Cancer incidence and prevalence among Métis Nation of Ontario citizens

Main Article Content

Noel Tsui
Abigail Simms
Kayla Bazinet
Ryah Heavens
Shelley Cripps
Sarah Edwards

Abstract

Background and objectives
Timely monitoring of cancer cases is important for identifying emerging patterns, targeting and evaluating prevention strategies, and supporting resource allocation. There is a demonstrated lack of timely tracking of Métis-specific cancer incidence and prevalence, despite studies suggesting a higher incidence of cancer, lower rate of cancer screening uptake, and higher prevalence of modifiable risk factors for Métis people across Canada. The Métis are one of three constitutionally recognized Indigenous Peoples in Canada and are represented by the Métis Nation of Ontario (MNO) in the province of Ontario. This study examines cancer incidence and prevalence in MNO citizens across 9 types of cancers.


Methods
The MNO holds a registry of citizens that is shared annually with ICES. MNO Registry data was deterministically linked to the Ontario Cancer Registry (OCR) which has information about all Ontario residents diagnosed with cancer. The cancers examined included breast, cervix, colon, gallbladder, larynx, liver, lung, prostate, and skin. Cancer case counts between 2019 and 2024 were used to calculate incidence and prevalence.


Results
Analyses are ongoing. In total, 29,997 of the 30,885 registered MNO citizens were linked with the OCR data. There were 365 MNO citizens (52% male) diagnosed with one of the nine cancers between 2019 and 2024. Colon, breast, lung, and colon cancers were most common types of cancers.


Implications
Ongoing and timely monitoring of Métis-specific cancer rates in Ontario is important to inform programs and services targeted to MNO citizens for their health and wellness.

Article Details

How to Cite
Tsui, N., Simms, A., Bazinet, K., Heavens, R., Cripps, S. and Edwards, S. (2026) “Cancer incidence and prevalence among Métis Nation of Ontario citizens”, International Journal of Population Data Science, 11(5). doi: 10.23889/ijpds.v11i5.3576.