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Three Interventions That Reduce Childhood Obesity Are Projected To Save More Than They Cost To Implement Gortmaker, Steven L; Wang, Y Claire; Long, Michael W; Giles, Catherine M; Ward, Zachary J; Barrett,

Jessica L; Kenney, Erica L; Sonneville, Kendrin R; Afzal, Amna Sadaf; Resch, Stephen C; Cradock, Angie L

. Health Affairs ; Chevy Chase  Vol. 34, Iss. 11,  (Nov 2015): 1932-65A.

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ABSTRACT (ENGLISH) Policy makers seeking to reduce childhood obesity must prioritize investment in treatment and primary prevention.

We estimated the cost-effectiveness of seven interventions high on the obesity policy agenda: a sugar-sweetened

beverage excise tax; elimination of the tax subsidy for advertising unhealthy food to children; restaurant menu

calorie labeling; nutrition standards for school meals; nutrition standards for all other food and beverages sold in

schools; improved early care and education; and increased access to adolescent bariatric surgery. We used

systematic reviews and a microsimulation model of national implementation of the interventions over the period

2015-25 to estimate their impact on obesity prevalence and their cost-effectiveness for reducing the body mass

index of individuals. In our model, three of the seven interventions-excise tax, elimination of the tax deduction, and

nutrition standards for food and beverages sold in schools outside of meals-saved more in health care costs than

they cost to implement. Each of the three interventions prevented 129,000-576,000 cases of childhood obesity in

2025. Adolescent bariatric surgery had a negligible impact on obesity prevalence. Our results highlight the

importance of primary prevention for policy makers aiming to reduce childhood obesity. FULL TEXT  

Headnote

ABSTRACT Policy makers seeking to reduce childhood obesity must prioritize investment in treatment and primary

prevention. We estimated the cost-effectiveness of seven interventions high on the obesity policy agenda: a sugar-

sweetened beverage excise tax; elimination of the tax subsidy for advertising unhealthy food to children;

restaurant menu calorie labeling; nutrition standards for school meals; nutrition standards for all other food and

beverages sold in schools; improved early care and education; and increased access to adolescent bariatric

surgery. We used systematic reviews and a microsimulation model of national implementation of the interventions

over the period 2015-25 to estimate their impact on obesity prevalence and their cost-effectiveness for reducing

the body mass index of individuals. In our model, three of the seven interventions-excise tax, elimination of the tax

deduction, and nutrition standards for food and beverages sold in schools outside of meals-saved more in health

care costs than they cost to implement. Each of the three interventions prevented 129,000-576,000 cases of

childhood obesity in 2025. Adolescent bariatric surgery had a negligible impact on obesity prevalence. Our results

highlight the importance of primary prevention for policy makers aiming to reduce childhood obesity.

The childhood obesity epidemic in the United States affects all segments of society. There is a clear need for

action by governments, foundations, and other relevant institutions to address this public health problem.

Controlling childhood obesity is complex because many risk behaviors are involved, shaped by multiple

environments and requiring multiple intervention strategies.1-4 However, simply asking what works without

considering costs has led to the proliferation of obesity treatment and prevention initiatives with limited evaluative

information. Little serious discussion has taken place about relative costs or cost-effectiveness. When we

searched the PubMed database of the National Library of Medicine for articles published through 2014 containing

the term child obesity, we found more than 31,000, but only 89 of these also contained the term cost-effectiveness.

Communities and health agencies have limited resources to address high rates of childhood obesity and need to

know how best to invest those resources.

There are two main approaches to altering the population prevalence of obesity in children: treating obesity after

onset and preventing excess weight gain (primary prevention). Many studies have documented the effectiveness

of interventions using these two different ap- proaches. For example, a meta-analysis of ado-lescent bariatric

surgery studies indicates an average reduction in body mass index (BMI) of 13.5 kg/m2 following this procedure.5

Some nonsurgical interventions to treat childhood obesity are effective, but effect sizes are small relative to the

high BMIs (or BMI z-scores-that is, BMI scores that are standardized for age and sex) of the children before the

intervention,6 and treatments may reach too few children to have a substantial population-level impact. For

example, bariatric surgery is used with only about 1,000 adolescents per year.7

The promise of primary prevention strategies during childhood has been bolstered by recent findings generated by

mathematical models of the physiological development of excess weight in children, adolescents, and adults.8,9

Modeling indicates that excess weight accumulates slowly, and excess weight gain among young children is dueto

relatively small changes in energy balance.

For example, among children ages 2-5, average excess weight gain is driven by an excess of about 33 extra

kilocalories per day.10 Changes needed to prevent excess weight gain and prevent obesity are thus quite small in

childhood. By adolescence, however, excess weight has accumulated for more than a decade, with an average

imbalance of almost 200 extra kcal/day.8,10 The typical adult with a BMI greater than 35 (about 14 percent of the

adult population) consumes 500 kcal/day more than is needed to maintain a healthy body weight.9 Improving

energy balance via improved diet and physical activity early in childhood thus requires much smaller changes than

those needed once obesity is established in adolescence and adulthood.

In addition, a large body of experimental evidence indicates that certain behavioral changes can reduce BMI and

obesity prevalence in children. For example, as documented in online Appendix A1,11 there is clear evidence of the

effectiveness of reducing the intake of sugarsweetened beverages on reducing BMI and obesity prevalence.

There is also strong evidence that reducing television viewing and other screen time leads to significant reductions

in BMI and obesity prevalence, mainly via dietary changes12 (also documented in Appendix A2).11 Despite

growing evidence that targeted interventions can improve diet and reduce BMI and obesity prevalence, there is

limited evidence concerning the cost-effectiveness of these approaches and the potential US population-level

impact of either treatment or preventive interventions.

In this article we present results of an evidence review and microsimulation modeling project concerning the cost-

effectiveness and population-level impact of seven interventions identified as potentially important strategies for

addressing childhood obesity. We conducted systematic evidence reviews of the interventions' effectiveness and

estimated costs and reach under specified implementation scenarios described in Appendices A1, A2, and A4-

A8.11 We developed a microsimulationmodel to assess key cost-effectiveness metrics of these interventions if

they were to be implemented nationally.

Study Data And Methods

We developed an evidence review process and microsimulation model to evaluate the costeffectiveness of

interventions for childhood obesity. Our modeling framework built on the Australian Assessing Cost-Effectiveness

approach13,14 in obesity15 and prevention studies.16 Our microsimulation model used US population, mortality,

and health care cost data. We focusedonoutcomes ofcostperBMIunitchange over two years following an

intervention and tenyear changes in obesity, health care costs, and net costs. We followed recommendations of

the US Panel on Cost-Effectiveness in Health and Medicine in reporting our results, including using a 3 percent

discount rate.17

Our approach has distinct methodological components designed to improve both the strength of evidence and the

applicability of results to real-world decision making.We created a stakeholder group of thirty-two US policy

makers, researchers, and nutrition and physical activity experts to provide advice concerning the selection of

interventions, evaluation of data, analyses, and implementation and equity issues. This group advised us to look

broadly for interventions to evaluate across settings and sectors. The clinical subgroup selected adolescent

bariatric surgery as an important benchmark clinical intervention to evaluate, since many insurers pay for this

treatment.18

Interventions Our stakeholder group selected for the study seveninterventions that are high on the treatment and

prevention policy agenda (further details about the interventions are provided in the Appendices).11 The

interventions are as follows: an excise tax of one cent per ounce on sugar-sweetened beverages, applied nationally

and administered at the state level; the elimination of the tax deductibility of advertising costs for television ads

seen by children and adolescents for nutritionally poor foods and beverages; restaurant menu calorie labeling,

modeled on the federal menu regulations to be implemented under the Affordable Care Act; implementation of

nutrition standards for federally reimbursable school meals sold through the National School Lunch and School

Breakfast Programs, modeled on US Department of Agriculture (USDA) regulations implemented under the

Healthy, Hunger-Free Kids Act of 2010; implementation of nutrition standards for all foods and beverages sold in

schools outside of reimbursable school meals, modeled on USDA regulations implemented under the Healthy,

Hunger-Free Kids Act; improved early childhood educationpolicies and practices, including the national

dissemination of the Nutrition and Physical Activity SelfAssessment for Child Care (NAP SACC) program; and a

nationwide fourfold increase in the use of adolescent bariatric surgery.

Intervention Specifications, Implementation Scenarios, And Costs We specified a national implementation

scenario for each of the interventions using the best available data for population eligibility and costs at each level

of implementation, from recruitment to outcomes. Costing followed standard guidelines19,20 (for details of

models and costing, see Appendix A3).11 All costs were calculated in 2014 dollars and adjusted for inflation using

the Consumer Price Index for all nonmedical costs and the Medical Care Consumer Price Index for medical costs.

Evidence Reviews Of Intervention Effects We estimated the effects of each of the seven interventions using an

evidence review process consistent with the Grading of Recommendations Assessment, Development, and

Evaluation (GRADE) approach21 and guidelines from the Cochrane Collaboration.22 Details of the evidence

reviews for the interventions are provided in Appendices A1, A2, and A4-A8.11

Microsimulation Model We developed a microsimulation model to calculate the costs and effectiveness of the

interventions through their impact on BMI changes, obesity prevalence, and obesity-related health care costs over

ten years (2015-25). This is a stochastic, discrete-time, individual-level microsimulation model of the US population

designed to simulate the experience of the population from 2015 to 2025.

The model used data from the Census Bureau, American Community Survey, Behavioral Risk Factor Surveillance

System, National Health and Nutrition Examination Surveys (NHANES), and National Survey of Children's Health. It

also used longitudinal data about weight and height from the National Longitudinal Survey of Youth, National

Longitudinal Study of Adolescent to Adult Health, Early Childhood Longitudinal Study-Kindergarten, Panel Survey

of Income Dynamics, and NHANES I Epidemiologic Followup Study.

We used smoking initiation and cessation rates from the National Health Interview Surveys and mortality rates by

smoking status and BMI from the NIH-AARP Diet and Health Study. Details of the data, analyses, and model are

provided in Appendix A3, and key model input parameters are listed in Appendix Exhibit A3.1.11

The estimated effects of the interventions on health care costs werebased on national analyses that

indicatedexcess health carecostsassociated with obesity among children and adults (see Appendix A3).11 We

assumed that each intervention took time-typically 18-36 months-to decrease the BMI of individuals who received

each intervention.8,9 Estimates of intervention costs included one-time start-up and ongoing costs, as well as

enforcement and compliance costs, but did not include costs of passing a policy. The annual costs for each

intervention are the average of its discounted total costs.

We used a "modified" societal perspective on costs. This means that we did not include several possible economic

impacts of the interventions, such as productivity losses associated with obesity or patient costs for items such

as transportation to clinic visits or the value of time spent seeking or receiving medical care. It was reasonable to

exclude these economic impacts because they are difficult to estimate systematically and likely to be small within

a ten-year period, relative to the intervention and health care costs.

We assumed that effects were sustained over the model's time frame-that is, eight years after two start-

upyears.For policy changes such asthe sugar-sweetened beverage excise tax, the elimination of the tax subsidy for

advertising unhealthy food to children, and restaurant menu calorie labeling, sustaining an effect for ten years is

reasonable, as the changed policy will continueoverthatperiod.For theinterventions that set nutrition standards for

school meals and other foods and beverages sold in schools, we can assume that most children will be exposed to

these for a substantial period of time-for example, from first through twelfth grades. For bariatric surgery, we can

also assume that the surgical change will persist over this time period.

Details of key input parameters for the interventions modeled where there is known variation from reviews of the

relevant literature, including the parameters' distributions and assumptions, are outlined in Appendices A1, A2, and

A4-A8.11 As explained above, all results are expressed in 2014 US dollars and discounted at 3 percent annually.

We calculated costs per BMI units reduced over two years (2015-17). We estimated health care costs, net costs,

and net costs saved per dollar spent over ten years (2015-25), since this is a time frame frequently used in policy

calculations.Weinflatedhealth carecoststo2014 dollars using the Medical Care Consumer Price Index. We

estimated obesity cases prevented and changes in childhood obesity prevalence in 2025, at the end of the period

of analysis.

Uncertainty And Sensitivity Analyses We calculated probabilistic sensitivity analyses by simultaneously sampling

all parameter values from predetermined distributions. We report 95 percent uncertainty intervals (around point

estimates) in Exhibits 1 and 2, taking 2.5 and 97.5 percentile values from simulated data.23 We calculated

uncertainty intervals using Monte Carlo simulations programmed in Java over one thousand iterations of the

model for a population of one million simulated individuals scaled to the national population size.

Consultation The stakeholder group assisted us in reviewing additional considerations, including quality of

evidence, equity, acceptability, feasibility, sustainability, side effects, and impacts on social and policy norms.

Limitations The study had several limitations. First, its results were based on a simulation model that incorporated

a broad range of data inputs. While we included the best available evidence on population characteristics, likely

trajectories of obesity prevalence, and obesity-related health care costs, our ability to forecast precise impacts of

all of the modeled interventionswas limited by the uncertainty around each of these inputs and by the

assumptions required to build the model (see Appendix A3).11

In previous publications we used a Markov cohort simulation model to estimate the impact of two of the

interventions modeled here, the sugar-sweetened beverage excise tax and the elimination of the tax subsidy for

advertising unhealthy food to children.24-26 The cohort model was limited in its ability to model heterogeneity of

individual differences, exposure to the intervention, and trajectories of BMI over the life course, and it could not

calculate population estimates for specific years. With the microsimulation model, we were able to estimate the

number of cases of obesity prevented. For both of these interventions, the estimated costs per BMI unit reduction

were similar under both modeling approaches, and both interventions were cost-saving.

Second, we modeled each of the interventions separately, which limited our ability to estimate their cumulative

effects. Future obesity prevention simulation modeling should begin to evaluate the impact of simultaneous

implementation of multiple interventions.

Third, there is limited evidence that directly links the interventions we evaluated to change in population-level

obesity prevalence. However, as detailed in Appendices A1, A2, and A4-A8,11 six of the interventions were

supported by randomized trials or natural or quasi-experimental evaluations27 that linked the intervention or

behavioral mechanism targeted by the intervention directly to reductions in BMI for recipients of each intervention.

We incorporated uncertainty for all of the underlying model inputs into the probabilistic uncertainty analyses (see

Appendix A3.1).11

Fourth, because we focused on obesity, we did not incorporate additional health improvements and health care

cost reductions due to improvements in diet and physical activity that were independent of reductions in BMI (for

example, reductions in diabetes and heart disease).28

Study Results

There were large differences in the projected populationreach of the interventions(Exhibit 1). The reach of bariatric

surgery, the smallest, was very limited, even assuming a fourfold increase in the number of adolescents who

receive the procedure. The most recent national data indicate that in 2012, among adolescents classified as

having grade 3 obesity (a BMI of roughly 40 or above), fewer than two in a thousand received the procedure

(Appendix A8).11 The largest population reaches occurred with interventions that would affect the whole

population, such as the sugar-sweetened beverage excise tax and restaurant menu calorie labeling-both of which

would reach 307 million people.

The annual costs of the interventions were driven by both the cost per person and the population reach and varied

greatly (Exhibit 1).

Differences across interventions in cost per BMI unit reduction varied more than 2,000-fold. Eliminating the tax

deduction for advertising nutritionally poor food to children would reduce a BMI unit for $0.66 per person, while

increasing access to bariatric surgery would reduce a BMI unit for $1,611.

Three of the interventions studied were found to be cost-saving across the range of modeled uncertainty: the

sugar-sweetened beverage excise tax, eliminating the tax subsidy for advertising unhealthy food to children, and

setting nutrition standards for food and beverages sold in schools outside of school meals (Exhibit 2). In other

words, these interventions were projected to save more in reduced health costs over the period studied than the

interventions would cost to implement. Perhaps more important, the interventions were projected to prevent

576,000, 129,100, and 345,000 cases of childhood obesity, respectively, in 2025. The net savings to society foreach

dollarspentwereprojectedtobe$30.78, $32.53, and $4.56, respectively.

Restaurant menu calorie labeling was also projected to be cost-saving (Exhibit 2), although on average the

uncertainty intervals were wide because of the wideuncertaintyintervalaround the estimated per meal reduction in

calories ordered or purchased as a result of the intervention (see Appendix A4).11 This uncertainty highlights the

need for ongoing monitoring of this policy when it is implemented nationwide in 2016. Of note, a study of

restaurant menu calorie labeling in King County, Washington, found that eighteen months after implementation of

menu calorie labeling regulations, restaurants had reduced their calorie content by 41 kilocalories per entrée,29 a

much larger effect than the reduction of 8 kilocalories per meal estimated in this study.

Setting nutrition standards for school meals would reach a very large population of children and have a substantial

impact: An estimated 1,816,000 cases of childhood obesity would be prevented, at a cost of $53 per BMI unit

change (Exhibits 1 and 2). Improved early care and educationpolicies and practices would reach a much smaller

segment of the population (1.18 million), preventing 38,400 childhood obesity cases if implemented nationally, at a

cost of $613 per BMI unit change.

The modeled preventive interventions could significantly reduce the overall prevalence of childhood obesity in the

United States. Currently, the prevalence of obesity among children and youth is about 17 percent.30 Based on our

model, the largest reduction in childhood obesity prevalence compared to no intervention would occur with the

implementation of nutrition standards for school meals (a reduction of 2.6 percent; data not shown), followed by

the sugar-sweetened beverage excise tax (0.8 percent). Adding in the two other cost-saving interventions

(elimination of the tax subsidy for advertising unhealthy food to children and setting nutrition standards for other

foods and beverages sold in schools) would reduce prevalence by an additional 0.7 percent.

These interventions would have a modest impact on obesity prevalence. Even if all were implemented and the

effects were additive, the overall impact would be a reduction of 4.1 percent, or 2.9 million cases of childhood

obesity prevented for the population in 2025.

Tax Revenue In addition to their effects on obesity, we estimated that both the sugar-sweetened beverage excise

tax and the elimination of the tax subsidy for advertising unhealthy food to children would lead to substantial

yearly tax revenues ($12.5 billion and $80 million, respectively). These revenues were not included in our

calculations of net costs.

Discussion

These results indicate that primary prevention of childhood obesity should be the remedy of choice. Four of the

interventions studied here have the potential for cost savings-that is, the interventions would cost less to

implement than they would save over the next ten years in health care costs-and would result in substantial

numbers of childhood obesity cases prevented.

The sugar-sweetened beverage excise tax- and, to a lesser extent, removing the tax deduction for advertising

unhealthy food to children- would also generate substantial revenue that could be used to fund other obesity

prevention interventions. The excise tax has been the focus of recent policy discussion,25,31 and the recent

enactment of an excise tax of one cent per ounce in Berkeley, California, and the national implementation of an

excise tax in Mexico indicate the growing political feasibility of this approach.

The improvements in meal standards in the National School Lunch and School Breakfast Programs as well as

implementation of the first meaningful national standards for all other foods and beverages sold in schools make

the Healthy, Hunger-Free Kids Act one of the most important national obesity prevention policy achievements in

recent decades. Although improving nutrition standards for school meals was not intended primarily as an obesity

reduction strategy, we estimated that this intervention-which includes improving the quality of school meals and

setting limits on portion sizes-would have the largest impact on reducing childhood obesity of any of the

interventions evaluated in this study.

The individual benefits of bariatric surgery and other intensive clinical interventions to treat obesity can be life

changing.32 Another promising new obesity treatment strategy employs lowcost technological approaches-

computerized clinical decision support-to effectively reduce excess childhood weight.33 Our study should in no

way discourage ongoing investment in advancing the quality, reach, and cost-effectiveness of clinical obesity

treatment. However, our results indicate that with current clinical practice, the United States will not be able to

treat its way out of the obesity epidemic. Instead, policy makers will need to expand investment in primary

prevention, focusing on interventions with broad population reach, proven individual effectiveness, and low cost of

implementation.

We modeled each intervention in this study separately to help policy makers prioritize investment in obesity

prevention. However, as the results show, none of the interventions by itself would be sufficient to reverse the

obesity epidemic. Instead, policy makers need todevelop a multifaceted prevention strategy that spans settings

and reaches individuals across the life course.

Because the energy gap that drives excess weight gain among young children is small, and adult obesity is difficult

to reverse, interventions early in the life course have the best chance of having a meaningful impact on long-term

obesity prevalence and related mortality and health care costs. However, early intervention will not

besufficientifyoungchildrenat ahealthyweight are subsequently introduced into environments that promote excess

weight gain later in childhood and in adulthood.

Increased access to adolescent bariatric surgery had the smallest reach and the highest cost per BMI unit

reduction. Of the other six interventions that we analyzed, improving early care and education using the NAP SACC

model both had the smallest reach, because of the intervention's relatively small age range and voluntary

implementation strategy, and was the most costly per BMI unit reduction. Nonetheless, this intervention might still

be a good investment, considering that even small changes among very young children can be important for

setting a healthier weight trajectory in childhood.

Additionally, the intervention focuses on improvements in nutrition, physical activity, and screen time for all

children and thus could have benefits for child development beyond reducing unhealthy weight gain. In contrast to

the tax policies we evaluated, which have been met with opposition from industry, the NAP SACC program is well

liked and has been widely adopted.

While policy makers should consider the longterm effectiveness of interventions that target young children,

substantially reducing health care expenditures due to obesity in the near term will require implementation of

strategies that target both children and adults. We estimated that over the decade 2015-25, the beverage excise

tax would save $14.2 billion in net costs, primarily due to reductions in adult health care costs. Interventions that

can achieve nearterm health cost savings among adults and reduce childhood obesity offer policy makers an

opportunity to make long-term investments in children's health while generating short-term returns. These results

are consistent with previous research that estimated the potential health cost savings and health gains from

reducing childhood obesity, much of which resulted from preventing obesity during adulthood.34

Conclusion

Reversing the tide of the childhood obesity epidemic will require sustained effort across all levels of government

and civil society for the foreseeable future. To make these efforts effective and sustainable during a period of

constrained public health resources, policy makers need to integrate the best available evidence on the potential

effectiveness, reach, and cost of proposed obesity strategies to prioritize the highest-value interventions.

We found that a number of preventive interventions would have substantial population-level impacts and would be

cost-saving. An important question for policy makers is, why are they not actively pursuing cost-effective policies

that can prevent childhood obesity and that cost less to implement than they would save for society?

Our results also highlight the critical impact that existing investments in improvements to the school food

environment would have on future obesity prevalence and indicate the importance of sustaining these preventive

strategies. Furthermore, while many of the preventive interventions inchildhood do not providesubstantial health

care cost savings (because most obesity-related health care costs occur later, in adulthood), childhood

interventions have the best chance of substantially reducing obesity prevalence and related mortality and health

care costs in the long run.

The focus of action for policy makers should be on implementing cost-effective preventive interventions, ideally

ones that would have broad population-level impact. Particularly attractive are interventions that affect both

children and adults, so that near-term health care cost savings can be achieved by reducing adult obesity and its

health consequences, while laying the groundwork for long-term cost savings by also reducing childhood and

adolescent obesity. ?

This work was supported in part by grants from The JPB Foundation; The Robert Wood Johnson Foundation (Grant

No. 66284); the Donald and Sue Pritzker Nutrition and Fitness Initiative; and the Centers for Disease Control and

Prevention (Grant No. U48/DP001946), including the Nutrition and Obesity Policy Research and Evaluation

Network. This work is solely the responsibility of the authors and does not represent the official views of the

Centers for Disease Control and Prevention or any of the other funders.

Footnote

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33 Taveras EM, Marshall R, Kleinman KP, Gillman MW, Hacker K, Horan CM, et al. Comparative effectiveness of

childhood obesity interventions in pediatric primary care: a clusterrandomized trial. JAMA Pediatr.

2015;169(6):535-42.

34 Trasande L. How much should we invest in preventing childhood obesity? Health Aff(Millwood). 2010;

29(3):372-8.

AuthorAffiliation

Steven L. Gortmaker ([email protected]) is a professor of the practice of health sociology at the

Harvard T.H. Chan School of Public Health, in Boston, Massachusetts.

Y. Claire Wang is an associate professor at the Mailman School of Public Health, Columbia University, in New York

City.

Michael W. Long is an assistant professor at the Milken Institute School of Public Health, the George Washington

University, in Washington, DC.

Catherine M. Giles is a program manager at the Harvard T.H. Chan School of Public Health.

Zachary J. Ward is a programmer analyst at the Harvard T.H. Chan School of Public Health.

Jessica L. Barrett is a research assistant IV at the Harvard T.H. Chan School of Public Health.

Erica L. Kenney is a postdoctoral research fellow at the Harvard T.H. Chan School of Public Health.

Kendrin R. Sonneville is an assistant professor at the University of Michigan School of Public Health, in Ann Arbor.

Amna Sadaf Afzal is an assistant professor at the Albert Einstein College of Medicine, in New York City.

Stephen C. Resch is deputy director of the Center for Health Decision Science at the Harvard T.H. Chan School of

Public Health.

Angie L. Cradock is a senior research scientist at the Harvard T.H. Chan School of Public Health.

Appendix

Appendix

Appendix A1. Sugar-]sweetened Beverage (SSB) Excise Tax Intervention Specification and Background

Modeled Intervention

We modeled the effect of an specific excise tax of $0.01/oz of SSBs administered at the state level and

implemented nationally based on recent proposals under consideration by federal, state and local governments.1

SSBs include all beverages with added caloric sweeteners. The modeled excise tax does not apply to 100% juice,

milk products, or artificially-] sweetened beverages.

Background

Despite recent declines, SSB consumption in the United States remains high among children and adults.2

Observational studies and randomized controlled trials have linked SSB consumption to excess weight gain,

diabetes, and cardiovascular disease.3-4 The Dietary Guidelines for Americans, 2010 recommends that individuals

reduce SSB intake in order to manage their body weight.5 In 2009, the Institute of Medicine suggested taxing SSBs

as a potential local strategy to reduce consumption of calorie-dense, nutrient-poor foods.6

Assessment of Benefit

The impact of a $0.01/oz SSB tax on individual body mass index (BMI) was modeled based on the logic model in

Appendix Exhibit A1.1. Key model input parameters based on this logic model are described below and are detailed

in Appendix Exhibit A1. Means and 95% uncertainty intervals are based on 1,000 simulations drawn from

parameter-]specific distributions.

Impact of Tax on Price to Consumers

Consistent with economic theory and international evidence, we assumed that the full price of the excise tax would

be passed on to consumers.7-12 The expected percent increase in SSB price was estimated based on the average

national retail price of $0.059/ounce in 2012 reported by Powell et al.,13 which was inflated to $0.0612 in July

2014 dollars to be consistent with recent modeling of the cost-effectiveness of an SSB excise tax.14 The

$0.01/ounce excise tax would then result in a 16.3% price increase (0.0712/0.0612). We assumed that the tax rate

would be adjusted annually for inflation to maintain the 16.3% price increase throughout the ten-year modeling

time frame.

Price Elasticity of Demand for SSBs

We estimated the potential reduction in current SSB purchases due to the tax based on a systematic review of

recent estimates of the price elasticity of demand for SSBs by Powell et al.15 The review estimated a mean own-

]price elasticity of demand for SSBs weighted by SSB category consumption shares of -]1.21, ranging from -]3.87

to -]0.69.

Change in SSB Intake in Response to Excise Tax

We modeled current SSB consumption using age and sex-specific mean daily intake (oz) estimated from the first

day 24-hour dietary recall from the 2011-2012 National Health and Nutrition Examination Survey. The change in

individual intake was estimated by multiplying current intake by the 16.3% price increase and the sampled price

elasticity of demand sampled in each model iteration. On average, we estimated that the 16.3% price increase

would result in a 20% decrease in consumption from current levels.

Effect of change in SSB consumption on change in BMI

Based on a review of studies included in thirteen systematic reviews,16-]28 we estimated the impact of reductions

in SSB consumption on weight or BMI based on four large longitudinal studies in adults29-]32(0.21-]0.57 BMI

units/12-]ounce serving) and a double-]blind, placebo controlled randomized trial in youth (1.01 kg/8-]ounce

serving).33 These studies provide the best available evidence of the impact of a change in SSB consumption on

weight and BMI accounting for any compensatory changes in other dietary intake or physical activity.

Reach

The intervention reaches all youth and adults aged 2 and older in the US. In the first year, the intervention would

reach 307 million people.

Costs

We estimated the cost of the intervention based on administrative data provided in 2010 from two states

(Washington and West Virginia) that had existing or planned excise taxes on SSBs. The states required between

0.10 and 0.54 full-]time equivalent (FTE) government tax agent time per year per million residents to administer the

tax and between 0.24 and 0.35 FTE per year per million residents to conduct audits. We applied these per capita

costs nationally assuming no economies of scale and estimated salary costs from the 2014 Bureau of Labor

statistics for tax examiners, collectors and revenue agents (BLS Occupation: 13-]2081). We assumed that industry

would require equivalent time to comply with audits and file new tax statements and applied salary costs from the

2014 Bureau of Labor statistics for accountants and auditors (BLS Occupation: 13-]2011). We assumed that the

time to administer and conduct audits would be twice the annual rate during the first year of implementation.

Additional limited costs estimated included field audit direct costs and limited tax certification system operating

costs.

Appendix A1 References

1. Chaloupka, F.J., L.M. Powell, and J.F. Chriqui, Sugar-sweetened Beverage Taxes and Public Health: A Research

Brief. Minneapolis, MN: Robert Wood Johnson Foundation. Healthy Eating Research, 2009.

2. Kit BK, Fakhouri TH, Park S, Nielsen SJ, Ogden CL. Trends in sugar-sweetened beverage consumption among

youth and adults: 1999-2010. Am J Clin Nutr. 2013;98(1):180-188.

3. Malik, V.S., et al., Sugar-sweetened beverages and weight gain in children and adults: a systematic review and

meta-analysis. Am J Clin Nutr. 2013;98(4):1084-1102.

4. Chen, L., et al., Reducing consumption of sugar-sweetened beverages is associated with reduced blood pressure

a prospective study among United States adults. Circulation, 2010;121(22):2398-2406.

5. U.S. Department of Agriculture. U.S. DHHS. Dietary Guidelines for Americans, 2010. Washington, D.C.: U.S.

Government Printing Office, 2010.

6. IOM, Local Government Actions to Prevent Childhood Obesity. Washington, DC: National Academies Press, 2009.

7. Fullerton, D. and G. E. Metcalf (2002). Chapter 26 Tax incidence. Handbook of Public Economics. J. A. Alan and

F. Martin, Elsevier. Volume 4: 1787-1872.

8. Besley TJ, Rosen HS. Sales taxes and prices: an empirical analysis. Natl Tax Journal. 1999;52(2):157-178

9. Berardi N, Sevestre P, Tepaut M, Vigneron A. The impact of a 'soda tax' on prices: Evidence from French micro

data. Working paper No. 415. Paris, France: Banque de France, 20- http://ssrn.com/abstract=2192470

10. Bergman, U. Michael, and Niels Lynggård Hansen. Are excise taxes on beverages fully passed through to

prices? The Danish evidence. Working Paper, University of Copenhagen, Denmark, 2010.

http://web.econ.ku.dk/okombe/MBNLH.pdf

11. Guthrie A. Mexico soda tax dents Coke bottler's sales: Coca-Cola Femsa says Mexican sales volume has fallen

more than 5% this year. New York, NY: WSJ.com. Dow Jones and Company, Inc., 2014. updated February 26

http://online.wsj.com/articles/SB10001424052702303801304579407322914779400

12. Hahn R. The potential economic impact of a U.S. excise tax on selected beverages: A report to the American

Beverage Association. Washington, D.C.: Georgetown University, Center for Business and Public Policy, 2009.

13. Powell, L.M., et al. Sugar-sweetened beverage prices: Estimates from a national sample of food outlets.

Chicago, IL: Bridging the Gap Program, Health Policy Center, Institute for Health Research and Policy, University of

Illinois at Chicago, 2014.

14. Long MW, Gortmaker SL, Ward ZJ, Resch SC, Moodie ML, Sacks G, Swinburn BA, Carter RC, Claire Wang Y. Cost

Effectiveness of a Sugar-Sweetened Beverage Excise Tax in the U.S. Am J Prev Med. 2015 Jul;49(1):112-23.

15. Powell, L.M., et al., Assessing the potential effectiveness of food and beverage taxes and subsidies for

improving public health: a systematic review of prices, demand and body weight outcomes. Obes Rev.

2013;14(2):110-28.

16. Te Morenga L, Mallard S, Mann J. Dietary sugars and body weight: systematic review and meta-analyses of

randomised controlled trials and cohort studies. BMJ. 2013;346:e7492.

17. Malik VS, Popkin BM, Bray GA, Despres JP, Hu FB. Sugar-sweetened beverages, obesity, type 2 diabetes

mellitus, and cardiovascular disease risk. Circulation. 2010;121(11):1356-64.

18. Bray GA. Softdrink consumption and obesity: it is all about fructose. Curr Opin Lipidol. 2010;21(1):51-7.

19. Libuda L, Kersting M. Softdrinks and body weight development in childhood: is there a relationship? Curr Opin

Clin Nutr Metab Care. 2009;12(6):596-600.

20. Malik VS, Willett WC, Hu FB. Sugar-sweetened beverages and BMI in children and adolescents: reanalyses of a

meta-analysis. Am J Clin Nutr. 2009;89(1):438-9; author reply 9-40.

21. van Baak MA, Astrup A. Consumption of sugars and body weight. Obes Rev. 2009;10 Suppl 1:9-23.

22. Olsen NJ, Heitmann BL. Intake of calorically sweetened beverages and obesity. Obes Rev. 2009;10(1):68-75.

23. Gibson S. Sugar-sweetened softdrinks and obesity: a systematic review of the evidence from observational

studies and interventions. Nutr Res Rev. 2008;21(2):134-47.

24. Forshee RA, Anderson PA, Storey ML. Sugar-sweetened beverages and body mass index in children and

adolescents: a meta-analysis. Am J Clin Nutr. 2008;87(6):1662-71.

25. Harrington S. The role of sugar-sweetened beverage consumption in adolescent obesity: a review of the

literature. J Sch Nurs. 2008;24(1):3-

26. Drewnowski A, Bellisle F. Liquid calories, sugar, and body weight. Am J Clin Nutr 2007;85(3):651-61.

27. Vartanian LR, Schwartz MB, Brownell KD. Effects of softdrink consumption on nutrition and health: a

systematic review and meta-analysis. Am J Public Health. 2007;97(4):667-75.

28. Levy DT, Friend KB, Wang YC. A review of the literature on policies directed at the youth consumption of sugar

sweetened beverages. Adv Nutr. 2011;2(2):182S-200S.

29. Chen, L., et al., Reduction in consumption of sugar-sweetened beverages is associated with weight loss: the

PREMIER trial. Am J Clin Nutr. 2009. 89(5): p. 1299-306.

30. Mozaffarian, D., et al., Changes in diet and lifestyle and long-term weight gain in women and men. N Engl J

Med. 2011. 364(25):2392-404.

31. Palmer, J.R., et al., Sugar-sweetened beverages and incidence of type 2 diabetes mellitus in African American

women. Arch Intern Med. 2008;168(14):1487-92.

32. Schulze, M.B., et al., Sugar-sweetened beverages, weight gain, and incidence of type 2 diabetes in young and

middle-aged women. JAMA. 2004;292(8):927-34.

33. de Ruyter JC, Olthof MR, Seidell JC, Katan MB. A trial of sugar-free or sugar-sweetened beverages and body

weight in children. N Engl J Med. 2012;367(15):1397-406.

Appendix A2. Advertising Tax Deduction Intervention Specification and Background

Modeled Intervention

We modeled the effect of eliminating the tax deductibility of TV advertising costs for nutritionally poor foods and

beverages advertised to children and adolescents ages 2-]19. The intervention applied to TV programming

watched on traditional TV and to TV advertising aired during childrenfs programming defined as >35% child-

]audience share.1 We did not model the effect of changes in advertising exposure to adults or the impact of

changes in non-]TV forms of digital advertising and marketing. The change in tax code would be administered at

the federal level and would result in limited auditing/monitoring activities conducted by the Internal Revenue

Service.

Background

Children and adolescents view thousands of food-related TV ads each year.2 These ads include extensive

promotion of nutritionally poor foods and beverages that are high in calories; contain significant amounts of

sodium, saturated fat, and added sugars; and are low in nutrients.3-5 Children are particularly vulnerable to

persuasive messages because of their inability to identify persuasive intent,6 and exposure to TV food advertising

is associated with increased consumption of nutritionally poor foods among both children and adolescents.7-10

In light of the limited effectiveness of self-regulation, the U.S. Constitution's protection of marketing as

commercial speech, and the reluctance of the current U.S. government to regulate even minimal restrictions on

advertising,6,11 alternative regulatory approaches have been considered. Tax incentives and disincentives are

known to be powerful tools for promoting the health and well-]being of the population.12 Accordingly,

eliminating13 or amending14 the tax deduction available to food companies for the costs of advertising to children

has been proposed by Senators Blumenthal and Harkin15 and in Congress by Representative Rosa DeLauro (H.R.

2831).16

Assessment of Benefit

The impact of eliminating the tax subsidy of TV advertising costs for nutritionally poor foods and beverages

advertised to children and adolescents was modeled using daily hours of TV viewed as our measure of food

advertising exposure based on the logic model in Appendix Exhibit A2.1. Key model input parameters based on this

logic model are described below and are detailed in Appendix Exhibit A2.2. Means and 95% uncertainty intervals

are based on sampling 1,000 iterations from the defined distributions for each parameter.

Impact of Change in Federal Tax Code on Advertising Price

The model assumes an effective corporate income tax rate of -6%, which will increase advertisement prices by

14.4%.17 Using estimates from a national analysis of TV advertising and childhood obesity, which found the price

elasticity of demand for TV advertising to be 0.74 for ages 2-]9 and 0.61 for ages 10-]19, we calculated an expected

reduction in actual advertising.18e

Impact of Change in Advertising Price on Advertising Exposure

The model estimates that 89%-]96% of all food advertisements will be impacted and combines the tax rate and

elasticity estimates from Chou et al. to project a 10.7% reduction among children and an 8.8% reduction among

adolescents in advertisement exposure.4,5

Impact of Change in Advertising Exposure on BMI

To estimate the impact of change in advertising on change in BMI, we reviewed studies included in recently

completed systematic reviews and meta analyses19-23 to identify those meeting the following criteria: RCTs of

screen time interventions (screen time includes TV, videotapes, videogames, computer time) that manipulated

screen time but not other aspects of children's diet or physical activity; ages included were from 2-18; measured

change in weight, BMI z-score or BMI was a reported outcome; significant change in screen time was measured in

hours per day; minimum duration of the study was six months. We identified two RCTs that met these criteria,

including one study that found significant changes in BMI associated with changes in TV time.24 This 7-month

cluster randomized trial with 192 children led to relative reductions of 1.37 hours of screen time per day and -0.45

BMI units (P= 0.002), or a reduction of -0.33 kg/m2 per hour/day of screen time. Although not statistically

significant due to the small sample size (n=70), the only other study identified found comparable results in a

younger sample: -]0.33 kg/m2 per hour/day of screen time.25 Based on the literature, this model has

conservatively reduced estimates of reductions in BMI due to reductions in TV time by 25% to account for any

potential effects of increased physical activity.

Reach

An elimination of Tax Deductibility among Targeted Advertising has the potential to reach 74 million children, ages

2- 19 years in all 50 states and DC.

Costs

Costs related to processing and auditing were included for the new tax, but not for enacting. Overhead costs of the

tax system included administrative costs (e.g., tax audits, litigation) and personnel responsible for these

undertakings. The model assumed that 20-25% of the 44 food companies responsible for the majority of

expenditures for food and beverage marketing to children would be audited for compliance.26 The model assumed

that each audit would demand 0.25-0.75 full-time equivalent (FTE). The model assumed that the costs and labor

associated with tax compliance by the food and beverage industry are equal to the cost of administration reported

by the government. The model assumed that, industry-wide, the reduction in sales of poor quality food will be

offset by the increase in sales of other foods and that a loss in revenue by commercial broadcasters will likely be

offset by new advertising contracts for other products.27

Appendix A2 References

1. The Children's Food &Beverage Advertising Initiative. A report on compliance and progress during 2011.

Arlington, VA: Council of Better Business Bureaus; 20-

2. Where children and adolescents view food and beverage ads on TV: Exposure by channel and program. New

Haven, CT: Yale Rudd Center for Food Policy and Obesity; 2013.

3. Institute of Medicine (US) Committee on Food Marketing and the Diets of Children and Youth. Food marketing to

children and youth threat or opportunity? Washington, DC: National Academies Press; 2006

http://www.iom.edu/Reports/2005/Food-]Marketing-]to-]Children-]and-] Youth-]Threat-]or-]Opportunity.aspx.

Accessed August 9, 2011.

4. Powell LM, Szczypka G, Chaloupka FJ, Braunschweig CL. Nutritional content of television food advertisements

seen by children and adolescents in the United States. Pediatrics. 2007;120(3):576-]583.

5. Powell LM, Schermbeck RM, Chaloupka FJ. Nutritional content of food and beverage products in television

advertisements seen on children's programming. Childhood Obesity. 2013; 9(6):524-]531.

6. Harris J, GraffS. Protecting Children From Harmful Food Marketing: Options for Local Government to Make a

Difference Prev Chronic Dis. 2011; 8(5):A92.

7. Wiecha JL, Peterson KE, Ludwig DS, Kim J, Sobol A, Gortmaker SL. When Children Eat What They Watch: Impact

of Television Viewing on Dietary Intake in Youth. Arch Pediatr Adolesc Med. April 1, 2006 2006; 160(4):436-]442.

8. Andreyeva T, Kelly IR, Harris JL. Exposure to food advertising on television: Associations with children's fast

food and softdrink consumption and obesity. Economics &Human Biology. 2011; 9(3):221-]233.

9. Mehta K, Coveney J, Ward P, Magarey A, Spurrier N, Udell T. Australian children's views about food advertising

on television. Appetite. 2010; 55(1):49-]55.

10. Falbe J, Willett WC, Rosner B, Gortmaker SL, Sonneville KR, Field AE. Longitudinal relations of television,

electronic games, and digital versatile discs with changes in diet in adolescents. The American Journal of Clinical

Nutrition. October 1, 2014 2014; 100(4):1173-] 1181.

11. Speers SE, Harris JL, Schwartz MB. Child and Adolescent Exposure to Food and Beverage Brand Appearances

During Prime-]Time Television Programming. American journal of preventive medicine. 2011; 41(3):291-]296.

12. Gostin L. Public Health Theory and Practice in the Constitutional Design. Health Matrix Clevel. 2001; 11 265-

]326.

13. Chou S-]Y, Rashad I, Grossman M. Fast-]Food Restaurant Advertising on Television and Its Influence on

Childhood Obesity. J Law Econ. 2008; 51(4):599-]618.

14. Fulwider V. Future benefits? Tax policy, advertising, and the epidemic of obesity in children. J Contemp Health

Law Policy. 2003; 20(1):217-]242.

15. S.2342. To amend the Internal Revenue Code of 1986 to protect children's health by denying any deduction for

advertising and marketing directed at children to promote the consumption of food of poor nutritional quality,

2013-]2014.

16. H.R.2831. To amend the Internal Revenue Code of 1986 to protect children's health by denying any deduction

for advertising and marketing directed at children to promote the consumption of food of poor nutritional quality,

2013-2014.

17. Report to congressional requesters. Corporate tax income: effective tax rates can differ significantly from the

statutory rate. Washington, DC: United States Government Accountability Office; 2013.

18. Chou SY, Rashad I, Grossman M. Fast-food restaurant advertising on television and its influence on childhood

obesity. Journal of Law &Economics. Nov 2008; 51(4):599-618.

19. The Guide to Community Preventive Services. Obesity prevention and control: Behavioral interventions that aim

to reduce recreational sedentary screen time among children. Washington, DC: Department of Health and Human

Services; 2009.

20. Tremblay M, LeBlanc A, Kho M, et al. Systematic review of sedentary behaviour and health indicators in school-

aged children and youth. International Journal of Behavioral Nutrition and Physical Activity. 2011;8(1):98.

21. LeBlanc AG, Spence JC, Carson V, et al. Systematic review of sedentary behaviour and health indicators in the

early years (aged 0-4 years). Applied Physiology, Nutrition, and Metabolism. 2012;37(4):753-772.

22. van Grieken A, Ezendam N, Paulis W, van der Wouden J, Raat H. Primary prevention of overweight in children

and adolescents: a meta-analysis of the effectiveness of interventions aiming to decrease sedentary behaviour.

International Journal of Behavioral Nutrition and Physical Activity. 2012; 9(1):61.

23. Wang Y, Wu Y, Wilson R, et al. Childhood Obesity Prevention Programs: Comparative Effectiveness Review and

Meta-Analysis. Comparative Effectiveness Review No. 115. Agency for Healthcare Research and Quality.

2013.;Publication No. 13-EHC081-EF.

24. Robinson TN. Reducing Children's Television Viewing to Prevent Obesity: A Randomized Controlled Trial.

JAMA. 1999; 282(16):1561-1567.

25. Epstein LH, Roemmich JN, Robinson JL, et al. A randomized trial of the effects of reducing television viewing

and computer use on body mass index in young children. Arch Pediatr Adolesc Med. 2008; 162(3):239-245.

26. Federal Trade Commission. Marketing food to children and adolescents: a review of industry expenditures,

activities, and self-regulation. Washington, DC: Federal Trade Commission;2008.

27. BauhoffS. The effect of school district nutrition policies on dietary intake and overweight: A synthetic control

approach. Econ Hum Biol. 2014 Jan; 12:45-55.

28. The Nielsen Company. State of the media: The cross-platform report. Quarter 1, 20- 20-

29. Zimmerman FJ, Bell JF. Associations of Television Content Type and Obesity in Children. Am J Public Health.

February 1, 2010 2010; 100(2):334-340.

30. Federal Trade Commission. A review of food marketing to children and adolescents: A follow-up report.

Washington, DC: 20-

Appendix A3: Microsimulation Model Description

We developed a stochastic, discrete-time, individual-level microsimulation model of the population in the United

States to simulate the experience of the population in the United States from 2015-2025. Key input parameters for

the model are detailed in Appendix Exhibit A3.1 and are described below.

Population Baseline Characteristics

Demographics

We simulated a population of 1,000,000 individuals using a simple random sample from the 2010 U.S. Census at

the census tract level and initiated the simulation in 2010. Using non-] parametric statistical matching,1-]3 we

assigned additional demographic variables (Exhibit A3.1) to individuals by sampling observations with

replacement from the 2008-]2012 American Community Survey (ACS) 5-]Year Estimates conditional on census

tract, age, sex and race/ethnicity. The matching algorithm employed dynamic strata definitions to achieve a

minimum sample size within each strata of the datasets used to assign additional data to the simulated

population.

Body Mass Index and Dietary Behavior

The microsimulation was designed to provide valid state-]level estimates of population obesity and related

mortality and healthcare expenditures. To capture state-]level variation in height and weight within demographic

subgroups, using the same non-]parametric matching techniques, adults sampled from the 2010 U.S. Census with

household income data from ACS were matched to individuals from the 2011 Behavioral Risk Factor Surveillance

System (BRFSS) to assign self-reported height and weight conditional on demographic variables and state

residence. After excluding observations with missing demographic variables and self-reported height and weight

(n=99,912) and excluding pregnant women because of possible effects on weight (n=2,758), data were sampled

with replacement proportion to sampling weights from 401,738 individuals to assign self-reported height and

weight to individuals in the simulation model.

Data on state-specific child and adolescent parent-reported height and weight from the 2003-2004 and 2007-2008

National Survey on Children's Health (NSCH) were used to incorporate state-level variation in childhood height and

weight conditional on demographic variables. The NSCH is a national and state-representative telephone survey

covering a range of children's health data conducted by the Centers for Disease Control and Prevention's National

Center for Health Statistics. Additional detail on the sampling methodology has been reported previously.4, 5 Data

from both waves of the surveys were available for 213,900 responses. After excluding observations with missing

demographic variables needed for the matching process (n=29,235) and those missing parent-reported height and

weight (n=51,452), 133,213 responses were used in this study's analysis. Sample weights were pooled across

survey rounds. Data on height and weight were not available in the 2011-2012 NSCH public use datasets, although

derived BMI values are available based on parent self-reported height and weight for participants aged 10-17 years.

Objectively-measured height and weight and selected dietary intake variables were assigned to individuals in the

simulated population by matching to individuals sampled with replacement from the 2005-2010 National Health

and Nutrition Examination Survey (NHANES) conditional on age, sex, race/ethnicity, household income and self or

parent-]reported height and weight from BRFSS and NSCH. After excluding observations with missing data for the

variables of interest (n=2885) and excluding pregnant women (n=415), the final sample from NHANES included

15,018 respondents aged 18 and older. After excluding individuals with missing demographic data (n=356) and

those with missing measured height and weight (n=224), data on height and weight were available from 9,377

individuals aged 2-]17. Sample weights for the pooled dataset were calculated following the NHANES analytic

guidelines.6 In contrast to estimates based on self-]reported BRFSS data, the resulting population closely

reproduced the body mass index (BMI) distribution, obesity prevalence and severe obesity prevalence of the U.S.

based on objectively-]measured data from NHANES.7 State-]level estimates of childhood obesity were validated

against objectively-]measured data from states that conducted a census of childhood obesity among

schoolchildren.8

Lifetime Height and Weight Trajectories

Building on previous studies,9 we developed a nationally-]representative set of lifetime height and weight

trajectories by combining objectively-]measured height and weight trajectories from the following longitudinal

cohort studies: National Longitudinal Survey of Youth (1986-]2010; n=9,402), the National Longitudinal Study of

Adolescent to Adult Health (Add Health) (1994-]2008; n=4,972), the Early Childhood Longitudinal Study-

]Kindergarten (1998-] 2007; n=15,180), the Panel Survey on Income Dynamics (n=4,792), and the NHANES I

Epidemiologic Follow-]up Study (NHEFS, n=7,221). For children and adolescents, we used CDC growth charts to

inform age-]specific, non-]linear interpolation between observed measurements of height and weight. For adults

(ages >20), height was assumed to remain constant and weight was linearly interpolated between observations.

Because none of the nationally-]representative height and weight trajectories includes data across the lifecourse,

synthetic trajectories were created by combining trajectories from the original datasets. We matched trajectories

conditional on age, sex, race/ethnicity, and overlapping segments of the underlying height and weight trajectories

using Bayesian optimization methods to minimize the distance between overlapping segments of matched

trajectories.10

While the nationally-]representative datasets capture individual heterogeneity in lifetime height and weight

trajectories, the resulting BMI distribution from these historical trajectories did not correspond to current

population estimates due to secular changes in obesity. To adjust for this difference, we used linear regression to

estimate recent time trends in age and sex-] specific mean BMI and obesity prevalence using data from the 1999-

]2012 NHANES. These estimates were used to calibrate the synthesized height and weight trajectories to projected

age/sex specific mean BMI and obesity prevalence from 2010-]2030 using a simulated annealing directed search.

The resulting height and weight trajectory sets thus capture the substantial heterogeneity in individual height and

weight changes while representing recent age and sex-] specific trends in BMI. We selected 50 good-]fitting

parameter sets from the calibration and generated 50 unique virtual populations to account for uncertainty in both

the statistical matching of cross-]sectional population data and the projections of future obesity trends.

Baseline Smoking Prevalence and Individual Smoking Trajectories

Baseline individual self-]reported smoking status was assigned using data from the same individuals matched

from the 2011 BRFSS when assigning self-]reported height and weight to individuals in the simulated population.

To model individual smoking trajectories, age and sex-] specific smoking initiation and cessation rates were

applied using the most recent cohort-] specific estimates based on U.S. National Health Interview Surveys

conducted from 1965 to 2009.11

Open Population Characteristics

Each cycle, the model simulates incoming infants to create an open population based on the number of births per

year projected in the U.S. Census 2014 National Population Projections. Race-]specific projections were used to

account for differences in fertility. Incoming infants were bootstrapped among existing model individuals of the

same race in order to maintain the covariance of demographic, anthropometric, and behavioral characteristics.

Mortality

Natural History Mortality

In each model cycle (i.e. every month), all-]cause mortality was simulated using the 2010 U.S. sex and

race/ethnicity-]specific period life tables. In the baseline scenario (i.e. no intervention), mortality rates were

adjusted simultaneously for smoking and BMI using published age-]standardized mortality rates for 313,000 men

and 214,000 women aged 50-]71 years followed for 10 years in the NIH-]AARP Diet and Health Study.12 Sex, age,

and race/ethnicity-]specific mortality rates were adjusted by BMI category (<18.5, 18.5-]20.9, 21.0-] 23.4,23.5-]24.9,

25.0 -]26.4,26.5-]27.9, 28.0-]29.9, 30-]34.9, 35.0-]39.9, 40+) and smoking status (current smoker, former smoker,

never smoker) for adults age 30-]100. These baseline mortality rates do not adjust for confounding of the observed

relationship between BMI, smoking and mortality. Instead, they represent the expected mortality for each of these

subgroups.

Mortality ShiftDue to Intervention Impact on BMI

To estimate the causal effect of reductions in BMI due to modeled interventions, individual-]level mortality rates

were shifted from baseline using published estimates of the hazard of mortality due to excess BMI from the

Prospective Studies Collaborative. The analysis was based on data from 57 prospective studies with 894,576

participants. After controlling for age, sex and baseline smoking status and excluding the first five years of follow-

]up to account for undiagnosed disease that may bias the relationship between BMI and mortality, the authors

found that each 5 BMI unit increase within the range of 25-]50 BMI units was associated with a 30% higher hazard

ratio for death (HR: 1.29; 95% CI: 1.27-]1.32).13 The estimated HR by age group was used to shiftindividual-]level

mortality risk due to BMI reductions compared to the individualfs risk in the baseline model.

Modeling the Time Course of Intervention Impact on BMI

The impact of each of the modeled interventions on individual BMI was estimated based on the best available

evidence linking the policy or program to change in BMI, weight, daily energy intake or physical activity using a

logic model developed for each intervention. For interventions that included evidence on the impact of the

intervention on BMI or weight, the duration of the study follow-]up was used to model the time course of weight

change for individuals receiving the intervention in the simulated population.

For interventions that resulted in a change in energy balance due either to reduced energy consumption or

increased energy expenditure, the full steady-]state impact of these interventions on individual weight was

modeled after 24 months for youth and 36 months for adults. The modeled time course of energy balance to

weight change is based on energy balance models developed by Hall et al.15, 16 If individuals in the simulated

population were not exposed to the intervention for the entire time needed to reach full effect, they were assigned

a portion of the full effect based on the duration of intervention received. Individuals were assumed to maintain the

full effect of the intervention relative to their baseline weight trajectories for the remainder of the ten-]year analytic

timeframe.

Cost Evaluation

We developed a cost evaluation protocol consistent with general practice in cost- effectiveness projects and

building on the work of the Assessing Cost-Effectiveness (ACE) studies. 17-20 All costs are reported in 2014

dollars with future costs discounted at 3% annually. Non-healthcare cost inputs were adjusted to 2014 dollars

using the Consumer Price Index.

Intervention Costing

The costing protocol entails three steps to evaluating the incremental cost of each of the modeled intervention: 1)

Identification of the types of resources used; 2) Measurement of the quantity of each resource used per person,

per state, or nationally for each model time period; and 3) Valuation of resource utilization in monetary terms. The

model employs a modified societal perspective that includes all opportunity costs regardless of payer except for

costs in time and other resources that program participants incur in order to participate in an intervention

program. Capital costs were amortized over their useful life for each intervention. Labor costs were based on the

2014 state-]specific annual or hourly wages by occupation from the Bureau of Labor Statistics. A fringe rate of

45.56% was applied to all labor costs based on data from the U.S. Bureau of Labor on the proportion of total

compensation due to wages. A description of the cost inputs for each modeled intervention is included in

Appendices A1-]A2 and A4 through A8.

Healthcare Costs

We estimated the annual total medical expenditures per person in the simulated population by obesity status

based on a published analysis of data from the 2001-]2003 Medical Expenditure Panel Surveys.21 The authors

estimated the incremental cost of healthcare among children and adults with obesity after controlling for age,

gender, race/ethnicity, insurance status and census region. The incremental cost for children 6-]19 was estimated

to be $220. Incremental costs for adults increased with age from $240 at age 20 to $2,147 for ages 74 and older.

Costs were inflated to 2014 dollars using the Medical Care Consumer Price Index. Healthcare cost savings were

estimated based on the lower annual age and sex-]specific obesity prevalence due to each intervention. The actual

inputs are described in Table A.3.2.

Model Outcomes

Over the 10 year period 2015-]2015, the model calculates a range of outcomes for each intervention scaled to the

U.S. 2010 Census population of 309 million individuals, including:

* Total and annualized intervention costs

* First year and total intervention reach

* Intervention cost per benefiting individual

* Mean BMI reduction among individuals in the benefiting population

. Intervention cost per BMI unit reduced per benefiting person

. Obesity-]related healthcare cost savings

. Net costs including intervention costs and healthcare cost savings

. Life years gained

. Years with obesity prevented

. Reduction in childhood obesity prevalence in the 2025 simulated population

. Cases of childhood obesity prevented in the 2025 simulated population

. Net cost per year with obesity prevented

. Net cost per case of childhood obesity prevented

. Healthcare cost savings per 1$ invested

Uncertainty Analysis

The model incorporates uncertainty by running 1,000 iterations of probabilistic sensitivity analysis around a range

of overall and intervention-]specific parameters. In each iteration, a population was sampled from the 50 generated

unique populations to account for baseline uncertainty. Incremental reductions in population obesity prevalence

and related reductions in mortality, morbidity and healthcare costs can then be calculated compared to the

selected populationfs baseline indicators.

Intervention-]specific model parameters were sampled from distributions in 1,000 model iterations, with correlation

induced between related recruitment, effectiveness, and cost parameters. Intervention outcomes are reported with

95% Uncertainty Intervals based on these 1,000 model iterations. Key model input parameters for each

intervention are included in Appendix Exhibits A1.2-]A2.2 and A3.2 through A8.2.

The Microsimulation Model compared to Markov Cohort Simulation Models

As noted in the main paper, in prior publications we used a Markov cohort simulation model to estimate the impact

of two of the interventions modeled here: The SSB Excise Tax and the Ad Tax Deduction.(22-]24) The cohort model

is more limited than a population-]based microsimulation in a number of ways:(25) in its ability to model

heterogeneity of individual differences, exposure to the intervention, the accuracy of modeling trajectories of BMI

over the lifecourse, and the inability to calculate population estimates for specific years. With the microsimulation

model we are able to estimate the number of cases of obesity prevented. For these interventions, the cost per BMI

unit reduction estimates were similar under both modeling approaches, and were cost-]saving. The

microsimulation also allows much more potential for future modeling, including combining interventions.

Appendix A3 References

1. D'Orazio M. Statistical Matching and Imputation of Survey Data with StatMatch. StatMatch R package vignette

[Internet]. 2014. Available from: http://cran.r-]

project.org/web/packages/StatMatch/vignettes/Statistical_Matching_with_StatMatch.pdf.

2. D'Orazio M, Di Zio M, Scanu M. Statistical matching : theory and practice. Chichester, England ; Hoboken, NJ:

Wiley; 2006. x, 256 p. p.

3. Vantaggi B. Statistical matching of multiple sources: A look through coherence. International Journal of

Approximate Reasoning. 2008;49:701-]11.

4. Blumberg SJ, Foster EB, Frasier AM, Satorius J, Skalland BJ, Nysse-]Carris KL, et al. Design and operation of the

National Survey of Children's Health, 2007. Vital and health statistics Ser 1, Programs and collection procedures.

2012(55):1-]149. Epub 2012/07/28.

5. Blumberg SJ, Olson L, Frankel MR, L. O, Srinath KP, Giambo P. Design and operation of the National Survey of

Children's Health, 2003. Vital Health Stat 2005;1(43):1-]124.

6. Statistics NCfH. When and How to Construct Weights When Combining Survey Cycles. [cited 2014]; Available

from: http://www.cdc.gov/nchs/tutorials/NHANES/SurveyDesign/Weighting/Task2.htm.

7. Ward ZW, Long MW, Resch SC, Gortmaker SL, Cradock AL, Hsiao A, Wang YC. Redrawing the US Obesity

Landscape: Bias-]corrected estimates of state-]specific adult obesity prevalence. Presented at American Public

Health Association, New Orleans, November 2014.

8. Blondin KJ, Ward Z, Resch SC, Cradock AL, Wang YC, Hsiao A, Gortmaker SL, Long, ML. Improving state obesity

surveillance: A review of current practices and recommendations for change.Presented at American Public Health

Association, New Orleans, November 2014.

9. Goldhaber-]Fiebert JD, Rubinfeld RE, Bhattacharya J, Robinson TN, Wise PH. The utility of childhood and

adolescent obesity assessment in relation to adult health. Medical decision making : an international journal of the

Society for Medical Decision Making. 2013;33(2):163-] 75. Epub 2012/06/01.

10. Resch S, Ward ZJ, Long MW, Goldhaber-]Fiebert J, Wang YC, Gortmaker SL. Using synthetic growth trajectories

to predict childhood obesity trends at the individual and population level. Presented at American Public Health

Association, New Orleans, November 2014.

11. Holford TR, Levy DT, McKay LA, Clarke L, Racine B, Meza R, et al. Patterns of birth cohort-]specific smoking

histories, 1965-]2009. American journal of preventive medicine. 2014;46(2):e31-]7. Epub 2014/01/21.

12. Adams KF, Schatzkin A, Harris TB, Kipnis V, Mouw T, Ballard-]Barbash R, et al. Overweight, obesity, and

mortality in a large prospective cohort of persons 50 to 71 years old. The New England journal of medicine.

2006;355(8):763-]78. Epub 2006/08/24.

13. Whitlock G, Lewington S, Sherliker P, Clarke R, Emberson J, Halsey J, et al. Body-]mass index and cause-

]specific mortality in 900 000 adults: collaborative analyses of 57 prospective studies. Lancet.

2009;373(9669):1083-]96. Epub 2009/03/21.

14. Muennig P, Lubetkin E, Jia H, Franks P. Gender and the burden of disease attributable to obesity. Am J Public

Health. 2006;96(9):1662-]8. Epub 2006/07/29.

15. Hall KD, Sacks G, Chandramohan D, Chow CC, Wang YC, Gortmaker SL, et al. Quantification of the effect of

energy imbalance on bodyweight. Lancet. 2011;378(9793):826-] 37. Epub 2011/08/30.

16. Hall KD, Butte NF, Swinburn BA, Chow CC. Dynamics of childhood growth and obesity: development and

validation of a quantitative mathematical model. The lancet Diabetes &endocrinology. 2013;1(2):97-105. Epub

2013/12/19.

17. Drummond M, Scuplher M, Torrance G, O'Brien B, Stoddard G. Methods for the economic evaluation of health

care programmes. Oxford: Oxford University Press; 2005.

18. Gold MR, Siegel JE, Russell LB, Weinstein MC. Cost-effectiveness in Health and Medicine: Oxford University

Press; 1996.

19. Carter R, Moodie M, Markwick A, Magnus A, Vos T, Swinburn B, et al. Assessing Cost- Effectiveness in Obesity

(ACE-Obesity): an overview of the ACE approach, economic methods and cost results. BMC Public Health.

2009;9:419.

20. Vos T, Carter R, Doran C, Anderson I, Lopez A, A W. ACE-Prevention Project 2005-09 Economic Evaluation

Protocol September 2007.

21. Finkelstein EA, Trogdon JG. Public health interventions for addressing childhood overweight: Analysis of the

business case. American Journal of Public Health. 2008;98(3):411-5.

22. Gortmaker SL, Long MW, Resch SC, Ward ZJ, Cradock AL, Barrett JL, Wright DR, Sonneville KR, Giles CM, Carter

RC, Moodie ML, Sacks G, Swinburn BA, Hsiao A, Vine S, Barendregt J, Vos T, Wang YC. Cost Effectiveness of

Childhood Obesity Interventions: Evidence and Methods for CHOICES. Am J Prev Med. 2015 Jul;49(1):102-11.

23. Long MW, Gortmaker SL, Ward ZJ, Resch SC, Moodie ML, Sacks G, Swinburn BA, Carter RC, Claire Wang Y. Cost

Effectiveness of a Sugar-Sweetened Beverage Excise Tax in the U.S. Am J Prev Med. 2015 Jul;49(1):112-23.

24. Sonneville KR, Long MW, Ward ZJ, Resch SC, Wang YC, Pomeranz JL, Moodie ML, Carter R, Sacks G, Swinburn

BA, Gortmaker SL. BMI and Healthcare Cost Impact of Eliminating Tax Subsidy for Advertising Unhealthy Food to

Youth. Am J Prev Med. 2015 Jul;49(1):124-34.

25. Ethgen O, Standaert B. Population- versus cohort-based modelling approaches. Pharmacoeconomics. 2012

Mar;30(3):171-81.

Appendix A4. Restaurant Menu Calorie Labeling Intervention Specification and Background Modeled Intervention

We modeled the effect on body mass index (BMI) of the final federal menu labeling regulations implemented under

section 4205 of the Patient Protection and Affordable Care Act of 2010.1 The final rule issued by the U.S. Food and

Drug Administration (FDA) in November 2014 requires that chain restaurants and similar retail food

establishments with 20 or more locations provide calories for standard menu items on menus and menu boards

along with a succinct statement concerning suggested daily caloric intake effective December 1, 2016.2

Background

In 2007-2008, fast food and full-service restaurants accounted for 14% of total energy among children and 24% of

total energy intake among adolescents and adults.3 The consumption of fast food and food away from home has

been associated with lower diet quality and higher body weight.4 The Dietary Guidelines for Americans, 2010

recommends reviewing posted calorie content at restaurants before eating as a strategy to reduce excess caloric

intake when consuming foods prepared away from home.5

Assessment of Benefit

The impact of federal restaurant menu calorie labeling on BMI was modeled based on the logic model in Appendix

Exhibit A4.1. Key model input parameters based on this logic model are described below and are detailed in

Appendix Exhibit A4.2. Means and 95% uncertainty intervals are based on 1,000 simulations drawn from

parameter-]specific distributions.

Meals per Week Impacted by Restaurant Menu Calorie Labeling

We modeled the individual frequency of meals consumed away from home based on data from the 2007-]2010

National Health and Nutrition Examination Survey (NHANES) Diet Behavior and Nutrition Questionnaires. See the

Microsimulation Model appendix for additional detail on how dietary variables from NHANES were matched to

individuals in the model. In line with the FDAfs preliminary and final regulatory impact analyses, we assumed that

95% of meals away from home were in restaurants and that 73% of all restaurant meals would be in chain

restaurants subject to the regulations.6-]7 Therefore, we assumed that 69% of all meals away from home based on

the NHANES questionnaire were would be impacted by restaurant menu calorie labeling. We did not estimate any

reduction in calories from meals away from home purchased in grocery stores, convenience stores or other

settings.

Impact of Restaurant Menu Calorie Labeling on Calories Purchased per Meal

We modeled the impact of restaurant menu calorie labeling on the calories purchased per restaurant meal based

on the summary effect in a recently published systematic review and meta-]analysis of the impact of restaurant

calorie menu labeling.8 The review found that customers reduced calories ordered or purchased per meal by 7.63

calories (95% CI: -]21.02, 5.76) based on six studies conducted in restaurant settings with control conditions.

Lacking information on heterogeneity of this effect size by age or demographic variables, we applied the same per

meal calorie reduction to all individuals in the simulation model.

Impact of per Meal Calorie Reductions on BMI

We assumed that changes in calories ordered and purchased would result in equivalent reductions in consumption

and that there would be no compensation for the small reduction in calories during meal at other times of the day,

which is consistent with the findings of an experiment involving menu labeling with a daily anchor statement.9 We

translated the resulting reduction in daily caloric intake into changes in body weight and BMI using methods

described in the Microsimulation Model appendix.

Reach

We evaluated the impact of the regulations on all children and adults aged 2 and older. The intervention would

reach 307 million people during the first year of implementation.

Cost

We based our evaluation of the cost of implementation on the FDA's final regulatory impact analysis.7 We revised

the FDA's assumptions as following:

* We included 10 FTE labor costs at the FDA to manager roll-]out of the regulations

* We assumed that restaurants would choose the least expensive nutritional analysis consistent with the

regulations such that each menu item analyzed would require a nutrition database fee ($56) and four hours of

dietitian time (BLS Occupation Code: 29-1031). This is consistent with the lower bound costing used by the FDA,

but one quarter of the cost of the laboratory analysis that the FDA used to assess the upper bound of the

uncertainty around nutritional analysis costs in their analysis.

* We assumed that all restaurants and other outlets nationally would incur costs of implementation regardless of

previous state or local regulations in order to align our cost estimates with benefits estimated nationally

* We included the cost of local public health department monitoring to ensure compliance, which we estimated

would require 229 FTEs of Public Health Inspectors (BLS Occupation Code: 13-]1041) nationally on an annual basis

based on the time cost of public health inspection per resident required to implement menu labeling requirements

in New York City

Appendix A4 References

1. Food labeling: Nutrition labeling of standard menu items in restaurants and similar retail food establishments,

final rule, 79 Federal Register 71155 (2014).

2. Food Labeling; Nutrition Labeling of Standard Menu Items in Restaurants and Similar Retail Food

Establishments; Extension of Compliance Date, 80 Federal Register 39675 (2015).

3. Powell LM, Nguyen BT, Han E. Energy intake from restaurants: demographics and socioeconomics, 2003-]2008.

Am J Prev Med. 2012; 43(5):498-]504.

4. Mesas AE, Munoz-]Pareja M, Lopez-]Garcia E, Rodriguez-]Artalejo F. Selected eating behaviours and excess body

weight: a systematic review. Obes Rev. 2012; 13(2):106-]135.

5. U.S. Department of Agriculture and U.S. Department of Health and Human Services. Dietary Guidelines for

Americans, 2010. Washington, D.C.: U.S. Government Printing Office, 2010.

6. Food labeling: nutrition labeling of standard menu items in restaurants and similar retail food establishments

notice of proposed rulemaking; preliminary regulatory impact analysis: FDA: FDA-]2011-]F-]0172. Washington, DC.

Food and Drug Administration, Department of Health and Human Services, 2011.

7. Food labeling: nutrition labeling of standard menu items in restaurants and similar retail food establishments;

final regulatory impact analysis: FDA-]2011-]F-]0172. Washington, DC. Food and Drug Administration, Department

of Health and Human Services, 2014.

8. Long MW, Tobias DK, Cradock AL, Batchelder H, Gortmaker SL. Systematic review and meta-]analysis of the

impact of restaurant menu calorie labeling. Am J Public Health. 2015; 105(5):e11-]e24.

9. Roberto CA, Larsen PD, Agnew H, Baik J, Brownell KD. Evaluating the impact of menu labeling on food choices

and intake. Am J Public Health. 2010; 100(2):312-]318

Appendix A5. Nutrition Standards for School Meals Intervention Specification and Background

Modeled Intervention

We modeled the effect of implementation of the federal nutrition standards for school meals for all grade levels. As

required by the Healthy, Hunger-]Free Kids Act of 2010, in January 2012 the USDA released a final rule updating

nutrition standards for school meals for the first time in 15 years,1 based largely on recommendations made by

the Institute of Medicine.2 The new standards went into effect in the 2012-]2013 school year and required schools

to increase the availability of fruits, vegetables, whole grains, and fat-]free and low-]fat milk; reduce levels of

sodium, saturated and trans fats; and for the first time set minimum and maximum calorie levels. All changes

except for sodium standards were implemented by the 2014-]15 school year.

Background

The USDA National School Lunch Program (NSLP) and School Breakfast Program (SBP) play a major role in

children's diets, providing up to half of the daily calories for over 30 million children at lunch and 13 million children

at breakfast each school day.3 Nearly all public schools participate in the NSLP and most (79%) participate in the

SBP.4 The programs aim to provide nutritious meals to children and youth, in accordance with national nutrition

guidance.

Assessment of Benefit

The impact of the school meal nutrition standards on BMI was modeled based on the logic model in Appendix

Exhibit A5.1. We used evidence from one natural experimental cross-] sectional study5 evaluating the difference in

weight status by school meal eligibility among 4,870 8th grade students residing in states with nutrition standards

exceeding the 1995 USDA standards (the last revision of the standards before 2012) compared to those whose

laws did not exceed USDA standards. The study found that the adjusted difference in mean BMI percentile

between students who obtained free/reduced price lunches at school was lower than those who did not obtain

lunch at school in states that exceeded USDA standards compared with those who did not (B -]11.0; 95% CI -]17.7, -

]4.3), and for students who obtained full price lunches at school the difference was smaller and not significant (B -

]6.0; 95% CI -]-7, 0.6).5 Corresponding differences in BMI units (kg/m2, not published in the manuscript) by gender

were obtained from the lead author (D. Taber, personal communication, June 8, 2015). Key model input parameters

are detailed in Appendix Exhibit A5.2.

Reach

The intervention reaches children and youth in grades kindergarten through 12 who attend public schools

participating in the NSLP and obtain meals reimbursable through the NSLP. Students are categorized according to

meal eligibility status, with on average 48% eligible for free or reduced price meals (range 26-]71% by state) and

52% eligible for full price meals.7 We estimate that 89% of free or reduced price meal eligible students and 35% of

full price meal eligible students participate in school meals, based on meal participation rates3 and the proportion

of all students in each meal eligibility category.

Costs

We estimated the cost of the intervention based on cost analyses done by the federal government in conjunction

with the passage of the regulation. We estimate that implementation of the intervention requires additional state

agency administrative labor for providing ongoing training and technical assistance, as well as coordinated review

effort and compliance monitoring, at a cost of $9.4 million per year.1 School districts would pay an additional

$414.8 million in food costs and $400 million in food service labor costs per year.1,8 The federal government

would spend an additional $396 million per year in reimbursements for meal costs at the 6 cents higher rate for

compliant programs8 and $25 million per year to provide grants to school districts for purchasing kitchen

equipment.9

Appendix A5 References

1. U.S. Department of Agriculture. Nutrition Standards in the National School Lunch and School Breakfast

Programs, final rule, 7 CFR Parts 210 and 220. Vol 77. Washington, D.C.: Federal Register; 2012:4088-]4167.

2. Institute of Medicine. School Meals: Building Blocks for Healthy Children. Washington, D.C.: Institute of

Medicine; 2010.

3. U.S. Department of Agriculture, Food and Nutrition Service. Child Nutrition Tables.

http://www.fns.usda.gov/pd/child-]nutrition-]tables. Accessed May 18, 2015.

4. May L, Standing K, Chu A, Gasper J, Rile J. Special Nutrition Program Operations Study: State and School Food

Authority Policies and Practices for School Meals Programs School Year 2011-]- Project Officer: John R. Endahl.

Prepared by Westat for the U.S. Department of Agriculture, Food and Nutrition Service; March 2014.

5. Taber DR, Chriqui JF, Powell L, Chaloupka FJ. Association between state laws governing school meal nutrition

content and student weight status: implications for new USDA school meal standards. JAMA Pediatr.

2013;167(6):513-]519.

6. Centers for Disease Control and Prevention, National Center for Health Statistics. CDC growth charts: United

States. http://www.cdc.gov/growthcharts/cdc_charts.htm. Accessed May 18, 2015.

7. U.S. Department of Education, National Center for Education Statistics. Common Core of Data (CCD), "Public

Elementary/Secondary School Universe Survey", 2012-]13 v.1a. Table generated May 14, 2015 using ElSi.

https://nces.ed.gov/ccd/elsi/. Accessed May 14, 2015.

8. Congressional Budget Office. Cost estimate for the Healthy, Hunger-]Free Kids Act of 2010, as ordered reported

by the Senate Committee on Agriculture, Nutrition, and Forestry on March 24, 2010. April 10, 2010;

http://www.cbo.gov/sites/default/files/healthyhungerfreekidsact.pdf. Accessed May 22, 2015.

9. USDA Office of Communications. USDA Awards Grants to Support Schools Serving Healthier Meals and Snacks,

Release No. 0058.15. March 6, 2015;

http://www.usda.gov/wps/portal/usda/usdahome?contentidonly=true&contentid=2015/03/00 58.xml. Accessed

May 22, 2015.

Appendix A6. Nutrition Standards for All Foods and Beverages Sold in Schools (Smart Snacks) Intervention

Specification and Background

Modeled Intervention

We modeled the effect of implementation of the national policy requiring nutrition standards for all foods and

beverages sold in schools, according to the USDA interim final rule issued in June 2013.1 As required by the

Healthy, Hunger-Free Kids Act of 2010, the USDA established nutrition standards for all foods sold in schools,

which were implemented beginning in the 2014-15 school year.1 These standards, which set allowable food and

beverage types and nutrient levels, replace the previous federal regulations restricting the sale of foods of minimal

nutritional value (FMNV; i.e., softdrinks, water ices, chewing gum, and certain candies). The food standards focus

on providing whole grains, fruits and vegetables, and key dietary nutrients while limiting calories, sodium, fats, and

sugar. Beverage standards restrict sugar-sweetened beverages and allow water, low-fat milk, and 100% juice.1

Background

The National School Lunch Program (NSLP) provides students with low-]cost or free meals in participating

schools, via federal reimbursements for qualifying meals. Foods and beverages other than federally reimbursable

school meals (aka "competitive foods") are also sold in vending machines, a la carte, and/or other venues in the

majority of schools . 65% of elementary,2 91% of middle,3 and 99% of high school students3 have access. Each

year, schools nationally earn $6.5 billion in revenue from these snacks, which is 16% of all school food service

revenue.1 Because snacks in school have been widely available and of typically poor nutritional quality,4 the

Institute of Medicine recommends strong nutrition standards for all foods and beverages sold or provided in

schools.5

Assessment of Benefit

The impact of Smart Snacks in School on BMI was modeled based on the logic model in Appendix Exhibit A6.1. We

used evidence from one retrospective cohort study6 that examined changes in state laws addressing snacks in

school between 2003 and 2006 from the elementary to middle school level and changes in objectively measured

BMI between spring 2004 (5th grade) and spring 2007 (8th grade) among the Early Childhood Longitudinal Study

Kindergarten Class (ECLS-]K) cohort. Compared to students in 15 states with consistently no laws, over 3 years,

students in 6 states with new laws that restricted snack sales beyond FMNV gained 0.10 fewer BMI units (95% CI: -

]0.33, 0.12), and students in 7 states with new weaker laws that recommended but did not require some standards

gained 0.39 fewer BMI units (95% CI: -]0.56, -] 0.22). In our model, we assume the BMI reduction for middle school

(MS) and high school (HS) students ranges from 0.10.0.39 (mean BMI reduction for MS/HS: 0.245), and for

elementary school (ES) students, that it is 53% of this effect (mean BMI reduction for ES: 0.13), corresponding to

the percentage of daily kilocalories from competitive foods they consume compared to middle school students.4

Key model input parameters are detailed in Appendix Exhibit A6.2.

Reach

The intervention reaches all children and youth in grades kindergarten through 12 attending schools that

participate in the National School Lunch Program (NSLP).

Costs

We estimated the cost of the intervention based on cost analyses done by the federal government in conjunction

with the passage of the regulation and expert stakeholder opinion. In order to implement the intervention,

additional labor will be required for food service staffto keep records of compliance with the nutrition standards

(e.g., receipts, nutrition labels, product specifications). According to the USDA,1 the recordkeeping cost is

estimated at $23.4 million per year. Additionally, we included the cost of one-]time trainings for district-]level food

service directors and for cafeteria managers in schools with a la carte venues.

Appendix A6 References

1. U.S. Department of Agriculture. National School Lunch Program and School Breakfast Program: nutrition

standards for all foods sold in school as required by the Healthy, Hunger- Free Kids Act of 2010, interim final rule, 7

CFR Parts 210 and 220. Vol 78: Federal Register; 2013:39068-39120.

2. Turner L, Chaloupka FJ, Sandoval A. School Policies and Practices for Improving Children's Health: National

Elementary School Survey Results: School Years 2006-07 through 2009-10. Chicago, IL: Bridging the Gap Program,

Health Policy Center, Institute for Health Research and Policy, University of Illinois at Chicago;20-

3. Terry-McElrath YM, Johnston LD, O'Malley PM. Trends in competitive venue beverage availability: findings from

US secondary schools. Archives of Pediatrics &Adolescent Medicine. 2012;166(8):776-778.

4. Fox MK, Gordon A, Nogales R, Wilson A. Availability and consumption of competitive foods in US public schools.

Journal of the American Dietetic Association. 2009;109(2):S57-S66.

5. Institute of Medicine. Nutrition Standards for Foods in Schools: Leading the Way Toward Healthier Youth.

Washington, D.C.: National Academies Press;2007.

6. Taber DR, Chriqui JF, Perna FM, Powell LM, Chaloupka FJ. Weight status among adolescents in States that

govern competitive food nutrition content. Pediatrics. 2012;130(3):437-444.

Appendix A7. Improving Nutrition, Physical Activity, and Screen Time Policies and Practices in Early Care and

Education through the Nutrition and Physical Activity Self-]Assessment for Child Care (NAP SACC) Program:

Intervention Specification and Background

Modeled Intervention

We modeled the effect of the nationwide, state-]by-]state incorporation of the Nutrition and Physical Activity Self-]

Assessment for Child Care (NAP SACC) program into state Quality Rating and Improvement Systems (QRIS) for

early care and education (ECE) programs, assuming completion of NAP SACC would be required for voluntarily

attaining QRIS certification. NAP SACC involves child care health consultants helping ECE program directors to

complete self-] assessments of current nutrition, physical activity (PA), and screen time practices and policies and

then implement improvements.

Background

Early care and education programs reach 64.3% of 3-]5 year olds in the U.S.,1 and can have a profound influence on

young childrenfs eating and PA habits.2-]6 However, ECE programs tend not to meet recommendations for healthy

nutrition, PA, and screen time practices.4-]15 The NAP SACC program, which has been frequently evaluated16-]18

and has been utilized by several different state and local organizations,19 is designed to help ECE programs

improve practices. Most states are developing Quality Rating and Improvement Systems (QRIS) for ECE programs,

which provide opportunities for programs to voluntarily earn levels of certification in return for completing certain

trainings and demonstrating other quality indicators.20 Although NAP SACC Is not currently incorporated into

QRIS, we model this implementation scenario given that QRIS could a promising mechanism for disseminating

NAP SACC widely.

Assessment of Benefit

The impact on child BMI of nationwide implementation of NAP SACC through QRIS certifications was modeled

based on the logic model in Appendix Exhibit A7.1. We used evidence from a group-]randomized, controlled trial of

NAP SACC implementation across three states that examined the impact of NAP SACC on the BMI z-]score of 3-]5

year old children;18 this study found the intervention was associated with a reduction in BMI z-]score of -]0.14

units (SE: 0.06, p=0.02) in intervention compared to control children 9 months after the intervention began. In our

model, we convert BMI z-]score to BMI by multiplying the z-]score change by the average standard deviation for

BMI for girls (SD=1.54) and boys (SD=1.39) aged 4.0 . 4.49 years in the CDC 2000 Growth Charts reference

population;21 this results in an estimated BMI effect of -]0.21 kg/m2 per child (SE: 0.09). Key model input

parameters based on this logic model are described below and are detailed in Appendix Exhibit A7.2.

Reach

The intervention reaches all 3-]5 year old children attending licensed ECE programs (both child care centers and

family daycare homes) that opt to participate in their statefs QRIS. Comparing estimates of licensed ECE program

capacity from a survey of state child care licensing agencies22 with Census estimates of the total 3-]5 year old

population in each state, we calculated the state-]specific probability of a 3-]5 year old attending a licensed child

care center or family daycare home (national average: 41.2%). We then internally surveyed each statefs licensing

agency to ascertain whether a QRIS was in place and, if so, how many centers and/or family daycares participated;

we then estimated the state-]specific probability of QRIS participation for ECE programs (average: 28.8% of

licensed programs). We assumed that all programs in QRIS would be eligible for the NAP SACC intervention, but

that 73% would complete the intervention;16 all children attending these 73% of eligible programs were then

assumed to benefit from the intervention.

Costs

We estimated the cost of the intervention based on data provided by both the authors of the study used to

estimate the BMI effect (A. Alkon, personal communication, March 18, 2015) and by staffat Go NAP SACC, which is

disseminating the NAP SACC intervention (E. Morris, personal communication, 9/19/14). NAP SACC requires

training of a cadre of child care health consultants (CCHCs) to work with ECE programs, which we assume is

overseen by each state. CCHCs then spend time consulting with each ECE program, which we also assume is paid

for by each state; we also estimate travel costs for CCHCs to consult with programs. ECE program directors spend

time implementing NAP SACC, while ECE program teachers are assumed to spend 5 hours in training for the

program. Participating ECE programs also purchase updated physical activity equipment upgrades and a binder of

NAP SACC materials. Lastly, we estimate the likely additional costs incurred by improving nutrition practices by

estimating the most likely changes to meal patterns, comparing to baseline meal service, and estimating the cost

difference per child per day using the USDA Center for Nutrition Policy and Promotion Food Cost Database.23

Additionally, we assume labor costs at the state level for QRIS administrators to monitor compliance with the

intervention.

Appendix A7 References

1. Snyder TD, Dillow SA. T.D., and Dillow, S.A. (2013). Digest of Education Statistics 2012 (NCES 2014-]015), Table

202.10. National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education.

Washington, DC: 2013. Accessed 2/26/15 at: http://nces.ed.gov/programs/digest/d13/tables/dt13_202.10.asp

2. Sigman-]Grant M, Christiansen E, Branen L, Fletcher J, Johnson SL. About feeding children: mealtimes in child-

]care centers in four western states. Journal of the American Dietetic Association. Feb 2008;108(2):340-]346.

3. Story M, Kaphingst KM, Robinson-]O'Brien R, Glanz K. Creating healthy food and eating environments: policy and

environmental approaches. Annual review of public health. 2008;29:253-]272.

4. Pate RR, Pfeiffer KA, Trost SG, Ziegler P, Dowda M. Physical activity among children attending preschools.

Pediatrics. Nov 2004;114(5):1258-]1263.

5. Finn K, Johannsen N, Specker B. Factors associated with physical activity in preschool children. J. Pediatr. Jan

2002;140(1):81-]85.

6. McWilliams C, Ball SC, Benjamin SE, Hales D, Vaughn A, Ward DS. Best-]practice guidelines for physical activity

at child care. Pediatrics. Dec 2009;124(6):1650-]1659.

7. Ball SC, Benjamin SE, Ward DS. Dietary intakes in North Carolina child-]care centers: are children meeting current

recommendations? Journal of the American Dietetic Association. Apr 2008;108(4):718-]721.

8. Erinosho T, Dixon LB, Young C, Brotman LM, Hayman LL. Nutrition practices and children's dietary intakes at 40

child-]care centers in New York City. Journal of the American Dietetic Association. Sep 2011;111(9):1391-]1397.

9. Ritchie LD, Boyle M, Chandran K, et al. Participation in the child and adult care food program is associated with

more nutritious foods and beverages in child care. Child Obes. Jun 2012;8(3):224-]229.

10. Tandon PS, Garrison MM, Christakis DA. Physical activity and beverages in home-] and center-]based child care

programs. Journal of nutrition education and behavior. Jul-]Aug 2012;44(4):355-]359.

11. Sisson SB, Campbell JE, May KB, et al. Assessment of food, nutrition, and physical activity practices in

Oklahoma child-]care centers. Journal of the Academy of Nutrition and Dietetics. Aug 2012;112(8):1230-]1240.

12. Frampton AM, Sisson SB, Horm D, Campbell JE, Lora K, Ladner JL. Whatfs for lunch? An analysis of lunch

menus in 83 urban and rural Oklahoma child care centers providing all-] day care to preschool children. J Acad Nutr

Diet 2014 Sep 10;114(9):1367-]74.

13. Sigman-]Grant M, Christiansen E, Fernandez G, Fletcher J, Johnson SL, Branen L, Price BA. Child care provider

training and a supportive feeding environment in child care settings in 4 states, 2003. Prev Chronic Dis

2011;8(5):A113.

14. Tandon PS, Saelens BE, Christakis DA. Active play opportunities at child care. Pediatrics. 2015 May 18. pii:

peds.2014-]2750. [Epub ahead of print]

15. Christakis DA, Garrison MM. Preschool-]aged children's television viewing in child care settings. Pediatrics

2009;124(6):1627-]1632.

16. Ward DS, Benjamin SE, Ammerman AS, Ball SC, Neelon B, Bangdiwala SI. Nutrition and physical activity in child

care: Results from an environmental intervention. Am J Prev Med. 2008;35(4):352.356.

17. Drummond RL, Staten LK, Sanford MR, Davidson CL, Magda Ciocazan M, Khor KN, Kaplan F. A pebble in the

pond: The ripple effect of an obesity prevention intervention targeting the child care environment. Health Promot

Pract. 2009 Apr;10(2 Suppl):156S. 167S

18. Alkon A, Crowley AA, Neelon SE, Hill S, Pan Y, Nguyen V et al. Nutrition and physical activity randomized control

trial in child care centers improves knowledge, policies, and children's body mass index. BMC Public Health. 2014

Mar 1;14:215

19. NAP SACC: About NAP SACC. Accessed 5/11/15 at: https://gonapsacc.org/about-]nap-] sacc.

20. Gabor V, Mantinan K. State efforts to address childhood obesity in child care quality rating and improvement

systems. Ann Arbor, MI: Altarum Institute, 20- Accessed 5/29/15 at

http://altarum.org/sites/default/files/uploaded-]related-]files/QRIS-]Report-] 22Feb12-]FIN_0.pdf.

21. Kuczmarski RJ, Ogden CL, Guo SS, et al. 2000 CDC growth charts for the United States: Methods and

development. National Center for Health Statistics. Vital Health Stat 11(246). 2002.

22. National Association for Regulatory Administration. 2011-]2013 Child Care Licensing Study. Accessed 3/27/15

at: http://www.naralicensing.org/Resources/Documents/2011-] 2013_CCLS.pdf

23. United States Department of Agriculture Center for Nutrition Policy and Promotion. Food Prices Database

2003-]2004.

Appendix A8. Bariatric Surgery Intervention Specification and Background Modeled Intervention.

We modeled the effect of a nationwide four-]fold increase in the use of bariatric surgery by eligible adolescents,

ages 13 to 19 years old, including Roux-]en-]Y gastric bypass, laparoscopic adjustable gastric banding, and sleeve

gastrectomy as currently performed. Intervention components include pre-]surgical evaluation, testing, and

multidisciplinary medical visits additional to the surgery itself. The proposed intervention would increase the rate

of bariatric surgery four-]fold over current levels, and will receive the three different surgeries at the higher rate but

the same relative proportion seen at baseline.

Background

Severe obesity, defined by a BMI .120% of the 95th percentile or .35 kg/m2 (considered Class 2 obesity in adults),

is the fastest growing subcategory of childhood obesity.1 A BMI .140% of the 95th percentile or . 40 kg/m2 can be

further defined as Class 3 obesity. The prevalence of Class 3 obesity among those 12-]19 is approximately 2.1% (so

approximately 640,000 adolescents ages 13-]19).2 Compared to the overweight and/ or obese, the severely obese

have a distinctly adverse cardiometabolic profile and are much less likely to respond to lifestyle modification.1,3

Bariatric surgery may be the only effective treatment option for a select group of severely obese adolescents. The

most commonly performed procedures in adolescents are laparoscopic Roux-]en-]Y gastric bypass [RYGB],

adjustable gastric banding [LAGB], and sleeve gastrectomy [SG].4 The American Society for Metabolic and

Bariatric Surgery pediatric committee best practice guidelines5 recommend that surgery be considered with a BMI

of .35 kg/m2 with major co-]morbidities or a BMI of .40 kg/m2 with minor co-]morbidities; however, other clinical

organizations (such as the American Academy of Pediatrics6) recommend a much more conservative approach

and many physicians are reluctant to refer adolescents for weight loss surgery, citing the invasiveness and

potential complications of the procedure.7 Recent evidence suggests that approximately 1,000 adolescents

undergo inpatient surgical weight loss procedures each year, which is far fewer than the total number of

adolescents with Class 3 obesity;8,9 more current estimates by the authors indicate 1150 cases per year. Since

there are approximately 640,000 fitting the criteria, less than 2 in 1,000 receive the procedure.

Assessment of Benefit

A systematic review of the literature was performed to identify all available studies. Of the yielded references, fifty-

]nine studies were reviewed to examine the effectiveness and cost-] effectiveness of surgical weight loss in

adolescents. Ultimately, an existing systematic review and meta-]analysis of adolescent bariatric surgery

performed by Black and colleagues10 was chosen to be applied to the model.

Effectiveness Estimates:

* Black et al.,10 reviewed 21 studies with a total of 637 patients; this included 1 randomized controlled trial, 12

retrospective studies, and 8 prospective observational studies.

Across surgery types (RYGB, LAGB, and SG) the average weighted mean change in BMI units was -]13.5 kg/ m2

(95% CI -]15.1, -]11.9).

Divided by surgical procedure, RYGB produced the largest change in BMI (-]17.2 kg m2; 95% CI -]20.1, -]14.3) and

LAGB produced the smallest BMI reduction (-]10.5 kg m2; 95% CI -]11.8, -9.1). Sleeve gastrectomy resulted in an

intermediate reduction of -]14.5 kg m2; (95%CI -]17.3, -] 11.7).10

Reach

We assumed that all adolescents with a BMI of >=40 are eligible, however, few will actually be offered the

procedure (about 2 in a 1000 currently receive the surgery).

We assumed that 1.56% of eligible adolescents actually have the evaluation for the procedure.

We assumed that only 60% of adolescents evaluated and offered the surgical procedure will actually follow

through to completion. (Note: Number referred to surgery 4600/0.6 = 7,667, which is a 40 percent drop-]out rate.)

We assumed that at baseline there is an estimated one quarter population coverage of this procedure by

Medicaid.12 Moreover, the rate of use among those eligible would increase if Medicaid (and other insurance

coverage) increased under policy change. We assumed this to be 4 times the current rate.

Based on our survey of adolescent bariatric centers (unpublished data), about 60% of referred patients ultimately

undergo surgery; extrapolated to the number of potentially eligible patients, about 4,600 severely obese

adolescents would undergo surgery in our primary scenario.

Costs

Previously published cost estimates for surgical weight loss vary by procedure type. In our scenario, we assume

that 43% of patients undergo RYGB, 51% undergo sleeve gastrectomy, and 6% undergo LAGB (authorfs

unpublished analysis of the most recent KIDS data). We include outpatient pre-]procedural evaluation and

diagnostic testing, the hospital admission, and physician fees.8,11 We estimate costs from the study of Weiner et

al.11 With data on the relative frequency of each procedure type, we estimate the overall cost of surgical weight

loss as a whole.

In the primary scenario, the surgical weight loss intervention would reach 4600 severely obese adolescents with a

BMI + 40 kg/m2 in the first year. To implement bariatric surgery for the benefitting population in the primary

scenario, the annual cost would be $26,174,836.00.

Appendix A8 References

1. Kelly AS, Barlow SE, Rao G, et al. Severe obesity in children and adolescents: identification, associated health

risks, and treatment approaches: a scientific statement from the American Heart Association. Circulation.

2013;128(15):1689-17-

2. Skinner AC, Skelton JA. Prevalence and Trends in Obesity and Severe Obesity Among Children in the United

States, 1999-20- JAMA Pediatr. 2014.

3. Zitsman JL, Inge TH, Reichard KW, Browne AF, Harmon CM, Michalsky MP. Pediatric and adolescent obesity:

management, options for surgery, and outcomes. J Pediatr Surg. 2014;49(3):491-494.

4. Zitsman JL, Fennoy I, Witt MA, Schauben J, Devlin M, Bessler M. Laparoscopic adjustable gastric banding in

adolescents: short-term results. J Pediatr Surg. 2011;46(1):157-162.

5. Michalsky M, Reichard K, Inge T, Pratt J, Lenders C, Surgery ASfMaB. ASMBS pediatric committee best practice

guidelines. Surg Obes Relat Dis. 2012;8(1):1-7.

6. Barlow SE, Committee E. Expert committee recommendations regarding the prevention, assessment, and

treatment of child and adolescent overweight and obesity: summary report. Pediatrics. 2007;120 Suppl 4:S164-

192.

7. Vanguri P, Lanning D, Wickham EP, Anbazhagan A, Bean MK. Pediatric health care provider perceptions of

weight loss surgery in adolescents. Clin Pediatr (Phila). 2014;53(1):60- 65.

8. Kelleher DC, Merrill CT, Cottrell LT, Nadler EP, Burd RS. Recent national trends in the use of adolescent inpatient

bariatric surgery: 2000 through 2009. JAMA Pediatr. 2013;167(2):126- 132.

9. Zwintscher NP, Azarow KS, Horton JD, Newton CR, Martin MJ. The increasing incidence of adolescent bariatric

surgery. J Pediatr Surg. 2013;48(12):2401-2407.

10. Black JA, White B, Viner RM, Simmons RK. Bariatric surgery for obese children and adolescents: a systematic

review and meta-analysis. Obes Rev. 2013.

11. Weiner JP, Goodwin SM, Chang HY, et al. Impact of bariatric surgery on health care costs of obese persons: a

6-year follow-up of surgical and comparison cohorts using health plan data. JAMA Surg. 2013;148(6):555-562

12. Yang YT and Pomeranz JL. States variations in the provision of bariatric surgery under affordable care act

exchanges. Surg Obes Relat Dis. 2015 May-Jun;11(3):715-20.

DETAILS

Subject: Studies; Access; Childhood; Schools; Prevention; Deduction; Effectiveness; Food;

Investment; Implementation; Taxation; Labeling; Nutrition; Obesity; Restaurants;

Standards; Surgery; School meals; Health care services policy; Excise tax; Health

care costs; Health care policy; Initiatives; Cost analysis; Early intervention;

Beverages; Health care; Sugar; Children; Body mass index; Meals; Body size; Body

mass

Location: United States--US

Classification: 9190: United States; 8320: Health care industry; 1200: Social policy; 9130:

Experiment/theoretical treatment

Publication title: Health Affairs; Chevy Chase

Volume: 34

Issue: 11

Pages: 1932-65A

Number of pages: 73

Publication year: 2015

Publication date: Nov 2015

Section: OBESITY &DIET

Publisher: The People to People Health Foundation, Inc., Project HOPE

Place of publication: Chevy Chase

Country of publication: United States, Chevy Chase

Publication subject: Insurance, Public Health And Safety

ISSN: 02782715

Source type: Scholarly Journals

Language of publication: English

Document type: Journal Article

Document feature: References Tables Equations Diagrams

DOI: http://dx.doi.org/10.1377/hlthaff.2015.0631

ProQuest document ID: 1731754743

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