Evidence map›Paper›PMID 35315547›Full record

ArticleJournal of gastroenterology and hepatology2022

Geospatial analysis of Helicobacter pylori infection in South Australia: Should location influence eradication therapy?

Jonathon P Schubert, Richard J Woodman, Arduino A Mangoni, Christopher K Rayner, Morgyn S Warner, Ian C Roberts-Thomson, Samuel P Costello, Robert V Bryant

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of gastroenterology and hepatology, 2022. 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
2.0field-weighted citation impact, top 15% 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

5 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Article
  3. Epidemiology ofAntibiotics (Basel, Switzerland) · 2023
    Review
  4. Mutations Associated to Antibiotic-ResistantJournal of clinical medicine · 2022
    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

8 authors at 5 institutions in 1 country.

Jonathon P SchubertAdelaide Medical School, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, South Australia, Australia.ORCID https://orcid.org/0000-0001-7201-1188
Richard J WoodmanFlinders Centre for Epidemiology and Biostatistics, Flinders University, Adelaide, South Australia, Australia.
Arduino A MangoniDepartment of Clinical Pharmacology, Flinders Medical Centre, Flinders University, Adelaide, South Australia, Australia.
Christopher K RaynerAdelaide Medical School, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, South Australia, Australia.
Morgyn S WarnerAdelaide Medical School, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, South Australia, Australia.
Ian C Roberts-ThomsonAdelaide Medical School, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, South Australia, Australia.
Samuel P CostelloAdelaide Medical School, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, South Australia, Australia.
Robert V BryantAdelaide Medical School, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, South Australia, Australia.
Queen Elizabeth Hospital · AUFlinders University · AURoyal Adelaide Hospital · AUSouth Australia Pathology · AUThe University of Adelaide · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

aimRates of antimicrobial-resistant Helicobacter pylori infection are rising globally; however, geospatial location and its interaction with risk factors for infection have not been closely examined.

methodsGastric biopsy specimens were collected to detect H. pylori infection at multiple centers in Adelaide, South Australia, between 1998 and 2017. The geospatial distribution of antibiotic-resistant H. pylori in the Greater Adelaide region was plotted using choropleth maps. Moran's I was used to assess geospatial correlation, and multivariate linear regression (MLR) was used to examine associations between migration status, socioeconomic status, age, gender, and rates of H. pylori positivity and antibiotic resistance. Geographically weighted regression (GWR) was used to determine the extent to which the associations varied according to geospatial location.

resultsOf 20 108 biopsies across 136 postcodes within the Greater Adelaide region, 1901 (9.45%) were H. pylori positive. Of these, 797 (41.9%) displayed clarithromycin, tetracycline, metronidazole, or amoxicillin resistance. In MLR, migration status was associated with the rate of H. pylori positivity (β = 3.85% per 10% increase in a postcode's migrant population; P < 0.001). H. pylori positivity and resistance to any antibiotic were geospatially clustered (Moran's I = 0.571 and 0.280, respectively; P < 0.001 for both). In GWR, there was significant geospatial variation in the strength of the migrant association for both H. pylori positivity and antibiotic resistance.

conclusionOur study demonstrates the heterogeneous geospatial distribution of H. pylori positivity and antibiotic resistance, as well as its interaction with migrant status. Geographic location and migrant status are important factors to consider for H. pylori eradication therapy.

Indexed as

Helicobacter InfectionsHelicobacter pyloriAmoxicillinAnti-Bacterial AgentsClarithromycinDrug Resistance, BacterialHumansMetronidazoleMicrobial Sensitivity TestsSouth AustraliaAmoxicillinAnti-Bacterial AgentsClarithromycinMetronidazoleantibioticsantimicrobialantimicrobial resistancegeospatialHelicobacter pyloriresistance

Identifiers

PMID35315547
PMCPMC9543722
OpenAlexW4220867995

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.