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Eun Jung Jang 3 Articles
Estimating the number of severe COVID-19 cases and COVID-19-related deaths averted by a nationwide vaccination campaign in Republic of Korea
Ji Hae Hwang, Ju Hee Lee, Eun Jung Jang, Ryu Kyung Kim, Kil Hun Lee, Seon Kyeong Park, Sang Eun Lee, Chungman Chae, Sangwon Lee, Young Joon Park
Osong Public Health Res Perspect. 2023;14(3):164-172.   Published online June 22, 2023
DOI: https://doi.org/10.24171/j.phrp.2023.0096
  • 2,593 View
  • 134 Download
  • 2 Web of Science
  • 3 Crossref
Graphical AbstractGraphical Abstract AbstractAbstract PDF
Objectives
The Korea Disease Control and Prevention Agency promotes vaccination by regularly providing information on its benefits for reducing the severity of coronavirus disease 2019 (COVID-19). This study aimed to analyze the number of averted severe COVID-19 cases and COVID-19-related deaths by age group and quantify the impact of Republic of Korea’s nationwide vaccination campaign.
Methods
We analyzed an integrated database from the beginning of the vaccination campaign on February 26, 2021 to October 15, 2022. We estimated the cumulative number of severe cases and COVID-19-related deaths over time by comparing observed and estimated cases among unvaccinated and vaccinated groups using statistical modeling. We compared daily age-adjusted rates of severe cases and deaths in the unvaccinated group to those in the vaccinated group and calculated the susceptible population and proportion of vaccinated people by age.
Results
There were 23,793 severe cases and 25,441 deaths related to COVID-19. We estimated that 119,579 (95% confidence interval [CI], 118,901–120,257) severe COVID-19 cases and 137,636 (95% CI, 136,909–138,363) COVID-19-related deaths would have occurred if vaccination had not been performed. Therefore, 95,786 (95% CI, 94,659–96,913) severe cases and 112,195 (95% CI, 110,870–113,520) deaths were prevented as a result of the vaccination campaign.
Conclusion
We found that, if the nationwide COVID-19 vaccination campaign had not been implemented, the number of severe cases and deaths would have been at least 4 times higher. These findings suggest that Republic of Korea’s nationwide vaccination campaign reduced the number of severe cases and COVID-19 deaths.

Citations

Citations to this article as recorded by  
  • Assessing the determinants of influenza and COVID-19 vaccine co-administration decisions in the elderly
    Seunghyun Lewis Kwon, So-Yeon Kim, Minju Song, Hyung-Min Lee, Seon-Hwa Ban, Mi-Soon Lee, Hyesun Jeong
    Human Vaccines & Immunotherapeutics.2024;[Epub]     CrossRef
  • 코로나바이러스감염증-19 대조 백신 및 연구용 백신 지원
    수봉 채, 미미소 이, 은영 조, 준구 박
    Public Health Weekly Report.2024; 17(32): 1378.     CrossRef
  • Comparative Effectiveness of COVID-19 Bivalent Versus Monovalent mRNA Vaccines in the Early Stage of Bivalent Vaccination in Korea: October 2022 to January 2023
    Ryu Kyung Kim, Young June Choe, Eun Jung Jang, Chungman Chae, Ji Hae Hwang, Kil Hun Lee, Ji Ae Shim, Geun-Yong Kwon, Jae Young Lee, Young-Joon Park, Sang Won Lee, Donghyok Kwon
    Journal of Korean Medical Science.2023;[Epub]     CrossRef
Presumed population immunity to SARS-CoV-2 in South Korea, April 2022
Eun Jung Jang, Young June Choe, Seung Ah Choe, Yoo-Yeon Kim, Ryu Kyung Kim, Jia Kim, Do Sang Lim, Ju Hee Lee, Seonju Yi, Sangwon Lee, Young-Joon Park
Osong Public Health Res Perspect. 2022;13(5):377-381.   Published online October 14, 2022
DOI: https://doi.org/10.24171/j.phrp.2022.0209
  • 2,997 View
  • 78 Download
  • 3 Web of Science
  • 4 Crossref
Graphical AbstractGraphical Abstract AbstractAbstract PDF
Objectives
We estimated the overall and age-specific percentages of the Korean population with presumed immunity against severe acute respiratory syndrome coronavirus 2 (SARSCoV-2) as of April 2022 using the national registry.
Methods
We used the national coronavirus disease 2019 (COVID-19) infection and vaccination registry from South Korea, as described to define individuals with a previous history of COVID-19 infection, vaccination, or both, as persons with presumed immunity.
Results
Of a total of 53,304,627 observed persons, 24.4% had vaccination and infection, 58.1% had vaccination and no infection, 7.6% had infection and no vaccination, and 9.9% had no immunity. The SARS-CoV-2 Omicron variant emerged at a time when the presumed population immunity ranged from 80% to 85%; however, nearly half of the children were presumed to have no immunity.
Conclusion
We report a gap in population immunity, with lower presumed protection in children than in adults. The approach presented in this work can provide valuable informed tools to assist vaccine policy-making at a national level.

Citations

Citations to this article as recorded by  
  • Realistic Estimation of COVID-19 Infection by Seroprevalence Surveillance of SARS-CoV-2 Antibodies: An Experience From Korea Metropolitan Area From January to May 2022
    In Hwa Jeong, Jong-Hun Kim, Min-Jung Kwon, Jayoung Kim, Hee Jin Huh, Byoungguk Kim, Junewoo Lee, Jeong-hyun Nam, Eun-Suk Kang
    Journal of Korean Medical Science.2024;[Epub]     CrossRef
  • Epidemiology of Coronavirus Disease 2019 in Infants and Toddlers, Seoul, South Korea
    JiWoo Sim, Euncheol Son, Young June Choe
    Pediatric Infection & Vaccine.2024; 31(1): 94.     CrossRef
  • Predicting adherence to COVID-19 preventive measures among South Korean adults aged 40 to 69 Years using the expanded health empowerment model
    Su-Jung Nam, Tae-Young Pak
    SSM - Population Health.2023; 22: 101411.     CrossRef
  • Acute COVID-19 in unvaccinated children without a history of previous infection during the delta and omicron periods
    Jee Min Kim, Ji Yoon Han, Seung Beom Han
    Postgraduate Medicine.2023; 135(7): 727.     CrossRef
Development of a Predictive Model for Type 2 Diabetes Mellitus Using Genetic and Clinical Data
Juyoung Lee, Bhumsuk Keam, Eun Jung Jang, Mi Sun Park, Ji Young Lee, Dan Bi Kim, Chang-Hoon Lee, Tak Kim, Bermseok Oh, Heon Jin Park, Kyu-Bum Kwack, Chaeshin Chu, Hyung-Lae Kim
Osong Public Health Res Perspect. 2011;2(2):75-82.   Published online June 30, 2011
DOI: https://doi.org/10.1016/j.phrp.2011.07.005
  • 3,342 View
  • 20 Download
  • 11 Crossref
AbstractAbstract PDFSupplementary Material
Objectives
Recent genetic association studies have provided convincing evidence that several novel loci and single nucleotide polymorphisms (SNPs) are associated with the risk of developing type 2 diabetes mellitus (T2DM). The aims of this study were: 1) to develop a predictive model of T2DM using genetic and clinical data; and 2) to compare misclassification rates of different models.
Methods
We selected 212 individuals with newly diagnosed T2DM and 472 controls aged in their 60s from the Korean Genome and Epidemiology Study. A total of 499 known SNPs from 87 T2DM-related genes were genotyped using germline DNA. SNPs were analyzed for significant association with T2DM using various classification algorithms including Quest (Quick, Unbiased, Efficient, Statistical tree), Support Vector Machine, C4.5, logistic regression, and K-nearest neighbor.
Results
We tested these models using the complete Korean Genome and Epidemiology Study cohort (n = 10,038) and computed the T2DM misclassification rates for each model. Average misclassification rates ranged at 28.2–52.7%. The misclassification rates for the logistic and machine-learning algorithms were lower than the statistical tree algorithms. Using 1-to-1 matched data, the misclassification rate of the statistical tree QUEST algorithm using body mass index and SNP variables was the lowest, but overall the logistic regression performed best.
Conclusions
The K-nearest neighbor method exhibited more robust results than other algorithms. For clinical and genetic data, our “multistage adjustment” model outperformed other models in yielding lower rates of misclassification. To improve the performance of these models, further studies using warranted, strategies to estimate better classifiers for the quantification of SNPs need to be developed.

Citations

Citations to this article as recorded by  
  • Population stratification in type 2 diabetes mellitus: A systematic review
    Sam Hodgson, Sukhmani Cheema, Zareena Rana, Doyinsola Olaniyan, Ellen O’Leary, Hermione Price, Hajira Dambha‐Miller
    Diabetic Medicine.2022;[Epub]     CrossRef
  • The Prediction of Diabetes
    Lalit Kumar, Prashant Johri
    International Journal of Reliable and Quality E-He.2022; 11(1): 1.     CrossRef
  • Hypertension: Constraining the Expression of ACE-II by Adopting Optimal Macronutrients Diet Predicted via Support Vector Machine
    Mohammad Farhan Khan, Gazal Kalyan, Sohom Chakrabarty, M. Mursaleen
    Nutrients.2022; 14(14): 2794.     CrossRef
  • Supervised and unsupervised algorithms for bioinformatics and data science
    Ayesha Sohail, Fatima Arif
    Progress in Biophysics and Molecular Biology.2020; 151: 14.     CrossRef
  • Medical Internet of things using machine learning algorithms for lung cancer detection
    Kanchan Pradhan, Priyanka Chawla
    Journal of Management Analytics.2020; 7(4): 591.     CrossRef
  • Perspective: Advancing Understanding of Population Nutrient–Health Relations via Metabolomics and Precision Phenotypes
    Stephanie Andraos, Melissa Wake, Richard Saffery, David Burgner, Martin Kussmann, Justin O'Sullivan
    Advances in Nutrition.2019; 10(6): 944.     CrossRef
  • Stacked classifiers for individualized prediction of glycemic control following initiation of metformin therapy in type 2 diabetes
    Dennis H. Murphree, Elaheh Arabmakki, Che Ngufor, Curtis B. Storlie, Rozalina G. McCoy
    Computers in Biology and Medicine.2018; 103: 109.     CrossRef
  • Machine Learning and Data Mining Methods in Diabetes Research
    Ioannis Kavakiotis, Olga Tsave, Athanasios Salifoglou, Nicos Maglaveras, Ioannis Vlahavas, Ioanna Chouvarda
    Computational and Structural Biotechnology Journal.2017; 15: 104.     CrossRef
  • Survey on clinical prediction models for diabetes prediction
    N. Jayanthi, B. Vijaya Babu, N. Sambasiva Rao
    Journal of Big Data.2017;[Epub]     CrossRef
  • Rule Extraction From Support Vector Machines Using Ensemble Learning Approach: An Application for Diagnosis of Diabetes
    Longfei Han, Senlin Luo, Jianmin Yu, Limin Pan, Songjing Chen
    IEEE Journal of Biomedical and Health Informatics.2015; 19(2): 728.     CrossRef
  • Depression among Korean Adults with Type 2 Diabetes Mellitus: Ansan-Community-Based Epidemiological Study
    Chan Young Park, So Young Kim, Jong Won Gil, Min Hee Park, Jong-Hyock Park, Yeonjung Kim
    Osong Public Health and Research Perspectives.2015; 6(4): 224.     CrossRef

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