Secondary Logo

Journal Logo

Feature Article/Online Only

The Contribution of Material, Behavioral, Psychological, and Social-Relational Factors to Income-Related Disparities in Cardiovascular Risk Among Older Adults

Lee, Chiyoung PhD, RN; Yang, Qing PhD; Im, Eun-Ok PhD, MPH, RN, CNS, FAAN; McConnell, Eleanor Schildwachter PhD, RN; Jung, Sin-Ho PhD; Kim, Hyeoneui PhD, MPH, RN, FAAN

Author Information
The Journal of Cardiovascular Nursing: 7/8 2021 - Volume 36 - Issue 4 - p E38-E50
doi: 10.1097/JCN.0000000000000808


Health disparities disproportionately affecting people of lower socioeconomic position (SEP) has been an ongoing concern in cardiovascular health globally.1 Promising approaches to reduce these disparities are increasingly described in the literature. In particular, researchers argue that understanding various factors underlying disparities is crucial to developing effective strategies to improve health equity.2,3

The conceptual framework of the social determinants of health proposed by the World Health Organization4 is particularly useful to address a range of factors that underlie cardiovascular disparities. The framework explains how SEP affects populations' health through a set of intermediary determinants of health; notably, material, behavioral, psychological, and social-relational factors have been identified as important modifiable intermediary factors.4 Indeed, studies have successfully explained socioeconomic disparities in cardiovascular health using these factors.5–12

Specifically, studies have shown that SEP primarily impacts cardiovascular health through material resources such as financial resources; working and housing conditions; or access to goods, services, and healthcare.5,6 Others argue that health-damaging lifestyle choices such as smoking, physical inactivity, and unhealthy diets (which are found more often in socioeconomically deprived people) are the main contributors to poor cardiovascular health among populations.5–8 Some research emphasizes that psychological or emotional distress such as stress, depression, or anxiety are also unevenly experienced by people of lower SEP, further contributing to social inequalities in cardiovascular health.6,8,9 Finally, social-relational factors such as marital status and living arrangement are important determinants of cardiovascular health10 and considered some of the main underlying mechanisms behind cardiovascular health disparities.11,12 Importantly, interventions addressing material, behavioral, psychological, and social-relational issues help reduce health disparities in low SEP individuals.2,3

Many established models support the additional influence of material, behavioral, psychological, and social-relational factors on cardiovascular health outside the influence of old age.13 Yet, to our knowledge, no Western studies have comprehensively investigated the influence of these intermediary factors on the effects of SEP on cardiovascular outcomes in older adults; existing research investigated only 1 or 2 intermediary factors to explain the mechanisms behind these disparities.14,15 Nursing research in South Korea (hereafter “Korea”) follows a similar pattern. As revealed by a recent review by Lee and Im,16 studies in Korea exploring the mechanisms linking SEP and cardiovascular health largely focus on the working-age population. Many Korean researchers have examined the influence of lifestyle behaviors,17 access to health services,18 depression,19 or suicidal ideation20 on cardiovascular health in older adults. However, they did not consider socioeconomic variation in these factors and its subsequent impact.

Because of the rapidly aging population of Korea, the rate of individuals with or at risk of cardiovascular diseases (CVDs) is increasing.21 A volume of studies has reported that such cardiovascular health conditions are closely related to low SEP among older Korean adults.22–25 In particular, Lee and colleagues revealed that socioeconomic disparities in cardiovascular risk among older adults have persisted from 2008 and have widened recently.25 Understanding the intermediary factors underlying these disparities may help develop effective strategies to improve cardiovascular health equity for older Korean communities.

With this in mind, this study aimed to explore how the material, behavioral, psychological, and social-relational factors to the association between SEP and cardiovascular outcomes among older adults in Korea. Considering that Korea is a rapidly aging society with widening socioeconomic gaps associated with various issues including cardiovascular health disparities, lessons learned from this analysis can provide useful insights into addressing health disparities in countries facing a similar challenge.



This study was based on the Korean National Health and Nutrition Examination Survey (KNHANES) conducted by the Korea Centers for Disease Control and Prevention. The KNHANES provides reliable statistics on the health and nutritional status of the Korean population through nationwide cross-sectional surveys. The target population consists of nationally representative noninstitutionalized civilians in Korea. Each survey year includes a new sample of approximately 10 000 individuals 1 year and older. As of 2007, the KNHANES is an annual rolling survey that uses a stratified, multistage, clustered sampling method to allow for annual analysis of nationally representative sample data. Sampling units were based on geographical area, gender, and age using household registries. The KNHANES is composed of 3 distinct sections: a health interview survey, a health examination survey, and a nutrition survey for dietary assessment. The present study uses the health interview and health examination survey.

Our study population was limited to adults 65 years or older. To explore the underlying factors contributing to cardiovascular health disparities, data collected from January 2013 to December 2017 were pooled; this period reflects a time when income disparities in cardiovascular risk generally persisted and even widened among older adults in Korea.25 We removed individuals with a history of CVD (n = 104) and or missing components of the cardiovascular risk assessment data (n = 397). This selection led to a final sample of 7311 (57.5%, women). This study received institutional review board approval from the institution with which the authors are affiliated.


Cardiovascular Risk

We operationalize the term “cardiovascular health” as the risk of developing CVD over a 10-year period. We determined the individual risk for CVD using a health risk appraisal model for coronary heart disease developed using data from the Korean Heart Study.26 This model was developed using a multivariate Cox proportional hazard model based on retrospective cohort data, in the same way as the Framingham risk assessment instrument.27 The proposed model was later validated through a test of a random representative sample of the Korean population enrolled in the national insurance system.28

The first step in estimating the future risk of CVD was to calculate the summation of categorical values for the following major CVD risk factors: (a) gender, (b) age, (c) total cholesterol, (d) high-density lipoprotein cholesterol, (e) systolic blood pressure, (f) smoking, and (g) diabetes (Supplemental Digital Content 1, This generates a continuous risk score, with lower scores indicating better cardiovascular health. We used the score to identify binary levels of CVD risk, defined as low-level and high-level risk groups. As suggested in the literature,29,30 we defined the high-risk group when the risk score was 90 percentile or higher in each gender, as the proposed model was validated by comparing actual events and prediction of events according to the decile of predicted risk for each gender.

Socioeconomic Position Measures

Income was used as a measure of SEP as it describes important aspects of the health disparities in Korea after the economic crisis in the 1990s. In particular, we used equivalized income, which takes into account the differences in a household's size and composition.31 Equivalized income was divided into quartiles following the criteria for categorizing the income level provided by the annual KNHANES report.

Intermediary Determinants

Material, behavioral, psychological, and social-relational intermediary factors were defined based on the items proposed in Moor et al32 and the availability of the relevant variables in the KNHANES dataset.

Material Factors

Material factors were defined with the variables that assessed home ownership, barriers to accessing healthcare, and having private health insurance. Home ownership was assessed with 3 categories of “1 home,” “more than 1 home,” or “renters.” The presence of barriers to accessing healthcare or private health insurance was each recorded using the binary answers of yes or no. Barriers to accessing healthcare were assessed with a binary question, “Have you encountered any barriers when accessing the healthcare system in the past year for any reason?” Having private health insurance was assessed with a binary question, “Do you have a private health insurance?”

Behavioral Factors

Three types of health behavior were considered: alcohol consumption, regular exercise, and attendance at health check-ups. The item “alcohol consumption” was categorized into 2 levels as moderate (≤1 time/week and ≤2 drinks/1 occasion for women; ≤2 time/week and ≤3 drinks/1 occasion for men) and heavy (≥2 times/week or >2 drinks/1 occasion for women; ≥2 time/week and >3 drinks/1 occasion for men) based on the National Institute on Alcohol Abuse and Alcoholism criteria.33 Regular exercise and attendance at health check-ups took a binary answer of “yes” or “no.” Health check-ups were measured by a single question: “‘Have you had a general health check-up over the past few years?” Lastly, regular physical activity was defined by the fulfillment of “sufficient activity” by the Korean version of the International Physical Activity Questionnaire.34

Psychological Factors

Psychological factors included perceived stress level, anxiety/depression, and suicidal ideation. Perceived level of stress was defined with the variable asking how much stress the participant usually experience. The participants answered using the following 4-point Likert scale response categories: never or rarely, sometimes, a lot of the time, and most or all of the time. Anxiety/depression was assessed using the anxiety-depression subscale from the EuroQol 5-dimensional questionnaire.35 This subscale takes 1 of 3 ordinal scale answers: “I do not feel anxious or depressed,” “I feel somewhat anxious or depressed,” and “I feel very anxious or depressed.” Suicidal ideation was defined with a variable asking “Have you had suicidal thoughts?” which took a binary answer of “yes” or “no.”

Social-Relational Factors

Two social-relational factors were defined using the marital status and living arrangement variables. Marital status was dichotomized as married/partnered or unmarried/unpartnered. For living arrangements, we used the categories as defined in the dataset: living alone or lived with others (1 vs 2 or more people in the household).

Statistical Analysis

Given the importance of the age- and gender-specific analysis in disparity research,36 all analyses were conducted separately by age (65–74 and ≥75 years) and gender groups (men, women). We first described the distribution of the 4 intermediary factors separately by the income levels and examined statistical associations between intermediary factors and income level using the χ2 test. In preliminary analyses, the prevalence and the relative risks (RRs) of high CVD risk were reported by the income levels and the intermediary factors. Generalized linear models for binomial data were used to estimate the RR, and the calculation of RR was adjusted for survey year (2013–2017).

In the main analyses, we used the following steps to assess the contribution of 4 groups of intermediary factors to the explanation of income disparities in cardiovascular risk: (1) The relative index of inequality (RII)37 was calculated to assess the magnitude of the association between income and the outcome variable (ie, binary levels of CVD risk); (2) we estimated the extent of the reduction in the RII adjusted for intermediary factors; and (3) several combinations of intermediary factor subsets were further adjusted to explore their direct and indirect contributions.

To calculate the RII, the population in each income stratum was assigned a Ridit score, calculated based on the midpoint of the range in the cumulative distribution of sample in each stratum.38 This score was then used in generalized linear models, with a logarithmic link function to model RIIs. Values of RII larger than 1 imply that the likelihood of being in a high-CVD risk group is higher in the lower income group. We calculated the RII adjusted for survey year (initial base model), adjusted separately for each intermediary factor group, and finally adjusted for all factors. The percentage reduction in the RII was used to calculate the mediation proportion (attenuation) for each adjustment using Equation 139:

RIIbaseRIImodel with the intermediary factor/RIIbase×100%

The direct contribution was assessed by subtracting the percentage reduction in the RII of a model including all factors except for the given factor, from a model including all factors; this indicates the percentage of contribution that is attributable to the given factor alone. This method has been previously described and recommended,40,41 as the separate analysis (Equation 1) does not reveal the actual contribution of the intermediary factor. The indirect contribution was subsequently calculated by subtracting the direct contribution (of the given factor) from the total contribution of the given factor. More details on this calculation are given in Supplemental Digital Content 2,

All statistical analysis was performed using SAS, version 9.4 (SAS Institute, Cary, North Carolina). Sampling weights, strata, and cluster identifiers were incorporated to produce valid population estimates that accounted for the complex survey design of KNHANES. For the merged data, we calculated an integrated weight in proportion to the total number of primary sample units of each year.

There were approximately less than 13% of cases with missing data points for all variables, and the Little test42 revealed that the data were not missing completely at random (P < .001). Participants with missing data did not differ from participants without missing data on cardiovascular risk or other baseline characteristics (bivariate analyses, P > .060). As data were not missing completely at random but showed no evidence of biased attrition, those data were considered missing at random. Although the multiple imputation technique is a recommended approach to deal with such data, it may not be well suited for the present analysis, which mainly comprises of categorical variables.43 Therefore, the single imputation technique was used to preserve sample size. In particular, missing values on continuous variables were replaced by means of the expectation-maximization method. Missing values on categorical variables were replaced with the most common response category for each variable.


Distribution of Intermediary Factors for Different Income Quartiles by Age and Gender (Table 1)

Groups Aged 65 to 74 Years

The distributions of the material, psychological, and social-relational factors differ significantly across the income levels for both genders (P < .001); those with advantages in these factors were concentrated among the higher income groups. Regarding the behavioral factors, the higher income group had more regular exercisers in both genders (P < .05). The higher income groups tended to have more regular heath check-ups than lower income groups in both genders, but this tendency was statistically significantly only in men (P < .001). No significant difference was observed in alcohol consumption across income levels.

TABLE 1 - Distribution of Intermediary Factors for Different Income Quartiles by Age and Gender
Intermediary Factors Groups Aged 65–74 Years Groups 75 Years or Older
Men Women Men Women
Income Quartilesa (%) Income Quartilesa (%) Income Quartilesa (%) Income Quartilesa (%)
1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4
Material factors
 Home ownership
  1 house 52.3 53.0 54.4 54.3 47.9 55.6 52.1 53.7 46.1 49.9 59.1 59.7 47.5 56.9 55.6 50.1
  >1 house 35.1 34.6 27.6 22.0 43.7 31.4 28.5 18.1 45.9 43.0 26.7 19.4 36.1 28.2 24.0 12.8
  Renters 12.5 12.4 18.0 23.7 8.4 13.0 19.4 28.2 8.0 7.1 14.2 20.8 16.4 14.9 20.4 37.1
   P <.001 <.001 <.001 <.001
 Barriers to accessing healthcare
  No 93.8 93.5 91.4 85.5 91.0 89.3 84.1 79.7 97.2 95.8 93.2 85.5 87.2 85.6 85.7 77.5
  Yes 6.2 6.5 8.6 14.5 9 10.7 15.9 20.3 2.7 4.2 6.8 14.5 12.8 14.4 14.3 22.5
   P <.001 <.001 <.001 .002
 Having private health insurance
  Yes 72.3 62.2 54.8 34.5 69.6 67.8 57.5 39.8 43.6 20.5 18.4 10.3 29.6 13.9 13.2 10.8
  No 27.7 37.8 45.2 65.5 30.4 32.2 42.5 60.2 56.4 79.5 81.6 89.7 70.4 86.1 87.8 89.2
   P <.001 <.001 <.001 <.001
Behavioral factors
 Alcohol consumption
  Moderate 41.8 50.4 49.6 47.4 86.5 88.2 87.2 86.2 37.1 54.4 57.2 61.8 92.1 91.2 90.0 87.3
  Heavy 58.2 49.6 50.4 52.6 13.5 11.8 12.8 13.8 62.9 45.6 42.7 38.2 7.9 8.8 10 12.7
   P .309 .856 <.001 .288
 Regular exercise
  Yes 52.9 44.5 43.2 41.6 38.5 41.5 36.4 28.2 44.6 34.9 33.1 33.3 29.5 24.2 23.1 19.6
  No 47.1 55.5 56.8 58.4 61.5 58.5 63.6 71.8 55.4 65.1 66.9 66.7 70.5 75.8 76.9 80.4
   P .026 <.001 .362 .124
 Attendance at health check-ups
  Yes 84.7 81.0 73.9 68.8 78.7 72.6 71.4 70.1 68.2 62.2 59.6 63.0 37.5 55.4 55.6 50.0
  No 15.3 19 26.1 31.2 21.3 27.4 28.6 29.9 31.8 37.8 40.4 37.0 62.5 44.6 44.4 50.0
   P <.001 .147 .858 .026
Psychological factors
 Perceived stress level
  Never or rarely 30.1 27.8 32.0 30.0 25.1 20.8 20.5 24.4 38.3 46.0 41.1 37.6 42.1 36.9 37.0 37.2
  Sometimes 57.7 58.3 58.3 52.1 53.5 56.2 56.8 46.8 50.1 41.0 48.1 45.7 46.8 41.7 43.1 37.0
  A lot of the time 11.1 12.3 7.5 14.2 15.9 18.8 16.9 23.9 8.1 10.2 7.3 12.9 9.0 18.5 17.4 20.3
  Most or all the time 1.1 1.6 2.3 3.7 5.5 4.3 5.7 6.0 3.5 2.8 3.5 3.8 2.1 2.8 2.5 5.5
   P .030 .020 .643 .068
  Not likely 89.6 90.8 87.9 82.1 86.5 85.3 78.5 74.2 90.5 86.1 86.6 81.6 84.7 87.1 77.4 77.8
  Somewhat likely 9.5 5.8 10.7 16.0 12.6 12.8 18.8 20.8 6.8 12.8 10.8 14.9 12.4 9.9 18.6 16.0
  Very likely 0.9 3.3 1.4 2.0 0.9 2.0 2.7 5.0 2.7 1.1 2.6 3.5 3.0 3.0 4.0 6.2
   P <.001 <.001 .247 .076
 Suicidal ideation
  No 98.5 98.5 94.9 88.0 95.0 93.0 89.6 85.8 91.5 95.8 90.6 86.9 94.0 84.8 90.0 85.7
  Yes 1.5 1.5 5.1 12.0 5 7 10.4 14.2 8.5 4.2 9.4 13.1 6.0 15.2 10.0 14.3
   P <.001 <.001 .142 .080
Social-relational factors
 Marital status
  Married/partnered 97.0 93.5 94.5 82.0 72.1 68.1 65.0 52.8 81.6 82.2 90.8 80.6 22.8 24.2 33.4 25.6
  Unmarried/unpartnered 3.0 6.5 5.5 18.0 27.9 31.9 35 47.2 18.4 17.8 9.2 19.4 77.2 75.8 66.6 74.4
   P <.001 <.001 .021 .035
 Living arrangement
  ≥2 people in the household 96.3 96.4 94.0 82.7 96.0 92.6 83.3 69.7 94.1 96.4 94.3 81.6 93.6 93.5 82.5 51.8
  Living alone 3.7 3.6 6.0 17.3 4 7.4 16.7 30.3 5.9 3.6 5.7 18.4 6.4 6.5 17.5 48.2
   P <.001 <.001 <.001 <.001
aQuartile 1: ≥$1801; quartile 2, $1051–$1800; quartile 3, $601–$1050; and quartile 4, ≤$600.
Bold indicates significance.

Groups 75 Years or Older

All material and social-relational factors differed significantly across income levels in both genders; the percentage of the disadvantaged conditions in material and social-relational factors was significantly higher in the lower income groups (P < .05). Among the behavioral factors, only moderate drinking showed a significant association with income level in men (P < .001), whereas only engaging in health check-ups was associated with income level in women (P = .026). There was no significant difference in the psychological factors across income levels in both genders.

Prevalence/RRs of High CVD Risk According to Different Income Quartiles and Intermediary Factors by Age and Gender (Table 2)

Groups Aged 65 to 74 Years

For both genders, lower income was associated with greater RR for a high CVD risk (P < .01). The RR was 2.96 times higher for men in the lowest income group than for men in the highest. The RR was 1.95 times higher for women in the lowest income group than for women in the highest.

TABLE 2 - Prevalence/Relative Risks of High Cardiovascular Disease Risk According to Different Income Quartiles and Intermediary Factors by Age and Gender
Groups Aged 65–74 Years Groups 75 Years or Older
Men Women Men Women
% RRa (95% CI) % RRa (95% CI) % RRa (95% CI) % RRa (95% CI)
 Quartile 1 (≥$1801) 4.8 1.00 6.7 1.00 2.4 1.00 6.7 1.00
 Quartile 2 ($1051–$1800) 8.1 1.71 (0.85–3.42) 5.4 0.80 (0.39–1.64) 6.3 2.60 (0.55–12.43) 10.0 1.52 (0.67–3.45)
 Quartile 3 ($601–$1050) 10.6 2.19 (1.19–4.03) 8.2 1.23 (0.66– 2.25) 8.1 3.23 (0.76–13.69) 6.6 0.97 (0.40–2.36)
 Quartile 4 (≤$600) 14.3 2.96 (1.64–5.33) 13.0 1.95 (1.11–3.44) 12.3 5.29 (1.24–22.61) 10.2 1.56 (0.59–4.12)
P for χ 2 trend .002 <.001 .008 .286
Intermediary factors
Material factors
 Home ownership
  1 house 10.0 1.00 10.0 1.00 10.5 1.00 8.3 1.00
  >1 house 10.2 0.98 (0.64–1.50) 7.2 0.72 (0.50–1.05) 7.6 0.67 (0.39–1.17) 8.8 1.07 (0.66–1.76)
  Renters 12.2 1.21 (0.83–1.76) 12.6 1.27 (0.90–1.79) 11.0 1.05 (0.62–1.77) 11.3 1.36 (0.93–1.98)
   P .639 .035 .399 .260
 Barriers to accessing healthcare
  No 10.2 1.00 9.4 1.00 8.8 1.00 9.4 1.00
  Yes 12.8 1.25 (0.69–2.26) 11.7 1.22 (0.83–1.78) 18.2 2.12 (1.32–3.40) 8.8 0.93 (0.60–1.45)
   P .457 .271 .003 .743
 Having private health insurance
  Yes 9.7 1.00 6.7 1.00 10.5 1.00 6.4 1.00
  No 11.3 1.19 (0.86–1.64) 13.2 1.96 (1.47–2.61) 9.7 0.95 (0.56–1.60) 9.7 1.53 (0.82–2.88)
   P .412 <.001 .340 .167
Behavioral factors
 Alcohol consumption
  Moderate 10.7 1.00 9.7 1.00 10.9 1.00 9.7 1.00
  Heavy 10.3 0.96 (0.70–1.33) 10.8 1.13 (0.76–1.67) 8.4 0.77 (0.50–1.19) 6.6 0.68 (0.41–1.14)
   P .824 .577 .241 .141
 Regular exercise
  Yes 9.4 1.00 7.5 1.00 8.1 1.00 9.2 1.00
  No 11.3 1.21 (0.88–1.66) 11.0 1.47 (1.06–2.04) 10.7 1.30 (0.85– 2.01) 9.3 1.02 (0.68–1.52)
   P .222 .020 .199 .940
 Attendance at health check-ups
  Yes 9.1 1.00 7.8 1.00 9.1 1.00 7.2 1.00
  No 14.7 1.62 (1.16– 2.26) 14.9 1.91 (1.44–2.53) 11.1 1.21 (0.79– 1.85) 11.5 1.60 (1.13– 2.26)
   P .005 .000 .370 .008
Psychological factors
 Perceived stress level
  Never or rarely 11.9 1.00 10.8 1.00 9.6 1.00 10.6 1.00
  Sometimes 9.9 0.83 (0.58–1.19) 8.9 0.83 (0.59–1.17) 8.3 0.87 (0.54–1.41) 7.7 0.73 (0.49–1.08)
  A lot of the time 7.8 0.66 (0.38–1.13) 10.5 0.97 (0.64– 1.47) 17.7 1.81 (1.08–3.01) 8.7 0.82 (0.51–1.31)
  Most or all the time 18.0 1.51 (0.63–3.64) 12.0 1.12 (0.63–1.99) 7.7 0.85 (0.33– 2.22) 15.7 1.49 (0.80–2.76)
   P .271 .563 .027 .130
  Not likely 9.9 1.00 10.0 1.00 9.7 1.00 9.3 1.00
  Somewhat likely 14.3 1.45 (0.88–2.40) 8.7 0.86 (0.60–1.24) 10.7 1.13 (0.66–1.89) 7.5 0.82 (0.47–1.40)
  Very likely 12.1 1.22 (0.53–2.78) 11.8 1.17 (0.59–2.29) 7.8 0.85 (0.31–2.35) 15.3 1.66 (0.93–2.95)
   P .235 .655 .850 .164
 Suicidal ideation
  No 10.1 1.00 9.3 1.00 9.9 1.00 8.6 1.00
  Yes 16.2 1.60 (0.85–3.08) 13.9 1.48 (1.01–2.19) 9.3 0.94 (0.50–1.74) 13.9 1.62 (1.05–2.47)
   P .166 .050 .857 .027
Social-relational factors
 Marital status
  Married/partnered 1.0 1.00 8.3 1.00 9.2 1.00 7.36 1.00
  Unmarried/unpartnered 18.9 1.97 (1.37–2.83) 12.2 1.47 (1.09–1.97) 13.0 1.41 (0.91–2.17) 10.0 1.36 (0.88– 2.09)
   P <.001 .009 .126 .163
 Living arrangement
  No, ≥2 people in the household 10.0 1.00 8.9 1.00 9.3 1.00 8.2 1.00
  Living alone 14.9 1.47 (1.02–2.14) 13.3 1.49 (1.13–1.97) 13.3 1.40 (0.86–2.27) 11.2 1.36 (0.99–1.89)
   P .044 .005 .151 .056
Bold indicates significance.
Abbreviations: RR, risk ratio; CI, confidence interval.
aAdjusted for survey year (2013–2017).

Among men, the RR was strongly associated with social-relational factors; the RR was 1.97 times greater for those who are unmarried or unpartnered (P < .001) and 1.47 times greater for those who lived alone (P = .044). Those who had not engaged in health check-ups tended to have an increased RR (P = .005). The material factors and the psychological factors did not show any significant associations with RR in men.

For women, disadvantages in the 2 material factors were associated with increased risk; the RR was 1.27 times higher for those with no home ownership (P = .035) than those with 1 house and was 1.96 times higher (P < .001) for those without a private health insurance than those with a private health insurance. Those who had not engaged in regular exercise (P = .02) or health check-ups (P < .001) showed greater RRs. Furthermore, higher RRs were observed for those with disadvantages in all social-relational factors (P < .01). Similar to men, no significant associations were found regarding psychological factors in women.

Groups 75 Years or Older

Similar to the groups aged 65 to 74 years, the RRs tended to increase as the income level decreases, but this trend was statistically significant only for men (P = .008). For men, the lowest income group had 5.29 times greater RRs than the highest income group. In addition, the RRs increased significantly for men with barriers to healthcare access and higher levels of stress (P = .003). The RRs were significantly higher for women who had not engaged in health check-ups (P = .008) or had a suicidal ideation (P = .027). Although not significant, the RRs were higher for those who did not have partners or lived alone.

Percentage Reduction in RII When Adjusted for Intermediary Factors by Age and Gender (Table 3)

Groups Aged 65 to 74 Years

Among men, social-relational factors showed the highest impact on RII. When these factors were adjusted for in the model, RII was reduced from 3.09 to 2.80; that is, RII was reduced by 13.7%. Adjusting for the behavioral factors resulted in a 13.5% reduction in RII. The direct contributions of social-relational and behavioral factors to RIIs were 6.9% and 5.4%, respectively. When all intermediary factors were included, RII declined by 24.4%. Among women, there was a 31.8% reduction in RII when the material factors were adjusted for. The material factors also showed the largest direct contribution to RII (19.3%). Behavioral factors and social-relational factors attenuated RII by 15.2% and 14.0%, respectively. Adjustments for psychological factors reduced RII by only less than 2.0%. When all factors were adjusted for, RII declined by 47.5%.

TABLE 3 - Percentage Reduction in Relative Index of Inequality When Adjusting for Intermediary Factors by Age and Gender
Groups Aged 65–74 Years Groups 75 Years or Older
RIIa (95% CI) % Reduction Direct Contributionsb Indirect Contributionsc RIIa (95% CI) % Reduction Direct Contributionsb Indirect Contributionsc
 Base model 3.09 (1.76–5.43) 4.16 (1.79–9.66)
  +Material factors 3.00 (1.75–5.17) 4.1 −0.1 4.2 3.90 (1.61–9.42) 8.3 6.8 1.5
  +Behavioral factors 2.81 (1.61–4.89) 13.5 5.4 8.1 3.93 (1.68–9.18) 7.4 6.2 1.2
  +Psychological factors 2.90 (1.67–5.03) 9.1 1.7 7.4 4.20 (1.82–9.67) −1.0 −1.3 0.2
  +Social-relational factors 2.80 (2.34–2.39) 13.7 6.9 6.8 4.08 (1.75–9.50) 2.6 2.8 −0.2
  All factors 2.58 (1.48–4.51) 24.4 3.63 (1.52–8.67) 16.8
 Base model 3.30 (1.87–5.80) 1.69 (0.82–3.45)
  +Material factors 2.56 (1.41– 4.66) 31.8 19.3 12.6 1.53 (0.76–3.32) 22.9 13.5 9.3
  +Behavioral factors 2.95 (1.67–5.18) 15.2 6.9 8.3 1.71 (0.83–3.35) −3.5 7.6 −11.1
  +Psychological factors 3.26 (1.84– 5.76) 1.7 0.1 1.6 1.62 (0.79–3.30) 9.2 6.4 2.8
  +Social-relational factors 2.97 (1.66–5.33) 14.0 8.8 5.2 1.47 (0.68–3.18) 32.0 23.3 8.7
  All factors 2.21 (1.19–4.08) 47.5 1.37 (0.63–2.99) 46.4
Abbreviations: RII, relative index of inequality; CI, confidence interval.
aAdjusted for survey year (2013–2017).
bThe direct contribution is calculated by [the percentage reduction in the RII from a model including all factors − the percentage reduction in the RII of a model including all factors except for the given factor]. The sum of direct contributions does not equal the percentage reductions shown by the full model as part of the total percentage reduction in the full model may be shared by multiple factors.
cThe indirect contribution was subsequently calculated by subtracting the direct contribution (of the given factor) from the total contribution of the given factor.

Groups 75 Years or Older

Among men, adjusting for the material factors led to the largest reductions in RII (8.3%), and their direct contribution was 6.8%. The RII dropped by 7.4% when adjusted for behavioral factors. However, adjustments for psychological and social-relational factors led to smaller reductions in RIIs. The RII was reduced by 16.8% when all factors were adjusted for. Among women, the largest reductions in RII were achieved after adjusting for the social-relational factors (32.0%), with the direct contributions of 23.3% reduction. The material factors reduced RII by 22.9%. Considering all intermediary factors, RII declined by 46.4%.


This study shows the presence of income-related disparities in cardiovascular risk among both men and women and demonstrates how a variety of potentially modifiable intermediary factors can influence those disparities across older Koreans. The importance of these factors differs by both gender and age group.

The Importance of Social-Relational Factors in Reducing Disparities

Among men aged 65 to 74 years, social-relational factors (ie, marital status and living arrangement) had an important influence on income disparities in cardiovascular risk. This was expected as all included variables were significantly associated with differentials in CVD risk while showing clear patterns based on income level. Our findings partly agree with other published studies on health disparities. For instance, Stringhini et al14 reported that low social integration (eg, family relations, positive/negative support, and loneliness) accounted for a partial degree of cumulative socioeconomic disparities in CVD mortality among British men older than 50 years. Khalatbari-Soltani et al15 revealed that being unmarried and living alone were both associated with a higher risk of CVD mortality among lower-income men older than 70 years in Australia.

Social-relational factors also had a significant role in income disparities of cardiovascular risk among women 75 years or older. These findings can be attributed to a strong association between social-relational factors and income seen in our analysis. Especially, most individuals who were unpartnered or lived alone were in the lower-income groups. This finding is somewhat intuitive considering the traditional paternal Korean culture where women tend to be subservient to and financially dependent on their husbands.44 Combined with less conjugal social support, such economic instability may negatively impact cardiovascular health management; similar findings have been reported in a previous Korean study.45

Although this study cannot be generalized, the present results help improve the understanding of social relationship's influence on cardiovascular health among disadvantaged older adults. In addition, the findings of this study also offer directions for future research and nursing practice. As suggested by the present results, interventional studies improving social relationships for older men are recommended to reduce health disparities. From clinical perspective, health practitioners should acknowledge the importance of social relations in improving cardiovascular health in the oldest-old women group.

The Importance of Material Factors in Reducing Disparities

Material factors explained the largest proportion of income disparities in cardiovascular risk among men 75 years or older, and this effect comes mainly from the contribution of access to healthcare. This finding extends upon previous Korean studies that showed that inadequate access to care has been associated with apparent disparities in cardiovascular health.46,47 Especially in Korea, many low-income oldest-old adults report inadequate access to their needed healthcare because of skyrocketing healthcare costs, limited noninsured services, and substantial out-of-pocket payments.48 Furthermore, they have not benefitted from the equitable utilization of primary care and medical services as Korean society increasingly suffers residential segregation based on SEP.49 According to previous evidence and the current findings, it is essential to implement targeted interventions or policies to improve access to healthcare in this age group. This is also an important global consideration in countries where social inequality in access to healthcare has been a great concern among the oldest-old population.50

Material factors also contributed to the largest portion of the income disparities in cardiovascular risk among women aged 65 to 74 years, which corroborates the findings of previous studies.6,41,51 Among the material factors analyzed, private health insurance seemed to most significantly affect these disparities. Because of the insufficient coverage provided by public health insurance, many Koreans pay for supplementary insurance to receive diverse benefit packages and customized plan choice in the event of critical illness (eg, stroke or cancer).52 However, inequalities to access or maintain private health insurance have persisted among older adults in Korea owing to high premiums; this is especially prevalent among older women, whose premiums are generally 15% to 40% higher than the men's.52 This disparity may partially explain the present results. Although there are many debates on the role of private health insurance within the Korean healthcare system, the present results reinforce the importance providing affordable coverage to low-income women. Germany successfully addressed this issue with an aging reserve policy.53

The Importance of Behavioral Factors in Reducing Disparities

Behavioral factors (ie, regular exercise and health check-ups) contributed to much of the income disparities in cardiovascular risk for both men and women aged 65 to 74 years. This finding indicates that behavioral interventions can be an effective means to improving cardiovascular outcomes and reduce health disparities. For instance, a social welfare program that included long-term exercise training successfully reduced the prevalence of metabolic syndrome among older Korean adults with low household incomes (average age, 67.5 ± 3.7 years).54 A community-based CVD prevention program aimed at increasing physical activity has demonstrated promising results among low-income older adults with hypertension in Korea (average age, 72.64 ± 8.17 years).55

Simultaneously, it is important to acknowledge that income levels also affect the behavioral factors investigated in this study. Interestingly, it seems that income impacts health check-up attendance, although all Korean adults older than 40 years are equally provided periodic health check-ups and screening programs without copayment. This suggests that greater efforts from multiple sectors should be carefully coordinated to ensure that individuals of all income levels, especially lower-income older adults, access their rightful health-promotion services.54

Limitations and Strengths

This study had some limitations. First, given the limitations of a cross-sectional design, no causal conclusions could be drawn between income and cardiovascular risk. Second, although this study covers a balanced selection of material, behavioral, psychological, and social-relational factors from the main social contexts of the elderly, including a different set of variables in each factor may result in different estimations of contribution. Third, the original variables included in the secondary dataset potentially lacked depth because they were operationally defined by a single survey item or subset of test items. This may have influenced their estimated strengths as intermediary factors for this analysis. For example, psychological factors did not exert the greatest influence, although previous studies have reported the widening socioeconomic disparities in mental illness, especially among older adults in Korea.56,57 Fourth, although missing data were a relatively minor problem as about 87% of the data were present, the single imputation method may have introduced a bias as it does not consider uncertainty among imputed values. Lastly, although the analysis shows that intermediary factors can contribute to RII reduction, our estimates of the effect size may be imprecise; the RIIs estimated in this study show wide confidence intervals, which overlap substantially between the base model and the factor adjusted models. Such observed wide confidence intervals are common in this type of analysis, which reported health disparities using RII.58,59 Therefore, the present interpretations should be with used with caution.

Despite these limitations, the present study used a large sample from a nationwide, population-based dataset to help minimize selection bias. In addition, all analyses were stratified by age and gender; therefore, the results should be useful for developing age- and gender-specific approaches that can effectively control cardiovascular risk among the elderly population across different socioeconomic statuses. Lastly, a better understanding of disparities in cardiovascular risk status could lead to early prevention strategies for preventing future disparities in CVD morbidity and mortality.


This study explored how material, behavioral, psychological, and social-relational factors contribute to disparities in cardiovascular risk among older Korean adults of differing incomes. The present results suggest that multiple factors are important for tackling these disparities and improving population health. In particular, the findings indicate that providing social-relational resources may be a valuable addition to strategies for reducing income disparities in cardiovascular risk among men aged 65 to 74 years and women 75 years or older. In addition, addressing material aspects such as access to healthcare services seems to be crucial when attempting to reduce cardiovascular risk among lower-income men 75 years or older and women aged 65 to 74 years. Lastly, behavioral interventions specifically targeted at lower income groups may also help reduce income disparities in cardiovascular risk for men and women aged 65 to 74 years.

Notably, our findings should be used to inform the current cardiovascular health-related interventions and policies in Korea, which historically emphasize behavioral aspects of health. Furthermore, the evidence presented should be used by healthcare professionals to identify and prioritize better CVD prevention and health promotion methods for older adults.

What’s New and Important

  • This is the first study to quantify the contribution of material, behavioral, psychological, and social-relational factors to cardiovascular risk among older Korean adults of differing SEPs.
  • The present study shows that interventions that improve social and material living conditions in lower-income old individuals can be an effective means of reducing cardiovascular disparities in old age.
  • This study's innovative use of an RII needs to be replicated with careful methodological considerations to confirm the influence of intermediary factors on cardiovascular health.


1. Coke LA. Eliminating cardiovascular health disparities: there has been progress, but there is more to do!J Cardiovasc Nurs. 2018;33(6):506–508. doi:10.1097/JCN.0000000000000529.
2. de Mestral C, Stringhini S. Socioeconomic status and cardiovascular disease: an update. Curr Cardiol Rep. 2017;19(11):115. doi:10.1007/s11886-017-0917-z.
3. Schultz WM, Kelli HM, Lisko JC, et al. Socioeconomic status and cardiovascular outcomes: challenges and interventions. Circulation. 2018;137(20):2166–2178. doi:10.1161/CIRCULATIONAHA.117.029652.
4. World Health Organization. A conceptual framework for action on the social determinants of health. Accessed August 3, 2020.
5. Piccolo RS, Subramanian SV, Pearce N, Florez JC, McKinlay JB. Relative contributions of socioeconomic, local environmental, psychosocial, lifestyle/behavioral, biophysiological, and ancestral factors to racial/ethnic disparities in type 2 diabetes. Diabetes Care. 2016;39(7):1208–1217. doi:10.2337/dc15-2255.
6. Robertson T, Benzeval M, Whitley E, Popham F. The role of material, psychosocial and behavioral factors in mediating the association between socioeconomic position and allostatic load (measured by cardiovascular, metabolic and inflammatory markers). Brain Behav Immun. 2015;45:41–49. doi:10.1016/j.bbi.2014.10.005.
7. Kershaw KN, Droomers M, Robinson WR, et al. Quantifying the contributions of behavioral and biological risk factors to socioeconomic disparities in coronary heart disease incidence: the MORGEN study. Eur J Epidemiol. 2013;28(10):807–814. doi:10.1007/s10654-013-9847.
8. Ruiz JM, Brondolo E. Introduction to the special issue disparities in cardiovascular health: examining the contributions of social and behavioral factors. Health Psychol. 2016;35(4):309–312. doi:10.1037/hea0000364.
9. Lockwood KG, Marsland AL, Matthews KA, Gianaros PJ. Perceived discrimination and cardiovascular health disparities: a multisystem review and health neuroscience perspective. Ann N Y Acad Sci. 2018;1428(1):170–207. doi:10.1111/nyas.13939.
10. Holt-Lunstad J, Smith TB, Layton JB. Social relationships and mortality risk: a meta-analytic review. PLoS Med. 2010;7(7):e1000316. doi:10.1371/journal.pmed.1000316.
11. Ryff CD, Singer BH, Palmersheim KA. Social inequalities in health and well-being: the role of relational and religious protective factors. In: Brim OG, Ryff CD, Kessler RC, eds. How Healthy Are We: A National Study of Well-being at Midlife.  The University Chicago Press; 2004:90–123.
12. Boen C, Yang YC. Social relationships and cardiovascular health: underlying mechanisms, life course processes, and future directions. In: Ronald RW, Sherma Z, eds. Lifestyle in Heart Health and Disease.  Academic Press; 2018:291–296.
13. Institute of Medicine. Examining the Health Disparities Research Plan of the National Institutes of Health: Unfinished Business. Washington, DC: The National Academies Press. 2006. doi:10.17226/11602.
14. Stringhini S, Zaninotto P, Kumari M, Kivimaki M, Lassale C, Batty GD. Socio-economic trajectories and cardiovascular disease mortality in older people: the English longitudinal study of ageing. Int J Epidemiol. 2018;47(1):36–46. doi:10.1093/ije/dyx106.
15. Khalatbari-Soltani S, Stanaway F, Cvejic E, et al. Contribution of psychosocial factors to socioeconomic inequalities in mortality among older Australian men: a population-based cohort study. Int J Equity Health. 2020;19(1):1–12. doi:10.1186/s12939-020-01277-2.
16. Lee CY, Im EO. Socioeconomic disparities in cardiovascular health in South Korea: a systematic review. J Cardiovasc Nurs. 2019;36(1):8–22. doi:10.1097/JCN.0000000000000624.
17. Kim K, Choi S, Hwang SE, et al. Changes in exercise frequency and cardiovascular outcomes in older adults. Eur Heart J. 2020;41(15):1490–1499. doi:10.1093/eurheartj/ehz768.
18. Lee CY. A two-step clustering approach for measuring socioeconomic factors associated with cardiovascular health among older adults in South Korea. Korean J Adult Nurs. 2020;32(6):551–559. doi:10.7475/kjan.2020.32.6.551.
19. Ra JS, Kim HS. Sex-based association between depression and metabolic syndrome in Korean middle-aged and older adults. Osong Public Health Res Perspect. 2017;8(2):130–137. doi:10.24171/j.phrp.2017.8.2.05.
20. Jee SH, Kivimaki M, Kang HC, Park IS, Samet JM, Batty GD. Cardiovascular disease risk factors in relation to suicide mortality in Asia: prospective cohort study of over one million Korean men and women. Eur Heart J. 2011;32(22):2773–2780. doi:10.1093/eurheartj/ehr229.
21. Kim H, Kim S, Han S, et al. Prevalence and incidence of atherosclerotic cardiovascular disease and its risk factors in Korea: a nationwide population-based study. BMC Public Health. 2019;19(1):1112. doi:10.1186/s12889-019-7439-0.
22. Khang YH, Kim HR. Socioeconomic inequality in mortality using 12-year follow-up data from nationally representative surveys in South Korea. Int J Equity Health. 2016;15(1):51. doi:10.1186/s12939-016-0341-9.
23. Hwang J, Lee EY, Lee CG. Measuring socioeconomic inequalities in obesity among Korean adults, 1998–2015. Int J Environ Res Public Health. 2019;16(9):1617. doi:10.3390/ ijerph16091617.
24. Seo SR, Kim SY, Lee SY, et al. The incidence of stroke by socioeconomic status, age, sex, and stroke subtype: a nationwide study in Korea. J Prev Med Public Health. 2014;47(2):104–112. doi:10.3961/jpmph.2014.47.2.104.
25. Lee C, Yang Q, Im EO, McConnell ES, Jung SH, Kim H. A 10-year trend in income disparity of cardiovascular health among older adults in South Korea. SSM Popul Health. 2020;12:100682. doi:10.1016/j.ssmph.2020.100682.
26. Jee SH, Batty GD, Jang Y, et al. The Korean heart study: rationale, objectives, protocol, and preliminary results for a new prospective cohort study of 430,920 men and women. Eur J Prev Cardiol. 2014;21(12):1484–1492. doi:10.1177/2047487313497602.
27. Anderson KM, Odell PM, Wilson PW, Kannel WB. Cardiovascular disease risk profiles. Am Heart J. 1991;121(1 pt 2):293–298. doi:10.1016/0002-8703(91)90861-B.
28. Jee SH, Jang Y, Oh DJ, et al. A coronary heart disease prediction model: the Korean heart study. BMJ Open. 2014;4(5):e005025. doi:10.1136/bmjopen-2014-005025.
29. Kang MY, Hong YC. Crossover effect of spouse weekly working hours on estimated 10-years risk of cardiovascular disease. PloS One. 2017;12(8):e0182010. doi:10.1371/journal.pone.0182010.
30. Lee DW, Hong YC, Min KB, Kim TS, Kim MS, Kang MY. The effect of long working hours on 10-year risk of coronary heart disease and stroke in the Korean population: the Korea National Health and Nutrition Examination Survey (KNHANES), 2007 to 2013. Ann Occup Environ Med. 2016;28(1):64. doi:10.1186/s40557-016-0149-5.
31. Organization for Economic Co-operation and Development. Adjusting household incomes: equivalence scales. Accessed August 2, 2020.
32. Moor I, Spallek J, Richter M. Explaining socioeconomic inequalities in self-rated health: a systematic review of the relative contribution of material, psychosocial and behavioural factors. J Epidemiol Community Health. 2017;71(6):565–575. doi:10.1136/jech-2016-207589.
33. National Institute on Alcohol Abuse and Alcoholism. Helping Patients Who Drink Too Much: A Clinician's Guide. Rockville, MD: National Institute on Alcohol Abuse and Alcoholism; 2007.
34. Oh JY, Yang YJ, Kim BS, Kang JH. Validity and reliability of Korean version of International Physical Activity Questionnaire (IPAQ) short form. J Korean Acad Fam. 2007;28(7):532–541.
35. EuroQol Group. EQ-5D instruments: EQ-5D-5L. 2009. Accessed December 1, 2020.
36. Khang YH, Lynch JW, Kaplan GA. Health inequalities in Korea: age- and sex-specific educational differences in the 10 leading causes of death. Int J Epidemiol. 2004;33(2):299–308. doi:10.1093/ije/dyg244.
37. Moreno-Betancur M, Latouche A, Menvielle G, Kunst AE, Rey G. Relative index of inequality and slope index of inequality: a structured regression framework for estimation. Epidemiology. 2015;26(4):518–527. doi:10.1097/EDE.0000000000000311.
38. Donaldson GW. Ridit scores for analysis and interpretation of ordinal pain data. Eur J Pain. 1998;2(3):221–227. doi:10.1016/S1090-3801(98)90018-0.
39. Khang YH, Lynch JW, Yang S, et al. The contribution of material, psychosocial, and behavioral factors in explaining educational and occupational mortality inequalities in a nationally representative sample of south Koreans: relative and absolute perspectives. Soc Sci Med. 2009;68(5):858–866. doi:10.1016/j.socscimed.2008.12.003.
40. Skalická V, Van Lenthe F, Bambra C, Krokstad S, Mackenbach J. Material, psychosocial, behavioural and biomedical factors in the explanation of relative socio-economic inequalities in mortality: evidence from the HUNT study. Int J Epidemiol. 2009;38(5):1272–1284. doi:10.1093/ije/dyp262.
41. Van Oort FV, van Lenthe FJ, Mackenbach JP. Material, psychosocial, and behavioural factors in the explanation of educational inequalities in mortality in the Netherlands. J Epidemiol Community Health. 2005;59(3):214–220. doi:10.1136/jech.2003.016493.
42. Little RJ. A test of missing completely at random for multivariate data with missing values. J Am Stat Assoc. 1998;83(404):1198–1202.
43. Allison PD. Imputation of categorical variables with PROC MI. 2005. Accessed December 13, 2020.
44. Chun H, Doyal L, Payne S, Il-Cho S, Kim IH. Understanding women, health, and social change: the case of South Korea. Int J Health Serv. 2006;36(3):575–592. doi:10.2190/6GUV-FBBA-W1B5-U79B.
45. Chu SH, Baek JW, Kim ES, et al. Gender differences in hypertension control among older Korean adults: Korean social life, health, and aging project. J Prev Med Public Health. 2015;48(1):38–47. doi:10.3961/jpmph.14.043.
46. Hong JS, Kang HC. Regional differences in treatment frequency and case-fatality rates in Korean patients with acute myocardial infarction using the Korea National Health Insurance Claims Database: findings of a large retrospective cohort study. Medicine. 2014;93(28):e287. doi:10.1097/MD.0000000000000287.
47. Kim Y, Oh J, Jha A. Contribution of hospital mortality variations to socioeconomic disparities in in-hospital mortality. BMJ Qual Saf. 2014;23(9):741–748. doi:10.1136/bmjqs-2013-002744.
48. Park EJ, Kwon JW, Lee EK, Jung YH, Park S. Out-of-pocket medication expenditure burden of elderly Koreans with chronic conditions. Int J Gerontol. 2015;9(3):166–171. doi:10.1016/j.ijge.2014.06.005.
49. Lee SE, Yeon M, Kim CW, Yoon TH, Kim D, Choi J. Neighborhood deprivation and unmet health care needs: a multilevel analysis of older individuals in South Korea. Osong Public Health Res Perspect. 2019;10(5):295–306. doi:10.24171/j.phrp.2019.10.5.06.
50. Almeida APSC, Nunes BP, Duro SMS, Facchini LA. Socioeconomic determinants of access to health services among older adults: a systematic review. Rev Saude Publica. 2017;51:50. doi:10.1590/S1518-8787.2017051006661.
51. Ploubidis GB, DeStavola BL, Grundy E. Health differentials in the older population of England: an empirical comparison of the materialist, lifestyle and psychosocial hypotheses. BMC Public Health. 2011;11:390. doi:10.1186/1471-2458-11-390.
52. Jeon B, Kwon S. Effect of private health insurance on health care utilization in a universal public insurance system: a case of South Korea. Health Policy. 2013;113(1-2):69–76. doi:10.1016/j.healthpol.2013.05.007.
53. Hofmann A, Browne M. One-sided commitment in dynamic insurance contracts: evidence from private health insurance in Germany. J Risk Uncertain. 2013;46(1):81–112. doi:10.1007/s11166-012-9160-6.
54. Choi SJ, Chang JS, Kong ID. Effects of a social welfare program for health promotion on cardiovascular risk factors. J Lifestyle Med. 2015;5(2):76–82. doi:10.15280/jlm.2015.5.2.76.
55. Yang SO, Kim SJ, Lee SH. Effects of a south Korean community-based cardiovascular disease prevention program for low-income elderly with hypertension. J Community Health Nurs. 2016;33(3):154–167. doi:10.1080/07370016.2016.1191872.
56. Kim JL, Kim JM, Choi Y, Lee TH, Park EC. Effect of socioeconomic status on the linkage between suicidal ideation and suicide attempts. Suicide Life Threat Behav. 2016;46(5):588–597. doi:10.1111/sltb.12242.
57. Jang SY, Choi B, Ju EY, et al. Association between restriction of activity related to chronic diseases and suicidal ideation in older adults in Korea. Geriatr Gerontol Int. 2014;14(4):983–988. doi:10.1111/ggi.12202.
58. Hiyoshi A, Fukuda Y, Shipley MJ, Brunner EJ. Health inequalities in Japan: the role of material, psychosocial, social relational and behavioural factors. Soc Sci Med. 2014;104:201–209. doi:10.1016/j.socscimed.2013.12.028.
59. Guarnizo-Herreño CC, Watt RG, Garzón-Orjuela N, Tsakos G. Explaining oral health inequalities in European welfare state regimes: the role of health behaviours. Community Dent Oral Epidemiol. 2019;47(1):40–48. doi:10.1111/cdoe.12420.

aged; cardiovascular diseases; health status disparities; income; Korea

Supplemental Digital Content

Copyright © 2021 The Authors. Published by Wolters Kluwer Health, Inc.