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Original Article
- The Recency Period for Estimation of Human Immunodeficiency Virus Incidence by the AxSYM Avidity Assay and BED-Capture Enzyme Immunoassay in the Republic of Korea
- Hye-Kyung Yu, Tae-Young Heo, Na-Young Kim, Jin-Sook Wang, Jae-Kyeong Lee, Sung Soon Kim, Mee-Kyung Kee
- Osong Public Health Res Perspect. 2014;5(4):187-192. Published online August 31, 2014
- DOI: https://doi.org/10.1016/j.phrp.2014.06.002
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Abstract
PDF - Objectives
Measurement of the incidence of the human immunodeficiency virus (HIV) is very important for epidemiological studies. Here, we determined the recency period with the AxSYM avidity assay and the BED-capture enzyme immunoassay (BED-CEIA) in Korean seroconverters.
Methods
Two hundred longitudinal specimens from 81 seroconverters with incident HIV infections that had been collected at the Korea National Institute of Health were subjected to the AxSYM avidity assay (cutoff = 0.8) and BED-CEIA (cutoff = 0.8). The statistical method used to estimate the recency period in recent HIV infections was nonparametric survival analyses. Sensitivity and specificity were calculated for 10-day increments from 120 days to 230 days to determine the recency period.
Results
The mean recency period of the avidity assay and BED-CEIA using a survival method was 158 days [95% confidence interval (CI), 135–181 days] and 189 days (95% CI, 170–208 days), respectively. Based on the use of sensitivity and specificity, the mean recency period for the avidity assay and BED-CEIA was 150 days and 200 days, respectively.
Conclusion
We determined the recency period to estimate HIV incidence in Korea. These data showed that the nonparametric survival analysis often led to shorter recency periods than analysis of sensitivity and specificity as a new method. These findings suggest that more data from seroconverters and other methodologies are needed to determine the recency period for estimating HIV incidence. -
Citations
Citations to this article as recorded by
- Western Blot-Based Logistic Regression Model for the Identification of Recent HIV-1 Infection: A Promising HIV-1 Surveillance Approach for Resource-Limited Regions
Jiegang Huang, Minlian Wang, Chunyuan Huang, Bingyu Liang, Junjun Jiang, Chuanyi Ning, Ning Zang, Hui Chen, Jie Liu, Rongfeng Chen, Yanyan Liao, Li Ye, Hao Liang
BioMed Research International.2018; 2018: 1. CrossRef
- Western Blot-Based Logistic Regression Model for the Identification of Recent HIV-1 Infection: A Promising HIV-1 Surveillance Approach for Resource-Limited Regions
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