Research Projects Using Growing Up Data

Health Disparities among Children of Immigrants: Evidence from Growing Up in New Zealand

Publication Date:
Lead Organisation:
University of Auckland
Lead Researcher:
Mehdi Rahimi, Ladan Hashemi, Allen Bartley
Access Type:
Primary Classification:
Secondary Classification:
Health and Wellbeing

This project is a proposed doctoral thesis in Social Work, which aims to assess the health disparities among children of immigrants. Health disparities are preventable differences in the burden of disease, injury, violence, or opportunities to achieve optimal health experienced by socially disadvantaged populations. Success in reducing these inequalities brings positive results for the individual, the economy, and society.


1. To explore disparities in the health status of children of immigrants (physical health and mental and social well-being) and their health care utilisation,

2. To explore a wide range of family and household, neighbourhood and societal, and migration-related factors that might mitigate or increase the risk of experiencing health disparities among children of immigrants.

Data analysis plan:

Data will come from GUiNZ. Children of foreign-born parents will be compared with those of native-born parents based on some health-related variables, including markers of physical health and mental/social well-being. Descriptive statistics will be used to describe the prevalence of each health marker and the health care utilisation by parent(s)’ migration status. Further, the potential role of household, societal, and migration-related factors in the risk of experiencing health inequities will be explored.

Univariate and multivariable logistic regression analyses adjusted for socio-demographic characteristic will be used to explore health disparities and investigate how the parent’s migration status influences the odds of experiencing health adversities beyond socioeconomic influences. In the case of continuous dependent variables, univariate and multiple linear regression analyses will be applied. All analyses will be conducted using R.