PrEPare for Work: A Pilot Randomized Controlled Trial of an Intervention to Optimize HIV PrEP Outcomes Among Male Sex Workers.

HIV pre-exposure prophylaxis (PrEP) use is limited among male sex workers, who are at exceptionally high-risk for HIV infection. We developed a theory-informed, two-pronged intervention ("PrEPare-for-Work") to optimize PrEP initiation and adherence among male sex workers, which was preliminarily evaluated in a two-stage pilot randomized controlled trial of 110 male sex workers in the US Northeast. Individuals randomized to the Stage 1 PrEPare-for-Work Case Management arm were three times as likely as those in the standard of care (SOC) arm to initiate PrEP (RR = 2.95, 95% CI = 1.57-5.57). Participants who initiated PrEP and were randomized to the Stage 2 PrEPare-for-Work Adherence Counseling arm had higher rates of prevention-effective adherence (measured via tenofovir in hair) compared to those in the SOC arm (RR = 1.7, 95% CI 0.64-4.77; 55.6% vs. 28.6%, respectively); though not statistically significant. Given the need and the promise of this pilot RCT, further efficacy testing is warranted and should be prioritized.

RESUMEN: El uso de la profilaxis prexposición (PrEP) para prevenir la adquisición del VIH es limitado entre trabajadores sexuales masculinos, que están en muy alto riesgo de contraerlo. Desarrollamos una intervención de dos partes basada en la teoría para optimizar el inicio y la observancia del tratamiento de la PrEP entre trabajadores sexuales masculinos, que se evaluó preliminarmente en un ensayo piloto controlado y aleatorizado (ECA) de dos fases de 110 trabajadores sexuales masculinos en el noreste de Estados Unidos. Las personas aleatorizadas al grupo de intervención (la primera fase de nuestro programa "PrEPare for Work" ­ la atención individualizada) eran tres veces más probable que las aleatorizadas al grupo control (la norma de atención) a iniciar la PrEP (RR = 2.95, 95% IC = 1.57­5.57). Los participantes que iniciaron la PrEP y se aleatorizaron al grupo de intervención (la segunda fase de "PrEPare for Work" ­ la terapia para aumentar la adherencia al tratamiento) tenían tasas más altas de adherencia al tratamiento (medido por tenofovir en el cabello) que los aleatorizados al grupo control (RR = 1.7, 95% IC 0.64­4.77; 55.6% vs. 28.6%, respectivamente); aunque la diferencia no fue estadísticamente significativa. En vista de la necesidad y el potencial de este ECA piloto, más pruebas de eficacia son necesarias y deben ser priorizadas.

Machine learning takes a village: Assessing neighbourhood-level vulnerability for an overdose and infectious disease outbreak.

BACKGROUND: Multiple areas in the United States of America (USA) are experiencing high rates of overdose and outbreaks of bloodborne infections, including HIV and hepatitis C virus (HCV), due to non-sterile injection drug use. We aimed to identify neighbourhoods at increased vulnerability for overdose and infectious disease outbreaks in Rhode Island, USA. The primary aim was to pilot machine learning methods to identify which neighbourhood-level factors were important for creating "vulnerability assessment scores" across the state. The secondary aim was to engage stakeholders to pilot an interactive mapping tool and visualize the results. METHODS: From September 2018 to November 2019, we conducted a neighbourhood-level vulnerability assessment and stakeholder engagement process named The VILLAGE Project (Vulnerability Investigation of underlying Local risk And Geographic Events). We developed a predictive analytics model using machine learning methods (LASSO, Elastic Net, and RIDGE) to identify areas with increased vulnerability to an outbreak of overdose, HIV and HCV, using census tract-level counts of overdose deaths as a proxy for injection drug use patterns and related health outcomes. Stakeholders reviewed mapping tools for face validity and community distribution. RESULTS: Machine learning prediction models were suitable for estimating relative neighbourhood-level vulnerability to an outbreak. Variables of importance in the model included housing cost burden, prior overdose deaths, housing density, and education level. Eighty-nine census tracts (37%) with no prior overdose fatalities were identified as being vulnerable to such an outbreak, and nine of those were identified as having a vulnerability assessment score in the top 25%. Results were disseminated as a vulnerability stratification map and an online interactive mapping tool. CONCLUSION: Machine learning methods are well suited to predict neighborhoods at higher vulnerability to an outbreak. These methods show promise as a tool to assess structural vulnerabilities and work to prevent outbreaks at the local level.

Public Health Approaches Toward Eliminating Hepatitis C Virus in Rhode Island.

Hepatitis C Virus (HCV) continues to be a cause of significant morbidity and mortality around the world surpassing HIV, Tuberculosis and Malaria as the leading cause of death by an infectious disease. In the United States, advances in screening, testing and treatment have put the goal set by the World Health Organization (WHO) to HCV elimination within reach. Rhode Island has taken an innovative public health approach to eliminating HCV by improving disease surveillance activities, supporting disease reduction strategies and removing barriers across the continuum of care, particularly for populations that are disproportionately impacted by the disease. Through the coordination of the Rhode Island Hepatitis C Action Coalition, the Rhode Island Department of Health (RIDOH), the Executive Office of Health and Human Services (EOHHS), community organizations, and clinical leaders, important steps have been taken to reduce transmission of the disease and work toward HCV elimination.