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7 results on '"Luan, N. T."'

1. Modeling Data Redundancy and Cost-Aware Task Allocation in MEC-Enabled Internet-of-Vehicles Applications

Author

Eui-Nam Huh, Md. Delowar Hossain, Van-Nam Pham, Luan N. T. Huynh, VanDung Nguyen, and Tri D.T. Nguyen

Subjects
Optimization problem, Computer Networks and Communications, business.industry, Computer science, Distributed computing, 020206 networking & telecommunications, 020302 automobile design & engineering, 02 engineering and technology, Computer Science Applications, Data modeling, Edge server, Task (computing), 0203 mechanical engineering, Hardware and Architecture, Data redundancy, Server, Signal Processing, 0202 electrical engineering, electronic engineering, information engineering, Redundancy (engineering), Computation offloading, Resource management, The Internet, business, Edge computing, Information Systems
Abstract

Multiaccess edge computing (MEC) enables autonomous vehicles to handle time-critical and data-intensive computational tasks for emerging Internet-of-Vehicles (IoV) applications via computation offloading. However, a massive amount of data generated by colocated vehicles is typically redundant, introducing a critical issue due to limited network bandwidth. Moreover, on the edge server side, these computation-intensive tasks further impose severe pressure on the resource-finite MEC server, resulting in low-performance efficiency of applications. To solve these challenges, we model the data redundancy and collaborative task computing scheme to efficiently reduce the redundant data and utilize the idle resources in nearby MEC servers. First, the data redundancy problem is formulated as a set-covering problem according to the spatiotemporal coverage of captured images. Next, we exploit the submodular optimization technique to design an efficient algorithm to minimize the number of images transferred to the MEC servers without degrading the quality of IoV applications. To facilitate the task execution in the MEC server, we then propose a collaborative task computing scheme, where an MEC server intentionally encourages nearby resource-rich MEC servers to participate in a collaborative computing group. Accordingly, a cost model is formulated as an optimization problem, the objective of which is to prompt the MEC server to judiciously allocate computing tasks to nearby MEC servers with the goal of achieving the minimal cost while the latency of tasks is guaranteed. Experimental results show that the proposed scheme can efficiently mitigate data redundancy, conserve network bandwidth consumption, and achieve the lowest cost for processing tasks.

Published
2021

2. Envisioning Edge Computing in Future 6G Wireless Networks

Author

Eui-Nam Huh and Luan N. T. Huynh

Subjects
business.industry, Computer science, Wireless network, Key (cryptography), Wireless, Augmented reality, Latency (engineering), Virtual reality, business, Edge computing, Mixed reality, Computer network
Abstract

The goal of sixth-generation (6G) wireless communication network is towards space-atmosphere-land-submarine connectivity. 6G communications are mainly characterized by massive connectivity, ultra-reliability, low latency, and extreme high bandwidth to meet the requirements of practical applications such as robotics and autonomous vehicles, transportation management systems, healthcare as well as telepresence, virtual reality (VR), augmented reality (AR), and mixed reality (MR) applications. Multi-Access edge computing (MEC) is a key component of future 6G wireless networks, enabling latency-sensitive, bandwidth-intensive, and compute-intensive devices. This article presents an overview of the technologies assisting, challenges, and opportunities for envisioning MEC in future 6G wireless networks.

Published
2021

3. Fuzzy Decision-Based Efficient Task Offloading Management Scheme in Multi-Tier MEC-Enabled Networks

Author

Luan N. T. Huynh, Tangina Sultana, Delowar Hossain, Eui-Nam Huh, Alamgir Hossain, Imtiaz Hossain, and Junyoung Park

Subjects
task offloading, Computer science, Cloud computing, 02 engineering and technology, lcsh:Chemical technology, Biochemistry, multi-access edge computing, Article, Analytical Chemistry, Task (project management), 0203 mechanical engineering, Server, 0202 electrical engineering, electronic engineering, information engineering, lcsh:TP1-1185, Orchestration (computing), Electrical and Electronic Engineering, Instrumentation, orchestrator, Edge computing, business.industry, Quality of service, Node (networking), 020206 networking & telecommunications, 020302 automobile design & engineering, Load balancing (computing), Atomic and Molecular Physics, and Optics, Enhanced Data Rates for GSM Evolution, fuzzy logic, business, Mobile device, 5G, Computer network
Abstract

Multi-access edge computing (MEC) is a new leading technology for meeting the demands of key performance indicators (KPIs) in 5G networks. However, in a rapidly changing dynamic environment, it is hard to find the optimal target server for processing offloaded tasks because we do not know the end users’ demands in advance. Therefore, quality of service (QoS) deteriorates because of increasing task failures and long execution latency from congestion. To reduce latency and avoid task failures from resource-constrained edge servers, vertical offloading between mobile devices with local-edge collaboration or with local edge-remote cloud collaboration have been proposed in previous studies. However, they ignored the nearby edge server in the same tier that has excess computing resources. Therefore, this paper introduces a fuzzy decision-based cloud-MEC collaborative task offloading management system called FTOM, which takes advantage of powerful remote cloud-computing capabilities and utilizes neighboring edge servers. The main objective of the FTOM scheme is to select the optimal target node for task offloading based on server capacity, latency sensitivity, and the network’s condition. Our proposed scheme can make dynamic decisions where local or nearby MEC servers are preferred for offloading delay-sensitive tasks, and delay-tolerant high resource-demand tasks are offloaded to a remote cloud server. Simulation results affirm that our proposed FTOM scheme significantly improves the rate of successfully executing offloaded tasks by approximately 68.5%, and reduces task completion time by 66.6%, when compared with a local edge offloading (LEO) scheme. The improved and reduced rates are 32.4% and 61.5%, respectively, when compared with a two-tier edge orchestration-based offloading (TTEO) scheme. They are 8.9% and 47.9%, respectively, when compared with a fuzzy orchestration-based load balancing (FOLB) scheme, approximately 3.2% and 49.8%, respectively, when compared with a fuzzy workload orchestration-based task offloading (WOTO) scheme, and approximately 38.6%% and 55%, respectively, when compared with a fuzzy edge-orchestration based collaborative task offloading (FCTO) scheme.

Published
2021

4. Fuzzy Based Collaborative Task Offloading Scheme in the Densely Deployed Small-Cell Networks with Multi-Access Edge Computing

Author

Luan N. T. Huynh, VanDung Nguyen, Tri D.T. Nguyen, Eui-Nam Huh, Delowar Hossain, Waqas ur Rahman, and Tangina Sultana

Subjects
Computer science, Cloud computing, 02 engineering and technology, Fuzzy logic, lcsh:Technology, multi-access edge computing, collaborative task offloading, Task (project management), lcsh:Chemistry, Server, 0202 electrical engineering, electronic engineering, information engineering, General Materials Science, Instrumentation, lcsh:QH301-705.5, Edge computing, Fluid Flow and Transfer Processes, business.industry, lcsh:T, Process Chemistry and Technology, Quality of service, General Engineering, 020206 networking & telecommunications, small-cell network, 021001 nanoscience & nanotechnology, lcsh:QC1-999, Computer Science Applications, lcsh:Biology (General), lcsh:QD1-999, lcsh:TA1-2040, Key (cryptography), Small cell, fuzzy logic, 0210 nano-technology, business, lcsh:Engineering (General). Civil engineering (General), lcsh:Physics, Computer network
Abstract

Accelerating the development of the 5G network and Internet of Things (IoT) application, multi-access edge computing (MEC) in a small-cell network (SCN) is designed to provide computation-intensive and latency-sensitive applications through task offloading. However, without collaboration, the resources of a single MEC server are wasted or sometimes overloaded for different service requests and applications, therefore, it increases the user&rsquo, s task failure rate and task duration. Meanwhile, the distinct MEC server has faced some challenges to determine where the offloaded task will be processed because the system can hardly predict the demand of end-users in advance. As a result, the quality-of-service (QoS) will be deteriorated because of service interruptions, long execution, and waiting time. To improve the QoS, we propose a novel Fuzzy logic-based collaborative task offloading (FCTO) scheme in MEC-enabled densely deployed small-cell networks. In FCTO, the delay sensitivity of the QoS is considered as the Fuzzy input parameter to make a decision where to offload the task is beneficial. The key is to share computation resources with each other and among MEC servers by using fuzzy-logic approach to select a target MEC server for task offloading. As a result, it can accommodate more computation workload in the MEC system and reduce reliance on the remote cloud. The simulation result of the proposed scheme show that our proposed system provides the best performances in all scenarios with different criteria compared with other baseline algorithms in terms of the average task failure rate, task completion time, and server utilization.

Published
2020
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5. Collaborative Task Offloading for Overloaded Mobile Edge Computing in Small-Cell Networks

Author

Delowar Hossain, Eui-Nam Huh, Choong Seon Hong, Luan N. T. Huynh, Tri D.T. Nguyen, Tangina Sultana, and Jae Ho Park

Subjects
020203 distributed computing, Mobile edge computing, Computer science, business.industry, 020206 networking & telecommunications, Cloud computing, 02 engineering and technology, Mobile cloud computing, 0202 electrical engineering, electronic engineering, information engineering, Computation offloading, Small cell, business, Mobile device, 5G, Computer network
Abstract

Mobile Edge Computing (MEC) enhances mobile cloud computing capabilities by fetching services close to the edge of the network, which adds 4C (Computing, Communication, Control and Caching) to the edges. MEC-enabled small cell network is regarded as the key technology in future 5G networks where for rapid task execution, a user offloads their tasks to the nearest small BS (SBS). The research work regarding MEC-enabled small cell network is still in its infancy. Recently, some researchers are trying to integrate MEC with small cell networks (SCNs) while ignoring the unlimited computation resource in a remote cloud and the computational capability of a single SBS-MEC server, which has the limited capacity for handling huge number of user request. To effectively handle latency-sensitive tasks and resources-hungry mobile applications in small-cell networks, two collaborative task offloading schemes of our proposed model is introduced in this paper. Our proposed collaborative model can make decision dynamically where the SBS-MEC server collaborate with mobile devices or remote cloud for executing the computation tasks. The simulation results confirm that collaborative task offloading between mobile with SBS-MEC scheme will reduce the average number of task failure more efficiently than other schemes and the collaborative task offloading between SBS-MEC with cloud scheme will provide lower task execution latency than others in small-cell networks.

Published
2020

6. Ontology-Based Recommender System for Sport Events

Author

Tuyen Pham Le, TaeChoong Chung, Quang Nguyen, and Luan N. T. Huynh

Subjects
business.industry, Process (engineering), Computer science, Event (computing), 020206 networking & telecommunications, 02 engineering and technology, Recommender system, Ontology (information science), computer.software_genre, Semantics, World Wide Web, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Mashup, The Internet, business, Set (psychology), computer
Abstract

Sport event participation has changed much recently with effective support of technology. Advanced developments in recommender systems and World Wide Web bring chances such as efficiently distantly booking to alone travelers which allows them to enjoy sport events without being dependent on expensive tourist agencies as in the past. However, currently, popular information sources for such a recommender system are isolated and mainly relied on Web 2.0 formats which are difficult to stored and processed, especially among different platforms and communities. To utilize huge resources of Web 2.0 as well as apply cutting-edge features of Web 3.0 and under-developing Web 4.0, the authors propose an implementation of a hybrid system which collects data from different sources in the Internet (Mashup), apply machine learning to process raw information (Natural Language Processing and Unsupervised Clustering), add semantics to the processed data and make it compatible to latest web generation (Ontology), and provide recommendations based on smart content-based filtering and social-network-based user profiles for sport events. Empirical results show promising applications of such a framework to the market portion of alone travelers and also set an example as a demonstration for the authors’ expectation toward Web 4.0 applications in the future.

Published
2019

7. Comparison of Kaposi Sarcoma Risk in Human Immunodeficiency Virus-Positive Adults Across 5 Continents: A Multiregional Multicohort Study

Author

Rohner E., Butikofer L., Schmidlin K., Sengayi M., Maskew M., Giddy J., Garone D., Moore R. D., D'Souza G., Goedert J. J., Achenbach C., Gill M. J., Kitahata M. M., Patel P., Silverberg M. J., Castilho J., McGowan C., Chen Y. -M. A., Law M., Taylor N., Paparizos V., Bonnet F., Verbon A., Fatkenheuer G., Post F. A., Sabin C., Mocroft A., Le Moing V., Dronda F., Obel N., Grabar S., Spagnuolo V., Antinori A., Quiros-Roldan E., Mussini C., Miro J. M., Meyer L., Hasse B., Konopnicki D., Roca B., Barger D., Raben D., Clifford G. M., Franceschi S., Brockmeyer N., Chakraborty R., Egger M., Bohlius J., Judd A., Zangerle R., Touloumi G., Warszawski J., Dabis F., Krause M. M., Ghosn J., Leport C., Wittkop L., Reiss P., Wit F., Prins M., Bucher H., Gibb D., Del Amo J., Thorne C., Kirk O., Stephan C., Perez-Hoyos S., Hamouda O., Bartmeyer B., Chkhartishvili N., Noguera-Julian A., Monforte A. D., Prieto L., Conejo P. R., Soriano-Arandes A., Battegay M., Kouyos R., Tookey P., Casabona J., Castagna A., Konopnick D., Goetghebuer T., Sonnerborg A., Teira R., Garrido M., Haerry D., De Wit S., Costagliola D., D'Arminio-Monforte A., Chene G., Schwimmer C., Termote M., Campbell M., Frederiksen C. M., Friis-Moller N., Kjaer J., Brandt R. S., Berenguer J., Bouteloup V., Cozzi-Lepri A., Davies M. -A., Dorrucci M., Dunn D., Furrer H., Guiguet M., Lambotte O., Leroy V., Lodi S., Matheron S., Monge S., Nakagawa F., Paredes R., Phillips A., Puoti M., Schomaker M., Smit C., Sterne J., Thiebaut R., Torti C., Van Der Valk M., Tanser F., Vinikoor M., MacEte E., Wood R., Stinson K., Fatti G., Phiri S., Chimbetete C., Malisita K., Eley B., Fritz C., Hobbins M., Kamenova K., Fox M., Prozesky H., Technau K., Sawry S., Benson C. A., Bosch R. J., Kirk G. D., Boswell S., Mayer K. H., Grasso C., Hogg R. S., Harrigan P. R., Montaner J. S. G., Yip B., Zhu J., Salters K., Gabler K., Buchacz K., Brooks J. T., Gebo K. A., Carey J. T., Rodriguez B., Horberg M. A., Thorne J. E., Rabkin C., Margolick J. B., Jacobson L. P., Klein M. B., Rourke S. B., Rachlis A. R., Cupido P., Hunter-Mellado R. F., Mayor A. M., Deeks S. G., Martin J. N., Saag M. S., Mugavero M. J., Willig J., Eron J. J., Napravnik S., Crane H. M., Drozd D. R., Haas D., Rebeiro P., Turner M., Bebawy S., Rogers B., Justice A. C., Dubrow R., Fiellin D., Gange S. J., Anastos K., Althoff K. N., McKaig R. G., Freeman A. M., Lent C., Van Rompaey S. E., Morton L., McReynolds J., Lober W. B., Abraham A. G., Lau B., Zhang J., Jing J., Modur S., Wong C., Hogan B., Desir F., Liu B., You B., Cahn P., Cesar C., Fink V., Sued O., Dell'Isola E., Perez H., Valiente J., Yamamoto C., Grinsztejn B., Veloso V., Luz P., De Boni R., Wagner S. C., Friedman R., Moreira R., Pinto J., Ferreira F., Maia M., De Menezes Succi R. C., MacHado D. M., De Fatima Barbosa Gouvea A., Wolff M., Cortes C., Rodriguez M. F., Allendes G., Pape J. W., Rouzier V., Marcelin A., Perodin C., Luque M. T., Padgett D., Madero J. S., Ramirez B. C., Belaunzaran P., Vega Y. C., Gotuzzo E., Mejia F., Carriquiry G., McGowan C. C., Shepherd B. E., Sterling T., Jayathilake K., Person A. K., Rebeiro P. F., Giganti M., Duda S. N., Maruri F., Vansell H., Ly P. S., Khol V., Zhang F. J., Zhao H. X., Han N., Lee M. P., Li P. C. K., Lam W., Chan Y. T., Kumarasamy N., Saghayam S., Ezhilarasi C., Pujari S., Joshi K., Gaikwad S., Chitalikar A., Merati T. P., Wirawan D. N., Yuliana F., Yunihastuti E., Imran D., Widhani A., Tanuma J., Oka S., Nishijima T., Choi J. Y., Na S., Kim J. M., Sim B. L. H., Gani Y. M., David R., Kamarulzaman A., Syed Omar S. F., Ponnampalavanar S., Azwa I., Ditangco R., Uy E., Bantique R., Wong W. W., Ku W. W., Wu P. C., Ng O. T., Lim P. L., Lee L. S., Ohnmar P. S., Avihingsanon A., Gatechompol S., Phanuphak P., Phadungphon C., Kiertiburanakul S., Sungkanuparph S., Chumla L., Sanmeema N., Chaiwarith R., Sirisanthana T., Kotarathititum W., Praparattanapan J., Kantipong P., Kambua P., Ratanasuwan W., Sriondee R., Nguyen K. V., Bui H. V., Nguyen D. T. H., Nguyen D. T., Cuong D. D., An N. V., Luan N. T., Sohn A. H., Ross J. L., Petersen B., Cooper D. A., Law M. G., Jiamsakul A., Boettiger D. C., Ellis D., Bloch M., Agrawal S., Vincent T., Allen D., Smith D., Rankin A., Baker D., Templeton D. J., Jackson E., McCallum K., Ryder N., Sweeney G., Cooper D., Carr A., MacRae K., Hesse K., Finlayson R., Gupta S., Langton-Lockton J., Shakeshaft J., Brown K., Idle S., Arvela N., Varma R., Lu H., Couldwell D., Eswarappa S., Smith D. E., Furner V., Cabrera G., Fernando S., Cogle A., Lawrence C., Mulhall B., Boyd M., Petoumenos K., Puhr R., Huang R., Han A., Gunathilake M., Payne R., O'Sullivan M., Croydon A., Russell D., Cashman C., Roberts C., Sowden D., Taing K., Marshall P., Orth D., Youds D., Rowling D., Latch N., Warzywoda E., Dickson B., Donohue W., Moore R., Edwards S., Boyd S., Roth N. J., Lau H., Read T., Silvers J., Zeng W., Hoy J., Watson K., Bryant M., Price S., Woolley I., Giles M., Korman T., Williams J., Nolan D., Allen A., Guelfi G., Mills G., Wharry C., Raymond N., Bargh K., Templeton D., Medical Microbiology & Infectious Diseases, Global Health, Infectious diseases, APH - Aging & Later Life, AII - Infectious diseases, APH - Global Health, Graduate School, APH - Digital Health, APH - Personalized Medicine, Bohlius, Julia, Cohere In, Eurocoord, Castagna, Antonella, Rohner, E, Butikofer, L, Schmidlin, K, Sengayi, M, Maskew, M, Giddy, J, Garone, D, Moore, R, D'Souza, G, Goedert, J, Achenbach, C, Gill, M, Kitahata, M, Patel, P, Silverberg, M, Castilho, J, Mcgowan, C, Chen, Y, Law, M, Taylor, N, Paparizos, V, Bonnet, F, Verbon, A, Fatkenheuer, G, Post, F, Sabin, C, Mocroft, A, Le Moing, V, Dronda, F, Obel, N, Grabar, S, Spagnuolo, V, Antinori, A, Quiros-Roldan, E, Mussini, C, Miro, J, Meyer, L, Hasse, B, Konopnicki, D, Roca, B, Barger, D, Raben, D, Clifford, G, Franceschi, S, Brockmeyer, N, Chakraborty, R, Egger, M, Bohlius, J, Judd, A, Zangerle, R, Touloumi, G, Warszawski, J, Dabis, F, Krause, M, Ghosn, J, Leport, C, Wittkop, L, Reiss, P, Wit, F, Prins, M, Bucher, H, Gibb, D, Del Amo, J, Thorne, C, Kirk, O, Stephan, C, Perez-Hoyos, S, Hamouda, O, Bartmeyer, B, Chkhartishvili, N, Noguera-Julian, A, Monforte, A, Prieto, L, Conejo, P, Soriano-Arandes, A, Battegay, M, Kouyos, R, Tookey, P, Casabona, J, Castagna, A, Konopnick, D, Goetghebuer, T, Sonnerborg, A, Teira, R, Garrido, M, Haerry, D, De Wit, S, Costagliola, D, D'Arminio-Monforte, A, Chene, G, Schwimmer, C, Termote, M, Campbell, M, Frederiksen, C, Friis-Moller, N, Kjaer, J, Brandt, R, Berenguer, J, Bouteloup, V, Cozzi-Lepri, A, Davies, M, Dorrucci, M, Dunn, D, Furrer, H, Guiguet, M, Lambotte, O, Leroy, V, Lodi, S, Matheron, S, Monge, S, Nakagawa, F, Paredes, R, Phillips, A, Puoti, M, Schomaker, M, Smit, C, Sterne, J, Thiebaut, R, Torti, C, Van Der Valk, M, Tanser, F, Vinikoor, M, Macete, E, Wood, R, Stinson, K, Fatti, G, Phiri, S, Chimbetete, C, Malisita, K, Eley, B, Fritz, C, Hobbins, M, Kamenova, K, Fox, M, Prozesky, H, Technau, K, Sawry, S, Benson, C, Bosch, R, Kirk, G, Boswell, S, Mayer, K, Grasso, C, Hogg, R, Harrigan, P, Montaner, J, Yip, B, Zhu, J, Salters, K, Gabler, K, Buchacz, K, Brooks, J, Gebo, K, Carey, J, Rodriguez, B, Horberg, M, Thorne, J, Rabkin, C, Margolick, J, Jacobson, L, Klein, M, Rourke, S, Rachlis, A, Cupido, P, Hunter-Mellado, R, Mayor, A, Deeks, S, Martin, J, Saag, M, Mugavero, M, Willig, J, Eron, J, Napravnik, S, Crane, H, Drozd, D, Haas, D, Rebeiro, P, Turner, M, Bebawy, S, Rogers, B, Justice, A, Dubrow, R, Fiellin, D, Gange, S, Anastos, K, Althoff, K, Mckaig, R, Freeman, A, Lent, C, Van Rompaey, S, Morton, L, Mcreynolds, J, Lober, W, Abraham, A, Lau, B, Zhang, J, Jing, J, Modur, S, Wong, C, Hogan, B, Desir, F, Liu, B, You, B, Cahn, P, Cesar, C, Fink, V, Sued, O, Dell'Isola, E, Perez, H, Valiente, J, Yamamoto, C, Grinsztejn, B, Veloso, V, Luz, P, De Boni, R, Wagner, S, Friedman, R, Moreira, R, Pinto, J, Ferreira, F, Maia, M, De Menezes Succi, R, Machado, D, De Fatima Barbosa Gouvea, A, Wolff, M, Cortes, C, Rodriguez, M, Allendes, G, Pape, J, Rouzier, V, Marcelin, A, Perodin, C, Luque, M, Padgett, D, Madero, J, Ramirez, B, Belaunzaran, P, Vega, Y, Gotuzzo, E, Mejia, F, Carriquiry, G, Shepherd, B, Sterling, T, Jayathilake, K, Person, A, Giganti, M, Duda, S, Maruri, F, Vansell, H, Ly, P, Khol, V, Zhang, F, Zhao, H, Han, N, Lee, M, Li, P, Lam, W, Chan, Y, Kumarasamy, N, Saghayam, S, Ezhilarasi, C, Pujari, S, Joshi, K, Gaikwad, S, Chitalikar, A, Merati, T, Wirawan, D, Yuliana, F, Yunihastuti, E, Imran, D, Widhani, A, Tanuma, J, Oka, S, Nishijima, T, Choi, J, Na, S, Kim, J, Sim, B, Gani, Y, David, R, Kamarulzaman, A, Syed Omar, S, Ponnampalavanar, S, Azwa, I, Ditangco, R, Uy, E, Bantique, R, Wong, W, Ku, W, Wu, P, Ng, O, Lim, P, Lee, L, Ohnmar, P, Avihingsanon, A, Gatechompol, S, Phanuphak, P, Phadungphon, C, Kiertiburanakul, S, Sungkanuparph, S, Chumla, L, Sanmeema, N, Chaiwarith, R, Sirisanthana, T, Kotarathititum, W, Praparattanapan, J, Kantipong, P, Kambua, P, Ratanasuwan, W, Sriondee, R, Nguyen, K, Bui, H, Nguyen, D, Cuong, D, An, N, Luan, N, Sohn, A, Ross, J, Petersen, B, Cooper, D, Jiamsakul, A, Boettiger, D, Ellis, D, Bloch, M, Agrawal, S, Vincent, T, Allen, D, Smith, D, Rankin, A, Baker, D, Templeton, D, Jackson, E, Mccallum, K, Ryder, N, Sweeney, G, Carr, A, Macrae, K, Hesse, K, Finlayson, R, Gupta, S, Langton-Lockton, J, Shakeshaft, J, Brown, K, Idle, S, Arvela, N, Varma, R, Lu, H, Couldwell, D, Eswarappa, S, Furner, V, Cabrera, G, Fernando, S, Cogle, A, Lawrence, C, Mulhall, B, Boyd, M, Petoumenos, K, Puhr, R, Huang, R, Han, A, Gunathilake, M, Payne, R, O'Sullivan, M, Croydon, A, Russell, D, Cashman, C, Roberts, C, Sowden, D, Taing, K, Marshall, P, Orth, D, Youds, D, Rowling, D, Latch, N, Warzywoda, E, Dickson, B, Donohue, W, Edwards, S, Boyd, S, Roth, N, Lau, H, Read, T, Silvers, J, Zeng, W, Hoy, J, Watson, K, Bryant, M, Price, S, Woolley, I, Giles, M, Korman, T, Williams, J, Nolan, D, Allen, A, Guelfi, G, Mills, G, Wharry, C, Raymond, N, and Bargh, K

Subjects
Male, viruses, HIV Infections, Men who have sex with men, Cohort Studies, 0302 clinical medicine, Risk Factors, Antiretroviral therapy, Cohort study, HIV, Kaposi sarcoma, Adolescent, Adult, Anti-Retroviral Agents, CD4 Lymphocyte Count, Female, HIV-1, Humans, Middle Aged, Sarcoma, Kaposi, Viral Load, Young Adult, 030212 general & internal medicine, Articles and Commentaries, Incidence (epidemiology), Hazard ratio, virus diseases, Sarcoma, 3. Good health, Infectious Diseases, 030220 oncology & carcinogenesis, Cohort, Coinfection, Microbiology (medical), antiretroviral therapy, 610 Medicine & health, Kaposi, 03 medical and health sciences, SDG 3 - Good Health and Well-being, 360 Social problems & social services, medicine, cohort study, business.industry, medicine.disease, Confidence interval, business, Demography
Abstract

Background We compared Kaposi sarcoma (KS) risk in adults who started antiretroviral therapy (ART) across the Asia-Pacific, South Africa, Europe, Latin, and North America. Methods We included cohort data of human immunodeficiency virus (HIV)-positive adults who started ART after 1995 within the framework of 2 large collaborations of observational HIV cohorts. We present incidence rates and adjusted hazard ratios (aHRs). Results We included 208140 patients from 57 countries. Over a period of 1066572 person-years, 2046 KS cases were diagnosed. KS incidence rates per 100000 person-years were 52 in the Asia-Pacific and ranged between 180 and 280 in the other regions. KS risk was 5 times higher in South African women (aHR, 4.56; 95% confidence intervals [CI], 2.73-7.62) than in their European counterparts, and 2 times higher in South African men (2.21; 1.34-3.63). In Europe, Latin, and North America KS risk was 6 times higher in men who have sex with men (aHR, 5.95; 95% CI, 5.09-6.96) than in women. Comparing patients with current CD4 cell counts ≥700 cells/µL with those whose counts were

Published
2017

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