<i>Notes from the Field:</i> HIV Diagnoses Among Persons Who Inject Drugs — Northeastern Massachusetts, 2015–2018

Cranston, Kevin; Alpren, Charles; John, Betsey; Dawson, Erica L.; Roosevelt, Kathleen; Burrage, Amanda; Bryant, Janice; Switzer, William M.; Breen, Courtney; Peters, Philip J.; Stiles, Tracy; Murray, Ashley; Fukuda, Hideki; Adih, William; Goldman, Linda; Panneer, Nivedha; Callis, Barry; Campbell, Ellsworth; Randall, Laura; France, Anne Marie; Klevens, R. Monina; Lyss, Sheryl; Onofrey, Shauna; Agnew-Brune, Christine; Goulart, Michael A.; Jia, Honglin; Tumpney, Matthew; McClung, Paul; Dasgupta, Sharoda; Bixler, Danae; Hampton, Kischa; Jaeger, Jenifer L.; Buchacz, Kate; DeMaria, Alfred

doi:10.15585/mmwr.mm6810a6

Cited by 147 publications

(100 citation statements)

References 2 publications

(2 reference statements)

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“…For cluster detection and response to be most effective it should incorporate robust core surveillance including an ongoing understanding of changes in HIV transmission trends and complete case and lab reporting, robust partner services activities with disease investigators that are familiar with the profile of their local cases, and efficient and complete molecular data collection and analysis [10,12,23]. There should also be transparency and dialogue with the community about the activities being undertaken and why they are important.…”

Section: Discussionmentioning

confidence: 99%

Characterization of Molecular Cluster Detection and Evaluation of Cluster Investigation Criteria Using Machine Learning Methods and Statewide Surveillance Data in Washington State

Erly

Herbeck

Kerani

et al. 2020

Viruses

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Molecular cluster detection can be used to interrupt HIV transmission but is dependent on identifying clusters where transmission is likely. We characterized molecular cluster detection in Washington State, evaluated the current cluster investigation criteria, and developed a criterion using machine learning. The population living with HIV (PLWH) in Washington State, those with an analyzable genotype sequences, and those in clusters were described across demographic characteristics from 2015 to2018. The relationship between 3- and 12-month cluster growth and demographic, clinical, and temporal predictors were described, and a random forest model was fit using data from 2016 to 2017. The ability of this model to identify clusters with future transmission was compared to Centers for Disease Control and Prevention (CDC) and the Washington state criteria in 2018. The population with a genotype was similar to all PLWH, but people in a cluster were disproportionately white, male, and men who have sex with men. The clusters selected for investigation by the random forest model grew on average 2.3 cases (95% CI 1.1–1.4) in 3 months, which was not significantly larger than the CDC criteria (2.0 cases, 95% CI 0.5–3.4). Disparities in the cases analyzed suggest that molecular cluster detection may not benefit all populations. Jurisdictions should use auxiliary data sources for prediction or continue using established investigation criteria.

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Section: Discussionmentioning

confidence: 99%

Characterization of Molecular Cluster Detection and Evaluation of Cluster Investigation Criteria Using Machine Learning Methods and Statewide Surveillance Data in Washington State

Erly

Herbeck

Kerani

et al. 2020

Viruses

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“…An HIV outbreak in Scott County, Indiana, was the most severe recent HIV outbreak in the United States, with more than 180 cases of HIV identified in a short period of time, caused by sharing of infectious syringe equipment among a network of predominantly prescription opioid–using persons . A similar HIV outbreak associated with opioid injection in Massachusetts was recently reported . In addition, CDC researchers have identified the connection between increasing rates of invasive methicillin‐resistant Staphylococcus aureus infections and injection drug use, including opioid injection .…”

Section: Introductionmentioning

confidence: 98%

Epidemiology of the U.S. opioid crisis: the importance of the vector

Compton

Jones

2019

Annals of the New York Academy of Sciences

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The roots of the remarkably lethal U.S. opioid crisis are complex and inextricably entangled with healthcare, especially in its treatment of another serious health problem: pain. Failures of the healthcare system—including lack of both training in pain management and caution in using an addictive class of medications—precipitated the rise in opioid misuse and addiction over the past two decades, but a wider range of social and economic forces has helped perpetuate the crisis and altered its character. The classic epidemiologic host–agent–environment triad can be augmented for the purpose of elucidating the current opioid crisis by addition of the “vector” to emphasize the importance of purveyors of opioids (licit and illicit), leading to our proposing an expanded host–agent–vector–environment model. Interventions addressing multiple components are needed, including solutions that account for behaviors of all vectors associated with the crisis. For prescription opioids, the vectors include clinicians and pharmaceutical‐related companies involved in marketing, prescribing, distributing, and dispensing opioid medications; for illicit opioids, they include drug manufacturing and distribution networks. Attending to the vectors of opioids, while simultaneously implementing a full range of public health, clinical, law enforcement, and other approaches to ending the opioid crisis, may help to improve public health outcomes.

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“…As such, this modality is associated with lower prevalence and incidence rates of HIV itself [16][17][18][19]. Many parts of the country, including Seattle, have witnessed outbreaks of HIV among persons who inject drugs related to the opioid crisis [20][21][22][23]. Given that medication treatment for opioid use disorder plays a critical role in protecting opioid users from HIV, ensuring continuous medication treatment for opioid use disorder treatment is imperative to help safeguard these individuals from acquiring HIV.…”

mentioning

confidence: 99%

Rapid Implementation of Service Delivery Changes to Mitigate COVID-19 and Maintain Access to Methadone Among Persons with and at High-Risk for HIV in an Opioid Treatment Program

et al. 2020

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Notes from the Field: HIV Diagnoses Among Persons Who Inject Drugs — Northeastern Massachusetts, 2015–2018

Cited by 147 publications

References 2 publications

Characterization of Molecular Cluster Detection and Evaluation of Cluster Investigation Criteria Using Machine Learning Methods and Statewide Surveillance Data in Washington State

Characterization of Molecular Cluster Detection and Evaluation of Cluster Investigation Criteria Using Machine Learning Methods and Statewide Surveillance Data in Washington State

Epidemiology of the U.S. opioid crisis: the importance of the vector

Rapid Implementation of Service Delivery Changes to Mitigate COVID-19 and Maintain Access to Methadone Among Persons with and at High-Risk for HIV in an Opioid Treatment Program

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