Re-examining the Reversal Hypothesis: A Nationwide Population-based Study of the Association Between Socioeconomic Status, and NCDs and Risk Factors in China

Overview

Journal SSM Popul Health

Date 2023 Jan 24

PMID 36691489

Authors

Xuemei Zhang

Tianxin Pan

Barbara McPake

Affiliations

Soon will be listed here.

Abstract

Background: According to the reversal hypothesis, as a country's economic and social development progresses, the burden of NCDs and risk factors shifts from rich to poor. The aim of this research is to examine the reversal hypothesis in the Chinese setting.

Methods: Using data from the China Health and Retirement Longitudinal Study (CHARLS) in 2015, we explored whether the reversal hypothesis applies at the subnational level. Participants aged 45 years and older in 2015 were included. We examined five risk factors (smoking, heavy drinking, physical inactivity, overweight, and obesity) and three objectively measured NCDs (diabetes, hypertension, dyslipidemia). Binary logistic regressions were performed to examine outcomes across people of differing SES in provincial level, in urban and rural areas, and across generations.

Results: Nationally, SES is positively associated with heavy drinking, obesity, diabetes and dyslipidemia, whereas it is negatively associated with physical inactivity. The association between SES and smoking and hypertension was not statistically significant. Except in the cases of diabetes and dyslipidemia, we found that risk factors of all kinds were more concentrated among richer people in rural than in urban areas. Across provinces with increasing GDP per capita, a downward trend in risk factors among those with high SES compared to those with low SES could be interpreted, while the opposite trend could be interpreted with respect to the metabolic syndrome conditions. Obesity and overweight exhibited slight downward trends (in line with those for risk factors) and upward trends (in line with those for metabolic syndrome conditions), respectively.

Conclusion: We conclude that China is at a relatively early stage of 'reversal', visible with respect to risk factors. If these patterns persist over time, the trend will likely feed through to metabolic disorders which will increasingly become diseases of the poor.

Citing Articles

Changes in pre-diabetes and diabetes prevalence and diabetes self-management behaviors across socioeconomic spectra in rural southwest China: 2013-2022.

Wu X, Li G, Liu L, Li H, Zhao Y, Golden A BMC Public Health. 2025; 25(1):594.

PMID: 39948504 PMC: 11823096. DOI: 10.1186/s12889-025-21763-y.

References

Im P, Millwood I, Chen Y, Guo Y, Du H, Kartsonaki C . Problem drinking, wellbeing and mortality risk in Chinese men: findings from the China Kadoorie Biobank. Addiction. 2019; 115(5):850-862. PMC: 7156287. DOI: 10.1111/add.14873. View

Aitsi-Selmi A, Chandola T, Friel S, Nouraei R, Shipley M, Marmot M . Interaction between education and household wealth on the risk of obesity in women in Egypt. PLoS One. 2012; 7(6):e39507. PMC: 3384649. DOI: 10.1371/journal.pone.0039507. View

Nikparvar M, Khaladeh M, Yousefi H, Farashah M, Moayedi B, Kheirandish M . Dyslipidemia and its associated factors in southern Iranian women, Bandare-Kong Cohort study, a cross-sectional survey. Sci Rep. 2021; 11(1):9125. PMC: 8080669. DOI: 10.1038/s41598-021-88680-z. View

Espirito Santo L, Faria T, Silva C, Xavier L, Reis V, Mota G . Socioeconomic status and education level are associated with dyslipidemia in adults not taking lipid-lowering medication: a population-based study. Int Health. 2019; 14(4):346-353. PMC: 10575599. DOI: 10.1093/inthealth/ihz089. View

Porhcisaliyan V, Wang Y, Tan N, Jafar T . Socioeconomic status and ethnic variation associated with type 2 diabetes mellitus in patients with uncontrolled hypertension in Singapore. BMJ Open Diabetes Res Care. 2021; 9(1). PMC: 8728350. DOI: 10.1136/bmjdrc-2020-002064. View

Ding X, Billari F, Gietel-Basten S . Health of midlife and older adults in China: the role of regional economic development, inequality, and institutional setting. Int J Public Health. 2017; 62(8):857-867. PMC: 5641278. DOI: 10.1007/s00038-017-0970-9. View

Wang Y, Wang J . Modelling and prediction of global non-communicable diseases. BMC Public Health. 2020; 20(1):822. PMC: 7268487. DOI: 10.1186/s12889-020-08890-4. View

Wang S, Kou C, Liu Y, Li B, Tao Y, DArcy C . Rural-urban differences in the prevalence of chronic disease in northeast China. Asia Pac J Public Health. 2014; 27(4):394-406. DOI: 10.1177/1010539514551200. View

Wang R, Bishwajit G, Zhou Y, Wu X, Feng D, Tang S . Intensity, frequency, duration, and volume of physical activity and its association with risk of depression in middle- and older-aged Chinese: Evidence from the China Health and Retirement Longitudinal Study, 2015. PLoS One. 2019; 14(8):e0221430. PMC: 6699736. DOI: 10.1371/journal.pone.0221430. View

10.

Jurj A, Wen W, Gao Y, Matthews C, Yang G, Li H . Patterns and correlates of physical activity: a cross-sectional study in urban Chinese women. BMC Public Health. 2007; 7:213. PMC: 2020478. DOI: 10.1186/1471-2458-7-213. View

11.

Moradi G, Majdzadeh R, Mohammad K, Malekafzali H, Jafari S, Holakouie-Naieni K . Is the status of diabetes socioeconomic inequality changing in Kurdistan province, west of Iran? A comparison of two surveys. Med J Islam Repub Iran. 2016; 30:375. PMC: 4972065. View

12.

Zhang X, Jiang X, Sha M, Zhou Q, Li W, Guo Y . Life-course pathways from childhood socioeconomic status to type 2 diabetes in mid-late Chinese adulthood. Sci Rep. 2021; 11(1):13051. PMC: 8219676. DOI: 10.1038/s41598-021-91768-1. View

13.

Ding L, Liang Y, Tan E, Hu Y, Zhang C, Liu Y . Smoking, heavy drinking, physical inactivity, and obesity among middle-aged and older adults in China: cross-sectional findings from the baseline survey of CHARLS 2011-2012. BMC Public Health. 2020; 20(1):1062. PMC: 7336642. DOI: 10.1186/s12889-020-08625-5. View

14.

Golestani M, Sadeghi-Bazargani H, Saadati M, Farahbakhsh M, Dalal K . Lifestyle Risk Factor Assessment Through WHO STEP Approach in Tabriz, Iran. Clinicoecon Outcomes Res. 2021; 13:487-492. PMC: 8203186. DOI: 10.2147/CEOR.S304189. View

15.

Zhou M, Wang H, Zeng X, Yin P, Zhu J, Chen W . Mortality, morbidity, and risk factors in China and its provinces, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2019; 394(10204):1145-1158. PMC: 6891889. DOI: 10.1016/S0140-6736(19)30427-1. View

16.

Goryakin Y, Suhrcke M . Economic development, urbanization, technological change and overweight: what do we learn from 244 Demographic and Health Surveys?. Econ Hum Biol. 2014; 14:109-27. PMC: 4330986. DOI: 10.1016/j.ehb.2013.11.003. View

17.

Corsi D, Subramanian S . Socioeconomic Gradients and Distribution of Diabetes, Hypertension, and Obesity in India. JAMA Netw Open. 2019; 2(4):e190411. PMC: 6450330. DOI: 10.1001/jamanetworkopen.2019.0411. View

18.

Thienemann F, Ntusi N, Battegay E, Mueller B, Cheetham M . Multimorbidity and cardiovascular disease: a perspective on low- and middle-income countries. Cardiovasc Diagn Ther. 2020; 10(2):376-385. PMC: 7225439. DOI: 10.21037/cdt.2019.09.09. View

19.

Monteiro C, Conde W, Popkin B . Independent effects of income and education on the risk of obesity in the Brazilian adult population. J Nutr. 2001; 131(3):881S-886S. DOI: 10.1093/jn/131.3.881S. View

20.

Miller V, Mente A, Dehghan M, Rangarajan S, Zhang X, Swaminathan S . Fruit, vegetable, and legume intake, and cardiovascular disease and deaths in 18 countries (PURE): a prospective cohort study. Lancet. 2017; 390(10107):2037-2049. DOI: 10.1016/S0140-6736(17)32253-5. View