Abstract

The obesity epidemic in the United States calls for action on a national level, yet the potential economic effects of 

such interventions are unclear. Researchers identified six randomized, controlled trials of community-based 

interventions to address childhood obesity at different ages that demonstrated a significant change in the prevalence of 

obesity in the target population. The effects obtained in those trials were applied to a new model of obesity through 

the life course (ages 3-65 years) in order to estimate the health and economic effects from such interventions scaled 

to the national level. Even the most effective intervention did not persistently affect the national prevalence of 

obesity more than 20 years after the intervention was completed. Furthermore, the economic impact of interventions 

was less than 10 percent of the total excess costs related to obesity for each birth cohort. This study underscores 

the challenges of addressing obesity at the national level and emphasizes how critical it will be to obtain long-term 

followup data on children enrolled in prior and future trials.

By Matthew M. Davis and Achamyeleh Gebremariam, Child Health Evaluation 

and Research Unit, Division of General Pediatrics, University of Michigan

Economic Long-Term Impacts of 

Interventions Aimed at Preventing or 

Reducing Obesity Among Children

Contractor and Cooperator Report No. 62

September 2010

This study was conducted by the University of Michigan under a cooperative 

research contract with USDA’s Economic Research Service (ERS) Food and 

Nutrition Assistance Research Program (FANRP): contract number 58-4000-7-

0037 (ERS project representative: Elizabeth Frazao). The views expressed are 

those of the authors and not necessarily those of ERS or USDA.
2

Introduction

As a major threat to the health of the US population, obesity is a target of considerable research regarding the 

potential impact of interventions on groups within the population. The investigators have demonstrated 

previously that it is possible to use models of the dynamics of body mass index throughout the life course to 

understand the impact of interventions on the prevalence of obesity. By using national data that relate obesity 

to the costs of health care and missed work, it is then possible to understand the potential impact of 

interventions on the costs of obesity resulting from changes in the population prevalence of obesity.

The work at the core of this project is based on a hypothetical birth cohort in the US, followed from age 3 

through age 65. This cohort was constructed in the investigators’ previous research, based on longitudinal data 

from the national Medical Expenditure Panel Survey and benchmarked against the National Health and 

Nutrition Examination Survey (NHANES). This cohort model allows the investigators to:

(a) Examine the dynamics of obesity through the life course 

(b) Examine the dynamics of health care and work loss costs accumulated through the life course 

associated with obesity, controlling for sex, race/ethnicity, income, and health insurance coverage

(c) Investigate and estimate the population-level effects on obesity prevalence and total costs for 

rigorously applied interventions reported in the peer-reviewed literature

(d) Investigate and estimate the population-level effects on obesity prevalence and total costs for 

hypothetical interventions applied at different ages and for differing durations

Investigators’ prior modeling of costs associated with BMI through the life course – Methods 

The Medical Expenditure Panel Survey (MEPS) is a complex national probability survey of the civilian noninstitutionalized population. The survey has been conducted on annual basis since 1996 by the Agency for 

Healthcare Research and Quality (AHRQ). The MEPS sample design is an overlapping panel design, with 

data collected over a two-year period for each MEPS sample. 

For the purpose of our model construction, we combined public-use data from MEPS panel data files for 

panels 5 through 8 (new panel years 2000 through 2003). The panel data permitted measurements of 

transitions in BMI group status at the individual level, and pooling data across years permitted stable annual 

estimates for BMI group transition probabilities. The study population was comprised of those aged 3 to 65 

years at the beginning of each panel of the MEPS survey. 

BMI categories were created using age- and sex-specific BMI percentiles for children 3 to 20 years 

(underweight=<5th percentile; healthy weight=5th up to 85th; overweight=85th to less than 95th and obesity ≥95th

percentile) and using the BMI values for those 20 and older (underweight= BMI<18.5; healthy weight=BMI 

18.5 to 24.9; overweight= BMI 25.0-29.9 and obese= BMI ≥30). In this study, the underweight group was 

incorporated in model development, but was excluded from data reporting and cost analysis due to small 

sample size.

As described below, data from concurrent public-use NHANES data were used to standardize distribution of 

the population across BMI groups at each age.

At each age, a one-year probability of transition from each BMI group to all others was generated, based on 

individual data from the MEPS. For example, for all 3-year-old children who were normal weight, 

probabilities were generated for transitioning to underweight, overweight, and obesity, and for remaining 

healthy weight by age 4; for all 3-year-old children who were overweight, probabilities were generated for 

transitioning to underweight, healthy weight, and obesity, and for remaining overweight by age 4. From 

transition probabilities, prevalence estimates for the different BMI categories were calculated for normal 

weight, at risk for overweight and for overweight categories.
3

To standardize BMI category prevalence estimates from the MEPS to the gold standard from concurrent 

NHANES, in rare instances in which they did not match exactly the transition probabilities were iteratively 

modified so that the BMI group prevalences at each age matched the corresponding NHANES prevalence 

values. 

Total health care expenditure was the primary outcome variable and was defined in the MEPS as the sum of 

direct payments for health care received during the year. In order to have a common reference for comparison 

of expenditures, expenditures were converted to 2006 USD after applying the appropriate inflation factor for 

each year’s expenditure data from the medical care Consumer Price Index.

In order to reduce the effect of extreme expenditure values, both high and low, we excluded 0.2% of the 

expenditure data (0.1% on the low end and 0.1% on the high end). On the low end, among zero values we 

randomly excluded 0.1% of the zero-value cases from the analysis. On the high end, no standard software can 

give us the 99.9th percentile. So, we identified the 99.9th percentile in one-year expenditures ($105,836) and 

removed observations in excess of this value.

In addition to health care expenditures, measures of overall costs also included estimated work loss costs. The 

latter were estimated using data from the MEPS about missed days of work, and multiplying the number of 

days by the mean wage for earners in each year of the MEPS from the Bureau of Labor Statistics, inflated to 

2006 USD.

Sample-weighted values were used in all analyses of costs. The sampling weights for the pooled data are the 

original sampling weights divided by four as we are pooling data across four years.

Given expected confounding by sociodemographic variables, we adjusted expenditures for age, sex, 

race/ethnicity, insurance status, income status, and age2 to capture the non-linear increase in expenditure with 

increased age. We used two-part multiple regression models to estimate the predicted total health care 

expenditures and overall costs, instead of using ordinary least squares models, because of skewed data. In the 

first part, we modeled the probability of having an expenditure of any positive amount using logistic 

regression. And in the second part, we performed a multiple linear regression on the log-transformed 

expenditure variable for those with positive expenditure. 

After each step, we obtained predicted marginals for each age and BMI category (the average predicted

response if all the observations had been in a given group (i.e., for a given age and BMI category)). From the 

logistic regression, we calculated an average predicted probability of having expenditure and from the linear 

regression the average predicted total expenditure after retransformation by exponentiation. The average of 

the exponentiated residuals from the linear regression formed the smearing estimate, which was used as an 

adjustment in the retransformation. Then we combined the estimate of the probability of having expenditure, 

the predicted expenditure and the smearing adjustment at each age and BMI to get the overall predicted mean 

expenditure at each age and BMI category. 

Bootstrap methods were used to obtain variance estimates and 95% percent confidence intervals for the overall 

predicted marginals. The prcoess required creating a predetermined number (in our case, 500) of bootstrap 

samples of the same size as the original data and performing the two-part models on each bootstrap sample. 

Then we computed the overall predicted marginals for each age and BMI category in each sample. Finally, we 

computed the variance as well as the 95% confidence intervals, based on the distribution of the bootstrapped 

overall predicted marginals.

Excess health care expenditures and excess overall costs associated with obesity were estimated by subtracting 

the adjusted mean value for individuals in the healthy weight group at each age from the mean value for 

individuals in the obese group at each age.

The national aggregate excess health care expenditures and excess overall costs were then estimated for a 

hypothetical birth cohort of 4 million individuals. To generate these estimates we multiplied the number of 
4

individuals in the cohort in the obese group at each age (a function of population adjusted for age-specific 

mortality) by the excess measure at each age, and then summed the age-specific, population-weighted 

estimates at each age over all the years of interest for the cohort, from age 3 through age 65.

Investigators’ prior modeling of costs associated with BMI through the life course – Principal Findings

The age-specific probabilities of healthy weight, overweight, and obesity are presented in Figure A1.

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Age of Cohort

Age-Specific Proportion

Proportion NW

Proportion Overweight

Proportion Obese

Figure A1. Age-specific prevalence proportions of normal weight, overweight, and obesity from age 3 

through 65, from NHANES 1999-2002.

Prevalence proportion for underweight is not shown (<3% each year).

These data illustrate the dynamic prevalence of different BMI designations of a cohort of 3-year-olds as they 

transit through adolescence into adulthood. The most remarkable feature of Figure A1 is the rapid decline in 

normal weight prevalence and rapid ascent of overweight and obesity prevalence through middle adolescence 

into young adulthood.

Transitions from one BMI category to another underlie the patterns in Figure A1. Those transitions are 

presented as 3 panels in Figure A2, for individuals who are overweight or obese in the first year of measure 

(e.g., age 3) and subsequently transition (e.g., by age 4) (Panel A2a), versus individuals who are normal weight 

in the first year and transition (Panel A2b).
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Prob OW -> NW

Prob OW -> OW

Prob OW -> OB

Prob OB -> NW

Prob OB -> OW

Prob OB -> OB

Figure A2a. Age-specific BMI transition probabilities for individuals overweight or obese at age N from 

age 3 through 65, based on longitudinal data from MEPS 2000-03.

Probabilities for individuals of healthy weight and underweight at age N not shown.

[go to next page]
6

Figure A2b. Age-specific BMI transition probabilities for individuals healthy weight at age N from age 

3 through 65, based on longitudinal data from MEPS 2000-03.

In Figure A2a, for children who are obese at age N, it is evident that the probability of remaining obese at age 

N+1 (brown circles) increases rapidly through childhood and adolescence, while the probability of 

transitioning to normal weight at age N+1 (red circles) falls rapidly through childhood and adolescence. For 

children obese at age N, the probability of transitioning to overweight at age N+1 (purple dots) is 

comparatively stable.

Similarly, it is apparent for children who are overweight at age N that the probability of remaining overweight 

at age N+1 (pink squares) increases rapidly through childhood and adolescence, whereas the probability of 

transitioning to normal weight at age N+1 (blue diamonds) decreases through childhood and adolescence. For 

children overweight at age N, the probability of transitioning to obesity at age N+1 (orange triangles) is 

comparatively stable.

In contrast, as shown in Figure A2b, for individuals at healthy weight at any age, the transition probabilities 

remain comparatively stable over the time course of the cohort.

The specific values for transition probabilities underlying the trends shown in the panels of Figure A2 are 

included in Appendix A.

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Age-specific transition prob.

Prob NW -> NW

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7

Current Project - Objectives

The work completed in the course of this project centered on three central goals:

1) Estimation of long-term costs of obesity for the hypothetical cohort, with data from the Medical 

Expenditure Panel Survey 2000-2004 and the NHANES 1999-2004

2) Development of models of “optimal” interventions during childhood, given a fixed intervention effect 

but varying the population ages at which such an intervention would be applied

3) Estimation of the long-term costs of obesity in the setting of population-level application of published 

interventions during childhood

Based on input from the USDA staff throughout the project, we made several modifications to our work that 

improved its interpretation and relevance; we are grateful for these suggestions. The final work as modified

and revised is presented here in this report.

Estimates of Long-term Costs of Obesity

Using the framework of the long-term model of cumulative excess costs of obesity that the investigators have 

developed from the Medical Expenditure Panel Survey (MEPS), the investigators estimated the cumulative 

long-term costs of obesity for a cohort of 4 million US children as they age from 3 years to 65 years. Direct 

and indirect costs (wages from lost work time associated with obesity) were estimated.

Total healthcare expenditure is the primary outcome variable and is defined as the sum of direct payments for 

healthcare received during the year. The distribution of total healthcare expenditure has a) a large number 

with no expenditures (~15% in our study data) and b) among those with some expenditure the distribution is 

skewed. Due to these two issues, ordinary least squares when used for modeling total expenditure will lead to 

imprecise estimates for the effects of the predictor variables on total expenditure. 

For this reason, we use two-part multiple regression models to estimate the predicted total healthcare 

expenditure instead of using ordinary least squares model. In the first part, we model the probability of having 

an expenditure of any positive amount using logistic regression. In the second part, we perform a multiple 

linear regression on the log-transformed expenditure variable for those with positive expenditure. In both 

models, we include age, BMI category, race/ethnicity, insurance status and income level. In addition to these 

variables, we added age2 to capture the non-linear increase in expenditure with increased age. 

After each step, we obtain predicted marginals for each age and BMI category. Predicted marginals are 

defined as the average predicted response if all the observations had been in a given group (i.e., for a given age 

and BMI category). From the logistic regression, we calculate an average predicted probability of having 

expenditure and from the linear regression the average predicted total expenditure after retransformation by 

exponentiation. The average of the exponentiated residuals from the linear regression form the smearing 

estimate which is used as an adjustment in the retransformation. Then we combine the estimate of the 

probability of having expenditure, the predicted expenditure and the smearing adjustment at each age and BMI 

to get the overall predicted mean expenditure at each age and BMI category. 

We compute the excess cost due to obesity at each age by subtracting the average predicted overall cost for 

someone in the normal weight from the predicted overall cost for someone in the obese category. The 

variance estimate for the average excess cost is obtained as the sum of the variances for average predicted cost 

for normal weight and obese/overweight. Total excess cost due to obesity at each age is obtained by 

multiplying the number of obese persons in the population and the excess average cost at each age. We will 

then cumulate across ages to get the cumulative total excess cost at each age.

The principal findings from the analysis appear below.
8

Figure 1. Mean health care expenditures associated with obesity from age 3 years-65 years, adjusted for 

sex, race/ethnicity, income, and health insurance status. 95% confidence intervals are shown.

Normal = normal BMI. AR/Ovwt = BMI in at risk (child) or overweight (adult) range. 

Ovwt/Obese = BMI in overweight (child) or obese (adult) range.

As shown in Figure 1, the mean health care expenditures begin to differ for overweight/obese versus normal 

weight individuals beginning at about age 30, and continue to differ at progressively larger amounts at older 

ages. These data represent a distinct approach to costs of obesity available in the literature, because: (a) we 

examine the incremental health care costs of obesity from early childhood through middle to late adulthood; 

(b) we adjust for covariates known to be associated with the predictor (obesity) and the outcome (health care 

costs).

Of note, there are no differences in the health care expenditures based on BMI category during childhood, 

based on these measurements. This observation is critical for understanding our other findings regarding the 

impact of hypothetical and published interventions in childhood.

Findings regarding the cumulative excess costs of obesity through the life course appear on the next page.

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Normal AR/Ovwt Ovwt/Obese
9

Figure 2. Cumulative excess direct costs associated with obesity for a cohort of US children from age 3 

through age 65, adjusted for sex, race/ethnicity, income, and health insurance status.

As shown in Figure 2, the estimated cumulative non-discounted excess direct medical costs associated with 

obesity from age 3 through age 65 are $48 billion. 

Figure 3. Cumulative total (direct + indirect) excess costs associated with obesity for a US birth cohort 

from age 3 through age 65, adjusted for sex, race/ethnicity, income, and health insurance status.

As shown in Figure 3, the total undiscounted cumulative excess costs (including lost wages) associated with 

obesity are $52 billion. In other words, consideration of lost wages adds only a modest amount to the total 

Cumulative total excess due to obesity: Direct

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10

excess costs associated with obesity. The majority of excess costs related to obesity are attributable to medical 

costs.

Development and Interpretation of Models of Hypothetical Interventions

To address the obesity epidemic, there is considerable policy interest in examining interventions that may be 

applicable at the population level. Because there have been no national interventions of this kind, the 

investigators wanted to model hypothetical interventions of similar magnitude and scale, to understand how 

the natural dynamics of BMI transitions during childhood might be affected by population-level programs.

In consideration of hypothetical periods of interventions for obesity during childhood, we considered

interventions that would be applied to a cohort for a period of 10 years, beginning at differing ages:

 Ages 4 to 13 years (“preschool”)

 Ages 6 to 15 years (“schoolage”)

 Ages 16 to 25 years (“adolescent and young adult”)

The first two interventions look very similar in their application time periods, but an important distinction is 

that the preschool intervention begins before the period of adiposity rebound which is thought to be so critical 

for childhood patterns of obesity. We chose 10 years as a uniform period of intervention in order to try to 

understand the comparative effects of population-level interventions of the same duration.

 Assumptions of the models include:

o Among those in the healthy weight category, we will see 5% increase in those who stay in the 

healthy weight category, by reducing those who transition to the at risk category from the 

healthy weight category. No changes in those who transition from healthy weight to 

overweight. These assumptions were based on existing published interventions.

o Among those in the at-risk weight category, 5% increase in those who transition to the healthy

weight category by reducing transition probabilities to the overweight and at-risk categories 

by 2.5%. These assumptions were based on existing published interventions.

o Among those in the overweight category, 5% increase in those who transition to the at-risk 

category by reducing the probability of staying in the overweight category. There were no 

changes in those who transition from overweight to healthy weight.

Those assumptions can be represented as follows:

Table of transition probability changes for the trial optimal intervention

Pnn Pna Pno Pan Paa Pao Pon Poa Poo

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0.025

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n=normal weight; a=at-risk; o=overweight

We applied these changes to the transition probabilities already established in our baseline model of obesity 

prevalence.

“Residual” effects: It is vitally important to consider the impact of “residual” effects of interventions – in 

other words, the persistent impact of interventions following their cessation. The magnitude and duration of 

residual effects of interventions are unknown in the literature, as there are no population-based cohorts that 

have been followed for longer than the duration of the interventions themselves. Therefore, to investigate the 

relative impact of intervention lengths, the investigators arbitrarily chose a 10-year durations for residual 

effects; in other words, a 10-year intervention + 10-year residual effect = 20 years of intervention-magnitude 

effects. For all residual effects, the investigators modeled a magnitude of the residual as the magnitude of the 

intervention itself, applied across the population.
11

Effect of intervention on prevalence of overweight/obesity

The prevalence of obesity in the setting of 10-year intervention + 10-year residual is demonstrated in the

panels of Figure 3.

[please go on to next page]
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Overweight/obese prevalence

intervention at ages 4 to 23

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Figure 3a. Prevalence of overweight/obesity in setting of a 10-year intervention + 10-year full residual 

effect from age 4-23. Upper trend line is smoothed prevalence of obesity in the absence of an 

intervention. Lower trend line is smoothed prevalence of obesity with the intervention.

Overweight/obese prevalence

intervention at ages 6 to 25

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Figure 3b. Prevalence of overweight/obesity in setting of a 10-year intervention + 10-year full residual 

effect from age 6-25. Upper trend line is smoothed prevalence of obesity in the absence of an 

intervention. Lower trend line is smoothed prevalence of obesity with the intervention.
13

Overweight/obese prevalence

intervention at ages 16 to 35

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Figure 3c. Prevalence of overweight/obesity in setting of a 10-year intervention + 10-year full residual 

effect from age 16-35. Upper trend line is smoothed prevalence of obesity in the absence of an 

intervention. Lower trend line is smoothed prevalence of obesity with the intervention.

There are some key observations from the obesity prevalence patterns evident in the panels of Figure 3:

a) This modest-size intervention (in terms of changes in BMI group transition probabilities) achieves 

differences in obesity prevalence of greater than 10 percentage points or more at the maximum. These 

are comparable to differences seen with published interventions (see later sections for direct 

comparisons).

b) After the termination of the residual effect, the obesity prevalence then returns to the “natural” 

baseline prevalence over a period of 12-15 years. This return to baseline, while it seems counterintuitive, happens to any two probability trend lines that start out at different points but have the same 

transition probabilities applied to them. The return to baseline happens slightly faster for the 

interventions applied at younger ages than the intervention applied during adolescence and young 

adulthood.

Effects of interventions on savings related to obesity

These differences in obesity prevalence translate into different levels of cumulative savings related to obesity, 

if applied to the hypothetical birth cohort, as shown in the 3 panels of Figure 4 below. The panels present 

findings from the 3 scenarios of interventions, first undiscounted and then discounted at 3% annually (standard 

in public health literature) and discounted at 7% annually (USDA and federal government standard, from the 

Federal Register). Discounting is applied to such analyses because of the natural human tendency to value 

money more today than the same amount of money in the future.

Of note, for the discounted analyses, we apply the discount rate from the first year of the intervention, in order 

to compare the relative savings of interventions as if they were applied at different ages of the cohort. In 
14

addition, we continue to apply the discount even after savings have stopped accruing from the intervention, 

because we assume that savings today are worth more than savings a year from now (ie, discounting would 

still apply).

Savings for interventions at selected age ranges

(no discount)

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ages 4 to 23

ages 6 to 25

ages 16 to 35

Figure 4a. Cumulative savings (undiscounted) with hypothetical interventions (10-year program + 10-

year residual) applied to the population for ages 4-23 (low), 6-25 (moderate), and 16-35 (high).

Savings for interventions at selected age ranges

(3% discount)

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ages 16 to 35

Figure 4b. Cumulative savings (3% discount) with hypothetical interventions (10-year program + 10-

year intervention) applied to the population for ages 4-23 (low), 6-25 (moderate), and 16-35 (high).
15

Savings for interventions at selected age ranges

(7% discount)

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Figure 4c. Cumulative savings (7% discount) with hypothetical interventions (10-year program + 10-

year residual) applied to the population for ages 4-23 (low), 6-25 (moderate), and 16-35 (high).

Key observations from these analyses of savings for the hypothetical interventions include:

a) If one assumes (as we do for these analyses, and as substantiated by the intervention literature) that it 

would be possible to achieve the same magnitude effect at later ages as at younger ages, then greater 

savings are achieved with interventions among adolescents/young adults than among schoolage and 

among preschool-age children. This is to be expected, given that there are no differences in mean 

annual health care expenditures related to obesity at younger ages (Figure 1). In other words, 

differences in obesity prevalence achieved with an intervention at younger ages do not translate into 

savings when there are no spending differences related to obesity at those ages.

If we assume, as we have in other analyses conducted during this project period but not shown here, 

that interventions at later ages cannot achieve the same magnitude of effect as programs applied at 

younger ages, then the savings curves do trend more closely together. However, the programs applied 

at older ages are still superior when assuming up to 20% worse performance in terms of changes in 

obesity prevalence at older ages.

b) Slightly greater savings are achieved with an intervention beginning at age 6 than at age 4. In 

discounted analyses, however, this difference is negligible by age 65.

c) The magnitude of savings (about $4.7 billion for the 16-35 year-old intervention in undiscounted 

analyses) amounts to less than 10% of the overall excess expenditures. It is unknown what the costs 

would be for a national program to achieve the magnitude of change in obesity observed in these 

model analyses.
16

Estimation of the Long-term Costs of Obesity in the Setting of Population-level Application of Published 

Interventions During Childhood

The investigators performed an extensive literature review in order to identify population-based, randomized 

trials of obesity interventions during childhood that could be assessed with their models of obesity prevalence

and related costs. During their earlier work, the investigators had identified 5 such studies. Details of these 

studies are presented below:

Interventions:

1. Reducing consumption of carbonated beverages through school-based education (James et al 2004)

a. Ages 7-11: reduced to 0 the probability that non-obese children will become obese

2. School-based behavioral education intervention regarding TV viewing, decreased consumption of high-fat foods, 

increasing fruit and veg intake, and increasing moderate and vigorous physical activity (Gortmaker et al 1999)

a. Ages 11-12 - 2-year longitudinal intervention: obesity prevalence in control increased 2.2% points, 

while obesity prevalence in intervention decreased 3.3% points

3. School-based behavioral education intervention to reduce TV viewing (Robinson 1999)

a. Ages 8-9, 1-year intervention: children in intervention group had .45kg/m2 LESS increase in BMI over 

the year (this study does not report % obese, so this is a difficult one to apply to our models)

4. School-based behavioral education intervention regarding nutrition and physical activity (Coleman et al 2005)

a. Age 8, 1-year intervention: By age 10 (5th grade, 1 year after intervention finished), prevalence of 

(overweight + obesity) was 8 points lower

5. Head Start (preschool)-based intervention regarding nutrition, physical activity, and TV watching (Fitzgibbon et 

al, 2005)

a. Ages 3-4, 1-year intervention: By age 6 (2 years after end of intervention), BMI Z-score was 0.18 

lower in the intervention group (this study does not present % obese, so it is challenging to apply to our 

models)

During this current project, the investigators identified 6 additional candidate studies with a focus on early 

childhood (as specified by USDA as an area of major interest); of these 6, only 1 (Hakenen M et al – Intl Jour 

Obesity 2006;30:618-26 ) satisfied the investigators’ criteria that the study showed a significant effect of the 

intervention and that specified effects with respect to early childhood subjects. This paper presented an 

intervention from infancy through age 10; we therefore considered this intervention as if it were applied 

beginning at age 3 (the first year of our cohort) through age 10, and then with a 10-year residual effect.

The prevalence of overweight/obesity for the Hakanen et al intervention is presented in Figure 5 below.
17

Overweight/obese prevalence

Hakanen intervention at ages 4 to 20

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proportion overweight/obese

Obese, baseline obese, Hakanen ages 4 to 20

Figure 5. Prevalence of overweight/obesity in setting of a 10-year intervention (Hakanen et al) + 10-year 

full residual effect from age 11-20. Upper trend line is smoothed prevalence of obesity in the absence of 

an intervention. Lower trend line is smoothed prevalence of obesity with the intervention.

Similar to the hypothetical interventions above, note that after the termination of the residual effect by age 20, 

the prevalence of obesity then converges with the “natural history” of obesity by age 35.

For this report, the investigators also present the summary obesity prevalence curves for all 6 interventions 

considered in our analyses to date (Figure 6, p. 19). Brief descriptions of all 6 interventions are presented for 

comparison in Table 1 (next page).
18

Table 1. Descriptions of randomized, controlled trials of obesity interventions modeled in this project.

Trial Description Ages

1) Reducing consumption of carbonated beverages through school-based education 7-11

2) School-based behavioral education intervention regarding TV viewing, decreased 

consumption of high-fat foods, increasing fruit and veg intake, and increasing moderate and 

vigorous physical activity

11-12

3) School-based behavioral education intervention to reduce TV viewing 8-9

4) School-based behavioral education intervention regarding nutrition and physical activity 8-9

5) Head Start (preschool)-based intervention regarding nutrition, physical activity, and TV 

watching

3-5

6) Infancy and childhood-based intervention regarding nutrition and physical activity Through 

age 10

The application of these RCT findings to a hypothetical population-level intervention scenario should be 

considered a best-case view of best evidence and practice. None of these RCTs were applied at the national 

level in their original forms, and no assessments of their scale-ability has been performed by the investigators 

or to the investigators’ knowledge.
19

Obesity prevalence at various intervention ages

-5%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

0 10 20 30 40 50 60 70

Age in years

Percent (%)

baseline Interv. 1 : ages 8 to 22 Interv. 2 : ages 11 to 22 Interv. 3 : ages 8 to 20

Interv. 4 : ages 8 to 20 Interv. 5 : ages 3 to 16 Hakanen : ages 4 to 20

Figure 6. Obesity prevalence trends with 6 different childhood interventions + 10-year residual effects. 

Top trend line is population prevalence of obesity in the absence of an intervention. Other trend lines 

are population prevalence of obesity with the interventions.

Key observations from Figure 6 include:

a) Published interventions yield a wide variety of effects on obesity prevalence, with most about 10 

percentage points lower than the prevalence without an intervention but outliers with even more 

substantial effects.

b) The obesity trend lines converge on the “natural history” line within 20 years of the termination of 

the residual effect, even in the case of the intervention with the most substantial effects.

These differences in obesity prevalence effects and timing translate into differences in expected savings with 

interventions (panels in Figure 7, next pages).
20

Savings for real interventions (no discount)

-$500

$0

$500

$1,000

$1,500

$2,000

$2,500

$3,000

$3,500

$4,000

0 10 20 30 40 50 60 70

Age in years

Savings($millions)

Interv. 1: ages 8 to 22 Interv. 2: ages 11 to 22 Interv. 3: ages 8 to 20

Interv. 4: ages 8 to 20 Interv. 5: ages 3 to 16 Hakanen: ages 4 to 20

Figure 7a. Cumulative savings (undiscounted) with published interventions (program + 10-year 

residual) applied to the population.

In the undiscounted results (Figure 7a), it is evident that the intervention with the effect on obesity prevalence 

of the greatest magnitude (“Real intervention 1”, smooth line without markers) would be expected to yield the 

greatest savings, of about $3.5 billion. Compare this with the magnitude of savings with the hypothetical 

intervention beginning in adolescence (Figure 4a, pg 13), and it is evident that even the strongest intervention 

applied at the population level may not meet the modest savings achieved under the hypothetical intervention

assumptions. 

The intervention with the second-largest savings magnitude was “Real intervention 2” (smooth line with 

square markers) beginning at age 11 and continued out (with residual effect) to age 22. One can see, looking 

at the obesity prevalence trend lines in Figure 6, that Real intervention 2 had the smallest magnitude effect on 

obesity prevalence of any of the published interventions we examined. Yet, it had the second-largest estimated 

savings – attributable, we believe, to its timing (beginning at the oldest age of any of the interventions we 

studied).

Of note, the authors conducted sensitivity analyses to evaluate how the magnitude of savings changed in the 

hypothetical cases for these interventions where the residual effect persisted at full magnitude until age 65. As 

expected, the savings did not plateau and continued to increase until age 65. Although these sensitivity 

analyses demonstrate the functional mechanics of the model, we do not present these data because the 

evidence is lacking to support such a residual effect.

Discounted savings estimates (3% and 7%) for the base case are presented on the following page.
21

Savings for real interventions (3% discount)

-$200

$0

$200

$400

$600

$800

$1,000

$1,200

$1,400

$1,600

$1,800

0 10 20 30 40 50 60 70

Age in years

Savings ($millions)

Interv. 1: ages 8 to 22 Interv. 2: ages 11 to 22 Interv. 3: ages 8 to 20

Interv. 4: ages 8 to 20 Interv. 5: ages 3 to 16 Hakanen: ages 4 to 20

Figure 7b. Cumulative savings (3% annual discount) with published interventions (program + 10-year 

residual) applied to the population.

Savings for real interventions (7% discount)

-$100

$0

$100

$200

$300

$400

$500

$600

$700

$800

0 10 20 30 40 50 60 70

Age in years

Savings ($millions)

Interv. 1: ages 8 to 22 Interv. 2: ages 11 to 22 Interv. 3: ages 8 to 20

Interv. 4: ages 8 to 20 Interv. 5: ages 3 to 16 Hakanen: ages 4 to 20

Figure 7c. Cumulative savings (7% annual discount) with published interventions (program + 10-year 

residual) applied to the population.

It is evident from Figures 7b and 7c that discounting diminishes the differences among the relative savings 

estimated to accrue with each intervention, as well as (as expected) diminishing the overall magnitude of 

savings with any of the interventions.
22

Summary Remarks 

The investigators are grateful for the continued support of the USDA Economic Research Service for this work 

regarding the potential economic effects of obesity prevention programs at the population level. 

There are 3 main domains of findings from this work:

1) Long-term costs of obesity – This is the only analysis of which we are aware that has attempted to 

estimate the long-term costs of obesity for a birth cohort of the US population. It is, therefore, 

difficult to compare our findings of >$50 billion in excess spending to other cross-sectional estimates 

of excess costs attributable to obesity. Nevertheless, because the estimates contained herein are 

derived from some of the same data used to derive cross-sectional estimates, the investigators believe 

they are comparable.

Of note, longitudinal estimates such as these provide a basis for near- and long-term programmatic 

impact evaluation, unlike cross-sectional estimates. The investigators believe these estimates may be 

helpful for other researchers and policymakers who wish to benchmark potential interventions against 

a long-term standard.

2) Models of hypothetical interventions – Models of interventions assisted the investigators greatly in 

understanding the dynamics of body mass index through the life course. In addition, these models 

emphasize the importance of understanding:

a) Convergence of post-intervention obesity trends with the “natural history” of obesity –

The investigators found empirically (and substantiated theoretically, in documents shared with 

the USDA program officer) that applying the same transition probabilities to two sets of 

prevalence data will lead the two trend lines to converge within 15-25 cycles. This 

phenomenon, not previously recognized, has profound implications for programmatic 

initiatives, which must have sustained effects in order to avoid “returning” to the natural state. 

In other words, sustaining effects to adolescence, or to young adulthood, and not any longer in 

the life course will not prevent a return to the natural trend toward obesity in later years. In 

other words, there does not seem to be a critical period for interventions, in terms of avoiding 

a return to the natural state. Rather than showing an optimal period, though, what our work 

indicates is that all interventions seem to be similar disadvantaged: obesity is an entrenched 

population-wide problem with origins early in the life course that appear to intensify with 

time.

b) Residual effects of interventions – Without residual effects, the convergence with the natural 

state of obesity prevalence would have occurred even earlier than in our models that assumed 

10-year full-magnitude residual effects. There must be more population-level research 

regarding the duration and magnitude of residual effects in order to better inform future 

modeling work such as that carried on in this project. Without the demonstration of residual 

effects on BMI trends, interventions during childhood will compare unfavorably with 

interventions later in life, when the excess costs of obesity are higher on a per person basis.

3) Models of published interventions – Evaluation of “real world” interventions as if they were applied 

across the US at the population level assisted greatly in examining the magnitude of savings that might 

be achieved with such programs. Principal conclusions from these comparisons were that programs 

with greater effects on obesity prevalence are not necessarily expected to yield greater savings; rather, 

the timing of the interventions may be more indicative – and later (at older ages, and for longer 

periods) appears generally better. However, much remains to be learned about the residual effects of 

actual interventions, and about how they might be implemented if scaled up to the national level.
23

The greatest limitation of this cost analysis is its hypothetical nature. The investigators examined entirely 

hypothetical interventions to investigate the importance of timing and duration of residual effects. Then they 

used data from published trials of the highest rigor (randomized, controlled trials) to understand how 

population-level (countrywide) interventions might affect BMI trends on a national level. Multiple 

assumptions needed to be incorporated in these models in order to estimate results from these models, and 

empiric support for many such assumptions is simply not available at this time. Where assumptions are made, 

the results of models may not be accurate.

One of the most hypothetical components of this model regards how BMI transition probabilities are likely 

changing over time. The model presented here contains BMI transition data for a hypothetical cohort born at 

one point in time – although the BMI transition probabilities over the life course reflect the probabilities for 

individuals born at different points in time. In other words, individuals in their 50s when the MEPS data were 

collected were born in the 1940s and early 1950s. Those in their teens when the MEPS data were collected 

were born in the 1980s and early 1990s. The investigators believe that it is highly likely that those born in the 

1980s will have different BMI transition probabilities when they reach their 50s than did the individuals born 

in the 1940s. Based on recent trajectories in population-level BMI, it appears likely that the probabilities will 

favor obesity even more for the 1980s birth cohort than the 1940s cohort. The data do not exist to even 

estimate such changes with any satisfactory precision, however. For that reason, the investigators opted not to 

modify the transition probabilities, but acknowledge that the economic impact of obesity may be even higher 

than what is measured in this study.

Another limitation of this analysis is that there are no cost estimates for the interventions themselves. 

Community-based interventions have not been attempted on this scale previously, but could potentially be part 

of obesity prevention and treamtent initiatives in the future. If there were estimates of program costs, that 

would permit funders (whether private or public) and program officials to anticipate what the net costs of the 

program(s) would be.

An additional limitation of this study is that the data source did not allow the investigators to measure other 

potential gains of preventing or reducing childhood obesity – namely, improvements in self-esteem and 

academic achievement that might result. In an applied implementation of a childhood obesity initiative, one 

would optimally include these metrics as outcomes as well.

In closing, by helping identify an agenda of meaningful data gathering going forward, the investigators hope 

that this work will help illuminate the nature of data that, if compiled in the future, will substantially advance 

the science and policy regarding obesity in childhood and adulthood in the United States and around the world. 

It is critical to emphasize that the findings in from this work do not indicate that investments in childhood 

obesity prevention and treatment are not worth making. Rather, the clear message from this work is that the 

health and economic benefits from childhood obesity initiatives will only be fully appreciated if there are 

concerted efforts to measure such benefits 10, 20, 30, and more years after the initial interventions were 

implemented.