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Determinants of unhealthy living by gender, age group, and chronic health conditions across districts in South Korea using the 2010-2017 Community Health Surveys

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OBJECTIVES: We investigated the prevalence and determinants of unhealthy living by gender, age, and comorbidities across Korean districts. METHODS: For 806,246 men and 923,260 women from 245 districts who participated in the 2010-2017 Korean Community Health Surveys, risk scores were calculated based on obesity, physical inactivity, smoking, and high-risk alcohol consumption, each scored from 0 (lowest risk) to 2 (highest risk). A risk score ≥ 4 was defined as indicating unhealthy living, and weighted proportions were calculated for each district. Using multivariate regression, an ecological model including community socioeconomic, interpersonal, and neighborhood factors was examined by gender, age, and comorbidities. RESULTS: The mean age-standardized rate of unhealthy living was 24.05% for men and 4.91% for women (coefficients of variation, 13.94% and 29.51%, respectively). Individuals with chronic diseases more frequently exhibited unhealthy lifestyles. Unhealthy lifestyles were associated with educational attainment (β-coefficients: men, -0.21; women, -0.15), high household income (β= 0.08 and 0.03, respectively), pub density (β= 0.52 and 0.22, respectively), and fast-food outlet density (β= 2.81 and 1.63, respectively). Negative associations were observed with manual labor, social activity participation, and hospital bed density. Unhealthy living was positively associated with living alone among women and with being unemployed among middle-aged men. Access to parks was negatively associated with unhealthy living among young men and women. The ecological model explained 32% of regional variation in men and 41% in women. CONCLUSIONS: Improving the neighborhood built and socioeconomic environment may reduce regional disparities in lifestyle behaviors; however, the impacts may vary according to socio-demographic traits and comorbidities.

Abstract

OBJECTIVES: We investigated the prevalence and determinants of unhealthy living by gender, age, and comorbidities across Korean districts. METHODS: For 806,246 men and 923,260 women from 245 districts who participated in the 2010-2017 Korean Community Health Surveys, risk scores were calculated based on obesity, physical inactivity, smoking, and high-risk alcohol consumption, each scored from 0 (lowest risk) to 2 (highest risk). A risk score ≥ 4 was defined as indicating unhealthy living, and weighted proportions were calculated for each district. Using multivariate regression, an ecological model including community socioeconomic, interpersonal, and neighborhood factors was examined by gender, age, and comorbidities. RESULTS: The mean age-standardized rate of unhealthy living was 24.05% for men and 4.91% for women (coefficients of variation, 13.94% and 29.51%, respectively). Individuals with chronic diseases more frequently exhibited unhealthy lifestyles. Unhealthy lifestyles were associated with educational attainment (β-coefficients: men, -0.21; women, -0.15), high household income (β= 0.08 and 0.03, respectively), pub density (β= 0.52 and 0.22, respectively), and fast-food outlet density (β= 2.81 and 1.63, respectively). Negative associations were observed with manual labor, social activity participation, and hospital bed density. Unhealthy living was positively associated with living alone among women and with being unemployed among middle-aged men. Access to parks was negatively associated with unhealthy living among young men and women. The ecological model explained 32% of regional variation in men and 41% in women. CONCLUSIONS: Improving the neighborhood built and socioeconomic environment may reduce regional disparities in lifestyle behaviors; however, the impacts may vary according to socio-demographic traits and comorbidities.

참고문헌 (50)

  1. Carey RM, "Prevention and control of hypertension : JACC health promotion series", J Am Coll Cardiol, 72, pp.1278~1293 (2018)[DOI]
  2. Pirani N, "Population attributable fraction for cardiovascular diseases risk factors in selected countries : a comparative study", Mater Sociomed, 29, pp.35~39 (2017)[DOI]
  3. "Global action plan for the prevention and control of noncommunicable diseases 2013-2020" (2013)[URL]
  4. GBD 2017 Risk Factor Collaborators, "Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990-2017 : a systematic analysis for the Global Burden of Disease Study 2017", Lancet, 392, pp.1923~1994 (2018)[DOI]
  5. Pholpark A, "Association between behavioral risk factors and self-rated health : data from national health surveys in South Korea and Thailand", Health Soc Welf Rev, 37, pp.498~524 (2017)[DOI]
  6. Park EJ, "The association of multiple risky health behaviors with self-reported poor health, stress, and depressive symptom", Health Soc Welf Rev, 35, pp.136~157 (2015)[DOI]
  7. Schuit AJ, "Clustering of lifestyle risk factors in a general adult population", Prev Med, 35, pp.219~224 (2002)[DOI]
  8. Meader N, "A systematic review on the clustering and co-occurrence of multiple risk behaviours", BMC Public Health, 16, pp.657~ (2016)[DOI]
  9. Jeong JY, "Factors related with regional variations of health behaviors and health status : based on community health survey and regional characteristics data", Korean Public Health Res, 43, pp.91~108 (2017)[DOI]
  10. Choi J, "Identifying patterns of lifestyle behaviours linked to sociodemographic characteristics and health conditions among young adults in South Korea", J Adv Nurs, 79, pp.2348~2359 (2023)[DOI]
  11. Ryu SY, "Associations between socio-demographic characteristics and healthy lifestyles in Korean adults : the result of the 2010 Community Health Survey", J Prev Med Public Health, 47, pp.113~123 (2014)[DOI]
  12. Seo AR, "Residential characteristics as factors related to healthy behavior practices-decision tree model analysis using a Community Health Survey from Korea", Int J Environ Res Public Health, 19, pp.7390~ (2022)[DOI]
  13. Mollborn S, "A gender framework for understanding health lifestyles", Soc Sci Med, 265, pp.113182~ (2020)[DOI]
  14. Jeon YJ, "Health behaviors in major chronic diseases patients : trends and regional variations analysis, 2008-2017, Korea", BMC Public Health, 20, pp.1813~ (2020)[DOI]
  15. Jeong CR, "Determinants of healthy living practice : county approach", Health Policy Manag, 30, pp.376~385 (2020)[DOI]
  16. Jang BN, "Effect of practicing health behaviors on unmet needs among patients with chronic diseases : a longitudinal study", Int J Environ Res Public Health, 18, pp.7977~ (2021)[DOI]
  17. "Health Plan 2020" (2015)[URL]
  18. Barbaresko J, "Lifestyle indices and cardiovascular disease risk : a meta-analysis", Am J Prev Med, 55, pp.555~564 (2018)[DOI]
  19. Kang YW, "Korea community health survey data profiles", Osong Public Health Res Perspect, 6, pp.211~217 (2015)[DOI]
  20. Kim I, "Income differences in smoking prevalences in 245 districts of South Korea : patterns by area deprivation and urbanity, 2008-2014", J Prev Med Public Health, 50, pp.100~126 (2017)[DOI]
  21. Kim E, "Factors influencing the practice of healthy living as an integrated indicator of the elderly’s drinking, smoking, and walking patterns : using Korea Community Health Surveys", Int J Environ Res Public Health, 19, pp.1936~ (2022)[DOI]
  22. Wu S, "Association of trajectory of cardiovascular health score and incident cardiovascular disease", JAMA Netw Open, 2, pp.e194758~ (2019)[DOI]
  23. "International guide for monitoring alcohol consumption and related harm" (2002)[URL]
  24. Yoo JI, "High prevalence of sarcopenia among binge drinking elderly women : a nationwide population-based study", BMC Geriatr, 17, pp.114~ (2017)[DOI]
  25. Ha S, "Clustering of four major lifestyle risk factors among Korean adults with metabolic syndrome", PLoS One, 12, pp.e0174567~ (2017)[DOI]
  26. Kim S, "Gender and tobacco epidemic in South Korea : implications from age-period-cohort analysis and the DPSEEA framework", BMJ Open, 12, pp.e058903~ (2022)[DOI]
  27. Sørensen M, "Perceived barriers to physical activity across Norwegian adult age groups, gender and stages of change", Scand J Med Sci Sports, 18, pp.651~663 (2008)[DOI]
  28. Nam GE, "Obesity fact sheet in Korea, 2020 : prevalence of obesity by obesity class from 2009 to 2018", J Obes Metab Syndr, 30, pp.141~148 (2021)[DOI]
  29. Villiers-Tuthill A, "The relationship of tobacco and alcohol use with ageing self-perceptions in older people in Ireland", BMC Public Health, 16, pp.627~ (2016)[DOI]
  30. Viinikainen J, "Does better education mitigate risky health behavior? A mendelian randomization study", Econ Hum Biol, 46, pp.101134~ (2022)[DOI]
  31. Pampel FC, "Socioeconomic disparities in health behaviors", Annu Rev Sociol, 36, pp.349~370 (2010)[DOI]
  32. Lee Y, "Slender women and overweight men : gender differences in the educational gradient in body weight in South Korea", Int J Equity Health, 16, pp.202~ (2017)[DOI]
  33. Tay L, "Social relations, health behaviors, and health outcomes : a survey and synthesis", Appl Psychol Health Well Being, 5, pp.28~78 (2013)[DOI]
  34. Dury S, "Gender-related differences in the relationship between social and activity participation and health and subjective well-being in later life", Soc Sci Med, 270, pp.113668~ (2021)[DOI]
  35. Roh HW, "Participation in physical, social, and religious activity and risk of depression in the elderly : a community-based three-year longitudinal study in Korea", PLoS One, 10, pp.e0132838~ (2015)[DOI]
  36. Cline KM, "Does religion increase the prevalence and incidence of obesity in adulthood?", J Sci Study Relig, 45, pp.269~281 (2006)[DOI]
  37. Salmoirago-Blotcher E, "Religion and healthy lifestyle behaviors among postmenopausal women : the women’s health initiative", J Behav Med, 34, pp.360~371 (2011)[DOI]
  38. Su JG, "Associations of green space metrics with health and behavior outcomes at different buffer sizes and remote sensing sensor resolutions", Environ Int, 126, pp.162~170 (2019)[DOI]
  39. Sun F, "Physical activity in older people : a systematic review", BMC Public Health, 13, pp.449~ (2013)[DOI]
  40. "Republic of Korea health system review" (2015)[URL]
  41. Park SM, "The association of health care access and utilization with self-perceived health in South Korea : the significance of age", J Biosoc Sci, 52, pp.1~13 (2020)[DOI]
  42. Lim JH, "Regional disparity and factors influencing unmet medical needs : a study based on the sixth Korea National Health and Nutrition Examination Survey(2015)", Osong Public Health Res Perspect, 8, pp.295~301 (2017)[DOI]
  43. Chun JD, "Comparisons of health status and health behaviors among the elderly between urban and rural areas", J Agric Med Community Health, 38, pp.182~194 (2013)[DOI]
  44. Park EJ, "Area deprivation, individual socioeconomic position and smoking among women in South Korea", Tob Control, 19, pp.383~390 (2010)[DOI]
  45. Kong KA, "Depressive symptoms and suicidal ideation in individuals living alone in South Korea", Diagnostics(Basel), 12, pp.603~ (2022)[DOI]
  46. Guan N, "Financial stress and depression in adults : a systematic review", PLoS One, 17, pp.e0264041~ (2022)[DOI]
  47. Wang Q, "Unemployment rate, smoking in China : are they related?", Int J Environ Res Public Health, 13, pp.113~ (2016)[DOI]
  48. Campbell CA, "The effectiveness of limiting alcohol outlet density as a means of reducing excessive alcohol consumption and alcohol-related harms", Am J Prev Med, 37, pp.556~569 (2009)[DOI]
  49. Katikireddi SV, "Socioeconomic status as an effect modifier of alcohol consumption and harm : analysis of linked cohort data", Lancet Public Health, 2, pp.e267~e276 (2017)[DOI]
  50. van Erpecum CL, "The association between fast-food outlet proximity and density and body mass index : findings from 147, 027 Lifelines Cohort Study participants", Prev Med, 155, pp.106915~ (2022)[DOI]

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