The cluster sampling method was adopted to collect the medical history, laboratory examinations and physical examinations for the permanent residents of Zunyi City, Guizhou Province. Taking education level as a key socioeconomic status (SES) indicator, multivariate logistic regression analysis was used to evaluate the risk factors of high-risk groups with different education levels.

Results

Among the included 4149 subjects, 494 were in the high-risk group and 3655 were in the non-high-risk group. The proportion of the high-risk population with education level ≥ high secondary school (8.7%) was significantly higher than that of the low-risk population. After adjusting for age, gender, and BMI, the OR of those with education leve l ≥ high secondary school was 2.8 (95% CI 1.9-4.2), which was significantly higher than those with education level of illiterate/primary school. In the model adjusted for all confounding factors, compared with illiterate/primary school, people with education level ≥ high secondary school were more likely to be at high risk of stroke (OR 3.0, 95% CI 1.9-4.6).

Conclusion

Education level ≥ high secondary school is an independent influencing factor for the high-risk population of stroke in Zunyi, which may be related to smoking and lipid metabolism abnormalities of people with high education level. Key interventions for high-risk populations with high education levels may have positive significance in reducing the incidence of stroke.

Introduction

Stroke is a clinical event in which acute cerebral circulation disorder rapidly leads to localized or diffuse brain function defect, and is a common and frequently occurring disease of the nervous system (Huxley et al., 2014). As is known to all, stroke has become the second leading cause of death in the world, and also the main cause of death and disability in China (M. Zhou et al., 2016). Statistics show that the incidence of stroke in middle-income countries is increasing rapidly at a rate of 100%, resulting in a continuous increase in medical expenses related to stroke (Meschia et al., 2014; Reddy et al., 2018) . According to the global burden of disease study in 2013, potential intervenable risk factors lead to more than 90% of the burden of stroke, and more than 75% of the burden of stroke can be reduced by controlling risk factors (Feigin et al., 2016; Pandian et al., 2018). Exploring the risk factors of stroke to find effective treatment measures may provide part of strategies so as to prevent more than 80% of strokes (Meschia et al., 2014). Therefore, early detection of individuals at high risk of stroke is the key to its prevention and treatment, whereas how to understand and master the exposure of risk factors in the local population continues to be changing.

Socioeconomic status (SES) is considered to be a predictor of stroke (Kerr et al., 2011; Kumar et al., 2015). Studies have shown that socioeconomic status is associated with stroke incidence, mortality, functional outcome, and risk of recurrence (Addo et al., 2012; Arrich, Lalouschek, & Müllner, 2005; Cox, McKevitt, Rudd, & Wolfe, 2006; Li et al., 2008). People with low SES experience a higher risk of stroke recurrence and mortality than people with high SES (J. Chen et al., 2019; G. Zhou et al., 2006). When discussing the impact of SES on stroke, the most commonly used evaluation indicators include education, occupation, income, medical insurance, property ownership, or wealth (Marshall et al., 2015). A study on the European population shows that the mortality of stroke is related to education level (Avendaño et al., 2004). Compared with people with higher education levels, the risk of death from stroke of men and women with lower education levels increases by 26% and 28% respectively.

However, previous studies have mostly focused on the relationship between SES and stroke incidence, mortality, functional outcome, and risk of recurrence. But there is no report based on SES-related research in high-risk populations of stroke to determine the of SES-related factors in high-risk populations of stroke which could help to find high-risk population of stroke, and to propose some prevention strategies to provide reference for the health management. Here, the author aims to explore the potential association between the high-risk population of stroke and education level in Guizhou, China.

Materials and Methods

Research objects

The cluster sampling method was used to select 7546 residents who participated in stroke screening in Zunyi City, Guizhou Province in 2015 as the research objects. Inclusion criteria: age ≥ 40 years old; permanent residents of Zunyi City, Guizhou Province, China; clear-minded and no speech communication barriers; willing to participate in this study and sign an informed consent form. Exclusion criteria: severe vision, hearing abnormalities and communication difficulties; combined with mental illness, cognitive-behavioral abnormalities; combined with severe physical diseases; incomplete clinical data. The final sample size of this study was 4149 cases, aged from 40 to 98 (61.13 ± 10.96) years, including 1630 males and 2519 females (Figure 1).This study was approved by the Ethics Committee of Affiliated Hospital of Zunyi Medical University (Date: 17.02.2016, No. 089), prior to initiation of the study.

Details are in the caption following the image — **Figure 1**
Open in figure viewer PowerPoint

*Flow chart of patient selection.*

Research methods

A cross-sectional survey method was used to collect and evaluate the basic information (social demographic characteristics, daily behavior and life, past medical history, family history) of the included research objects. Meanwhile, physical examinations and laboratory examinations were carried out on residents who were assessed as high-risk groups of stroke. The personnel participating in the investigation had received strict training and passed the assessment. All measuring instruments had been rigorously calibrated. During the screening process, quality control personnel were assigned to perform quality control.

Laboratory examination

The subjects were required to fast for more than 12 hours, and 5 ml of cubital venous blood was collected in the morning. The levels of fasting blood glucose (FBG), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) were detected within 6 hours. The detection instrument was OLYMPUS AU5400 automatic biochemical analyzer and supporting reagents.

Screening criteria

The “8+2” stroke risk elements were used to assess the risk stratification of patients, of which “8” includes the following 8 aspects: 1) Hypertension: systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg without the use of hypertensive drugs, or have been diagnosed as hypertension by the community and above hospitals; 2) diabetes; 3) atrial fibrillation or valvular heart disease; 4) smoking history (continuous or cumulative smoking for more than 6 months); 5) dyslipidemia: TG ≥ 2.26 mmol/L, or TC ≥ 6.22 mmol/L, or LDL-C ≥ 4.14 mmol/L, or HDL-C ≤ 1.04 mmol/L; 6) lack of exercise: exercise frequency < 3 times/week, time < 30 mins/time, duration <1 year; 7) obviously overweight or obese: BMI of 24~28 kg/m2 is regarded as overweight, ≥ 28 kg/m2 as obesity; 8) family history of stroke. “2” refers to a past history of stroke and transient ischemic attack (TIA).

The high-risk population was defined as those with three or more of the “8” stroke risk factors, or a history of TIA, or a history of stroke. And the non-high-risk group was defined as meeting less than 3 elements. Total 494 were in the high-risk group and 3655 were in the non-high-risk group.

Statistical Analysis

The SPSS 23.0 software was utilized for statistical analysis. The measurement data was expressed as mean ± standard deviation. Independent sample t-test was used for comparison between two groups, and one-way analysis of variance was used for comparison between multiple groups. χ2 test or Fisher’s exact probability method was adopted for enumeration data. The association of SBP, Smoking, TC, LDL-C and BMI with education level was estimated by odds ratio (OR) and 95% confidence interval (CI) in the logistic regression model. The difference was statistically significant with p < 0.05.

Results

Comparison of clinical data between the high-risk and non-high-risk groups in Zunyi

A total of 4149 subjects were enrolled in this study, including 3655 cases of the high-risk group of stroke and 494 cases of the non-high-risk group (Figure 1). For the high-risk group, the top five exposure rates of risk factors were hypertension (76.5%), obesity (59.5%), smoking history (43.9%), dyslipidemia (35.4%), and lack of exercise (30.8%) (Figure 2 and Table s1).

There was no statistical significance in age, pulse, occupation and marriage status between the high-risk and non-high-risk groups (Table 1). However, the proportion of men in the high-risk group (53.8%) was significantly higher than that of women (46.2%) (p < 0.001). In addition, the BMI of the high-risk group was significantly higher than that of the non-high-risk group, especially those with BMI ≥ 28 kg/m² (p < 0.001). There were significant differences in smoking, drinking, FBG, TG, TC, LDL-C and HDL-C levels between the high-risk and non-high-risk groups (p < 0.001). For socioeconomic status, there was no significant difference in main occupations between the two groups. However, compared with the non-high-risk group, the proportion of the high-risk group in high secondary school and monthly income > 5001 was significantly higher, while the proportion of illiterate/primary school was significantly lower (p < 0.001). Moreover, the proportion of people with an education level ≥ high secondary school in the high-risk group of stroke (8.7%) was significantly higher than that of the non-high-risk group (3.1%) (p < 0.001) (Table 1).

Table 1. Baseline characteristics, between non-high and high-risk populations

Variable	Non-high n=3655	(%)	High n=494	(%)	t /χ²	p value
Age (years)	61.2	11.0	60.8	10.4	0.624	0.5
Categorical age (years)					4.450	0.1
40-59	1551	42.4	205	41.5
60-69	1198	32.8	183	37.0
≥ 70	906	24.8	106	21.5
Gender					50.0	<0.001*
Men	1364	37.3	266	53.8
Women	2291	62.7	228	46.2
BMI (kg/m2)	23.53	3.6	26.5	4.0	17.0	<0.001*
Categorical BMI (kg/m2)					279.6	<0.001*
＜24	2249	61.5	132	26.7
24~28	1062	29.1	212	42.9
≥ 28	344	9.4	150	30.4
SBP	135.0	20.6	144.5	21.8	9.5	<0.001*
DBP	84.4	11.9	89.5	12.3	8.8	<0.001*
Pulse	75.6	9.1	76.0	8.4	1.1	0.3
Smoking					188.1	<0.001*
Never-smoking	115	3.1	10	2.0
Ex-smoking	2905	79.5	267	54.0
Current-smoking	635	17.4	217	43.9
Alcohol drinking					36.743	<0.001*
Large/frequent	3391	92.8	419	84.8
Rarely/None	264	7.2	75	15.2
Laboratory Indicator
FBG (mmol/L)	5.2	1.8	5.9	2.7	8.0	<0.001*
TG (mmol/L)	2.0	1.6	2.5	1.8	6.6	<0.001*
TC (mmol/L)	4.9	1.0	5.0	1.0	3.0	0.002*
LDL-C (mmol/L)	2.3	0.9	2.5	0.8	4.9	<0.001*
HDL-C (mmol/L)	1.5	0.6	1.3	0.6	6.2	<0.001*
Socioeconomic status
Educational level					41.3	<0.001*
Illiterate/Primary school	2799	76.6	339	68.6
Secondary school	743	20.3	112	22.7
≥ High secondary school	113	3.1	43	8.7
Main occupation					7.2	0.07
Officer/teacher	61	1.7	14	2.8
Business/Mechanic	127	3.5	24	4.9
Manual labourer	2890	79.1	370	74.9
Other	577	15.8	86	17.4
Income (¥ /month)					13.7	0.003*
≤ 1000	1784	48.8	249	50.4
1001-3000	1042	28.5	114	23.1
3001-5000	507	13.9	66	13.4
＞5001	322	8.8	65	13.2
Social network and support and mental status
Marital status					0.3	0.8
Married	3315	90.7	452	91.5
Never married/Divorced	33	0.9	4	0.8
Widowed	307	8.4	38	7.7

Note: systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting blood glucose (FBG), triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C), body mass index (BMI).
* p < 0.05, The difference was statistically significant.

Logistic regression analysis of influencing factors in the high-risk population of stroke

According to education level and monthly income, the OR values of the high-risk group under different adjustment models for baseline factors were compared (Table 2). In the univariate model, the OR of those with education level ≥ high secondary school was 3.1 (95% CI 2.2-4.5), which was significantly higher than those with education level of illiterate/primary school. After adjusting for age, gender, and BMI, the OR of those with an education level ≥ high secondary school was 2.8 (95% CI 1.9-4.2), which was significantly higher than those with education level of illiterate/primary school. In the model adjusted for all factors (Model 4), compared with illiterate/primary school, education level ≥ high secondary school would increase the high risk of stroke (OR 3.0, 95% CI 1.9-4.6).

Table 2. Odds ratio for population with different levels of education and income

Variable	Model 1	p value	Model 2	p value	Model 3	p value	Model 4	p value
Educational level
Illiterate/Primary school	1.0		1.0		1.0		1.0
Secondary school	1.2 (1.0-1. 6)	0.06	1.2 (1.0-1.6)	0.1	1.3(1.0-1. 7)	0.07	1.3 (1.0-1.7)	0.04*
≥ High secondary school	3.1 (2.2-4.5)	<0.001*	2.8 (1.9-4.2)	<0.001*	3.0 (2.0-4.6)	<0.001*	3.0(1.9-4.6)	<0.001*
Income (¥ /month)
≤ 1000	1.0		1.0		1.0		1.0
1001-3000	0.8(0.6-0.9)	0.04*	0.8 (0.70-1.1)	0.2	0.9 (0.7-1.2)	0.4	0.8 (0.6-1.1)	0.2
3001-5000	0.9 (0.7-1.2)	0.6	0.9 (0.6-1.2)	0.4	1.0(0.7-1.3)	0.8	0.90 (0.6-1.2)	0.5
＞5001	1.4 (1.1-1.9)	0.02*	1.3 (0.9-1.8)	0.1	1.4 (1.0-2.0)	0.05	1.3 (0.9-1.9)	0.1

Model 1: A univariate modeling.Model 2: Adjusted for age, gender and body mass index(BMI).Model 3: Adjusted for age, gender, BMI, smoking and drinking. Model 4: Adjusted for age, gender, BMI, smoking, drinking, systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting blood glucose (FBG), triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C). * p < 0.05, The difference was statistically significant.

In the univariate model, the OR of persons with a monthly income of ¥1001-3000 was 0.8 (95%CI 0.6-1.0), which was significantly lower than that of persons with monthly income ≤ ¥1000; while the OR of persons with monthly income > ¥5001 was 1.4 (95% CI 1.1-1.9), significantly higher than those with monthly income ≤ ¥ 1000. In the model adjusted for all factors, there was no significant change in the ratio of people with different monthly incomes in the high-risk group of stroke (Table 2).

The relationship between education level and SBP, smoking, TC, LDL-C, and BMI

Among men and women, multiple logistic regression analysis showed that compared with those in the group of illiterate/primary school, those with education level ≥ high secondary school had the most significant association with smoking (OR 2.2, 95% CI 1.1-4.3), while the group of secondary school had the most significant association with TC ≥ 6.22 mmol/L (OR 0.26, 95% CI 0.07-0.6) and BMI ≥ 28 kg/m² (Table 3). Women with education levels of secondary school and ≥ high secondary school were more inclined to smoke, while no similar situation was observed in male smokers (Table 3).

Table 3. ORs for SBP, Smoking, TC, LDL-C, and BMI associated with education levels

SBP	OR^**	95% CI	p value	OR^*	95% CI	p value	OR_#	95% CI	p value
Variable		Total			Men			Women
Illiterate/Primary school	1.0			1.0			1.0
Secondary school	0.8	0.52-1.3	0.4	0.8	0.5-1.4	0.5	1.0	0.4-2.2	0.9
≥ High secondary school	1.2	0.6-2.4	0.6	1.0	0.5-2.2	0.9	2.4	0.4-14.2	0.3
Smoking
Illiterate/Primary school	1.0			1.0			1.0
Secondary school	1.5	0.9-2.3	0.09	0.6	0.3-1.2	0.1	1.6	1.03-2.4	0.04^*
≥ High secondary school	2.2	1.1-4.3	0.02^*	0.6	0.3-1.4	0.3	2.3	1.2-4.5	0.01^*
TC
Illiterate/Primary school	1.0			1.0			1.0
Secondary school	0.26	0.07-0.6	0.004^*	0.3	0.09-1.3	0.1	0.1	0.02-1.0	0.04^*
≥High secondary school	0.96	0. 4-2.4	0.90	1.7	0.6-5.0	0.3	NA
LDL-C
Illiterate/Primary school	1.0			1.0			1.0
Secondary school	0.3	0.07-1.4	0.1	0.5	0.05-4.6	0.6	0.4	0.04-2.7	0.3
≥ High secondary school	NA		1.0			1.0	NA
BMI
Illiterate/Primary school	1.0			1.0			1.0
Secondary school	0.6	0.3-1.0	0.03^*	0.5	0.3-1.0	0.07	0.7	0.3-1.6	0.3
≥ High secondary school	0.6	0.3-1.3	0.2	0.8	0.4-1.8	0.6	NA

The cut-points for systolic blood pressure (SBP) ≥ 140 mm/Hg; for total cholesterol (TC) ≥ 6.22 mmol/L; for low-density lipoprotein cholesterol (LDL-C) ≥ 4.14 mmol/L; for body mass index (BMI) ≥ 28 kg/m₂. ^**OR adjusted for age and gender. #OR adjusted for age. ^*p < 0.05, The difference was statistically significant.

Characteristics of the high-risk population by education level

For the high-risk population, the exposure rates of dyslipidemia (51.2%), stroke history (60.5%) and family history of stroke (32.6%) were significantly higher in those with education level ≥ high secondary school than those with education level of illiterate/ primary school (Figure 3 and Table s2). Besides, there were significant differences in age, gender, SBP, smoking, TC and LDL-C among the high-risk population with different education levels (Table 4). Among these factors, as the level of education increased, the age distribution of the high-risk population showed a downward trend (p < 0.001). In the high-risk population, the proportion of males (83.7%) with education level ≥ high secondary school was significantly higher than that of women (16.3%), while the proportion of females with education level of illiterate/primary school (55.5%) was significantly higher than men (44.5%). The proportion of current smoking (60.5%) among those with an education level≥high secondary school was significantly higher than those with illiterate/primary school (39.5%) (p = 0.008). Although the TC and LDL-C levels of people from the illiterate/primary school and ≥ high secondary school groups were not significantly different, they were still significantly higher than those of the secondary school group (p < 0.001 for TC and p = 0.04 for LDL-C) (Table 4).

Table 4. Baseline characteristics, by education level, among high-risk population

Variable	Education level Illiterate/Primary		Secondary school		≥ High secondary school		F	p value
	n=339	%	n=112	%	n=43	%
Age (years)	63.2	9.8	55.3	9.4	56.7	10.6	32.0	<0.001*
Categorical age (years)							53.7	<0.001*
40-59	104	30.7	73	65.2	28	65.1
60-69	145	42.8	29	25.9	9	20.9
≥ 70	90	26.5	10	8.9	6	14.0
Gender							39.8	<0.001*
Men	151	44.5	79	70.5	36	83.7
Women	188	55.5	33	29.5	7	16.3
Categorical BMI (kg/m2)							11.5	0.02*
＜24	98	28.9	27	24.1	7	16.3
24~28	129	38.1	58	51.8	25	58.1
≥ 28	112	33.0	27	24.1	11	25.6
SBP	146.7	22.7	138.8	19.3	141.8	17.9	5.9	0.003*
DBP	89.7	12.5	89.2	11.9	88.6	11.8	0.20	0.8*
Pulse	76.3	8.2	76.0	8.7	74.0	9.2	1.5	0.2*
Smoking							9.644	0.008*
Never-smoking/Ex-smoking	205	60.5	55	49.1	17	39.5
Current-smoking	134	39.5	57	50.9	26	60.5
Alcohol drinking							4.8	0.09*
Large/frequent	294	86.7	93	83.0	32	74.4
Rarely/None	45	13.3	19	17.0	11	25.6
Laboratory indicator
FBG	6.0	3.0	5.4	1.8	5.9	1.8	2.2	0.1
TG	2.4	1.8	2.4	1.4	3.1	2.5	2.7	0.07
TC	5.1	1.0	4.6	1.0	5.1	1.1	14.4	<0.001*
LDL-C	2.6	0.8	2.3	0.9	2.5	0.7	3.2	0.04*
HDL-C	1.3	0.6	1.3	0.6	1.3	0.6	0.01	1.0

* p < 0.05, The difference was statistically significant.

Distribution of other social factors among the high-risk population of different education levels

In the high-risk group, main occupation, income and marital status were significantly different among the high-risk population with different education levels (Table 5). Among them, the proportion of manual labourers in the illiterate/primary school group was significantly higher than that of the secondary school and ≥ high secondary school group (p = 0.01).

Table 5. Associations of other social factors among high-risk populations with different education level

Socioeconomic status	Education level Illiterate/Primary		Secondary school		≥ High secondary school		F	p value
	n=339	%	n=112	%	n=43	%
Main occupation							179.8	<0.001*
Officer/teacher	0	0	3	2.7	11	25.6
Business/Mechanic	1	0.3	12	10.7	11	25.6
Manual labourer	291	85.8	69	61.6	10	23.3
Other	47	13.9	28	25.0	11	25.6
Income (¥ /month)							162.2	<0.001*
≤ 1000	209	61.7	33	29.5	7	25.6
1001-3000	80	23.6	30	26.8	4	9.3
3001-5000	40	11.8	22	19.6	4	9.3
＞5001	10	2.9	27	24.1	28	65.1
Social network and support and mental status
Marital status							12.8	0.01*
Married	301	88.8	110	98.2	41	95.3
Never married/Divorced	3	0.9	-	-	1	2.3
Widowed	35	10.3	2	1.8	1	2.3

Note: P represents the P value calculated by Fisher’s exact probability method; * p < 0.05, The difference was statistically significant.

Discussion

The impact of SES on stroke risk has become an important public health issue in low- and middle-income countries, including China. This study adopted the classic indicator of education to measure the relationship between SES and the population at high risk of stroke. We firstly compared clinical data between the high-risk and non-high-risk groups in Zunyi. The high-risk groups were higher in following factors: male proportion, smoking, drinking, FBG, TG, TC, LDL-C and HDL-C levels, and education level ≥ high secondary school. So, we assumed that education level may be an independent influencing factor for the high-risk population of stroke in Zunyi. By correlation analysis, the results showed that population with higher education level were associated with higher systolic blood pressure, more smoker, higher TC, higher LDL-C, and higher BMI. Thus, we focused on the relationship between education level and high-risk group. In the model adjusted for all confounding factors, compared with illiterate/primary school, people with education level ≥ high secondary school were more likely to be at high risk of stroke.

Education level ≥ high secondary school may be related to the high risk of stroke in Zunyi

In 2013, the national special survey of cerebrovascular disease epidemiology showed that the most common risk factors for stroke patients in the Chinese population are hypertension (84.2%), smoking (47.6%) and drinking (43.9%) (W. Wang et al., 2017). In the current study, we found that hypertension (76.5%), obesity (59.5%), smoking history (43.9%), dyslipidemia (35.4%) and lack of exercise (30.8%) are the main exposure factors for the high-risk population in Zunyi. From the data collected in the 2017 Global Burden of Disease, Injury, and Risk Factors Study (GBD), the global age-standardized stroke incidence rate dropped from 169.6/100,000 in 1990 to 150.5/100,000, which can be partially explained by active preventive measures and control of risk factors (Avan et al., 2019). A combination of a population-wide strategy and a high-risk population strategy has been adopted for stroke prevention and control in China, to reduce the exposure of the entire population while focusing on reducing or eliminating risk factors for high-risk populations (Lü & Li, 2010). Therefore, in view of the current exposure to stroke risk factors, it is recommended that Zunyi City, Guizhou Province should actively adjust and formulate health education and intervention measures that meet the needs of residents in the region.

The prevalence of stroke is associated with increased levels of all SES indexes, including average family income, education and occupation (Xu, Ah Tse, Yin, Yu, & Griffiths, 2008) . Most studies at home and abroad have shown that stroke is more common (Seo et al., 2014) (Avendano & Glymour, 2008) (Jackson, Jones, & Mishra, 2014) (Xiuyun, Qian, Minjun, Weidong, & Lizhen, 2020), more severe (Arrich et al., 2008), and more likely to die in people with lower income or education (Kerr et al., 2011) (Li et al., 2008), and education level may be a very important predictor of secondary stroke prevention (Liu et al., 2011). However, Avendano et al.(Avendano et al., 2006) found that people with higher education and income after the age of 75 have a higher incidence of stroke. Besides, Wang et al. (S. Wang, Shen, Wu, Chen, & Wang, 2019) also pointed out that education level, income, and occupation are all positively correlated with the risk of ischemic stroke. These studies suggested that factors such as the education level do play an important role in stroke. However, there are no relevant studies on people at high risk for stroke. In this study, we measured the relationship between SES and high-risk population of stroke based on education, and found that the proportion (8.7%) of the high-risk population of stroke with education level ≥ high secondary school was significantly higher than that of the non-high-risk population (3.1%). In the model adjusted for confounding factors, it was also observed that compared with the group of illiterate/primary school, education level ≥ high secondary school may be related to the high risk of stroke in the Zunyi population (OR 2.962, 95% CI 1.915-4.581).

The underlying mechanism of the relationship between SES and stroke is still unclear, and may be related to known risk factors, especially lifestyle, biological factors and cardiovascular risk factors (Arrich et al., 2008; Avendano et al., 2006; Honjo et al., 2015; Jackson et al., 2014; Liu et al., 2011; S. Wang et al., 2019; Xiuyun et al., 2020). Chen et al.（M. Chen et al., 2015) found that there are significant differences in the type and intensity of different types of physical activity (PA) in SES: the occupational activity intensity of people with moderate SES is higher, while people with high SES tend to sit for longer. Kubota et al. proposed that moderate physical activity is the best choice to prevent stroke(Kubota, Iso, Yamagishi, Sawada, & Tsugane, 2017). In this study, the proportion of manual labourer in the high-risk population with education level ≥ high secondary school is significantly lower than that of illiterate/primary school, which may explain, to some extent, why the high-risk group in Zunyi are more inclined to be high-educated. Furthermore, people with higher SES show significantly higher social or life pressure (Aginsky, Constantinou, Delport, & Watson, 2017). In addition, stress can increase the risk of cerebrovascular disease by regulating sympathomimetic activity, affecting blood pressure responsiveness, brain endothelium, coagulation or heart rhythm (Kotlęga, Gołąb-Janowska, Masztalewicz, Ciećwież, & Nowacki, 2016). At the same time, stress-driven dieters tend to consume more alcohol and high-calorie foods than others (Laitinen, Ek, & Sovio, 2002), which may also increase the risk of obesity and cerebrovascular diseases (Zagorsky, Smith, & Biology, 2017). Studies have shown that potential mechanisms of healthy eating patterns that reduce the risk of stroke include helping to control body weight and blood vessels, and improving vascular function, blood lipids and lipoproteins (Dreher, 2018). In addition, people with higher social status are more likely to lack physical exercise or smoke (Avan et al., 2019), both of which are independent risk factors for stroke (Larsson, Burgess, & Michaëlsson, 2019). The current study found that the exposure rates of dyslipidemia (51.2%), smoke history (60.5%) and family history of stroke (32.6%) among the high-risk group with education level ≥ high secondary school were significantly higher than those of illiterate/primary school. Therefore, smoking and an unhealthy diet may be the reason for the higher detection rate of high-risk population with education level ≥ high secondary school in Zunyi.

Intervention to manage the high-risk population with higher education.

For the population at high risk of stroke in Zunyi City, key interventions for groups with a high level of education may have positive significance in reducing the incidence of stroke. Recent studies have shown that BMI, SBP, and smoking behavior largely mediate the protective effect of education on the outcome risk of coronary heart disease, stroke, myocardial infarction, and cardiovascular disease (Carter et al., 2019). Dégano et al. found that BMI can explain 7% to 14% of the association between education and cardiovascular disease (Dégano et al., 2017). Wen et al. (Xiuyun et al., 2020) believed that BMI and age will affect the causal relationship between education and stroke, and the protective effect of education on stroke lies in the BMI (range 24-28) category and age < 60 years. We also found that although there was no significant difference in the proportion of obese people (BMI ≥ 28 kg/m2) among the high-risk group with education level ≥ high secondary school and illiterate/primary school, the proportion of overweight people (BMI 24-28 kg/m2) among those ≥ high secondary school was significantly higher. And the proportion of those with education level ≥ high secondary school who are younger than 60 years was significantly lower than that of the illiterate/primary school group. Both high levels of TC and LDL-C are associated with an increased risk of ischemic stroke (Kilander, Berglund, Boberg, Vessby, & Lithell, 2001). Genetically elevated LDL-C is associated with the risk of ischemic stroke, but not with the risk of hemorrhagic stroke (Hindy et al., 2018). It has been reported that drugs that lower LDL-C can reduce the incidence of ischemic stroke, indicating that there is a causal relationship between lowering LDL-C and preventing ischemic stroke (Hackam & Hegele, 2019). The sex-stratified analysis conducted by Veronesi et al. (Veronesi et al., 2016) found that smoking behavior has a mediating effect on men and women. Kershaw et al. (Kershaw et al., 2013) attributed nearly 27% of the association between education and coronary heart disease to smoking behavior, while 10% and 5% were attributed to obesity and hypertension respectively. Multiple logistic regression analysis in the present study also showed that the level of education was significantly correlated with smoking and TC levels. At the same time, women with education levels of secondary school and ≥high secondary school were more inclined to smoke, but similar situations had not been observed among male smokers. A follow-up study in Japan showed that better adherence to physical activities and exercise programs in the stroke population is significantly correlated with better functional recovery (Gunnes et al., 2019). In addition, compliance with taking statins is an independent protective factor against cerebrovascular events (Wu et al., 2018). Therefore, it is necessary to strengthen publicity on weight, blood lipids and sports management, and change the risk factors through intervention to manage the high-risk population with higher education levels in Zunyi.

Strengths and limitations

The main contribution of this study is to explore the relationship between education level and the high-risk of stroke population in Zunyi. These findings may be helpful for the formulation of intervention measures for people with different education levels in the prevention and control of stroke diseases. However, there are still some limitations in this study. First of all, the included study population is from the same region with small sample size and obvious regional characteristics. Second, this study focuses on the high-risk and non-high-risk populations of stroke, without considering the prognosis or functional recovery of stroke patients. Third, this study has not made statistics on the intervention methods and drug use of people at high risk of stroke. Education, income and occupation respectively reflect different aspects of SES and are not interchangeable (Marshall et al., 2015). However, this study only focuses on the intensity of education, while the joint relationship between the three is ignored. Although education level is recognized as the most stable indicator of SES, previous studies have included health insurance as an indicator of SES and confirmed that the presence of health insurance is correlated with the severity of ischemic stroke (Rey, Faouzi, Huchmand-Zadeh, & Michel, 2011). Larger, multi-center, comprehensive studies are still needed.

Conclusion

In conclusion, this study shows that education level ≥ high secondary school is an independent influencing factor for the high-risk population of stroke in Zunyi, among which smoking and abnormal lipid metabolism may be the reasons for the higher detection rate of high-risk population with education level ≥ high secondary school in Zunyi. Active and focused interventions for high-educated and high-risk populations with smoking and dyslipidemia may reduce the incidence of stroke in these people. Therefore, in addition to active intervention in the traditional risk factors of stroke, disease prevention and control from the perspective of SES may be an important measure to reduce the disease burden of residents in Zunyi.

Ethical statement

Approval for the study was granted by the Ethics Committee of Affiliated Hospital of Zunyi Medical University (Date: 17.02.2016, No. 089), prior to initiation of the study.

Consent to participate

The patients has signed the informed consent.

Acknowledgements

We would like to thank the researchers and study participants for their contributions.

Conflict of interest

There is no conflict of interest.

Funding

None.

Transparency statement

The authors affirm that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.

Authors’ contribution

Yanyan Yu designed the work that led to the submission. Dan Lei and Qiong He drafted and revised the manuscript. Wei Chen approved the final version of this work.

Supplementary table

Table s1. Exposure rates of risk factors among stroke high-risk and non-high-risk populations

Risk factors	Non-high (n=3655)		High (n=494)		p
Hypertension	865	23.7	378	76.5	<0.001
Dyslipidemia	164	4.5	175	35.4	<0.001
DM	104	2.8	116	23.5
AF	2	0.1	1	0.2	0.316
Smoke history	635	17.4	217	43.9	<0.001
Obesity	688	18.8	294	59.5	<0.001
Lack of exercise	556	15.2	152	30.8	<0.001
fhStroke	109	3.0	87	17.6	<0.001
Stroke/TIA	-		134	27.1	<0.001

Table s2. Exposure rates of risk factors, by education level, among high-risk population

Risk factors	Illiterate/Primary		Secondary school		≥ High secondary school		F/χ²	p
Risk factors	n=339	%	n=112	%	n=43	%	F/χ²	p
Hypertension	266	78.5	78	69.6	34	79.1	3.818	0.148
Dyslipidemia	110	32.4	43	38.4	22	51.2	6.400	0.041
DM	85	25.1	20	17.9	11	25.6	2.556	0.279
AF	1	0.3	-	-	-	-	0.754	0.686
Smoke history	134	39.5	57	50.9	26	60.5	9.644	0.008
Obesity	205	60.5	65	58.0	24	55.8	0.475	0.789
Lack of exercise	112	33.0	29	25.9	11	25.6	2.613	0.271
fhStroke	50	14.7	23	20.5	14	32.6	9.195	0.010
Stroke/TIA	97	28.6	28	25.0	9	20.9	1.471	0.479

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Volume7, Issue3

September 2021

Pages 181-191

A cohort study on the relationship between education level and high-risk population of stroke