INTRODUCTION

Hypertension is a major risk factor for non-communicable diseases, such as cardiovascular diseases, diabetes, and chronic kidney disease [1]. The prevalence of hypertension increased from 24.5% in 2007 in Korea to 27.2% in 2019 [2], and the rate pISSN 1975-8375 eISSN 2233-4521 of hypertension control is below 50%, indicating the need to manage hypertension risk factors in Korea [3].

Socioeconomic status (SES) is associated with a high prevalence of hypertension [4]. Demographic factors (e.g., age or sex), socioeconomic factors (e.g., income, marital status, education, etc.), and health behavior factors (smoking, drinking, body mass index [BMI], etc.) are well-known risk factors associated with hypertension [5]. Marital status was an important risk factor for hypertension in previous studies, and the results of studies on the relationship between marital status and hypertension were not consistent [6-9]. In studies such as that of Defianna et al. [5], socioeconomic factors and sex differences in marital status were observed to affect hypertension risk.

Many international studies have reported relationships be tween hypertension control and socioeconomic levels, but there are few studies related to hypertension control in Korea, and those studies are limited to research on mortality according to some socioeconomic levels [10]. A previous study found that marital status was not associated with hypertension control [10]. However, the results of Lim [10] were different from those of previous studies [11,12]. The results of previous studies showed that hypertension control was high in married subjects—that is, those living with a spouse [11] regardless of race and age. Shah and Cook [12] reported that people living alone did not have well-controlled hypertension. In some Korean studies, income was associated with all causes of hypertension, increases in cardiovascular mortality and cardiovascular events [13], as well as hypertension diagnoses [14].

Although income was reported to have a large effect on hypertension [13,15-20], few Korean studies have investigated the effect of marriage on hypertension, especially the correlation between marriage and income, have been reported. We need to figure out how the interactions of socio-political issues such as income and marriage affect hypertension. It is also necessary to consider marital status by subdividing it into groups of married, unmarried, separated, divorced, and those with deceased spouses. The purpose of this study was to investigate how the income, marital status, and health behavior factors of male and female over 40 years of age were correlated.

The hypotheses of this study were: (1) the prevalence rate of hypertension is associated with demographic characteristics, income, marital status, and health behaviors, and (2) marital status and income level affect the prevalence of hypertension more than health behavior risk factors.

METHODS

Research Materials and Targets

The data in this study were obtained from the Korean Genome and Epidemiology Study (KoGES; 4851-302), National Institute of Health, Korea Disease Control and Prevention Agency, Korea. The population-based cohorts in the KoGES, including the KoGES Ansan and Ansung study, the KoGES Health Examinee Study, and the KoGES Cardiovascular Disease Association Study, consisted of community-dwellers and male and female participants, aged ≥40 years at baseline recruited from the national health examinee registry. The purpose of the KoGES survey data was to identify lifestyle, diet, and environmental factors in people between the ages of 40 and 69 with chronic diseases in rural and medium sized city populations.

A total of 211 576 participants were collected by the KoGES as the general cohort between 2001 and 2013. Finally, 210 413 were included in this study, excluding 1163 who did not respond to information from demographic characteristics, income, marital status, and health behavior factors.

Hypertension

The definition of hypertension in this study was a measured systolic blood pressure of over 140 mmHg or diastolic blood pressure of 90 mmHg, or when the participant was diagnosed with hypertension by a physician.

Demographic characteristics

The demographic characteristics included sex (male and female) and age (40-44, 45-49, 50-54, 55-59, 60-64 years, and over).

Income level

The income level was investigated as the average monthly income of the family, and was divided into <0.500 million Korean won (KRW), 0.500-0.999 million KRW, <1.000-1.499 million KRW, 1.500-1.999 million KRW, 2.000-2.999 million KRW, 3.000-3.999 million KRW, and ≥6.000 million KRW.

Marital status

The current marital status was categorized by the response to the question “What is your current marital status?” Participants were divided into married, unmarried, separated, divorced, deceased spouse, and other groups.

Health behavior factors

The health behaviors were smoking, drinking, and BMI. Smoking was investigated through the question “Have you ever smoked?” The responses were divided into “no,” “yes (past smoker),” and “yes (current smoker).” Pack-years were classified as 1-9, 10-19, 20-39, 40-59, and 60 or more.” Drinking was investigated through the question “Haven’t you ever consumed alcohol?” The responses were divided into “yes” or “no (past drinking),” and “no (current drinking)”. Total alcohol consumption (g/day) was obtained by the consumption of alcoholic beverages including makgeolli, beer, jeongjong (cheongju), wine, soju, and liquor. The density of ethanol is 0.7893 g/mL, and the alcohol concentration (%) was 6% in Juru makgeolli, 4.5% in beer, 15% in jeongjong (cheongju), 13% in wine, 22% in soju, and 40% in liquor. The alcohol intake was calculated according to the following formula: drinking frequency×one drink×alcohol content (g/drink). The total alcohol intake was classified as 0.05-0.09, 1.00-9.99, 10.00-19.99, and 20.00-29.99 g/day, or more. BMI is a statistical index that uses a person’s weight and height to provide an estimate of body fat and is a value obtained by dividing a person’s weight (kg) by his or her height (m²). A BMI of <18.5 kg/m² was considered underweight, normal was 18.5-22.9 kg/m², pre-obese was 23.0-24.9 kg/m², and stage 1 obesity was 25.0-29.9 kg/m². Stage 2 obesity was classified as a BMI of 30.0-34.9 kg/m², and stage 3 obesity was classified as over 35.0 kg/m² [21].

Statistical Analysis

The prevalence of hypertension was analyzed according to the demographic characteristics, income levels, marital status, and health behavior factors of the study population. Logistic regression was used to analyze the effects of income level, marital status, and health behaviors on hypertension among male and female, adjusting for age, health behavior (smoking, alcohol, and BMI), and nutrition, and the odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. Trend analysis using the likelihood ratio test was conducted in relation to the relationship between demographic characteristics and income level, marital status, and health behaviors of patients with hypertension. All statistical analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA).

Ethics Statement

This study was approved by the Kangwon National University Institutional Review Board (approval No. KWNUIRB-2021-02-002) and performed in accordance with the principles of the Declaration of Helsinki.

RESULTS

The Prevalence of Hypertension According to Demographic Characteristics, Income Level, Marital Status, and Health Behavior Factors

The prevalence of hypertension was 19.28% in the total study population, 16.74% in female and 23.94% in male. Older people showed a higher prevalence of hypertension, with the highest at 29.78% for those aged 65 years or older.

Lower income levels were associated with a higher prevalence of hypertension. The prevalence of hypertension was 12.51% for those with an income of over 6.0 million KRW, while it was 29.67% for those with an income below 0.5 million KRW (male, 18.37% and 32.14%; female, 8.14% and 28.66%, respectively). The prevalence of hypertension was the lowest among unmarried people at 15.04% (male, 21.58%; female, 10.80%), followed by 25.63% for those with a deceased spouse, and 19.79% for separated and 16.81% for divorced individuals (male, 28.41%, 26.92%, and 26.31%; female, 25.43%, 17.13, and 13.69%, respectively).

In terms of health behavior factors, the prevalence of hypertension was 24.40% in past smokers, 20.28% in current smokers, and 24.57% in ≥60 pack-year smokers. The prevalence was 29.12% in the ≥30.00 g/day total alcohol group and 19.26% in the stage 3 obesity group. The prevalence of hypertension was higher in male and the older age, lower-income level, deceased spouse, divorced, presently drinking, higher total alcohol intake (g/day), and stage 3 obesity groups (Table 1).

The Relationship of Demographic Characteristics and Income Levels, Marital Status, and Health Behaviors in People With Hypertension

The OR for hypertension among male was 1.98 (95% CI, 1.60 to 2.45) compared to female. The OR for developing hypertension in people with an income of <0.5 million KRW compared to ≥6.0 million KRW was 1.55 (95% CI, 1.25 to 1.93) in the total population, 1.58 (95% CI, 1.27 to 1.98) in male, and 1.07 (95% CI, 0.35 to 3.28) in female. The risk of hypertension related to marital status was higher for those who were separated, divorced, and those with deceased spouses. The risk of hypertension related to health behaviors was higher among those with past and present smoking, higher pack-years, current drinking, higher total alcohol intake (g/day), and stage 3 obesity. In further tests for trend (likelihood ratio tests), we identified trends in the association with income for male (p for _trend<0.001), female (p for _trend<0.001), and the total population (p for _trend <0.001) (Table 2).

The OR of hypertension was 1.58 (95% CI, 1.30 to 1.96) in the total population, 1.61 (95% CI, 1.28 to 2.01) in male, and 0.93 (95% CI, 0.31 to 2.83) in female with an income of <0.5 million KRW compared to those with ≥6.0 million KRW. The effect of income on hypertension was stronger in male than female. The trends in the association with income for male (p for _trend <0.001), female (p for _trend<0.001), and the total population (p for _trend<0.001) were also identified using Test for trend (likelihood ratio tests) (Table 3).

Regarding the relationship between marital status and hypertension, divorce showed the most strongest association, with 1.30 times (95% CI, 1.04 to 1.62) higher odds of hypertension in the total population and 1.40 times (95% CI, 1.10 to 1.79) in male. However, the marital status of female had a weaker association with hypertension than that of male. In the case of single male, there was a 1.27 times (95% CI, 1.02 to 1.57) higher likelihood of hypertension, but in female, there was no significant correlation with being single, at 0.99 times (95% CI, 0.49 to 1.97). In the association between marital status and hypertension, when a spouse died, the association with hypertension increased in the other spouse (total group: unadjusted OR, 1.48; 95% CI, 1.43 to 1.54), and this association was attenuated (OR, 1.23; 95% CI, 1.18 to 1.28) when adjusted for sex and age. The health behavior variables and nutritional variables did not significantly attenuate this association. Finally, when all sex, age, nutrition, and health behavior variables were adjusted, the OR was 1.29 (95% CI, 0.95 to 1.74). Thus, as a result of adjusting several covariates in the relationship between marital status and high blood pressure, sex and age acted more as confounding variables than the health behavior and nutrition variables (Table 4).

According to the income level and marital status in the total population, hypertension was associated with an income of ≥3.0 million KRW and having a deceased spouse (2.69 times; 95% CI, 1.13 to 6.40). Hypertension was also associated with an income of 1.0 million KRW to <3.0 million KRW and being unmarried (1.58 times; 95% CI, 1.24 to 2.02), and an income of <1.0 million KRW and being separated (4.92 times; 95% CI, 1.50 to 16.19). In male, low income and divorce were most associated with hypertension (1.76 times; 95% CI, 1.01 to 3.08). However, in female, the low-income, married group was most associated with hypertension (1.83 times; 95% CI, 1.71 to 1.97]). Further test for trend (likelihood ratio tests) showed trends in the association between income and marital status, for male (p for _trend<0.001), female (p for _trend<0.001), and the total population (p for _trend<0.001) (Table 5).

DISCUSSION

The key result of this study is that the association between income level and marital status and hypertension was stronger in male than in female. Divorce was most influential in the overall population and for male, whereas female was less strongly affected by marriage. The association between marital status and hypertension was strongest in the lower-income group. Low-income groups showed the strongest overall impact of separation on hypertension, whereas divorce in male, and being married in female were most strongly associated with hypertension.

The Prevalence Rate of Hypertension

The countries with the lowest prevalence of hypertension in female are Switzerland (17%), Peru (18%), Canada (20%), and Taiwan and Spain (21%), whereas, for male, it is Eritrea (22%) and Peru (23%), and Bangladesh and Canada (24%) [22]. In this study, the prevalence of hypertension in female in Korea was 16.74% in female and 23.94% in male.

Income Level and Hypertension

Currently, studies on the relationship between income level and hypertension [13,19,20,23] and the relationship between marital status and hypertension [5-9] are being conducted. In this study, the risk of hypertension was found to be higher in the low-income group, and the effect was stronger in male. The relationship between average monthly income and hypertension remained statistically significant even after adjustments for sex, age, health behaviors, and nutritional factors. Similar to this study, in domestic studies, a low-income level was related to the risk of hypertension [15,16], and low SES combined with education and income level was also reported as a risk factor for hypertension [17].

In an international study, for each increase in SES, the risk of hypertension increased by 1.31 times [24]. There was no difference in hypertension between male with higher and lower economic statuses, whereas female showed a 1.6-2.6 times higher risk of hypertension than the low or middle-economic groups [18]. Female with poor SES had a 1.67 times higher risk of hypertension, unlike what was found in this study because of the more sedentary lifestyle and less physically active workers [5].

Low SES may increase sympathetic nervous system activity due to occupational and financial stress, increasing the risk of hypertension [25]. Hypertension risk factors associated with low SES include health care access and the quality of care [26], the lack of early screening and access to medicine [27,28], poor living conditions, social support, emotional stress [29], high salt consumption [30], and a diet low in vegetables [31,32]. For example, the proportion of hypertension increased with decreasing levels of individual patient wealth [19,20]. Other studies showed that the prevalence of hypertension increased with lower SES (education, occupation, and social environment) [18], as well as education and income [17].

We found that although income was a factor influencing hypertension, lower income was a more important risk factor in male. The income level was an important determinant of cardiovascular mortality in the treatment of hypertension [13], suggesting the need to establish policies suitable for health equity to prevent health inequalities in low-income groups in Korea.

Marital Status and Hypertension

Single male have a higher risk of hypertension than married male, and unmarried female have a lower risk of hypertension than married female; thus, health status differs according to marital status and sex [6]. The prevalence of hypertension was higher in married female or female who were separated, divorced or widowed than in female living together with their spouses, and the prevalence of hypertension differed according to marital status and sex [8]. Economic difficulties and low SES, as well as divorce or a deceased spouse, were important risk factors for hypertension in female [5].

The marital status that most strongly affected hypertension was divorce. No Korean studies have investigated the risk of hypertension according to marital status. However, some studies have reported results different from those in this study, finding that the risk of hypertension was 1.76 times higher [33] in unmarried and single groups and 2.34 times higher [24] in married individuals.

In this study, divorce had a strong association with high blood pressure in male. However, in female, marital status did not show a statistically significant association with hypertension. The results of this study are different from the reported effects of marital status and sex on hypertension. In a study on the effect of male marital status on hypertension, unmarried male had a high risk of hypertension [6,9]. A study on the effect of female’s marital status on hypertension found that separation, divorce, and a deceased spouse affected hypertension in female [8]. In some studies, divorced female had a higher risk of hypertension [6].

The Combined Effect of Income Level and Marital Status on Hypertension

When looking at the combined effect of income level and marital status, low income (<1.0 million KRW) and separation had the greatest impact on hypertension in the entire population, whereas low-income and divorce in male and low-income and married status in female were most associated with hypertension. Similar to our study, a previous study found a higher incidence of hypertension in male with a prior spouse than in unmarried and married male, with additional effects of employment status and educational background [7]. In an Indonesian study, female were economically highly dependent on male, and being divorced and poor were found to be important risk factors for hypertension in female, unlike our study, in which being poor and married were identified as risk factors for hypertension in female [5]. In this study, as a result of confirming the interaction effect that combines income and marital status as socioeconomic factors affecting high blood pressure, in male, lower income levels were associated with stronger the interactions between income, marital status, and hypertension. In the future, based on the results of this study, individualized intervention studies for each group with low socioeconomic factors are needed to prevent health inequality from occurring.

Metabolic risk factors such as high systolic blood pressure, high lactate dehydrogenase and cholesterol levels, and high BMI have historically been viewed as problems in high-income settings, but now a trend toward increased exposure to these metabolic risk factors is also being seen in middle-income and low-income settings [34]. Some reports have stated that health behavior factors affected hypertension [34-39], but it is necessary to understand the magnitude of the influence of health behavioral variables on the relationship between socioeconomic factors and hypertension. Singh et al. [24] suggested that female, unmarried people, young people, and highly educated people avoid all kinds of addictions, including tobacco and alcohol, which are risk factors for hypertension. SES is an important determinant of health status and the outcome of various diseases, and low SES, which contributes to chronic stress such as discrimination, crime, noise, and other risk factors affects the prevalence of health problems [40]. In our study, marriage in female and divorce in male with low-income levels were also found to be important risk factors for hypertension. Economic hardship and poor marital status may contribute to chronic stress and affect hypertension. Prior studies have reported that health behavior factors affected hypertension [34-39]. Smoking and drinking in male have been correlated with poor control of hypertension [38], and in another study, smoking [35,36], drinking, and BMI were associated with the presence of hypertension [37,39].

However, it is necessary to understand the difference between marital status and sex in how health behavior variables affect the relationship between socioeconomic factors and hypertension. Health care policies should be established that consider health equity, including socioeconomic factors such as marital status and income level. A limitation of this study is that the variables related to the prevalence of hypertension used in this study were self-reported, which may have led to under-reporting or over-reporting due to recall errors affecting the subject’s questionnaire entry process. Despite these limitations, it is meaningful that this study used long-term cohort data from a large group of ≥210 000 people for 10 years, and the risk of developing hypertension varied depending on demographic characteristics such as sex, age, income level, and marital status. In addition, this study is makes a meaningful contribution by confirming the interaction effect of combined income and marital status with socioeconomic factors affecting hypertension. In this study, ORs were calculated through various analyses to identify influencing factors such as income, marriage, and sex. In the future, it will be necessary to develop a customized intervention program according to sex with adequate consideration of income level and marital status.

DATA AVAILABILITY

Data in this study were from the Korean Genome and Epidemiology Study (KoGES; 4851-302), National Research Institute of Health, Centers for Disease Control and Prevention, Ministry for Health and Welfare, Republic of Korea.

Notes

CONFLICT OF INTEREST

The authors have no conflicts of interest associated with the material presented in this paper.

FUNDING

This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant No. HI19C1320). This study was supported by 2016 Research Grant from Kangwon National University (No. 520160280). This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1B03035890).

ACKNOWLEDGEMENTS

Notes

AUTHOR CONTRIBUTIONS

Conceptualization: Son M. Data curation: Son M. Formal analysis: Son M. Funding acquisition: Son M. Methodology: Son M, Heo YJ. Project administration: Son M. Visualization: Son M, Heo YJ. Writing – original draft: Son M, Heo YJ. Writing – review & editing: Son M, Heo YJ, Hyun HJ, Kwak HJ.

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Figure & Data

References

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