Evidence map›Paper›PMID 30213791›Full record

ArticleCancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology2019

Development and Validation of Lifestyle-Based Models to Predict Incidence of the Most Common Potentially Preventable Cancers.

Juliet A Usher-Smith, Stephen J Sharp, Robert Luben, Simon J Griffin

Abstract readValidation Study
In one paragraph

Article in Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 3 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

10 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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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

4 authors.

Juliet A Usher-SmithThe Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom. jau20@medschl.cam.ac.uk.
Stephen J SharpMRC Epidemiology Unit, University of Cambridge, Institute of Metabolic Science, Cambridge, United Kingdom.ORCID 0000-0003-2375-1440
Robert LubenDepartment of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, United Kingdom.ORCID 0000-0002-5088-6343
Simon J GriffinThe Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom.ORCID 0000-0002-2157-4797

Funding

Cancer Research UK 21464Cancer Research UK C55650/A21464Cancer Research UK C864/A14136Cancer Research UK C864/A8257Department of HealthMedical Research Council G0401527Medical Research Council G1000143Medical Research Council MC_ UU_12015/1Medical Research Council MC_UU_12015/1Medical Research Council MC_UU_12015/4
6 · The paper itself

Abstract

backgroundMost risk models for cancer are either specific to individual cancers or include complex or predominantly non-modifiable risk factors.

methodsWe developed lifestyle-based models for the five cancers for which the most cases are potentially preventable through lifestyle change in the UK (lung, colorectal, bladder, kidney, and esophageal for men and breast, lung, colorectal, endometrial, and kidney for women). We selected lifestyle risk factors from the European Code against Cancer and obtained estimates of relative risks from meta-analyses of observational studies. We used mean values for risk factors from nationally representative samples and mean 10-year estimated absolute risks from routinely available sources. We then assessed the performance of the models in 23,768 participants in the EPIC-Norfolk cohort who had no history of the five selected cancers at baseline.

resultsIn men, the combined risk model showed good discrimination [AUC, 0.71; 95% confidence interval (CI), 0.69-0.73] and calibration. Discrimination was lower in women (AUC, 0.59; 95% CI, 0.57-0.61), but calibration was good. In both sexes, the individual models for lung cancer had the highest AUCs (0.83; 95% CI, 0.80-0.85 for men and 0.82; 95% CI, 0.76-0.87 for women). The lowest AUCs were for breast cancer in women and kidney cancer in men.

conclusionsThe discrimination and calibration of the models are both reasonable, with the discrimination for individual cancers comparable or better than many other published risk models. IMPACT: These models could be used to demonstrate the potential impact of lifestyle change on risk of cancer to promote behavior change.

Indexed as

Life StyleModels, TheoreticalAdultAgedCohort StudiesFemaleFollow-Up StudiesHealth BehaviorHumansIncidenceMaleMiddle AgedNeoplasmsPrognosisRisk AssessmentRisk Factors

Identifiers

PMID30213791
PMCPMC6330056

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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.