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18 results on '"Konikoff, Jacob"'

1. Identification and validation of a multi-assay algorithm for cross-sectional HIV incidence estimation in populations with subtype C infection

Author

Laeyendecker, Oliver, Konikoff, Jacob, Morrison, Douglas E., Brookmeyer, Ronald, Wang, Jing, Celum, Connie, Morrison, Charles S., Karim, Quarraisha Abdool, Pettifor, Audrey E., and Eshleman, Susan H.

Subjects
Algorithms -- Usage -- Health aspects, HIV infections -- Prevention -- Research, HIV seroprevalence -- Analysis, Sentinel surveillance -- Research, Algorithm, Health
Abstract

Introduction: Cross-sectional methods can be used to estimate HIV incidence for surveillance and prevention studies. We evaluated assays and multi-assay algorithms (MAAs) for incidence estimation in subtype C settings. Methods: We analysed samples from individuals with subtype C infection with known duration of infection (2442 samples from 278 adults; 0.1 to 9.9 years after seroconversion). MAAs included 1-4 of the following assays: Limiting Antigen Avidity assay (LAg-Avidity), BioRad-Avidity assay, CD4 cell count and viral load (VL). We evaluated 23,400 MAAs with different assays and assay cutoffs. We identified the MAA with the largest mean window period, where the upper 95% confidence interval (CI) of the shadow was 1000 copies/mL), and two MAAs previously optimized for subtype B settings. We compared these cross-sectional incidence estimates to observed incidence in an independent longitudinal cohort. Results: The optimal MAA was LAg-Avidity 400 copies/mL. This MAA had a mean window period of 248 days (95% CI: 218, 284), a shadow of 306 days (95% CI: 255, 359), and provided the most accurate and precise incidence estimate for the independent cohort. The widely used LAg algorithm had a shorter mean window period (142 days, 95% CI: 118, 167), a longer shadow (410 days, 95% CI; 318, 491), and a less accurate and precise incidence estimate for the independent cohort. Conclusions: An optimal MAA was identified for cross-sectional HIV incidence in subtype C settings. The performance of this MAA is superior to a testing algorithm currently used for global HIV surveillance. Keywords: Cross-sectional incidence testing; Southern Africa; Subtype C; Women; Epidemiology, 1 | INTRODUCTION Accurate methods for estimating HIV incidence are critical for HIV surveillance and for evaluating the effectiveness of HIV prevention efforts [1]. Traditional longitudinal cohort studies are costly [...]

Published
2018
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2. Times to key events in Zika virus infection and implications for blood donation: a systematic review/Chronometrage des evenements cles de l'infection a virus Zika et consequences sur le don de sang: examen systematique/Cronograma de los eventos clave de la infeccion por el virus de Zika e implicaciones para la donacion de sangre: una revision sistematica

Author

Lessler, Justin, Ott, Cassandra T., Carcelen, Andrea C., Konikoff, Jacob M., Williamson, Joe, Bi, Qifang, Kucirka, Lauren M., Cummings, Derek A.T., Reich, Nicholas G., and Chaisson, Lelia H.

Subjects
Medical research, Medicine, Experimental, Infectious disease incubation period -- Research, Blood donors -- Health aspects, Health
Abstract

Objective To estimate the timing of key events in the natural history of Zika virus infection. Methods In February 2016, we searched PubMed, Scopus and the Web of Science for [...]

Published
2016
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3. HIV Incidence Determination in the United States: A Multiassay Approach

Author

Laeyendecker, Oliver, Brookmeyer, Ron, Cousins, Matthew M., Mullis, Caroline E., Konikoff, Jacob, Donnell, Deborah, Celum, Connie, Buchbinder, Susan P., Seage,, George R., Kirk, Gregory D., Menta, Shruti H., Astemborski, Jacquie, Jacobson, Lisa P., Margolick, Joseph B., Brown, Joelle, Quinn, Thomas C., and Eshleman, Susan H.

Published
2013
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4. Cross-Sectional HIV Incidence Estimation: Techniques and Challenges

Author

Konikoff, Jacob

Subjects
Biostatistics, Cross-Sectional, HIV, Incidence, MCMC, Multivariate, Sample Size
Abstract

Tracking and surveillance of the HIV epidemic depend on accurate estimation of the number of new infections in the population. The rate at which these infections occur, known as the incidence, is also critical for effectively designing, targeting, and evaluating prevention efforts. Incidence can be estimated through cross-sectional surveys by using biomarkers, such as HIV viral load, CD4 cell count, and recently developed serologic assays, which define and mark people in an early disease stage. The total number of individuals found in this stage, that is possessing markers of recent infection, gives a snapshot of how the epidemic is progressing. We explore how these biomarkers should best be combined to define this early disease stage by examining how the definition influences the bias and variability of the cross-sectional incidence estimator. These calculations depend on estimating the probability that persons will remain in the early disease stage $t$ years after seroconversion. We present two different approaches for estimating this probability curve. Once we have defined viable methods for combining these biomarkers we derive the sample sizes needed to conduct one or more cross-sectional surveys and explore how missing biomarker data should be handled in the context of implementing these surveys.

Published
2015

12. A Comparison of Two Measures of HIV Diversity in Multi-Assay Algorithms for HIV Incidence Estimation

Author

Cousins, Matthew M., primary, Konikoff, Jacob, additional, Sabin, Devin, additional, Khaki, Leila, additional, Longosz, Andrew F., additional, Laeyendecker, Oliver, additional, Celum, Connie, additional, Buchbinder, Susan P., additional, Seage, George R., additional, Kirk, Gregory D., additional, Moore, Richard D., additional, Mehta, Shruti H., additional, Margolick, Joseph B., additional, Brown, Joelle, additional, Mayer, Kenneth H., additional, Kobin, Beryl A., additional, Wheeler, Darrell, additional, Justman, Jessica E., additional, Hodder, Sally L., additional, Quinn, Thomas C., additional, Brookmeyer, Ron, additional, and Eshleman, Susan H., additional

Published
2014
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13. HIV Diversity as a Biomarker for HIV Incidence Estimation: Including a High-Resolution Melting Diversity Assay in a Multiassay Algorithm

Author

Cousins, Matthew M., primary, Konikoff, Jacob, additional, Laeyendecker, Oliver, additional, Celum, Connie, additional, Buchbinder, Susan P., additional, Seage, George R., additional, Kirk, Gregory D., additional, Moore, Richard D., additional, Mehta, Shruti H., additional, Margolick, Joseph B., additional, Brown, Joelle, additional, Mayer, Kenneth H., additional, Koblin, Beryl A., additional, Wheeler, Darrell, additional, Justman, Jessica E., additional, Hodder, Sally L., additional, Quinn, Thomas C., additional, Brookmeyer, Ron, additional, and Eshleman, Susan H., additional

Published
2014
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14. Performance of a Limiting-Antigen Avidity Enzyme Immunoassay for Cross-Sectional Estimation of HIV Incidence in the United States

Author

Konikoff, Jacob, primary, Brookmeyer, Ron, additional, Longosz, Andrew F., additional, Cousins, Matthew M., additional, Celum, Connie, additional, Buchbinder, Susan P., additional, Seage, George R., additional, Kirk, Gregory D., additional, Moore, Richard D., additional, Mehta, Shruti H., additional, Margolick, Joseph B., additional, Brown, Joelle, additional, Mayer, Kenneth H., additional, Koblin, Beryl A., additional, Justman, Jessica E., additional, Hodder, Sally L., additional, Quinn, Thomas C., additional, Eshleman, Susan H., additional, and Laeyendecker, Oliver, additional

Published
2013
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15. HIV Incidence Determination in the United States: A Multiassay Approach

Author

Laeyendecker, Oliver, primary, Brookmeyer, Ron, additional, Cousins, Matthew M., additional, Mullis, Caroline E., additional, Konikoff, Jacob, additional, Donnell, Deborah, additional, Celum, Connie, additional, Buchbinder, Susan P., additional, Seage, George R., additional, Kirk, Gregory D., additional, Mehta, Shruti H., additional, Astemborski, Jacquie, additional, Jacobson, Lisa P., additional, Margolick, Joseph B., additional, Brown, Joelle, additional, Quinn, Thomas C., additional, and Eshleman, Susan H., additional

Published
2012
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18. Performance of a Limiting-Antigen Avidity Enzyme Immunoassay for Cross-Sectional Estimation of HIV Incidence in the United States

Author

Konikoff, Jacob, Brookmeyer, Ron, Longosz, Andrew F., Cousins, Matthew M., Celum, Connie, Buchbinder, Susan P., Seage III, George R., Kirk, Gregory D., Moore, Richard D., Mehta, Shruti H., Margolick, Joseph B., Brown, Joelle, Mayer, Kenneth H., Koblin, Beryl A., Justman, Jessica E., Hodder, Sally L., Quinn, Thomas C., Eshleman, Susan H., and Laeyendecker, Oliver

Subjects
Epidemiology, Confidence intervals, FOS: Clinical medicine, Immunology, otorhinolaryngologic diseases, chemical and pharmacologic phenomena, 3. Good health, HIV infections
Abstract

Background A limiting antigen avidity enzyme immunoassay (HIV-1 LAg-Avidity assay) was recently developed for cross-sectional HIV incidence estimation. We evaluated the performance of the LAg-Avidity assay alone and in multi-assay algorithms (MAAs) that included other biomarkers. Methods and Findings Performance of testing algorithms was evaluated using 2,282 samples from individuals in the United States collected 1 month to >8 years after HIV seroconversion. The capacity of selected testing algorithms to accurately estimate incidence was evaluated in three longitudinal cohorts. When used in a single-assay format, the LAg-Avidity assay classified some individuals infected >5 years as assay positive and failed to provide reliable incidence estimates in cohorts that included individuals with long-term infections. We evaluated >500,000 testing algorithms, that included the LAg-Avidity assay alone and MAAs with other biomarkers (BED capture immunoassay [BED-CEIA], BioRad-Avidity assay, HIV viral load, CD4 cell count), varying the assays and assay cutoffs. We identified an optimized 2-assay MAA that included the LAg-Avidity and BioRad-Avidity assays, and an optimized 4-assay MAA that included those assays, as well as HIV viral load and CD4 cell count. The two optimized MAAs classified all 845 samples from individuals infected >5 years as MAA negative and estimated incidence within a year of sample collection. These two MAAs produced incidence estimates that were consistent with those from longitudinal follow-up of cohorts. A comparison of the laboratory assay costs of the MAAs was also performed, and we found that the costs associated with the optimal two assay MAA were substantially less than with the four assay MAA. Conclusions The LAg-Avidity assay did not perform well in a single-assay format, regardless of the assay cutoff. MAAs that include the LAg-Avidity and BioRad-Avidity assays, with or without viral load and CD4 cell count, provide accurate incidence estimates.

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