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Healthcare costs attributable to abnormal weight in China: evidence based on a longitudinal study



The prevalence of abnormal weight is on the rise, presenting serious health risks and socioeconomic problems. Nonetheless, there is a lack of studies on the medical cost savings that can be attained through the mitigation of abnormal weight. The aim of this study was to estimate the impact of abnormal weight on healthcare costs in China.


The study employed a 4-wave panel data from China Family Panel Studies (CFPS) between 2012 and 2018 (11,209 participants in each wave). Inpatient, non-inpatient and total healthcare costs were outcome variables. Abnormal weight is categorized based on body mass index (BMI). Initially, the two-part model was employed to investigate the impact of overweight/obesity and underweight on healthcare utilisation and costs, respectively. Subsequently, the estimated results were utilised to calculate the overweight/obesity attributable fraction (OAF) and the underweight attributable fraction (UAF).


In 2018, healthcare costs per person for overweight and obese population were estimated to be $607.51 and $639.28, respectively, and the underweight population was $755.55. In comparison to people of normal weight, individuals who were overweight/obese (OR = 1.067, p < 0.05) was more likely to utilise healthcare services. Overweight/obesity attributable fraction (OAF) was 3.90% of total healthcare costs and 4.31% of non-inpatient costs. Overweight/obesity does not result in additional healthcare expenditures for young people but increases healthcare costs for middle-aged adults (OAF = 7.28%) and older adults (OAF = 6.48%). The non-inpatient cost of underweight population was significantly higher than that of normal weight population (β = 0.060,p < 0.1), but the non-inpatient health service utilisation was not significantly affected.


Abnormal weight imposes a huge economic burden on individuals, households and the society. Abnormal weight in Chinese adults significantly increased healthcare utilisation and costs, particular in non-inpatient care. It is recommended that government and relevant social agencies provide a better social environment to enhance individual self-perception and promote healthy weight.

Peer Review reports


The issue of abnormal body weight poses a major concern for population health. Presently, there are current more than 2 billion individuals globally who are either overweight or obese [1, 2], while underweight is prevalent among the elderly and women [3,4,5]. According to a study published in 2021, China has the largest population of overweight individuals (34.3%) and obese individuals (16.4%) worldwide, totaling over 600 million people [6]. Notably, the majority of underweight adults are concentrated in the under-30 and over-60 age groups, particularly among women and older adults residing in rural areas [7]. This holds significance as variations in body mass index (BMI) have been found to impact the mortality risk in adults, with both higher and lower BMI values being associated with an increased mortality risk. Specifically, the risk of mortality linked to being underweight is attributed to neurological diseases and accidents [8].

The financial implications stemming from abnormal weight extend beyond individual patients [9, 10], encompassing the healthcare system as well, despite the fact that certain expenses are covered by health insurance [11, 12]. Abnormal weight not only constitutes an independent disease, resulting in healthcare costs [13], but it also serves as a major risk factor for a number of chronic conditions, such as hypertension, type 2 diabetes, cerebrovascular disease, and cancer [14], thereby contributing to substantial healthcare expenditures [15, 16]. Examining the policy regarding the management of abnormal weight requires a comprehensive understanding of the healthcare costs associated with being overweight/obese or underweight, as well as the proportion of these costs attributable to this condition. These figures aid in converting the negative health effects of abnormal weight into measurable costs and ratios that more precisely depict their impact [17, 18].

Prior research conducted by Chinese academics has endeavored to increase awareness among healthcare providers regarding overweight and obesity by estimating the substantial economic burden of prevalent chronic diseases resulting from excessive weight [19, 20]. However, there are several issues about the analysis. Firstly, the effects of abnormal weight on physical health are not well-defined and vary significantly among different groups of population. In addition, considering only the healthcare costs associated with a limited number of major chronic diseases may not provide a comprehensive evaluation of the overall financial burden caused by abnormal body weight. Using mixed cross-sectional data from the years 2000 to 2009, Qin et al. (2016) estimated that overweight/obesity contributed 24.35 billion CNY to the healthcare costs of Chinese adults [21]. Nevertheless, it is important to note that the data used in that study are relatively out of date, and given the escalating prevalence of overweight/obesity, the findings may not accurately reflect the current situation [22]. For instance, a 2018 study of Japanese adults between the ages of 40 and 69 found that 9.62% of healthcare costs in this group were attributable to overweight and obesity, which is a significant increase from 3.20% found in a 2002 study [23]. In addition, previous studies have paid less attention to underweight populations, and there is a lack of reports concerning underweight populations in China.

This study investigates the impact of overweight/obesity and underweight on healthcare expenditures, specifically focusing on inpatient costs, non-inpatient costs, and total healthcare costs. The study utilises data from the China Family Panel Study (CFPS), a nationally representative longitudinal dataset, to provide recent insights. Notably, this study contributes to the existing literature by providing updated estimates of medical costs attributable to overweight/obesity as well as addressing the dearth of information regarding the prevalence and associated costs of underweight among Chinese adults. In addition, we utilised econometric modeling, a reliable technique extensively employed in studies estimating healthcare costs. The results of this study will serve as a foundation for designing and implementing more effective weight control measures.


Data source

This study utilised the panel data provided by the latest four waves of data (2012, 2014, 2016, 2018) of China Family Panel Studies (CFPS). CFPS is a national comprehensive family social tracking survey conducted by the Institute of Social Science Survey (ISSS) of Peking University. The survey was conducted using a multi-stage, equal probability sampling technique, covering 25 provinces and 94.5% of the adult population in China, collecting information on education, health behaviors and economic activities. The Peking University Biomedical Ethics Review Committee provided ethical approval of the survey (IRB00001052-14010). All respondents read a statement describing the purpose of the study and provided their agreement to continue. More details about the CFPS are available from studies [24,25,26,27].

In 2010, The CFPS initiated a baseline survey and has subsequently followed individuals every two years. The individual-level data sets currently available to the publicly include the years 2010, 2012, 2014, 2016, 2018 and 2020. In this study, we mainly used information on healthcare utilisation, healthcare costs, health behaviors, sociodemographic characteristics, as well as socioeconomic status for the analysis. The 2010 survey on healthcare information only included the utilisation and costs of inpatient services, the 2012–2020 survey was expanded to include non-inpatient services. In addition, the COVID-19 pandemic had a significant impact on the likelihood and cost of utilising healthcare services in 2020. To rule out unreasonable changes in healthcare utilisation and expenditures caused by the COVID-19 pandemic, we utilised data from 2012, 2014, 2016, and 2018. Following the removal of missing values for key variables, the final sample used for analysis consisted of 44,836 observations. (11,209 individuals each year).


Healthcare utilisation and costs

The main dependent variables in this study were annual healthcare utilisation (individuals with positive healthcare expenses) and total healthcare costs. Annual total healthcare cost was calculated by adding the inpatient costs and non-inpatient costs. Inpatient costs include expenses for laboratory tests, consultations, medicines, bed tariffs, and nursing care. The non-inpatient costs refer to the expenses incurred as a result of the patient’s illness other than the inpatient costs. For healthcare costs, first, costs were inflation-adjusted to the year 2018 Chinese Yuan (CNY) by using national Consumer Price Index (CPI) of each corresponding year [28]. Then, to facility cross-country comparisons, the costs were converted into USD of the year 2018.The currency exchange rate between US dollars and Chinese Yuan was: 1.0 USD = 6.6174 CNY in 2018.

Abnormal weight

The key independent variables in this study were overweight/obesity and underweight based on BMI category. BMI is defined as the weight in kilograms divided by the height in meters squared. The criteria for the BMI were applicate according to the National Health and Family Planning Commission of the People’s Republic of China [29]. Specifically, it is divided into four categories: underweight (< 18.5 kg/m2), normal weight (18.5–23.9 kg /m2), overweight (24.0-27.9 kg /m2) and obesity (≥ 28.0 kg /m2). In this study, we focused on the healthcare costs associated with abnormal weight as compared with normal weight. We combined obesity and overweight into a category termed overweight/obesity.


In the model, we accounted for the factors that may affect the utilisation and cost of healthcare services for individuals based on previous studies [23, 30, 31]. Included were demographic characteristics (age, gender, location, marital status and educational level), health behaviors (smoking status, drinking status, health status), socioeconomic status (health insurance and income level).

Geographical locations are classified into two distinct categories, namely urban and rural, based on the criteria for the division by the National Bureau of Statistics; Marital status is divided into two categories: cohabited (married or cohabitating) and other (single or separated or divorced or widowed); Educational level is divided into three categories: primary school and below, junior high school, and senior high school and above; Smoking status, drinking status were all dichotomous variables (0 = No, 1 = Yes); Health status is the self-rated health status of the respondent (1 = good, 2 = general, 3 = bad); health insurance is a multicategorical variable (0 = None, 1 = Publicly-funded medical care, 2 = Urban employee basic medical insurance, 3 = Urban resident basic medical insurance, 4 = Supplementary medical insurance, 5 = New rural cooperative medical scheme); Considering the consistency of indicators in CFPS each wave, the income level adopted in this study is primarily based on self-reported subjective income level, which is evaluated by individuals based on their local location of household income, ranging from very low “1” to very high “5”.

Statistical analysis

First, the mean (standard deviation) and frequency (percentage) were used to conduct a descriptive statistical analysis of each variable year by year. Second, in order to explore the impact of abnormal weight on healthcare utilisation and costs among adults, and to predict the healthcare costs attributable to abnormal weight, we developed the following two-part model based on existing studies [32], which is a standard health economics method for estimating healthcare demand [33].

Notably, for healthcare costs for which there is a large amount of zero-valued data, the two-part model does not rely on assumptions of homoskedasticity and normality of the outcome variable [25, 33]. Using as an example the estimated equation for overweight/obesity:

$$\text{D}\text{E}{\text{x}\text{p}}_{it}={\gamma }_{0}+{\gamma }_{1}{\text{O}}_{it}+{\gamma }_{2}{X}_{it}+{\alpha }_{i}+{u}_{it}$$
$$\text{I}\text{n}{Exp}_{it}={\beta }_{0}+{\beta }_{1}{\text{O}}_{it}+{\beta }_{2}{X}_{it}+{{\alpha }^{{\prime }}}_{i}+{{u}^{{\prime }}}_{it}$$

A logit model was established for \(\text{D}\text{E}{\text{x}\text{p}}_{it}\) (Eq. (1)), denoting \(\text{D}\text{E}{\text{x}\text{p}}_{it}\) as the healthcare services utilisation of the ith indivudual in year t: when the individual utilised healthcare services (that is, positive healthcare costs occurred), the value of \(\text{D}\text{E}{\text{x}\text{p}}_{it}\) was 1, and 0 otherwise. \({\text{O}}_{it}\) is the primary independent variable Overweight/Obese, \({X}_{it}\) represents the covariate matrix made up of individual demographic characteristics, health behaviors and socioeconomic status, \({\alpha }_{i}\) is the random heterogeneity of the ith observation that does not vary over time, and \({\text{u}}_{\text{i}\text{t}}\) is the random error term.

And then, a linear regression model (Eq. (2)) is established for \(\text{I}\text{n}{Exp}_{it}\). Under the condition of \(\text{D}\text{E}{\text{x}\text{p}}_{it}\)=1, that is, among the population with healthcare utilisation, denoted \(\text{I}\text{n}{Exp}_{it}\) as the natural logarithm of the healthcare costs of the ith individual in year t, the remaining variables are specified identically to those in Eq. 1.

The parameters estimated by the two preceding equations allowed us to predict the healthcare costs attribute to overweight/obesity. First, the healthcare costs of each observation in the actual situation can be predicted based on the estimated coefficient of each variable and the actual characteristics of the individual, which is called the actual predicted value (\({\widehat{\text{Y}}}_{\text{i}}^{\text{A}}\)); Second, it was assumed that the body weight of overweight/obesity individuals in the sample was within the normal range (i.e. overweight/obesity = 0), and other characteristics remained unchanged. The estimated parameters of the model can be used to obtain the healthcare costs of each individual under normal weight, which is the counterfactual value of healthcare costs \({\widehat{\text{Y}}}_{\text{i}}^{\text{C}\text{F}}\); Finally, the overweight/obesity attributable fraction (OAF) in healthcare costs can be calculated by Eq. (3) using the predicted actual and counterfactual values.


Similarly, the effect of underweight on healthcare costs can be explored and the underweight attributable fraction (UAF) calculated by assigning the primary independent variable to underweight (\({\text{U}}_{it}\)). Notably, healthcare costs attributable to overweight/obesity and underweight were estimated separately. In order to estimating healthcare costs associated with overweight/obesity, we excluded underweight samples and used normal weight as the reference group. Similarly, when evaluating healthcare costs attributable to underweight, we excluded the overweight/obese sample and used the normal weight as the reference group. The heterogeneity of different age groups was then analyzed. We divided age into three groups (18–44, 45–59, and 60 and above) to estimate the parameters of healthcare service utilisation and cost of abnormal weight and the OAF value, respectively. All data were analyzed using Stata software 16.0 (Stata Corp., College Station, TX, USA).


Characteristics of participants

Descriptive statistics of variables are presented in Table 1. At the baseline, the mean (SD) age of the sample was 47.66(12.92) years; 51.54% (5,777/11,209) of the participants are male; 28.20% of the participants are urban residents; 50.88% of the participants achieved primary school education or below; 91.61% of the participants live with their spouses, and 90.08% of them were covered by health insurance. In terms of BMI, 27.56% and 7.27% of adults were overweight and obese respectively in 2012, both of which exhibited an upward tendency, with the proportions rising to 33.05% and 9.80% in 2018. In contrast, the prevalence of underweight decreased from 7.20% in 2012 to 5.33% in 2018. In terms of the healthcare utilisation, the inpatient services utilisation rate was 8.36% in 2012, indicating a general upward trend, and it was 15.21% in 2018. From 2012 to 2018, the non-inpatient healthcare utilisation rate fell from 77.26 to 67.65%, and total healthcare utilisation rate decreased from 79.45 to 71.07%.

Table 1 Descriptive statistics of study sample [n (%)] (China. 2012–2018)

From 2012 to 2018, both inpatient and non-inpatient costs exhibited an upward trend (Table 1). The same pattern was observed across different BMI categories. Particularly, the inpatient costs of underweight individuals were higher than those of normal weight and overweight/obese individuals (Table 2).

Table 2 The average healthcare cost of different types of health services among different BMI groups ($)

The impact of overweight/obesity on healthcare utilisation and healthcare costs

Using a two-part model as previously described, we analyzed the impact of overweight/obesity on healthcare utilisation and costs (Table 3). Overweight/obesity individuals were more likely to utilise healthcare services (OR = 1.067, p < 0.05) and non-inpatient services (OR = 1.070, p < 0.05). In terms of healthcare costs, those with overweight/obesity have higher healthcare costs (β = 0.082, p < 0.01) and non-inpatient costs (β = 0.088, p < 0.01) than people of normal weight. However, the effect of overweight/obesity on the increase in inpatient costs was not statistically significant (p > 0.1).

Table 3 Parametric estimates of healthcare utilisation and expenditure due to overweight/obesity (China. 2012–2018)

The impact of underweight on healthcare utilisation and healthcare costs

Similarly, we utilised a two-part model to analyze the impact of underweight on healthcare utilisation and costs (Table 4). There was no significant difference in the probability of healthcare utilisation between the underweight population and the normal weight population. In the cost estimation, the underweight population had significantly higher non-inpatient costs (β = 0.060, p < 0.1) than the normal weight group, but there was no statistically significant difference in inpatient costs and healthcare costs (p > 0.1).

Table 4 Parametric estimates of healthcare utilisation and expenditure due to underweight (China. 2012–2018)

Heterogeneity analysis of parameter estimates for different age groups

For young adults (18–44), overweight/obesity had no additional healthcare costs compared to normal-weight individuals, and underweight individuals had higher utilisation of inpatient services (OR = 0.706, p < 0.05). For middle-aged adults (44–59), overweight/obese individuals had higher utilisation (OR = 1.107, p < 0.05) and costs (β = 0.133, p < 0.01) of non-inpatient services, as well as utilisation (OR = 1.148, p < 0.01) and costs (β = 0.145, p < 0.01) of healthcare services. Underweight was associated with a higher cost of non-inpatient services (β = 0.103, p < 0.1). For older adults (60 and above), overweight/obese individuals had higher utilisation of non-inpatient services (OR = 1.207, p < 0.01) and costs (β = 0.151, p < 0.01), and utilisation of healthcare services (OR = 1.169, p < 0.05) and costs (β = 0.130, p < 0.01). Underweight did not increase additional healthcare service utilisation and costs. (Table 5).

Table 5 Estimation of healthcare utilisation and expenditure on the heterogeneity of the age group

Overweight/obesity attributable fraction (OAF) prediction

Based on the estimation results from the two-part model, we further predicted the healthcare costs caused by abnormal weight, including actual and counterfactual values, from which OAFs were calculated (Table 6). Using overweight/obesity as an illustration, the predicted value of healthcare cost for overweight/obesity groups is 149.429 USD, whereas the counterfactual value is 143.600 USD (assuming that the weight of obese or overweight people is normal). In other words, the impact of overweight/obesity on healthcare cost per capita is an increase of 5.829 USD, accounting for 3.90% of individual healthcare cost. The OAF of healthcare costs for overweight/obese individuals was 7.28% for middle-aged adults and 6.48% for older adults.

Table 6 Estimated annual healthcare costs attribute to overweight/obesity (China. 2012–2018)


Based on nationally representative datasets and econometric models, this study estimates the most recent values for the impact of abnormal weight on healthcare costs in Chinese adults. The results show that the prevalence of overweight and obesity among Chinese adults is comparable to that reported in the China Nutrition and Chronic Disease Report (2020), validating the representativeness of the data and the reliability of the findings. To the best of our knowledge, this is the most comprehensive and nationally representative study of overweight/obese and underweight people in China using the most recent data.

Healthcare costs for overweight/obese people have increased significantly in recent years. Using data from the China Health and Nutrition Survey (CHNS) from 2000 to 2009, a study found that the per capita medical cost of overweight and obesity was 6.18 CNY (0.90 USD), equivalent to 24.35 billion CNY (3.53 USD) of annual national medical expenditure [32], which is significantly lower than the estimate in this study. In addition, overweight/obese adults had significantly higher healthcare costs compared to adults of normal weight, with an OAF value of 3.90%. Notably, the utilisation rate and cost value of hospitalisation in the attributable costs of overweight/obesity were not significantly different from those with normal weight. Shi et al. (2011), using data from the China Health and Retirement Longitudinal Study (CHARLS), found that there were no significant differences in inpatient utilisation or inpatient costs for overweight/obese individuals over the age of 45 [32]. In accordance with the findings of this study, more additional costs for overweight/obese individuals are primarily non-inpatient costs.

Further, heterogeneity analyses by age groups revealed that middle-aged (45–59 years) and older adults (≥ 60 years) are more affected by overweight/obesity and their non-inpatient costs may be more related to outpatient and medication costs associated with chronic diseases [34,35,36]. Examples include cardiovascular disease treatment and prevention [37, 38] and anti-inflammatory medications [39, 40].

Lifestyle intervention is the first-line treatment for obesity in China, but research into its clinical efficacy is still largely insufficient [41]. Interventions for overweight and obesity must be regarded systematically and comprehensively for the time being. Firstly, improving public perception of unhealthy weight and the health risks associated with overweight and obesity is a prerequisite for promoting healthy lifestyles [42]. Secondly, the most effective intervention for obese individuals is the provision of readily accessible professional advice, including effective interventions developed by healthcare facilities, such as planned physical activity, healthy eating, and cognitive behavioral therapy [43]. Improving the abnormal weight status of Chinese population depends not only on self-perceptions of weight, risk perceptions, and a better grasp of existing interventions, but also on an awareness of the interdependence of biological, social, and political settings [41, 44]. For instance, Xiong et al. (2021) demonstrated that in recent decades, individuals tended to purchase low-nutrition, high-energy food and drink at a lower price. This transition in consumption had a substantial impact on the diet health of individuals, especially for those with low socioeconomic status [6]. Consequently, governments should allocate more financial resources towards food, with the aim of increasing the price of unhealthy foods and decrease the cost of healthy foods rich in fiber. Equally essential environmental drivers include urbanization, urban planning and built environments, food systems, and natural environments that shape obesity risk factors at the individual level [45, 46].

On the other hand, the results of underweight individuals did not show a statistically significant difference, which could be attributed to the small sample size of underweight individuals in the dataset. This suggests that more targeted studies on underweight people are warranted in future studies. Current relative economic inequality in the world impedes efforts to improve the nutrition of underweight individuals [47, 48]. Relative economic inequalities in societies undermine nutrition in numerous ways, including public education, diet, physical activity, food systems, health infrastructure, etc. [49, 50]. Additionally, relative economic inequality may explain why the distribution of underweight populations varied by location [51]. Low-income countries prioritize overweight, obesity and diet-related chronic diseases, and governments frequently lack adequate nutrition coordination [52, 53]. To estimate the spatial concentration of underweight people, a detailed study of the food and nutritional state of the population is required immediately. Accordingly, nutritional resources should be reallocated to the regions and populations most affected.

The study has some limitations. Firstly, this study focuses on individuals over the age of 18 and lacks a sample of minors. The self-reported height and weight of the respondents may differ marginally from the real situation. Secondly, obesity-related complications may occur in overweight/obese individuals, but due to the limited biological indicators, this study was unable to identify and answer the question of which characteristics of individuals are most likely to develop obesity-related complications, regardless of their weight status. This is what we can investigate and study in the future. Finally, due to the lack of information, indirect medical costs associated with abnormal weight were not considered in this study, which may lead to an underestimation of the economic burden.


The study aimed to examine the impact of abnormal weight on the likelihood and cost of healthcare utilisation among Chinese adults. The results showed that among Chinese adults, overweight/obese individuals were more likely to utilise healthcare services than normal-weight individuals, especially non-inpatient services. In addition, overweight/obesity increases healthcare costs for middle-aged and older adults to varying degrees. These findings have substantial implications for how governments and healthcare institutions allocate resources for disease prevention and treatment.

Data availability

The data used in this paper is publicly available and could be accessible via the website of China Family Panel Studies ( The data used during the current study are available from the first author or corresponding author on reasonable request.


  1. Mehta NK. Obesity as a main threat to future improvements in Population Health: Policy Opportunities and Challenges. Milbank Q. 2023;101:460–77.

    PubMed  Google Scholar 

  2. Caballero B. Humans against Obesity: Who Will Win? Advances in Nutrition. 2019;10 suppl_1:S4–9.

  3. NCD Risk Factor Collaboration. Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19·2 million participants. The Lancet. 2016;387:1377–96.

    Google Scholar 

  4. Strube-Lahmann S, Müller-Werdan U, Norman K, Skarabis H, Lahmann NA. Underweight in nursing Homes: differences between men and women. Gerontology. 2021;67:211–9.

    PubMed  Google Scholar 

  5. Bailly M, Boscaro A, Pereira B, Courteix D, Germain N, Galusca B, et al. Underweight but not underfat: is fat-free mass a key factor in constitutionally thin women? Eur J Clin Nutr. 2021;75:1764–70.

    CAS  PubMed  Google Scholar 

  6. Xiong F, Wang L, Pan A. Epidemiology and determinants of obesity in China. The Lancet Diabetes & Endocrinology. 2021;9:373–92.

    Google Scholar 

  7. National Health Commission Disease Prevention and Control Bureau. Report on Nutrition and Chronic Diseases in China 2020. Beijing: People ’s Health Publishing House; 2022. p. 160.

    Google Scholar 

  8. Bhaskaran K, dos-Santos-Silva I, Leon DA, Douglas IJ, Smeeth L. Association of BMI with overall and cause-specific mortality: a population-based cohort study of 3·6 million adults in the UK. The Lancet Diabetes & Endocrinology. 2018;6:944–53.

    Google Scholar 

  9. Berkowitz SA, Seligman HK, Rigdon J, Meigs JB, Basu S. Supplemental Nutrition Assistance Program (SNAP) participation and Health Care expenditures among low-income adults. JAMA Intern Med. 2017;177:1642–9.

    PubMed  PubMed Central  Google Scholar 

  10. Hoque ME, Mannan M, Long KZ, Al Mamun A. Economic burden of underweight and overweight among adults in the Asia-Pacific region: a systematic review. Trop Med Int Health. 2016;21:458–69.

    PubMed  Google Scholar 

  11. Ma M, Tian W, Kang J, Li Y, Xia Q, Wang N, et al. Does the medical insurance system play a real role in reducing catastrophic economic burden in elderly patients with cardiovascular disease in China? Implication for accurately targeting vulnerable characteristics. Global Health. 2021;17:36.

    PubMed  PubMed Central  Google Scholar 

  12. Meng Q, Fang H, Liu X, Yuan B, Xu J. Consolidating the social health insurance schemes in China: towards an equitable and efficient health system. Lancet. 2015;386:1484–92.

    PubMed  Google Scholar 

  13. Okunogbe A, Nugent R, Spencer G, Powis J, Ralston J, Wilding J. Economic impacts of overweight and obesity: current and future estimates for 161 countries. BMJ Glob Health. 2022;7:e009773.

    PubMed  PubMed Central  Google Scholar 

  14. Jia G, Shu X-O, Liu Y, Li H-L, Cai H, Gao J, et al. Association of Adult Weight Gain with Major Health Outcomes among Middle-aged chinese persons with low body weight in early adulthood. JAMA Netw Open. 2019;2:e1917371.

    PubMed  PubMed Central  Google Scholar 

  15. Zhao Y, He L, Marthias T, Ishida M, Anindya K, Desloge A, et al. Out-Of-Pocket expenditure Associated with Physical Inactivity, Excessive Weight, and obesity in China: Quantile Regression Approach. Obes Facts. 2022;15:416–27.

    PubMed  PubMed Central  Google Scholar 

  16. d’Errico M, Pavlova M, Spandonaro F. The economic burden of obesity in Italy: a cost-of-illness study. Eur J Health Econ. 2022;23:177–92.

    PubMed  Google Scholar 

  17. Anis AH, Zhang W, Bansback N, Guh DP, Amarsi Z, Birmingham CL. Obesity and overweight in Canada: an updated cost-of-illness study. Obes Rev. 2010;11:31–40.

    CAS  PubMed  Google Scholar 

  18. Finkelstein EA, Trogdon JG, Cohen JW, Dietz W. Annual medical spending attributable to obesity: payer-and service-specific estimates. Health Aff (Millwood). 2009;28:w822–831.

    PubMed  Google Scholar 

  19. Zhao W, Zhai Y, Hu J, Wang J, Yang Z, Kong L, et al. Economic burden of obesity-related chronic diseases in Mainland China. Obes Rev. 2008;9(Suppl 1):62–7.

    PubMed  Google Scholar 

  20. Zhang J, Shi X, Liang X. Economic costs of both overweight and obesity among chinese urban and rural residents, in 2010. Zhonghua Liu Xing Bing Xue Za Zhi. 2013;34:598–600.

    PubMed  Google Scholar 

  21. Qin X, Pan J. The medical cost attributable to obesity and overweight in China: Estimation based on longitudinal surveys. Health Econ. 2016;25:1291–311.

    PubMed  Google Scholar 

  22. Mahase E. Global cost of overweight and obesity will hit $4.32tn a year by 2035, report warns. BMJ. 2023;380:523.

    PubMed  Google Scholar 

  23. Fujita M, Sato Y, Nagashima K, Takahashi S, Hata A. Medical costs attributable to overweight and obesity in japanese individuals. Obes Res Clin Pract. 2018;12:479–84.

    PubMed  Google Scholar 

  24. Xie Y, Hu J. An introduction to the China Family Panel Studies (CFPS). Chin Sociol Rev. 2014;47:3–29.

    Google Scholar 

  25. Zhou M, Sun X, Huang L. Chronic disease and medical spending of chinese elderly in rural region. Int J Qual Health Care. 2021;33:mzaa142.

    PubMed  Google Scholar 

  26. Sun X, Zhou M, Huang L, Nuse B. Depressive costs: medical expenditures on depression and depressive symptoms among rural elderly in China. Public Health. 2020;181:141–50.

    CAS  PubMed  Google Scholar 

  27. Zhou L, Zhong Q, Yang J. Air Pollution and Household Medical expenses: evidence from China. Front Public Health. 2022;9:798780.

    PubMed  PubMed Central  Google Scholar 

  28. National Bureau of Statistics of China. 2021 China Health Statistical Yearbook. 2021.

  29. Wang Y, Mi J, Shan X-Y, Wang QJ, Ge K-Y. Is China facing an obesity epidemic and the consequences? The trends in obesity and chronic disease in China. Int J Obes (Lond). 2007;31:177–88.

    CAS  PubMed  Google Scholar 

  30. Cawley J, Biener A, Meyerhoefer C, Ding Y, Zvenyach T, Smolarz BG, et al. Direct medical costs of obesity in the United States and the most populous states. J Manag Care Spec Pharm. 2021;27:354–66.

    PubMed  Google Scholar 

  31. Li C, Mao Z, Yu C. The effects of smoking, regular drinking, and unhealthy weight on health care utilization in China. BMC Public Health. 2021;21:2268.

    PubMed  PubMed Central  Google Scholar 

  32. Shi J, Wang Y, Cheng W, Shao H, Shi L. Direct health care costs associated with obesity in chinese population in 2011. J Diabetes Complications. 2017;31:523–8.

    PubMed  Google Scholar 

  33. Duan N, Manning WG, Morris CN, Newhouse JP. A comparison of alternative models for the demand for Medical Care. J Bus Economic Stat. 1983;1:115–26.

    Google Scholar 

  34. Li C, Young B-R, Jian W. Association of socioeconomic status with financial burden of disease among elderly patients with cardiovascular disease: evidence from the China Health and Retirement Longitudinal Survey. BMJ Open. 2018;8:e018703.

    PubMed  PubMed Central  Google Scholar 

  35. Gorasso V, Moyersoen I, Van der Heyden J, De Ridder K, Vandevijvere S, Vansteelandt S, et al. Health care costs and lost productivity costs related to excess weight in Belgium. BMC Public Health. 2022;22:1693.

    PubMed  PubMed Central  Google Scholar 

  36. van den Broek-Altenburg E, Atherly A, Holladay E. Changes in healthcare spending attributable to obesity and overweight: payer- and service-specific estimates. BMC Public Health. 2022;22:962.

    PubMed  PubMed Central  Google Scholar 

  37. Fretheim A, Odgaard-Jensen J, Brørs O, Madsen S, Njølstad I, Norheim OF, et al. Comparative effectiveness of antihypertensive medication for primary prevention of cardiovascular disease: systematic review and multiple treatments meta-analysis. BMC Med. 2012;10:33.

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Karmali KN, Lloyd-Jones DM, Berendsen MA, Goff DC, Sanghavi DM, Brown NC, et al. Drugs for primary Prevention of Atherosclerotic Cardiovascular Disease: an overview of systematic reviews. JAMA Cardiol. 2016;1:341.

    PubMed  PubMed Central  Google Scholar 

  39. Aletaha D, Smolen JS. Diagnosis and management of rheumatoid arthritis: a review. JAMA. 2018;320:1360.

    PubMed  Google Scholar 

  40. Nurmagambetov T, Kuwahara R, Garbe P. The Economic Burden of Asthma in the United States, 2008–2013. Annals ATS. 2018;15:348–56.

    Google Scholar 

  41. The Lancet Diabetes & Endocrinology. Obesity in China: time to act. The Lancet Diabetes & Endocrinology. 2021;9:407.

    Google Scholar 

  42. Kwak YE, McMillan R, McDonald EK. Trends in overweight and obesity self-awareness among adults with overweight or obesity in the United States, 1999 to 2016. Ann Intern Med. 2021;174:721–3.

    PubMed  Google Scholar 

  43. Gómez Puente JM, Martínez-Marcos M. Sobrepeso y obesidad: eficacia de las intervenciones en adultos. Enfermería Clínica. 2018;28:65–74.

    Google Scholar 

  44. Siddiqui MZ, Donato R. Overweight and obesity in India: policy issues from an exploratory multi-level analysis. Health Policy Plan. 2016;31:582–91.

    PubMed  Google Scholar 

  45. Cecchini M, Sassi F, Lauer JA, Lee YY, Guajardo-Barron V, Chisholm D. Tackling of unhealthy diets, physical inactivity, and obesity: health effects and cost-effectiveness. The Lancet. 2010;376:1775–84.

    Google Scholar 

  46. Holdsworth M, El Ati J, Bour A, Kameli Y, Derouiche A, Millstone E, et al. Developing national obesity policy in middle-income countries: a case study from North Africa. Health Policy Plann. 2013;28:858–70.

    Google Scholar 

  47. Pysmenna O, Anderson KM. Income and health perceptions in an economically disadvantaged community: a qualitative case study from Central Florida. Int J Com WB. 2022.

    Article  Google Scholar 

  48. Shimonovich M, Pearce A, Thomson H, McCartney G, Katikireddi SV. Assessing the causal relationship between income inequality and mortality and self-rated health: protocol for systematic review and meta-analysis. Syst Rev. 2022;11:20.

    PubMed  PubMed Central  Google Scholar 

  49. Adeyanju O, Tubeuf S, Ensor T. Socio-economic inequalities in access to maternal and child healthcare in Nigeria: changes over time and decomposition analysis. Health Policy Plann. 2017;32:1111–8.

    Google Scholar 

  50. Namugumya BS, Candel JJL, Termeer CJAM, Talsma EF. The framing of malnutrition by parliamentarians in Uganda. Health Policy Plann. 2021;36:585–93.

    Google Scholar 

  51. Bhandari P, Gayawan E, Yadav S. Double burden of underweight and overweight among indian adults: spatial patterns and social determinants. Public Health Nutr. 2021;24:2808–22.

    PubMed  Google Scholar 

  52. Brixi H, Mu Y, Targa B, Hipgrave D. Engaging sub-national governments in addressing health equities: challenges and opportunities in China’s health system reform. Health Policy Plann. 2013;28:809–24.

    Google Scholar 

  53. Bristol UK. Development Initiatives. 2020 Global Nutrition Report: action on equity to end malnutrition. Bristol, UK: Development Initiatives; 2020.

    Google Scholar 

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We thank the China Family Panel Studies (CFPS) data source conducted by the Chinese Social Science Survey Center (ISSS) of Peking University.


This study was supported by the Science and Technology Climbing Engineering-Scientific Research Innovation Project-Innovative Research Cultivation Project, Nanjing Medical University, grant number 06.

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Authors and Affiliations



HY and XPX designed the study and critically reviewed and commented important intellectual content. SQZ performed the final statistical analyses and interpretation of data, and drafted and revised the manuscript. XPX revised the draft grammatical sentences. JJG and QFW made important suggestions for the revision of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Xinpeng Xu or Hua You.

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The Peking University Biomedical Ethics Review Committee provided ethical approval of the survey (IRB00001052-14010). Informed consent was obtained from all individual participants included in the study. ALL methods were carried out in accordance with relevant guidelines and regulations.

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Not applicable.

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Zhao, S., Xu, X., You, H. et al. Healthcare costs attributable to abnormal weight in China: evidence based on a longitudinal study. BMC Public Health 23, 1927 (2023).

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