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Investigating the Dynamics of Migration and Health in Australia: A Longitudinal Study

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Abstract

There have been few longitudinal studies investigating the immigrant health and changes in their health with longer residency in the host country. Additionally, the pathways and mechanisms by which transition of health over time occurs are poorly understood, limiting the ability to implement policies that will result in improved health for all, including immigrants. We assessed differences in health outcomes among foreign-born people from English speaking countries and non-English speaking countries relative to native-born Australians over a 10-year period using a large representative longitudinal dataset. We also explored English language proficiency, socio-economic factors and health behaviour factors as possible mechanisms through which health outcomes change over time post-migration. Conventional multilevel mixed and hybrid regression models were used to evaluate health outcomes in 9558 native-born and 3067 foreign-born people from the Household, Income and Labour Dynamics in Australia survey. There were clear differences in physical health, mental health and self-assessed health between foreign-born subgroups in comparison with native-born Australians. Foreign-born people from English speaking countries typically had a health advantage relative to native-born people, and foreign-born people from non-English speaking countries had a health disadvantage with respect to native-born people for all health outcomes. There was no evidence that these differences changed by duration of residence except for self-assessed health amongst foreign-born people from non-English speaking countries when duration of residence exceeded 20 years. English language proficiency mediated the relationship between duration of residence and health for foreign-born people from non-English speaking countries.

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Acknowledgements

This paper is based on research being conducted as part of the research project “Investigating the dynamics of migration and health in Australia: A Longitudinal study”. It is supported by an Australian Research Council Discovery Grant (DP DP120104604) to the lead author. The paper uses the unit record data from the Household, Income and Labour Dynamics in Australia (HILDA) survey, which is funded by the Commonwealth Department of Families, Housing, Community Services and Indigenous Affairs (FaHCSIA) and is managed by the Melbourne Institute of Applied Economic and Social research at the University of Melbourne. The research findings and views reported in this paper, however, are those of the authors and should not be attributed to either FaHCSIA or the Melbourne Institute. An earlier version of this paper was presented at HILDA Survey 11th Anniversary Conference in 2013 in Melbourne, 27th International Population Conference, organised by International Union for Scientific Study of Population (IUSSP), in Busan, South Korea, in 2013 and the Australian Population Association 16th Biennial Conference in Melbourne in 2012. The authors wish to thank the participants and the discussants for their useful comments that helped improve the manuscript.

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Authors and Affiliations

  1. Centre for Social Impact Swinburne, Faculty of Business and Law, Swinburne University of Technology, PO Box 218, Mail 23, Cnr John and Wakefield Streets, Hawthorn, VIC, 3122, Australia

    Santosh Jatrana

  2. Department of Public Health, School of Medicine and Health Sciences Wellington, University of Otago, PO Box 7343, Wellington, New Zealand

    Ken Richardson

  3. Population Studies Unit, Indian Statistical Institute, Kolkata, India

    Samba Siva Rao Pasupuleti

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  1. Santosh Jatrana
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Appendix (Full Regression Results)

Appendix (Full Regression Results)

Tables 4, 5, 6, 7, 8 and 9.

Table 4 Multilevel linear mixed (model I) and hybrid (Models II, III and IV) regression model results showing estimates and their 95% confidence intervals (CI), with physical health as the outcome variable and country of birth as the main exposure variable
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Table 5 Multilevel linear mixed (model I) and hybrid (Models II, III and IV) regression model results showing estimates and their 95% confidence intervals (CI), with mental health as the outcome variable and country of birth as the main exposure variable
Full size table
Table 6 Multilevel linear mixed (model I) and hybrid (Models II, III and IV) regression model results showing estimates and their 95% confidence intervals (CI), with self-assessed health as the outcome variable and country of birth as the main exposure variable
Full size table
Table 7 Multilevel linear mixed (model I) and hybrid (Models II, III and IV) regression model results showing estimates and their 95% confidence intervals (CI), with physical health as the outcome variable and duration of residence as the main exposure variable
Full size table
Table 8 Multilevel linear mixed (model I) and hybrid (Models II, III and IV) regression model results showing estimates and their 95% confidence intervals (CI), with mental health as the outcome variable and duration of residence as the main exposure variable
Full size table
Table 9 Multilevel linear mixed (model I) and hybrid (Models II, III and IV) regression model results showing estimates and their 95% confidence intervals (CI), with self-assessed health as the outcome variable and duration of residence as the main exposure variable
Full size table

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Jatrana, S., Richardson, K. & Pasupuleti, S.S.R. Investigating the Dynamics of Migration and Health in Australia: A Longitudinal Study. Eur J Population 34, 519–565 (2018). https://doi.org/10.1007/s10680-017-9439-z

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