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Marley G, Fu G, Zhang Y, Li J, Tucker JD, Tang W, Yu R. Willingness of Chinese Men Who Have Sex With Men to Use Smartphone-Based Electronic Readers for HIV Self-testing: Web-Based Cross-sectional Study. J Med Internet Res 2021; 23:e26480. [PMID: 34806988 PMCID: PMC8663451 DOI: 10.2196/26480] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Revised: 04/21/2021] [Accepted: 10/08/2021] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND The need for strategies to encourage user-initiated reporting of results after HIV self-testing (HIVST) persists. Smartphone-based electronic readers (SERs) have been shown capable of reading diagnostics results accurately in point-of-care diagnostics and could bridge the current gaps between HIVST and linkage to care. OBJECTIVE Our study aimed to assess the willingness of Chinese men who have sex with men (MSM) in the Jiangsu province to use an SER for HIVST through a web-based cross-sectional study. METHODS From February to April 2020, we conducted a convenience web-based survey among Chinese MSM by using a pretested structured questionnaire. Survey items were adapted from previous HIVST feasibility studies and modified as required. Prior to answering reader-related questions, participants watched a video showcasing a prototype SER. Statistical analysis included descriptive analysis, chi-squared test, and multivariable logistic regression. P values less than .05 were deemed statistically significant. RESULTS Of 692 participants, 369 (53.3%) were aged 26-40 years, 456 (65.9%) had ever self-tested for HIV, and 493 (71.2%) were willing to use an SER for HIVST. Approximately 98% (483/493) of the willing participants, 85.3% (459/538) of ever self-tested and never self-tested, and 40% (46/115) of unwilling participants reported that SERs would increase their HIVST frequency. Engaging in unprotected anal intercourse with regular partners compared to consistently using condoms (adjusted odds ratio [AOR] 3.04, 95% CI 1.19-7.74) increased the odds of willingness to use an SER for HIVST. Participants who had ever considered HIVST at home with a partner right before sex compared to those who had not (AOR 2.99, 95% CI 1.13-7.90) were also more willing to use an SER for HIVST. Playing receptive roles during anal intercourse compared to playing insertive roles (AOR 0.05, 95% CI 0.02-0.14) was associated with decreased odds of being willing to use an SER for HIVST. The majority of the participants (447/608, 73.5%) preferred to purchase readers from local Centers of Disease Control and Prevention offices and 51.2% (311/608) of the participants were willing to pay less than US $4.70 for a reader device. CONCLUSIONS The majority of the Chinese MSM, especially those with high sexual risk behaviors, were willing to use an SER for HIVST. Many MSM were also willing to self-test more frequently for HIV with an SER. Further research is needed to ascertain the diagnostic and real-time data-capturing capacity of prototype SERs during HIVST.
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Affiliation(s)
- Gifty Marley
- School of Public Health, Nanjing Medical University, Nanjing, China.,The Social Entrepreneurship to Spur Health Project, The University of North Carolina Project-China, Guangzhou, China
| | - Gengfeng Fu
- Section of STD/AIDS Prevention and Control, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Ye Zhang
- Kirby Institute, The University of New South Wales, Sydney, Australia
| | - Jianjun Li
- Section of STD/AIDS Prevention and Control, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Joseph D Tucker
- The Social Entrepreneurship to Spur Health Project, The University of North Carolina Project-China, Guangzhou, China.,Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Weiming Tang
- The Social Entrepreneurship to Spur Health Project, The University of North Carolina Project-China, Guangzhou, China.,Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Rongbin Yu
- School of Public Health, Nanjing Medical University, Nanjing, China
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Turbé V, Herbst C, Mngomezulu T, Meshkinfamfard S, Dlamini N, Mhlongo T, Smit T, Cherepanova V, Shimada K, Budd J, Arsenov N, Gray S, Pillay D, Herbst K, Shahmanesh M, McKendry RA. Deep learning of HIV field-based rapid tests. Nat Med 2021; 27:1165-1170. [PMID: 34140702 PMCID: PMC7611654 DOI: 10.1038/s41591-021-01384-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 05/06/2021] [Indexed: 02/04/2023]
Abstract
Although deep learning algorithms show increasing promise for disease diagnosis, their use with rapid diagnostic tests performed in the field has not been extensively tested. Here we use deep learning to classify images of rapid human immunodeficiency virus (HIV) tests acquired in rural South Africa. Using newly developed image capture protocols with the Samsung SM-P585 tablet, 60 fieldworkers routinely collected images of HIV lateral flow tests. From a library of 11,374 images, deep learning algorithms were trained to classify tests as positive or negative. A pilot field study of the algorithms deployed as a mobile application demonstrated high levels of sensitivity (97.8%) and specificity (100%) compared with traditional visual interpretation by humans-experienced nurses and newly trained community health worker staff-and reduced the number of false positives and false negatives. Our findings lay the foundations for a new paradigm of deep learning-enabled diagnostics in low- and middle-income countries, termed REASSURED diagnostics1, an acronym for real-time connectivity, ease of specimen collection, affordable, sensitive, specific, user-friendly, rapid, equipment-free and deliverable. Such diagnostics have the potential to provide a platform for workforce training, quality assurance, decision support and mobile connectivity to inform disease control strategies, strengthen healthcare system efficiency and improve patient outcomes and outbreak management in emerging infections.
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Affiliation(s)
- Valérian Turbé
- London Centre for Nanotechnology, University College London, London, UK.
| | - Carina Herbst
- Africa Health Research Institute, Nelson R. Mandela Medical School, Durban, South Africa
| | - Thobeka Mngomezulu
- Africa Health Research Institute, Nelson R. Mandela Medical School, Durban, South Africa
| | | | - Nondumiso Dlamini
- Africa Health Research Institute, Nelson R. Mandela Medical School, Durban, South Africa
| | - Thembani Mhlongo
- Africa Health Research Institute, Nelson R. Mandela Medical School, Durban, South Africa
| | - Theresa Smit
- Africa Health Research Institute, Nelson R. Mandela Medical School, Durban, South Africa
| | | | - Koki Shimada
- Department of Computer Science, University College London, London, UK
| | - Jobie Budd
- London Centre for Nanotechnology, University College London, London, UK
- Division of Medicine, University College London, London, UK
| | - Nestor Arsenov
- London Centre for Nanotechnology, University College London, London, UK
| | - Steven Gray
- UCL Centre for Advanced Spatial Analysis, London, UK
| | - Deenan Pillay
- Africa Health Research Institute, Nelson R. Mandela Medical School, Durban, South Africa
- Division of Infection and Immunity, University College London, London, UK
| | - Kobus Herbst
- Africa Health Research Institute, Nelson R. Mandela Medical School, Durban, South Africa.
- DSI-MRC South African Population Research Infrastructure Network, Durban, South Africa.
| | - Maryam Shahmanesh
- Africa Health Research Institute, Nelson R. Mandela Medical School, Durban, South Africa.
- Institute for Global Health, University College London, London, UK.
| | - Rachel A McKendry
- London Centre for Nanotechnology, University College London, London, UK.
- Division of Medicine, University College London, London, UK.
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Ding X, Mauk MG, Yin K, Kadimisetty K, Liu C. Interfacing Pathogen Detection with Smartphones for Point-of-Care Applications. Anal Chem 2019; 91:655-672. [PMID: 30428666 PMCID: PMC6867037 DOI: 10.1021/acs.analchem.8b04973] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Xiong Ding
- Department of Biomedical Engineering, University of Connecticut Health Center, Farmington, Connecticut 06030, USA
| | - Michael G. Mauk
- Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Kun Yin
- Department of Biomedical Engineering, University of Connecticut Health Center, Farmington, Connecticut 06030, USA
| | - Karteek Kadimisetty
- Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Changchun Liu
- Department of Biomedical Engineering, University of Connecticut Health Center, Farmington, Connecticut 06030, USA
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