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Bayesian Spatio-temporal Modeling of Ontario Lung Cancer Incidence and Survival: Findings from the MOSAIC Study
Lung cancer affects many Ontarians, and there are large differences in the cancer rates across the province. We evaluated small area disparities in incidence and 5-year relative survival over time to help inform cancer control.
This study explored how socioeconomic status, urban-rural-remoteness, and, for incidence, risk factor prevalence can influence inequalities the burden of lung cancer among Ontarians, 1999-2018. Full Bayesian space-time hierarchical models with conditionally autoregressive smoothing across space and time dimensions were used. Poisson linear mixed models were employed to estimate incidence rate ratios. Excess mortality proportional odds models with restricted cubic splines for the baseline hazard functions were employed for relative survival.
Overall, lung cancer incidence rates declined, and 5-year survival improved in Ontario. Poorer outcomes were associated with lower socioeconomic status and residing outside large urban areas. Residual disparities were evident and may be related to other factors such as access to primary care.
Date and Time
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Additional Authors and Speakers (not including you)
Todd Norwood
Ontario Health, Cancer Care Ontario
Laura Seliske
Ontario Health, Cancer Care Ontario
Eric J Holowaty
Dalla Lana School of Public Health
Inthuja Selvaratnam
Ontario Health, Cancer Care Ontario
Prithwish De
Ontario Health, Cancer Care Ontario
Amidu Olalekan Raifu
Ontario Health, Cancer Care Ontario
Linda Rabeneck
Ontario Health, Cancer Care Ontario
Simron Singh
Sunnybrook Health Sciences Centre
Jill Tinmouth
Sunnybrook Health Sciences Centre
Zeinab El-Masri
Ontario Health, Cancer Care Ontario
Language of Oral Presentation
English
Language of Visual Aids
English

Speaker

Edit Name Primary Affiliation
Shabnam Balamchi Ontario Health, Cancer Care Ontario