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Health Medical

Social behavior theory

West Virginia University

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Study Guide Final Assessment SBHS 601 1. How does product price influence our health decisions? Think about different types of impact across the SEM levels 2. What is behavioral economics and why is it needed? 3. How do we organize a community? 4. When are helping relationships useful? What are they and how do they differ by stages of change? 5. Know types of social and economic policy and how they impact individual behaviors. 6. How are policies developed? 7. Review the Ford and Dzewaltowski (2008) article for their economic suggestions. 8. Describe how social determinants influence individual behavior. 9. What are the qualities of an effective helper? Nutrition Science↔Policy Disparities in obesity prevalence due to variation in the retail food environment: three testable hypotheses Paula B Ford and David A Dzewaltowski Although the overall population in the United States has experienced a dramatic increase in obesity in the past 25 years, ethnic/racial minorities, and socio­ economically disadvantaged populations have a greater prevalence of obesity, as compared to white, and/or economically advantaged populations. Disparities in obesity are unlikely to be predominantly due to individual psychosocial or biological differences, and they may reflect differences in the built or social environment. The retail food environment is a critical aspect of the built environment that can contribute to observed disparities. This paper reviews the literature on retail food environments in the United States and proposes interrelated hypotheses that geographic, racial, ethnic, and socioeconomic disparities in obesity within the United States are the result of disparities in the retail food environment. The findings of this literature review suggest that poor-quality retail food environments in disadvantaged areas, in conjunction with limited individual economic resources, contribute to increased risk of obesity within racial and ethnic minorities and socioeconomically disadvantaged populations. © 2008 International Life Sciences Institute INTRODUCTION Prevalence of overweight (BMI 225 kg/m2) and obesity (BMI 230 kg/m2) has increased dramatically in the United States in the past 25 years, with recent surveys reporting approximately 23% of adults categorized as obese.1 Among children and adolescents, the prevalence of overweight has increased even more dramatically, having almost tripled since 1980.2 While most interna­ tional obesity rates are not as high as those reported in the United States, similar trends have been reported in other industrialized countries.2,3 Although overweight and obesity has increased across almost all racial, ethnic and socioeconomic levels, there are significant disparities within the overall US population, with higher BMIs associated with socioeco­ nomic disadvantage and non-white race and ethnicity.2,4–6 Employing multivariate regression techniques on reported height and weight data from the 2000 National Health Interview Study, Denney et al.4 identified dispari­ ties in relative risks associated with overweight and obesity that persisted even after controlling for sex, age, marital status, region, family income, education, employ­ ment, smoking, biking/walking habits, and weekly vigor­ ous activities. The relative risk ratios and 95% confidence intervals (95% CIs) for overweight among various racial/ ethnic groups were as follows: 1.60 (95% CI, 1.44–1.76) for non-Hispanic blacks; 2.14 (95% CI, 1.32–3.47) for Native Americans; 0.5 (95% CI, 0.40–0.61) for Asian Americans; 1.21 (95% CI, 0.93–1.58) for Puerto Ricans; 1.54 (95% CI, 1.36–1.76) for Mexican Americans; and 1.57 (95% CI, 2.16–2.45) for Cuban Americans. It is important to note, however, that when stratified by sex, disparities by race and ethnicity are more consistently observed among women, as compared to men.4,7–9 Disparities in obesity prevalence by race and ethnicity Affiliations: PB Ford is with the Department of Human Nutrition and the North Central Sustainable Agriculture Research and Education (SARE) Program, Kansas State University, Manhattan, Kansas, USA. DA Dzewaltowski is with the Department of Kinesiology and Community Health Institute, Kansas State University, Manhattan, Kansas, USA. Correspondence: PB Ford, Department of Human Nutrition, 4A Edwards Hall, Kansas State University, Manhattan, KS 66506-4810, USA. E-mail:; Phone: +1-785-532-5328, Fax: +1-785-532-6532. Key words: disparities, food access, food environment, neighborhood effects, obesity 216 doi:10.1111/j.1753-4887.2008.00026.x Nutrition Reviews® Vol. 66(4):216–228 that persist even after controlling for socioeconomic position have been reported elsewhere.5,6,10–12 Low socioeconomic status (SES) has also been inde­ pendently associated with increased risk for obesity in industrialized countries, particularly in women. In a recently published review of the literature on SES and obesity, McLaren9 identified inverse associations between SES and obesity among women in 63% of cross-sectional studies conducted in industrialized countries. In contrast, the pattern of association between SES and obesity was less consistent among men in industrialized countries, with a general pattern of non-significance or curvilinear­ ity with most socioeconomic indicators (income, material possessions, and occupation) and an inverse association with other socioeconomic indicators (education). The central proximal causes for racial, ethnic, and socioeconomic disparities in the prevalence of obesity have traditionally been attributed to individual differ­ ences in health behaviors influencing calorie balance. Spe­ cifically, health behavior research in this area has found racial/ethnic, and socioeconomic differences in physical activity,13 fresh fruit and vegetable consumption,14 and dietary fat intake.15 However, social ecological theory sug­ gests that individual health decisions are determined by multiple levels of influence, including institutional, com­ munity, and broader physical, economic, and cultural environmental levels.16 Recent attention to the contribu­ tion of built environments to obesity (“obesogenic envi­ ronments”) has led to the development of several frameworks for empirically describing retail food envi­ ronments with respect to the availability, accessibility and pricing of foods associated with healthy eating behaviors.17–21 These models identify environmental vari­ ables hypothesized to influence eating behaviors at the contextual level, a critical prerequisite for systematically examining nutrition environments using multilevel models that include information gathered at both the individual level and the environmental level. The present report proposes three hypotheses that can serve as a framework for empirically testing the asso­ ciation between neighborhood retail food environments and obesity, and for examining the role environmental disparities may play in the prevalence of obesity among different racial/ethnic and socioeconomic groups within the United States. The proposed hypotheses to be tested include: 1) geographic differences in the access and avail­ ability of foods result in disparities in the retail food envi­ ronment; 2) neighborhoods of low SES with high concentrations of racial/ethnic minorities have limited accessibility to and availability of healthy foods (poor­ quality retail food environment), as compared to neigh­ borhoods of relatively high SES and low concentrations of ethnic/racial minorities; and 3) individuals exposed to poor-quality retail food environments are more likely to Nutrition Reviews® Vol. 66(4):216–228 have diets that include foods of low nutritional quality and high caloric density and to have higher rates of obesity, as compared to individuals exposed to highquality food environments. To provide preliminary evidence to test these hypotheses, a PubMed (National Library of Medicine, Bethesda, Maryland) search was conducted for the period 1992–2007 using the search terms “food environment”, “nutrition environment”, “food access”, “food availabil­ ity”, and “obesity”. Studies found through the electronic search were supplemented with others that were brought to our attention through the literature review. Abstracts of selected papers were screened and the study was included in the review if it was conducted in the United States and included a characterization of the retail food environment. Of the 13 studies included in the review, six employed an ecological research design, four used a cross-sectional approach, and three were multilevel studies. The studies are organized and discussed by hypothesis, and summarized in Tables 1–3. HYPOTHESIS 1 Geographic differences in the access and availability of foods result in disparities in the retail food environment The question of whether food environments differ geo­ graphically has been addressed by several investigations in a host of disciplines.22–25 It is important to note, however, that differences in the retail food environment do not always represent disparities. Consistent with the definition of health disparities as outlined by Braveman,26 disparities in the food environment refer to avoidable differences in the access and availability of healthful foods that systematically place socially disadvantaged groups at a further disadvantage for achieving healthy diets. Although it has been well documented that there are regional variations associated with food preference and price among ethnic groups and by region, disparities in retail food environments across neighborhoods are not well understood. However, observational measures of the quality of retail food environments, as characterized by availability, accessibility, and pricing, provide a useful method for comparing food environments between neighborhoods. A selective summary of recent research examining geographic differences in retail food environ­ ments using observational measures is presented in Table 1. First introduced as a concept to examine disparities in food access and pricing in the United Kingdom, the term “food desert” has been used to describe areas with limited access to retail grocery stores.27 Early research on food deserts was primarily concerned with exploring the 217 Table 1 Summary of studies related to hypothesis 1 – geographic differences in the access and availability of foods result in disparities in the retail food environment. Reference Location/setting Food environment Key findings measure/method Morris et al. National (direct observation Store type a) Average food costs 20% higher in small/ (1992)72 in rural areas) medium grocery stores as compared to supermarkets Market basket b) Fruit and vegetable availability limited in small/medium grocery stores c) 32% of residents in persistently poor rural counties redeemed food stamps at small/medium grocery stores as compared to 20% redemption rates in small/medium grocery stores Chung et al. Minneapolis, MN (urban) Store type a) Chain stores prices significantly lower (1993)44 with greater variety of foods available as compared to convenience and small grocery stores Market basket b) Chain stores less prevalent in urban core areas c) Gap between urban core and suburban TFP basket significant and due primarily to presence of chain stores (chain stores $16 price reduction) with net impact of poverty to increase price of basket by approximately 3% Horowitz et al. New York City – paired Market basket a) 18% of grocery stores in low SES (2004)49 comparison: East Harlem (low neighborhoods stocked foods SES, high ethnic minority pop.) associated with recommended diet, as and Upper East Side (high SES compared to 58% of grocery stores in and low ethnic minority pop.) high SES neighborhoods b) Only 9% of low SES bodegas carried recommended foods as compared to 48% of high SES bodegas Block et al. Chicago – paired comparison Market basket, including a) Affluent neighborhoods had more chain (2006)43 quality characteristics grocery stores and supermarkets, while (participatory, direct less affluent neighborhoods had more observation) “low-cost” retail grocery chains 1. Austin (low SES, high ethnic b) Price differentials between minority pop.) neighborhoods not significant when controlling for store type 2. Oak Park (high SES and low c) Produce in Austin neighborhood rated ethnic minority pop.) as lower quality as compared to produce in Oak Park impact of retail flight from the urban core, but it has since been extended to include rural areas that have experi­ enced reductions in populations and concomitant reductions in the retail sector, including small-town supermarkets.28–30 Research in this area examined the availability of supermarkets by store type (supermarket chain versus small grocer or convenience store) and pricing differentials among stores.27,31 Of the four studies identified in this review (Table 1), there is relatively con­ sistent evidence that the quality of the retail food envi­ ronment (as measured by access and availability of healthy foods) varies geographically, and that low-quality food environments are associated with neighborhood 218 deprivation. This contrasts with recently reported food­ environment studies from the United Kingdom in which the association between the quality of the food environ­ ment and the sociodemographic structure of the neigh­ borhood is mixed,32 casting some doubt on the existence of “food deserts” within the United Kingdom.30,33,34 While some of the variance associated with the relationship between retail food environment and neighborhood demographics in the United States and the United Kingdom can be linked with different patterns of residen­ tial segregation among countries, additional sources of variance may be associated with Modifiable Areal Unit Problems (MAUP) in which both scale and zoning influ­ Nutrition Reviews® Vol. 66(4):216–228 Nutrition Reviews® Vol. 66(4):216–228 219 New York City – East Harlem (low SES, high ethnic minority pop.) and Upper East Side (high SES and low ethnic minority pop.) Detroit, MI North Carolina (n = 75 census tracts) Horowitz et al. (2004)49 Zenk et al. (2005)25 and Zenk et al. (2006)52 Moore et al. (2006)48 Maryland (n = 276 census tracts) New York (n = 334 census tracts) Location/setting Reference Cross-sectional design, Poisson regression, and multilevel analysis Cross-sectional design, chi-square and spatial regression, geographic information systems (GIS) Research design, methods, and analysis Ecological design, direct observation of food environment measures Store type c) Price and availability of fruits and vegetables b) Distance to supermarket a) Store type Price and availability of core foods needed for diabetic diet Store type Outcome variable a) 18% of grocery stores in East Harlem stocked foods associated with recommended diet, as compared to 58% of grocery stores in the Upper East Side b) Only 9% of East Harlem bodegas carried recommended foods as compared to 48% of Upper East Side bodegas a) Quality of fresh produce lower in predominantly African American low SES (AA-low SES) communities as compared to racially heterogeneous middle-income communities (RH-mid SES), even after adjusting for store type b) 97% of AA-low SES live within 1 mile of > 8 liquor stores, as compared to 87.9% in RH-low SES, 59.3% AA-mid SES, and 0% RH-mid SES c) Selection (#) and price of produce did not vary significantly by store type or neighborhood d) Within lowest SES group, African American neighborhoods have 2.7 fewer supermarkets within 3-mile radius as compared to white neighborhoods e) Within lowest SES group, African Americans resided 1.1 miles further from supermarket as compared to white residents f) Interaction between race/ethnicity significant and inclusion of interaction term improved spatial regression model fit (c2 = 15.83, p < 0) a) Minority and racially mixed neighborhoods, after adjusting for population ratio, had more grocery stores and fewer supermarkets than white neighborhoods (African American tracts SR = 0.5; 95% CI 0.3–0.7; mixed tracts SR = 0.7, 95% CI 0.5–0.9) b) Lower income neighborhoods had half as many supermarkets as compared to affluent neighborhoods (SR = 0.5; 95% CI 0.3–0.8) Neighborhood-level variables: income, race/ethnicity Neighborhood variables: average income and racial composition Model adjusted for confounders, including population density Individual-level variables: income and race/ethnicity Neighborhood-level SES. Race- average income and racial composition Individual-level variables: income, race/ethnicity Key findings Explanatory variables Table 2 Summary of studies related to hypothesis 2 – neighborhoods of low SES with high concentrations of racial/ethnic minorities have limited accessibility and availability of healthy foods (poor-quality retail food environment). 220 Nutrition Reviews® Vol. 66(4):216–228 Location/setting St. Louis, MO (n = 220 census tracts) National Brooklyn, NY Reference Baker et al. (2006)47 Powell et al. (2006)40 Morland et al. (2007)53 Table 2 Continued Cross-sectional design, direct observation, Poisson regression Ecological design, multivariate analysis Research design, methods, and analysis Ecological design, direct observation of food environments, spatial clustering statistics a) Neighborhood racial segregation b) Neighborhood confounders: population density and neighborhood wealth (median house value) b) Availability of fresh, canned, frozen and prepared produce b) Regional/other confounders: population density, region, degree of urbanization a) Neighborhood variables: income, race/ethnicity a) Neighborhood variables: % below poverty level and race/ethnicity at census tract Explanatory variables a) Store type a) Store type a) Supermarket audit tool and creation of z score Outcome variable a) Spatial clustering of supermarkets (unadjusted and without including quality ranking) was not significant (p < 0.50); however, clustering by race/ ethnicity was observed b) Spatial clustering of supermarkets using quality scores (z score from audit) was significant (p < 0.01; p < 0.03) with supermarkets in highest two quality tertiles clustered in census tracts with >75% white and <10% below poverty a) Low-income neighborhoods had 25% fewer supermarkets as compared to middle-income neighborhoods (p < 0.01) b) After controlling for income and other covariates, the availability of supermarkets in African-American neighborhoods was only 48% of white neighborhoods (p < 0.01). c) Hispanic neighborhoods have 32% as many supermarkets as compared to non-Hispanic neighborhoods (p < 0.01) a) Prevalence of supermarket varied by neighborhood composition, with white, racially mixed, and black areas having 0.33, 0.27, and 0.0 supermarkets per census tract, respectively b) 64% of fresh produce surveyed had a higher presence in predominantly white areas, as compared to 31% in racially mixed and 5% in predominantly black areas Key findings Nutrition Reviews® Vol. 66(4):216–228 221 Location setting North Carolina, Maryland, New York (n = 10,623) North Carolina, Maryland, New York (n = 10,623) Reference Morland et al. (2002)58 Morland et al. (2006)42 Cross-sectional design, multilevel analysis, geographic information systems Research design and method of analysis Cross-sectional design, multilevel analysis, geographic information systems b) Neighborhood-level variables: store type, race/ethnicity, income b) Hypertension c) Other CVD risk factors a) Individual-level variables: income, race/ethnicity b) Neighborhood variables: store type, SES, race/ethnicity Explanatory and confounding variables a) Individual-level variables: income, educational attainment, region, race/ ...
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