Evidence map›Paper›PMID 34447507›Full record

ArticleInternational journal of preventive medicine2021

Joint Disease Mapping of Breast, Uterine, and Ovarian Cancers in Cities of Isfahan Province from 2005 to 2010 Using Spatial Shared Component Model.

Marzieh Nasr, Behzad Mahaki, Mehdi Kargar, Pejman Aghdak

Open access · goldAbstract read
In one paragraph

Article in International journal of preventive medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 48% 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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 citations in OpenAlex.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors at 2 institutions in 4 countries.

Marzieh NasrDepartment of Biostatistics, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran.
Behzad MahakiDepartment of Biostatistics and Epidemiology, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran.
Mehdi KargarHealth Education and Health Promotion, Health Promotion Department, Health School, Shiraz, Iran.
Pejman AghdakSocial Department of Health Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.
Isfahan University of Medical Sciences · IRKorea Health Promotion Institute · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBreast, uterine, and ovarian cancers are the most prevalent types of cancers among women. The aim of this study was to estimate the relative risk of these cancers and recognizing spatial patterns of their shared and specific risk factors in cities of Isfahan province, one of the most populated provinces of Iran, using spatial shared component model.

methodsIn this ecological study, the population consisted of all the registered patients having breast, ovarian, and uterine cancers in the cities of Isfahan from 2005 to 2010. In order to simultaneously analyze these diseases and clarify common and specific patterns of disease, spatial Shared component model was applied. Model fitting was done using Bayesian inference in OpenBUGS software.

resultsThe highest relative risk of breast cancer was seen in Isfahan (4.96), Shahreza (2.37), Dehaghan (5.01), Lenjan (2.33), and Najafabad (2.68), respectively. For ovarian cancer, Isfahan (4.29), Shahreza (2.51), Dehaghan (5.02), Lenjan (2.06), Najafabad (2.00), and Borkhar (2.39) had the highest relative risk, respectively. However, no significant difference was seen among the cities for uterine cancer (

conclusionsSince ovarian and uterine cancers are the less prevalent disease in comparison with breast cancer, the preciseness of these estimates were improved remarkably over simple mapping models. Based on this model, the estimates were done according to the correlation between the diseases. After recognizing the spatial patterns of the shared and specific risk factors and reviewing of previous studies, regardless of risk factors data, environmental pollution arises as a potential risk factor.

Indexed as

Breast neoplasmsmappingovarian neoplasmsuterine neoplasms

Identifiers

PMID34447507
PMCPMC8356957
OpenAlexW3196070389

What OpenQuestion holds

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LicenceCC BY-NC-SA
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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.