Multilevel Modeling of Health Inequalities at the Intersection of Multiple Social Identities in Canada

ABSTRACT

Health inequities in Canada are pervasive. Intersectional theory and novel quantitative methods can be used to understand health inequities. Drawing on a sample of adults from the 2015 and 2016 Canadian Community Health Survey, this study uses multilevel analysis individual heterogeneity and discriminatory accuracy (MAIHDA) to examine the intersectional effect of race, sex, income and immigration status on perceived health and perceived mental health. Small variance partition coefficients of the final models suggest that most of the variance across social strata is explained by the main effects for the four variables. Intersectional interaction effects for each social strata are reported.

KEYWORDS:

• Canadian community health survey
• intersectionality
• intersectional multilevel analysis individual heterogeneity and discriminatory accuracy (MAIHDA)
• perceived health
• perceived mental health

Acknowledgments

Although the research and analysis are based on data from Statistics Canada, the opinions expressed herein do not represent the views of Statistics Canada.

Disclosure Statement

The authors have no conflicts of interest to disclose.

Additional information

Funding

This project was not funded.

Notes on contributors

Carla Ickert

Carla Ickert recently completed a Master of Public Health degree specialization in Applied Biostatistics from the School of Public Health at the University of Alberta, Edmonton, Canada. She has a BA and MA in Sociology from the University of Alberta. Her research interests include intersectionality, quantitative methods, and health equity.

Ambikaipakan Senthilselvan

Ambikaipakan Senthilselvan is a Professor and Program Director for Biostatistics at the School of Public Health, University of Alberta, Edmonton, Canada. His interests are in biostatistics, epidemiology of respiratory diseases including asthma and genetic epidemiology. His methodological expertise includes analysis of survival data and correlated data including clustered, longitudinal data and multilevel data.

Gian S. Jhangri

Gian S. Jhangri is a Teaching Professor of Biostatistics at the School of Public Health, University of Alberta, Edmonton, Canada. Gian’s primary focus is on teaching and training graduate students in the theoretical and practical aspects of biostatistical methods. He has experience in simple to advanced/complex statistical analyses, such as descriptive and exploratory analyses, longitudinal analyses, mixed effect modeling, generalized linear modeling, multilevel modeling, logistic regression analyses, and survival analyses.