Osong Public Health and Research Perspectives

Original Article

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

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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

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

Brief Report

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

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Graphical Abstract Abstract 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

Original Article

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

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Abstract PDF Supplementary 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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