Hormonal contraception and risk of STIs and bacterial vaginosis in South African adolescents: secondary analysis of a randomised trial.

OBJECTIVES: Young women in sub-Saharan Africa are at high risk of STIs and unintended pregnancies, yet hormonal contraceptive (HC) use may affect STI risk. We compared the influence of three HCs on the incidence and prevalence of STIs and bacterial vaginosis (BV) in South African adolescents. METHODS: One hundred and thirty adolescents between 15 and 19 years were randomised to the injectable norethisterone enanthate (Net-En), combined oral contraceptives (COC) (Triphasil or Nordette) or a combined contraceptive vaginal ring (CCVR; NuvaRing) for 16 weeks (clinicaltrials.gov/NCT02404038). Vaginal samples were collected at baseline and 16 weeks post contraceptive initiation for STI and BV testing. RESULTS: In an intention-to-treat analysis, no significant differences in BV prevalence were found between study arms. The overall incidence of any STI at follow-up was high: 16.2% in the COC arm; 25.7% in the Net-En arm; and 37.1% in the CCVR arm. The incidence rate (IR) of any STI was similar between Net-En (IR 0.74 (95% CI 0.34 to 1.41)) and the oestrogen-containing contraceptives (IR 0.78 (95% CI 0.47 to 1.22)). A lower IR of Chlamydia trachomatis (incidence rate ratio (IRR) 0.68 (95% CI 0.19 to 1.99)) and Neisseria gonorrhoeae (IRR 0.25 (95% CI 0.01 to 1.35)) but a higher IR of Mycoplasma genitalium (IRR 16.0 (95% CI 2.96 to 400)), was observed in the Net-En arm compared with the oestrogen-containing contraceptives, although the overall incidence of M. genitalium was low (4.7%). In an exploratory analysis, the risk of any STI and N. gonorrhoeae was lower in the COC arm compared with CCVR. A per-protocol analysis yielded similar results. CONCLUSION: Our results suggest that use of Net-En may be associated with increased risk of M. genitalium compared with oestrogen-containing contraceptives but not with overall STI risk. COC use may decrease STI risk relative to CCVR.

Improved Indicators for Subnational Unmet Antiretroviral Therapy Need in the Health System: Updates to the Naomi Model in 2023.

BACKGROUND: Quantifying subnational need for antiretroviral therapy (ART) for HIV is challenging because people living with HIV (PLHIV) access health facilities in areas that may differ from their residence. We defined and demonstrated new indicators for PLHIV treatment needed to guide health system target setting and resource allocation. SETTING: Botswana. METHODS: We extended Naomi, a Bayesian small-area model for estimating district-level HIV indicators from national household survey and HIV service delivery data. We used model outputs for ART seeking probabilities in neighboring districts to define the "PLHIV (attending)" indicator representing the estimated number of PLHIV who would seek treatment at health facilities in a district, and "Untreated PLHIV attending" representing gaps in ART service provision. Botswana 2021 district HIV estimates were used to demonstrate new outputs and assess the sensitivity to uncertainty in district population sizes. RESULTS: Across districts of Botswana, estimated adult ART coverage in December 2021 ranged 90%-96%. In the capital city Gaborone, there were 50,400 resident PLHIV and 64,200 receiving ART, of whom 24% (95% CI: 20 to 32) were estimated to reside in neighboring districts. Applying ART attendance probabilities gave a "PLHIV attending" denominator of 68,300 and unmet treatment need of 4100 adults (95% CI: 3000 to 5500) for Gaborone health facilities. The facility-based "PLHIV attending" denominator was less-sensitive to fluctuations in district population size assumptions. CONCLUSIONS: New indicators provided more consistent targets for HIV service provision, but are limited by ART data quality. This challenge will increase as treatment coverage reaches high levels and treatment gaps are smaller.

Historical visit attendance as predictor of treatment interruption in South African HIV patients: Extension of a validated machine learning model.

Retention of antiretroviral (ART) patients is a priority for achieving HIV epidemic control in South Africa. While machine-learning methods are being increasingly utilised to identify high risk populations for suboptimal HIV service utilisation, they are limited in terms of explaining relationships between predictors. To further understand these relationships, we implemented machine learning methods optimised for predictive power and traditional statistical methods. We used routinely collected electronic medical record (EMR) data to evaluate longitudinal predictors of lost-to-follow up (LTFU) and temporal interruptions in treatment (IIT) in the first two years of treatment for ART patients in the Gauteng and North West provinces of South Africa. Of the 191,162 ART patients and 1,833,248 visits analysed, 49% experienced at least one IIT and 85% of those returned for a subsequent clinical visit. Patients iteratively transition in and out of treatment indicating that ART retention in South Africa is likely underestimated. Historical visit attendance is shown to be predictive of IIT using machine learning, log binomial regression and survival analyses. Using a previously developed categorical boosting (CatBoost) algorithm, we demonstrate that historical visit attendance alone is able to predict almost half of next missed visits. With the addition of baseline demographic and clinical features, this model is able to predict up to 60% of next missed ART visits with a sensitivity of 61.9% (95% CI: 61.5-62.3%), specificity of 66.5% (95% CI: 66.4-66.7%), and positive predictive value of 19.7% (95% CI: 19.5-19.9%). While the full usage of this model is relevant for settings where infrastructure exists to extract EMR data and run computations in real-time, historical visits attendance alone can be used to identify those at risk of disengaging from HIV care in the absence of other behavioural or observable risk factors.

Validation and Improvement of a Machine Learning Model to Predict Interruptions in Antiretroviral Treatment in South Africa.

INTRODUCTION: Machine learning algorithms are increasingly being used to inform HIV prevention and detection strategies. We validated and extended a previously developed machine learning model for patient retention on antiretroviral therapy in a new geographic catchment area in South Africa. METHODS: We compared the ability of an adaptive boosting algorithm to predict interruption in treatment (IIT) in 2 South African cohorts from the Free State and Mpumalanga and Gauteng and North West (GA/NW) provinces. We developed a novel set of predictive features for the GA/NW cohort using a categorical boosting model. We evaluated the ability of the model to predict IIT over all visits and across different periods within a patient's treatment trajectory. RESULTS: When predicting IIT, the GA/NW and Free State and Mpumalanga models demonstrated a sensitivity of 60% and 61%, respectively, able to correctly predict nearly two-thirds of all missed visits with a positive predictive value of 18% and 19%. Using predictive features generated from the GA/NW cohort, the categorical boosting model correctly predicted 22,119 of a total of 35,985 missed next visits, yielding a sensitivity of 62%, specificity of 67%, and positive predictive value of 20%. Model performance was highest when tested on visits within the first 6 months. CONCLUSIONS: Machine learning algorithms may be useful in informing tools to increase antiretroviral therapy patient retention and efficiency of HIV care interventions. This is particularly relevant in developing countries where health data systems are being strengthened to collect data on a scale that is large enough to apply novel analytical methods.

Spatio-temporal estimates of HIV risk group proportions for adolescent girls and young women across 13 priority countries in sub-Saharan Africa.

The Global AIDS Strategy 2021-2026 identifies adolescent girls and young women (AGYW) as a priority population for HIV prevention, and recommends differentiating intervention portfolios geographically based on local HIV incidence and individual risk behaviours. We estimated prevalence of HIV risk behaviours and associated HIV incidence at health district level among AGYW living in 13 countries in sub-Saharan Africa. We analysed 46 geospatially-referenced national household surveys conducted between 1999-2018 across 13 high HIV burden countries in sub-Saharan Africa. Female survey respondents aged 15-29 years were classified into four risk groups (not sexually active, cohabiting, non-regular or multiple partner[s] and female sex workers [FSW]) based on reported sexual behaviour. We used a Bayesian spatio-temporal multinomial regression model to estimate the proportion of AGYW in each risk group stratified by district, year, and five-year age group. Using subnational estimates of HIV prevalence and incidence produced by countries with support from UNAIDS, we estimated new HIV infections in each risk group by district and age group. We then assessed the efficiency of prioritising interventions according to risk group. Data consisted of 274,970 female survey respondents aged 15-29. Among women aged 20-29, cohabiting (63.1%) was more common in eastern Africa than non-regular or multiple partner(s) (21.3%), while in southern countries non-regular or multiple partner(s) (58.9%) were more common than cohabiting (23.4%). Risk group proportions varied substantially across age groups (65.9% of total variation explained), countries (20.9%), and between districts within each country (11.3%), but changed little over time (0.9%). Prioritisation based on behavioural risk, in combination with location- and age-based prioritisation, reduced the proportion of population required to be reached in order to find half of all expected new infections from 19.4% to 10.6%. FSW were 1.3% of the population but 10.6% of all expected new infections. Our risk group estimates provide data for HIV programmes to set targets and implement differentiated prevention strategies outlined in the Global AIDS Strategy. Successfully implementing this approach would result in more efficiently reaching substantially more of those at risk for infections.

Prediction of HIV status based on socio-behavioural characteristics in East and Southern Africa.

INTRODUCTION: High yield HIV testing strategies are critical to reach epidemic control in high prevalence and low-resource settings such as East and Southern Africa. In this study, we aimed to predict the HIV status of individuals living in Angola, Burundi, Ethiopia, Lesotho, Malawi, Mozambique, Namibia, Rwanda, Zambia and Zimbabwe with the highest precision and sensitivity for different policy targets and constraints based on a minimal set of socio-behavioural characteristics. METHODS: We analysed the most recent Demographic and Health Survey from these 10 countries to predict individual's HIV status using four different algorithms (a penalized logistic regression, a generalized additive model, a support vector machine, and a gradient boosting trees). The algorithms were trained and validated on 80% of the data, and tested on the remaining 20%. We compared the predictions based on the F1 score, the harmonic mean of sensitivity and positive predictive value (PPV), and we assessed the generalization of our models by testing them against an independent left-out country. The best performing algorithm was trained on a minimal subset of variables which were identified as the most predictive, and used to 1) identify 95% of people living with HIV (PLHIV) while maximising precision and 2) identify groups of individuals by adjusting the probability threshold of being HIV positive (90% in our scenario) for achieving specific testing strategies. RESULTS: Overall 55,151 males and 69,626 females were included in the analysis. The gradient boosting trees algorithm performed best in predicting HIV status with a mean F1 score of 76.8% [95% confidence interval (CI) 76.0%-77.6%] for males (vs [CI 67.8%-70.6%] for SVM) and 78.8% [CI 78.2%-79.4%] for females (vs [CI 73.4%-75.8%] for SVM). Among the ten most predictive variables for each sex, nine were identical: longitude, latitude and, altitude of place of residence, current age, age of most recent partner, total lifetime number of sexual partners, years lived in current place of residence, condom use during last intercourse and, wealth index. Only age at first sex for male (ranked 10th) and Rohrer's index for female (ranked 6th) were not similar for both sexes. Our large-scale scenario, which consisted in identifying 95% of all PLHIV, would have required testing 49.4% of males and 48.1% of females while achieving a precision of 15.4% for males and 22.7% for females. For the second scenario, only 4.6% of males and 6.0% of females would have had to be tested to find 55.7% of all males and 50.5% of all females living with HIV. CONCLUSIONS: We trained a gradient boosting trees algorithm to find 95% of PLHIV with a precision twice higher than with general population testing by using only a limited number of socio-behavioural characteristics. We also successfully identified people at high risk of infection who may be offered pre-exposure prophylaxis or voluntary medical male circumcision. These findings can inform the implementation of new high-yield HIV tests and help develop very precise strategies based on low-resource settings constraints.

Naomi: a new modelling tool for estimating HIV epidemic indicators at the district level in sub-Saharan Africa.

INTRODUCTION: HIV planning requires granular estimates for the number of people living with HIV (PLHIV), antiretroviral treatment (ART) coverage and unmet need, and new HIV infections by district, or equivalent subnational administrative level. We developed a Bayesian small-area estimation model, called Naomi, to estimate these quantities stratified by subnational administrative units, sex, and five-year age groups. METHODS: Small-area regressions for HIV prevalence, ART coverage and HIV incidence were jointly calibrated using subnational household survey data on all three indicators, routine antenatal service delivery data on HIV prevalence and ART coverage among pregnant women, and service delivery data on the number of PLHIV receiving ART. Incidence was modelled by district-level HIV prevalence and ART coverage. Model outputs of counts and rates for each indicator were aggregated to multiple geographic and demographic stratifications of interest. The model was estimated in an empirical Bayes framework, furnishing probabilistic uncertainty ranges for all output indicators. Example results were presented using data from Malawi during 2016-2018. RESULTS: Adult HIV prevalence in September 2018 ranged from 3.2% to 17.1% across Malawi's districts and was higher in southern districts and in metropolitan areas. ART coverage was more homogenous, ranging from 75% to 82%. The largest number of PLHIV was among ages 35 to 39 for both women and men, while the most untreated PLHIV were among ages 25 to 29 for women and 30 to 34 for men. Relative uncertainty was larger for the untreated PLHIV than the number on ART or total PLHIV. Among clients receiving ART at facilities in Lilongwe city, an estimated 71% (95% CI, 61% to 79%) resided in Lilongwe city, 20% (14% to 27%) in Lilongwe district outside the metropolis, and 9% (6% to 12%) in neighbouring Dowa district. Thirty-eight percent (26% to 50%) of Lilongwe rural residents and 39% (27% to 50%) of Dowa residents received treatment at facilities in Lilongwe city. CONCLUSIONS: The Naomi model synthesizes multiple subnational data sources to furnish estimates of key indicators for HIV programme planning, resource allocation, and target setting. Further model development to meet evolving HIV policy priorities and programme need should be accompanied by continued strengthening and understanding of routine health system data.

Modelling the impact of screening for chlamydia and gonorrhoea in youth and other high-prevalence groups in a resource-limited setting.

OBJECTIVES: Modelling the potential impact of screening for chlamydia and gonorrhoea in youth and other populations in a resource-limited setting. METHODS: We extended an agent-based model of heterosexual chlamydia and gonorrhoea transmission in South Africa to investigate the impact of screening strategies in key populations including youth, patients in HIV care, pregnant women and female sex workers (FSWs). Additionally, we compared the modelled impact of a standardised screening programme to results obtained from other published mathematical models of chlamydia screening. RESULTS: All strategies resulted in reductions in general and targeted population chlamydia and gonorrhoea transmission. Opportunistic screening of patients in youth and HIV care was shown to be the most effective, and FSW screening was shown to be the most efficient strategy. Differences between models could be attributed to differences in the modelled heterogeneity in sexual behaviour as well as differences in assumptions about immunity following chlamydia recovery. CONCLUSIONS: Taking modelling assumptions into account, opportunistic chlamydia and gonorrhoea screening of youth and those in HIV care represents a viable intervention for reducing sexually transmitted infections in the South African population.

Relationship between the Oral and Vaginal Microbiota of South African Adolescents with High Prevalence of Bacterial Vaginosis.

Bacterial vaginosis (BV) and periodontal disease (PD) are characterised as bacterial dysbioses. Both are associated with an increased risk of poor pregnancy outcomes, yet it is unknown whether PD and BV are related. We characterised the oral microbiota of young South African females with a high prevalence of BV and investigated the association between oral communities and vaginal microbiota. DNA was extracted from vaginal lateral wall, saliva and supragingival plaque samples from 94 adolescent females (aged 15-19 years). 16S rRNA gene sequencing of the V4 hypervariable region was performed for analysis of the oral and vaginal microbiota and BV status was determined by Nugent scoring. The core oral microbiota was predominately comprised of Firmicutes followed by Proteobacteria and Bacteroidetes. The salivary microbiota of participants with BV was more diverse than those with lactobacillus-dominated communities (p = 0.030). PD-associated bacterial species, including Prevotella intermedia and Porphyromonas endodontalis were enriched in the supragingival microbiota of women with non-optimal vaginal communities compared to those with Lactobacillus-dominant communities, while Pseudomonas aeruginosaand Prevotella intermedia were enriched in the saliva of women with non-optimal vaginal microbiota. These data suggest a relationship between oral and vaginal dysbiosis, warranting further investigation into whether they are casually related.

Hormonal contraception alters vaginal microbiota and cytokines in South African adolescents in a randomized trial.

Young women in sub-Saharan Africa are disproportionally affected by HIV infection and unintended pregnancies. However, hormonal contraceptive (HC) use may influence HIV risk through changes in genital tract microbiota and inflammatory cytokines. To investigate this, 130 HIV negative adolescent females aged 15-19 years were enrolled into a substudy of UChoose, an open-label randomized crossover study (NCT02404038), comparing acceptability and contraceptive product preference as a proxy for HIV prevention delivery methods. Participants were randomized to injectable norethisterone enanthate (Net-En), combined oral contraceptives (COC) or etonorgesterol/ethinyl estradiol combined contraceptive vaginal ring (CCVR) for 16 weeks, then crossed over to another HC for 16 weeks. Cervicovaginal samples were collected at baseline, crossover and exit for characterization of the microbiota and measurement of cytokine levels; primary endpoints were cervical T cell activation, vaginal microbial diversity and cytokine concentrations. Adolescents randomized to COCs had lower vaginal microbial diversity and relative abundance of HIV risk-associated taxa compared to Net-En or CCVR. Cervicovaginal inflammatory cytokine concentrations were significantly higher in adolescents randomized to CCVR compared to COC and Net-En. This suggests that COC use may induce an optimal vaginal ecosystem by decreasing bacterial diversity and inflammatory taxa, while CCVR use is associated with genital inflammation.

Does HIV Exploit the Inflammatory Milieu of the Male Genital Tract for Successful Infection?

In many parts of the World, medical male circumcision (MMC) is used as standard prevention of care against HIV infection. This is based on seminal reports made over 10 years ago that removal of the foreskin provides up to 60% protection against HIV infection in males and seems currently the best antiretroviral-free prevention strategy yet against the global epidemic. We explore the potential mechanisms by which MMC protects against HIV-1 acquisition and that one of the oldest, albeit re-invented, rituals of removing a foreskin underscores the exploitative nature of HIV on the anatomy and tissue of the uncircumcised penis. Furthermore, foreskin removal also reveals how males acquire HIV, and in reality, the underlying mechanisms of MMC are not known. We argue that the normal sequelae of inflammation in the male genital tract (MGT) for protection from sexually transmitted infections (STI)-induced pathology represents a perfect immune and microbial ecosystem for HIV acquisition. The accumulation of HIV-1 target cells in foreskin tissue and within the urethra in response to STIs, both during and after resolution of infection, suggests that acquisition of HIV-1, through sexual contact, makes use of the natural immune milieu of the MGT. Understanding immunity in the MGT, the movement of HIV-1 target cells to the urethra and foreskin tissue upon encounter with microbial signals would provide more insight into viral acquisition and lay the foundation for further prevention strategies in males that would be critical to curb the epidemic in all sexual partners at risk of infection. The global female-centric focus of HIV-1 transmission and acquisition research has tended to leave gaps in our knowledge of what determines HIV-1 acquisition in men and such understanding would provide a more balanced and complete view of viral acquisition.