Evidence map›Paper›PMID 16557371›Full record

ArticleDiabetologia2006

Space-time clustering analyses of type 1 diabetes among 0- to 29-year-olds in Yorkshire, UK.

R J Q McNally, R G Feltbower, L Parker, H J Bodansky, F Campbell, P A McKinney

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Article in Diabetologia, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.7field-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

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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

11 citing papers in PubMed, 26 citations in OpenAlex.

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  11. Spatial-temporal analysis of breast cancer in upper Cape Cod, Massachusetts.International journal of health geographics · 2008
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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

6 authors at 4 institutions in 1 country.

R J Q McNallySchool of Clinical Medical Sciences (Child Health), University of Newcastle upon Tyne, Newcastle upon Tyne, UK. Richard.McNally@ncl.ac.uk
R G Feltbower
L Parker
H J Bodansky
F Campbell
P A McKinney
Newcastle University · GBUniversity of Leeds · GBLeeds General Infirmary · GBLeeds Teaching Hospitals NHS Trust · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aims/hypothesisInfections have been suggested to play a role in the aetiology of type 1 diabetes. The presence of space-time clustering is consistent with the notion of an environmental component in disease aetiology, possibly linked to infections. We tested for evidence of space-time clustering among children and young adults under 30 years of age using data from a population-based register in Yorkshire, UK. SUBJECTS AND

methodsTwo data sets of children and young people diagnosed with type 1 diabetes were analysed: (1) children aged 0-14 years and resident in Yorkshire during 1978-2002; (2) those aged 15-29 years and resident in West Yorkshire during 1991-2002. Tests for space-time interactions between cases were applied. Addresses at diagnosis were geo-coded and used as the basis for the analyses.

resultsThe study analysed 3,019 type 1 diabetic patients in the 0-14 years age group and 989 patients in the 15-29 years group. Statistically significant space-time clustering based on place and time of diagnosis was detected both for the 10-14-year-olds (p=0.04) and for the 15-19-year-olds (p=0.01). CONCLUSIONS/

interpretationPrevious studies of clustering of type 1 diabetes have generally been restricted to childhood. Our results from a data set that includes teenagers and young adults show that space-time clustering was limited to young people aged 10-19 years. This finding is consistent with an aetiology involving late exposure to infection. However, the question of whether this is directly diabetogenic or unmasks latent diabetes cannot be addressed by this methodology.

Indexed as

AdolescentAdultAge DistributionChildChild, PreschoolDiabetes Mellitus, Type 1EnglandFemaleGeographyHumansInfantMaleRegistries

Identifiers

PMID16557371
OpenAlexW2051211685

What OpenQuestion holds

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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.