates of hospitalizations and greater use of physician services than their nonobese peers (Thomson Medstat, 2006). The percentage of Americans living in poverty increased from 11.3 percent in 2000 to 12.5 percent in 2003. The 2004 poverty rate among children under 6 years of age was 21 percent (Annie E. Casey Foundation, 2006). In 2004, 38.2 million individuals (an estimated 11.9 percent of the total population), including 13.9 million children, lived in households with food insecurity (Nord et al., 2005). Several studies examining the relationships among food insecurity, SES, and obesity in children or youth have not been able to demonstrate a strong association or causal effect after adjustment for other factors (Hofferth and Curtin, 2005; Kaiser et al., 2002; Matheson et al., 2002; Whitaker and Orzol, 2006). 3 Food insecurity describes households that have limited or an uncertain availability of nutritionally adequate and safe foods or that have the inability to acquire such foods in a socially acceptable way. 4 Federal poverty guidelines are issued annually in the Federal Register by the U.S. Department of Health and Human Services and are used to determine the financial eligibility of individuals and households for federal assistance programs (DHHS, 2006a).

OCR for page 74
Progress in Preventing Childhood Obesity: How Do We Measure Up? Many racial/ethnic minority subpopulations have experienced social, political, and historical contextual events that continue to have long-lasting effects on their physical health, psychosocial well-being, and economic livelihoods (Duran et al., 1998; NRC, 2006b; Williams and Collins, 1995). The challenges associated with understanding the relationship between SES and obesity risk are discussed in Box 3-1. Immigration and Acculturation Immigrants are the fastest-growing segment of the U.S. population. As a percentage of the total population, the foreign-born population increased BOX 3-1 Challenges in Understanding the Relationship Between Socioeconomic Status and Obesity Risk Despite the substantial variation in BMI that exists as a function of both SES and race/ethnicity, uncertainties remain as to whether these rates can be attributed solely to SES, because obesity disparities are not the same across ethnic groups and they do not emerge at comparable times during childhood (Parsons et al., 1999). There is no consensus about the reasons for these disparities, although recent research provides certain insights. Variation in obesity risk by race/ethnicity and SES appears to occur early in life. An assessment of 16,000 preschool children, ages 2 to 4 years, enrolled in the Head Start Program in New York City found that 27 percent were obese and 15 percent were at risk for obesity. An estimated one in four Head Start children in that sample were found to be obese by the age of 2 years, and one in three children were obese by the age of 4 years. Although obesity was identified as a problem among all Head Start children in New York City, Hispanic/Latino and African-American preschoolers are disproportionately affected (New York City Department of Health and Mental Hygiene, 2006). Moreover, socioeconomic deprivation in childhood has been found to be both a strong predictor of obesity in adulthood for African-American adult women (James et al., 2006a) and adult hypertension in adulthood for African-American men (James et al., 2006b). Mexican-American children and youth living along the U.S.-Mexico border experience higher levels of economic disadvantages and special challenges in accessing foods that contribute to a healthful diet, regular physical activity, and health care services (Abarca and Ramachandran, 2005; Ruiz-Beltran and Kamau, 2001). Low-SES Mexican school-aged children living along the U.S. border in Tijuana, adjacent to San Diego County in California, have been found to be at increased risk of obesity and related chronic diseases, which may be related to less healthful food choices for children attending schools in low-SES neighborhoods (Villa-Caballero et al., 2006). In contrast, among Mexican children and adolescents, particularly those living in urban areas, obesity is increasing among higher SES groups (IOM, 2007).

OCR for page 74
Progress in Preventing Childhood Obesity: How Do We Measure Up? Analyses of nationally representative longitudinal data—the National Longitudinal Survey of Youth (Strauss and Knight, 1999; Strauss and Pollack, 2001) and the National Longitudinal Study of Adolescent Health (NLSAH) (Goodman, 1999)—have suggested that family SES is inversely related to obesity prevalence in children and that the effects of SES and race/ethnicity are independent of other variables. A more recent analysis of a nationally representative sample of adolescents enrolled in the NLSAH examined trends in racial/ethnic disparities for leading health indicators from Healthy People 2010 (Harris et al., 2006) across multiple domains from adolescence to young adulthood. The results revealed that the health risk increased for 15 of 20 indicators among racially/ethnically diverse adolescents. Access to health care decreased from the teen to the adult years for most U.S. racial/ethnic groups, and the disparity was particularly high for American Indians (Harris et al., 2006). Analyses of nationally representative cross-sectional data reveal additional findings that can help to provide an understanding of the relationship between SES and obesity. An examination of the 1988 to 1994 NHANES data showed that the prevalence of obesity in white adolescents was higher for those in low-income families, but there was no clear relationship between family income and obesity in individuals in other age or racial/ethnic subgroups (Ogden et al., 2003; Troiano and Flegal, 1998). A more recent analysis of trends in the association between poverty and adolescents’ obesity risk was conducted for four cross-sectional NHANES surveys conducted from 1971 to 2004 (Miech et al., 2006). Although the obesity prevalence did not differ by SES or family poverty status for teens through age 14 years, a widening disparity was observed for 15- to 17-year-olds, especially boys, girls, non-Hispanic whites, and non-Hispanic African Americans. There was a 50 percent higher risk of obesity among adolescents in poor families compared with that among adolescents in non-poor families. Possible mechanisms that contributed to the obesity risk for adolescents were physical inactivity, higher levels of consumption of sweetened beverages, and skipping breakfast (Miech et al., 2006). from 4.7 percent in 1970 to 11.5 percent in 2002 (Dey and Lucas, 2006). The children of immigrant families are thus among the fastest-growing and the most ethnically diverse segment of the American child population. The median age of the individuals who make up the Hispanic/Latino second generation, for example, is just over 12 years (NRC, 2006b). Significant differences in the physical health status exist among U.S.-born and foreign-born individuals. Differences in the lengths of stay of immigrants in the United States suggest that the role of acculturation on immigrant health is complex and differs for various racial/ethnic groups (Dey and Lucas, 2006; Goel et al., 2004; Gordon-Larsen et al., 2003). However, what is clear from the available evidence is that the acculturation of young and adult immigrant populations is associated with the adoption of lifestyle behaviors and social norms that promote weight gain and obesity. An analysis of a nationally representative sample of 13,783 adolescents from the National Longitudinal Study of Adolescent Health found that

OCR for page 74
Progress in Preventing Childhood Obesity: How Do We Measure Up? proaches into the delivery of health care and social services (Anderson et al., 2003a; Andrulis, 2005). Address multiple health issues pertinent to a range of stakeholders in order to build momentum and community support for interventions (e.g., the Harlem Fitness Zone and the Cherokee Choices projects) and the Active Living by Design interventions (Sallis et al., 2006). Recognize that the use of nonintervention control groups may not be acceptable to ethnic minority communities; rather, delayed intervention designs may be more tenable (Boon and Clydesdale, 2005). Involve researchers who are knowledgeable about the racial/ethnic groups or cultures being studied or evaluated (American College of Epidemiology, 1995; Anderson et al., 2003a; Gil and Bob, 1999; Hopson, 2003; IOM, 1994; Kumanyika et al., 2005). Focus attention on the process of intervening in addition to achieving outcomes. This may include particular attention to delivery channels, messengers, materials and messages, and other cultural adaptations or targeting. Subgroup analyses are critical, and, when differences are found, further examination is needed to explore why the interventions are not effective for certain subgroups (e.g., characteristics of the intervention itself or how it was implemented) (Kreuter and McClure, 2004; Yancey et al., 2006b). Expanding Surveillance to Identify Areas With High Obesity Burdens and Related Chronic Disease Data collection and analyses for surveillance and monitoring are core functions of governmental public health practices. However, methodological limitations to the identification and documentation of health disparities must be addressed. The public health infrastructure has the capacity to monitor aggregate racial/ethnic groups (e.g., categories defined by the U.S. Bureau of the Census). However, some individuals in racially/ethnically diverse communities may not participate in formal national data-gathering efforts because of logistical issues and concerns about how the data might be used. These challenges may result in the underrepresentation of racial/ ethnic minority populations in national surveillance systems. Representative surveillance and monitoring systems must be established to allow the monitoring of minority populations at potential health risk. The REACH 2010 Risk Factor Survey, for example, is conducted annually in African-American, Hispanic/Latino, Asian/Pacific Islander, and American Indian communities throughout the United States (CDC, 2004). Data from this survey demonstrate that residents in racial/ethnic minority communities experience greater disease risk and burden than individuals in the general population living in similar areas or states.

OCR for page 74
Progress in Preventing Childhood Obesity: How Do We Measure Up? The continuous and expanded surveillance of health status in racial/ ethnic minority communities is an important measurement challenge for evaluators, yet it is needed to provide accurate disease prevalence estimates for evaluating culturally targeted prevention strategies for smaller geographic areas (e.g., for certain zip codes, school catchments areas, or census tracts). Expanding access to surveillance data would decrease the burden placed on community-based organizations, school districts, and other local government agencies to monitor and evaluate interventions, thereby allowing them to focus on the service delivery missions that motivate their activities (Yancey et al., 2005). The federal and state governments can expand their roles in collecting data by race/ethnicity and refining the definitions of race/ethnicity categories (Lurie et al., 2005). Surveillance and monitoring systems may not provide the data needed for a comprehensive assessment of program quality. Community-based participatory research is one qualitative research approach to inquiry that emphasizes community partnerships and action for social change and the reduction of health disparities as an integral component of the research process (McAllister et al., 2003). Indeed, qualitative indicators require more precise definitions. Yet, effective programs and services will depend on the ability to measure and evaluate these indicators and integrate an understanding of the indicators into interventions. The ability to measure an array of indicators, both qualitative and quantitative, for a variety of diverse populations and outcomes is central to the elimination of health disparities and the prevention of childhood obesity in high-risk communities. SUMMARY AND RECOMMENDATIONS Quantitative assessment of progress over the past few years in preventing childhood obesity in diverse population groups is difficult. Examples are provided throughout the chapter of localized successes and innovative programs that are being implemented and evaluated across the nation. Large-scale initiatives focused on disproportionately affected groups are needed and should incorporate participatory approaches into their design, implementation, and evaluation. Making progress toward closing the childhood obesity and health disparity gaps in high-risk racial/ethnic minority populations and diverse low-income populations will depend on several factors. These include a national commitment to substantially improve the social and built environments of high-risk communities; defining the contexts and mechanisms that lead to and perpetuate childhood obesity; and designing, implementing, and evaluating effective and culturally competent interventions, evaluation tools, and outcome measures.

OCR for page 74
Progress in Preventing Childhood Obesity: How Do We Measure Up? Childhood obesity prevention efforts should creatively identify and build upon community assets, collective efficacy, and other leverage points where the needs of diverse populations are served, such as federal nutrition safety-net programs and shared cultural values and traditions. Attention to these factors will promote the implementation and evaluation of promising interventions that can help identify future best practices that support childhood obesity prevention efforts. Recommendations 2 and 3 discussed in Chapter 2 explicitly propose further action focused on strengthening the implementation and the evaluation of obesity prevention interventions, policies, and initiatives relevant to culturally and ethnically diverse populations. This emphasis highlights the need to carefully consider and involve the people most affected in the policy change or intervention. Recommendation 2: Policy makers, program planners, program implementers, and other interested stakeholders—within and across relevant sectors—should evaluate all childhood obesity prevention efforts, strengthen the evaluation capacity, and develop quality interventions that take into account diverse perspectives, that use culturally relevant approaches, and that meet the needs of diverse populations and contexts. Recommendation 3: Government, industry, communities, and schools should expand or develop relevant surveillance and monitoring systems and, as applicable, should engage in research to examine the impact of childhood obesity prevention policies, interventions, and actions on relevant outcomes, paying particular attention to the unique needs of diverse groups and high-risk populations. Additionally, parents and caregivers should monitor changes in their family’s food, beverage, and physical activity choices and their progress toward healthier lifestyles. REFERENCES Abarca J, Ramachandran S. 2005. Using community indicators to assess nutrition in Arizona-Mexico border communities. Prev Chronic Dis [Online]. Available: http://www.cdc.gov/Pcd/issues/2005/jan/04_0082.htm [accessed July 23, 2006]. Ackard DM, Neumark-Sztainer D, Story M, Perry C. 2003. Overweight among adolescents: Prevalence and associations with weight-related characteristics and psychological health. Pediatrics 111(1):67–74. Acton KJ, Burrows NR, Moore K, Querec L, Geiss LS, Engelgau MM. 2002. Trends in diabetes prevalence among American Indian and Alaska Native children, adolescents, and young adults. Am J Public Health 92(9):1485–1490. ADA (American Diabetes Association). 2000. Type 2 diabetes in children and adolescents. Pediatrics 105(3):671–680. American College of Epidemiology. 1995. Committee on Minority Affairs Statement of Principles on Epidemiology and Minority Populations. Ann Epidemiol 5:505–508.

OCR for page 74
Progress in Preventing Childhood Obesity: How Do We Measure Up? Anderson LM, Scrimshaw SC, Fullilove MT, Fielding JE, Normand J. Task Force on Community Preventive Services. 2003a. Culturally competent healthcare systems. A systematic review. Am J Prev Health 24(3 Suppl):68–79. Anderson LM, Scrimshaw SC, Fullilove MT, Fielding JE, Task Force on Community Preventive Services. 2003b. The community guide’s model for linking the social environment to health. Am J Prev Med 24(3 Suppl):12–20. Anderson LM, Brownson RC, Fullilove MT, Teutsch SM, Novick LF, Fielding J, Land GH. 2005. Evidence-based public health policy and practice: Promises and limits. Am J Prev Med 28(5 Suppl):226–230. Andrulis DP. 2005. Moving beyond the status quo in reducing racial and ethnic disparities in children’s health. Public Health Rep 120(4):370–377. Annie E. Casey Foundation. 2006. 2006 Kids Count Data Book. [Online]. Available: http://www.aecf.org/kidscount/sld/databook.jsp [accessed July 10, 2006]. Baruffi G, Hardy CJ, Waslien CI, Uyehara SJ, Krupitsky D. 2004. Ethnic differences in the prevalence of overweight among young children in Hawaii. J Am Diet Assoc 104(11): 1701–1707. Beech BM, Klesges RC, Kumanyika SK, Murray DM, Klesges L, McClanahan B, Slawson D, Nunnally C, Rochon J, McLain-Allen B, Pree-Cary J. 2003. Child- and parent-targeted interventions: The Memphis GEMS Pilot Study. Ethnic Dis 13(1 Suppl 1):S40–S53. Boon CS, Clydesdale FM. 2005. A review of childhood and adolescent obesity interventions. Crit Rev Food Sci Nutr 45(7–8):511–525. Brach C, Fraserirector I. 2000. Can cultural competency reduce racial and ethnic health disparities? A review and conceptual model. Med Care Res Rev 57(Suppl 1):181–217. Burdette HL, Whitaker RC. 2005. A national study of neighborhood safety, outdoor play, television viewing, and obesity in preschool children. Pediatrics 116(3):657–662. Burgeson CR, Wechsler H, Brener ND, Young JC, Spain CG. 2001. Physical education and activity: Results from the school health policy and program study 2000. J School Health 7(17):279–293. Caballero B, Himes JH, Lohman T, Davis SM, Stevens J, Evans M, Going S, Pablo J. 2003. Body composition and overweight prevalence in 1704 schoolchildren from 7 American Indian communities. Am J Clin Nutr 78(2):308–312. Cardon G, De Clercq D, De Bourdeaudhuij I, Breithecker D. 2004. Sitting habits in elementary schoolchildren: A traditional versus a “Moving School.” Patient Educ Couns 54(2): 133–142. CDC (Centers for Disease Control and Prevention). 2004. REACH 2010 surveillance for health status in minority communities—United States, 2001–2002. MMWR 53(SS-6): 1–36. Chen E, Martin AD, Matthews KA. 2006. Understanding health disparities: The role of race and socioeconomic status in children’s health. Am J Public Health 96(4):702–708. Cohen DA, Finch BK, Bower A, Sastry N. 2006. Collective efficacy and obesity: The potential influence of social factors on health. Soc Sci Med 62(3):769–778. Dabelea D, Hanson RL, Lindsay RS, Pettit DJ, Imperatore G, Gabir MM, Roumain J, Bennett PH, Knowler WC. 2000. Intrauterine exposure to diabetes conveys risks for type 2 diabetes and obesity: A study of discordant sibships. Diabetes 49(12):2208–2211. Datar A, Sturm R. 2004. Physical education in elementary school and BMI: Evidence from the Early Childhood Longitudinal Study. Am J Public Health 94(9):1501–1506. Day K. 2006. Active living and social justice: Planning for physical activity in low-income, black, and Latino communities. J Am Planning Assoc 72(1):88–99. DeNavas-Walt C, Proctor BD, Lee CH. 2005. Income, Poverty, and Health Insurance Coverage in the United States: 2004. Current Population Reports, P60-229. U.S. Bureau of the Census. Washington, DC: U.S. Government Printing Office. [Online]. Available: http://www.census.gov/prod/2005pubs/p60-229.pdf [accessed July 12, 2006].

OCR for page 74
Progress in Preventing Childhood Obesity: How Do We Measure Up? Dey AN, Lucas JW. 2006. Physical and mental health characteristics of U.S.- and foreign-born adults: United States, 1998–2003. Advance Data from Vital and Health Statistics. No. 369. Hyattsville, MD: National Center for Health Statistics. DHHS (U.S. Department of Health and Human Services). 2001. Mental Health: Culture, Race, and Ethnicity. A Supplement to Mental Health: A Report of the Surgeon General. Rockville, MD: Office of the Surgeon General. Public Health Service. DHHS. 2006a. 2006 Federal Poverty Guidelines. [Online]. Available: http://www.dhhs.state.nh.us/DHHS/PIO/LIBRARY/Policy-Guideline/federal-poverty-guidelines.htm [accessed May 29, 2006]. DHHS. 2006b. Best Practice Initiative: Latino Health Access Latino Childhood Obesity Prevention Initiative Demonstration Project. [Online]. Available: http://phs.os.dhhs.gov/ophs/BestPractice/LatinoObesity.htm [accessed May 29, 2006]. DHHS and AHRQ (Agency for Healthcare Research and Quality). 2005. 2005 National Healthcare Disparities Report. [Online]. Available: http://www.ahrq.gov/qual/nhdr05/nhdr05.pdf [accessed April 13, 2006]. Diez-Roux AV, Nieto FJ, Muntaner C, Tyroler HA, Comstock GW, Shahar E, Cooper LS, Watson RL, Szklo M. 1997. Neighborhood environments and coronary heart disease: A multilevel analysis. Am J Epidemiol 146(1):48–63. Dirks LG, Ross-Tsilkowski A, Ballew C. 2006. Rural Alaska Native Pediatric Height and Weight Survey 2005. Alaska Native Tribal Health Consortium, Anchorage, AK. Indian Health Service 17th Annual Research Conference, April 24–26. Abstract. Donnelly JE. 2005 (June 29). Physical Activity Across the Curriculum/Take 10! Presentation at the Institute of Medicine Symposium Progress in Preventing Childhood Obesity: Focus on Schools, Wichita, Kansas. Institute of Medicine Committee on Progress in Preventing Childhood Obesity. Duncan GE. 2006. Prevalence of diabetes and impaired fasting glucose levels among U.S. adolescents. Arch Pediatr Adolesc Med 160(5):523–528. Duran E, Duran B, Brave Heart MYH, Yellow-Horse-Davis S. 1998. Healing the American Indian soul wound. In: Danieli Y, ed. International Handbook of Multigenerational Legacies of Trauma. New York: Plenum Press. Finkelstein EA, Khavjou OA, Mobley LR, Haney DM, Will JC. 2004. Racial/ethnic disparities in coronary heart disease risk factors among WISEWOMAN enrollees. J Women’s Health 13(5):503–518. Freedman DS, Khan LK, Serdula MK, Ogden CL, Dietz WH. 2006. Racial and ethnic differences in secular trends for childhood BMI, weight, and height. Obesity 14(2):301–308. Gahagan S, Silverstein J. American Academy of Pediatrics Committee on Native American Child Health. American Academy of Pediatrics Section on Endocrinology. 2003. Prevention and treatment of type 2 diabetes mellitus in children, with special emphasis on American Indian and Alaska Native children. American Academy of Pediatrics Committee on Native American Child Health. Pediatrics 112(4):e328. Garden Mosaics. 2006. TRUCE Carrie McCracken Community Garden. New York, NY: Garden Mosaics. [Online]. Available: http://www.gardenmosaics.cornell.edu/pgs/data/inventoryread.aspx?garden=84 [accessed June 3, 2006]. Gil EF, Bob S. 1999. Culturally competent research: An ethical perspective. Clin Psychol Rev 19(1):45–55. Gillman MW, Rifas-Shiman S, Berkey CS, Field AE, Colditz GA. 2003. Maternal gestational diabetes, birth weight, and adolescent obesity. Pediatrics 111(3):e221–e226. Glanz K, Sallis JF, Saelens BE, Frank LD. 2005. Healthy nutrition environments: Concepts and measures. Am J Health Promot 19(5):330–333. Goel MS, McCarthy EP, Phillips RS, Wee CC. 2004. Obesity among U.S. immigrant subgroups by duration of residence. J Am Med Assoc 292(23):2860–2867.

OCR for page 74
Progress in Preventing Childhood Obesity: How Do We Measure Up? Goodman E. 1999. The role of socioeconomic status gradients in explaining differences in U.S. adolescents health. Am J Public Health 89(10):1522–1528. Goodman E. 2003. Letting the “gini” out of the bottle: Social causation and the obesity epidemic. J Pediatr 142(3):228–230. Goodman E, Whitaker RC. 2002. A prospective study of the role of depression in the development and persistence of adolescent obesity. Pediatrics 109(3):497–504. Gordon-Larsen P, Harris KM, Ward DS, Popkin BM. 2003. Acculturation and overweight-related behaviors among Hispanic immigrants to the U.S.: The National Longitudinal Study of Adolescent Health. Soc Sci Med 57(11):2023–2034. Gordon-Larsen P, Nelson MC, Page P, Popkin BM. 2006. Inequality in the built environment underlies key health disparities in physical activity and obesity. Pediatrics 117(2):417– 424. Gostin LO, Powers M. 2006. What does social justice require for the public’s health? Public health ethics and policy imperatives. Health Aff 25(4):1053–1060. Green LW, Mercer SL. 2001. Can public health researchers and agencies reconcile the push from funding bodies and the pull from communities? Am J Public Health 91(12):1926– 1943. Halfon N, Hochstein M. 2002. Life course health development: An integrated framework for developing health, policy, and research. Milbank Q 80(3):433–479. Halfon N, Inkelas M. 2003. Optimizing the health and development of children. J Am Med Assoc 290(23):3136–3138. Harris KM, Gordon-Larsen P, Chantala K, Udry JR. 2006. Longitudinal trends in race/ethnic disparities in leading health indicators from adolescence to young adulthood. Arch Pediatr Adolesc Med 160(1):74–81. Hedley AA, Ogden CL, Johnson CL, Carroll MD, Curtin LR, Flegal KM. 2004. Prevalence of overweight and obesity among U.S. children, adolescents, and adults, 1999–2002. J Am Med Assoc 291(23):2847–2850. Hobbs F, Stoops N. 2002. Demographic Trends in the 20th Century. Census 200 Special Reports. [Online]. Available: http://www.census.gov/prod/2002pubs/censr-4.pdf [accessed July 31, 2006]. Hofferth S, Curtin S. 2005. Poverty, food programs, and childhood obesity. J Policy Anal Manage 24(4):703–726. Hopson R. 2003. Overview of Multicultural and Culturally Competent Program Evaluation. Oakland, CA: Social Policy Research Associates. [Online]. Available: http://www.calendow.org/reference/publications/pdf/evaluations/TCE0 509-2004_Overview_of_Mu.pdf [accessed April 18, 2006]. Huang ZJ, Yu SM, Ledsky R. 2006. Health status and health service access and use among children in U.S. immigrant families. Am J Public Health 96(4):634–640. IHS (Indian Health Service). National Diabetes Program, Department of Health and Human Services. 2004. Interim Report to Congress: Special Diabetes Program for Indians. [Online]. Available: http://www.ihs.gov/MedicalPrograms/diabetes/resources/r_rtc2004index.asp [accessed July 12, 2006]. ILSI (International Life Sciences Institute). 2006. What Is Take 10!®? [Online]. Available: http://www.take10.net/whatistake10.asp [accessed May 23, 2006]. ILSI Center for Health Promotion. 2005. A General Overview of Physical Activity and Nutrition Intervention Programs. [Online]. Available: http://chp.ilsi.org/NR/rdonlyres/1287022A-3300-426A-ACCF-C87D5CEA1348/0/SchoolHCCommtyProgramsListILSICHP041206.pdf [accessed May 31, 2006]. IOM (Institute of Medicine). 1994. Balancing the Scales of Opportunity: Ensuring Racial and Ethnic Diversity in the Health Professions. Washington DC: National Academy Press.

OCR for page 74
Progress in Preventing Childhood Obesity: How Do We Measure Up? IOM. 2001. Health and Behavior: The Interplay of Biological, Behavioral, and Societal Influences. Washington DC: National Academy Press. IOM. 2003. The Future of the Public’s Health in the 21st Century. Washington, DC: The National Academies Press. IOM. 2005. Preventing Childhood Obesity: Health in the Balance. Washington DC: The National Academies Press. IOM. 2006. Examining the Health Disparities Research Plan of the National Institutes of Health: Unfinished Business. Washington DC: The National Academies Press. IOM. 2007. Joint U.S.–Mexico Workshop on Preventing Obesity in Children and Youth of Mexican Origin. Washington, DC: The National Academies Press. Jackson MY. 1993. Height, weight, and body mass index of American Indian school children, 1990–1991. J Am Diet Assoc 93(10):1136–1140. James SA, Fowler-Brown A, Raghunathan TE, Van Hoewyk J. 2006a. Life-course socioeconomic position and obesity in African American women: The Pitt County study. Am J Public Health 96(3):554–560. James SA, Van Hoewyk J, Belli RF, Strogatz DS, Williams DR, Raghunathan TE. 2006b. Life-course socioeconomic position and hypertension in African American men: The Pitt County study. Am J Public Health 96(5):812–817. Jargowsky PA. 1997. Poverty and Place: Ghettos, Barrios and the American City. New York, NY: Russell Sage Foundation. Jenkins C, McNary S, Carlson BA, King MG, Hossler CL, Magwood G, Zheng D, Hendrix K, Beck LS, Linnen F, Thomas V, Powell S, Ma’at I. 2004. Reducing disparities for African Americans with diabetes: Progress made by the REACH 2010 Charleston and Georgetown Diabetes Coalition. Public Health Rep 119(3):322–330. Jetter KM, Cassady DL. 2005. The availability and cost of healthier food alternatives. Am J Prev Med 30(1):38–44. Kaiser LL, Melgar-Quinonez HR, Lamp CL, Johns MC, Sutherlin JM, Harwood JO. 2002. Food security and nutritional outcomes of preschool-age Mexican-American children. J Am Diet Assoc 102(7):924–929. Kreuter MW, McClure SM. 2004. The role of culture in health communication. Annu Rev Public Health 25:439–455. Krieger N. 1994. Epidemiology and the web of causation: Has anyone seen the spider? Soc Sci Med 39(7):887–903. Krieger N. 2001. A glossary for social epidemiology. J Epidemiol Community Health 55(10): 693–700. Krieger N, Davey Smith G. 2004. “Bodies count” and body counts: Social epidemiology and embodying inequality. Epidemiol Rev 26(1):92–103. Kumanyika SK. 1994. Obesity in minority populations: An epidemiologic assessment. Obes Res 2(2):166–182. Kumanyika SK. 2005. Obesity, health disparities, and prevention paradigms: Hard questions and hard choices. Prev Chronic Dis [Online]. Available: http://www.cdc.gov/Pcd/issues/2005/oct/05_0025.htm [accessed July 23, 2006]. Kumanyika SK, Golden PM. 1991. Cross-sectional differences in health status in U.S. racial/ ethnic minority groups: Potential influence of temporal changes, disease, and lifestyle transitions. Ethn Dis 1(1):50–59. Kumanyika SK, Grier S. 2006. Targeting interventions for ethnic minority and low-income populations. In: Paxon C, ed. Future Child 16(1):187–207. Kumanyika SK, Gary TL, Lancaster KJ, Samuel-Hodge CD, Banks-Wallace J, Beech BM, Hughes-Halbert C, Karanja N, Odoms-Young AM, Prewitt TE, Whitt-Glover MC. 2005. Achieving healthy weight in African-American communities: Research perspectives and priorities. Obes Res 13(12):2037–2047.

OCR for page 74
Progress in Preventing Childhood Obesity: How Do We Measure Up? Lavizzo-Mourey R, Richardson WC, Ross RK, Rowe JW. 2005. A tale of two cities. Health Aff 24(2):313–315. Levy SR, Anderson EE, Issel LM, Willis MA, Dancy BL, Jacobson KM, Fleming SG, Copper ES, Berrios NM, Sciammarella E, Ochoa M, Hebert-Beirne J. 2004. Using multilevel, multisource needs assessment data for planning community interventions. Health Promot Pract 5(1):59–68. Li X, Li S, Ulusoy E, Chen W, Srinivasan SR, Berenson GS. 2004. Childhood adiposity as a predictor of cardiac mass in adulthood: The Bogalusa Heart Study. Circulation 110(22): 3488–3492. Liburd LC, Jack L Jr, Williams S, Tucker P. 2005. Intervening on the social determinants of cardiovascular disease and diabetes. Am J Prev Med 29(5 Suppl 1):18–24. Lloyd LK, Cook CL, Kohl HW. 2005. A pilot study of teachers’ acceptance of a classroom-based physical activity curriculum tool: Take 10!® Texas Assoc Health Phys Educ Rec Dance J 73(3):8–11. Lumeng JC, Gannon K, Cabral HJ, Frank DA, Zuckerman B. 2003. Association between clinically meaningful behavior problems and overweight in children. Pediatrics 112(5): 1138–1145. Lurie N, Jung M, Lavizzo-Mourey R. 2005. Disparities and quality improvement: Federal policy levers. Health Aff 24(2):354–364. Lynch JW, Kaplan GA, Shema SJ. 1997. Cumulative impact of sustained economic hardship on physical, cognitive, psychological, and social functioning. N Engl J Med 337(26): 1889–1895. Ma’at I, Owens M, Hughes M. 2002. Observations from the CDC. REACH 2010 coalitions: Reaching for ways to prevent cardiovascular disease and diabetes. J Womens Health 11(10):829–839. Matheson DM, Varady J, Varady A, Killen JD. 2002. Household food security and nutritional status of Hispanic children in the fifth grade. Am J Clin Nutr 76(1):210–217. MBDA (Minority Business Development Agency). U.S. Department of Commerce. 1999. Minority Population Growth: 1995–2050. [Online]. Available: http://www.mbda.gov/documents/mbdacolor.pdf [accessed June 16, 2006]. McAllister CL, Green BL, Terry MA, Herman V, Mulvey L. 2003. Parents, practitioners, and researchers: Community-based participatory research with early Head Start. Am J Public Health 93(10):1672–1679. McEwen BS. 2001. From molecules to mind: Stress, individual differences, and the social environment. Ann NY Acad Sci 935:42–49. Miech RA, Kumanyika SK, Stettler N, Link BG, Phelan JC, Chang VW. 2006. Trends in the association of poverty with overweight among U.S. adolescents, 1971–2004. J Am Med Assoc 295(20):2385–2393. Narayan KM, Hoskin M, Kozak D, Kriska AM, Hanson RL, Pettitt DJ, Nagi DK, Bennett PH, Knowler WC. 1998. Randomized clinical trial of lifestyle interventions in Pima Indians: A pilot study. Diabet Med 15(1):66–72. National Diabetes Information Clearinghouse. 2005. National Diabetes Statistics. [Online]. Available: http://diabetes.niddk.nih.gov/dm/pubs/statistics/index.htm [accessed July 12, 2006]. NCHS (National Center for Health Statistics). 2005. Health United States, 2005 with Chartbook on Trends in the Health of Americans. Hyattsville, MD: NCHS. [Online]. Available: http://www.cdc.gov/nchs/data/hus/hus05.pdf [accessed February 11, 2006]. New York City Department of Health and Mental Hygiene. 2006. Obesity in Early Childhood: More Than 40% of Head Start Children in NYC Are Overweight or Obese. [Online]. Available: http://www.nyc.gov/html/doh/downloads/pdf/survey/survey-2006 childobesity.pdf [accessed April 9, 2006].

OCR for page 74
Progress in Preventing Childhood Obesity: How Do We Measure Up? Nord M, Andrews M, Carlson S. 2005. Household Food Security in the United States, 2004. Economic Research Report Number 11. Washington, DC: Economic Research Service, U.S. Department of Agriculture. North KE, MacCluer JW, Williams JT, Welty TK, Best LG, Lee ET, Fabsitz RR, Howard BV. 2003. Evidence for distinct genetic effects on obesity and lipid-related CVD risk factors in diabetic compared to nondiabetic American Indians: The Strong Heart Family Study. Diabetes Metab Res Rev 19(2):140–147. NRC (National Research Council). 2001. New Horizons in Health: An Integrative Approach. Washington DC: National Academy Press. NRC. 2006a. Hispanics and the Future of America. Washington DC: The National Academies Press. NRC. 2006b. Multiple Origins, Uncertain Destinies: Hispanics and the American Future. Washington DC: The National Academies Press. NRC and IOM. 2000. From Neurons to Neighborhoods: The Science of Early Childhood Development. Washington DC: National Academy Press. NRC and IOM. 2004. Children’s Health, the Nation’s Wealth. Washington, DC: The National Academies Press. Oeltmann JE, Liese AD, Heinze HJ, Addy CL, Mayer-Davis EJ. 2003. Prevalence of diagnosed diabetes among African-American and non-Hispanic white youth, 1999. Diabetes Care 26(9):2531–2535. Ogden CL, Carroll MD, Flegal KM. 2003. Epidemiologic trends in overweight and obesity. Endocrinol Metab Clin N Am 32(4):741–760, vii. Ogden CL, Carroll MD, Curtin LR, McDowell MA, Tabak CJ, Flegal KM. 2006. Prevalence of overweight and obesity in the United States, 1999–2004. J Am Med Assoc 295(13): 1549–1555. Ogunwole SU. 2006. We the People: American Indians and Alaska Natives in the United States. Census 2000 Special Reports. U.S. Census Bureau. [Online]. Available: http://www.census.gov/prod/2006pubs/censr-28.pdf [accessed July 31, 2006]. Parsons TJ, Power C, Logan S, Summerbell CD. 1999. Childhood predictors of adult obesity: A systematic review. Int J Obes Relat Metab Disord 23(Suppl 8):S1–S107. Pinhas-Hamiel O, Zeitler P. 2005. The global spread of type 2 diabetes mellitus in children and adolescents. J Pediatr 6(5):693–700. Popkin BM, Udry JR. 1998. Adolescent obesity increases significantly in second and third generation U.S. immigrants: The National Longitudinal Study of Adolescent Health. J Nutr 128(4):701–706. Powell LM, Slater S, Chaloupka FJ. 2004. The relationship between community physical activity settings and race, ethnicity and socioeconomic status. Evidence-Based Prev Med 1(2):135–144. Pyramid Communications. 2003. Communities Helping Children Be Healthy: A Guide to Reducing Childhood Obesity in Low-income African-American, Latino and Native American Communities. [Online]. Available: http://www.rwjf.org/files/publications/HealthyChildren.pdf [accessed March 13, 2006]. Reilly JJ, Armstrong J, Dorosty AR, Emmett PM, Ness A, Rogers I, Steer C, Sherriff A. 2005. Early life risk factors for obesity in childhood: Cohort study. Br Med J 330(7504):1357– 1364. Robert SA, House JS. 2000. Socioeconomic inequalities in health: Integrating individual-, community-, and societal-level theory and research. In: Abrecht GL, ed. Handbook of Social Studies in Health and Medicine. New York: Sage Publications.

OCR for page 74
Progress in Preventing Childhood Obesity: How Do We Measure Up? Robinson TN, Killen JD, Kraemer HC, Wilson DM, Matheson DM, Haskell WL, Pruitt LA, Powell TM, Owens AS, Thompson NS, Flint-Moore NM, Davis GJ, Emig KA, Brown RT, Rochon J, Green S, Varady A. 2003. Dance and reducing television viewing to prevent weight gain in African-American girls: The Stanford GEMS pilot study. Ethnic Dis 13(1 Suppl 1):S65–S77. Rogers EM. 2003. Diffusion of Innovations, 5th ed. New York, NY: Free Press. Rosenbloom AL. 2002. Fetal nutrition and insulin sensitivity: The genetic and environmental aspects of “thrift.” J Pediatr 141(4):459–462. Rosenbloom AL, Joe JR, Young RS, Winter WE. 1999. Emerging epidemic of type 2 diabetes in youth. Diabetes Care 22(2):345–354. Ruiz-Beltran M, Kamau JK. 2001. The socio-economic and cultural impediments to well-being along the US-Mexico border. J Community Health 26(2):123–132. Sallis JF, Glanz K. 2006. The role of built environments in physical activity, eating, and obesity in childhood. In: Paxon C, ed. Future Child 16(1):89–108. Sallis JF, Cervero RB, Ascher W, Henderson KA, Kraft MK, Kerr J. 2006. An ecological approach to creating active living communities. Annu Rev Public Health 27:297–322. Sherry B, Mei Z, Scanlon KS, Mokdad AH, Grummer-Strawn LM. 2004. Trends in state-specific prevalence of overweight and underweight in 2- through 4-year-old children from low-income families from 1989 through 2000. Arch Pediatr Adolesc Med 158(12): 1116–1124. Sloane D, Nascimento L, Yancey AK, Flynn G, Lewis LB, Guinyard JJ, Diamant A, Galloway-Gilliam L, Yancey AK. 2006. Assessing resource environments to target prevention interventions in community chronic disease control. J Health Care Poor Underserved 17(2):146–159. Srinivasan SR, Myers L, Berenson GS. 2002. Predictability of childhood adiposity and insulin for developing insulin resistance syndrome (syndrome X) in young adulthood: The Bogalusa Heart Study. Diabetes 51(1):204–209. Stewart JA, Dennison DA, Kohl HW, Doyle A. 2004. Exercise level and energy expenditure in the Take 10!® in-class physical activity program. J School Health 74(10):397–400. Strauss RS, Knight J. 1999. Influence of the home environment on the development of obesity in children. Pediatrics 103(6):e85. Strauss RS, Pollack HA. 2001. Epidemic increase in childhood overweight, 1986–1998. J Am Med Assoc 286(22):2845–2848. TFAH (Trust for America’s Health). 2005. F as in Fat: How Obesity Policies are Failing in America 2005. Washington, DC: The Trust for America’s Health. [Online]. Available: http://healthyamericans.org/reports/obesity2005/Obesity2005Report.pdf [accessed May 9, 2006]. Thomson Medstat. 2006. Childhood Obesity: Costs, Treatment Patterns, Disparities in Care, and Prevalent Medical Conditions. Research brief. [Online]. Available: http://www.medstat.com/pdfs/childhood_obesity.pdf [accessed February 2, 2006]. Troiano RP, Flegal KM. 1998. Overweight children and adolescents: Description, epidemiology, and demographics. Pediatrics 101(3):497–504. Tucker P, Liao Y, Giles WH, Liburd L. 2006. The REACH 2010 logic model: An illustration of expected performance. Prev Chronic Dis [Online]. Available: http://www.cdc.gov/Pcd/issues/2006/jan/05_0131.htm [accessed July 23, 2006]. Unger JB, Reynolds K, Shakib S, Spruijt-Metz D, Sun P, Johnson CA. 2004. Acculturation, physical activity, and fast-food consumption among Asian-American and Hispanic adolescents. J Community Health 29(6):467–481. USDA (U.S. Department of Agriculture). 2006. WIC Farmers’ Market Nutrition Program. [Online]. Available: http://www.fns.usda.gov/wic/FMNP/FMNPfaqs.htm [accessed June 13, 2006].

OCR for page 74
Progress in Preventing Childhood Obesity: How Do We Measure Up? van der Kolk BA, Fisler RE. 1994. Childhood abuse and neglect and loss of self-regulation. Bull Menninger Clin 58(2):145–168. Villa-Caballero L, Caballero-Solano V, Chavarria-Gamboa M, Linares-Lomeli P, Torres-Valencia E, Medina-Santillan R, Palinkas LA. 2006. Obesity and socioeconomic status in children of Tijuana. Am J Prev Med 30(3):197–203. Whitaker RC, Orzol SM. 2006. Obesity among US urban preschool children: Relationships to race, ethnicity, and socioeconomic status. Arch Pediatr Adolesc Med 160(6):578–584. Williams DR, Collins C. 1995. U.S. socioeconomic and racial differences in health: Patterns and explanations. Annu Rev Sociol 21(1):349–386. Williams DE, Cadwell BL, Cheng YJ, Cowie CC, Gregg EW, Geiss LS, Engelgau MM, Narayan KM, Imperatore G. 2005. Prevalence of impaired fasting glucose and its relationship with cardiovascular disease risk factors in U.S. adolescents, 1999–2000. Pediatrics 116(5):1122–1126. Williamson DF, Thompson TJ, Anda RF, Dietz WH, Felitti V. 2002. Body weight and obesity in adults and self-reported abuse in childhood. Int J Obes Relat Metab Disord 26(8): 1075–1082. Wong MD, Shapiro MF, Boscardin WJ, Ettner SL. 2002. Contribution of major diseases to disparities in mortality. N Engl J Med 347(20):1585–1592. Yancey AK, Lewis LB, Sloane DC, Guinyard JJ, Diamant AL, Nascimento LM, McCarthy WJ. 2004. Leading by example: A local health department-community collaboration to incorporate physical activity into organizational practice. J Public Health Manage Pract 10(2):116–123. Yancey AK, Robinson RG, Ross RK, Washington R, Goodell HR, Goodwin NJ, Benjamin ER, Langie RG, Galloway JM, Carroll LN, Kong BW, Leggett CJ, Williams RA, Wong MJ, American Heart Association Advocacy Writing Group. 2005. Discovering the full spectrum of cardiovascular disease: Minority Health Summit 2003: Report of the Advocacy Writing Group. Circulation 111(10):e140–e149. Yancey AK, Ortega AN, Kumanyika SK. 2006a. Effective recruitment and retention of minority research participants. Annu Rev Pub Health 27:1–28. Yancey AK, Ory M, Davis SM. 2006b. Dissemination of physical activity promotion interventions in underserved populations. Am J Prev Med. 31(4S):82–91. Yancey AK, Simon PA, McCarthy WJ, Lightstone AS, Fielding JE. 2006c. Ethnic and sex variations in overweight self-perception: Relationship to sedentariness. Obesity 14(5):1–9. Yancy CW, Benjamin EJ, Fabunmi RP, Bonow RO. 2005. Discovering the full spectrum of cardiovascular disease: Minority Health Summit 2003: Executive summary. Circulation 111(10):1339–1349. Yu S. 2006. The life-course approach to health. Am J Public Health 96(5):768. Zephier E, Himes JH, Story M, Zhou X. 2006. Increasing prevalences of overweight and obesity in Northern Plains American Indian children. Arch Pediatr Adolesc Med 160(1): 34–39. Zimring C, Joseph A, Nicoll GL, Tsepas S. 2005. Influences of building design and site design on physical activity: Research and intervention opportunities. Am J Prev Med 28(2 Suppl 2):186–193.