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Does Social Context Matter? Income Inequality, Racialized Identity, and Health Among Canada’s Aboriginal Peoples Using a Multilevel Approach

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Abstract

Objectives

Debates surrounding the importance of social context versus individual level processes have a long history in public health. Aboriginal peoples in Canada are very diverse, and the reserve communities in which they reside are complex mixes of various cultural and socioeconomic circumstances. The social forces of these communities are believed to affect health, in addition to individual level determinants, but no large scale work has ever probed their relative effects. One aspect of social context, relative deprivation, as indicated by income inequality, has greatly influenced the social determinants of health landscape. An investigation of relative deprivation in Canada’s Aboriginal population has never been conducted. This paper proposes a new model of Aboriginal health, using a multidisciplinary theoretical approach that is multilevel.

Methods

This study explored the self-rated health of respondents using two levels of determinants, contextual and individual. Data were from the 2001 Aboriginal Peoples Survey. There were 18,890 Registered First Nations (subgroup of Aboriginal peoples) on reserve nested within 134 communities. The model was assessed using a hierarchical generalized linear model.

Results

There was no significant variation at the contextual level. Subsequently, a sequential logistic regression analysis was run. With the sole exception culture, demographics, lifestyle factors, formal health services, and social support were significant in explaining self-rated health.

Conclusions

The non-significant effect of social context, and by extension relative deprivation, as indicated by income inequality, is noteworthy, and the primary role of individual level processes, including the material conditions, social support, and lifestyle behaviors, on health outcomes is illustrated. It is proposed that social structure is best conceptualized as a dynamic determinant of health inequality and more multilevel theoretical models of Aboriginal health should be developed and tested.

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Notes

  1. Composing 4.3 % of the population or 1.4 million people [2], “Aboriginal peoples” or “Indigenous peoples” applies to the first peoples of Canada as well as their descendants. According to the Constitution, the three subgroups are as follows: Indian (First Nations), Métis, and Inuit. Furthermore, Indians are categorized as Registered or Treaty, which are sometimes referred to as “Status Indians,” and non-Registered or non-Status Indians. Registered Indians are registered under the Indian Act and are entitled to specific rights and benefits, and Treaty Indians are individuals who are part of a First Nation or Indian band that signed a treaty with the Crown. Non-Status or non-Registered Indians are people who self-identify as Indians, but cannot register under the Indian Act. Métis refers to individuals of mixed ancestry (First Nations and European), and Inuit are people of the circumpolar region in Arctic Canada [3].

  2. Given the data, this paper is not about structure versus agency but structural inequality and relative deprivation, as well as the relative explanatory value of macrolevel versus microlevel processes. Social inequality is a structural level variable in this work.

  3. There is debate surrounding the appropriate way to theorize and capture the effects of structure [15]. While this work adopts a theoretically driven quantitative approach, which is the first step in assessing the systematic effects of structure on Aboriginal peoples in a large-scale manner, there is no doubt that certain processes cannot be captured, such as how structure is manifested, challenged, and reproduced through everyday practices. This is a product of methodological approach and limitations of the dataset.

  4. Although this is not the most up to date administration of the Aboriginal Peoples Survey, the 2001 data are still relevant for this work as coverage of the “on-reserve” population was stopped after this version of the survey, which is the focus of analysis [40].

  5. The strategy was to conduct the survey on the largest reserves of each province, which resulted in 44 % of the entire on-reserve population being surveyed [41]. Some of the largest reserves did not participate in the survey and smaller reserves were then selected. In British Columbia, coverage of the reserve population was reduced because of the significant number of small reserves in the province, which would have been costly to sample [41]. Ergo, there was no randomness in the sampling strategy or the reserves that refused to participate; coupled with exclusion of smaller reserves, the data are not representative of the entire on-reserve population. A study was completed to evaluate the comparability of the data collected on the Aboriginal Peoples Survey selected reserves to the entire on-reserve population. An examination of seven demographic variables found differences in the distributions of variables were very small. The differences varied by region, and the greatest differences were in Quebec and Ontario [41].

  6. The variable was categorized as follows:

    • Low = no schooling/less than high school diploma

    • Medium = high school diploma/some trade school/some other non-university institution

    • High = some university/diploma or certificate from trade school/diploma or certificate from other non-university institution/university certificate or diploma below bachelor’s level/bachelor’s level/university certificate or diploma above bachelor’s level/master’s degree/degree in medicine, dentistry, veterinary medicine, or optometry/earned doctorate

  7. An ordinal confirmatory factor analysis supported use of the proposed social support scale, operationalized by combining four items in the survey, based on the question, could you tell me how often each of the following kinds of support are available to you when you need it? Someone who shows you love and affection, someone to have a good time with, someone to do something enjoyable with, someone to get together with for relaxation, with possible responses scored as almost none of the time (1), some of the time (2), most of the time (3), all of the time (4).

  8. The Gini coefficient has a range between zero and one; higher numbers indicate greater inequality [63]: \( G=\frac{{\displaystyle {\sum}_{i=1}^x{\displaystyle {\sum}_{j=1}^x\left|{x}_i-{x}_j\right|}}}{2\;{n}^2\mu } \).

    It has been claimed that a threshold effect of income inequality may exist, with a pronounced risk of decreased health outcomes when the Gini coefficient is above 0.30 [64]. There is some evidence that total adult mortality could be decreased by 9.6 % among 15 to 60 year olds across 30 OECD countries, if the Gini coefficients were brought below the threshold [64].

  9. Variables in this analysis did not have a large percentage of missing data (i.e., much less than 10 %), with the exception of access to traditional medicine, healing, and wellness practices in a community at 22 %. The process of multiple imputation in this analysis can be summarized as follows: (1) impute (validity of the imputation model was assessed using various diagnostic functions, such as overimputation, overdispersed starting values, comparing densities, missingness maps), (2) perform the statistical analysis of interest, (3) combine the results. Please see the literature for further details [65].

  10. With a Bernoulli sampling model and a logit link function, the level 1 model is given by the following:

    Probability of (poor health ij  = 1│β j ) = φ ij , where ij refers to individual “i” in community “j.”

    Log [φ ij /(1 − φ ij )] = η ij and, therefore, the level 1 model is η ij  = β oj

    The level 2 model is β oj  = γ oo  + u oj where u oj  ∼ N(0, τ oo ).

    The mixed model is given by η ij  = γ oo  + u oj .

    φ ij :

    is the conditional probability of poor health of individual “i” in community “j’

    β oj :

    is the intercept of community j

    τ oo :

    is the variance of β oj

    γ oo :

    is the grand mean of poor health for communities

    u oj :

    is the unique effect of community j on the mean of poor health; it is assumed to be normally distributed with a mean of 0 and variance τ oo .

  11. The intra-class correlation (proportion of contextual variance divided by the total variance in mixed models) quantifies the relative contribution of context. While not statistically significant, this study has an intra-class correlation of 0.63 %, which is within the range of empirical work on nonlinear models for health data that are usually less than 2 % and infrequently in the range of 3–5 % [68].

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Acknowledgments

This research was supported with funds from the Social Sciences and Humanities Research Council of Canada.

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Conflict of Interest

Nicholas D. Spence declares that he has no conflict of interest.

Research Involving Human Participants

This research used a public dataset from Statistics Canada.

Informed Consent

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000 (5). Informed consent was obtained from all participants for being included in the study.

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Spence, N.D. Does Social Context Matter? Income Inequality, Racialized Identity, and Health Among Canada’s Aboriginal Peoples Using a Multilevel Approach. J. Racial and Ethnic Health Disparities 3, 21–34 (2016). https://doi.org/10.1007/s40615-015-0108-9

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