Health and Economic Value of Eliminating Socioeconomic Disparities in US Youth Physical Activity

This study aims to determine the potential public health and economic effects of eliminating disparities in physical activity levels among US youth socioeconomic status groups.


Introduction
There are considerable socioeconomic status (SES) disparities in youth physical activity (PA) levels in the US.For example, in low-SES schools, only 24.6% of eighth graders play sports. 1 Another study reports that school sport participation correlates negatively with SES and was lower among low-SES youth. 2 This study also found that lower-SES students were less likely to have school physical education requirements and that lower SES was associated with lower participation in physical education. 2Research on Wisconsin students found that those in lower-income schools were less active than those in higher-income schools (68.2 vs 73.3 minutes) and scored lower on fitness tests. 3study of children in Seattle, Washington, and San Diego, California, found that lower-income youth had lower access to portable play equipment (eg, bicycles, jump ropes) compared with higherincome children. 4 Such disparities in PA during childhood/adolescence can contribute to health disparities, with lower-income youth being at higher risk for negative health outcomes in adulthood (eg, coronary heart disease [CHD], 5 stroke, 6,7 diabetes, 8 and cancer [9][10][11] ).12,13 There is one remaining question: what would happen if we eliminated these PA disparities and achieved parity in PA levels across SES groups?Understanding and quantifying the potential health and economic benefits of this could help policymakers better allocate their limited resources toward interventions aimed at eliminating PA disparities, which could subsequently reduce long-term health care expenditures and improve population-level health.Therefore, this study uses an agent-based model (ABM) to demonstrate and quantify the estimated health and economic effect of achieving parity in PA across SES groups for US children and adolescents aged 6 to 17 years.

ABM Overview
Using the previously described Virtual Population for Obesity Prevention ABM, [14][15][16][17][18] we represented all 50 million US children and adolescents 6 to 17 years old (starting in 2023), their growth over time, daily levels of PA, and physical health outcomes.The model represents each child as a computational agent and simulates each day of their childhood/adolescent years until age 18 years, then each year for the rest of their lives.Similar to a real child, each agent has a set of sociodemographic characteristics including age, sex, and SES (represented as 4 categories, defined based on household income percentage compared with the federal poverty level [FPL]), as well as clinical characteristics (fat-free mass, fat mass 19 ). Figure 1 outlines how each agent proceeds through the model.Each day, each agent grows in height based on nationally representative growth charts 20 and consumes calories to maintain a constant body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) percentile if their PA level was unchanged.This study was exempt from institutional review board approval, as it does not involve active data collection nor interaction with human participants.This study adheres to the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) 2022 checklist for decision analytical models.

Metabolic Model Embedded in Each Agent
Each agent has an embedded metabolic model specific to their age, sex, and weight.The metabolic model tracks their caloric consumption and energy expenditure and translates caloric surplus or deficit into weight gain or weight loss each day. 21,22presenting Each Agent's PA As Figure 1 shows, at the simulation start, each agent has a range of daily PA levels based on their age, sex, and SES, which translates to caloric expenditures.Each week, each agent draws a certain number of days per week that they get 60 minutes of PA, 23 which varies by age, sex, and SES.We assumed that youth get 30 minutes of PA on the remaining days in the week (varied in sensitivity analyses).In general, youth with a higher SES get more PA and are active for 60 minutes on more days per week compared with youth in the lower-SES groups (Table 1).Thus, changes in PA for any agent result in weight gain or loss, each day up until age 18 years, specific to that person's change in PA and their BMI.

Representing the Physical Health Outcomes for Each Agent
Starting at age 18 years, agents enter a Markov model (Figure 1), described in previous publications, [15][16][17] which consists of 15 mutually exclusive health states accounting for both anthropometric measures (eg, BMI) and the presence and severity of risk factors associated with weight.Agents start at a metabolically healthy state and assume 1 of 3 states based on their BMI category (normal weight, overweight, or with obesity) at the end of childhood.As the simulation progresses, each agent has an opportunity to remain in their current state, move to a new state, or potentially die, based on the probability of developing additional health issues.
Each state is associated with weight-related health conditions (eg, diabetes), QALYs, and corresponding direct medical costs and productivity losses that accrue as the agent ages.

Direct medical costs Productivity losses QALYs
Eliminating MVPA disparities between SES groups for a given age and sex

Economic Outcomes
The third-party payer perspective includes direct medical costs, while the societal perspective includes direct and indirect (ie, productivity losses due to presenteeism) costs.Daily wage attenuated by the individual's health condition-specific utility weight for the duration of their condition serves as a proxy for productivity losses and is calculated as follows: Daily wage × (1 − utility weight) × duration of outcome All individuals accrue productivity losses, regardless of age or employment status, since everyone contributes to society.We report all costs in 2023 US dollars, converting all past and future costs using a 3% annual rate.Similarly, all future quality-adjusted life-years are presented in net present value, discounted with a 3% rate.

Estimated Physical Health Effects of Reducing Disparities in PA Levels Among Youth in Different SES Groups Nationwide
Table 2 summarizes the physical health outcomes that result from reducing disparities in PA levels among youth in different SES groups.For example, this generates a decrease in absolute overweight/ obesity prevalence by 0.826% (95% CI, 0.821%-0.832%)among youth 6 to 17 years old (a reduction from 35.6%, the cohort's baseline overweight/obesity prevalence), which results in approximately 383 000 (95% CI, 368 000-399 000) fewer cases of overweight and obesity.Additionally, eliminating the SES disparities within all youth age and sex groups averts 101 000 (95% CI, 98 000-105 000) cases of weight-related diseases over their lifetime, reducing health disparities such as diabetes prevalence by 0.26%, cancer by 0.05%, CHD deaths by 0.49%, and diabetes deaths by 0.75% across the cohort.
Knowing that it may be ambitious to fully eliminate PA disparities among SES groups, Table 2 summarizes the estimated effect of reducing the disparity by different degrees.For example, closing the SES disparity gap by 25% results in approximately 27 000 (95% CI, 24 000-30 000) fewer total cases of weight-related diseases over the cohort's lifetime.This further increases to 55 000 (95% CI, 52 000-59 000) fewer cases of weight-related diseases when closing the gap by 50% and up to 81 000 (95% CI, 78 000-85 000) fewer cases when closing it by 75%.
However, different age and sex groups do not have equal health benefits from reducing disparities, as different groups have different disparities in PA levels, overweight/obesity prevalence, and population sizes.For example, adolescent boys aged 14 to 17 years at 100% to 199% FPL experience the greatest reduction in overweight and obesity with 63 000 fewer cases and 12 000 (95% CI, 11 000-13 000) weight-related disease cases averted when eliminating the disparity.On the other hand, adolescent girls aged 14 to 17 years at 200% to 399% FPL experience the least amount of health benefits from reducing the disparity, with 2700 fewer cases of overweight and obesity and 410 (95% CI, −500 to 1300) fewer cases of weight-related diseases.

JAMA Health Forum | Original Investigation
Eliminating Socioeconomic Disparities in US Youth Physical Activity Estimated Economic Effect of Reducing Disparities in PA Levels Among Youth in Different SES Groups Nationwide Figure 2 shows the resulting savings from the societal perspective for the different age and sex groups.The improved physical health outcomes achieved by eliminating the disparity in PA levels among SES groups also results in more than $15.60 (95% CI, $15.01-$16.10) billion in cost savings over   32,33 ).[36] Additionally, it is important to note that increases in resources may not overcome all barriers to changing weight across different SES groups.The sensitivity analyses attempted to account for some potential differences among various SES groups in the relationship between PA and overweight and obesity (eg, changes in compensatory eating and dietary habits).However, there are some other potential differences such as access to health care and alcohol and drug use.
To our knowledge, this is the first study of its kind to quantify the health and economic benefits that can be achieved by eliminating PA disparities among youth in different SES groups.While previous studies have quantified the consequences of insufficient PA and its association with increased health-related problems and costs, 37-39 as well as examined specific PA interventions and found that they often result in uneven gains among SES groups, 40 they have not yet attempted to quantify the potential long-term benefits of programs targeting these disparities.The present study is critical to understanding the scale of potential benefits of programs addressing these inequalities and guiding the prioritization of efforts to reduce SES disparities in youth PA.
To actually address these disparities, there are a number of policies and interventions that policymakers can consider.For example, allocating funding to school physical education systems so underresourced children can engage in quality PA, investing in the development of PA facilities in lower-income neighborhoods (eg, green spaces, recreational gyms), or by increasing neighborhood maintenance of existing PA resources to improve neighborhood perceptions and the physical environment. 34Additionally, transit-oriented development and mixed-use zoning can help connect individuals to PA infrastructure as well as encourage active transport (eg, walking, biking, using wheelchair). 35Policymakers and community leaders can also develop joint-use agreements that allow youth to use existing infrastructure (eg, schools, playgrounds, pools) after regularly scheduled programming. 36Future studies can further examine the drivers of PA disparities by SES and quantify the effectiveness of these policies and programs in different social and built-environment contexts to inform the design of effective interventions.

Limitations
All models, by definition, are simplifications of reality and cannot account for all factors.To remain conservative about the potential positive effect of PA, this study focused on BMI and weight-related chronic health conditions and did not include other potential outcomes (eg, osteoporosis).The model also did not include the potential effect of PA on mental health or the other potential benefits of PA and sports for youth (eg, improved academic performance, social skills, emotional regulation, mood).

Figure 2 .
Figure 2. Economic and Clinical Outcomes of Reducing Physical Activity (PA) Disparities Among US Youth by Socioeconomic Status Group Each simulated year, the agent has probabilities of staying in the same health state or moving to a new health state based on state-, age-, and sex-specific probabilities.While in each health state, The model increases MVPA levels necessary to eliminate the disparities between SES groups MVPA levels Legend Initial level of MVPA Additional MVPA needed to reduce disparities SES with the lowest MVPA level SES with the highest MVPA level SES with the secondlowest MVPA level Different SES groups within each age and sex group have different MVPA levels There is no longer a disparity in MVPA levels between SES groups within the age and sex group Agent turns 18 y old

Table 1 .
Youth Physical Activity and Anthropometric Measures by Age, Sex, and Socioeconomic Status a

Experimental Scenarios and Sensitivity Analysis Different
26,27iments simulated the effect of reducing, to varying degrees, the disparities in PA levels between different age, sex, and SES groups.The first set of scenarios brings each age, sex, and SES group to the highest level of PA observed across their sex and age group.For example, for boys 6 to 10 years old, the highest PA level in that group was among those 400% FPL or higher at 4.4 days per week physically active.Therefore, we brought all boys 6 to 10 years old up to an average of 4.4 days per week to eliminate the disparity within that group.We repeated this for the other groups.Next, we simulated the effect of reducing disparities among SES groups within each age and sex group by 75%, 50%, and 25%.The baseline scenario assumed that the influence of additional PA on the weight of individuals was not affected by SES.Studies have shown that those in lower SES groups may face additional barriers to losing weight (eg, living in neighborhoods oversaturated with unhealthy food options).24,25Atthesametime, evidence exists showing that increasing PA can result in other lifestyle, behavioral, and social changes (eg, healthier eating behaviors) that can facilitate weight loss.26,27Therefore,sensitivity analyses varied the accompanying changes in calorie consumption that may occur with increases in PA, ranging from a 2% decrease in caloric consumption (representing healthier diets) to an increase in caloric consumption equivalent to 50% of calories expended from additional PA (representing compensatory eating or consumption of more caloric dense food/beverages such as soda and ultraprocessed food).Sensitivity analyses also varied the amount of PA that the agents got on the days when they were not getting at least 60 minutes (eg, 0-45 minutes).Since the goal of this study was to determine what would happen if SES disparities in average amounts of PA were eliminated or reduced, we assumed in all experimental scenarios that individuals maintained increases in PA until age 18 years.Analyses were completed using Python, version 3.7 (Python Software Foundation), and NumPy, version 1.19.

Table 2 .
Number and Relative Percentage of Clinical Outcomes Averted When Reducing Physical Activity Disparities Among US Youth a

Table 2 .
Number and Relative Percentage of Clinical Outcomes Averted When Reducing Physical Activity Disparities Among US Youth a

Estimated Effect of Varying the Effect PA Has on the Weight of Individuals in Lower SES Groups
The savings generated vary across the SES, age, and sex groups.For example, eliminating the disparity saves $847.77(95% CI, $756.93-$939.61) million in societal costs for girls 11 to 13 years old at 100% to 199% FPL (societal perspective) but saves only $41.48 (95% CI, −$91.15 to $170.41) million for adolescent girls aged 14 to 17 years at 200% to 399% FPL.Similar to the health benefits, these cost-savings values are affected by the size of the disparity between groups, the starting overweight/ obesity prevalence in each group, and the group's size.
Estimated Effect

of Varying the Amount of PA on Days Not Getting 60 Minutes
As indicated in the Methods section, because the goal of this study was to determine what would happen if we eliminated or reduced PA disparities, the experiments assumed that any increases in PA would be maintained throughout childhood/adolescence.This would mean that policies or interventions to increase PA among those of lower SES may have to be maintained or even adapted from age 6 through 17 years.It cannot be assumed that an increase in PA at an earlier age will be sustainable throughout a child's subsequent years (eg, evidence suggests that many youth drop out of sports participation at age 11 not clear where such funds would come from and whether they would be shifted from other areas of need.Nevertheless, it is also important to remember that PA influences health and subsequent costs through other benefits besides weight status (eg, bone density, muscle strengthening, likelihood of anxiety, depression); thus, results may underestimate the potential cost savings of eliminating such disparities.