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Development of the Policy Indicator Checklist: A Tool to Identify and Measure Policies for Calorie-Dense Foods and Sugar-Sweetened Beverages Across Multiple Settings Rebecca E. Lee, PhD, Allen M. Hallett, BS, Nathan Parker, MPH, Ousswa Kudia, MPH, Dennis Kao, MSW, PhD, Maria Modelska, MPA, Hanadi Rifai, PhD, and Daniel P. O’Connor, PhD
There has been increasing attention on policyfocused approaches to reduce the prevalence of childhood obesity1---6 at the federal (e.g., Public Law 111---296),7---9 state,10---17 and local6,16,18 levels of government. Policies are a promising strategy at all levels for sustainable improvements in health, because an effective policy can change contextual cues and, in turn, affect many individual behavioral choices with very little individual effort.1,19,20 Despite the promise of these approaches, there is a lack of comprehensive, systematic, and valid measurement protocols that may be applied across multiple settings. Settings are places where policies can improve child behavior or the behavior of adults that affects children; these settings include communities, schools, and early care and education centers (ECECs), among others. Policies have the potential to change community food environments,21,22 to improve food access and availability in schools and ECECs,23---31 and to affect the pricing and marketing of food (e.g., vending machines), all of which could have an important impact on dietary habits.32,33 Taken together, most studies are descriptive in nature, do not actually investigate the policy itself, are often regionally specific, and do not account for the broad range of policies that may make up the child nutrition policy portfolio across multiple settings. These limitations make research that combines the effects of policies across settings impossible, so it is unknown how policies in different settings may complement or confound each other. There is little understanding of how, when, or why policies may work at one level together with policies at another level to produce sustainable systems changes. This hampers the ability of policymakers within and across settings to partner together and to enact policies at multiple levels that might yield the greatest impact for improving health.
Objectives. We developed the policy indicator checklist (PIC) to identify and measure policies for calorie-dense foods and sugar-sweetened beverages to determine how policies are clustered across multiple settings. Methods. In 2012 and 2013 we used existing literature, policy documents, government recommendations, and instruments to identify key policies. We then developed the PIC to examine the policy environments across 3 settings (communities, schools, and early care and education centers) in 8 communities participating in the Childhood Obesity Research Demonstration Project. Results. Principal components analysis revealed 5 components related to calorie-dense food policies and 4 components related to sugar-sweetened beverage policies. Communities with higher youth and racial/ethnic minority populations tended to have fewer and weaker policy environments concerning calorie-dense foods and healthy foods and beverages. Conclusions. The PIC was a helpful tool to identify policies that promote healthy food environments across multiple settings and to measure and compare the overall policy environments across communities. There is need for improved coordination across settings, particularly in areas with greater concentration of youths and racial/ethnic minority populations. Policies to support healthy eating are not equally distributed across communities, and disparities continue to exist in nutrition policies. (Am J Public Health. 2015;105: 1036–1043. doi:10.2105/AJPH.2015.302559)
Policy enactment often outpaces available evidence. Policies originate from a broad range of places, including the immediate concerns of constituent or advocacy groups, best practices or models, or knee-jerk responses to current events or political pressures.34 As a consequence, policymakers may produce policies that fall short of efficacy evidence and understanding of what is needed for implementation. To date, research has been limited by the lack of standardized measures and protocols that are widely applicable across municipal, state, and federal levels, as well as across settings, such as community, school, and ECEC settings. There is need for development of instruments that incorporate multiple levels of analysis and settings to measure policies that may influence these behaviors.35 This is vital to enhance monitoring and evaluation of policies that might create supportive nutrition
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environments and improve the readiness of communities to deliver childhood obesity prevention programs. The Childhood Obesity Research Demonstration (CORD) project, which was funded by the Centers for Disease Control and Prevention, tested whether integrating primary care interventions and public health interventions within multiple settings (community, schools, and ECEC settings) that targeted obesityrelated behaviors and health outcomes among participants could provide lasting impact to reduce childhood obesity.36 The demonstration sites spanned 8 communities in 3 states, which allowed the CORD evaluation center to assess policies to provide comprehensive recommendations on how future programs should be designed nationwide. The project targeted children 2 to 12 years old who were eligible for services provided by Medicare
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and the Children’s Health Insurance Program.37 As part of the evaluation, we developed the policy indicator checklist (PIC) to measure the policy environments of the 8 CORD communities with special attention to community, school, and ECEC settings. We described the development and testing of 2 PIC subscales that focused on calorie-dense foods and sugar-sweetened beverages. We used factor analysis to determine common policy components or dimensions that represented a community’s overall policy environment. Last, we aimed to compare the CORD communities on these policies by sociodemographic dimensions of educational attainment, youth population, and racial/ethnic composition.
METHODS The development and testing of the PIC occurred in 2012 and 2013. Eight communities from 3 states were included in this study: Brawley, Calexico, and El Centro in Imperial County, California; Fitchburg, Lowell, and New Bedford in Worcester, Middlesex, and Bristol Counties, Massachusetts; and Austin and Houston in Travis and Harris Counties, Texas. Each community was a site of a CORD demonstration project. Interventions were implemented across full towns or cities in California and Massachusetts, whereas those in Texas were implemented in defined catchment areas within larger cities (36 census tracts in Austin and 79 census tracts in Houston). In the present study, we varied the geographic units for community comparisons to match intervention activities: characteristics in California and Massachusetts described full towns or cities, and characteristics in Texas described selections of census tracts that represented intervention catchment areas. Communities ranged in population size from 24 952 to 327 667, with an average population of 103 957.
Sociodemographics We used data from the American Community Survey38 to provide estimates of the communities’ sociodemographic characteristics, including educational attainment, youth population, and racial/ethnic composition. These characteristics were selected based on commonly reported associations with disparities in behavioral and health outcomes and
their potential to be associated with differences in the policy environment. The percentage of adults aged 25 years and older with at least a high school diploma represented educational attainment. The percentage of the total population younger than 18 years represented the youth population. Racial/ethnic composition was considered the percentage of the population other than non-Hispanic White (100% minus % non-Hispanic White39; Table 1).
Policy Indicator Checklist We examined existing policy checklists and recommendations by focusing on community and youths (e.g., the 2009 Centers for Disease Control and Prevention report on community strategies,40 the National Cancer Institute’s Classification of Laws Associated with School Students,41 the Wellness Child Care Assessment Tool,41 the Let’s Move! Child Care Checklist,42 and the Nutrition and Physical Activity Self-Assessment for Child Care43,44). We also reviewed existing state and local policies in the 8 communities to determine whether existing checklists captured all possible policies. We compiled items from the reviewed checklists together with additional items drawn from observations of community policies. We checked items for duplication, refined them for appropriateness for settings, and vetted them for clarity. Finally, we completed a final review of items and verified their face validity by ensuring each item matched one of the existing policy checklists or recommendations. The PIC contained 2 subscales related to calorie-dense foods (69 items) and sugar-sweetened beverages (25 items; see data available as a supplement to the online version of this article at http://www.ajph.org). We scored items from ranges of zero to a maximum number between 2 and 6. A score of zero indicated that neither a requirement nor a recommendation was present, and the maximum score indicated the presence of a governmental or community mandate or requirement that covered all aspects of the given policy area. An example of an item scored from a range of zero to 6 detailed guidelines for nutritional content in school meals, where scoring guidelines ranged from no provisions about nutritional content in schools (0) to a mandate enforcing several specific nutrient guidelines (6). Items with ranges of zero to 2 had scoring options of
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no provision found (0), a policy recommendation (1), and a policy mandate (2).
Procedure We trained 4 research assistants to conduct systematic searches using policy tracking systems and Web sites to collect the most accurate and complete information on all rules, regulations, and recommendations at city, county, and state levels of government. The training included searches across publicly available databases, including the National Resource for Health and Safety in Child Care and Early Education State Licensing and Regulation Information database, the Conference of State Legislature’s Health Promotion Program State Legislation and State Database, and the National Association of State Boards of Education’s State-Level School Health Policies Database. State Department of Health Services Web sites, local government Web sites, comprehensive master plans, and news releases were also searched. We extracted relevant policy information using a PIC coding sheet for standard characteristics, including Web sites authorship, date accessed, update date, url, and policy strength. All coders were given 2 example policy indicators to complete independently. We discussed these as a group to promote a clear understanding of how to navigate policy tracking systems and accurately complete the coding sheet. Two pairs of coders completed independent retrieval and review. Each member of the first pair coded all calorie-dense food and sugarsweetened beverage policies for 4 of the communities, and each member of the second pair coded the same policies for the other 4 communities. To ensure that intervention activities did not affect policy environments within sample communities, we only reviewed policies that existed before interventions (i.e., before 2011). Interrater reliability was strong with intraclass correlation coefficients of 0.91 (calorie-dense foods) and 0.87 (sugar-sweetened beverages). Coders then compared coding sheets within pairs. We resolved discrepancies within pairs in a convergent manner, with partners referring one another to the sources of information they used to arrive at their independent decisions. Coders from the other pair were consulted to resolve discrepancies in a convergent manner, with coding pairs referring one another to their documented sources until consensus was achieved.
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TABLE 1—Sociodemographic Characteristics of Childhood Obesity Research Demonstration Communities: California, Massachusetts, and Texas; 2012–2013 Characteristics Total population, no.
Brawley, CA
Calexico, CA
El Centro, CA
Fitchburg, MA
Lowell, MA
New Bedford, MA
Austin, TXa
Houston, TXb
24 952
38 572
42 598
40 318
106 519
95 072
155 959
327 667
Non-Hispanic White, %
14.8
3.4
13.7
56.8
54.1
69.8
28.4
14.0
Non-Hispanic Black, %
3.8
0.0
3.1
4.1
5.9
7.2
16.8
29.8
Hispanic, %
80.1
95.5
81.2
35.1
16.1
15.1
50.6
49.9
Racial/ethnic composition, % Population younger than 18 y, %
85.2 30.2
96.6 29.9
86.4 28.6
43.2 21.7
45.9 23.7
30.2 21.2
71.6 21.7
86.1 24.7
High school graduate or higher, %
62.8
55.0
67.8
83.7
78.3
67.3
72.3
65.9
a
Thirty-six census tracts forming treatment and control catchment areas. Seventy-nine census tracts forming treatment and control catchment areas.
b
Analyses PIC items with no intercommunity variability (i.e., all communities had the same score for a given policy item) would not load on any component, and we dropped these from the principal components analyses. This included 12 calorie-dense food items (3 for schools and 9 for ECECs) and 4 sugar-sweetened beverage items (3 for schools and 1 for ECECs). We performed factor analyses using principal components and orthogonal (varimax) rotation separately on the PIC calorie-dense foods and sugarsweetened beverages subscales (SPSS Statistics version 22, IBM, Armonk, NY). We normalized each PIC item score by dividing it by the total possible score for the item, and we calculated component scale scores by adding the scaled item scores for all policies in each component. We created a percentage of the total score attained for each PIC item component score by dividing each community’s component score by the total possible score for that component. We conducted comparisons of communities’ policy environments and characteristics (educational attainment, youth population, and racial/ ethnic composition) using bivariate (Spearman) correlation coefficients because of the small number of communities in the sample. We assumed non-normality of the component scores.
RESULTS A principal components factor analysis with varimax rotation produced 5 components with eigenvalues of 1 or greater, which explained 98.1% of the variance in the PIC items regarding policies about calorie-dense food
(Table 2). A second principal components factor analysis with varimax rotation produced 4 components with eigenvalues of 1 or greater, which explained 94.1% of the variance in the PIC items regarding policies about sugarsweetened beverages (Table 3). The first component for calorie-dense foods was “guidelines for food availability,” which accounted for 36.4% of the PIC item variability (Table 2). Examples of items loading highly to this component included “School: a la carte entrée requirements” (0.97) and “School: Child and Adult Care Food Program lunch requirements” (–0.97). In particular, some of these items loaded highly in opposite directions and might have showed that settings had either 1 comprehensive policy or separate enumerated policies that constituted the larger policy. The second component for calorie-dense foods was “food program operations,” which accounted for 28.4% of the PIC item variability (Table 2). Examples of items loading high to this component included “Community: limit number of less healthy foods in public service venues” (0.96) and “School: nutrition info for meals” (–0.96). The third component for calorie-dense foods was “nutrition practices in early care and education,” which accounted for 16.9% of the PIC item variability (Table 2). Examples of items loading highly on this component included “Early Care and Education: standards for foods from home” (0.95) and “Early Care and Education: fruits/vegetables as a snack” (0.91). The fourth component for calorie-dense foods was “lifestyle and behaviors,” which accounted for 10.2% of the PIC item variability (Table 2). Examples of items loading highly on
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this component included “Community: purchasing farm foods promotion” (0.70) and “Community: grocery stores in underserved areas” (–0.80). The fifth component for calorie-dense foods was “healthy foods in public places,” which accounted for 6.3% of the PIC item variability (Table 2). Examples of items loading highly on this component included “Community: affordable healthy foods are available” (0.75) and “Community: portion size limits in public service venues” (0.75). The first component for sugar-sweetened beverages was “healthy beverage promotion,” which accounted for 34.8% of the PIC item variability (Table 3). Examples of items loading highly to this component included “School: cafeteria beverage requirements” (0.94) and “School: healthy options in vending machines” (0.94). The second component for sugarsweetened beverages was “alternatives to less healthy beverages in public venues,” which accounted for 22.9% of the PIC item variability (Table 3). Examples of items loading high to this component included “Early Care and Education: juice quantity” (0.94) and “Early Care and Education: limiting fat content of milk” (0.94). The third component for sugar-sweetened beverages was “restrict less healthy beverages in public venues,” which accounted for 18.5% of the PIC item variability (Table 3). Examples of items loading highly on this component included “Community: availability of healthy beverages” (0.98) and “Community: restriction of unhealthy beverages” (0.91). The fourth component for sugar-sweetened beverages was “incentives and restrictions to improve healthy beverage consumption,” which accounted for 17.9% of the
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TABLE 2—Component Matrix Displaying the Rotated Solutions for Each Calorie-Dense Food Component and the Percentage of Variance Explained by Each Component: California, Massachusetts, and Texas; 2012–2013
Item
Guidelines for Food Availability
Community: food retailer incentives for underserved areas Community: healthy lifestyles campaigns
0.54 –0.65
Community: link between farm and food service purchasers
0.59
Community: limit less healthy food advertisements
0.98
Community: nutrition assistance program promotion
0.74
Community: purchasing farm foods promotion School: a la carte entre´e requirements
0.59
School: vending machines nonentre´e requirements School: school stores/snack bars nonentre´e requirements School: fundraiser nonentre´e food/snacks requirements School: CACFP reimbursable lunch requirements School: meal environment requirements
Nutrition Practices in Early Care and Education
–0.67 0.68 0.59 0.65 0.70
0.97
–0.97 0.73
School: nutrition education requirements
–0.75
School: nutrition-based marketing requirements
0.93
School: preferential pricing in marketing requirements
0.97
0.66 0.65
0.73 –0.87
0.66
School: access to drinking water
0.73
0.66
Early care and education: replacing SFA with MUFA, PUFA
0.69
Early care and education: regulating TFA
0.69
0.57
Early care and education: children can self-regulate
0.69
0.57
0.57
Early care and education: teachers gauge child fullness
0.69
0.57
Early care and education: regulate all class celebration foods
0.53
–0.76
Early care and education: restrict less healthy choice marketing Early care and education: standards on caloric content
0.53 0.97
–0.76
Early care and education: standards on sodium
0.97
Early care and education: standards on FA/TFA
0.97
Community: food retailers provide F/V in underserved areas Community: available purchasing farm foods mechanisms
Healthy Foods in Public Places
0.97 0.90
–0.86
Community: affordable healthy foods are available
Lifestyle and Behaviors
0.97
School: food service director qualification requirements
School: promotes CACFP School: healthy lifestyle education
Food Program Operations
0.57
0.75
–0.52
–0.63
0.88
Community: incentive for foods from local farms
0.88
Community: limit less healthy foods in public service venues Community: portion size limits in public service venues
0.96 0.57
Community: farmers market promotion School: a la carte (nonentre´e) snack requirements
0.56
0.75
–0.96
School: nutrition info for meals
–0.96
School: standards on caloric content
–0.96
School: standards on sodium content
–0.96
School: healthy snacks promoted
–0.96
Early care and education: limit sugar content Early care and education: variety of foods
0.90 –0.57
Early care and education: food as reward/punishment
0.96
Early care and education: standards for foods served at events
0.67
–0.79 0.51 Continued
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TABLE 2—Continued Early care and education: limit milk fat content
0.67
0.51
Community: geographic supermarket availability
–0.84
Early care and education: F/V as snack
0.91
Early care and education: access to drinking water
0.61
Early care and education: written menus provided to parents
–0.77
Early care and education: mealtime nutrition concepts Early care and education: staff training in cooking/food prep
0.91 0.91
Early care and education: standards for foods from home
0.95
Community: grocery stores in underserved areas
–0.60
–0.80
Early care and education: nutrition standards beyond CACFP
–0.67
Early care and education: parent nutrition/health consultation
–0.67
Early care and education: parents engaged in wellness/health
–0.67
Early care and education: healthy food availability in public service venues Variance explained, %
0.82 36.37
28.41
16.90
10.18
6.27
Note. CACFP = Child and Adult Care Food Program; FA = fatty acids; F/V = fruits and vegetables; MUFA = monounsaturated fatty acids; PUFA = polyunsaturated fatty acids; SFA = saturated fatty acids; TFA = trans fatty acids.
PIC item variability (Table 3). Examples of items loading highly on this component included “Early Care and Education: restriction of beverages during class celebrations” (0.75) and “Community: soda or junk food tax” (0.62). Table 4 shows raw total scale scores and the percentages of total score attainment for each component in each community. Sociodemographic characteristics for the 8 CORD communities are presented in Table 1. On average, two thirds of people living in CORD communities attained a high school diploma or a higher level of education. People younger than 18 years made up nearly one quarter of CORD communities. Across all sites, communities averaged 31.9% non-Hispanic White, 8.8% non-Hispanic Black, and 52.9% Hispanic population, with an average racial/ ethnic composition of 68.2%. Communities with higher educational attainment had higher scores on the healthy beverage promotion component (r = 0.788; P < .05). Communities with higher youth composition had lower scores on the guidelines for food availability (r = –0.910; P < .05), nutrition practices in early care and dducation (r = –0.759; P < .05), healthy foods in public places (r = –0.861; P < .05), and healthy beverage promotion (r = –0.861; P < .05) components. Communities with higher minority composition had lower scores on the guidelines for food availability (r = –0.838; P < .05) and healthy
beverage promotion (r = –0.897; P < .05) components. The calorie-dense food components food program operations and the sugarsweetened beverage components alternatives to less healthy beverages, restricting less healthy beverages in public venues, and incentives and restrictions to improve healthy beverage consumption showed no significant associations with community sociodemographic characteristics.
DISCUSSION Policy approaches to behavior change posit that macrolevel policy has the capacity to improve and sustain the healthy behavior of individuals throughout the population. However, a main challenge in this arena is the lack of adequate measurement protocols and instruments. In this study, we constructed the PIC to measure policies that regulate calorie-dense foods and sugar-sweetened beverages across 8 different communities in 3 different states, and we compared these policy environments by key community-level demographic characteristics. Our analysis identified 5 calorie-dense food components and 4 sugar-sweetened beverage policy components that cut across all 3 settings. From the items that measure calorie-dense food related policies, component I focused on guidelines for food availability and consisted of a variety of factors at all levels that included
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policies focused on increasing access to healthy and fresh foods. Component II, food program operations, emphasized operations of food administration, including farm to table regulations and nutrition guidelines in schools. Component III, nutrition standards and practices in Early Care and Education, focused almost exclusively on early childcare, which reflected, in part, recent interest and rapid recognition that early childhood is a critical time in the development of disease later in life.45,46 Component IV, lifestyle practices and behaviors, addressed policies that aided community members, in particular parents, in maintaining a healthy lifestyle. Component V, healthy foods in public places, was comprised entirely of community-level policies that promoted availability and access of healthy foods. For the components derived from the sugar-sweetened beverages policy components, component I was exclusively about healthy beverage promotion, including policies related to availability, access, and marketing at all levels. Component II, offering alternatives to less healthy beverages, contained items that addressed policies about specific beverages that were alternatives to sugar-sweetened beverages, such as water, juice, and milk. Component III, restricting less healthy beverages in public service venues, included items that were primarily community-focused policies aimed at reducing the amount of available
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TABLE 3—Component Matrix Displaying the Rotated Solutions for Each Sugar-Sweetened Beverage Component and the Percentage of Variance Explained by Each Component: California, Massachusetts, and Texas; 2012–2013
Item Community: availability of affordable healthy beverages Community: restriction of unhealthy beverage advertising
Healthy Beverage Promotion
Alternatives to Less Healthy Beverages
0.59 0.80
–0.61
Schools: cafeteria beverage requirements
0.94
Schools: vending machine beverage requirements
0.94
Schools: school store beverage requirements
0.94
Schools: fundraiser beverage requirements
0.94
Schools: healthy options in vending machines
0.94
Schools: availability of beverage nutrition information
0.74
Schools: access to free drinking water Early childcare: limiting sugar-sweetened beverages
0.69 –0.53
Community: retail incentives for offering healthy beverages
Restrict Less Healthy Beverages in Public Venues
0.55 0.74 –0.81
Early childcare: juice quantity
Incentives and Restrictions to Improve Healthy Beverage Consumption
0.55
0.94
Early childcare: provision of 100% juice
0.92
Early childcare: limiting fat content of milk
0.94
Community: beverage portion sizes
0.91
Community: availability of healthy beverages
0.98
Community: restriction of unhealthy beverages Early childcare: restriction of juice additives and sweeteners
0.91 0.55
0.51
Community: soda or junk food tax
0.62
Early childcare: access to drinking water
0.82
Early childcare: nutrition standards for flavored milk
0.84
Early childcare: restriction of beverages during class celebrations Variance explained, %
sugar-sweetened beverages. Component IV, incentives and restrictions to improve beverage consumption, included community taxes as well as early care and education regulations. Several of the policy components varied by community, with communities that had higher proportions of youth and racial/ethnic minorities having weaker policy environments related to calorie-dense foods and sugar-sweetened beverages. This was particularly troubling and seemed to echo the behavioral and health disparities in the food environment and concomitant higher rates of obesity among high minority populations.47,48 Although correlational in nature, these findings might suggest the need for stronger guidelines and regulations, along with improved strategies to promote healthful food in areas with more children and minorities to improve policy environments. Previous studies in single settings (e.g., schools) found that better policy environments that
0.75 34.81
22.87
18.53
17.87
improved food quality, access, and availability might lead to improved dietary habits, at least during the school day. If these were extended to the policy environment of a community, children might be positively affected outside of school. Future work is needed to determine how stable policies are over time and the effects of changes in policy environments across settings.
differ if there were a wider and larger array of geographic regions, where recommended policies that are not commonly implemented might be present. Nevertheless, the PIC explained more than 90% of the variance in both analyses. These findings suggested the feasibility of such a measure, which has important implications for policy development and comparative analysis.
Limitations
Conclusions
The small number of communities might limit the generalizability of the results; in particular, future studies including data on more communities would provide greater power to determine the general component structure of the PIC. We excluded several items from the original instrument, because they did not have any variability among the communities. In many cases, this indicated that the policy under investigation was simply not observed. However, it was possible that variability of these items might
Although not an explicit aim of this study, an interesting finding that emerged was that different communities might prioritize different approaches toward improving nutrition in the community, school, and ECECs. Communities in California, for example, demonstrated the strongest policy environment related to improving food-related lifestyles and behaviors and the weakest policy environment involving guidelines for food availability. Massachusetts communities demonstrated the exact opposite
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TABLE 4—Policy Indicator Checklist Subcomponent Scores by Community: California, Massachusetts, and Texas; 2012–2013 Brawley, CA; Calexico, CA; El Centro, CA; Fitchburg, MA; Lowell, MA; New Bedford, Austin, TX; Houston, TX; % (score) % (score) % (score) % (score) % (score) MA; % (score) % (score) % (score)
Policy Indicator Checklist Component Scores Calorie-dense foods Guidelines for food availability (28 items, max. 28)
26.1 (7.3)
29.6 (8.3)
26.1 (7.3)
56.4 (15.8)
50.4 (14.1)
53.9 (15.1)
Food program operations (22 items, max. 22)
37.3 (8.2)
37.7 (8.3)
39.5 (8.7)
49.5 (10.9)
55.0 (12.1)
53.2 (11.7)
69.5 (15.3) 58.2 (12.8)
Nutrition practices in early care and education (16 items, max. 16) Lifestyle and behaviors (8 items, max. 8)
23.1 (3.7) 57.5 (4.6)
14.4 (2.3) 18.8 (1.5)
23.1 (3.7) 57.5 (4.6)
28.8 (4.6) 15.0 (1.2)
30.1 (4.9) 22.5 (1.8)
32.3 (5.2) 21.3 (1.7)
33.8 (5.4) 22.5 (1.8)
30.6 (4.9) 22.5 (1.8)
0.0 (0.0)
10.0 (0.5)
0.0 (0.0)
30.0 (1.5)
16.0 (0.8)
30.0 (1.5)
56.0 (2.8)
10.0 (0.5)
Healthy beverage promotion (10 items, max. 10)
37.0 (3.7)
34.0 (3.4)
37.0 (3.7)
85.0 (8.5)
80.0 (8.0)
80.0 (8.0)
43.0 (4.3)
43.0 (4.3)
Alternatives to less healthy beverages in public venues (6 items,
16.7 (1.0)
8.3 (0.5)
16.7 (1.0)
33.3 (2.0)
53.3 (3.2)
53.3 (3.2)
56.7 (3.4)
56.7 (3.4)
7.5 (0.3)
0.0 (0.0)
7.5 (0.3)
7.5 (0.3)
0.0 (0.0)
0.0 (0.0)
60.0 (2.4)
17.5 (0.7)
58.6 (4.1)
0.0 (0.0)
65.7 (4.6)
7.1 (0.5)
7.1 (0.5)
7.1 (0.5)
45.7 (3.2)
45.7 (3.2)
Healthy foods in public places (5 items, max. 5)
39.3 (11.0) 38.6 (10.8)
Sugar sweetened beverages
max. 6) Restrict less healthy beverages in public venues (4 items, max. 4) Incentives and restrictions to improve healthy beverage consumption (7 items, max. 7)
trend, suggesting they prioritized guidelines for food availability over improving food-related lifestyles and behaviors. Both components of the policy environment served to improve nutrition for constituents, and each might provide an equally important method of accomplishing this goal. Although based on a limited sample, these differences demonstrated the value of systematic tools that measured communities’ nutritionrelated policies across settings to investigate the overall policy environment. Our study of the PIC offered an instrument that could provide a comprehensive look at the policy environment of an entire community. Policies have typically been understudied, particularly at the community level and in early care and education. The strengths of this system approach included allowing for future comparison across communities and settings. Future work is needed to confirm our findings, because with a sample of 8 communities, some of the trends were likely the result of clustering (e.g., the high proportions of Hispanic populations in the California communities). In addition, because many of the policies were at the state level, the communities in a state, such as California, would be affected by the same policies. One additional caveat was that the comprehensive nature of the instrument lent itself to time-intensive protocol training and data collection.
We described a carefully crafted instrument and protocol that demonstrated high interrater reliability. Future research is needed to investigate how the PIC might capture the policy environment in other communities, and how these environments might be associated with childhood obesity prevalence. The PIC allowed for the measurement of policies across multiple settings, and future work should analyze and compare how policies in multiple settings work together to complement efforts to reduce and prevent childhood obesity. Future studies might also compare how the policy environment in the United States, as captured by the PIC, compares with the policy environment in other countries, to investigate improvements and innovations that might be working in other countries that might be applied to the US policy environment. The PIC provided a reliable and feasible method of comprehensively capturing communities’ policy environments that might inform future policymaking and studies of policy impact. j
Houston. Dennis Kao is with the Department of Social Work, California State University, Fullerton. Maria Modelska and Hanadi Rifai are with the Texas Obesity Research Center and the Department of Civil and Environmental Engineering, University of Houston. Correspondence should be sent to Rebecca E. Lee, PhD, Arizona State University, 550 N. 3rd Street, Phoenix, AZ 85004 (e-mail: [email protected]). Reprints can be ordered at http://www.ajph.org by clicking the “Reprints” link. This article was accepted January 7, 2015.
Contributors R. E. Lee led the methodological design, oversaw the collection of data, and led the writing of the article. A. M. Hallett, N. Parker, and O. Kudia led the collection and analysis of data and assisted in writing the article. D. Kao, M. Modelska, and H. Rifai participated in the article’s methodological design. D. P. O’Connor was the principal investigator at the Evaluation Center for the Childhood Obesity Research Demonstration (CORD) Project, oversaw the methodological design, oversaw the collection and analysis of data, and assisted in writing the article. All authors provided subject matter expertise, participated in the interpretation of the findings, provided extensive article commentary and review, and approved the final version of the article.
Acknowledgments About the Authors Rebecca E. Lee is with the College of Nursing and Health Innovation, Arizona State University, Phoenix. Allen M. Hallett, Nathan Parker, and Daniel P. O’Connor are with the Texas Obesity Research Center and the Department of Health and Human Performance, University of Houston. Ousswa Kudia is with the Department of Behavioral Science, University of Texas MD Anderson Cancer Center,
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This work was supported in part by cooperative agreement RFA-DP-11-007 (award U18DP003350) from the Centers for Disease Control and Prevention (CDC). Note. The findings and interpretations in this article are those solely of the authors and are not necessarily those of the CDC.
Human Participant Protection No protocol approval was required because no data were collected from human participants in this study.
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competitive food and beverage policies align with the 2010 Dietary Guidelines for Americans: implications for federal regulations. J Acad Nutr Diet. 2012;112(6):892---896. 18. Chriqui JF. Obesity prevention policies in US states and localities: lessons from the field. Curr Obes Rep. 2013;2(3):200---210. 19. Story M, Kaphingst KM, Robinson-O’Brien R, Glanz K. Creating healthy food and eating environments: policy and environmental approaches. Annu Rev Public Health. 2008;29:253---272. 20. Frieden TR. A framework for public health action: the health impact pyramid. Am J Public Health. 2010;100 (4):590---595. 21. Larson NI, Story MT, Nelson MC. Neighborhood environments: disparities in access to healthy foods in the US. Am J Prev Med. 2009;36(1):74–81. 22. Morland K, Wing S, Diez Roux A, Poole C. Neighborhood characteristics associated with the location of food stores and food service places. Am J Prev Med. 2002;22(1):23---29. 23. Trost SG, Messner L, Fitzgerald K, Roths B. Nutrition and physical activity policies and practices in family child care homes. Am J Prev Med. 2009;37(6):537---540. 24. O’Toole TP, Anderson S, Miller C, Guthrie J. Nutrition services and foods and beverages available at school: results from the School Health Policies and Programs Study 2006. J Sch Health. 2007;77(8):500---521. 25. L’Abbé M, Schermel A, Minaker L, et al. Monitoring foods and beverages provided and sold in public sector settings. Obes Rev. 2013;14(suppl 1):96---107.
35. Blanck HM, Kim SA. Creating supportive nutrition environments for population health impact and health equity: an overview of the Nutrition and Obesity Policy Research and Evaluation Network’s efforts. Am J Prev Med. 2012;43(3, suppl 2):S85---S90. 36. Dooyema CA, Belay B, Foltz JL, Williams N, Blanck HM. The childhood obesity research demonstration project: a comprehensive community approach to reduce childhood obesity. Childhood Obesity. 2013;9(5):454--459. 37. Children’s Health Insurance Program Reauthorization Act of 2009 (CHIPRA). 111---3 PL, US Congress. Vol 155. Washington, DC: US Government Printing Office; 2009. 38. US Census Bureau. American Community Survey. 2007-2011 5-year estimates. Available at: http://www. census.gov/acs/www/data_documentation/ 2011_release. Accessed December 1, 2014. 39. Centers for Disease Control and Prevention. Minority health. Definitions. Available at: http://www.cdc. gov/minorityhealth/populations/REMP/definitions. html. Accessed December 1, 2014. 40. Khan LK, Sobush K, Keener D, et al. Recommended community strategies and measurements to prevent obesity in the United States. MMWR Recomm Rep. 2009;58(RR-7):1---26. 41. National Cancer Institute. Classification of Laws Associated with School Students. Available at: http:// class.cancer.gov. Accessed December 1, 2014. 42. Falbe J, Kenney EL, Henderson KE, Schwartz MB. The Wellness Child Care Assessment Tool: a measure to assess the quality of written nutrition and physical activity policies. J Am Diet Assoc. 2011;111(12):1852--1860.
9. United States Department of Agriculture, Food and Nutrition Service (FNS). Cooperation in USDA studies and evaluations, and full use of federal funds in nutrition assistance programs nondiscretionary provisions of the Healthy, Hunger-Free Kids Act of 2010, Public Law 111-296. Final rule. Fed Regist. 2011;76(125): 37979---37983.
26. Hood NE, Colabianchi N, Terry-McElrath YM, O’Malley PM, Johnston LD. School wellness policies and foods and beverages available in schools. Am J Prev Med. 2013;45(2):143---149.
10. Foltz JL, Harris DM, Blanck HM. Support among US adults for local and state policies to increase fruit and vegetable access. Am J Prev Med. 2012;43(3, suppl 2): S102---S108.
28. Greathouse KL, Chriqui J, Moser RP, Agurs-Collins T, Perna FM. The association of soda sales tax and school nutrition laws: a concordance of policies. Public Health Nutr. 2013;1---6.
44. Ammerman AS, Ward DS, Benjamin SE, et al. An intervention to promote healthy weight: Nutrition and Physical Activity Self-Assessment for Child Care (NAP SACC) theory and design. Prev Chronic Dis. 2007;4(3): A67.
11. Kim SA, Blanck HM. State legislative efforts to support fruit and vegetable access, affordability, and availability, 2001 to 2009: a systematic examination of policies. J Hunger Environ Nutr. 2011;6(1):99---113.
29. Hirschman J, Chriqui JF. School food and nutrition policy, monitoring and evaluation in the USA. Public Health Nutr. 2013;16(6):982---988.
45. Birch LL, Parker L, Burns A. Early Childhood Obesity Prevention Policies. Washington, DC: National Academies Press; 2011.
30. Chriqui JF, Pickel M, Story M. Influence of school competitive food and beverage policies on obesity, consumption, and availability: a systematic review. JAMA Pediatr. 2014;168(3):279---286.
46. Campbell F, Conti G, Heckman JJ, et al. Early childhood investments substantially boost adult health. Science. 2014;343(6178):1478---1485.
12. Sacks G, Swinburn BA, Lawrence MA. A systematic policy approach to changing the food system and physical activity environments to prevent obesity. Aust New Zealand Health Policy. 2008;5:13. 13. Taber DR, Chriqui JF, Chaloupka FJ. Geographic disparities in state and district policies targeting youth obesity. Am J Prev Med. 2011;41(4):407---414. 14. Taber DR, Chriqui JF, Chaloupka FJ. Association and diffusion of nutrition and physical activity policies on the state and district level. J Sch Health. 2012;82(5):201-- 209. 15. Fleischhacker S. Food fight: the battle over redefining competitive foods. J Sch Health. 2007;77(3):147--152. 16. Chriqui JF, Turner L, Taber DR, Chaloupka FJ. Association between district and state policies and US public elementary school competitive food and beverage environments. JAMA Pediatr. 2013;167(8):714---722. 17. Schneider LM, Schermbeck RM, Chriqui JF, Chaloupka FJ. The extent to which school district
27. Harris JL, Fox T. Food and beverage marketing in schools: putting student health at the head of the class. JAMA Pediatr. 2014;168(3):206---208.
31. Ouellette JA, Wood W. Habit and intention in everyday life: the multiple processes by which past behavior predicts future behavior. Psychol Bull. 1998;124(1):54---74. 32. French SA, Jeffery RW, Story M, Hannan P, Snyder MP. A pricing strategy to promote low-fat snack choices through vending machines. Am J Public Health. 1997;87 (5):849---851.
43. The Nemours Foundation. Let’s Move! Child Care Checklist Quiz. Jacksonville, FL: Nemours Foundation; 2011.
47. Ogden CL, Carroll MD, Curtin LR, McDowell MA, Tabak CJ, Flegal KM. Prevalence of overweight and obesity in the United States, 1999-2004. JAMA. 2006;295(13):1549---1555. 48. Ogden CL, Carroll MD, Flegal KM. High body mass index for age among US children and adolescents, 20032006. JAMA. 2008;299(20):2401---2405.
33. Byrd-Bredbenner C, Johnson M, Quick VM, et al. Sweet and salty. An assessment of the snacks and beverages sold in vending machines on US postsecondary institution campuses. Appetite. 2012;58 (3):1143---1151. 34. Weissert CS, Weissert WG. Governing Health: The Politics of Health Policy. Baltimore, MD: Johns Hopkins University Press; 2008.
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Development of the Policy Indicator Checklist: A Tool to Identify and Measure Policies for Calorie-Dense Foods and Sugar-Sweetened Beverages Across Multiple Settings Rebecca E. Lee, PhD, Allen M. Hallett, BS, Nathan Parker, MPH, Ousswa Kudia, MPH, Dennis Kao, MSW, PhD, Maria Modelska, MPA, Hanadi Rifai, PhD, and Daniel P. O’Connor, PhD
There has been increasing attention on policyfocused approaches to reduce the prevalence of childhood obesity1---6 at the federal (e.g., Public Law 111---296),7---9 state,10---17 and local6,16,18 levels of government. Policies are a promising strategy at all levels for sustainable improvements in health, because an effective policy can change contextual cues and, in turn, affect many individual behavioral choices with very little individual effort.1,19,20 Despite the promise of these approaches, there is a lack of comprehensive, systematic, and valid measurement protocols that may be applied across multiple settings. Settings are places where policies can improve child behavior or the behavior of adults that affects children; these settings include communities, schools, and early care and education centers (ECECs), among others. Policies have the potential to change community food environments,21,22 to improve food access and availability in schools and ECECs,23---31 and to affect the pricing and marketing of food (e.g., vending machines), all of which could have an important impact on dietary habits.32,33 Taken together, most studies are descriptive in nature, do not actually investigate the policy itself, are often regionally specific, and do not account for the broad range of policies that may make up the child nutrition policy portfolio across multiple settings. These limitations make research that combines the effects of policies across settings impossible, so it is unknown how policies in different settings may complement or confound each other. There is little understanding of how, when, or why policies may work at one level together with policies at another level to produce sustainable systems changes. This hampers the ability of policymakers within and across settings to partner together and to enact policies at multiple levels that might yield the greatest impact for improving health.
Objectives. We developed the policy indicator checklist (PIC) to identify and measure policies for calorie-dense foods and sugar-sweetened beverages to determine how policies are clustered across multiple settings. Methods. In 2012 and 2013 we used existing literature, policy documents, government recommendations, and instruments to identify key policies. We then developed the PIC to examine the policy environments across 3 settings (communities, schools, and early care and education centers) in 8 communities participating in the Childhood Obesity Research Demonstration Project. Results. Principal components analysis revealed 5 components related to calorie-dense food policies and 4 components related to sugar-sweetened beverage policies. Communities with higher youth and racial/ethnic minority populations tended to have fewer and weaker policy environments concerning calorie-dense foods and healthy foods and beverages. Conclusions. The PIC was a helpful tool to identify policies that promote healthy food environments across multiple settings and to measure and compare the overall policy environments across communities. There is need for improved coordination across settings, particularly in areas with greater concentration of youths and racial/ethnic minority populations. Policies to support healthy eating are not equally distributed across communities, and disparities continue to exist in nutrition policies. (Am J Public Health. 2015;105: 1036–1043. doi:10.2105/AJPH.2015.302559)
Policy enactment often outpaces available evidence. Policies originate from a broad range of places, including the immediate concerns of constituent or advocacy groups, best practices or models, or knee-jerk responses to current events or political pressures.34 As a consequence, policymakers may produce policies that fall short of efficacy evidence and understanding of what is needed for implementation. To date, research has been limited by the lack of standardized measures and protocols that are widely applicable across municipal, state, and federal levels, as well as across settings, such as community, school, and ECEC settings. There is need for development of instruments that incorporate multiple levels of analysis and settings to measure policies that may influence these behaviors.35 This is vital to enhance monitoring and evaluation of policies that might create supportive nutrition
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environments and improve the readiness of communities to deliver childhood obesity prevention programs. The Childhood Obesity Research Demonstration (CORD) project, which was funded by the Centers for Disease Control and Prevention, tested whether integrating primary care interventions and public health interventions within multiple settings (community, schools, and ECEC settings) that targeted obesityrelated behaviors and health outcomes among participants could provide lasting impact to reduce childhood obesity.36 The demonstration sites spanned 8 communities in 3 states, which allowed the CORD evaluation center to assess policies to provide comprehensive recommendations on how future programs should be designed nationwide. The project targeted children 2 to 12 years old who were eligible for services provided by Medicare
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and the Children’s Health Insurance Program.37 As part of the evaluation, we developed the policy indicator checklist (PIC) to measure the policy environments of the 8 CORD communities with special attention to community, school, and ECEC settings. We described the development and testing of 2 PIC subscales that focused on calorie-dense foods and sugar-sweetened beverages. We used factor analysis to determine common policy components or dimensions that represented a community’s overall policy environment. Last, we aimed to compare the CORD communities on these policies by sociodemographic dimensions of educational attainment, youth population, and racial/ethnic composition.
METHODS The development and testing of the PIC occurred in 2012 and 2013. Eight communities from 3 states were included in this study: Brawley, Calexico, and El Centro in Imperial County, California; Fitchburg, Lowell, and New Bedford in Worcester, Middlesex, and Bristol Counties, Massachusetts; and Austin and Houston in Travis and Harris Counties, Texas. Each community was a site of a CORD demonstration project. Interventions were implemented across full towns or cities in California and Massachusetts, whereas those in Texas were implemented in defined catchment areas within larger cities (36 census tracts in Austin and 79 census tracts in Houston). In the present study, we varied the geographic units for community comparisons to match intervention activities: characteristics in California and Massachusetts described full towns or cities, and characteristics in Texas described selections of census tracts that represented intervention catchment areas. Communities ranged in population size from 24 952 to 327 667, with an average population of 103 957.
Sociodemographics We used data from the American Community Survey38 to provide estimates of the communities’ sociodemographic characteristics, including educational attainment, youth population, and racial/ethnic composition. These characteristics were selected based on commonly reported associations with disparities in behavioral and health outcomes and
their potential to be associated with differences in the policy environment. The percentage of adults aged 25 years and older with at least a high school diploma represented educational attainment. The percentage of the total population younger than 18 years represented the youth population. Racial/ethnic composition was considered the percentage of the population other than non-Hispanic White (100% minus % non-Hispanic White39; Table 1).
Policy Indicator Checklist We examined existing policy checklists and recommendations by focusing on community and youths (e.g., the 2009 Centers for Disease Control and Prevention report on community strategies,40 the National Cancer Institute’s Classification of Laws Associated with School Students,41 the Wellness Child Care Assessment Tool,41 the Let’s Move! Child Care Checklist,42 and the Nutrition and Physical Activity Self-Assessment for Child Care43,44). We also reviewed existing state and local policies in the 8 communities to determine whether existing checklists captured all possible policies. We compiled items from the reviewed checklists together with additional items drawn from observations of community policies. We checked items for duplication, refined them for appropriateness for settings, and vetted them for clarity. Finally, we completed a final review of items and verified their face validity by ensuring each item matched one of the existing policy checklists or recommendations. The PIC contained 2 subscales related to calorie-dense foods (69 items) and sugar-sweetened beverages (25 items; see data available as a supplement to the online version of this article at http://www.ajph.org). We scored items from ranges of zero to a maximum number between 2 and 6. A score of zero indicated that neither a requirement nor a recommendation was present, and the maximum score indicated the presence of a governmental or community mandate or requirement that covered all aspects of the given policy area. An example of an item scored from a range of zero to 6 detailed guidelines for nutritional content in school meals, where scoring guidelines ranged from no provisions about nutritional content in schools (0) to a mandate enforcing several specific nutrient guidelines (6). Items with ranges of zero to 2 had scoring options of
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no provision found (0), a policy recommendation (1), and a policy mandate (2).
Procedure We trained 4 research assistants to conduct systematic searches using policy tracking systems and Web sites to collect the most accurate and complete information on all rules, regulations, and recommendations at city, county, and state levels of government. The training included searches across publicly available databases, including the National Resource for Health and Safety in Child Care and Early Education State Licensing and Regulation Information database, the Conference of State Legislature’s Health Promotion Program State Legislation and State Database, and the National Association of State Boards of Education’s State-Level School Health Policies Database. State Department of Health Services Web sites, local government Web sites, comprehensive master plans, and news releases were also searched. We extracted relevant policy information using a PIC coding sheet for standard characteristics, including Web sites authorship, date accessed, update date, url, and policy strength. All coders were given 2 example policy indicators to complete independently. We discussed these as a group to promote a clear understanding of how to navigate policy tracking systems and accurately complete the coding sheet. Two pairs of coders completed independent retrieval and review. Each member of the first pair coded all calorie-dense food and sugarsweetened beverage policies for 4 of the communities, and each member of the second pair coded the same policies for the other 4 communities. To ensure that intervention activities did not affect policy environments within sample communities, we only reviewed policies that existed before interventions (i.e., before 2011). Interrater reliability was strong with intraclass correlation coefficients of 0.91 (calorie-dense foods) and 0.87 (sugar-sweetened beverages). Coders then compared coding sheets within pairs. We resolved discrepancies within pairs in a convergent manner, with partners referring one another to the sources of information they used to arrive at their independent decisions. Coders from the other pair were consulted to resolve discrepancies in a convergent manner, with coding pairs referring one another to their documented sources until consensus was achieved.
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TABLE 1—Sociodemographic Characteristics of Childhood Obesity Research Demonstration Communities: California, Massachusetts, and Texas; 2012–2013 Characteristics Total population, no.
Brawley, CA
Calexico, CA
El Centro, CA
Fitchburg, MA
Lowell, MA
New Bedford, MA
Austin, TXa
Houston, TXb
24 952
38 572
42 598
40 318
106 519
95 072
155 959
327 667
Non-Hispanic White, %
14.8
3.4
13.7
56.8
54.1
69.8
28.4
14.0
Non-Hispanic Black, %
3.8
0.0
3.1
4.1
5.9
7.2
16.8
29.8
Hispanic, %
80.1
95.5
81.2
35.1
16.1
15.1
50.6
49.9
Racial/ethnic composition, % Population younger than 18 y, %
85.2 30.2
96.6 29.9
86.4 28.6
43.2 21.7
45.9 23.7
30.2 21.2
71.6 21.7
86.1 24.7
High school graduate or higher, %
62.8
55.0
67.8
83.7
78.3
67.3
72.3
65.9
a
Thirty-six census tracts forming treatment and control catchment areas. Seventy-nine census tracts forming treatment and control catchment areas.
b
Analyses PIC items with no intercommunity variability (i.e., all communities had the same score for a given policy item) would not load on any component, and we dropped these from the principal components analyses. This included 12 calorie-dense food items (3 for schools and 9 for ECECs) and 4 sugar-sweetened beverage items (3 for schools and 1 for ECECs). We performed factor analyses using principal components and orthogonal (varimax) rotation separately on the PIC calorie-dense foods and sugarsweetened beverages subscales (SPSS Statistics version 22, IBM, Armonk, NY). We normalized each PIC item score by dividing it by the total possible score for the item, and we calculated component scale scores by adding the scaled item scores for all policies in each component. We created a percentage of the total score attained for each PIC item component score by dividing each community’s component score by the total possible score for that component. We conducted comparisons of communities’ policy environments and characteristics (educational attainment, youth population, and racial/ ethnic composition) using bivariate (Spearman) correlation coefficients because of the small number of communities in the sample. We assumed non-normality of the component scores.
RESULTS A principal components factor analysis with varimax rotation produced 5 components with eigenvalues of 1 or greater, which explained 98.1% of the variance in the PIC items regarding policies about calorie-dense food
(Table 2). A second principal components factor analysis with varimax rotation produced 4 components with eigenvalues of 1 or greater, which explained 94.1% of the variance in the PIC items regarding policies about sugarsweetened beverages (Table 3). The first component for calorie-dense foods was “guidelines for food availability,” which accounted for 36.4% of the PIC item variability (Table 2). Examples of items loading highly to this component included “School: a la carte entrée requirements” (0.97) and “School: Child and Adult Care Food Program lunch requirements” (–0.97). In particular, some of these items loaded highly in opposite directions and might have showed that settings had either 1 comprehensive policy or separate enumerated policies that constituted the larger policy. The second component for calorie-dense foods was “food program operations,” which accounted for 28.4% of the PIC item variability (Table 2). Examples of items loading high to this component included “Community: limit number of less healthy foods in public service venues” (0.96) and “School: nutrition info for meals” (–0.96). The third component for calorie-dense foods was “nutrition practices in early care and education,” which accounted for 16.9% of the PIC item variability (Table 2). Examples of items loading highly on this component included “Early Care and Education: standards for foods from home” (0.95) and “Early Care and Education: fruits/vegetables as a snack” (0.91). The fourth component for calorie-dense foods was “lifestyle and behaviors,” which accounted for 10.2% of the PIC item variability (Table 2). Examples of items loading highly on
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this component included “Community: purchasing farm foods promotion” (0.70) and “Community: grocery stores in underserved areas” (–0.80). The fifth component for calorie-dense foods was “healthy foods in public places,” which accounted for 6.3% of the PIC item variability (Table 2). Examples of items loading highly on this component included “Community: affordable healthy foods are available” (0.75) and “Community: portion size limits in public service venues” (0.75). The first component for sugar-sweetened beverages was “healthy beverage promotion,” which accounted for 34.8% of the PIC item variability (Table 3). Examples of items loading highly to this component included “School: cafeteria beverage requirements” (0.94) and “School: healthy options in vending machines” (0.94). The second component for sugarsweetened beverages was “alternatives to less healthy beverages in public venues,” which accounted for 22.9% of the PIC item variability (Table 3). Examples of items loading high to this component included “Early Care and Education: juice quantity” (0.94) and “Early Care and Education: limiting fat content of milk” (0.94). The third component for sugar-sweetened beverages was “restrict less healthy beverages in public venues,” which accounted for 18.5% of the PIC item variability (Table 3). Examples of items loading highly on this component included “Community: availability of healthy beverages” (0.98) and “Community: restriction of unhealthy beverages” (0.91). The fourth component for sugar-sweetened beverages was “incentives and restrictions to improve healthy beverage consumption,” which accounted for 17.9% of the
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TABLE 2—Component Matrix Displaying the Rotated Solutions for Each Calorie-Dense Food Component and the Percentage of Variance Explained by Each Component: California, Massachusetts, and Texas; 2012–2013
Item
Guidelines for Food Availability
Community: food retailer incentives for underserved areas Community: healthy lifestyles campaigns
0.54 –0.65
Community: link between farm and food service purchasers
0.59
Community: limit less healthy food advertisements
0.98
Community: nutrition assistance program promotion
0.74
Community: purchasing farm foods promotion School: a la carte entre´e requirements
0.59
School: vending machines nonentre´e requirements School: school stores/snack bars nonentre´e requirements School: fundraiser nonentre´e food/snacks requirements School: CACFP reimbursable lunch requirements School: meal environment requirements
Nutrition Practices in Early Care and Education
–0.67 0.68 0.59 0.65 0.70
0.97
–0.97 0.73
School: nutrition education requirements
–0.75
School: nutrition-based marketing requirements
0.93
School: preferential pricing in marketing requirements
0.97
0.66 0.65
0.73 –0.87
0.66
School: access to drinking water
0.73
0.66
Early care and education: replacing SFA with MUFA, PUFA
0.69
Early care and education: regulating TFA
0.69
0.57
Early care and education: children can self-regulate
0.69
0.57
0.57
Early care and education: teachers gauge child fullness
0.69
0.57
Early care and education: regulate all class celebration foods
0.53
–0.76
Early care and education: restrict less healthy choice marketing Early care and education: standards on caloric content
0.53 0.97
–0.76
Early care and education: standards on sodium
0.97
Early care and education: standards on FA/TFA
0.97
Community: food retailers provide F/V in underserved areas Community: available purchasing farm foods mechanisms
Healthy Foods in Public Places
0.97 0.90
–0.86
Community: affordable healthy foods are available
Lifestyle and Behaviors
0.97
School: food service director qualification requirements
School: promotes CACFP School: healthy lifestyle education
Food Program Operations
0.57
0.75
–0.52
–0.63
0.88
Community: incentive for foods from local farms
0.88
Community: limit less healthy foods in public service venues Community: portion size limits in public service venues
0.96 0.57
Community: farmers market promotion School: a la carte (nonentre´e) snack requirements
0.56
0.75
–0.96
School: nutrition info for meals
–0.96
School: standards on caloric content
–0.96
School: standards on sodium content
–0.96
School: healthy snacks promoted
–0.96
Early care and education: limit sugar content Early care and education: variety of foods
0.90 –0.57
Early care and education: food as reward/punishment
0.96
Early care and education: standards for foods served at events
0.67
–0.79 0.51 Continued
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TABLE 2—Continued Early care and education: limit milk fat content
0.67
0.51
Community: geographic supermarket availability
–0.84
Early care and education: F/V as snack
0.91
Early care and education: access to drinking water
0.61
Early care and education: written menus provided to parents
–0.77
Early care and education: mealtime nutrition concepts Early care and education: staff training in cooking/food prep
0.91 0.91
Early care and education: standards for foods from home
0.95
Community: grocery stores in underserved areas
–0.60
–0.80
Early care and education: nutrition standards beyond CACFP
–0.67
Early care and education: parent nutrition/health consultation
–0.67
Early care and education: parents engaged in wellness/health
–0.67
Early care and education: healthy food availability in public service venues Variance explained, %
0.82 36.37
28.41
16.90
10.18
6.27
Note. CACFP = Child and Adult Care Food Program; FA = fatty acids; F/V = fruits and vegetables; MUFA = monounsaturated fatty acids; PUFA = polyunsaturated fatty acids; SFA = saturated fatty acids; TFA = trans fatty acids.
PIC item variability (Table 3). Examples of items loading highly on this component included “Early Care and Education: restriction of beverages during class celebrations” (0.75) and “Community: soda or junk food tax” (0.62). Table 4 shows raw total scale scores and the percentages of total score attainment for each component in each community. Sociodemographic characteristics for the 8 CORD communities are presented in Table 1. On average, two thirds of people living in CORD communities attained a high school diploma or a higher level of education. People younger than 18 years made up nearly one quarter of CORD communities. Across all sites, communities averaged 31.9% non-Hispanic White, 8.8% non-Hispanic Black, and 52.9% Hispanic population, with an average racial/ ethnic composition of 68.2%. Communities with higher educational attainment had higher scores on the healthy beverage promotion component (r = 0.788; P < .05). Communities with higher youth composition had lower scores on the guidelines for food availability (r = –0.910; P < .05), nutrition practices in early care and dducation (r = –0.759; P < .05), healthy foods in public places (r = –0.861; P < .05), and healthy beverage promotion (r = –0.861; P < .05) components. Communities with higher minority composition had lower scores on the guidelines for food availability (r = –0.838; P < .05) and healthy
beverage promotion (r = –0.897; P < .05) components. The calorie-dense food components food program operations and the sugarsweetened beverage components alternatives to less healthy beverages, restricting less healthy beverages in public venues, and incentives and restrictions to improve healthy beverage consumption showed no significant associations with community sociodemographic characteristics.
DISCUSSION Policy approaches to behavior change posit that macrolevel policy has the capacity to improve and sustain the healthy behavior of individuals throughout the population. However, a main challenge in this arena is the lack of adequate measurement protocols and instruments. In this study, we constructed the PIC to measure policies that regulate calorie-dense foods and sugar-sweetened beverages across 8 different communities in 3 different states, and we compared these policy environments by key community-level demographic characteristics. Our analysis identified 5 calorie-dense food components and 4 sugar-sweetened beverage policy components that cut across all 3 settings. From the items that measure calorie-dense food related policies, component I focused on guidelines for food availability and consisted of a variety of factors at all levels that included
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policies focused on increasing access to healthy and fresh foods. Component II, food program operations, emphasized operations of food administration, including farm to table regulations and nutrition guidelines in schools. Component III, nutrition standards and practices in Early Care and Education, focused almost exclusively on early childcare, which reflected, in part, recent interest and rapid recognition that early childhood is a critical time in the development of disease later in life.45,46 Component IV, lifestyle practices and behaviors, addressed policies that aided community members, in particular parents, in maintaining a healthy lifestyle. Component V, healthy foods in public places, was comprised entirely of community-level policies that promoted availability and access of healthy foods. For the components derived from the sugar-sweetened beverages policy components, component I was exclusively about healthy beverage promotion, including policies related to availability, access, and marketing at all levels. Component II, offering alternatives to less healthy beverages, contained items that addressed policies about specific beverages that were alternatives to sugar-sweetened beverages, such as water, juice, and milk. Component III, restricting less healthy beverages in public service venues, included items that were primarily community-focused policies aimed at reducing the amount of available
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TABLE 3—Component Matrix Displaying the Rotated Solutions for Each Sugar-Sweetened Beverage Component and the Percentage of Variance Explained by Each Component: California, Massachusetts, and Texas; 2012–2013
Item Community: availability of affordable healthy beverages Community: restriction of unhealthy beverage advertising
Healthy Beverage Promotion
Alternatives to Less Healthy Beverages
0.59 0.80
–0.61
Schools: cafeteria beverage requirements
0.94
Schools: vending machine beverage requirements
0.94
Schools: school store beverage requirements
0.94
Schools: fundraiser beverage requirements
0.94
Schools: healthy options in vending machines
0.94
Schools: availability of beverage nutrition information
0.74
Schools: access to free drinking water Early childcare: limiting sugar-sweetened beverages
0.69 –0.53
Community: retail incentives for offering healthy beverages
Restrict Less Healthy Beverages in Public Venues
0.55 0.74 –0.81
Early childcare: juice quantity
Incentives and Restrictions to Improve Healthy Beverage Consumption
0.55
0.94
Early childcare: provision of 100% juice
0.92
Early childcare: limiting fat content of milk
0.94
Community: beverage portion sizes
0.91
Community: availability of healthy beverages
0.98
Community: restriction of unhealthy beverages Early childcare: restriction of juice additives and sweeteners
0.91 0.55
0.51
Community: soda or junk food tax
0.62
Early childcare: access to drinking water
0.82
Early childcare: nutrition standards for flavored milk
0.84
Early childcare: restriction of beverages during class celebrations Variance explained, %
sugar-sweetened beverages. Component IV, incentives and restrictions to improve beverage consumption, included community taxes as well as early care and education regulations. Several of the policy components varied by community, with communities that had higher proportions of youth and racial/ethnic minorities having weaker policy environments related to calorie-dense foods and sugar-sweetened beverages. This was particularly troubling and seemed to echo the behavioral and health disparities in the food environment and concomitant higher rates of obesity among high minority populations.47,48 Although correlational in nature, these findings might suggest the need for stronger guidelines and regulations, along with improved strategies to promote healthful food in areas with more children and minorities to improve policy environments. Previous studies in single settings (e.g., schools) found that better policy environments that
0.75 34.81
22.87
18.53
17.87
improved food quality, access, and availability might lead to improved dietary habits, at least during the school day. If these were extended to the policy environment of a community, children might be positively affected outside of school. Future work is needed to determine how stable policies are over time and the effects of changes in policy environments across settings.
differ if there were a wider and larger array of geographic regions, where recommended policies that are not commonly implemented might be present. Nevertheless, the PIC explained more than 90% of the variance in both analyses. These findings suggested the feasibility of such a measure, which has important implications for policy development and comparative analysis.
Limitations
Conclusions
The small number of communities might limit the generalizability of the results; in particular, future studies including data on more communities would provide greater power to determine the general component structure of the PIC. We excluded several items from the original instrument, because they did not have any variability among the communities. In many cases, this indicated that the policy under investigation was simply not observed. However, it was possible that variability of these items might
Although not an explicit aim of this study, an interesting finding that emerged was that different communities might prioritize different approaches toward improving nutrition in the community, school, and ECECs. Communities in California, for example, demonstrated the strongest policy environment related to improving food-related lifestyles and behaviors and the weakest policy environment involving guidelines for food availability. Massachusetts communities demonstrated the exact opposite
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TABLE 4—Policy Indicator Checklist Subcomponent Scores by Community: California, Massachusetts, and Texas; 2012–2013 Brawley, CA; Calexico, CA; El Centro, CA; Fitchburg, MA; Lowell, MA; New Bedford, Austin, TX; Houston, TX; % (score) % (score) % (score) % (score) % (score) MA; % (score) % (score) % (score)
Policy Indicator Checklist Component Scores Calorie-dense foods Guidelines for food availability (28 items, max. 28)
26.1 (7.3)
29.6 (8.3)
26.1 (7.3)
56.4 (15.8)
50.4 (14.1)
53.9 (15.1)
Food program operations (22 items, max. 22)
37.3 (8.2)
37.7 (8.3)
39.5 (8.7)
49.5 (10.9)
55.0 (12.1)
53.2 (11.7)
69.5 (15.3) 58.2 (12.8)
Nutrition practices in early care and education (16 items, max. 16) Lifestyle and behaviors (8 items, max. 8)
23.1 (3.7) 57.5 (4.6)
14.4 (2.3) 18.8 (1.5)
23.1 (3.7) 57.5 (4.6)
28.8 (4.6) 15.0 (1.2)
30.1 (4.9) 22.5 (1.8)
32.3 (5.2) 21.3 (1.7)
33.8 (5.4) 22.5 (1.8)
30.6 (4.9) 22.5 (1.8)
0.0 (0.0)
10.0 (0.5)
0.0 (0.0)
30.0 (1.5)
16.0 (0.8)
30.0 (1.5)
56.0 (2.8)
10.0 (0.5)
Healthy beverage promotion (10 items, max. 10)
37.0 (3.7)
34.0 (3.4)
37.0 (3.7)
85.0 (8.5)
80.0 (8.0)
80.0 (8.0)
43.0 (4.3)
43.0 (4.3)
Alternatives to less healthy beverages in public venues (6 items,
16.7 (1.0)
8.3 (0.5)
16.7 (1.0)
33.3 (2.0)
53.3 (3.2)
53.3 (3.2)
56.7 (3.4)
56.7 (3.4)
7.5 (0.3)
0.0 (0.0)
7.5 (0.3)
7.5 (0.3)
0.0 (0.0)
0.0 (0.0)
60.0 (2.4)
17.5 (0.7)
58.6 (4.1)
0.0 (0.0)
65.7 (4.6)
7.1 (0.5)
7.1 (0.5)
7.1 (0.5)
45.7 (3.2)
45.7 (3.2)
Healthy foods in public places (5 items, max. 5)
39.3 (11.0) 38.6 (10.8)
Sugar sweetened beverages
max. 6) Restrict less healthy beverages in public venues (4 items, max. 4) Incentives and restrictions to improve healthy beverage consumption (7 items, max. 7)
trend, suggesting they prioritized guidelines for food availability over improving food-related lifestyles and behaviors. Both components of the policy environment served to improve nutrition for constituents, and each might provide an equally important method of accomplishing this goal. Although based on a limited sample, these differences demonstrated the value of systematic tools that measured communities’ nutritionrelated policies across settings to investigate the overall policy environment. Our study of the PIC offered an instrument that could provide a comprehensive look at the policy environment of an entire community. Policies have typically been understudied, particularly at the community level and in early care and education. The strengths of this system approach included allowing for future comparison across communities and settings. Future work is needed to confirm our findings, because with a sample of 8 communities, some of the trends were likely the result of clustering (e.g., the high proportions of Hispanic populations in the California communities). In addition, because many of the policies were at the state level, the communities in a state, such as California, would be affected by the same policies. One additional caveat was that the comprehensive nature of the instrument lent itself to time-intensive protocol training and data collection.
We described a carefully crafted instrument and protocol that demonstrated high interrater reliability. Future research is needed to investigate how the PIC might capture the policy environment in other communities, and how these environments might be associated with childhood obesity prevalence. The PIC allowed for the measurement of policies across multiple settings, and future work should analyze and compare how policies in multiple settings work together to complement efforts to reduce and prevent childhood obesity. Future studies might also compare how the policy environment in the United States, as captured by the PIC, compares with the policy environment in other countries, to investigate improvements and innovations that might be working in other countries that might be applied to the US policy environment. The PIC provided a reliable and feasible method of comprehensively capturing communities’ policy environments that might inform future policymaking and studies of policy impact. j
Houston. Dennis Kao is with the Department of Social Work, California State University, Fullerton. Maria Modelska and Hanadi Rifai are with the Texas Obesity Research Center and the Department of Civil and Environmental Engineering, University of Houston. Correspondence should be sent to Rebecca E. Lee, PhD, Arizona State University, 550 N. 3rd Street, Phoenix, AZ 85004 (e-mail: [email protected]). Reprints can be ordered at http://www.ajph.org by clicking the “Reprints” link. This article was accepted January 7, 2015.
Contributors R. E. Lee led the methodological design, oversaw the collection of data, and led the writing of the article. A. M. Hallett, N. Parker, and O. Kudia led the collection and analysis of data and assisted in writing the article. D. Kao, M. Modelska, and H. Rifai participated in the article’s methodological design. D. P. O’Connor was the principal investigator at the Evaluation Center for the Childhood Obesity Research Demonstration (CORD) Project, oversaw the methodological design, oversaw the collection and analysis of data, and assisted in writing the article. All authors provided subject matter expertise, participated in the interpretation of the findings, provided extensive article commentary and review, and approved the final version of the article.
Acknowledgments About the Authors Rebecca E. Lee is with the College of Nursing and Health Innovation, Arizona State University, Phoenix. Allen M. Hallett, Nathan Parker, and Daniel P. O’Connor are with the Texas Obesity Research Center and the Department of Health and Human Performance, University of Houston. Ousswa Kudia is with the Department of Behavioral Science, University of Texas MD Anderson Cancer Center,
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This work was supported in part by cooperative agreement RFA-DP-11-007 (award U18DP003350) from the Centers for Disease Control and Prevention (CDC). Note. The findings and interpretations in this article are those solely of the authors and are not necessarily those of the CDC.
Human Participant Protection No protocol approval was required because no data were collected from human participants in this study.
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