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.

An Automated Bioinformatics Pipeline Informing Near-Real-Time Public Health Responses to New HIV Diagnoses in a Statewide HIV Epidemic.

Molecular HIV cluster data can guide public health responses towards ending the HIV epidemic. Currently, real-time data integration, analysis, and interpretation are challenging, leading to a delayed public health response. We present a comprehensive methodology for addressing these challenges through data integration, analysis, and reporting. We integrated heterogeneous data sources across systems and developed an open-source, automatic bioinformatics pipeline that provides molecular HIV cluster data to inform public health responses to new statewide HIV-1 diagnoses, overcoming data management, computational, and analytical challenges. We demonstrate implementation of this pipeline in a statewide HIV epidemic and use it to compare the impact of specific phylogenetic and distance-only methods and datasets on molecular HIV cluster analyses. The pipeline was applied to 18 monthly datasets generated between January 2020 and June 2022 in Rhode Island, USA, that provide statewide molecular HIV data to support routine public health case management by a multi-disciplinary team. The resulting cluster analyses and near-real-time reporting guided public health actions in 37 phylogenetically clustered cases out of 57 new HIV-1 diagnoses. Of the 37, only 21 (57%) clustered by distance-only methods. Through a unique academic-public health partnership, an automated open-source pipeline was developed and applied to prospective, routine analysis of statewide molecular HIV data in near-real-time. This collaboration informed public health actions to optimize disruption of HIV transmission.

Beyond HIV outbreaks: protocol, rationale and implementation of a prospective study quantifying the benefit of incorporating viral sequence clustering analysis into routine public health interventions.

INTRODUCTION: HIV continues to have great impact on millions of lives. Novel methods are needed to disrupt HIV transmission networks. In the USA, public health departments routinely conduct contact tracing and partner services and interview newly HIV-diagnosed index cases to obtain information on social networks and guide prevention interventions. Sequence clustering methods able to infer HIV networks have been used to investigate and halt outbreaks. Incorporation of such methods into routine, not only outbreak-driven, contact tracing and partner services holds promise for further disruption of HIV transmissions. METHODS AND ANALYSIS: Building on a strong academic-public health collaboration in Rhode Island, we designed and have implemented a state-wide prospective study to evaluate an intervention that incorporates real-time HIV molecular clustering information with routine contact tracing and partner services. We present the rationale and study design of our approach to integrate sequence clustering methods into routine public health interventions as well as related important ethical considerations. This prospective study addresses key questions about the benefit of incorporating a clustering analysis triggered intervention into the routine workflow of public health departments, going beyond outbreak-only circumstances. By developing an intervention triggered by, and incorporating information from, viral sequence clustering analysis, and evaluating it with a novel design that avoids randomisation while allowing for methods comparison, we are confident that this study will inform how viral sequence clustering analysis can be routinely integrated into public health to support the ending of the HIV pandemic in the USA and beyond. ETHICS AND DISSEMINATION: The study was approved by both the Lifespan and Rhode Island Department of Health Human Subjects Research Institutional Review Boards and study results will be published in peer-reviewed journals.

Statewide Longitudinal Trends in Transmitted HIV-1 Drug Resistance in Rhode Island, USA.

BACKGROUND: HIV-1 transmitted drug resistance (TDR) remains a global challenge that can impact care, yet its comprehensive assessment is limited and heterogenous. We longitudinally characterized statewide TDR in Rhode Island. METHODS: Demographic and clinical data from treatment-naïve individuals were linked to protease, reverse transcriptase, and integrase sequences routinely obtained over 2004-2020. TDR extent, trends, impact on first-line regimens, and association with transmission networks were assessed using the Stanford Database, Mann-Kendall statistic, and phylogenetic tools. RESULTS: In 1123 individuals, TDR to any antiretroviral increased from 8% (2004) to 26% (2020), driven by non-nucleotide reverse transcriptase inhibitor (NNRTI; 5%-18%) and, to a lesser extent, nucleotide reverse transcriptase inhibitor (NRTI; 2%-8%) TDR. Dual- and triple-class TDR rates were low, and major integrase strand transfer inhibitor resistance was absent. Predicted intermediate to high resistance was in 77% of those with TDR, with differential suppression patterns. Among all individuals, 34% were in molecular clusters, some only with members with TDR who shared mutations. Among clustered individuals, people with TDR were more likely in small clusters. CONCLUSIONS: In a unique (statewide) assessment over 2004-2020, TDR increased; this was primarily, but not solely, driven by NNRTIs, impacting antiretroviral regimens. Limited TDR to multiclass regimens and pre-exposure prophylaxis are encouraging; however, surveillance and its integration with molecular epidemiology should continue in order to potentially improve care and prevention interventions.

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.

Investigation and modeling of the residential infiltration of fine particulate matter in Beijing, China.

The objective of this study was to estimate the residential infiltration factor (Finf) of fine particulate matter (PM2.5) and to develop models to predict PM2.5 Finf in Beijing. Eighty-eight paired indoor-outdoor PM2.5 samples were collected by Teflon filters for seven consecutive days during both non-heating and heating seasons (from a total of 55 families between August, 2013 and February, 2014). The mass concentrations of PM2.5 were measured by gravimetric method, and elemental concentrations of sulfur in filter deposits were determined by energy-dispersive x-ray fluorescence (ED-XRF) spectrometry. PM2.5 Finf was estimated as the indoor/outdoor sulfur ratio. Multiple linear regression was used to construct Finf predicting models. The residential PM2.5 Finf in non-heating season (0.70 ± 0.21, median = 0.78, n = 43) was significantly greater than in heating season (0.54 ± 0.18, median = 0.52, n = 45, p < 0.001). Outdoor temperature, window width, frequency of window opening, and air conditioner use were the most important predictors during non-heating season, which could explain 57% variations across residences, while the outdoor temperature was the only predictor identified in heating season, which could explain 18% variations across residences. The substantial variations of PM2.5 Finf between seasons and among residences found in this study highlight the importance of incorporating Finf into exposure assessment in epidemiological studies of air pollution and human health in Beijing. The Finf predicting models developed in this study hold promise for incorporating PM2.5 Finf into large epidemiology studies, thereby reducing exposure misclassification. IMPLICATIONS: Failure to consider the differences between indoor and outdoor PM2.5 may contribute to exposure misclassification in epidemiological studies estimating exposure from a central site measurement. This study was conducted in Beijing to investigate residential PM2.5 infiltration factor and to develop a localized predictive model in both nonheating and heating seasons. High variations of PM2.5 infiltration factor between the two seasons and across homes within each season were found, highlighting the importance of including infiltration factor in the assessment of exposure to PM2.5 of outdoor origin in epidemiological studies. Localized predictive models for PM2.5 infiltration factor were also developed.

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.