IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0249585.html
   My bibliography  Save this article

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

Author

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

Abstract

Background: People 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. Methods: We 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. Results: Of 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. Conclusion: While 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.

Suggested Citation

  • Ali Mirzazadeh & Yea-Hung Chen & Jess Lin & Katie Burk & Erin C Wilson & Desmond Miller & Danielle Veloso & Willi McFarland & Meghan D Morris, 2021. "Progress toward closing gaps in the hepatitis C virus cascade of care for people who inject drugs in San Francisco," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-11, April.
    • Handle: RePEc:plo:pone00:0249585
    DOI: 10.1371/journal.pone.0249585
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0249585
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0249585&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0249585?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Gile, Krista J., 2011. "Improved Inference for Respondent-Driven Sampling Data With Application to HIV Prevalence Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 135-146.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ian E. Fellows & Mark S. Handcock, 2023. "Modeling of networked populations when data is sampled or missing," METRON, Springer;Sapienza Università di Roma, vol. 81(1), pages 21-35, April.
    2. Fatemi, Samira & Salehi, Mostafa & Veisi, Hadi & Jalili, Mahdi, 2018. "A fuzzy logic based estimator for respondent driven sampling of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 42-51.
    3. Chien-Min Huang & F. Jay Breidt, 2023. "A dual-frame approach for estimation with respondent-driven samples," METRON, Springer;Sapienza Università di Roma, vol. 81(1), pages 65-81, April.
    4. Lisa Avery & Alison Macpherson & Sarah Flicker & Michael Rotondi, 2021. "A review of reported network degree and recruitment characteristics in respondent driven sampling implications for applied researchers and methodologists," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-19, April.
    5. Dongah Kim & Krista J. Gile & Honoria Guarino & Pedro Mateu‐Gelabert, 2021. "Inferring bivariate association from respondent‐driven sampling data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(2), pages 415-433, March.
    6. Florence Samkange-Zeeb & Ronja Foraita & Stefan Rach & Tilman Brand, 2019. "Feasibility of using respondent-driven sampling to recruit participants in superdiverse neighbourhoods for a general health survey," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 64(3), pages 451-459, April.
    7. Malmros Jens & Masuda Naoki & Britton Tom, 2016. "Random Walks on Directed Networks: Inference and Respondent-Driven Sampling," Journal of Official Statistics, Sciendo, vol. 32(2), pages 433-459, June.
    8. Yakir Berchenko & Jonathan D. Rosenblatt & Simon D. W. Frost, 2017. "Modeling and analyzing respondent‐driven sampling as a counting process," Biometrics, The International Biometric Society, vol. 73(4), pages 1189-1198, December.
    9. Merli, M. Giovanna & Moody, James & Smith, Jeffrey & Li, Jing & Weir, Sharon & Chen, Xiangsheng, 2015. "Challenges to recruiting population representative samples of female sex workers in China using Respondent Driven Sampling," Social Science & Medicine, Elsevier, vol. 125(C), pages 79-93.
    10. Barash Vladimir D. & Cameron Christopher J. & Spiller Michael W. & Heckathorn Douglas D., 2016. "Respondent-Driven Sampling – Testing Assumptions: Sampling with Replacement," Journal of Official Statistics, Sciendo, vol. 32(1), pages 29-73, March.
    11. Nicky McCreesh & Andrew Copas & Janet Seeley & Lisa G Johnston & Pam Sonnenberg & Richard J Hayes & Simon D W Frost & Richard G White, 2013. "Respondent Driven Sampling: Determinants of Recruitment and a Method to Improve Point Estimation," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-9, October.
    12. Aronow, Peter M. & Crawford, Forrest W., 2015. "Nonparametric identification for respondent-driven sampling," Statistics & Probability Letters, Elsevier, vol. 106(C), pages 100-102.
    13. Thespina J. Yamanis & M. Giovanna Merli & William Whipple Neely & Felicia Feng Tian & James Moody & Xiaowen Tu & Ersheng Gao, 2013. "An Empirical Analysis of the Impact of Recruitment Patterns on RDS Estimates among a Socially Ordered Population of Female Sex Workers in China," Sociological Methods & Research, , vol. 42(3), pages 392-425, August.
    14. Mart L Stein & Vincent Buskens & Peter G M van der Heijden & Jim E van Steenbergen & Albert Wong & Martin C J Bootsma & Mirjam E E Kretzschmar, 2018. "A stochastic simulation model to study respondent-driven recruitment," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-19, November.
    15. Hernández, Hugo & Quiroz, Gabriel & Zambrano, Omar & Zanoni, Wladimir, 2023. "Measuring Labor Market Discrimination against LGTBQ+ in the Case of Ecuador: A Field Experiment," IDB Publications (Working Papers) 12977, Inter-American Development Bank.
    16. Zanoni, Wladimir & Fabregas, Raissa, 2024. "The Migrant Penalty in Latin America: Experimental Evidence from Job Recruiters," IDB Publications (Working Papers) 13804, Inter-American Development Bank.
    17. Mark S. Handcock & Krista J. Gile & Corinne M. Mar, 2015. "Estimating the size of populations at high risk for HIV using respondent-driven sampling data," Biometrics, The International Biometric Society, vol. 71(1), pages 258-266, March.
    18. Schonlau, Matthias & Liebau, Elisabeth, 2012. "Respondent-Driven Sampling," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 12(1), pages 72-93.
    19. Lee Sunghee & Suzer-Gurtekin Tuba & Wagner James & Valliant Richard, 2017. "Total Survey Error and Respondent Driven Sampling: Focus on Nonresponse and Measurement Errors in the Recruitment Process and the Network Size Reports and Implications for Inferences," Journal of Official Statistics, Sciendo, vol. 33(2), pages 335-366, June.
    20. Matthias Schonlau & Elisabeth Liebau, 2012. "Respondent-driven sampling," Stata Journal, StataCorp LP, vol. 12(1), pages 72-93, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0249585. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.