CLINICAL RESEARCH
Income level is associated with differences in primary and secondary stroke prevention in China
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1
Department of Rehabilitation Medicine, Ningbo No. 2 Hospital, Ningbo, China
2
Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou City, Guangdong Province, China
3
Department of Global Health, Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo City, Zhejiang Province, China
4
Tianjin Cerebral Vascular and Neural Degenerative Disease Key Laboratory, Tianjin Neurosurgery Institute, Tianjin Huanhu Hospital, Tianjin, China
5
University of Sydney School of Health Sciences, Australia
6
Department of Neurosurgery, Ningbo No. 2 Hospital, University of Chinese Academy of Sciences, Ningbo City, Zhejiang Province, China
7
Department of Neurology, Shenzhen Qianhai Shekou Free Trade Zone Hospital, Shenzhen City, Guangdong Province, China
Submission date: 2023-09-18
Final revision date: 2023-12-24
Acceptance date: 2024-01-04
Online publication date: 2024-10-28
Corresponding author
Qiang Li
Department of
Neurosurgery,
Hwa Mei Hospital,
University of Chinese,
Academy of Sciences
Ningbo City,
Zhejiang Province,
China
Xiaojie Wang
Department of Neurology, Shenzhen Qianhai Shekou Free Trade Zone Hospital, No. 128, Seventh Road, Shekou Industrial Zone, She, Shenzhen, China
KEYWORDS
TOPICS
ABSTRACT
Introduction:
The aim of this study was to assess differences in the effects of income level on the primary and secondary prevention of stroke in the Chinese population.
Material and methods:
This was a population-based study using data from a China Kadoorie Biobank survey that began in 2004 in 10 geographical regions. Community residents (n = 512,715) aged 30–79 years were recruited. Stroke was determined by the self-reporting of a doctor’s diagnosis, and participants with a high risk of stroke were identified using the model developed in the Prediction for ASCVD Risk in China study.
Results:
The final numbers of people included in this study were 8,884 with stroke and 218,972 with a high risk of stroke. The participants’ income level was positively associated with high levels of physical activity and the consumption of a healthy diet, but negatively associated with the control of alcohol consumption (all p < 0.05). In addition, positive associations were observed between the control of smoking and the use of antiplatelet and antihypertensive medication for primary prevention (all p < 0.05), but there was a negative association with the control of blood pressure (p < 0.001).
Conclusions:
Low-income individuals were less likely to control smoking and their diet and use preventive medications, while high-income individuals were less likely to control their alcohol consumption and blood pressure. Moreover, medication use was low for both primary and secondary prevention in high-income individuals.
REFERENCES (31)
1.
Chun M, Clarke R, Zhu T, et al. Utility of single versus sequential measurements of risk factors for prediction of stroke in Chinese adults. Sci Rep 2021; 11: 17575.
2.
Hu H, Bi C, Lin T, et al. Sex difference in the association between plasma selenium and first stroke: a community-based nested case-control study. Biol Sex Differ 2021; 12: 39.
3.
Ding Q, Liu S, Yao Y, et al. Global, Regional, and National Burden of Ischemic Stroke, 1990-2019. Neurology 2022; 98: e279-90.
4.
Ma Q, Li R, Wang L, et al. Temporal trend and attributable risk factors of stroke burden in China, 1990-2019: an analysis for the Global Burden of Disease Study 2019. Lancet Public Health 2021; 6: e897-906.
5.
Hong Z, Xu L, Zhou J, et al. The relationship between self-rated economic status and falls among the elderly in Shandong Province, China. Int J Environ Res Public Health 2020; 17: 2150.
6.
Braveman PA, Cubbin C, Egerter S, et al. Socioeconomic status in health research: one size does not fit all. JAMA 2005; 294: 2879-88.
7.
Laaksonen M, Prattala R, Helasoja V, et al. Income and health behaviours. Evidence from monitoring surveys among Finnish adults. J Epidemiol Community Health 2003; 57: 711-7.
8.
Geyer S, Hemström O, Peter R, Vågerö D. Education, income, and occupational class cannot be used interchangeably in social epidemiology. Empirical evidence against a common practice. J Epidemiol Community Health 2006; 60: 804-10.
9.
Ecob R, Smith GD. Income and health: what is the nature of the relationship? Soc Sci Med 1999; 48: 693-705.
10.
O’ Donnell M, Hankey GJ, Rangarajan S, et al. Variations in knowledge, awareness and treatment of hypertension and stroke risk by country income level. Heart 2020; heartjnl-2019-316515.
11.
Andersen KK, Olsen TS. Social inequality by income in short- and long-term cause-specific mortality after stroke. J Stroke Cerebrovasc Dis 2019; 28: 1529-36.
12.
Caprio FZ, Sorond FA. Cerebrovascular disease: primary and secondary stroke prevention. Med Clin North Am 2019; 103: 295-308.
13.
Chen Z, Lee L, Chen J, et al. Cohort profile: the Kadoorie Study of Chronic Disease in China (KSCDC). Int J Epidemiol 2005; 34: 1243-9.
14.
Yang X, Li J, Hu D, et al. Predicting the 10-year risks of atherosclerotic cardiovascular disease in Chinese population: the China-PAR Project (Prediction for ASCVD Risk in China). Circulation 2016; 134: 1430-40.
15.
Darmon N, Drewnowski A. Contribution of food prices and diet cost to socioeconomic disparities in diet quality and health: a systematic review and analysis. Nutr Rev 2015; 73: 643-60.
16.
da Silva IC, Van Hees VT, Ramires VV, et al. Physical activity levels in three Brazilian birth cohorts as assessed with raw triaxial wrist accelerometry. Int J Epidemiol 2014; 43: 1959-68.
17.
Borkoles E, Reynolds N, Ski CF, et al. Relationship between type-D personality, physical activity behaviour and climacteric symptoms. BMC Women’s Health 2015; 15: 18.
18.
Mui Y, Ballard E, Lopatin E, et al. A community-based system dynamics approach suggests solutions for improving healthy food access in a low-income urban environment. PLoS One 2019; 14: e0216985.
19.
Feng Q, Fan S, Wu Y, et al. Adherence to the dietary approaches to stop hypertension diet and risk of stroke: a meta-analysis of prospective studies. Medicine 2018; 97: e12450.
20.
Crespi CM, Ganz PA, Petersen L, et al. Refinement and psychometric evaluation of the impact of cancer scale. J Natl Cancer Inst 2008; 100: 1530-41.
21.
Lewer D, McKee M, Gasparrini A, et al. Socioeconomic position and mortality risk of smoking: evidence from the English Longitudinal Study of Ageing (ELSA). Eur J Public Health 2017; 27: 1068-73.
22.
Lee AJ, Kane S, Herron LM, et al. A tale of two cities: the cost, price-differential and affordability of current and healthy diets in Sydney and Canberra, Australia. Int J Behav Nutr Phys Activity 2020; 17: 80.
23.
Rabat Y, Sibon I, Berthoz S. Implication of problematic substance use in poststroke depression: an hospital-based study. Sci Rep 2021; 11: 13324.
24.
Fernández Ruiz I. Risk factors: alcohol intake, MI, and income level. Nat Rev Cardiol 2015; 12: 682.
25.
Auld MC. Smoking, drinking, and income. J Human Res 2005; XL: 505-18.
26.
Sundell L, Salomaa V, Vartiainen E, et al. Increased stroke risk is related to a binge-drinking habit. Stroke 2008; 39: 3179-84.
27.
Yang W, Kang DW, Ha SY, Lee SH. Drinking patterns and risk of ischemic stroke in middle-aged adults: do beneficial drinking habits indeed exist? Stroke 2021; 52: 164-71.
28.
Wu B, Mao ZF, Rockett IRH, Yue Y. Socioeconomic status and alcohol use among urban and rural residents in China. Substance Use Misuse 2008; 43: 952-66.
29.
Taylor DJ, Lichstein KL, Durrence HH, et al. Epidemiology of insomnia, depression, and anxiety. Sleep 2005; 28: 1457-64.
30.
Toivanen S. Social determinants of stroke as related to stress at work among working women: a literature review. Stroke Res Treat 2012; 2012: 873678.
31.
Chen R, Zhang Y, Yang C, et al. Acute effect of ambient air pollution on stroke mortality in the China air pollution and health effects study. Stroke 2013; 44: 954-60.