Evidence map›Paper›PMID 34425772›Full record

ArticleBMC cancer2021

Geographic disparities in Saskatchewan prostate cancer incidence and its association with physician density: analysis using Bayesian models.

Mustafa Andkhoie, Michael Szafron

Abstract read
In one paragraph

Article in BMC cancer, 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
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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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

2 authors.

Mustafa AndkhoieUniversity of Saskatchewan, 104 Clinic Place, Saskatoon, SK, S7N 2Z4, Canada.
Michael SzafronUniversity of Saskatchewan, 104 Clinic Place, Saskatoon, SK, S7N 2Z4, Canada. michael.szafron@usask.ca.ORCID http://orcid.org/0000-0002-7448-2386

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSaskatchewan has one of the highest incidence of prostate cancer (PCa) in Canada. This study assesses if geographic factors in Saskatchewan, including location of where patients live and physician density are affecting the PCa incidence. First, the objective of this study is to estimate the PCa standardized incidence ratio (SIRs) in Saskatchewan stratified by PCa risk-level. Second, this study identifies clusters of higher than and lower than expected PCa SIRs in Saskatchewan. Lastly, this study identifies the association (if any) between family physician density and estimated PCa SIRs in Saskatchewan.

methodsFirst, using Global Moran's I, Local Moran's I, and the Kuldorff's Spatial Scan Statistic, the study identifies clusters of PCa stratified by risk-levels. Then this study estimates the SIRs of PCa and its association with family physician density in Saskatchewan using the Besag, York, and Mollie (BYM) Bayesian method.

resultsHigher than expected clusters of crude estimated SIR for metastatic PCa were identified in north-east Saskatchewan and lower than expected clusters were identified in south-east Saskatchewan. Areas in north-west Saskatchewan have lower than expected crude estimated SIRs for both intermediate-risk and low-risk PCa. Family physician density was negatively associated with SIRs of metastatic PCa (IRR: 0.935 [CrI: 0.880 to 0.998]) and SIRs of high-risk PCa (IRR: 0.927 [CrI: 0.880 to 0.975]).

conclusionsThis study identifies the geographical disparities in risk-stratified PCa incidence in Saskatchewan. The study identifies areas with a lower family physician density have a higher-than-expected incidences of metastatic and high-risk PCa. Hence policies to increase the number of physicians should ensure an equitable geographic distribution of primary care physicians to support early detection of diseases, including PCa.

Indexed as

Bayes TheoremHealthcare DisparitiesAgedFollow-Up StudiesGeographyHumansIncidenceMaleMiddle AgedPhysiciansPractice Patterns, Physicians'PrognosisProstatic NeoplasmsRisk FactorsSaskatchewanEpidemiologyGeographyHealthcare accessPhysician supplyProstate cancerProstatic neoplasmsSpatial analysis

Identifiers

PMID34425772
PMCPMC8383452

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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.