ArticleInternational journal of health geographics2011
Developing the atlas of cancer in Queensland: methodological issues.
Article in International journal of health geographics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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.
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Who cites it
20 citing papers in PubMed.
- Generation of machine-learning derived cancer vulnerability indicator to determine the spatial burden of cancer outcomes.PloS one · 2026Article
- Designing the Australian Cancer Atlas: visualizing geostatistical model uncertainty for multiple audiences.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- ACEMID cohort study: protocol of a prospective cohort study using 3D total body photography for melanoma imaging and diagnosis.BMJ open · 2023Article
- Assessing the effect of environmental and socio-economic factors on skin melanoma incidence: an island-wide spatial study in Gran Canaria (Spain), 2007-2018.Cancer causes & control : CCC · 2022Article
- Mapping the Morbidity Risk Associated with Coal Mining in Queensland, Australia.International journal of environmental research and public health · 2022Article
- Environmental and sociodemographic risk factors associated with environmentally transmitted zoonoses hospitalisations in Queensland, Australia.One health (Amsterdam, Netherlands) · 2021Article
- Modelling the Relationship between Rainfall and Mental Health Using Different Spatial and Temporal Units.International journal of environmental research and public health · 2021Article
- Augmenting disease maps: a Bayesian meta-analysis approach.Royal Society open science · 2020Article
- Development of the Australian Cancer Atlas: spatial modelling, visualisation, and reporting of estimates.International journal of health geographics · 2019Article
- Area-Level Walkability and the Geographic Distribution of High Body Mass in Sydney, Australia: A Spatial Analysis Using the 45 and Up Study.International journal of environmental research and public health · 2019Article
- Does Walkability Contribute to Geographic Variation in Psychosocial Distress? A Spatial Analysis of 91,142 Members of the 45 and Up Study in Sydney, Australia.International journal of environmental research and public health · 2018Article
- The contribution of area-level walkability to geographic variation in physical activity: a spatial analysis of 95,837 participants from the 45 and Up Study living in Sydney, Australia.Population health metrics · 2017Article
- Spatio-Temporal Pattern of Breast Cancer - Case Study of Southern Karnataka, India.Journal of clinical and diagnostic research : JCDR · 2016Article
- Article
- Spatially Enabling the Health Sector.Frontiers in public health · 2016Article
- Maps and atlases of cancer mortality: a review of a useful tool to trigger new questions.Ecancermedicalscience · 2016Review
- Missing in space: an evaluation of imputation methods for missing data in spatial analysis of risk factors for type II diabetes.International journal of health geographics · 2014Article
- Comparing multilevel and Bayesian spatial random effects survival models to assess geographical inequalities in colorectal cancer survival: a case study.International journal of health geographics · 2014Article
- The Spatial Distribution of Cancer Incidence in Fars Province: A GIS-Based Analysis of Cancer Registry Data.International journal of preventive medicine · 2013Article
- Identification of area-level influences on regions of high cancer incidence in Queensland, Australia: a classification tree approach.BMC cancer · 2011Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAchieving health equity has been identified as a major challenge, both internationally and within Australia. Inequalities in cancer outcomes are well documented, and must be quantified before they can be addressed. One method of portraying geographical variation in data uses maps. Recently we have produced thematic maps showing the geographical variation in cancer incidence and survival across Queensland, Australia. This article documents the decisions and rationale used in producing these maps, with the aim to assist others in producing chronic disease atlases.
methodsBayesian hierarchical models were used to produce the estimates. Justification for the cancers chosen, geographical areas used, modelling method, outcome measures mapped, production of the adjacency matrix, assessment of convergence, sensitivity analyses performed and determination of significant geographical variation is provided.
conclusionsAlthough careful consideration of many issues is required, chronic disease atlases are a useful tool for assessing and quantifying geographical inequalities. In addition they help focus research efforts to investigate why the observed inequalities exist, which in turn inform advocacy, policy, support and education programs designed to reduce these inequalities.
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Registered trials
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.