Evidence map›Paper›PMID 39247147›Full record

ArticleStatistics in biosciences2024

Modeling Historic Arsenic Exposures and Spatial Risk for Bladder Cancer.

Joseph Boyle, Mary H Ward, Stella Koutros, Margaret R Karagas, Molly Schwenn, Alison T Johnson, Debra T Silverman, David C Wheeler

Abstract read
In one paragraph

Article in Statistics in biosciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Trace elements in freshwater killifishToxicology reports · 2025
    Article
  4. 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.

Joseph BoyleDepartment of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA.
Mary H WardOccupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
Stella KoutrosOccupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
Margaret R KaragasDepartment of Epidemiology, Dartmouth Geisel School of Medicine, Hanover, NH, USA.
Molly SchwennFormerly of the Maine Department of Health and Human Services, Maine Cancer Registry, Augusta, ME, USA.
Alison T JohnsonVermont Department of Health, Burlington, VT, USA.
Debra T SilvermanOccupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
David C WheelerDepartment of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA.ORCID 0000-0001-8121-5182

Funding

Training Program in Cancer Prevention and Control and Cancer Health Equity (CPC-CHE)T32CA093423 · NCI · VIRGINIA COMMONWEALTH UNIVERSITY · PI Bernard F Fuemmeler, Oxana G Palesh · 2018 to 2026
$3.0M
Modeling cancer risk and environmental and socio-spatial exposures using residential historiesU01CA259376 · NCI · VIRGINIA COMMONWEALTH UNIVERSITY · PI SABO, ROY TRAVIS · 2021 to 2024
$1.2M
NCI NIH HHS T32 CA093423NCI NIH HHS U01 CA259376
6 · The paper itself

Abstract

Arsenic is a bladder carcinogen though less is known regarding the specific temporal relationship between exposure and bladder cancer diagnosis. In this study, we modeled time-varying mixtures of arsenic exposures at many historic temporal windows to evaluate their association with bladder cancer risk in the New England Bladder Cancer Study. We used arsenic exposure estimates up to 60 years prior to study entry and compared the goodness of fit of models using these mixtures to those using summary measures of arsenic exposures. We used the Bayesian index low rank kriging multiple membership model (LRK-MMM) to estimate the associations of these mixtures with bladder cancer and estimate cumulative spatial risk for bladder cancer using participants' residential histories. We found consistent evidence that modeling arsenic exposures as a time-varying mixture provided better fit to the data than using a single arsenic exposure summary measure. We estimated several positive though not significant associations of the time-varying arsenic mixtures with bladder cancer having odds ratios (ORs) of 1.03-1.14 and identified many significant and positive associations for an interaction among those who consumed water from a private dug well (ORs 1.28-1.60). Arsenic exposures 40-50 years before study entry received elevated importance weights in these mixtures. Additionally, we found two small areas of elevated cumulative spatial risk for bladder cancer in southern New Hampshire and in south central Maine. These results emphasize the importance of considering time-varying mixtures of exposures for diseases with long latencies such as bladder cancer.

Indexed as

ArsenicBayesianBladder cancerMixture analysisResidential historySpatial analysis

Identifiers

PMID39247147
PMCPMC11378980

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.