Comparing Different BMI Categories for Projecting Obesity Trends

Comparing Different BMI Categories for Projecting Obesity Trends | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Technical Report Comparing Different BMI Categories for Projecting Obesity Trends Barbora Tacheyzova, Roland Sturm, Jakub Hlávka This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7996398/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objectives Obesity trends are typically monitored using a single BMI ≥ 30 threshold. This common practice may be inadequate if there are differential changes between levels of obesity, in particular disproportional acceleration of severe (BMI 35–40, class 2), and morbid obesity (BMI over 40, class 3). We study this question for a central European country to identify whether differential changes alter conclusions about future public health burden related to obesity. Study Design Trend analysis using longitudinal survey data. Methods Data from the Czech portion of the Survey of Health, Ageing and Retirement in Europe (SHARE) from 2006–2022 were analyzed. We modeled prevalence trends for obesity thresholds (BMI ≥ 30, ≥ 35, and ≥ 40), using natural cubic splines for the time trend, while controlling for age, sex and education and holding population composition constant for the 2022 demographic structure. Projections were forecasted through 2030, with 10th -90th percentile intervals generated via parametric bootstrap. To estimate the cost impact, projected prevalence rates in mutually exclusive obesity classes were multiplied by published excess healthcare cost estimates for each respective class. Results The prevalence of severe and morbid obesity is increasing at a much more rapid rate than moderate obesity. This trend is the primary driver of projected growth in excess costs. Conclusions Relying solely on general obesity measure (BMI ≥ 30) is insufficient and misleading, causing an underestimation of the trends and impact of the obesity epidemic. Differentiating between obesity classes is essential for effective policymaking. Health sciences/Health care/Public health Health sciences/Risk factors Obesity Severe obesity BMI categories SHARE Economic burden Figures Figure 1 Introduction Modifiable risk factors impact health care expenditures, disability, social insurance programs, and long-term care needs. While some risk factors have become less prevalent – smoking being the most prominent example – other risk factors deteriorated. Far more citizens of developed countries have obesity than they did in the past. Even the poorest countries have undergone a nutrition transition where excess weight is a more common health risk than being underweight. Standard population statistics of average BMI or the percentage of individuals with a body mass index (BMI) of ≥ 30 obscure differential changes across obesity classes. This paper demonstrates this by studying differential trajectories of obesity classes and their impact on health care cost growth in the Czech Republic. The typical definition of obesity, a body mass index (BMI) of over 30 (which is about 16 kg overweight for a person 1.75m), obscures the heterogeneity within obesity. Individuals who are 50 kg overweight (a BMI over 40 for most people) have much more serious health problems and associated health care costs than individuals with moderate obesity. Individuals with a BMI over 40 (class 3 or morbid obesity) have a hazard ratio of about three for cardiovascular disease and all-cause mortality compared to individuals with overweight (BMI 25–30), after adjusting for baseline demographic and cardiovascular risk factors. 1 Among middle-aged adults in the US, a body mass index (BMI) of 30–35 (moderate obesity, class 1) was associated with a 25% increase in health care costs. A body mass index of 35–40 (class 2) doubled those excess costs, and a BMI of over 40 (class 3) doubled those excess costs yet again. 2 In the past, severe levels of obesity were so rare (fractions of a percent) that no meaningful trend estimates could be made at the population level. It also was believed that morbid obesity primarily had genetic causes, resulting in a stable percentage. Even now, trend estimates remain sparse. 3 The first evidence about a disproportionate increase of extreme weight categories came from the US 4–6 , but similar effects have since been reported in Spain, Brazil, and Sweden. 7 – 9 Despite these reports, a measure of only BMI ≥ 30 remains a standard method when projecting economic costs of obesity. 10 This report calculates trends for the Czech Republic and may be the first such estimate for a region of central Europe. Methods We analyze data from the Czech Republic as provided in the Gateway harmonized Survey of Health, Ageing and Retirement in Europe (SHARE). 11 The dependent variable is an individual’s BMI group and covariates include age, sex, and education in addition to calendar time, which is modeled using natural cubic splines. The numbers shown correspond to the demographic composition of Czech adults age 50 + in 2022 and should be interpreted as the change in obesity holding population composition constant. The crosshatched area reflects the 10th and 90th range based on a parametric bootstrap (where we bootstrap new parameter values of trends based on a multi-variate normal distribution using the original covariance matrix). We use three (mutually non-exclusive) thresholds BMI ≥ 30, BMI ≥ 35, and BMI ≥ 40. The model is estimated on data from 2006–2022 and forecasts values through 2030. We show both absolute levels of prevalence and relative changes of prevalence using these thresholds. To calculate the share of excess medical costs related to obesity that is attributable to different obesity classes, we use magnitudes reported for the US based on the US Health and Retirement Survey by Tatiana Andreyeva and colleagues. 2 Compared to normal weight (BMI 18–25), moderate obesity (class 1) increased health care costs by 24%, class 2 obesity by 51%, and class 3 obesity by 108%. We recalculate the prevalence rates into mutually exclusive BMI categories and multiply prevalence by the associated percentage increase reported by Andreyeva et al. 2 These are summed separately for the baseline year 2006 and for the projected year 2030. Results Figure 1 shows predicted trends in absolute prevalence (panel a) and relative changes over time (panel b), setting 2006 as the index year. The dot-dashed line shows the typically reported rate, which includes all BMI levels over 30. In absolute prevalence, this line shows an increase from around 22% in 2006 to around 30% in 2016–2018, the typical “obesity epidemic” that has been reported worldwide. The vertical dotted line marks the start of our predictions beyond the last available data with 10th and 90th percentile predictions crosshatched. Obesity rates defined as BMI ≥ 30 seem to have plateaued in recent years for that age group and the point estimate even suggests a small decline. The middle dashed line shows BMI ≥ 35 the solid line BMI ≥ 40. In comparison, the relative change shows that extreme BMI categories continue to increase rapidly. In fact, the shift toward the extreme BMI categories is so strong that if we were to remove those from the BMI ≥ 30 group, the latter would have seen hardly any increase since 2006. By applying excess healthcare cost estimates 2 to our prevalence projections, we can quantify the rising financial impact of severe obesity, as shows in Table 1 . These costs are projected to nearly double from 6.6% in 2006 to 12.3% in 2030. This overall increase of 5.7 percentage points is not driven by all classes of obesity equally. In 2006, the most prevalent category was class 1 (BMI 30–35) at 18.24%, which accounted for the largest share of costs (4.4%). However, this group’s contribution to future cost growth is negligible (-0.1%). Instead, the projected increase is caused by the rising prevalence of more severe obesity. Individuals with morbid obesity (class 3, BMI > 40) are the primary driver, accounting for 3.3 percentage points of the total cost growth, as their prevalence is predicted to increase from 0.5% in 2006 to 3.6% in 2030. Class 2 (BMI 35–40) also contributes significantly, adding 2.4 percentage points to the growth in excess costs, with predicted prevalence rising from 3.2% in 2006 to 8% in 2030. Table 1 Estimated excess medical costs by BMI Class in 2006 and 2030 2006 2030 Prevalence (Czech Republic) Estimated Effect on Medical Costs Prevalence (Czech Republic) Estimated Effect on Medical Costs Difference in excess medical costs between 2004 and 2030 BMI 30–35 18.2% 4.4% 17.8% 4.3% -0.1% BMI 35–40 3.2% 1.6% 8.0% 4.0% 2.4% BMI > 40 0.5% 0.6% 3.6% 3.9% 3.3% Total 6.6% 12.3% 5.7% Note: Our calculations using SHARE data for Czech Republic and estimates from Andreyeva et al. 2 Discussion Our analysis demonstrates that the standard BMI ≥ 30 threshold is an insufficient metric for monitoring obesity trends. We found that extreme levels of obesity are increasing rapidly in the Czech Republic even if the prevalence of obesity when defined as a BMI ≥ 30 has stabilized among middle age Czech adults. This is not an isolated finding. In the US, stabilized obesity rates (but not stabilized rates of severe obesity) between 2013 and 2023 have been reported. 12 Data from Brazil showed a similar pattern in that the fastest increases are in severe obesity, although the absolute prevalence rates of all levels of obesity in Brazil are lower and unlike in the Czech Republic or the US, there is no sign of a plateauing for BMI > 30 in Brazil. 8 It would be premature to claim that the obesity epidemic has been stopped based on trend estimates that use the standard BMI ≥ 30 cutoff to calculate prevalence. Instead, the severity of obesity appears to be increasing and this change remains poorly documented. 3 For the Czech Republic, we estimate that almost all of the increased share in excess medical costs related to obesity between 2006 and 2030 among middle aged adults is due to more severe levels of obesity. Population aging and cohort effects will add to the cost pressure and our calculations held population characteristics constant. For example, more recent cohorts in the Czech SHARE data are at a higher risk of obesity (at the same age) than earlier cohorts (results not shown). Limitation of this analysis is that we rely on survey data for the Czech Republic that includes only middle aged or older adults. The survey relies on self-reported weight and height, which generally results in BMI values lower than objective measurements, but that is the same approach used in other countries. For the excess cost calculation, we use older estimates from the US as there are no estimates available for the Czech Republic, but those estimates were based on a similarly structured survey and age composition and so the relative impact of each obesity class is comparable. It would be more problematic to mix data based on self-reported and objective measurements or estimates for other age groups. Conclusion Studies based on the obesity measure of BMI ≥ 30 fail to identify the heterogeneity within obesity. In the Czech Republic, severe levels of obesity are increasing at a much more rapid pace than moderate obesity and similar results have been reported elsewhere. This is leading to a disproportional increase in the excess healthcare costs related to obesity. Given the delayed onset of obesity-related chronic conditions – including diabetes and its severe complications such as kidney disease, heart disease, stroke, blindness, and amputations – this shift indicates escalating healthcare demands and costs in the coming decades. Consequently, public health policies based on the overarching measure of obesity (BMI ≥ 30) are effectively blind to this underlying shift, leaving the healthcare system unprepared for the growing burden of chronic diseases. Declarations DATA AVAILABILITY Data was obtained from a third party and cannot be made publicly available. More specifically, data for the study came from the SHARE project and are available to all researchers for purely scientific purposes upon request on their website (https://share-eric.eu/). Contact data: SHARE-ERIC, Chausseestraße 111, 10115 Berlin, Germany, [email protected] Acknowledgments The work was supported from ERDF/ESF project Ageing of the population and related challenges for health and social systems (AGEING-CZ) (No. CZ.02.01.01/00/23_025/0008743). COMPETING INTERESTS The authors have no relevant financial or non-financial interests to disclose. References Iyen B, Weng S, Vinogradova Y, Akyea RK, Qureshi N, Kai J. Long-term body mass index changes in overweight and obese adults and the risk of heart failure, cardiovascular disease and mortality: a cohort study of over 260,000 adults in the UK. BMC Public Health . 2021;21(1):576. doi:10.1186/s12889-021-10606-1 Andreyeva T, Sturm R, Ringel JS. Moderate and severe obesity have large differences in health care costs. Obesity Research . 2004;12(12):1936-1943. doi:10.1038/oby.2004.243 Williamson K, Nimegeer A, Lean M. Rising prevalence of BMI ≥40 kg/m2: A high-demand epidemic needing better documentation. Obesity Reviews . 2020;21(4):e12986. doi:10.1111/obr.12986 Sturm R. Increases in Clinically Severe Obesity in the United States, 1986-2000. Archives of Internal Medicine . 2003;163(18):2146-2148. doi:10.1001/archinte.163.18.2146 Sturm R. Increases in morbid obesity in the USA: 2000-2005. Public Health . 2007;121(7):492-496. doi:10.1016/j.puhe.2007.01.006 Sturm R, Hattori A. Morbid obesity rates continue to rise rapidly in the United States. Int J Obes (Lond) . 2013;37(6):889-891. doi:10.1038/ijo.2012.159 Basterra-Gortari FJ, Bes-Rastrollo M, Ruiz-Canela M, Gea A, Martinez-Gonzalez MÁ. Prevalencia de obesidad y diabetes en adultos españoles, 1987-2012. Medicina Clínica . 2017;148(6):250-256. doi:10.1016/j.medcli.2016.11.022 Brum M, Sturm R. Severe obesity increases more rapidly in Brazil than moderate obesity: analysis of Vigitel 2006-2021. Revista Brasileira De Epidemiologia = Brazilian Journal of Epidemiology . 2025;28:e250011. doi:10.1590/1980-549720250011 Neovius M, Teixeira-Pinto A, Rasmussen F. Shift in the composition of obesity in young adult men in Sweden over a third of a century. International Journal of Obesity . 2008;32(5):832-836. doi:10.1038/sj.ijo.0803784 Nagi MA, Ahmed H, Rezq MAA, et al. Economic costs of obesity: a systematic review. Int J Obes . 2024;48(1):33-43. doi:10.1038/s41366-023-01398-y Börsch-Supan A, Brandt M, Hunkler C, et al. Data Resource Profile: the Survey of Health, Ageing and Retirement in Europe (SHARE). International Journal of Epidemiology . 2013;42(4):992-1001. doi:10.1093/ije/dyt088 Emmerich SD, Fryar CD, Stierman B, Gu Q, Afful J, Ogden CL. Trends in Obesity-Related Measures Among US Children, Adolescents, and Adults. JAMA . 2025;333(12):1082-1084. doi:10.1001/jama.2024.27676 Additional Declarations There is NO conflict of interest to disclose Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7996398","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Technical Report","associatedPublications":[],"authors":[{"id":539773003,"identity":"3d953308-b5f3-4c90-b107-4b4fa2b29631","order_by":0,"name":"Barbora 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1","display":"","copyAsset":false,"role":"figure","size":75792,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredicted Obesity Prevalence in the Czech Republic\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: Our calculations using SHARE data for Czech Republic\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7996398/v1/bc908929141ebf35b22e444d.png"},{"id":109219646,"identity":"5f34cbe0-bfee-48ce-96d5-b2461bc1b1a2","added_by":"auto","created_at":"2026-05-13 19:58:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":201572,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7996398/v1/192506fb-59f6-494e-a335-6ae4cfb8d187.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Comparing Different BMI Categories for Projecting Obesity Trends","fulltext":[{"header":"Introduction","content":"\u003cp\u003eModifiable risk factors impact health care expenditures, disability, social insurance programs, and long-term care needs. While some risk factors have become less prevalent \u0026ndash; smoking being the most prominent example \u0026ndash; other risk factors deteriorated. Far more citizens of developed countries have obesity than they did in the past. Even the poorest countries have undergone a nutrition transition where excess weight is a more common health risk than being underweight. Standard population statistics of average BMI or the percentage of individuals with a body mass index (BMI) of \u0026ge;\u0026thinsp;30 obscure differential changes across obesity classes. This paper demonstrates this by studying differential trajectories of obesity classes and their impact on health care cost growth in the Czech Republic.\u003c/p\u003e\u003cp\u003eThe typical definition of obesity, a body mass index (BMI) of over 30 (which is about 16 kg overweight for a person 1.75m), obscures the heterogeneity within obesity. Individuals who are 50 kg overweight (a BMI over 40 for most people) have much more serious health problems and associated health care costs than individuals with moderate obesity. Individuals with a BMI over 40 (class 3 or morbid obesity) have a hazard ratio of about three for cardiovascular disease and all-cause mortality compared to individuals with overweight (BMI 25\u0026ndash;30), after adjusting for baseline demographic and cardiovascular risk factors.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Among middle-aged adults in the US, a body mass index (BMI) of 30\u0026ndash;35 (moderate obesity, class 1) was associated with a 25% increase in health care costs. A body mass index of 35\u0026ndash;40 (class 2) doubled those excess costs, and a BMI of over 40 (class 3) doubled those excess costs yet again.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eIn the past, severe levels of obesity were so rare (fractions of a percent) that no meaningful trend estimates could be made at the population level. It also was believed that morbid obesity primarily had genetic causes, resulting in a stable percentage. Even now, trend estimates remain sparse.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e The first evidence about a disproportionate increase of extreme weight categories came from the US \u003csup\u003e4\u0026ndash;6\u003c/sup\u003e, but similar effects have since been reported in Spain, Brazil, and Sweden.\u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Despite these reports, a measure of only BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 remains a standard method when projecting economic costs of obesity.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e This report calculates trends for the Czech Republic and may be the first such estimate for a region of central Europe.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe analyze data from the Czech Republic as provided in the Gateway harmonized Survey of Health, Ageing and Retirement in Europe (SHARE).\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e The dependent variable is an individual\u0026rsquo;s BMI group and covariates include age, sex, and education in addition to calendar time, which is modeled using natural cubic splines. The numbers shown correspond to the demographic composition of Czech adults age 50\u0026thinsp;+\u0026thinsp;in 2022 and should be interpreted as the change in obesity holding population composition constant. The crosshatched area reflects the 10th and 90th range based on a parametric bootstrap (where we bootstrap new parameter values of trends based on a multi-variate normal distribution using the original covariance matrix). We use three (mutually non-exclusive) thresholds BMI\u0026thinsp;\u0026ge;\u0026thinsp;30, BMI\u0026thinsp;\u0026ge;\u0026thinsp;35, and BMI\u0026thinsp;\u0026ge;\u0026thinsp;40. The model is estimated on data from 2006\u0026ndash;2022 and forecasts values through 2030. We show both absolute levels of prevalence and relative changes of prevalence using these thresholds.\u003c/p\u003e\u003cp\u003eTo calculate the share of excess medical costs related to obesity that is attributable to different obesity classes, we use magnitudes reported for the US based on the US Health and Retirement Survey by Tatiana Andreyeva and colleagues.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Compared to normal weight (BMI 18\u0026ndash;25), moderate obesity (class 1) increased health care costs by 24%, class 2 obesity by 51%, and class 3 obesity by 108%. We recalculate the prevalence rates into mutually exclusive BMI categories and multiply prevalence by the associated percentage increase reported by Andreyeva et al. \u003csup\u003e2\u003c/sup\u003e These are summed separately for the baseline year 2006 and for the projected year 2030.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows predicted trends in absolute prevalence (panel a) and relative changes over time (panel b), setting 2006 as the index year. The dot-dashed line shows the typically reported rate, which includes all BMI levels over 30. In absolute prevalence, this line shows an increase from around 22% in 2006 to around 30% in 2016\u0026ndash;2018, the typical \u0026ldquo;obesity epidemic\u0026rdquo; that has been reported worldwide. The vertical dotted line marks the start of our predictions beyond the last available data with 10th and 90th percentile predictions crosshatched. Obesity rates defined as BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 seem to have plateaued in recent years for that age group and the point estimate even suggests a small decline. The middle dashed line shows BMI\u0026thinsp;\u0026ge;\u0026thinsp;35 the solid line BMI\u0026thinsp;\u0026ge;\u0026thinsp;40. In comparison, the relative change shows that extreme BMI categories continue to increase rapidly. In fact, the shift toward the extreme BMI categories is so strong that if we were to remove those from the BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 group, the latter would have seen hardly any increase since 2006.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBy applying excess healthcare cost estimates \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e to our prevalence projections, we can quantify the rising financial impact of severe obesity, as shows in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. These costs are projected to nearly double from \u003cb\u003e6.6%\u003c/b\u003e in 2006 to \u003cb\u003e12.3%\u003c/b\u003e in 2030. This overall increase of 5.7 percentage points is not driven by all classes of obesity equally. In 2006, the most prevalent category was class 1 (BMI 30\u0026ndash;35) at 18.24%, which accounted for the largest share of costs (4.4%). However, this group\u0026rsquo;s contribution to future cost growth is negligible (-0.1%). Instead, the projected increase is caused by the rising prevalence of more severe obesity. Individuals with morbid obesity (class 3, BMI\u0026thinsp;\u0026gt;\u0026thinsp;40) are the primary driver, accounting for \u003cb\u003e3.3 percentage points\u003c/b\u003e of the total cost growth, as their prevalence is predicted to increase from \u003cb\u003e0.5%\u003c/b\u003e in 2006 to \u003cb\u003e3.6%\u003c/b\u003e in 2030. Class 2 (BMI 35\u0026ndash;40) also contributes significantly, adding \u003cb\u003e2.4\u003c/b\u003e percentage points to the growth in excess costs, with predicted prevalence rising from \u003cb\u003e3.2%\u003c/b\u003e in 2006 to \u003cb\u003e8%\u003c/b\u003e in 2030.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEstimated excess medical costs by BMI Class in 2006 and 2030\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e2006\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e2030\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrevalence (Czech Republic)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEstimated Effect on Medical Costs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePrevalence (Czech Republic)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEstimated Effect on Medical Costs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDifference in excess medical costs between 2004 and 2030\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI 30\u0026ndash;35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI 35\u0026ndash;40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u0026thinsp;\u0026gt;\u0026thinsp;40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.7%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: Our calculations using SHARE data for Czech Republic and estimates from Andreyeva et al.\u003csup\u003e2\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur analysis demonstrates that the standard BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 threshold is an insufficient metric for monitoring obesity trends. We found that extreme levels of obesity are increasing rapidly in the Czech Republic even if the prevalence of obesity when defined as a BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 has stabilized among middle age Czech adults. This is not an isolated finding. In the US, stabilized obesity rates (but not stabilized rates of severe obesity) between 2013 and 2023 have been reported.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e Data from Brazil showed a similar pattern in that the fastest increases are in severe obesity, although the absolute prevalence rates of all levels of obesity in Brazil are lower and unlike in the Czech Republic or the US, there is no sign of a plateauing for BMI\u0026thinsp;\u0026gt;\u0026thinsp;30 in Brazil.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eIt would be premature to claim that the obesity epidemic has been stopped based on trend estimates that use the standard BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 cutoff to calculate prevalence. Instead, the severity of obesity appears to be increasing and this change remains poorly documented.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eFor the Czech Republic, we estimate that almost all of the increased share in excess medical costs related to obesity between 2006 and 2030 among middle aged adults is due to more severe levels of obesity. Population aging and cohort effects will add to the cost pressure and our calculations held population characteristics constant. For example, more recent cohorts in the Czech SHARE data are at a higher risk of obesity (at the same age) than earlier cohorts (results not shown).\u003c/p\u003e\u003cp\u003eLimitation of this analysis is that we rely on survey data for the Czech Republic that includes only middle aged or older adults. The survey relies on self-reported weight and height, which generally results in BMI values lower than objective measurements, but that is the same approach used in other countries. For the excess cost calculation, we use older estimates from the US as there are no estimates available for the Czech Republic, but those estimates were based on a similarly structured survey and age composition and so the relative impact of each obesity class is comparable. It would be more problematic to mix data based on self-reported and objective measurements or estimates for other age groups.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eStudies based on the obesity measure of BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 fail to identify the heterogeneity within obesity. In the Czech Republic, severe levels of obesity are increasing at a much more rapid pace than moderate obesity and similar results have been reported elsewhere. This is leading to a disproportional increase in the excess healthcare costs related to obesity. Given the delayed onset of obesity-related chronic conditions \u0026ndash; including diabetes and its severe complications such as kidney disease, heart disease, stroke, blindness, and amputations \u0026ndash; this shift indicates escalating healthcare demands and costs in the coming decades. Consequently, public health policies based on the overarching measure of obesity (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30) are effectively blind to this underlying shift, leaving the healthcare system unprepared for the growing burden of chronic diseases.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData was obtained from a third party and cannot be made publicly available. More specifically, data for the study came from the SHARE project and are available to all researchers for purely scientific purposes upon request on their website (https://share-eric.eu/). Contact data: SHARE-ERIC, Chausseestra\u0026szlig;e 111, 10115 Berlin, Germany, [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe work was supported from ERDF/ESF project Ageing of the population and related challenges for health and social systems (AGEING-CZ) (No. CZ.02.01.01/00/23_025/0008743).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOMPETING INTERESTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eIyen B, Weng S, Vinogradova Y, Akyea RK, Qureshi N, Kai J. Long-term body mass index changes in overweight and obese adults and the risk of heart failure, cardiovascular disease and mortality: a cohort study of over 260,000 adults in the UK. \u003cem\u003eBMC Public Health\u003c/em\u003e. 2021;21(1):576. doi:10.1186/s12889-021-10606-1\u003c/li\u003e\n\u003cli\u003eAndreyeva T, Sturm R, Ringel JS. Moderate and severe obesity have large differences in health care costs. \u003cem\u003eObesity Research\u003c/em\u003e. 2004;12(12):1936-1943. doi:10.1038/oby.2004.243\u003c/li\u003e\n\u003cli\u003eWilliamson K, Nimegeer A, Lean M. Rising prevalence of BMI \u0026ge;40 kg/m2: A high-demand epidemic needing better documentation. \u003cem\u003eObesity Reviews\u003c/em\u003e. 2020;21(4):e12986. doi:10.1111/obr.12986\u003c/li\u003e\n\u003cli\u003eSturm R. Increases in Clinically Severe Obesity in the United States, 1986-2000. \u003cem\u003eArchives of Internal Medicine\u003c/em\u003e. 2003;163(18):2146-2148. doi:10.1001/archinte.163.18.2146\u003c/li\u003e\n\u003cli\u003eSturm R. Increases in morbid obesity in the USA: 2000-2005. \u003cem\u003ePublic Health\u003c/em\u003e. 2007;121(7):492-496. doi:10.1016/j.puhe.2007.01.006\u003c/li\u003e\n\u003cli\u003eSturm R, Hattori A. Morbid obesity rates continue to rise rapidly in the United States. \u003cem\u003eInt J Obes (Lond)\u003c/em\u003e. 2013;37(6):889-891. doi:10.1038/ijo.2012.159\u003c/li\u003e\n\u003cli\u003eBasterra-Gortari FJ, Bes-Rastrollo M, Ruiz-Canela M, Gea A, Martinez-Gonzalez M\u0026Aacute;. Prevalencia de obesidad y diabetes en adultos espa\u0026ntilde;oles, 1987-2012. \u003cem\u003eMedicina Cl\u0026iacute;nica\u003c/em\u003e. 2017;148(6):250-256. doi:10.1016/j.medcli.2016.11.022\u003c/li\u003e\n\u003cli\u003eBrum M, Sturm R. Severe obesity increases more rapidly in Brazil than moderate obesity: analysis of Vigitel 2006-2021. \u003cem\u003eRevista Brasileira De Epidemiologia = Brazilian Journal of Epidemiology\u003c/em\u003e. 2025;28:e250011. doi:10.1590/1980-549720250011\u003c/li\u003e\n\u003cli\u003eNeovius M, Teixeira-Pinto A, Rasmussen F. Shift in the composition of obesity in young adult men in Sweden over a third of a century. \u003cem\u003eInternational Journal of Obesity\u003c/em\u003e. 2008;32(5):832-836. doi:10.1038/sj.ijo.0803784\u003c/li\u003e\n\u003cli\u003eNagi MA, Ahmed H, Rezq MAA, et al. Economic costs of obesity: a systematic review. \u003cem\u003eInt J Obes\u003c/em\u003e. 2024;48(1):33-43. doi:10.1038/s41366-023-01398-y\u003c/li\u003e\n\u003cli\u003eB\u0026ouml;rsch-Supan A, Brandt M, Hunkler C, et al. Data Resource Profile: the Survey of Health, Ageing and Retirement in Europe (SHARE). \u003cem\u003eInternational Journal of Epidemiology\u003c/em\u003e. 2013;42(4):992-1001. doi:10.1093/ije/dyt088\u003c/li\u003e\n\u003cli\u003eEmmerich SD, Fryar CD, Stierman B, Gu Q, Afful J, Ogden CL. Trends in Obesity-Related Measures Among US Children, Adolescents, and Adults. \u003cem\u003eJAMA\u003c/em\u003e. 2025;333(12):1082-1084. doi:10.1001/jama.2024.27676\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Obesity, Severe obesity, BMI categories, SHARE, Economic burden","lastPublishedDoi":"10.21203/rs.3.rs-7996398/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7996398/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eObesity trends are typically monitored using a single BMI ≥ 30 threshold. This common practice may be inadequate if there are differential changes between levels of obesity, in particular disproportional acceleration of severe (BMI 35–40, class 2), and morbid obesity (BMI over 40, class 3). We study this question for a central European country to identify whether differential changes alter conclusions about future public health burden related to obesity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTrend analysis using longitudinal survey data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData from the Czech portion of the Survey of Health, Ageing and Retirement in Europe (SHARE) from 2006–2022 were analyzed. We modeled prevalence trends for obesity thresholds (BMI ≥ 30, ≥ 35, and ≥ 40), using natural cubic splines for the time trend, while controlling for age, sex and education and holding population composition constant for the 2022 demographic structure. Projections were forecasted through 2030, with 10th -90th percentile intervals generated via parametric bootstrap. To estimate the cost impact, projected prevalence rates in mutually exclusive obesity classes were multiplied by published excess healthcare cost estimates for each respective class.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe prevalence of severe and morbid obesity is increasing at a much more rapid rate than moderate obesity. This trend is the primary driver of projected growth in excess costs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRelying solely on general obesity measure (BMI ≥ 30) is insufficient and misleading, causing an underestimation of the trends and impact of the obesity epidemic. Differentiating between obesity classes is essential for effective policymaking.\u003c/p\u003e","manuscriptTitle":"Comparing Different BMI Categories for Projecting Obesity Trends","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-11 10:43:01","doi":"10.21203/rs.3.rs-7996398/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"02587357-ff2d-4be4-9d45-0559a73060e1","owner":[],"postedDate":"November 11th, 2025","published":true,"recentEditorialEvents":[{"type":"decision","content":"Reject after peer review","date":"2026-05-13T11:05:52+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57410230,"name":"Health sciences/Health care/Public health"},{"id":57410231,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-05-13T11:20:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-11 10:43:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7996398","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7996398","identity":"rs-7996398","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}