Skip to content

Advertisement

  • Research article
  • Open Access
  • Open Peer Review

Ethnic differences in overweight and obesity and the influence of acculturation on immigrant bodyweight: evidence from a national sample of Australian adults

BMC Public HealthBMC series – open, inclusive and trusted201616:932

https://doi.org/10.1186/s12889-016-3608-6

  • Received: 25 February 2016
  • Accepted: 29 August 2016
  • Published:
Open Peer Review reports

Abstract

Background

Despite growing international migration and documented ethnic differences in overweight and obesity in developed countries, no research has described the epidemiology of immigrant overweight and obesity at a national level in Australia, a country where immigrants comprise 28.1 % of the population. The aim of this study was to examine ethnic differences in body mass index (BMI) and overweight/obesity in Australia and the influence of acculturation on bodyweight among Australian immigrants.

Methods

Data from the national Household Income and Labour Dynamics in Australia (HILDA) survey were used to examine mean BMI and odds of overweight/obesity comparing immigrants (n = 2 997) with Australian born (n = 13 047). Among immigrants, acculturation differences were examined by length of residence in Australia and age at migration. Data were modelled in a staged approach using multilevel linear and logistic regression, controlling for demographic and socioeconomic variables.

Results

Relative to Australian born, men from North Africa/Middle East and Oceania regions had significantly higher BMIs, and men from North West Europe, North East Asia and Southern and Central Asia had significantly lower BMIs. Among women, the majority of foreign born groups had significantly lower BMIs compared with Australian born. Male and female immigrants living in Australia for 15 years or more had significantly higher BMIs and increased odds of being overweight/obese respectively, compared with immigrants living in Australia for less than 5 years. Male immigrants arriving as adolescents were twice more likely to be overweight/obese and had significantly higher BMIs than immigrants who arrived as adults. Male and female immigrants who arrived as children (≤11 years) had significantly higher odds of adult overweight/obesity and BMIs.

Conclusions

This study provides evidence of ethnic differences in overweight and obesity in Australia with male immigrants from North Africa/Middle East and Oceania regions being particularly vulnerable. In addition, this study suggests that greater acculturation may negatively impact immigrant bodyweight and recently arrived immigrants as well as those who arrive as children or adolescents may benefit from obesity prevention intervention. Public health policy targeted at and tailored to these immigrant cohorts will assist in the multi-pronged approach required to address the obesity epidemic.

Keywords

  • Obesity
  • BMI
  • Bodyweight
  • Ethnicity
  • Immigrant
  • Minority
  • Acculturation
  • Prevention
  • Inequality
  • Australia

Background

Obesity is a significant global health challenge impacting both developing and developed countries [1]. Worldwide, international migration has increased 41 % from 2000 to 2015, with over 244 million people now living in a country other than where they were born [2]. The prevalence of overweight and obesity is ethnically [36] as well as socioeconomically patterned [710] and understanding the nature of these relationships is important in designing effective obesity prevention policy.

Ethnicity and bodyweight research is dominated by studies from the United States and Europe which demonstrate stark, multi-generational inequalities in overweight and obesity among some ethnic minority groups [3, 4, 1115]. Few studies however have focused on the Asia Pacific region and no published studies have defined ethnic differences in bodyweight in a national sample of Australian adults. Australia has high rates of overweight and obesity (70.3 % of men and 56.2 % of women) [16] and a particularly high immigrant population with 28.1 % of the population born overseas [17] (in contrast for example, to the United States which has 12.5 % born overseas [18]). It is somewhat surprising therefore, that epidemiological studies of ethnic difference in bodyweight in Australia have focused largely on children [1922] and the three known studies that focused on adults [2325] have a number of substantive and methodological limitations. In particular, previous Australian studies have been based on single State surveys, were not inclusive of all ethnic groups, two studies did not address expected gender differences [23, 24] and two studies were limited to older adults [23, 25].

Alongside ethnic differences in bodyweight, a related body of research has examined the influence of acculturation on immigrant bodyweight. Acculturation is defined as a change in cultural patterns arising from exposure to the host country’s lifestyle, environment and culture [26, 27]. Studies primarily from the United States and United Kingdom have consistently shown that upon arrival, immigrants have lower BMI, overweight and obesity relative to their host-country born counterparts [5, 12, 2831] however longer residence has been shown to be associated with higher BMI [28, 30, 32, 33] – often attributed to acculturation [32, 33]. Acculturation can be assessed with scale measures (typically measuring language, use of media in the host country, values, lifestyle, attitudes and ethnic social relations and networks), as well as via temporal measures such as length of residence in the host country and age at arrival [32, 34]. While scale measures more sensitively measure social structural and cultural changes, temporal measures are more readily available and commonly used in population immigrant health research [32, 34]. Length of residence is thought to influence immigrant overweight and obesity through behavioural change such as adoption of unhealthy dietary habits [35]; contextual effects, such as ethnic social network [5] and neighbourhood effects [36]; and a range of individual differences - in household income, English proficiency, acculturative stress, experiences of discrimination [37] and education, gender and racial/ethnic group [28]. Age at arrival may influence adult obesity risk due to the different adaptive capabilities of children vs adults [38], English language proficiency [39], wage earning potential [39], and the level of acculturation to behaviours such as physical activity, diet and smoking [40]. While this topic has received some attention in the Australian context [24, 25, 35], there are no known studies to date, which have examined at a national level, whether acculturation and obesity relationships hold true in Australia’s unique immigration history and immigration policy environment.

The aim of this present study therefore, is to present for the first time, national-level findings on the gender-specific ethnic differences in BMI and overweight/obesity in Australian adults and the influence of acculturation on bodyweight among immigrants to Australia.

Methods

Study design and sample

This paper uses Wave 11 (2011) data from the Household Income and Labour Dynamics in Australia (HILDA) survey. HILDA is a national, household-based longitudinal survey about life in Australia that includes a range of ethnicity and migration related variables and information on economic, social and demographic characteristics. The HILDA methodology is described in detail elsewhere [41]. Briefly, the scope and coverage of the survey are Australian households (and usual residents) in private dwellings, excluding very remote and sparsely populated areas [41]. The panel in wave 1 of the survey consisted of 7 682 responding households and 19 914 individuals. The sample was topped up in wave 11 with an additional 2 153 responding households. The selection method for the top-up sample was similar to the original sample methodology [42]. The survey research team have examined the issue of cross-sectional representativeness and found that combining the main sample with the top up sample served to improve the quality of the cross-sectional estimates (compared to just using the main sample), particularly for estimates of country of birth and year of arrival [43]. The survey researchers also found that the combined sample resulted in estimates which more closely reflected data benchmarked from the Australian Bureau of Statistics Labour Force Survey [43]. The majority of wave 11 interviews were conducted during the period August to November, 2011. Data were collected using personal interviews with each member of the household aged ≥15 years, followed by a self-completion questionnaire which included questions on lifestyle and health habits. In 2011, 10 440 households were included in the study with 64.9 % of these being fully responding households: this resulted in a sample of 17 612 responding individuals.

Measures

Anthropometric measurements

Two common indicators of population weight, mean BMI and prevalence of overweight/obesity were used in this study. Weight and height were self-reported and BMI was calculated as weight in kilograms divided by the square of height in metres and outliers removed [44]. The dichotomous variable for overweight/obesity (or not) was derived as per WHO cut offs (BMI >25 kg m−2) [45]. Overweight/obesity as a combined category is clinically relevant due to the established health consequences of exceeding a body mass index of 25 kg m−2 (see Ng et al. [1] for an overview). It is also a policy-relevant categorisation reflecting international obesity reduction targets and indicators [45].

Ethnicity

The ethnicity variable used in this study was Country of birth (sometimes referred to as Nativity), categorised into regions using the Standard Australian Classification of Countries (based on geographic proximity and similarities in economic, social and political characteristics) [46].

Acculturation

There were two acculturation variables. Length of residence in Australia was calculated by subtracting the year the person first came to Australia to live from the year of the survey; and Age at arrival, calculated by subtracting the year of birth from the year the person first came to Australia to live. Consistent with previous research [33, 40], both variables were transformed into categorical variables for the analysis (see Table 1 for definitions).
Table 1

Socio-demographic and bodyweight characteristics of men and women: ethnicity and bodyweight analytic sample (n = 13 047)

 

Men

(n = 6 216, 47.6 %)

27.1 (5.0) Mean BMI (SD)

65.2%Owt/Obese

Women

(n = 6 831, 52.4 %) 26.6 (6.3) Mean BMI (SD)

52.8%Owt/Obese

 

%

Mean BMI (SD)

%Owt/Obese

%

Mean BMI (SD)

%Owt/Obese

Country of birth

 Australian born

76.5

27.1 (4.9)

65.3

77.4

26.8 (6.4)

54.2

 Oceania (excluding Australia)

3.3

28.3 (5.5)

71.6

2.6

27.0 (5.7)

58.5

 North-West Europe

9.5

26.9 (4.4)

67.3

8.4

26.6 (5.9)

53.1

 Southern & Eastern Europe

2.6

27.7 (4.3)

74.7

2.5

27.5 (5.7)

64.5

 North Africa & The Middle East

1.0

28.8 (8.5)

68.8

0.7

28.0 (6.2)

62.2

 South-East Asia

1.7

26.3 (5.6)

53.3

2.7

24.7 (6.2)

35.3

 North-East Asia

1.1

25.2 (5.7)

39.4

1.8

21.5 (3.0)

11.6

 Southern & Central Asia

1.9

25.7 (6.1)

51.7

1.6

25.0 (6.4)

42.6

 Americas

1.1

28.2 (5.5)

71.4

1.4

25.3 (6.3)

43.3

 Sub-Saharan Africa

1.2

26.0 (3.5)

58.9

1.0

24.5 (5.1)

35.7

Age

 18–24 years

13.2

24.8 (4.7)

40.3

12.4

23.7 (5.2)

26.7

 25–34 years

14.4

26.4 (4.5)

58.8

13.8

25.4 (6.3)

41.8

 35–44 years

16.8

27.6 (5.1)

68.3

17.7

27.2 (6.6)

55.1

 45–54 years

20.3

27.8 (4.9)

73.3

19.7

27.2 (6.5)

56.1

 55–64 years

16.6

28.3 (5.2)

74.2

17.3

28.0 (6.3)

63.5

 65–74 years

11.4

27.5 (4.8)

72.3

11.0

27.8 (6.0)

64.4

  > 75 years

7.2

26.2 (4.4)

61.3

8.1

26.1 (5.5)

54.7

Remoteness

 Major City

63.4

26.9 (4.9)

63.3

63.8

26.3 (6.2)

50.0

 Inner Regional Australia

24.1

27.3 (5.0)

67.4

24.7

27.1 (6.2)

57.3

 Outer Regional Australia

10.9

27.6 (5.3)

69.4

9.9

27.6 (7.0)

58.6

 Remote and Very Remote Australia

1.6

28.5 (4.6)

78.6

1.6

27.5 (6.4)

57.8

Highest attained education level

 Bachelor +

23.5

26.7 (4.6)

63.5

26.1

25.6 (5.6)

45.4

 Diploma

9.4

27.3 (4.5)

68.0

9.5

26.4 (6.4)

51.2

 Certificate (trade/business)

28.6

27.7 (4.9)

71.2

16.2

27.3 (6.5)

57.7

 School - Year 12 and below

38.4

26.9 (5.3)

61.0

48.2

27.0 (6.5)

55.4

Occupation

 Managers and professionals

27.7

27.1 (4.3)

68.3

23

26.0 (5.6)

47.1

 White Collar

13.6

27.0 (5.2)

60.7

30.8

26.3 (6.3)

50.2

 Blue Collar

29.8

27.2 (4.9)

65.1

6.9

26.8 (6.8)

52.1

 Unemp/Not in Labour Force

28.8

27.0 (5.4)

64.4

39.3

27.2 (6.6)

58.2

Household Income

  > $130,000 k per annum

19.4

27.0 (4.6)

64.4

17.2

25.5 (5.6)

44.5

 $72,800–$129,999

35.6

27.2 (4.8)

67.5

32.9

26.4 (5.9)

50.9

 $52,000–$72,799

17.1

26.8 (4.9)

63.2

16.0

27.2 (6.6)

55.8

 $41,600–$51,999

8.2

27.2 (5.1)

65.4

8.0

27.3 (7.1)

53.4

 $26,000–$41,599

11.9

27.0 (5.6)

63.4

13.4

27.3 (6.5)

58.7

 $0–$25,999

7.8

27.0 (5.6)

63.4

12.5

27.0 (6.7)

58.1

Neighbourhood Disadvantage

 Quintile 5 (least disadvantage)

22.0

26.4 (4.0)

61.4

21.2

25.4 (5.6)

44.3

 Quintile 4

22.1

26.8 (4.7)

63.8

22.2

26.3 (5.8)

51.4

 Quintile 3

19.5

27.5 (5.3)

68.2

20.1

26.7 (6.3)

54.2

 Quintile 2

18.9

27.5 (5.2)

68.3

19.3

27.0 (6.4)

54.9

 Quintile 1 (most disadvantage)

17.5

27.4 (5.7)

64.9

17.2

28.0 (7.2)

60.8

Countries of birth of respondents comprising >5 % of region sample

Oceania: New Zealand, Fiji, Papua New Guinea

North-West Europe: United Kingdom, Netherlands, Germany

Southern & Eastern Europe: Italy, Poland, Croatia, Federal Republic of Yugoslavia, Romania, Former Yugoslav Republic of Macedonia

North Africa & Middle East: Lebanon, Egypt, Turkey, Iraq, Iran

South-East Asia: Philippines, Vietnam, Malaysia, Indonesia

North-East Asia: China, Hong Kong, Japan, Taiwan

Southern & Central Asia: India, Sri Lanka, Nepal, Bangladesh, Pakistan

Americas: USA, Canada, Chile, Colombia

Sub-Saharan Africa: South Africa, Mauritius, Zimbabwe

Abbreviations: BMI body mass index, Owt overweight, SD standard deviation

Controls

Six demographic and socioeconomic variables were included in the models as controls to address potential sources of confounding as identified in the literature [5, 7, 9, 28]. Variables were categorised as shown in Table 1 and included age (date of birth), highest educational qualification, occupation and annual household income, with data collected through interviewer-administered questionnaires [41]. Neighbourhood disadvantage was derived by the data provider from a ranking based on the Australian Bureau of Statistics’ methods of compiling a range of indicators of socio-economic disadvantage into a single ‘SEIFA’ (Socio-Economic Indexes for Areas) index [47]. For this study the SEIFA 2011 Decile of Index of Relative Socio-economic Disadvantage (IRSD) was used to calculate quintiles of neighbourhood disadvantage. Area remoteness was derived by the data-provider and based on the Australian Standard Geographical Classification (ASGC) [48]. Remoteness was included as a control variable as it is an element of disadvantage that has been linked to obesity in Australia [49, 50].

Analysis

The analysis comprised two stages: the first examined the relationship between ethnicity (country of birth) and bodyweight; and the second stage examined acculturation (length of stay and age at arrival) and bodyweight.

Ethnicity and bodyweight

Those who were aged < 18 years or were pregnant in the last year were removed from the sample (n = 1 751) as out of scope for this study. Those who had no self-completed questionnaire, had missing or implausible BMI data or missing data for the predictor variables (n = 2 814) were also excluded from the analysis: of these, predictors for non-inclusion were younger age (18–24 and 25–34 years) (p < 0.001) and those born in Oceania (p = 0.041), Southern & Eastern Europe (p < 0.001), North Africa & Middle East (p < 0.001) and South East Asia (p = 0.001). The final analytic sample included 13 047 adults (6 216 men and 6 831 women). We examined the relationship between ethnicity and bodyweight using a staged modelling approach and stratifying by gender. The staged approach included a base model (model 1) with only country of birth and subsequently adjusted for age (model 2) and adjusted for socioeconomic variables and area remoteness (model 3). The reference group was Australian born. Multilevel linear and logistic regression techniques were selected due to the multi-level structure of the data and to account for clustering at the individual, household, neighbourhood and area levels. Regression analyses were used to examine associations between the outcome variables (BMI, overweight/obesity) and the predictor variables. We tested for an interaction between ethnicity and sex on BMI. The parameters for the multilevel logistic models were estimated using Markov Chain Monte Carlo (MCMC) simulation [51]. Results are presented as odds ratios and their 95 % credible intervals (CrI). All models were run with sufficient iterations to meet the minimum estimation requirements. The statistical analyses were performed using STATA 12 and MLwiN [52].

Acculturation and bodyweight

For the second stage of the analysis, we took the analytic sample from the first stage and excluded those born in Australia, resulting in a sample of 2 997 foreign-born adults (1 457 men and 1 540 women). The modelling approach was the same as previous, with the addition of a further model (model 4) adjusting for country of birth. The hypothesised least acculturated group was used as the reference category: length of residence < 5 years, and age at arrival  25 years. Linear and logistic regression analyses were used to model BMI and odds overweight/obesity as per previous.

Results

Ethnicity and bodyweight

Table 1 describes the summary characteristics of the analytic sample (n = 13 047). The majority of the sample were Australian born (76.5 and 77.4 % for men and women), which is broadly reflective of their proportion in the Australian population. The majority were middle aged (35–64 years) and lived in either major cities or inner regional centres. Nearly half of the women (48.2 %) had low educational attainment. The highest proportions of overweight and obesity were seen in men and women born in Southern and Eastern Europe (74.7 and 64.5 % respectively). Male and female immigrants from North Africa/Middle East had the highest mean BMIs of 28.8 kg m−2 (SD 8.5) and 28.0 kg m−2 (SD 6.2) respectively. Significant interaction effects for gender and ethnicity on BMI (p < 0.001), and on percent overweight/obese (p = 0.004) were found, therefore analyses were stratified by gender.

Among men, after adjustment for age, area remoteness, education, occupation, household income and neighbourhood disadvantage, BMI was significantly higher for immigrants from North Africa/Middle East (β = 1.42, 95 % confidence interval (CI) = 0.19, 2.64) and Oceania (β = 0.84, CI = 0.16, 1.52), compared with Australian born (Table 2). Men from North West Europe (β = −0.47, CI = −0.89, −0.05), North East Asia (β = −1.48, CI = −2.63, −0.34) and Southern and Central Asia (β = −1.24, CI = −2.17, −0.32) had significantly lower BMIs relative to Australian born. In the fully adjusted models, the odds of being overweight/obese were significantly less among Asian ethnic groups.
Table 2

Country of birth by BMI and Odds Overweight/Obesity, men and women

 

BMI

Odds Overweight/Obesity

 

Model 1a

Model 2b

Model 3c

Model 1a

Model 2b

Model 3c

 

Coeff

95 % CI

Coeff

95 % CI

Coeff

95 % CI

OR

95 % CrI

OR

95 % CrI

OR

95 % CrI

Men

 Australian born

Reference

Reference

Reference

Reference

Reference

Reference

 Oceania (excluding Australia)

1.31

(0.62, 2.01)

0.89

(0.21, 1.58)

0.84

(0.16, 1.52)

1.35

(1.00, 1.87)

1.14

(0.83, 1.59)

1.12

(0.81, 1.57)

 North-West Europe

−0.10

(−0.53, 0.32)

−0.52

(−0.94, −0.10)

−0.47

(−0.89, −0.05)

1.10

(0.92, 1.33)

0.87

(0.72, 0.88)

0.88

(0.73, 1.08)

 Southern & Eastern Europe

0.67

(−0.10, 1.45)

0.23

(−0.53, 1.00)

0.24

(−0.52, 1.01)

1.59

(1.11, 2.31)

1.29

(0.89, 1.91)

1.33

(0.91, 1.97)

 N. Africa & The Middle East

1.69

(0.43, 2.94)

1.25

(0.03, 2.47)

1.42

(0.19, 2.64)

1.20

(0.70, 2.11)

0.99

(0.56, 1.81)

1.12

(0.63, 2.08)

 South-East Asia

−0.73

(−1.70, 0.23)

−1.03

(−1.97, −0.09)

−0.90

(−1.84, 0.05)

0.61

(0.41, 0.90)

0.53

(0.35, 0.79)

0.57

(0.37, 0.86)

 North-East Asia

−1.77

(−2.94, −0.59)

−1.75

(−2.89, −0.60)

−1.48

(−2.63, −0.34)

0.35

(0.21, 0.57)

0.33

(0.20, 0.55)

0.36

(0.21, 0.61)

 Southern & Central Asia

−1.25

(−2.19, −0.32)

−1.44

(−2.36, −0.52)

−1.24

(−2.17, −0.32)

0.58

(0.39, 0.84)

0.53

(0.36, 0.78)

0.59

(0.39, 0.88)

 Americas

1.18

(0.02, 2.35)

0.69

(−0.45, 1.83)

0.97

(−0.16, 2.11)

1.37

(0.81, 2.37)

1.11

(0.65, 1.97)

1.23

(0.72, 2.16)

 Sub-Saharan Africa

−0.94

(−2.09, 0.21)

−0.95

(−2.07, 0.18)

−0.77

(−1.89, 0.35)

0.78

(0.48, 1.28)

0.78

(0.47, 1.30)

0.81

(0.49, 1.37)

Women

 Australian born

Reference

Reference

Reference

Reference

Reference

Reference

 Oceania (excluding Australia)

0.27

(−0.67, 1.22)

−0.19

(−1.11, 0.73)

−0.22

(−1.13, 0.70)

1.22

(0.90, 1.68)

1.05

(0.77, 1.45)

1.05

(0.76, 1.45)

 North-West Europe

−0.25

(−0.79, 0.28)

−0.91

(−1.44, −0.38)

−0.80

(−1.33, −0.28)

0.95

(0.79, 1.13)

0.72

(0.60, 0.87)

0.75

(0.62, 0.90)

 Southern & Eastern Europe

0.69

(−0.26, 1.64)

−0.07

(−1.00, 0.86)

−0.12

(−1.05, 0.80)

1.59

(1.15, 2.21)

1.21

(0.87, 1.70)

1.20

(0.87, 1.69)

 N. Africa & The Middle East

1.47

(−0.37, 3.31)

0.89

(−0.91, 2.69)

0.73

(−1.06, 2.52)

1.52

(0.81, 2.92)

1.36

(0.72, 2.60)

1.31

(0.69, 2.55)

 South-East Asia

−1.96

(−2.89, −1.04)

−2.35

(−3.27, −1.45)

−2.34

(−3.25, −1.43)

0.45

(0.33, 1.60)

0.41

(0.29, 0.56)

0.41

(0.30, 0.57)

 North-East Asia

−4.92

(−6.08, −3.76)

−4.99

(−6.13, −3.85)

−4.93

(−6.06, −3.79)

0.11

(0.06, 0.19)

0.11

(0.06, 0.18)

0.11

(0.06, 0.19)

 Southern & Central Asia

−1.36

(−2.57, −0.15)

−1.54

(−2.73, −0.36)

−1.60

(−2.77, −0.42)

0.67

(0.44, 1.01)

0.63

(0.41, 0.96)

0.64

(0.42, 0.97)

 Americas

−1.27

(−2.52, −0.02)

−1.81

(−3.03, −0.59)

−1.67

(−2.88, −0.45)

0.73

(0.48, 1.12)

0.62

(0.41, 0.95)

0.66

(0.43, 1.00)

 Sub-Saharan Africa

−2.34

(−3.82, −0.86)

−2.54

(−3.99, −1.09)

−2.37

(−3.81, −0.93)

0.45

(0.27, 0.75)

0.42

(0.24, 0.71)

0.44

(0.26, 0.74)

Abbreviations: BMI body mass index, CI confidence interval, OR odds ratio, CrI credible interval

aBase model, no adjustment. bAdjusted for age. cAdjusted for age, area remoteness, education, occupation, household income, neighbourhood disadvantage. Bold p < 0.05

Among women, six of the nine ethnic immigrant groups had significantly lower BMIs compared with the Australian-born reference group. The results for odds overweight/obesity showed similar patterns. Immigrants from North East Asia had the largest (lower) BMI difference compared with Australian-born (β = −4.93, CI = −6.06, −3.79).

Acculturation and bodyweight

Table 3 describes the summary characteristics of the foreign born sample (n = 2 997), which includes small proportions in the youngest age category (18–24 years) and the majority living in major cities. Over 40 % of women were in the lowest category for individual measures of socioeconomic position (education and occupation). Over 75 % of the foreign born sample had lived in Australia for >15 years and the majority arrived as adults (25 years). Those who had resided in Australia for 15 or more years, had the highest BMIs in both males and females (27.3 (5.1) and 26.4 (5.8) respectively). Men who arrived during adolescence had a relatively high BMI of 28.0 (6.2) and both men and women who arrived as children (0–11 years), also had high BMIs compared with the other categories. The %overweight/obese descriptive results for the acculturation variables showed the same patterns as for mean BMI.
Table 3

Socio-demographic and bodyweight characteristics of men and women in the acculturation and bodyweight sample (n = 2 997)

 

Men (FB Only)

(n = 1 457, 48.6 %)

27.1(5.2) mean BMI(SD)

64.9%Owt/Obese

Women (FB Only)

(n = 1 540, 51.4 %)

25.9 (6.0) mean BMI(SD)

47.8%Owt/Obese

%

Mean BMI (SD)

%Owt/ Obese

%

Mean BMI (SD)

%Owt/ Obese

Length of residence in Australia

  < 5 years

6.7

25.3 (4.1)

49.0

9.1

23.2 (4.2)

25.7

 5–9 years

6.2

25.8 (4.6)

57.1

5.8

24.9 (9.0)

31.5

 10–14 years

7.8

26.7 (6.2)

56.6

9.2

24.7 (5.2)

41.1

  ≥ 15 years

79.3

27.3 (5.1)

67.6

76.0

26.4 (5.8)

52.4

Age at Arrival

  ≥ 25 years (arrived as adult)

44.9

26.7 (5.1)

62.6

45.1

25.5 (5.8)

46.2

 18–24 years (arrived as young adult)

18.8

26.7 (4.6)

62.6

19.7

25.2 (5.1)

44.2

 12–17 years (arrived as an adolescent)

8.0

28.0 (6.2)

70.1

9.0

26.2 (5.5)

50.0

 0–11 years (arrived as young child)

28.3

27.5 (5.3)

68.5

26.2

27.0 (6.8)

52.5

Age

 18–24 years

5.4

24.9 (4.2)

40.5

4.7

22.4 (4.1)

23.3

 25–34 years

11.9

25.9 (4.6)

50.0

12.3

23.9 (5.7)

28.6

 35–44 years

15.0

27.2 (4.2)

65.3

16.9

25.6 (5.9)

43.8

 45–54 years

22.5

27.3 (5.0)

68.9

21.8

25.7 (5.8)

46.7

 55–64 years

20.6

28.0 (5.5)

73.7

21.9

27.0 (6.7)

54.3

 65–74 years

15.5

27.5 (5.6)

69.0

13.8

27.2 (5.4)

60.6

  > 75 years

9.0

26.2 (4.6)

61.1

8.6

26.7 (4.9)

62.1

Remoteness

 Major City

76.9

27.0 (5.1)

64.5

77.9

25.8 (6.0)

46.7

 Inner Regional Australia

15.8

27.3 (5.7)

64.1

13.9

26.1 (5.2)

51.4

 Outer Regional Australia

5.9

27.2 (4.9)

68.6

6.8

27.0 (6.7)

52.4

 Remote and Very Remote Australia

1.3

27.9 (4.0)

78.9

1.4

24.9 (5.1)

48.6

Highest attained education level

 Bachelor +

32.3

26.7 (5.3)

61.1

33.6

24.6 (5.1)

38.8

 Diploma

11.0

26.7 (4.8)

60.6

10.7

25.1 (6.2)

41.2

 Certificate (trade/business)

24.9

27.3 (4.4)

70.8

13.4

26.7 (6.4)

53.4

 School - Year 12 and below

31.8

27.4 (5.6)

65.5

42.3

26.9 (6.2)

54.8

Occupation

 Managers and professionals

29.2

26.8 (4.7)

65.0

22.5

25.0 (4.9)

40.9

 White Collar

12.8

26.9 (5.5)

61.5

23.6

25.4 (6.1)

41.8

 Blue Collar

25.0

27.3 (4.8)

65.1

7.3

26.0 (6.5)

42.9

 Unemp/Not in Labour Force

32.9

27.2 (5.7)

65.8

46.6

26.5 (6.2)

54.9

Household Income

  > $130,000 k per annum

18.3

26.8 (4.4)

61.3

15.7

24.7 (5.4)

38.4

 $72,800–$129,999

35.4

27.4 (5.2)

68.6

33.1

25.6 (5.5)

46.9

 $52,000–$72,799

16.3

26.2 (4.5)

58.6

15.9

25.8 (6.3)

43.3

 $41,600–$51,999

7.6

27.1 (5.7)

63.1

7.5

25.9 (5.4)

44.8

 $26,000–$41,599

14.1

27.6 (6.1)

68.0

14.3

26.8 (6.7)

55.4

 $0–$25,999

8.3

27.0 (5.4)

65.3

13.4

27.0 (6.4)

59.9

Neighbourhood Disadvantage

 Quintile 5 (least disadvantage)

24.8

26.5 (3.7)

62.7

23.6

24.3 (4.7)

36.1

 Quintile 4

20.4

26.7 (4.8)

63.8

21.3

25.8 (5.4)

48.2

 Quintile 3

16.7

28.0 (6.3)

68.7

18.1

26.1 (6.5)

48.2

 Quintile 2

20.0

27.5 (5.7)

70.8

19.5

25.9 (5.9)

48.2

 Quintile 1 (most disadvantage)

18.0

26.8 (5.4)

58.9

17.5

27.8 (7.0)

62.2

Abbreviations: BMI body mass index, FB foreign born, Owt overweight, SD standard deviation

Length of residence in Australia: After adjustment for age, area remoteness, education, occupation, household income, neighbourhood disadvantage and nativity, male immigrants who had lived in Australia for ≥ 15 years had significantly higher BMIs (β = 1.27, CI = 0.10, 2.44) compared with their counterparts residing in Australia less than 5 years (Table 4). Among female immigrants, as length of residence increased, so too did odds of overweight/obesity, however the relationship reached significance only for those living in Australia for ≥ 15 years (odds ratio (OR) = 1.59, 95 % credible interval (CrI) = 1.04, 2.46). Odds ratios were not significant for men and BMI results were not significant for women.
Table 4

Length of residence in Australia by BMI and Odds Overweight/Obesity, men and women

 

BMI

Odds Overweight/Obesity

 

Model 1a

Model 2b

Model 3c

Model 4d

Model 1a

Model 2b

Model 3c

Model 4d

 

Coeff

95 % CI

Coeff

95 % CI

Coeff

95 % CI

Coeff

95 % CI

OR

95 % CrI

OR

95 % CrI

OR

95 % CrI

OR

95 % CrI

Men (FB only)

  < 5 years

Reference

Reference

Reference

Reference

Reference

Reference

Reference

Reference

 5–9 years

0.63

(−0.84, 2.10)

0.63

(−0.82, 2.08)

0.71

(−0.73, 2.16)

0.61

(−0.82, 2.05)

1.43

(0.87, 2.37)

1.41

(0.84, 2.34)

1.56

(0.90, 2.73)

1.56

(0.91, 2.70)

 10–14 years

1.47

(0.05, 2.88)

1.18

(−0.25, 2.61)

1.31

(−0.11, 2.74)

0.95

(−0.47, 2.37)

1.37

(0.85, 2.22)

1.06

(0.65, 1.73)

1.15

(0.67, 2.0)

1.03

(0.61, 1.78)

  ≥ 15 years

2.05

(0.96, 3.14)

1.45

(0.28, 2.61)

1.54

(0.37, 2.71)

1.27

(0.10, 2.44)

2.20

(1.53, 3.16)

1.43

(0.95, 2.16)

1.54

(0.98, 2.44)

1.33

(0.86, 2.08)

Women (FB only)

  < 5 years

Reference

Reference

Reference

Reference

Reference

Reference

Reference

Reference

 5–9 years

1.53

(−0.01, 3.07)

0.97

(−0.57, 2.52)

0.86

(−0.67, 2.40)

1.13

(−0.38, 2.64)

1.28

(0.75, 2.15)

1.09

(0.64, 1.83)

1.06

(0.61, 1.82)

1.17

(0.66, 2.06)

 10–14 years

1.24

(−0.14, 2.64)

0.56

(−0.85, 1.97)

0.34

(−1.06, 1.73)

0.19

(−1.17, 1.56)

1.97

(1.24, 3.11)

1.54

(0.96, 2.42)

1.47

(0.91, 2.37)

1.43

(0.87, 0.42)

  ≥ 15 years

2.86

(1.81, 3.91)

1.32

(0.13, 2.50)

1.13

(−0.06, 2.31)

0.77

(−0.40, 1.93)

3.17

(2.19, 4.60)

1.79

(1.21, 2.61)

1.75

(1.17, 2.63)

1.59

(1.04, 2.46)

Abbreviations: BMI body mass index, Coeff coefficient, CI confidence interval, CrI credible interval, FB foreign born, OR odds ratio

aBase model, no adjustment. bAdjusted for age. cAdjusted for age, area remoteness, education, occupation, household income, neighbourhood disadvantage. dAdjusted for age, area remoteness, education, occupation, household income, neighbourhood disadvantage, country of birth. Bold p < 0.05

Age at arrival

After full adjustment, male immigrants who arrived as a young child or an adolescent had significantly higher BMIs (β = 1.27, CI = 0.59, 1.95 and β = 2.01, CI = 1.03, 3.06 respectively) and odds overweight/obesity (OR = 1.65, CrI = 1.26, 2.16 and OR = 2.09, CrI = 1.38, 3.18 respectively) compared with immigrants who arrived as adults (Table 5). Among women, immigrants who arrived as a young child also had significantly higher BMIs (β = 1.67, CI = 0.92, 2.42) and odds overweight/obesity (OR = 1.45, CrI = 1.12, 1.86) compared with those who arrived as adults.
Table 5

Age at arrival in Australia by BMI and Odds Overweight/Obesity, men and women

 

BMI

Odds Overweight/Obesity

 

Model 1a

Model 2b

Model 3c

Model 4d

Model 1a

Model 2b

Model 3c

Model 4d

 

Coeff

95 % CI

Coeff

95 % CI

Coeff

95 % CI

Coeff

95 % CI

OR

95 % CrI

OR

95 % CrI

OR

95 % CrI

OR

95 % CrI

Men (FB only)

  ≥ 25 years (arr. as adult)

Reference

Reference

Reference

Reference

Reference

Reference

Reference

Reference

 18–24 years (arr. as young adult)

−0.08

(−0.81, 0.64)

0.23

(−0.49, 0.95)

0.07

(−0.66, 0.79)

−0.08

(−0.80, 0.64)

1.01

(0.78, 1.30)

1.23

(0.90, 1.71)

1.11

(0.84, 1.47)

1.07

(0.81, 1.42)

 12–17 years (arr. as adolescent)

1.20

(0.20, 2.21)

1.86

(0.85, 2.88)

1.92

(0.91, 2.94)

2.01

(1.03, 3.06)

1.42

(0.98, 2.08)

2.31

(1.43, 3.88)

2.09

(1.38, 3.18)

2.09

(1.38, 3.18)

 0–11 years (arr. as young child)

0.63

(0.01, 1.26)

1.23

(0.58, 1.89)

1.19

(0.53, 1.86)

1.27

(0.59, 1.95)

1.28

(1.02, 1.62)

1.92

(1.42, 2.66)

1.78

(1.37, 2.33)

1.65

(1.26, 2.16)

Women (FB only)

  ≥ 25 years (arr. as adult)

Reference

Reference

Reference

Reference

Reference

Reference

Reference

Reference

 18–24 years (arr. as young adult)

−0.38

(−1.17, 0.40)

0.36

(−0.42, 1.14)

0.42

(−0.36, 1.20)

0.17

(−0.61, 0.94)

0.90

(0.71, 1.15)

1.15

(0.90, 1.48)

1.18

(0.91, 1.53)

1.05

(0.81, 1.37)

 12–17 years (arr. as adolescent)

0.29

(−0.78, 1.35)

0.57

(−0.47, 1.61)

0.53

(−0.50, 1.56)

0.30

(−0.73, 1.32)

1.08

(0.78, 1.50)

1.22

(0.88, 1.72)

1.22

(0.86, 1.72)

1.08

(0.76, 1.52)

 0–11 years (arr. as young child)

1.25

(0.53, 1.96)

2.17

(1.45, 2.90)

2.16

(1.44, 2.89)

1.67

(0.92, 2.42)

1.24

(0.99, 1.55)

1.73

(1.38, 2.19)

1.77

(1.39, 2.27)

1.45

(1.12, 1.86)

Abbreviations: Arr arrived, BMI body mass index, Coeff coefficient, CI confidence interval, CrI credible interval, FB foreign born, OR odds ratio

aBase model, no adjustment. bAdjusted for age. cAdjusted for age, area remoteness, education, occupation, household income, neighbourhood disadvantage. dAdjusted for age, area remoteness, education, occupation, household income, neighbourhood disadvantage, country of birth. Bold p < 0.05

Discussion

This study revealed gender-specific ethnic differences in bodyweight in a national sample of Australian adults. Adjustment for socioeconomic factors had minimal and variable impact on regression coefficients and odds ratios, suggesting that these constructs do not explain ethnic differences in bodyweight in Australia. Two ethnic groups had significantly higher BMI compared with Australian born - male immigrants born in North Africa/Middle East, and Oceania. This contrasts to results from single-State Australian studies, which identified immigrants born in Southern European countries as having significantly higher BMIs compared with Australian born after full adjustment [23, 24]. Published results from these earlier studies were not stratified by gender and comparisons are difficult due to methodological constraints (as described in the introduction). There is a paucity of international studies focused on immigrants from Oceania and North Africa/Middle Eastern regions. International prevalence data has shown in excess of 50 % obesity rates in countries of these regions [1], however it cannot be assumed that nationals living in their own countries have the same characteristics as those who immigrate, underscoring the importance of further research on ethnic differences in bodyweight among existing and emerging immigrant cohorts.

Findings from this study of lower BMI among male and female Asian immigrants compared with Australian born are generally consistent with State-based research [23, 24]. It remains important however, to include Asian immigrants in obesity monitoring and prevention efforts, as using Asian BMI cut-offs for overweight/obesity has revealed higher levels of health risks, [25] and generational studies from multiple countries have shown a rapid upward assimilation of Asian immigrants’ BMI to the host country’s BMI over the course of one generation [14, 24, 53].

The acculturation results demonstrated that Australian immigrants are no exception to international evidence of immigrants having lower BMI on arrival and increasing BMI with longer durations of residence. A number of contributing factors have been postulated to explain these phenomena. These include, strict immigrant health entry requirements [54]; protective biological, behavioural and sociocultural factors [54, 55]; and immigrant self-selection, that is, only those who are healthy, educated and have the financial means to migrate, do so [56]. Most studies from developed countries found that women are particularly susceptible to increasing BMI with longer duration of residence [28, 29, 57, 58], although others have found the opposite [28]. The modelling in this study showed that the relationship between duration of residence and BMI was significant only for men living in Australia for ≥ 15 years and that among women, socioeconomic factors and nativity explained the increased BMI with increased length of residence. The odds of overweight/obesity remained significantly higher after full adjustment among female immigrants residing in Australia ≥ 15 years, which suggests that conclusions on gender acculturation differences may vary depending on the measures used to assess adiposity.

Age at arrival results from this study supported the length of residence results which in turn is consistent with findings that arrival < 20 years of age (compared with arrival at later ages), placed immigrants at higher risk of overweight/obesity [40]. This study is unique in showing that men who arrived as adolescents are at particularly high risk in terms of their adult BMI and likelihood of overweight/obesity, suggesting an important area for policy attention.

Strengths, limitations and areas for further research

This study had a number of limitations. Country of birth is the most commonly used indicator of ethnicity in Australian datasets and other, more sensitive measures such as self-identified ethnicity [59] are not routinely gathered. Country of birth may be only one of several factors which influence a person’s ethnicity [60] and in this study, aggregating countries into regions may mask important heterogeneity both within countries and within regions. The self-completed questionnaire was only available in English and analysis of reasons for exclusion, revealed that birth in non-English speaking regions may be an important predictor of questionnaire non-return and may have introduced selection bias into the sample. Self-reported BMI is known to be subject to error [61] and further research is needed to confirm the presence and direction of weight-reporting biases among adults in different ethnic groups [28, 62]. In this study, as we were comparing ethnic differences in overweight and obesity relative to Australian born, the WHO standard overweight and obesity cut-off points were used. This may underestimate overweight and obesity amongst some Asian ethnic groups [63].

Acknowledging the constraints of this paper, the complexity of ethnicity as a construct and the under-representation of ethnic minority groups in health research, we echo the calls of other researchers [54, 59] for increased population-level research on migrant health trends and the inclusion of a greater range of ethnicity variables and appropriate data collection techniques to enable this to occur. Findings from this study, along with research from other developed countries, suggest that the complex and intertwining nature of ethnicity, acculturation, gender and socioeconomic status requires further context specific research. In particular longitudinal studies will build on our findings and reveal trends which take into account cohort effects and secular and age-related increases in obesity [64].

Conclusions

This paper was the first study of its kind to examine ethnic differences in BMI and overweight/obesity and the influence of acculturation on the bodyweight of immigrants in a national sample of Australian adults. Our findings emphasise the importance of targeted and tailored obesity prevention intervention aimed at ethnic groups at high risk of overweight and obesity. In the Australian context this includes male immigrants from North Africa/Middle East and Oceania regions. Our findings also highlight the need for public health policy directed at immigrants in the early years post-arrival and to those who arrive as young children or adolescents, in order to combat acculturation-related weight gain. The study adds to the international literature by demonstrating the pervasiveness of ethnic differences in immigrant bodyweight and the consistency and speed of immigrant acculturation to a country’s unhealthy weight profile in the face of obesogenic environments present in developed countries.

Abbreviations

BMI: 

Body mass index

CrI: 

Credible interval

HILDA: 

Household, income and labour dynamics in Australia

MCMC: 

Markov chain Monte Carlo

OR: 

Odds ratio

WHO: 

World Health Organisation

Declarations

Acknowledgements

This paper uses unit record data from the Household, Income and Labour Dynamics in Australia (HILDA) Survey. The HILDA Project was initiated and is funded by the Australian Government Department of Social Services (DSS) (formerly FaHCSIA), and is managed by the Melbourne Institute of Applied Economic and Social Research (Melbourne Institute). The findings and views reported in this paper, however, are those of the author and should not be attributed to either DSS or the Melbourne Institute.

Funding

GT is supported by an Australian National Health and Medical Research Council Senior Research Fellowship (ID 1003710).

Availability of data and materials

The data that support the findings of this study are available from the Melbourne Institute of Applied Economic and Social Research but restrictions apply to the availability of these data which were used under license for the current study, and so are not publicly available. Those interested in accessing the data must contact the Melbourne Institute of Applied Economic and Social Research, Level 5, Business and Economics Building, 111 Barry Street, The University of Melbourne, VIC 3010, Australia, Email hilda-inquiries@unimelb.edu.au.

Author’s contributions

GT and KM initiated the study. KM conducted the data analysis and drafted the manuscript with GT and AN providing advice, critical comments and editing of the draft. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

This study has human research ethics approval from the QUT University Human Research Ethics Committee as a Negligible-Low Risk Category. Approval number: 1500000836.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
School of Public Health and Social Work, Queensland University of Technology (QUT), Victoria Park Road, Kelvin Grove, QLD, 4059, Australia
(2)
Institute for Health & Ageing, Australian Catholic University (ACU), Level 6, 215 Spring Street, Melbourne, VIC, 3000, Australia

References

  1. Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2014;384:766–81. http://dx.doi.org/10.1016/S0140-6736(14)60460-8.
  2. United Nations Department of Economic and Social Affairs Population Division. Trends in International Migration, 2015. In: Population Facts. vol. 2015/4: United Nations; 2015: 1–4.Google Scholar
  3. Kumar BN, Meyer HE, Wandel M, Dalen I, Holmboe-Ottesen G. Ethnic differences in obesity among immigrants from developing countries, in Oslo. Norway Int J Obes. 2006;30(4):684–90. doi:10.1038/sj.ijo.0803051;.View ArticleGoogle Scholar
  4. Wang Y, Beydoun MA. The obesity epidemic in the United States - gender, age, socioeconomic, racial/ethnic, and geographic characteristics: a systematic review and meta-regression analysis. Epidemiol Rev. 2007;29(1):6–28. doi:10.1093/epirev/mxm007.View ArticlePubMedGoogle Scholar
  5. McDonald JT, Kennedy S. Is migration to Canada associated with unhealthy weight gain? Overweight and obesity among Canada’s immigrants. Soc Sci Med. 2005;61(12):2469–81. doi:10.1016/j.socscimed.2005.05.004.View ArticlePubMedGoogle Scholar
  6. El-Sayed A, Scarborough P, Galea S. Ethnic inequalities in obesity among children and adults in the UK: a systematic review of the literature. Obes Rev. 2011;12(5):e516–34. doi:10.1111/j.1467-789X.2010.00829.x.View ArticlePubMedGoogle Scholar
  7. El-Sayed A, Scarborough P, Galea S. Unevenly distributed: a systematic review of the health literature about socioeconomic inequalities in adult obesity in the United Kingdom. BMC Public Health. 2012;12(1):18. doi:10.1186/1471-2458-12-18.View ArticlePubMedPubMed CentralGoogle Scholar
  8. Gearon E, Backholer K, Stevenson C, Magliano DJ, Keating C, Ball K, et al. Trends in body mass index according to educational attainment for urban Australian adults between 1980 and 2007. Int J Obes. 2015;39(6):1019–26. doi:10.1038/ijo.2015.27.View ArticleGoogle Scholar
  9. McLaren L. Socioeconomic status and obesity. Epidemiol Rev. 2007;29(1):29–48. doi:10.1093/epirev/mxm001.View ArticlePubMedGoogle Scholar
  10. Ball K, Crawford D. Socioeconomic status and weight change in adults: a review. Soc Sci Med. 2005;60(9):1987–2010. doi:10.1016/j.socscimed.2004.08.056.View ArticlePubMedGoogle Scholar
  11. Flegal KM, Carroll MD, Kit BK, Ogden CL. Prevalence of obesity and trends in the distribution of body mass index among US adults, 1999–2010. JAMA. 2012;307(5):491–7. doi:10.1001/jama.2012.39.View ArticlePubMedGoogle Scholar
  12. Krueger PM, Coleman‐Minahan K, Rooks RN. Race/ethnicity, nativity and trends in BMI among US adults. Obesity. 2014;22(7):1739–46. doi:10.1002/oby.20744.View ArticlePubMedPubMed CentralGoogle Scholar
  13. Zilanawala A, Davis-Kean P, Nazroo JY, Sacker A, Simonton S, Kelly YJ. Race/ethnic disparities in early childhood BMI, obesity and overweight in the United Kingdom and United States. Int J Obes. 2014;39:520–9. doi:10.1038/ijo.2014.171.View ArticleGoogle Scholar
  14. Smith NR, Kelly YJ, Nazroo JY. The effects of acculturation on obesity rates in ethnic minorities in England: evidence from the Health Survey for England. Eur J Public Health. 2012;22(4):508–13. doi:10.1093/eurpub/ckr070.View ArticlePubMedGoogle Scholar
  15. Alkerwi AA, Sauvageot N, Pagny S, Beissel J, Delagardelle C, Lair M-L. Acculturation, immigration status and cardiovascular risk factors among Portuguese immigrants to Luxembourg: findings from ORISCAV-LUX study. BMC Public Health. 2012;12(1):1–10. doi:10.1186/1471-2458-12-864.View ArticleGoogle Scholar
  16. Australian Bureau of Statistics: Australian Health Survey: first results, 2011–12. 2012. http://www.ausstats.abs.gov.au/ausstats/subscriber.nsf/0/1680ECA402368CCFCA257AC90015AA4E/$File/4364.0.55.001.pdf. Accessed 19 Aug 2015.
  17. Australian Bureau of Statistics: Australia’s population by country of birth. 2012. http://www.abs.gov.au/ausstats/abs@.nsf/Products/84074889D69E738CCA257A5A00120A69?opendocument. Accessed 19 Aug 2015.
  18. United States Census Bureau: Statistical Abstract of the United States: 2012. http://www.census.gov/library/publications/2011/compendia/statab/131ed.html. Accessed 5 Sept 2015.
  19. Achat HM, Stubbs JM. Socio-economic and ethnic differences in the prevalence of overweight and obesity among school children. J Paediatr Child Health. 2012;50:E77–84. doi:10.1111/j.1440-1754.2012.02474.x.View ArticlePubMedGoogle Scholar
  20. Hardy LL, King L, Hector D, Baur LA. Socio-cultural differences in Australian primary school children’s weight and weight-related behaviours. J Paediatr Child Health. 2013;49(8):641–8. doi:10.1111/jpc.12263.View ArticlePubMedGoogle Scholar
  21. O’Dea JA. Gender, ethnicity, culture and social class influences on childhood obesity among Australian schoolchildren: implications for treatment, prevention and community education. Health Soc Care Community. 2008;16(3):282–90. doi:10.1111/j.1365-2524.2008.00768.x.View ArticlePubMedGoogle Scholar
  22. Waters E, Ashbolt R, Gibbs L, Booth M, Magarey A, Gold L, et al. Double disadvantage: the influence of ethnicity over socioeconomic position on childhood overweight and obesity: findings from an inner urban population of primary school children. Int J Pediatr Obes. 2008;3(4):196–204. doi:10.1080/17477160802141846.View ArticlePubMedGoogle Scholar
  23. Astell-Burt T, Feng X, Croteau K, Kolt GS. Influence of neighbourhood ethnic density, diet and physical activity on ethnic differences in weight status: a study of 214,807 adults in Australia. Soc Sci Med. 2013;93:70–7. doi:10.1016/j.socscimed.2013.06.006.View ArticlePubMedGoogle Scholar
  24. Hauck K, Hollingsworth B, Morgan L. BMI differences in 1st and 2nd generation immigrants of Asian and European origin to Australia. Health & Place. 2011;17(1):78–85. doi:10.1016/j.healthplace.2010.08.016.View ArticleGoogle Scholar
  25. Guo S, Lucas RM, Joshy G, Banks E. Cardiovascular disease risk factor profiles of 263,356 older Australians according to region of birth and acculturation, with a focus on migrants born in Asia. PLoS One. 2015;10(2), e0115627. doi:10.1371/journal.pone.0115627.View ArticlePubMedPubMed CentralGoogle Scholar
  26. Redfield R, Linton R, Herskovits MJ. Memorandum for the study of acculturation. Am Anthropol. 1936;38(1):149–52.View ArticleGoogle Scholar
  27. Schwartz SJ, Unger JB, Zamboanga BL, Szapocznik J. Rethinking the concept of acculturation: implications for theory and research. Am Psychol. 2010;65(4):237–51. doi:10.1037/a0019330.View ArticlePubMedPubMed CentralGoogle Scholar
  28. Sanchez-Vaznaugh EV, Kawachi I, Subramanian S, Sánchez BN, Acevedo-Garcia D. Differential effect of birthplace and length of residence on body mass index (BMI) by education, gender and race/ethnicity. Soc Sci Med. 2008;67(8):1300–10. doi:10.1016/j.socscimed.2008.06.015.View ArticlePubMedGoogle Scholar
  29. Barcenas CH, Wilkinson AV, Strom SS, Cao Y, Saunders KC, Mahabir S, et al. Birthplace, years of residence in the United States, and obesity among Mexican‐American adults. Obesity. 2007;15(4):1043–52.View ArticlePubMedGoogle Scholar
  30. Albrecht SS, Diez-Roux AV, Aiello AE, Schulz AJ, Abraido-Lanza AF. Secular trends in the association between nativity/length of US residence with body mass index and waist circumference among Mexican-Americans, 1988–2008. Int J Public Health. 2013;58(4):573–81. doi:10.1007/s00038-012-0414-5.View ArticlePubMedGoogle Scholar
  31. Bennett GG, Wolin KY, Askew S, Fletcher R, Emmons KM. Immigration and obesity among lower income blacks. Obesity. 2007;15(6):1391–4.View ArticlePubMedGoogle Scholar
  32. Delavari M, Sønderlund AL, Swinburn B, Mellor D, Renzaho AMN. Acculturation and obesity among migrant populations in high income countries: a systematic review. BMC Public Health. 2013;13(1):458. doi:10.1186/1471-2458-13-458.View ArticlePubMedPubMed CentralGoogle Scholar
  33. Oza‐Frank R, Cunningham SA. The weight of US residence among immigrants: a systematic review. Obes Rev. 2010;11(4):271–80. doi:10.1111/j.1467-789X.2009.00610.x.View ArticlePubMedGoogle Scholar
  34. Salant T, Lauderdale DS. Measuring culture: a critical review of acculturation and health in Asian immigrant populations. Soc Sci Med. 2003;57(1):71–90. doi:10.1016/S0277-9536(02)00300-3.View ArticlePubMedGoogle Scholar
  35. Wan-Ping L, Lingard J, Bermingham M. Change in diet and body mass index in Taiwanese women with length of residence in Australia. Asia Pac J Clin Nutr. 2007;16(1):56–65.Google Scholar
  36. Albrecht SS, Osypuk TL, Kandula NR, Gallo LC, Lê-Scherban F, Shrager S, et al. Change in waist circumference with longer time in the United States among Hispanic and Chinese immigrants: the modifying role of the neighborhood built environment. Ann Epidemiol. 2015;25(10):767–72. http://dx.doi.org/10.1016/j.annepidem.2015.07.003.View ArticlePubMedPubMed CentralGoogle Scholar
  37. Ro A, Bostean G. Duration of U.S. stay and body mass index among Latino and Asian immigrants: a test of theoretical pathways. Soc Sci Med. 2015;144:39–47. doi:10.1016/j.socscimed.2015.09.001.View ArticlePubMedGoogle Scholar
  38. Gray L, Harding S, Reid A. Evidence of divergence with duration of residence in circulatory disease mortality in migrants to Australia. Eur J Public Health. 2007;17(6):550–4. doi:10.1093/eurpub/ckm008.View ArticlePubMedGoogle Scholar
  39. Guven C, Islam A. Age at migration, language proficiency and socio-economic outcomes: evidence from Australia. In: Discussion Paper 18/13. Melbourne: Department of Economics, Monash University; 2013.Google Scholar
  40. Roshania R, Narayan K, Oza‐Frank R. Age at arrival and risk of obesity among US immigrants. Obesity. 2008;16(12):2669–75. doi:10.1038/oby.2008.425.View ArticlePubMedGoogle Scholar
  41. Watson N, Wooden M. The Household, Income and Labour Dynamics in Australia (HILDA) survey: wave 1 survey methodology. 2002. http://www.melbourneinstitute.com/downloads/hilda/Bibliography/HILDA_Technical_Papers/htec102.pdf. Accessed 26 Apr 2015.
  42. Watson N. Methodology for the HILDA top-up Sample. 2011. http://www.melbourneinstitute.com/hilda/biblio/HILDA_technical_papers.html. Accessed 22 Jul 2016.Google Scholar
  43. Watson N. Longitudinal and Cross-Sectional Weighting Methodology for the HILDA Survey. 2012. http://www.melbourneinstitute.com/hilda/biblio/HILDA_technical_papers.html. Accessed 22 Jul 2016.Google Scholar
  44. Wooden M, Watson N, Agius P, Friedin S. Assessing the quality of the height and weight data in the HILDA survey. 2008. http://www.melbourneinstitute.com/hilda/biblio/HILDA_technical_papers.html. Accessed 24 Aug 2015.Google Scholar
  45. World Health Organisation: Global database on body mass index. 2013. http://apps.who.int/bmi/index.jsp?introPage=intro_3.html. Accessed 19 Jul 2015.
  46. Australian Bureau of Statistics: Standard Australian Classification of Countries (SACC). 2011. http://www.abs.gov.au/ausstats/abs@.nsf/mf/1269.0. Accessed 21 Apr 2015.
  47. Australian Bureau of Statistics: Socio-Economic Indexes for Areas. 2013. http://www.abs.gov.au/websitedbs/censushome.nsf/home/seifa?opendocument&navpos=260. Accessed 22 July 2016.
  48. Australian Bureau of Statistics: Australian Standard Geographical Classification (ASGC). 2001. http://www.abs.gov.au/AUSSTATS/abs@.nsf/DetailsPage/1216.02001?OpenDocument. Accessed 21 Apr 2015.
  49. Cleland V, Hume C, Crawford D, Timperio A, Hesketh K, Baur LA, et al. Urban-rural comparison of weight status among women and children living in socioeconomically disadvantaged neighbourhoods. Med J Aust. 2010;192(3):137.PubMedGoogle Scholar
  50. Lau R, Morse CA. Health and wellbeing of older people in Anglo‐Australian and Italian‐Australian communities: a rural-urban comparison. Aust J Rural Health. 2008;16(1):5–11.View ArticlePubMedGoogle Scholar
  51. Browne WJ. MCMC Estimation in MLwiN Version 2.13. United Kingdom: Centre for Multilevel Modelling, University of Bristol; 2009.Google Scholar
  52. Leckie G, Charlton C. Runmlwin-a program to run the MLwiN multilevel modelling software from within Stata. J Stat Softw. 2013;52(11):1–40.Google Scholar
  53. Bates LM, Acevedo-Garcia D, Alegria M, Krieger N. Immigration and generational trends in body mass index and obesity in the United States: results of the National Latino and Asian American Survey, 2002–2003. Am J Public Health. 2008;98(1):70–7. doi:10.2105/AJPH.2006.102814.View ArticlePubMedPubMed CentralGoogle Scholar
  54. Anikeeva O, Bi P, Hiller JE, Ryan P, Roder D, Han G-S. The health status of migrants in Australia: a review. Asia Pac J Public Health. 2010;22(2):159–93. doi:10.1177/1010539509358193.View ArticlePubMedGoogle Scholar
  55. Krueger P, Reither E. Mind the gap: race/ethnic and socioeconomic disparities in obesity. Current Diabetes Reports. 2015;15(11):1–9. doi:10.1007/s11892-015-0666-6.View ArticleGoogle Scholar
  56. Kennedy S, McDonald JT, Biddle N. The healthy immigrant effect and immigrant selection: evidence from four countries. In: SEDAP Research Paper #164. Ottawa: Social and Economic Dimensions of an Ageing Population (SEDAP) Research Program; 2006.Google Scholar
  57. Antecol H, Bedard K. Unhealthy assimilation: why do immigrants converge to American health status levels? Demography. 2006;43(2):337–60. doi:10.1353/dem.2006.0011.View ArticlePubMedGoogle Scholar
  58. Kaplan MS, Huguet N, Newsom JT, McFarland BH. The association between length of residence and obesity among Hispanic immigrants. Am J Prev Med. 2004;27(4):323–6. doi:10.1016/j.amepre.2004.07.005.View ArticlePubMedGoogle Scholar
  59. Abouzeid M, Bhopal RS, Dunbar JA, Janus ED. The potential for measuring ethnicity and health in a multicultural milieu: the case of type 2 diabetes in Australia. Ethn Health. 2013;19(4):424–39. doi:10.1080/13557858.2013.828828.View ArticlePubMedGoogle Scholar
  60. Ford CL, Harawa NT. A new conceptualization of ethnicity for social epidemiologic and health equity research. Soc Sci Med. 2010;71(2):251–8. doi:10.1016/j.socscimed.2010.04.008.View ArticlePubMedPubMed CentralGoogle Scholar
  61. Gorber SC, Tremblay MS, Moher D, Gorber B. A comparison of direct vs. self‐report measures for assessing height, weight and body mass index: a systematic review. Obes Rev. 2007;8(4):307–26. doi:10.1111/j.1467-789X.2007.00347.x.View ArticlePubMedGoogle Scholar
  62. Richmond TK, Thurston I, Sonneville K, Milliren CE, Walls CE, Austin SB. Racial/ethnic differences in accuracy of body mass index reporting in a diverse cohort of young adults. Int J Obes. 2015;39(3):546–8. doi:10.1038/ijo.2014.147.View ArticleGoogle Scholar
  63. Stella SY, Kwon SC, Wyatt L, Islam N, Trinh-Shevrin C. Weighing in on the hidden Asian American obesity epidemic. Prev Med. 2015;73:6–9. http://dx.doi.org/10.1016/j.ypmed.2015.01.007.View ArticleGoogle Scholar
  64. Albrecht SS, Roux AVD, Kandula NR, Osypuk TL, Ni H, Shrager S. Immigrant assimilation and BMI and waist size: a longitudinal examination among hispanic and chinese participants in the multi-ethnic study of atherosclerosis. Obesity. 2013;21(8):1695–703. doi:10.1002/oby.20104.View ArticlePubMedPubMed CentralGoogle Scholar

Copyright

© The Author(s). 2016

Advertisement