Genotypic Resistance Tests Sequences Reveal the Role of Marginalized Populations in HIV-1 Transmission in Switzerland.

Targeting hard-to-reach/marginalized populations is essential for preventing HIV-transmission. A unique opportunity to identify such populations in Switzerland is provided by a database of all genotypic-resistance-tests from Switzerland, including both sequences from the Swiss HIV Cohort Study (SHCS) and non-cohort sequences. A phylogenetic tree was built using 11,127 SHCS and 2,875 Swiss non-SHCS sequences. Demographics were imputed for non-SHCS patients using a phylogenetic proximity approach. Factors associated with non-cohort outbreaks were determined using logistic regression. Non-B subtype (univariable odds-ratio (OR): 1.9; 95% confidence interval (CI): 1.8-2.1), female gender (OR: 1.6; 95% CI: 1.4-1.7), black ethnicity (OR: 1.9; 95% CI: 1.7-2.1) and heterosexual transmission group (OR:1.8; 95% CI: 1.6-2.0), were all associated with underrepresentation in the SHCS. We found 344 purely non-SHCS transmission clusters, however, these outbreaks were small (median 2, maximum 7 patients) with a strong overlap with the SHCS'. 65% of non-SHCS sequences were part of clusters composed of >= 50% SHCS sequences. Our data suggests that marginalized-populations are underrepresented in the SHCS. However, the limited size of outbreaks among non-SHCS patients in-care implies that no major HIV outbreak in Switzerland was missed by the SHCS surveillance. This study demonstrates the potential of sequence data to assess and extend the scope of infectious-disease surveillance

Unsupervised machine learning predicts future sexual behaviour and sexually transmitted infections among HIV-positive men who have sex with men.

Machine learning is increasingly introduced into medical fields, yet there is limited evidence for its benefit over more commonly used statistical methods in epidemiological studies. We introduce an unsupervised machine learning framework for longitudinal features and evaluate it using sexual behaviour data from the last 20 years from over 3'700 participants in the Swiss HIV Cohort Study (SHCS). We use hierarchical clustering to find subgroups of men who have sex with men in the SHCS with similar sexual behaviour up to May 2017, and apply regression to test whether these clusters enhance predictions of sexual behaviour or sexually transmitted diseases (STIs) after May 2017 beyond what can be predicted with conventional parameters. We find that behavioural clusters enhance model performance according to likelihood ratio test, Akaike information criterion and area under the receiver operator characteristic curve for all outcomes studied, and according to Bayesian information criterion for five out of ten outcomes, with particularly good performance for predicting future sexual behaviour and recurrent STIs. We thus assess a methodology that can be used as an alternative means for creating exposure categories from longitudinal data in epidemiological models, and can contribute to the understanding of time-varying risk factors

Unsupervised machine learning predicts future sexual behaviour and sexually transmitted infections among HIV-positive men who have sex with men

Machine learning is increasingly introduced into medical fields, yet there is limited evidence for its benefit over more commonly used statistical methods in epidemiological studies. We introduce an unsupervised machine learning framework for longitudinal features and evaluate it using sexual behaviour data from the last 20 years from over 3'700 participants in the Swiss HIV Cohort Study (SHCS). We use hierarchical clustering to find subgroups of men who have sex with men in the SHCS with similar sexual behaviour up to May 2017, and apply regression to test whether these clusters enhance predictions of sexual behaviour or sexually transmitted diseases (STIs) after May 2017 beyond what can be predicted with conventional parameters. We find that behavioural clusters enhance model performance according to likelihood ratio test, Akaike information criterion and area under the receiver operator characteristic curve for all outcomes studied, and according to Bayesian information criterion for five out of ten outcomes, with particularly good performance for predicting future sexual behaviour and recurrent STIs. We thus assess a methodology that can be used as an alternative means for creating exposure categories from longitudinal data in epidemiological models, and can contribute to the understanding of time-varying risk factors

IP-10 Levels as an Accurate Screening Tool to Detect Acute HIV Infection in Resource-Limited Settings.

Acute HIV infection (AHI) is the period prior to seroconversion characterized by high viral replication, hyper-transmission potential and commonly, non-specific febrile illness. AHI detection requires HIV-RNA viral load (VL) determination, which has very limited access in low-income countries due to restrictive costs and implementation constraints. We sought to identify a biomarker that could enable AHI diagnosis in scarce-resource settings, and to evaluate the feasibility of its implementation. HIV-seronegative adults presenting at the Manhiça District Hospital, Mozambique, with reported-fever were tested for VL. Plasma levels of 49 inflammatory biomarkers from AHI (n = 61) and non-HIV infected outpatients (n = 65) were determined by Luminex and ELISA. IP-10 demonstrated the best predictive power for AHI detection (AUC = 0.88 [95%CI 0.80-0.96]). A cut-off value of IP-10 ≥ 161.6 pg/mL provided a sensitivity of 95.5% (95%CI 85.5-99.5) and a specificity of 76.5% (95%CI 62.5-87.2). The implementation of an IP-10 screening test could avert from 21 to 84 new infections and save from US$176,609 to US$533,467 to the health system per 1,000 tested patients. We conclude that IP-10 is an accurate biomarker to screen febrile HIV-seronegative individuals for subsequent AHI diagnosis with VL. Such an algorithm is a cost-effective strategy to prevent disease progression and a substantial number of further HIV infections

The Cumulative Impact of Harm Reduction on the Swiss HIV Epidemic: Cohort Study, Mathematical Model, and Phylogenetic Analysis.

Human immunodeficiency virus (HIV) transmission among injecting drug users (IDUs) is increasing in the United States due to the recent opioid epidemic and is the leading mode of transmission in Eastern Europe. To evaluate the overall impact of HIV harm reduction, we combined (1) data from the Swiss HIV Cohort Study and public sources with (2) a mathematical model expressed as a system of ordinary differential equations. The model reconstructs the national epidemic from the first case in 1980 until 2015. Phylogenetic cluster analysis of HIV-1 pol sequences was used to quantify the epidemic spillover from IDUs to the general population. Overall, harm reduction prevented 15903 (range, 15359-16448) HIV infections among IDUs until the end of 2015, 5446 acquired immune deficiency syndrome (AIDS) deaths (range, 5142-5752), and a peak HIV prevalence of 50.7%. Introduction of harm reduction 2 years earlier could have halved the epidemic, preventing 3161 (range, 822-5499) HIV infections and 1468 (range, 609-2326) AIDS deaths. Suddenly discontinuing all harm reduction in 2005 would have resulted in outbreak re-emergence with 1351 (range, 779-1925) additional HIV cases. Without harm reduction, the estimated additional number of heterosexuals infected by HIV-positive IDUs is estimated to have been 2540 (range, 2453-2627), which is equivalent to the total national reported incidence among heterosexuals in the period of 2007 to 2015. Our results suggest that a paramount, population-level impact occurred because of the harm reduction package, beyond factors that can be explained by a reduction in risk behavior and a decrease in the number of drug users over time

Unsupervised machine learning predicts future sexual behaviour and sexually transmitted infections among HIV-positive men who have sex with men

Machine learning is increasingly introduced into medical fields, yet there is limited evidence for its benefit over more commonly used statistical methods in epidemiological studies. We introduce an unsupervised machine learning framework for longitudinal features and evaluate it using sexual behaviour data from the last 20 years from over 3'700 participants in the Swiss HIV Cohort Study (SHCS). We use hierarchical clustering to find subgroups of men who have sex with men in the SHCS with similar sexual behaviour up to May 2017, and apply regression to test whether these clusters enhance predictions of sexual behaviour or sexually transmitted diseases (STIs) after May 2017 beyond what can be predicted with conventional parameters. We find that behavioural clusters enhance model performance according to likelihood ratio test, Akaike information criterion and area under the receiver operator characteristic curve for all outcomes studied, and according to Bayesian information criterion for five out of ten outcomes, with particularly good performance for predicting future sexual behaviour and recurrent STIs. We thus assess a methodology that can be used as an alternative means for creating exposure categories from longitudinal data in epidemiological models, and can contribute to the understanding of time-varying risk factors