Evidence map›Paper›PMID 39972312›Full record

SynthesisBMC public health2025

Insights into the spatial epidemiology of hepatitis C infection: systematic synthesis of area-level determinants and spatiotemporal analyses.

Jacob Mugoya Gizamba, Brian Karl Finch, Siqin Wang, Jeffrey D Klausner

Abstract readSystematic Review
In one paragraph

Synthesis in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
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

4 authors.

Jacob Mugoya GizambaSpatial Science Institute, University of Southern California, Los Angeles, CA, USA. gizamba@usc.edu.
Brian Karl FinchCenter for Economic and Social Research, University of Southern California, Los Angeles, CA, USA.
Siqin WangSpatial Science Institute, University of Southern California, Los Angeles, CA, USA.
Jeffrey D KlausnerDepartment of Population and Public Health, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHepatitis C virus (HCV) stands at the forefront of global elimination endeavors by 2030, highlighting the need for a nuanced exploration into disparities and vulnerabilities using innovative spatial epidemiological approaches. This study aimed to systematically review existing literature to summarize area-level factors associated with HCV infection and to examine the application of spatial and spatiotemporal analyses in HCV research.

methodsA systematic search following PRISMA guidelines was conducted for peer-reviewed literature published between 2000 and 2023 using PubMed, Web of Science, Scopus, and Embase databases. The synthesis of area-level factors was organized according to four distinct categories of risk environments: social, economic, policy, and physical environments.

resultsSixty-five studies were selected for this systematic review. 60% of the studies focused on the general population, while 20% of the studies targeted people who inject drugs. The area-level factors explored predominantly were characteristics of the social and economic risk environments. For instance, areas with a higher level of socioeconomic disadvantage, lower education attainment, higher population density, and located more remotely were associated with higher HCV infection rates. Additionally, some studies noted a significant correlation between the accessibility to harm reduction and healthcare services and HCV occurrence, testing, and treatment rates. Furthermore, spatial data exploration and cluster analysis methods were the predominant methods used to explore the nuanced spatial distribution of HCV infection.

conclusionThis review emphasizes the imperative of deciphering the complex interplay of area-level factors in HCV infection dynamics. Understanding the potential risk environment landscape of HCV could facilitate identifying vulnerable areas and communities. Additionally, the limited application of spatial analytics in HCV research highlights the untapped potential, emphasizing the need for enhanced spatial techniques to pinpoint priority areas for intervention.

Indexed as

Hepatitis CSpatio-Temporal AnalysisHumansRisk FactorsSocioeconomic FactorsArea-level factorsGlobal eliminationHepatitis CSpatial epidemiologySpatial models

Identifiers

PMID39972312
PMCPMC11841175

What OpenQuestion holds

Textmetadata
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.