Reducing Childhood Obesity through US Federal Policy

Reducing Childhood Obesity through US Federal Policy

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  Reducing Childhood Obesitythrough U.S. Federal Policy A Microsimulation Analysis Alyson H. Kristensen, MPH, Thomas J. Flottemesch, PhD, Michael V. Maciosek, PhD, Jennifer Jenson, MPH, MPP, Gillian Barclay, DDS, MPH, DrPH, Marice Ashe, JD, MPH, Eduardo J. Sanchez, MD, MPH, Mary Story, PhD, RD, Steven M. Teutsch, MD, MPH, Ross C. Brownson, PhD Background: Childhood obesity prevalence remains high in the U.S., especially among racial/ ethnic minorities and low-income populations. Federal policy is important in improving public health given its broad reach. Information is needed about federal policies that could reduce childhood obesity rates and by how much. Purpose: To estimate the impact of three federal policies on childhood obesity prevalence in 2032, after 20 years of implementation. Methods: Criteria were used to select the three following policies to reduce childhood obesity from 26 recommended policies: afterschool physical activity programs, a $0.01/ounce sugar-sweetened beverage (SSB) excise tax, and a ban on child-directed fast food TV advertising. For each policy, the literature was reviewed from January 2000 through July 2012 to find evidence of effectiveness and create average effect sizes. In 2012, a Markov microsimulation model estimated each policy’s impact on diet or physical activity, and then BMI, in a simulated school-aged population in 2032. Results: The microsimulation predicted that afterschool physical activity programs would reduce obesity the most among children aged 6–12 years (1.8 percentage points) and the advertising ban would reduce obesity the least (0.9 percentage points). The SSB excise tax would reduce obesity the most among adolescents aged 13–18 years (2.4 percentage points). All three policies would reduce obesity more among blacks and Hispanics than whites, with the SSB excise tax reducing obesity disparities the most. Conclusions: All three policies would reduce childhood obesity prevalence by 2032. However, a national $0.01/ounce SSB excise tax is the best option. (Am J Prev Med 2014;47(5):604–612) & 2014 American Journal of Preventive Medicine Introduction A lthough recent data suggest that childhood obes- ity has plateaued or begun to decline, pre- valence remains high.1,2 In 2009–2010, nearly one in three U.S. youth aged 2–19 years were overweight or obese and 17% were obese.3 Significant disparities in obesity prevalence persist among racial/ethnic groups and by SES. More Hispanic (21.2%) and non-Hispanic black (24.3%) youth were obese in 2009–2010 than non- Hispanic white youth (14.0%).3 Obesity is also higher among lower-income children than higher-income chil- dren.4 Further, obese adolescents tend to remain obese as adults,5,6 making childhood the ideal time to prevent obesity. For these reasons, policymakers are interested in effective programs and policies to reduce childhood obesity. States and localities are increasingly using laws, regulations, and other policy tools to promote healthy eating and physical activity (PA).7 However, federal policies can reach larger populations and fund programs that benefit populations at risk for obesity, and thus play From Partnership for Prevention (Kristensen, Jenson), Washington, District of Columbia; HealthPartners Institute for Education and Research (Flotte- mesch, Maciosek), Minneapolis, Minnesota; Aetna Foundation Inc. (Barclay), Hartford, Connecticut; ChangeLab Solutions (Ashe), Oakland; Los Angeles County Department of Public Health (Teutsch, retired), Los Angeles, California; American Heart Association (Sanchez), Dallas, Texas; Community and Family Medicine and Global Health (Story), Duke University, Durham, North Carolina; and Brown School and Division of Public Health Sciences (Brownson), Washington University in St. Louis, St. Louis, Missouri Address correspondence to: Alyson Kristensen, MPH, Partnership for Prevention, 1015 18th St. NW, Ste 300, Washington DC 20036. E-mail: [email protected]. 0749-3797/$36.00 http://dx.doi.org/10.1016/j.amepre.2014.07.011 604 Am J Prev Med 2014;47(5):604–612 & 2014 American Journal of Preventive Medicine Published by Elsevier Inc.
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  an essential rolein improving public health. Information is needed about which federal policies could reduce childhood obesity rates and by how much. The purpose of this study is to estimate the impact of three federal policies on childhood obesity prevalence in 2032, after 20 years of implementation. Methods The methods used in this analysis are summarized below; see the Appendix for more details. Microsimulation models are useful in informing health policy decision making.8,9 In 2012, a microsimulation model (developed in TreeAge, TreeAge Software, Inc.) examined how three federal policies affect obesity-related behaviors (PA and diet); BMI; and obesity prevalence in a simulated school-aged U.S. population. The model generated annual values for these measures based on demographic and behavioral variables and then aggregated indi- vidual estimates to create population-level results. The initial population was drawn randomly from a sample of simulated school-aged children (6–12 years) and adolescents (13–18 years) with demographic characteristics matching that of the U.S., using 2010 U.S. Census data. The model’s primary outcome variables were BMI and changes in the percentage of overweight or obese youth. Obesity and overweight were determined by comparing BMI values from the model to BMI values from CDC growth charts. The current CDC definitions of obesity (BMI at or above the 95th percentile for age and sex) and overweight (BMI at or above the 85th percentile and below the 95th percentile for age and sex) were used.10 The microsimulation model estimated yearly changes in PA, diet, and BMI using multivariable equations developed using 2001– 2010 continuous National Health and Nutrition Examination Survey (NHANES) data. The equations included measures of PA expressed in METs and dietary recall measures, including total daily calories and grams of fat, carbohydrates, and sugar. NHANES data were used to assign initial health and BMI measurements to the simulated population and then to estimate the impact of changes in health behaviors on changes in BMI over time. Each simulated agent was initialized using a two-step process. First, age, sex, and ethnicity were assigned with the distribution of these demographic variables across the simulated population set equal to those in the 2010 U.S. Census. Second, each agent’s initial BMI, level of PA, and diet were set conditioned on that agent’s demographics. The distribution of each factor across the simulated population was set equal to the distribution observed for that agent’s corresponding demographic group in the NHANES sample, scaled to the U.S. child and adolescent population. The model represents changes in BMI for a simulated popula- tion over time, but the NHANES provides a series of cross- sectional estimates. To account for this difference, annual changes in BMI were estimated on the basis of age, sex, and ethnicity trends. The relative BMI remains constant until a policy inter- vention causes it to trend downward to a new level consistent with expected changes in behavior from the intervention. Each policy was introduced in the model independently. Next, a systematic process was used to search and review the literature on 26 recommended policies for preventing childhood obesity. The list was narrowed to three policies in a two-step process using multiple criteria, including effectiveness, potential reach into the general population and high-risk groups, feasibility, acceptability, precision of information for modeling, and potential impact on childhood obesity. Effectiveness was rated as “unknown,” “emerging,” “promising,” “effective (second tier),” or “effective (first tier).”11 The other criteria were rated low, medium/moderate, or high. The policies would (1) strengthen and expand federally funded afterschool programs to promote PA; (2) enact a $0.01/ounce excise tax on sugar-sweetened beverages (SSBs); and (3) ban fast food TV advertising targeting children aged 12 years and under. These policies target key obesity-related behaviors through the federal policy mechanisms of appropriation, taxation, and regulation. Table 1 summarizes the policies. Estimating Effect Sizes PubMed and journal article references were searched from January 2000 through July 2012 to find effectiveness data for the three policies and create average effect sizes. A systematic strategy and policy definition limited the scope of the literature search and identified key data elements for the population groups targeted by each policy. The literature search and abstraction process followed methods previously described.12 Owing to the varied nature of evidence, the general process to determine each policy’s average effect size was modified as needed. Table 2 lists model inputs. Effects of interventions enter the model through increases in PA or reductions in calories, both of which are determinants of BMI z scores. Afterschool Physical Activity Programs The literature search generated 55 potentially relevant articles. Inclusion and exclusion criteria identified 16 articles suitable for abstraction. To be abstracted, studies needed to evaluate an afterschool intervention or program, report measures that could be used in the model, have a sample size of at least 50, describe an intervention that resembled a typical afterschool program, use randomization or a pre-test/post-test design, require participants to engage in PA, and be conducted in an Organization for Economic Cooperation and Development country. One article was eliminated during abstraction because the intervention was unclear. Effect sizes for the 15 articles13–27 were summarized by sample size; sex; race; location; age (school grade); baseline BMI (normal or obese); program adherence; intervention design; and interven- tion intensity. Because most studies did not provide sample demographics, effect sizes were averaged across all racial/ethnic groups. The policy was modeled such that all programs were offered to all youth for the entire year. The modeled intervention combined individual interventions using sample sizes and demo- graphic breakdowns as weighting factors. Sugar-Sweetened Beverage Excise Tax No well-controlled trials were found that directly assessed an excise tax’s impact on youth SSB consumption or the relationship between SSB consumption and childhood obesity. Instead, the literature search yielded well-controlled, econometric studies using observational data that showed a negative association between increased taxes on SSBs and SSB purchases. Few studies quantified Kristensen et al / Am J Prev Med 2014;47(5):604–612 605 November 2014
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  the impact ofa SSB tax or price increase on childhood obesity prevalence, and those that did were based on econometric forecasts. However, two key associations were found in the litera- ture28–33 : (1) between SSB consumption and per-ounce excise taxes and (2) between BMI and existing state-level soda taxes. From these studies, it was estimated that SSB consumption decreased by 25% among children and 35% among adolescents because of reduced purchasing resulting from the excise tax. The policy’s impact on obesity resulted from reduced consumption of sugar and total calories, assuming complete substitution with a zero-calorie beverage. A composite SSB was created on the basis of the nutritional content of ten calorically sweetened beverages. Ban on Fast Food Television Advertising Targeting Children No studies were found that directly evaluated the impact of restricting advertising on youth consumption of high-calorie, low-nutrient foods/beverages. The literature search also did not identify any observational or experimental studies in the U.S. that examined the same issue. Instead, it yielded observational studies that explored the association between exposure to advertising and consumption of certain types of food, food purchases, or BMI. Direct evidence appears limited to observational data assessing the impact of Quebec’s ban on child-directed advertising on fast food purchases.34 Therefore, this analysis limits the impact of an advertising ban to fast food and uses an age limit of 12 years because research shows children under this age do not understand advertising’s persuasive intent and cannot cognitively defend themselves from its effects.35,36 Two estimates of changes in purchasing following the advertising ban were derived and applied to modeled individuals consuming two or more fast food meals per week. The ban’s impact was assumed to be two to three fewer meals per week for children, whose parents purchase most fast food, and three to five fewer meals per week for adolescents, who can purchase fast food themselves. Although the policy targets children, the analysis included adoles- cents to show that the effect continues into adolescence. The policy’s impact on obesity was modeled through dietary change (reduced sugar, carbohydrates, fat, and total calories). A composite fast food meal was created by averaging adult or children’s portions of main dishes, sides, and beverages from 23 large fast food chain restaurants. Adult portions were included because adolescents eat adult meals. These restaurant-specific meals were then averaged to create the composite meal. A meal meeting the 2012 National School Lunch Program standards was assumed to be substituted for each fast food meal not eaten, which would result in an average of 504 fewer calories and 94 fewer grams of sugar consumed per week. Results Table 3 summarizes baseline obesity rates and behaviors for the simulated school-aged population. Consistent with the underlying data, nearly 20% are obese and considerable racial and ethnic disparities exist. Just over one quarter of children meet the recommended 1 hour of daily PA, whereas only one in five adolescents do. Approximately one third of the simulated youth con- sume SSBs at least twice per day (32.3%) and fast food at least twice a week (35.4%). Table 4 summarizes each policy’s predicted impact on PA or diet in 2032. Afterschool PA programs would increase the number of children and adolescents who met the daily PA recommendation by 7.7% and 7.4%, respectively. This change would represent an addi- tional 25 minutes of moderate-to-vigorous PA per day. A $0.01/ounce SSB excise tax would reduce the num- ber of children and adolescents consuming two or more SSBs per day by 11.4% and 16.6%, respectively, Table 1. Summary of U.S. federal policies to reduce childhood obesity Afterschool physical activity $0.01/ounce excise tax on SSBs Ban on fast food television advertising targeting children Policy description Programs that incorporate 60–90 minutes of moderate-to-vigorous physical activity 3–5 days per week and are delivered after school A 1-cent-per-ounce excise tax placed on beverages containing added caloric sweeteners A ban on fast food television advertising that targets children aged 12 and under Targeted population(s) Children and adolescents aged 6–18 years Children and adolescents aged 6–18 years Children aged 6–12 years Summary impact The policy results in a potential average increase in moderate-to- vigorous physical activity of 22.6% across both age groups The potential impact is smaller among children aged 6–12 years (15.3%) than adolescents aged 13–18 years (25.3%) because of differences in baseline activity levels There are two potential impacts: For children (aged 6–12 years), whose parents purchase most SSBs, the tax results in a 25% average decrease in SSB consumption For adolescents (aged 13–18 years), who can independently purchase SSBs, the tax results in a 35% average decrease in SSB consumption There are two potential impacts: For children (aged 6–12 years), whose parents purchase most fast food, the advertising ban results in 2–3 fewer fast food meals consumed per week For adolescents (aged 13–18 years), who have their own purchasing power, the advertising ban results in 3–5 fewer fast food meals consumed per week Kristensen et al / Am J Prev Med 2014;47(5):604–612606 www.ajpmonline.org
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  Table 2. Microsimulationmodel parameters Model inputs Baseline value Range (þ/À) for sensitivity analysis Data source DEMOGRAPHICS, BMI AND HEALTH BEHAVIORS Age U.S. population distribution NA 2010 U.S. Census Race and ethnicity BMIa 21.4 5.8 2001–2010 Continuous NHANES Physical activity level (METs)a,c 1,902.7 471.9 Dieta Daily calories 2,027.0 723.3 2001–2010 Continuous NHANES Daily grams of sugar 131.2 59.3 Daily grams of fat 75.3 31.9 Daily grams of carbohydrates 269.4 99.7 AFTERSCHOOL PHYSICAL ACTIVITY Percentage change in METs/week (policy effect size)b 22.6% 5.8% Abstracted articles Program adherence b 52.5% 2.3% $0.01/OUNCE EXCISE TAX ON SSBS Composite SSB (per 8-ounce serving) Abstracted from beverage labelsd Calories 101.9 16.6 Grams of sugar 26.3 5.4 Current daily 8-ounce servingsa 2.2 1.4 2001–2010 Continuous NHANES; YRBS Percent change in consumption (policy effect size) SSB literature Children (6–12 years) 25% 12.5%, 37.5% Adolescents (13–18 years) 35% 17.5%, 52.5% BAN ON FAST FOOD TELEVISION ADVERTISING TARGETING CHILDREN Composite fast food meal Abstracted from menuse Calories 744.0 46.6 Grams of sugar 52.0 10.1 Grams of fat 29.0 3.0 Current weekly servingsf 2.5 2.8 2003–2010 Continuous NHANES Reduced servings of fast food (policy effect size)g Marketing literature Children (6–12 years) 2.5 1.25, 3.75 Adolescents (13–18 years) 4 2, 6 (continued on next page) Kristensen et al / Am J Prev Med 2014;47(5):604–612 607 November 2014
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  which translates intoan average daily reduction of 1.5 beverages in children and 2.2 beverages in adolescents. The microsimulation predicted that among the three policies, the ban on child-directed fast food TV advertis- ing would impact behaviors the most. The number of children eating two or more fast food meals per week would drop by almost 20%, and for adolescents by 18%. The policy would reduce consumption in children more than adolescents because children consumed more fast food at baseline and thus would have a greater potential for change. Table 5 summarizes the policies’ potential impact on rates of overweight and obesity in 2032. Obesity would decline, but overweight would increase slightly as obese individuals lose weight. The afterschool policy would reduce obesity prevalence in children by 1.8 percentage points and by 1.9 points in adolescents. The Table 2. Microsimulation model parameters (continued) Model inputs Baseline value Range (þ/À) for sensitivity analysis Data source Substituted healthy meal National School Lunch Program Calories 600 NA Grams of sugar 25 NA Grams of fat 10 NA a Listed value is the average across the entire simulated population. Unique values for each simulated individual are drawn from age-, gender-, and ethnicity-specific distributions fit to the 2001–2010 continuous NHANES data. b Adjusted by age and grade in school according to abstracted literature. c Expressed in metabolic equivalents (METs) derived from CDC tables and assuming a sedentary floor of 1,200 (8 sleeping hours and 16 inactive hours). d Abstracted beverages were Coke, Sprite, Snapple, Juicy Juice, Hi-C, Capri Sun, Country Time Lemonade, Powerade, Gatorade, and Sunny D. e Abstracted restaurants were Arby’s, Burger King, McDonald’s, Wendy’s, Taco Bell, Dairy Queen, Subway, Hardee’s, Carl's Jr., In-N-Out Burger, Jack in the Box, White Castle, Krystal, Popeyes, Chick-fil-A, KFC, Chipotle, Sonic, Domino’s Pizza, Pizza Hut, Little Caesars Pizza, Papa John's Pizza, and CiCi's Pizza. f The average for the entire modeled population where almost 2/3 (64.6% [Table 3]) consumed fewer than 2 fast food meals/week. g Those averaging fewer fast food meals per week than the modeled effect size experienced a complete elimination of fast food consumption. Table 3. Baseline weight and behavior distributions of simulated population, % Age Race/ ethnicitya Normal weight (o85th percentile of BMI distribution) Overweight (Z85th– o95th percentile of BMI distribution) Obese (Z95th percentile of BMI distribution) Meets physical activity recommendationb Consumes Z2 SSBs/ dayc Consumes Z2 fast food meals/ weekc All All 64.3 15.9 19.8 24.3 32.3 35.4 6–12 All 65.6 15.5 19.0 26.8 30.5 37.9 White 68.3 15.4 16.3 24.4 29.3 36.8 Black 61.8 15.6 22.7 31.5 30.1 37.7 Hispanic 57.5 17.5 25.1 24.1 30.0 41.5 13–18 All 63.0 16.3 20.7 21.5 34.2 32.7 White 67.9 15.0 17.1 19.7 35.8 37.2 Black 59.6 15.6 24.8 17.0 28.2 21.3 Hispanic 56.0 18.6 25.4 19.0 29.6 32.3 a Other was removed from race/ethnicity. b At least 1 hour of daily physical activity (CDC’s recommendation for children aged 6–17 years). c Extrapolated from the NHANES 2-day food frequency questionnaire. Kristensen et al / Am J Prev Med 2014;47(5):604–612608 www.ajpmonline.org
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  microsimulation predicted thatdisparities would decrease for black and Hispanic adolescents, with obesity decreasing by 2.3 and 2.4 percentage points, respectively, compared to 1.6 for whites. The change would be similar for children. The predicted impact of afterschool PA programs on disparities reflects higher baseline rates of obesity among blacks and Hispanics and the non-linear relationship between behavior change and BMI. For children, after- school PA programs would have the largest impact on obesity of the three policies. For adolescents, the $0.01/ounce SSB excise tax had the largest predicted overall impact on obesity, resulting in a 2.4 percentage point decrease. Obesity would decrease by 1.6 percentage points for children. The tax would also reduce disparities, especially in adolescents. Obesity in blacks and Hispanics would drop by 3.0 and 2.9 percentage points, respectively, compared to 2.0 percentage points in whites. The greater predicted impact for black and Hispanic adolescents was despite whites’ higher baseline SSB consumption. This was due to two factors: Compared to blacks and Hispanics, whites had higher income levels and are therefore less affected by a price increase, and had a lower baseline rate of obesity. The child-directed ban on fast food TV advertising has the greatest predicted behavioral impact, but would reduce obesity prevalence the least. This is due to the substitution effect and the policy’s narrow focus on fast food. Although its predicted impact on obesity is small, the large behavioral result shows that TV advertising affects what children eat. Like the other policies, its estimated impact is greater for blacks and Hispanics, who watch more TV and are more heavily targeted by food marketers than whites, and thus are more impacted by reduced advertising.37 Although this policy targets children aged 12 years and younger, it would also reduce obesity in adolescents because their baseline consumption of fast food is decreased because of less exposure to advertising as children. The Appendix describes the key univariate results of a sensitivity analysis. Discussion This microsimulation analysis suggests that long-term implementation of three federal policies could reduce childhood obesity in the U.S. To our knowledge, this study provides the first quantitative estimate of the potential impact of afterschool PA programs on U.S. childhood obesity prevalence. The use of microsimulation contributes to the childhood obesity literature because behavior change can be modeled over time in the simulated population. This approach differs from models that derive estimates from population-level trends, and provides valuable information as to how policies may impact known disparities in health behaviors and obesity in different populations. For the SSB and fast food advertising policies, this study’s effect sizes are consistent with, but smaller than, prior work owing to the use of microsimulation, narrower policy definitions, and different assumptions. In this study, a $0.01/ounce excise tax on SSBs would reduce obesity by 1.6 percentage points among 6–12-year-olds and 2.4 percentage points among 13–18-year-olds in 2032. Smith et al.28 estimated that a 20% price increase (roughly equivalent to a $0.01/ounce excise tax) on all SSBs could reduce childhood obesity prevalence by 2.9 percentage points. Sturm and colleagues29 estimated that an 18% differential soda tax (i.e., the difference between the regular state sales tax and the higher soda tax) would correspond to a 20% reduction of the excess BMI gain seen between the third and fifth grades if the tax impact is linear. Table 4. Change in childhood obesity-related behaviors in 2032, % Ages Race/ ethnicitya Change in youth meeting physical activity recommendation Change in youth consuming Z2 SSBs/day Change in youth consuming Z2 fast food meals/week All All 7.5 –13.9 –18.8 6–12 All 7.7 –11.4 –19.6 White 7.1 –10.6 –18.0 Black 8.7 –11.2 –18.5 Hispanic 7.0 –11.3 –22.4 13–18 All 7.4 –16.6 –18.0 White 6.9 –15.9 –16.2 Black 6.7 –14.5 –12.1 Hispanic 6.6 –14.5 –21.3 a Other was removed from race/ethnicity. Kristensen et al / Am J Prev Med 2014;47(5):604–612 609 November 2014
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  According to thepresent study, a ban on child- directed fast food TV advertising would reduce obesity among children and adolescents by nearly 1 percentage point in 2032. Chou et al.38 estimated that banning all TV fast food advertising would reduce the number of obese children aged 3–11 years by 18%. The model of Veerman and col- leagues39 predicts that a ban on all TV food advertising would reduce obesity prevalence among U.S. children aged 6–12 years by 2.5 percentage points. The bans in both studies are much broader than in this study. All three policies could reduce childhood obesity prevalence, particularly among blacks and His- panics, who have higher rates of obesity than whites, thus demonstrating that federal policy could alter the childhood obesity epidemic. Although the microsimulation predicts that each policy would reduce obesity in children and ado- lescents, the $0.01/ounce SSB excise tax has char- acteristics that make it the best option. It reduces obesity while generating significant revenue for additional obesity prevention activ- ities. Andreyeva et al.40 estimated that a national $0.01/ounce SSB excise tax would have generated $13.25 billion in 2010. It would also reduce obesity among adults who consume SSBs, does not req- uire substantial federal funding to implement (unlike the afterschool policy), and would not face the legal hurdles that new regulations often encounter. Unfortunately, reduced federal spending, industry lobbying, a contentious political environment, and legal protection for commercial speech hinder near- term implementation of any of these policies. However, over the long time frame included in this analysis, the infeasible may become feasible as the evidence base for these policies grows and changes in public knowledge increase calls for stronger governmental action. Research showing the harm of consuming SSBs coupled with the need for new revenue sources may spur Congress to consider a national SSB excise tax. The courts may recognize that young children need protection against the damaging influence of junk food advertising, as was done previously for tobacco and alcohol advertising, or the federal Interagency Working Group’s volun- tary marketing guidelines could be implemented. In the meantime, the findings support state- and local-level action to enact SSB excise taxes, pro- mote PA in afterschool settings, and reduce mar- keting and advertising of unhealthy foods and beverages in public schools. Table5.Changeinchildhoodweightdistributionsin2032,% Afterschoolphysicalactivity$0.01/ounceexcisetaxonSSBs Banonfastfoodtelevisionadvertising targetingchildren Age Race/ ethnicitya Changein normalweight Changein overweight Changein obesity Changein normalweight Changein overweight Changein obesity Changein normalweight Changein overweight Changein obesity AllAll1.70.2–1.91.70.2–1.90.60.2–0.9 6–12All1.60.2–1.81.20.3–1.60.70.2–0.9 White1.60.1–1.61.10.2–1.30.70.0–0.7 Black1.70.5–2.21.40.5–1.90.70.3–1.0 Hispanic1.90.4–2.31.50.5–2.00.80.4–1.2 13–18All1.80.1–1.92.30.1–2.40.60.3–0.8 White1.8–0.2–1.62.2–0.2–2.00.60.1–0.7 Black1.80.6–2.32.50.6–3.00.50.5–1.0 Hispanic2.00.4–2.42.70.2–2.90.60.5–1.1 a Otherwasremovedfromrace/ethnicity. Kristensen et al / Am J Prev Med 2014;47(5):604–612610 www.ajpmonline.org
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  Limitations This study hasseveral limitations. Modeling childhood obesity is challenging and others believe attempts should stop at energy balance owing to insufficient data on the association between changes in behaviors and changes in BMI z scores.41 We agree that the challenges are significant, but attempts to examine policy impact on childhood obesity have relevance. These results are only as accurate as the method used for translating short-term study results into multiple-year effects and the reliability of cross-sectional data in determining how changes in PA and diet impact BMI z scores. Although strong survey surveillance systems allow robust estimation of baseline trends, there are little effectiveness data for the SSB and advertising policies, particularly in children, and existing data often come from observational studies. To broaden the evidence base, international studies were included in this analysis, which may limit applicability in the U.S. In addition, the estimated policy impact is sensitive to the model assumptions. For instance, substituting a caloric bever- age, rather than a zero-calorie beverage, can reduce the SSB policy’s estimated impact by more than 60%. In the absence of data on substitution effects in food consumption resulting from an advertising ban, it was assu- med that a lower-calorie meal would be available and con- sumed instead. Another limitation is the inability to assess interaction effects among the three policies or with existing policies, such as state-level physical education policies. In this analysis, policies were assessed independently, but to reverse the childhood obesity epidemic, a comprehensive set of national policies would need to be implemented. Conclusions The three federal policies in this analysis could each reduce childhood obesity prevalence by 2032. However, a national $0.01/ounce SSB excise tax is the best option given its ability to generate revenue for additional obesity prevention activities and reduce obesity among SSB- consuming adults. The authors thank Jud Richland, MPH, for conceptualizing this work and developing the proposal; Ana Lindsay, DrPH, MPH, for contributing to policy selection; Dana McGree for providing administrative and project management support; and Sheena De Freitas, MPH, and Amanda Asgeirsson, MPH, for their research assistance. The work of each author was supported entirely by the Aetna Foundation. No financial disclosures were reported by the authors of this paper. References 1. CDC. Vital signs: obesity among low-income, preschool-aged children— U.S., 2008-2011. MMWR Morb Mortal Wkly Rep 2013;62(31):629–34. 2. Ogden CL, Carroll MD, Curtin LR, Lamb MM, Flegal KM. Prevalence of high body mass index in US children and adolescents, 2007-2008. JAMA 2010;303(3):242–9. 3. Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of obesity and trends in body mass index among US children and adolescents, 1999- 2010. JAMA 2012;307(5):483–90. 4. Rossen LM, Schoendorf KC. Measuring health disparities: trends in racial-ethnic and socioeconomic disparities in obesity among 2- to 18- year old youth in the U.S., 2001-2010. Ann Epidemiol 2012;22(10): 698–704. 5. Serdula MK, Ivery D, Coates RJ, Freedman DS, Williamson DF, Byers T. Do obese children become obese adults? A review of the literature. Prev Med 1993;22(2):167–77. 6. Singh AS, Mulder C, Twisk JW, van Mechelen W, Chinapaw MJ. Tracking of childhood overweight into adulthood: a systematic review of the literature. Obes Rev 2008;9(5):474–88. 7. Eyler AA, Nguyen L, Kong J, Yan Y, Brownson R. Patterns and predictors of enactment of state childhood obesity legislation in the U.S.: 2006-2009. Am J Public Health 2012;102(12):2294–302. 8. Abraham JM. Using microsimulation models to inform U.S. health policy making. 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