Dataset Information

When "the Cure" Is the Risk: Understanding How Substance Use Affects HIV and HCV in a Layered Risk Environment in San Juan, Puerto Rico.

ABSTRACT: BACKGROUND:Substance use, particularly injection drug use, continues to fuel the HIV/HCV (hepatitis C virus) epidemics in San Juan, Puerto Rico (PR). AIM:This article examines individual and sociostructural factors that affect HIV/HCV risk among people who use drugs (PWUD) living with or at risk for HIV/HCV in San Juan, PR. Findings were used to inform a community-level intervention to enhance HIV care access and retention for this population. METHOD:A rapid ethnographic assessment in collaboration with a community-based organization was conducted. Data collection took place between June and December 2013 and included field observations, 49 unstructured interviews with PWUD, and 19 key informant interviews with community stakeholders. Fieldnotes, photographs, and interview transcripts were analyzed for recurrent themes and to address the intervention-planning needs. Study results are presented as fieldnote excerpts, direct quotes from interviews, and photographs. RESULTS:Findings suggest that PWUD in PR face myriad challenges that affect HIV/HCV risk and hinder linkage to and retention in care. Results describe a layered risk environment where PWUD encounter many barriers to prevention, care, and treatment such as transience, social isolation, stigma, limited housing options, and inadequate medical and substance use disorder treatment services. DISCUSSION:These observed circumstances provide an empirical basis for the development and evaluation of comprehensive interventions that may serve to reduce barriers to care and link individuals to other supportive services. CONCLUSION:New approaches and comprehensive interventions are needed to break the structures that perpetuate risk and lack of engagement and retention in HIV care and substance use disorder treatment in San Juan.

SUBMITTER: Hernandez D

PROVIDER: S-EPMC5868741 | biostudies-other | 2017 Oct

REPOSITORIES: biostudies-other

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Publications

When "the Cure" Is the Risk: Understanding How Substance Use Affects HIV and HCV in a Layered Risk Environment in San Juan, Puerto Rico.

Hernández Diana D Castellón Pedro C PC Fernández Yohansa Y Torres-Cardona Francisco A FA Parish Carrigan C Gorshein Danielle D Vargas Vidot Jose J Miranda de Leon Sandra S Rodriguez Allan A Santana Bagur Jorge J Feaster Daniel J DJ Schackman Bruce R BR Metsch Lisa R LR

Health education & behavior : the official publication of the Society for Public Health Education 20170909 5

<h4>Background</h4>Substance use, particularly injection drug use, continues to fuel the HIV/HCV (hepatitis C virus) epidemics in San Juan, Puerto Rico (PR).<h4>Aim</h4>This article examines individual and sociostructural factors that affect HIV/HCV risk among people who use drugs (PWUD) living with or at risk for HIV/HCV in San Juan, PR. Findings were used to inform a community-level intervention to enhance HIV care access and retention for this population.<h4>Method</h4>A rapid ethnographic as ...[more]

PMID: 28891344

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Project description:BackgroundPredictive models can serve as early warning systems and can be used to forecast future risk of various infectious diseases. Conventionally, regression and time series models are used to forecast dengue incidence, using dengue surveillance (e.g., case counts) and weather data. However, these models may be limited in terms of model assumptions and the number of predictors that can be included. Machine learning (ML) methods are designed to work with a large number of predictors and thus offer an appealing alternative. Here, we compared the performance of ML algorithms with that of regression models in predicting dengue cases and outbreaks from 4 to up to 12 weeks in advance. Many countries lack sufficient health surveillance infrastructure, as such we evaluated the contribution of dengue surveillance and weather data on the predictive power of these models.MethodsWe developed ML, regression, and time series models to forecast weekly dengue case counts and outbreaks in Iquitos, Peru; San Juan, Puerto Rico; and Singapore from 1990-2016. Forecasts were generated using available weekly dengue surveillance, and weather data. We evaluated the agreement between model forecasts and actual dengue observations using Mean Absolute Error and Matthew's Correlation Coefficient (MCC).ResultsFor near term predictions of weekly case counts and when using surveillance data, ML models had 21% and 33% less error than regression and time series models respectively. However, using weather data only, ML models did not demonstrate a practical advantage. When forecasting weekly dengue outbreaks 12 weeks in advance, ML models achieved a maximum MCC of 0.61.ConclusionsOur results identified 2 scenarios when ML models are advantageous over regression model: 1) predicting dengue weekly case counts 4 weeks ahead when dengue surveillance data are available and 2) predicting weekly dengue outbreaks 12 weeks ahead when dengue surveillance data are unavailable. Given the advantages of ML models, dengue early warning systems may be improved by the inclusion of these models.

Dataset Information

When "the Cure" Is the Risk: Understanding How Substance Use Affects HIV and HCV in a Layered Risk Environment in San Juan, Puerto Rico.

Publications

When "the Cure" Is the Risk: Understanding How Substance Use Affects HIV and HCV in a Layered Risk Environment in San Juan, Puerto Rico.

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