Self-rated health in Canadian immigrants: Analysis of the Longitudinal Survey of Immigrants to Canada

Health & Place 17 (2011) 658–670

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Self-rated health in Canadian immigrants: Analysis of the Longitudinal Survey of Immigrants to Canada Maninder Singh Setia a, John Lynch b,c, Michal Abrahamowicz a, Pierre Tousignant a, Amelie Quesnel-Vallee a,d,e,n a

Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada School of Health Sciences, University of South Australia, Adelaide, Australia c Department of Social Medicine, University of Bristol, UK d Department of Sociology, McGill University, Montreal, Quebec, Canada e International Research Infrastructure on Social Inequalities in Health (IRIS), McGill University, Montreal, Quebec, Canada b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 22 July 2010 Received in revised form 31 December 2010 Accepted 15 January 2011 Available online 26 January 2011

Using a multi-level random effects logistic model, we examine the contribution of source country, individual characteristics and post-migration experiences to the self-rated health (SRH) of 2468 male and 2614 female immigrants from the Longitudinal Survey of Immigrants to Canada (2001–2005). Sex/gender differences were found for all categories of health determinants. Source country characteristics explained away some ethnic differentials in health and had independent negative effects, particularly among women. Thus, women from countries lower on the development index appear at greater risk of poor SRH, and should be at the forefront of public health programmes aimed at new immigrants in Canada. & 2011 Elsevier Ltd. All rights reserved.

Keywords: Immigrants Self-rated health Longitudinal analysis Immigration experiences

1. Introduction Canada has a longstanding history as a major immigrant country globally; currrently, it admits more than 250,000 immigrants per year (Citizenship and Immigration Canada, 2009). The pattern of immigration, however, has changed over the past century in tandem with a reform in immigration policies. The introduction of a point system in 1967, in particular, led to a shift in the source countries for Canada’s immigrant population from ‘‘traditional’’ source countries (UK, Europe, and US) to a preponderance of immigrants from China and India in 2008 (Citizenship and Immigration Canada, 2009). Another consequence of the point system is that non-refugee immigrants to Canada are a highly selected population: the majority tend to be young and within the economically active age group. Further, the Immigrant and Refugee Protection Act of Canada (2001 c.27) requires that immigrants be screened for certain infectious diseases (e.g. tuberculosis) and chronic conditions before being granted permanent resident status (Citizenship and Immigration Canada, 2002; Department of Justice, 2001). Thus, new immigrants have fewer chronic conditions and better n Corresponding author at: Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada. Tel.: + 1 514 398 2758; fax: + 1 514 398 4503. E-mail address: [email protected] (A. Quesnel-Vallee).

1353-8292/$ - see front matter & 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.healthplace.2011.01.006

health compared with the average for the host population at the time of entry in the country, a phenomenon known as the healthy immigrant effect (Antecol and Bedard, 2006; Chen et al., 1996; Deri, 2004; Strong et al., 1998). As immigrants start to settle in a new country, however, this initial advantage may be challenged by the stressful social, cultural, economic, and emotional experiences they face (Bagely, 1992; Beiser et al., 2002; Brink and Saunders, 1976; DeVoretz, 2001; Glazier et al., 2004; Ponizovsky and Ritsner, 2004). Usually, these challenges appear to diminish with the passage of time in the new country: immigrants’ economic condition improves; they develop social relationships, and face fewer settlement problems (Duleep and Dowhan, 2008; Schellenberg and Maheux, 2007). Nevertheless, certain individual characteristics may make some immigrants vulnerable and may impede their integration process in the new society. For example, inability to communicate in either of Canada’s two official languages may limit an immigrant’s participation in social programmes, while members of visible ethnic minorities may experience discrimination or racism (individual or structural). As a result of a combination of individual and post-settlement factors, immigrants in a new society may start losing their initial health advantage (Agudelo-Suarez et al., 2009; De Maio and Kemp, 2010; Derose et al., 2009; Newbold, 2009; Pottie et al., 2008; Scheppers et al., 2006). Indeed, it has been shown that immigrants’ duration of stay in Canada was associated inversely with the health

M.S. Setia et al. / Health & Place 17 (2011) 658–670

of immigrants: immigrants who had been in Canada for fewer than 10 years had better health than the Canadian-born, whereas they had similar or poorer health after 10 years of stay in Canada (Ali, 2002; Bergeron et al., 2009; Chen et al., 1996; Gee et al., 2004; Pernice and Brook, 1996). In addition to time-since-immigration, some studies have highlighted the importance of other socio-demographic factors in understanding the health of immigrants. Dunn and Dyck, for instance, found that single immigrants were more likely to rate their health as poor compared with those that were married or were in commonlaw partnerships. They also found that immigrants from Asia/Africa/ South America were more likely to rate their health as poor compared with their counterparts from Europe/Australia/United States (Dunn and Dyck, 2000). Another important association – poor economic conditions and poor health – has also been reported by various immigrant studies (Dunn and Dyck, 2000; Newbold, 2009; Newbold and Danforth, 2003). In sum, post-migration factors such as income inadequacy, being unemployed, having faced discrimination, or having poor language proficiency were all associated with a poor health status in recent immigrants (De Maio and Kemp, 2010; Pottie et al., 2008). However, as integration to the host country proceeds, some of these social determinants of health (such as income) may change as well; thus, treating these determinants as dynamic is important for assessment of health of immigrants in host societies. Moreover, data limitations often preclude the study of heterogeneity among the immigrant population. For instance, it has been argued that immigrants coming from richer countries may be more likely to find jobs with higher economic benefits, whereas the work experiences of those coming from less developed countries may not be adequately valued (Piche´ et al., 1999). Thus, the initial experience and relative changes in immigrant earnings may differ between these immigrant groups, and this may in turn have deleterious effects on the health advantage of some of these immigrant groups. Yet, it is also possible that social conditions in the new host society may be sufficiently better than those in the country of origin that any potential post-immigration health loss is mitigated (Dean and Wilson, 2010; Razum and Twardella, 2002; Razum and Zeeb, 2004). Because this relationship depends on the level of development of the country of origin, the latter may then be another factor associated with the health of immigrants in the new country. Finally, adding further complexity to this picture is the possibility that men and women experience and respond to post-immigration social, cultural, and economic challenges in different ways, making gender a possible effect modifier of immigrants’ health (Llacer et al., 2007; Martins and Reid, 2007; Thurston and Vissandje´e, 2005; Vissandje´e et al., 2004). With this background in mind, we designed the following longitudinal analyses, separately in male and female immigrants to Canada, over a four-year period to evaluate the following questions: (1) which socio-demographic (including ethnicity) and economic factors are associated with self-reported poor health over a four-year period of recent immigration to Canada, (2) which post-settlement factors may be associated with poor health in these immigrants, and (3) does the health of new immigrants vary according to characteristics of the country they arrive from. All these analyses were performed by accounting for the country of origin of these immigrants.

national survey that prospectively collects information on the citizenship and immigration status, socio-demographics, employment and income, linguistic skills, housing, settlement experiences, social networks, values, and attitudes of a sample of immigrants who arrived in Canada between October 2000 and September 2001. Initially, immigrating units (individuals, couples, or families) were selected from the administrative database of Citizenship and Immigration Canada. From these units, an individual who was over the age of 15 years at the time of landing and had landed from abroad was selected. Thus, individuals who applied for any type of permanent residency or asylum from within Canada were excluded from the survey. Individuals included had to have been followed for the three data waves to be included in our analytic sample. The first wave of LSIC had 12,040 individuals, among whom 7716 (64%) were followed up for all the three waves. From this longitudinal sample of 7716 (3819 men and 3897 women), we excluded the following individuals: (1) less than 18 years of age at baseline, (2) immigrant status of refugee, (3) country of birth and last residence (considered as country of origin for our analyses) were different, and (4) missing information on all the three waves or country-level parameters. Thus, our final sample comprised 2468 men and 2614 women. Details of the sample selection are provided in Fig. 1. Our study data follow a multi-level structure, with individual observations at a specific wave representing level-1. Multiple (up to three) observations over time are then clustered within persons (level-2), who are in turn clustered within the countries of origin (level-3). For men, we had 7404 observations (level-1) for 2468 individuals (level-2) and these were from 97 source

Total longitudinal population for all the three waves: 7716 3819 males & 3897 females Excluded 403 (5%) individuals < 18 years at baseline Total population >18 years at baseline: 7313 3609 males & 3704 females Excluded 1035 (14%) immigrants who had a refugee status Total longitudinal population: 6278 3096 males & 3182 females

Total longitudinal population: 5387 2626 males & 2761 females

2. Methods 2.1. Data source We used three waves of data collected by Statistics Canada in the Longitudinal Survey of Immigrants to Canada (LSIC) for our longitudinal analyses (Statistics Canada, 2007). The LSIC is a

659

Final sample for the present analysis: 5082 2468 males & 2614 females

Excluded 891 (14%) immigrants in whom the country of birth and country of last residence were different

Excluded 305 (6%) immigrants in whom data were missing for all the three waves and/or the country level variables

Fig. 1. Sample size for our present analyses of the Longitudinal Survey of Immigrants to Canada (2001–2005).

660

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countries (level-3). For women, we had 7842 observations for 2614 individuals, and they also came from 97 source countries.

2.2. Variables The outcome variable of interest was self-rated health. Although this information was collected on a 5-point scale (excellent to poor), we dichotomised the responses as ‘‘good’’ health (combining excellent, very good, and good responses), and as ‘‘poor’’ health (combining fair and poor responses). Berry’s framework for acculturation (Berry, 1997) is a useful framework for research on immigration and health. Some of the individual factors discussed in this framework are age, gender, education, migration motivation, cultural distance, length of stay in the new society, attitude, social support, prejudices, and discrimination and the group level variables are prior experiences in the society of origin (Berry, 1997). In addition, another framework for immigrant health suggested by Hyman discusses the individual factors (such as income, education, employment, language fluency, and length of stay), pre-migration factors (some macro-level factors such as economy and political environment), and experiences (some macro-level factors such as racism) in the new society as determinants of health of immigrants. Further, other variables such as gender and socio-economic status have also been highlighted in this framework (Hyman, 2010). Taking into account different variables discussed in the above mentioned frameworks, we used three categories of independent variables: (1) individual socio-demographics, economic situation, and language skills; (2) post-immigration experiences and beliefs; and (3) source country parameters. We included ethnic grouping (White, Chinese, South Asian, Arab, and others, as reported in the LSIC), marital status (single/never married, married and principal applicant, married and spousal applicant, separated), province of destination (Ontario, Quebec, British Columbia, and others), education (up to high school, CEGEP/college/trade, some University/Bachelors, Masters and above). A variable for income adequacy was created based on the total household income and number of persons in the household and individuals were classified as in the highest, upper middle, lower middle, or lowest group. A variable for linguistic majority was created based on the linguistic ability score (French for Quebec and English for the other provinces); the score (as suggested by Statistics Canada in the LSIC data file: ‘‘The English and French linguistic ability scores are measures that were developed a posteriori from several LSIC questions. The goal of these scores is to measure the respondent’s ability to function in each of the two official languages. These measures take into account the respondent’s reported ability to speak, read and write in these two languages, the ability to do certain daily life activities in these languages, as well as the use of these languages at work and while studying to obtain their highest education level. Each score takes on values between 0 and 1, the higher values indicating a greater ability’’) was then divided into quartiles and used in the models. The variables for post-immigration experiences and beliefs included current employment status (yes/no); interaction with friends (daily, once or more per week, less than that/no friends); faced discrimination (yes/no); importance of keeping the values of home country (no/yes); perception of life in Canada (neither satisfied/nor dissatisfied, satisfied/completely satisfied, dissatisfied/ completely dissatisfied); experience in Canada (about/somewhat/ much better than expected, somewhat/much worse than expected). We used a ‘time variable’ to represent calendar time in years. Finally, we created a dataset with the country-of-origin level variables and linked this to the LSIC. Using internationally comparable data (United Nations Development Program, United Nations

Population Division, World Bank) (DFID Health Systems Resource Centre, 2003; Government of Hong Kong, 2000; State Statistical Office – Republic of Macedonia, 2008; United Nations Children’s Fund, 2004; United Nations Development Program, 2009; United Nations Population Project, 2008; World Bank, 2009a, 2009b), we gathered indicators on the human development index (HDI), gross national income per capita (GNI Per capita), political stability measure (PSM), and infant mortality rate (IMR). These four variables provide a broad picture of the source countries’ context in terms of general development, financial strength, political stability, and population health. HDI is a composite measure composed of healthy lifespan (measured as life expectancy), knowledge skills (measured as adult literacy and combined primary, secondary, and tertiary enrolment), and standard of living (measured as Gross Domestic Product per capita) (Watkins, 2007). GNI per capita measures the total national income divided by the mid-year population and is considered to be a good measure of living standards (Konjunkturinstitutet: National Institute of Economic Research, 2005; United Nations Children’s Fund, 2009). The PSM essentially indicates the stability of the government, violence, and absence of terrorism (Kaufmann et al., 2009). The IMR is considered to be a good measure of population health and provides information comparable to disability adjusted life expectancy (Reidpath and Allotey, 2003). We used data from 2000 for these variables. If the measure was not available for this year, we used the nearest available measure (preferably the one before 2000). We prepared gender-specific data files for these indicators, as the values for IMR differed in men and women. We used quintiles for HDI, GNI per capita, PSM, and IMR in the model. The measures were used as indicators to measure the contextual experiences of these immigrants in these source countries.

2.3. Statistical analyses We analysed the data for male and female immigrants separately. The initial descriptive statistics included means (and medians, standard deviations, and IQR) for continuous variables and frequency distributions for categorical, baseline socio-demographic, and economic characteristics in these immigrants. We also calculated the proportions of people reporting poor health for various subgroups, individual characteristics, post-immigration experiences, and the country-level variables. These descriptive data were weighted and bootstrapped using the sampling weights recommended by Statistics Canada (Statistics Canada, 2007). We used multi-level random effect (RE) models for the analyses of the associations between socio-demographic and economic variables, post-immigration experiences, source country variables, and the binary outcome of self-rated poor health (Rabe-Hesketh and Skrondal, 2008; Snijders and Bosker, 2004). Unlike the usual logistic regression models, RE models can measure both the between-subject and within-subject variances in the dataset (Snijders and Bosker, 2004). The three levels in the RE model were as follows: multiple observations (level-1) in the same individual (level-2) from different source countries (level-3). As discussed earlier, we used three waves of LSIC data for our analyses. Thus, we had up to three observations for each individual. In the subsequent waves – waves two and three – some of the covariates (such as ethnicity) remained the same, whereas others (such as income category, health outcomes) potentially changed in these individuals. However, in the codes for our models we indicated that these observations belong to the same group (i.e. the individual in our case) and thus are correlated. Consequently, the estimates and standard errors from our RE models accounted for both between-subject and within-subject correlations of individuals. The models were built in the

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following sequence: null models with no explanatory variables estimate the variances at higher levels; crude estimates of association between various independent variables and self-rated health; and multivariable models that adjusted these associations for a priori selected subset of independent variables. The first multivariable model included only individual socio-demographic and economic variables. Then in separate models, we added each of the post-immigration variables, one at a time, to these sociodemographic and economic variables. For the final set of models we added each of the country-level variables separately to the socio-demographic and economic variables. All the analyses were performed in Stata Version 11 (StataCorp, College Station, Texas, US). We used the gllamm procedure in Stata for the present analysis (Rabe-Hesketh and Skrondal, 2008). We used Akaike Information Criteria (AIC) to compare the fit of models while accounting for additional variables in the models (Akaike, 1974). We had complete information for all the variables in about 98% of observations in males and 97% of observations in females. The information on the ‘discrimination’ variable was available only for two waves (2003 and 2005). Though, we estimated models with ‘discrimination’ as one of the variables in both men and women, we did not compare it to the other estimated models. The study was approved for secondary data analysis by the Institutional Review Board of McGill University, Montreal, Canada.

3. Results The weighted mean ages (95% confidence intervals (CI)) of the male and female immigrants were 36.0 (95% CI: 35.8–36.3) and 35.4 (95% CI: 35.1–35.7) years, respectively. The weighted proportions for ethnicities among men were 20% white, 23% ethnically Chinese, and 28% ethnically South Asian. For women, these weighted proportions were 17% white, 24% ethnically Chinese, and 28% ethnically South Asian. Selected baseline demographic characteristics of male and female immigrants are presented in Table 1. We have also described the weighted proportion of men and women reporting excellent to poor health in Appendix A. In the next sections, we describe results for men followed by those for women. 3.1. Correlates of poor self-reported health among male immigrants In the univariate models (Table 1), both ethnically Chinese and South Asian men had higher (unadjusted) odds of reporting health as poor compared with White men. However, after adjusting for all the individual level variables, only Chinese men had significantly higher odds of reporting their health as poor (Table 2, Model I). In the same multivariable model, the adjusted odds of reporting poor health increased with both age and calendar time. Thus, overall, at any given time, older men were more likely to report their health as poor as shown by the ‘age effect’ and the ‘time effect’, suggesting a statistically significant secular trend for increased reporting of poor health in men of the same age group. Interestingly, married men who had entered as the primary applicant were least likely to report their health as poor. In Model II (Table 2), which adjusted for all the socio-demographic and economic parameters, men who were not currently employed were twice as likely to report their health as poor than those who were employed and had the same values of all other parameters (Table 2, Model II). The frequency with which male immigrants had social interactions with friends was not significantly associated with their health, but men who felt the need to retain the values of the home culture were significantly less likely to report their health as poor compared with those who did not.

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Table 1 Baseline sample characteristics (weighted and bootstrapped proportions) in male and female immigrants from the Longitudinal Survey of Immigrants to Canada (2001). Variables

Males (%)a

Females (%)a

Mean age (95% confidence interval)b

36.0 (35.8–36.3)

35.4 (35.1–35.7)

Ethnicity White Chinese South Asian Arab Others

20 23 28 6 23

17 24 28 5 25

Marital status Single/never married Married: primary applicant Married: dependant Separated

19 70 10 1

11 35 48 6

Income adequacy Highest Upper middle Lower middle Lowest

3 4 14 79

4 7 17 72

Province Others Ontario Quebec British Columbia

10 57 17 16

10 56 15 19

Education Up to high school CEGEP/college/trade/other University/Bachelor’s Master’s and above

16 12 49 23

25 18 42 15

a The proportions shown here are weighted and bootstrapped proportions in the respective categories. b The means and standard deviations shown over here are weighted and bootstrapped.

Men who did not report any form of discrimination in Canada were also less likely to report poor health than those who did (adjusted odds ratio (OR) 0.53, 95% confidence intervals 0.36–0.79). As seen the adjusted models (Table 2, Models V and VI), even after adjustments for socio-demographic and economic parameters, men who were dissatisfied with their life in Canada or who rated their experience worse than what they had expected were more likely to report poor health than those who did not. When we added the country-level variables to the sociodemographic and economic variables (Table 3, Models I–IV), the odds of reporting poor health were reduced for South Asian and Chinese men, becoming statistically insignificant for the latter. While the odds of reporting poor health were generally higher in male immigrants coming from countries with lower HDI and GNI per capita, some of these associations failed to reach statistical significance (Table 3, Models I and II). We found that men coming from countries in the lowest HDI quintile and highest IMR quintile were significantly more likely to report their health as poor compared with those coming from the highest HDI quintile and lowest IMR quintile, respectively. However, there were no significant differences between the self-rated health of male immigrants arriving from countries ranked in different quintiles of the PSM. 3.2. Correlates of poor self-reported health among female immigrants In the univariate models (Table 4), we found that Chinese and South Asian women were more likely to report their health as

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M.S. Setia et al. / Health & Place 17 (2011) 658–670

Table 2 Unadjusted and adjusted associations of individual socio-demographics, post-immigration experiences, and self-rated health in 2468 male immigrants from the Longitudinal Survey of Immigrants to Canada (2001–2005). Variables

Ethnicity White Chinese South Asian Arab Others Age categories 18–29 years 30–39 years 40–49 years 50 years and above Time (per year)

Unadjusted estimates

Adjusted estimates Model I

Model II

Model III

Model IV

Model V

Model VI

Reference 3.01 (1.70–5.32) 2.24 (1.29–3.88) 1.88 (0.81–4.36) 1.45 (0.79–2.65)

Reference 2.57 (1.40–4.72) 1.72 (0.94–3.13) 1.90 (0.77–4.68) 1.21 (0.66–2.24)

Reference 2.29 (1.25–4.21) 1.70 (0.94–3.09) 1.66 (0.68–4.09) 1.20 (0.65–2.20)

Reference 2.38 (1.29–4.38) 1.67 (0.92–3.04) 1.89 (0.77–4.64) 1.20 (0.65–2.21)

Reference 2.56 (1.38–4.76) 1.97 (1.06–3.67) 2.14 (0.85–5.34) 1.37 (0.73–2.56)

Reference 2.13 (1.06–4.32) 1.66 (0.81–3.39) 1.54 (0.60–3.91) 1.14 (0.60–3.91)

Reference 2.36 (1.29–4.35) 1.59 (0.87–2.90) 1.70 (0.69–4.20) 1.16 (0.63–2.15)

Reference 1.28 (0.73–2.24) 2.27 (1.48–4.80) 8.42 (4.51– 15.73)

Reference 2.14 (1.10–4.20) 4.06 (1.96–8.41) 9.98 (4.66– 21.35)

Reference 2.16 (1.10–4.24) 3.98 (1.92–8.25) 8.29 (3.86– 17.81)

Reference 2.05 (1.05–4.02) 3.80 (1.83–7.88) 9.14 (4.26– 19.61)

Reference 2.03 (1.03–4.00) 3.86 (1.85–8.07) 9.23 (4.30– 20.06)

Reference 1.91 (0.97–3.76) 3.60 (1.73–7.51) 9.05 (4.24– 19.32)

Reference 1.91 (0.97–3.75) 3.66 (1.77–7.59) 9.09 (4.25–19.42)

1.33 (1.22–1.46)

1.29 (1.16–1.44)

1.31 (1.17–1.46)

1.29 (1.15–1.44)

1.30 (1.16–1.46)

1.30 (1.16–1.46)

1.28 (1.14–1.43)

Marital status Single/never married Married: primary applicant Married: dependant Separated

Reference 1.07 (0.64–1.81)

Reference 0.40 (0.21–0.75)

Reference 0.43 (0.23–0.82)

Reference 0.40 (0.21–0.75)

Reference 0.43 (0.22–0.82)

Reference 0.41 (0.22–0.79)

Reference 0.42 (0.22–0.79)

1.86 (0.95–3.64) 1.70 (0.57–5.10)

0.80 (0.38–1.67) 0.48 (0.15–1.56)

0.84 (0.40–1.77) 0.54 (0.17–1.74)

0.80 (0.38–1.69) 0.48 (0.15–1.54)

0.88 (0.41–1.88) 0.50 (0.15–1.66)

0.83 (0.40–1.76) 0.50 (0.15–1.62)

0.79 (0.37–1.67) 0.51 (0.16–1.64)

Income adequacy Highest Upper middle Lower middle Lowest

Reference 0.92 (0.55–1.55) 1.44 (0.87–2.38) 0.61 (0.37–1.02)

Reference 0.99 (0.59–1.68) 1.73 (1.03–2.90) 1.19 (0.67–2.10)

Reference 1.02 (0.60–1.74) 1.69 (1.00–2.83) 1.01 (0.57–1.80)

Reference 1.00 (0.59–1.70) 1.74 (1.04–2.93) 1.22 (0.69–2.16)

Reference: 1.02 (0.60–1.74) 1.73 (1.03–2.94) 1.22 (0.68–2.18)

Reference 0.97 (0.57–1.64) 1.53 (0.91–2.58) 1.07 (0.60–1.91)

Reference 0.96 (0.57–1.64) 1.53 (0.91–2.58) 1.09 (0.61–1.94)

Province Others Ontario Quebec British Columbia

Reference 1.12 (0.62–2.01) 1.00 (0.47–2.11) 2.43 (1.30–4.56)

Reference 1.14 (0.64–2.04) 1.06 (0.47–2.37) 1.68 (0.89–3.18)

Reference 1.07 (0.60–1.90) 0.84 (0.37–1.89) 1.59 (0.84–2.99)

Reference 1.15 (0.64–2.06) 1.03 (0.46–2.32) 1.67 (0.88–3.17)

Reference 1.16 (0.64–2.09) 1.13 (0.50–2.57) 1.68 (0.88–3.22)

Reference 1.04 (0.58–1.86) 0.99 (0.44–2.24) 1.59 (0.84–3.00)

Reference 1.06 (0.59–1.90) 0.99 (0.44–2.22) 1.57 (0.83–2.98)

Reference 0.72 (0.40–1.29)

Reference 0.96 (0.51–1.82)

Reference 1.00 (0.53–1.88)

Reference 0.97 (0.51–1.82)

Reference 0.94 (0.49–1.80)

Reference 0.89 (0.47–1.67)

Reference 0.88 (0.46–1.67)

0.49 (0.31–0.79) 0.32 (0.18–0.57)

0.88 (0.51–1.52) 0.59 (0.31–1.14)

0.90 (0.52–1.54) 0.59 (0.31–1.14)

0.89 (0.52–1.53) 0.59 (0.31–1.14)

0.92 (0.53–1.60) 0.62 (0.32–1.22)

0.78 (0.45–1.35) 0.50 (0.26–0.98)

0.79 (0.46–1.37) 0.53 (0.27–1.02)

Linguistic major quartiles 1st (lowest) Reference 2nd 0.51 (0.32–0.81) 3rd 0.35 (0.21–0.58) 4th (highest) 0.36 (0.22–0.58)

Reference 0.84 (0.51–1.39) 0.67 (0.39–1.14) 0.74 (0.42–1.28)

Reference 0.88 (0.54–1.44) 0.71 (0.41–1.21) 0.86 (0.49–1.49)

Reference 0.83 (0.51–1.37) 0.68 (0.39–1.16) 0.75 (0.43–1.31)

Reference 0.82 (0.49–1.35) 0.64 (0.37–1.11) 0.68 (0.39–1.19)

Reference 0.78 (0.48–1.29) 0.65 (0.38–1.12) 0.71 (0.41–1.23)

Reference 0.81 (0.49–1.33) 0.67 (0.39–1.15) 0.73 (0.42–1.28)

Education Up to high school CEGEP/college/trade/ other University/Bachelor’s Master’s and above

Currently employed Yes No Interaction with friends Daily Once or more per week Less than that/does not have any friends

Reference 2.20 (1.59–3.03)

Reference 2.23 (1.56–3.20)

Reference 0.98 (0.65–1.47) 1.94 (1.25–3.00)

Reference 0.89 (0.59–1.36) 1.44 (0.91–2.27)

Important to keep values of the home country No Reference Yes 0.56 (0.38–0.84)

Reference 0.54 (0.36–0.81)

Perception of life in Canada Neither satisfied/ Reference nor dissatisfied Satisfied/completely 0.85 (0.56–1.41) satisfied Dissatisfied/completely 1.90 (1.03–3.51) dissatisfied Experience in Canada About/somewhat/much better than expected Somewhat/much worse than expected Variance (level 2) Variance (level 3) AIC

Reference 0.64 (0.40–1.02) 2.02 (1.07–3.79)

Reference

Reference

1.94 (1.38–2.73)

1.96 (1.38–2.79) 3.63 (0.68) o 0.01 2037.3

3.48 (0.70) o 0.01 2020.3

3.60 (0.68) o 0.01 2033.6

3.86 (0.73) o 0.01 2016.9

3.42 (0.66) 0.04 (0.11) 1998.6

3.59 (0.68) o 0.01 2011.3

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Table 3 Adjusted models describing the association between country-level variables and self-rated health in 2468 male immigrants from the Longitudinal Survey of Immigrants to Canada (2001–2005). Variables

Model I

Model II

Model III

Model IV

Ethnicity White Chinese South Asian Arab Others

Reference 1.67 (0.54–5.18) 0.70 (0.21–2.28) 1.46 (0.47–4.52) 0.71 (0.31–1.67)

Reference 1.12 (0.47–2.63) 1.01 (0.42–2.39) 2.00 (0.78–5.14) 1.23 (0.64–2.37)

Reference 1.88 (0.89–3.98) 1.81 (0.86–3.80) 1.47 (0.56–3.82) 1.00 (0.52–1.96)

Reference 1.86 (0.93–3.73) 1.29 (0.58–2.85) 1.87 (0.68–5.16) 1.08 (0.52–2.26)

Age categories 18–29 years 30–39 years 40–49 years 50 years and above

Reference 2.07 (1.05–4.07) 3.93 (1.89–8.19) 9.58 (4.46–20.56)

Reference 2.16 (1.10–4.25) 4.21 (2.02–8.77) 10.30 (4.78–22.18)

Reference 2.09 (1.07–4.11) 4.07 (1.96–8.45) 9.96 (4.64–21.37)

Reference 2.10 (1.07–4.13) 4.11 (1.97–8.54) 10.46 (4.86–22.53)

Time (per year)

1.28 (1.15–1.43)

1.28 (1.15–1.43)

1.28 (1.15–1.43)

1.28 (1.15–1.43)

Marital status Single/never married Married: primary applicant Married: dependant Separated

Reference 0.41 (0.22–0.78) 0.79 (0.37–1.67) 0.50 (0.16–1.63)

Reference 0.40 (0.20–0.75) 0.76 (0.36–1.60) 0.48 (0.15–1.54)

Reference 0.41 (0.22–0.78) 0.79 (0.37–1.66) 0.51 (0.16–1.65)

Reference 0.41 (0.22–0.78) 0.78 (0.37–1.66) 0.49 (0.15–1.59)

Income adequacy Highest Upper middle Lower middle Lowest

Reference 0.97 (0.57–1.65) 1.63 (0.97–2.75) 1.12 (0.63–1.99)

Reference 0.98 (0.58–1.67) 1.65 (0.98–2.77) 1.13 (0.63–2.00)

Reference 0.98 (0.58–1.67) 1.66 (0.99–2.78) 1.13 (0.64–2.01)

Reference 0.98 (0.58–2.85) 1.65 (0.98–2.77) 1.13 (0.64–2.01)

Province Others Ontario Quebec British Columbia

Reference 1.06 (0.59–1.90) 0.97 (0.42–2.27) 1.73 (0.92–3.27)

Reference 1.02 (0.57–1.83) 0.85 (0.37–1.95) 1.67 (0.88–3.16)

Reference 1.06 (0.59–1.90) 1.01 (0.45–2.25) 1.68 (0.89–3.17)

Reference 1.06 (0.59–1.90) 0.94 (0.41–2.14) 1.71 (0.90–3.23)

Education Up to high school CEGEP/college/trade/other University/Bachelor’s Master’s and above

Reference 0.98 (0.52–1.86) 0.90 (0.52–1.57) 0.59 (0.29–1.15)

Reference 0.99 (0.52–1.87) 0.88 (0.51–1.52) 0.57 (0.29–1.10)

Reference 0.97 (0.51–1.82) 0.88 (0.51–1.51) 0.58 (0.30–1.12)

Reference 0.96 (0.51–1.82) 0.86 (0.50–1.49) 0.56 (0.29–1.08)

Linguistic major quartiles 1st (lowest) 2nd 3rd 4th (highest)

Reference 0.83 (0.51–1.37) 0.63 (0.36–1.09) 0.67 (0.37–1.21)

Reference 0.87 (0.53–1.42) 0.69 (0.40–1.19) 0.78 (0.44–1.36)

Reference 0.83 (0.50–1.36) 0.63 (0.36–1.08) 0.69 (0.39–1.20)

Reference 0.83 (0.51–1.37) 0.64 (0.37–1.10) 0.72 (0.40–1.27)

Human development index quintile 5th (highest) 4th 3rd 2nd 1st (lowest)

Reference 1.44 (0.59–3.51) 4.56 (1.14–18.25) 2.04 (0.69–6.07) 3.85 (1.05–14.16)

Gross national income per capita quintiles 5th (highest) 4th 3rd 2nd 1st (lowest)

Reference 1.44 (0.71–2.91) 1.08 (0.45–2.63) 3.28 (1.44–7.48) 1.99 (0.81–4.88)

Political stability measure quintile 5th (highest) 4th 3rd 2nd 1st (lowest) Infant mortality rate quintile 1st (lowest) 2nd 3rd 4th 5th (highest) Variance (level 2) Variance (level 3) AIC

Reference 1.16 (0.40–3.35) 1.64 (0.83–3.25) 0.88 (0.39–2.02) 1.70 (0.87–3.32)

3.45 (0.66) 0.16 (0.17) 2038.2

3.57 (0.68) o0.01 2036.2

3.58 (0.68) o 0.01 2039.5

Reference 1.83 (0.88–3.83) 1.51 (0.63–3.59) 1.37 (0.64–2.90) 2.98 (1.21–7.37) 3.59 (0.68) o 0.01 2037.2

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Table 4 Unadjusted and adjusted associations of individual socio-demographics, post-immigration experiences, and self-rated health in 2614 female immigrants from the Longitudinal Survey of Immigrants to Canada (2001–2005). Variables

Unadjusted estimates

Adjusted estimates Model I

Model II

Model III

Model IV

Model V

Model VI

Reference 4.28 (1.76– 10.39) 2.38 (1.15–4.92) 1.45 (0.57–3.66) 1.40 (0.74–2.65)

Reference 3.27 (1.35–7.88)

Reference 3.25 (1.35–7.81)

Reference 3.13 (1.30–7.49)

Reference 3.40 (1.38–8.36)

Reference 2.82 (1.19–6.64)

Reference 3.21 (1.37–7.51)

1.82 (0.89–3.73) 1.54 (0.60–3.93) 1.33 (0.69–2.56)

1.78 (0.87–3.62) 1.48 (0.58–3.76) 1.32 (0.69–2.53)

1.78 (0.88–3.62) 1.54 (0.60–3.93) 1.30 (0.68–2.49)

1.95 (0.94–4.06) 1.61 (0.62–4.17) 1.44 (0.74–2.80)

1.81 (0.90–3.64) 1.54 (0.61–3.87) 1.26 (0.66–2.42)

1.89 (0.95–3.79) 1.60 (0.64–4.04) 1.34 (0.71–2.55)

Reference 1.84 (1.27–2.66) 5.12 (3.43–7.65) 11.69 (7.70– 17.75)

Reference 1.63 (1.08–2.45) 3.75 (2.40–5.87) 5.96 (3.65–9.75)

Reference 1.62 (1.08–2.43) 3.77 (2.41–5.90) 5.65 (3.45–9.25)

Reference 1.62 (1.08–2.44) 3.72 (2.38–5.83) 5.88 (3.59–9.62)

Reference 1.63 (1.08–2.45) 3.81 (2.43–5.98) 5.89 (3.60–9.65)

Reference 1.53 (1.01–2.32) 3.65 (2.32–5.76) 6.01 (3.64–9.91)

Reference 1.54 (1.02–2.31) 3.58 (2.29–5.61) 5.74 (3.52–9.38)

1.45 (1.36–1.55)

1.45 (1.34–1.56)

1.46 (1.35–1.58)

1.45 (1.34–1.56)

1.45 (1.34–1.57)

1.48 (1.37–1.60)

1.44 (1.33–1.56)

Reference 1.45 (0.82–2.57)

Reference 0.95 (0.52–1.73)

Reference 0.93 (0.51–1.69)

Reference 0.94 (0.51–1.71)

Reference 0.94 (0.51–1.72)

Reference 1.01 (0.50–1.87)

Reference 0.99 (0.54–1.81)

2.60 (1.49–4.52) 5.09 (2.63–9.89)

1.01 (0.55–1.86) 1.06 (0.51–2.18)

0.98 (0.53–1.79) 1.01 (0.49–2.09)

1.01 (0.55–1.85) 1.05 (0.51–2.16)

1.01 (0.55–1.86) 1.05 (0.51–2.16)

1.01 (0.54–1.90) 1.15 (0.55–2.42)

1.01 (0.55–1.86) 1.07 (0.52–2.21)

Income adequacy categories Highest Reference Upper middle 0.99 (0.68–1.44) Lower middle 1.16 (0.81–1.67) Lowest 0.62 (0.43–0.90)

Reference 1.11 (0.76–1.63) 1.40 (0.97–2.04) 1.53 (1.02–2.30)

Reference 1.13 (0.77–1.66) 1.40 (0.96–2.03) 1.49 (0.99–2.24)

Reference 1.11 (0.76–1.63) 1.40 (0.97–2.04) 1.53 (1.02–2.30)

Reference: 1.10 (0.75–1.62) 1.38 (0.95–2.01) 1.52 (1.01–2.29)

Reference 1.10 (0.75–1.62) 1.27 (0.87–1.86) 1.38 (0.91–2.09)

Reference 1.07 (0.73–1.57) 1.29 (0.89–1.88) 1.38 (0.92–2.09)

Province Others Ontario Quebec British Columbia

Reference 0.91 (0.60–1.37) 0.96 (0.54–1.70) 1.70 (1.11–2.61)

Reference 0.95 (0.62–1.45) 1.23 (0.66–2.32) 1.34 (0.86–2.09)

Reference 0.94 (0.61–1.42) 1.12 (0.59–2.13) 1.32 (0.85–2.05)

Reference 0.96 (0.63–1.47) 1.22 (0.65–2.30) 1.35 (0.87–2.11)

Reference 0.94 (0.61–1.43) 1.25 (0.67–2.37) 1.33 (0.85–2.08)

Reference 0.85 (0.56–1.31) 1.18 (0.62–2.23) 1.24 (0.79–1.95)

Reference 0.86 (0.57–1.31) 1.15 (0.61–2.16) 1.25 (0.80–1.94)

Reference 0.48 (0.33–0.69)

Reference 0.85 (0.56–1.28)

Reference 0.85 (0.56–1.28)

Reference 0.85 (0.56–1.29)

Reference 0.84 (0.56–1.27)

Reference 0.77 (0.51–1.18)

Reference 0.80 (0.53–1.21)

0.35 (0.25–0.48) 0.34 (0.22–0.53)

0.81 (0.56–1.18) 0.83 (0.50–1.35)

0.82 (0.56–1.19) 0.84 (0.51–1.38)

0.81 (0.56–1.19) 0.83 (0.51–1.36)

0.82 (0.56–1.20) 0.83 (0.51–1.37)

0.75 (0.51–1.11) 0.71 (0.43–1.18)

0.77 (0.53–1.13) 0.76 (0.46–1.25)

Linguistic major quartiles 1st (lowest) Reference 2nd 0.32 (0.23–0.44) 3rd 0.23 (0.16–0.32) 4th (highest) 0.23 (0.16–0.33)

Reference 0.48 (0.34–0.70) 0.47 (0.32–0.70) 0.42 (0.26–0.67)

Reference 0.48 (0.34–0.70) 0.48 (0.32–0.72) 0.46 (0.29–0.74)

Reference 0.49 (0.34–0.70) 0.47 (0.32–0.70) 0.43 (0.27–0.68)

Reference 0.47 (0.33–0.68) 0.48 (0.32–0.71) 0.42 (0.26–0.67)

Reference 0.48 (0.33–0.69) 0.47 (0.31–0.70) 0.41 (0.25–0.65)

Reference 0.49 (0.34–0.70) 0.48 (0.32–0.72) 0.43 (0.27–0.68)

Ethnicity White Chinese South Asian Arab Others Age categories 18–29 years 30–39 years 40–49 years 50 years and above Time (per year) Marital status Single/never married Married: primary applicant Married: dependant Separated

Education Up to high school CEGEP/college/trade/ other University/Bachelor’s Master’s and above

Currently employed Yes No Interaction with friends Daily Once or more per week Less than that/does not have any friends

Reference 1.42 (1.13–1.78)

Reference 1.29 (0.99–1.69)

Reference 1.08 (0.80–1.44) 1.53 (1.12–2.10)

Reference 0.91 (0.67–1.24) 1.17 (0.84–1.62)

Important to keep values of the home country No Reference Yes 0.77 (0.56–1.07)

Reference 0.77 (0.55–1.08)

Perception of life in Canada Neither satisfied/nor Reference dissatisfied Satisfied/completely 0.74 (0.54–1.00) satisfied Dissatisfied/completely 1.19 (0.76–1.85) dissatisfied Experience in Canada About/somewhat/much better than expected Somewhat/much worse than expected Variance (level 2) Variance (level 3) AIC

Reference 0.49 (0.35–0.68) 1.43 (0.90–2.27)

Reference

Reference

1.56 (1.22–1.99)

1.77 (1.37–2.29) 2.65 (0.39) 0.30 (0.25) 3553.2

2.61 (0.38) 0.30 (0.24) 3551.6

2.65 (0.39) 0.29 (0.24) 3551.5

2.64 (0.39) 0.33 (0.26) 3516.9

2.67 (0.40) 0.25 (0.23) 3438.7

2.59 (0.39) 0.25 (0.23) 3498.3

M.S. Setia et al. / Health & Place 17 (2011) 658–670

poor compared with White immigrant women. However, after adjusting for all the individual socio-demographic and economic variables, this association remained only for Chinese women (Table 4, Model I). After adjusting for other variables, older women, as older men, were more likely to report poor health at any given point in time. Similarly, the time effect for women indicated a secular trend, as it did for men: in any given age group women were more likely to report poor health in more recent years. Women in the higher quartiles (2nd, 3rd, and 4th) of linguistic majority were significantly less likely to rate their health as poor compared with women in the lowest quartile. There were no significant differences in health across various marital status conditions in women. After adjusting for all the individual socio-demographic and economic variables (Model II: Table 4), though women who were not currently employed were more likely to report their health as poor compared with those who were, the odds were lower than for men and not statistically significant (OR ¼1.29 for females vs. OR ¼2.23 for males). For women, as for men, poor self-rated health did not differ across various categories of social

665

interaction. However, women who found their experience in Canada to be worse than expected were more likely to rate their health as poor than those who found the experience to be as good as or better than expected. Moreover as observed in men, women who did not report any form of discrimination in Canada were less likely to rate their health as poor than those who reported discrimination (OR: 0.62, 95% CI: 0.45–0.85). After the country-level variables (HDI and GNI per capita) were added to the socio-demographic and economic variables (Table 5, Models I and II), the odds ratios for reporting poor health were reduced in all the ethnic groups when compared with white women, and lost statistical significance for Chinese women (in these models). Women in the lowest HDI and GNI per capita quintile were more likely to report poor health than those in the highest; moreover, the odds gradually increased in the lower quintiles in what appears to be a dose–response relationship (Table 5, Models I and II). A similar effect was seen across IMR quintiles: women arriving from countries with higher IMRs were more likely to report poor health than those arriving from those in the lowest IMR quintile. As was the case with male immigrants,

Table 5 Adjusted models describing the association between country-level variables and self-rated health in 2614 female immigrants from the Longitudinal Survey of Immigrants to Canada (2001–2005). Variables

Model I

Model II

Model III

Model IV

Ethnicity White Chinese South Asian Arab Others

Reference 1.77 (0.85–3.69) 1.05 (0.49–2.26) 1.56 (0.65–3.71) 0.95 (0.54–1.66)

Reference 2.03 (0.71–5.75) 0.91 (0.42–1.98) 1.74 (0.66–4.54) 1.14 (0.61–2.14)

Reference 2.88 (1.08–7.65) 1.51 (0.69–3.32) 1.00 (0.37–2.69) 1.21 (0.61–2.39)

Reference 2.24 (1.18–4.25) 0.90 (0.48–1.70) 1.18 (0.52–2.70) 0.87 (0.53–1.43)

Age categories 18–29 years 30–39 years 40–49 years 50 and above

Reference 1.61 (1.07–2.41) 3.80 (2.43–5.93) 5.92 (3.63–9.67)

Reference 1.67 (1.11–2.51) 3.95 (2.53–6.19) 6.18 (3.78–10.10)

Reference 1.65 (1.10–2.48) 3.85 (2.46–6.03) 6.04 (3.69–9.88)

Reference 1.62 (1.08–2.43) 3.84 (2.46–5.99) 5.85 (3.61–9.50)

Time (per year)

1.45 (1.34–1.56)

1.44 (1.34–1.56)

1.44 (1.34–1.56)

1.45 (1.34–1.56)

Marital status Single/never married Married: primary applicant Married: dependant Separated

Reference 0.92 (0.51–1.67) 0.98 (0.54–1.79) 1.06 (0.52–2.18)

Reference 0.89 (0.49–1.63) 0.95 (0.52–1.74) 1.01 (0.49–2.08)

Reference 0.94 (0.51–1.71) 0.99 (0.54–1.82) 1.05 (0.51–2.17)

Reference 0.93 (0.51–1.70) 0.99 (0.54–1.80) 1.06 (0.52–2.17)

Income adequacy Highest Upper middle Lower middle Lowest

Reference 1.09 (0.75–1.60) 1.37 (0.95–1.99) 1.51 (1.01–2.27)

Reference 1.09 (0.75–1.60) 1.36 (0.94–1.97) 1.49 (0.99–2.24)

Reference 1.09 (0.75–1.61) 1.37 (0.94–1.99) 1.50 (1.00–2.25)

Reference 1.10 (0.75–1.60) 1.38 (0.95–2.00) 1.52 (1.01–2.28)

Province Others Ontario Quebec British Columbia

Reference 0.95 (0.63, 1.44) 1.13 (0.61–2.08) 1.36 (0.87–2.11)

Reference 0.92 (0.60–1.40) 1.13 (0.60–2.13) 1.39 (0.89–2.16)

Reference 0.91 (0.60–1.39) 1.18 (0.62–2.23) 1.33 (0.86–2.08)

Reference 0.93 (0.61–1.41) 1.12 (0.61–2.07) 1.34 (0.86–2.09)

Education Up to high school CEGEP/college/trade/other University/Bachelor’s Master’s and above

Reference 0.81 (0.54–1.22) 0.77 (0.53–1.22) 0.76 (0.47–1.24)

Reference 0.86 (0.57–1.29) 0.80 (0.55–1.16) 0.77 (0.47–1.27)

Reference 0.85 (0.57–1.29) 0.79 (0.54–1.15) 0.78 (0.48–1.29)

Reference 0.84 (0.56–1.27) 0.80 (0.55–1.15) 0.79 (0.48–1.27)

Linguistic major quartiles 1st (lowest) 2nd 3rd 4th (highest)

Reference 0.50 (0.35–0.72) 0.49 (0.33–0.72) 0.43 (0.27–0.68)

Reference 0.50 (0.35–0.73) 0.49 (0.33–0.74) 0.45 (0.28–0.72)

Reference 0.50 (0.34–0.71) 0.49 (0.33–0.73) 0.44 (0.28–0.71)

Reference 0.49 (0.34–0.71) 0.46 (0.31–0.69) 0.40 (0.25–0.63)

Human development index quintile 5th (highest) 4th 3rd 2nd 1st (lowest)

Reference 1.10 (0.63–1.92) 1.08 (0.57–2.07) 1.58 (0.81–3.07) 2.46 (1.23–4.94)

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Table 5 (continued ) Variables

Model I

Gross national income per capita quintiles 5th (highest) 4th 3rd 2nd 1st (lowest)

Model II

Model IV

Reference 1.86 (0.77–4.45) 1.06 (0.34–3.33) 3.18 (1.48–6.83) 4.37 (1.72–11.06)

Political stability measure quintile 5th (highest) 4th 3rd 2nd 1st (lowest) Infant mortality rate quintile 1st (lowest) 2nd 3rd 4th 5th (highest) Variance (level 2) Variance (level 3) AIC

Model III

Reference 1.37 (0.34–5.61) 3.01 (1.34–6.76) 2.20 (0.69–6.98) 1.58 (0.70–3.56)

2.71 (0.39) o 0.01 3553.6

2.61 (0.38) 0.16 (0.22) 3545.2

women showed no specific pattern of self-reported health across various PSM quintiles.

4. Discussion Our analyses revealed several social determinants of immigrant health that were common to men and women, as well as some that were gender-specific: marital/applicant status and attachment to values from the home society were related to self-reported health in men, whereas host province language fluency was associated with self-reported health in women. Overall, ethnically Chinese immigrants were most likely to rate their health as poor over the four-year post-immigration period studied, as were immigrants who were not currently employed, with the relationship stronger in men than in women. Generally speaking, immigrants who found their Canadian experience to be worse than expected were more likely to report poor health. Interestingly, we found that source country characteristics appeared to affect health, in particular by removing the effect noted for Chinese ethnic immigrants when added to our models. In addition, we found that women who had emigrated from the lowest GNI per capita countries were more likely to report poor health as were both women and men from the highest IMR and lowest HDI countries. 4.1. Gender differences Married men who had entered Canada as the principal applicant were least likely to rate their health as poor, whereas the self-rated health of women showed no variation across various categories of marital status. Various authors have indicated that married men, presumably, have a better control over their decisions, usually are the primary decision makers for immigration within the family unit, and the process of migration more often than not has a better economic outcome for men rather than their wives (Bielby and Bielby, 1992; Boyle et al., 1999, 2001; Cooke, 2003). Other authors have indicated that immigrating women, however, are often on the lower end of the labour market in their host country; contrary to men, their economic situation often does not improve and may even deteriorate (Boyd, 1984; De Jong and Madamba, 2001; McKinnish,

2.61 (0.38) 0.32 (0.24) 3551.5

Reference 1.21 (0.73–1.98) 1.23 (0.69–2.19) 2.97 (1.67–5.28) 3.19 (1.64–6.19) 2.67 (0.39) o 0.01 3546.6

2008). Many married women may in fact be ‘feminised’ and end up doing household chores in the new society even if they were a part of the labour force in their home countries (Ho, 2006); all these factors may contribute to the health disadvantage in these women. In general, studies have shown that marriage is more protective in men compared with women (Cleary and Mechanic, 1983; Fuller et al., 2004; Gove, 1984). Furthermore, Markey et al. (2005) also found that married men have more ‘proactive health beliefs’ compared with single men, whereas this was reversed in women. Nonetheless, even after adjusting for individual socio-demographic factors, post-immigration experiences, and other country-level variables, we found that married men who had come in as the principal applicant had the most favourable self-rated health outcomes. Another individual characteristic that had different effects between immigrating men and women was fluency with the province of residence’s main language. While this was definitely advantageous for women, no such effect was seen among men. Knowledge of language may increase the independence (ability to move around, shopping, access governmental services, or socialising with others to name a few activities) of immigrating women, particularly for those who enter as the dependant applicant. Conversely, linguistic barriers may be particularly detrimental to the health of these women who are otherwise dependant on a spouse for contact with the host society. They may underutilise health services, experience problems in understanding health communication, or may be disadvantaged in their occupational settings (Bradshaw et al., 2007; Jang et al., 1998; Pottie, 2007; Premji et al., 2008). As observed in our data, men who were unemployed at the time of data collection were more likely to rate their health as poor; this effect was attenuated in terms of magnitude and not significant among women. Given that men were more likely to be the principal economic class immigrant, the responsibility of finding a job and ensuring a living may have weighted more heavily on them. New immigrants may face difficulties in entering the labour market or may not find jobs matching their educational status in the home countries; this may have deleterious health effects, particularly as it may result in financial hardship (Dean and Wilson, 2009). Interestingly, male immigrants who were more eager to keep values from their home country were less likely to rate their

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health as poor, though this effect was not significant in female immigrants. It has been discussed that it is easy to hold on the ‘home culture’ and identity in this increasingly globalised era with the help of technology; many of them tend to keep significant contacts with their societies of origin (Isaacs, 2010; Li and Teixeira, 2007; Patterson, 2006). This form of identification may be related to health views, medical treatment, or lifestyle factors (such as eating habits) (Barimah and van Teijlingen, 2008; Belliard and Ramirez-Johnson, 2005; Hilton et al., 2001; Isaacs, 2010; Lai and Chappell, 2007; Varghese and Moore-Orr, 2002). Previous studies have indicated that immigrants who were less integrated in the society had better sleeping patterns than those who were more integrated (Voss and Tuin, 2008a, 2008b). Various authors have stated that ethnic identity may be a part of the social capital; it may help immigrants adjust to the new society and deal with the settlement stressors and these traditional norms may be useful for the overall health of immigrants (Abdelhady, 2006; Acharya and Northcott, 2007; Evergeti and Zontini, 2006; Guglani et al., 2000; Isaacs, 2010; Lieber et al., 2001; Noh et al., 1999; Wu et al., 2003). 4.2. Factors operating for both genders While all ethnic minority groups were more likely to rate their health as poor compared with white immigrants, only ethnically Chinese immigrants (both male and female) were significantly more likely to report poor health after adjustment for other sociodemographic characteristics. These findings replicate in longitudinal data those reported in previous cross-sectional Canadian studies (Kobayashi et al., 2008; Wu and Schimmele, 2005). Previous research has suggested that Chinese immigrants may particularly be disadvantaged linguistically compared with other immigrant groups, and this may be especially salient in health care settings and lead to dissatisfaction with these services (Ahmad et al., 2004; Wang et al., 2008). Some of them with cultural beliefs in Chinese identity and medicine are likely to use traditional forms of medicine; furthermore, they may underutilise the health services for non-physical factors. These may contribute in part to poor access to general health care in Canada (Aroian et al., 2005; Lai and Chappell, 2007; Ma, 1999; Tiwari and Wang, 2008; Wang et al., 2008). While some of these factors may be important for other immigrant groups as well, in our analyses only the Chinese immigrants were significantly more likely to rate their health as poor – thus making them an important group for targeted health care intervention immediately after immigration. Beyond ethnic identification, a host of post-settlement experiences, such as social interactions, and discrimination to name a few, may influence the health of newcomers. Men and women who reported facing discrimination were more likely to rate their health as poor – a finding discussed by other authors as well (Beiser, 1988; De Maio and Kemp, 2010; Hyman, 2009; Noh et al., 1999, 2007). Finally, we did not find a significant effect of regular interactions with friends, either in men or women. Given that married couples were numerous in this sample; this finding could be explained by a recent report by Almeida et al. (2009), who found that support by family members was more protective for health outcomes than support by friends. 4.3. Source country Finally, we evaluated the role of the source countries in explaining poor health in the immigrants from them. Whereas the political stability of the home country was not associated with health outcomes in Canada for men or women, coming to Canada from a country with a low human development index (HDI), low

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gross national income (GNI), or high infant mortality rate (IMR) were all significant determinants of poor health among women; only HDI and IMR reached statistical significance for men. Moreover, when these country-level variables were added to our models, the effects noted above for the Chinese ethnic immigrants were removed in many of the models. Thus, it appears not only that immigrants carry with them health disadvantages from the source country, but that women are particularly vulnerable to these influences, at least initially (Boyd, 1984; Ho, 2006; Merali, 2009; Raj et al., 2005). Jasso et al. (2004) had concluded that the variance in health of new immigrants from top 17 source countries to the US is relatively smaller than the variance experienced by the source countries. However, they also found that a higher proportion of immigrants from top five healthiest countries reported better self-rated health.

5. Conclusion Using the longitudinal data available, we were able to identify a number of variables that appear to play a role in the health of immigrants to Canada. We were only able to use a self-related measure of health, and others have raised concerns about the reliability and validity of this kind of measure, particularly when it is applied across different settings (Crossley and Kennedy, 2002; De Maio, 2007; Jurges, 2007). Despite this limitation, however, self-reported health has been fully documented to be a good predictor of health outcomes such as morbidity, mortality, and health control strategies, and may represent a cognitive appreciation of health status that is not dependant on access to health care – a common outcome in reports of chronic conditions (Benyamini and Idler, 1999; DeSalvo et al., 2006; Idler and Benyamini, 1997; Jylha, 2009; Menec et al., 1999). Furthermore, we used a repeated measure of self-rated health in the same individual; these longitudinal trajectories of health are also a good measure of mortality through their influence on the current measure of health (Miller and Wolinsky, 2007). Another potential limitation of our study stems from our grouping of individuals. Though we could classify a number of immigrants according to their ethnic categories, we had to group many immigrants in the ‘other’ ethnic category. This grouping underestimates the heterogeneity within the group; however, small numbers did not allow us to categorise them further. Moreover, as happens in longitudinal studies, there was attrition in the LSIC sample between the first and third waves of data collection, and this could lead to some self-selection bias: individuals who were not satisfied with their lives, who were not doing well economically, or who experienced poor health may have moved out of Canada. Regarding the country of origin measurements, while we excluded immigrants for whom the country of birth and the last country of residence were different to allow for a fair assessment of country-level contextual effects, it is possible that some may have moved out of their country of birth, stayed in some other country and returned back to their country of birth before migrating to Canada. Moreover, the contextual level variables for the countries of origin are likely poor proxies for the actual life condition of the individuals (e.g. an individual migrating from a low GNI per capita country may never have experienced material deprivation). Nevertheless, these measures are reflective of the population at large and the social context of the country of origin, and should be interpreted as such. Finally, we restricted our analyses to the economic and family class immigrants only; refugees in general are a different group socially and economically, and we felt that merely adjusting them in the models may not provide an accurate picture of their health. Nonetheless, it is an important health issue within the larger scope of

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immigrant health (Beiser, 1988; Beiser and Edwards, 1994; Lindert et al., 2009), and LSIC will be useful to explore the health outcomes in refugees in Canada. Furthermore, though we have tried to address various individual, post-immigration experiences, and country-level variables in the present manuscript, neighbourhoods and ethnic density of these neighbourhoods are other important associations of health outcomes in immigrants and need to be studied in this population (Bhugra and Arya, 2005; Veling et al., 2008). Despite these limitations, the study has several strengths and we believe it to be a useful contribution to literature. We studied the association between various factors and self-rated health in Canadian immigrants who had only been in Canada for about five years. Though some studies have highlighted that immigrants who have been in Canada for a longer period of time (usually 10 years or more) are more likely to report poor health, others have noted poor health even in recent immigrants (Dean and Wilson, 2010; Newbold, 2009; Ng et al., 2005). Our longitudinal analysis of three waves of the LSIC allowed us to take into account the between- and within-subject correlation of individuals. We have also grouped immigrants according to their countries of origin and accounted for this in our analysis. As discussed earlier, immigrants in Canada today represent a diverse group of individuals from source countries with varying economic growths, social disadvantages, political situations, and health outcomes. In addition, the ethnic composition of the traditional source countries (such as the UK, US, and France) itself has changed over the past few decades. Thus, we may have an ethnically Chinese individual migrating who has been born and brought up in the UK, the USA, or China; these variations need to be accounted for in the analysis of health outcomes in these ‘newcomers’. This is an issue that has largely been neglected in previous analyses. Further, we have studied the associations of three different types of variables – individual characteristics, post-settlement experiences, and country of origin characteristics – thus providing a global view of factors associated with poor health of immigrants in the first four years of their stay in Canada. Since all the immigrants had arrived in Canada in the same period, we minimised the possibility of a ‘‘cohort effect’’ on the health of these immigrants. In totality, we found that certain individual characteristics emerged as particularly salient for both men and women: ethnically Chinese men and women were most likely to rate their health as poor. In contrast, others were specific to each group: being married and being a principal applicant for men and linguistic capabilities for women were, respectively, associated with better self-rated health. Concerning the post-settlement factors, sex/gender differences were even more evident: being unemployed was significantly associated with poor health in male immigrants, but only moderately for women. Furthermore, male immigrants who had a tendency to retain the values of the home country were more likely to report good health than those who were not very eager to retain them, but this effect was not significant for women. Finally, after adjusting for individual level characteristics, we found that immigrants, and particularly women, who had migrated from underdeveloped countries, were more likely to report poor health at least in the first four years of their stay in Canada. Thus, our analyses indicate that this group is more vulnerable than others for loss of the health advantage – if any – in the new country and should be at the forefront of public health programmes aimed at new immigrants in Canada.

Acknowledgements We would like to thank Dr. Abby Lippman for comments on the article. The study was funded by the Canadian Institutes of

Table A1 Self-rated health

Excellent Very good Good Fair Poor

Wave 1

Wave 2

Wave 3

Males

Females

Males

Females

Males

Females

47.8 34.6 15.3 – –

37.2 37.0 22.2 2.9 0.8

32.2 44.0 20.3 3.0 0.5

26.9 38.2 27.8 6.2 1.3

25.7 38.9 30.6 4.6 0.5

18.9 36.7 33.8 9.2 1.8

NB: The missing values indicate that the data cannot be released due to restrictions by Statistics Canada.

Health Research (MOP 77800 PI AQV). MSS is funded by CIHR-IHSPR Fellowship and CIHR-RRSPQ Public Health Training Programme for his doctoral studies at McGill University. Additional funding was provided by the QICSS Matching Grant 2007.

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