Background: HIV pre-exposure prophylaxis (PrEP), the use of antiretroviral drugs by uninfected individuals to prevent HIV infection, has demonstrated effectiveness in preventing acquisition in a high-risk population of men who have sex with men (MSM). Cons

Background

The growing availability of big data spontaneously generated by social media platforms allows us to leverage natural language processing (NLP) methods as valuable tools to understand the opioid crisis.

Objective

We aimed to understand how NLP has been applied to Reddit (Reddit Inc) data to study opioid use.

Methods

We systematically searched for peer-reviewed studies and conference abstracts in PubMed, Scopus, PsycINFO, ACL Anthology, IEEE Xplore, and Association for Computing Machinery data repositories up to July 19, 2022. Inclusion criteria were studies investigating opioid use, using NLP techniques to analyze the textual corpora, and using Reddit as the social media data source. We were specifically interested in mapping studies' overarching goals and findings, methodologies and software used, and main limitations.

Results

In total, 30 studies were included, which were classified into 4 nonmutually exclusive overarching goal categories: methodological (n=6, 20% studies), infodemiology (n=22, 73% studies), infoveillance (n=7, 23% studies), and pharmacovigilance (n=3, 10% studies). NLP methods were used to identify content relevant to opioid use among vast quantities of textual data, to establish potential relationships between opioid use patterns or profiles and contextual factors or comorbidities, and to anticipate individuals' transitions between different opioid-related subreddits, likely revealing progression through opioid use stages. Most studies used an embedding technique (12/30, 40%), prediction or classification approach (12/30, 40%), topic modeling (9/30, 30%), and sentiment analysis (6/30, 20%). The most frequently used programming languages were Python (20/30, 67%) and R (2/30, 7%). Among the studies that reported limitations (20/30, 67%), the most cited was the uncertainty regarding whether redditors participating in these forums were representative of people who use opioids (8/20, 40%). The papers were very recent (28/30, 93%), from 2019 to 2022, with authors from a range of disciplines.

Conclusions

This scoping review identified a wide variety of NLP techniques and applications used to support surveillance and social media interventions addressing the opioid crisis. Despite the clear potential of these methods to enable the identification of opioid-relevant content in Reddit and its analysis, there are limits to the degree of interpretive meaning that they can provide. Moreover, we identified the need for standardized ethical guidelines to govern the use of Reddit data to safeguard the anonymity and privacy of people using these forums.

Pathogenic and likely pathogenic variants in the TECRL gene are known to be associated with recessive catecholaminergic polymorphic ventricular tachycardia 3, which can include prolonged QT intervals (MIM#614021). We report a case of cardiac arrest in a previously healthy adolescent male in the community. The patient was found to have a novel maternally inherited likely pathogenic variant in TECRL (c.915T>G [p.Tyr305Ter]) and an additional 19-kb duplication encompassing multiple exons of TECRL (chr4:65165944-65185287, dup [4q13.1]) not identified in the mother. Genetic results were revealed via rapid whole-genome sequencing, which allowed appropriate treatment and prognostication.

Introduction. Needle and syringe programs (NSP) reduce syringe sharing and HIV transmission among people who inject drugs (PWID). However, their effectiveness relies on sufficient individual and population level coverage. In Tijuana, Mexico, the Global Fund (GF) supported NSP during 10/2011-12/2013, but withdrew funds at the end of 2013 following Mexico’s re-classification as an upper-middle income country. Objective. We tested the hypothesis of higher NSP access and lower receptive syringe sharing among PWID in Tijuana during the GF support period compared to pre-GF initiation and post-GF withdrawal. Method. We used data from an ongoing cohort study of PWID in Tijuana (03/2011-10/2015) to implement a segmented regression analysis investigating changes in the self-reported probability of NSP access, reported difficulty in finding sterile syringes and receptive syringe sharing before GF initiation and after GF discontinuation. Results. We found a large significant increase in the probability of NSP access (+.07) and decrease in receptive syringe sharing (-.23) right after GF initiation, which continued over the GF period. Subsequently, we found a significant decline in NSP access (-.05) and an increase in receptive syringe sharing (+.02) right after post-GF withdrawal, which continued thereafter. Discussion and conclusion. We demonstrated significant temporal changes in NSP access and receptive syringe sharing among PWID in Tijuana after GF initiation and withdrawal consistent with our hypothesis. Coordinated efforts with local authorities are needed to sustain major coverage NSP in settings receiving GF or external aid to guarantee continuity of harm reduction services and prevent reinside in risk behaviors associated with HIV transmission.

In 2019, Mexico was one of the first countries in Latin America to commit resources to achieve hepatitis C elimination by 2030. One year after this commitment, the global COVID-19 pandemic diverted attention to address immediate health needs to combat the spread of the disease. As a result, efforts to implement hepatitis C prevention and management programs were indefinitely postponed. Furthermore, populations at high risk of contracting the hepatitis C virus (HCV) and who bear the greatest burden of HCV national epidemic, including people who inject drugs and people who live with human immunodeficiency virus infection, remain exposed to extreme health disparities, which have potentially been exacerbated during the COVID-19 pandemic. In this article, we discuss the potential impact the COVID-19 pandemic has had on HCV elimination efforts in Mexico and the urgent need to resume them, since without these efforts, HCV elimination goals are likely not be achieved in the country by 2030.

Background

There is no recognized gold standard method for estimating the number of individuals with substance use disorders (SUDs) seeking help within a given geographical area. This presents a challenge to policy makers in the effective deployment of resources for the treatment of SUDs. Internet search queries related to help seeking for SUDs using Google Trends may represent a low-cost, real-time, and data-driven infoveillance tool to address this shortfall in information.

Objective

This paper assesses the feasibility of using search query data related to help seeking for SUDs as an indicator of unmet treatment needs, demand for treatment, and predictor of the health harms related to unmet treatment needs. We explore a continuum of hypotheses to account for different outcomes that might be expected to occur depending on the demand for treatment relative to the system capacity and the timing of help seeking in relation to trajectories of substance use and behavior change.

Methods

We used negative binomial regression models to examine temporal trends in the annual SUD help-seeking internet search queries from Google Trends by US state for cocaine, methamphetamine, opioids, cannabis, and alcohol from 2010 to 2020. To validate the value of these data for surveillance purposes, we then used negative binomial regression models to investigate the relationship between SUD help-seeking searches and state-level outcomes across the continuum of care (including lack of care). We started by looking at associations with self-reported treatment need using data from the National Survey on Drug Use and Health, a national survey of the US general population. Next, we explored associations with treatment admission rates from the Treatment Episode Data Set, a national data system on SUD treatment facilities. Finally, we studied associations with state-level rates of people experiencing and dying from an opioid overdose, using data from the Agency for Healthcare Research and Quality and the CDC WONDER database.

Results

Statistically significant differences in help-seeking searches were observed over time between 2010 and 2020 (based on P<.05 for the corresponding Wald tests). We were able to identify outlier states for each drug over time (eg, West Virginia for both opioids and methamphetamine), indicating significantly higher help-seeking behaviors compared to national trends. Results from our validation analyses across different outcomes showed positive, statistically significant associations for the models relating to treatment need for alcohol use, treatment admissions for opioid and methamphetamine use, emergency department visits related to opioid use, and opioid overdose mortality data (based on regression coefficients having P≤.05).

Conclusions

This study demonstrates the clear potential for using internet search queries from Google Trends as an infoveillance tool to predict the demand for substance use treatment spatially and temporally, especially for opioid use disorders.

The opioid crisis in the United States has been defined by waves of drug- and locality-specific Opioid use-Related Epidemics (OREs) of overdose and bloodborne infections, among a range of health harms. The ability to identify localities at risk of such OREs, and better yet, to predict which ones will experience them, holds the potential to mitigate further morbidity and mortality. This narrative review was conducted to identify and describe quantitative approaches aimed at the "risk assessment," "detection" or "prediction" of OREs in the United States. We implemented a PubMed search composed of the: (1) objective (eg, prediction), (2) epidemiologic outcome (eg, outbreak), (3) underlying cause (ie, opioid use), (4) health outcome (eg, overdose, HIV), (5) location (ie, US). In total, 46 studies were included, and the following information extracted: discipline, objective, health outcome, drug/substance type, geographic region/unit of analysis, and data sources. Studies identified relied on clinical, epidemiological, behavioral and drug markets surveillance and applied a range of methods including statistical regression, geospatial analyses, dynamic modeling, phylogenetic analyses and machine learning. Studies for the prediction of overdose mortality at national/state/county and zip code level are rapidly emerging. Geospatial methods are increasingly used to identify hotspots of opioid use and overdose. In the context of infectious disease OREs, routine genetic sequencing of patient samples to identify growing transmission clusters via phylogenetic methods could increase early detection capacity. A coordinated implementation of multiple, complementary approaches would increase our ability to successfully anticipate outbreak risk and respond preemptively. We present a multi-disciplinary framework for the prediction of OREs in the US and reflect on challenges research teams will face in implementing such strategies along with good practices.