Evidence map›Paper›PMID 21781342›Full record

ArticleBMC cancer2011

Identification of area-level influences on regions of high cancer incidence in Queensland, Australia: a classification tree approach.

Susanna M Cramb, Kerrie L Mengersen, Peter D Baade

Abstract read
In one paragraph

Article in BMC cancer, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

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

  1. Pooled it
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  6. Using Latent Class Modeling to Jointly Characterize Economic Stress and Multipollutant Exposure.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2020
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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

3 authors.

Susanna M CrambViertel Centre for Research in Cancer Control, Cancer Council Queensland, Gregory Tce, Fortitude Valley, Australia. susannacramb@cancerqld.org.au
Kerrie L Mengersen
Peter D Baade

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundStrategies for cancer reduction and management are targeted at both individual and area levels. Area-level strategies require careful understanding of geographic differences in cancer incidence, in particular the association with factors such as socioeconomic status, ethnicity and accessibility. This study aimed to identify the complex interplay of area-level factors associated with high area-specific incidence of Australian priority cancers using a classification and regression tree (CART) approach.

methodsArea-specific smoothed standardised incidence ratios were estimated for priority-area cancers across 478 statistical local areas in Queensland, Australia (1998-2007, n = 186,075). For those cancers with significant spatial variation, CART models were used to identify whether area-level accessibility, socioeconomic status and ethnicity were associated with high area-specific incidence.

resultsThe accessibility of a person's residence had the most consistent association with the risk of cancer diagnosis across the specific cancers. Many cancers were likely to have high incidence in more urban areas, although male lung cancer and cervical cancer tended to have high incidence in more remote areas. The impact of socioeconomic status and ethnicity on these associations differed by type of cancer.

conclusionsThese results highlight the complex interactions between accessibility, socioeconomic status and ethnicity in determining cancer incidence risk.

Indexed as

Breast NeoplasmsFemaleGeographyHealth Services AccessibilityHumansIncidenceLung NeoplasmsMaleMelanomaNeoplasmsProstatic NeoplasmsQueenslandRegistriesRegression AnalysisRisk AssessmentRisk Factors

Identifiers

PMID21781342
PMCPMC3155913

What OpenQuestion holds

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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.