A Prediction Risk Score for HIV among Adolescent Girls and Young Women in South Africa: Identifying those in Need of HIV Pre-Exposure Prophylaxis

Author:

Moyo Reuben Christopher1,Govindasamy Darshini2,Manda Samuel3,Nyasulu Peter Suwilakwenda1

Affiliation:

1. Stellenbosch University

2. South African Medical Research Council

3. University of Pretoria

Abstract

Abstract Background: In sub-Saharan Africa (SSA), adolescent girls and young women (AGYW) have the highest risk of acquiring HIV. This has led to several studies aimed at identifying risk factors for HIV in AGYM. However, a combination of the purported risk variables in a multivariate risk model could be more useful in determining HIV risk in AGYW than one at a time. The purpose of this study was to develop and validate an HIV risk prediction model for AGYW. Methods: We analysed HIV-related HERStory survey data on 4,399 AGYW from South Africa. We identified 16 purported risk variables from the data set. The HIV acquisition risk scores were computed by combining coefficients of a multivariate logistic regression model of HIV positivity. The performance of the final model at discriminating between HIV positive and HIV negative was assessed using the area under the receiver-operating characteristic curve (AUROC). The optimal cut-point of the prediction model was determined using the Youden index. We also used other measures of discriminative abilities such as predictive values, sensitivity, and specificity. Results: The estimated HIV prevalence was 12.4% (11.7% – 14.0) %. The score of the derived risk prediction model had a mean and standard deviation of 2.36 and 0.64 respectively and ranged from 0.37 to 4.59. The prediction model’s sensitivity was 16. 7% and a specificity of 98.5%. The model’s positive predictive value was 68.2% and a negative predictive value of 85.8%. The prediction model’s optimal cut-point was 2.43 with sensitivity of 71% and specificity of 60%. Our model performed well at predicting HIV positivity with training AUC of 0.78 and a testing AUC of 0.76. Conclusion: A combination of the identified risk factors provided good discrimination and calibration at predicting HIV positivity in AGYW. This model could provide a simple and low-cost strategy for screening AGYW in primary healthcare clinics and community-based settings. In this way, health service providers could easily identify and link AGYW to HIV PrEP services.

Publisher

Research Square Platform LLC

Reference33 articles.

1. United Nations Children’s Fund (UNICEF). Aldolescent HIV prevention. http://dataunicef/topic/hivaids/adolescents-youth-Accessed on 19/10/2020. 2020.

2. World Health Organization (WHO). Maternal, newborn child and adolescent health.. http://www.who.int/maternal_child_adolescent/topics/maternal/adolescent_HIV/en/. Accessed on 19/10/2020.

3. National Department of Health (NDoH). Statistics South Africa (Stats SA), South African Medical Research Council (SAMRC), and ICF. Pretoria, South Africa, and Rockville, Maryland, USA: NDoH, Stats SA, SAMRC and ICF. South Africa Demographic and Health Survey 2016. 2019.

4. SAMRC. Evaluation of a South African Combination HIV Prevention Programme for Adolescent Girls and Young Women. 2020;(August):128.

5. The DREAMS core package of interventions: A comprehensive approach to preventing HIV among adolescent girls and young women;Saul J;PLoS ONE,2018

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