Utility of a Machine-Guided Tool for Assessing Risk Behavior Associated With Contracting HIV in Three Sites in South Africa: Protocol for an In-Field Evaluation.

HIV HIV status South Africa algorithm machine learning modeling predictive risk risk assessment

Journal

JMIR research protocols
ISSN: 1929-0748
Titre abrégé: JMIR Res Protoc
Pays: Canada
ID NLM: 101599504

Informations de publication

Date de publication:
02 Dec 2021
Historique:
received: 10 05 2021
accepted: 10 09 2021
revised: 25 08 2021
entrez: 3 12 2021
pubmed: 4 12 2021
medline: 4 12 2021
Statut: epublish

Résumé

Mobile technology has helped to advance health programs, and studies have shown that an automated risk prediction model can successfully be used to identify patients who exhibit a high probable risk of contracting human immunodeficiency virus (HIV). A machine-guided tool is an algorithm that takes a set of subjective and objective answers from a simple questionnaire and computes an HIV risk assessment score. The primary objective of this study is to establish that machine learning can be used to develop machine-guided tools and give us a deeper statistical understanding of the correlation between certain behavioral patterns and HIV. In total, 200 HIV-negative adult individuals across three South African study sites each (two semirural and one urban) will be recruited. Study processes will include (1) completing a series of questions (demographic, sexual behavior and history, personal, lifestyle, and symptoms) on an application system, unaided (assistance will only be provided upon user request); (2) two HIV tests (one per study visit) being performed by a nurse/counselor according to South African national guidelines (to evaluate the prediction accuracy of the tool); and (3) communicating test results and completing a user experience survey questionnaire. The output metrics for this study will be computed by using the participants' risk assessment scores as "predictions" and the test results as the "ground truth." Analyses will be completed after visit 1 and then again after visit 2. All risk assessment scores will be used to calculate the reliability of the machine-guided tool. Ethical approval was received from the University of Witwatersrand Human Research Ethics Committee (HREC; ethics reference no. 200312) on August 20, 2020. This study is ongoing. Data collection has commenced and is expected to be completed in the second half of 2021. We will report on the machine-guided tool's performance and usability, together with user satisfaction and recommendations for improvement. Machine-guided risk assessment tools can provide a cost-effective alternative to large-scale HIV screening and help in providing targeted counseling and testing to prevent the spread of HIV. South African National Clinical Trial Registry DOH-27-042021-679; https://sanctr.samrc.ac.za/TrialDisplay.aspx?TrialID=5545. DERR1-10.2196/30304.

Sections du résumé

BACKGROUND BACKGROUND
Mobile technology has helped to advance health programs, and studies have shown that an automated risk prediction model can successfully be used to identify patients who exhibit a high probable risk of contracting human immunodeficiency virus (HIV). A machine-guided tool is an algorithm that takes a set of subjective and objective answers from a simple questionnaire and computes an HIV risk assessment score.
OBJECTIVE OBJECTIVE
The primary objective of this study is to establish that machine learning can be used to develop machine-guided tools and give us a deeper statistical understanding of the correlation between certain behavioral patterns and HIV.
METHODS METHODS
In total, 200 HIV-negative adult individuals across three South African study sites each (two semirural and one urban) will be recruited. Study processes will include (1) completing a series of questions (demographic, sexual behavior and history, personal, lifestyle, and symptoms) on an application system, unaided (assistance will only be provided upon user request); (2) two HIV tests (one per study visit) being performed by a nurse/counselor according to South African national guidelines (to evaluate the prediction accuracy of the tool); and (3) communicating test results and completing a user experience survey questionnaire. The output metrics for this study will be computed by using the participants' risk assessment scores as "predictions" and the test results as the "ground truth." Analyses will be completed after visit 1 and then again after visit 2. All risk assessment scores will be used to calculate the reliability of the machine-guided tool.
RESULTS RESULTS
Ethical approval was received from the University of Witwatersrand Human Research Ethics Committee (HREC; ethics reference no. 200312) on August 20, 2020. This study is ongoing. Data collection has commenced and is expected to be completed in the second half of 2021. We will report on the machine-guided tool's performance and usability, together with user satisfaction and recommendations for improvement.
CONCLUSIONS CONCLUSIONS
Machine-guided risk assessment tools can provide a cost-effective alternative to large-scale HIV screening and help in providing targeted counseling and testing to prevent the spread of HIV.
TRIAL REGISTRATION BACKGROUND
South African National Clinical Trial Registry DOH-27-042021-679; https://sanctr.samrc.ac.za/TrialDisplay.aspx?TrialID=5545.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) UNASSIGNED
DERR1-10.2196/30304.

Identifiants

pubmed: 34860679
pii: v10i12e30304
doi: 10.2196/30304
pmc: PMC8686409
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e30304

Informations de copyright

©Mohammed Majam, Mothepane Phatsoane, Keith Hanna, Charles Faul, Lovkesh Arora, Sarvesh Makthal, Akhil Kumar, Kashyap Jois, Samanta Tresha Lalla-Edward. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 02.12.2021.

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Auteurs

Mohammed Majam (M)

Ezintsha, Faculty of Health Sciences, University of Witswatersrand, Johannesburg, South Africa.

Mothepane Phatsoane (M)

Ezintsha, Faculty of Health Sciences, University of Witswatersrand, Johannesburg, South Africa.

Keith Hanna (K)

IPRD Solutions, New York, NY, United States.

Charles Faul (C)

IPRD Solutions, New York, NY, United States.

Lovkesh Arora (L)

IPRD Solutions, New York, NY, United States.

Sarvesh Makthal (S)

IPRD Solutions, New York, NY, United States.

Akhil Kumar (A)

IPRD Solutions, New York, NY, United States.

Kashyap Jois (K)

IPRD Solutions, New York, NY, United States.

Samanta Tresha Lalla-Edward (ST)

Ezintsha, Faculty of Health Sciences, University of Witswatersrand, Johannesburg, South Africa.

Classifications MeSH