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.

Inadequate Engagement in HIV Care Among People With HIV Newly Diagnosed With a Sexually Transmitted Disease: A Multijurisdictional Analysis.

Background: A key challenge of HIV surveillance-based HIV care reengagement is locating people living with HIV (PLWH) who seem to be out of care to reengage them in care. Providing reengagement services to PLWH diagnosed with a sexually transmitted disease (STD)-individuals who are in jurisdiction and connected to the health care system-could be an efficient means of promoting HIV treatment and reducing HIV transmission.

Methods: Early and late syphilis (ES/LS) and gonorrhea (GC) cases diagnosed in 2016 and 2017 in Louisiana, Michigan, Mississippi, Oregon, Rhode Island, and Texas were matched to each state's HIV surveillance data to determine the proportion of PLWH with these infections who (1) did not have evidence of a CD4 count or viral load in the prior ≥13 months (out of care) or (2) had a viral load ≥1500 copies/mL on their most recent HIV RNA test before STD diagnosis (viremic).

A 10-Year Review of Total Hospital-Onset ICU Bloodstream Infections at an Academic Medical Center.

Background: The rates of central line-associated bloodstream infections (CLABSIs) in U.S. ICUs have decreased significantly, and a parallel reduction in the rates of total hospital-onset bacteremias in these units should also be expected.