Understanding and Addressing Hepatitis C Virus Reinfection Among Men Who Have Sex with Men

Hepatitis C virus reinfection rates among men who have sex with men are high. Factors associated with infection point to varied sexual and drug-related risks that could be targeted for interventions to prevent infection/reinfection. Modeling indicates that tackling increasing incidence and high reinfection rates requires high levels of hepatitis C virus treatment combined with behavioral interventions. Enhanced testing strategies and prompt retreating of reinfection may be required to promptly diagnosed reinfections. Behavioral interventions studies addressing reinfection are required. Other interventions include traditional harm reduction interventions, adapted behavioral interventions, and interventions to prevent harms related to ChemSex and other risk factors

Extending outbreak investigation with machine learning and graph theory: Benefits of new tools with application to a nosocomial outbreak of a multidrug-resistant organism.

OBJECTIVE From January 1, 2018, until July 31, 2020, our hospital network experienced an outbreak of vancomycin-resistant enterococci (VRE). The goal of our study was to improve existing processes by applying machine-learning and graph-theoretical methods to a nosocomial outbreak investigation. METHODS We assembled medical records generated during the first 2 years of the outbreak period (January 2018 through December 2019). We identified risk factors for VRE colonization using standard statistical methods, and we extended these with a decision-tree machine-learning approach. We then elicited possible transmission pathways by detecting commonalities between VRE cases using a graph theoretical network analysis approach. RESULTS We compared 560 VRE patients to 86,684 controls. Logistic models revealed predictors of VRE colonization as age (aOR, 1.4 (per 10 years), with 95% confidence interval [CI], 1.3-1.5; P < .001), ICU admission during stay (aOR, 1.5; 95% CI, 1.2-1.9; P < .001), Charlson comorbidity score (aOR, 1.1; 95% CI, 1.1-1.2; P < .001), the number of different prescribed antibiotics (aOR, 1.6; 95% CI, 1.5-1.7; P < .001), and the number of rooms the patient stayed in during their hospitalization(s) (aOR, 1.1; 95% CI, 1.1-1.2; P < .001). The decision-tree machine-learning method confirmed these findings. Graph network analysis established 3 main pathways by which the VRE cases were connected: healthcare personnel, medical devices, and patient rooms. CONCLUSIONS We identified risk factors for being a VRE carrier, along with 3 important links with VRE (healthcare personnel, medical devices, patient rooms). Data science is likely to provide a better understanding of outbreaks, but interpretations require data maturity, and potential confounding factors must be considered

The impact of public health interventions on the future prevalence of ESBL-producing Klebsiella pneumoniae: a population based mathematical modelling study.

BACKGROUND Future prevalence of colonization with extended-spectrum betalactamase (ESBL-) producing K. pneumoniae in humans and the potential of public health interventions against the spread of these resistant bacteria remain uncertain. METHODS Based on antimicrobial consumption and susceptibility data recorded during > 13 years in a Swiss region, we developed a mathematical model to assess the comparative effect of different interventions on the prevalence of colonization. RESULTS Simulated prevalence stabilized in the near future when rates of antimicrobial consumption and in-hospital transmission were assumed to remain stable (2025 prevalence: 6.8% (95CI%:5.4-8.8%) in hospitals, 3.5% (2.5-5.0%) in the community versus 6.1% (5.0-7.5%) and 3.2% (2.3-4.2%) in 2019, respectively). When overall antimicrobial consumption was set to decrease by 50%, 2025 prevalence declined by 75% in hospitals and by 64% in the community. A 50% decline in in-hospital transmission rate led to a reduction in 2025 prevalence of 31% in hospitals and no reduction in the community. The best model fit estimated that 49% (6-100%) of observed colonizations could be attributable to sources other than human-to-human transmission within the geographical setting. CONCLUSIONS Projections suggests that overall antimicrobial consumption will be, by far, the most powerful driver of prevalence and that a large fraction of colonizations could be attributed to non-local transmissions

Triggers of change in sexual behavior among people with HIV: The Swiss U = U statement and Covid-19 compared.

We assessed changes in sexual behaviour among people with HIV (PWH) over 20 years. Condom use with stable partners steadily declined from over 90% to 29% since the Swiss U = U statement with similar trajectories between men who have sex with men (MSM) and heterosexuals. Occasional partnership remained higher among MSM compared to heterosexuals even during COVID-19 social distancing

Sexual Behaviour and STI Incidence in Sexually Active MSM Living With HIV in Times of COVID-19.

Despite decreased numbers of sexual partners, the COVID-19 pandemic had limited impact on the prevalence of attending private sex parties, traveling for sex within Switzerland, and practicing chemsex in men with HIV who have sex with men. COVID-19 risk perception was low, and STI-diagnosis incidence rates remained stable over time

Sexual Behaviour and STI Incidence in Sexually Active MSM Living With HIV in Times of COVID-19

Despite decreased numbers of sexual partners, the COVID-19 pandemic had limited impact on the prevalence of attending private sex parties, traveling for sex within Switzerland, and practicing chemsex in men with HIV who have sex with men. COVID-19 risk perception was low, and STI-diagnosis incidence rates remained stable over time

An Approach to Quantifying the Interaction between Behavioral and Transmission Clusters.

We hypothesize that patterns of sexual behavior play a role in the conformation of transmission networks, i.e., the way you behave might influence whom you have sex with. If that was the case, behavioral grouping might in turn correlate with, and potentially predict transmission networking, e.g., proximity in a viral phylogeny. We rigorously present an intuitive approach to address this hypothesis by quantifying mapped interactions between groups defined by similarities in sexual behavior along a virus phylogeny while discussing power and sample size considerations. Data from the Swiss HIV Cohort Study on condom use and hepatitis C virus (HCV) sequences served as proof-of-concept. In this case, a strict inclusion criteria contrasting with low HCV prevalence hindered our possibilities to identify significant relationships. This manuscript serves as guide for studies aimed at characterizing interactions between behavioral patterns and transmission networks. Large transmission networks such as those of HIV or COVID-19 are prime candidates for applying this methodological approach

Impact of Direct-Acting Antivirals on the Burden of HCV Infection Among Persons Who Inject Drugs and Men Who Have Sex With Men in the Swiss HIV Cohort Study.

In the Swiss HIV Cohort Study, the number of people who inject drugs with replicating hepatitis C virus (HCV) infection decreased substantially after the introduction of direct-acting antivirals (DAAs). Among men who have sex with men, the increase in DAA uptake and efficacy was counterbalanced by frequent incident HCV infections

Mental Health, ART Adherence, and Viral Suppression Among Adolescents and Adults Living with HIV in South Africa: A Cohort Study.

We followed adolescents and adults living with HIV aged older than 15 years who enrolled in a South African private-sector HIV programme to examine adherence and viral non-suppression (viral load > 400 copies/mL) of participants with (20,743, 38%) and without (33,635, 62%) mental health diagnoses. Mental health diagnoses were associated with unfavourable adherence patterns. The risk of viral non-suppression was higher among patients with organic mental disorders [adjusted risk ratio (aRR) 1.55, 95% confidence interval (CI) 1.22-1.96], substance use disorders (aRR 1.53, 95% CI 1.19-1.97), serious mental disorders (aRR 1.30, 95% CI 1.09-1.54), and depression (aRR 1.19, 95% CI 1.10-1.28) when compared with patients without mental health diagnoses. The risk of viral non-suppression was also higher among males, adolescents (15-19 years), and young adults (20-24 years). Our study highlights the need for psychosocial interventions to improve HIV treatment outcomes-particularly of adolescents and young adults-and supports strengthening mental health services in HIV treatment programmes

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