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Systematic review and meta-analysis of risk prediction models for HIV testing in key populations.

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Title: Systematic review and meta-analysis of risk prediction models for HIV testing in key populations.
Authors: Xie, Xin1,2 (AUTHOR), Zhang, Chaowen3 (AUTHOR), Han, Shuyu3 (AUTHOR) 2116393033@bjmu.edu.cn, Shi, Yirong4 (AUTHOR), Ming, Jinyang3 (AUTHOR), Chen, Weimei4 (AUTHOR), Hu, Jingxian2 (AUTHOR), Zhou, Bo3 (AUTHOR), Zhang, Lili2 (AUTHOR) zhanglili2020@mail.ccmu.edu.cn
Source: AIDS Reviews. Apr-Jun2026, Vol. 28 Issue 2, p56-70. 15p.
Subjects: Risk assessment, Model validation, Public health, Evidence synthesis, Demography, Disease risk factors, Diagnosis of HIV infections
Abstract: HIV testing is a critical tool for preventing HIV transmission, with early identification in key populations reducing onward spread. This study aims to evaluate risk prediction models, identify factors influencing HIV testing, and provide recommendations to enhance testing among key populations. Electronic databases were searched for peer-reviewed and gray literature published in English and Chinese from January 1, 1996, to November 14, 2025. Two reviewers independently assessed methodological quality and extracted data. The prediction model risk of bias assessment tool was used to evaluate bias and applicability. Of 3693 initially identified studies, seven met the inclusion criteria. Reported area under the curve (AUC) values ranged from 0.72 to 0.82, and the pooled AUC of validated models was 0.77 (95% confidence interval: 0.70-0.84), indicating moderate discriminative performance. However, most of the studies were assessed as having a high risk of bias, primarily due to insufficient reporting in the analysis domain, limiting the reliability of existing models for clinical or public health applications. Predictors of HIV testing were broadly grouped into sociodemographic, behavioral, knowledge-related, and structural factors, and predictors of HIV testing varied considerably across models, reflecting differences in study populations, behavioral characteristics, and contextual factors across settings. Overall, although existing models demonstrate moderate predictive ability, their methodological limitations and lack of external validation restrict their generalizability and practical utility. Therefore, reliable prediction models remain limited. Future research should develop high-quality models with larger sample sizes, robust designs, and multi-center external validation to support clinical application, improve practical relevance, and inform strategies to advance HIV testing. Beyond HIV testing prediction, this study highlights the need for broader prevention approaches, including sexuality education, risk awareness, and safe sexual behaviors, alongside targeted testing interventions. [ABSTRACT FROM AUTHOR]
Copyright of AIDS Reviews is the property of Publicidad Permanyer SLU and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Systematic review and meta-analysis of risk prediction models for HIV testing in key populations.
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  Data: <searchLink fieldCode="AR" term="%22Xie%2C+Xin%22">Xie, Xin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Chaowen%22">Zhang, Chaowen</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Han%2C+Shuyu%22">Han, Shuyu</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> 2116393033@bjmu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Shi%2C+Yirong%22">Shi, Yirong</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ming%2C+Jinyang%22">Ming, Jinyang</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Weimei%22">Chen, Weimei</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hu%2C+Jingxian%22">Hu, Jingxian</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhou%2C+Bo%22">Zhou, Bo</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Lili%22">Zhang, Lili</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> zhanglili2020@mail.ccmu.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22AIDS+Reviews%22">AIDS Reviews</searchLink>. Apr-Jun2026, Vol. 28 Issue 2, p56-70. 15p.
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  Data: <searchLink fieldCode="DE" term="%22Risk+assessment%22">Risk assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Model+validation%22">Model validation</searchLink><br /><searchLink fieldCode="DE" term="%22Public+health%22">Public health</searchLink><br /><searchLink fieldCode="DE" term="%22Evidence+synthesis%22">Evidence synthesis</searchLink><br /><searchLink fieldCode="DE" term="%22Demography%22">Demography</searchLink><br /><searchLink fieldCode="DE" term="%22Disease+risk+factors%22">Disease risk factors</searchLink><br /><searchLink fieldCode="DE" term="%22Diagnosis+of+HIV+infections%22">Diagnosis of HIV infections</searchLink>
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  Label: Abstract
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  Data: HIV testing is a critical tool for preventing HIV transmission, with early identification in key populations reducing onward spread. This study aims to evaluate risk prediction models, identify factors influencing HIV testing, and provide recommendations to enhance testing among key populations. Electronic databases were searched for peer-reviewed and gray literature published in English and Chinese from January 1, 1996, to November 14, 2025. Two reviewers independently assessed methodological quality and extracted data. The prediction model risk of bias assessment tool was used to evaluate bias and applicability. Of 3693 initially identified studies, seven met the inclusion criteria. Reported area under the curve (AUC) values ranged from 0.72 to 0.82, and the pooled AUC of validated models was 0.77 (95% confidence interval: 0.70-0.84), indicating moderate discriminative performance. However, most of the studies were assessed as having a high risk of bias, primarily due to insufficient reporting in the analysis domain, limiting the reliability of existing models for clinical or public health applications. Predictors of HIV testing were broadly grouped into sociodemographic, behavioral, knowledge-related, and structural factors, and predictors of HIV testing varied considerably across models, reflecting differences in study populations, behavioral characteristics, and contextual factors across settings. Overall, although existing models demonstrate moderate predictive ability, their methodological limitations and lack of external validation restrict their generalizability and practical utility. Therefore, reliable prediction models remain limited. Future research should develop high-quality models with larger sample sizes, robust designs, and multi-center external validation to support clinical application, improve practical relevance, and inform strategies to advance HIV testing. Beyond HIV testing prediction, this study highlights the need for broader prevention approaches, including sexuality education, risk awareness, and safe sexual behaviors, alongside targeted testing interventions. [ABSTRACT FROM AUTHOR]
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  Label:
  Group: Ab
  Data: <i>Copyright of AIDS Reviews is the property of Publicidad Permanyer SLU and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.24875/AIDSRev.26000004
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        Text: English
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        PageCount: 15
        StartPage: 56
    Subjects:
      – SubjectFull: Risk assessment
        Type: general
      – SubjectFull: Model validation
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      – SubjectFull: Public health
        Type: general
      – SubjectFull: Evidence synthesis
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      – SubjectFull: Demography
        Type: general
      – SubjectFull: Disease risk factors
        Type: general
      – SubjectFull: Diagnosis of HIV infections
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              Text: Apr-Jun2026
              Type: published
              Y: 2026
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