Next Article in Journal
Facets of Impulsivity in Relation to Diet Quality and Physical Activity in Adolescence
Previous Article in Journal
Sensory Processing Sensitivity and Compassion Satisfaction as Risk/Protective Factors from Burnout and Compassion Fatigue in Healthcare and Education Professionals
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJERPH
Volume 18
Issue 2
10.3390/ijerph18020612
ijerph-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Effects of Behavioral Risk Factors and Social-Environmental Factors on Non-Communicable Diseases in South Korea: A National Survey Approach

by
Nam Jeong Jeong
1,
Eunil Park
1,* and
Angel P. del Pobil
1,2,*
1
Department of Interaction Science, College of Computing, Sungkyunkwan University, Seoul 03063, Korea
2
Robotic Intelligence Laboratory, Engineering and Computer Science Deparment, University Jaume-I, 12071 Castellon, Spain
*
Authors to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2021, 18(2), 612; https://doi.org/10.3390/ijerph18020612
Submission received: 2 December 2020 / Revised: 9 January 2021 / Accepted: 9 January 2021 / Published: 12 January 2021
(This article belongs to the Section Digital Health)
Browse Figures
Versions Notes

Abstract

:
Non-communicable diseases (NCDs) are one of the major health threats in the world. Thus, identifying the factors that influence NCDs is crucial to monitor and manage diseases. This study investigates the effects of social-environmental and behavioral risk factors on NCDs as well as the effects of social-environmental factors on behavioral risk factors using an integrated research model. This study used a dataset from the 2017 Korea National Health and Nutrition Examination Survey. After filtering incomplete responses, 5462 valid responses remained. Items including one’s social-environmental factors (household income, education level, and region), behavioral factors (alcohol use, tobacco use, and physical activity), and NCDs histories were used for analyses. To develop a comprehensive index of each factor that allows comparison between different concepts, the researchers assigned scores to indicators of the factors and calculated a ratio of the scores. A series of path analyses were conducted to determine the extent of relationships among NCDs and risk factors. The results showed that social-environmental factors have notable effects on stroke, myocardial infarction, angina, diabetes, and gastric, liver, colon, lung, and thyroid cancers. The results indicate that the effects of social-environmental and behavioral risk factors on NCDs vary across the different types of diseases. The effects of social-environmental factors and behavioral risk factors significantly affected NCDs. However, the effect of social-environmental factors on behavioral risk factors was not supported. Furthermore, social-environmental factors and behavioral risk factors affect NCDs in a similar way. However, the effects of behavioral risk factors were smaller than those of social-environmental factors. The current research suggests taking a comprehensive view of risk factors to further understand the antecedents of NCDs in South Korea.

1. Background

Non-communicable diseases (NCDs) have been highlighted as a significant global health threat. According to the World Health Organization’s (WHO) report, NCDs accounted for approximately 71% of global deaths in 2016. The four major types of NCDs—cardiovascular disease (CVD), cancer, diabetes, and chronic pulmonary disease—are responsible for the majority of deaths by NCDs [1].
The South Korean government reported that over 60% of deaths in 2017 were significantly associated with NCDs [2]. Among them, cancer, CVD, and diabetes were the top three causes of NCD deaths, accounting for 27.6%, 20.8%, and 17.9%, respectively [2].
Prior studies on NCDs indicated that both environmental and behavioral risk factors play critical roles in the development of NCDs [3,4]. The environment can be divided into physical and social dimensions [5]. The social environment includes one’s social belonging, neighborhood, and overall circumstances [5], while it determines several indicators of NCDs (e.g., mortality, incidence, or prevalence) [6].
In the case of behavioral risk factors, the WHO reports that tobacco use, physical inactivity, unhealthy diet, and harmful use of alcohol are four major determinants of NCDs [7]. The WHO indicates that all these behavioral risk factors have considerable effects on NCDs. For instance, tobacco use accounts for nearly 6 million deaths worldwide. Both physical inactivity and harmful use of alcohol account for about 3.2 million and 2.3 million deaths, respectively [3].
In an attempt to reduce the global incidence rate of NCDs, the WHO presented “the Global Action Plan for the Prevention and Control of NCDs” [7]. The action plan provides instructions for managing major behavioral risk issues, which are defined as common modifiable risk factors, through local and national healthcare systems.
NCDs are conceptualized as chronic diseases in South Korea [8]. In addition to their continually growing threat to national health and other medical areas, the government has strengthened healthcare policies to decrease the burden of NCDs [8]. For example, the government expanded the coverage of national insurance plans to include four major disease categories: cancer, heart diseases, cerebrovascular disease, and rare or intractable diseases [9].
However, concerning the fact that the national plans and policies in South Korea generally focus on treating illnesses rather than preventing them, attention to monitoring and preventing NCDs is needed [10,11]. Thus, the current study aims to explore the effects of social-environmental and behavioral risk factors on NCDs in South Korea, such as stroke, myocardial infarction (MI), diabetes, and cancer.

1.1. Social-Environmental Factors

The environment includes not only global environmental changes such as urbanization and the aging population but also the social and physical environments surrounding individuals [12]. Since the 1980s, the relationship between an individual’s social environment and their health has been extensively studied [6]. The importance of social-environmental factors’ effect on the population’s health has been elaborated by epidemiological studies [13,14]. This line of study highlights the role of social-environmental factors such as socioeconomic status and social support as the determinants of health. The studies have discovered that low socioeconomic status leads to poor health outcomes and even raises the possibility of deaths [13]. Social status works as a fundamental cause of disease as it is linked tightly to the resources such as money, knowledge, and social supports [15]. Individuals in higher social status get more benefits from the resources, thus gain positive health outcomes [13,15].
As noted in several previous studies [6,16], the social environment has notable effects on the social standing of individuals and shows a consistent correlation with individual health. This correlation means that an individual’s health status can be predicted if their social standing is known.
Over the years, the relationship between NCDs and social-environmental factors has also been widely addressed. Grotto et al. (2008) argued that lower social status is associated with higher blood pressure, based on 22 research articles on the connection between hypertension and the social environment [17]. Moreover, Dinca-Panaitescu et al. (2011) analyzed the 2005 Canadian Community Health Survey and found strong ties between an individual’s income and prevalence of type II diabetes [18].
Preceding studies on the effects of social-environmental factors on cancer survival also indicated that low-income and poorly educated populations have a lower survival rate than other populations [19,20]. However, a recent study found that the relationship between social-environmental factors and incidence of cancer can vary by cancer type or ethnic group. Based on cancer diagnosis data from California from 1998 to 2002, Yin et al. (2010) found that a more significant number of individual incidence rates of female breast cancer and prostate cancer are triggered by higher social status. While, the relationship between social status and incidence rates of other cancer types, such as colorectal, cervical, and lung cancers, varies across races and ethnic groups [21].
In another example, Robert et al. (2004) found a notable association between social-environmental factors and female breast cancer. They investigated the effects of community-level social status and living areas on developing female breast cancer based on the breast cancer case-control study in Wisconsin between 1988 and 1995 [22]. Four indices of community-level social status, including family income and community education levels, were measured while urban or rural areas categorized the living areas. The results showed that individuals with higher community-level social status and living in urban areas are more likely to be associated with breast cancer than those with lower community-level social status and living in rural areas.
Thus, based on the findings of the previous studies mentioned above on social-environmental factors, this study proposes the following hypothesis:
Hypothesis 1 (H1).
Social-environmental factors will directly affect NCDs.

1.2. Behavioral Risk Factors

1.2.1. Alcohol Use

There are mixed findings on the relationship between alcohol use and NCD risk. Hong et al. (2015) used the dataset of the 2017 Korea National Health and Nutrition Examination Survey (KNHANES) from 2010 to 2012 and found that excessive alcohol consumption is related to a higher risk of diabetes in South Korean males [23]. Xi et al. (2017) claimed that light or moderate alcohol intake decreases the potential risk of CVD mortality by introducing a J-shaped curve phenomenon, based on the United States (US) National Health Interview Surveys from 1997 to 2009 and the US National Death Index Records in 2011 [24].
Meanwhile, based on 156 research articles on alcohol use, Corrao et al. (2004) argued that the potential risk of developing major NCDs, including hypertension and cancer, increases due to moderate alcohol use [25]. Most prior studies also indicated that heavy or binge drinking leads to an increased risk of developing NCDs [25,26].

1.2.2. Tobacco Use

Using the South Korean National Cohort Data from 1992 to 2006, Jee et al. (2010) explored the effects of tobacco use on diabetes incidence and mortality rates in South Korean adults (aged 30–95 years) [27]. Using the Cox proportional hazards model, they found that smoking contributes to increased incidence and mortality rates of diabetes.
Further, several studies identified increased cancer risks due to consistent smoking behavior [28,29,30]. Inoue-Choi and colleagues investigated the dataset of the National Institutes of Health—American Association of Retired Persons Diet and Health Study in 2004 and 2005 conducted by the National Cancer Institute and found that smoking 1–10 cigarettes per day over a lifetime leads to a more than twofold increase in smoking-related cancer risk compared to non-smokers [30]. Thus, based on the findings of previous studies on the effects of smoking behavior, the current study assumed that tobacco use is related to the incidence of NCDs.

1.2.3. Physical Inactivity

The relationship between physical activity and health outcomes has been extensively examined. Morris and Crawford (1958) found a meaningful connection between physical activity at work and coronary heart diseases in the 1950s based on the National Necropsy Survey in England [31]. Specifically, they found that men with physically inactive jobs tend to be more prone to coronary heart disease. Recent research has pointed out that different physical activities significantly affect various health outcomes [32]. Based on the Third National Health and Nutrition Examination Survey dataset in the US, leisure-time physical activities were negatively associated with all-cause mortality. In contrast, occupational, physical activities were positively associated with all-cause mortality in Mexican-American workers [32].
As presented above, both social-environmental and behavioral risk factors have been recognized as significant determinants of health status. Thus, many scholars have attempted to identify the relationship between these factors [33,34,35,36]. However, even though the relationships among the factors have been investigated, the possibility of societal and temporal variations of the relationships underscores the empirical testing on different temporal and spatial contexts [35].
Therefore, based on the findings of previous studies on behavioral risk factors, the current study proposes the following hypotheses:
Hypothesis 2 (H2).
Behavioral risk factors will directly affect NCDs.
Hypothesis 3 (H3).
Social-environmental factors will directly affect behavioral risk factors.
To investigate the hypotheses, we constructed the following research model (Figure 1).

2. Method

2.1. Data Description

We utilized the 2017 dataset of the KNHANES for this study [37,38]. Since 1998, the Korea Centers for Disease Control and Prevention (KCDC) has conducted the KNHANES to monitor the general health and nutritional status. The survey used interviews, examinations, and nutrition questionnaire items, including socio-demographic information, health and biochemical status, health-related behaviors, and dietary habits. Both the interviews and examinations were carried out by professional medical staff. In addition, whole survey procedures were conducted with the respondents’ consent. The anonymized dataset of 2017 KNHANES was published in January 2019 and offers public access.

2.2. Sample

All respondents were South Korean. The sample for the survey was extracted using multi-stage clustering probability sampling based on the 2010 Population and Housing Census, with a sample size of 10,430 [38]. Those who responded to at least one of the interviews were 8128 (77.9%). Of the 8128 responses, 6519 (80.2%) were adults. After filtering the responses (e.g., excluding incomplete responses), 5462 (67.2%) validated responses were obtained and used in the analyses.

2.3. Measurements

2.3.1. Social-Environmental and Behavioral Risk Factors

Three indicators were used to measure each factor. Measurements for social-environmental factors were monthly household income, education level, and region. Alcohol use, tobacco use, and physical activity level were used for measuring behavioral risk factors. Since the KNHANES employed the second version of the Global Physical Activity Questionnaire (GPAQ) as the physical activity measurement item, total physical activity Metabolic Equivalent (MET) was calculated following the GPAQ analysis guide provided by the WHO [39]. Overall, a higher score indicated a higher social-environmental status and positive behaviors, while a lower score indicated the opposite. A detailed summary of the factors is presented in Table 1.

2.3.2. Non-Communicable Diseases

With regard to data availability, NCDs, excluding diabetes, were categorized based on self-reports. Accordingly, those who reported having been diagnosed with MI, angina, stroke, and cancer were categorized as having the corresponding diseases (yes: 0, no: 1). Cancers were first divided into general cancers and female cancers. Gastric, liver, colon, lung, and thyroid cancers composed the cancer model. We considered breast and cervical cancers as exclusive to females and investigated them within the female responses dataset. In the case of diabetes, respondents who have taken oral hypoglycemic drugs or insulin injections or have fasting blood glucose higher than 126 mg/dL were categorized to have diabetes.

2.4. Data Analyses

To develop a comprehensive index of the factors for statistical analyses, each factor’s ratio index was calculated based on the following process.
First, the scores were assigned for each component of both social-environmental and behavioral risk factors. A score of one point was given to the lowest level of each indicator, and a point was added as the level went up. For example, the first quartile (Q1), the lowest level of household income, was assigned a score of one, while the fourth quartile (Q4), the highest level, was assigned a score of four. The scores for the indicators with a binary scale (e.g., region, alcohol use, and tobacco use) were adjusted to the highest scores in each factor. Between the two categories, one that represents undesirable status (e.g., current or former use of alcohol) was allocated a lower score than the rest. The scores for each component are given in Appendix A (Table A1).
Next, we calculated the ratio index by dividing the total score by the perfect score of each factor. If a participant is in the second quartile of income, the score was 2; and if a high school graduate, the score was 3. Whereas if the participant lived in the metropolitan city, the score totaled 4, thus the social-environmental factor index of this person was 0.75. The distributions of the ratio scores are presented in Appendix A (Figure A1).
Each NCD was analyzed based on the research model. The research model examines the effects of social-environmental factors on NCDs (H1) and behavioral risk factors (H3) as well as the effect of behavioral risk factors on NCDs (H2).
A series of path analysis was employed for stroke, MI, angina, diabetes, cancers, breast cancer, and cervical cancer with AMOS 18.0.

3. Results

3.1. Research Model Results

Table 2 shows the results of the research model. Social-environmental factors were significantly related to the prevalence of stroke ( β = 0.101, p < 0.001), MI ( β = 0.058, p < 0.001), angina ( β = 0.077, p < 0.001), diabetes ( β = 0.201, p < 0.001), and cancers ( β = 0.069, p < 0.001), which include gastric, colon, lung, and thyroid cancers. However, there was no significant relationship between social-environmental factors and breast and cervical cancers. Analysis on the second hypothesis showed that behavioral risk factors significantly affect stroke ( β = 0.031, p = 0.045), MI ( β = 0.030, p = 0.046), angina ( β = 0.039, p = 0.009), and diabetes ( β = 0.049, p < 0.001). Finally, the significant negative effect of social-environmental factors on behavioral risk factors was observed in female cancer models ( β = −0.053, p = 0.004), while no significant effect was found in models with a complete dataset ( β = −0.024, p = 0.08).

3.2. Supplemental Analyses

Further, we conducted path analyses to comprehensively examine each factor’s effects by adding age and gender in the analyses. The supplemental analyses indicated that the models’ overall validation effects were decreased. However, there were no notable changes in tendencies.

4. Discussion

The current study investigated the effects of social-environmental and behavioral risk factors on NCDs using the national healthcare survey in South Korea. A series of path analyses were employed to examine the research model developed based on previous healthcare and NCD studies [17,18,19].
As indicated by the results, the effects of social-environmental factors on NCDs were validated. Thus, Hypothesis 1 was supported for stroke, MI, angina, diabetes, and cancers. These results support the previous literature that presents the causal linkage between social contexts and health outcomes [13,14,15]. Furthermore, Hypothesis 2 explored the relationship between behavioral risk factors and NCDs and was validated for stroke, MI, angina, and diabetes. Our findings suggest that social-environmental and behavioral risk factors are significantly correlated with the development of most NCDs, including cardiovascular diseases, diabetes, and several cancers. The results are consistent with the findings of earlier studies that focused on the effects of each risk factor on NCDs [18,21,25,27,32].
Regarding cancer, behavioral risk factors did not have notable effects. Following the findings of earlier studies that addressed the impact of behavioral risk factors on each cancer [25,40], the current study also confirmed the relationship between behavioral risk factors and cancers. For instance, Coups and Ostroff (2005) examined US national survey data and found that the effects of behavioral risk factors differ according to cancer types [40].
Social-environmental factors did not significantly affect behaviors in stroke, MI, angina, diabetes, and cancers models. Considering the strong relationship between social-environmental factors and behaviors suggested by preceding literature [13,14,33,34], the results possibly indicate that social-environmental factors other than income and education would affect the behaviors. Another possible interpretation is that scoring, which was an attempt to develop the comprehensive indices for each factor, might have diluted the intensity of the relationship.
Interestingly, social-environmental factors have a negative effect on behavioral risk factors in female cancer models. This result implies the possibility of the gender effects on the relationship between social-environmental factors and behavioral factors, as the earlier studies indicated [41,42].
From a practical perspective, the current study sheds light on the potentiality of using the national healthcare survey to address several risk factors and NCDs. Moreover, it employs a comprehensive view of social-environmental and behavioral risk factors as antecedents of NCDs. By integrating social-environmental and behavioral risk factors in a unified model, this study adds to discovering the sequential relationships of the factors.
Additionally, as an individual’s health status is one of the most complicated concepts in healthcare and medical areas, our findings can provide a better understanding of the antecedents of NCDs in South Korea. This approach allows us profound knowledge of NCDs since measuring an individual’s health status is a complex task that requires a variety of factors to be concerned [7,43].
Furthermore, as the current study employs the national healthcare survey, the findings can be easily generalized in South Korea. However, because each country has different social, economic, and cultural backgrounds, it is necessary to consider national healthcare policies [7]. Moreover, various risk factors should be considered when investigating NCDs from a national perspective.

5. Conclusions

In conclusion, the effects of social-environmental and behavioral risk factors on NCDs vary across the different types of diseases. The impacts of social-environmental factors on NCDs were significant in most of the NCDs examined but not in female cancers. Behavioral risk factors affect NCDs in similar patterns with social-environmental elements, but the effects were smaller than the previous factors. The relationship between social-environmental and behavioral risk factors was supported only in female cancer, which implies the gender effect. This study suggests including various preceding factors when investigating the national health status, which provides further understanding of the diseases.

6. Limitations and Future Research

Even though the current study provides several notable implications, some limitations remain. First, there may be other dominant factors that have significant effects on the development of NCDs [44]. This study did not adjust for gender, age, and diet, which have shown effects on NCDs in several studies that used larger datasets [45,46,47,48,49]. Nevertheless, some studies included the factors in the analyses with relatively smaller datasets [50,51,52]. Therefore, future studies should consider adjusting for the variables by adopting a larger dataset. Second, the current study examined data from the annual national survey of South Korea. A long-term follow-up time-series survey can provide a better understanding of NCDs from a national perspective. Furthermore, as the original data provide self-reports on the NCDs diagnosis except for diabetes, there is a possibility of inaccuracies in categorizing the diseases due to the recall bias. Next, even though the comprehensive approach can give benefits stated in the discussion section, the process of scoring might have attenuated the effects of each component. Lastly, there is a chance of bias due to the list-wise deletion method used in the data filtering process. Using more robust techniques could improve this issue. Future research should address these limitations to the extent of this stream of research.

Author Contributions

N.J.J. and E.P. proposed the research model and analyzed the datasets. N.J.J. performed the data pre-processing and filtering procedures and wrote the first draft of the manuscript. E.P. and A.P.d.P. revised the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2020R1C1C1004324, NRF-2020R1F1A1048225). The funding bodies had no role in study design, data collection, analysis, interpretation, or writing of this manuscript.

Institutional Review Board Statement

The Ethical Committee and Institutional Review Board of Sungkyunkwan University approved this study (No. 2020-11-012).

Informed Consent Statement

Not applicable.

Data Availability Statement

The data that support the findings of this study are publicly available on the KNHANES website, https://knhanes.cdc.go.kr/knhanes/eng/index.do.

Conflicts of Interest

The authors declare that they have no competing interests.

Abbreviations

The following abbreviations are used in this manuscript:
NCDNon-Communicable Disease
KNHANESKorea National Health and Nutrition Examination Survey
WHOWorld Health Organization
CVDCardiovascular Disease
MIMyocardial Infarction
USUnited States
KCDCKorea Centers for Disease Control and Prevention
GPAQGlobal Physical Activity Questionnaire
METMetabolic Equivalent
GFIGoodness of Fit Index
AGFIAdjusted Goodness of Fit Index
RMSEARoot Mean Square Error of Approximation

Appendix A

Ijerph 18 00612 g0a1 550
Figure A1. The distribution of the ratio scores.
Figure A1. The distribution of the ratio scores.
Ijerph 18 00612 g0a1
Table
Table A1. Scores for each factor.
Table A1. Scores for each factor.
FactorsScoresFactorsScores
Social-environmental factors Behavioral risk factors
 Household income  Alcohol use
  Q11  Current or former use1
  Q22  Never use3
  Q33 Tobacco use
  Q44  Current or former smoker1
 Education level   Non-smoker3
  Elementary school or less1 Physical activity (MET level)
  Middle school2  Low1
  High school3  Moderate2
  University or more4  High3
 Region
  Non-metropolitan city2
  Metropolitan city4

References

  1. World Health Organization. Noncommunicable Diseases Country Profiles 2018; WHO Press: Geneva, Switzerland, 2018; Available online: https://apps.who.int/iris/handle/10665/274512 (accessed on 5 August 2020).
  2. Korean Statistical Information Service. Causes of Death Statistics in 2017. Available online: http://kostat.go.kr/portal/eng/pressReleases/1/index.board?bmode=read&aSeq=371140 (accessed on 5 August 2020).
  3. World Health Organization. Global Status Report on Noncommunicable Diseases 2010: Description of the Global Burden of NCDs, Their Risk Factors and Determinants; WHO Press: Geneva, Switzerland, 2011; Available online: https://www.who.int/nmh/publications/ncd_report2010/en (accessed on 5 August 2020).
  4. Alcántara, C.; Diaz, S.V.; Cosenzo, L.G.; Loucks, E.B.; Penedo, F.J.; Williams, N.J. Social determinants as moderators of the effectiveness of health behavior change interventions: Scientific gaps and opportunities. Health Psychol. Rev. 2020, 14, 132–144. [Google Scholar] [CrossRef] [PubMed]
  5. Yen, I.H.; Syme, S.L. The social environment and health: A discussion of the epidemiologic literature. Annu. Rev. Public Health 1999, 20, 287–308. [Google Scholar] [CrossRef] [PubMed]
  6. Adler, N.E.; Ostrove, J.M. Socioeconomic status and health: What we know and what we don’t. Ann. N. Y. Acad. Sci. 1999, 896, 3–15. [Google Scholar] [CrossRef] [PubMed]
  7. World Health Organization. Global Action Plan for the Prevention and Control of Noncommunicable Diseases 2013–2020; WHO Press: Geneva, Switzerland, 2013; Available online: https://www.who.int/nmh/publications/ncd-action-plan/en (accessed on 5 August 2020).
  8. Korea Centers for Disease Control & Prevention. Chronic Disease Management Research & Development. Available online: http://www.cdc.go.kr/contents.es?mid=a30322000000 (accessed on 5 August 2020).
  9. Kim, Y. Towards universal coverage: An evaluation of the benefit enhancement plan for four major conditions in Korean National Health Insurance. J. Korean Med. Assoc. 2014, 57, 198–203. [Google Scholar] [CrossRef] [Green Version]
  10. Khang, Y.H. Burden of noncommunicable diseases and national strategies to control them in Korea. J. Prev. Med. Public Health 2013, 46, 155. [Google Scholar] [CrossRef]
  11. Korea Centers for Disease Control & Prevention. 2019 Status and Issues of Chronic Diseases. Available online: http://www.cdc.go.kr/gallery.es?mid=a20503020000&bid=0003&act=view&list_no=144581 (accessed on 3 August 2020).
  12. World Health Organization. Global Status Report on Noncommunicable Diseases 2014; WHO Press: Geneva, Switzerland, 2014; Available online: https://www.who.int/iris/bitstream/10665/148114/1/9789241564854_eng.pdf?ua=1 (accessed on 5 August 2020).
  13. Berkman, L.F.; Kawachi, I.; Glymour, M.M. Social Epidemiology; Oxford University Press: New York, NY, USA, 2014. [Google Scholar]
  14. Krieger, N. Epidemiology and the People’s Health: Theory and Context; Oxford University Press: New York, NY, USA, 2011. [Google Scholar]
  15. Phelan, J.C.; Link, B.G. Fundamental cause theory. In Medical Sociology on the Move: New Directions in Theory; Cockerham, W.C., Ed.; Springer: Birmingham, UK, 2013; pp. 105–125. [Google Scholar]
  16. Adler, N.E.; Boyce, T.; Chesney, M.A.; Cohen, S.; Folkman, S.; Kahn, R.L.; Syme, S.L. Socioeconomic status and health: The challenge of the gradient. Am. Psychol. 1994, 49, 15. [Google Scholar] [CrossRef]
  17. Grotto, I.; Huerta, M.; Sharabi, Y. Hypertension and socioeconomic status. Curr. Opin. Cardiol. 2008, 23, 335–339. [Google Scholar] [CrossRef]
  18. Dinca-Panaitescu, S.; Dinca-Panaitescu, M.; Bryant, T.; Daiski, I.; Pilkington, B.; Raphael, D. Diabetes prevalence and income: Results of the Canadian Community Health Survey. Health Policy 2011, 99, 116–123. [Google Scholar] [CrossRef]
  19. Cella, D.F.; Orav, E.J.; Kornblith, A.B.; Holland, J.C.; Silberfarb, P.M.; Lee, K.W.; Comis, R.L.; Perry, M.; Cooper, R.; Maurer, L.H. Socioeconomic status and cancer survival. J. Clin. Oncol. 1991, 9, 1500–1509. [Google Scholar] [CrossRef]
  20. Smith, D.; Taylor, R.; Coates, M. Socioeconomic differentials in cancer incidence and mortality in urban New South Wales, 1987–1991. Aust. New Zealand J. Public Health 1996, 20, 129–137. [Google Scholar] [CrossRef]
  21. Yin, D.; Morris, C.; Allen, M.; Cress, R.; Bates, J.; Liu, L. Does socioeconomic disparity in cancer incidence vary across racial/ethnic groups? Cancer Causes Control 2010, 21, 1721–1730. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  22. Robert, S.A.; Strombom, I.; Trentham-Dietz, A.; Hampton, J.M.; McElroy, J.A.; Newcomb, P.A.; Remington, P.L. Socioeconomic risk factors for breast cancer: Distinguishing individual- and community-level effects. Epidemiology 2004, 15, 442–450. [Google Scholar] [CrossRef] [PubMed]
  23. Hong, S.W.; Linton, J.A.; Shim, J.Y.; Kang, H.T. High-risk drinking is associated with a higher risk of diabetes mellitus in Korean men, based on the 2010–2012 KNHANES. Alcohol 2015, 49, 275–281. [Google Scholar] [CrossRef] [PubMed]
  24. Xi, B.; Veeranki, S.P.; Zhao, M.; Ma, C.; Yan, Y.; Mi, J. Relationship of alcohol consumption to all-cause, cardiovascular, and cancer-related mortality in US adults. J. Am. Coll. Cardiol. 2017, 70, 913–922. [Google Scholar] [CrossRef]
  25. Corrao, G.; Bagnardi, V.; Zambon, A.; La Vecchia, C. A meta-analysis of alcohol consumption and the risk of 15 diseases. Prev. Med. 2004, 38, 613–619. [Google Scholar] [CrossRef]
  26. Shield, K.D.; Parry, C.; Rehm, J. Chronic diseases and conditions related to alcohol use. Alcohol Res. Curr. Rev. 2014, 35, 155. [Google Scholar]
  27. Jee, S.H.; Foong, A.W.; Hur, N.W.; Samet, J.M. Smoking and risk for diabetes incidence and mortality in Korean men and women. Diabetes Care 2010, 33, 2567–2572. [Google Scholar] [CrossRef] [Green Version]
  28. Gandini, S.; Botteri, E.; Iodice, S.; Boniol, M.; Lowenfels, A.B.; Maisonneuve, P.; Boyle, P. Tobacco smoking and cancer: A meta-analysis. Int. J. Cancer 2008, 122, 155–164. [Google Scholar] [CrossRef]
  29. Jha, P. Avoidable global cancer deaths and total deaths from smoking. Nat. Rev. Cancer 2009, 9, 655–664. [Google Scholar] [CrossRef]
  30. Inoue-Choi, M.; Hartge, P.; Liao, L.M.; Caporaso, N.; Freedman, N.D. Association between long-term low-intensity cigarette smoking and incidence of smoking-related cancer in the national institutes of health-AARP cohort. Int. J. Cancer 2018, 142, 271–280. [Google Scholar] [CrossRef]
  31. Morris, J.N.; Crawford, M.D. Coronary heart disease and physical activity of work. Br. Med. J. 1958, 2, 1485. [Google Scholar] [CrossRef] [PubMed]
  32. Richard, A.; Martin, B.; Wanner, M.; Eichholzer, M.; Rohrmann, S. Effects of leisure-time and occupational physical activity on total mortality risk in NHANES III according to sex, ethnicity, central obesity, and age. J. Phys. Act. Health 2015, 12, 184–192. [Google Scholar] [CrossRef] [PubMed]
  33. Lantz, P.M.; House, J.S.; Lepkowski, J.M.; Williams, D.R.; Mero, R.P.; Chen, J. Socioeconomic factors, health behaviors, and mortality: Results from a nationally representative prospective study of US adults. JAMA 1998, 279, 1703–1708. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Mehta, N.K.; House, J.S.; Elliott, M.R. Dynamics of health behaviours and socioeconomic differences in mortality in the USA. J. Epidemiol. Community Health 2015, 69, 416–422. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Dunn, J.R. Health behavior vs. the stress of low socioeconomic status and health outcomes. JAMA 2010, 303, 1199–1200. [Google Scholar] [CrossRef]
  36. Chokshi, D.A.; Farley, T.A. Changing behaviors to prevent noncommunicable diseases. Science 2014, 345, 1243–1244. [Google Scholar] [CrossRef]
  37. Kweon, S.; Kim, Y.; Jang, M.; Kim, Y.; Kim, K.; Choi, S.; Chun, C.; Khang, Y.H.; Oh, K. Data resource profile: The Korea national health and nutrition examination survey (KNHANES). Int. J. Epidemiol. 2014, 43, 69–77. [Google Scholar] [CrossRef] [Green Version]
  38. Korea Centers for Disease Control & Prevention. Korea National Health and Nutrition Examination Survey. Available online: https://knhanes.cdc.go.kr/knhanes/eng/index.do (accessed on 5 August 2020).
  39. World Health Organization. Global Physical Activity Questionnaire Analysis Guide. Available online: https://www.who.int/ncds/surveillance/steps/GPAQ/en (accessed on 5 August 2020).
  40. Coups, E.J.; Ostroff, J.S. A population-based estimate of the prevalence of behavioral risk factors among adult cancer survivors and noncancer controls. Prev. Med. 2005, 40, 702–711. [Google Scholar] [CrossRef]
  41. Denton, M.; Walters, V. Gender differences in structural and behavioral determinants of health: An analysis of the social production of health. Soc. Sci. Med. 1999, 48, 1221–1235. [Google Scholar] [CrossRef]
  42. Varì, R.; Scazzocchio, B.; D’Amore, A.; Giovannini, C.; Gessani, S.; Masella, R. Gender-related differences in lifestyle may affect health status. Ann. Dell’Istituto Super. Di Sanità 2016, 52, 158–166. [Google Scholar]
  43. Lleras, C. Path analysis. Encycl. Soc. Meas. 2005, 3, 25–30. [Google Scholar]
  44. World Health Organization. Preventing Noncommunicable Diseases (NCDs) by Reducing Environmental Risk Factors; WHO Press: Geneva, Switzerland, 2017; Available online: https://www.who.int/quantifying_ehimpacts/publications/preventing-ncds/en (accessed on 5 August 2020).
  45. Kim, G.R.; Nam, C.M. Temporal trends in educational inequalities in non-communicable diseases in Korea, 2007–2015. PLoS ONE 2017, 12, e0190143. [Google Scholar]
  46. Lee, H.; Lee, H.H.; Kang, A.; Cha, Y.; Operario, D. Psychological stress, smoking, and hazardous drinking behaviors in South Korea: Findings from the Korea National Health and Nutrition Examination Survey. J. Subst. Use 2020. [Google Scholar] [CrossRef]
  47. Kim, S.R.; Han, K.; Choi, J.Y.; Ersek, J.; Liu, J.; Jo, S.J.; Lee, K.S.; Yim, H.W.; Lee, W.C.; Park, Y.G.; et al. Age-and sex-specific relationships between household income, education, and diabetes mellitus in Korean adults: The Korea National Health and Nutrition Examination Survey, 2008–2010. PLoS ONE 2015, 10, e0117034. [Google Scholar]
  48. Kim, D.Y.; Ahn, A.; Lee, H.; Choi, J.; Lim, H. Dietary patterns independent of fast food are associated with obesity among Korean adults: Korea National Health and Nutrition Examination Survey 2010–2014. Nutrients 2019, 11, 2740. [Google Scholar] [CrossRef] [Green Version]
  49. Kang, Y.; Son, H. Gender differences in factors associated with colorectal cancer screening: A national cross-sectional study in Korea. Asia Pac. J. Public Health 2017, 29, 495–505. [Google Scholar] [CrossRef]
  50. Noh, J.W.; Jo, M.; Huh, T.; Cheon, J.; Kwon, Y.D. Gender differences and socioeconomic status in relation to overweight among older Korean people. PLoS ONE 2014, 9, e97990. [Google Scholar]
  51. Baek, T.H.; Lee, H.Y.; Lim, N.K.; Park, H.Y. Gender differences in the association between socioeconomic status and hypertension incidence: The Korean Genome and Epidemiology Study (KoGES). BMC Public Health 2015, 15, 852. [Google Scholar] [CrossRef] [Green Version]
  52. Villegas, R.; Kearney, P.M.; Perry, I.J. The cumulative effect of core lifestyle behaviours on the prevalence of hypertension and dyslipidemia. BMC Public Health 2008, 8, 210. [Google Scholar] [CrossRef] [Green Version]
Ijerph 18 00612 g001 550
Figure 1. The research model.
Figure 1. The research model.
Ijerph 18 00612 g001
Table
Table 1. Characteristics of the sample.
Table 1. Characteristics of the sample.
Characteristicsn (%)Characteristicsn (%)
Age Social-environmental factors
 19–30726 (13.3) Household income
 31–40866 (15.9)  Q11018 (18.6)
 41–501022 (18.7)  Q21303 (23.9)
 51–601093 (20)  Q31515 (27.7)
 61–70963 (17.6)  Q41626 (29.8)
 71–80792 (1.5) Education level
Gender   Elementary school or less1108 (20.3)
 Men2438 (44.6)  Middle school548 (10.0)
 Women3024 (55.4)  High school1686 (30.9)
Behavioral risk factors   University or more2120 (38.8)
 Alcohol use  Region
  Current or former use4884 (89.4)  Non-metropolitan city2961 (54.2)
  Never use578 (10.6)  Metropolitan city3305 (45.8)
 Tobacco use
 Current or former smoker2157 (39.5)
 Non-smoker3305 (60.5)
 Physical activity (MET level)
 Low3487 (63.8)
 Moderate1554 (28.5)
 High421 (7.7)
Table
Table 2. The results of the research models.
Table 2. The results of the research models.
HypothesesStandardized Coefficient ( β )Standard ErrorCritical RatioResults
H1. Social-environment→ Stroke0.101 ***0.0126.482Supported
H2. Behavior→ Stroke0.031*0.0142.005Supported
H1. Social-environment→ MI0.058 ***0.0083.941Supported
H2. Behavior→ MI0.030 *0.0101.995Supported
H1. Social-environment→ Angina0.077 ***0.0115.252Supported
H2. Behavior→ Angina0.039 **0.0132.614Supported
H1. Social-environment→ Diabetes0.201 ***0.02513.952Supported
H2. Behavior→ Diabetes0.049 ***0.0313.375Supported
H1. Social-environment→ Cancers0.069 ***0.0134.606Supported
H2. Behavior→ Cancers−0.0060.015−0.448Not supported
H1. Social-environment→ Breast cancer0.0180.0091.104Not supported
H2. Behavior→ Breast cancer−0.0300.016−1.750Not supported
H1. Social-environment→ Cervical cancer0.0090.0100.470Not supported
H2. Behavior→ Cervical cancer0.0170.0140.979Not supported
H3. Social-environment→ Behavior  a −0.0240.011−1.740Not supported
H3. Social-environment→ Behavior  b −0.053 **0.012−2.869Supported
*** p < 0.001, ** p < 0.01, * p < 0.05. a Stroke, MI, angina, diabetes, and cancers model. b Breast cancer and cervical cancer model.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Jeong, N.J.; Park, E.; del Pobil, A.P. Effects of Behavioral Risk Factors and Social-Environmental Factors on Non-Communicable Diseases in South Korea: A National Survey Approach. Int. J. Environ. Res. Public Health 2021, 18, 612. https://doi.org/10.3390/ijerph18020612

AMA Style

Jeong NJ, Park E, del Pobil AP. Effects of Behavioral Risk Factors and Social-Environmental Factors on Non-Communicable Diseases in South Korea: A National Survey Approach. International Journal of Environmental Research and Public Health. 2021; 18(2):612. https://doi.org/10.3390/ijerph18020612

Chicago/Turabian Style

Jeong, Nam Jeong, Eunil Park, and Angel P. del Pobil. 2021. "Effects of Behavioral Risk Factors and Social-Environmental Factors on Non-Communicable Diseases in South Korea: A National Survey Approach" International Journal of Environmental Research and Public Health 18, no. 2: 612. https://doi.org/10.3390/ijerph18020612

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop