Evidence map›Paper›PMID 33798243›Full record

ArticlePloS one2021

Progress toward closing gaps in the hepatitis C virus cascade of care for people who inject drugs in San Francisco.

Ali Mirzazadeh, Yea-Hung Chen, Jess Lin, Katie Burk, Erin C Wilson, Desmond Miller, Danielle Veloso, Willi McFarland, Meghan D Morris

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.8field-weighted citation impact, top 14% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 16 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 1 country.

Ali MirzazadehDepartment of Epidemiology and Biostatistics, University of California, San Francisco, California, United States of America.ORCID 0000-0002-0478-3220
Yea-Hung ChenDepartment of Epidemiology and Biostatistics, University of California, San Francisco, California, United States of America.
Jess LinCenter for Public Health Research, San Francisco Department of Public Health, San Francisco, California, United States of America.
Katie BurkCommunity Health Equity and Promotion Branch, San Francisco Department of Public Health, San Francisco, California, United States of America.
Erin C WilsonCenter for Public Health Research, San Francisco Department of Public Health, San Francisco, California, United States of America.
Desmond MillerCenter for Public Health Research, San Francisco Department of Public Health, San Francisco, California, United States of America.
Danielle VelosoCenter for Public Health Research, San Francisco Department of Public Health, San Francisco, California, United States of America.
Willi McFarlandInstitute for Global Health Sciences, University of California, San Francisco, California, United States of America.
Meghan D MorrisDepartment of Epidemiology and Biostatistics, University of California, San Francisco, California, United States of America.
San Francisco Department of Public Health · USUniversity of California, San Francisco · US

Funding

Making Better Decisions: Policy Modeling for AIDS and Drug AbuseR37DA015612 · NIDA · STANFORD UNIVERSITY · PI DOUGLAS K OWENS · 2019 to 2026
$6.3M
National HIV Behavioral Surveillance SystemU1BPS003247 · PS · SAN FRANCISCO DEPARTMENT OF PUBLIC HEALTH · PI RAYMOND, HENRY FISHER · 2011 to 2015
$2.2M
Informing hepatitis C virus elimination strategies with epidemiological data from highest need populationsR21DA046809 · NIDA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI MORRIS, MEGHAN D · 2019 to 2020
$385k
NCHHSTP CDC HHS U1B PS003247NIDA NIH HHS R21 DA046809NIDA NIH HHS R37 DA015612
6 · The paper itself

Abstract

backgroundPeople who inject drugs (PWID) are disproportionately affected by hepatitis C virus (HCV). Data tracking the engagement of PWID in the continuum of HCV care are needed to assess the reach, target the response, and gauge impact of HCV elimination efforts.

methodsWe analyzed data from the National HIV Behavioral Surveillance (NHBS) surveys of PWID recruited via respondent driven sampling (RDS) in San Francisco in 2018. We calculated the number and proportion who self-reported ever: (1) tested for HCV, (2) tested positive for HCV antibody, (3) diagnosed with HCV, (4) received HCV treatment, (5) and attained sustained viral response (SVR). To assess temporal changes, we compared 2018 estimates to those from the 2015 NHBS sample.

resultsOf 456 PWID interviewed in 2018, 88% had previously been tested for HCV, 63% tested antibody positive, and 50% were diagnosed with HCV infection. Of those diagnosed, 42% received treatment. Eighty-one percent of those who received treatment attained SVR. In 2015 a similar proportion of PWID were tested and received an HCV diagnosis, compared to 2018. However, HCV treatment was more prevalent in the 2018 sample (19% vs. 42%, P-value 0.01). Adjusted analysis of 2018 survey data showed having no health insurance (APR 1.6, P-value 0.01) and having no usual source of health care (APR 1.5, P-value 0.01) were significantly associated with untreated HCV prevalence.

conclusionWhile findings indicate an improvement in HCV treatment uptake among PWID in San Francisco, more than half of PWID diagnosed with HCV infection had not received HCV treatment in 2018. Policies and interventions to increase coverage are necessary, particularly among PWID who are uninsured and outside of regular care.

Indexed as

AdolescentAdultAgedCross-Sectional StudiesDrug UsersFemaleHepacivirusHepatitis CHumansMaleMiddle AgedPrevalenceSan FranciscoSubstance Abuse, IntravenousYoung Adult

Identifiers

PMID33798243
PMCPMC8018615
OpenAlexW3149212799

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.