544
Letters to the Editor
[10] Safar ME, Thuilliez C, Richard V, Benetos A. Pressure-independent contribution of sodium to large artery structure and function in hypertension. Cardiovasc Res 2000;46:269–76.
[11] Brooks VL, Scrogin KE, McKeogh DF. The interaction of angiotensin II and osmolality in the generation of sympathetic tone during changes in dietary salt intake. An hypothesis. Ann N Y Acad Sci 2001;940:380–94.
http://dx.doi.org/10.1016/j.ijcard.2014.03.083 0167-5273/© 2014 Elsevier Ireland Ltd. All rights reserved.
Relation of socioeconomic status to hypertension occurrence Zhida Wang a,b,1, Xiaofei Yue a,1, Huili Wang a, Cuiping Bao c, Weili Xu a,d, Liming Chen b, Xiuying Qi a,⁎ a
Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China 2011 Collaborative Innovation Center of Tianjin for Medical Epigenetics, the Key Laboratory of Hormones and Development (Ministry of Health), Metabolic Diseases Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, China c Haihe Hospital, Tianjin, China d Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institute and Stockholm University, Stockholm, Sweden b
a r t i c l e
i n f o
Article history: Received 24 January 2014 Accepted 12 March 2014 Available online 20 March 2014 Keywords: Socioeconomic status Prevalence Hypertension
In recent years, along with economic development, nutritional improvements and advances in medical technology, the prevalence of chronic noncommunicable diseases including hypertension, which is the major factor of chronic diseases, continues to increase, especially in developing countries [1]. Extensive research has shown that socioeconomic status (SES) was associated with health outcomes [2]. However, studies on the relationship between SES and hypertension have been sparse, and results are inconclusive and conflicting. Further investigations with large sample are needed. The present study aimed to examine the relationship between SES and hypertension, using data from a large population-based cross-sectional study in Tianjin, China, in July, 2005, which has been described in detail elsewhere [3,4]. After exclusion of 496 subjects aged 15–19 years, 252 students due to no stable income and occupation and 324 participants due to no information of blood pressure status, 7037 participants aged 20 to 79 years were left for the current analysis. Informed consent was obtained from all participants. The ethics committee at the Tianjin Medical University approved the study. Data on age, gender, marital status, SES (including average monthly income, education, and occupation), lifestyle and health status were collected from participants through the interview following a structured questionnaire. Average monthly income was categorized as b1000 Yuan ($121.70 according to the Yuan-Dollar rate in 2005), 1000 to 1999 Yuan ($121.70 to $243.20), and ≥2000 Yuan ($243.30). Education level was categorized as ≤6 years (illiterate and primary school), 7–9 years (junior high school), and N9 years (senior high school and higher). Occupation was classified into four categories, i.e. non-manual work, manual work, retirement and unemployment. In addition, height, weight, blood pressure and blood glucose were measured by trained examiners. Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters. Hypertension was defined as systolic blood pressure ≥140 mm Hg and/or diastolic blood pressure ≥90 mm Hg, having a history of hypertension, or the use of anti-hypertensive ⁎ Corresponding author. Tel./fax: + 86 22 83336727. E-mail address: [email protected] (X. Qi). 1 These two authors contributed equally to this study.
medication. Type 2 diabetes mellitus was assessed as having previous diagnosed diabetes, or fasting plasma glucose ≥7.0 mmol/L or postprandial 2-h plasma glucose ≥11.1 mmol/L according to the WHO criteria (1999). The characteristics of participants between two groups were compared using Chi-square tests for categorical variables and independent-samples t tests for continuous variables. Binary logistic regression analysis was performed to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) of hypertension in relation to SES adjusting for potential confounders. All statistical analyses were performed using SPSS 17.0 (SPSS Inc., Chicago, IL, USA). Among the 7037 participants, 2415 (34.3%) participants had hypertension. Compared to those without hypertension, patients with hypertension were significantly older (52.53 ± 12.57 years vs. 46.08 ± 13.80 years, P b 0.001) and more likely to be married currently (91.4% vs. 88.4%, P b 0.001), have higher BMI (25.09 ± 3.54 kg/m2 vs. 23.87 ± 3.26 kg/m2, P b 0.001) and fasting plasma glucose (5.87 ±1.84 mmol/L vs. 5.25 ± 1.30 mmol/L, P b 0.001), more smokers and type 2 diabetes. Furthermore, there was a significant difference in education, monthly Table 1 Characteristics of the study participants by hypertension. Characteristics Age (years) Male sex Married currentlya Education (years)a ≤6 7–9 N9 Monthly income ()a b1000 1000– 2000– Occupationa Manual work Non-manual work Unemployment Retirement Cigarette smokinga Alcohol drinkinga Type 2 diabetes mellitus Hyperlipidemiaa Body mass index (kg/m2) Fasting plasma glucose (mmol/L) Systolic blood pressure (mm Hg) Diastolic blood pressure (mm Hg)
Non-hypertension (n = 4622)
Hypertension (n = 2415)
46.08 (13.80) 2223 (48.1) 4069 (88.4)
52.53 (12.57) 1184 (49.0) 2197 (91.4)
1225 (26.5) 1736 (37.6) 1656 (35.9)
1046 (43.4) 799 (33.2) 564 (23.4)
1117 (24.3) 1984 (43.1) 1499 (32.6)
872 (36.4) 982 (41.0) 541 (22.6)
P value b 0.001 0.458 b 0.001 b 0.001
b 0.001
b 0.001 1621 (35.2) 1331 (28.9) 827 (17.9) 830 (18.0) 2144 (46.4) 1293 (28.6) 294 (6.4) 2172 (50.6) 23.87 (3.26) 5.25 (1.30) 115.65 (10.92) 75.79 (6.99)
722 (30.0) 426 (17.7) 528 (22.0) 727 (30.3) 1226 (50.8) 703 (30.1) 404 (16.7) 1122 (49.0) 25.09 (3.54) 5.87 (1.84) 141.49 (18.49) 88.93 (10.89)
0.001 0.208 b 0.001 0.205 b 0.001 b 0.001 b 0.001 b 0.001
Data are mean/number (SD or %). a Numbers of subjects with missing values were 27 for marital status, 11 for education, 42 for monthly income, 25 for occupation, 6 for cigarette smoking, 180 for alcohol drinking, and 452 for hyperlipidemia.
Letters to the Editor
545
Table 2 Odds ratios (ORs) with 95% confidence intervals (CIs) of socioeconomic status related to hypertension. Socioeconomic status Education (years) ≤6 7–9 N9 Monthly income () b1000 1000– 2000– Occupation Manual work Non-manual work Unemployment Retirement a b
n
OR (95% CI)
OR (95% CI)a
OR (95% CI)b
1046 799 564
1.00 (Ref.) 0.54 (0.48–0.61) 0.40 (0.35–0.45)
1.00 (Ref.) 0.64 (0.56–0.72) 0.46 (0.40–0.53)
1.00 (Ref.) 0.62 (0.54–0.71) 0.45 (0.38–0.52)
872 982 541
1.00 (Ref.) 0.63 (0.56–0.71) 0.46 (0.40–0.53)
1.00 (Ref.) 0.75 (0.65–0.85) 0.60 (0.52–0.70)
1.00 (Ref.) 0.75 (0.65–0.86) 0.68 (0.58–0.80)
722 426 528 727
1.00 (Ref.) 0.72 (0.62–0.83) 1.43 (1.25–1.65) 1. 97 (1.72–2.25)
1.00 (Ref.) 0.81 (0.70–0.95) 1.38 (1.17–1.61) 1.56 (1.34–1.82)
1.00 (Ref.) 1.05 (0.90–1.24) 1.25 (1.06–1.47) 2.08 (1.78–2.45)
Adjusted for age, sex, marital status, BMI, cigarette smoking, alcohol drinking, type 2 diabetes mellitus and hyperlipidemia. Adjusted for age, sex, marital status, BMI, cigarette smoking, alcohol drinking, type 2 diabetes mellitus, hyperlipidemia and the other socioeconomic factors.
income and occupation between hypertensive individuals and nonhypertensive individuals. However, there was no significant difference between the two groups in terms of gender, alcohol drinking and hyperlipidemia (Table 1). The results of logistic regressions show that education, monthly income and occupation were significantly associated with hypertension (Table 2). After adjusting for age, sex, marital status, BMI, cigarette smoking, alcohol drinking, type 2 diabetes and hyperlipidemia, participants with the highest level of education (N9 years) and monthly income (≥2000 Yuan or $243.30) had a 54% and 40% risk reduction of hypertension compared with those with the lowest level of education (≤6 years) and monthly income (b1000 Yuan or $121.70), respectively. Further adjustment for the other socioeconomic factors, 55% and 32% risk reduction of hypertension in participants with the highest level of education and monthly income respectively, was found, which suggests that income and occupation had little impact on the relationship between education and hypertension. Participants with retirement and unemployment had higher risk of hypertension than those in the manual work group after adjusting for other risk factors, including age. The previous studies on the relationship between SES and hypertension have been conflicting results. Most studies indicated that socioeconomically disadvantaged people were the most vulnerable to having hypertension [5–7]. We also found that participants with higher education or higher monthly income had a lower risk of hypertension. However, no significant associations between education or income and hypertension were found in another study [8]. Very few studies have evaluated the relationship between occupational status and prevalence of hypertension, thus the possible relationship remains to be established. We found that both retired and unemployed people had higher odds of hypertension than manual workers after adjusting for other risk factors including age, which is in agreement with the findings from the study in Nepal [5]. A previous research suggests that the risk of hypertension was not statistically significant higher in blue-collar and low-level white-collar men workers than intermediate- and high-level white-collar men workers, while the risk increased in women workers [9]. Our study also shows that the risk of hypertension in manual workers was not significantly different from that in non-manual workers after adjusting for other risk factors including age. Three possible mechanisms can explain these results. First, people with higher SES can live in safer environments and have more chance accessing to better social support, health care and positive psychology http://dx.doi.org/10.1016/j.ijcard.2014.03.082 0167-5273/© 2014 Elsevier Ireland Ltd. All rights reserved.
conditions, while people of low SES are more likely to be exposed to stressful events, which contribute to higher risk of hypertension [10]. Second, lower SES populations may have less knowledge and risk perception regarding hypertension and have unhealthful behaviors or lifestyle including smoking, inactivity, and higher salt intake, which can increase blood pressure [7]. Third, retired people may be older and lack of physical activity, which are not good for blood pressure control. In conclusion, we found that lower SES (e.g., income, education, occupation) was associated with increased odds of hypertension in the present study. These findings suggest that further population-based longitudinal studies are needed to better understand the causal relationship between SES and hypertension and to develop more effective hypertension prevention strategies for persons with lower income and education. The work was supported by grant (30471490 to X.Q.) from the National Natural Science Foundation of China. References [1] Capingana DP, Magalhaes P, Silva AB, et al. Prevalence of cardiovascular risk factors and socioeconomic level among public-sector workers in Angola. BMC Public Health 2013;13:732. [2] Mackenbach JP, Stirbu I, Roskam AJ, et al. Socioeconomic inequalities in health in 22 European countries. N Engl J Med 2008;358:2468–81. [3] Qi X, Sun J, Wang J, et al. Prevalence and correlates of latent autoimmune diabetes in adults in Tianjin, China: a population-based cross-sectional study. Diabetes Care 2011;34:66–70. [4] Xu W, Xu Z, Jia J, Xie Y, Wang H, Qi X. Detection of prediabetes and undiagnosed type 2 diabetes: a large population-based study. Can J Diab 2012;36:108–13. [5] Thawornchaisit P, de Looze F, Reid CM, Seubsman SA, Sleigh A. Health-risk factors and the prevalence of hypertension: cross-sectional findings from a national cohort of 87,143 Thai Open University students. Glob J Health Sci 2013;5:126–41. [6] Grotto I, Huerta M, Sharabi Y. Hypertension and socioeconomic status. Curr Opin Cardiol 2008;23:335–9. [7] Miyaki K, Song Y, Taneichi S, et al. Socioeconomic status is significantly associated with dietary salt intakes and blood pressure in Japanese workers (J-HOPE study). Int J Environ Res Public Health 2013;10:980–93. [8] Kershaw KN, Diez Roux AV, Carnethon M, et al. Geographic variation in hypertension prevalence among blacks and whites: the multi-ethnic study of atherosclerosis. Am J Hypertens 2010;23:46–53. [9] Palta P, Page G, Piferi RL, et al. Evaluation of a mindfulness-based intervention program to decrease blood pressure in low-income African-American older adults. J Urban Health 2012;89:308–16. [10] Ploubidis GB, Mathenge W, De Stavola B, Grundy E, Foster A, Kuper H. Socioeconomic position and later life prevalence of hypertension, diabetes and visual impairment in Nakuru, Kenya. Int J Public Health 2013;58:133–41.
Letters to the Editor
[10] Safar ME, Thuilliez C, Richard V, Benetos A. Pressure-independent contribution of sodium to large artery structure and function in hypertension. Cardiovasc Res 2000;46:269–76.
[11] Brooks VL, Scrogin KE, McKeogh DF. The interaction of angiotensin II and osmolality in the generation of sympathetic tone during changes in dietary salt intake. An hypothesis. Ann N Y Acad Sci 2001;940:380–94.
http://dx.doi.org/10.1016/j.ijcard.2014.03.083 0167-5273/© 2014 Elsevier Ireland Ltd. All rights reserved.
Relation of socioeconomic status to hypertension occurrence Zhida Wang a,b,1, Xiaofei Yue a,1, Huili Wang a, Cuiping Bao c, Weili Xu a,d, Liming Chen b, Xiuying Qi a,⁎ a
Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China 2011 Collaborative Innovation Center of Tianjin for Medical Epigenetics, the Key Laboratory of Hormones and Development (Ministry of Health), Metabolic Diseases Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, China c Haihe Hospital, Tianjin, China d Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institute and Stockholm University, Stockholm, Sweden b
a r t i c l e
i n f o
Article history: Received 24 January 2014 Accepted 12 March 2014 Available online 20 March 2014 Keywords: Socioeconomic status Prevalence Hypertension
In recent years, along with economic development, nutritional improvements and advances in medical technology, the prevalence of chronic noncommunicable diseases including hypertension, which is the major factor of chronic diseases, continues to increase, especially in developing countries [1]. Extensive research has shown that socioeconomic status (SES) was associated with health outcomes [2]. However, studies on the relationship between SES and hypertension have been sparse, and results are inconclusive and conflicting. Further investigations with large sample are needed. The present study aimed to examine the relationship between SES and hypertension, using data from a large population-based cross-sectional study in Tianjin, China, in July, 2005, which has been described in detail elsewhere [3,4]. After exclusion of 496 subjects aged 15–19 years, 252 students due to no stable income and occupation and 324 participants due to no information of blood pressure status, 7037 participants aged 20 to 79 years were left for the current analysis. Informed consent was obtained from all participants. The ethics committee at the Tianjin Medical University approved the study. Data on age, gender, marital status, SES (including average monthly income, education, and occupation), lifestyle and health status were collected from participants through the interview following a structured questionnaire. Average monthly income was categorized as b1000 Yuan ($121.70 according to the Yuan-Dollar rate in 2005), 1000 to 1999 Yuan ($121.70 to $243.20), and ≥2000 Yuan ($243.30). Education level was categorized as ≤6 years (illiterate and primary school), 7–9 years (junior high school), and N9 years (senior high school and higher). Occupation was classified into four categories, i.e. non-manual work, manual work, retirement and unemployment. In addition, height, weight, blood pressure and blood glucose were measured by trained examiners. Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters. Hypertension was defined as systolic blood pressure ≥140 mm Hg and/or diastolic blood pressure ≥90 mm Hg, having a history of hypertension, or the use of anti-hypertensive ⁎ Corresponding author. Tel./fax: + 86 22 83336727. E-mail address: [email protected] (X. Qi). 1 These two authors contributed equally to this study.
medication. Type 2 diabetes mellitus was assessed as having previous diagnosed diabetes, or fasting plasma glucose ≥7.0 mmol/L or postprandial 2-h plasma glucose ≥11.1 mmol/L according to the WHO criteria (1999). The characteristics of participants between two groups were compared using Chi-square tests for categorical variables and independent-samples t tests for continuous variables. Binary logistic regression analysis was performed to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) of hypertension in relation to SES adjusting for potential confounders. All statistical analyses were performed using SPSS 17.0 (SPSS Inc., Chicago, IL, USA). Among the 7037 participants, 2415 (34.3%) participants had hypertension. Compared to those without hypertension, patients with hypertension were significantly older (52.53 ± 12.57 years vs. 46.08 ± 13.80 years, P b 0.001) and more likely to be married currently (91.4% vs. 88.4%, P b 0.001), have higher BMI (25.09 ± 3.54 kg/m2 vs. 23.87 ± 3.26 kg/m2, P b 0.001) and fasting plasma glucose (5.87 ±1.84 mmol/L vs. 5.25 ± 1.30 mmol/L, P b 0.001), more smokers and type 2 diabetes. Furthermore, there was a significant difference in education, monthly Table 1 Characteristics of the study participants by hypertension. Characteristics Age (years) Male sex Married currentlya Education (years)a ≤6 7–9 N9 Monthly income ()a b1000 1000– 2000– Occupationa Manual work Non-manual work Unemployment Retirement Cigarette smokinga Alcohol drinkinga Type 2 diabetes mellitus Hyperlipidemiaa Body mass index (kg/m2) Fasting plasma glucose (mmol/L) Systolic blood pressure (mm Hg) Diastolic blood pressure (mm Hg)
Non-hypertension (n = 4622)
Hypertension (n = 2415)
46.08 (13.80) 2223 (48.1) 4069 (88.4)
52.53 (12.57) 1184 (49.0) 2197 (91.4)
1225 (26.5) 1736 (37.6) 1656 (35.9)
1046 (43.4) 799 (33.2) 564 (23.4)
1117 (24.3) 1984 (43.1) 1499 (32.6)
872 (36.4) 982 (41.0) 541 (22.6)
P value b 0.001 0.458 b 0.001 b 0.001
b 0.001
b 0.001 1621 (35.2) 1331 (28.9) 827 (17.9) 830 (18.0) 2144 (46.4) 1293 (28.6) 294 (6.4) 2172 (50.6) 23.87 (3.26) 5.25 (1.30) 115.65 (10.92) 75.79 (6.99)
722 (30.0) 426 (17.7) 528 (22.0) 727 (30.3) 1226 (50.8) 703 (30.1) 404 (16.7) 1122 (49.0) 25.09 (3.54) 5.87 (1.84) 141.49 (18.49) 88.93 (10.89)
0.001 0.208 b 0.001 0.205 b 0.001 b 0.001 b 0.001 b 0.001
Data are mean/number (SD or %). a Numbers of subjects with missing values were 27 for marital status, 11 for education, 42 for monthly income, 25 for occupation, 6 for cigarette smoking, 180 for alcohol drinking, and 452 for hyperlipidemia.
Letters to the Editor
545
Table 2 Odds ratios (ORs) with 95% confidence intervals (CIs) of socioeconomic status related to hypertension. Socioeconomic status Education (years) ≤6 7–9 N9 Monthly income () b1000 1000– 2000– Occupation Manual work Non-manual work Unemployment Retirement a b
n
OR (95% CI)
OR (95% CI)a
OR (95% CI)b
1046 799 564
1.00 (Ref.) 0.54 (0.48–0.61) 0.40 (0.35–0.45)
1.00 (Ref.) 0.64 (0.56–0.72) 0.46 (0.40–0.53)
1.00 (Ref.) 0.62 (0.54–0.71) 0.45 (0.38–0.52)
872 982 541
1.00 (Ref.) 0.63 (0.56–0.71) 0.46 (0.40–0.53)
1.00 (Ref.) 0.75 (0.65–0.85) 0.60 (0.52–0.70)
1.00 (Ref.) 0.75 (0.65–0.86) 0.68 (0.58–0.80)
722 426 528 727
1.00 (Ref.) 0.72 (0.62–0.83) 1.43 (1.25–1.65) 1. 97 (1.72–2.25)
1.00 (Ref.) 0.81 (0.70–0.95) 1.38 (1.17–1.61) 1.56 (1.34–1.82)
1.00 (Ref.) 1.05 (0.90–1.24) 1.25 (1.06–1.47) 2.08 (1.78–2.45)
Adjusted for age, sex, marital status, BMI, cigarette smoking, alcohol drinking, type 2 diabetes mellitus and hyperlipidemia. Adjusted for age, sex, marital status, BMI, cigarette smoking, alcohol drinking, type 2 diabetes mellitus, hyperlipidemia and the other socioeconomic factors.
income and occupation between hypertensive individuals and nonhypertensive individuals. However, there was no significant difference between the two groups in terms of gender, alcohol drinking and hyperlipidemia (Table 1). The results of logistic regressions show that education, monthly income and occupation were significantly associated with hypertension (Table 2). After adjusting for age, sex, marital status, BMI, cigarette smoking, alcohol drinking, type 2 diabetes and hyperlipidemia, participants with the highest level of education (N9 years) and monthly income (≥2000 Yuan or $243.30) had a 54% and 40% risk reduction of hypertension compared with those with the lowest level of education (≤6 years) and monthly income (b1000 Yuan or $121.70), respectively. Further adjustment for the other socioeconomic factors, 55% and 32% risk reduction of hypertension in participants with the highest level of education and monthly income respectively, was found, which suggests that income and occupation had little impact on the relationship between education and hypertension. Participants with retirement and unemployment had higher risk of hypertension than those in the manual work group after adjusting for other risk factors, including age. The previous studies on the relationship between SES and hypertension have been conflicting results. Most studies indicated that socioeconomically disadvantaged people were the most vulnerable to having hypertension [5–7]. We also found that participants with higher education or higher monthly income had a lower risk of hypertension. However, no significant associations between education or income and hypertension were found in another study [8]. Very few studies have evaluated the relationship between occupational status and prevalence of hypertension, thus the possible relationship remains to be established. We found that both retired and unemployed people had higher odds of hypertension than manual workers after adjusting for other risk factors including age, which is in agreement with the findings from the study in Nepal [5]. A previous research suggests that the risk of hypertension was not statistically significant higher in blue-collar and low-level white-collar men workers than intermediate- and high-level white-collar men workers, while the risk increased in women workers [9]. Our study also shows that the risk of hypertension in manual workers was not significantly different from that in non-manual workers after adjusting for other risk factors including age. Three possible mechanisms can explain these results. First, people with higher SES can live in safer environments and have more chance accessing to better social support, health care and positive psychology http://dx.doi.org/10.1016/j.ijcard.2014.03.082 0167-5273/© 2014 Elsevier Ireland Ltd. All rights reserved.
conditions, while people of low SES are more likely to be exposed to stressful events, which contribute to higher risk of hypertension [10]. Second, lower SES populations may have less knowledge and risk perception regarding hypertension and have unhealthful behaviors or lifestyle including smoking, inactivity, and higher salt intake, which can increase blood pressure [7]. Third, retired people may be older and lack of physical activity, which are not good for blood pressure control. In conclusion, we found that lower SES (e.g., income, education, occupation) was associated with increased odds of hypertension in the present study. These findings suggest that further population-based longitudinal studies are needed to better understand the causal relationship between SES and hypertension and to develop more effective hypertension prevention strategies for persons with lower income and education. The work was supported by grant (30471490 to X.Q.) from the National Natural Science Foundation of China. References [1] Capingana DP, Magalhaes P, Silva AB, et al. Prevalence of cardiovascular risk factors and socioeconomic level among public-sector workers in Angola. BMC Public Health 2013;13:732. [2] Mackenbach JP, Stirbu I, Roskam AJ, et al. Socioeconomic inequalities in health in 22 European countries. N Engl J Med 2008;358:2468–81. [3] Qi X, Sun J, Wang J, et al. Prevalence and correlates of latent autoimmune diabetes in adults in Tianjin, China: a population-based cross-sectional study. Diabetes Care 2011;34:66–70. [4] Xu W, Xu Z, Jia J, Xie Y, Wang H, Qi X. Detection of prediabetes and undiagnosed type 2 diabetes: a large population-based study. Can J Diab 2012;36:108–13. [5] Thawornchaisit P, de Looze F, Reid CM, Seubsman SA, Sleigh A. Health-risk factors and the prevalence of hypertension: cross-sectional findings from a national cohort of 87,143 Thai Open University students. Glob J Health Sci 2013;5:126–41. [6] Grotto I, Huerta M, Sharabi Y. Hypertension and socioeconomic status. Curr Opin Cardiol 2008;23:335–9. [7] Miyaki K, Song Y, Taneichi S, et al. Socioeconomic status is significantly associated with dietary salt intakes and blood pressure in Japanese workers (J-HOPE study). Int J Environ Res Public Health 2013;10:980–93. [8] Kershaw KN, Diez Roux AV, Carnethon M, et al. Geographic variation in hypertension prevalence among blacks and whites: the multi-ethnic study of atherosclerosis. Am J Hypertens 2010;23:46–53. [9] Palta P, Page G, Piferi RL, et al. Evaluation of a mindfulness-based intervention program to decrease blood pressure in low-income African-American older adults. J Urban Health 2012;89:308–16. [10] Ploubidis GB, Mathenge W, De Stavola B, Grundy E, Foster A, Kuper H. Socioeconomic position and later life prevalence of hypertension, diabetes and visual impairment in Nakuru, Kenya. Int J Public Health 2013;58:133–41.
