Effect of socioeconomic disparities on the risk of COVID-19 in eight metropolitan cities, the Korea: a community-based study
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OBJECTIVES: Socioeconomic disparities have been reported as the main risk factors contributing to the spread of coronavirus disease 2019 (COVID-19) at the community level. We conducted an epidemiological study on the risk of COVID-19 incidence using area deprivation indices (DIs) depending on the characteristics of the susceptible population. METHODS: The database of the confirmed COVID-19 cases in eight metropolitan cities, the Republic of Korea, from January 20, 2020 to December 31, 2021, was combined with the area DIs and standardized prevalence of diabetes and hypertension from the community health survey. The relative risk (RR) was estimated using a generalized linear model with Poisson distribution by age group. RESULTS: The risk of COVID-19 incidence increased with the increasing age group, especially in patients aged ≥75 years. The RR per interquartile range increment of total social deprivation index (total SDI) was 1.54 (95% confidence interval [CI]: 1.34-1.70) in the COVID-19 incidence. Especially, the risk of COVID-19 incidence in the first wave was about three times higher in the region belonging to the lowest socioeconomic status than in the region with the highest socioeconomic status. The risk was 3.08 (95% CI: 2.42 to 3.78) based on the total SDI and 3.13 (95% CI: 2.53 to 3.83) based on the social deprivation index. CONCLUSIONS: This study provides scientific evidence that socioeconomic inequity is an important risk factor for the spread of COVID-19. This finding suggests that a mid-to-long-term strategy is needed for the susceptible population to reduce the burden of COVID-19 in the community.
Abstract
OBJECTIVES: Socioeconomic disparities have been reported as the main risk factors contributing to the spread of coronavirus disease 2019 (COVID-19) at the community level. We conducted an epidemiological study on the risk of COVID-19 incidence using area deprivation indices (DIs) depending on the characteristics of the susceptible population. METHODS: The database of the confirmed COVID-19 cases in eight metropolitan cities, the Republic of Korea, from January 20, 2020 to December 31, 2021, was combined with the area DIs and standardized prevalence of diabetes and hypertension from the community health survey. The relative risk (RR) was estimated using a generalized linear model with Poisson distribution by age group. RESULTS: The risk of COVID-19 incidence increased with the increasing age group, especially in patients aged ≥75 years. The RR per interquartile range increment of total social deprivation index (total SDI) was 1.54 (95% confidence interval [CI]: 1.34-1.70) in the COVID-19 incidence. Especially, the risk of COVID-19 incidence in the first wave was about three times higher in the region belonging to the lowest socioeconomic status than in the region with the highest socioeconomic status. The risk was 3.08 (95% CI: 2.42 to 3.78) based on the total SDI and 3.13 (95% CI: 2.53 to 3.83) based on the social deprivation index. CONCLUSIONS: This study provides scientific evidence that socioeconomic inequity is an important risk factor for the spread of COVID-19. This finding suggests that a mid-to-long-term strategy is needed for the susceptible population to reduce the burden of COVID-19 in the community.
참고문헌 (26)
- Wilkinson A, "Local response in health emergencies : Key considerations for addressing the COVID-19 epidemic in informal urban settlements", Environment and Ubrnaization, 32, pp.503~522 (2020)
- Rethinking intersectional approaches to vulnerability reduction and COVID19 risks in low-income communities, "Kim SJ", Korea Research Institute for Human Settlements, ~ (2020)
- "Cities, crowding and the coronavirus: Predicting contagion risk hotspots" (2020)[URL]
- Wasdani KP, "The impossibility of social distancing among the urban poor : the case of an Indian slum in the times of COVID-19", Local Environment, 25, pp.414~418 (2020)
- Rostila M, "Disparities in Coronavirus Disease 2019 Mortality by Country of Birth in Stockholm, Sweden : A Total-PopulationBased Cohort Study", Am J Epidemiol, 190, pp.1510~1518 (2021)
- Northridge ME, "Sorting out the connections between the built environment and health : a conceptual framework for navigating pathways and planning healthy cities", J Urban Health, 80, pp.556~568 (2003)
- Nathan A, "The Role of the Built Environment on Health Across the Life Course : A Call for CollaborACTION", Am J Health Promot, 32, pp.1460~1468 (2018)
- Algren MH, "HealthRisk Behaviour in Deprived Neighbourhoods Compared with Non-Deprived Neighbourhoods : A Systematic Literature Review of Quantitative Obser vational Studies", PLoS One, 10, pp.e0139297~ (2015)
- Jackson RJ, "The impact of the built environment on health : an emerging field", Am J Public Health, 93, pp.1382~1384 (2003)
- 이승은, "The Association Among Individual and Contextual Factors and Unmet Healthcare Needs in South Korea: A Multilevel Study Using National Data", 예방의학회지, 49(5), pp.308~322 (2016), 대한예방의학회
- Developing Health Inequalities Indicators and Monitoring the Status of Health Inequalities in Korea, "Lee DJ", Korea Institution for Health and Social Affairs, 286~298 (2013)
- 김영택, "지역사회건강조사의 조사 기획과 수행", 대한의사협회지, 55(1), pp.74~83 (2012), 대한의사협회
- Chetty R, "The association between income and life expectancy in the United States, 2001-2014", JAMA, 315, pp.1750~1766 (2016)
- Marmot M, "Social determinants of health inequalities", Lancet, 365, pp.1099~1104 (2005)
- Lee GB, "Socioeconomic inequality in precautions compliance and health behavior changes during COVID-19 outbreak : analysis of the Korean Community Health Sur vey 2020", Eidemiol Health, 44, pp.e2022013~ (2022)
- Jeong HE, "Socioeconomic disparities in Korea by health insurance type during the COVID-19 epidemic : a nationwide study", Epidemiol Health, 43, pp.e2021007~ (2021)
- Lundon DJ, "Social determinants predict outcomes in data from a multi-ethnic cohort of 20, 899 patients investigated for COVID-19", Front Public Health, 8, pp.571364~ (2020)
- KC M, "The effect of area deprivation on COVID-19 risk in Louisiana", PLoS One, 15, pp.e0243028~ (2020)
- Hatef E, "Assessing the impact of neighborhood socioeconomic characteristics on COVID-19 prevalence across seven states in the United States", Front Public Health, 8, pp.571808~ (2020)
- Reichberg SB, "Rapid emergence of SARS-CoV-2 in the greater New York metropolitan area: geolocation, demographics, positivity rates, and hospitalization for 46 793 persons tested by northwell health", Clin Infect Dis, 71, pp.3204~3213 (2020)
- Dalstra JA, "Socioeconomic differences in the prevalence of common chronic diseases : an overview of eight European countries", Int J Epidemiol, 34, pp.316~326 (2005)
- Shah BR, "Quantifying the risk of infectious diseases for people with diabetes", Diabetes Care, 26, pp.510~513 (2003)
- Escobedo-de la Peña J, "Hypertension, diabetes and obesity, major risk factors for death in patients with COVID-19 in Mexico", Arch Med Res, 52, pp.443~449 (2021)
- Jeong IK, "Diabetes and COVID-19 : Global and regional perspectives", Diabetes Res Clin Pract, 166, pp.108303~ (2020)
- Barrera FJ, "Prevalence of diabetes and hypertension and their associated risks for poor outcomes in Covid-19 patients", J Endocr Soc, 4, pp.bvaa102~ (2020)
- Doroshenko A, "The combined effect of vaccination and nonpharmaceutical public health interventions—ending the COVID-19 epidemic", JAMA Netw Open, 4, pp.e2111675~ (2021)