Our results demonstrate large differences in prevalence rates of T2DM within and between migrant groups in Victoria, and a large migrant health gap compared with the Australian-born population. Differences between groups persist even after allowing for effects of socio-economic disadvantage. The proportion with diagnosed T2DM aged under 50 years varies markedly by region of birth.
Over 20% of registrants born in Oceania and in Southern and Central Asia were aged under 50 years when NDSS data were extracted. Disease onset and diagnosis may have occurred much earlier when these subjects were even younger. At diagnosis in Australia, 22.5% of people with T2DM were aged less than 45 years . Our results suggest that this proportion may vary by region of origin. We support recommendations for earlier commencement of diabetes screening, at age 35, for Pacific Islanders, Indians and Chinese .
Even after adjusting for age and SES, prevalence odds of T2DM were higher in all migrant groups compared with the Australian-born group. The reasons underlying this cannot be elucidated from our data. International literature suggests that effects and mediators of the nature/nurture interaction may be contextual – for example, prevalence of diabetes among male Tunisian migrants to France was lower than rates in Tunisia but similar to that of French-born men in France. The apparently protective effect of migration among this group was partly mediated by factors such as physical activity and smoking . Studies of Japanese immigrants and their offspring in the United States have, however, demonstrated the complex interplay between lifestyle factors promoting visceral adiposity and insulin resistance, and unmasking impaired beta-cell function in genetically susceptible individuals . Second generation Japanese Americans had higher prevalence of diabetes than both Japanese in Tokyo and the rest of the USA adult population . It is possible that similar factors are at play for some migrant groups in Victoria. Future research should seek to elucidate any such influences in the local context.
In this study prevalence differed by region of birth and was further influenced by SES level (Figures 1 and 2). In many developing and transitional countries, diabetes prevalence increases with SES whereas the reverse is true in developed nations. For some migrant groups in our study, these diabetes–SES associations are not as clear but it is not possible to infer causation. There are multiple factors at work. Circumstances underpinning relocation may influence migrants’ demographic characteristics (e.g. skilled workers vs. refugees). Age at and time of migration may determine extent of acculturation and attendant lifestyle and behavioural risk exposures, choice of residential area, and socio-economic position. Migrants exhibit much mobility in the first decade after relocation to Australia, and large differences exist between migrants on some socio-economic indicators, such as employment status, based on time of migration . Additionally, numerous factors other than SES may influence where people live. Migrant groups may cluster in particular residential areas, for reasons unrelated to SES.
Comparison with other studies
Our findings for Southern and Eastern European born people agree with higher reported diabetes prevalence among Greeks and Italians compared with Australian-born people . Our higher age-adjusted T2DM prevalence rates for all migrant groups, however, contrasts with recent reports from the Victorian Health Monitor  showing no differences. Some of our results also differ from earlier Australian analyses including reports of higher diabetes prevalence rates among some but not all migrant groups in the 2000 AusDiab study  and recent analyses of the New South Wales  and National Health Surveys [7, 12, 16]. Comparison between studies is difficult as immigrant categories and age ranges differed and there are some differences in inclusion criteria for diabetes while we used clinician-validated data for NDSS registrants with diagnosed T2DM. Rising prevalence of obesity , and changing migratory patterns altering Australia’s demographic landscape  since studies such as AusDiab were conducted, also hinder comparisons.
Our findings confirm a strong area-based social gradient in T2DM prevalence overall [10, 22, 23], but differ from another recent Victorian study . SES was based on that of the residential area at the time of data extraction and not current individual circumstance, which has been reported to be associated with diabetes prevalence in Victoria . Notably, social factors other than those considered in our study may influence disease risk. SES is a multifaceted construct and its various aspects, such as educational attainment, assets and access to healthcare, may well exert differential effects.
Strengths and limitations
In the NDSS, clinician validated diagnosis renders misclassification of presence or type of diabetes less likely than patient self-report. NDSS is a contemporary database, continuously updated with new registrants. Interstate migrations are identified by monitoring the State in which NDSS access occurred. Deceased registrants are removed through annual linkages with the National Death Index or following notification by relatives (pers. comm.). NDSS is considered among the best available national data sources for estimating overall prevalence of diagnosed diabetes , but recent migrants may be under-represented as only Australian or New Zealand citizens and Australian permanent residents qualify for government Medicare Cards, an NDSS eligibility requirement. Temporary NDSS registration may be granted to visitors from countries with reciprocal healthcare agreements with Australia. Additionally, those unaware of the scheme or managed by diet alone may be under-represented .
There are further reasons for possible underestimation of prevalence. NDSS only captures diagnosed cases registered with the scheme. Recent Victorian data indicate that for every three people with diagnosed diabetes, there is one undiagnosed . On the other hand the Victorian population denominator was based on 2006 census data, and may result in overstated prevalence rates. Given the proportion with missing birthplace data, our migrant-specific prevalence rates represent minimum estimates and may further underestimate the true prevalence of diagnosed T2DM in Victoria. It is possible that rate of diagnosis may differ by region of birth, possibly influenced by factors such as health-seeking behaviours and contact with the local healthcare system, and that among those with diagnosed diabetes, the rate of registration with NDSS may also vary.
Our Australian-born reference population includes ATSIs, a subgroup known to have a higher burden [10, 29] and earlier age at diagnosis of T2DM . Analysing them separately was not possible due to uncertainty surrounding the size and demographic characteristics of the Victorian Indigenous population. Census counts indicate that only 0.6% of the total Victorian population identify as Indigenous; including ATSIs in our referent group is therefore unlikely to markedly influence our findings.
This study captures only first-generation immigrants, and it is not possible to infer ethnicity, ancestral background or any other ethnic parameter that may influence diabetes risk. The Australian-born group contains second and later generation Australians, who may retain behavioural and genetic risk profiles of the ancestral ethnicity. An example of that comes from a study of Indians in Singapore reporting higher diabetes prevalence among those born in Singapore to Indian-born parents than in the Indian-born immigrants .
Heterogeneity of regional classifications may also mask differences within groups as some regions comprise culturally, linguistically, religiously and developmentally diverse nations. In Australia, self-assigned ethnicity is not widely collected. As it was not available in the NDSS data set, analysing or interpreting our data in the context of population racial or ethnic composition within each migrant group is not possible.