Factors associated with syphilis incidence in the HIV-infected in the era of highly active antiretrovirals.

After several years of steady decline, syphilis is reemerging globally as a public health hazard, especially among people living with human immunodeficiency virus (HIV). Syphilis resurgence is observed mainly in men who have sex with men (MSM), yet other transmission groups are affected too. In this manuscript, we study the factors associated with syphilis incidence in the Swiss HIV cohort study in the era of highly effective antiretrovirals. Using parametric interval censored models with fixed and time-varying covariates, we studied the immunological, behavioral, and treatment-related elements associated with syphilis incidence in 3 transmission groups: MSM, heterosexuals, and intravenous drug users. Syphilis incidence has been increasing annually since 2005, with up to 74 incident cases per 1000 person-years in 2013, with MSM being the population with the highest burden (92% of cases). While antiretroviral treatment (ART) in general did not affect syphilis incidence, nevirapine (NVP) was associated with a lower hazard of syphilis incidence (multivariable hazard ratio 0.5, 95% confidence interval 0.2-1.0). We observed that condomless sex and younger age were associated with higher syphilis incidence. Moreover, time-updated CD4, nadir CD4, and CD8 cell counts were not associated with syphilis incidence. Finally, testing frequency higher than the recommended once a year routine testing was associated with a 2-fold higher risk of acquiring syphilis. Condomless sex is the main driver of syphilis resurgence in the Swiss HIV Cohort study; ART and immune reconstitution provide no protection against syphilis. This entails targeted interventions and frequent screening of high-risk populations. There is no known effect of NVP on syphilis; therefore, further clinical, epidemiological, and microbiological investigation is necessary to validate our observation

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

Inferring the age difference in HIV transmission pairs by applying phylogenetic methods on the HIV transmission network of the Swiss HIV Cohort Study.

Age-mixing patterns are of key importance for understanding the dynamics of human immunodeficiency virus (HIV)-epidemics and target public health interventions. We use the densely sampled Swiss HIV Cohort Study (SHCS) resistance database to study the age difference at infection in HIV transmission pairs using phylogenetic methods. In addition, we investigate whether the mean age difference of pairs in the phylogenetic tree is influenced by sampling as well as by additional distance thresholds for including pairs. HIV-1 pol-sequences of 11,922 SHCS patients and approximately 240,000 Los Alamos background sequences were used to build a phylogenetic tree. Using this tree, 100 per cent down to 1 per cent of the tips were sampled repeatedly to generate pruned trees (N = 500 for each sample proportion), of which pairs of SHCS patients were extracted. The mean of the absolute age differences of the pairs, measured as the absolute difference of the birth years, was analyzed with respect to this sample proportion and a distance criterion for inclusion of the pairs. In addition, the transmission groups men having sex with men (MSM), intravenous drug users (IDU), and heterosexuals (HET) were analyzed separately. Considering the tree with all 11,922 SHCS patients, 2,991 pairs could be extracted, with 954 (31.9 per cent) MSM-pairs, 635 (21.2 per cent) HET-pairs, 414 (13.8 per cent) IDU-pairs, and 352 (11.8 per cent) HET/IDU-pairs. For all transmission groups, the age difference at infection was significantly (P < 0.001) smaller for pairs in the tree compared with randomly assigned pairs, meaning that patients of similar age are more likely to be pairs. The mean age difference in the phylogenetic analysis, using a fixed distance of 0.05, was 9.2, 9.0, 7.3 and 5.6 years for MSM-, HET-, HET/IDU-, and IDU-pairs, respectively. Decreasing the cophenetic distance threshold from 0.05 to 0.01 significantly decreased the mean age difference. Similarly, repeated sampling of 100 per cent down to 1 per cent of the tips revealed an increased age difference at lower sample proportions. HIV-transmission is age-assortative, but the age difference of transmission pairs detected by phylogenetic analyses depends on both sampling proportion and distance criterion. The mean age difference decreases when using more conservative distance thresholds, implying an underestimation of age-assortativity when using liberal distance criteria. Similarly, overestimation of the mean age difference occurs for pairs from sparsely sampled trees, as it is often the case in sub-Saharan Africa

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

Dissecting HIV Virulence: Heritability of Setpoint Viral Load, CD4+ T-Cell Decline, and Per-Parasite Pathogenicity.

Pathogen strains may differ in virulence because they attain different loads in their hosts, or because they induce different disease-causing mechanisms independent of their load. In evolutionary ecology, the latter is referred to as "per-parasite pathogenicity". Using viral load and CD4+ T-cell measures from 2014 HIV-1 subtype B-infected individuals enrolled in the Swiss HIV Cohort Study, we investigated if virulence-measured as the rate of decline of CD4+ T cells-and per-parasite pathogenicity are heritable from donor to recipient. We estimated heritability by donor-recipient regressions applied to 196 previously identified transmission pairs, and by phylogenetic mixed models applied to a phylogenetic tree inferred from HIV pol sequences. Regressing the CD4+ T-cell declines and per-parasite pathogenicities of the transmission pairs did not yield heritability estimates significantly different from zero. With the phylogenetic mixed model, however, our best estimate for the heritability of the CD4+ T-cell decline is 17% (5-30%), and that of the per-parasite pathogenicity is 17% (4-29%). Further, we confirm that the set-point viral load is heritable, and estimate a heritability of 29% (12-46%). Interestingly, the pattern of evolution of all these traits differs significantly from neutrality, and is most consistent with stabilizing selection for the set-point viral load, and with directional selection for the CD4+ T-cell decline and the per-parasite pathogenicity. Our analysis shows that the viral genotype affects virulence mainly by modulating the per-parasite pathogenicity, while the indirect effect via the set-point viral load is minor