7

Disparities and Measurement

Key Points Noted in Presentations

•    Tackling the disparities in obesity rates among population subgroups is an important component of the overall goal of preventing and reducing obesity.

•    A community approach to improving nutrition among minority and low-income populations with particularly high rates of obesity—an approach in which the food environment and community attitudes are addressed from multiple vantage points—shows promise.

•    Measurement and evaluation must be highly adaptive to the local and sometimes changing conditions throughout an intervention. Modifications may be needed in order for measures to remain relevant to the evaluation.

•    Accurately measuring physical activity is challenging, and as a result, disparities in this area are not fully understood. Objective measures are accurate but quantify only amount of activity; subjective measures do not reflect total energy expended and can easily be misinterpreted.

•    It is important to look across both populations groups and types of data to understand physical activity patterns and ways to increase activity levels.

•    Food marketers have extremely sophisticated means of understanding the interests and needs of various populations and are particularly adept at targeting ethnic and racial minority groups.



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7 Disparities and Measurement Key Points Noted in Presentations • Tackling the disparities in obesity rates among population sub- groups is an important component of the overall goal of pre- venting and reducing obesity. • A community approach to improving nutrition among minor- ity and low-income populations with particularly high rates of obesity—an approach in which the food environment and com- munity attitudes are addressed from multiple vantage points— shows promise. • Measurement and evaluation must be highly adaptive to the local and sometimes changing conditions throughout an inter- vention. Modifications may be needed in order for measures to remain relevant to the evaluation. • Accurately measuring physical activity is challenging, and as a result, disparities in this area are not fully understood. Objec- tive measures are accurate but quantify only amount of activity; subjective measures do not reflect total energy expended and can easily be misinterpreted. • It is important to look across both populations groups and types of data to understand physical activity patterns and ways to increase activity levels. • Food marketers have extremely sophisticated means of under- standing the interests and needs of various populations and are particularly adept at targeting ethnic and racial minority groups. 91

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92 MEASURING PROGRESS IN OBESITY PREVENTION There are significant disparities among racial and ethnic groups in rates of obesity. Obesity has been rising more steeply among African Ameri- can and Hispanic children than among children in other ethnic groups, explained Shiriki Kumanyika, professor of epidemiology in the Depart- ments of Biostatistics and Epidemiology as well as Pediatrics (Section on Nutrition) and associate dean for health promotion and disease prevention at the University of Pennsylvania Perelman School of Medicine, in intro- ducing a discussion of disparities and their implications for measurement. African American girls and Hispanic boys are particularly likely to have weight levels in the obese or very obese range, she added. Adult African American and Hispanic women both had high levels of obesity before the current epidemic began, and these levels have continued to increase with the epidemic in the general population. Obesity rates also are generally higher among populations of low socioeconomic status. Health disparities are defined by the Centers for Disease Control and Prevention (CDC) as “differences in health outcomes that reflect social inequalities,” and CDC finds that such disparities are “both unacceptable and correctable” (CDC, 2011, p. 1). Thus, Kumanyika pointed out, “part of addressing the [obesity] epidemic has to include closing that gap.” To address the gap, she added, it is important to recognize that environmental, social, and cultural contexts for addressing obesity vary just as does its prevalence, and that solutions that will be effective within these different contexts also vary. Moreover, she noted, narrowing the gap will require attention to two goals: “one is to make everybody better off and the other is to help those who are worse off catch up.” These issues present measurement challenges, Kumanyika observed. It is important to ask whether existing measures are sensitive enough “to pick up nuances or even big-picture issues that differ for population subgroups defined by ethnicity or socio-economic status,” she explained. Also impor- tant is to consider whether the measures focus on the right questions for each group, given potential differences in sociocultural contexts for food and physical activity. Kumanyika also emphasized that disparities in obesity rates are not new. A 1985 report on the health of minority groups from the Department of Health and Human Services (HHS, 1985) identified obesity as one of the modifiable risk factors that could, if addressed, lead to a closing of the gap between white and minority populations in rates of cardiovascular disease and diabetes. Some data Kumanyika presented illustrate how obesity prevalence and trajectories differ for ethnic minority compared with non-Hispanic white populations. Figure 7-1 shows changes in the population percentage at or above a body mass index (BMI) of 30 for African American, Mexican American, and white males and females between 1960 and 2004. Figures

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93 DISPARITIES AND MEASUREMENT White Men White Women Black Men Black Women Mex Am Men Mex Am Women 60 50 40 30 20 10 0 1960–1962 1971–1974 1976–1980 1988–1994 1999–2000 2001–2004 FIGURE 7-1 Obesity prevalence trends in three ethnic groups. NOTES: Mex Am = Mexican American. Obesity is defined for adults as a body mass 7-1.eps index at or above 30 kg/m2. Data reported for whites and blacks in 1960-1962 (Na- tional Health Examination Survey) and 1971-1974 (National Health and Nutrition Examination Survey) include persons of Hispanic and non-Hispanic origin. Persons of Hispanic origin were excluded from the data for whites and blacks from 1976 onward. Data for Mexican Americans shown for 1976-1980 are from the Hispanic Health and Nutrition Examination Survey (1980-1982). Data are for adults aged 20-74, age-adjusted to the 2000 standard population. SOURCE: NCHS, 2002 (for 1960 through 2000) and NCHS, 2006 (for 2001-2004). 7-2 and 7-3 focus on trends among girls (showing data for adolescents) and boys (showing data for school-age children), respectively, between 1976 and 2006. Figure 7-4 shows rates of obesity in children by both their poverty status and their racial/ethnic group, and highlights the differences in the patterns across three groups. What is most important, in Kumanyika’s view, is that, regardless of the prevalence rates, “the conditions for addressing obesity are not as good in ethnic minority and low-income communities.” She closed by presenting a model that guides research in the African-American Collaborative Obesity Research Network (Figure 7-5). The traditional focus of research on the energy balance issues that cause obesity, she noted, is one of the elements in the middle of the diagram, but a more community-oriented approach takes into consideration the role of the history and social context of each popula- tion, as well as the physical and economic environment and the cultural and psychosocial processes that influence personal perceptions and behaviors.

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94 MEASURING PROGRESS IN OBESITY PREVENTION 30 27.7 23.5 25 20 16.3 % Obese White 15 Black Mex Am 10.7 10 5 0 1976–1980 1988–1994 1999–2002 2003–2006 FIGURE 7-2 Obesity trends in 12- to 19-year-old girls in three ethnic groups. NOTE: Obesity is defined for children and adolescents as a body mass index (BMI) 7-2.eps at or above the 95th percentile on the age- and sex-specific 2000 Centers for Disease Control and Prevention (CDC) BMI growth charts. SOURCE: NCHS, 2009. 30 27.5 26.5 25 20 17.5 % Obese 15 White 13.3 Black Mex Am 10 5 0 1976–1980 1988–1994 1999–2002 2003–2006 FIGURE 7-3 Obesity trends in 6- to 11-year-old boys in three ethnic groups. NOTE: Obesity is defined for children and adolescents as a body mass index (BMI) at or above the 95th percentile on the age- and sex-specific 2000 Centers for Disease 7-3.eps Control and Prevention (CDC) BMI growth charts. SOURCE: NCHS, 2009.

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95 DISPARITIES AND MEASUREMENT Non-Hispanic white Non-Hispanic black Mexican American 30 27 25 22 21 21 21 19 20 18 18 17 17 % Obese 15 15 14 15 12 11 10 5 0 <1 9 9 9 4+ <1 9 9 9 4+ <1 9 9 9 4+ 2. 3. 1. 2. 3. 1. 2. 3. 1. 1- 1- 1- 2- 2- 2- 3- 3- 3- Poverty–Income Ratio FIGURE 7-4 Percentage of children and adolescents aged 2-19.9 who are obese, by family poverty–income ratio. NOTES: Obesity is defined for children and adolescents as a body mass index (BMI) Figure 7-4.eps at or above the 95th percentile on the sex- and age-specific 2000 Centers for Disease Control and Prevention (CDC) BMI growth charts. The poverty–income ratio is the ratio of the income of the family to family income at the poverty level. Families with an income ratio of less than 1 are below the poverty threshold. SOURCE: Freedman et al., 2007. In other words, she explained “focus on the people and help them with the problem as opposed to focusing on the problem and trying to squeeze everybody into a very narrow box”—referring to the relatively limited perspective derived from the strictly biomedical view of energy balance. With those thoughts as background, presenters addressed disparities in three specific areas. Sarah Samuels, president of Samuels & Associates, spoke about disparities related to diet. Carlos J. Crespo, professor and director of the School of Community Health, Portland State University, focused on disparities related to physical activity. Finally, Sonya Grier, associate professor of marketing, Kogod School of Business, American University, looked at the role of marketing in these disparities. DISPARITIES RELATED TO DIET Presenter: Sarah Samuels The availability of healthy and unhealthy foods is a key factor in dis- parities in weight and health, explained Samuels. Despite efforts to curtail the availability of unhealthy foods, one need not travel far in the United

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96 • Aesthetic, moral, religious, and social values • Collective psychology Cultural and • Literary expressions Psychosocial Processes Balance • Historical legacy of • Social processes • Media and Calorie in families and marketing Intake communities • Built environment and • Food costs Output Historical Physical and and Economic Social Environments Contexts FIGURE 7-5 Community perspective on obesity influences. 7-5.eps SOURCE: Adapted from Kumanyika et al., 2007. landscape

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97 DISPARITIES AND MEASUREMENT States to find an area that is densely populated with fast-food outlets. Some U.S. schools have benefited from efforts to improve the nutritional profile of the foods and beverages available to children during the school day, but many communities are lacking nutrition policies or standards to guide what is available. In such schools, there is “a sea of marketing and promotion of unhealthy foods,” Samuels observed. Samuels believes that an environmental approach to improving diets is particularly important for low-income communities, where resources are limited and obesity risk is concentrated. Individuals make many decisions about diet within the contexts of school, neighborhood, and the workplace, and unhealthy foods are dominant among the available choices in many communities. Community-wide programs, Samuels explained, can create a seamless environment for children, promoting healthier choices wherever they go, and can have a much broader reach than programs focused on changing behavior at the individual level. Evaluation and Measurement Samuels and her colleagues have developed a theory of change to guide evaluations of programs designed to improve access to healthy foods. This theory defines the steps required for improvement (Samuels & Associates, 2008): • Step 1—Change the environment to create greater access to healthy foods. • Step 2—Change norms so that healthier choices become the easier choices. • Step 3—Residents make healthier choices. • Step 4—Health indicators, such as BMI, improve. To measure the progress these steps describe, she added, means mea- suring improvements in food environments, tracking the adoption and implementation of policies and their strength, measuring the changing atti- tudes and practices of both policy makers and community residents, and measuring health outcomes. Samuels and her colleagues have developed several tools for these mea- surements, which they have used in communities across the country. One is FoodBEAMS, a database of information about the competitive food envi- ronment that contains data on more than 5,000 food, beverage, and snack items sold in vending machines and other places in schools outside the school meals programs.1 It allows users to analyze the foods and beverages 1 See http://www.foodbeams.com/ (accessed September 2011) for more information.

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98 MEASURING PROGRESS IN OBESITY PREVENTION available in a particular school to determine whether they comply with state nutrition standards (the database currently is based on California’s stan- dards, but links to other standards are under development). Another tool developed by Samuels and colleagues is the Store Assessment Tool, used for documenting the presence, placement, quality, promotion, and price of healthy and unhealthy foods in store settings. As Table 7-1 and Figure 7-6 illustrate, this tool allows researchers to see food choices through the eyes of consumers and to document the ratio of healthy to unhealthy choices. The example in Figure 7-6 quantifies the experience of shopping in a store where small amounts of fresh fruit, yogurt, and other healthy foods are overshadowed by a large volume of candy, chips, and cookies. Assessment of Two Example Programs One community initiative Samuels and her colleagues have evaluated— Healthy Eating, Active Communities (HEAC)—focused on reducing dis- parities in obesity and diabetes by improving the food and physical activity environments for school-age children in six low-income California commu- nities (Samuels & Associates, 2010b).2 The program targeted policies and organizational practices in five sectors: school, after-school time, neighbor- hood, health care/public health, and marketing and advertising. Each of the communities received supplemental funding over 5 years for their schools, community organizations, and local public health departments. A second initiative evaluated—the Central California Regional Obesity Prevention Program (CCROPP)—worked in a similar way in eight counties in the Central Valley of California, an area in which there are significant health disparities related to access to both healthy foods and physical activity opportunities, exacerbated by issues of racism and immigration (Samuels & Associates, 2010b; Schwarte et al., 2010). Samuels and her colleagues used a variety of measures to assess changes resulting from these two programs. The primary goal with regard to the food environment (they also evaluated physical activity effects) was to assess the extent to which access to healthful foods in schools, after school, and in neighborhoods had improved. Among the tools they used were • assessment of the competitive food environment (foods and bever- ages sold on school campuses outside of school meals programs), • survey of students’ nutrition and physical activity, • environmental assessment of neighborhood foods and beverage marketing, 2 See http://www.partnershipph.org/projects/heac/ (accessed September 2011) for more information.

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99 DISPARITIES AND MEASUREMENT • in-store assessment of available foods and beverages, • environmental assessment of farmers’ markets, • policy tracking, • surveys of community residents, • focus groups with students and parents, and • surveys of policy makers. The researchers provided ongoing feedback to the community, Samuels noted, so the community could use the data and information to help advo- cate for its own local programs. Samuels highlighted several key findings. The adoption of nutrition standards did yield an overall improvement in the nutritional value of foods available in school and after-school environments, and school district food services did not lose money when healthier foods were sold on school campuses. Students did continue to purchase competitive foods, but were more likely to participate in school meals programs. Students reported making healthier choices and said they supported the changes in the foods available to them. Looking at effects in the wider community, Samuels noted that innova- tion resulting from the programs created new venues for the sale of fresh and locally grown produce. Community residents reported making use of farmers’ markets and produce stands, and the proportion of advertisements for healthy foods inside stores increased three-fold (although the percentage of such advertisements outside of stores decreased). In Samuels’ view, the greatest achievement was the mobilization of residents, especially young people, around nutrition and physical activity. In both studies, youth and other residents reported support for the new strategies, and a shift occurred from thinking about obesity as an individual problem to thinking about it as a community problem, Samuels observed. The two programs also influenced policy makers’ attitudes and prac- tices. Both liberal and conservative policy makers supported policy solutions designed to improve community environments, although they expressed some concern about finding the resources needed to implement changes. New relationships forged among grantees involved in the programs and community partners have the potential to influence local and school poli- cies. Health departments and health care workers also reported greater engagement with community efforts and support for policy strategies. Samuels emphasized that the evaluations were able to use quantitative and qualitative measures to capture a diverse array of outcomes, which could be reported to diverse audiences. By providing standardized baseline, midpoint, and endpoint measures, she and her colleagues developed a pic- ture of change that was easy to communicate. The results provided strong evidence for the extent to which policies lead to environmental change. The

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100 TABLE 7-1 Excerpt from the Store Assessment Tool Check (ü) All Locations Where Foods Are Found Inside the Store Outside of Front Cash Side Back Total Food Categories Store Wall Register Walls (2) Wall Center Varieties Notes Bread - Whole wheat/whole grain Bread - Refined flour/white bread Cereal - <7g sugar per serving and whole grain Cereal - ≥7g sugar per serving and not whole grain Cheese - Regular Cheese - Light/Reduced Fat Fruit- Dried (no sugar added) Fruit - Fresh whole fruit Fruit - Fresh ready-to-eat cut up fruit

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Fruit - Canned (packed in water) Fruit - Frozen (no sugar added) Rice - Brown Rice - White Tortilla - Whole wheat/whole grain Tortilla - Refined flour Vegetables - Fresh whole vegetables Vegetables - Fresh ready-to-eat cut-up vegetables Vegetables - Canned (packed in water, no added fat) Vegetables - Frozen (no added fat) Yogurt - Low-fat Yogurt - Regular SOURCE: Samuels & Associates, 2010a. Reproduced with permission of Samuels & Associates, Oakland, CA. 101

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102 FIGURE 7-6 Food choices available in a store analyzed using the Store Assessment Tool. SOURCE: Samuels & Associates, 2010a. Reproduced with permission of Samuels & Associates, Oakland, CA. 7-7.eps bitmap, landscape

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103 DISPARITIES AND MEASUREMENT evaluations had to be flexible, Samuels noted: “We had to make modifica- tions as we went along based on what people were doing. The outcome measures really needed to be tied to the interventions and what it was really realistic to expect.” Moreover, she added, many environmental strategies take a long time to be fully implemented and to show effects. It can be diffi- cult to capture the full scope of change within a confined evaluation period. Samuels closed with a few recommendations for the field. Standardized approaches to monitoring policy adoption and implementation that could be used across the country would make it much easier for researchers to collect and compare information. “We really need to be able to measure the strength of a policy,” she emphasized, and “the more the measures can be standardized, and policies can be standardized, the easier it’s going to be.” Specifically, she added, “we need to measure the changes in the envi- ronment, know the impact of the intervention on the environment, and learn whether the change in the environment is strong enough to have an impact on behavior.” Measures that could be used to track the perceptions, attitudes, and opinions of policy makers, youth, and community residents would also be valuable in assessing the impact of environmental change, she added. In conclusion, Samuels said that in conducting evaluations, it is impor- tant to look across communities, sectors, and strategies. “Ultimately what we want to learn is whether there’s synergy with all of these efforts com- bined that is enough to tip a community—especially a low-income commu- nity where resources are limited—into being a place that promotes health and provides access to healthy choices,” she observed. Because resources in low-income communities are limited, she added, it is important to deter- mine which policies and practices must be in place in order to impact health outcomes. DISPARITIES RELATED TO PHYSICAL ACTIVITY Presenter: Carlos J. Crespo Obesity “shows up at the doctor’s office,” Crespo noted, “but the solu- tion is a community solution.” There is little a doctor can do for an indi- vidual, but the community can do much more, he added, echoing a major theme of the workshop. Changing behaviors and environments at the com- munity level is as complex as is applying the standard scientific approach of using randomized controlled trials to identify the most effective interven- tions. “We know we have an obesity problem,” he commented. “We have disparities, and we know the risk factors.” The difficulty, he added, is that

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104 MEASURING PROGRESS IN OBESITY PREVENTION “we are not rats; we are living in a free market society where we buy what we want and do what we want to do.” Researchers have examined numerous categories of people to discern patterns—focusing primarily on race and ethnicity, age, gender, geographic region, and health status (e.g., those with chronic diseases). The differences in prevalence are evident, yet the data are not detailed enough to answer many questions about what is taking place within groups or smaller sub- groups. Gaps exist in measures of physical activity, Crespo added. There are degrees of inactivity, and measures of, for example, sedentary activities such as travel, sitting at work, or television watching may not capture dif- ferences in the degree of movement that may be significant. In particular, Crespo noted, “we have engineered physical activity out of our jobs,” and the degree of physical activity required for different modes of transporta- tion is not typically viewed as a domain of health. Yet, these are potentially important opportunities for physical activity. Data Overview Despite the measurement challenges, a few points are clear, Crespo explained. Current data indicate that during their leisure time, members of most minority groups are more inactive than whites, and women are less active than men (Crespo et al., 2000). The data on occupational physical activity are inconsistent, Crespo added. In general, people who are active at work are more likely to exercise during leisure time, but this pattern does not hold across genders, racial and ethnic groups, and regions. Additionally, as people age they are likely to be less and less active, although rates in this regard vary by ethnicity. Children are more likely to be obese the more television they watch per day—approximately 18 percent of those who watch 4-5 hours per day are obese, compared with only 8 percent of those who watch 1 hour per day or less (Crespo et al., 2001a). Non-Hispanic black children are the most likely to watch 4 or more hours of television per day—nearly 40 percent do so as compared with 25-30 percent of Mexican American children, approximately 12 percent of non-Hispanic white girls, and approximately 17 percent of non-Hispanic white boys (there is little gender difference for the other two groups). Children tend to take in more calories the more tele- vision they watch, Crespo added, with those who watch 5 or more hours per day consuming an average of 150 calories per day more than those who watch 1 hour or less (Crespo et al., 2001a). Data also indicate that people with less educational attainment engage in less physical activity than those with greater attainment, and here also there is variation by race and ethnicity (Crespo et al., 2000). Looking at men, more than 20 percent of whites, more than 30 percent of blacks, and approximately 40 percent of Mexican Americans who have had fewer

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105 DISPARITIES AND MEASUREMENT than 12 years of education engage in no leisure-time physical activity; the comparable figures for those with 16 years or more of educational attain- ment are less than 10 percent of whites and approximately 15 percent of the other two groups. The trends are the same for women, although they are more likely than men at each educational level to report engaging in no leisure-time physical activity. Other data show, however, that as Mexican Americans grow more acculturated (as measured by languages spoken in the home) in the United States, their rates of inactivity decline (Crespo et al., 2001b). On the other hand, inactivity is prevalent across economic classes, Crespo added, with blue-collar workers only modestly more likely to report no leisure-time activity than white-collar or white-collar profes- sional workers (Crespo et al., 2000). Geographic differences are significant, as Figure 7-7 shows. Adults who live in the Pacific Northwest, for example, are among the least likely to be physically inactive during their leisure time, and those who live in rural areas are more likely than those in urban areas to report no leisure-time physical activity. Activity levels also vary by season, but in different ways for different groups (CDC, 1997). Measurement Issues To capture information about the complex nature of physical activ- ity, researchers use both subjective measures (questionnaires and direct observation) and objective measures (e.g., activity monitors, pedometers, indirect calorimetry [to measure calories burned]), but assessments vary in validity and reliability, Crespo explained. “There are multiple ways we move as humans, and we still have rudimentary instrumentation for mea- suring them,” he explained. The objective measures are accurate, but they are complicated to use, and “all you get are counts,” he added. They reveal nothing about behaviors, and thus are of limited value for the develop- ment of policy and program implementation, in Crespo’s view. Moreover, technical issues, such as the challenge of using heart rate to measure physi- cal activity in older people who are taking medication to control cardiac problems, can limit the usefulness of some objective measures for some purposes. Subjective measures have more practical applicability, Crespo suggested, but they do not reflect total energy expenditure and can easily be misinterpreted. Thus to understand physical activity patterns, it is important to look across groups (for example, gender, age, and race/ethnicity), as well as types of data (for example, calorimetry or questionnaires) for all of those groups. To obtain a complete picture, it is also important to examine results for various age groups with diverse capabilities and to capture the activity that takes place during non-leisure time, such as during transportation and

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106 MEASURING PROGRESS IN OBESITY PREVENTION Age-adjusted percent 0–19.9 20.0–24.1 24.2–27.9 28.0–32.5 ≥ 32.6 FIGURE 7-7 County-level map for leisure-time physical inactivity among adults aged 20 and older, 2008. SOURCE: CDC, 2011. See http://apps.nccd.cdc.gov/DDT_STRS2/NationalDiabetes PrevalenceEstimates.aspx?mode=PHY (accessed October 4, 2011). work, as well as incidental physical activity. If diverse patterns are not cap- tured, Crespo explained, the information will be incomplete. For example, if some groups get much more of their physical activity in the work setting but that activity is particularly difficult to measure, researchers’ picture of those groups may be less accurate than that of other groups. Crespo concluded with several suggested goals for the field. Collecting data at the school level would be a valuable way to expand information about children and youth, in his view, and more community-level data would also be beneficial. Moreover, “we need to be able to better capture physical activity and energy expenditure in the workplace,” he observed, and “we need to do a much better job of calculating different types of physical activity in different populations.” THE ROLE OF MARKETING IN DISPARITIES Presenter: Sonya Grier Marketing is a system designed to influence consumers’ choices and consumption, Grier explained. Marketing shapes awareness of and access to food and beverage products, as well as the prices consumers pay. Market- ing strategies tend to focus on particular groups of consumers, and ethnic minorities are attractive target markets, Grier added. They are the fastest-

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107 DISPARITIES AND MEASUREMENT growing segments of the population, and their buying power has been increasing. Surveys of advertising agencies and food marketing companies indicate that multicultural marketing is a high priority for these groups, so understanding this influence is important for researchers. Marketers work from the characteristics of the groups they are target- ing, Grier noted. Thus, for example, they know that black and Hispanic youth are particularly reachable through television advertising because they spend so much time in this activity. These groups of young people are also regarded as trendsetters in the marketplace, so marketers see them as a means of targeting other groups. Marketers know how to reach groups locally (e.g., using billboards) as well as nationally, through television and the Internet. Research suggests that targeted marketing may predispose minority consumers to poor-quality diets and also limit the effectiveness of general prevention initiatives, Grier noted (Grier and Kumanyika, 2008). Market- ers use research to identify groups that are both homogeneous and distinct from other groups, and select them as targets. They “position” products to appeal to such groups using design features (type of food, packaging, portion size); price (actual or relative); placement in retail outlets; and promotion (sampling, cross-promotion, and links to social causes) (Grier and Kumanyika, 2010). Other tools for targeting ethnic minorities, Grier noted, include event sponsorship, cultural symbols, product placement in movies and songs, street teams, giveaways, websites, mobile marketing, social networking, and custom products. Each of these tools is effective on its own, Grier added, but the com- bination is “greater than the sum of the parts.” Through their research, companies understand quite well who their consumers are and what they need, Grier noted. For example, the dollar menu was a strategy based on research that suggested a need for low-cost food in particular communities. “The dollar menu appeals to lower-income ethnic consumers. It’s people who don’t always have $6 in their pocket,” a vice president for U.S. busi- ness research at McDonald’s has been quoted as saying (Warner, 2006). Such marketing is not new, Grier added. A 1930s article discussed new urban consumers—meaning African Americans—who could be targeted. This example illustrates that marketing is linked to complex issues such as civil rights—“the right to be a consumer,” Grier suggested. Identifying the causal chain linking such marketing practices to health outcomes is challenging, Grier explained. Measuring the “marketing mix” targeting particular groups is a challenge—there are no standardized mea- sures of differential targeted marketing. To understand the big picture, Grier explained, multiple perspectives need to be considered. Marketers are focused on their own brands and on how to get people to buy them. Consumers are thinking about their own desires and how to handle the

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108 MEASURING PROGRESS IN OBESITY PREVENTION information and messages they are receiving. It is policy makers who take an aggregate view and think about multiple buyers, sellers, and groups, as well as such issues as fairness and accuracy. The complexity of the picture, Grier added, is likely one reason why standardized measures do not exist in this area. Research Grier and Kumanyika (2010) suggest some types of evidence that would be useful for assessing the influence of targeted marketing on dispari- ties and health. First, one would want to know whether a given product is harmful, although that seemingly simple question is controversial given prevailing advice that any food, in moderation, could be part of a balanced diet. Second, one would want to know whether a particular group is the target of excessive marketing and whether that exposure is influencing the group’s behavior. With these questions in mind, Grier and Kumanyika conducted a sys- tematic review of the marketing environments of African American con- sumers, looking particularly at whether they are more likely than white consumers to be targeted by marketing of unhealthy foods (Grier and Kumanyika, 2008). Because marketing, food access, and other important aspects of the issue generally are studied in different venues, they reviewed empirical research from a variety of disciplines published from 1992 to 2006, using eight databases that cover economics, sociology, business, med- icine, and related fields. They found 20 relevant interdisciplinary articles: 8 on product promotions, 11 on food distribution, and 3 on food prices. These studies used diverse methods and measures, including content analysis of advertising and in-store promotion (e.g., promotion of healthy verses unhealthy products and the ethnicity of product endorsers); spatial and statistical analysis of retail food outlet locations and prices using geo- graphic information system (GIS) and secondary data (e.g., comparisons of travel distances to certain types of outlets in different neighborhoods); market basket studies, market inventories, and menu audits within retail food outlets; and community-based participatory research. By linking this range of information, the researchers hoped to gain a comprehensive pic- ture of the marketing environment. Grier and Kumanyika found that across these studies, with their diverse approaches, the findings showed a great deal of consistency. The studies of product promotion demonstrated that low-cost, low-nutrition products such as candy, soda, and snacks were the predominant subjects of promo- tion to low-income neighborhoods and those with primarily minority popu- lations. Positive nutritional messages were a smaller proportion of food marketing for these groups than for white or mainstream audiences. Other

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109 DISPARITIES AND MEASUREMENT studies showed that predominantly black neighborhoods had fewer super- markets and healthy food options than predominantly white neighborhoods and a higher density of fast-food restaurants, even though food prices might be somewhat higher on average (because of distribution and other issues). There were limitations to the data reviewed, Grier noted, which help identify areas in which further research is needed. Much of the data is cross-sectional, and socioeconomic status was frequently confounded with race in these studies. Moreover, there was a greater focus on advertising and distribution than on price, which is also important, Grier added. These limitations meant that it was challenging to assess the validity, reliability, and representativeness of these 20 diverse studies. A new study by the Federal Trade Commission will provide a view of marketing strategies for food and beverages.3 For this study, researchers are examining materials supplied by 48 companies—including expenditure records, samples of marketing activities, and research studies—related to targeted marketing of foods to children (ages 2 to 11) and adolescents (ages 12 to 18). The researchers are looking at product placement, content (e.g., use of cartoon characters or celebrities), and whether any of the companies targeted messages about healthy diets to young people. They are consider- ing digital advertising, word of mouth, and the use of philanthropy (e.g., corporate sponsorships), and they are also examining marketing that targets subgroups defined by gender, race, ethnicity, or income (see Box 7-1 for a partial list of measures used in this study). Discussion Grier explained that it is important to ask how consumers respond to such marketing, and existing research suggests that ethnic minority consumers tend to respond more favorably than their white peers (Aaker et al., 2000). She suggested that the reasons have to do with the fact that being a member of a minority group (even if the group has social minority status but is not necessarily a minority in the numeric sense) makes people more likely to identify with distinctive traits or personalities they associate with their group, and thus respond more favorably to targeted advertising. It may also be, Grier added, that ethnic minority consumers respond to nontargeted advertising differently as well. It has been suggested that members of minority groups may tend to seek traits relevant to them in response to a wider context in which members of their own group are not well represented. On the other hand, Grier noted in response to a question, population subgroups are not homogeneous, and prevalent attitudes may 3See http://www.ftc.gov/os/6b_orders/foodmktg6b/P094511/P094511order.pdf (accessed September 30, 2011).

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110 MEASURING PROGRESS IN OBESITY PREVENTION BOX 7-1 Sample Measures Used in the Federal Trade Commission’s Study of Food Marketing to Youth • T elevision, radio, and print advertising • C ompany-sponsored Internet sites • O ther digital advertising • P ackaging and labeling • M ovie theater/video/video game advertising • I n-store advertising and product promotions • S pecialty item or premium distribution (items other than food products that are distributed in connection with the sale of food products, such as a toy) • S ponsorship of public entertainment events • P roduct placements • C haracter licensing, toy co-branding, and cross-promotions • S ports sponsorship • W ord-of-mouth and viral marketing • C elebrity endorsements • I n-school marketing • A dvertising via philanthropic endeavors shift over time. Survey results are somewhat mixed, she added, and there have been no nationally representative studies of how people perceive and react to targeted marketing. A participant suggested that it is important to consider that the products being marketed may play different roles in the lives of different populations. While nutrition researchers think poorly of fast-food restaurants, for example, they can provide play spaces, places to congregate, and employment within their neighborhoods, and community members may value them and their products in ways that research does not capture. One participant asked whether there is clear evidence that targeted marketing results directly in obesity, and another asked whether targeted marketing of healthful behaviors could work as well as the marketing of unhealthy foods appears to work. In response, Grier and Kumanyika suggested it is clear that the pervasiveness of targeted marketing makes preventing obesity difficult. Thus, any counter advertising must address that competitive environment. It is an uphill battle, Grier noted. “While advertising [of healthy alternatives] might create awareness, marketing [of unhealthy products] might be used to reinforce norms and help maintain current behavior—so that’s what you’re competing against.” Kumanyika noted the difficulty of reaching people with messages or interventions

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111 DISPARITIES AND MEASUREMENT regarding healthy foods when a higher proportion of what is available and what is advertised are products that are not recommended. Thus, Grier concluded, there are many measures for understanding consumer behavior. The challenge, in her view, is to take the research fur- ther to explore factors that may contribute to the demand for unhealthy foods and how habitual environments may shape that demand. At the same time, researchers must keep pace with new marketing tools, particularly digital media. For example, consumers who use mobile electronic devices can now be “hypertargeted” using geolocation technology. Grier showed an example in which messages in Spanish sent only to Hispanic consum- ers within a particular zone in New York City guided them to a nearby McDonald’s outlet for a promotion directly targeting them. Apart from the challenge of measuring the influence of such finely targeted marketing, a participant noted, new technologies complicate the research goal of com- paring “apples to apples”—already a challenge when products, brands, and advertising evolve so rapidly. More broadly, Grier added, the challenge is to measure the “synergistic and cumulative effects” of these influences at the individual, community, society, and national levels. REFERENCES Aaker, J. L., A. M. Brumbaugh, and S. A. Grier. 2000. Nontarget markets and viewer distinc- tiveness: The impact of target marketing on advertising attitudes. Journal of Consumer Psychology 9(3):127-140. CDC (Centers for Disease Control and Prevention). 1997. Monthly estimates of leisure- time physical inactivity—United States, 1994 Morbidity and Mortality Weekly Report 46(18):393-397. CDC. 2011. CDC health disparities and inequalities report: United States, 2011. Morbidity and Mortality Weekly Report 60(Suppl.). Crespo, C. J., E. Smit, R. E. Andersen, O. Carter-Pokras, and B. E. Ainsworth. 2000. Race/ ethnicity, social class and their relation to physical inactivity during leisure time: Results from the Third National Health and Nutrition Examination Survey, 1988-1994. Ameri- can Journal of Preventive Medicine 18(1):46-53. Crespo, C. J., E. Smit, R. P. Troiano, S. J. Bartlett, C. A. Macera, and R. E. Andersen. 2001a. Television watching, energy intake, and obesity in US children: Results from the Third National Health and Nutrition Examination Survey, 1988-1994. Archives of Pediatrics and Adolescent Medicine 155(3):360-365. Crespo, C. J., E. Smit, O. Carter-Pokras, and R. Andersen. 2001b. Acculturation and leisure- time physical inactivity in Mexican American adults: Results from NHANES III, 1988- 1994. American Journal of Public Health 91(8):1254-1257. Freedman, D. S., C. L. Ogden, K. M. Flegal, L. K. Khan, M. K. Serdula, and W. H. Dietz. 2007. Childhood overweight and family income. Medscape General Medicine 9(2). Grier, S. A., and S. K. Kumanyika. 2008. The context for choice: Health implications of tar- geted food and beverage marketing to African Americans. American Journal of Public Health 98(9):1616-1629. Grier, S. A., and S. Kumanyika. 2010. Targeted marketing and public health. Annual Review of Public Health 31:349-369.

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112 MEASURING PROGRESS IN OBESITY PREVENTION HHS (U.S. Department of Health and Human Services). 1985. Report of the Secretary’s task force on black and minority health. Bethesda, MD: National Institutes of Health. Kumanyika, S. K., M. C. Whitt-Glover, T. L. Gary, T. E. Prewitt, A. M. Odoms-Young, J. Banks-Wallace, B. M. Beech, C. H. Halbert, N. Karanja, K. J. Lancaster, and C. D. Samuel-Hodge. 2007. Expanding the obesity research paradigm to reach African Ameri- can communities. Preventing Chronic Disease 4(4). NCHS (National Center for Health Statistics). 2002. Health, United States, 2002 with chart- book on trends in the health of Americans. Hyattsville, MD: National Center for Health Statistics. NCHS. 2006. Health, United States, 2006 with chartbook on trends in the health of Ameri- cans. Hyattsville, MD: National Center for Health Statistics. NCHS. 2009. Health, United States, 2008 with chartbook. Hyattsville, MD: National Center for Health Statistics. Samuels & Associates. 2008. Healthy eating, active communities program: Phase 1 evaluation findings, 2005-2008. Oakland, CA: Samuels & Associates. Samuels & Associates. 2010a. Food retail assessment tool. Oakland, CA: Samuels & Associates. Samuels & Associates. 2010b. Healthy eating, active communities and central California regional obesity prevention program: Final evaluation synthesis report. Oakland, CA: Samuels & Associates. Schwarte, L., S. E. Samuels, J. Capitman, M. Ruwe, M. Boyle, and G. Flores. 2010. The central California regional obesity prevention program: Changing nutrition and physi- cal activity environments in California’s heartland. American Journal of Public Health 100(11):2124-2128. Warner, M. 2006. Salads or no, cheap burgers revive McDonald’s. New York Times, April 19.