Background
Reports such as the Canada Health Survey [
1] and the Canadian Community Health Survey [
2] indicated that inequalities of health resulting from socioeconomic status (SES) required urgent scrutiny [
3]. Because the majority of health data is released in area-level form in comparison to individual-level form as a result of privacy concerns, geographical proxies, where the SES for small areas is linked to health data from administrative databases are often utilized [
3]. Most of these studies have used neighbourhood income as the indicator of social disparity and mortality as the health indicator [
3]. Measuring SES using a single indicator, however, is unlikely to completely reflect its complexity. Deprivation indices including other measures such as unemployment, social class, income, marital status, occupation, and education have been developed for Great Britain [
4], Spain [
5], and Italy [
6].
Until recently, only two deprivation indices for Canada have been developed, each with a specific purpose. Matheson et al. [
7] proposed an index called the “Can-Marg” using Census 2006 data, in which they focussed on examining inequalities in health and other social problems. Four deprivation criteria: residential instability, material deprivation, dependency and ethnic concentration were defined and inequalities in 18 health and behavioural problems from the Canadian Community Health Survey (CCHS) reported [
7]. However, the index that is mostly used in Canadian research has been the Pampalon index, developed in Quebec. Pampalon et al. illustrated its value by linking it to overall Canadian premature mortality rates in 2001 [
3]. The group developed their index based on Townsend’s definition of deprivation [
8] and included variables such as education and marital status. More specifically, their index was divided into two components: social and material. The Pampalon index only included six variables in the analyses: employment, income, education, marital status, single parent family, and living alone, while the Canadian Census form from which the index was developed, contains over 200 variables.
Among other factors like “individual susceptibility” (e.g. genetic polymorphisms), environmental stressors such as radiation, chemicals, and viruses, as well as dietary habits, psycho-social stress, and social characteristics are known to contribute to the occurrence of common childhood conditions. There recently has been growing interest in environmental injustice, a concept suggesting that those populations with lower SES may be vulnerable to greater exposure to environmental pollutants than their higher SES counterparts, and consequently experiencing potentially increased health risks. Building on this concept, the U.S. Institute of Medicine coined the term “double jeopardy” to emphasize the combined risk often faced by socially disadvantaged groups. Specifically, groups experiencing higher environmental exposure are often more susceptible because they have higher rates of smoking, obesity, poor nutrition, and adverse occupational exposures [
9].
Thus a need exists for a comprehensive index of socioeconomic status that is indicative of the Canadian population, which can be used for research involving environmental pollution and health outcomes. For that purpose, we aimed to develop a novel SES index that is comprehensive and more encompassing of the Canadian population, by incorporating cultural identities, examining factors relevant to health outcomes from environmental pollution, and considering other variables used in previous environmental injustice studies.
Discussion
Although our SES index is not the first to be developed for Canada, it likely reflects more fully the dimensions of SES in Canada for purposes of examining health outcomes from environmental pollution. While our index similarly includes aspects of social and material deprivation, it is novel in that we explored the contribution of: 1) age of homes as a proxy for age of neighbourhood which may in turn be an indicator of potential indoor environmental pollution; and 2) cultural identities with special attention to First Nations groups. Additionally, since our index is comprised of a single scale unlike the Pampalon indices, it is more easily communicated and better suited for presenting data directed toward studies investigating health outcomes and environmental pollution, for instance using maps.
More specifically, we examined the age of the homes as a proxy for the age of the neighbourhood. Older neighbourhoods more likely contain asbestos [
14], lead paint [
15] or increased indoor infiltration of fine outdoor particles [
22]. Interestingly, age of the homes was not included in any of the three components for our SES index. This observation may be explained by Canada’s relatively strong social programs, which may have weakened correlations between older homes and living in poverty as seen in the United States. For example, advances geared toward the development of newer government subsidized accommodations in an effort to decrease poverty have been in place with programs such as the Newfoundland and Labrador Poverty Reduction Strategy [
28]. Another explanation may include a possible trend in Canada toward middle class or wealthy populations living in older, more established neighbourhoods and homes. This may also have diluted the relationship seen in the US between inhabiting older homes and living in poverty.
Cultural differences were strongly evident with our SES index. Our index differs from Matheson et al.’s “Can-Marg” in that they utilized visible minority and recent immigration status (within 5 years). We grouped cultural origins by examining “ethnic origins”, which takes into account the ancestry of the Canadian population. This may be a more accurate indication of ethnicity, as recent immigration has mostly been from skilled workers from China, who generally have higher SES [
29]. This effect was illustrated in the “Can-Marg” index, where ethnicity was positively associated with better health outcomes and more healthy behaviours [
7]. We also examined “visible minority” and “recent immigration” in the development of our index (data not shown), and these variables were not associated with any of our components. This pattern was also seen in attempts to include ethnicity through “recent immigration” or “visible minorities” by Jerrett et al. [
17]. Thus, by utilizing “ethnic origins”, we may be able to overcome this potential characteristic of the population. Inclusion of aboriginal groups with HDI categories in our index was novel. As 4 % of Canada’s population (1.2 million people) in 2006, aboriginal groups in Canada represent the second largest population in a country internationally [
30]. Although there is a large population of aboriginals living in Canada (5 % in Alberta, 14 % in Saskatchewan and Manitoba, 85 % in Nunavut, 51 % in the Northwest Territories, 23 % in the Yukon) [
30], this variable interestingly did not appear in any of the three components of our SES index. Historically, aboriginal groups (especially in First Nations communities) have low response to the Census and this may be a source of bias in that this population’s responses may be missing. Nonetheless, aboriginal identity is important to consider because aboriginal families are more likely to experience poverty than the overall population of Canada [
30]. For example, those with aboriginal cultural identities are more likely than other Canadians to consist of single parent families (50 % of children in census metropolitan areas) [
30]. Another explanation for the lack of aboriginal cultural identities contributing to any of the three components of our SES index may include dilution of the relationship with variables associated with poverty, as there may be different definitions of social and economic advantages for aboriginals living in Northern Canada, where aboriginals comprise a large proportion of the population (Yukon: 25 %, NWT: 50.3 %, Nunavut: 83.6 % in 2006). The aspects of “wealth” and “deprivation” could easily be obscured in these areas, as the attainment of education or even the use of a vehicle in comparison to other forms of transportation may be influenced by traditional forms of living.
Because the index is novel, it was important to test its validity against an extensively used index, the Pampalon deprivation index, exploring an outcome for which associations with SES are well documented such as adverse birth outcomes and exposure to PM
2.5. Adverse birth outcomes with low SES relationships are a heavily researched area and our results showing increasing prevalence of PTB, LBW, and SGA with lower SES corroborate what has been published previously [
23,
24]. Additionally, we observed a more consistent gradient of the occurrence of the outcomes with lower values of our index compared to the Pampalon index, while the reverse was true for PM
2.5 exposures during pregnancy. We established the validity of our index based on several evaluative criteria: 1) demonstrated similar findings to those reported in the literature showing correlations between SES and adverse birth outcomes; 2) showed potential for supporting our hypothesis of environmental injustice in Canada by demonstrating associations of low index values with increased PM
2.5 exposure; and 3) showed similar, but stronger findings in comparison with an older index. A clear advantage of our index is that it consists of a single value and is therefore simpler to interpret. A limitation to our index is that while a single value may be useful for easier interpretation, the Pampalon index would allow for independent analyses of material and social deprivation for public health policy and intervention purposes. However, another advantage to utilizing our index is that a background in using past indices such as the Townsend index for interpretation of the Pampalon index is also not required. A limitation of working with indices based on Census data in Canada is the lower number of variables collected in the most recent Census [
31]. It is also assumed that SES will be stable over time, serving as a proxy in population based studies using data from other years.
Acknowledgments
We would like to thank Bernard Beckerman (University of California, Berkeley, United States) for providing PM2.5 data, and Dr. Walter Omariba and Michael Tjepkema (Statistics Canada, Ottawa, Canada) for assistance in accessing pregnancy outcome data.
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Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
EC contributed to study design, data acquisition, data analysis, and drafting the manuscript. JS carried out statistical analyses. LC and DS contributed to pregnancy outcome data acquisition and statistical analyses. DS and MJ participated in helping draft the manuscript. AOV participated in the design of the study, coordination and helped draft the manuscript. All authors read and approved the final manuscript.