Exploring Intersectionality of Race and Newcomer Status with Material and Social Deprivation in Ontario Census Data: A Comparative Analysis of the ON-MARG Deprivation Index and Machine-Learning Derived Demographic Clusters
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Background
The Ontario Marginalization Index (ON-MARG) is widely used to assess health inequalities in Ontario by measuring four dimensions of marginalization at the dissemination area (DA) level. However, averaging these dimensions into an overall deprivation score can obscure important information, in particular information on intersectionality of material and social deprivation and race and immigrant status.
Objective
To use machine learning algorithms to uncover relationships among the four ON-MARG dimensions across DAs as demographic clusters and to compare the use of these clusters to understand and map marginalization and to describe health inequities.
Methods
We applied K-means clustering to 2021 ON-MARG data on the four On-MARG dimensions — Households and Dwellings (HD), Material Resources (MR), Age and Labour Force (AL), and Racialized and Newcomer Populations (RN) across 20,123 DAs. We then compared these clusters to ON-MARG average index scores in terms of mapping marginalization in Toronto and examined how these clusters were associated with inequities in mental health service as compared specific dimensions of the ON-MARG index.
Results
We identified four clusters: (1) Advantaged White Canadians, (2) Disadvantaged White Canadians, (3) Advantaged Visible Minorities and Immigrants, and (4) Disadvantaged Visible Minorities and Immigrants. The clustering approach revealed nuanced patterns not captured by the ON-MARG summary scores alone. Disadvantaged White Canadians exhibited the highest outpatient mental health visit rates, particularly among females (250–300 visits per 100,000). Disadvantaged Visible Minorities and Immigrants followed with elevated rates, while both advantaged clusters showed significantly lower utilization. The clusters provided better discrimination of health service disparities than ON-MARG quintiles alone, highlighting that disadvantaged groups, regardless of racial composition, had higher rates of mental health service use.
Conclusions
Combining ON-MARG with machine learning clustering offers a more comprehensive understanding of marginalization’s intersectionality, revealing disparities in health service utilization not apparent from the index alone. This approach underscores the need for targeted, intersectional policies to address the specific needs of diverse populations, ultimately contributing to more equitable healthcare interventions in Ontario.
