Evidence map›Paper›PMID 32303231›Full record

ArticleInternational journal of health geographics2020

Bayesian spatial modelling of childhood cancer incidence in Switzerland using exact point data: a nationwide study during 1985-2015.

Garyfallos Konstantinoudis, Dominic Schuhmacher, Roland A Ammann, Tamara Diesch, Claudia E Kuehni, Ben D Spycher, Swiss Paediatric Oncology Group, Swiss National Cohort Study Group

Open access · goldAbstract read
In one paragraph

Article in International journal of health geographics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 2 pooled it
0.8field-weighted citation impact, top 30% 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

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

5 citing papers in PubMed, 2 syntheses or guidelines pooled it, 13 citations in OpenAlex.

  1. Pooled it
  2. Exposure to outdoor air pollution at different periods and the risk of leukemia: a meta-analysis.Environmental science and pollution research international · 2021
    Pooled it
  3. Review
  4. Article
  5. 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

8 authors at 3 institutions in 3 countries.

Garyfallos KonstantinoudisInstitute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland. g.konstantinoudis@imperial.ac.uk.
Dominic SchuhmacherInstitute for Mathematical Stochastics, University of Göttingen, Göttingen, Germany.
Roland A AmmannDepartment of Paediatrics Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
Tamara DieschDivision of Paediatric Oncology/Haematology, University Children's Hospital Basel, Basel, Switzerland.
Claudia E KuehniInstitute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland.
Ben D SpycherInstitute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland.
Swiss Paediatric Oncology Group
Swiss National Cohort Study Group
University of Bern · CHInstitute of Social and Preventive Medicine · CHUniversity Children’s Hospital Basel · CH

Funding

Medical Research Council MR/T025352/1
6 · The paper itself

Abstract

backgroundThe aetiology of most childhood cancers is largely unknown. Spatially varying environmental factors such as traffic-related air pollution, background radiation and agricultural pesticides might contribute to the development of childhood cancer. This study is the first investigation of the spatial disease mapping of childhood cancers using exact geocodes of place of residence.

methodsWe included 5947 children diagnosed with cancer in Switzerland during 1985-2015 at 0-15 years of age from the Swiss Childhood Cancer Registry. We modelled cancer risk using log-Gaussian Cox processes and indirect standardisation to adjust for age and year of diagnosis. We examined whether the spatial variation of risk can be explained by modelled ambient air concentration of NO

resultsFor all childhood cancers combined, the posterior median relative risk (RR), compared to the national level, varied by location from 0.83 to 1.13 (min to max). Corresponding ranges were 0.96 to 1.09 for leukaemia, 0.90 to 1.13 for lymphoma, and 0.82 to 1.23 for central nervous system (CNS) tumours. The covariates considered explained 72% of the observed spatial variation for all cancers, 81% for leukaemia, 82% for lymphoma and 64% for CNS tumours. There was weak evidence of an association of CNS tumour incidence with modelled exposure to background ionising radiation (RR per SD difference 1.17; 0.98-1.40) and with SEP (1.6; 1.00-1.13).

conclusionOf the investigated diagnostic groups, childhood CNS tumours showed the largest spatial variation. The selected covariates only partially explained the observed variation of CNS tumours suggesting that other environmental factors also play a role.

Indexed as

Environmental ExposureNeoplasmsResidence CharacteristicsAdolescentBayes TheoremChildChild, PreschoolGeographic Information SystemsHumansIncidenceInfantInfant, NewbornRegistriesRisk FactorsSwitzerlandBayesian spatial modellingCancer clustersCentral nervous system cancerChildhood cancerPoint processes

Identifiers

PMID32303231
PMCPMC7165384
OpenAlexW3017209681

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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.