dc.contributor.author |
Moyo, Reuben Christopher
|
|
dc.contributor.author |
Govindasamy, Darshini
|
|
dc.contributor.author |
Manda, S.O.M. (Samuel)
|
|
dc.contributor.author |
Nyasulu, Peter
|
|
dc.date.accessioned |
2024-07-10T11:49:37Z |
|
dc.date.available |
2024-07-10T11:49:37Z |
|
dc.date.issued |
2023-06-08 |
|
dc.description |
AVAILABILITY 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.za |
en_US |
dc.description |
South African Medical Research Council (SAMRC) for authorizing HERStory data to be used in the development of this risk prediction model. |
en_US |
dc.description.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 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.department |
Statistics |
en_US |
dc.description.librarian |
am2024 |
en_US |
dc.description.sdg |
SDG-03:Good heatlh and well-being |
en_US |
dc.description.uri |
https://www.tandfonline.com/journals/YHCT |
en_US |
dc.identifier.citation |
Reuben 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.issn |
2578-7489 (print) |
|
dc.identifier.issn |
2578-7470 (online) |
|
dc.identifier.other |
10.1080/25787489.2023.2221377 |
|
dc.identifier.uri |
http://hdl.handle.net/2263/96914 |
|
dc.language.iso |
en |
en_US |
dc.publisher |
Taylor and Francis Group |
en_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.subject |
Risk score |
en_US |
dc.subject |
Human immunodeficiency virus (HIV) |
en_US |
dc.subject |
Pre-exposure prophylaxis (PrEP) |
en_US |
dc.subject |
Sub-Saharan Africa (SSA) |
en_US |
dc.subject |
Adolescent girls and young women (AGYW) |
en_US |
dc.subject |
SDG-03: Good health and well-being |
en_US |
dc.title |
A prediction risk score for HIV among adolescent girls and young women in South Africa : identifying those in need of HIV pre-exposure prophylaxis |
en_US |
dc.type |
Article |
en_US |