Our Health Counts Toronto: using respondent-driven sampling to unmask census undercounts of an urban indigenous population in Toronto, Canada

Abstract

Objectives

To provide evidence of the magnitude of census undercounts of ‘hard-to-reach’ subpopulations and to improve estimation of the size of the urban indigenous population in Toronto, Canada, using respondent-driven sampling (RDS).

Design

Respondent-driven sampling.

Setting

The study took place in the urban indigenous community in Toronto, Canada. Three locations within the city were used to recruit study participants.

Participants

908 adult participants (15+) who self-identified as indigenous (First Nation, Inuit or Métis) and lived in the city of Toronto. Study participants were generally young with over 60% of indigenous adults under the age of 45 years. Household income was low with approximately two-thirds of the sample living in households which earned less than $C20 000 last year.

Primary and secondary outcome measures

We collected baseline data on demographic characteristics, including indigenous identity, age, gender, income, household type and household size. Our primary outcome asked: ‘Did you complete the 2011 Census Canada questionnaire?’

Results

Using RDS and our large-scale survey of the urban indigenous population in Toronto, Canada, we have shown that the most recent Canadian census underestimated the size of the indigenous population in Toronto by a factor of 2 to 4. Specifically, under conservative assumptions, there are approximately 55 000 (95% CI 45 000 to 73 000) indigenous people living in Toronto, at least double the current estimate of 19 270.

Conclusions

Our indigenous enumeration methods, including RDS and census completion information will have broad impacts across governmental and health policy, potentially improving healthcare access for this community. These novel applications of RDS may be relevant for the enumeration of other ‘hard-to-reach’ populations, such as illegal immigrants or homeless individuals in Canada and beyond.