Epidemiological and viral characteristics of undiagnosed HIV infections in Botswana

Lynnette Bhebhe, Sikhulile Moyo, Simani Gaseitsiwe, Molly Pretorius-Holme, Etienne K Yankinda, Kutlo Manyake, Coulson Kgathi, Mompati Mmalane, Refeletswe Lebelonyane, Tendani Gaolathe, Pamela Bachanas, Faith Ussery, Mpho Letebele, Joseph Makhema, Kathleen E Wirth, Shahin Lockman, Max Essex, Vlad Novitsky, Manon Ragonnet-Cronin, Lynnette Bhebhe, Sikhulile Moyo, Simani Gaseitsiwe, Molly Pretorius-Holme, Etienne K Yankinda, Kutlo Manyake, Coulson Kgathi, Mompati Mmalane, Refeletswe Lebelonyane, Tendani Gaolathe, Pamela Bachanas, Faith Ussery, Mpho Letebele, Joseph Makhema, Kathleen E Wirth, Shahin Lockman, Max Essex, Vlad Novitsky, Manon Ragonnet-Cronin

Abstract

Background: HIV-1 is endemic in Botswana. The country's primary challenge is identifying people living with HIV who are unaware of their status. We evaluated factors associated with undiagnosed HIV infection using HIV-1 phylogenetic, behavioural, and demographic data.

Methods: As part of the Botswana Combination Prevention Project, 20% of households in 30 villages were tested for HIV and followed from 2013 to 2018. A total of 12,610 participants were enrolled, 3596 tested HIV-positive at enrolment, and 147 participants acquired HIV during the trial. Extensive socio-demographic and behavioural data were collected from participants and next-generation sequences were generated for HIV-positive cases. We compared three groups of participants: (1) those previously known to be HIV-positive at enrolment (n = 2995); (2) those newly diagnosed at enrolment (n = 601) and (3) those who tested HIV-negative at enrolment but tested HIV-positive during follow-up (n = 147). We searched for differences in demographic and behavioural factors between known and newly diagnosed group using logistic regression. We also compared the topology of each group in HIV-1 phylogenies and used a genetic diversity-based algorithm to classify infections as recent (< 1 year) or chronic (≥ 1 year).

Results: Being male (aOR = 2.23) and younger than 35 years old (aOR = 8.08) was associated with undiagnosed HIV infection (p < 0.001), as was inconsistent condom use (aOR = 1.76). Women were more likely to have undiagnosed infections if they were married, educated, and tested frequently. For men, being divorced increased their risk. The genetic diversity-based algorithm classified most incident infections as recent (75.0%), but almost none of known infections (2.0%). The estimated proportion of recent infections among new diagnoses was 37.0% (p < 0.001).

Conclusion: Our results indicate that those with undiagnosed infections are likely to be young men and women who do not use condoms consistently. Among women, several factors were predictive: being married, educated, and testing frequently increased risk. Men at risk were more difficult to delineate. A sizeable proportion of undiagnosed infections were recent based on a genetic diversity-based classifier. In the era of "test and treat all", pre-exposure prophylaxis may be prioritized towards individuals who self-identify or who can be identified using these predictors in order to halt onward transmission in time.

Keywords: HIV; Phylogenetics; Recent HIV infection; Undiagnosed infection.

Conflict of interest statement

SM is an editorial Board Member for BMC Infectious Diseases. All other authors report no competing interests.

© 2022. The Author(s).

Figures

Fig. 1
Fig. 1
Recency prediction based on diversity-based classification algorithm. The distribution of data is skewed around the median, shown by the middle line. Boxes represent quartiles (25%, 75%) and whiskers represent the range of the data. Dots represent outliers in the plot. The Kruskal–Wallis test was used to calculate the p-value (p < 10–16). Incident cases mean probability of recency was 0.847, for new cases it was 0.252 and for known cases it was 0.005

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