Osong Public Health and Research Perspectives

Eun Jung Jang

2 Articles

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.

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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11 Citations

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

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