Evidence map›Paper›PMID 32433663›Full record

ArticlePloS one2020

Cancer morbidity trends and regional differences in England-A Bayesian analysis.

Ayşe Arık, Erengul Dodd, George Streftaris

Abstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Observational
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. 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

3 authors.

Ayşe ArıkMaxwell Institute for Mathematical Sciences, Heriot-Watt University, Edinburgh, Scotland, United Kingdom.ORCID 0000-0001-8190-6768
Erengul DoddMathematical Sciences, University of Southampton, Southampton, England, United Kingdom.
George StreftarisMaxwell Institute for Mathematical Sciences, Heriot-Watt University, Edinburgh, Scotland, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Reliable modelling of the dynamics of cancer morbidity risk is important, not least due to its significant impact on healthcare and related policies. We identify morbidity trends and regional differences in England for all-cancer and type-specific incidence between 1981 and 2016. We use Bayesian modelling to estimate cancer morbidity incidence at various age, year, gender, and region levels. Our analysis shows increasing trends in most rates and marked regional variations that also appear to intensify through time in most cases. All-cancer rates have increased significantly, with the highest increase in East, North West and North East. The absolute difference between the rates in the highest- and lowest-incidence region, per 100,000 people, has widened from 39 (95% CI 33-45) to 86 (78-94) for females, and from 94 (85-104) to 116 (105-127) for males. Lung cancer incidence for females has shown the highest increase in Yorkshire and the Humber, while for males it has declined in all regions with the highest decrease in London. The gap between the highest- and lowest-incidence region for females has widened from 47 (42-51) to 94 (88-100). Temporal change in in bowel cancer risk is less manifested, with regional heterogeneity also declining. Prostate cancer incidence has increased with the highest increase in London, and the regional gap has expanded from 33 (30-36) to 76 (69-83). For breast cancer incidence the highest increase has occurred in North East, while the regional variation shows a less discernible increase. The analysis reveals that there are important regional differences in the incidence of all-type and type-specific cancers, and that most of these regional differences become more pronounced over time. A significant increase in regional variation has been demonstrated for most types of cancer examined here, except for bowel cancer where differences have narrowed.

Indexed as

AdolescentAdultAgedAged, 80 and overAge DistributionBayes TheoremBreast NeoplasmsChildChild, PreschoolEnglandFemaleGeography, MedicalHumansIncidenceInfantInfant, Newborn

Identifiers

PMID32433663
PMCPMC7239391

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.