Evidence map›Paper›PMID 22798256›Full record

ArticleBMJ open2012

Campylobacter epidemiology: a descriptive study reviewing 1 million cases in England and Wales between 1989 and 2011.

Gordon L Nichols, Judith F Richardson, Samuel K Sheppard, Chris Lane, Christophe Sarran

Abstract read
In one paragraph

Article in BMJ open, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
64citing papers in PubMed, 5 pooled it
–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

64 citing papers in PubMed, 5 syntheses or guidelines pooled it.

  1. Pooled it
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  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Observational
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Evidence of potentialMicrobial genomics · 2023
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  13. Pathogens (Basel, Switzerland) · 2023
    Article
  14. Article
  15. Article
  16. Genomic Screening of Antimicrobial Resistance Markers in UK and USAntimicrobial agents and chemotherapy · 2022
    Article
  17. Article
  18. Article
  19. AMicrobial genomics · 2021
    Article
  20. Article

4 more citing papers are in PubMed but not listed here.

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

5 authors.

Gordon L NicholsHPA Colindale, Health Protection Agency, London, UK.
Judith F Richardson
Samuel K Sheppard
Chris Lane
Christophe Sarran

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo review Campylobacter cases in England and Wales over 2 decades and examine the main factors/mechanisms driving the changing epidemiology.

designA descriptive study of Campylobacter patients between 1989 and 2011. Cases over 3 years were linked anonymously to postcode, population density, deprivation indices and census data. Cases over 5 years were anonymously linked to local weather exposure estimates.

settingPatients were from general practice, hospital and environmental health investigations through primary diagnostic laboratories across England and Wales.

participantsThere were 1 109 406 cases. OUTCOME MEASURES: Description of changes in Campylobacter epidemiology over 23 years and how the main drivers may influence these.

resultsThere was an increase in Campylobacter cases over the past 23 years, with the largest increase in people over 50 years. Changes in the underlying population have contributed to this, including the impacts of population increases after World War I, World War II and the 'baby boom' of the 1960s. A recent increase in risk or ascertainment within this population has caused an increase in cases in all age groups from 2004 to 2011. The seasonal increase in cases between weeks 18 (Early May) and 22 (Early June) was consistent across ages, years and regions and was most marked in children and in more rural regions. Campylobacter prevalence by week in each region correlated with temperature 2 weeks before. There were higher prevalences in areas with a low population density, low deprivation and lower percentage of people of ethnic origin. Data from sero-phage and multilocus sequence typing show a few common types and many uncommon types.

conclusionsThe drivers/mechanisms influencing seasonality, age distribution, population density, socioeconomic and long-term differences are diverse and their relative contributions remain to be established. Surveillance and typing provide insights into Campylobacter epidemiology and sources of infection, providing a sound basis for targeted interventions.

Identifiers

PMID22798256
PMCPMC3400078

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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.