Bayesian Space-Time Analysis of Brain Cancer Incidence in Southern Ontario, Canada: 2010-2013.

Bayesian Canada cancer head injury obesity risk space–time spatio-temporal

Journal

Medical sciences (Basel, Switzerland)
ISSN: 2076-3271
Titre abrégé: Med Sci (Basel)
Pays: Switzerland
ID NLM: 101629322

Informations de publication

Date de publication:
14 Dec 2019
Historique:
received: 12 11 2019
revised: 09 12 2019
accepted: 11 12 2019
entrez: 19 12 2019
pubmed: 19 12 2019
medline: 19 12 2019
Statut: epublish

Résumé

Canada has one of the highest incidence rates of brain cancer in the world. This study investigates the space-time variation of brain cancer risk across Southern Ontario, Canada. A Bayesian spatio-temporal regression model is used to estimate the relative risk of brain cancer in the 12 spatial health units of Southern Ontario over a four-year period (2010-2013). This work also explores the association between brain cancer and two potential risk factors: traumatic head injury (THI) and excess body fat (EBF). Across all areal units from 2010-2013, results show that the relative risk of brain cancer ranged from 0.83 (95% credible interval (CI) 0.74-0.91) to 1.26 (95% CI 1.13-1.41). Over the years, the eastern and western health units had persistently higher risk levels compared to those in the central areas. Results suggest that areas with elevated THI rates and EBF levels were also potentially associated with higher brain cancer relative risk. Findings revealed that the mean temporal trend for cancer risk progression in the region smoothly decreased over time. Overall, 50% of the health units displayed area-specific trends which were higher than the region's average, thus indicating a slower decrease in cancer rates for these areas in comparison to the mean trend.

Identifiants

pubmed: 31847406
pii: medsci7120110
doi: 10.3390/medsci7120110
pmc: PMC6950658
pii:
doi:

Types de publication

Journal Article

Langues

eng

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Auteurs

Ravi Ancil Persad (RA)

Independent Researcher, Toronto, ON M2J 4E2, Canada.

Classifications MeSH