{
  "abstract": "BackgroundState-level estimates of hepatitis C virus (HCV) prevalence are needed to guide resource allocation and to provide a baseline against which to measure progress toward elimination goals. Population prevalence is not directly observable, requiring estimation from indirect data sources.\n\nMethodsWe adapted a Bayesian spatial integrated abundance model to estimate state-level HCV prevalence during 2017-2020 across 48 states and the District of Columbia. The model integrated six HCV-related outcomes: acute and chronic surveillance cases, HCV-related deaths, observations of HCV in [MarketScan] administrative claims data, diagnoses of HCV in Medicaid recipients, and treatment with direct-acting antivirals in Medicaid recipients. Estimates were anchored to a national prevalence estimate, and the model accounted for data source-specific selection, heterogeneity in HCV risk factors across states, and geospatial correlation.\n\nResultsEstimated average prevalence was 1.32% (95% credible interval [CrI]: 0.94%-1.80%), corresponding to 3.33 million (95% CrI: 2.37-4.65 million) adults with HCV infection across 48 states and DC. State estimates ranged from 0.74% in North Dakota to 2.22% in Oklahoma (median state-specific prevalence, 1.27%), with higher prevalence concentrated in South Central states, Appalachia, and the West, and the lower prevalence in the upper Midwest, Southeast, and New England. Ten states accounted for 56% of estimated infections. Estimates were stable in sensitivity analyses (most differences <1 percentage point).\n\nConclusionsThese estimates quantify state-level HCV burden prior to the 2021 federal expansion of HCV surveillance funding, providing a baseline for monitoring elimination progress. Geographic variation indicates that resource needs will differ by jurisdiction.",
  "abstract_shown": true,
  "affiliations": [
    "Emory University"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "H. Bradley"
    },
    {
      "affs": [
        0
      ],
      "name": "Y.-H. Yu"
    },
    {
      "name": "L. Li"
    },
    {
      "name": "S. N. Kapadia"
    },
    {
      "name": "P. S. Sullivan"
    },
    {
      "name": "E. W. Hall"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6pxtzkzz1grfb.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.29.26364309",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.09.29.26364309v1.full",
  "id": "6pxtzkzz1grfb",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.09.29.26364309v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "epidemiology"
    ],
    [
      "openalex",
      "2721|Hepatitis C virus research"
    ]
  ],
  "openalex": "W7220397471",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.09.29.26364309v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6pxtzkzz1grfb.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:10Z",
      "native_id": "10.64898/2026.09.29.26364309",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.09.29.26364309v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:epidemiology",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Estimating Hepatitis C Virus Prevalence in US States and the District of Columbia, 2017-2020",
  "title_text": "Estimating Hepatitis C Virus Prevalence in US States and the District of Columbia, 2017-2020",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:10Z",
  "url": "https://research.draconitic.com/paper/6pxtzkzz1grfb",
  "venue": "medRxiv",
  "versions": []
}