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Hepatitis01:25

Hepatitis

Hepatitis is an inflammatory condition of the liver most commonly caused by hepatotropic viruses (A–E), though non-infectious causes such as alcohol and drugs also exist.Hepatitis AHepatitis A virus (HAV) is a non-enveloped RNA virus of the Picornaviridae family. It is primarily transmitted via the fecal-oral route, typically through ingestion of contaminated food or water. After ingestion, HAV enters the bloodstream through the oropharynx or intestinal epithelium and reaches the liver. The...

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Related Experiment Video

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A Protocol for Analyzing Hepatitis C Virus Replication
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Novel Models to Identify Census Tracts for Hepatitis C Screening Interventions.

Thomas Ludden1, Lindsay Shade2, Jeremy Thomas2

  • 1From Department of Family Medicine, Atrium Health, Charlotte, NC (TL, LS, JT, SM, HT); Department of Hepatology, Atrium Health, Charlotte, NC (MWR, PJZ); Department of Infectious Diseases, Atrium Health, Charlotte, NC (ML); Community Health, Atrium Health, Charlotte, NC (BUH); School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA (CGP). Tom.Ludden@atriumhealth.org.

Journal of the American Board of Family Medicine : JABFM
|May 21, 2020
PubMed
Summary

Geospatial modeling identified high-risk areas for undiagnosed hepatitis C (HCV) by analyzing transmission risk and social determinants of health. This approach aids in targeted screening and intervention for at-risk populations.

Keywords:
Hepatitis CRegression AnalysisRisk FactorsSocial Determinants of Health

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Area of Science:

  • Public Health
  • Epidemiology
  • Geospatial Analysis

Background:

  • Hepatitis C (HCV) screening and treatment have improved outcomes, yet many remain undiagnosed in the US.
  • Geospatial modeling can identify at-risk populations using social determinants of health (SDOH) and transmission risk factors.

Purpose of the Study:

  • To identify high-risk census tracts for undiagnosed HCV using geospatial modeling.
  • To map populations in need of HCV screening and linkage-to-care interventions.

Main Methods:

  • Literature review to identify HCV transmission risk and SDOH variables.
  • Creation of combined indices and mapping at the census tract level (n=233).
  • Validation using multiple linear regression and Pearson correlation.

Main Results:

  • Four HCV transmission risk and 12 SDOH variables were identified.
  • 21 high-risk census tracts were identified (≥2 standard deviations above the mean).
  • Significant correlations found between HCV infection rate and drug use prevalence, limited English proficiency, lack of car access, poverty, and median household income.

Conclusions:

  • Geospatial models effectively identify high-priority census tracts for HCV.
  • This mapping facilitates targeted screening and linkage-to-care for unrecognized high-risk populations.