Literature DB >> 11416078

Population mixing and childhood diabetes.

R C Parslow1, P A McKinney, G R Law, H J Bodansky.   

Abstract

BACKGROUND: Exposure to infections, particularly in early life, may modify the risk of developing childhood diabetes. Population mixing, based on the number and diversity of incoming migrants to an area can be used as a proxy measure for exposure to infections. We tested the hypothesis that incidence of childhood Type 1 diabetes is higher in areas of low population mixing.
METHODS: Children (<15 years) diagnosed with diabetes between 1986--1994 in Yorkshire, UK (n = 994) were analysed with demographic data and denominator populations from the 1991 UK Census. Population mixing was estimated separately for 'any age' (>1 year) and children (1--15 years) for each area, using the proportion of migrants and an index of diversity based on numbers and origins of migrants. Regression models calculated the effect of 'any age' and childhood population mixing on the incidence of diabetes, controlling for population density, ethnicity and proportion of migrants.
RESULTS: Areas with low levels of population mixing of children (bottom decile), were significantly associated with higher incidence of childhood diabetes for 0-14 years (incidence rate ratio [IRR] = 1.46, 95% CI : 1.01--2.11). When stratified by age different effects were observed for childhood population mixing with raised IRR for ages 5-9 (2.23, 95% CI : 1.20--4.11) and 10-14 (1.47, 95% CI : 0.89--2.42), and decreased IRR for 0--4-year-olds (0.56, 95% CI : 0.17--1.82).
CONCLUSION: The incidence of childhood diabetes is highest in areas where limited childhood population mixing occurs and the diversity of origins of incoming children is low; those over 4 years are at greatest risk. This is consistent with an infectious hypothesis where absence of stimulation to the developing immune system increases vulnerability to late infectious exposure, which may precipitate diabetes.

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Year:  2001        PMID: 11416078     DOI: 10.1093/ije/30.3.533

Source DB:  PubMed          Journal:  Int J Epidemiol        ISSN: 0300-5771            Impact factor:   7.196


  11 in total

1.  Does population mixing measure infectious exposure in children at the community level?

Authors:  John C Taylor; Graham R Law; Paul J Boyle; Zhiqiang Feng; Mark S Gilthorpe; Roger C Parslow; Gavin Rudge; Richard G Feltbower
Journal:  Eur J Epidemiol       Date:  2008-08-14       Impact factor: 8.082

2.  Relationship between the incidence of type 1 diabetes and maternal enterovirus antibodies: time trends and geographical variation.

Authors:  H Viskari; J Ludvigsson; R Uibo; L Salur; D Marciulionyte; R Hermann; G Soltesz; M Füchtenbusch; A-G Ziegler; A Kondrashova; A Romanov; B Kaplan; Z Laron; P Koskela; T Vesikari; H Huhtala; M Knip; H Hyöty
Journal:  Diabetologia       Date:  2005-05-19       Impact factor: 10.122

3.  Neighborhood context and incidence of type 1 diabetes: the SEARCH for Diabetes in Youth study.

Authors:  Robin C Puett; Archana P Lamichhane; Michele D Nichols; Andrew B Lawson; Debra A Standiford; Lenna Liu; Dana Dabelea; Angela D Liese
Journal:  Health Place       Date:  2012-02-27       Impact factor: 4.078

4.  Modeling type 1 and type 2 diabetes mellitus incidence in youth: an application of Bayesian hierarchical regression for sparse small area data.

Authors:  Hae-Ryoung Song; Andrew Lawson; Ralph B D'Agostino; Angela D Liese
Journal:  Spat Spatiotemporal Epidemiol       Date:  2011-03

5.  Evaluating geographic variation in type 1 and type 2 diabetes mellitus incidence in youth in four US regions.

Authors:  Angela D Liese; Andrew Lawson; Hae-Ryoung Song; James D Hibbert; Dwayne E Porter; Michele Nichols; Archana P Lamichhane; Dana Dabelea; Elizabeth J Mayer-Davis; Debra Standiford; Lenna Liu; Richard F Hamman; Ralph B D'Agostino
Journal:  Health Place       Date:  2010-01-15       Impact factor: 4.078

6.  Cancer incidence among the south Asian and non-south Asian population under 30 years of age in Yorkshire, UK.

Authors:  M van Laar; P A McKinney; R C Parslow; A Glaser; S E Kinsey; I J Lewis; S V Picton; M Richards; G Shenton; D Stark; P Norman; R G Feltbower
Journal:  Br J Cancer       Date:  2010-09-14       Impact factor: 7.640

7.  Daycare attendance, breastfeeding, and the development of type 1 diabetes: the diabetes autoimmunity study in the young.

Authors:  Katelyn Hall; Brittni Frederiksen; Marian Rewers; Jill M Norris
Journal:  Biomed Res Int       Date:  2015-03-25       Impact factor: 3.411

Review 8.  Environmental trigger(s) of type 1 diabetes: why so difficult to identify?

Authors:  Kjersti S Rønningen
Journal:  Biomed Res Int       Date:  2015-03-25       Impact factor: 3.411

9.  Higher parental occupational social contact is associated with a reduced risk of incident pediatric type 1 diabetes: Mediation through molecular enteroviral indices.

Authors:  Anne-Louise Ponsonby; Angela Pezic; Fergus J Cameron; Christine Rodda; Andrew S Kemp; John B Carlin; Heikki Hyoty; Amirbabak Sioofy-Khojine; Terence Dwyer; Justine A Ellis; Maria E Craig
Journal:  PLoS One       Date:  2018-04-17       Impact factor: 3.240

10.  How do childhood diagnoses of type 1 diabetes cluster in time?

Authors:  Colin R Muirhead; Timothy D Cheetham; Simon Court; Michael Begon; Richard J Q McNally
Journal:  PLoS One       Date:  2013-04-03       Impact factor: 3.240

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