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). RESULTS: Previously diagnosed HIV infection was common among persons diagnosed with ES (n = 6942; 39%), LS (n = 4329; 27%), and GC (n = 9509; 6%). Among these ES, LS, and GC cases, 26% (n = 1543), 33% (n = 1113), and 29% (n = 2391) were out of HIV medical care or viremic at the time of STD diagnosis. CONCLUSIONS: A large proportion of STD cases with prior HIV diagnosis are out of care or viremic. Integrating relinkage to care activities into STD partner services and/or the use of matching STD and HIV data systems to prioritize data to care activities could be an efficient means for relinking patients to care and promoting viral suppression.

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.

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. We report 10-year trends for total hospital-onset ICU-associated bacteremias at a tertiary-care academic medical center. METHODS: This was a retrospective analysis of all positive-result blood cultures among patients admitted to seven adult ICUs for fiscal year 2005 (FY2005) through FY2014 according to Centers for Disease Control and Prevention National Healthcare Safety Network definitions. The rate of change for primary and secondary hospital-onset BSIs was determined, as was the distribution of organisms responsible for these BSIs. Data from three medical, two general surgical, one combined neurosurgical/trauma, and one cardiac/cardiac surgery adult ICU were analyzed. RESULTS: Across all ICUs, the rates of primary BSIs progressively fell from 2.11/1,000 patient days in FY2005 to 0.32/1,000 patient days in FY2014; an 85.0% decrease (P < .0001). Secondary BSIs also progressively decreased from 3.56/1,000 to 0.66/1,000 patient days; an 81.4% decrease (P < .0001). The decrease in BSI rates remained significant after controlling for the number of blood cultures obtained and patient acuity. CONCLUSIONS: An increased focus on reducing hospital-onset infections at the academic medical center since 2005, including multimodal multidisciplinary efforts to prevent central line-associated BSIs, pneumonia, Clostridium difficile disease, surgical site infections, and urinary tract infections, was associated with progressive and sustained decreases for both primary and secondary hospital-onset BSIs.