https://orcid.org/0000-0002-1122-3264
?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.ABSTRACT
In 2024, the economic burden of obesity remains a paramount global concern due to its escalating prevalence, which imposes substantial pressures on healthcare systems, hampers productivity, and detracts from the quality of life. This paper explores the evolving obesity epidemic and its economic ramifications, emphasizing the necessity of precise measurement techniques to inform policy development and resource allocation. The study adopts the Value of a Statistical Life (VSL) methodology, which provides an innovative approach to quantifying the monetary value of mortality risk reductions associated with obesity-related health conditions. This approach surpasses traditional cost analyses that focus on direct medical expenses by incorporating a willingness-to-pay metric, reflecting societal preferences for risk mitigation. Our findings present a comprehensive cost analysis for the year 2024, utilizing both total deaths and Disability-Adjusted Life Years (DALYs) within the VSL framework, highlighting the importance of such an approach in understanding the full economic impact of obesity on a global scale.
Background
In 2024, the economic burden of obesity remains a critical global concern, with increasing prevalence rates placing a significant strain on healthcare systems, impacting productivity, and affecting quality of life. As the obesity epidemic evolves, accurately measuring its economic impact is imperative for effective policy development and resource allocation. The Value of a Statistical Life (VSL) has gained traction as an approach to estimate the monetary value of reducing mortality risks associated with obesity (Viscusi & Aldy, Citation2003). VSL transcends direct medical costs by integrating the willingness-to-pay metric to preserve life, reflecting societal preferences for risk reduction (Robinson, Citation2007).
The preference for VSL over other tools like cost-of-illness or quality-adjusted life years (QALYs) is due to its ability to capture the intangible benefits of health interventions, such as increased life expectancy and enhanced well-being (Kniesner & Viscusi, Citation2005). Although QALYs and disability-adjusted life years (DALYs) are instrumental in measuring life quality and quantity, they often fall short of incorporating societal value of risk reductions as VSL does, making it a preferred tool for assessing benefits of obesity interventions that yield mortality risk reductions (Aldy & Viscusi, Citation2008).
Additionally, VSL aligns with the emerging emphasis on preventive care, underscoring the economic rationale for investing in obesity prevention programs that yield significant long-term benefits (Trogdon et al., Citation2008). For individuals, these benefits include improved health outcomes, enhanced quality of life, and reduced personal healthcare costs due to the prevention of obesity-related conditions. Society benefits through decreased healthcare expenditures, increased productivity from a healthier workforce, and enhanced economic efficiency as public and private resources are not disproportionately spent on obesity-related healthcare needs. The adaptability of VSL across different demographic and regional contexts ensures that these economic benefits are understood and maximized, supporting the development of health policies that are both targeted and efficient. By allocating funds to preventative care based on VSL calculations, policies not only address the economic burden of obesity but also contribute to the overall well-being and equity of the population, thereby fostering a healthier society over the long term.
The adaptability of VSL to different demographic and regional contexts enables a more tailored understanding of the economic burden of obesity, supporting targeted and efficient health policy frameworks (Cawley, Citation2015).
The financial burdens of obesity arise primarily from managing health conditions associated with it, like heart diseases, diabetes, and specific cancers, which involve medical care, medication, and hospital stays. Additionally, there are costs related to lost productivity when people miss work, are less productive while at work, or die prematurely (Tremmel et al., Citation2017).
The impact of obesity extends beyond healthcare, affecting society and the economy. Public health initiatives and preventive strategies are necessary to mitigate obesity’s prevalence and costs.
Research indicates that strategies promoting healthy living and better monitoring are cost-effective for long-term health (Benmarhnia et al., Citation2017). The financial impact of obesity varies worldwide, suggesting the necessity for customized approaches and policy initiatives (Pineda et al., Citation2018).
Overall, obesity’s economic load comprises both the direct healthcare expenses and the less obvious costs due to productivity loss, underscoring the need for comprehensive countermeasures.
Though not typically utilized in evaluating obesity’s economic costs, the Value of a Statistical Life (VSL) brings a quantifiable perspective by assigning a monetary value to health outcomes. This supports informed policy-making and delivers a full assessment of economic effects. It also broadens public understanding, refines cost-benefit analyses, and permits comparisons across different countries and sectors. Nevertheless, the use of VSL requires judicious consideration due to its broader implications (Aldy & Viscusi, Citation2008; Cawley, Citation2015; Kniesner & Viscusi, Citation2005; Robinson, Citation2007; Trogdon et al., Citation2008; Viscusi & Aldy, Citation2003).
The application of the Value of a Statistical Life (VSL) should be approached with caution for several ethical and societal reasons:
Ethical Challenges: Assigning a monetary value to human life through VSL involves moral complexities. It raises concerns about commodifying life and questions of whether it implies some lives are valued more than others. This could lead to utilitarian policies that might neglect the inherent worth of each individual.
Cultural and Moral Values: Different societies have varying approaches to valuing life, influenced by cultural, economic, and social factors. High-income countries often have higher VSLs, reflecting a greater ability to pay for risk reduction. The diverse values and norms across societies mean that a one-size-fits-all approach to VSL can be problematic.
Implications for Welfare and Equity: Policies based on VSL can significantly affect societal welfare and fairness. There’s a risk that such policies might disproportionately favor certain groups, particularly if VSL doesn’t account for demographic differences. For example, using a uniform VSL could result in less investment in lower-income areas, increasing inequality. Ensuring that VSL-based policies are equitable and beneficial to all parts of society is essential.
In summary, while VSL can be a valuable tool in policymaking, it must be applied with sensitivity to ethical considerations and the diverse values and needs of different societal groups.
Objectives: The primary objectives of this research are as follows:
To estimate the economic burden of obesity for each country in 2024
To examine the relationship between obesity rates and VSL, considering variations in income levels
Research Questions: To achieve the above objectives, this study will address the following research questions:
What is the economic burden of obesity in 2024 measured through the VSL approach?
To what extent does the economic burden of obesity vary across different countries and income levels, and how is it related to the VSL?
Methods
To estimate the future disease burden of obesity in 2024, we adapted a methodology grounded in the analytical framework provided by the Institute for Health Metrics and Evaluation’s (IHME) Global Burden of Diseases (GBD). While IHME provides annual estimates for total deaths and Disability-Adjusted Life Years (DALYs) related to obesity from 2010 up to 2019, there is a lack of direct projections for obesity beyond this point (Institute for Health Metrics and Evaluation, Citation2019).
For each country, we initiated our analysis by determining the average annual growth rate of per capita deaths, years of life lost from mortality (YLLs) and Years Lived with Disability (YLDs) from obesity for the years 2020–2024 using GBD data. These per capita rates were then applied to population projections from the United Nations’ World Population Prospects (medium variant) to calculate the total Deaths, YLLs and YLDs attributable to obesity (United Nations, Citation2019, Citation2022; World Bank, Citation2021)
In a sensitivity analysis, we employed IHME’s projections of deaths YLLs to infer future burdens of obesity. The ratio of YLLs to YLDs from 2019 served as a constant factor to extrapolate YLDs for 2020 and 2024. We postulated that the growth rates for YLLs and YLDs post-2020 would parallel those observed during 2010–2019 and projected these figures accordingly for the year 2024. These projections were subject to variability in a probabilistic uncertainty analysis.
Estimates of the Value of Statistical Life (VSL)
The economic literature on the VSL is extensive, particularly within the context of the US and other high-income nations. For the purpose of our study, we adopted the international methodology for VSL calculation as delineated by Sweis (Citation2022). This VSL was then tailored to each country as delineated in , thereby aligning our economic assessment of obesity’s burden with recognized international standards. The VSL was measured by using the formula below developed by Sweis (Citation2022) that gives an estimate of the value of a statistical life that basically equals full wealth, adjusted upwards for the degree of concavity in the single period utility function (1/γ).
Table 1. Economic burden of obesity in 2024 using DALYs.
Table 2. Economic burden of obesity in 2024 using total deaths.
=
Where r: is the discount rate, γ: is the degree of concavity in the single period utility function, x1: is the total spending on goods X, L1: is leisure time, W1: is the wage rate, and C1: is the full wealth.
We will use the same assumptions by Sweis (Citation2022) and Becker (Citation2007) that the total time available for work and leisure is approximately 5200 hour per year (excluding sleep of roughly 68 hour per week). Therefore, the annual hours for work and leisure per year are 1900 and 3300, respectively. The marginal utility of consumption is diminishing and γ = 0.5 while the interest rate equals 5%. And the wages were estimated by using the income per capita for each country to estimate the hourly rate.
For example, the VSL for an American is equal to 7.91 million dollars and this was calculated as follows:
Gross national Income per capita= $72230.98
Wage per hour = $72230.98/1900 = $38.016
VSL = (1/r)* (1/γ) *income per hour *5200 = (1/0.5)*(1/0.5)* 38.016 x 5200 = 7.91 million dollar.
Economic burden of obesity using value of a statistical life year
We applied the concept of value of a statistical life (VSL) to estimate the value of a year of life (VSLY) lost to obesity due to disability and quality of life by using life expectancy and median age for each country. The VSLY was multiplied by the expected years of DALYs for each country in 2024 and total deaths were multiplied by the VSL for each country. shows the results of the economic burden of obesity by using DALYs and total deaths respectively.
The results shows that the United States has the highest economic burden of obesity using the DALYs and total deaths approach and total burden was 1603.78 billion dollars and 3411.55 billion dollars respectively.
China ranked second of total burden using DALYs of 1012.95 billion dollars and 1874.87 billion dollars using total deaths.
The economic burden results were higher using the total deaths approach compared to using DALYs.
Discussion
The results presented indicate that the Value of a Statistical Life (VSL) and the Value of a Statistical Life Year (VSLY) have been used to estimate the economic burden of obesity in terms of Disability-Adjusted Life Years (DALYs) and total deaths for each country in 2024. Here’s a breakdown of why these results might have been observed:
Use of VSL and VSLY
VSL estimates the monetary value people place on reducing their risk of dying. VSLY extends this concept to a year of life, adjusting for factors such as life expectancy and median age. These measures are particularly useful for capturing the broader economic impacts of health risks like obesity that lead to premature mortality or reduced quality of life.
United States’ economic burden
The United States has the highest economic burden according to both DALYs and total deaths. This could be attributed to a higher VSL due to the country’s higher per capita income and willingness to pay for risk reduction. Additionally, the U.S. may have a higher prevalence of obesity-related health issues and associated costs.
China’s economic burden
China’s ranking as second in terms of the economic burden could be due to its large population and the growing prevalence of obesity in the country. Even if the VSL is lower than in the U.S., the sheer number of affected individuals could result in a high total economic burden.
Comparison between DALYs and total Deaths
The results being higher using the total deaths approach compared to DALYs suggest that the loss of life due to obesity is valued more highly than the disability or quality of life reductions. This may reflect a societal preference for life preservation over the quality of life, or it could indicate that the years lost due to premature mortality are more numerous than the years lived with disability.
Total burden Figures
The figures of $1603.78 billion and $3411.55 billion for the U.S. highlight the enormous financial implications of obesity. This underscores the importance of obesity prevention and treatment strategies from an economic standpoint.
The findings indicating the use of the Value of a Statistical Life (VSL) and the Value of a Statistical Life Year (VSLY) to estimate the economic burden of obesity through Disability-Adjusted Life Years (DALYs) and total deaths are consistent with the established literature that uses VSL and VSLY as measures to monetize health outcomes. This method is widely recognized for its ability to translate the non-market impacts of health risks, such as obesity, into economic terms, which is essential for policy evaluation and resource allocation.
The observation that the United States bears the highest economic burden of obesity aligns with its high per capita income, which correlates with a higher willingness to pay for risk reduction, as indicated by the higher VSL. This is consistent with the literature suggesting that wealthier societies tend to place a higher monetary value on life and health improvements.
China’s substantial economic burden from obesity, despite possibly having a lower VSL than the United States, reflects findings from other studies that consider the large population and the rapid increase in obesity prevalence as significant contributors to national economic impacts.
The higher valuation using total deaths compared to DALYs resonates with the literature that often values the prevention of mortality more than the avoidance of morbidity, which may be due to societal preferences or the relative magnitude of years lost to death compared to disability.
The enormous financial figures quoted for the United States corroborate other studies highlighting the critical need for effective obesity prevention and management strategies from both a public health and an economic perspective.
Overall, these findings underscore the utility of VSL and VSLY as comprehensive tools for economic impact assessment, aligning with other literature that emphasizes their value in health policy and economics for crafting effective strategies to combat obesity.
When comparing the results of the Value of a Statistical Life (VSL) and Value of a Statistical Life Year (VSLY) from our findings with other methodologies estimating the global burden of obesity, we look at various aspects such as the economic impact, the population affected, and the rate of increase in obesity-related costs.
For instance, our findings suggest that the economic impact of obesity in the United States is substantial, with figures amounting to billions of dollars. This aligns with data from the World Obesity Federation, which anticipates a global economic impact of overweight and obesity to reach $4.32 trillion annually by 2035 if no improvements are made in prevention and treatment measures. This impact is nearly 3% of the global GDP, likened to the financial repercussions of the COVID-19 pandemic in 2020. This prediction is based on the current trend that might see over half of the global population living with overweight or obesity within the next 12 years, doubling the rate of childhood obesity from current levels.
Furthermore, the Brookings Institution highlights that the economic consequences of obesity extend across direct medical costs, productivity costs, transportation costs, and human capital costs. This comprehensive view encompasses not just the healthcare-related expenditures but also the broader socio-economic implications of obesity, such as its impact on workforce productivity and educational outcomes.
The divergence in methodologies can primarily be seen in how different models account for these costs. While some focus primarily on direct medical expenditures, others like VSL and VSLY account for the broader societal impacts of reduced life expectancy and quality of life. To get a holistic view, one could compare the total figures, the accounting methods for direct and indirect costs, and how different countries value these economic burdens based on income levels and societal preferences for health outcomes.
In summary, these results emphasize the grave economic consequences of obesity and the importance of considering both mortality and morbidity when assessing its financial impact. They also illustrate the usefulness of the VSL and VSLY in quantifying these effects in monetary terms, which is critical for informing health policy and allocating resources effectively.
Conclusion
The application of the Value of a Statistical Life (VSL) and the Value of a Statistical Life Year (VSLY) to estimate the economic burden of obesity provides a compelling conclusion about the financial impact of this health issue in 2024. The staggering figures revealed in the analysis – with the United States facing the highest economic burden, followed by China – highlight the severe economic consequences tied to the obesity epidemic. The use of DALYs and total deaths as metrics not only reflects the healthcare costs but also the value society places on life and well-being.
The fact that the economic burden is higher when using the total deaths approach suggests that the loss of life carries a greater economic impact than the loss of quality of life due to disability. This could inform policymakers about the importance of investing in interventions that not only improve the quality of life but also, and more crucially, reduce mortality.
These findings underline the urgency for robust public health strategies that prioritize obesity prevention and management, taking into account both the direct and indirect costs to the healthcare system and the economy at large. The variation in economic burden between countries also calls for tailored interventions that consider local contexts and resource availability.
The comparison between methodologies is essential for understanding the full scope of obesity’s economic burden and for policymakers to allocate resources effectively. Each method provides unique insights that can guide comprehensive strategies for mitigating the economic impact of obesity on both a national and global scale.
In conclusion, this economic evaluation serves as a clarion call for immediate and sustained action against obesity, emphasizing the dual benefits of improved public health and economic savings. By leveraging the insights provided by VSL and VSLY, health policies can be designed to allocate resources more efficiently and make a significant impact on the global fight against obesity.
Authors’ contributions
Nadia Sweis, the sole author, was involved in the contribution in the conception, design, analysis, interpretation of the data; the drafting of the paper, revising it critically for intellectual content; and the final approval of the version to be published; and the author agree to be accountable for all aspects of the work.
Disclosure statement
No potential conflict of interest was reported by the author.
Data availability statement
Data is shared in supplementary files and all questions regarding data can be answered upon request.
Additional information
Funding
References
- Aldy, J. E., & Viscusi, W. K. (2008). Adjusting the value of a statistical life for age and cohort effects. Review of Economics and Statistics, 90(3), 573–581. https://doi.org/10.1162/rest.90.3.573
- Becker, G. S. (2007). Health as human capital: Synthesis and extensions. Oxford Economic Papers, 59(3), 379–410. https://doi.org/10.1093/oep/gpm020
- Benmarhnia, T., Dionne, P. A., Tchouaket, É., Fansi, A. K., & Brousselle, A. (2017). Investing in a healthy lifestyle strategy: Is it worth it? International Journal of Public Health, 62(1), 3–13. https://doi.org/10.1007/s00038-016-0884-y
- Cawley, J. (2015). An economy of scales: A selective review of obesity’s economic causes, consequences, and solutions. Journal of Health Economics, 43, 244–268. https://doi.org/10.1016/j.jhealeco.2015.03.001
- Institute for Health Metrics and Evaluation. (2019). Global burden of diseases results tool. GBD Results Tool. http://ghdx.healthdata.org/gbd-results-tool.
- Kniesner, T. J., & Viscusi, W. K. (2005). Value of a statistical life: Relative position vs. relative age. Applied Economics, 37(4), 371–388. https://doi.org/10.2139/ssrn.668383
- Pineda, E., Sanchez-Romero, L. M., Brown, M., Jaccard, A., Jewell, J., Galea, G., Webber, L., & Breda, J. (2018). Forecasting future trends in obesity across Europe: The value of improving surveillance. Obesity Facts, 11(5), 360–371.. https://doi.org/10.1159/000492115
- Robinson, L. A. (2007). How U.S. Government agencies value mortality risk reductions. Review of Environmental Economics and Policy, 1(2), 283–299. https://doi.org/10.1093/reep/rem018
- Sweis, N. J. (2022). Revisiting the value of a statistical life: An international approach during COVID-19. Risk Management, 24(3), 259–272. https://doi.org/10.1057/s41283-022-00094-x
- Tremmel, M., Gerdtham, U. G., Nilsson, P. M., & Saha, S. (2017). Economic burden of obesity: A systematic literature review. International Journal of Environmental Research and Public Health, 14(4), 435.. https://doi.org/10.3390/ijerph14040435
- Trogdon, J. G., Finkelstein, E. A., & Hylands, T. (2008). Economic impact of obesity in the U.S. American Journal of Preventive Medicine, 35(1), 72–82.
- United Nations. (2019). Department of economic and social affairs, population division. World Population Prospects. https://population.un.org/wpp/Publications
- United Nations. (2022). Undata. https://data.un.org/Default.aspx
- Viscusi, W. K., & Aldy, J. E. (2003). The value of a statistical life: A critical review of market estimates throughout the world. Journal of Risk and Uncertainty, 27(1), 5–76. https://doi.org/10.1023/A:1025598106257
- World Bank. (2021). DataBank | The World Bank. Retrieved September 23, 2021, from https://databank.worldbank.org/home.aspx