Evidence map›Paper›PMID 41692841›Full record

ArticleDiabetologia2026

Geospatial clustering of type 1 diabetes in Sweden: a cohort study based on all residential locations from birth to diagnosis.

Samy Sebraoui, Oskar Englund, Fredrik Nyberg, Annelie Carlsson, Olle Korsgren, Gun Forsander, Katarina Eeg-Olofsson, Björn Eliasson, Hanne K Carlsen, Karin Åkesson and 1 more

Abstract read
In one paragraph

Article in Diabetologia, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Samy SebraouiDepartment of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. samy.sebraoui@wlab.gu.se.ORCID http://orcid.org/0000-0002-2096-0055
Oskar EnglundDepartment of Natural Sciences, Design and Sustainable Development, Mid-Sweden University, Östersund Campus, Sweden.ORCID http://orcid.org/0000-0002-1662-6951
Fredrik NybergSchool of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.ORCID http://orcid.org/0000-0003-0892-5668
Annelie CarlssonLund University Diabetes Centre, Department of Clinical Sciences, Skåne University Hospital, Lund University, Lund, Sweden.ORCID http://orcid.org/0000-0002-5608-3437
Olle KorsgrenDepartment of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.ORCID http://orcid.org/0000-0002-8524-9547
Gun ForsanderDepartment of Paediatrics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.ORCID http://orcid.org/0000-0002-0266-9651
Katarina Eeg-OlofssonDepartment of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.ORCID http://orcid.org/0000-0002-3376-4707
Björn EliassonDepartment of Endocrinology and Metabolism, Sahlgrenska University Hospital, Gothenburg, Sweden.ORCID http://orcid.org/0000-0003-2569-4160
Hanne K CarlsenThe Swedish National Diabetes Register, Centre of Registers, Gothenburg, Sweden.ORCID http://orcid.org/0000-0003-1656-9624
Karin ÅkessonThe Swedish National Diabetes Register, Centre of Registers, Gothenburg, Sweden.ORCID http://orcid.org/0000-0003-2556-5094
Soffia GudbjörnsdottirDepartment of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.ORCID http://orcid.org/0000-0002-6583-5581

Funding

ALF Västra Götaland ALFGBG-1005511Barndiabetesfonden K8220019072Swedish Research Council ALFGBG-1005511
6 · The paper itself

Abstract

aims/hypothesisType 1 diabetes develops gradually, and previous exposures may influence incidence. We aimed to assess the geographical variation in type 1 diabetes incidence in Sweden by considering all residential locations from birth to diagnosis in individuals aged 0-30 years, diagnosed between 2005 and 2022. Significant high- and low-risk clusters were identified for different life stage exposure windows.

methodsIn 21,774 individuals with type 1 diabetes, all residential geographical locations from birth to diagnosis were geocoded. Geostatistical analysis of the incidence of type 1 diabetes was conducted at the municipality level using the most common residential location during four life stage-specific exposure windows (at diagnosis, the first 5 years after birth, 5 years prior to diagnosis, and from birth to diagnosis). Spatial scan statistics were used to identify statistically significant high- and low-risk clusters for each window. Land use and land cover within these clusters were also characterised.

resultsSignificant geographical variation in the incidence of type 1 diabetes was observed. The incidence was consistently higher in rural, low-population-density areas, particularly in central Sweden, and lower in major urban areas. The largest number of spatial clusters of both high risk (RR 1.29-16.0) and low risk (RR 0.32-0.73) was identified when using the most common residential location during the first 5 years after birth. High-risk clusters for this exposure window were characterised by forested and agricultural land, while low-risk clusters were characterised by urban land and open land other than agricultural land. CONCLUSIONS/

interpretationOur findings suggest that the development of type 1 diabetes in Sweden varies geographically and is associated with specific features of the local surroundings in early childhood. This is important knowledge as a basis for identifying possible environmental risk factors and the relationship with risk of type 1 diabetes in future studies.

Indexed as

Diabetes Mellitus, Type 1AdolescentAdultChildChild, PreschoolCluster AnalysisCohort StudiesFemaleHumansIncidenceInfantInfant, NewbornMaleRisk FactorsSwedenYoung AdultDisease mappingEnvironmental exposuresEnvironment-wide association studyEpidemiologyGeographical variationIncidenceRisk factorsSpace-time clusteringType 1 diabetes

Identifiers

PMID41692841
PMCPMC13005807

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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.