A prediction risk score for HIV among adolescent girls and young women in South Africa: identifying those in need of HIV pre-exposure prophylaxis
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...
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| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | HIV Research & Clinical Practice |
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| Online Access: | http://dx.doi.org/10.1080/25787489.2023.2221377 |
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| author | Reuben Christopher Moyo Darshini Govindasamy Samuel Om Manda Peter Suwirakwenda Nyasulu |
| author_facet | Reuben Christopher Moyo Darshini Govindasamy Samuel Om Manda Peter Suwirakwenda Nyasulu |
| author_sort | Reuben Christopher Moyo |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-91791a25371d4fb19f5e2a7403969db4 |
| institution | Kabale University |
| issn | 2578-7470 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | HIV Research & Clinical Practice |
| spelling | doaj-art-91791a25371d4fb19f5e2a7403969db42025-01-20T14:37:59ZengTaylor & Francis GroupHIV Research & Clinical Practice2578-74702023-12-0124110.1080/25787489.2023.22213772221377A prediction risk score for HIV among adolescent girls and young women in South Africa: identifying those in need of HIV pre-exposure prophylaxisReuben Christopher Moyo0Darshini Govindasamy1Samuel Om Manda2Peter Suwirakwenda Nyasulu3Faculty of Medicine and Family Health, Division of Epidemiology and Biostatistics, Stellenbosch UniversityHealth Systems Research Unit, South African Medical Research CouncilDepartment of Statistics, University of PretoriaFaculty of Medicine and Family Health, Division of Epidemiology and Biostatistics, Stellenbosch UniversityBackground 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.http://dx.doi.org/10.1080/25787489.2023.2221377hivrisk scoreadolescent girls and young women & pre-exposure prophylaxis |
| spellingShingle | Reuben Christopher Moyo Darshini Govindasamy Samuel Om Manda Peter Suwirakwenda Nyasulu 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 hiv risk score adolescent girls and young women & pre-exposure prophylaxis |
| 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 |
| title_full | A prediction risk score for HIV among adolescent girls and young women in South Africa: identifying those in need of HIV pre-exposure prophylaxis |
| title_fullStr | A prediction risk score for HIV among adolescent girls and young women in South Africa: identifying those in need of HIV pre-exposure prophylaxis |
| title_full_unstemmed | A prediction risk score for HIV among adolescent girls and young women in South Africa: identifying those in need of HIV pre-exposure prophylaxis |
| title_short | A prediction risk score for HIV among adolescent girls and young women in South Africa: identifying those in need of HIV pre-exposure prophylaxis |
| title_sort | prediction risk score for hiv among adolescent girls and young women in south africa identifying those in need of hiv pre exposure prophylaxis |
| topic | hiv risk score adolescent girls and young women & pre-exposure prophylaxis |
| url | http://dx.doi.org/10.1080/25787489.2023.2221377 |
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