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Socioeconomic disparities in Korea by health insurance type during the COVID-19 pandemic: a nationwide study

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This study explored socioeconomic disparities in Korea using health insurance type as a proxy during the ongoing coronavirus disease 2019 (COVID-19) pandemic. METHODS: We conducted a retrospective cohort study using Korea’s nationwide healthcare database, which contained all individuals who received a diagnostic test for COVID-19 (n=232,390) as of May 15, 2020. We classified our cohort by health insurance type into beneficiaries of the National Health Insurance (NHI) or Medicaid programs. Our study outcomes were infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and COVID-19-related outcomes, a composite of all-cause death, intensive care unit admission, and mechanical ventilation use. We estimated age-, sex-, and Charlson comorbidity index score–adjusted odds ratios (aORs) with 95% confidence intervals (CIs) using a multivariable logistic regression analysis. RESULTS: Of the 218,070 NHI and 14,320 Medicaid beneficiaries who received COVID-19 tests, 7,777 and 738 tested positive, respectively. The Medicaid beneficiaries were older (mean age, 57.5 vs. 47.8 years), more likely to be males (47.2 vs. 40.2%), and had a higher comorbidity burden (mean CCI, 2.0 vs. 1.7) than NHI beneficiaries. Compared to NHI beneficiaries, Medicaid beneficiaries had a 22% increased risk of SARS-CoV-2 infection (aOR, 1.22; 95% CI, 1.09 to 1.38), but had no significantly elevated risk of COVID-19-related outcomes (aOR 1.10, 95% CI 0.77 to 1.57); the individual events of the composite outcome yielded similar findings. CONCLUSIONS: As socioeconomic factors, with health insurance as a proxy, could serve as determinants during the current pandemic, pre-emptive support is needed for high-risk groups to slow its spread.

Abstract

This study explored socioeconomic disparities in Korea using health insurance type as a proxy during the ongoing coronavirus disease 2019 (COVID-19) pandemic. METHODS: We conducted a retrospective cohort study using Korea’s nationwide healthcare database, which contained all individuals who received a diagnostic test for COVID-19 (n=232,390) as of May 15, 2020. We classified our cohort by health insurance type into beneficiaries of the National Health Insurance (NHI) or Medicaid programs. Our study outcomes were infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and COVID-19-related outcomes, a composite of all-cause death, intensive care unit admission, and mechanical ventilation use. We estimated age-, sex-, and Charlson comorbidity index score–adjusted odds ratios (aORs) with 95% confidence intervals (CIs) using a multivariable logistic regression analysis. RESULTS: Of the 218,070 NHI and 14,320 Medicaid beneficiaries who received COVID-19 tests, 7,777 and 738 tested positive, respectively. The Medicaid beneficiaries were older (mean age, 57.5 vs. 47.8 years), more likely to be males (47.2 vs. 40.2%), and had a higher comorbidity burden (mean CCI, 2.0 vs. 1.7) than NHI beneficiaries. Compared to NHI beneficiaries, Medicaid beneficiaries had a 22% increased risk of SARS-CoV-2 infection (aOR, 1.22; 95% CI, 1.09 to 1.38), but had no significantly elevated risk of COVID-19-related outcomes (aOR 1.10, 95% CI 0.77 to 1.57); the individual events of the composite outcome yielded similar findings. CONCLUSIONS: As socioeconomic factors, with health insurance as a proxy, could serve as determinants during the current pandemic, pre-emptive support is needed for high-risk groups to slow its spread.

참고문헌 (28)

  1. Zimmerman FJ, "Trends in health equity in the United States by race", ethnicity, sex, and income, 1993-2017, 2019, pp.2, e196386~
  2. Chetty R, "The association between income and life expectancy in the United States, 2001-2014", JAMA, 315, pp.1750~1766 (2016)
  3. Kim CW, "Inequalities in cancer incidence and mortality across income groups and policy implications in South Korea", Public Health, 122, pp.229~236 (2008)
  4. O’Connor JM, "Factors associated with cancer disparities among low-, medium-, and high-income US counties", JAMA Netw Open, 1, pp.e183146~ (2018)
  5. Downing NS, "Association of racial and socioeconomic disparities with outcomes among patients hospitalized with acute myocardial infarction, heart failure, and pneumonia : an analysis of within-and betweenhospital variation", JAMA Netw Open, 1, pp.e182044~ (2018)
  6. Kim HJ, "Socioeconomic disparities in behavioral risk factors and health outcomes by gender in the Republic of Korea", BMC Public Health, 10, pp.195~ (2010)
  7. Marmot M, "Social determinants of health inequalities", Lancet, 365(9464), pp.1099~1104 (2005)
  8. Farmer PE, "Structural violence and clinical medicine", PLoS Med, 3, pp.e449~ (2006)
  9. "Medicaid statistics 2018" (2019)[URL]
  10. Shah BR, "Quantifying the risk of infectious diseases for people with diabetes", Diabetes Care, 26, pp.510~513 (2003)
  11. Bagshaw SM, "Epidemiology of intensive care unitacquired urinary tract infections", Curr Opin Infect Dis, 19, pp.67~71 (2006)
  12. Bouchardy C, "Social class is an important and independent prognostic factor of breast cancer mortality", Int J Cancer, 119, pp.1145~1151 (2006)
  13. Weissman JS, "The quality of hospital care for Medicaid and private pay patients", Med Care, 51, pp.389~395 (2013)
  14. Yang B, "Changing disparities in COVID-19 burden in the ethnically homogeneous population of Hong Kong through pandemic waves: an observational study", Clin Infect Dis, pp.ciab002~ (2021)
  15. Little C, "The impact of socioeconomic status on the clinical outcomes of COVID-19; a retrospective cohort study", J Community Health (2021)[DOI]
  16. Ahmad Khanijahani, "Racial, ethnic, and socioeconomic disparities in confirmed COVID-19 cases and deaths in the United States: a county-level analysis as of November 2020", Ethnicity & Health, 26(1), pp.22~35 (2021), Informa UK Limited[DOI]
  17. Lundon DJ, "Social determinants predict outcomes in data from a multiethnic cohort of 20, 899 patients investigated for COVID-19", Front Public Health, 8, pp.571364~ (2020)
  18. "Evaluation and consideration methods of consistency between health insurance claims diagnostic codes and medical records; 2017"[URL]
  19. "Laboratory testing for 2019 novel coronavirus (2019-nCoV) in suspected human cases: interim guidance; 2020"[URL]
  20. Quan H, "Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries", Am J Epidemiol, 173, pp.676~682 (2011)
  21. D’Hoore W, "Practical considerations on the use of the Charlson comorbidity index with administrative data bases", J Clin Epidemiol, 49, pp.1429~1433 (1996)
  22. Brookhart MA, "Variable selection for propensity score models", Am J Epidemiol, 163, pp.1149~1156 (2006)
  23. Kurth T, "Results of multivariable logistic regression, propensity matching, propensity adjustment, and propensity-based weighting under conditions of nonuniform effect", Am J Epidemiol, 163, pp.262~270 (2006)
  24. Desai RJ, "Alternative approaches for confounding adjustment in observational studies using weighting based on the propensity score: a primer for practitioners", BMJ, 367, pp.l5657~ (2019)
  25. Chapel JM, "Prevalence and medical costs of chronic diseases among adult Medicaid beneficiaries", Am J Prev Med, 53(6S2), pp.S143~S154 (2017)
  26. Byrne DD, "Prevalence of diagnosed chronic hepatitis B infection among U. S. Medicaid enrollees, 2000-2007", Ann Epidemiol, 24, pp.418~423 (2014)
  27. Baicker K, "The effect of Medicaid on management of depression : evidence from the Oregon Health Insurance Experiment", Milbank Q, 96, pp.29~56 (2018)
  28. Bahk J, "Trends in life expectancy among medical aid beneficiaries and National Health Insurance beneficiaries in Korea between 2004 and 2017", BMC Public Health, 19, pp.1137~ (2019)

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