A prediction risk score for HIV among adolescent girls and young women in South Africa : identifying those in need of HIV pre-exposure prophylaxis

dc.contributor.authorMoyo, Reuben Christopher
dc.contributor.authorGovindasamy, Darshini
dc.contributor.authorManda, S.O.M. (Samuel)
dc.contributor.authorNyasulu, Peter
dc.date.accessioned2024-07-10T11:49:37Z
dc.date.available2024-07-10T11:49:37Z
dc.date.issued2023-06-08
dc.descriptionAVAILABILITY OF DATA AND MATERIAL : The datasets analyzed during the current study are not publicly available due Data protection policy of South African Medical Research Council (SAMRC) which only makes the data available on request. Data can be requested from SAMRC through Dr Darshini Govindasamy on darshini. govindasamy@mrc.ac.zaen_US
dc.descriptionSouth African Medical Research Council (SAMRC) for authorizing HERStory data to be used in the development of this risk prediction model.en_US
dc.description.abstractBACKGROUND : 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 analyzed 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.en_US
dc.description.departmentStatisticsen_US
dc.description.librarianam2024en_US
dc.description.sdgSDG-03:Good heatlh and well-beingen_US
dc.description.urihttps://www.tandfonline.com/journals/YHCTen_US
dc.identifier.citationReuben Christopher Moyo, Darshini Govindasamy, Samuel Om Manda & Peter Suwirakwenda Nyasulu (2023) A prediction risk score for HIV among adolescent girls and young women in South Africa: identifying those in need of HIV pre-exposure prophylaxis, HIV Research & Clinical Practice, 24:1, 2221377, DOI: 10.1080/25787489.2023.2221377.en_US
dc.identifier.issn2578-7489 (print)
dc.identifier.issn2578-7470 (online)
dc.identifier.other10.1080/25787489.2023.2221377
dc.identifier.urihttp://hdl.handle.net/2263/96914
dc.language.isoenen_US
dc.publisherTaylor and Francis Groupen_US
dc.rights© 2023 The Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution License.en_US
dc.subjectRisk scoreen_US
dc.subjectHuman immunodeficiency virus (HIV)en_US
dc.subjectPre-exposure prophylaxis (PrEP)en_US
dc.subjectSub-Saharan Africa (SSA)en_US
dc.subjectAdolescent girls and young women (AGYW)en_US
dc.subjectSDG-03: Good health and well-beingen_US
dc.titleA prediction risk score for HIV among adolescent girls and young women in South Africa : identifying those in need of HIV pre-exposure prophylaxisen_US
dc.typeArticleen_US

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