Author(s)

Aurora Zhao ¹

Affiliation(s)

¹ West Essex High School, North Caldwell, New Jersey, United States

Corresponding Author

Aurora Zhao

ABSTRACT

Obesity rates among adults have been increasing in the US to concerning levels. This poses a substantial public health risk because obese people have a higher chance of contracting multiple conditions, including cardiovascular disease, diabetes, and obstructive sleep apnea. Predictive modeling is needed because it can help develop mitigation policies. In this study, data from ten cycles of the NHANES survey were used to create a 10-year Markov transition matrix. The 10-year matrix was then decomposed into a 2-year matrix, which was used to project future obesity prevalence. Separate matrices were created for racial/ethnic and income categories. A bootstrap-based Monte Carlo simulation was utilized to determine uncertainty in the future obese adult population. By 2040, the obesity prevalence is projected to be 54.0% (51.4% to 55.6%), and the total obese population is forecasted to be 131.8 million (121.7 to 141.7 million). Additionally, the transition matrix demonstrates that obesity has a high level of persistence and that transitioning to a higher weight category is more likely than moving to a lower one. These projections are consistent with previous studies. Minority groups are disproportionately affected by obesity, and obesity rates among Black individuals are projected to be the highest in 2040. These results demonstrate the importance of public policy to address obesity as a persistent, population-level risk and to target disparities among different groups.

KEYWORDS

Obesity; Markov model; Population projection; Adult obesity; United States

CITE THIS PAPER

Aurora Zhao. Projected Trends in Adult Obesity Prevalence in the United States through 2040: A Markov Chain Analysis. Social Medicine and Health Management (2026). Vol. 7, No. 2, 38-47. DOI: http://dx.doi.org/10.23977/socmhm.2026.070205.

REFERENCES

[1] Emmerich, S., Fryar, C., Stierman, B., & Ogden, C. (2024, September). Obesity and severe obesity prevalence in adults: United States, August 2021–August 2023. National Center for Health Statistics (U.S.). https://doi.org/10.15620/cdc/159281
[2] Flegal, K. M. (2002, October). Prevalence and trends in obesity among US adults, 1999–2000. JAMA, 288(14), 1723. https://doi.org/10.1001/jama.288.14.1723
[3] Purnell, J. Q. (n.d.). Definitions, classification, and epidemiology of obesity. In Endotext. MDText.com. Retrieved May 16, 2026, from https://www.ncbi.nlm.nih.gov/books/NBK279167/
[4] Kolotkin, R. L., Meter, K., & Williams, G. R. (2001, November). Quality of life and obesity. Obesity Reviews, 2(4), 219–229. https://doi.org/10.1046/j.1467-789X.2001.00040.x
[5] Vidra, N., Trias-Llimós, S., & Janssen, F. (2019, July). Impact of obesity on life expectancy among different European countries: Secondary analysis of population-level data over the 1975–2012 period. BMJ Open, 9(7), e028086. https://doi.org/10.1136/bmjopen-2018-028086
[6] Huang, A. A., & Huang, S. Y. (2023, December). Stochastic modeling of obesity status in United States adults using Markov chains: A nationally representative analysis of population health data from 2017–2020. Obesity Science & Practice, 9(6), 653–660. https://doi.org/10.1002/osp4.697
[7] Rocco, S. M., & Zio, E. (2005). Bootstrap-based techniques for computing confidence intervals in monte carlo system reliability evaluation. In Annual Reliability and Maintainability Symposium, 2005. Proceedings. (pp. 303–307). IEEE. https://doi.org/10.1109/RAMS.2005.1408379
[8] Ward, Z. J., et al. (2019, December). Projected U.S. state-level prevalence of adult obesity and severe obesity. New England Journal of Medicine, 381(25), 2440–2450. https://doi.org/10.1056/NEJMsa1909301
[9] Ng, M., et al. (2024, December). National-level and state-level prevalence of overweight and obesity among children, adolescents, and adults in the USA, 1990–2021, and forecasts up to 2050. The Lancet, 404(10469), 2278–2298. https://doi.org/10.1016/S0140-6736(24)01548-4
[10] National Center for Health Statistics. (n.d.). NHANES questionnaires, datasets, and related documentation. Retrieved from https://wwwn.cdc.gov/nchs/nhanes/default.aspx#print
[11] The GBD 2015 Obesity Collaborators. (2017, July). Health effects of overweight and obesity in 195 countries over 25 years. New England Journal of Medicine, 377(1), 13–27. https://doi.org/10.1056/NEJMoa1614362
[12] DeCleene, N. K., et al. (2026, March). US state-level prevalence of adult obesity by race and ethnicity from 1990 to 2022 and forecasted to 2035. JAMA, 335(11), 975. https://doi.org/10.1001/jama.2025.26817
[13] Byrd, A. S., Toth, A. T., & Stanford, F. C. (2018, June). Racial disparities in obesity treatment. Current Obesity Reports, 7(2), 130–138. https://doi.org/10.1007/s13679-018-0301-3
[14] Rosas, L. G., et al. (2015, April). The effectiveness of two community-based weight loss strategies among obese, low-income US Latinos. Journal of the Academy of Nutrition and Dietetics, 115(4), 537–550.e2. https://doi.org/10.1016/j.jand.2014.10.020
[15] Kumanyika, S. K., et al. (2009, October). Trial of family and friend support for weight loss in African American adults. Archives of Internal Medicine, 169(19). https://doi.org/10.1001/archinternmed.2009.337
[16] Li, P., Huang, Y., & Wong, A. (2024, February). Behavioural strategies to reduce obesity among lower socio-economic adults living in high-income countries: A Grades of Recommendation, Assessment, Development and Evaluation-assessed systematic review and meta-analysis of randomised controlled trials. British Journal of Nutrition, 131(3), 544–552. https://doi.org/10.1017/S0007114523001940
[17] Dee, A., et al. (2014, December). The direct and indirect costs of both overweight and obesity: A systematic review. BMC Research Notes, 7(1), 242. https://doi.org/10.1186/1756-0500-7-242
[18] Ramasamy, A., et al. (2019, November). Direct and indirect cost of obesity among the privately insured in the United States: A focus on the impact by type of industry. Journal of Occupational & Environmental Medicine, 61(11), 877–886. https://doi.org/10.1097/JOM.0000000000001693
[19] Simmonds, M., Llewellyn, A., Owen, C. G., & Woolacott, N. (2016, February). Predicting adult obesity from childhood obesity: A systematic review and meta‐analysis. Obesity Reviews, 17(2), 95–107. https://doi.org/10.1111/obr.12334

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