Importance: Eating disorders (EDs) are common psychiatric disorders associated with high mortality. However, data on ED disease dynamics and treatment coverage are sparse. Objectives: To model the individual-level disease dynamics of ED from birth to age 40 years and to estimate the association of increased treatment coverage with ED-related mortality. Design, Setting, and Participants: In this decision analytical model study, an individual-level Markov state transition model was empirically calibrated in April 2019 using a Bayesian approach to synthesize available clinical and epidemiologic ED data. The simulation model was calibrated to nationally representative US survey data from 2007 and 2011. A virtual cohort of 100 000 individuals (50 000 [50%] male) was modeled from birth to age 40 years for 4 ED diagnoses: anorexia nervosa, bulimia nervosa, binge eating disorder, and other specified feeding and eating disorders. Exposures: Age-specific ED incidence and mortality rates and background (all-cause) mortality. Main Outcomes and Measures: The main outcomes were age-specific 12-month and lifetime ED prevalence and number of deaths per 100 000 general population individuals by age 40 years. The mean and 95% uncertainty intervals (UIs) of 1000 simulations, accounting for stochastic and parameter uncertainty, are reported. Results: The highest estimated mean annual prevalence of ED occurred at approximately age 21 years for both male individuals (7.4%; 95% UI, 3.5%-11.5%) and female individuals (10.3%; 95% UI, 7.0%-14.2%), with lifetime mean prevalence estimates increasing to 14.3% (95% UI, 9.7%-19.0%) for male individuals and 19.7% (95% UI, 15.8%-23.9%) for female individuals by age 40 years. Ninety-five percent of first-time cases occurred by age 25 years. Current treatment coverage averts an estimated mean of 41.7 deaths per 100 000 people (95% UI, 13.0-82.0 deaths per 100 000 people) by age 40 years, whereas increasing treatment coverage for all patients with ED could avert an estimated mean of 70.5 deaths per 100 000 people by age 40 years (95% UI, 26.0-143.0 deaths per 100 000 people). Conclusions and Relevance: In this simulation modeling study, the estimated lifetime prevalence of ED was high, with approximately 1 in 7 male and 1 in 5 female individuals having an ED by age 40 years. The initial onset of EDs was highly concentrated during adolescence and young adulthood, suggesting that this is a critical period for prevention efforts. However, the high estimated prevalence of recurring ED later in life highlights the importance of identification and treatment of ED at older ages as well. These findings suggest that increasing treatment coverage could substantially reduce ED-related mortality.

OBJECTIVE: To evaluate the potential cost-effectiveness of and stakeholder perspectives on a sugar-sweetened beverage (SSB) excise tax and a Supplemental Nutrition Assistance Program (SNAP) policy that would not allow SSB purchases in Maine, US. DESIGN: A cost-effectiveness simulation model combined with stakeholder interviews. SETTING: Maine, US. PARTICIPANTS: Microsimulation of the Maine population in 2015 and interviews with stakeholders (n = 14). Study conducted from 2013 to 2017. MAIN OUTCOME MEASURES: Health care cost savings, net costs, and quality-adjusted life-years (QALYs) from 2017 to 2027. Stakeholder positions on policies. Retail SSB cost and implementation cost data were collected. ANALYSIS: Childhood Obesity Intervention Cost-Effectiveness Study project microsimulation model with uncertainty analysis to estimate cost-effectiveness. Thematic stakeholder interview coding. RESULTS: Over 10 years, the SSB and SNAP policies were projected to reduce health care costs by $78.3 million (95% uncertainty interval [UI], $31.7 million-$185 million) and $15.3 million (95% UI, $8.32 million-$23.9 million), respectively. The SSB and SNAP policies were projected to save 3,560 QALYs (95% UI, 1,447-8,361) and 749 QALYs (95% UI, 415-1,168), respectively. Stakeholders were more supportive of SSB taxes than the SNAP policy because of equity concerns associated with the SNAP policy. CONCLUSIONS AND IMPLICATIONS: Cost-effectiveness analysis provided evidence of potential health improvement and cost savings to state-level stakeholders weighing broader implementation considerations.

BACKGROUND: Accurate childhood cancer survival estimates are crucial for policy makers and clinicians for priority-setting and planning decisions. However, observed survival estimates are lacking for many countries, and when available, wide variation in outcomes is reported. Understanding the barriers to optimising survival can help improve childhood cancer outcomes. We aimed to provide estimates of global childhood cancer survival, accounting for the impact of multiple factors that affect cancer outcomes in children. METHODS: We developed a microsimulation model to simulate childhood cancer survival for 200 countries and territories worldwide, accounting for clinical and epidemiologic factors, including country-specific treatment variables, such as availability of chemotherapy, radiation, and surgery. To ensure model results were consistent with reported survival data, we calibrated the model to estimates from the CONCORD-2 and CONCORD-3 studies using an Approximate Bayesian Computation approach. We estimated 5-year net survival for diagnosed cases of childhood cancer in each country and territory and estimated potential survival gains of seven policy interventions focused on improving treatment availability and delivery (ie, increasing the availability of chemotherapy, radiation, general surgery, neurosurgery, or ophthalmic surgery, reducing treatment abandonment, and improving the quality of care to the mean of high-income countries) implemented in isolation or as packages. FINDINGS: Our model estimated that, for diagnosed cases, global 5-year net childhood cancer survival is currently 37·4% (95% uncertainty interval 34·7-39·8), with large variation by region, ranging from 8·1% (4·4-13·7) in eastern Africa to 83·0% (81·6-84·4) in North America. Among the seven policy interventions modelled, each individually provided small gains, increasing global 5-year net survival to between 38·4% (35·8-40·9) and 44·6% (41·7-47·4). 5-year net survival increased more substantially when policy interventions were bundled into packages that improved service delivery (5-year net survival 50·2% [47·3-53·0]) or that expanded treatment access (54·1% [50·1-58·5]). A comprehensive systems approach consisting of all policy interventions yielded superadditive gains with a global 5-year net survival of 53·6% (51·5-55·6) at 50% scale-up and 80·8% (79·5-82·1) at full implementation. INTERPRETATION: Childhood cancer survival varies widely by region, with especially poor survival in Africa. Although expanding access to treatment (chemotherapy, radiation, and surgery) and addressing financial toxicity are essential, investments that improve the quality of care, at both the health-system and facility level, are needed to improve childhood cancer outcomes globally. FUNDING: Boston Children's Hospital, Dana-Farber Cancer Institute, Harvard TH Chan School of Public Health, Harvard Medical School, National Cancer Institute, SickKids, St Jude Children's Research Hospital, Union for International Cancer Control, Children with Cancer UK Davidson and O'Gorman Fellowship.

BACKGROUND: Accurate estimates of childhood cancer incidence are important for policy makers to inform priority setting and planning decisions. However, many countries do not have cancer registries that quantify the incidence of childhood cancer. Moreover, even when registries do exist, they might substantially underestimate the true incidence, since children with cancer might not be diagnosed. We therefore aimed to provide estimates of total childhood cancer incidence accounting for underdiagnosis. METHODS: We developed a microsimulation model to simulate childhood cancer incidence for 200 countries and territories worldwide, taking into account trends in population growth and urbanicity, geographical variation in cancer incidence, and health system barriers to access and referral that contribute to underdiagnosis. To ensure model results were consistent with epidemiological data, we calibrated the model to publicly available cancer registry data using a Bayesian approach in which the observed data are fixed and the model parameters (cancer incidence and probabilities of health system access and referral) are random variables. We estimated the total incidence of childhood cancer (diagnosed and undiagnosed) in each country in 2015 and projected the number of cases from 2015 to 2030. FINDINGS: Our model estimated that there were 397 000 (95% uncertainty interval [UI] 377 000-426 000) incident cases of childhood cancer worldwide in 2015, of which only 224 000 (95% UI 216 000-237 000) were diagnosed. This finding suggests that 43% (172 000 of 397 000) of childhood cancer cases were undiagnosed globally, with substantial variation by region, ranging from 3% in western Europe (120 of 4300) and North America (300 of 10 900) to 57% (43 000 of 76 000) in western Africa. In south Asia (including southeastern Asia and south-central Asia), the overall proportion of undiagnosed cases was estimated to be 49% (67 000 of 137 000). Taking into account population projections, we estimated that there will be 6·7 million (95% UI 6·3-7·2) cases of childhood cancer worldwide from 2015 to 2030. At current levels of health system performance, we estimated that 2·9 million (95% UI 2·7-3·3) cases of childhood cancer will be missed between 2015 and 2030. INTERPRETATION: Childhood cancer is substantially underdiagnosed, especially in south Asia and sub-Saharan Africa (including western, eastern, and southern Africa). In addition to improving treatment for childhood cancer, health systems must be strengthened to accurately diagnose and effectively care for all children with cancer. As countries expand universal health coverage, these estimates of total incidence will hopefully help guide efforts to appropriately increase health system capacity to ensure access to effective childhood cancer care. FUNDING: Boston Children's Hospital, Dana-Farber Cancer Institute, Harvard T H Chan School of Public Health, Harvard Medical School, National Cancer Institute, SickKids, St Jude Children's Research Hospital, and Union for International Cancer Control.

BACKGROUND: Although the current obesity epidemic has been well documented in children and adults, less is known about long-term risks of adult obesity for a given child at his or her present age and weight. We developed a simulation model to estimate the risk of adult obesity at the age of 35 years for the current population of children in the United States. METHODS: We pooled height and weight data from five nationally representative longitudinal studies totaling 176,720 observations from 41,567 children and adults. We simulated growth trajectories across the life course and adjusted for secular trends. We created 1000 virtual populations of 1 million children through the age of 19 years that were representative of the 2016 population of the United States and projected their trajectories in height and weight up to the age of 35 years. Severe obesity was defined as a body-mass index (BMI, the weight in kilograms divided by the square of the height in meters) of 35 or higher in adults and 120% or more of the 95th percentile in children. RESULTS: Given the current level of childhood obesity, the models predicted that a majority of today's children (57.3%; 95% uncertainly interval [UI], 55.2 to 60.0) will be obese at the age of 35 years, and roughly half of the projected prevalence will occur during childhood. Our simulations indicated that the relative risk of adult obesity increased with age and BMI, from 1.17 (95% UI, 1.09 to 1.29) for overweight 2-year-olds to 3.10 (95% UI, 2.43 to 3.65) for 19-year-olds with severe obesity. For children with severe obesity, the chance they will no longer be obese at the age of 35 years fell from 21.0% (95% UI, 7.3 to 47.3) at the age of 2 years to 6.1% (95% UI, 2.1 to 9.9) at the age of 19 years. CONCLUSIONS: On the basis of our simulation models, childhood obesity and overweight will continue to be a major health problem in the United States. Early development of obesity predicted obesity in adulthood, especially for children who were severely obese. (Funded by the JPB Foundation and others.).

OBJECTIVES: To estimate the cost-effectiveness and population impact of the national implementation of the Study of Technology to Accelerate Research (STAR) intervention for childhood obesity. METHODS: In the STAR cluster-randomized trial, 6- to 12-year-old children with obesity seen at pediatric practices with electronic health record (EHR)-based decision support for primary care providers and self-guided behavior-change support for parents had significantly smaller increases in BMI than children who received usual care. We used a microsimulation model of a national implementation of STAR from 2015 to 2025 among all pediatric primary care providers in the United States with fully functional EHRs to estimate cost, impact on obesity prevalence, and cost-effectiveness. RESULTS: The expected population reach of a 10-year national implementation is ∼2 million children, with intervention costs of $119 per child and $237 per BMI unit reduced. At 10 years, assuming maintenance of effect, the intervention is expected to avert 43 000 cases and 226 000 life-years with obesity at a net cost of $4085 per case and $774 per life-year with obesity averted. Limiting implementation to large practices and using higher estimates of EHR adoption improved both cost-effectiveness and reach, whereas decreasing the maintenance of the intervention's effect worsened the former. CONCLUSIONS: A childhood obesity intervention with electronic decision support for clinicians and self-guided behavior-change support for parents may be more cost-effective than previous clinical interventions. Effective and efficient interventions that target children with obesity are necessary and could work in synergy with population-level prevention strategies to accelerate progress in reducing obesity prevalence.

BACKGROUND: Evidence on immunization costs is a critical input for cost-effectiveness analysis and budgeting, and can describe variation in site-level efficiency. The Expanded Program on Immunization Costing and Financing (EPIC) Project represents the largest investigation of immunization delivery costs, collecting empirical data on routine infant immunization in Benin, Ghana, Honduras, Moldova, Uganda, and Zambia. METHODS: We developed a pooled dataset from individual EPIC country studies (316 sites). We regressed log total costs against explanatory variables describing service volume, quality, access, other site characteristics, and income level. We used Bayesian hierarchical regression models to combine data from different countries and account for the multi-stage sample design. We calculated output elasticity as the percentage increase in outputs (service volume) for a 1% increase in inputs (total costs), averaged across the sample in each country, and reported first differences to describe the impact of other predictors. We estimated average and total cost curves for each country as a function of service volume. RESULTS: Across countries, average costs per dose ranged from $2.75 to $13.63. Average costs per child receiving diphtheria, tetanus, and pertussis ranged from $27 to $139. Within countries costs per dose varied widely-on average, sites in the highest quintile were 440% more expensive than those in the lowest quintile. In each country, higher service volume was strongly associated with lower average costs. A doubling of service volume was associated with a 19% (95% interval, 4.0-32) reduction in costs per dose delivered, (range 13% to 32% across countries), and the largest 20% of sites in each country realized costs per dose that were on average 61% lower than those for the smallest 20% of sites, controlling for other factors. Other factors associated with higher costs included hospital status, provision of outreach services, share of effort to management, level of staff training/seniority, distance to vaccine collection, additional days open per week, greater vaccination schedule completion, and per capita gross domestic product. CONCLUSIONS: We identified multiple features of sites and their operating environment that were associated with differences in average unit costs, with service volume being the most influential. These findings can inform efforts to improve the efficiency of service delivery and better understand resource needs.

Participation in recommended levels of physical activity promotes a healthy body weight and reduced chronic disease risk. To inform investment in prevention initiatives, we simulate the national implementation, impact on physical activity and childhood obesity and associated cost-effectiveness (versus the status quo) of six recommended strategies that can be applied throughout childhood to increase physical activity in US school, afterschool and childcare settings. In 2016, the Childhood Obesity Intervention Cost Effectiveness Study (CHOICES) systematic review process identified six interventions for study. A microsimulation model estimated intervention outcomes 2015-2025 including changes in mean MET-hours/day, intervention reach and cost per person, cost per MET-hour change, ten-year net costs to society and cases of childhood obesity prevented. First year reach of the interventions ranged from 90,000 youth attending a Healthy Afterschool Program to 31.3 million youth reached by Active School Day policies. Mean MET-hour/day/person increases ranged from 0.05 MET-hour/day/person for Active PE and Healthy Afterschool to 1.29 MET-hour/day/person for the implementation of New Afterschool Programs. Cost per MET-hour change ranged from cost saving to $3.14. Approximately 2500 to 110,000 cases of children with obesity could be prevented depending on the intervention implemented. All of the six interventions are estimated to increase physical activity levels among children and adolescents in the US population and prevent cases of childhood obesity. Results do not include other impacts of increased physical activity, including cognitive and behavioral effects. Decision-makers can use these methods to inform prioritization of physical activity promotion and obesity prevention on policy agendas.

Health utility, a summary measure of quality of life, has not been previously used to compare outcomes among childhood cancer survivors and individuals without a cancer history. We estimated health utility (0, death; 1, perfect health) using the Short Form-6D (SF-6D) in survivors (n = 7105) and siblings of survivors (n = 372) (using the Childhood Cancer Survivor Study cohort) and the general population (n = 12 803) (using the Medical Expenditures Panel Survey). Survivors had statistically significantly lower SF-6D scores than the general population (mean = 0.769, 95% confidence interval [CI] = 0.766 to 0.771, vs mean = 0.809, 95% CI = 0.806 to 0.813, respectively, ITALIC! P< .001, two-sided). Young adult survivors (age 18-29 years) reported scores comparable with general population estimates for people age 40 to 49 years. Among survivors, SF-6D scores were largely determined by number and severity of chronic conditions. No clinically meaningful differences were identified between siblings and the general population (mean = 0.793, 95% CI = 0.782 to 0.805, vs mean = 0.809, 95% CI = 0.806 to 0.813, respectively). This analysis illustrates the importance of chronic conditions on long-term survivor quality of life and provides encouraging results on sibling well-being. Preference-based utilities are informative tools for outcomes research in cancer survivors.

OBJECTIVE: To estimate the cost-effectiveness of noncardia gastric adenocarcinoma (NCGA) screening strategies based on new biomarker and endoscopic technologies. DESIGN: Using an intestinal-type NCGA microsimulation model, we evaluated the following one-time screening strategies for US men: (1) serum pepsinogen to detect gastric atrophy (with endoscopic follow-up of positive screen results), (2) endoscopic screening to detect dysplasia and asymptomatic cancer (with endoscopic mucosal resection (EMR) treatment for detected lesions) and (3) Helicobacter pylori screening and treatment. Screening performance, treatment effectiveness, cancer and cost data were based on published literature and databases. Subgroups included current, former and never smokers. Outcomes included lifetime cancer risk and incremental cost-effectiveness ratios (ICERs), expressed as cost per quality-adjusted-life-year (QALY) gained. RESULTS: Screening the general population at age 50 years reduced the lifetime intestinal-type NCGA risk (0.24%) by 26.4% with serum pepsinogen screening, 21.2% with endoscopy and EMR and 0.2% with H. pylori screening/treatment. Targeting current smokers reduced the lifetime risk (0.35%) by 30.8%, 25.5%, and 0.1%, respectively. For all subgroups, serum pepsinogen screening was more effective and more cost-effective than all other strategies, although its ICER varied from $76,000/QALY (current smokers) to $105,400/QALY (general population). Results were sensitive to H. pylori prevalence, screen age and serum pepsinogen test sensitivity. Probabilistic sensitivity analysis found that at a $100,000/QALY willingness-to-pay threshold, the probability that serum pepsinogen screening was preferred was 0.97 for current smokers. CONCLUSIONS: Although not warranted for the general population, targeting high-risk smokers for serum pepsinogen screening may be a cost-effective strategy to reduce intestinal-type NCGA mortality.

BACKGROUND: State-level estimates from the Centers for Disease Control and Prevention (CDC) underestimate the obesity epidemic because they use self-reported height and weight. We describe a novel bias-correction method and produce corrected state-level estimates of obesity and severe obesity. METHODS: Using non-parametric statistical matching, we adjusted self-reported data from the Behavioral Risk Factor Surveillance System (BRFSS) 2013 (n = 386,795) using measured data from the National Health and Nutrition Examination Survey (NHANES) (n = 16,924). We validated our national estimates against NHANES and estimated bias-corrected state-specific prevalence of obesity (BMI≥30) and severe obesity (BMI≥35). We compared these results with previous adjustment methods. RESULTS: Compared to NHANES, self-reported BRFSS data underestimated national prevalence of obesity by 16% (28.67% vs 34.01%), and severe obesity by 23% (11.03% vs 14.26%). Our method was not significantly different from NHANES for obesity or severe obesity, while previous methods underestimated both. Only four states had a corrected obesity prevalence below 30%, with four exceeding 40%-in contrast, most states were below 30% in CDC maps. CONCLUSIONS: Twelve million adults with obesity (including 6.7 million with severe obesity) were misclassified by CDC state-level estimates. Previous bias-correction methods also resulted in underestimates. Accurate state-level estimates are necessary to plan for resources to address the obesity epidemic.

BACKGROUND/OBJECTIVES: State-specific obesity prevalence data are critical to public health efforts to address the childhood obesity epidemic. However, few states administer objectively measured body mass index (BMI) surveillance programs. This study reports state-specific childhood obesity prevalence by age and sex correcting for parent-reported child height and weight bias. SUBJECTS/METHODS: As part of the Childhood Obesity Intervention Cost Effectiveness Study (CHOICES), we developed childhood obesity prevalence estimates for states for the period 2005-2010 using data from the 2010 US Census and American Community Survey (ACS), 2003-2004 and 2007-2008 National Survey of Children's Health (NSCH) (n=133 213), and 2005-2010 National Health and Nutrition Examination Surveys (NHANES) (n=9377; ages 2-17). Measured height and weight data from NHANES were used to correct parent-report bias in NSCH using a non-parametric statistical matching algorithm. Model estimates were validated against surveillance data from five states (AR, FL, MA, PA and TN) that conduct censuses of children across a range of grades. RESULTS: Parent-reported height and weight resulted in the largest overestimation of childhood obesity in males ages 2-5 years (NSCH: 42.36% vs NHANES: 11.44%). The CHOICES model estimates for this group (12.81%) and for all age and sex categories were not statistically different from NHANES. Our modeled obesity prevalence aligned closely with measured data from five validation states, with a 0.64 percentage point mean difference (range: 0.23-1.39) and a high correlation coefficient (r=0.96, P=0.009). Estimated state-specific childhood obesity prevalence ranged from 11.0 to 20.4%. CONCLUSION: Uncorrected estimates of childhood obesity prevalence from NSCH vary widely from measured national data, from a 278% overestimate among males aged 2-5 years to a 44% underestimate among females aged 14-17 years. This study demonstrates the validity of the CHOICES matching methods to correct the bias of parent-reported BMI data and highlights the need for public release of more recent data from the 2011 to 2012 NSCH.

PURPOSE: Best-worst scaling (BWS) is a survey method for assessing individuals' priorities. It identifies the extremes-best and worst items, most and least important factors, biggest and smallest influences-among sets. In this article, we demonstrate an application of BWS in a primary care setting to illustrate its use in identifying patient priorities for services. METHODS: We conducted a BWS survey in 2014 in Boston, Massachusetts, to assess the relative importance of 10 previously identified attributes of Papanicolaou (Pap) testing services among women experiencing homelessness. Women were asked to evaluate 11 sets of 5 attributes of Pap services, and identify which attribute among each set would have the biggest and smallest influence on promoting uptake. We show how frequency analysis can be used to analyze results. RESULTS: In all, 165 women participated, a response rate of 72%. We identified the most and least salient influences on encouraging Pap screening based on their frequency of report among our sample, with possible standardized scores ranging from+1.0 (biggest influence) to -1.0 (smallest influence). Most important was the availability of support for issues beyond health (+0.39), while least important was the availability of accommodations for personal hygiene (-0.27). CONCLUSIONS: BWS quantifies patient priorities in a manner that is transparent and accessible. It is easily comprehendible by patients and relatively easy to administer. Our application illustrates its use in a vulnerable population, showing that factors beyond those typically provided in health care settings are highly important to women in seeking Pap screening. This approach can be applied to other health care services where prioritization is helpful to guide decisions.

INTRODUCTION: Food and beverage TV advertising contributes to childhood obesity. The current tax treatment of advertising as an ordinary business expense in the U.S. subsidizes marketing of nutritionally poor foods and beverages to children. This study models the effect of a national intervention that eliminates the tax subsidy of advertising nutritionally poor foods and beverages on TV to children aged 2-19 years. METHODS: We adapted and modified the Assessing Cost Effectiveness framework and methods to create the Childhood Obesity Intervention Cost Effectiveness Study model to simulate the impact of the intervention over the 2015-2025 period for the U.S. population, including short-term effects on BMI and 10-year healthcare expenditures. We simulated uncertainty intervals (UIs) using probabilistic sensitivity analysis and discounted outcomes at 3% annually. Data were analyzed in 2014. RESULTS: We estimated the intervention would reduce an aggregate 2.13 million (95% UI=0.83 million, 3.52 million) BMI units in the population and would cost $1.16 per BMI unit reduced (95% UI=$0.51, $2.63). From 2015 to 2025, the intervention would result in $352 million (95% UI=$138 million, $581 million) in healthcare cost savings and gain 4,538 (95% UI=1,752, 7,489) quality-adjusted life-years. CONCLUSIONS: Eliminating the tax subsidy of TV advertising costs for nutritionally poor foods and beverages advertised to children and adolescents would likely be a cost-saving strategy to reduce childhood obesity and related healthcare expenditures.

INTRODUCTION: Reducing sugar-sweetened beverage consumption through taxation is a promising public health response to the obesity epidemic in the U.S. This study quantifies the expected health and economic benefits of a national sugar-sweetened beverage excise tax of $0.01/ounce over 10 years. METHODS: A cohort model was used to simulate the impact of the tax on BMI. Assuming ongoing implementation and effect maintenance, quality-adjusted life-years gained and disability-adjusted life-years and healthcare costs averted were estimated over the 2015-2025 period for the 2015 U.S. POPULATION: Costs and health gains were discounted at 3% annually. Data were analyzed in 2014. RESULTS: Implementing the tax nationally would cost $51 million in the first year. The tax would reduce sugar-sweetened beverage consumption by 20% and mean BMI by 0.16 (95% uncertainty interval [UI]=0.06, 0.37) units among youth and 0.08 (95% UI=0.03, 0.20) units among adults in the second year for a cost of $3.16 (95% UI=$1.24, $8.14) per BMI unit reduced. From 2015 to 2025, the policy would avert 101,000 disability-adjusted life-years (95% UI=34,800, 249,000); gain 871,000 quality-adjusted life-years (95% UI=342,000, 2,030,000); and result in $23.6 billion (95% UI=$9.33 billion, $54.9 billion) in healthcare cost savings. The tax would generate $12.5 billion in annual revenue (95% UI=$8.92, billion, $14.1 billion). CONCLUSIONS: The proposed tax could substantially reduce BMI and healthcare expenditures and increase healthy life expectancy. Concerns regarding the potentially regressive tax may be addressed by reduced obesity disparities and progressive earmarking of tax revenue for health promotion.

INTRODUCTION: Many American children do not meet recommendations for moderate to vigorous physical activity (MVPA). Although school-based physical education (PE) provides children with opportunities for MVPA, less than half of PE minutes are typically active. The purpose of this study is to estimate the cost effectiveness of a state "active PE" policy implemented nationally requiring that at least 50% of elementary school PE time is spent in MVPA. METHODS: A cohort model was used to simulate the impact of an active PE policy on physical activity, BMI, and healthcare costs over 10 years for a simulated cohort of the 2015 U.S. population aged 6-11 years. Data were analyzed in 2014. RESULTS: An elementary school active PE policy would increase MVPA per 30-minute PE class by 1.87 minutes (95% uncertainty interval [UI]=1.23, 2.51) and cost $70.7 million (95% UI=$51.1, $95.9 million) in the first year to implement nationally. Physical activity gains would cost $0.34 per MET-hour/day (95% UI=$0.15, $2.15), and BMI could be reduced after 2 years at a cost of $401 per BMI unit (95% UI=$148, $3,100). From 2015 to 2025, the policy would cost $235 million (95% UI=$170 million, $319 million) and reduce healthcare costs by $60.5 million (95% UI=$7.93 million, $153 million). CONCLUSIONS: Implementing an active PE policy at the elementary school level could have a small impact on physical activity levels in the population and potentially lead to reductions in BMI and obesity-related healthcare expenditures over 10 years.

INTRODUCTION: The childhood obesity epidemic continues in the U.S., and fiscal crises are leading policymakers to ask not only whether an intervention works but also whether it offers value for money. However, cost-effectiveness analyses have been limited. This paper discusses methods and outcomes of four childhood obesity interventions: (1) sugar-sweetened beverage excise tax (SSB); (2) eliminating tax subsidy of TV advertising to children (TV AD); (3) early care and education policy change (ECE); and (4) active physical education (Active PE). METHODS: Cost-effectiveness models of nationwide implementation of interventions were estimated for a simulated cohort representative of the 2015 U.S. population over 10 years (2015-2025). A societal perspective was used; future outcomes were discounted at 3%. Data were analyzed in 2014. Effectiveness, implementation, and equity issues were reviewed. RESULTS: Population reach varied widely, and cost per BMI change ranged from $1.16 (TV AD) to $401 (Active PE). At 10 years, assuming maintenance of the intervention effect, three interventions would save net costs, with SSB and TV AD saving $55 and $38 for every dollar spent. The SSB intervention would avert disability-adjusted life years, and both SSB and TV AD would increase quality-adjusted life years. Both SSB ($12.5 billion) and TV AD ($80 million) would produce yearly tax revenue. CONCLUSIONS: The cost effectiveness of these preventive interventions is greater than that seen for published clinical interventions to treat obesity. Cost-effectiveness evaluations of childhood obesity interventions can provide decision makers with information demonstrating best value for the money.

INTRODUCTION: Child care facilities influence diet and physical activity, making them ideal obesity prevention settings. The purpose of this study is to quantify the health and economic impacts of a multi-component regulatory obesity policy intervention in licensed U.S. child care facilities. METHODS: Two-year costs and BMI changes resulting from changes in beverage, physical activity, and screen time regulations affecting a cohort of up to 6.5 million preschool-aged children attending child care facilities were estimated in 2014 using published data. A Markov cohort model simulated the intervention's impact on changes in the U.S. population from 2015 to 2025, including short-term BMI effects and 10-year healthcare expenditures. Future outcomes were discounted at 3% annually. Probabilistic sensitivity analyses simulated 95% uncertainty intervals (UIs) around outcomes. RESULTS: Regulatory changes would lead children to watch less TV, get more minutes of moderate and vigorous physical activity, and consume fewer sugar-sweetened beverages. Within the 6.5 million eligible population, national implementation could reach 3.69 million children, cost $4.82 million in the first year, and result in 0.0186 fewer BMI units (95% UI=0.00592 kg/m(2), 0.0434 kg/m(2)) per eligible child at a cost of $57.80 per BMI unit avoided. Over 10 years, these effects would result in net healthcare cost savings of $51.6 (95% UI=$14.2, $134) million. The intervention is 94.7% likely to be cost saving by 2025. CONCLUSIONS: Changing child care regulations could have a small but meaningful impact on short-term BMI at low cost. If effects are maintained for 10 years, obesity-related healthcare cost savings are likely.

Efforts to expand Medicaid while controlling spending must be informed by a deeper understanding of the extent to which the high medical costs associated with severe obesity (having a body mass index of [Formula: see text] or higher) determine spending at the state level. Our analysis of population-representative data indicates that in 2013, severe obesity cost the nation approximately $69 billion, which accounted for 60 percent of total obesity-related costs. Approximately 11 percent of the cost of severe obesity was paid for by Medicaid, 30 percent by Medicare and other federal health programs, 27 percent by private health plans, and 30 percent out of pocket. Overall, severe obesity cost state Medicaid programs almost $8 billion a year, ranging from $5 million in Wyoming to $1.3 billion in California. These costs are likely to increase following Medicaid expansion and enhanced coverage of weight loss therapies in the form of nutrition consultation, drug therapy, and bariatric surgery. Ensuring and expanding Medicaid-eligible populations' access to cost-effective treatment for severe obesity should be part of each state's strategy to mitigate rising obesity-related health care costs.