An epidemiological study of type 2 diabetes in - born

Duong Thuy Tran School of Medicine University of Western

This thesis is presented for the degree Doctor of Philosophy

March 2013

Acknowledgements

This thesis would not have been possible without the full support and assistance of many people. First, I would like to express my great appreciation to my principal supervisor Professor Louisa Jorm for her knowledge, expertise, clear guidance and ongoing support. I owe my deep gratitude to Professor Maree Johnson for encouraging me to embark on the PhD journey and spending time to comment on my thesis. I am extremely grateful to Associate Professor Hilary Bambrick for her valuable feedback and enthusiastic encouragement during my candidature. I am fortunate to have such a dedicated team of supervisors to guide me through this research project.

Sincere thanks are due to Sanja Lujic for her patience and advice on methods and statistical issues, Louise Francis for her administrative assistance, Associate Professor Elizabeth Comino for sharing her knowledge and experience in diabetes research, and Philomena Kaarma for proofreading my chapters. I also appreciate other colleagues and friends, both past and present, for their support and insight. My acknowledgement also goes to the University of Western Sydney and staff, particularly for the Postgraduate Research Awards and the University Scholarship, which provided financial assistance for this research.

Finally, I wish to thank my entire extended family, especially my husband Dan, my daughters Isabelle and Catherine, my parents and my parents-in-law, for their love and endless support while I have been concentrating on my research studies.

Declaration

I hereby declare that this thesis presents work carried out by myself and does not contain any materials which have been accepted for the award to the candidate of any other degree or diploma at any educational institution. To the best of my knowledge, the thesis does not contain any materials which have been previously published or written by any other person, except where due reference is made in the text. All substantive contributions by others to the work presented, including jointly authored publications, are clearly acknowledged.

...... (Signature)

Contents

Tables ...... iv Figures ...... vi Acronyms and Abbreviations ...... vii Abstract ...... ix

CHAPTER 1 INTRODUCTION ...... 1

1.1. Overview ...... 1 1.2. Research plan and hypotheses ...... 4 1.3. Ethics approval ...... 6 1.4. Outline of thesis chapters ...... 6

CHAPTER 2 VIETNAMESE HISTORY, CULTURE, HEALTH BELIEFS AND IMMIGRATION ...... 8

2.1. History, religion and culture ...... 8 2.1.1. Religious beliefs ...... 9 2.1.2. Vietnamese culture ...... 10 2.2. Health beliefs and practices ...... 12 2.3. Vietnam-born people in developed countries ...... 13 2.3.1. Migration history ...... 13 2.3.2. Socio-economic status ...... 15 2.3.3. Health status ...... 16 2.3.4. Use of health care services ...... 18

CHAPTER 3 THE 45 AND UP STUDY AND SELECTION OF VIETNAM- AND -BORN PARTICIPANTS ...... 20

3.1. The 45 and Up Study ...... 20 3.2. Selection of Vietnam- and Australia-born participants ...... 22 3.3. Participants in the 45 and Up Study compared to NSW population at the 2006 Census ...... 24 3.3.1. Australian Bureau of Statistics 2006 Census data ...... 24 3.3.2. Comparability of demographic and socio-economic variables between the questionnaire and Census data ...... 25 3.3.3. Results ...... 26

i 3.4. Discussion of findings ...... 31

CHAPTER 4 EFFECTS OF ACCULTURATION ON LIFESTYLE AND HEALTH ...... 34

4.1. Introduction ...... 34 4.2. Acculturation theory ...... 35 4.2.1. Concepts ...... 35 4.2.2. Acculturation orientation ...... 36 4.3. Acculturation measurement ...... 39 4.3.1. Scale measures ...... 39 4.3.2. Non-scale measures ...... 41 4.4. Conceptualisation of acculturation process and effects ...... 42 4.4.1. Conceptualisation of acculturation process and effects ...... 42 4.4.2. Some evidence of effects of acculturation on health ...... 46 4.5. Aims and hypotheses ...... 47 4.6. Method ...... 47 4.6.1. Measures of acculturation ...... 48 4.6.2. Measures of lifestyle ...... 49 4.6.3. Measures of health status ...... 50 4.6.4. Statistical analysis ...... 52 4.7. Results ...... 53 4.7.1. Descriptive results ...... 53 4.7.2. Logistic regression modelling of acculturation and outcomes ...... 60 4.8. Discussion of findings ...... 73

CHAPTER 5 DIABETES PREVALENCE AND RISK FACTORS ...... 79

5.1. Introduction ...... 79 5.2. Diabetes in the research context ...... 80 5.2.1. Diabetes definitions ...... 80 5.2.2. Types of diabetes ...... 80 5.2.3. Health risk factors of diabetes ...... 81 5.2.4. Impact of diabetes ...... 85 5.2.5. Diabetes prevalence and incidence in Australia...... 89 5.3. Diabetes among the Vietnam-born population ...... 90 5.4. Aims and hypotheses ...... 92 5.5. Method ...... 92

ii 5.5.1. Identifying type 2 diabetes and use of diabetes medications ...... 93 5.5.2. Additional study variables ...... 96 5.5.3. Statistical analysis ...... 97 5.6. Results ...... 98 5.6.1. Overall comparisons between Vietnam- and Australia-born participants (everyone) ...... 98 5.6.2. Diabetes-specific comparisons between Vietnam and Australia-born participants (with type 2 diabetes) ...... 102 5.6.3. Prevalence of type 2 diabetes ...... 107 5.6.4. Likelihood of type 2 diabetes by risk factors and health status ...... 109 5.7. Discussion of findings ...... 116

CHAPTER 6 HOSPITALISATION AND MORTALITY ...... 121

6.1. The Behavioral Model of Health Service Use ...... 122 6.2. Aims and hypotheses ...... 123 6.3. Method ...... 124 6.3.1. Data sources and data linkage ...... 124 6.3.2. Reliability of country of birth recording in APDC data ...... 128 6.3.3. Identifying cohort of Vietnam- and Australia-born patients with type 2 diabetes from APDC data ...... 131 6.3.4. Study variables ...... 135 6.3.5. Statistical analysis ...... 138 6.4. Results ...... 140 6.4.1. Descriptive statistics at baseline and follow-up ...... 140 6.4.2. Rates of readmission and length of stay for diabetes and comorbidities .. 144 6.4.3. Time to readmission for non-dialysis reasons ...... 147 6.4.4. Time to death due to all causes, and due to diabetes ...... 151 6.4.5. Sensitivity analyses ...... 156 6.5. Discussion of findings ...... 162

CHAPTER 7 DISCUSSION AND CONCLUSION ...... 168

REFERENCE LIST ...... 180

APPENDICES ...... 203

iii

Tables

Table 1 Information collected in the 45 and Up Study baseline questionnaire .. 21

Table 2 Demographic comparisons between the 45 and Up Study and 2006 Census for Vietnam- and Australia-born people ...... 29

Table 3 Socio-economic status comparisons between the 45 and Up Study and 2006 Census for Vietnam- and Australia-born people ...... 30

Table 4 BMI cut-off values: WHO convention and Vietnamese population ..... 50

Table 5 Demography and SES of 797 Vietnam-born participants by gender .... 54

Table 6 Acculturation measures of 797 Vietnam-born participants by gender .. 56

Table 7 Dietary patterns of 797 Vietnam-born participants by gender ...... 57

Table 8 Health-related behaviours of 797 Vietnam-born participants by gender ...... 58

Table 9 Health status of 797 Vietnam-born participants by gender ...... 59

Table 10 Associations between acculturation measures and dietary patterns: crude and adjusted odds ratio (95%CI) ...... 63

Table 11 Associations between acculturation measures and health-related behaviours: crude and adjusted odds ratio (95%CI) ...... 67

Table 12 Associations between acculturation measures and health status: crude and adjusted odds ratio (95%CI) ...... 71

Table 13 Overall comparisons by demography and SES between Vietnam- born (N=797) and Australia-born participants (N=199,917) ...... 99

Table 14 Overall comparisons by diabetes risk factors between Vietnam-born (N=797) and Australia-born participants (N=199,917) ...... 100

Table 15 Overall comparisons by health status and quality of life between Vietnam-born (N=797) and Australia-born participants (N=199,917) ...... 101

Table 16 Diabetes-specific comparisons by demography and SES between Vietnam-born (N=103) and Australia-born participants (N=15,221) . 103

Table 17 Diabetes-specific comparisons by diabetes risk factors, duration of diabetes, and use of medications between Vietnam-born (N=103) and Australia-born participants (N=15,221) ...... 105

Table 18 Diabetes-specific comparisons by health status and quality of life between Vietnam-born (N=103) and Australia-born participants (N=15,221) ...... 106

iv Table 19 Prevalence of type 2 diabetes in Vietnam- and Australia-born populations: crude and age-standardised (95%CI) ...... 108

Table 20 Diabetes risk by demographic characteristics: crude, adjusted odds ratio, and ratio of odds ratios (95%CI) ...... 111

Table 21 Diabetes risk by socio-economic status: crude, adjusted odds ratio, and ratio of odds ratios (95%CI) ...... 112

Table 22 Diabetes risk by family history and lifestyle factors: crude, adjusted odds ratio, and ratio of odds ratios (95%CI) ...... 114

Table 23 Diabetes risk by health status and quality of life: crude, adjusted odds ratio, and ratio of odds ratios (95%CI) ...... 115

Table 24 Reliability of Vietnam and Australia country of birth recording in the Admitted Patient Data Collection ...... 130

Table 25 Cohort characteristics at baseline ...... 141

Table 26 Cohort characteristics during follow-up ...... 143

Table 27 Number of readmissions by principal reasons of admission ...... 143

Table 28 Principal diagnoses of readmissions for diabetes and comorbidities .. 144

Table 29 Readmission rate ratios: crude and adjusted models ...... 145

Table 30 Readmission for diabetes and comorbidities by predisposing, enabling and need factors: crude and adjusted rate ratios (95%CI) .... 146

Table 31 Time to non-dialysis readmission hazard ratios: crude and four adjusted models ...... 148

Table 32 Time to non-dialysis readmissions by predisposing, enabling and need factors: crude and adjusted hazard ratios (95%CI) ...... 149

Table 33 All-cause mortality risk: crude and four adjusted models ...... 153

Table 34 Diabetes-specific mortality risk: crude and four adjusted models ...... 153

Table 35 Time to mortality for all causes of death by predisposing, enabling and need factors: crude and adjusted hazard ratios (95%CI) ...... 154

Table 36 Time to mortality due to diabetes by predisposing, enabling and need factors: crude and adjusted hazard ratios (95%CI) ...... 155

Table 37 Comparisons of results from nil-clearance cohort with results of 18- month and 30-month clearance cohorts...... 159

Table 38 Comparisons of risk factors identified by a three-year lookback period and the single index admission record ...... 160

Table 39 Comparisons of adjusted outcome estimates by methods used to identify health risk factors ...... 161

v

Figures

Figure 1 Selection of Vietnam- and Australia-born participants in the 45 and Up Study ...... 23

Figure 2 Year of arrival in Australia of Vietnam-born people ...... 28

Figure 3 Gordon’s uni-dimensional or bipolar acculturation model ...... 37

Figure 4 Berry’s bi-dimensional acculturation model ...... 38

Figure 5 Conceptualisation of the acculturation process and effects on lifestyle and health ...... 45

Figure 6 Classifying diabetes status among the 45 and Up Study participants ... 95

Figure 7 Age-specific prevalence of type 2 diabetes among Vietnam- and Australia-born participants in the 45 and Up Study ...... 107

Figure 8 Andersen and Newman’s Behavioral Model of Health Service Use- Phase 4 ...... 123

Figure 9 Kaplan-Meier graph of time to non-dialysis readmission ...... 148

Figure 10 Kaplan-Meier graph of survival time for all-cause mortality ...... 152

Figure 11 Kaplan-Meier graph of survival time for diabetes-specific mortality . 152

vi

Acronyms and Abbreviations

ABS Australian Bureau of Statistics AIHW Australian Institute of Health and Welfare ANZDATA Australian and New Zealand Dialysis and Transplant Registry APDC Admitted Patient Data Collection AR-DRG Australian Refined Diagnosis Related Group ARIA+ Accessibility Remoteness Index of Australia plus AusDiab Australian Diabetes, Obesity and Lifestyle Study BMI Body mass index CALD Culturally and linguistically diverse CHeReL Centre for Health Record Linkage CI Confidence interval Diabetes Diabetes mellitus DSSI Duke Social Support Index DVA Department of Veterans’ Affairs EDDC Emergency Department Data Collection ESKD End-stage kidney disease GDM Gestational diabetes mellitus GI Glycaemic index GL Glycaemic load GP General practitioner HR Hazard ratio International Statistical Classification of Diseases and Related ICD-10-AM Health Problems, Tenth Revision, Australian Modification IRSD Index of Relative Socio-Economic Disadvantage K10 Kessler-10 scale LGA Local Government Area LoS Length of stay LOTE Language other than English MBS Medical Benefits Schedule Medicare Medicare Australia

vii

NDSS National Diabetes Services Scheme NHS National Health Survey NSW OHA Oral hypoglycaemic agent OR Odds ratio PBS Pharmaceutical Benefits Scheme PDC Perinatal Data Collection PHS Population Health Survey PPN Project personal number r Correlation coefficient RBDM Registry of Births, Deaths and Marriages ROR Ratio of odds ratios RR Rate ratio SACC Standard Australian Classification of Countries SAS Statistical Analysis Software SD Standard deviation SEIFA Socio-Economic Indexes For Areas SES Socio-economic status SF36-PF Short Form 36 Physical Functioning scale SLA Statistical Local Area UK United Kingdom USA United States of America WHO World Health Organization

viii Abstract

Understanding the relationships between culture, associated health beliefs and lifestyle, and ethnic disparities in health is of particular importance in Australia, where one in four people was born overseas. Vietnam-born Australians (N=159,849 at the 2006 Census) are among the top five overseas-born population groups. Most arrived as refugees (50%, 1977-1986) and family reunion immigrants (42%, 1987- 1996), thus many have poorer socio-economic status than other population groups. Vietnam-born Australians also share a distinct Oriental culture and traditional health beliefs that largely differ from Western biomedicine perspectives. Changes in diet among new immigrants have been reported but the impact of acculturation on various lifestyle factors and, importantly, health status of Vietnam-born Australians has not been examined extensively. Research evidence shows that people of Vietnamese ethnicity are at higher risk of diabetes. However, there is little existing information about diabetes among Vietnam-born Australians. Therefore, this thesis aimed to investigate two interrelated aspects of health in this population: the impact of acculturation on health-related behaviours and health status; and the prevalence of type 2 diabetes, its risk factors and hospitalisation and mortality outcomes.

Baseline questionnaire data (2006 to 2008) from the 45 and Up Study, a cohort study of more than 266,000 residents of New South Wales (NSW), Australia aged 45 years and over, were used to investigate relationships between acculturation (duration of residence, age at immigration, density of Vietnam-born population in residential areas, and social interactions) and lifestyle, health status, and prevalence of and risk factors for type 2 diabetes. Analytic techniques included descriptive statistics, direct age-standardisation and logistic regression modelling. Among 797 Vietnam-born participants in the Study (390 men and 407 women), higher levels of acculturation were associated with increased consumption of red meat, white meat and seafood, higher levels of physical activities, and lower prevalence of overweight and obesity, and type 2 diabetes. Likelihood of smoking was lower among Vietnam-born men living in areas with low proportion of Vietnam-born population (<2%). The age- standardised prevalence of self-reported type 2 diabetes was 11.2% (crude prevalence 12.9%), which was 1.6 times (95%CI=1.31-1.90) higher than in Australia-born participants. Strong risk factors for type 2 diabetes in Vietnam-born participants included family history of diabetes (adjusted odds ratio [OR]=7.07, ix 95%CI=4.14-12.07) and older age (OR≥2.49, p<0.001). Overweight or obesity based on body mass index (≥23.0 kg/m2) was not a strong predictor (OR=1.64, 95%CI=0.99-2.74). Vietnam-born people with type 2 diabetes were more likely to have a health care concession card, high blood pressure, heart disease, and poorer self-rated general health and quality of life.

The NSW Admitted Patient Data Collection (APDC, 1/7/2000 to 31/12/2008), an administrative database of all hospital stays in NSW, was linked to NSW death registrations (1/7/2000 to 30/12/2009) and Australian Bureau of Statistics mortality data (1/7/2000 to 30/12/2007) to investigate diabetes-related hospitalisation and mortality. One hundred and fifty-two Vietnam-born patients admitted between 1/7/2000 and 31/12/2008 for treatment of type 2 diabetes were followed prospectively for readmissions and mortality. Statistical techniques included Poisson and Cox proportional hazard regression modelling. Vietnam-born patients had lower rates of readmission for diabetes and comorbidities (450.7, 95%CI=394.4-515.0 per 1,000 person-years) than Australia-born counterparts (528.5, 95%CI=522.2-535.0) but the difference was not statistically significant (adjusted rate ratio [RR]=0.81, 95%CI=0.64-1.03). However, Vietnam-born patients had significantly higher risk of death from all causes (adjusted hazard ratio [HR]=1.42, 95%CI=1.07-1.88) and for diabetes-related causes (HR=1.58, 95%CI=1.05-2.38). The prevalence of hypertension, chronic kidney disease, and other comorbidities was significantly higher in Vietnam-born than in Australia-born patients.

The findings of this thesis have implications for education about healthy lifestyle and for proactive management of diabetes in this population. Early diagnosis and optimal control of diabetes and comorbid conditions are important for Vietnam-born Australians given their high risk of diabetes. Family members’ participation in patient-centred management of people with diabetes could provide additional positive outcomes. This research has demonstrated the value of record linkage of already available, population-based health administrative data for investigating diabetes management and associated health outcomes among overseas-born Australians.

x

Chapter 1

Introduction

1.1. OVERVIEW

Australia is one of the most culturally and linguistically diverse (CALD) countries in the world.1 At the 2006 Census, one in four Australians reported being born in another country, and among the Australia-born population, one in four had at least one overseas-born parent.2 Vietnam was the fifth most commonly reported birthplace (3.6%) following the United Kingdom (UK, 23.3%), New Zealand (9.6%), Italy (4.4%), and China (4.1%).2, 3 There were 159,849 Vietnam-born Australians at the 2006 Census,3 indicating a four-fold increase from 40,700 in 1981,4 and representing the second largest Vietnam-born population in developed countries, following the United States of America (USA).5 Vietnamese immigrants share distinct Oriental cultural heritage, traditional health beliefs and practices that completely differ from the Western biomedicine and conceptualisation of health.6-11 settled in developed countries also share the humanitarian migration patterns.12 The majority of Vietnam-born Australians arrived in Australia as refugees (50%) and family reunion immigrants (42%).12

Culture and lifestyle are important social determinants of health.13, 14 People from CALD backgrounds arrive in a new country with distinctive pre-existing health patterns from their country of origin and particular risk factors such as diet and cultural practices that may affect health.15 During the acculturation process, changes in beliefs and knowledge about health and diseases, dietary habits and lifestyles can occur, and immigrants often develop chronic disease patterns resembling those of the new country.16-19 Most studies of immigrant health have reported a “healthy immigrant effect” whereby immigrants are healthier than native-born populations,19-

1 22 which is believed to be largely explained by formal and informal health screening in the selective migration processes.15, 19 However, the health status of immigrants varies according to the circumstances of migration.19 Humanitarian immigrants such as refugees and family reunion entrants12, 21 often have poorer health status compared to native-born populations.19 Additionally, these immigrants may face socio- economic and cultural challenges that impact on health, such as limited education, disrupted careers, problems with communication due to low levels of proficiency in the language of their new country, financial difficulties, social discrimination, changes in family structures, conflict between generations and reduced access to health services.23 CALD communities in Australia also have diverse age structures, reflecting patterns of migration. People born in some countries, for example, Italy, Greece, Germany, Lebanon, East Timor, Cambodia and Vietnam have older median age and are ageing more rapidly than Australia-born people.24 Meanwhile, the median age of people born in China, India, Pakistan, Sudan, and Kenya has decreased gradually over the last decade, and become younger than that of Australia- born population.24

Vietnam-born populations in Australia and elsewhere25-27 tend to have lower socio- economic status (SES) than the general population of the new country, and are less likely to report good or excellent health status than some other CALD communities.27-30 A number of health disadvantages of the Vietnam-born population may relate to the high prevalence of communicable diseases in Vietnam (for example, tuberculosis,31 hepatitis B,32 and sexually transmitted diseases33), the circumstances of migration, and subsequent changes in lifestyle, health beliefs and practices in the new country.

Acculturation research conducted in Australia and internationally reported some changes in lifestyle among newly arrived Vietnamese immigrants in early 1990s.34-36 To date, the Vietnamese community in Australia, being a large CALD population, has almost 35 years of settlement in the new country. It is unknown how acculturation impacts on a number of key determinants of health, such as foods consumption, physical activity and alcohol drinking in this population. Changes in health status including subjective assessment (self-rated general health, physical function, and psychological well-being), prevalence of biological markers (body 2 mass index, high blood pressure), and prevalence of medical conditions (type 2 diabetes, heart diseases) as a result of acculturation has not been examined. The validity of the “healthy immigrant effect” in Vietnam-born immigrants could be questionable because of their refugee and humanitarian background12 in contrast to selective migration processes.15, 19 The relationship between acculturation and health is complex, and generally accepted theoretical models explaining the effects of acculturation on physical health are not yet available.37-39 This thesis provides a conceptualisation of the acculturation process and its effects on lifestyle and health, which assist the analyses of relationships between degrees of acculturation and a range of lifestyle and health status factors in Vietnam-born Australians. Findings of this research can potentially inform health professionals and the Vietnam-born community about health care practice, education, and lifestyle modifications.

The global prevalence of diabetes mellitus (diabetes) in adults aged 25 years and over increased from 8.3% in 1980 to 9.8% in 2008.40 In 2008, diabetes caused 1.3 million deaths world-wide.41 The number of people with diabetes world-wide is estimated to double between 2005 and 2030.42 Type 2 diabetes has become a major global public health challenge because of its rising prevalence and associated economic and health burdens.40, 43, 44 Although the aetiology of type 2 diabetes has not been fully understood,43 factors such as population growth and ageing, longer life expectancy, changes in the environment and lifestyles, and obesity have contributed to its rising prevalence.40, 43-45 The acceleration of industrialisation, urbanisation, economic development and market globalisation over the past decades has mediated the growth of diabetes through the increased availability and consumption of energy- dense foods and decreased levels of physical activity.41, 46

Research evidence has suggested that people of Vietnamese ethnicity are at increased risk of diabetes,47-52 but have inadequate levels of diabetes knowledge and diabetes management.53-55 Vietnam-born Australians are approaching middle age (median age 42.8 years in 2011),24 when chronic diseases are more likely to develop. However, information about the prevalence of, risk factors for and health-related outcomes of type 2 diabetes among Vietnam-born people in Australia, Vietnam and other counties is limited.56-66 Data for diabetes-related hospitalisation and mortality capture more severe aspects of diabetes, providing an indication of the health system burden of 3 diabetes and the characteristics of people who are most affected by their diabetes. A comparison with the general population (Australia-born) in terms of prevalence of type 2 diabetes, health risk profiles, hospitalisation and mortality provides details on where health disparities exist. Such research evidence can inform health promotion and interventions to address differences in health status and burden of diabetes between Vietnam-born and the general population.

Therefore, this program of research aims to investigate two interrelated aspects of health in this population: the impact of acculturation on health-related behaviours and health status; and the prevalence of type 2 diabetes, risk factors, hospitalisation, and mortality. Research findings will assist to inform targeted programs in health education and practice, thus contributing to improving the health of Vietnam-born Australians.

1.2. RESEARCH PLAN AND HYPOTHESES

This program of research consists of three main components involving analysis of data. The first component examines the effects of acculturation on dietary patterns, lifestyle factors and health status in Vietnam-born Australians. The second component compares general health and diabetes-specific profiles, prevalence of, and risk factors for type 2 diabetes between Vietnam- and Australia-born people. The final component investigates hospitalisation and mortality outcomes in Vietnam-born Australians who were admitted to hospital for reasons relating to type 2 diabetes, in comparison to their Australia-born counterparts.

The following hypotheses are tested: Hypothesis One: Lifestyle factors, including dietary patterns and health-related behaviours of Vietnam-born Australians, are associated with levels of acculturation. Hypothesis Two: Health status, including physical health and psychological distress of Vietnam-born Australians is associated with levels of acculturation.

4 Hypothesis Three: Prevalence of type 2 diabetes and risk factors for diabetes of Vietnam-born people differ from those of Australia-born people. Hypothesis Four: Vietnam-born people with type 2 diabetes have higher rates of hospital admissions for diabetes and its complications and comorbidities than their Australia-born counterparts. Hypothesis Five: Vietnam-born people with type 2 diabetes have higher risk of all- cause and diabetes-specific mortality than their Australia-born counterparts.

This program of research used already collected data. The first and second components used baseline questionnaire data collected by the 45 and Up Study,67 a large-scale population-based cohort study. The third component used routinely collected administrative data from the New South Wales (NSW) Admitted Patient Data Collection (APDC) linked with mortality data from the NSW Registry of Births, Deaths and Marriages (RBDM) and the Australian Bureau of Statistics (ABS).

In addition to the above major components, two supplementary analyses were conducted to assess the representativeness of Vietnam- and Australia-born participants in the 45 and Up Study, and to make a decision on how to select Vietnam- and Australia-born patients from hospital morbidity data. In the first supplementary analysis, demographic and socio-economic characteristics of Vietnam- and Australia-born participants in the 45 and Up Study were compared to those characteristics of the Vietnam- and Australia-born populations of NSW, using aggregated data obtained from the ABS 2006 Census. The second supplementary analysis evaluated the quality of country of birth recording in hospital morbidity data for people born in Vietnam and Australia, using data from the 45 and Up Study baseline questionnaire linked to NSW APDC data.

All data linkage was performed by the NSW Centre for Health Record Linkage (CHeReL). Descriptions of each data source and procedures in health record linkage are provided in relevant chapters.

5 All data preparation and analyses were conducted using Statistical Analysis Software (SAS)68 version 9.1. All statistical tests were two-sided, using conventional alpha of 0.05. Conclusions were drawn based on statistical significance and effect size.

1.3. ETHICS APPROVAL

Ethics approval for the research components were obtained from the relevant ethics committees as following: (i) Use of 45 and Up Study baseline questionnaire data was approved by the 45 and Up Study Scientific Advisory Committee (Project number 09006).

(ii) Use of 45 and Up Study baseline questionnaire data linked with NSW APDC was approved by NSW Population and Health Services Research Ethics Committee (Reference HREC/10/CIPHS/35).

(iii) Use of NSW APDC linked with the NSW RBDM and ABS mortality data was approved by NSW Population and Health Services Research Ethics Committee (Reference HREC/10/CIPHS/10).

1.4. OUTLINE OF THESIS CHAPTERS

The thesis comprises seven chapters. Chapter 2 is a description of the culture, health beliefs, health care practices, and migration history of Vietnamese immigrants to Australia as well as their socio-economic and health status. Chapter 3 introduces the 45 and Up Study and evaluates the representatives of the Vietnam- and Australia- born participants in the Study. Chapter 4 summarises theoretical concepts and models of acculturation, presents a conceptualisation framework and assesses effects of acculturation on lifestyle and health in Vietnam-born Australians. Chapter 5 further reviews literature relating to diabetes and compares general health and diabetes-specific profiles, prevalence and risk factors of type 2 diabetes between Vietnam- and Australia-born people. Chapter 6 presents a theoretical model of health service use and describes data sources and health record linkage procedures. Chapter 6 also includes a supplementary analysis of validity of country of birth recording in the NSW APDC and investigation of diabetes and comorbidities hospitalisation and mortality among Vietnam-born admitted patients, in comparison to their Australia- born counterparts. Chapter 7 concludes the thesis with a summary of the major 6 findings and discussion of the limitations and strengths of the research, and implications of research findings for practice and future research.

7

Chapter 2

Vietnamese history, culture, health beliefs

and immigration

Lifestyle or health-related behaviours are strong determinants of health, and perceptions of health and illness play an important role in health-care-seeking behaviours.13, 14 In a wider social context, lifestyle and health perceptions have their roots in people’s history, religion and culture.9, 10, 13 The fundamental focus of this thesis was health-related behaviours, diabetes risk factors and outcomes such as hospitalisation and mortality among Vietnam-born Australians. This chapter presents Vietnamese history, religion and culture in relation to conceptualisations of health, illness, and health care practices. It also describes migration history of Vietnamese immigrants, and their socio-economic and health status in Australia and internationally. This background information provides a solid foundation for the hypotheses and research questions.

2.1. HISTORY, RELIGION AND CULTURE

Archaeological work provides evidence of human existence and civilisation on Vietnamese territory as early as the Paleolithic era (10,000 BC).9, 10, 69 There were three major ancient cultural epochs, including Hòa Bình (9,000-7,000 BC), Bắc Sơn (unknown-3,000 BC), and Phùng Nguyên cultures (2,500-1,500 BC).69 Vietnam was known as Văn Lang during the legendary Hồng Bàng dynasty of the 18 rulers known as Hùng kings approximately 4000 years ago.9 Since then, the nation’s name has changed many times, and finally became Việt Nam in 1945 with the establishment of the first, official national political government.10

8 In Vietnamese history, there were a number of long periods when the country was under the domination of a foreign nation, which had profound and long-lasting influences on Vietnamese culture, religion and health beliefs.9, 10 China dominated Vietnam for over one thousand years (111 BC-938 AD, 1407-1427, and 1778- 1792).10 Mongol emperors invaded Vietnam twice, in 1257 and 1284, but were quickly defeated.69 In 1859, France started military conquests in Vietnam. In 1884, Vietnam officially became a French protectorate, and a part of French Indochina until 1954.69 Between 1940 and 1945, some parts of Vietnam were also under the control of Japanese army forces.69

On 2 September 1945, Hồ Chí Minh proclaimed the Democratic Republic of Vietnam.9, 10 The revolutions against French domination became successful on 7 May 1954 when French forces were defeated by Việt Minh forces at the Dien Bien Phu military area.10 This marked the end of over eighty years of French domination. Following this historical event, countries participating in the 1954 Indochina Peace Conference in Geneva, Switzerland signed the Geneva Accords. According to the Accords, Vietnam was partitioned into and at the 17th parallel Vietnamese demilitarised zone. The Democratic Republic of Vietnam governed North Vietnam with the support of communist countries such as the People’s Republic of China, the former Union of Soviet Socialist Republics, and North Korea.9, 10, 69 South Vietnam was under the Republic of Vietnam government, supported by the USA, South Korea, Australia, New Zealand, Canada and some European countries. The commonly known Vietnam War (1955-1975) was between North and South Vietnam. On 30 April 1975, North Vietnam military forces took control of South Vietnam. On 2 July 1976 the Socialist Republic of Vietnam was officially established,9, 10, 69 and remains today.

2.1.1. Religious beliefs

There are several religions in Vietnam. The earliest established religions include , (originating from China) and (originating from both India and China).9, 10 Other religions introduced later into Vietnam included , Islam and Hinduism. Religions indigenous to Vietnam were (Cao Đài) and Hòa Hảo.9, 10 In mountainous and remote areas, different religious

9 beliefs exist among minority tribal communities. Currently, six religions are officially recognised by the Vietnamese government: Buddhism, Catholicism, Protestantism, Islam, Caodaism and Hòa Hảo are.70

Among many religions practised in Vietnam, Buddhism is the most popular. Approximately 85% of Vietnam’s population identify themselves as Buddhist although they may not practise Buddhism on a regular basis.70 Buddhism, Confucianism, and Taoism have been co-existing in the country for centuries, and integrate with the Vietnamese tradition of worshipping ancestors and national heroes.10, 70 Confucianism can be described as a philosophy that encodes ethical and moral aspects of human life, emphasising filial piety and ancestry. Taoism seeks to achieve perfection and harmony by allowing things to assume their natural course. Buddhism’s concept of life is an attainment of enlightenment through experiential learning to overcome ego and desire which are believed to cause suffering in life. The combination of the three religions dictates morality, order, harmony and pragmatism, consequently emphasising the virtues of obedience, hierarchical social interactions, and a shy and modest personality.8-10, 71 The mindset or philosophy of life paradigms have implications for the culture and conceptualisation of health and illness by Vietnamese people.

2.1.2. Vietnamese culture

Vietnamese culture has an Oriental cultural heritage.9, 72 While Western culture often stresses individualism,72 Vietnamese culture emphasises family tradition, values the welfare of family over the welfare of individuals, and expresses deep respect to ancestors, elders, and the whole family unit.8-10 In contrast to the nuclear family structure of Western culture,72 the typical traditional Vietnamese extended families have three or more generations living in the same household.7, 10, 71, 72 The roles of each family member are clearly defined, reflecting a strong solidarity, mutual helpfulness, patriarchal structure and filial piety.71 Traditionally, the father or eldest son is the representative of the family with the highest level of interaction with other people outside the family. Men are expected to carry out heavy physical household tasks, make final decisions and support the rest of the family in times of family crisis. Women are expected to do housework, nurture the children, and they tend to support

10 the belief that the father or husband has the legitimate right to make final decisions. Women are the primary carers of ill family members. Children are taught to obey and honour their parents and expected to look after their ageing parents. Conflict within the family is generally avoided. However, when conflict arises women usually withdraw from such situations to maintain harmony in the family. The elderly receive high levels of respect from other people, and grandparents often assist with housework such as preparing meals or taking care of grandchildren. The elderly are normally cared for at home, and as such institutionalising an aged family member in a nursing home is believed to be disrespectful to them.72 Nevertheless, the traditional extended family model is increasingly being replaced by a nuclear family structure, especially in urban areas, which relates to a rapid socio-economic growth as a result of the economic reform since late the 1980s and early 1990s.73 In contemporary nuclear families, individualism is more common.7

Reflecting religious beliefs, attitudes toward life among Vietnamese people are characterised by passivity and personal reserve with an emphasis on proper form and appearance.8-10, 71 Hugging or holding hands between people of the same sex in public is regarded as normal, but it is unusual for a male and a female to show their affection in public.7, 8 The head of the human body is considered to be the sacred part of the body, thus patting the head of an older person is regarded as impolite and disrespectful behaviour. Feet are the lowest body part and should never be directed at another individual. It is also considered an improper behaviour to place feet on the chair or desk while sitting.7, 8

Self-control is another Vietnamese cultural value, in which emotions are typically kept to oneself; as such, it is uncommon for a person to express their strong emotions in public. There are also deep cultural restraints against showing “weakness of the mind” because this interferes with self-control. It is a common practice that people try to forget pain, sorrow and loss. Difficult events are often rationalised as “destiny” or “fate”.8 In contrast to the goal-oriented Western culture,8 Vietnamese people prefer activities that develop the human essence, such as philosophy, poetry and meditation,10 and this relates to their conceptualisations of health.

11 2.2. HEALTH BELIEFS AND PRACTICES

Vietnamese health beliefs and practices are greatly influenced by Chinese traditional medicine,6, 7 and to a lesser extent by Western biomedicine7 as a result of a long-term Chinese and French domination. Religions such as Confucianism and Taoism also play a predominant role in the conceptualisation of health and illness.6 Illness is conceptualised in three different, although overlapping, models of health beliefs.

The first and least common model of health beliefs is supernatural or spiritual. This model explains that illness can be brought on by a curse, sorcery or non-observance of religious ethics. Mental and physical illness is associated with spiritual ailments.74, 75 People who have strong beliefs in supernatural forces often seek consultations from Buddhist monks or traditional medical practitioners for amulets or other forms of spiritual protection, and even exorcism.74, 75

The second model is the âm-dương equilibrium theory which is similar to yin (âm) and yang (dương) in Chinese medicine. In this model, âm is translated as “cold” and dương is translated as “hot”, although these concepts are not necessarily referring to temperature. Illness occurs as a result of an imbalance between the two elements. An âm-dương imbalance can be caused by internal factors such as a high emotional state or pregnancy and external factors such as weather and seasonal changes and excessive consumption of “cold” or “hot” foods.6, 7, 76 To restore the balance, many folk treatments are applied, for example eating or drinking countering foods, using traditional herbal medicine, applying techniques such as coin rubbing, skin pinching, cup suctioning, herbal steaming, balm, minor bloodletting, acupressure, acupuncture and massage to expel the “bad” forces out of the body.7, 8, 74 According to the Vietnamese Ministry of Health,77 Vietnamese traditional medicine has been recognised officially since 1957 and included in curricula of medical universities. The use of herbal medicines and acupuncture is the most common form of traditional health practice.77 Vietnam-born people in Australia and USA report high levels of using complementary and alternative medical therapies such as herbal medicine, coining, massage and cupping.54, 78-82

12 The third health belief model is Western biomedicine, which was introduced into Vietnam in the nineteenth century during the French domination period. However, biomedicine beliefs are often combined with âm-dương beliefs. Diseases are believed to be caused by germs or contaminants in the environment.7 People seeking health care from biomedical doctors often expect an immediate diagnosis and some form of drastic treatment. Invasive diagnostic or surgical procedures are not welcome due to a perception that blood loss in such procedures will worsen the illness.7 Surgical operations are perceived as the last option from a fear that operations would alter the internal balance.7, 8 Western biomedicine medications are perceived as a strong and hot remedy, and therefore preferred for acute illnesses only. It is a common practice for Vietnamese patients to vary the dosage and course of Western medications according to their subjective feelings or to combine prescribed medications with traditional herbal medicines or vitamin C for a “cooling” effect, which causes high levels of concerns about treatment compliance.54, 75, 76, 82-86 It is common in Vietnam for people with acute sickness to self-prescribe antibiotics, and antibiotics can be purchased from chemists without a doctor’s prescription. This practice is believed the leading cause of antibiotic resistance in Vietnam.87

In summary, Vietnam-born people view health from different perspectives and interpret illness as a result of interaction between spiritual factors and internal inequities, as well as the effects of bacteria and environment. These health conceptualisations lead to a mix of health-seeking behaviours in which various diagnostic and treatment methods can be used to obtain perceived health benefits.7 Such beliefs and practices still remain popular among first generation of Vietnam- born people after decades of living in the Western countries.83, 88

2.3. VIETNAM-BORN PEOPLE IN DEVELOPED COUNTRIES

2.3.1. Migration history

According to the ABS 2006 Census, there were 159,849 Vietnam-born Australians in the country,3 representing an increase from approximately 40,700 in 1981, to 122,000 in 1991, and 155,000 in 2001.12 Vietnam-born Australians reside mostly in urban areas in NSW (63,790; 39.9%), (58,874; 36.8%), Queensland (13,084;

13 8.2%), and (10,547; 6.6%).3 The Vietnamese community of Australia is diverse in many respects, largely due to characteristics of migration movements,7 together with various religious beliefs.75 The migration circumstances of Vietnam-born people can be divided into three main migration movements.

The first wave of migration happened about a week prior to 30 April 1975 when North Vietnam took over South Vietnam. In a short period, only high-ranking staff of South Vietnam government and military could be evacuated by air from Saigon – the former capital city of South Vietnam, which is now Ho Chi Minh city – to the offshore American fleet, then settled in the USA, Australia and Canada.10, 69 Therefore, this group of Vietnamese immigrants had high levels of education and were familiar with Western lifestyles. Most of them resumed their professional career in the new country.7

The second migration movement of “boat people” occurred between 1977 and 1986. Factors driving this migration wave included economic and financial constraints and prosecution and political unrest in Vietnam post-war.89 The majority of migrants made their way to United Nations refugee camps in small homemade boats and wooden vessels while some left Vietnam by land. This group consisted of people from a broad socio-economic spectrum such as ex-government soldiers and officers, merchants, farmers, fishermen, and inhabitants of rural areas.7, 71 Many spent months or years in refugee camps under appalling and regimented conditions before being granted entry to Australia, the USA and other countries.89 Compared to immigrants in the first wave, people in the second wave generally had lower SES, and suffered hardship and loss, and the deaths of loved ones during the journey.7, 69, 90 These traumas meant that they were more likely to suffer psychological problems and issues with post-immigration adjustment.90 This group was also less likely to be familiar with the Western lifestyle.7 In addition to the arrival of Vietnamese refugees (1977-1986), a proportion of Vietnam-born Australians migrated under the Orderly Departure Program (1983-1984), which was an agreement between the Australian and Vietnamese governments for family members of Vietnamese immigrants who had settled in Australia.12, 91

14 The third migration movement occurred between 1987 and 1996 under the family reunion scheme of the Australian government. This group made up 42% of Vietnam- born Australians, including family members such as elderly parents, spouses and dependent children of the Vietnam-born Australians.12

Prior to 1975, there were approximately 700 Vietnam-born people in Australia, including orphans from the Vietnam War, Vietnamese wives of Vietnam veterans, and tertiary students.25 The number of refugees seeking asylum in Australia declined from the mid-1990s as the result of the Australian government implementing the Comprehensive Plan of Action and streamlining the Vietnamese Family Migration Program.25 Since 2003, approximately 2,500 Vietnamese people were granted Australian permanent residence each year as family reunion or skilled immigrants.92

2.3.2. Socio-economic status

Vietnam-born Australians are an ageing population. The median age increased from 33.4 in 1996 to 37.5 in 2001, 40.7 years in 2006, and 42.8 years in 2011.24 Most speak Vietnamese (78.0%) and Cantonese (15.7%) at home and have limited English proficiency. Forty percent of Vietnam-born Australians report difficulty in speaking English or do not speak English at all.25

The ABS 2006 Census25 reveals that Vietnam-born people have poorer SES compared to the Australian general population, such as a lower proportion having post-school qualifications (35.1% vs 52.5%), lower weekly individual income ($349 median vs $466), and slightly lower rate of labour force participation (61.9% vs 64.6%) but significantly higher rate of unemployment (11.4% vs 5.2%).

In comparisons with people born in China (the fourth largest CALD community in Australia, 4.1% as at the 2006 Census), India (the sixth largest, 3.1%) and the Philippines (the seventh largest, 2.7%),4 the Vietnam-born population had the lowest proportions of people who can speak English well or very well (56.7% Vietnam- born, 65.4% China-born, 95.0% India-born, and 96.8% Philippines-born) and who have post-school qualifications (35.1% Vietnam-born, 55.0% China-born, 76.1% India-born, and 64.9% Philippines-born).25 The weekly individual income (median)

15 among Vietnam-born people ($349) was higher than that among China-born ($242) but lower than for those born in India ($543) and the Philippines ($538). Sixty two percent of people born in Vietnam participated in the labour force compared to 56.3%, 72.3%, and 73.1% of people born China, India, and the Philippines, respectively.25 However, the unemployment rate was higher in both the Vietnam- born (11.4%) and China-born groups (11.2%), compared to 7.2% in India-born and 5.2% among Philippines-born people.25 Reflecting the recent growth of migration to Australia from China and India, 27% of China- and 25% of India-born people were non-Australian citizens compared to only 5% and 8% of the Vietnam- and Philippines-born people.25

International studies conducted in the USA26, 27 and Canada55 have also reported a lower SES of Vietnam-born people compared to native-born or other overseas-born groups. About 31% of Vietnam-born Americans do not finish high school compared to 17% of Chinese Americans, and 6% of other Asian Americans.26, 27

2.3.3. Health status

Most immigrant health studies have found a “healthy immigrant effect” whereby those immigrants selected on the basis of their economic success are generally healthier than their native-born counterparts at the time of immigration.19-22 This health advantage is a result of the health screening in the selective migration process, and healthier diets or lifestyles in the country of origin.15, 19 However, the health status of immigrants varies according to the circumstances of migration.19 In contrast to selective immigrants, refugees and family reunion visa holders generally have a poorer health status than the native-born population.19 This is also reflected in the health of Vietnamese immigrants in the developed world.

Vietnam-born people in Australia and other countries are less likely to rate their health positively than native- and other overseas-born populations.21, 27, 29, 30, 63 For example, 69% of Vietnam-born adults in NSW have reported good, very good or excellent health compared to 80% of the NSW population, 89% of India-born, and 87% of Philippines-born counterparts.63 Similarly, positive health status has been reported by only 60% of Vietnam-born people in California, USA in comparison to

16 87% of non-Hispanic Caucasians, 86% of Filipino Americans and 85% of Koreans in California.27

A high prevalence of mental health disorders such as post-traumatic stress disorders and depression has been reported among Vietnam-born people, especially among refugees and former political prisoners, which could be associated with pre-migration trauma suffered in communist labour and refugee camps.29, 88, 90, 93 Trauma and post- traumatic stress disorders continue to affect the mental health of Vietnam-born people even after a decade of resettlement in a new country.93, 94 According to the 2010 NSW Population Health Survey (PHS),63 12.3% of Vietnam-born people reported high and very high levels of psychological distress compared to 11.1% of the NSW population. The NSW PHS is an on-going, computer assisted telephone interviewing survey of randomly selected NSW residents living in households with private telephones. The NSW PHS reports health status (age and gender weighted) of adult, overseas-born residents aged 16 years and over.63

Vietnam-born women in developed countries are reported to have a higher rate of cervical cancer,28, 95 and poorer levels of knowledge about cervical and breast cancer95-97 than the general population. Inadequate knowledge about sexually transmitted infections among Vietnamese men has been reported.73, 98, 99 Further studies have also revealed a lack of knowledge about diabetes,53-55 hypertension,100 hypercholesterolemia,101 hepatitis,102, 103 and smoking104 in Vietnam-born people. Health statistics for NSW show elevated rates of tuberculosis, hepatitis B, hepatitis C, and liver cancer among Vietnam-born people.28, 105, 106

In terms of health-related behaviours and risk factors, the 2010 NSW PHS63 reported that only half of Vietnam-born adults (51.8%) had adequate daily intake of fruits (≥2 serves/day) compared to 55.4% of NSW adults, and that a minor proportion (5.6%) had adequate daily intake of vegetables (≥5 serves/day) compared to 10.1% of NSW adults. Vietnam-born people were also less likely to have adequate levels of physical activity (≥150 minutes per week) than the NSW average (34.9% vs 55.1%) or consume reduced fat milk (25.9% vs 47.5%). On a positive note, in comparison to the general NSW population, Vietnam-born people consistently had lower rates of: alcohol consumption at risky levels (10.0% vs 32.2%), overweight or obesity (19.6% 17 vs 51.9%), asthma (7.8% vs 10.6%), high blood pressure (14.3% vs 30.2%), and high cholesterol (25.5% vs 26.7%).

2.3.4. Use of health care services

There is research evidence that Vietnam-born people in developed countries are less likely to use health services than the general and many other overseas-born populations. Vietnamese women have low rates of participation in cervical screening,63, 95, 96 and perinatal care during pregnancy.107 Despite a high prevalence of mental health problems, Vietnam-born people are less likely to seek help from health professionals for psychological problems than the Caucasian population.29, 93, 94, 108, 109

Understanding barriers to the use of health care services among Vietnam-born people could provide insight for intervention programs. Given that traditional Vietnamese medicine differs greatly from Western biomedicine as discussed earlier, researchers continue to raise concerns about whether the traditional health beliefs and practices of Vietnam-born people may prevent them from accessing and using health services.8, 84, 110-112 However, two empirical studies have reported that the underutilisation of health services did not relate to cultural beliefs and practices.75, 83 Strong determinants of the use of health services by Vietnam-born people are language barrier, communication between patient and health professional, socio- economic status, access to the health care system, and cultural mores.86, 110, 113

Many Vietnam-born people in English-speaking countries are linguistically isolated because of their limited English proficiency. Language barrier has been identified as the most important barrier to accessing health care services,55, 86, 103, 110, 114, 115 which can be associated with inadequate interpreter services available in the health care system.86, 99, 116 Vietnam-born patients generally prefer to visit Vietnamese-speaking health professionals.117 Those seeking care from non-Vietnamese-speaking health professionals often express a concern about effective communication between patient and health professional.78, 85, 86, 116 Vietnam-born patients who use health interpreter services often associate the quality of care and understanding of their conditions and health professional instructions with the quality of the interpreter.118

18 The difficulty in discussing traditional medicine with physicians has also been identified.78, 85, 86, 119 Vietnam-born patients are often reluctant to inform their physicians about their use of traditional medicines because of a fear of disapproval119 or that this would affect the patient-doctor relationship.78, 85, 86 Researchers recommend that health care professionals should be aware of the patient’s traditional health beliefs and remedies as part of quality of care.78, 85, 86 Having no private health insurance, and low levels of education and incomes have been found to predict an underutilisation of health services.83, 99, 103, 107 It is not uncommon for Vietnam-born people to not have one regular family doctor, and this could contribute to a lack of access to health care system.83, 99, 116

In most Asian cultures, it is unusual to have open discussions about intimate and sensitive subjects such as sexuality.120-122 Vietnamese culture is not an exception, which may prevent patients from exchanging information or discussing their sexual health issues with clinicians.123 These cultural norms are still evident in the second generation of Vietnamese Australians.124

In summary, Vietnam-born Australians are among the top five overseas-born populations of Australia. Most Vietnam-born immigrants in Australia and other countries share non-voluntary and humanitarian migration backgrounds; consequently, they also share socio-economically and health disadvantaged positions relative to the general populations of their new homelands. In addition, they possess distinct traditional perceptions of health and health care practice and tend to underutilise health services. In the following chapters, the influence of immigration measured by acculturation indicators on lifestyle and health status is explored, followed by an investigation of the health status and outcomes of type 2 diabetes among Vietnam-born Australians. The next chapter, Chapter 3, presents the 45 and Up Study from which the sample of Vietnam-born Australians were selected for this program of research, and also evaluates the representativeness of the 45 and Up Study participants. The results of Chapter 3 will facilitate interpretations of findings in Chapter 4 and Chapter 5.

19

Chapter 3

The 45 and Up Study and selection of Vietnam- and

Australia-born participants

This chapter describes the 45 and Up Study (referred to as the Study) and its baseline questionnaire data, and details how Vietnam- and Australia-born participants were selected. Demographic and socio-economic characteristics of Vietnam- and Australia-born participants in the Study are compared to those of Vietnam- and Australia-born populations in NSW as of the Census 2006 to assess the representativeness of the Study’s participants.

3.1. THE 45 AND UP STUDY

The 45 and Up Study (website: http://www.45andup.org.au) is the largest population- based cohort study ever undertaken in the Southern Hemisphere. It is managed by the Sax Institute in partnership with Cancer Council NSW, the Heart Foundation (NSW Division), Beyondblue: The National Depression Initiative, NSW Health, Ageing, Disability and Home Care, Department of Human Services NSW and UnitingCare Ageing. Initial recruitment into the Study commenced in February 2006. By December 2009, 266,848 residents of NSW aged 45 years and over had joined the Study. The participants’ health will be followed-up longitudinally via questionnaires and there is regular linkage to their health records collected by authorities such as the NSW Department of Health, Medicare Australia (Medicare), NSW RBDM and other agencies.

Potential participants were randomly sampled from the Medicare enrolment database and were mailed an invitation to join the Study, an information leaflet, a study questionnaire and a consent form. There was over sampling of individuals aged 80

20 years and over and of residents of rural areas. Participants joined the Study by completing the questionnaire in English and providing signed consent, including consent to have their health followed prospectively and retrospectively through health record linkage. People could also volunteer to join the Study by calling the Study helpline and requesting an invitation pack.

The baseline questionnaire (response rate 18%) collected information about demographic and socio-economic characteristics, personal health behaviours, medical conditions and treatment, and other general health-related issues (Table 1). The questionnaire was gender-specific, so that the blue questionnaire contained male-specific questions and pink questionnaire had female-specific questions. The completed questionnaire with valid written consent was double entered into the database. The questionnaires were completed with minimal missing data, ranging from 0% for gender to 6.6% for body mass index.125 The sample questionnaire is presented in Appendix 1.

Table 1 Information collected in the 45 and Up Study baseline questionnaire

Medical conditions and Demographic and socio- Personal health general health-related economic characteristics behaviours information

 Gender  Smoking  Disease and surgical history  Date of birth  Alcohol Family history of illness  Marital status  Physical activity  Medication  Country of birth,  Fruit and vegetable  ancestry, year of consumption  Functional capacity arrival to Australia  Other dietary  Psychological distress Education information   Cancer screening history Household income Sleep habits    Falls Working status and   Oral health retirement  Skin pigmentation and  Social connectedness response to sunlight

 Reproductive history  Incontinence  Prostate symptoms and sexual functioning in men

21 3.2. SELECTION OF VIETNAM- AND AUSTRALIA-BORN PARTICIPANTS

The selection of Vietnam- and Australia-born participants was based on the following items in the baseline questionnaire and presented in Figure 1.

(i) Country of birth: Question 7 asked “In which country were you born?”. Tick boxes were listed for the 14 most common countries of birth, and a space was provided for specifying other country of birth. The country of birth responses were numerically coded according to Standard Australian Classification of Countries (SACC).67, 126

(ii) Year of arrival in Australia: Question 8 asked “What year did you first come to Australia to live for one year or more?”. The responses were coded as a four-digit number, for example 1970.

(iii) Ancestry: Question 9 asked “What is your ancestry?” Tick boxes were listed for the 16 most common ancestries, and a space was provided for specifying other ancestry.

It is possible that the 45 and Up Study participants of non-Vietnamese ethnicities were born in Vietnam, in particular during the French domination and Vietnam War periods as discussed in Chapter 2. In addition, a proportion of people who were born in Vietnam may also have both Vietnamese and Chinese ancestries because of a long history of Chinese domination in Vietnam. The migration movements of Vietnamese people started from April 1975. Therefore, combinations of responses to country of birth, ancestry and year of arrival in Australia (Figure 1) were used to identify Vietnam-born participants who were of Vietnamese ethnicity (referred to subsequently as Vietnam-born).

(i) Country of birth was Vietnam (SACC code=5105), ancestry was Vietnamese and/or Chinese, and year of arrival could be any year.

(ii) Country of birth was Vietnam and year of arrival was 1975 and later if ancestry was not reported.

(iii) Ancestry was Vietnamese and year of arrival was 1975 and later if country of birth was not reported.

22

Figure 1 Selection of Vietnam- and Australia-born participants in the 45 and Up Study

Country of birth Ancestry Year of arrival in Australia

Vietnam Vietnamese N=764 SACC code=5105 and/or Chinese Any year

Vietnam N=29 Vietnam-born SACC code=5105 Not reported 1975 or later N=797

N=4 Not reported Vietnamese 1975 or later

Australia Australia-born SACC code=1101 N=199,917

23 Using these criteria, 797 participants were identified as Vietnam-born, accounting for 0.3% of the entire cohort of the Study. The Australia-born group consisted of 199,917 participants who reported country of birth as Australia (SACC code=1101), accounting for 75.0% of the Study’s cohort.

3.3. PARTICIPANTS IN THE 45 AND UP STUDY COMPARED TO NSW POPULATION AT THE 2006 CENSUS

The 45 and Up Study participants was recruited from the general population of NSW, but the baseline questionnaire was available only in English and the response rate was low (18%). This warrants an assessment of whether the participants in the Study represent the underlying general population. This section compares demographic and socio-economic characteristics of Vietnam- and Australia-born participants in the 45 and Up Study with those of Vietnam- and Australia-born populations of NSW using aggregated 2006 Census data. This Census year was the nearest in year to the questionnaire completion (2006 to 2008).

3.3.1. Australian Bureau of Statistics 2006 Census data

The ABS conducts Census every five years with the aim to accurately measure the number of people and dwellings in Australia and a range of key demography indicators on the Census night.127 For each Census, the ABS provides various Census products, for example the community profiles, Socio-Economic Indexes for Areas (SEIFA),128 data packs and the Social Atlas series.127 The 2006 Census data of population and housing are arranged according to topics such as geographical location, age, gender, education, marital and relationship status, housing, income, language, migration, transport, religion, ethnicity, occupation and workforce.3 Aggregated data can be retrieved in a tabular format from the ABS website via an online mapping tool, TableBuilder.3

For this chapter, the 2006 Census data for NSW geographical location, and Australia and Vietnam country of birth were obtained from ABS using the TableBuilder tool and saved in a Microsoft Excel spread sheet. Each spread sheet contains age in single years as row headings, and other information, including gender, marital status,

24 educational qualification, income, working status, remoteness, postcodes, and year of arrival in Australia as column headings. Data on Index of Relative Socio-Economic Disadvantage (IRSD)128 by country of birth were not available from TableBuilder, thus IRSD scores for all postcodes in NSW were obtained from another section of the ABS website. Lower IRSD scores indicate higher levels of socio-economic disadvantage.128 The IRSD score were divided into quintiles: the 1st quintile (639- 930, most disadvantaged), 2nd quintile (931-971), 3rd quintile (972-1005), 4th quintile (1006-1063), and 5th quintile (≥1064, least disadvantaged). These IRSD scores and quintiles were then merged into the postcode spread sheet using postcode as a matching variable.

3.3.2. Comparability of demographic and socio-economic variables between the questionnaire and Census data

Directly comparable variables The following variables derived from the 45 and Up Study baseline questionnaire and the 2006 Census data are considered to be identical, allowing a direct comparison. (i) Gender as male and female. (ii) Age as continuous and categorical data. (iii) Relationship as living with a partner or having no partner. (iv) Working status as not working and working. (v) IRSD quintiles. (vi) Accessibility Remoteness Index of Australia plus (ARIA+): The remoteness was classified as major cities (ARIA+≤0.2), inner regional (0.22.4).129 (vii) Year of arrival in Australia (not applicable to people born in Australia).

Non-directly comparable variables The educational qualification and income variables were not directly comparable between the two data sources, thus requiring data manipulation. The health insurance variable was only available in the questionnaire data.

25 (i) Educational qualification: The 2006 Census data classified educational qualifications into postgraduate degree, bachelor degree, graduate diploma and graduate certificate, advanced diploma and diploma, certificate levels, and not-stated or inadequately described category. Meanwhile, the Study baseline questionnaire categorised educational qualifications into less than high school certificate, high school/trade, certificate/diploma, and university and higher degrees. Thus, the postgraduate and bachelor degree in Census data were combined into the category “University or higher” to match the similar category of the questionnaire. The new category “Certificate/Diploma” was created to accommodate graduate diploma and graduate certificate level, advanced diploma and diploma categories of the Census data and trade/apprenticeship and certificate/diploma categories of questionnaire data.

(ii) Income: The Census data collated individual weekly income but the questionnaire contained annual household income; therefore, weekly household income was estimated for questionnaire data by dividing the annual household income by 52.

(iii) Health insurance: Health insurance status was coded as having private health insurance, Department of Veterans’ Affairs card (DVA), health care concession card, and none of these. Because this variable was available only in the questionnaire data, distribution of health insurance among participants in the Study is presented in Chapter 3 and Chapter 4.

3.3.3. Results

In NSW at the 2006 Census night, there were 63,558 Vietnam-born persons; of these, 26,298 (41%) were aged 45 years and over. There were 4,521,154 Australia-born persons; of these, 1,534,934 (34%) were aged 45 years and over. The Census data of people under 45 years of age were excluded from analysis. Data were presented as frequency and percentage. Differences between the 45 and Up Study participants and the underlying general populations were assessed by chi-square test for proportion (categorical data) and Student’s t-test (for age as continuous data).

26 3.3.3.1. Demographic characteristics and socio-economic status

Table 2 shows distributions of gender, age and marital status among Vietnam- and Australia-born participants in the 45 and Up Study and the underlying general populations in NSW. There were minor differences in gender such as 48.9% of Vietnam-born participants were males compared to 47.4% of the general population (p=0.41). Similarly, males accounted for 45.4% of Australia-born participants compared to 46.9% of the Australia-born general population (p<0.001). The Vietnam-born participants in the Study were 2.7 years older than the Vietnam-born general population (95% confidence interval [CI]=1.97-2.70, p<0.001). The Australia-born participants in the Study were also one year older (95%CI=0.94-1.05, p<0.001). The Vietnam- and Australia-born participants were more likely to live with a partner than the general populations (74.4% vs 68.9% for Vietnam-born, 75.2% vs 61.8% for Australia-born).

Socio-economic status of the Vietnam- and Australia-born participants and NSW general populations are presented in Table 3. The Vietnam- and Australia-born participants in the Study were more likely to be working than the general populations (49.1% vs 42.4% for Vietnam-born, 50.2% vs 48.3% for Australia-born). Nineteen percent of Vietnam-born participants in the Study had a university or higher degree (vs 7.8% of the general population) and 13.6% had certificates or diplomas (vs 4.0% of the general population). Similarly, 21.6% of the Australia-born participants in the Study had a university or higher degree vs 11.5% of the general population; and 20.7% had certificates or diplomas vs 9.3% of the general population. Weekly income was not directly comparable between the questionnaire (household income) and Census data (individual income). However, the patterns of the distribution of income in both Vietnam- and Australia-born participants relative to those of the general populations were similar, in which higher proportions of the Study’ participants reported a high weekly income (≥$1,000) than the general populations.

For area-based SES measures such as IRSD and remoteness, Vietnam-born participants in the Study were more likely to live in the 4th and 5th IRSD quintile areas (less socio-economically disadvantaged) than the general Vietnam-born population (18.8% vs 13.7%, p=0.001). In contrast, Australia-born participants were

27 less likely to reside in these areas (the 4th and 5th IRSD quintiles, 36.5% vs 39.9%, p=0.001). The 45 and Up Study over-sampled individuals from rural and remotes areas. This was reflected in the findings that a higher percentage of Vietnam- and Australia-born participants in the Study lived in regional and remote areas of NSW than those of the general populations (1.8% vs 0.6% for Vietnam-born and 60.1% vs 40.3% for Australia-born).

3.3.3.2. Year of arrival in Australia

The distribution of year of arrival in Australia among Vietnam-born participants in the 45 and Up Study was consistent with the Vietnamese immigration waves as shown in Figure 2. The first peak of immigration was between 1977 and 1982, corresponding to the arrival of refugees. The second peak was between 1983 and 1984, reflecting the Orderly Departure Program (for family members of Vietnamese Australians) in addition to refugee arrivals. The third peak was between 1987 and 1992, coinciding the arrival of Vietnamese people under the Family Reunion Scheme.12, 91

Figure 2 Year of arrival in Australia of Vietnam-born people

28

Table 2 Demographic comparisons between the 45 and Up Study and 2006 Census for Vietnam- and Australia-born people

People born in Vietnam People born in Australia Demographic 45 and Up Study 2006 Census P 45 and Up Study 2006 Census P characteristics χ χ N=797 N=26,298 value N=199,917 N=1,534,934 value Gender Male 390 (48.9%) 12,477 (47.4%) 0.41 90,667 (45.4%) 720,383 (46.9%) <0.001 Female 407 (51.1%) 13,821 (52.6%) 109,250 (54.6%) 814,551 (53.1%) Age (years) Median 55 52 61 59 Mean (SD) 58.6 (10.4) 55.9 (10.2) 62.5 (11.1) 61.5 (12.2) 45-54 377 (47.3%) 15,511 (59.0%) <0.001 60,254 (30.1%) 554,577 (36.1%) <0.001 55-64 231 (29.0%) 5,837 (22.2%) 64,054 (32.0%) 428,761 (27.9%) 65-74 110 (13.8%) 2,854 (10.9%) 43,009 (21.5%) 274,841 (17.9%) 75-84 64 (8.0%) 1,696 (6.4%) 27,112 (13.6%) 205,170 (13.4%) ≥85 15 (1.9%) 400 (1.5%) 5,488 (2.7%) 71,585 (4.7%) Relationship∑ No partner 202 (25.6%) 8,183 (31.1%) 0.001 49,256 (24.8%) 585,922 (38.2%) <0.001 Partner 586 (74.4%) 18,116 (68.9%) 149,517 (75.2%) 949,006 (61.8%)

χ: Chi-square test for proportions ∑: Missing data were excluded from percentages

29 Table 3 Socio-economic status comparisons between the 45 and Up Study and 2006 Census for Vietnam- and Australia-born people

People born in Vietnam People born in Australia Socio-economic status 45 and Up Study 2006 Census 45 and Up Study 2006 Census P valueχ P valueχ N=797 N=26,298 N=199,917 N=1,534,934 Working∑ Not working 387 (50.9%) 14,806 (57.6%) <0.001 97,930 (49.8%) 775,050 (51.7%) <0.001 Working 410 (49.1%) 10,919 (42.4%) 98,806 (50.2%) 723,151 (48.3%) Qualification∑^ Less than high school 291 (36.5%) - N/A 73,408 (36.7%) - N/A High school certificate/Trade 222 (27.9%) - 39,068 (19.5%) - Certificate/Diploma 108 (13.6%) 1,058 (4.0%) 41,360 (20.7%) 144,056 (9.3%) University or higher degree 154 (19.3%) 2,057 (7.8%) 43,265 (21.6%) 178,324 (11.5%) Others/Unknown 22 (2.8%) 23,183 (88.2%) 2,816 (1.4%) 1,212,554 (78.5%) Weekly <$400 333 (52.9%) 16,494 (65.0%) N/A 37,666 (23.9%) 704,422 (47.9%) N/A income $400-$799 111 (17.6%) 5,616 (22.1%) 35,558 (22.6%) 358,168 (24.4%) ($AUS)∑^≠ $800-$999 62 (9.8%) 1,386 (5.5%) 14,878 (9.5%) 107,570 (7.3%) $1,000-$1,300 51 (8.1%) 915 (3.6%) 21,386 (13.6%) 108,325 (7.4%) ≥$1,300 73 (11.6%) 952 (3.8%) 47,880 (30.4%) 191,439 (13.0%) IRSD 1st: most disadvantaged 445 (55.8%) 15,275 (58.1%) 0.001 22,805 (11.4%) 186,983 (12.4%) <0.001 (quintile)∑ 2nd quintile 137 (17.2%) 5,173 (19.7%) 48,467 (24.3%) 335,762 (22.2%) 3rd quintile 65 (8.2%) 2,222 (8.5%) 55,542 (27.8%) 386,666 (25.6%) 4th quintile 95 (11.9%) 2,398 (9.1%) 35,154 (17.6%) 273,455 (18.1%) 5th: least disadvantaged 55 (6.9%) 1,217 (4.6%) 37,800 (18.9%) 329,885 (21.8%) Remoteness∑ Major cities 783 (98.2%) 26,101 (99.4%) <0.001e 79,739 (39.9%) 915,582 (59.7%) <0.001 Inner regional 14 (1.8%) 140 (0.5%) 74,644 (37.4%) 448,618 (29.3%) Outer regional, remote 0 (0.0%) 24 (0.1%) 45,396 (22.7%) 168,651 (11.0%) χ: Chi-square test for proportions ∑: Missing data were excluded from percentages ^: Questionnaire and Census data were not directly comparable ≠: Household income in questionnaire but individual income in Census data N/A: Not applicable e: Outer regional and remote were excluded from chi-square test 30 3.4. DISCUSSION OF FINDINGS

This chapter has described the 45 and Up Study from which the study populations for subsequent sections of this thesis were selected. Supplementary descriptive analyses were undertaken to assess the representativeness of the Study’s Vietnam- and Australia-born participants for the underlying general populations from the same country of birth. The findings of this chapter will assist in interpreting the results of subsequent chapters.

Although the 45 and Up Study is a large population-based cohort, the availability of the baseline questionnaire in English only and the low response rate (18%) indicate that there were limited opportunities for people with low levels of English proficiency to participate, and high levels of motivation among the Study participants. These were reflected in the results of comparisons. On average, both Vietnam- and Australia-born participants were older than the respective general populations (2.7 years for Vietnam-born, 1.0 year for Australia-born). This cannot be explained solely by the oversampling of individuals aged 80 years and older by the 45 and Up Study.67 The percentages of the 45-54 age group among the Study participants were lower than those of the general populations (47.3% vs 59.0% for Vietnam-born and 30.1% vs 36.1% for Australia-born). This was indicative of selective under-response by people of working age. The 45 and Up Study participants were more likely to have a partner than the general population, suggesting higher levels of social support among participants.

The Vietnam- and Australia-born participants were more likely to have better SES than the general populations, demonstrated by greater proportions of the Study participants holding university degrees and being in the workforce despite their older age. Almost all Vietnam-born people in NSW (99.4%) and those who participated in the Study (98.2%) were living in major cities. According to the NSW Social Health Atlas,130 Vietnam-born communities are concentrated mainly in metropolitan Local Government Areas (LGA) of Sydney such as Fairfield, , Auburn, Marrickville as well as Canterbury, Liverpool and Strathfield. Comparisons by IRSD quintiles for Vietnam-born people further supported a higher SES of those

31 participated in the 45 and Up Study, although IRSD is an indicator for geographical areas rather than individual people. The oversampling of individuals in rural areas in the 45 and Up Study possibly explained the fact that 60.1% of Australia-born participants were residents of regional and remote areas of NSW compared to 40.3% of the Australia-born population of NSW.

The year of arrival in Australia of the Vietnam-born participants was consistent with the migration waves of Vietnamese immigrants, suggesting that the Vietnam-born participants in the 45 and Up Study could be as diverse in many respects as the underlying Vietnam-born general population, as discussed in Chapter 2. This could explain the inconsistent differences in SES between the Vietnam-born and Australia- born participants such as marginal differences in working status (working 49.1% vs 50.2% respectively), educational qualification (less than high school 36.5% vs 36.7%; university or higher degree 19.3% vs 21.6%) but substantial differences in household income (70.5% vs 46.5% having less than $800 per week).

Census data included little health information, thus the representativeness of the Study participants in terms of health characteristics could not be explored here. However, the 45 and Up Study research team has assessed health status of the Study participants against health information collected in the 2006/07 NSW PHS, and published those findings elsewhere.131 The 2006/07 NSW PHS (60% response rate)132 contained survey items that were highly comparable to the Study baseline questionnaire.131 The distributions of fruit consumption and body mass index were similar between the Study participants and NSW PHS survey respondents but the Study participants had slightly lower prevalence of smoking, psychological distress, hypertension, diabetes and asthma.131 However, the analyses were not stratified by country of birth.

The representativeness and non-response bias in surveys could be a concern for estimations of disease prevalence.133, 134 However, in epidemiological studies the representativeness of the study population does not affect the relative risk or odds ratio (OR) estimates based on internal comparisons within study populations.135, 136 It has been reported that given the different survey method and lower response rate, the 45 and Up Study data yielded similar shapes, directions and point of estimates for 32 associations of 20 pairs of exposure and outcome measures in comparison to those generated from the NSW PHS.131 For example, the Study baseline questionnaire data yielded OR of having diabetes was 0.95 (95%CI=0.74-1.21) for underweight, 1.66 (95%CI=1.56-1.76) for overweight, and 4.17 (95%CI=3.92-4.44) for obesity compared to normal weight (adjusted for age, sex, and remoteness of residence). The NSW PHS data produced the respective ORs of 0.93 (95%CI=0.48-1.80, underweight), 1.81 (95%CI=1.49-2.20, overweight) and 4.80 (95%CI=3.95-5.83, obesity).131

In conclusion, findings in this chapter and from a previous study131 indicate that the participants in the 45 and Up Study are more likely to be older, wealthier and healthier than the average NSW populations. These differences could limit the generalisability of certain research findings using the Study baseline questionnaire. However, any departures from the general populations of the Vietnam- and Australia-born participants are unlikely to bias the magnitudes of exposure and outcome relationship expressed as relative measures (that is, relative risks or ORs) which are to be estimated in Chapter 4 for acculturation effects and Chapter 5 for diabetes risk factors.

33

Chapter 4

Effects of acculturation on lifestyle and health

4.1. INTRODUCTION

The last few decades have seen a growth of research on the health of immigrants in relation to acculturation.20, 37, 137, 138 This phenomenon was possibly driven by the increasing migration movements around the world. The concept “healthy immigrant effect”, whereby immigrants are healthier than native-born populations, emerged from immigrant health research.19-22 However, whether or not acculturation is a health risk or a protective factor continues to be a debated topic due to a lack of consensus among researchers, and this is reflected in various theories conceptualising the processes of acculturation, a wide range of acculturation proxies and scales, and varying effects of acculturation.20, 39, 139

A shift among immigrants from traditional diet and lifestyles, which were usually “healthy”, towards these of the host country has been reported.18, 140-144 The Vietnamese traditional diet is nutritionally adequate, high in carbohydrates and low in fats and refined sugars and contains marginal amounts of some vitamins and minerals.36, 145, 146 Research conducted among Vietnamese immigrants has demonstrated mixed evidence of changes in lifestyles,34-36 such as an increased consumption of takeaway foods and decreased intake of fruits and vegetables,34 less smoking, consistently low levels of alcohol consumption, and increased daily physical activity,35 although traditional cooking was maintained.34 Factors contributing to changes in dietary habits among Vietnamese immigrants may include affordability of foods that are expensive in Vietnam (red meat, white meat, and seafood), availability of new foods (snacks, takeaway foods, and other foods that do not reflect traditional home cooking), lack of traditional seasoning and spices, and

34 the difficulty in growing tropical vegetables and fruits in cold climate regions such as southern Australia, northern America and Europe. These studies were conducted in the 1980s focussing on newly arrived Vietnamese immigrants at the beginning of their settlement in their new countries. By now, Vietnamese communities of Australia have had almost 35 years of settlement in the new homeland. Vietnam-born Australians are also ageing (age median of 33.4 years in 1996, 37.5 in 2001, 40.7 in 2006, and 42.8 years in 2011).24 The assumption of the “healthy immigrant effect” could be invalid in the Vietnam-born population due to non-voluntary characteristics of immigration. In the absence of theoretical models of acculturation and physical health outcomes,37 it is unclear whether the acculturation process is still operating in the ageing Vietnam-born Australians. Importantly, it is unknown what impact acculturation has on key social and biological determinants of health such as food consumption, physical activity, smoking and alcohol drinking, and presence of medical conditions and biological markers.

This chapter consists of two main sections. The first section presents a theoretical framework on acculturation including concepts and measurements of acculturation, and conceptualisation of the impact of acculturation on lifestyle and health. The second analytic section includes aims, method, analysis and discussion of findings.

THEORETICAL FRAMEWORK

4.2. ACCULTURATION THEORY

4.2.1. Concepts

The acculturation concept originated in anthropology and was defined by the anthropologists Redfield, Linto, and Herkovits147 as: "Acculturation comprehends those phenomena which result when groups of individuals having different cultures come into continuous first-hand contact, with subsequent changes in the original culture patterns of either or both group” (page 149).

35 Since this early explication of the construct, many definitions of acculturation have arisen.37, 137 The concept has gradually evolved in other disciplines such as sociology, psychology and epidemiology, and has developed into a number of domains such as attitudes, behaviours, cultural values and identity.

4.2.2. Acculturation orientation

Numerous theoretical and empirical studies have been conducted since the mid- 1960s to further understand the construct and operational mechanism of acculturation, and to develop methodologies in acculturation research. To date, there are two predominant orientations or models of acculturation: (i) assimilative, uni- dimensional, bipolar model; and (ii) culturally plural, bi-dimensional model.

4.2.2.1. Uni-dimensional or bipolar model

With the sociological emphasis on social structures, the sociologist Milton Gordon in 1964148 developed a theory of assimilation that focused on immigrants’ integration in the host society and distinguished between cultural or behavioural assimilation (acculturation) and social structural or institutional assimilation. The uni-dimensional model assumes that the acculturation process takes place along a single continuum over the course of time, thus indicating a shift from maintenance of the original culture to full adaptation of the host culture148 (Figure 3). In this model, there are seven stages of assimilation:148

(i) cultural assimilation (adoption of language, dress, and daily customs);

(ii) structural assimilation (formation of cliques, clubs and institutions in the host society);

(iii) marital assimilation (widespread intermarriage);

(iv) identification assimilation (feeling bonded to the host culture);

(v) attitude reception assimilation (absence of prejudice);

(vi) behaviour reception assimilation (absence of discrimination);

(vii) civic assimilation (absence of values and power struggles).

36 Terms such as “Westernisation”, “urbanisation”, “Americanisation”, and “modernisation” were subsequently derived from the development of this model and have since been used frequently in epidemiology and sociology.39

Figure 3 Gordon’s uni-dimensional or bipolar acculturation model

Maintenance of the Full adaptation of host original culture culture

“Not acculturated” “Acculturated”

There have been several criticisms of the uni-dimensional model.39, 149 First, the model assumes cultural mutual exclusions while in fact individuals can be highly involved in both cultures, for example having fluency in both languages.149 Second, this model is biased towards the host culture as it implies that the ultimate goal for all immigrants is to replace their traditional culture with the host culture.39 It assumes a distinctive outcome rather than a complex and multidirectional process.39 Third, the bipolar model fails to account for the increasing demographic diversity with many ethnic communities and resources which can offer choices other than assimilation. Finally, it does not satisfactorily differentiate between the integration of the two cultures with the marginalisation from both cultures.149 Despite these limitations, the bipolar model still dominates within acculturation studies.39, 149

4.2.2.2. The bi-dimensional model

In late 1980s, Berry and associates had developed the culturally plural theoretical framework based on earlier work by Graves.150-152 In contrast to Gordon’s uni- dimensional model, Berry’s acculturation model (Figure 4) has two independent dimensions (cultural maintenance and cultural adaptation). These two dimensions are not necessarily polar opposites, thus creating four acculturation strategies depending on the variability of the maintenance of the culture of origin and adaptation of the new culture, including:

37 (i) integration (high maintenance and high adaptation);

(ii) separation (high maintenance and low adaptation);

(iii) assimilation (low maintenance and high adaptation); and

(iv) marginalisation (low maintenance and low adaptation).

Figure 4 Berry’s bi-dimensional acculturation model

High adaptation of host culture

Assimilation Integration Low maintenance of High maintenance of original culture original culture Marginalisation Separation

Low adaptation of host culture

In this model, acculturation can occur at the micro (individual) and macro (group) levels and generally in overlapping stages. At the micro level, acculturation refers to individual psychological changes expressed in both perceptions and behaviours due to changes of environment (residential area, accommodation, density of population), biology (nutritional and disease status) and psychology.151 At the macro level, acculturation could lead to cultural changes such as political, economic, linguistic, religious, and social institutional alterations, which then result in the development of new sets of social relationships.153

38 This model has also been subjected to criticism on conceptual and methodological grounds. Theoretically, the interdependent nature of the scales implies that a high score on one strategy should be accompanied by low scores on the other three; however, the reported scores for strategies often contradicted these theoretical expectations.154, 155 Berry’s original bi-dimensional theoretical framework has been further developed, and its comprehensiveness over the uni-dimensional model has been widely discussed in the literature.39, 149, 154 However, these supporting arguments remained largely theoretically-based rather than empirically-based,37, 154 with the exception of Ryder’s work in 2000.154 Ryder and colleagues154 used several acculturation measurement scales to compare the uni- and bi-dimensional models and suggested two cultural identities coexisted and could vary independently. However, Ryder’s study was constrained within personality, self-identity, and post- immigration adjustment aspects of acculturation.

4.3. ACCULTURATION MEASUREMENT

Measuring acculturation is a controversial topic138 due to the complexity of the acculturation concept itself and potential conceptual errors in measuring acculturation.37 Many measures of acculturation have been introduced in the last few decades,39 which can be classified as scale or non-scale measures.

4.3.1. Scale measures

Numerous psychometric scales have been developed to measure multiple aspects of acculturation, such as language, food, music, clothing and media preference, social networking, geographic history, attitudes, values, and ethnic identity.37-39, 156 These scales have been validated mainly in social science, psychology, health services use, and dietary acculturation.37-39, 138, 141, 152

Most of the scales (60%) target a specific group in the USA, for example, Mexican- Americans, Hispanic-Americans, Cuban-Americans, Southeast Asian-Americans, Vietnamese-Americans, Puerto Rican-Americans, Hawaiian-Americans, and Native Americans.38 Examples of these scales are the Language Acculturation Scale for

39 Mexican Americans,157 American International Relations Scale,158 Cultural Orientation Scale,159 Suinn-Lew Asian Self Identity Acculturation Scale,160, 161 Acculturation Scale for Southeast Asians,162 Short Acculturation Scale for Hispanics,163 Acculturation Rating Scale for Mexican Americans,164, 165 Dietary Acculturation Scale,166 Acculturation Scale for Vietnamese Adolescents,71 and the Multigroup Ethnic Identity Measure.167

There are number of scales developed and validated among ethnic groups in other countries. For instance, the Acculturation Attitudes Scales developed by Berry in Canada168 and its modified versions in other studies in Canada,169 Norway,170 and Japan.171 The Acculturation Scale in South Asian young people which was first established in Britain by Ghuman in 1975172 has been further validated in Canada, USA, and Australia.173 Some other examples include the Vancouver Index of Acculturation in Canada,154 a scale of acculturation in Australia,17 and the Lowlands Acculturation Scale in the Netherlands.174

The majority (80%) of the psychometric scales demonstrate adequate internal consistency (Cronbach’s alpha ≥0.70).38, 175 Scales that have been frequently used in research studies include the Acculturation Rating Scale for Mexican Americans (alpha=0.81-0.88),164, 165 Suinn-Lew Asian Self Identity Acculturation Scale (alpha=0.88-0.91),160, 161 Acculturation Attitudes Scales (alpha=0.73-0.87 for Assimilation, 0.70-0.78 for Integration, 0.71-0.90 for Separation, and 0.67-0.87 for Marginalisation subscale),168 Language Acculturation Scale for Mexican Americans (alpha=0.97),157 Short Acculturation Scale for Hispanics(alpha=0.90-0.96),163 and the Vancouver Index of Acculturation (alpha=0.79).154

These scale measures are based on either uni-dimensional or bi-dimensional theoretical models, measuring a single or multiple domains of acculturation.38, 175 However, the acculturation orientation (uni-dimensional vs bi-dimensional) and acculturation process has not always been explicitly described.38, 39 Cross-cultural validity of the measures and applicability in other groups than the target group is rarely reported.38

40 Studies using scale measures often calculate a composite score, which is then categorised to summarise individuals’ overall acculturation status. Although the categorisation technique is often necessary at the data analysis stage,155 it consequently reduces the benefits of measuring multiple dimensions of acculturation and limits the ability to assess separate domains of acculturation.37, 39 The middle scores could also be problematic in terms of interpretability and predictability. A wide range of outcomes such as attitudes, behaviours and cultural identity could be a result of various acculturation conditions, orientations and strategies but share similar middle scores of acculturation.137 In addition, other factors, including historical, cultural, social and political conditions, can affect migration circumstances, acculturation processes and health-related outcomes; however, these potential confounders are not included in the acculturation scales. Thus, the sole use of acculturation measures cannot fully explain acculturation and associated outcomes.

The heterogeneity of acculturation measures further indicates that the use of acculturation scales appears to be limited to a specific ethnicity or cultural identity. Moreover, the increasing multiculturalism in countries such as the USA and Australia may raise a further question about the relevance of the term “Australian acculturated” or “American acculturated” as it is possible that many aspects of an individual’s traditional culture can be viewed as “Westernised” by some but as “ethnic” by others.149 On this basis, several researchers have challenged the appropriateness of using acculturation as a variable in health research,138 while others have suggested acculturation is a “latent variable” with various indicators.37 It has been further recommended that identifying the relationship between specific aspects of acculturation and particular health issues would be advanced by using relevant available variables other than monolithic acculturation scales.37

4.3.2. Non-scale measures

Non-scale measures include nativity, generation, length of residence, age at immigration, proportion of life spent in the host country, residential area, and use of language.37, 39, 176 These indices are often presented as either direct or proxy measures of acculturation39, 176, 177 with an underlying assumption of the direction of relationship between the indices and acculturation orientations.39 For example, longer

41 duration of residence increases social contacts and interactions, and younger age at immigration is associated with better communication skills and improved ability to navigate the new society. Living in areas with many people of the same cultural background can hinder socialisation within the host country and can facilitate the maintenance of the original cultures and practices, irrespective of years of residence. This assumption provided the grounds for the segmented assimilation theory, explaining divergent health outcomes among some groups of immigrants.178-180

These non-scale measures are simple, easy to assess, and often available in large- scale, routine data collections such as national health surveys. In contrast to psychometric measures of acculturation, these non-scale measures are not ethnicity- specific, offering greater flexibility in investigations of acculturation effects on health. These measures, therefore have remained predominant in epidemiology and public health research.37, 39, 149 The major concern for the use of these non-scale measures is that these measures do not adequately capture all aspects of acculturation.39, 176 However, Cruz and colleagues176 have demonstrated the validity of a Proxy Acculturation Scale which incorporates non-scale measures including language spoken (during interview or at home), proportion of life lived in the USA, and generational status among Hispanic population in the USA. Researchers have suggested the use of proxy measures such as language usage, duration of residence, generation, and country of birth when comprehensive assessments of acculturation are unfeasible or unavailable.176, 177

4.4. CONCEPTUALISATION OF ACCULTURATION PROCESS AND EFFECTS

4.4.1. Conceptualisation of acculturation process and effects

This section proposes a conceptualisation of the acculturation process and its effects on lifestyle and health (Figure 5). As a result of international migration, immigrants become exposed to a different environment with a new set of living conditions, health-related lifestyles and behaviours, cultural behaviours and attitudes as well as new societal and political structure,20 thus triggering the acculturation process.147 The

42 acculturation process is then driven by continuous interactions of various predisposing and environmental conditions.

Predisposing conditions refer to social, cultural, contextual and individual circumstances which could be grouped into demographic and socio-economic, migration, cultural and personality factors. These predisposing factors, such as age, level of education, levels of familiarity with the lifestyle and language of the new country prior to migration, duration of residence in the new country, and interpersonal attributes, might determine the pace of acculturation. For example, skilled immigrants who are young and self-motivated may settle and adapt more quickly than others. Environmental conditions refer to availability of new foods, working or leisure conditions, health care system, standard of living, and household composition.

Migration from a developing country, for example Vietnam, to a developed country, for example Australia, could provide the immigrant with a better standard of living, greater availability of transportation, more affordable and accessible health care services and health information. Possible alterations in household characteristics may include cross-cultural marriage and a departure from an extended family structure towards the nuclear structure. Being parents or guardians of children at school age may increase the adult immigrants’ contacts with the new society and culture. The continuous interaction between predisposing and exposure factors over the course of time could determine the acculturation process and its associated outcomes.

Acculturation can have effects on immigrants’ health in a complex, multidimensional, and multistage process in which lifestyle and health-related behaviours could be either outcomes of acculturation or mediating factors in the causal relationship between acculturation and health (Figure 5). The behaviour acculturation model39 posits that an individual’s knowledge, attitudes and behaviours may change during a continuously direct contact with another different culture as a response to discrimination and poverty, loss of social networks, exposure to various behaviours, beliefs, values and norms.37 Over time, it is possible that the individual continues to maintain pre-migration traditional behaviours and beliefs, combines

43 both traditional and the new society’s behaviours at different levels or fully adopts the new society’s behaviours.

Regardless of acculturation strategies, the individual’s behaviours could be directed towards maintaining current practices or towards a healthier or less healthy lifestyle. Such behaviours can be manifested in diet, physical activity, substance use (tobacco, alcohol and illicit drugs), and uptake of preventative medicine. Previous studies have reported changes in dietary habits in relation to acculturation.18, 141-144 Living in a new geographical location and climate can mean a lack of traditional foods or seasoning, availability of new foods and cooking methods, and increased affordability of formerly expensive foods. Combined with an individual’s food preference, these factors can cumulatively affect dietary behaviours in three principal ways: (i) maintain the traditional diet; (ii) completely adopt the new country’s dietary patterns; or (iii) incorporate new dietary patterns into traditional dietary practices.

Lifestyle behaviour is a strong determinant of health.14 During the acculturation process, it is possible that individuals with a healthy lifestyle might continue these protective health behaviours and maintain their health status. However, healthy individuals may also start to practise unhealthy behaviours under specific contextual, social, health, and personal circumstances. On the other hand, people with health issues may actively modify lifestyle in order to achieve health benefits.

44 Figure 5 Conceptualisation of the acculturation process and effects on lifestyle and health

PROCESS EFFECTS Predisposing factors Environmental factors Lifestyle* Health

Demographic & socio- economic factors Food environment Age, gender, marital Traditional foods status Food affordability Number of children New foods and recipes Education Income Toward a more healthy Better Employment lifestyle health status Working/ Living standards Migration factors Distance Traditional maintenance Country of origin Transportation Traditional & host culture Migration circumstances Continuous Leisure facilities combined Age at immigration interaction Host culture adopt Duration of residence Health care environment Health knowledge and Worse Cultural factors Toward a information less healthy lifestyle health status Religion Health care practice Cultural beliefs, Health care cost attitudes, values Live in ethnic enclave

Internal (personality) Household environment factors Offspring schooling * Lifestyle refers to dietary patterns, physical activity, cigarette smoking, alcohol drinking Self-esteem Intermarriage Self-motivation Positive association between lifestyle and health status Negative association between lifestyle and health status

45 4.4.2. Some evidence of effects of acculturation on health

The question of how acculturation impacts on the health of immigrants has received a great deal of research attention.37, 137, 138 Although most of the immigrant health studies have found a “healthy immigrant effect”, which relates to the formal and informal health screening in the selective immigration processes,19-22 this health advantage tends to decline over time.21, 180, 181 However, the acculturation process does not occur at the same rate or to the same degree in all individuals and the effects vary according to ethnic identity and health outcomes.39, 139, 141

A Canadian study has reported that levels of physical activity in adult immigrants differed according to ethnicity and duration of residence.182 Among Asian Americans, prevalence of cigarette smoking and rates of smoking cessation,183 and limitations in physical functioning21 also vary by countries of birth. Acculturated Asian women in the USA are more likely to smoke cigarettes than other women.183

Higher levels of acculturation among Haitian immigrants in the USA184 or Vietnamese immigrants in Australia185 have been associated with better oral health such as reduction in missing and decayed teeth,184, 185 decreased rates of periodontal detachment,184 improved levels of dental health knowledge and higher use of dental health services.185 Hispanic women who lived longer in the USA have shown a higher uptake of breast cancer screening,186 with those living in the ethnic enclave less likely to have infants with low birth weight.178

Higher levels of acculturation in Latino Americans have been associated with improved mortality and life expectancy but increased prevalence of cancer, mental health disorders, smoking, and alcohol consumption,37 and mixed results in prevalence of diabetes and obesity.37, 187, 188 Immigrants to Australia have been found to have lower mortality from circulatory disease and diabetes relative to the Australia-born population.180

These inconsistent findings regarding the health impact of acculturation might be explained by the complexity of acculturation itself, the great diversity of culture and

46 ethnicity, variations in migration circumstances, and a wide range of health-related outcomes. The next section will focus on the impact of acculturation on lifestyles and health of Vietnam-born Australians.

ANALYTICAL FRAMEWORK

4.5. AIMS AND HYPOTHESES

This chapter aims to investigate associations between acculturation and lifestyle factors and health status among Vietnam-born Australians. The following hypotheses are tested.

Hypothesis One: Lifestyle factors, including dietary patterns and health-related behaviours of Vietnam-born Australians, are associated with levels of acculturation. Hypothesis Two: Health status, including physical health and psychological distress of Vietnam-born Australians is associated with levels of acculturation.

4.6. METHOD

The Vietnam-born participants in the 45 and Up Study were included in the analyses for this chapter. Chapter 3 has presented the distribution of age, gender, relationship status, educational qualifications, IRSD quintiles, and remoteness of residential areas. This chapter further describe additional study variables including acculturation indicators, dietary patterns, health-related behaviours, medical conditions and psychological distress which were derived from the baseline questionnaire of the Study. This section also details statistical analysis methods applied in this chapter.

47 4.6.1. Measures of acculturation

Acculturation measures available from the 45 and Up Study baseline questionnaire are duration of residence in Australia, age at immigration, speaking a language other than English (LOTE) at home, density of the Vietnam-born population in residential areas, and levels of social interaction.

(i) Duration of residence in Australia was computed based on the year that the participant joined the Study (Question 2 in the base questionnaire, mainly 2008 and 2009) and the year of arrival in Australia (Question 8). Reflecting the migration waves of Vietnamese immigrants into Australia (Chapter 2), duration of residence was categorised as ≥25 years (first wave), 20-24 years (second wave), and <20 years (third wave and later).

(ii) Age at immigration was computed based on age (years) at enrolment into the Study (Question 1) and year of arrival in Australia. Most of Vietnam- born Australians migrated in their adulthood (Chapter 2), thus age at immigration was grouped as ≥40 years old, 30-39 years old, and <30 years old.

(iii) Density of the Vietnam-born population in residential areas was based on the participant’s LGA of residence. Information about the proportion of Vietnam-born populations in each LGA of NSW was extracted from Social Health Atlas of Australia, 2010.130 In NSW, there is a high concentration of Vietnam-born residents in south western suburbs of Sydney (Chapter 2 and Chapter 3), thus this variable was categorised as low Vietnam-born density (<2.0%), medium (2.0%-6.9%), and high (≥7.0%) and merged with the questionnaire data using the participants’ LGA as the matching key.

(iv) Speaking a LOTE at home was asked in Question 10 and dichotomised as Yes or No. The questionnaire did not specify the language that participants spoke at home.

(v) Levels of social interaction were measured by the four-item social interaction subscale of the Duke Social Support Index (DSSI) (Questions 55 and 56).The DSSI-social interaction subscale quantified the individual’s weekly interaction with friends, relatives and others, and the number of

48 people living within one hour’s travel that the person can depend on. The total scores ranged from 4 to 12 with higher scores indicating more social interaction, and were classified as low (4-6), medium (7-9), and high (10- 12). The four-item DSSI-social interaction scale has been validated among Australian men and women aged 70 years and older (Cronbach’s alpha=0.60).189, 190 Its reliability and validity in the younger population (45- 70 years old) is unknown.

4.6.2. Measures of lifestyle

The lifestyle variables available from the 45 and Up Study baseline questionnaire included dietary patterns and health-related behaviours.

(i) Dietary patterns: Questions 40 to 45 in the baseline questionnaire asked types, frequency and quantity of food consumption. These items were adapted from the questionnaire of the Million Women Study in the UK.191 Responses for food consumption were categorised into:

 Red meat (pork, beef, lamb) as number of times per week;  White meat (chicken, turkey, duck) as number of times per week;  Seafood (fish, other seafood) as number of times per week;  Vegetables as number of serves per day;  Fruits as number of serves per day; and  Dairy products (cheese, cow’s milk) as yes (eating) or no (non-eating). (ii) Smoking status: Question 11 in the questionnaire sought information about whether the participant had been a regular smoker, age when started smoking, age when stopped smoking, and number of cigarettes/pipes/cigars per day. The smoking status was recoded as current smoker or non-smoker.

(iii) Alcohol consumption was based on the number of alcoholic drinks per week (Question 12) and categorised as <2 and ≥2 drinks per week.

(iv) Physical activity was measured by the Active Australia Questionnaire192 (Question 17), based on the number of times or sessions in the previous week that the participant walked for at least 10 minutes and did any moderate and vigorous physical activities. The weighted total number of physical activity sessions per week was quantified with vigorous activities 49 receiving twice the weighting of moderate activities or walking,192 and categorised into <5 and ≥5 sessions per week

(v) Body mass index (BMI kg/m2) was calculated based on the self-reported body weight (Question 4) and height (Question 3). The BMI cut-off values for normal weight, overweight and obesity recommended for the Vietnamese population193, 194 differ from World Health Organization (WHO) conventional cut-off values195 as presented in Table 4. For this chapter, the BMI cut-off values recommended for the Vietnamese population were used.

Table 4 BMI cut-off values: WHO convention and Vietnamese population

Categories WHO convention Vietnamese Underweight BMI <18.5 BMI <18.5 Normal weight 18.5 ≤BMI <25.0 18.5 ≤BMI <23.0 Overweight 25.0 ≤BMI <30.0 23.0 ≤BMI <27.5 Obese 30.0 ≤BMI 27.5 ≤BMI

4.6.3. Measures of health status

Health status variables derived from the 45 and Up Study baseline questionnaire included the following:

(i) Type 2 diabetes: An algorithm was developed to identify participants with type 2 diabetes and is presented in Chapter 5. In brief, type 2 diabetes was identified according to responses to the question “Has a doctor EVER told you that you have diabetes?”, age at diabetes diagnosis (Question 24), free- text description of other illnesses (Question 26), age at giving birth to the last child (Question 19 for women), and diabetes medications (insulin and oral hypoglycaemic agents [OHA] in Question 23).

(ii) Heart disease: Having heart disease was identified based on responses to the question “Has a doctor EVER told you that you have heart disease?” (Question 24), and free-text description of other illnesses (Question 26).

50 (iii) High blood pressure: Having high blood pressure was identified according to responses to the question “Has a doctor EVER told you that you have high blood pressure?” for males, “Has a doctor EVER told you that you have high blood pressure when not pregnant?” for females (Question 24), and free-text description of other illnesses (Question 26).

(iv) Self-rated overall health status: The generic item “In general, how would you rate your overall health?” in Question 31 asked participants to rate their health status as excellent, very good, good, fair and poor.

(v) Physical functioning: Physical functional capacity was measured in Question 28 by the Medical Outcomes Study, Short Form 36 Physical Functioning scale (SF36-PF, Cronbach’s alpha 0.93).196, 197 The SF36-PF had 10 items asking the level of limitation (a lot, a little, and not at all) in performing vigorous (for example, running, strenuous sports), moderate (for example, pushing a vacuum cleaner, playing golf) and less intensive activities (for example, self-dressing, self-bathing). The summarising score was between zero and 100, with higher scores indicating better physical functioning. The reported norms of the SF36-PF scale in the general Australian population (aged 15 years or older) are mean score (85.4), standard deviation (21.6), 25th percentile (80.0), median (95.0) and 75th percentile score (100.0).198 These norms decrease with older age. Compared to the 45-54 age group, people who aged 75 years and older had lower mean (52.4 vs 86.9), 25th percentile (25.0 vs 80.0), median (55.0 vs 95.0) and 75th percentile score (80.0 vs 100.0).198 Physical capacity was classified as no limitation (SF36-PF score 100), minor limitation (90-99), moderate limitation (60-89) and severe limitation (0-59).199

(vi) Psychological distress: Non-specific psychological distress was assessed by the Kessler-10 scale (K10) in Question 57. The K10 scale contains 10 items about negative emotional feelings in the previous four weeks (Cronbach’s alpha 0.93).200 The K10 scores range from 10 to 50, with higher scores indicating a higher level of distress, and were categorised as low (10-15), moderate (16-21), high (22-29) and very high (30-50).201

51 4.6.4. Statistical analysis

Analyses in this chapter were conducted for 797 Vietnam-born participants in the 45 and Up Study. Descriptive results are presented as frequencies and percentages. Differences between men and women were assessed by chi-square test for proportions. Logistic regression analyses were used to assess the relationship between acculturation indicators and lifestyle and health. It is assumed that longer duration of residence, younger age at immigration and higher levels of social interaction would facilitate the acculturation process. On the other hand, living in areas with a high density of the Vietnam-born populations and speaking at home is expected to hinder the integration or assimilation into Australian society. Almost all Vietnam-born participants reported speaking a LOTE at home, and there was no measure of English language proficiency. Therefore, the assessment of language acculturation was not possible due to a lack of variability. Four acculturation indicators (duration of residence, age at immigration, Vietnam-born density, and DSSI social interaction) were modelled. For each acculturation indicator, the category representing a lower level of acculturation such as shorter duration of residence (<20 years), older age at immigration (≥40 years), higher density of Vietnam-born population (≥7.0%) and lower DSSI social interaction (DSSI 4-6) was chosen as the reference group.

There were three models for each acculturation measure-outcome logistic regression analyses. Model 1 was an unadjusted model, Model 2 adjusted for age and gender, and Model 3 adjusted for gender, age, marital status, qualification, household income, employment, and other acculturation covariates. Unadjusted and adjusted ORs and 95%CI were calculated. The collinearity among age, length of residence and age at immigration (age = length of residence + age at immigration) warrants the simultaneous inclusion of at most two of these three variables in the multivariate models. The examination of bivariate correlation coefficients indicated that age and age at immigration were highly correlated (Pearson correlation coefficient [r]=0.862, p<0.001) followed by duration of residence and age at immigration (r=-0.561, p<0.001). Age and duration of residence were not correlated (r=-0.064, p=0.08). Therefore, age and duration of residence were included as covariates in the adjusted models for Vietnam-born density and DSSI social interaction indicators.

52 Outcome variables were dichotomised as Yes/No for the logistic regression analyses. Dietary patterns were categorised as eating red meat ≥3 times/week, white meat ≥3 times/week, seafood ≥3 times/week, vegetables ≥5 serves/day, fruits ≥2 serves/day, and having dairy products. Health-related behaviours were dichotomised as doing physical activity ≥5 sessions/week, being overweight or obese (BMI≥23.0kg/m2), current smoker, and drinking ≥2 alcoholic drinks per week. Health status variables were grouped as excellent, very good or good self-rated general health, having type 2 diabetes, heart disease, high blood pressure, physical functioning limitation (SF36- PF score 0-99), and very high, high or moderate psychological distress (K10 score 15-50). Analyses for cigarette smoking and alcohol drinking were performed among only male Vietnam-born participants because Vietnam-born women very rarely smoked cigarettes or drank alcohol.

For the Vietnam-born participants in the 45 and Up study, data were fully completed for half of the variables (n= 12, 50%). Variables containing ≤5% missing values include duration of residence (n=17, 2.1%), age at immigration (n=17, 2.1%), marital status (n=9, 1.1%), education (22, 2.8%), private health insurance (n=30, 3.8%), vegetable intake (n=32, 4.0%), and BMI (n=39, 4.9%). Variables containing >5% missing values were fruit intake (n=51, 6.4%), self-rated general health (n=71, 8.9%), self-rated quality of life (n=72, 9.0%), DSSI social interaction (n=109, 13.7%), SF36-PF (n=130, 16.3%), and K10 (n=135, 16.9%). The levels of missing data are evenly distributed across age groups and gender, except SF-36PF (60.7% missing data in females) and K10 (61.5% missing data in females). Missing data were excluded from the calculation of percentages and from the logistic regression analyses.

4.7. RESULTS

4.7.1. Descriptive results

Demographic and socio-economic characteristics of 797 Vietnam-born participants in the 45 and Up Study were partially presented in Chapter 3. In brief, the mean age was 58.6 years (standard deviation [SD] 10.4). A majority (74.4%) were living with a partner. There were 154 (19.9%) participants with a university or higher degree,

53 while 222 (28.6%) had a high school or trade certificate. Sixty-four percent had an annual household income less than AUD$50,000; and almost half (N=387, 48.6%) were not working. Four in ten Vietnam-born participants had private health insurance (N=318, 41.5%); and 34.0% (N=261) had a health care concession card (Table 5).

Table 5 Demography and SES of 797 Vietnam-born participants by gender

Male Female P Total Demography and SES N=390 N=407 valueχ N=797 Age (years) Mean (SD) 59.5 (10.6) 57.8 (10.3) 58.6 (10.4) 45-54 168 (43.1%) 209 (51.4%) 0.02 377 (47.3%) 55-64 115 (29.5%) 116 (28.5%) 231 (29.0%) ≥65 107 (27.4%) 82 (20.1%) 189 (23.7%) Relationship∑ No partner 57 (14.8%) 145 (36.0%) <0.001 202 (25.6%) Partner 328 (85.2%) 258 (64.0%) 586 (74.4%) Educational qualification∑ Less than high school 121 (32.2%) 170 (42.6%) <0.001 291 (37.5%) High school/Trade 110 (29.3%) 112 (28.1%) 222 (28.6%) Certificate /Diploma 50 (13.3%) 58 (14.5%) 108 (13.9%) University or higher 95 (25.3%) 59 (14.8%) 154 (19.9%) Household income ($AUD) <$20,000 148 (37.9%) 185 (45.5%) <0.001 333 (41.8%) $20,000-$49,999 90 (23.1%) 83 (20.4%) 173 (21.7%) ≥$50,000 89 (22.8%) 35 (8.6%) 124 (15.6%) Won’t disclose 63 (16.2%) 104 (25.6%) 167 (21.0%) Working status Not working 159 (40.8%) 228 (56.0%) <0.001 387 (48.6%) Working 231 (59.2%) 179 (44.0%) 410 (51.4%) Health insurance∑ None 82 (21.8%) 61 (15.6%) 0.01 143 (18.6%) Private health insurance 157 (41.6%) 161 (41.3%) 318 (41.5%) DVA card 27 (7.2%) 18 (4.6%) 45 (5.9%) Health care concession 111 (29.4%) 150 (38.5%) 261 (34.0%) χ: Chi-square test for proportions ∑: Missing data were excluded from percentages

54 There were significant differences in demographic and socio-economic characteristics between Vietnam-born men and women. Compared to female participants, male participants were older (1.7 years, p=0.02), more likely to have partner (85.2% vs 64.0%, p<0.001), higher levels of educational qualification (25.3% with university or higher degree vs 14.8%, p<0.001), and higher levels of household income (22.8% with income ≥$50,000 vs 8.6%, p<0.001). Men were also more likely to be working (59.2% vs 44.0%, p<0.001), and were less likely to hold a health care concession card (29.4% vs 38.5%, p=0.01) (Table 5).

Table 6 shows the distribution of acculturation measures. Overall, the mean duration of residence was 23.3 years (SD 6.4) and age at immigration was 35.3 years (SD 4.4). There were 391 (50.1%) participants who had resided in Australia for 25 years or longer, and 323 (41.4%) participants immigrated at less than 30 years of age. In terms of spatial distribution, the Vietnam-born participants lived in 39 LGAs of urban NSW. There were 302 (37.9%) participants living in LGAs with a low Vietnam-born population density (<2.0%) and 214 (26.9%) participants living in LGAs with a high Vietnam-born population density (≥7.0%). The mean DSSI social interaction score was 7.7 (SD 1.8) out of possible scores between 4 and 12. High level of social interaction (DSSI 10-12) were reported by 16.4%, and low level of social interaction (DSSI 4-6) accounted for 24.6% of the participants. Most of the Vietnam-born participants (94.0%) spoke a LOTE at home, which was assumed to be the Vietnamese language. These acculturation measures were similar between Vietnam-born men and women, except duration of residence (p=0.004). The difference in duration of residence between male (mean 23.9 years, SD 6.0) and female participants (mean 22.8 years, SD 6.8) could be explained by the common phenomenon that Vietnamese men migrated first then sponsored their wives and children to migrate to Australia.

55 Table 6 Acculturation measures of 797 Vietnam-born participants by gender

Male Female P Total Acculturation indicators N=390 N=407 valueχ N=797 Duration of residence (years)∑ Mean (SD) 23.9 (6.0) 22.8 (6.8) 23.3 (6.4) <20 years 90 (23.5%) 134 (33.8%) 0.004 224 (28.7%) 20-24 years 93 (24.3%) 72 (18.1%) 165 (21.2%) ≥25 years 200 (52.2%) 191 (48.1%) 391 (50.1%) Age at immigration (years)∑ Mean (SD) 35.7 (9.2) 34.9 (8.6) 35.3 (4.4) <30 years 156 (40.7%) 167 (42.1%) 0.33 323 (41.4%) 30-39 years 108 (28.2%) 125 (31.5%) 233 (29.9%) ≥40 years 119 (31.1%) 105 (26.4%) 224 (28.7%) Vietnam-born density in LGA Low (<2.0%) 135 (34.6%) 167 (41.0%) 0.17 302 (37.9%) Medium (2.0%-6.9%) 146 (37.4%) 135 (33.2%) 281 (35.3%) High (≥7.0%) 109 (27.9%) 105 (25.8%) 214 (26.9%) DSSI social interaction∑ Mean (SD) 7.7 (1.8) 7.7 (1.8) 7.7 (1.8) Low (4-6) 87 (25.4%) 82 (23.7%) 0.76 169 (24.6%) Medium (7-9) 197 (57.6%) 209 (60.4%) 406 (59.0%) High (10-12) 58 (17.0%) 55 (15.9%) 113 (16.4%) Speaking a LOTE at home No 20 (5.1%) 28 (6.9%) 0.30 48 (6.0%) Yes 370 (94.9%) 379 (93.1%) 749 (94.0%) χ: Chi-square test for proportions ∑: Missing data were excluded from percentages

Dietary patterns are presented in Table 7. Overall, Vietnam-born participants reported eating red meat (beef, lamb or pork) three times a week (SD 2.6), white meat (chicken, turkey or duck) 2.5 times a week (SD 1.9) and seafood (fish or other seafood) approximately 3 times a week (SD 2.3). The average daily consumption of vegetables was 3.5 serves (SD 2.9), and of fruits was 2.1 serves (SD 1.7). Based on the Dietary Guidelines for Australian Adults,202 only 150 (19.6%) participants had an adequate daily vegetable intake (≥5 serves/day) and 443 (59.4%) had an adequate daily fruit intake (≥2 serves/day). Most participants (73.4%) reported eating cheese or drinking cow’s milk. Food intakes were similar between men and women, except for fruits. Female participants were more likely to have adequate intake of fruits (≥2 serves/day) than men (66.6% vs 51.8%, p<0.001).

56 Table 7 Dietary patterns of 797 Vietnam-born participants by gender

Male Female P Total Dietary patterns N=390 N=407 valueχ N=797 Red meat (times/week)∑ Mean (SD) 3.1 (2.2) 2.9 (2.9) 3.1 (2.6) <3 times/week 185 (49.1%) 209 (54.3%) 0.15 394 (51.7%) ≥3 times/week 192 (50.9%) 176 (45.7%) 368 (48.3%) White meat (times/week)∑ Mean (SD) 2.7 (2.0) 2.4 (1.8) 2.5 (1.9) <3 times/week 217 (58.3%) 245 (64.3%) 0.09 462 (61.4%) ≥3 times/week 155 (41.7%) 136 (35.7%) 291 (38.6%) Seafood (times/week)∑ Mean (SD) 2.8 (2.0) 2.8 (2.6) 2.8 (2.3) <3 times/week 209 (55.6%) 220 (57.0%) 0.70 429 (56.3%) ≥3 times/week 167 (44.4%) 166 (43.0%) 333 (43.7%) Vegetables (serves/day)∑ Mean (SD) 3.3 (2.8) 3.6 (3.0) 3.5 (2.9) <5 serves/day 315 (83.1%) 300 (77.7%) 0.06 615 (80.4%) ≥5 serves/day 64 (16.9%) 86 (22.3%) 150 (19.6%) Fruits (serves/day)∑ Mean (SD) 1.9 (1.7) 2.4 (1.8) 2.1 (1.7) <2 serves/day 175 (48.2%) 128 (33.4%) <0.001 303 (40.6%) ≥2 serves/day 188 (51.8%) 255 (66.6%) 443 (59.4%) Dairy products No 101 (25.9%) 111 (27.3%) 0.66 212 (26.6%) Yes 289 (74.1%) 296 (72.7%) 585 (73.4%) χ: Chi-square test for proportions ∑: Missing data were excluded from percentages

Regarding lifestyle behaviours (Table 8), 68.4% of Vietnam-born men and women had ≥5 sessions of physical activity per week (mean 10.6, SD 13.7). According to BMI cut-off values recommended for the Vietnamese population, 339 (44.7%) participants were overweight (23.0≤BMI<27.5) and 58 (7.7%) participants were obese (BMI ≥27.5). The majority of the participants were not current smokers (N=735, 92.2%) and consumed less than 2 alcoholic drinks per week (N=519, 81.3%). There was no difference in the level of physical activity between men and women (≥5 sessions/week, 67.2% vs 69.5%, p=0.48). However, being overweight or obese was more common in Vietnam-born men than in women (55.3% vs 34.6% overweight, 8.1% vs 7.2% obese, p<0.001). Among the 62 current cigarette smokers and 119 alcohol drinkers (≥2 drinks/week), smoking and alcohol drinking was 57 predominantly reported in men (N=60, and N=105 respectively). On average, Vietnam-born male participants had 2.2 (SD 5.7) alcoholic drinks per week.

Table 8 Health-related behaviours of 797 Vietnam-born participants by gender

Male Female P Total Health-related behaviours N=390 N=407 valueχ N=797 Physical activity (session/week) Mean (SD) 11.4 (16.2) 9.9 (10.7) 10.6 (13.7) <5 sessions/week 128 (32.8%) 124 (30.5%) 0.48 252 (31.6%) ≥5 sessions/week 262 (67.2%) 283 (69.5%) 545 (68.4%) BMI (kg/m2)∑ § Mean (SD) 23.6 (2.8) 22.8 (3.2) 23.2 (3.1) Normal weight 136 (36.7%) 225 (58.1%) <0.001 361 (47.6%) Overweight 205 (55.3%) 134 (34.6%) 339 (44.7%) Obese 30 (8.1%) 28 (7.2%) 58 (7.7%) Current smoker No 330 (84.6%) 405 (99.5%) N/A 735 (92.2%) Yes 60 (15.4%) 2 (0.5%) 62 (7.8%) Alcohol (drinks/week)∑ Mean (SD) 2.2 (5.7) 0.3 (2.7) 1.2 (4.5) <2 drinks/week 260 (71.2%) 259 (94.9%) <0.001 519 (81.3%) ≥2 drinks/week 105 (28.8%) 14 (5.1%) 119 (18.7%) χ: Chi-square test for proportions ∑: Missing data were excluded from percentages N/A: Chi-square test could not be computed §: BMI recommended for the Vietnamese population: normal weight (≥18.5 to <23.0), overweight (≥23.0 to <27.5), obese (≥27.5)

As shown in Table 9, 469 (64.6%) Vietnam-born participants rated their overall health status as good or better (excellent 4.7%, very good 16.4%, and good 43.5%). There were 103 (12.9%) participants who reported having type 2 diabetes, 46 (5.8%) with heart disease, and 242 (30.4%) with high blood pressure. There were 457 (68.5%) participants who had limited physical functioning according to the SF36-PF, including minor (N=120, 18.0%), moderate (N=176, 26.4%) and severe levels (N=161, 24.1%). The mean K10 score was 16.1 (SD 6.9) out of possible scores from 10 to 50. Low and moderate levels of psychological distress were found in 407 (61.5%) and 136 (20.5%) participants, respectively. High or very high levels of psychological distress were prevalent in 119 participants (18.0%). Analysis by

58 gender showed that compared to female counterparts, Vietnam-born men were more likely to have high blood pressure (37.2% vs 23.8%, p<0.001) but were less likely to have limited physical functioning (62.8% with limitation vs 74.4%, p<0.001) and psychological distress (30.5% with moderate, high or very high levels vs 46.9%, p<0.001) (Table 9).

Table 9 Health status of 797 Vietnam-born participants by gender

Male Female P Total Health status N=390 N=407 valueχ N=797 Self-rated general health ∑ Excellent 20 (5.6%) 14 (3.8%) 0.29 34 (4.7%) Very good 64 (18.0%) 55 (14.8%) 119 (16.4%) Good 157 (44.2%) 159 (42.9%) 316 (43.5%) Fair 90 (25.4%) 117 (31.5%) 207 (28.5%) Poor 24 (6.8%) 26 (7.0%) 50 (6.9%) Diabetes Without diabetes 332 (85.1%) 352 (86.5%) 0.10F 684 (85.8%) Type 2 diabetes 56 (14.4%) 47 (11.5%) 103 (12.9%) Other types 2 (0.5%) 8 (2.0%) 10 (1.3%) Heart disease No 364 (93.3%) 387 (95.1%) 0.29 751 (94.2%) Yes 26 (6.7%) 20 (4.9%) 46 (5.8%) High blood pressure No 245 (62.8%) 310 (76.2%) <0.001 555 (69.6%) Yes 145 (37.2%) 97 (23.8%) 242 (30.4%) Physical functioning SF36-PF∑ Mean (SD) 79.8 (26.2) 72.3 (28.2) 76.1 (27.4) No limitation (100) 126 (37.2%) 84 (25.6%) <0.001 210 (31.5%) Minor limitation (90-99) 73 (21.5%) 47 (14.3%) 120 (18.0%) Moderate limitation (60-89) 73 (21.5%) 103 (31.4%) 176 (26.4%) Severe limitation (0-59) 67 (19.8%) 94 (28.7%) 161 (24.1%) Psychological distress K10∑ Mean (SD) 14.9 (6.3) 17.1 (7.3) 16.1 (6.9) Low (10-15) 235 (69.5%) 172 (53.1%) <0.001 407 (61.5%) Moderate (16-21) 56 (16.6%) 80 (24.7%) 136 (20.5%) High (22-29) 33 (9.8%) 53 (16.4%) 86 (13.0%) Very high (30-50) 14 (4.1%) 19 (5.9%) 33 (5.0%) χ: Chi-square test for proportions ∑: Missing data were excluded from percentages F: Fisher’s exact test

59

4.7.2. Logistic regression modelling of acculturation and outcomes

4.7.2.1. Acculturation effects on lifestyles

Table 10 shows estimates for the relationship between acculturation measures and dietary patterns. In unadjusted models, the likelihood of eating red meat and white meat (≥3 times/week), fruits (≥2 serves/day) and dairy products (cow’s milk and cheese) increased with longer duration of residence in Australia. Compared to Vietnam-born participants who were living in Australia for less than 20 years (reference group), those who had been in Australia for a longer period were more likely to eat red meat (OR=1.34, 95%CI=0.89-2.03 for 20-24 years of residence; OR=1.62, 95%CI=1.15-2.29 for ≥25 years of residence, Model 1). The respective ORs for having white meat for ≥3 times/week were 1.68 (95%CI=1.08-2.62) and 2.11 (95%CI=1.46-3.04). The likelihood of using dairy products increased by 37% (OR=1.37, 95%CI=0.88-2.13, 20-24 years or residence) to 74% (OR=1.74, 95%CI=1.21-2.51, ≥25 years of residence). The intake of vegetables (≥5 serves/day) tended to be lower in participants with 20-24 years of residence (OR=0.75, 95%CI=0.44-1.28). However, the daily intakes of fruits (≥2 serves/day) increased among people with ≥25 years of residence (OR=1.42, 95%CI=1.01-2.01, Model 1).

In model 2 (age and gender adjusted), duration of residence (≥25 years of residence) was strongly associated with white meat consumption for (OR=2.01, 95%CI=1.38- 2.93) and marginally associated with red meat consumption (OR=1.44, 95%CI=1.02- 2.50) and using diary product (95%CI=1.60, 95%CI=1.10-2.32). Following adjustment for demographic, socio-economic and acculturation covariates (Model 3), only the association between duration of residence and white meat consumption remained statistically significant (p=0.001). Compared to the reference group, the odds of eating white meat for ≥3 times/week among participants who had resided in Australia for 20-24 years was 1.73 (95%CI=1.05-2.86, p=0.03) and ≥25 years was 2.21 (95%=1.44-3.35, p<0.001) (Table 10, Model 3). Age was strongly associated with red meat consumption (p=0.01, Model 2).

60 Similar associations between age at immigration and dietary patterns were found. Vietnam-born participants who immigrated at a younger age (<40 years old) were more likely to eat red and white meat for ≥3 times/week and have dairy products, but less likely to eat seafood for ≥3 times/week than participants aged ≥40 years at immigration (reference group). The respective unadjusted ORs for eating red and white meat for participants aged 30-39 years at immigration were 1.94 (95%CI=1.35- 2.77), and 1.37 (95%CI=0.92-2.05). Participants who immigrated at a younger age (<30 years old) were 52% (OR=1.52, 95%CI=1.07-2.21) more likely to have ≥2 serves of fruit daily than the others.

The relationship between age at immigration and white meat remained statistically significant in Model 2 (p=0.05) and Model 3 (p=0.04). Participants who immigrated before 30 years of age reported having white meat more frequently than the reference group (OR=2.21, 95%CI=1.13-4.32, p=0.02, Model 3) (Table 10). The association between age at immigration and red meat became statistically non-significant in Model 3 (p=0.35) mainly due to effect of the covariate Vietnam-born density (Model 3, p=0.02).

Participants who lived in LGAs with a medium (2.0%-6.9%) and low (<2.0%) density of Vietnam-born population were more likely to eat red meat (unadjusted OR=1.65, 95%CI=1.14-2.37 for LGAs with medium density; OR=1.05, 95%CI=0.73-1.50 for low density LGAs) than participants in high density areas (≥7.0%, the reference group). These participants were also more likely to eat white meat for ≥3 times/week (unadjusted OR=1.44, 95%CI=0.99-2.09 for medium density LGAs) and fruits for ≥2 serves/day (unadjusted OR=1.44, 95%CI=1.01-2.11 for low density LGAs). However, the likelihood of having ≥5 serves of vegetables daily decreased by 34% (OR=0.66, 95%CI=0.42-1.04 unadjusted) for medium density LGAs, and by 22% (OR=0.78, 95%CI=0.51-1.21 unadjusted) for low density LGAs.

As shown in Table 10, results generated from Model 2 were similar to those from Model 3 regarding association between Vietnam-born density and food consumption. Following adjustment of covariates, the consumption of red and white meat was statistically significantly higher in participants living in areas with medium Vietnam-

61 born population density (Model 3, OR=1.61, 95%CI=1.07-2.45; OR=1.57, 95%CI=1.03-2.38, respectively) (Table 10).

In relation to the DSSI social interaction, Table 10 shows that the likelihood of having seafood (≥3 times/week) and dairy products increased with higher levels of social interaction. In unadjusted models, compared to the reference group (DSSI 4- 6), participants with medium (DSSI 7-9) and high levels of social interaction (DSSI 10-12) were 56% (OR=1.56, 95%CI=1.07-2.27) and 44% (OR=1.44, 95%CI=0.88- 2.36) more likely to eat seafood for ≥3 times/week, respectively. The unadjusted ORs of having dairy products were 1.44 (95%CI=0.96-2.14) for medium DSSI, and 1.84 (95%CI=1.04-3.25) for high DSSI. Similar to the other three acculturation measures, the Vietnam-born participants with higher levels of social interaction were less likely to have adequate daily intake of vegetables. The unadjusted OR was 0.83 (95%CI=0.53-1.31) for medium, and 0.51 (95%CI=0.25-0.99) for high level of interaction.

Model 2 (age and gender adjusted) revealed similar patterns of associations between DSSI social interaction and diets as the unadjusted models (Table 10). In fully adjusted model (Model 3), the medium level of social interaction remained statistically significantly associated with eating seafood for ≥ 3 times/week (OR=1.68, 95%CI=1.13-2.49).

62 Table 10 Associations between acculturation measures and dietary patterns: crude and adjusted odds ratio (95%CI)

Red meat‡ White meat‡ Seafood‡ Vegetables‡ Fruits‡ Dairy products‡ Acculturation measures ≥3 times/week ≥3 times/week ≥3 times/week ≥5 serves/day ≥2 serves/day Yes MODEL 1 - UNADJUSTED Duration of residence (<20 years‡) 20-24 years 1.34 (0.89-2.03) 1.68 (1.08-2.62)* 0.83 (0.55-1.26) 0.75 (0.44-1.28) 1.16 (0.76-1.76) 1.37 (0.88-2.13) ≥25 years 1.62 (1.15-2.29)** 2.11 (1.46-3.04)*** 0.71 (0.51-0.99)* 0.91 (0.61-1.37) 1.42 (1.01-2.01) 1.74 (1.21-2.51)** Age at immigration (≥40years‡) 30-39 years 1.94 (1.35-2.77)*** 1.37 (0.92-2.05) 0.79 (0.54-1.15) 0.77 (0.46-1.24) 1.07 (0.73-1.57) 1.44 (0.97-2.16) <30 years 1.66 (1.13-2.44)** 1.63 (1.13-2.37)** 0.61 (0.43-0.87)** 0.98 (0.64-1.51) 1.52 (1.07-2.21)* 1.86 (1.27-2.72)** Vietnam-born density (High ≥7.0%‡) Medium (2.0%-6.9%) 1.65 (1.14-2.37)** 1.44 (0.99-2.09) 1.09 (0.72-1.61) 0.66 (0.42-1.04) 1.15 (0.81-1.67) 1.02 (0.69-1.52) Low (<2.0%) 1.05 (0.73-1.50) 0.95 (0.65-1.39) 0.88 (0.61-1.27) 0.78 (0.51-1.21) 1.44 (1.01-2.11)* 0.98 (0.66-1.46) DSSI social interaction (Low 4-6‡) Medium (7-9) 1.18 (0.82-1.69) 1.33 (0.91-1.94) 1.56 (1.07-2.27) * 0.83 (0.53-1.31) 1.13 (0.78-1.64) 1.44 (0.96-2.14) High (10-12) 1.15 (0.71-1.87) 1.04 (0.63-1.73) 1.44 (0.88-2.36) 0.51 (0.25-0.99)* 1.32 (0.81-2.19) 1.84 (1.04-3.25)*

MODEL 2- AGE AND GENDER ADJUSTED Duration of residence (<20 years‡) 20-24 years 1.20 (0.78-1.83) 1.59 (1.02-2.50)* 0.85 (0.56-1.30) 0.79 (0.46-1.35) 1.26 (0.82-1.94) 1.28 (0.82-2.00) ≥25 years 1.44 (1.02-2.05)* 2.01 (1.38-2.93)*** 0.73 (0.52-1.04) 0.93 (0.61-1.43) 1.48 (1.03-2.13) 1.60 (1.10-2.32)* Age at immigration (≥40years‡) 30-39 years 1.82 (1.03-3.19)* 2.02 (1.10-3.71)* 0.76 (0.46-1.27) 0.99 (0.51-1.92) 0.98 (0.57-1.67) 1.39 (0.80-2.41) <30 years 1.46 (0.87-2.47) 1.61 (0.91-2.84) 0.57 (0.33-0.99) 0.68 (0.36-1.29) 1.61 (0.89-2.89) 2.10 (1.15-3.84)* Vietnam-born density (High ≥7.0%‡) Medium (2.0%-6.9%) 1.63 (1.13-2.36)** 1.43 (0.98-2.07) 1.09 (0.75-1.56) 0.66 (0.42-1.03) 1.16 (0.80-1.69) 0.95 (0.63-1.42) Low (<2.0%) 1.02 (0.70-1.47) 0.94 (0.64-1.38) 0.90 (0.63-1.30) 0.76 (0.49-1.19) 1.38 (0.95-2.01) 0.99 (0.66-1.48) DSSI social interaction (Low 4-6‡) Medium (7-9) 1.13 (0.78-1.64) 1.31 (0.89-1.91) 1.61 (1.10-2.35)* 0.81 (0.51-1.28) 1.10 (0.75-1.60) 1.38 (0.93-2.07) High (10-12) 1.15 (0.70-1.88) 1.05 (0.63-1.75) 1.46 (0.89-2.40) 0.48 (0.24-0.96)* 1.32 (0.79-2.19) 1.80 (1.02-3.20)*

63 Red meat‡ White meat‡ Seafood‡ Vegetables‡ Fruits‡ Dairy products‡ Acculturation measures ≥3 times/week ≥3 times/week ≥3 times/week ≥5 serves/day ≥2 serves/day Yes (continued) MODEL 3- FULL ADJUSTMENT# Duration of residence (<20 years‡) 20-24 years 1.19 (0.74-1.92) 1.73 (1.05-2.86)* 0.86 (0.54-1.39) 0.82 (0.43-1.55) 1.19 (0.74-1.93) 1.08 (0.64-1.82) ≥25 years 1.42 (0.96-2.11) 2.21 (1.44-3.35)*** 0.71 (0.48-1.04) 1.06 (0.64-1.76) 1.42 (0.95-2.15) 1.31 (0.84-2.02) Age at immigration (≥40years‡) 30-39 years 1.21 (0.68-2.11) 1.55 (0.84-2.86) 0.82 (0.46-1.44) 0.99 (0.46-2.08) 0.93 (0.51-1.65) 1.54 (0.81-2.93) <30 years 1.53 (0.82-2.86) 2.21 (1.13-4.32)* 0.57 (0.31-1.06) 1.53 (0.66-3.44) 1.49 (0.78-2.86) 1.61 (0.81-3.24) Vietnam-born density (High ≥7.0%‡) Medium (2.0%-6.9%) 1.61 (1.07-2.45)* 1.57 (1.03-2.38)* 1.06 (0.72-1.65) 0.71 (0.42-1.19) 1.26 (0.83-1.91) 0.89 (0.56-1.42) Low (<2.0%) 1.01 (0.67-1.53) 1.04 (0.67-1.61) 0.94 (0.62-1.44) 0.76 (0.45-1.28) 1.38 (0.91-2.12) 1.03 (0.64-1.65) DSSI social interaction (Low 4-6‡) Medium (7-9) 1.08 (0.72-1.55) 1.21 (0.81-1.81) 1.68 (1.13-2.49)** 0.81 (0.51-1.31) 1.08 (0.73-1.61) 1.41 (0.92-2.14) High (10-12) 0.95 (0.56-1.61) 0.85 (0.49-1.46) 1.60 (0.95-2.71) 0.51 (0.25-1.04) 1.11 (0.65-1.89) 1.73 (0.95-3.16)

‡: Reference category. Red meat, white meat, seafood (<3times/week), vegetable (<5serves/day), fruit (<2serves/day), dairy products (no) and others in parentheses #: Adjusted for age, gender, relationship, educational qualification, income, working and other acculturation covariates *: p<0.05; **: p<0.01; ***: p<0.001

64 Table 11 summarises associations between acculturation measures and health-related behaviours. Regarding duration of residence, compared to the reference group (<20 years of residence), Vietnam-born participants with 20-24 years of residence were more likely to smoke cigarettes (unadjusted Model 1, OR=2.63, 95%CI=1.17-5.91; adjusted Model 3 OR=2.48, 95%CI=0.96-6.41), and have ≥2 alcoholic drinks per week (unadjusted OR=1.57, 95%CI=0.81-3.08; adjusted Model 3, OR=1.17, 95%CI=0.53-2.55). Levels of physical activity and BMI varied little according to duration of residence in unadjusted and adjusted models. Participants with 20-24 years of residence had the unadjusted OR of 1.09 (95%CI=0.71-1.68), and adjusted OR of 1.07 (95%CI=0.64-1.81, Model 3) for ≥5 sessions of physical activity per week. The risk of being overweight or obese among participants with ≥25 years of residence was 0.94 (95%CI=0.63-1.41) in unadjusted and 0.93 (95%CI=0.67-1.31) in fully adjusted Model 3.

As shown in the unadjusted models in Table 11, younger age at immigration was associated with cigarette smoking and alcohol drinking (≥2 drinks/week). The likelihood of smoking increased by 58% (95%CI=0.69-3.62) for male participants who immigrated at 30-39 years of age, and doubled (OR=2.34, 95%CI=1.12-4.89) for participants who immigrated before 30 years of age compared to the older immigrants (≥40 years). The association of duration of residence and age at immigration with cigarettes smoking became statistically non-significant in the fully- adjusted models (Model 3), mainly due to the effect of the Vietnam-born density (p=0.03). The unadjusted OR for alcohol drinking among Vietnam-born men was 3.11 (95%CI=1.62-5.91) for 30-39 years of age at immigration and 2.23 (95%CI=1.21-4.13) for <30 years of age at immigration. However, when age and gender were adjusted for (Model 2), association between age at immigration and alcohol drinking was less evident (OR=3.00, 95%CI=1.11-8.12 for 30-39 years old; OR=1.78, 95%CI=0.58-5.44 for <30 years old). The likelihood of drinking ≥2 alcoholic drinks per week reduced among men aged 65 years and older in Model 3 (p=0.02).

The likelihood of being overweight or obese, smoking, and drinking alcohol tended to decrease with lower density of Vietnam-born population in LGAs in unadjusted and adjusted models. As presented in Table 11, unadjusted ORs for being overweight 65 or obese in participants living in LGAs with medium and low Vietnam-born population density was 0.82 (95%CI=0.57-1.18) and 0.57 (95%CI=0.40-0.82) respectively. The respective adjusted ORs (Model 3) were 0.81 (95%CI=0.52-1.21) and 0.61 (95%CI=0.41-0.93). Vietnam-born men in medium and low density LGAs were 35% (95%CI=0.31-1.42) and 64% (95%CI=0.15-0.87) less likely to be current smokers than participants in high density areas. The level of physical activity was not associated with the Vietnam-born population density indicator (Model 3, OR=1.01, 95%CI=0.64-1.58 for medium density, OR=0.94, 95%CI=0.61-1.48 for low density).

Social interaction was found to be positively associated with levels of physical activity. Adjusted ORs (Model 3) for doing ≥5 sessions/week for medium level of DSSI (7-9) was 1.86 (95%CI=1.24-2.77), and 3.31 (95%CI=1.77-6.15) for high DSSI (10-12). Vietnam-born male participants with medium and high levels of social interaction were less likely to smoke (Model 3, OR=0.83, 95%CI=0.38-1.81; OR=0.63, 95%CI=0.21-1.96 respectively). However, following adjustment of other covariates (Model 3), the likelihood of being overweight or obese increased by 22% (95%CI=0.82-1.81) and 44% (95%CI=0.84-2.45) in participants with medium and high DSSI scores, respectively (Table 11). Models 2 and Models 3 provided similar patterns of relationship between acculturation indicators and lifestyle factors. The effects of age (65 years and older) and Vietnam-born density (low) explained the attenuation in the association of duration of residence and age at immigration with cigarettes smoking and alcohol drinking.

66 Table 11 Associations between acculturation measures and health-related behaviours: crude and adjusted odds ratio (95%CI)

Physical activity‡ BMI‡ Current smoker‡♂ Alcohol drinking‡♂ Acculturation measures ≥5 sessions/week Overweight/ Obese Yes ≥2 drinks/week MODEL 1 - UNADJUSTED MODELS Duration of residence (<20 years‡) 20-24 years 1.09 (0.71-1.68) 0.96 (0.63-1.46) 2.63 (1.17-5.91)* 1.57 (0.81-3.08) ≥25 years 1.16 (0.82-1.66) 0.93 (0.67-1.31) 1.04 (0.47-2.29) 1.26 (0.71-2.28) Age at immigration (≥40years‡) 30-39 years 1.38 (0.91-2.08) 1.19 (0.82-1.74) 1.58 (0.69-3.62) 3.11 (1.62-5.91)** <30 years 0.73 (0.51-1.05) 0.97 (0.68-1.38) 2.34 (1.12-4.89)* 2.23 (1.21-4.13)*** Vietnam-born density (High ≥7.0%‡) Medium (2.0%-6.9%) 1.09 (0.74-1.61) 0.82 (0.57-1.18) 0.74 (0.39-1.41) 0.86 (0.51-1.49) Low (<2.0%) 0.89 (0.61-1.31) 0.57 (0.40-0.82)** 0.49 (0.24-0.98)* 0.65 (0.36-1.16) DSSI social interaction (Low 4-6‡) Medium (7-9) 1.83 (1.26-2.68)** 1.21 (0.83-1.73) 0.83 (0.43-1.61) 0.86 (0.49-1.52) High (10-12) 3.13 (1.77-5.55)*** 1.33 (0.82-2.17) 0.61 (0.21-1.61) 0.89 (0.42-1.87)

MODEL 2- AGE AND GENDER ADJUSTED Duration of residence (<20 years‡) 20-24 years 1.41 (0.74-1.77) 0.81 (0.52-1.24) 2.28 (1.01-5.20)* 1.42 (0.72-2.82) ≥25 years 1.22 (0.85-1.75) 0.81 (0.57-1.16) 0.85 (0.38-1.90) 1.06 (0.57-1.96) Age at immigration (≥40years‡) 30-39 years 1.54 (0.88-2.69) 1.20 (0.71-2.03) 0.79 (0.24-2.60) 3.00 (1.11-8.12)* <30 years 0.85 (0.47-1.52) 0.98 (0.56-1.72) 0.97 (0.27-3.48) 1.78 (0.58-5.44) Vietnam-born density (High ≥7.0%‡) Medium (2.0%-6.9%) 1.09 (0.74-1.60) 0.79 (0.54-1.15) 0.70 (0.36-1.34) 0.80 (0.45-1.40) Low (<2.0%) 0.89 (0.61-1.30) 0.58 (0.40-0.84) 0.45 (0.22-0.93)* 0.58 (0.32-1.05) DSSI social interaction (Low 4-6‡) Medium (7-9) 1.83 (1.25-2.68)** 1.19 (0.82-1.73) 0.75 (0.38-1.47) 0.79 (0.44-1.41) High (10-12) 3.05 (1.72-5.42)*** 1.30 (0.79-2.15) 0.62 (0.24-1.63) 0.91 (0.43-1.94)

67 Physical activity‡ BMI‡ Current smoker‡♂ Alcohol drinking‡♂ Acculturation measures ≥5 sessions/week Overweight/ Obese Yes ≥2 drinks/week (continued) MODEL 3 – FULL ADJUSTMENT# Duration of residence (<20 years‡) 20-24 years 1.07 (0.64-1.81) 1.01 (0.62-1.63) 2.48 (0.96-6.41) 1.17 (0.53-2.55) ≥25 years 1.01 (0.65-1.55) 0.94 (0.63-1.41) 0.84 (0.33-2.15) 1.06 (0.52-2.16) Age at immigration (≥40years‡) 30-39 years 1.49 (0.76-2.92) 1.35 (0.76-2.42) 0.69 (0.18-2.66) 2.78 (0.91-8.45) <30 years 0.57 (0.28-1.16) 1.21 (0.64-2.28) 1.05 (0.24-4.67) 1.74 (0.48-6.34) Vietnam-born density (High ≥7.0%‡) Medium (2.0%-6.9%) 1.01 (0.64-1.58) 0.81 (0.52-1.21) 0.65 (0.31-1.42) 0.88 (0.46-1.68) Low (<2.0%) 0.94 (0.61-1.48) 0.61 (0.41-0.93)* 0.36 (0.15-0.87)* 0.66 (0.33-1.31) DSSI social interaction (Low 4-6‡) Medium (7-9) 1.86 (1.24-2.77)** 1.22 (0.82-1.81) 0.83 (0.38-1.81) 0.83 (0.44-1.55) High (10-12) 3.31 (1.77-6.15)*** 1.44 (0.84-2.45) 0.63 (0.21-1.96) 1.05 (0.46-2.41) ‡: Reference group: physical activity (<5 sessions/week), BMI (normal weight), current smoker (no), alcohol (<2drinks/week), others in parentheses. ♂: Men only. #: Adjusted for age, gender, relationship, educational qualification, income, working and other acculturation covariates *: p<0.05; **: p<0.01; ***: p<0.001

68

4.7.2.2. Acculturation effects on health status

Table 12 presents unadjusted and adjusted ORs for associations between the four acculturation measures and self-rated general health, medical conditions and psychological distress of the Vietnam-born participants. In unadjusted models, longer duration of residence related to good and better self-rated general health (excellent, very good or good), lower prevalence of type 2 diabetes, high blood pressure, SF36- PF limitation and K10 psychological distress (very high, high and moderate). The unadjusted OR for good or better self-rated general health among participants with 20-24 and ≥25 years of residence, respectively, was 1.30 (95%CI=0.84-2.01) and 2.08 (95%CI=1.45-3.00), compared to the reference group (<20 years). The likelihood of having psychological distress reduced between 25% and 30% respectively. The risk of having heart disease in participants with 20-24 years of residence decreased (unadjusted OR=0.41, 95%CI=0.13-1.26) while the risk slightly increased among those with ≥25 years living in Australia (unadjusted OR=1.25, 95%CI=0.63-2.47).

Unadjusted models shown that younger age at immigration was strongly associated with self-rated general health, medication conditions, and psychological distress among Vietnam-born participants. Participants aged 30-39 years and under 30 years at immigration were more likely to rate their health as excellent, very good or good (unadjusted OR=2.25, 95%CI=1.51-3.35; OR=3.81, 95%CI=2.59-5.61, respectively). They were less likely to have type 2 diabetes (OR=0.44, 95%CI=0.26-0.73; OR=0.24, 95%CI=0.14-0.41), heart disease (OR=0.24, 95%CI=0.11-0.54; OR=0.17, 95%CI=0.08-0.38), high blood pressure (OR=0.61, 95%CI=0.41-0.88; OR=0.26, 95%CI=0.18-0.39) and physical functioning limitations (OR=0.34, 95%CI=0.21- 0.56; OR=0.26, 95%CI=0.16-0.41). People lived in low Vietnam-born density areas also rate their health status as good or excellent (unadjusted OR=1.69, 95%CI=1.15- 2.49). People with medium and high level of social interaction were also less likely to report signs of psychological distress (unadjusted OR=0.68, 95%CI=0.46-1.01, and OR=0.56, 95%CI=0.33-0.96, respectively).

69

As shown in Table 12, when age and gender were adjusted (Model 2), the strength of the relationship between all four acculturation measures with self-rated general health, between age at immigration and type 2 diabetes, and between social interaction and psychological distress remained statistically significant. However, in the full adjustment models (Model 3), only type 2 diabetes remained statistically significantly associated with younger age at immigration (<30 years, OR=0.34, 95%CI=0.14-0.84) and Vietnam-born density (OR=0.51, 95%CI=0.28-0.92 for medium, OR=0.51, 95%CI=0.29-0.98 for low density). The likelihood of having heart disease decreased by half for participants with 20-24 years of residence (adjusted OR=0.46, 95%CI=0.12-1.78, Model 3) while doubling for those with ≥25 years of residence (adjusted OR=2.16, 95%CI=0.94-4.94, Model 3) (Table 12), but these associations were not statistically significant.

The attenuated effects of acculturation measures on self-rated general health in Model 3 were related to the confounding or mediating effects of household income (p=0.001) and working status (p<0.001). People who had higher annual household income, and were working were more likely to rate their health status as good or excellent. In addition, older age was strongly associated with higher odds of having type 2 diabetes (p=0.02), heart disease (p<0.01), high blood pressure (p<0.001) and limited physical function (p<0.001) but lower odds of having psychological distress (p<0.001). Higher levels of household income were also associated with lower odds of having diabetes (p=0.03) and psychological distress (p=0.008) (ORs not shown).

70 Table 12 Associations between acculturation measures and health status: crude and adjusted odds ratio (95%CI)

Self-rated general health ‡ Type 2 diabetes‡ Heart disease‡ High blood pressure‡ SF36-PF‡ K10 distress‡ Acculturation measures Excellent/very good/good Yes Yes Yes Physical limitation Very high/high/moderate

MODEL 1 - UNADJUSTED MODELS Duration of residence (<20 years‡) 20-24 years 1.30 (0.84-2.01) 0.79 (0.45-1.41) 0.41 (0.13-1.26) 0.81 (0.52-1.24) 0.92 (0.56-1.52) 0.71 (0.45-1.12) ≥25 years 2.08 (1.45-3.00)*** 0.59 (0.37-0.96)* 1.25 (0.63-2.47) 0.77 (0.54-1.09) 0.68 (0.47-1.01) 0.75 (0.52-1.08) Age at immigration (≥40years‡) 30-39 years 2.25 (1.51-3.35)*** 0.44 (0.26-0.73)** 0.24 (0.11-0.54)*** 0.61 (0.41-0.88)** 0.34 (0.21-0.56)*** 0.99 (0.67-1.47) <30 years 3.81 (2.59-5.61)*** 0.24 (0.14-0.41)*** 0.17 (0.08-0.38)*** 0.26 (0.18-0.39)*** 0.26 (0.16-0.41)*** 1.36 (0.91-2.07) Vietnam-born density (High ≥7%‡) Medium (2.0%-6.9%) 1.23 (0.84-1.81) 1.01 (0.69-1.49) 0.64 (0.31-1.39) 1.01 (0.69-1.46) 0.74 (0.49-1.13) 0.81 (0.55-1.22) Low (<2.0%) 1.69 (1.15-2.49)** 0.78 (0.53-1.16) 0.84 (0.41-1.71) 0.74 (0.51-1.09) 0.84 (0.55-1.29) 0.82 (0.54-1.19) DSSI social interaction (Low 4-6‡) Medium (7-9) 1.31 (0.89-1.91) 1.16 (0.77-1.72) 1.17 (0.53-2.55) 1.13 (0.76-1.68) 1.01 (0.66-1.51) 0.68 (0.46-1.01) High (10-12) 1.66 (0.99-2.79) 1.37 (0.82-2.31) 1.35 (0.51-3.62) 1.37 (0.82-2.29) 1.13 (0.65-1.96) 0.56 (0.33-0.96)*

MODEL 2- AGE AND GENDER ADJUSTED Duration of residence (<20 years‡) 20-24 years 1.07 (0.68-1.69) 0.83 (0.49-1.50) 0.53 (0.17-1.71) 0.83 (0.52-1.31) 1.24 (0.73-2.09) 0.84 (0.46-1.55) ≥25 years 1.71 (1.17-2.50)** 0.65 (0.39-1.07) 2.11 (1.02-4.37)* 0.85 (0.58-1.25) 0.87 (0.57-1.32) 0.93 (0.58-1.51) Age at immigration (≥40years‡) 30-39 years 1.79 (1.04-3.07)* 0.28 (0.13-0.60)** 0.87 (0.29-2.58) 1.01 (0.59-1.74) 0.63 (0.32-1.22) 1.42 (0.65-3.12) <30 years 2.87 (1.58-5.21)*** 0.39 (0.20-0.75)** 0.97 (0.25-3.81) 0.55 (0.30-1.01) 0.56 (0.28-1.13) 1.03 (0.44-2.37) Vietnam-born density (High ≥7.0%‡) Medium (2.0%-6.9%) 1.22 (0.82-1.80) 0.62 (0.37-1.04) 0.69 (0.31-1.52) 1.03 (0.69-1.52) 0.90 (0.58-1.41) 1.04 (0.63-1.72) Low (<2.0%) 1.65 (1.10-2.45)* 0.61 (0.37-1.03) 1.02 (0.49-2.13) 0.82 (0.55-1.22) 0.80 (0.51-1.24) 0.91 (0.55-1.53) DSSI social interaction (Low 4-6‡) Medium (7-9) 1.20 (0.84-1.84) 1.02 (0.60-1.75) 1.44 (0.64-3.25) 1.29 (0.84-1.96) 1.06 (0.69-1.63) 0.44 (0.0.28-0.71)*** High (10-12) 1.79 (1.05-3.08)* 0.56 (0.25-1.25) 1.36 (0.49-3.77) 1.34 (0.78-2.31) 1.04 (0.58-1.84) 0.50 (0.25-0.97)*

71 Self-rated general health ‡ Type 2 diabetes‡ Heart disease‡ High blood pressure‡ SF36-PF‡ K10 distress‡ Acculturation measures Excellent/very good/good Yes Yes Yes Physical limitation Very high/high/moderate (continued) MODEL 3- FULL ADJUSTMENT# Duration of residence (<20 years‡) 20-24 years 0.84 (0.49-1.43) 1.12 (0.57-2.22) 0.46 (0.12-1.78) 0.89 (0.52-1.55) 1.31 (0.73-2.32) 0.84 (0.49-1.42) ≥25 years 1.27 (0.82-1.97) 0.85 (0.47-1.52) 2.16 (0.94-4.94) 1.01 (0.64-1.55) 0.92 (0.57-1.48) 0.86 (0.56-1.33) Age at immigration (≥40years‡) 30-39 years 1.31 (0.71-2.41) 0.49 (0.23-1.03) 0.77 (0.24-2.45) 0.61 (0.31-1.22) 1.08 (0.58-1.99) 1.47 (0.76-2.88) <30 years 1.51 (0.76-2.99) 0.34 (0.14-0.84)* 0.87 (0.21-3.86) 1.06 (0.57-1.95) 0.59 (0.31-1.22) 1.01 (0.49-2.052) Vietnam-born density (High ≥7.0%‡) Medium (2.0%-6.9%) 1.03 (0.65-1.63) 0.51 (0.28-0.92)* 0.52 (0.22-1.23) 1.02 (0.65-1.61) 0.84 (0.51-1.37) 0.75 (0.48-1.19) Low (<2.0%) 1.28 (0.81-2.05) 0.54 (0.29-0.98)* 0.71 (0.31-1.61) 0.84 (0.53-1.33) 0.72 (0.44-1.18) 0.85 (0.53-1.34) DSSI social interaction (Low 4-6‡) Medium (7-9) 1.15 (0.74-1.78) 1.19 (0.66-2.13) 1.27 (0.53-3.07) 1.39 (0.89-2.17) 1.11 (0.71-1.75) 0.65 (0.42-1.02) High (10-12) 1.54 (0.85-2.79) 0.59 (0.24-1.41) 1.16 (0.38-3.53) 1.53 (0.85-2.73) 1.04 (0.57-1.91) 0.56 (0.31-1.01) ‡: Reference group. Self-rated general health (fair/ poor), diabetes (without diabetes), heart disease, high blood pressure (no), SF36-PF (no limitation), K10 (low distress), others in parentheses #: Adjusted for age, gender, relationship, educational qualification, income, working and other acculturation covariates *: p<0.05; **: p<0.01; ***: p<0.001

72 4.8. DISCUSSION OF FINDINGS

This chapter investigated relationships between four measures of acculturation including duration of residence in Australia, age at immigration, density of Vietnam- born population in residential areas and level of social interactions, and measures of lifestyle, physical health, and psychological distress in Vietnam-born participants in the 45 and Up Study.

There are some evidence for effects of acculturation measures. Hypothesis One, that lifestyle profiles including dietary patterns and health-related behaviours of Vietnam- born Australians are associated with levels of acculturation, was partially supported. In particular, results from adjusted Model 3 showed that likelihood of having red meat ≥3 times per week increased in those living in medium Vietnam-born density LGAs. Consumption of white meat (≥3 times per week) increased among Vietnam- born individuals who immigrated at younger age (<30 years), had longer duration of residence (≥20 years) and lived in medium Vietnam-born density LGAs (Models 3). Individuals with medium level of social interaction were more likely to eat seafood ≥3 times per week. People with medium or high levels of social interaction were more likely to exercise ≥5 sessions of physical activity per week. Likelihood of being current smoker and overweight and obese was lower in Vietnam-born people living in areas with low concentration of Vietnam-born population (<2%) (Model 3). The odds of being overweight and obese slightly increased with younger age at immigration and higher levels of DSSI social interaction, although these relationships were not statistically significant (Model 3). However, these associations could have been mediated by correlations between BMI and consumption of meat, especially white meat (r=0.106, p=0.004), and BMI and high blood pressure (p<0.001).

Effects of Vietnam-born density on smoking and overweight and obesity could relate to socio-economic status of the areas because LGAs with low Vietnam-born density have higher IRSD scores128 than high density areas. Prevalence of smoking, obesity and diabetes was lower in high IRSD areas than in disadvantaged areas.14 The rates of smoking and overweight or obesity among Vietnam-born participants might

73 reflect the norms in their neighbourhood areas. Although the relationship of duration of residence and age at immigration with smoking and alcohol drinking attenuated from unadjusted to fully adjusted models, the adjusted ORs between smoking and duration of residence (OR=2.48, 95%CI=0.96-6.41, 20-24 years of residence, Model 3), and between alcohol drinking and age at immigration (OR=2.78, 95%CI=0.91- 8.45 for 30-39 years at immigration, and 1.74, 95%CI=0.48-6.34 for <30 years of age at immigration, Model 3) still elevated. Given the mean age of Vietnam-born men was 59.5, and the correlation between duration of residence and age at immigration (r=-0.561, p<0.001), the higher rates of smoking in men with 20-24 years of residence and alcohol drinking in men who immigrated at 30-39 years of age may be due to the “age, cohort, and period effects”203, 204 in which historical differences in smoking rates and influences of smoking cessation campaigns may exist among groups of Vietnam-born participants. It may be possible that the smoking rate was higher among those who immigrated to Australia between 1982 and 1988.

In relation to the effects of acculturation measures on health status of Vietnam-born Australians as per Hypothesis Two, the likelihood of having type 2 diabetes decreased in people immigrated before 30 years of age and those living in low Vietnam-born density (Model 3). Although the relationship between age at immigration and other health status indicators attenuated following adjustment of covariates, there was a trend of better self-rated general health and lower likelihood of having heart disease, high blood pressure and limited physical functioning. Nevertheless, participants who were socially interactive had slightly increased risk of having heart disease (fully adjusted OR=1.27, 95%CI=0.53-3.07 for medium DSSI) and high blood pressure (fully adjusted OR=1.39, 95%CI=0.89-2.17 for medium DSSI; 1.53, 95%CI=0.85-2.73 for high DSSI). Given the use of cross-sectional data, and age being a strong confounding factor in models for heart disease (p<0.001) and high blood pressure (p<0.001), the negative associations between DSSI and heart disease and high blood pressure were likely to be due to a higher level of dependence on other people among ageing Vietnam-born participants with heart disease and high blood pressure.

74 Among the demographic and socio-economic variables, age, household income, and working status were strongly associated with most of the outcome measures in both age and gender adjusted (Model 2) and fully adjusted (Model 3) logistic regression models. These covariates explained the attenuation of the relationship between acculturation measures and some lifestyle and health status outcomes in comparison to unadjusted models. For example, the relationship between duration of residence and age at immigration and good or excellent self-rated general health were statistically significant in both Model 1 (unadjusted) and Model 2 (age and gender adjusted) but non-significant in Model 3 due to the effects of household income (p=0.001) and working status (p<0.001). Participants with a higher income were more likely to rate their health as excellent, very good and good (p=0.001), and less likely to report psychological distress (p=0.008) and type 2 diabetes (p=0.04). Older age was associated with lower rates of cigarette smoking (for men, p=0.008), alcohol drinking (for men, p=0.05), and lower psychological distress (p<0.001) but higher rates of type 2 diabetes (p=0.02), heart disease (p<0.001) and high blood pressure (p<0.001). The effects of age and income were in line with literature that smoking and alcohol drinking decline with older age,203-205 and that the prevalence of chronic conditions such as diabetes, hypertension and heart disease increases with age.14, 205, 206

Almost 40% of the Vietnam-born participants lived in areas other than the South Western region of Sydney where there was a high concentration of Vietnam-born population. This finding was consistent with a previous report that the spatial distribution of the Vietnam-born population in Sydney, NSW and , Victoria has dispersed into the broader Australian community over the last two decades.207 It was not an unexpected finding that the majority of Vietnam-born participants in the 45 and Up Study reported speaking a LOTE at home, because these participants represented the first generation of Vietnamese immigrants in Australia who migrated in their adulthood (mean age at immigration 35.3 years). However, this does not indicate a lack of English skill among the participants. In fact, these participants may have represented a subgroup of the Vietnam-born Australians who have already been acculturated to some extent or have reached a “saturated” level of acculturation. As discussed in Chapter 3, Vietnam-born participants in the 45 and Up Study had better SES and linguistic acculturation

75 compared to the general Vietnam-born population. This might explain the lack of statistical significance in most of the logistic regression models testing the relationships between acculturation indicators and lifestyle and health conditions. Another finding of this chapter, that Vietnam-born participants with higher degrees of acculturation were more likely to eat cheese or drink cow’s milk, also lends support to a previous study208 that found increased consumption of dairy products was an indicator of acculturation.

In contrast to the “healthy immigrant effect”,19-22 this chapter found that a higher degree of acculturation was beneficial towards the health status of Vietnam-born Australians, which could be due to a better living standard and health care for the Vietnamese humanitarian immigrants. Other studies among Vietnam-born Australians also reported that compared to new Vietnamese immigrants, those with a longer duration of residence appeared to have lower risks of cardiovascular disease and diabetes, higher levels of physical exercises,35, 208 lower prevalence of abdominal obesity and hypercholesterolemia,208 and consistently low levels of alcohol drinking and smoking.35

The baseline questionnaire data of the 45 and Up Study used in this chapter allowed only cross-sectional analyses of associations between degrees of acculturation and lifestyles and health status. However, the 45 and Up Study data contained comprehensive information on demography, SES, lifestyles and health, thus allowing investigations of multiple relationships between measures of acculturation and health-related outcomes. Longitudinal data from the Study will further provide opportunities to assess the impact of acculturation on the health of the ageing Vietnam-born Australians. As previously discussed, measures of acculturation presented in this chapter have frequently been used in epidemiology and public health research,37, 39, 149 however, their limitations should be acknowledged. Time- related indices such as duration of residence and age at immigration are considered as a proxy of acculturation because these measures do not capture various aspects of acculturation.39, 176 Age at immigration in this chapter operated as a moderator of the acculturation process, not as an indicator of exposure to Australian culture as duration of residence. The measure Vietnam-born density of LGAs relates to geographic areas rather than each individual person. The DSSI social interaction did 76 not provide information about the culture network (Vietnamese, Australian, or other CALD culture). Nevertheless, Vietnam-born people in this chapter are first generation, speaking a LOTE at home. In the absence of a comprehensive assessment of acculturation in the 45 and Up Study baseline questionnaire, the use of proxy measures of acculturation in this chapter is provides the best available information.176, 177

Although the majority of variables used contain minimal missing values (less than 5%), there were considerable percentages of missing value for the exposure variable (DSSI social interaction, 13.7%), and some outcome variables such as self-rated general health (8.9%), SF36-PF (16.3%), and K10 (16.9%). The exclusion of missing cases from multivariable logistic regression could potentially introduce bias. It is unknown whether the nature of missing data was random or non-random, despite the even distribution by age and gender of missing cases for the DSSI-social interaction (an exposure variable).

This chapter raises several implications for multicultural research and health promotion. The amount of food consumption cannot be directly quantified from questions such as “About how many times each week do you eat beef, lamb or pork?” as in the 45 and Up Study baseline questionnaire. This is because food intake relates not only to frequency of food consumption but also how food is served, which varies by culture. The Vietnamese meal structure differs from Western meals. There are three meals a day. Breakfast can be as little as a cup of coffee or as much as a bowl of rice with vegetables, or noodle soup with meat or fish. Lunch and dinner are of equal importance and both consist of four basic dishes including steamed rice, stock soup, boiled or stir fried vegetables, and marinated small pieces of meat or seafood. Bread, cereal, and dairy products are uncommon in Vietnamese meals. At meal time, foods are served in communal serving dishes. It is very common that meat or seafood is served every day or on alternate days, but in small portions. Thus, it would be more appropriate to ask questions about food intake in measurable units such as serving size which would then facilitate drawing research conclusions on nutrition or comparing across cultures.

77 The findings in this chapter have highlighted the necessity of culture-specific analysis due to the complexity of the acculturation concept itself, heterogeneity in culture-driven lifestyles and health disparities among immigrant populations. The link between degrees of acculturation and health can only be discussed in depth within a specific culture, health belief and practice, and immigration circumstances, which is reflected in the conclusion by Frisbie21 that immigrant status is crucial to health in general and to the validity of acculturation hypotheses.

Health education targeting Vietnam-born population should emphasise the importance of a traditional diet, especially vegetable consumption. Vietnam-born Australians should continue to avoid smoking and alcohol, and increase physical activity, so that living in Australia can further contribute to positive health status and lower the risk of chronic diseases including diabetes. The likelihood of having type 2 diabetes tend be lower in Vietnam-born participants who had higher levels of acculturation, such as longer duration of residence, younger age at immigration, and living in LGAs with a low density Vietnam-born population. The next chapter will further present prevalence and risk factors of type 2 diabetes in Vietnam-born participants relative to those among Australia-born people.

78

Chapter 5

Diabetes prevalence and risk factors

5.1. INTRODUCTION

There have been concerns raised about a “global diabetes epidemic”40, 44, 45, 209 because of the rise in the number of people with diabetes. In 2008, approximately 347 million adults worldwide had diabetes, which meant that diabetes affected almost one in ten people (9.8%), and this was an increase in diabetes prevalence from 8.2% in 1980.40 The wide-ranging effects of diabetes are of paramount concern, such as increased morbidity, mortality, substantial costs to the health system, and societal consequences for individuals and their families.42, 44, 206, 209 Factors contributing to the increased prevalence and incidence of diabetes include population growth, ageing, obesity, physical inactivity, and acceleration of market globalisation.40, 43-45

As found in Chapter 4, higher degree of acculturation among Vietnam-born people was associated with lower risk of type 2 diabetes. Previous research has suggested that Vietnamese people have a higher risk of diabetes,47-52 but have inadequate levels of diabetes knowledge and diabetes management.53-55 Vietnam-born Australians are getting older, indicating an increasing need of health services for chronic illnesses. Limited information is available on the health risk profile of diabetes among Vietnam-born Australians. To address this gap, this chapter will review relevant literature and investigate prevalence and risk factors of type 2 diabetes among Vietnam-born participants in the 45 and Up Study in comparison to Australia-born participants. A better understanding of diabetes-related health status could potentially inform health prevention, education, and diabetes management for the Vietnam-born community. 79

THEORETICAL FRAMEWORK

5.2. DIABETES IN THE RESEARCH CONTEXT

5.2.1. Diabetes definitions

Diabetes is defined as a chronic disease characterised by hyperglycaemia with disorders of carbohydrate, fat and protein metabolism resulting from defective insulin production or insulin action.210 Uncontrolled diabetes usually leads to long- term damage, dysfunction and failure of various organs.206, 210

5.2.2. Types of diabetes

There are four types of diabetes: type 1, type 2, gestational and other specific diabetes.

(i) Type 1 diabetes: Type 1 diabetes is prevalent in 10% of people with diabetes.42, 205, 211 Clinically, it is characterised by little or no insulin secretion which can be indicated by a low or undetectable level of plasma C-peptide.212 The onset is often before the age of 30;212 however, up to ten percent of people with type 1 are diagnosed at a later age, depending on the autoimmune destruction of the islet beta-cells of the pancreas.210, 212 People with type 1 diabetes often become insulin dependent for survival, and thus are at higher risk of severe acute complications such as ketoacidosis.212 Type 1 diabetes can be further classified into type 1A (autoimmune) and type 1B (idiopathic).210, 213

(ii) Type 2 diabetes: Type 2 diabetes is the most common form of diabetes, accounting for approximately 90% of people with diabetes.42, 205, 211 It is characterised by relative insulin deficiency or resistance to the action of insulin, for which the specific reasons are not yet known. It often occurs in people aged 40 years and over.206, 212 Management of type 2 diabetes initially involves lifestyle modification including diet, body weight control and regular physical activity. Medications for blood sugar management involve the use of OHAs and/or insulin.214 80 (iii) Gestational diabetes (GDM): Gestational diabetes describes a hyperglycaemia status in pregnant women who have not previously been diagnosed with other forms of diabetes. Glucose intolerance may exist before pregnancy but not be detected. GDM usually disappears after giving birth; however, it can recur in later pregnancies.206, 210 According to the Australasian Diabetes in Pregnancy Society,215 GDM can be managed primarily by a dietary therapy, then by insulin injection.

(iv) Other specific types: These forms of diabetes are less common and have specific underlying causes including defects or diseases such as genetic defects of islet beta-cell function and actions of insulin, exocrine pancreas diseases, and endocrinopathies.210 One type of diabetes, known as drug- induced diabetes, can also result from long-term use of certain drugs and chemicals, for example glucocorticoids and antipsychotics.210, 214

5.2.3. Health risk factors of diabetes

Factors associated with development of diabetes often co-exist and interact with each other.206 Identifying these factors and understanding their interactions can assist targeted intervention to prevent development of diabetes and its complications.

5.2.3.1. Risk factors of type 1 diabetes

A number of non-modifiable, genetic and environmental factors were identified as triggers in development of type 1 diabetes.206 A person can inherit a genetic predisposition, for example the Human Leukocyte Antigen genotype, which increases the person’s susceptibility to type 1 diabetes.213, 216-218 Environmental determinants of type 1 diabetes are not fully understood.43 To date, there are three major hypotheses explaining causes of type 1 diabetes, including infection with certain viruses and bacteria,219, 220 young infants consuming cow’s milk, cereals or gluten, where certain proteins may trigger an autoimmune response,221-224 and exposure to food-borne chemical toxins such as N-nitroso compounds.225 A high prevalence of serum vitamin D insufficiency has been reported in adolescents with type 1 diabetes but a causal relationship is not yet confirmed.226

81 5.2.3.2. Risk factors of type 2 diabetes

In addition to genetic predispositions such as family history, ethnic background and ageing,206 several modifiable behavioural and biomedical factors play important roles in the onset of type 2 diabetes. Behavioural factors include physical inactivity, unhealthy diet, alcohol drinking and tobacco smoking, while biomedical factors include overweight or obesity, high blood pressure, high blood cholesterol and impaired glucose regulation.

(i) Physical inactivity: Physical inactivity alone contributes to one-quarter of disability-adjusted life years from diabetes in Australia.227 Sufficient physical activity can delay diabetes development in people with impaired glucose regulation,228 improve glucose control in people with diabetes even without weight loss,229 and reduce morbidity and mortality associated with complications of diabetes.206 According to the National Physical Activity Guidelines for Australians,230, 231 sufficient physical activity is defined as 30 minutes of moderate physical activity on at least five days of the week, or 150 minutes spread out over five sessions in a week. A 30 minute session can be divided into three blocks of ten minutes, especially for those aged 65 years and over.231 Moderate intensity activities include, for example, brisk walking, swimming, doubles tennis, and medium paced cycling. Vigorous activities include jogging and active sports such as football and basketball.230

(ii) Unhealthy diet: According to WHO,46 the acceleration of industrialisation, urbanisation, economic development and market globalisation over the past decades has shifted the population’s diet towards less healthy eating, such as increased consumption of energy-dense foods, animal-based saturated fats, and sugary drinks and decreased intakes of complex carbohydrates and dietary fibre, fruits and vegetables. Energy-dense foods are often highly processed, thus containing inadequate amounts of non-starch polysaccharides and micronutrients.46 Eating high amounts of saturated fats causes overweight and obesity and insulin resistance.228 Foods containing high amounts of fibre, such as wholegrain cereals, legumes, fruits and vegetables, are associated with weight loss, fewer cardiovascular events and

82 improved insulin sensitivity.232, 233 Australian health guidelines recommend that adults and children should consume a wide variety of nutritious foods, including a large amount of plant foods, lean meat, fish, poultry while limiting their intake of salt, saturated fats and excessive alcohol.202

Many studies have also been conducted to determine whether the long-term intake of carbohydrates that are rapidly absorbed as glucose (measured by glycaemic index [GI]a and glycaemic load [GL]b) increases the risk of type 2 diabetes,232-241 as a high intake of high GI carbohydrates could increase insulin resistance and impair pancreatic function.236, 240 However, research evidence is inconsistent. Nevertheless, these studies have confirmed that fibre-rich diets could reduce the risk of type 2 diabetes and improve glycaemic control in people with diabetes,232, 233, 236, 241 and that a low GI diet is recommended for people with diabetes.232, 236, 239, 240

(iii) Alcohol drinking: Excessive alcohol drinking can exacerbate diabetes complications and cause hypoglycaemia in people taking diabetes medications.242 In contrast, drinking alcohol at light and moderate levels can be protective against diabetes243-245 and coronary heart diseases.244, 246-250 Similar to recommendations to the general population, men and women with diabetes should not drink more than 20g of alcohol (equal to two Australian standard drinks) in a day, and pregnant and breastfeeding women with diabetes should not drink alcohol.251

(iv) Tobacco smoking: Tobacco smoking increases the risk of diabetes complications such as coronary heart disease, stroke, peripheral vascular and kidney diseases.206, 210 A number of studies have suggested that tobacco smoking may lead to the development of type 2 diabetes particularly in men;252-255 however, more research evidence is required to confirm tobacco smoking as a risk factor of type 2 diabetes.206

a GI is defined as the percentage increase of blood glucose level following ingestion of 25-50g carbohydrates from a reference food such as glucose or white bread.232 b GL is calculated as the amount of carbohydrate in one serving multiplied by the GI of the food.232

83 (v) Overweight and obesity: Overweight and obesity describes excessive body weight due to a sustained energy imbalance, resulting from dietary energy intake exceeding energy expenditure over a period of time.206 Being overweight or obese is an important risk factor for diabetes, and is strongly associated with energy-dense food consumption and a sedentary lifestyle. In 2003, high BMI was the largest contributor (55%) to disability-adjusted life years from diabetes in Australia.227 In people with impaired glucose tolerance, excessive weight increases the risk of progression to type 2 diabetes.46, 206, 228 BMI is a conventional measure of overweight and obese status;195 however, using BMI to determine the risk of diabetes has limitations because the composition and distribution of body fat vary by ethnic groups,46 and BMI fails to distinguish weight attributable to fat and weight attributable to muscle.206 Waist circumference is an additional measure as abdominal fat varies greatly within a narrow range of total body fat or BMI. For adults (age ≥18 years), waist circumference values for abdominal overweight and obesity are 94-101 cm, and ≥102 cm respectively for men; and 80-87 cm and ≥88 cm for women.195 Similar to BMI, waist circumference cut-off values vary by ethnic group.195

(vi) High blood pressure: High blood pressure (≥140/90 mmHg)256 is a major risk factor for diabetes complications.256, 257 The risk of having cardiovascular disease doubles in people who have both diabetes and high blood pressure.257 Good management of blood pressure in people with diabetes can substantially reduce the risk for cardiovascular events and deaths.257, 258

(vii) High blood cholesterol: High blood cholesterol (total cholesterol ≥5.5 mmol/L)206 is significantly associated with cardiovascular disease and vascular complications of diabetes such as coronary heart disease and stroke.206, 259 Diabetic cholesterol abnormalities are often characterised by increased levels of triglycerides and low-density lipoprotein cholesterol, and decreased levels of high-density lipoprotein cholesterol, and associated with insulin resistance.260 Maintaining a healthy lifestyle through diet, exercise and body weight control is essential in managing lipid disorders260 and can reduce cardiovascular events.259

84 (viii) Impaired glucose regulation: Impaired glucose regulation (also known as pre-diabetes) describes an intermediate metabolic stage between non- diabetes and diabetes, and can be manifested in two forms: impaired glucose tolerance and impaired fasting glycaemia.261 People with impaired glucose tolerance are six times more likely to progress to type 2 diabetes (rate 35.8 to 87.3 per 1,000 person-years) than those with normal glucose tolerance.262, 263 In people with both impaired glucose tolerance and impaired fasting glycaemia, incidence of type 2 diabetes is elevated by 12 folds.261

5.2.3.3. Risk factors for gestational diabetes

Risk factors for GDM include older maternal age, family history of diabetes, being a member of certain ethnic groups (such as and people from the Indian subcontinent, Pacific Islands, Asia and the Middle East), being overweight or obese before pregnancy, and having a history of impaired glucose tolerance, GDM and having “large for gestational age” babies.210, 215, 264 The Australasian Diabetes in Pregnancy Society215 recommends that all pregnant women should be screened for GDM at weeks 26-28 of gestation, and mothers with GDM should have a maternal follow-up at 6-8 weeks postpartum then at least biannually for development of type 2 diabetes.

5.2.4. Impact of diabetes

5.2.4.1. Diabetes complications

Diabetes can result in various complications that are mostly responsible for diabetes- related morbidity and mortality.206

Acute complications The most common and severe acute complications of diabetes are ketoacidosis and hyperosmolar hyperglycaemia. Ketoacidosis occurs more often in people with type 1 diabetes while hyperosmolar hyperglycaemia occurs mainly in people with type 2 diabetes. These two complications are life threatening unless promptly treated, and are caused by an absolute or relative insulin deficiency, blood volume depletion,

85 acid-base abnormalities, and sometimes by errors in administration of insulin or OHAs.212 People with poorly controlled diabetes may also encounter increased susceptibility to infections and delayed wound healing.212, 265

Chronic complications In the long term, diabetes can damage small or large blood vessels leading to micro- vascular and macro-vascular complications.206, 212 When small arteries of the eyes are affected, retinopathy, cataracts and glaucoma can develop and cause impaired vision and even blindness.212 In kidneys, glomerular filtration can be weakened (nephropathy), releasing albumin or even protein into urine. In severe cases, diabetic nephropathy can lead to chronic kidney failure and end-stage kidney diseases, requiring regular dialysis or kidney transplantation.206, 212 Another micro-vascular effect of diabetes is neuropathy of peripheral and autonomic nervous systems.212 People with neuropathy can experience pain, tingling and numbness in the arms or legs when peripheral sensory nerves are affected and can also suffer from weakened muscle strength and an inability to control body movement if nerves controlling motor movement are damaged. Chronic autonomic neuropathy can result in dysfunctions of multiple organs of cardiovascular, gastrointestinal, genitourinary and metabolic systems, causing symptoms such as dizziness, fainting, nausea, vomiting, diarrhoea, constipation, loss of bladder control and sexual dysfunction.206, 212

Macro-vascular complications of diabetes can be manifested by cardiovascular, cerebrovascular and peripheral artery diseases.206 People with diabetes have twice the risk of myocardial infarction and stroke, higher mortality and poorer recovery from cardiovascular events than people without diabetes.266 A combination of peripheral neuropathy and peripheral artery diseases can increase the risk of foot ulcers, deformity and chronic infections which may lead to lower limb amputation.206

For pregnant women, diabetes is potentially harmful to mothers and their babies. Mothers may suffer from pre-eclampsia, pre-term delivery, postpartum haemorrhage, caesarean section and maternal mortality.267-271 Foetal effects could include congenital defects, perinatal death, low birth weight, jaundice, respiratory distress, birth trauma and stillbirth.269, 272, 273 Children of women with GDM or type 1 diabetes

86 have an increased risk of developing obesity and type 2 diabetes.268, 273-275 Other long-term complications of diabetes are gastroparesis and changes of skin.206, 212

5.2.4.2. Impact of diabetes on quality of life and disability

Quality of life for individuals with diabetes can be compromised due to long-term complications,276-279 physical and mental health comorbidities,280, 281 dependence on insulin, which can be further mediated by duration of diabetes and the impact on social and financial circumstances.282-284 Lifestyle modifications, such as smoking cessation, physical exercise, body weight control and healthy diet, therapeutic and surgical treatment of complications, and good compliance with diabetes management, have been found to not only improve psychological well-being and quality of life for people with diabetes,285, 286 but also empower family members and health professionals.287-289

Due to cardiovascular and neuropathic complications, diabetes can lead to mobility reduction, physical disability and limitation of daily activities.290, 291 The ability to perform daily activities such as stair climbing, shopping, cooking and other regular household work can reduce by half or worse in people with diabetes.292 It has been estimated that more than half (56%) of Australians with diabetes also have a physical disability and of these 42% require assistance for their daily activities, mobility and communication.293

5.2.4.3. Burdens of diabetes and impact on economy and society

Diabetes is one of the major causes of premature illness and death worldwide.40 In 2004, approximately 3.4 million people died from consequences of high blood sugar,42 and the number of deaths due to diabetes in 2011 reached 4.6 million.209 More than 80% of diabetes deaths occur in low and middle income countries.42, 209 WHO has projected that deaths due to diabetes will double between 2005 and 2030.42

In Australia, diabetes is among the top ten leading causes of death.206 Between 1997 and 2005, on average there were 10,609 deaths per year where diabetes was an underlying or associated cause of death.206 In 2007 alone, nearly 7,500 Australians

87 died from diabetes and related causes.294 A decreasing trend of Australian diabetes mortality was observed between 1997 (38.8 deaths per 100,000 people), 2003 (34.3 deaths per 100,000 people) and 2007 (32.4 deaths per 100,000 people).294 Diabetes- related deaths are mainly attributable to a combination of diabetes with its complications or with comorbidity illnesses. It is common that when diabetes is recorded as the underlying cause of death, the associated causes of death are coronary heart disease, kidney disease, stroke and heart failure. When diabetes was an associated cause of death, the underlying causes of death included coronary heart disease, cancer and stroke.206 Diabetes accounts for 5.5% of the total Australian burden of disease and injury206 and is expected to increase to 7.0% in 2013 and 8.7% in 2023.295

In terms of economy and society, the direct costs of diabetes to the health care system include increased expenditure on hospital treatment, medications, visits to health care professionals and use of diagnostic facilities. People with diabetes, especially those with complications, use health services more frequently than others.206, 296 According to Australian statistics on disease expenditure, direct health care expenditure on diabetes in 2004/05 was $989 million206 compared to $784 million in 2000/01.297 This accounted for 1.9% of the total recurrent health expenditure in 2004/05206 compared to 1.7% in 2000/01.297 Of expenditure on diabetes in 2004/05, type 2 diabetes made up 84% and type 1 diabetes made up 14%. Almost 40% of this expenditure was on hospital services, 30% on out-of-hospital medical services and 30% for diabetes-related pharmaceutical products.206

The indirect costs of diabetes consist of mortality, loss of productivity, loss of income, disability, loss of life years and impaired quality of life.44, 206, 296 When those indirect costs are taken into account, the total economic costs of type 2 diabetes in 2008 may have been $34.6 billion, an increase of 57% compared to 2005 ($22.0 billion). Of these costs, 64% were due to loss of wellbeing, 14% due to loss of productivity, 15% due to costs to carers, and 3.6% due to costs to the health system.296 The substantial social and economic burdens of diabetes present a significant challenge to public health internationally and nationally.206, 227

88 5.2.5. Diabetes prevalence and incidence in Australia

In Australia, different estimates of diabetes prevalence and methods of calculation have been reported. The ABS National Health Survey (NHS)298 estimated that 3.6% of Australian population had diabetes in 2004/05 based on self-reported diagnosis of diabetes. The Australian Diabetes, Obesity and Lifestyle Study (AusDiab Study) in 2001 reported higher estimates (7.5%) based on blood glucose measurement.205, 263 Recently, the Australian Institute of Health and Welfare (AIHW)299 assessed five national data sources including the AusDiab Study, National Diabetes Services Scheme (NDSS), ABS NHS, Medicare Benefits Schedule (MBS) and Pharmaceutical Benefits Scheme (PBS) databases that could be used to estimate diagnosed cases of diabetes, and recommended that the ABS NHS and NDSS databases were the best available data sources for monitoring diagnosed diabetes.

The prevalence of diabetes in Australia (age adjusted) has been increasing from 2.4% in 1995, to 3.0% in 2001, 3.6% in 2004/05 and 4.4% in 2007/08 according to the ABS NHS.211, 298 Approximately 900,000 Australians have been diagnosed with diabetes as at 2007/08,211 compared to 700,000 persons as at 2003/04.298 Among people with diabetes, 9.7% had type 1 diabetes and 87.6% had type 2 diabetes.211 Rates of diabetes were higher in men (4.9%) than in women (3.8%) and in older age groups (4.3% in 45-49, 10.7% in 55-59, 15.8% in 65-69 and 14.5% in 75-79 age groups).211 Across Australian states and territories, diabetes prevalence was 5.2% in South Australia, 4.5% in NSW, 4.4% in Queensland, 4.0% in Victoria, 4.0% in Tasmania, 3.8% in Western Australia and 3.0% in Australian Capital Territory. The estimate for Northern Territory (10.6%) did not include persons living in the very remote areas that comprise one-fifth of the Northern Territory population.211 In addition, a high proportion of the Australian population had diabetes but their diabetes was not detected. The rates of undiagnosed diabetes among people attending general practices could be as high as 18.1%,300 and 11.0% among hospitalised patients.301

According to the AusDiab Study, the overall annual incidence of diabetes (excluding GDM) was 0.8% (men 0.9% and women 0.7%), peaking at 3.0% in males aged 55- 74 years and 2.6% in females aged 65 years and over.263 The incidence of GDM in

89 2005/06 increased from 3.7% in 2000/01 to 4.6% in 2005/06.264 The GDM incidence was higher among mothers aged 45 to 49 years (13.2%) and those born in South Asian countries (27.8%).264 Given the current rising rate of diabetes and obesity, approximately three million Australians will have diabetes by the year 2005.302 Among people with diabetes, only half achieve the optimal level of diabetes control,303 which further indicates a higher risk of diabetes complications and greater burden of diabetes in Australia.

5.3. DIABETES AMONG THE VIETNAM-BORN POPULATION

Information about the magnitude of diabetes in Vietnam has become increasingly available since 2001 when diabetes was recognised as one of Vietnam’s national public health priorities.304 The Vietnam’s first national diabetes survey conducted in 2002 showed that diabetes was prevalent in 2.7% of Vietnamese people aged 30 to 64 years, ranging from 2.1% in remote areas to 4.4% in major cities.65, 66 This indicated an increase of diabetes in urban areas, as previous studies showed diabetes prevalence was 1.4% in Hanoi (in 1991)64 and 2.5% in Ho Chi Minh City (in 1993).58 A 2010 study in Ho Chi Minh City reported the prevalence of type 2 diabetes was 10.8% in men and 11.7% in women (30-72 years age group).59 In addition, approximately 7.3% of the Vietnamese population had impaired glucose tolerance66, 305 and between 64% and 80% of persons with diabetes were undiagnosed.304-306 It was estimated that there were approximately 1.7 million Vietnamese adults between 20 and 79 years of age with diabetes in 2010, and that this number will double by the year 2030.307 The rise of diabetes in Vietnam could be due to the recent economic growth during which higher prevalence and incidence of obesity have also been reported.58, 65, 194, 305, 308

Information about diabetes among Vietnam-born populations in Australia and internationally is patchy. According to AIHW,56 there were 2,761 Vietnam-born people registered in the NDSS by 2001. The NSW PHS surveys reported inconsistent rates of diabetes and high blood sugar among Vietnam-born residents: 4.3% in 2002- 05 (compared to 6.3% of Australia-born)62 but 9.7% in 2006-09 (compared to 7.2% of Australia-born).63 Among Vietnam-born people living in California, which is the state of USA with the highest number of Vietnam-born Americans,309 diabetes

90 prevalence estimates range between 4.3% and 7.3%.57, 61, 310, 311 In a convenience sample of Vietnamese psychiatric patients in Oregon, USA, 13% were found to have diabetes. Among Vietnam-born people aged 30 to 60 years in Norway, the prevalence of diabetes was 8.0%.312

The reported rates of GDM in Vietnam-born women range from 5.3% to 10.6%,47-52 which is 1.4 to 4.0 times higher than in Australia-born women.47, 51, 313 Among Vietnam-born Australian women aged 40 years and older, GDM incidence in 2005/06 was 27.8% compared to 18.6% in other South Asian mothers who were also at higher risk of GDM.313 A previous study in Victoria, Australia, which followed up Vietnam-born women for nine years, found that the incidence of GDM increased over time (2.7% in 1984-86 to 5.7% in 1987/88 and 8.9% in 1989/90).47 In the follow-up period, 25% of Vietnam-born mothers with GDM developed type 2 diabetes compared to 9% in Australia-born mothers.47 In addition, Vietnam-born mothers with GDM are more likely to be hospitalised than their Australia-born counterparts.28

In terms of diabetes risk factors, previous studies in the Vietnamese population have found that sex, age, overweight and sedentary lifestyle are strong predictors of diabetes.47, 58, 194, 312 Compared to BMI, waist-hip ratio is more indicative of overweight and risk of diabetes in this population because Vietnamese people with diabetes often have BMI within a normal range but higher waist-hip ratios.59, 193, 194, 312 Similarly, GDM in Vietnamese mothers is independent of BMI.314 In terms of diet, rice is the Vietnamese traditional main source of carbohydrates,36, 145, 146 and Vietnamese rice is reported to have high GI values (between 86 and 109).315 Given the lack of clear evidence of the relationship between high GI foods and diabetes as presented earlier, it is unknown whether regular rice consumption plays any role in diabetes trajectory among the Vietnam-born population.

The level of understanding and knowledge of diabetes and its management among Vietnam-born people is reported as inadequate.53-55 Causes of diabetes are often interpreted by âm-dương or spiritual beliefs.54 People with chronic disease such as diabetes tend to use complementary and alternative medicine, spiritual healing and relaxation techniques in addition to conventional medicines.80, 81 The use of 91 traditional medicines to manage diabetes instead of doctor-prescribed medications and a lack of compliance with diabetes treatment regimens among Vietnam-born people with diabetes have been reported.54, 82, 119 These factors may relate to poor glycaemic control in a proportion of Vietnam-born people with diabetes.54, 316, 317

The previous chapter investigated lifestyle and health status including risk of diabetes in relation to acculturation among Vietnam-born people. This chapter will further investigate health status in relation to type 2 diabetes in Vietnam-born people in comparison to Australia-born people.

ANALYTICAL FRAMEWORK

5.4. AIMS AND HYPOTHESES

This chapter investigates and compares demographic, socio-economic and health status between Vietnam- and Australia-born populations for everyone (overall) and for those with type 2 diabetes (diabetes-specific). It also estimates the prevalence of type 2 diabetes and assesses risk factors of type 2 diabetes in these two populations.

Hypothesis Three: Prevalence of type 2 diabetes and risk factors for diabetes of Vietnam-born people differ from those of Australia-born people.

5.5. METHOD

The Vietnam- and Australia-born participants in the 45 and Up Study are included in this chapter. This section describes the algorithm and procedures used to classify diabetes status and use of diabetes medications, based on self-reported information in the Study baseline questionnaire, and the methods for statistical analysis.

92 5.5.1. Identifying type 2 diabetes and use of diabetes medications

The following items in the baseline questionnaire were used to classify 45 and Up Study participants according to diabetes status. (i) Diagnosis of diabetes indicated in the tick box to Question 24: “Has a doctor EVER told you that you have diabetes” Tick the box if Yes. (ii) Age at diabetes diagnosis in Question 24: “Age when condition was first found”. (iii) Diagnosis of diabetes written in the text box: “Are you NOW suffering from any other important illness?” and “Please describe this illness and its treatment” (Question 26). (iv) Diabetes medications indicated in the tick box to the item: “Have you taken Diabex, Diaformin, Metformin for most of the last 4 week?” in the Question 23. Tick the box if Yes. (v) Diabetes medications written in the text box: “Please list any other regular medications or supplements here” (Question 23). (vi) Gender. (vii) Age at giving birth to the last child: “How old were you when you gave birth to your LAST child?” (Question 19 for women).

The Study baseline questionnaire did not specifically elicit types of diabetes, thus an algorithm (Figure 6) was developed to classify types of diabetes, as follows:

Step 1: Identifying use of diabetes medications

Lists of different types and brand names for insulin products (Appendix 2) and OHA (Appendix 3) available in Australia were created based on clinical guidelines published by Diabetes Australia.214, 318 The keywords for insulin products and each class of OHA as listed in Appendix 2 and Appendix 3 were searched in the medication free-text description field using the SAS Perl regular expression functions, which matches and allocates text patterns in string variables.319

93 Records for all resultant insulin products and OHAs and for all participants who self- reported a diagnosis of diabetes but who apparently took nil diabetes medications were manually reviewed. The false positive rates were from 0% to 1.06% and the false negative rate was 1.69% (Appendix 4).

Step 2: Identifying self-reported diagnosis of diabetes in the free-text field

For the participants who answered No to Question 24: “Has a doctor EVER told you that you have diabetes” but wrote the keyword “diabetes” or “blood sugar” in the text box of Question 26: “Please describe this illness and its treatment”, the written text was reviewed manually to confirm self-reported diagnosis of diabetes. On review, the participants described their diabetes status using terms such as diabetes, sugar diabetes, non-insulin dependent diabetes, diabetes 2, grade 2 diabetes, type 2 diabetes, diabetes self-management, controlled diabetes, diabetes under control, diabetes on tablets, diabetes exercise and diabetes diet. These participants were flagged as “Having diabetes in free text”. There were also descriptions such as pre- diabetes, borderline diabetes, diabetes insipidus, diabetes not yet confirmed, diabetes 1, and diabetes type 1 and insulin dependent diabetes, and these were flagged as “Type of diabetes other than type 2”.

Step 3: Classifying types of diabetes

The participants were classified as having “Type 2 diabetes” if one of the following criteria was met.

(i) Criteria 1: Reported having diabetes in tick box (Question 24) AND age at diagnosis ≥31 yearsc AND age at diagnosis was greater than age at giving birth to the last child.d

(ii) Criteria 2: Reported having diabetes in tick box AND male gender AND taking any OHAs where age at diagnosis was not provided.

(iii) Criteria 3: Texts indicating type 2 diabetes reported in the free text field.

c Most type 1 diabetes occurs before the age of 30.212 d Only applicable to females and excluded possible GDM 94 The participants were classified as “Without diabetes” if they did not indicate having diabetes in both tick box and text box fields AND did not report taking insulin and OHAs.

The remaining participants were classified as “Type of diabetes other than type 2”. These included those whose diabetes was uncertain, such as pre-diabetes and unconfirmed diabetes, and those who reported OHAs but did not report of having diabetes.

Figure 6 Classifying diabetes status among the 45 and Up Study participants

Age at diagnosis ≥31

and

Type of diabetes

Others others than type 2 Diabetes in tick box

No Type 2 diabetes■ Type 2 diabetes

Yes Others▲ Diabetes Type of diabetes in text others than type 2 Insulin or OHAs

No Without No insulin + No OHAs diabetes

■ For example: grade 2 diabetes, type 2 diabetes, diabetes self-management, controlled diabetes, diabetes under control, diabetes on tablets, diabetes exercise and diabetes diet ▲ For example: pre-diabetes, borderline diabetes, diabetes insipidus, diabetes not yet confirmed, type 1 diabetes

95 5.5.2. Additional study variables

Chapter 3 described demographic and socio-economic measures and Chapter 4 further presented measures of lifestyle and health status. Of these measures, the following variables will be carried forward into this chapter:

(i) Demographic: gender, age, and marital status.

(ii) Socio-economic status: education, household income, and working status.

(iii) Lifestyle: BMI, consumption of vegetables and fruits, levels of physical activity. Alcohol drinking and cigarette smoking were not included in the analyses because these behaviours were mainly reported by Vietnam-born male participants, as discussed in Chapter 4.

(iv) Health status: self-rated general health, psychological distress K10, physical limitation SF36-PF, having heart disease and high blood pressure.

Additional variables included in this chapter are:

(i) Having family history of diabetes was based on responses to Question 18, which asked: “Have your mother, father, brother(s) or sister(s) ever had diabetes?”. The participants were coded as having a family history of diabetes if they indicated that their mother or father or sibling/s had diabetes.

(ii) Duration of diabetes was computed based on age of the participants at recruitment into the Study and age when diabetes was first found.

(iii) Use of medications for diabetes was categorised as insulin and OHAs.

(iv) Quality of life was based on responses (excellent, very good, good, fair, and poor) to the single, generic item: “In general, how would you rate your quality of life?” of Question 31.

96 5.5.3. Statistical analysis

Descriptive analyses such as frequencies, percentages, and chi-square or Student’s t- tests were used to compare Vietnam- and Australia-born participants for everyone (overall) and for the subset of participants with type 2 diabetes (diabetes-specific). Both crude and directly age-standardised prevalence of type 2 diabetes was calculated for Vietnam- and Australia-born groups. The standard populations used were the Vietnam- and Australia-born populations in Australia as at the 2006 Census (≥45 years), obtained from the ABS website using TableBuilder.3 To compare the prevalence of type 2 diabetes between Vietnam- and Australia-born populations, the age-standardised prevalence ratio was calculated. The equations used to estimate crude prevalence, age-standardised prevalence, age-standardised prevalence ratio and 95%CIs were based on methods of Breslow and Day,320 and are presented in Appendix 5.

To assess the risk of diabetes according to various risk factors, health status and quality of life, logistic regression models were built separately for the Vietnam- and Australia-born participants in the 45 and Up Study. Both crude and adjusted ORs were generated with adjustment for demographic (gender, age and marital status) and socioeconomic (educational qualification, household income, working status and health insurance) factors, and family history of diabetes. Participants whose diabetes status was classified as “Type of diabetes other than type 2” were excluded from logistic regression models because these participants may include those with type 1 diabetes, pre-diabetes and GDM, which have different risk factors to type 2 diabetes.

To compare patterns of associations between diabetes and explanatory variables between the Vietnam- and Australia-born participants, ratios of adjusted odds ratios (ROR) and 95%CI were calculated according to Altman and Bland.321 A ROR statistically differs from 1 indicates the presence of an effect modifier (country of birth) when comparing the risk of diabetes between the two groups.321 Details of ROR calculation are summarised in Appendix 5.

The levels of missing data in the Vietnam-born group have been presented in Chapter 4. For the Australia-born group (n=199,917), similar patterns of missing values were

97 observed. Variables with more than 5% of missing values includes BMI (7.8%), SF36-PF (9.6%) and K10 (10.9%). Missing data were excluded from descriptive and regression analyses.

The Power and Sample Size program322 was used to calculate the minimum detectable odds ratio of diabetes between Vietnam- and Australia-born groups. Assuming the prevalence of diabetes among Australia-born population is 7.0%,63 the sample sizes of 797 Vietnam-born and 199,917 Australia-born individuals are able to detect true odds ratios of 1.42 with 80% power and 5% significance level.322

5.6. RESULTS

5.6.1. Overall comparisons between Vietnam- and Australia-born participants (everyone)

The distribution of demographic and socio-economic characteristics of Vietnam- and Australia-born participants in the 45 and Up Study were partially reported in previous chapters. This chapter further presents the differences between the two groups in terms of demographic and socio-economic characteristics, lifestyle and risk factors of diabetes, physical health, psychological distress and quality of life.

In comparison to all participants born in Australia, Vietnam-born participants had a younger mean age (58.6 vs 62.5 years, mean difference=3.9 years, 95%CI=3.1-4.6, p<0.001). They had lower SES, demonstrated by lower levels of education (33.8% with certificate, diploma or university degrees in Vietnam-born participants vs 43.0% in Australia-born participants, p<0.001), lower levels of annual household income (15.6% with ≥$50,000 vs 34.7%, p<0.001) and a lower proportion of private health insurance (41.5% vs 66.5%, p<0.001) (Table 13). Ninety-eight percent of the Vietnam-born participants lived in the major cities of NSW in compared to 39.9% of the Australia-born participants, reflecting the urban concentration of Vietnamese communities as discussed in previous chapters.

98 Table 13 Overall comparisons by demography and SES between Vietnam- born (N=797) and Australia-born participants (N=199,917)

Vietnam-born Australia-born Demographic and socio-economic (N=797) (N=199,917) P valueχ characteristics Number (%) Number (%) Gender Male 390 (48.9%) 90,667 (45.4%) 0.04 Female 407 (51.1%) 109,250 (54.6%) Age (years) Mean (SD) 58.6 (10.4) 62.5 (11.1) <0.001t 45-54 377 (47.3%) 60,254 (30.1%) <0.001 55-64 231 (29.0%) 64,054 (32.0%) ≥65 189 (23.7%) 75,609 (37.8%) Relationship∑ No partner 202 (25.6%) 49,256 (24.8%) 0.58 Partner 586 (74.4%) 149,517 (75.2%) Educational qualification∑ Less than high school 291 (37.5%) 73,408 (37.2%) <0.001 High school certificate/Trade 222 (28.6%) 39,068 (19.8%) Certificate/Diploma 108 (13.9%) 41,360 (21.0%) University or higher degree 154 (19.9%) 43,265 (22.0%) Household income ($AUD) <$20,000 333 (41.8%) 37,666 (18.8%) <0.001 $20,000-$49,999 173 (21.7%) 50,436 (25.2%) ≥$50,000 124 (15.6%) 69,266 (34.7%) Won’t disclose 167 (21.0%) 42,549 (21.3%) Working status Not working 387 (48.6%) 97,930 (49.0%) 0.81 Working 410 (51.4%) 101,987 (51.0%) Private health insurance None 143 (18.6%) 28,095 (14.3%) <0.001 Private health insurance 318 (41.5%) 130,777 (66.5%) Department of Veterans’ Affairs 45 (5.9%) 3,917 (2.0%) Concession card 261 (34.0%) 33,748 (17.2%) IRSD of residence (quintile) 1st most disadvantaged 445 (55.8%) 22,805 (11.4%) <0.001 2nd 137 (17.2%) 48,467 (24.3%) 3rd 65 (8.2%) 55,542 (27.8%) 4th 95 (11.9%) 35,154 (17.6%) 5th least disadvantaged 55 (6.9%) 37,800 (18.9%) Remoteness of residence Major cities 783 (98.2%) 79,739 (39.9%) <0.001 Other areas 14 (1.8%) 120,178 (60.1%) χ: Chi-square test for proportions t: Student’s t-test ∑: Missing data excluded from percentages

99 In terms of diabetes risk factors (Table 14), around one in four participants born in both Vietnam (24.7%) and Australia (22.8%, p=0.15) had a family history of diabetes. Slightly more than half of the participants from both backgrounds had adequate daily intake of fruits (≥2 serves/day, 59.4% Vietnam-born vs 57.6% Australia-born, p=0.34). However, Vietnam-born participants were less likely than Australia-born participants to have adequate daily consumption of vegetables (≥5 serves/day, 19.6% vs 34.0%, p<0.001) and adequate weekly physical activity (≥5 sessions/week, 68.4% vs 78.4%, p<0.001). Mean BMI was significantly lower in the Vietnam-born participants (23.2 vs 27.1 kg/m2; mean difference=3.9, 95%CI=3.5- 4.2, p<0.001). According to WHO conventional BMI cut-off values, only 21.6% of the Vietnam-born participants were overweight or obese compared to 63.3% of the Australia-born participants.

Table 14 Overall comparisons by diabetes risk factors between Vietnam-born (N=797) and Australia-born participants (N=199,917)

Vietnam-born Australia-born Diabetes risk factors (N=797) (N=199,917) P valueχ Number (%) Number (%) Family history of diabetes No 598 (75.4%) 154,249 (77.2%) 0.15 Yes 199 (24.7%) 45,668 (22.8%) BMI (kg/m2) ∑ Mean (SD) 23.2 (3.1) 27.1 (4.9) <0.001t Normal weight (BMI<25.0) 594 (78.4%) 67,600 (36.7%) <0.001 Overweight (25.0≤BMI<30.0) 148 (19.5%) 73,241 (39.7%) Obese (BMI≥30.0) 16 (2.1%) 43,489 (23.6%) Vegetable intake∑ Mean (SD) 3.5 (2.9) 4.0 (2.7) <5 serves/day 615 (80.4%) 129,373 (66.0%) <0.001 ≥5 serves/day 150 (19.6%) 66,589 (34.0%) Fruit intake∑ Mean (SD) 2.1 (1.7) 1.9 (1.4) <2 serves/day 303 (40.6%) 81,537 (42.4%) 0.34 ≥2 serves/day 443 (59.4%) 110,921 (57.6%) Physical activity Mean (SD) 10.6 (13.7) 12.1 (16.8) <5 sessions/week 252 (31.6%) 43,075 (21.6%) <0.001 ≥5 sessions/week 545 (68.4%) 156,842 (78.4%) χ: Chi-square test for proportions t: Student’s t-test ∑: Missing data excluded from percentages

100 Table 15 Overall comparisons by health status and quality of life between Vietnam-born (N=797) and Australia-born participants (N=199,917)

Vietnam-born Australia-born Health status and quality of life (N=797) (N=199,917) P valueχ Number (%) Number (%) Self-rated general health ∑ Excellent 34 (4.7%) 29,028 (15.0%) <0.001 Very good 119 (16.4%) 72,657 (37.6%) Good 316 (43.5%) 64,899 (33.6%) Fair 207 (28.5%) 22,922 (11.9%) Poor 50 (6.9%) 3,940 (2.0%) Self-rated quality of life∑ Excellent 42 (5.8%) 45,995 (24.2%) <0.001 Very good 127 (17.5%) 72,657 (38.1%) Good 320 (44.1%) 52,391 (27.6%) Fair 201 (27.7%) 16,213 (8.5%) Poor 35 (4.8%) 2,974 (1.6%) Diabetes Without diabetes 684 (85.8%) 181,645 (90.9%) <0.001 Diabetes type 2 103 (12.9%) 15,221 (7.6%) Other type 10 (1.3%) 3,051 (1.5%) Heart disease No 751 (94.2%) 175,993 (88.0%) <0.001 Yes 46 (5.8%) 23,924 (12.0%) High blood pressure No 555 (69.6%) 127,447 (63.8%) <0.001 Yes 242 (30.4%) 72,470 (36.2%) Physical limitation SF36-PF∑ Mean (SD) 76.1 (27.4) 81.9 (24.7) <0.001t No limitation (100) 210 (31.5%) 58,513 (32.4%) <0.001 Minor limitation (90-99) 120 (18.0%) 50,780 (28.1%) Moderate limitation (60-89) 176 (26.4%) 43,663 (24.2%) Severe limitation (0-59) 161 (24.1%) 27,764 (15.4%) Psychological distress K10∑ Mean (SD) 16.1 (6.9) 13.9 (5.1) <0.001t Low (10-15) 407 (61.5%) 137,246 (77.0%) <0.001 Moderate (16-21) 136 (20.5%) 27,851 (15.6%) High (22-29) 86 (13.0%) 9,333 (5.2%) Very high (30-50) 33 (5.0%) 3,747 (2.1%) χ: Chi-square test for proportions t: Student’s t-test ∑: Missing data excluded from percentages

101 It can be seen in Table 15 that health and quality of life disparities existed between the two groups. Vietnam-born participants were three times more likely than Australia-born participants to rate their general health and quality of life as poor or fair (35.4% vs 13.9%, and 32.5% vs 10.1%, respectively, p<0.001). The crude prevalence of type 2 diabetes in Vietnam-born was 12.9%, significantly higher than in Australia-born participants (7.6%). Vietnam-born participants had a lower SF36- PF mean score (76.1 vs 81.9, mean difference=-5.7, 95%CI=-7.6 to -3.8, p<0.001), indicating a higher prevalence of severe limitation of physical functioning (24.1% vs 15.4%). They also had a higher mean K10 score (16.1 vs 13.9, mean difference=2.1, 95%CI=1.7-2.5), corresponding to a higher proportion of individuals with high or very high levels of psychological distress (18.0% vs 7.3%). Nevertheless, prevalence of heart disease and high blood pressure was lower in Vietnam-born participants (5.8% vs 12.0% p<0.001 and 30.4% vs 36.2% p<0.001, respectively).

5.6.2. Diabetes-specific comparisons between Vietnam and Australia-born participants (with type 2 diabetes)

This section assesses demographic, SES, risk factors and health status of 103 Vietnam-born participants with type 2 diabetes relative to characteristics of 15,221 Australia-born counterparts. Vietnam-born participants with type 2 diabetes on average 3.5 years younger (95%CI=1.44-5.45, p<0.001) than Australia-born participants with type 2 diabetes (Table 16). Vietnam-born participants also had lower levels of annual household income (≥$50,000, 8.7% vs 20.1%, p<0.001) and private health insurance (24.0% vs 55.0%, p<0.001) but a slightly higher proportion had a university or higher degree (20.2% vs 13.6%, p=0.04). The Vietnam- and Australia-born groups, respectively, had similar distributions of males (54.4% vs 57.5%, p=0.52), those who were living with partner (62.8% vs 70.5%, p=0.09) and working (33.0% vs 30.4%, p=0.56). The majority of Vietnam-born participants with type 2 diabetes resided in IRSD disadvantaged areas (1st and 2nd quintile, 88.3% vs 41.0%, p<0.001) (Table 16).

102 Table 16 Diabetes-specific comparisons by demography and SES between Vietnam-born (N=103) and Australia-born participants (N=15,221)

Vietnam-born Australia-born Demographic and socio-economic (N=103) (N=15,221) P valueχ characteristics Number (%) Number (%) Gender Male 56 (54.4%) 8,753 (57.5%) 0.52 Female 47 (45.6%) 6,468 (42.5%) Age (years) Mean (SD) 63.4 (10.6) 66.9 (10.3) <0.001t 45-54 27 (26.2%) 2,117 (13.9%) <0.001 55-64 38 (36.9%) 4,600 (30.2%) ≥65 38 (36.9%) 8,504 (55.8%) Relationship∑ No partner 38 (37.2%) 4,464 (29.5%) 0.09 Partner 64 (62.8%) 10,661 (70.5%) Educational qualification∑ Less than high school 42 (42.2%) 7,003 (46.9%) 0.04 High school certificate/Trade 27 (27.3%) 3,222 (21.6%) Certificate/Diploma 10 (10.1%) 2,670 (17.9%) University or higher degree 20 (20.2%) 2,035 (13.6%) Household income ($AUD) <$20,000 62 (60.2%) 4,786 (31.4%) <0.001 $20,000 to $49,999 12 (11.7%) 4,053 (26.6%) $50,000 or more 9 (8.7%) 3,062 (20.1%) Won’t disclose 20 (19.4%) 3,320 (21.8%) Working status Not working 69 (67.0%) 10,601 (69.7%) 0.56 Working 34 (33.0%) 4,620 (30.4%) Private health insurance∑ None 9 (9.0%) 1,769 (11.9%) <0.001 Private health insurance 24 (24.0%) 8,189 (55.0%) Department of Veterans’ Affairs 12 (12.0%) 523 (3.5%) Concession card 55 (55.0%) 4,405 (29.6%) IRSD of residence (quintile) ∑ 1st most disadvantaged 75 (72.8%) 2,267 (14.9%) <0.001 2nd 16 (15.5%) 3,971 (26.1%) 3rd 6 (5.8%) 4,381 (28.8%) 4th 3 (2.9%) 2,525 (16.6%) 5th least disadvantaged 3 (2.9%) 2,068 (13.6%) Remoteness of residence Major cities 100 (97.1%) 5,907 (38.8%) <0.001 Other areas 3 (2.9%) 9,314 (61.2%) χ: Chi-square test for proportions t: Student’s t-test ∑: Missing data excluded from percentages

103 The profile of diabetes risk factors in Vietnam- and Australia-born participants with type 2 diabetes reflected those of participants overall. Table 17 shows that there were no differences between Vietnam- and Australia-born participants in terms of family history of diabetes (49.5% vs 46.0% respectively, p=0.48), daily fruit intake (59.4% having ≥2 serves/day vs 62.5%, p=0.53) and weekly physical exercise (71.8% doing ≥5 sessions/week vs 69.9%, p=0.67). Daily intake of vegetables was low for both groups: 19.0% and 37.3% of Vietnam- and Australia-born participants respectively having ≥5 serves of vegetables a day. The mean BMI of Vietnam-born participants with type 2 diabetes was also significantly lower than of the Australia-born group (mean difference=5.7, 95%CI=4.5-6.8, p<0.001). Duration of diabetes and use of diabetes medications were similar between the two groups. On average, Vietnam- and Australia-born participants had had their diagnosis of diabetes for nine years; one in five of the participants (18.8%) had diabetes for 15 years or more. Around two-thirds (64%) of the participants with type 2 diabetes reported taking insulin or OHAs (Table 17).

Table 18 compares health status and quality of life between Vietnam- and Australia- born participants with type 2 diabetes. The major differences between the two groups were in self-rated quality of life, self-rated general health and psychological distress. Half of the Vietnam-born participants (54.8%) reported poor or fair general health status compared to 32.3% of the Australia-born participants (p<0.001), poor or fair quality of life (50.6% vs 20.7%, p<0.001) and moderate, high and very high levels of psychological distress (51.2% vs 28.2%, p<0.001). A majority of both Vietnam- and Australia-born participants with type 2 diabetes reported physical functioning limitations (80.5% vs 84.4%, p=0.08) and high blood pressure (62.1% vs 62.7%, p=0.90). Vietnam-born participants with type 2 diabetes were less likely to have heart disease than Australia-born participants (14.6% vs 23.8%, p=0.03).

104 Table 17 Diabetes-specific comparisons by diabetes risk factors, duration of diabetes, and use of medications between Vietnam-born (N=103) and Australia- born participants (N=15,221)

Vietnam-born Australia-born Diabetes risk factors (N=103) (N=15,221) P valueχ Number (%) Number (%) Family history of diabetes No 52 (50.5%) 8,220 (54.0%) 0.48 Yes 51 (49.5%) 7,001 (46.0%) BMI (kg/m2) ∑ Mean (SD) 24.4 (3.8) 30.1 (5.7) <0.001t Normal weight (BMI<25.0) 60 (61.9%) 2,469 (17.8%) Overweight (25.0≤BMI<30.0) 30 (30.9%) 5,100 (36.8%) Obese (BMI≥30.0) 7 (7.2%) 6,306 (45.4%) Vegetable intake∑ Mean (SD) 3.3 (2.9) 4.1 (2.7) <5 serves/day 81 (81.0%) 9,475 (62.7%) <0.001 ≥5 serves/day 19 (19.0%) 5,626 (37.3%) Fruit intake∑ Mean (SD) 2.0 (1.5) 2.0 (1.4) <2 serves/day 39 (40.6%) 5,551 (37.5%) 0.53 ≥2 serves/day 57 (59.4%) 9,252 (62.5%) Physical activity Mean (SD) 9.6 (9.9) 10.3 (17.7) <5 sessions/week 29 (28.2%) 4,577 (30.1%) 0.67 ≥5 sessions/week 74 (71.8%) 10,644 (69.9%) Duration of diabetes∑ Mean (SD) 9.2 (7.2) 9.1 (7.6) 0.96t Under 5 years 28 (29.2%) 5,367 (35.7%) 0.39 5-9 years 33 (34.4%) 4,073 (27.1%) 10-14 years 17 (17.7%) 2,779 (18.5%) ≥15 years 18 (18.8%) 2,813 (18.7%) Diabetes medications Insulin or OHAs 66 (64.1%) 9,834 (64.6%) 0.91 None 37 (35.9%) 5,387 (35.4%) χ: Chi-square test for proportions t: Student’s t-test ∑: Missing data excluded from percentages

105 Table 18 Diabetes-specific comparisons by health status and quality of life between Vietnam-born (N=103) and Australia-born participants (N=15,221)

Vietnam-born Australia-born Health status and quality of life (N=103) (N=15,221) P valueχ Number (%) Number (%) Self-rated general health ∑ Excellent 0 460 (3.1%) <0.001 Very good 4 (4.3%) 3,261 (22.0%) Good 38 (40.8%) 6,316 (42.6%) Fair 35 (37.6%) 3,854 (26.0%) Poor 16 (17.2%) 930 (6.3%) Self-rated quality of life∑ Excellent 2 (2.2%) 1,657 (11.5%) <0.001 Very good 6 (6.6%) 4,350 (30.1%) Good 37 (40.6%) 5,463 (37.7%) Fair 38 (41.8%) 2,486 (17.2%) Poor 8 (8.8%) 510 (3.5%) Heart disease No 88 (85.4%) 11,592 (76.2%) 0.03 Yes 15 (14.6%) 3,629 (23.8%) High blood pressure No 39 (37.9%) 5,671 (37.3%) 0.90 Yes 64 (62.1%) 9,550 (62.7%) Physical limitation SF36-PF∑ Mean (SD) 64.3 (32.0) 68.0 (30.4) 0.27t No limitation (100) 16 (19.5%) 2,170 (15.6%) 0.08 Minor limitation (90-99) 12 (14.6%) 2,987 (21.5%) Moderate limitation (60-89) 19 (23.2%) 4,277 (30.7%) Severe limitation (0-59) 35 (42.7%) 4,485 (32.2%) Psychological distress K10∑ Mean (SD) 17.8 (7.9) 14.7 (5.9) <0.001t Low (10-15) 38 (48.7%) 9,189 (71.8%) <0.001 Moderate (16-21) 20 (25.6%) 2,211 (17.3%) High (22-29) 14 (17.9%) 936 (7.3%) Very high (30-50) 6 (7.7%) 464 (3.6%) χ: Chi-square test for proportions t: Student’s t-test ∑: Missing data excluded from percentages

106 5.6.3. Prevalence of type 2 diabetes

There were 103 Vietnam-born participants with type 2 diabetes (out of 797) and 15,221 Australia-born participants with type 2 diabetes (out of 199,917). Respective crude prevalence was 12.9% (95%CI=10.6-15.3) and 7.6% (95%CI=7.5-7.7) (Table 19). As illustrated in Figure 7, prevalence of type 2 diabetes in Vietnam- and Australia-born participants increased with age. In the Vietnam-born group, prevalence increased from 3.5% in the 45-49 year age group to 11.7% in 55-59, 26.0% in 60-64, and 22.8% in the ≥75 year age groups. In the Australia-born participants, prevalence was 2.7% in the 45-49 year age group, rising to 6.1% in 55- 59 and 12.2% in the 70-74 year age groups. The age-specific prevalence in the Vietnam-born group was consistently higher than that in the Australia-born group.

Figure 7 Age-specific prevalence of type 2 diabetes among Vietnam- and Australia-born participants in the 45 and Up Study

40% Vietnam-born

Australia-born 30% 26.0% 22.2% 22.8% 20% 14.3% 12.2% 11.3% 11.7% 10.5% 10.3% 8.4% 10% 6.1% 4.2%

Age-specific prevalence prevalence Age-specific (%) 3.5% 2.7%

0% 45-49 50-54 55-59 60-64 65-69 70-74 75+

Age group (years)

Following adjustment for age differences between the Vietnam- and Australia-born participants in the 45 and Up Study and the respective Vietnam- and Australia-born populations of Australia as at the 2006 Census, age-standardised prevalence of type 2 diabetes in the Vietnam-born population was 11.2% (95%CI=9.2-13.3) and 7.1% (95%CI=7.0-7.2) in the Australia-born population. The age-standardised prevalence ratio for the Vietnam-born population was 1.60 (95%CI=1.31-1.90) (Table 19).

107 Table 19 Prevalence of type 2 diabetes in Vietnam- and Australia-born populations: crude and age-standardised (95%CI)

Vietnam-born population Australia-born population In the 45 and Up Study In Australia, 2006 Census In the 45 and Up Study In Australia, 2006 Census Age Having Age- Having Age- group Total Total Expected Total Total Expected type 2 specific type 2 specific number number number number number number diabetes prevalence diabetes prevalence

ni ri pi Ni Ni * pi ni ri pi Ni Ni * pi 45-49 173 6 0.035 21,247 737 26,992 723 0.027 929,709 24,903 50-54 204 21 0.103 16,424 1,691 33,262 1,394 0.042 838,595 35,145 55-59 154 18 0.117 9,209 1,076 34,489 2,102 0.061 744,865 45,397 60-64 77 20 0.260 4,993 1297 29,565 2,498 0.084 576,019 48,669 65-69 56 8 0.143 3,713 530 24,983 2,614 0.105 437,672 45,794 70-74 54 12 0.222 3,087 686 18,026 2,195 0.122 354,229 43,134 ≥75 79 18 0.228 4,882 1,112 32,600 3,695 0.113 768,680 87,125 Total 797 103 63,555 7,130 199,917 15,221 4,649,769 330,167

Crude prevalence 12.9% (95%CI=10.6-15.3) 7.6% (95%CI=7.5-7.7)

Age-standardised 11.2% (95%CI=9.2-13.3) 7.1% (95%CI=7.0-7.2) prevalence Age-standardised 1.60 (95%CI=1.31-1.90) prevalence ratio

108 5.6.4. Likelihood of type 2 diabetes by risk factors and health status

This section presents the risk of type 2 diabetes (reference group=without diabetes) by demographic and socio-economic characteristics, lifestyle and health status, and quality of life. Participants classified as having “Type of diabetes other than type 2” (10 Vietnam- and 3,051 Australia-born participants) were excluded from regression models leaving 787 Vietnam- and 196,866 Australia-born participants for these analyses. Results of comparisons between adjusted ORs are expressed as ROR.

As shown in Table 20, the crude and adjusted risk of type 2 diabetes in Vietnam- and Australia-born participants by gender, age and marital status was similar. After controlling for other demographic and socio-economic characteristics, and family history of diabetes, the risk of type 2 diabetes was higher in Vietnam-born men (adjusted OR=1.63, 95%CI=0.96-2.76) and male Australia-born participants (adjusted OR=1.94, 95%CI=1.87-2.02). The risk increased significantly with older age. In the Vietnam-born group, the adjusted OR was 2.49 (95%CI=1.34-4.65) for the 55-64 year age group and 3.27 (95%CI=1.61-6.62) for those aged 65 years and older, compared to the reference group (45-54 years old). For the Australia-born group, the respective adjusted ORs were 1.84 (95%CI=1.74-1.95) and 2.28 (95%CI=2.14-2.42). The risk decreased by half in Vietnam-born individuals who lived with a partner (adjusted OR=0.49, 95%CI=0.29-0.86) and by 12% in Australia- born participants (adjusted OR=0.88, 95%CI=0.85-0.92).

The comparison of adjusted ORs indicates that the magnitude of risk of type 2 diabetes by gender and age in Vietnam-born participants was similar to that in Australia-born participants (ROR=0.84, 95%CI=0.49-1.42 for males, ROR=1.35, 95%CI=0.72-2.53 for the 55-64 year age group, and ROR=1.43, 95%CI=0.71-2.91 for ≥65 year age group) (Table 20). However, for participants with a partner, the risk of type 2 diabetes was marginally lower in the Vietnam-born group, with ROR of 0.56 (95%CI=0.32-0.97, p=0.04).

109 Regarding SES indicators (Table 21), there was little variation in the risk of type 2 diabetes by educational qualification. Compared to people with less than high school qualifications (reference group), the adjusted OR for high school and trade certificates was 1.05 (95%CI=0.58-1.92) for Vietnam-born and 0.88 (95%CI=0.84- 0.92) for Australia-born participants. For people with university and higher degrees, the adjusted OR was 1.42 (95%CI=0.68-2.96) in Vietnam-born and 0.74 (95%CI=0.70-0.78) in Australia-born participants. The risk of type 2 diabetes tended to be lower in individuals with higher household incomes, although this was not statistically significant for the Vietnam-born group. The adjusted OR was 0.55 (95%CI=0.23-1.32) and 0.80 (95%CI=0.76-0.84) for an annual income between $20,000 and $49,999 in Vietnam- and Australia-born participants respectively. For people with an annual household income ≥$50,000, the adjusted risk of type 2 diabetes reduced by 36% (OR=0.64, 95%CI=0.22-1.85) for the Vietnam-born and 33% (OR=0.67, 95%CI=0.63-0.71) for the Australia-born group. There was no difference in diabetes risk by working status in Vietnam-born group (OR=1.03, 95%CI=0.51-2.08) while working Australia-born participants were less likely to report type 2 diabetes (OR=0.67, 95%CI=0.64-0.70). In terms of health insurance, the risk of type 2 diabetes in those with private health insurance for both Vietnam- born (adjusted OR=1.12, 95%CI=0.47-2.66) and Australia-born participants (OR=0.97, 95%CI=0.91-1.02) was similar to that of the reference group. However, DVA veterans had higher risk of type 2 diabetes (adjusted OR=5.17, 95%CI=1.71- 15.69 for Vietnam-born, 1.26, 95%CI=1.12-1.41 for Australia-born) as well as health care concession card holders (adjusted OR=3.14, 95%CI=1.33-7.39 for Vietnam- born, 1.37, 95%CI=1.29-1.46 for Australia-born).

Ratios of adjusted ORs for SES indicators showed that the patterns of risk in Vietnam- and Australia-born participants did not differ by levels of educational qualification, annual household income, working status and private health insurance. However, the risk was four times higher in Vietnam-born DVA veterans (ROR=4.12, 95%CI=1.35-12.56, p=0.01) and doubled in Vietnam-born participants with health care concession cards (ROR=2.29, 95%CI=0.97-5.41, p=0.06).

110

Table 20 Diabetes risk by demographic characteristics: crude, adjusted odds ratio, and ratio of odds ratios (95%CI)

Vietnam-born Australia-born Demographic ROR Type 2 # Type 2 # characteristics Total Crude OR Adjusted OR Total Crude OR Adjusted OR (95%CI) N (%)∆ (95%CI) (95%CI) N (%)∆ (95%CI) (95%CI) Gender Female‡ 399 47 (11.8%) 1 1 107,416 6,468 (6.2%) 1 1 1 Male 388 56 (14.4%) 1.26 (0.83-1.91) 1.63 (0.96-2.76) 89,450 8,753 (9.8%) 1.69 (1.64-1.75) 1.94 (1.87-2.02) 0.84 (0.49-1.42) Age (years) 45-54‡ 373 27 (7.2%) 1 1 59,488 2,117 (3.6%) 1 1 1 55-64 230 38 (16.5%) 2.54 (1.49-4.28) 2.49 (1.34-4.65) 63,223 4,600 (7.3%) 2.13 (2.02-2.24) 1.84 (1.74-1.95) 1.35 (0.72-2.53) ≥65 184 38 (20.1%) 3.34 (1.96-5.67) 3.27 (1.61-6.62) 74,155 8,504 (11.5%) 3.51 (3.34-3.69) 2.28 (2.14-2.42) 1.43 (0.71-2.91) Relationship No partner‡ 200 38 (19.0%) 1 1 48,296 4,464 (9.2%) 1 1 1 Partner 578 64 (11.0%) 0.53 (0.34-0.82) 0.49 (0.29-0.86) 147,452 10,661 (7.2%) 0.77 (0.74-0.79) 0.88 (0.85-0.92) 0.56 (0.32-0.97)*

0.1 1 10 0.1 1 10 ∆: Row percentage #: Adjusted for age, sex, relationship, qualification, income, working, health insurance and family history of diabetes ‡: Reference category *: p<0.05 Forest plots: the dots represent adjusted ORs, the horizontal bars represent 95%CIs, the larger dots represent reference categories.

111 Table 21 Diabetes risk by socio-economic status: crude, adjusted odds ratio, and ratio of odds ratios (95%CI)

Vietnam-born Australia-born Socio-economic ROR Type 2 Crude OR Adjusted OR# Type 2 Crude OR Adjusted OR# status Total Total (95%CI) N (%)∆ (95%CI) (95%CI) N (%)∆ (95%CI) (95%CI) Education

0.1 1 10 0.1 1 10 ∆: Row percentage #: Adjusted for age, sex, relationship, qualification, income, working, health insurance and family history of diabetes ‡: Reference category *: p<0.05 £: Private health insurance §: Department of Veterans' Affairs Forest plots: the dots represent adjusted ORs, the horizontal bars represent 95%CIs, the larger dots represent reference categories.

112 Table 22 presents the risk of type 2 diabetes by factors such as family history of diabetes, BMI and health-related behaviours. Having a family history of diabetes was a strong risk factor for type 2 diabetes; the risk increased by seven-fold in Vietnam- born (adjusted OR=7.07, 95%CI=4.14-12.07) and over three-fold in Australia-born participants (adjusted OR=3.77, 95%CI=3.64-3.91). This indicated that the effect of having a family history of diabetes in the Vietnam-born group was almost double that in the Australia-born group (ROR=1.88, 95%CI=1.10-3.21, p=0.02) Being overweight or obese (WHO conventional BMI ≥25 kg/m2) increased the risk by 2.35 times (adjusted, 95%CI=1.38-4.02) in the Vietnam-born and 2.68 times (adjusted, 95%CI=2.56-2.81) in the Australia-born group, compared to normal weight (18.5≤ BMI<25 kg/m2). With the lower BMI cut-off value recommended for the Vietnamese population, the adjusted risk of diabetes in the Vietnam-born participants for overweight or obesity (BMI≥ 23 kg/m2) was 1.64 (95%CI=0.99-2.74). For both Vietnam- and Australia-born participants, the adjusted risk of type 2 diabetes varied slightly by daily intake of vegetables (OR=0.91, 95%CI=0.49-1.69 and OR=1.19, 95%CI=1.14-1.23 respectively) and intake of fruits (OR=1.12, 95%CI=0.68-1.86 and OR=1.34, 95%CI=1.29-1.39 respectively). Weekly physical activity did not relate to the risk of type 2 diabetes in Vietnam-born participants but was negatively associated with diabetes in the Australia-born group (adjusted OR=0.66, 95%CI=0.63-0.69).

Participants born in Vietnam and Australia had similar patterns of risk according to self-rated general health, quality of life and diabetes co-morbidities (Table 23). Following adjustment for covariates, people with type 2 diabetes were more likely to report poor or fair health status (Vietnam-born OR=1.83, 95%CI=1.07-3.14; Australia-born OR=2.62, 95%CI=2.51-2.73) and poor or fair quality of life (respectively adjusted OR=1.70, 95%CI=1.00-2.86 and OR=1.85, 95%CI=1.76- 1.94). Having type 2 diabetes was also associated with higher prevalence of heart disease in Vietnam- and Australia-born groups respectively (adjusted OR=2.12, 95%CI=0.98-4.60; OR=1.76, 95%CI=1.68-1.83), high blood pressure (adjusted OR=3.66, 95%CI=2.18-6.17; OR=2.63, 95%CI=2.54-2.73), limitation of physical functioning (adjusted OR=1.71, 95%CI=0.87-3.36; OR=1.88, 95%CI=1.78-1.97) and psychological distress (adjusted OR=1.47, 95%CI=0.83-2.58; OR=1.36, 95%CI=1.29-1.42).

113 Table 22 Diabetes risk by family history and lifestyle factors: crude, adjusted odds ratio, and ratio of odds ratios (95%CI)

Family history Vietnam-born Australia-born ROR and lifestyle Type 2 Crude OR Adjusted OR# Type 2 Crude OR Adjusted OR# Total Total (95%CI) factors N (%)∆ (95%CI) (95%CI) N (%)∆ (95%CI) (95%CI) Family history of diabetes No‡ 593 52 (8.8%) 1 1 152,521 8,220 (5.4%) 1 1 1 Yes 194 51 (26.3%) 3.71 (2.42-5.69) 7.07 (4.14-12.07) 44,345 7,001 (15.8%) 3.29 (3.18-3.41) 3.77 (3.64-3.91) 1.88 (1.10-3.21)* BMI (Conventional)Ф Normal‡ 589 60 (10.2%) 1 1 66,941 2,469 (3.7%) 1 1 1 Overweight/obese 160 37 (23.1%) 2.65 (1.68-4.18) 2.35 (1.38-4.02) 114,672 11,406 (10.0%) 2.88 (2.76-3.02) 2.68 (2.56-2.81) 0.88 (0.51-1.50) BMI (Vietnamese)Ψ Normal‡ 358 35 (9.8%) 1 1 34,432 1,054 (3.1%) 1 1 1 Overweight/obese 391 62 (15.9%) 1.74 (1.12-2.71) 1.64 (0.99-2.74) 147,181 12,821 (8.7%) 3.02 (2.83-3.22) 2.70 (2.53-2.89) 0.61 (0.36-1.02) Vegetable intake <5 serves/day‡ 607 81 (13.3%) 1 1 127,635 9,475 (7.4%) 1 1 1 ≥5 serves/day 148 19 (12.8%) 0.96 (0.56-1.63) 0.91 (0.49-1.69) 65,506 5,626 (8.6%) 1.17 (1.13-1.21) 1.19 (1.14-1.23) 0.76 (0.41-1.42) Fruit intake <2 serves/day‡ 298 39 (13.1%) 1 1 80,492 5,551 (6.9%) 1 1 1 ≥2 serves/day 438 57 (13.0%) 0.99 (0.64-1.54) 1.12 (0.68-1.86) 109,194 9,252 (8.5%) 1.25 (1.21-1.29) 1.34 (1.29-1.39) 0.84 (0.51-1.39) Physical activity <5 sessions/week‡ 249 29 (11.7%) 1 1 42,075 4,577 (10.9%) 1 1 1 ≥5 sessions/week 538 74 (13.8%) 1.21 (0.77-1.91) 0.97 (0.57-1.67) 154,791 10,644 (6.9%) 0.61 (0.58-0.63) 0.66 (0.63-0.69) 1.48 (0.86-2.54)

0.1 1 10 0.1 1 10 ∆ # Ф : Row percentage : Adjusted for age, sex, relationship, qualification, income, working, health insurance and family history of diabetes : Overweight 25.0≤BMI<30.0, obese 30.0≤BMI *: p<0.05 Ψ ‡ : Overweight 23.0≤BMI<27.5, obese 27.5≤BMI Forest plots: the dots represent adjusted ORs, the horizontal bars represent 95%CIs, the larger dots represent reference categories : Reference group. 114 Table 23 Diabetes risk by health status and quality of life: crude, adjusted odds ratio, and ratio of odds ratios (95%CI)

Vietnam-born Australia-born Health profile and ROR Type 2 # Type 2 # quality of life Total Crude OR Adjusted OR Total Crude OR Adjusted OR (95%CI) N (%)∆ (95%CI) (95%CI) N (%)∆ (95%CI) (95%CI) Self rated health status ¥ ‡ ≥ Good 468 42 (9.0%) 1 1 164,696 10,037 (6.1%) 1 1 1 Poor, fair 250 51 (20.4%) 2.61 (1.67-4.04) 1.83 (1.07-3.14) 25,991 4,784 (18.4%) 3.48 (3.35-3.61) 2.62 (2.51-2.73) 0.70 (0.41-1.20) Self-rated quality of life ¥ ‡ ≥ Good 483 45 (9.3%) 1 1 168,537 11,470 (6.8%) 1 1 1 Poor, fair 233 46 (19.7%) 2.39 (1.53-3.74) 1.70 (1.00-2.86) 18,643 2,996 (16.1%) 2.62 (2.51-2.74) 1.85 (1.76-1.94) 0.92 (0.54-1.55) Heart disease No ‡ 743 88 (11.8%) 1 1 173,652 11,592 (7%) 1 1 1 Yes 44 15 (34.1%) 3.85 (1.99-7.46) 2.12 (0.98-4.60) 23,214 3,629 (16%) 2.59 (2.49-2.71) 1.76 (1.68-1.83) 1.21 (0.56-2.62) High blood pressure No ‡ 548 39 (7.1%) 1 1 126,029 5,671 (4.5%) 1 1 1 Yes 239 64 (26.8%) 4.77 (3.09-7.36) 3.66 (2.18-6.17) 70,837 9,550 (13.5%) 3.31 (3.21-3.42) 2.63 (2.54-2.73) 1.39 (0.82-2.35) Physical limitation No limitation ‡ 209 16 (7.7%) 1 1 58,011 2,170 (3.7%) 1 1 1 Limitation 451 66 (14.6%) 2.07 (1.17-3.67) 1.71 (0.87-3.36) 120,164 11,749 (9.8%) 2.79 (2.66-2.92) 1.88 (1.78-1.97) 0.91 (0.46-1.79) Psychological distress Low ‡ 404 38 (9.4%) 1 1 135,578 9,189 (6.8%) 1 1 1 € ≥ Moderate 251 40 (15.9%) 1.83 (1.14-2.94) 1.47 (0.83-2.58) 40,166 3,612 (9.0%) 1.36 (1.31-1.42) 1.36 (1.29-1.42) 1.08 (0.61-1.91)

0.1 1 10 0.1 1 10 ∆: Row percentage #: Adjusted for age, sex, relationship, qualification, income, working, health insurance and family history of diabetes ‡: Reference category *: p<0.05 ¥ € : Good, very good, excellent :Moderate, high, very high Forest plots: the dots represent adjusted ORs, the horizontal bars represent 95%CIs, the larger dots represent reference categories.

115 5.7. DISCUSSION OF FINDINGS

This chapter synthesised relevant literature of diabetes risk factors, prevalence and incidence, and burdens and impacts of diabetes, and then presented analyses of overall and diabetes-specific profiles, prevalence of type 2 diabetes and diabetes risk in Vietnam- and Australia-born populations.

The findings of this chapter that Vietnam-born participants in the 45 and Up Study were disadvantaged relative to their Australia-born counterparts in terms of socio- economic and health status, are consistent with previous reports.25-27, 62 Vietnam-born people had lower levels of education, income, private health insurance and lived in socio-economically disadvantaged areas. Vietnam-born people were less likely than Australia-born participants to assess their own health as good or better (64.6% vs 86.2%), which was also reflected in the results of 2002-05 NSW PHS (68.3% Vietnam-born population vs 80.4% of the NSW general population).62 The daily vegetable consumption and weekly physical activity levels in the Vietnam-born participants were significantly lower than in the Australia-born participants, which further supports findings of the 2002-05 NSW PHS62 and international studies.57, 61 There is anecdotal evidence that vegetables in developed countries are considered as an expensive grocery item by Vietnamese people given their low SES.57, 61 These findings indicate a need to increase the awareness and practice of healthy lifestyles, especially vegetable intake and physical activity, among the Vietnam-born population.

Almost 40% of the Vietnam-born participants manifested moderate and more severe levels of psychological distress compared to 22% of the Australia-born group. This may be explained by a high prevalence and prolonged effects of mental health disorders, such as post-traumatic stress disorder and depression among Vietnamese refugees, especially among former political prisoners.93, 94 Among the Vietnam-born participants, psychological distress highly correlated with poor or fair health status (r=0.40, p<0.001) or quality of life (r=0.36, p<0.001).

116 The age-standardised prevalence estimate of type 2 diabetes for people born in Australia in this chapter is similar to the estimate from the 2006-09 NSW PHS63 (7.1% vs 7.2%). For the Vietnam-born group, the age-standardised estimate (11.2%) is higher than the estimates for this population in NSW (9.7%),63 in the USA (between 4.3% and 7.3%),57, 61, 310, 311 and Norway (8.0%)312 but is similar to the reported data from Vietnam (10.8% in men and 11.7% in women).59 Because diabetes prevalence increases with age, the inclusion of only people aged 45 years and older could have inflated the prevalence estimates when compared to estimates of Vietnam-born residents of NSW (aged ≥16 years),63 in the USA (aged ≥18 years310 or ≥35 years),311 in Norway (30-60 age group),312 and in Vietnam (30-72 age group).59 Rising prevalence of diabetes over time and differences in lifestyles of people in Australia and Vietnam may also have contributed to discrepancies in findings. The universal health insurance scheme in Australia that provides free or subsidised treatment by medical practitioners, and certain diagnostic and therapeutic procedures and services14 possibly has resulted in early detection of diabetes cases. Forty-three Vietnam-born participants migrating at an older age (≥ 40 years) were diagnosed with diabetes in Australia. Of these, 30% were diagnosed within four years of arrival.

Characteristics of the 45 and Up Study participants such as age (≥45 years), socio- economic representativeness and non-response bias133, 134 (as discussed in Chapter 3) could limit the generalisability into the wider community of the current findings regarding the prevalence of type 2 diabetes. Nevertheless, the ratio of age- standardised prevalence was less likely to be biased by these characteristics of the Vietnam- and Australia-born samples.131, 135, 136 The age-standardised prevalence of type 2 diabetes in the ageing Vietnamese population (≥45 years) was 11.2% which was 1.6 times (95%CI=1.31-1.90) higher than the prevalence in the ageing Australia- born population (7.1%). The sample sizes used in this chapter were more than adequate to detect a ratio of 1.60 (95%CI=1.31-1.90), which is larger than the minimum detectable ratio of 1.42.322 In fact the power to detect a ratio of 1.60 was 96%.323

117 Regarding the risk of type 2 diabetes according to various health risk factors and health conditions, the findings outlined in this chapter are consistent with current knowledge of diabetes risk factors. Strong indicators of type 2 diabetes included having family history of diabetes, being overweight or obese, and ageing. Other factors were gender (male) and lower SES.206, 263, 282-284, 324 The mean BMI in Vietnam-born participants with type 2 diabetes (24.4 kg/m2) and without diabetes (23.0 kg/m2) was similar to reported data from Vietnam59 (mean BMI was 24.4 kg/m2 in people with type 2 diabetes; 22.6 kg/m2 in people without diabetes). Vietnam-born participants with type 2 diabetes had a mean BMI remarkably lower than that of Australia-born participants with diabetes (30.1 kg/m2). These findings support the conclusion by the WHO46, 195 that BMI in a proportion of Asian populations at risk of diabetes and cardiovascular disease is substantially below the WHO conventional cut-off points. Accordingly, measures such as body fat percentages or waist-hip ratio are more accurate indicators of diabetes risks in these populations.46, 59, 195 Previous anthropometric studies in the Vietnam-born population have confirmed higher levels of body fat while BMI is in a normal range,59, 312 and further suggested the use of waist-hip ratio and systolic blood pressure to identify individuals at high risk of undiagnosed type 2 diabetes.59

The comparisons of adjusted ORs indicate that the patterns of risk of type 2 diabetes according to demographic and socio-economic characteristics, health risk factors, health status, and quality of life in Vietnam- and Australia-born people are similar, except for family history of diabetes, and having DVA or health care concession cards. Among Vietnam-born individuals with a family history of diabetes, 26.3% had type 2 diabetes in comparison to 15.8% in the Australia-born group (ROR=1.88, 95%CI=1.10-3.21, p=0.02). This suggests a genetic influence among Vietnam-born people, and is consistent with conclusions around Vietnamese ethnicity per se being a risk factor for diabetes,47-52 especially when the comparison Australia-born group may include people of different ethnic backgrounds. Nevertheless, the use of self- reported and cross-sectional data could be subject to recall bias in which Vietnam- born participants without diabetes may have under-reported family history of diabetes. As discussed in Chapter 2, Vietnamese traditional health beliefs are different from the Western models of health. Vietnamese immigrants in developed countries have been found to have lower levels of health knowledge than the general 118 native-born populations. It is possible that in the absence of the disease the person is less likely to be aware of any family members with that disease, thus under-reporting their family history of the disease.

Given the same patterns of diabetes risk in Vietnam- and Australia-born participants according to educational qualifications, household income, working status and intake of vegetables and fruits, the lack of statistical significance of regression coefficients for these variables in the Vietnam-born group is possibly due to a smaller sample size of the Vietnam-born participants (N=787) relative to the Australia-born group (N=196,866). Adjusted ORs for adequate daily intakes of vegetables (OR=1.19, 95%CI=1.14-1.23) and fruits (OR=1.34, 95%CI=1.29-1.39) in the Australia-born group slightly increased, indicating a possibility of lifestyle modifications such as eating more vegetables and fruits in Australia-born people with type 2 diabetes.

As presented earlier, similar patterns of missing data were observed in both Vietnam- and Australia-born groups. Considerable missing cases were present in the SF36-PF, K10, and BMI variables. Exclusion of missing cases from regression analyses for Vietnam- and Australia-born groups could generate bias in adjusted ORs, thus resulting in biased RORs.

Overall and diabetes-specific comparisons between the Vietnam- and Australia-born participants in the 45 and Up Study coupled with results from logistical regression modelling, indicate that having diabetes may have a greater impact on SES in Vietnam-born participants than in Australia-born participants, although the two groups had similar duration of diabetes (mean 9.1 years). In the Vietnam-born group, the percentage of concession card holders increased from 34.0% (overall) to 55.0% in participants with type 2 diabetes while the respective percentages in Australia- born participants were 17.2% (overall) and 29.6% (with type 2 diabetes). These corresponded to ROR=4.12 (95%CI=1.35-12.56, p=0.01) for DVA cards and ROR=2.29 (95%CI=0.97-5.41, p=0.06) for concession cards. Both Vietnam- and Australia-born participants with diabetes demonstrated higher prevalence of self- rated poor or fair general health and quality of life, psychological distress, physical limitation, heart disease and high blood pressure. Slightly over 60% of participants

119 with type 2 diabetes had high blood pressure, consistent with results of the AusDiab Study (69%)205 and data from Vietnam (50%-60%).305

In summary, the Hypothesis Three, that general and diabetes-specific profiles of the Vietnam-born participants differ from those of their Australia-born counterparts, was successfully tested in this chapter. Overall, Vietnam-born Australians had poorer socio-economic status and health status relative to Australia-born population. The greatest health disparities were in self-rated general health, self-rated quality of life and psychological distress. The gap between the two populations tended to be wider for people with type 2 diabetes. The age-standardised prevalence of type 2 diabetes in middle-aged Vietnam-born people was 11.2%, which was 1.6 times higher than in Australia-born people (7.1%), which relates to a higher rate of family history of diabetes in Vietnam-born people with type 2 diabetes. The findings of this chapter support reports in the literature that people of Vietnamese ethnicity are at increased risk of diabetes. Age was found to be an important risk factor in Vietnam-born people while BMI was not a sensitive predictor of diabetes. Vietnam-born people with diabetes are at a substantial risk of hypertensive cardiovascular disease because of a high prevalence of high blood pressure. Additionally, there were also high rates of limited physical functioning and psychological distress symptoms. The findings of this chapter have implications for the investigation of health services use, which is presented in the next chapter.

120

Chapter 6

Hospitalisation and mortality

Advanced stages of diabetes complications often require treatment in hospitals.206 Thus, hospitalisation capture more severe aspects of diabetes, providing an indication of the health system burden of diabetes and the characteristics of people who are most affected by their diabetes. Disparities in diabetes-related hospitalisation and mortality exist among Australian populations. An analysis by the AIHW using National Hospital Morbidity data reported that Indigenous Australians were eleven times more likely to be hospitalised and nine times more likely to die from any diabetes-related cause than the Australian general population.206 People living in socio-economically disadvantaged areas had significantly higher rates of risk factors, hospitalisations, and deaths from diabetes than residents of more affluent areas. People born in North Africa, Middle East, Southeast Asia, Oceania, and Southern and Eastern Europe had a higher prevalence of diabetes and diabetes-related mortality than those born in Australia.15, 206

As found in Chapter 5, Vietnam-born Australians had poorer socio-economic status and health status, including higher prevalence of type 2 diabetes compared to Australia-born counterparts. The discrepancies were wider in people with type 2 diabetes. This chapter further investigates the gap in research evidence in relation to outcomes of diabetes including hospitalisation and mortality in Vietnam-born people in comparison to Australia-born population.

121 THEORETICAL FRAMEWORK

6.1. THE BEHAVIORAL MODEL OF HEALTH SERVICE USE

The widely-used Andersen and Newman’s Behavioral Model of Health Service Use325, 326 formed the conceptual framework for this chapter. The model was first introduced in the 1960s and has since evolved through four phases. The model can either predict or explain the use of health services. It addresses factors that either facilitate or impede health service use and classifies factors that influence the use of health care as predisposing, enabling and need factors as follows:326

(i) Predisposing factors include demographic characteristics (age, gender), social structure (education, occupation, ethnicity, social networks, social interactions, and culture) and health beliefs (attitudes, values, and knowledge towards health care).

(ii) Enabling factors refer to the logistical aspects of obtaining care mainly at person, family and community levels. “Access” to health services is conceptualised as relating to the person and family while “availability” of services relates to the community.

(iii) Need factors have perceived and evaluated need components. The former represents the view of the health service user about their health and their decision to seek care from health professionals. The latter component refers to professional judgement about an individual’s health status and the appropriate amount of care to be provided. Need factors are the most immediate cause of health service use, resulting from functional and health problems.

Andersen and Newman’s Phase-4 model (Figure 8) further emphasises the dynamic nature of the model with the inclusion of health outcomes. It describes multiple influences on health service use, health status and health outcomes. Health outcomes in turn affect predisposing and need factors as well as health behaviours.

122 Figure 8 Andersen and Newman’s Behavioral Model of Health Service Use- Phase 4

Source: Andersen 1995

ANALYTICAL FRAMEWORK

6.2. AIMS AND HYPOTHESES

This chapter aims to investigate the use of hospital services for diabetes-related reasons and all-cause and diabetes-specific mortality among Vietnam-born people with type 2 diabetes, in comparison to their Australia-born counterparts.

The use of hospital services often reflects the occurrence of more serious problems and conditions that are primarily driven by need and demographic characteristics.326 As discussed in previous chapters, Vietnam-born Australians have Oriental health beliefs and most maintain traditional health care practices (Chapter 2), while they have considerably poorer socio-economic and health status (Chapter 4 and Chapter 5) and higher prevalence of type 2 diabetes (Chapter 5) than Australia-born people. These population characteristics would suggest a higher use of hospital services and higher mortality among Vietnam-born people with diabetes than in the equivalent Australia-born population. Thus, this chapter aims to test the following hypotheses.

123 Hypothesis Four: Vietnam-born people with type 2 diabetes have higher rates of hospital admissions for diabetes and its complications and comorbidities than their Australia-born counterparts. Hypothesis Five: Vietnam-born people with type 2 diabetes have higher risk of all- cause and diabetes-specific mortality than their Australia-born counterparts.

6.3. METHOD

This chapter applied a cohort study design using health record linkage of the NSW APDC, RBDM, and ABS mortality data. The cohort members were residents of NSW who were born in Vietnam (study group) and born in Australia (comparison group) and admitted to hospitals for reasons relating to type 2 diabetes during the observation period from 1 July 2000 to 30 June 2008.

This section describes the data sources, rationale for their use and an overview of procedures in health record linkage. It then presents the reliability of “Vietnam” and “Australia” country of birth recording in the APDC data, followed by a description of data manipulation and preparation (APDC and RBDM datasets), the cohort exclusion and inclusion criteria, and construction of study variables. Finally, this section details the statistical analysis methods used for this chapter.

6.3.1. Data sources and data linkage

6.3.1.1. NSW Admitted Patient Data Collection

Previous chapters (Chapter 4 and Chapter 5) used cross-sectional data from the 45 and Up Study baseline questionnaire. The 45 and Up Study participants provided consent for their health to be followed up via access to their health records, including hospitalisation records held by the NSW Department of Health. Among the 103 Vietnam-born participants in the Study with type 2 diabetes, 53 (50%) had been admitted to hospitals for all reasons between 2000 and 2009. This small sample size provided insufficient statistical power to test the proposed hypotheses. Thus, this

124 chapter used population-based APDC data which capture all hospital admissions for all residents of NSW.

The APDC is a mandatory health administrative data collection of all inpatient separations in all public, private and repatriation hospitals, private day procedure centres, and public nursing homes in NSW.327, 328 A hospital separation (referred to as “hospital admission” or “hospitalisation” in this chapter) is a process of care by which an admitted patient completes an episode of care by: being discharged, being transferred to another hospital, having a change of type of care (for example, from acute to rehabilitation) or dying.327 Information recorded for each hospital separation includes the following.328

(i) Patient-related demographic and socio-economic characteristics: sex, date of birth, country of birth, marital status, language spoken at home, interpreter required, address, location of residence according to ABS Census Statistical Local Area (SLA), patient public/private status.

(ii) Hospital-related characteristics: name, location, facility type, hospital role and peer group.

(iii) Admission-related information: date and time of admission, date and time of discharge, source of referral, service referred to on discharge, mode of separation, the principal diagnosis and up to 54 additional diagnoses, the principal procedure and up to 50 additional procedures, external causes of injury or poisoning, Australian Refined Diagnosis Related Group (AR- DRG), and length of stay for the individual episode of care.

Diagnoses were coded according to the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification (ICD-10-AM).329 Procedures were coded according to the Australian Classification of Health Interventions.330

The NSW Department of Health routinely collates data on a financial year basis. The APDC data for this chapter contain admissions between 1 July 2000 and 30 June 2008.

125 6.3.1.2. NSW Registry of Births, Deaths, and Marriages, and ABS mortality data

The RBDM compiles death registrations for NSW but these do not include NSW residents who die interstate. Most deaths are registered within four weeks of the date of death. Deaths referred to a Coroner are registered by the Coroner at the conclusion of inquiry into the circumstances of death. The RBDM death registration contains date of birth, date of death, and year of death registration.

The RBDM forwards the free-text causes of death recorded in the death certificate to the ABS, where underlying and associated causes of death are assigned according to ICD-10-AM codes.328 Underlying cause of death is defined as the disease or injury initiating the sequence of events leading to death, while associated cause of death (up to 20 associated causes) are diseases or injuries that are considered to have contributed to the death.206 The ABS mortality data containing coded causes of death are available later than the RBDM death registration due to the time needed for coding and cleaning of the data.328 This chapter include RBDM records between 1 July 2000 and 31 December 2009 and ABS mortality data between 1 July 2000 and 31 December 2007.

6.3.1.3. Health record linkage

Administrative data that are routinely and electronically collected, such as hospital admissions, emergency department attendances, claims of medical and pharmaceutical benefits, and death registrations, provide a powerful and cost effective resource for health research. Data linkage is a process that connects records belonging to an individual from one data collection to other records for the same person held in another data collection, thus yielding a longitudinal sequence of health events for the individual.331

126 Researchers and policy makers in Western Australia have pioneered health record linkage in Australia and successfully established a health linkage system over the last few decades.331 In 2006, building on Western Australia’s experience in data linkage, CHeReL332 was launched to create and maintain a record linkage system for health and human services in NSW. Data linkage at the CHeReL uses ChoiceMaker software332 to probabilistically link records of two or more files based on the probabilities of matching personal identifiers such as name, gender, date of birth, address and postcode. Linkage procedures follow strict “privacy preserving protocols”.332, 333 According to these protocols, data custodians send encrypted local source record numbers and personal identifiers to the CHeReL to create a “master linkage key” that contains links to records belonging to one person across different data sources. The master linkage key is used to generate a “project personal number” (PPN) for each individual project. After identifying the records required for each project, the CHeReL returns the original encrypted local source record numbers to each data custodian together with the newly created PPN. Data custodians use their local source record number to extract health information from their main database and supply these health data and the PPN to the researchers. As per protocols, the CHeReL does not have access to health information and researchers do not have access to personal identifiers. The CHeReL destroys the PPN six months following completion of the data linkage process and data being successfully sent to the researchers. This time period allows the researchers to review the data and forward any queries. The linkage process involves both manual and automated steps and regular quality assurance checks. The false positive linkage rate is less than 0.5% and false negative is less than 0.1%.334 Prior to July 2000, APDC data did not contain patients’ names and addresses, thus linked APDC data are only available for the period of July 2000 onwards.332

127 6.3.2. Reliability of country of birth recording in APDC data

The routine collection of hospital data is primarily for administrative purposes rather than for research; thus, it is important for researchers to understand the reliability of information recorded in these data. There have been reports on the accuracy of the recording of sex; age; and race as “black” and “white” designations,335-337 as broad categories such as Asian/Pacific Islander, American Indian, Hispanic and Native American;335, 337 Aboriginality status;338, 339 and clinical conditions (for example maternal and neonatal diagnoses).340, 341 For this chapter, Vietnam- and Australia- born patients were identified from a single country of birth variable in the APDC. However, the reliability of this variable has not been reported in Australia and internationally. Therefore, a supplementary assessment of the quality of country of birth recording in the APDC was undertaken to inform the defining of Vietnam- and Australia-born patient cohorts.

Data from the 45 and Up Study baseline questionnaire linked with APDC records were used to determine quality of country of birth recording in hospital morbidity data. Of the 266,848 participants in the Study, 170,038 (63.7%) had at least one hospital admission between 1 July 2001 and 30 June 2009. The validity of the country of birth reported by participants in the Study was ensured by double data entry and cross-checking of data entered.67 Self-report country of birth from the 45 and Up Study was used as a “gold standard” to calculate sensitivity, specificity, positive and negative predictive values for country of birth recorded in the APDC. For each participant, the most recent APDC record was selected. Analyses were repeated with the two alternative methods of APDC record selection: a random record, and the record nearest in date to questionnaire completion.

Table 24 shows that the most recent APDC record selection yielded sensitivities of 85.7% for the country of birth Vietnam and 98.2% for the country of birth Australia. Positive predictive values were 94.2% and 97.3% and the specificities were 100% and 91%, respectively. These results were similar to those generated from the other two methods of APDC record selection (random and nearest in date to the questionnaire completion). These findings indicate that the reliability of recording of the countries of birth Vietnam and Australia is reasonably high in the APDC data.

128 Among 130,479 Australia-born participants in the 45 and Up Study, only two were recorded as Vietnam-born in the APDC data, thus this misclassification is unlikely to bias research findings using the APDC data to identify Vietnam-born patients. Of 378 Vietnam-born participants in the 45 and Up Study, 37 were classified as Australia-born in the APDC data. Given the much larger number of Australia-born patients, a small proportion of Vietnam-born people being classified as Australia- born in APDC data is also unlikely to cause bias in results for Australia-born patients.

In brief, the recording of Vietnam and Australia country of birth in APDC data was reliable. Minor misclassifications of country of birth are unlikely to introduce bias in analyses which compare relative risks of hospital admission and mortality between Vietnam- and Australia-born patients, and any resulting bias will be towards the null value (no-effect).342 Research findings on the reliability of recording of Vietnam, Australia and 38 other countries of birth in APDC data, undertaken as part of this program of research, have been published in a peer-reviewed journal article,343 which is included as Appendix 6 to this thesis.

129

Table 24 Reliability of Vietnam and Australia country of birth recording in the Admitted Patient Data Collection

45+ Study¶: Yes 45+ Study¶: No Positive Negative Record Sensitivity Specificity APDC: APDC: APDC: APDC: predictive value predictive value selection (95%CI) (95%CI) Yes No Yes No (95%CI) (95%CI) Vietnam country of birth Most recent 324 54 20 169,640 85.7 (81.8-89.1) 94.2 (91.1-96.4) 99.9 (99.9-100) 99.9 (99.9-100) ¤ Nearest 321 57 20 169,640 84.9 (80.9-88.4) 94.1 (91.1-96.4) 99.9 (99.9-100) 99.9 (99.9-100) Random 247 47 17 134,005 84.3 (79.6-88.3) 93.6 (89.9-96.2) 99.9 (99.9-100) 99.9 (99.9-100) Australia country of birth Most recent 128,121 2,358 3,565 35,994 98.2 (98.1-98.3) 97.3 (97.2-97.4) 91.0 (90.7-91.3) 93.9 (93.6-94.1) ¤ Nearest 127,830 2,649 3,599 35,960 97.9 (97.9-98.1) 97.3 (97.2-97.4) 90.9 (90.6-91.2) 93.1 (92.9-93.4) Random 100,503 2,468 2,936 28,408 97.6 (97.5-97.7) 97.2 (97.1-97.3) 90.6 (90.3-90.9) 92.0 (91.7-92.3) ¶: Self-reported country of birth in the 45 and Up Study was used as “gold standard” ¤: Admission record was nearest in date to the questionnaire completion

130 6.3.3. Identifying cohort of Vietnam- and Australia-born patients with type 2 diabetes from APDC data

The entire population-based APDC dataset used in this chapter contained records for 17,362,843 hospital admissions, belonging to 5,304,535 residents of all ages in NSW between 1 July 2000 and 30 June 2008. The APDC dataset was linked to RBDM death registrations (1 July 2000 to 30 December 2009) and ABS mortality data (1 July 2000 to 30 December 2007) using the unique PPN. In order to identify the study cohort, the following data preparation and manipulation steps were undertaken.

Step 1: Logical data checking and deletion The following people and all of their APDC records were removed from the datasets due to possible linkage errors. (i) APDC data indicated the person died at hospital discharge but no death record was linked. (ii) People had hospital admissions after date of death. (iii) People had three or more years of birth recorded. Year of birth was derived from age and date of admission. People who were not residents of NSW were also excluded because their hospital admission information was likely to be incomplete.

Step 2: Identify people born in Vietnam and Australia Country of birth was assigned according to the country of birth recorded in the most recent admission. (i) Vietnam-born group was created for those who had Vietnam recorded as a country of birth in the most recent hospital admission record. (ii) Australia-born group was created for those who had Australia recorded as a country of birth in the most recent hospital admission record.

131 Step 3: Identify people with type 2 diabetes and index admission Type of diabetes was identified from ICD-10-AM codes listed in Chapter IV- Endocrine, nutritional and metabolic diseases of the ICD-10-AM book,329 as below. (i) E10.XX for type 1 diabetes. (ii) E12.XX for type 2 diabetes. (iii) E13.XX for other specified diabetes. (iv) E14.XX for unspecified diabetes. (v) O24.XX for diabetes during pregnancy, including pre-existing and GDM. X denotes one numeric digit indicating diabetes complication, anatomic site and severity.329 The detailed ICD-10-AM codes for diabetes with and without complications are presented in Appendix 7.

For the purpose of identifying people with type 2 diabetes, the above diagnostic codes were searched in the principal and across all 54 diagnosis fields in the APDC data. On examination of data, there were cases with inconsistent recording of diabetes codes that could not be satisfactorily explained. For example, some patients had other specified diabetes diagnosis (E13.XX) recorded predominantly in their diabetes-related hospital admissions but these patients also had admissions in which type 1 (E10.XX) or type 2 (E12.XX) were also recorded. In a few cases, both type 1 and type 2 were recorded in the same admission. A strategy was developed to identify people with type 2 diabetes, taking into account the prevalence and common age at onset of type 2 diabetes, potential errors during clinical coding, and the large size of the dataset. As such, the following steps were executed. (i) Flag patients who had at least one admission in which type 2 diabetes (E11.XX or O24.1) was recorded in any diagnosis field. (ii) Create a subset of data, containing all separations (regardless of diagnoses) belonging to these flagged patients. (iii) Count the total number of separations by type of diabetes: other specified diabetes (E13.XX or O24.2), type 1 (E10.XX or O24.2) and type 2 for each patient. Minimum count was zero. (iv) If the count of other specified diabetes was greater than both counts of type 1 and type 2, then the patient was categorised as “Other specified diabetes”. (v) If the count of type 1 was greater than both count of type 2 and other specified diabetes, then the patients was categorised as “Type 1”. 132 (vi) The remaining patients were categorised as “Type 2” provided that age was ≥31 yearse when type 2 was first recorded (between 1 July 2000 and 30 June 2008). Those patients whose age was <31 years when type 2 was first recorded during the observation period were categorised as “Diabetes type could not be categorised” and were not included in the analysis.

Step 4: Identify medical hospital admissions The information recorded in each hospital admission included AR-DRG categories. The AR-DRG system classifies the hospital admission episodes into 23 major diagnostic categories based on health conditions, procedures and other factors. Each AR-DRG category code has four characters: one alphabetical followed by two numerical and one alphabetical characters, for example, “K60A” code indicating diabetes with catastrophic or severe complication or co-morbidity. The AR-DRG categories can be partitioned into three major groups including surgical (the numeric characters range between 01 and 39), medical (60 to 99) and other (40 to 59).344

Diabetes is a chronic condition; surgical procedures – for example, laser surgery for retinopathy, extraction of cataract,345 and lower extremity amputations,206 – are more likely to be planned admissions346 for long-term complications of diabetes. Surgical patients generally undergo a comprehensive assessment prior to surgery.345, 346 For the Vietnam- and Australia-born patient cohorts, the majority of surgical admissions (84% Vietnam- and 77% Australia-born patients) were for cataract procedures. Differences in rates of such elective procedures are likely to be strongly related to socio-economic factors, especially private health insurance. Meanwhile, hospitalisations for medical reasons are often due to acute metabolic complications of diabetes, poor metabolic control of established diabetes, and acute episodes of chronic cardiovascular, neurological, renal and other diabetic complications.347 As such, the clinical characteristics of medical and surgical patients are likely to be very different. Because the focus of this chapter was to identify differences between Vietnam- and Australia-born patients with diabetes that might reflect differences in disease severity and management, the analyses were restricted to medical hospital admissions only. e Most type 1 diabetes occurs before the age of 30,212 thus reducing the risk of including type 1 diabetes which was accidentally recorded as type 2. 133 Step 5: Cohort selection criteria and follow-up Vietnam- and Australia-born patients with type 2 diabetes who met the following criteria formed the study and comparison groups. (i) Type 2 diabetes (with or without complications) was the principal reason for admission. The earliest such admission during the study observation period was referred to as the “index admission” and functioned as the baseline entry into the cohort. (ii) The index admission belonged to an AR-DRG medical diagnostic group. (iii) The index admission was prior to 1 July 2007, allowing for a minimum of 12 months of follow-up. (iv) The patient survived at least 30 days after discharge from the index admission. Each Vietnam- and Australia-born cohort member was followed up from the index admission to the end of the observation period (30 June 2008) for hospital readmissions and mortality.

The index admission identified between 1 July 2000 and 30 June 2007 may not, of course, be the patient’s first hospitalisation for diabetes which might have predated the study observation period. The term “prevalent pool effect” refers to the inclusion of prevalent cases that had hospitalisations prior to the study observation period when using hospital data to calculate estimates of disease incidence rates.348 In order to assess the potential impact of prevalent cases on the outcomes, a sensitivity analysis approach was conducted in which three cohorts were formed using different clearance periods. The term “clearance period” means a retrospective observation period that was free of diabetes hospitalisations.349 In particular, these cohorts were: (i) Nil-clearance cohort included patients whose index admission was between 1 July 2000 and 30 June 2007. This cohort had the longest follow-up period. (ii) 18-month clearance cohort included patients whose index admission was between 1 January 2002 and 30 June 2007 and who had no admission for diabetes prior to 1 January 2002. (iii) 30-month clearance cohort included patients whose index admission was between 1 January 2003 and 30 June 2007 and who had no admission for diabetes prior to 1 January 2003. This cohort had the shortest follow-up period. 134 6.3.4. Study variables

6.3.4.1. Baseline measures

Potential determinants of hospitalisation were grouped into predisposing, enabling, and need factors according to Andersen and Newman’s model,326 as below.

(i) Predisposing factors were derived from information recorded in the index admission.

 Sex  Age at index admission  Marital status (ii) Enabling factors were derived from information recorded in the index admission.

 Patient public or private status.  Socio-economic disadvantage of residence classified according to IRSD quintile scores128 for the patient’s residential ABS SLA.

 Remoteness of residence was assigned according to the ARIA+ score129 of patient’s SLA.

 Hospital peer group: The AIHW classification of public hospital peer group is based on size of hospital (number of acute casemix weighted separations), demographic characteristics of major patient groups (such as children’s hospital), primary role (teaching and research status), and remoteness of the hospital.327 The variable was grouped as principal referral, major, and others.

 Hospital public or private status. 129  Hospital remoteness was assigned according to the ARIA+ score of hospital SLA.

(iii) Need factors were derived from the principal and all 54 additional diagnoses recorded in the index and preceding admissions. Identification of particular conditions or events from administrative health records that precede a specific event of interest is referred to as use of a “lookback period”.350-352 The following measures of need were derived:

135  Charlson comorbidity index. The popular Charlson comorbidity index353 was initially developed to predict mortality. The index was calculated as the weighted accumulative score taking into account both the number and the severity of comorbid diseases, thus a higher index indicates worse health status.353 For this chapter, diabetes was excluded from the index score to avoid double counting. The index was then grouped into three categories (zero, 1 to 2, and ≥3). A one- year lookback period (365 days) was used to calculate the Charlson index. Previous studies have validated the use of the one-year lookback period and reported that a longer lookback period does not improve the index’s capacity in mortality prediction.351, 352 The ICD- 10-AM codes used for the Charlson comorbid diseases and weights are listed in Appendix 7.

 Health risk factors. High blood pressure, high cholesterol, smoking, and obesity were ascertained using a three-year lookback period (1095 days). The use of a three-year lookback period to identify risk factors would introduce potential bias, where patients who had multiple admissions during the lookback period had a higher chance of risk factor information being recorded. A previous study350 has reported that among women giving births in NSW, the ascertainment of risk factors and chronic diseases in APDC data improved the most within a three year lookback period, but the length of the lookback period did not have impact on the values of odds ratios between risk factors and the postpartum haemorrhage maternal health outcome. People with chronic disease such as diabetes are more likely to differ from obstetric populations. Therefore, to assess the extent, to which the use of a three-year lookback period impacts on the outcomes, another sensitivity analysis was conducted. Risk factors were also ascertained based on only information recorded the index admission record. The ICD-10-AM codes used to identify these health risk factors are listed in Appendix 7.

 Emergency or planned status of admission.

136 6.3.4.2. Outcome measures

The main variables used to define patient outcomes during follow-up included the following:

(i) Length of follow-up (years) was calculated as number of years between the discharge date from the index admission and 30 June 2008. For people who died before 30 June 2008, the length of follow-up was censored at date of death. A person contributed one person-year of follow-up for each full year (365 days) within the person’s observation period.

(ii) Number of hospital readmissions. Each hospital separation following discharge from the index admission was counted as one hospital readmission.

(iii) Principal reasons for the readmission were categorised into diabetes (with and without complications), diabetes comorbidities, dialysis, and other reasons. Diabetes comorbidities were defined according to previous national and international studies,206, 354 including cardiovascular diseases, renal disease, eye and vision disorders, neuropathy, and chronic skin problems and cellulitis. The ICD-10-AM codes for diabetes, diabetes complications, comorbidities and dialysis are presented in Appendix 7.

(iv) Weighted length of hospital stays (LoS) for diabetes and comorbidities. Length of each hospital stay was defined as the number of days between the admission date and the discharge date, taking into account any transfers or leave occurring during the hospital stay. LoS was deemed as one day for same day discharge. Weighted LoS for diabetes and comorbidities was calculated by dividing the sum of LoS for all diabetes and comorbidities admissions by number of person-years of follow up.

(v) Time to non-dialysis readmission (days). This was calculated as the number of days between the discharge date from index admission and the date of the next hospital admission for reasons other than regular dialysis, referred to as “non-dialysis”. Dialysis readmission was not counted in readmission because most patients on dialysis attend a hospital or satellite centre attached to a hospital about three times a week for dialysis procedures.355

137 (vi) Number of deaths. Deceased cohort members were identified from RBDM death registrations. Deaths after 30 June 2008 (the last day of observation) were ignored.

(vii) Causes of death. For deaths for which ABS mortality data were available, underlying causes of death were categorised into diabetes, cardiovascular disease, renal disease, cancer, and others. Diabetes as an associated cause of death was further identified if diabetes was not the underlying cause of death.

(viii) Time to death (days). This was calculated as the number of days between the index admission discharge date and date of death.

6.3.5. Statistical analysis

Descriptive statistics were presented as mean (SD) for continuous data and frequencies and percentages for categorical data. Chi-square test or Student’s t-tests were used to compare the characteristics of the Vietnam- and Australia-born groups at baseline.

The risks of readmission for diabetes and comorbidities in Vietnam-born patients relative to Australia-born patients (reference group) were assessed using both the Cox proportional hazard and Poisson regression methods. In the Cox proportional hazard regression models, crude and adjusted hazard ratios (HR) were calculated to compare time to non-dialysis admission outcome. Cox proportional hazard regression deals with time-to-event (readmission) data but ignores consequent events, thus it was supplemented by the Poisson regression method which accommodates count data. Crude rates of hospital readmissions for diabetes and comorbidities for the Vietnam-and Australia-born groups were calculated by dividing the total number of readmissions for diabetes and comorbidities by the total number of person-years of follow-up. In Poisson regression models, the offset term was the logarithm of the person-years of follow-up. There was evidence of over-dispersion in the standard Poisson regression model (likelihood ratio test356=17622.52, degree of freedom=1, p<0.001). Both values of deviance or Pearson’s chi-square divided by the degrees of freedom were much higher than one (3.2984 and 4.9750,

138 respectively), suggesting a greater variability among counts of admission than would be expected for a Poisson distribution, due to repeated readmissions per person.357 Accordingly, the over-dispersed Poisson model was used, which includes the over- dispersion scaling parameter.357, 358 The over-dispersed models generate the same rate ratio (RR) estimates but a larger variance estimator compared to the standard Poisson regression model.357, 358

The difference in LoS for diabetes and comorbidities between the two groups was determined by the non-parametric Wilcoxon rank-sum test. The risks of death due to all causes and due to diabetes (underlying or associated causes of death) were assessed by Cox proportional hazard regression which models time to death outcome.

There were four models for each of the regression analyses (Poisson and Cox). Model 1 (crude) included only the country of birth variable. Model 2 adjusted for other predisposing factors. Model 3 included country of birth, predisposing and enabling factors. Model 4 (full adjustment) included country of birth, predisposing, enabling and need factors. For both crude and adjusted estimates, 95%CIs were calculated. The choice of predisposing, enabling and need factor variables for inclusion in the adjusted models was based on the distribution of the variables (see Table 25), and their correlations. The variable hospital peer group was highly correlated with remoteness of residence, hospital public or private, and hospital remoteness, thus only hospital peer group was included in the fully adjusted models.

To assess the robustness of findings, results generated from the nil-clearance cohort were compared with those generated from 18-month and 30-month clearance cohorts. In addition, adjusted risk estimates (RRs and HRs generated from Models 4, based on the nil-clearance cohort) were compared between the use of a three-year lookback period and the single index admission record to identify health risk factors.

The Power and Sample Size program322 was used to calculate minimum detectable HRs for time to readmission and time to mortality between the two groups. Using parameters including the sample sizes of 152 Vietnam- and 14,197 Australia-born patients, accrual time of 7 years (index admission between 1 July 2000 and 30 June 139 2007), additional follow-up time of at least 1 year (a minimum of 12 months of follow-up from the index admission to end of the study 30 June 2008), and the median survival time ranging between 1 and 4 years, 80% power, and 5% significance, the minimum HRs could be detected ranged between 1.28 to 1.40.

6.4. RESULTS

Findings presented in this section were generated from the cohort without a clearance period, which included 152 Vietnam- and 14,197 Australia-born admitted patients.

6.4.1. Descriptive statistics at baseline and follow-up

At baseline (Table 25), compared to Australia-born admitted patients, the Vietnam- born patients were approximately 1.5 years older (mean 69.1 years, SD 13.2 vs 67.6 years, SD 14.4, p=0.23). The Vietnam-born group had a higher proportion of females (56.6% vs 46.1%, p=0.01), public patients (94.0% vs 71.7%, p<0.001) and residents of socio-economically disadvantaged areas (85.5% vs 55.2%, p<0.001).

The Charlson comorbidity index indicated poorer health status among the Vietnam- born patients. More than half (51.9%) the Vietnam-born patients had a Charlson index of one or more, compared to 42.1% of Australia-born patients (p=0.03). Of the Charlson comorbid diseases, Vietnam-born patients had significantly higher prevalence of renal (25.6% vs 14.2%, p<0.001) and liver diseases (11.8% vs 2.8%, p<0.001) but slightly lower prevalence of cardiovascular disease (19.7% vs 23.9%, p=0.23), pulmonary diseases (6.6% vs 10.6%, p=0.10) and cancer (4.0% vs 4.7%, p=0.67). In terms of health risk factors, Vietnam-born patients had significantly higher rates of high blood pressure (73.7% vs 54.9%, p<0.001) and high cholesterol (33.6% vs 20.6%, p<0.001) but lower rates of current or past smoking (19.1% vs 36.1%, p<0.001) and obesity (3.3% vs 13.2%, p<0.001). A higher proportion of Vietnam-born patients had their index admission as an emergency admission (91.5% vs 79.3%, p<0.001).

140 Table 25 Cohort characteristics at baseline

Vietnam-born Australia-born Characteristics at baseline (N=152) (N=14,197) P valueχ Number (%) Number (%) Predisposing factors Sex Male 66 (43.4%) 7,647 (53.9%) 0.01 Female 86 (56.6%) 6,541 (46.1%) Age (years) Mean (SD) 69.1 (13.2) 67.6 (14.4) 0.23t Under 50 19 (12.5%) 1,957 (13.8%) 0.02 50-59 15 (9.9%) 2,231 (15.7%) 60-69 29 (19.1%) 3,017 (21.3%) 70-79 60 (39.5%) 3,884 (27.4%) 80+ 29 (19.1%) 3,108 (21.8%) Marital status No partner 60 (41.1%) 6,736 (49.1%) 0.05 Partner 86 (58.9%) 6,980 (50.9%) Enabling factors Patient public/private Public patient 142 (94.0%) 10,175 (71.7%) <0.001 Private patient/Other 9 (6.0%) 4,019 (28.3%) IRSD of residence 1st-3rd quintiles less disadvantaged 22 (14.5%) 6,325 (44.8%) <0.001 4th-5th quintiles more disadvantaged 130 (85.5%) 7,801 (55.2%) Remoteness of residence Major cities 152 (100%) 6,003 (42.5%) N/A Inner regional 0 (0%) 4,449 (31.5%) Outer regional /Remote 0 (0%) 3,674 (26.0%) Hospital peer group Principal referral 73 (48.0%) 3,576 (25.2%) <0.001 Major 67 (44.1%) 3,437 (24.2%) Other 12 (7.9%) 7,183 (50.6%) Hospital public/private Public hospital 151 (99.3%) 12,552 (88.4%) N/A Private hospital 1 (0.7%) 1,645 (11.6%) Hospital remoteness Major cities 152 (100%) 6,440 (45.4%) N/A Inner regional 0 (0%) 4,614 (32.5%) Outer regional /Remote 0 (0%) 3,143 (22.6%)

141 Vietnam-born Australia-born Characteristics at baseline (N=152) (N=14,197) P valueχ Number (%) Number (%) (continued) Need factors Charlson index Zero 73 (48.1%) 8,232 (57.9%) 0.03 1 to 2 49 (32.2%) 3,949 (27.8%) ≥3 30 (19.7%) 2,016 (14.3%) High blood pressure Yes 112 (73.7%) 7,795 (54.9%) <0.001 No/Unknown 40 (26.3%) 6,402 (45.1%) High cholesterol Yes 51 (33.6%) 2,923 (20.6%) <0.001 No/Unknown 101 (66.4%) 11,274 (79.4%) Current or ex-smoking Yes 29 (19.1%) 5,122 (36.1%) <0.001 No/Unknown 123 (80.9%) 9,075 (63.9%) Obese Yes 5 (3.3%) 1,868 (13.2%) <0.001F No/Unknown 147 (96.7%) 12,329 (86.8%) Emergency status Emergency 139 (91.5%) 11,254 (79.3%) <0.001 Planned/Other 13 (8.5%) 2,943 (20.7%) χ: Chi-square test for proportions t: Student’s t-test F: Fisher’s exact test N/A: Not applicable

By the end of the observation period (30 June 2008), the total length of follow-up for the Vietnam- and Australia-born groups was 479.3 and 49,440.9 person-years, respectively (Table 26). The two groups had similar average duration of follow-up (3.2 vs 3.5 person-years, respectively, p=0.06). Over 80% of patients in both groups had at least one readmission for any reason (80.9% and 84.7%, respectively, p=0.20). The two groups had similar proportions of people being readmitted for diabetes without complications (1.3% vs 2.4%, p=0.38), diabetes complications (36.2% vs 37.9%, p=0.66), and diabetes comorbidities (34.2% vs 37.9%, p=0.35) as the principal reason for admission. However, the Vietnam-born patients were almost four times more likely to have regular dialysis than the Australia-born patients (N=19, 12.5% vs N=518, 3.7%, p<0.001). Fifty-two (34.2%) Vietnam-born and 5,026 (35.4%) Australia-born patients died during the follow-up period (p=0.76).

142 Table 26 Cohort characteristics during follow-up

Vietnam-born Australia-born Characteristics at follow-up (N=152) (N=14,197) P valueχ Number (%) Number (%) Length of follow-up (person-years) Total 479.3 49,440.9 Mean (SD) 3.2 (2.2) 3.5 (2.2) 0.06t Median 2.7 3.1 Range 0.1-7.9 0.1-8.0 Had at least one hospital readmission For any reasons 123 (80.9%) 12,020 (84.7%) 0.20 For diabetes no complication 2 (1.3%) 342 (2.4%) 0.59F For diabetes complications 55 (36.2%) 5,384 (37.9%) 0.66 For diabetes comorbidities 52 (34.2%) 5,386 (37.9%) 0.35 For dialysis 19 (12.5%) 518 (3.7%) <0.001 For other reasons 109 (71.7%) 10,692 (75.3%) 0.31 Mortality Number of deaths 52 (34.2%) 5,026 (35.4%) 0.76 χ: Chi-square test for proportions t: Student’s t-test F: Fisher’s exact test

Following discharge from the index admission, the Vietnam- and Australia-born patients recorded a total of 2,953 and 190,263 hospital readmissions (Table 27). Dialysis as the principal diagnosis accounted for 80.2% (Vietnam-born) and 56.5% (Australia-born) of the total readmissions. Vietnam-born patients had 99 (3.4%) readmissions due to diabetes complications and 115 (3.9%) due to diabetes comorbidities.

Table 27 Number of readmissions by principal reasons of admission

Vietnam-born Australia-born Principal reasons of admission P valueχ Number (%) Number (%) Diabetes without complications 2 (0.1%) 456 (0.2%) <0.001 Diabetes complications 99 (3.4%) 12,622 (6.6%) Diabetes comorbidities 115 (3.9%) 13,054 (6.9%) Dialysis 2,368 (80.2%) 107,614 (56.5%) Other reasons 369 (12.5%) 56,517 (29.7%) TOTAL 2,953 (100%) 19,0263 (100%) χ: Chi-square test for proportions

143 6.4.2. Rates of readmission and length of stay for diabetes and comorbidities

Following discharge from the index admission, 83 (54.6%) Vietnam-born and 8,217 (57.9%) Australia-born patients were readmitted for diabetes and comorbidity reasons with a total of 216 and 26,132 readmission episodes, respectively. Table 28 lists the principal diagnoses causing readmissions for diabetes and comorbidities among the Vietnam- and Australia-born patients. The most common diagnosis for both groups was cardiovascular disease (37.5% vs 44.2%). Diabetic nephropathy or chronic kidney diseases excluding dialysis were the second most common diagnoses among Vietnam-born patients (32.4% vs 7.5%), followed by diabetic retinopathy or other eye or vision disorders (13.8% vs 10.3%). The two groups had similar proportions of readmissions due to acute complications or hypoglycaemia (6.0% vs 8.2%) or poor diabetes control (4.6% vs 6.6%) but Vietnam-born patients were less likely to be readmitted for diabetic foot ulcer or other chronic skin problems than Australia-born patients (2.3% vs 13.8%).

Table 28 Principal diagnoses of readmissions for diabetes and comorbidities

Vietnam-born Australia-born Principal diagnoses P valueχ Number (%) Number (%) Cardiovascular disease 81 (37.5%) 11,555 (44.2%) <0.001 Diabetic nephropathy, chronic kidney 70 (32.4%) 1,960 (7.5%) diseases (excluding dialysis) Diabetic retinopathy, other eye 30 (13.8%) 2,689 (10.3%) or vision disorders Acute complications, hypoglycaemia 13 (6.0%) 2,157 (8.2%) Poor control 10 (4.6%) 1,722 (6.6%) Diabetic foot ulcer, chronic skin 5 (2.3%) 3,611 (13.8%) problems No complications 2 (0.9%) 456 (1.7%) Others 5 (2.3%) 1,982 (7.6%) TOTAL 216 (100%) 26,132 (100%) χ: Chi-square test for proportions

144 The person-year based analyses yielded a crude readmission rate for diabetes and comorbidities in the Vietnam-born group of 450.7 per 1,000 person-years of follow- up (95%CI=394.4-515.0). This was lower than the crude rate among the Australia- born patients (528.5 per 1,000 person-years, 95%CI=522.2-535.0). However, the difference between these crude rates was not statistically significant (crude RR=0.85, 95%CI=0.67-1.09, p=0.20) as presented in Table 29. When predisposing, enabling and need factors were taken into account (Model 2, Model 3 and Model 4), the likelihood of being readmitted for diabetes and comorbidity among the Vietnam-born patients was slightly lower than for the Australia-born patients (RR=0.86 Model 1, RR=0.80 Model 2, and RR=0.81 Model 4), but the differences remained statistically non-significant.

Table 29 Readmission rate ratios: crude and adjusted models

Rate Ratio‡ Poisson regression modelsō P value (95%CI)

Model 1 Crude 0.85 (0.67-1.09) 0.20 Model 2 Adjusted for predisposing factors 0.86 (0.68-1.10) 0.23 Model 3 Adjusted predisposing and enabling factors 0.80 (0.63-1.02) 0.08 Model 4 Adjusted for all predisposing, enabling and 0.81 (0.64-1.03) 0.09 need factors ō Overdispersion scaling parameter was included in the models to adjust for the over-dispersion ‡ Reference category: Australia-born

Table 30 summarises the crude and fully adjusted RR (Model 4) for each of the predisposing, enabling and need factors. The risk of readmissions for diabetes and comorbidities increased significantly with older age and higher Charlson index. When other variables were controlled for (Model 4), the readmission risk increased by 38% (50-59 year age group), 67% (60-69 year age group), 90% (70-79 year age group) and 84% (≥80 year age group) compared to the risk in patients under 50 years of age (p<0.001). Among people with a Charlson index of 1 to 2, the risk of readmission increased by 40% (95%CI=1.33-1.47, p<0.001), and people with index ≥3 had the risk increased by 71% (95%CI=1.60-1.83, p<0.001) compared to people with an index of zero. The risk of readmission was slightly lower for females (RR=0.87) or for those living with a partner (RR=0.86), while it was marginally higher for people with high blood pressure (RR=1.08), high cholesterol (RR=1.14) or among ex/current smokers (RR=1.11) or obese patients (RR=1.13). 145 Table 30 Readmission for diabetes and comorbidities by predisposing, enabling and need factors: crude and adjusted rate ratios (95%CI)

Model 1-Crude Model 4-Full adjustment# Country of birth Australia-born‡ 11 Vietnam-born 0.85 (0.67-1.09) 0.81 (0.64-1.03) Sex Male‡ 11 Female 0.90 (0.86-0.94) 0.87 (0.83-0.91) Age (years) Under 50‡ 11 50-59 1.45 (1.33-1.59) 1.38 (1.27-1.51) 60-69 1.81 (1.67-1.97) 1.67 (1.53-1.81) 70-79 2.06 (1.90-2.23) 1.90 (1.75-2.07) 80+ 1.96 (1.80-2.14) 1.84 (1.68-2.01) Relationship No partner‡ 11 Partner 0.87 (0.83-0.91) 0.86 (0.82-0.90) Patient public/private Public patient‡ 11 Private patient/Other 1.08 (1.03-1.14) 0.95 (0.90-1.00) IRSD of residence Less disadvantaged‡ 11 More disadvantaged 1.06 (1.02-1.11) 1.12 (1.06-1.17) Hospital peer group Principal referral‡ 11 Major 1.02 (0.95-1.08) 1.00 (0.94-1.07) Other 0.98 (0.93-1.03) 0.99 (0.93-1.05) Charlson index Zero‡ 11 1 to 2 1.57 (1.49-1.65) 1.40 (1.33-1.47) ≥3 2.06 (1.94-2.19) 1.71 (1.60-1.83) High blood pressure No/Unknown‡ 11 Yes 1.33 (1.28-1.39) 1.08 (1.03-1.13) High cholesterol No/Unknown‡ 11 Yes 1.34 (1.27-1.41) 1.14 (1.08-1.21) Smoking No/Unknown‡ 11 Current/Ex-smoker 1.21 (1.16-1.27) 1.11 (1.04-1.18) Obese No/Unknown‡ 11 Yes 1.16 (1.08-1.23) 1.13 (1.07-1.18) Emergency status Emergency‡ 11 Planned/Other 1.11 (1.06-1.17) 1.12 (1.06-1.18) 0.25 1 2 4 0.25 1 2 4 #: Adjusted for predisposing, enabling and need factors ‡: Reference category Forest graphs: The dots represent rate ratios, the horizontal bars represent the 95%CIs, the bigger dots represent reference categories. 146 On average, the weighted length of hospital stay for diabetes and comorbidity readmissions among Vietnam-born patients was 12.6 days for each year of follow-up (SD 23.1, median 4.4, range 0.1 to 150 days). The Australia-born patients had a mean stay of 13.5 days (SD 29.7, median 3.9, range 0.1 to 358 days). The length of stay did not differ between groups (z-test=0.469, p=0.64 Wilcoxon non-parametric test).

6.4.3. Time to readmission for non-dialysis reasons

Following the index admission, 122 Vietnam-born (80.3%) and 11,895 Australia- born patients (83.8%) were readmitted for reasons other than dialysis (p=0.48). The average time to readmission for Vietnam-born patients was 249.4 days (SD 344.7, median 57, range 1 to 1,577 days) in comparison to 290.6 days (SD 395.3, median 135, range 1 to 2,806 days) of the Australia-born patients.

The unadjusted and adjusted Cox proportional hazard models indicated that the risk of being readmitted to hospitals following discharge of the index admission was similar for Vietnam- and Australia-born patients (Figure 9). As shown in Table 31, the crude HR (Model 1) was 1.01 (95%CI=0.84-1.20) and adjusted HRs were 0.98 (95%CI=0.82-1.17, Model 2), 0.93 (95%CI=0.78-1.12, Model 3) and 0.94 (95%CI=0.79-1.13, Model 4).

Among the predisposing, enabling and need factors, age and Charlson index were strongly associated with the risk of readmission for any non-dialysis reason as shown in Table 32. When other factors were taken into account (Model 4), the risk increased from 22% (95%CI=1.14-1.32) for patients aged 50-59 years to 76% (95%CI=1.64-1.89) for patients aged 80 years and older. The risk for people with a Charlson index of 1-2 increased by 38%, and for obese patients the risk increased by 14% (95%CI=1.09-1.19). There were minor or no effects of gender, marital status, private or public patient status, IRSD of patients’ residential area, hospital peer group, and health risk factors.

147

Figure 9 Kaplan-Meier graph of time to non-dialysis readmission

Table 31 Time to non-dialysis readmission hazard ratios: crude and four adjusted models

Hazard ratios‡ Cox proportional hazard regression models P value (95%CI)

Model 1 Crude 1.01 (0.84-1.20) 0.95

Model 2 Adjusted for predisposing factors 0.98 (0.82-1.17) 0.82

Model 3 Adjusted predisposing and enabling factors 0.93 (0.78-1.12) 0.46

Model 4 Adjusted for all predisposing, enabling and 0.94 (0.79-1.13) 0.54 need factors

‡ Reference category: Australia-born

148

Table 32 Time to non-dialysis readmissions by predisposing, enabling and need factors: crude and adjusted hazard ratios (95%CI)

149 Model 1-Crude Model 4-Full adjustment# Country of birth Australia-born‡ 11 Vietnam-born 1.01 (0.84-1.20) 0.94 (0.79-1.13) Sex Male‡ 11 Female 1.05 (1.01-1.09) 1.00 (0.96-1.04) Age (years) Under 50‡ 11 50-59 1.27 (1.19-1.36) 1.22 (1.14-1.32) 60-69 1.52 (1.42-1.62) 1.40 (1.31-1.50) 70-79 1.91 (1.79-2.03) 1.71 (1.60-1.83) 80+ 1.97 (1.85-2.11) 1.76 (1.64-1.89) Relationship No partner‡ 11 Partner 0.90 (0.87-0.93) 0.94 (0.90-0.98) Patient public/private Public patient‡ 11 Private patient/Other 1.14 (1.10-1.19) 1.01 (0.97-1.06) IRSD of residence Less disadvantaged‡ 11 More disadvantaged 0.99 (0.96-1.03) 1.07 (1.02-1.11) Hospital peer group Principal referral‡ 11 Major 0.94 (0.90-0.99) 0.95 (0.90-1.00) Other 0.91 (0.87-0.95) 0.93 (0.88-0.97) Charlson index Zero‡ 11 1 to 2 1.56 (1.49-1.62) 1.38 (1.32-1.44) ≥3 2.01 (1.91-2.12) 1.67 (1.58-1.77) High blood pressure No/Unknown‡ 11 Yes 1.26 (1.21-1.30) 1.04 (1.00-1.09) High cholesterol No/Unknown‡ 11 Yes 1.21 (1.16-1.27) 1.08 (1.03-1.13) Smoking No/Unknown‡ 11 Current/Ex-smoker 1.15 (1.11-1.19) 1.05 (0.99-1.11) Obese No/Unknown‡ 11 Yes 1.07 (1.01-1.12) 1.14 (1.09-1.19) Emergency status Emergency‡ 11 Planned/Other 1.08 (1.03-1.13) 1.07 (1.02-1.12) 0.25 1 2 4 0.25 1 2 4 #: Adjusted for predisposing, enabling and need factors ‡: Reference category Forest graphs: The dots represent hazard ratios, the horizontal bars represent the 95%CIs, the bigger dots represent reference categories.

150 6.4.4. Time to death due to all causes, and due to diabetes

Following the index admission discharge, 52 patients in the Vietnam-born group died within a mean of 569.4 days (median 469, range 31 to 1,509 days) for all causes of death. The Australia-born group had 5,026 deaths (all causes) within a mean of 792.1 days (median 617.5, range 30 to 2,854 days). The causes of death extracted from ABS mortality data were available for 49 deaths (94%) of the Vietnam-born patients and 4,594 deaths (91%) among Australia-born patients. The underlying causes of these deaths for Vietnam- and Australia-born patients respectively included diabetes (N=11, 22% vs N=813, 18%), cardiovascular disease (N=11, 22% vs N=1,556, 34%), renal disease (N=4, 8% vs N=179, 4%), cancer (N=8, 16% vs N=760, 17%) and other causes (N=15, 31% vs N=1,286, 28%). Among deaths for which diabetes was not the underlying cause of death, diabetes was recorded as an associated cause of death in 14 Vietnam-born and 1,831 Australia-born patients. This section presents results of survival analysis for all-cause and diabetes-specific (underlying or associated causes) mortality.

As illustrated in Figure 10 and Figure 11, the crude survival time following discharge from the index admission for both all-causes and diabetes-specific mortality was significantly shorter in the Vietnam-born patients. The crude HR for all-cause mortality was 1.53 (95%CI=1.17-2.02) as in Table 33 and for diabetes-specific mortality was 1.72 (95%CI=1.16-2.56) as in Table 34. Following adjustment for other predisposing, enabling and need factors, Vietnam-born patients had a risk of all-cause mortality 1.42 times higher than that of the Australia-born group (95%CI=1.07-1.88, p=0.02, Table 33, Model 4). The risk of diabetes-specific mortality among Vietnam-born patients was even higher (HR=1.58, 95%CI=1.05- 2.38, p=0.03, Table 34, Model 4).

151

Figure 10 Kaplan-Meier graph of survival time for all-cause mortality

Figure 11 Kaplan-Meier graph of survival time for diabetes-specific mortality

152 Table 33 All-cause mortality risk: crude and four adjusted models

Hazard ratios‡ Cox proportional hazard regression models P value (95%CI) Model 1 Crude 1.53 (1.17-2.02) 0.002

Model 2 Adjusted for predisposing factors 1.52 (1.16-2.01) 0.003

Model 3 Adjusted predisposing and enabling factors 1.47 (1.11-1..94) 0.008

Model 4 Adjusted for all predisposing, enabling and 1.42 (1.07-1.88) 0.02 need factors ‡ Reference category: Australia-born

Table 34 Diabetes-specific mortality risk: crude and four adjusted models

Hazard ratios‡ Cox proportional hazard regression models P value (95%CI)

Model 1 Crude 1.72 (1.16-2.56) 0.007

Model 2 Adjusted for predisposing factors 1.62 (1.08-2.42) 0.02

Model 3 Adjusted predisposing and enabling factors 1.53 (1.02-2..30) 0.04

Model 4 Adjusted for all predisposing, enabling and 1.58 (1.05-2.38) 0.03 need factors ‡ Reference category: Australia-born

Table 35 and Table 36 show that the risks of all-cause and diabetes-specific mortality were strongly related to Charlson comorbidity index and age. For all-cause mortality, compared to people with a Charlson index of zero, those with an index of 1 to 2 had the all-cause mortality risk (Model 4) increased by 30% (95%CI=1.21-1.38) and those with an index ≥3 had a 75% increase in risk (95%CI=1.62-1.89). The respective HRs for diabetes-specific mortality were 1.27 (95%CI=1.16-1.40) and 1.76 (95%CI=1.59-1.96). In terms of age, compared to patients under 50 years of age, the risk for all-cause mortality for those aged 70-79 years was 1.19 (95%CI=1.01-1.41), and 1.51 (95%CI=1.27-1.78) for patients aged ≥80 years. The effects of age on diabetes-specific mortality were slightly greater, for example in adjusted Model 4, HR=1.44 for the 70-79 year age group, and adjusted HR=1.77 for age ≥80 years. Both all-cause and diabetes-specific mortality risk increased approximately by 17% for obese patients but decreased approximately by 10% for those whose index admission was for a non-emergency reason.

153 Table 35 Time to mortality for all causes of death by predisposing, enabling and need factors: crude and adjusted hazard ratios (95%CI)

Model 1-Crude Model 4-Full adjustment# Country of birth Australia-born‡ 11 Vietnam-born 1.53 (1.17-2.02) 1.42 (1.07-1.88) Sex Male‡ 11 Female 0.99 (0.94-1.05) 0.97 (0.91-1.03) Age (years) Under 50‡ 11 50-59 1.00 (0.84-1.20) 0.96 (0.80-1.16) 60-69 1.11 (0.94-1.31) 1.06 (0.90-1.26) 70-79 1.25 (1.07-1.46) 1.19 (1.01-1.41) 80+ 1.51 (1.29-1.77) 1.51 (1.27-1.78) Relationship No partner‡ 11 Partner 0.99 (0.93-1.04) 0.98 (0.92-1.04) Patient public/private Public patient‡ 11 Private patient/Other 1.07 (1.00-1.13) 1.01 (0.94-1.08) IRSD of residence Less disadvantaged‡ 11 More disadvantaged 0.94 (0.89-0.99) 1.00 (0.94-1.06) Hospital peer group Principal referral‡ 11 Major 0.99 (0.92-1.07) 0.99 (0.91-1.07) Other 0.91 (0.85-0.97) 0.97 (0.90-1.04) Charlson index Zero‡ 11 1 to 2 1.34 (1.26-1.43) 1.30 (1.21-1.38) ≥3 1.87 (1.74-2.00) 1.75 (1.62-1.89) High blood pressure No/Unknown‡ 11 Yes 1.24 (1.17-1.31) 1.08 (1.01-1.15) High cholesterol No/Unknown‡ 11 Yes 1.20 (1.12-1.29) 1.05 (0.97-1.14) Smoking No/Unknown‡ 11 Current/Ex-smoker 1.20 (1.14-1.27) 1.03 (0.94-1.14) Obese No/Unknown‡ 11 Yes 1.03 (0.94-1.12) 1.17 (1.10-1.25) Emergency status Emergency‡ 11 Planned/Other 0.88 (0.82-0.95) 0.89 (0.82-0.95)

0.25 1 2 4 0.25 1 2 4 # ‡ : Adjusted for predisposing, enabling and need factors : Reference category Forest graphs: The dots represent hazard ratios, the horizontal bars represent the 95%CIs, the bigger dots represent reference categories. 154 Table 36 Time to mortality due to diabetes by predisposing, enabling and need factors: crude and adjusted hazard ratios (95%CI)

Model 1-Crude Model 4-Full adjustment# Country of birth Australia-born‡ 11 Vietnam-born 1.72 (1.16-2.56) 1.58 (1.05-2.38) Sex Male‡ 11 Female 0.98 (0.91-1.06) 0.93 (0.85-1.01) Age (years) Under 50‡ 11 50-59 1.03 (0.79-1.33) 0.99 (0.76-1.30) 60-69 1.18 (0.93-1.50) 1.15 (0.91-1.47) 70-79 1.45 (1.15-1.82) 1.44 (1.14-1.83) 80+ 1.66 (1.32-2.09) 1.77 (1.39-2.24) Relationship No partner‡ 11 Partner 0.96 (0.89-1.04) 0.96 (0.88-1.04) Patient public/private Public patient‡ 11 Private patient/Other 1.05 (0.97-1.14) 0.97 (0.89-1.06) IRSD of residence Less disadvantaged‡ 11 More disadvantaged 0.95 (0.88-1.03) 1.02 (0.93-1.10) Hospital peer group Principal referral‡ 11 Major 0.99 (0.89-1.10) 0.96 (0.86-1.08) Other 0.90 (0.82-0.98) 0.97 (0.88-1.08) Charlson index Zero‡ 11 1 to 2 1.31 (1.20-1.43) 1.27 (1.16-1.40) ≥3 1.87 (1.69-2.06) 1.76 (1.59-1.96) High blood pressure No/Unknown‡ 11 Yes 1.21 (1.12-1.31) 1.08 (0.99-1.17) High cholesterol No/Unknown‡ 11 Yes 1.21 (1.09-1.33) 1.05 (0.95-1.17) Smoking No/Unknown‡ 11 Current/Ex-smoker 1.18 (1.09-1.28) 1.13 (0.99-1.28) Obese No/Unknown‡ 11 Yes 1.10 (0.98-1.25) 1.17 (1.07-1.28) Emergency status Emergency‡ 11 Planned/Other 0.85 (0.77-0.93) 0.86 (0.77-0.95)

0.25 1 2 4 0.25 1 2 4 # ‡ : Adjusted for predisposing, enabling and need factors : Reference category Forest graphs: The dots represent hazard ratios, the horizontal bars represent the 95%CIs, the bigger dots represent reference categories. 155 6.4.5. Sensitivity analyses

Table 37 compares major outcome estimates generated from the clearance cohorts with nil, 18-month and 30-month clearance periods. Compared to the nil-clearance cohort, there were fewer patients included in the cohorts with a clearance period: 120 Vietnam- and 10,778 Australia-born patients in 18-month clearance cohort, and 98 Vietnam- and 8,701 Australia-born patients in the 30-month clearance cohort.

In terms of readmission, the clearance cohorts had similar patterns of outcomes to the nil-clearance cohort. Vietnam-born patients had lower crude readmission rates for diabetes and comorbidities (per 1,000 person-year of follow-up), than the Australia- born patients, such as 405.4 (95%CI=341.3-481.4) vs 522.2 (95%CI=514.3-530.1) for the 18-month clearance cohort; and 411.0 (95%CI=336.5-502.1) vs 512.9 (95%CI=503.7-522.2) for the 30-month clearance cohort. These differences in the readmission rates were statistically non-significant. There were also similar magnitudes of crude rate ratios generated from the 18-month, 30-month and nil- clearance cohorts (RR=0.78, 0.80, and 0.85 respectively) as well as fully-adjusted rate ratios (RR=0.76, 0.80, and 0.81 respectively). The risk of being readmitted for non-dialysis reasons among Vietnam-born patients was the same with that of Australia-born patients across the three cohorts. The adjusted HR was 1.02 (95%CI=0.82-1.26) for the 18-month clearance, 1.04 (95%CI=0.82-1.31) for the 30- month clearance, and 0.94 (95%CI=0.79-1.13) for the nil-clearance cohort.

In terms of mortality, in the 18-month clearance cohort there were 38 Vietnam-born (31.7%) and 3,257 Australia-born (30.2%) patients who died during the follow-up period; of those, 19 (Vietnam-born) and 1,634 (Australia-born) deaths were due to diabetes. Vietnam-born patients had a marginally higher risk of all-cause mortality (crude HR=1.37, 95%CI=1.01-1.89, and adjusted HR=1.20, 95%CI=0.86-1.68), and diabetes-specific mortality (crude HR=1.49, 95%CI=0.95-2.35, and adjusted HR=1.39, 95%CI=0.86-2.23) than the Australia-born patients.

156 In the 30-month clearance cohort there were 28 (28.6%) and 2,339 (26.9%) deaths due to all-causes; of those, 15 (Vietnam-born) and 1,127 (Australia-born) deaths were due to diabetes. The risk of all-cause mortality (crude HR=1.10, 95%CI=0.76- 1.60, and adjusted HR=0.91, 95%CI=0.62-1.34) and diabetes-specific mortality (crude HR=1.15, 95%CI=0.69-1.92, and adjusted HR=1.09, 95%CI=0.63-1.87) was not significantly different for the Vietnam- and Australia-born groups.

Table 38 compares the ascertainment of health risk factors, using the three-year lookback period and the single index admission record in Vietnam- and Australia- born patients of the nil-clearance cohort. Using extra information recorded in the three-year lookback period, more patients with health risk factors were identified. In the Vietnam-born group, the ascertainment increased from 88 (57.9%) to 112 (73.7%) for high blood pressure, 29 (19.1%) to 51 (33.6%) for high cholesterol, and 23 (15.1%) to 29 (19.1%) for current or ex-smoking. In the Australia-born group, the risk factor enumeration also increased, including 14.7% improvement (5,702 to 7,795) for high blood pressure, 9.0% improvement (1,645 to 2,923) for high cholesterol, 12.8% increase (3,312 to 5,122) for smoking status, and 5.4% increase (1,103 to 1,868) for obesity. However, the patterns of differences in health risk factors between Vietnam- and Australia-born groups were similar. The use of a three-year lookback period improved statistical power to detect the differences between the two groups.

Table 39 presents adjusted RRs and HRs for Vietnam-born group and each of health risk factors, using alternative methods in identifying health risk factors. For the Vietnam-born group, after adjustment for other predisposing, enabling and need factors, the two alternative methods for health risk factors ascertainment yielded the same adjusted rate ratios for the readmission for diabetes and comorbidities (RR=0.81, 95%CI=0.64-1.03, using a three-year lookback period vs RR=0.81, 95%CI=0.64-1.03 using the single index admission record); and the same hazard ratios for time to non-dialysis readmission (HR=0.94, 95%CI=0.79-1.13 vs HR=0.93, 95%CI=0.78-1.12), for all-cause mortality (HR=1.42, 95%CI=1.07-1.88 vs HR=1.40, 95%CI=1.06-1.86), and for diabetes-specific mortality (HR=1.58, 95%CI=1.05-2.38 vs HR=1.53, 95%CI=1.02-2.3).

157 For health risk factors, the two methods also generated similar RRs and HRs for the readmission and mortality outcomes. Among patients with high blood pressure, adjusted RR for the outcome readmission for diabetes and comorbidities was 1.08 (95%CI=1.03-10.13) using the three-year lookback period, and 0.99 (95%CI=0.94- 1.04) using the single index admission record. Respective adjusted HRs for diabetes- specific mortality among those with high blood pressure were 1.08 (95%CI=0.99- 1.17) and 1.03 (95%CI=0.94-1.12).

The comparisons of outcome estimates for other predisposing, enabling and need factors (Appendix 8, Appendix 9, Appendix 10, and Appendix 11) indicate that impacts of these factors on readmission and mortality outcomes were independent of methods used to enumerate health risk factors.

158 Table 37 Comparisons of results from nil-clearance cohort with results of 18-month and 30-month clearance cohorts

Cohorts with clearance period, Outcomes estimates (95%CI) and P value Nil clearance 18-month clearance 30-month clearance Vietnam-born Australia-born Vietnam-born Australia-born Vietnam-born Australia-born N=152 N=14,197 N=120 N=10,778 N=98 N=8,701 Follow up-period 1 July 2000 to 30 June 2008 1 Jan 2002 to 30 June 2008 1 Jan 2003 to 30 June 2008 Readmission for diabetes and comorbidities Number of patients readmitted 83 (54.6%) 8,217 (57.9%) 61 (50.8%) 5,874 (54.5%) 49 (50.0%) 4,523 (51.9%) Crude rate per 1,000 person-year 450.7 528.5 405.4 522.2 411.0 512.9

(394.4-515.0) (522.2-535.0) (341.3-481.4) (514.3-530.1) (336.5-502.1) (503.7-522.2) Crude RR 0.85 (0.67-1.09, p=0.20) 0.78 (0.58-1.04, p=0.09) 0.80 (0.58-1.11, p=0.18) Adjusted RR# 0.81 (0.64-1.03, p=0.09) 0.76 (0.57-1.02, p=0.07) 0.80 (0.58-1.10, p=0.16) Time to readmission (non-dialysis) Number of patients readmitted 122 (80.3%) 11,895 (83.8%) 93 (77.5%) 8,764 (81.3%) 74 (75.5%) 6,904 (79.3%) Crude HR 1.01 (0.84-1.20, p=0.95) 1.02 (0.83-1.25, p=0.88) 1.00 (0.80-1.26, p=0.97) Adjusted HR# 0.94 (0.79-1.13, p=0.54) 1.02 (0.82-1.26, p=0.87) 1.04 (0.82-1.31, p=0.77) All-cause mortality Number of deaths 52 (34.2%) 5,026 (35.4%) 38 (31.7%) 3,257 (30.2%) 28 (28.6%) 2,339 (26.9%) Crude HR 1.53 (1.17-2.02, p=0.002) 1.37 (1.01-1.89, p=0.05) 1.10 (0.76-1.60, p=0.61) Adjusted HR# 1.42 (1.07-1.88, p=0.02) 1.20 (0.86-1.68, p=0.28) 0.91 (0.62-1.34, p=0.63) RR: Rate ratio HR: Hazard ratio # Model 4: Adjusted for all predisposing, enabling and need factors

159 Cohorts with clearance period, Outcomes estimates (95%CI) and P value Nil clearance 18-month clearance 30-month clearance Vietnam-born Australia-born Vietnam-born Australia-born Vietnam-born Australia-born N=152 N=14,197 N=120 N=10,778 N=98 N=8,701 (continued) Diabetes-specific mortality Number of deaths with known causes 49 4,594 35 2,895 25 2027 of death Number of diabetes-specific deaths 25 2,644 19 1,634 15 1,127 Crude HR 1.72 (1.16-2.56, p=0.007) 1.49 (0.95-2.35, p=0.08) 1.15 (0.69-1.92, p=0.59) Adjusted HR# 1.58 (1.05-2.38, p=0.03) 1.39 (0.86-2.23, p=0.18) 1.09 (0.63-1.87, p=0.76) RR: Rate ratio HR: Hazard ratio # Model 4: Adjusted for all predisposing, enabling and need factors

Table 38 Comparisons of risk factors identified by a three-year lookback period and the single index admission record

Three-year lookback period Index admission Health risk factors Vietnam-born Australia-born Vietnam-born Australia-born P valueχ P valueχ (N=152) (N=14,197) (N=152) (N=14,197) High blood pressure 112 (73.7%) 7,795 (54.9%) <0.001 88 (57.9%) 5,702 (40.2%) <0.001 High cholesterol 51 (33.6%) 2,923 (20.6%) <0.001 29 (19.1%) 1,645 (11.6%) 0.004 Current or ex-smoking 29 (19.1%) 5,122 (36.1%) <0.001 23 (15.1%) 3,312 (23.3%) 0.02 Obese 5 (3.3%) 1,868 (13.2%) <0.001 4 (2.6%) 1,103 (7.8%) 0.02 χ: Chi-square test for proportions

160 Table 39 Comparisons of adjusted outcome estimates by methods used to identify health risk factors

Three-year lookback period Index admission Estimates Estimates P value P value (95%CI) (95%CI)

Readmission for diabetes and comorbidities: Adjusted RR#

Vietnam-born ‡ 0.81 (0.64-1.03) 0.09 0.81 (0.64-1.03) <0.001 High blood pressure ‡ 1.08 (1.03-1.13) 0.002 0.99 (0.94-1.04) 0.69 High cholesterol ‡ 1.14 (1.08-1.21) <0.001 1.03 (0.96-1.10) 0.46 Current or ex-smoking ‡ 1.11 (1.04-1.18) <0.001 1.08 (1.02-1.14) 0.005 Obese ‡ 1.13 (1.07-1.18) 0.002 0.99 (0.90-1.07) 0.74

Time to readmission (non-dialysis): Adjusted HR#

Vietnam-born ‡ 0.94 (0.79-1.13) 0.54 0.93 (0.78-1.12) 0.46 High blood pressure ‡ 1.04 (1.00-1.09) 0.04 0.96 (0.92-1.00) 0.03 High cholesterol ‡ 1.08 (1.03-1.13) 0.003 0.99 (0.94-1.05) 0.81 Current or ex-smoking ‡ 1.05 (0.99-1.11) <0.001 1.04 (1.00-1.09) 0.06 Obese ‡ 1.14 (1.09-1.19) 0.09 1.02 (0.95-1.09) 0.63

All-cause mortality: Adjusted HR#

Vietnam-born ‡ 1.42 (1.07-1.88) 0.02 1.40 (1.06-1.86) 0.02 High blood pressure ‡ 1.08 (1.01-1.15) 0.02 1.03 (0.96-1.09) 0.42 High cholesterol ‡ 1.05 (0.97-1.14) 0.20 1.03 (0.93-1.14) 0.54 Current or ex-smoking ‡ 1.03 (0.94-1.14) <0.001 1.07 (0.99-1.15) 0.09 Obese ‡ 1.17 (1.10-1.25) 0.52 0.96 (0.84-1.09) 0.48

Diabetes-specific mortality: Adjusted HR#

Vietnam-born ‡ 1.58 (1.05-2.38) 0.03 1.53 (1.02-2.3) 0.04 High blood pressure ‡ 1.08 (0.99-1.17) 0.09 1.03 (0.94-1.12) 0.54 High cholesterol ‡ 1.05 (0.95-1.17) 0.35 0.94 (0.82-1.08) 0.40 Current or ex-smoking ‡ 1.13 (0.99-1.28) 0.001 1.06 (0.96-1.17) 0.28 Obese ‡ 1.17 (1.07-1.28) 0.07 1.00 (0.84-1.20) 0.97 ‡: Reference category. Country of birth (Australia-born), high blood pressure, high cholesterol, smoking, obese (No/Unknown) RR: Rate ratio HR: Hazard ratio # Model 4: Adjusted for all predisposing, enabling and need factors

161 6.5. DISCUSSION OF FINDINGS

This chapter used record linkage of population-based health administrative data sources to investigate hospitalisation and mortality among Vietnam-born people who were admitted to hospital in NSW between 1 July 2000 and 30 June 2008 for reasons related to type 2 diabetes, in comparison to their Australia-born counterparts.

The demographic and socio-economic characteristics of Vietnam-born patients with type 2 diabetes found in this chapter are consistent with the findings of previous chapters. The Vietnam-born patients were older (1.5 years) than Australia-born patients and had lower SES with higher proportions of public patients, admissions to public hospitals, and residents of socio-economically disadvantaged areas. The Vietnam-born patients were less likely to be past or current smokers or be obese, but had worse health status than the Australia-born patients, including higher percentages with high blood pressure, high cholesterol, and comorbidities. These findings reflect those reported in Chapter 5, that Vietnam-born participants in the 45 and Up Study with type 2 diabetes were more likely to report poorer health status, poorer quality of life and higher levels of psychological distress than Australia-born participants with type 2 diabetes. Consistent with the finding presented in Chapter 4, current or past smoking was predominant in Vietnam-born male patients (24 men vs 5 women), but smoking rates among Vietnam-born admitted patients in this analysis were higher than smoking rates among Vietnam-born participants in the 45 and Up Study for both men (36.4% vs 15.4%) and women (5.8% vs 0.5%). These differences in smoking rates likely reflect a “healthy cohort effect” in Vietnam-born participants in the 45 and Up Study (as discussed in Chapter 3).

Compared to Australia-born patients, Vietnam-born patients had similar length of time to non-dialysis readmission following discharge from index admission (adjusted HR=0.94). In terms of readmission, the Vietnam-born patients had lower person-year rates of readmission for diabetes complications and comorbidities (adjusted RR=0.81). According to the 2008 report of the NSW Chief Health Officer,28 rates of hospitalisation for type 1 and type 2 diabetes and GDM combined among Vietnam- born residents were slightly higher than that among Australia-born residents (341.5 vs 328.1 per 100,000 population between 2002 and 2007, ratio=1.04), however, this

162 was largely accounted for by hospitalisation for GDM in Vietnam-born women which was approximately triple the rate among Australia-born women. A 2010 report from the Queensland Health Department119 indicated a lower rate of hospitalisation for diabetes of all types in Vietnam-born people compared to the overall Queensland population. Given that type 2 diabetes is the most common form of diabetes, the findings of this chapter regarding rates of hospitalisation can be regarded as reasonably consistent with Australian published data,28, 119 in the absence of comprehensive international and national data on hospitalisation among Vietnam- born people with type 2 diabetes.

However, the current analyses found that Vietnam-born patients had a significantly higher risk of mortality due to all-causes of death and diabetes, following adjustment for demographic and socio-economic characteristics and risk factors. This finding could be explained by diabetes status being more severe (the Charlson index as a proxy measure) in Vietnam-born patients at baseline (index admission) as a consequence of late access to health care. It might also be due to the underlying higher mortality from diabetes in Vietnam-born population than in Australia-born population. According to the AIHW,15 in 2000, Asia-born Australians had significantly higher mortality due to diabetes than the Australia-born population, with a standardised mortality ratio of 1.36 for men and 1.67 for women. Unfortunately, national data specific to the Vietnam-born population are not available. Further investigation of population-based mortality from diabetes in people of CALD would be beneficial for better understanding and tackling health inequality issues.

These analyses found that 12.5% of Vietnam-born patients were regularly admitted for dialysis, indicating a higher prevalence of end-stage kidney disease (ESKD) in Vietnam-born patients than in Australia-born patients (3.7%). People with ESKD often require dialysis three times a week,355, 359 explaining the higher percentage of diabetes-related admissions that were for dialysis (80.0%) in Vietnam-born patients than in Australia-born patients (56.5%). Diabetes and its nephropathy complications (Chapter 5), and high blood pressure are leading causes of chronic kidney disease.355 In hypertensive people, the wall of kidney blood vessels becomes thick while the internal diameter narrows.355 The current analyses found that Vietnam-born patients 163 had a higher proportion of readmissions for non-dialysis diabetic nephropathy and renal diseases than their Australia-born counterparts (38.5% vs 8.6%). The prevalence of high blood pressure among Vietnam-born patients (73.7%) was higher than in Vietnam-born participants with type 2 diabetes in the 45 and Up Study (61.2%) and in Australia-born admitted patients (54.9%).

The age-standardised prevalence of treated ESKD in Australia was 67.5 per 100,000 population in 2003 (total number 13,625 people) based on ESKD registrations in the Australian and New Zealand Dialysis and Transplant (ANZDATA) Registry.360 The age-standardised incidence was 20 per 100,000 between 2003 and 2007 (total 21,370 new cases).355 There were approximately 165 new Vietnam-born people registered in ANZDATA between 1993 and 2001.361 Compared to non-Indigenous people born in Australia, those born in Asian countries (including China, Vietnam, Philippines, Indonesia and Malaysia) had a five-fold incidence of ESKD caused by type 2 diabetes nephropathy.361 A study conducted in Vietnam in 2006 reported a prevalence of ESKD of 3.1%.362 Individuals with ESKD in Vietnam had a high rate of hypertension (30.5%) and most of them were not aware of their hypertension and thus did not receive appropriate treatment and management.362 However, the rates of ESKD in Vietnam-born patients in the current analyses possibly do not represent ESKD in the wider community because data were based on hospital morbidity data, while approximately 33% of dialysis-dependent patients choose to have dialysis at home,360 thus are not captured in hospital data.359 Information about the uptake of home dialysis among subgroups of the Australian population such as country of birth, SES and remoteness of residence is currently unavailable. However, the advantages that home dialysis can offer, such as flexibility in dialysis schedules and reduced needs of travelling to dialysis centre,363 may suggest a higher uptake of home dialysis among Australia-born people with ESDK in rural and remote areas.

Results of sensitivity analyses in relation to the clearance cohorts and three-year lookback period indicate robustness of findings. The three cohorts (nil-, 18-month and 30-month clearance periods) yielded similar patterns of results. The statistical non-significance in mortality risks in the 18-month and 30-month clearance cohorts could be explained by a lack of statistical power to detect a difference due to smaller sample sizes compared to the nil-clearance cohort. Brameld and colleagues348 164 estimated incidence trends from linked hospital morbidity data and reported that the prevalent pool effect persisted for 13 years for diabetes cases. The relatively short clearance periods that could be achieved with APDC data used in this chapter meant that the findings essentially relate to prevalent cases of diabetes patients who may have had multiple prior admissions, rather than to people who had their first admission for diabetes. It is not known how the selection of the first admission for diabetes might have influenced comparisons between the Vietnam- and Australia- born groups. Consistent with the previous study,350 the use of a three-year lookback period to identify health risk factors improved the enumeration of patients having risk factors but did not have impact on outcome estimates. Post-hoc power calculation322 indicated that the current study had strong power (≥97%) to detect HR=1.42 (95%CI=1.07-1.88, Model 4) for all-cause mortality, and HR=1.58 (95%CI=1.05-2.38, Model 4) for diabetes-specific mortality. However, the study was under-powered (20%) to detect HR=0.94 (95%CI=0.79-1.13) for non-dialysis readmission, which required a need for precaution in the interpretation of time to readmission results.

The use of routinely collected hospital morbidity data for research purposes has some limitations that should be acknowledged. Chronic conditions are often under- ascertained from hospital data340, 341, 350 because only diagnoses affecting the management of the current admission are required to be coded in the discharge summary.364 Other information such as level of education, income, duration of residence in Australia for overseas-born people, lifestyle factors, duration and management of diabetes are not available in hospital morbidity data. However, the sensitivity of identifying Vietnam- and Australia-born individuals from the APDC data was high (85% and 98%). This, coupled with the consistency between findings in this chapter and previous chapters, supports the reliability and generalisability of the findings.

The findings of these analyses did not support Hypothesis Four, that Vietnam-born people with type 2 diabetes have higher rates of hospital admissions for diabetes complications and comorbidities than the Australia-born population. In contrast, the use of hospital services for diabetes and comorbidities among the Vietnam-born people with type 2 diabetes tended to be less (but not statistically significantly) than 165 their Australia-born counterparts. Hypothesis Five, that Vietnam-born people are at higher risk of all-cause and diabetic-specific mortality than their Australia-born counterparts, was supported. The risk of all-cause and diabetes-specific mortality was significantly higher among Vietnam-born admitted patients.

According to Andersen and Newman’s Behavioral Model of Health Service Use,326 a wide range of factors may influence the use of health services, including ethnicity, culture and health beliefs, accessibility and availability of services, and perceived health needs and health needs evaluated by health professionals. Given Australia’s universal health system and the spatial distribution in urban regions of NSW of Vietnam-born people, the issue of availability of hospital-based health services is more likely to impact on Australia-born population, especially those in rural and remote areas rather than Vietnam-born people. However, accessibility to these services for Vietnam-born patients may have been affected by language barriers, availability of Vietnamese interpreter services,365 lack of knowledge of health and the health system,115, 365 and lack of transportation.115, 365 Compared to Australia-born patients, Vietnam-born patients had poorer health status, including a higher prevalence of high blood pressure, high cholesterol, ESKD and other comorbidities. These indicated a higher level of evaluated health needs in Vietnam-born patients. Previous studies have reported low levels of health literacy (discussed in Chapter 3), inadequate knowledge about diabetes, and poor compliance with diabetes treatment regimens (presented in Chapter 5) among Vietnam-born people. It is possible that there is a lower level of perceived health needs among Vietnam-born patients with diabetes, leading to delays seeking care from health professionals and thus delays in referrals to specialist, allied health and tertiary health services. This explanation is supported by a report of an Australian study365 that general practitioners (GPs) of Vietnam-born people with diabetes felt that referrals to health services were compromised by their patient’s failure to realise the importance of other services, lack of linguistically and culturally appropriate services, and the cost of services. A significantly higher proportion of index admissions in the Vietnam-born patients were emergency admissions (91.5%) compared to the Australia-born group (79.3%), again potentially indicating delays in seeking or accessing care.

166 Findings in this chapter have implications for patient education to raise awareness among Vietnam-born people with diabetes about the condition and its complications, and proactive management to prevent complications. In addition to delivering quality care for patients with diabetes according to guidelines,214 health care providers for Vietnam-born patients could be encouraged to provide brief booster patient education sessions during regular consultations. Such patient education should also include family members, because of the important roles of family members in health decisions and care in Vietnamese culture. Further large-scale research into how Vietnam-born patients monitor and manage their diabetes and the quality of diabetes care that they receive is needed, especially as Vietnam-born Australians are ageing rapidly, with a consequent increase in the burden of diabetes.

167

Chapter 7

Discussion and Conclusion

This thesis investigated two interrelated aspects of health in Vietnam-born Australians: the impact of acculturation on lifestyle behaviours and health; and prevalence, risk factors, hospitalisation and mortality outcomes of type 2 diabetes. Vietnam-born Australians represent a significant CALD population of Australia, and many have had a stressful migration and refugee history. Research has not yet delivered a consensus on the effects of acculturation on health-related outcomes of immigrants, beyond the “healthy immigrant effect”, which is attributable to selective immigration. Prior to the current program of research, the effects of acculturation on lifestyle and health of Vietnam-born Australians over a 35-year settlement period in Australia were unknown.

Diabetes prevention and management is one of Australia’s national health priorities.206 People of Vietnamese ethnicity are at increased risk of diabetes47-52 but risk factors, hospitalisation and mortality related to type 2 diabetes in Vietnam-born Australians have not yet been addressed. This PhD thesis is the first population-based study investigating this topic. Findings of the thesis will make a significant contribution to the understanding of the health and outcomes of diabetes among Vietnam-born Australians with diabetes. The thesis provides relevant insights for health services planning and provision of optimal care and informs targeted health education programs and interventions for Vietnam-born Australians with diabetes. Comparisons with Australia-born counterparts have important implications for health inequality research and practice. This final chapter summarises the major findings of the program of research, describes strengths and limitations of the analyses, presents implications for research and practice, and draws general conclusions.

168 Major findings

Vietnamese traditional conceptualisations of health and illness and consequently health-seeking behaviours largely differ from Western biomedicine perspectives. The cultural health beliefs and practices have their roots from Oriental culture, Chinese traditional medicine, and a mixture of religious beliefs. These beliefs and practices are well maintained among Vietnam-born people living in Western countries, regardless of their duration of residence.53, 54, 79, 83, 88, 366 Vietnam-born people in developed countries are less likely to use health care services, especially preventative medicine and mental health services, than other immigrant populations because of communication, language and socio-economic barriers, cultural norms and stigma.86, 103, 110 Vietnamese traditional health beliefs and practices might not pose direct barriers to the use of health services,75, 83 but whether cultural health beliefs and practices mediate the use of health services and thus impact on health outcomes is not known.

It has been demonstrated throughout this program of research that Vietnam-born Australians are socio-economically disadvantaged compared to native-born Australians across indicators including level of educational qualifications, household income, and possession of private health insurance. Most Vietnam-born residents of NSW live in metropolitan but socio-economically disadvantaged areas such as Fairfield, Bankstown, Auburn, Marrickville, Canterbury, Liverpool and Strathfield LGAs. However, it should be acknowledged that the Vietnam-born population could also be socio-economically diverse, depending on the migration circumstances, as discussed in Chapter 3 and Chapter 4. Consistent with previous reports,62, 63 this thesis found health inequalities between the Vietnam- and Australia-born populations in perceived quality of life, health status, and mortality.

Chapter 4 provided some evidence of the impact of acculturation on lifestyle and health of Vietnam-born people. Higher levels of acculturation were associated with a higher frequency of eating meat, fruits and dairy products but less seafood and vegetables. Higher levels of social interaction related to higher levels of physical activity. Acculturated individuals were less likely to report poor health, psychological distress, physical limitation, type 2 diabetes and high blood pressure.

169 Although most of the relationships between acculturation measures and health status were not statistically significant, overall the findings suggested health benefits of migration to Australia for Vietnam-born people. Similar positive effects of acculturation might not necessarily be expected for voluntary immigrants or those whose country of origin had a similar or higher standard of living to the country of arrival. For example, acculturated Latino Americans have been found to have increased prevalence of cancer, mental illness, cigarette smoking, alcohol use and overweight and obesity.37 Japanese immigrants in the USA experience increased risks and occurrence of coronary heart disease.20 Australian researchers have reported a “morbidity mortality paradox”180, 367 among Greek and Italian immigrants in Australia whereby these immigrants have low mortality rates from many cancers and cardiovascular disease despite a high prevalence of cardiovascular risk factors such as obesity, diabetes, high blood pressure and high cholesterol.180, 367 These health advantages are largely explained by retention of traditional Mediterranean cuisine and foods, which are believed to contain large amount of antioxidants.367, 368 The practices of traditional diets among these immigrants is also believed to be a benefit of the immigrants clustering in the same neighbourhood areas which creates supportive environments.180

Consistent with earlier studies of other Vietnam-born populations,35, 208 Vietnamese men in the current research demonstrated low levels of alcohol drinking and smoking, but acculturated men (≤40 years at immigration) were more likely to smoke cigarettes and drink alcohol. However, this finding could relate to “age, cohort and period effects”, whereby smoking rates could be higher among Vietnam men immigrating between 1982 and 1988, or that smoking cessation campaigns have been effective among groups of Vietnam-born participants.

In the context of the global increase in diabetes, this thesis found that the prevalence of type 2 diabetes (crude rate 12.9%, age-standardised 11.2%) in Vietnam-born Australians (age ≥45 years) was higher than recently reported estimates for Vietnam- born adults in NSW, Australia (9.7%),63 USA (7.3%),310, 311 and Norway (8.0%),312 but similar to figures from Vietnam (10.8% in men and 11.7% in women).59 These disparities in prevalence estimates of diabetes could be attributable to different age distributions of the populations under investigations and greater opportunities of 170 diabetes being detected under the Australian universal health care system. Following adjustment for age, the aged-standardised prevalence of type 2 diabetes in ageing Vietnam-born Australians was 1.6 times (95%CI=1.31-1.90) higher than that of native-born Australians (11.2% vs 7.1%). A greater percentage of Vietnam-born people with a family history of diabetes reported having type 2 diabetes than Australia-born people (26.3% vs 15.8%). Although no causal inferences could be made using these cross-sectional data, this finding supports Vietnamese ethnicity itself being a risk factor of diabetes.47-52 The findings of Chapter 5 were in line with existing literature on the health and quality of life burden of diabetes. Having diabetes was negatively and significantly associated with poorer SES, and poorer health status of both Vietnam- and Australia-born populations, but to a greater extent in the Vietnam-born group given the similar duration of diabetes. Among Vietnam- and Australia-born people with type 2 diabetes, there was a high prevalence of self- rated fair and poor health (54.8% and 32.3%), fair and poor quality of life (50.6% and 20.7%), high blood pressure (62.1% and 62.7%), and moderate and severe limitation of physical functioning (65.9% and 62.9%). Consistent with other studies in Vietnam-born populations,59, 193, 194, 312 this chapter also found that BMI-based overweight and obesity (BMI ≥23.0 kg/m2) among Vietnam-born people was not a strong risk factor for diabetes. Researchers have suggested that instead the waist-hip ratio should be used to identify Vietnam-born people at risk of diabetes.59

Longitudinal analyses of population-based hospitalisation and mortality data in Chapter 6 found that compared to Australia-born counterparts, Vietnam-born patients were less likely to use hospital services for diabetes complications and comorbidities but had significantly higher risks of all-cause and diabetes-specific mortality. The Vietnam-born patients had substantially poorer health status, especially higher rates of high blood pressure, ESKD and other diabetes comorbidities. However, interpretations of ESKD prevalence in Vietnam-born patients require precautions because the estimate was based on people admitted for only medical reasons relating to type 2 diabetes. There is also possibility that people were carrying out dialysis procedures at home, and thus rates of ESKD derived from hospital morbidity data could be underestimated.

171 Results of Chapter 6 were interpreted in the context of the Vietnam-born people’s Oriental cultural heritage, traditional conceptualisation of health and practices (Chapter 2), research evidence about the inadequate levels of knowledge and management of diabetes,53-55 and the Andersen and Newman’s Behavioral Model of Health Service Use.325, 326 The findings of Chapter 6 suggest that issues of late access to health care may contribute to the underlying higher mortality from diabetes in this population. A lack of accessibility of health services for Vietnam-born people could be another explanation for the disparities between Vietnam- and Australia-born patients with type 2 diabetes.

Limitations and strengths

Although using a population-based approach, the program of research presented in this PhD thesis is not without limitations. The use of cross-sectional data (45 and Up Study baseline questionnaire) to investigate the impact of acculturation limited the inferences that could be drawn about causal relationships. Duration of residence, age at immigration and density of Vietnam-born populations in LGAs were used as proxies of acculturation based on the assumption of a linear relationship between these variables and levels of acculturation. The use of the DSSI social interaction scale could not differentiate the specific cultural networks of Vietnam-born people. It was unknown whether Vietnam-born participants in the 45 and Up Study maintained their social networks with people from Vietnamese speaking backgrounds or had networks with other people who were born in Australia or in another country. It was not possible to model the impact of English language proficiency because all of the Vietnam-born participants had completed the 45 and Up Study questionnaire in English and no rating of English proficiency was available. Vietnam-born participants in the 45 and Up Study were not a random population sample of Vietnam-born Australians, being older, wealthier and healthier than average Vietnam-born people in NSW (Chapter 3). Therefore, it is uncertain if a replication of analyses in Chapter 4 using a different sample would provide the same results.

The prevalence of type 2 diabetes in Vietnam-born people reported in Chapter 5 could be best generalised in the ageing population of Vietnam-born Australians (≥45 years). Diabetes status was defined based on self-report data, which may result in an 172 underestimated prevalence due to non-reporting of undiagnosed cases. According to the AusDiab Study,205 there was one undiagnosed case of diabetes for every known case. However, the population-based coverage and comprehensive baseline questionnaire data of the 45 and Up Study enabled this research to investigate multiple relationships among a wide range of explanatory variables and health- related outcomes in Vietnam-born Australians. In addition, questionnaire data can be further linked to other health-related records such as hospital admissions, emergency department attendances, mortality and Medicare claims data, thus allowing complex longitudinal and other research questions. One of these benefits is the validation of country of birth information recorded in hospital morbidity data.343

In chapter 6, Vietnam- and Australia-born admitted patients were identified from a single field, country of birth, in APDC data. As presented in Appendix 6, the accuracy of country of birth recording in APDC data was generally high, but varied according to characteristics of hospitals, countries, and overseas-born people’s duration of residence and age at immigration. The validity of methods (the most recent hospitalisation record) used to ascertain Vietnam and Australia country of birth in APDC data remains uncertain. Misclassification of Vietnam and Australia country of birth exist, but to a minor degree, thus are unlikely to introduce bias in relative analyses between Vietnam- and Australia-born patients. In addition, precaution should be taken while interpreting results relating readmission due to a lack of adequate power of this particular analysis.

The use of comparison groups of Australia-born people, who represent the majority of the Australian population (75%),2 not only facilitated the interpretation of findings and thus generalisability, but also assisted to unpack inequality issues relating to socio-economic positions, lifestyles and health status between Vietnam-born and the mainstream population, therefore targeting potential interventions. The use of whole- of-population data about hospitalisation and death from NSW – the Australian state with the largest Vietnam-born population3 – in Chapter 6 enabled the recruitment of the maximum number of Vietnam-born residents in NSW who were admitted to hospital for reasons related to type 2 diabetes during the study observation period and their mortality information, thus providing strong statistical power while minimising

173 bias. This is the largest reported study to date of hospitalisation and mortality among Vietnam-born Australians with type 2 diabetes.

Despite a longitudinal design to assess hospitalisation and mortality among Vietnam- born people with type 2 diabetes, the average length of follow-up (3.5 years) in the current research was relatively short given the chronic nature of diabetes. However, during this follow-up period, approximately 35% of Vietnam- and Australia-born patients with type 2 diabetes died, indicating that hospitalisation is a marker of severe and end-stage illness in people with diabetes. Health risk factors presented in Chapter 6 were identified from diagnoses recorded in hospital admissions, thus these factors may have been under-enumerated due to rules of clinical coding of the discharge summary.364 Nevertheless, the use of a three-year lookback period improved the ascertainment of health risk factors while having no impacts on the outcome estimates.

Hospitalisation data available for this study covered only an eight year period from 1 July 2000 to 30 June 2008, therefore, a 13-year clearance period as previously reported348 could not be implemented in these analyses. The cohorts of Vietnam- and Australia-born patients were more likely to represent prevalent cases who may have had prior hospital admissions for type 2 diabetes. It is not known whether there are differences between Vietnam- and Australia-born prevalent cases in key features such as severity and duration of diabetes. Therefore, different results may have been obtained if the cohorts of patients had been selected on the basis of first admission to hospital for reasons related to type 2 diabetes.

Important confounding and mediating factors such as duration of diabetes and management of diabetes were not taken into account in the analyses relating to hospitalisation and mortality. However, the use of independent sources of data in this program of research (the 45 and Up Study, population-based hospital morbidity and mortality data) allowed a determination of the consistency and robustness of findings across indicators including demographic and socio-economic characteristics, and health status of Vietnam-born people against Australia-born people.

174 Implications for practice and future research

Findings from this thesis have a number of implications for practice and further research. Consistent with a previous report,62 Vietnam-born people in this research were less likely to have an adequate intake of vegetables and physical activity compared to the general Australian population. Health education programs to promote healthy lifestyle among Vietnam-born Australians are important. The Multicultural Health Week program was launched in 2009 by the NSW Department of Multicultural Communication in partnership with the NSW Department of Health, State and local libraries (State Library and MyLanguage) and Special Broadcasting Service, SBS.369 This program aims to raise public awareness of the health of CALD communities, promote a healthy lifestyle such as no smoking, physical activity and healthy diets, and inform CALD groups about the availability and accessibility of multicultural services. Leaflets in community languages including Vietnamese were available during the Week and can be downloaded from the website (http://www.multiculturalhealthweek.com). The Open Day for the Vietnamese community was well-received.370 Education on regular basis via community media sources, local libraries, health stalls at community events, outreach programs of Community Health Services of Local Health Districts, and especially by GPs, can provide further benefits to increase the awareness and understanding of the importance of healthy lifestyle among Vietnam-born Australians, and thus modifications of health-related behaviours.

Early detection and optimal control of diabetes can reduce its burden on people with diabetes and their family. Being of Vietnamese ethnicity might increase the risk of diabetes in not only the first but also the second and following generations of Vietnam-born people. In addition to a healthy lifestyle, a patient-centred proactive approach to diabetes is important in Vietnam-born people who are at risk of diabetes including those who are overweight or have a high waist-hip ratio; women with a history of gestational diabetes or family history of diabetes; those with cardiovascular disease such as myocardial infarction, angina, stroke or peripheral vascular disease; and those on certain medications such as glucocorticoids and antipsychotics.214 Regular visits to a family doctor for health checks are beneficial for early detection of impaired glucose tolerance, diabetes and other illnesses. 175 The current research suggests that compared to Australia-born people with diabetes, Vietnam-born people are at a higher risk of cardiovascular and chronic kidney disease due to higher prevalence of high blood pressure. Monitoring and controlling blood sugar levels, blood pressure, cholesterol and lifestyle modifications are major goals of diabetes management and prevention of micro- and macro-vascular disease. According to the current Australian guidelines for diabetes management,214 people with diabetes should be reviewed quarterly for glycaemic control (HbA1c), blood pressure, body weight, and lifestyle modification, and annually for diabetes management goal achievement and examination for diabetes complications by a GP or practice nurse. GP referrals to an optometrist or ophthalmologist, dietician and diabetes educator, podiatrist, pharmacist for medication reviews, oral health professionals, and diabetes specialist should be made annually, bi-annually, or when necessary.214

The findings that Vietnam-born patients had poorer health status and mortality outcomes but were less likely to use hospital services than their Australia-born counterparts suggest a lack of accessibility to health services or low levels of perceived health needs among Vietnam-born people with diabetes. These possible reasons are well supported by previous research evidence and highlight a need for health education delivered to Vietnam-born people with diabetes should not only focus on explanations of diabetes, risk factors and complications but also on the importance of self-management and team care management to prevent diabetes complications. A high level of needs for diabetes self-management education by Vietnam-born Americans has recently been reported.22

Vietnamese culture emphasises the family unit, which has a hierarchical structure. This suggests that both Vietnam-born people with diabetes and their family members should be involved and encouraged to actively participate in diabetes management plans, so that any social issues such as language, finance, transport and social support can be identified and dealt with appropriately. For Vietnam-born men with diabetes, education given to their wives and daughters about diet is very beneficial, because preparing and cooking meals remains the responsibility of Vietnamese women. Diets for Vietnam-born people with diabetes are of particular importance because rice (which has a high GI)315 is the main source of carbohydrates in the Vietnamese 176 meal,36, 145, 146 yet a low GI diet is recommended for people with diabetes.232, 236, 239, 240 In addition, given a limited level of English proficiency among many Vietnam- born people, communication with non-Vietnamese-speaking health professionals requires a professional interpreter or an English-speaking family member (children or grandchildren).365 A previous study reported that Vietnam-born people were more likely to be satisfied with care, and receive health education in visits with either professional health interpreters or amateur interpreters.117 Furthermore, there is a culture-driven fear or avoidance of confronting health professionals among many Vietnamese immigrants, especially the elderly, thus the involvement of another family member, who is often younger and more acculturated, as a patient advocate could improve doctor-patient communication and promote better patient outcomes. When caring for Vietnam-born people with diabetes, health professionals should also consider the patient’s cultural beliefs and health practices such as use of complementary medicines. There is also a need for GPs to not only provide expert medical advice but also encourage and show professional interest in the outcomes of patient self-management. There is research evidence that Vietnam-born patients largely rely on their GPs for health education.106, 115 People with type 2 diabetes tend to decrease self-monitoring blood sugar due to a belief that health professionals become less interested in the results of self-monitoring.371

Research into how Vietnam-born people living in developed countries manage their diabetes has mainly used qualitative approaches54, 365 or cross-sectional designs with small sample sizes55 Given that Vietnam-born Australians are ageing, at higher risk of diabetes, and generally have lower socio-economic and poorer health status, there is an impetus for future quantitative studies to focus on self-management of diabetes (such as self-monitoring of blood sugar, diets and physical activity); receipt of primary care for diabetes (such as regular visits to GPs or practice nurses, quarterly and annual reviews of diabetes); use of multidisciplinary health care services for diabetes (such as an optometrist or ophthalmologist, diabetes specialist, podiatrist and other allied health professionals) whether initiated by patients or referred by a GP; compliance with diabetes treatment plans (such as blood sugar monitoring, use of medications for diabetes, high blood pressure and cholesterol); and health-related outcomes (such as health status, quality of life, emergency attendance or admission to hospitals, and mortality). 177 Routinely collected administrative health data such as data on hospital admissions and emergency department attendance, registries of diseases, general practice databases, and health services payment and claims data, coupled with health record linkage techniques, provide a powerful and inexpensive resource for disease surveillance, epidemiological and health services research,337, 372, 373 and studies of the effectiveness of interventions.374, 375 In addition to the administrative and research data sources used in this program of research including the 45 and Up Study, APDC and RBDM, other data sources such as NDSS registry database, NSW Emergency Department Data Collection (EDDC), NSW Perinatal Data Collection (PDC), MBS and PBS databases could potentially be used to investigate diabetes management and health outcomes in Vietnam-born Australians. The NDSS registry database contains information about the delivery of diabetes-related products and support services to people who are registered on the NDSS.318 The EDDC captures presentations to public hospital emergency departments for a substantial proportion of the NSW population. As of 2010, there were 90 emergency departments (out of 150 in NSW) participating in the EDDC. The recorded information includes patient demographics, mode of arrival, triage category, mode of separation, service referred to on separation, diagnoses and procedures.328 The PDC covers all births in NSW public and private hospitals, as well as homebirths. Apart from maternal demographic characteristics, the data contain medical and obstetric diagnoses, and information about the labour and delivery and condition of the infant.328 The MBS and PBS data include patient demographics (age, gender, postcode), date of the service, item number for the service (for example GP, specialist, allied health consultation, pathology, type of prescription, name and dose of medications) and the amount charged by the provider and Medicare benefit for the service. Vietnam-born people with diabetes can be identified retrospectively and prospectively from linked records (NDSS, APDC, EDDC, PDC and RBDM). Management of diabetes could be derived from NDSS, MBS and PBS data. A range of health-related outcomes could be identified from APDC, EDDC, PDC and RBDM data.

This thesis has found that Vietnam-born patients with diabetes had a higher risk of diabetes-related mortality than Australia-born patients. This finding indicates that the underlying rates of mortality from diabetes in Vietnam-born patients might be higher than in Australia-born patients. Currently, this information is unknown; thus, further 178 investigations of variations in diabetes-related mortality rates according to country of birth, using population-based data such as ABS mortality and Census data, are important to inform health equality intervention programs.

Conclusions

Vietnam-born Australians represent an important CALD community who share the distinct Oriental traditional conceptualisation of health and illness, and health practices. In contrast to findings from other groups of selective and voluntary immigrants, acculturation was found, in this program of research, to be a beneficial factor towards the health and perceived quality of life of ageing Vietnam-born Australians, who shared forced migration circumstances and substantial length of residence in their new homeland. The effect of acculturation on health-related behaviours including changes in dietary patterns, physical activity, cigarette smoking and alcohol drinking were also evident. Although the majority of Vietnam-born people reside in urban areas, this population is socio-economically and health-wise disadvantaged compared to their Australia-born counterparts. Vietnam-born people had significantly higher rates of type 2 diabetes, and substantially poorer diabetes- related health status and mortality, but were less likely to be admitted to hospital for diabetes complications and comorbidities than Australia-born counterparts. The findings of this program of research have implications for health education about healthy lifestyle and proactive diabetes management in this population. Given diabetes is a chronic condition, early detection and quality of care can be highly beneficial for Vietnam-born Australians with diabetes. A patient-centred approach to the management of diabetes with the involvement of family members could provide additional positive outcomes. This research has demonstrated the value of record linkage of already available, population-based health administrative data for investigating diabetes management and associated health outcomes among overseas- born Australians.

179

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Appendices

Appendix 1 The 45 and Up Study baseline questionnaire

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Appendix 2 Insulin products available in Australia

Keywords to Insulin type Commercial name searchҚ NovoRapid (insulin aspart) Rapid onset–fast acting Insulin Humalog (insulin lispro) insulin NovoRapid Apidra (insulin gluisine) Aspart Humalog Actrapid Lispro Short acting insulin Humulin R Apidra Hypurin Neutral (beef) Gluisine Actrapid Hypurin Isophane (beef) Humulin Intermediate acting insulin Protaphane Hypurin Humulin NPH Isophane Protaphane NovoMix 30 NovoMix Humalog Mix 25 Humalog Pre-mixed insulin Mixtard 30/70 Mixtard Lantus Mixtard 50/50 Glargine Humulin 30/70 Levemir Detemir Lantus (insulin glargine) Long acting insulin NovoPen Levemir (insulin detemir) AutoPen NovoPen® 3 HumaPen InnoLet NovoPen® 3 Demi Durable devices FlexPen AutoPen® NovoLet HumaPen Luzura Full and Half Solostar Insulin Dose® KwikOen

delivery InnoLet® devices FlexPen® Pre-filled NovoLet® disposable Solostar® KwikOen® Қ: The search for the keyword also allowed variations in spelling

209 Appendix 3 Oral hypoglycaemic agents available in Australia

Commercial / Keywords to Class and Effects Chemical name Brand names searchҚ Diabex Biguanides Diaformin ‐ Reduce glucose Formet released by the liver Glucohexal Diabex Slow intestine glucose ‐ Glucomet Diaformin absorption Metformin Glucophage Formet Increase insulin ‐ (immediate release) Metformin-BC Glucohexal sensitivity Genrx metformin Glucomet Genepharm Glucophage Metformin Metformin Metforbell Metforbell Metex Metex

Metformin Diabex XR (Extended Release) Diaformin XR

Diamicron Sulphonylureas Glyade ‐ Stimulates insulin Mellihexal Gliclazide secretion Nidem Gliclazide Diamicron Genrx gliclazide Glyade Diamicron MR Mellihexal Glyade MR Nidem Oziclide Oziclide Glibenclamide Daonil Daonil Glibenclamide Glimel Glimel Glipizide Melizide Melizide Glipizide Minidiab Minidiab Glimepiride Amaryl Amaryl Dimirel Dimirel Aylide Glimepiride Aylide Diapride Diapride Glimepiride

Thiazolidinediones Rosiglitazone Avandia (glitazones) Avandia Increase insulin effects ‐ Actos ‐ Improve insulin Pioglitazone Actos resistance

Meglitinides Novonorm Novonorm Stimulate insulin Repaglinide ‐ Prandin Prandin production

210 Commercial / Keywords to Class and Effects Chemical name Brand names searchҚ

Alpha Glucosidase Glucobay Glucobay Inhibitor Acarbose Precose Precose Slow intestine glucose ‐ Glyset Glyset absorption

Inhibitor Dipeptidyl Peptisase IV (DPP-4) Sitagliptin Januvia ‐ Enhance the strength and increase active Januvia levels of incretin Galvus hormones which

stimulate pancreas insulin production and Vildagliptin Galvus reduce liver glucose production

Exenatide An “incretin mimetic”, Byetta ‐ Exenatide Byetta (injection) stimulating the GLP-1 Exenatide receptor.

Metformin AND Glucovance Pre-mixed tables Glibenclamide

Metformin AND Avandamet Glucovance Rosiglitazone Avandamet Janumet Metformin AND Janumet Galvumet Sitagliptin

Metformin AND Galvumet Vildagliptin

Қ: The search for the keyword also allowed variations in spelling

211 Appendix 4 Sensitivity and specificity in identifying insulin products and oral hypoglycaemic agents from the free-text field

False positive for medications detected by SAS syntax

Number of Number of False positive

records reviewed false positives rates

Insulin products 2,263 3 0.13% Biguanides 1,093 4 0.37% Sulphonylureas 3,616 7 0.19% Thiazolidinediones 848 9 1.06% Meglitinides 8 0 0.00% Alpha glucosidase inhibitor 72 0 0.00% DPP-4 inhibitor 50 0 0.00% Exenatide 17 0 0.00% Pre-mixed tablets 187 1 0.53%

False negative for nil medications detected by SAS syntax in participants who reported diabetes

Number of record, Number of False negative

reviewed false negative, rates

Insulin products and OHAs 9,446 160 0.13%

212 Appendix 5 Equations of crude prevalence, age-standardised prevalence, age- standardised prevalence ratio, ratio of odds ratios and 95%CIs

Calculation of crude prevalence, age-standardised prevalence, age-standardised prevalence ratio and variance

ri Crude prevalence pi = (1) ni

N pii Age-standardised prevalence P =  (2) N  i N 2 p 1( p /) n Variance of age-standardised  ii  i i  var(P) = 2 (3) prevalence N  i P Age-standardised prevalence ratio SPR =  (4) P  var P var P Variance of log (SPR) var(logSPR)=    +  (5) P 2 P 2  

Where:

pi is the age-group specific rate for age group i in the study sample

ri is the number of observed cases for the age group i in the study sample

ni is the number of people for the age group i in the study sample

Ni is the total population for the age group i in the standard population

PA is the age-standardised prevalence for population A as per equation (2)

var(PA) is the age-standardised prevalence for population A as per equation (3)

PB is the age-standardised prevalence for population B as per equation (2)

var(PB) is the age-standardised prevalence for population B as per equation (3)

213 Calculation ratio of odds ratios, 95% CI and P value

Population A Population B

Steps in calculation Adjusted ORA Adjusted ORB

95%CIA(ORA): LLA-ULA 95%CIB(ORB): LLB-ULB

Transforming to log scale

Natural logarithms of odds 1. E = log (OR ) E = log (OR ) ratio (E) A A B B

2. Width of CI of odds ratio (W) WA = log LLA - logULA WB = log LLB - logULB

W W 3. Standard error (SE) SEA =  SEB =  2  96.1 2  96.1 Difference of log odds ratios 4. d = E – E (d) A B Standard error of log 5. SE(d) = SE 2 SE 2 difference (SE(d))    95%CI of log difference 6. 95%CI(d) = d ± 1.96 SE(d) (CI(d)) 

Test of the log difference

Standardised normal deviate d 7. z = (z) SE d)(

8. P value Table of normal distribution (two-tailed)

Transforming to odd ratio scale OR 9. Ratio of odds ratios (ROR) ROR = exponential(d) or ROR =  OR  95%CI (ROR) = exponential lower and upper limits 10. 95%CI for ROR of CI(d) as per step 6

Where: A and B denote population A and B respectively LL and UL denote lower and upper limit of 95%CI

214 Appendix 6 A publication from thesis findings

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221 Appendix 7 ICD-10-AM diagnostic codes

Conditions ICD-10-AM codes Weight♦ Diabetes and comorbidities Diabetes without complications E10.9, E11.9, E13.9, E14.9 Diabetes complications Hyperosmolarity E10.0, E11.0, E13.0, E14.0 Ketoacidosis E10.1, E11.1, E13.1, E14.1 Renal complications E10.2, E11.2, E13.2, E14.2 Ophthalmic complications E10.3, E11.3, E13.3, E14.3 Neurological complications E10.4, E11.4, E13.4, E14.4 Circulatory complications E10.5, E11.5, E13.5, E14.5 Musculoskeletal and connective E10.61, E11.61, E13.61, E14.61,

tissue complications E10.62, E11.62, E13.62, E14.62 Periodontal complications E10.63, E11.63, E13.63, E14.63 Hypoglycemia E10.64, E11.64, E13.64, E14.64 Poor control E10.65, E11.65, E13.65, E14.65 Multiple micro-vascular E10.71, E11.71, E13.71, E14.71 complications Foot ulcer due to multiple causes E10.73, E11.73, E13.73, E14.73 E10.69, E11.69, E13.69, E14.69, Other specified complications E10.72, E11.72, E13.72, E14.72 Unspecified complication E10.8, E11.8, E13.8, E14.8 Cardiovascular disease Ischemic heart disease I20 - I25 Congestive heart failure I50, I11 Cerebrovascular disease I60 - I67, I69, G45, H34.0, R47.0, G46 I70 - I79, I87.2, I99, R02, Peripheral vascular disease Z95.5, Z95.8, Z95.9 Cardiovascular disease I51.6 unspecified Cataract, glaucoma H25, H26, H28.0, H40, H42.8 I12, N00, N01, N03, N04, N05, Glomerulonephritis, nephropathy N06, N07, N08, N18, N19 Neuropathy G59 Chronic skin ulcer, cellulitis I89, L03, L97, L98.4, R02 Dialysis Z49 Health risk factors Smoking F17, Z71.6, Z72.0, Z86.43 Hypertension I10 - I15, O10, O11 Cholesterol E78.0, E78.2, E78.4, E78.5 Obesity E65, E66 222 Conditions ICD-10-AM codes Weight♦ Charlson index conditions Acute myocardial infarction I21, I22, I25.2 1 Congestive heart failure I50 1 Peripheral vascular disease I71, I79.0, I73.9, R02, Z95.8, Z95.9 1 G46, G45.0, G45.1, G45.2, G45.4, Cerebral vascular disease G45.8, G45.9, I60 - I66, I67.0 - I67.2, 1 I67.4 - I67.9, I68.1, I68.2, I68.8, I69 Dementia F00, F01, F02, F05.1 1 Pulmonary disease J40 - J47, J60 - J67 1 M05.0 - M05.3, M05.8 - M06.0, M32, Connective tissue disorder 1 M34, M33.2, M06.3, M06.9, M35.3 Peptic ulcer K25 - K28 1 K70.2, K70.3, K71.7, K73, Liver disease 1 K74.0, K74.2 - K74.6 E10.9, E11.9, E13.9, E14.9, Diabetes complications♪ E10.1, E11.1, E13.1, E14.1, 2 E10.5, E11.5, E13.5, E14.5 Paraplegia G04.1, G81, G82.0 - G82.2 2 N01, N03, N05.2 - N05.6, Renal disease 2 N07.2 - N07.4, N18, N19, N25 Severe liver disease K72.1, K72.9, K76.6, K76.7 3 C00 - C39, C40, C41, C43, C45 - C49, C50-C59, C60-C69, C70-C76, Cancer C80-C85, C88.3, C88.7, C88.9, 6 C90.0, C90.1, C91-C93, C94.0-C94.3, C94.51,C94.7, C95, C96 Metastatic cancer C77 - C80 6 HIV B20 - B24 6 ♦: Weighted accumulative score for Charlson index condition ♪: Excluded in the calculation of the Charlson index score in this thesis

223 Appendix 8 Comparison of adjusted rate ratios (95%CI) of readmissions for diabetes and comorbidities by methods used to identify health risk factors

Full adjustment model# Three-year lookback period Index admission Country of birth Australia-born‡ 11 Vietnam-born 0.81 (0.64-1.03) 0.81 (0.64-1.03) Sex Male‡ 11 Female 0.87 (0.83-0.91) 0.87 (0.83-0.91) Age (years) Under 50‡ 11 50-59 1.38 (1.27-1.51) 1.41 (1.29-1.54) 60-69 1.67 (1.53-1.81) 1.69 (1.55-1.83) 70-79 1.90 (1.75-2.07) 1.90 (1.75-2.06) 80+ 1.84 (1.68-2.01) 1.80 (1.64-1.97) Relationship No partner‡ 11 Partner 0.86 (0.82-0.90) 0.86 (0.82-0.90) Patient public/private Public patient‡ 11 Private patient/Other 0.95 (0.90-1.00) 0.95 (0.90-1.00) IRSD of residence Less disadvantaged‡ 11 More disadvantaged 1.12 (1.06-1.17) 1.12 (1.07-1.17) Hospital peer group Principal referral‡ 11 Major 1.00 (0.94-1.07) 1.00 (0.93-1.06) Other 0.99 (0.93-1.05) 0.96 (0.91-1.02) Charlson index Zero‡ 11 1 to 2 1.40 (1.33-1.47) 1.46 (1.39-1.53) ≥3 1.71 (1.60-1.83) 1.85 (1.74-1.97) High blood pressure No/Unknown‡ 11 Yes 1.08 (1.03-1.13) 0.99 (0.94-1.04) High cholesterol No/Unknown‡ 11 Yes 1.14 (1.08-1.21) 1.03 (0.96-1.10) Smoking No/Unknown‡ 11 Current/Ex-smoker 1.11 (1.04-1.18) 1.08 (1.02-1.14) Obese No/Unknown‡ 11 Yes 1.13 (1.07-1.18) 0.99 (0.90-1.07) Emergency status Emergency‡ 11 Planned/Other 1.12 (1.06-1.18) 1.11 (1.05-1.17) 0.25 1 2 4 0.25 1 2 4 #: Adjusted for predisposing, enabling and need factors ‡: Reference category Forest graphs: The dots represent rate ratios, the horizontal bars represent the 95%CIs, the bigger dots represent reference categories.

224 Appendix 9 Comparison of adjusted hazard ratios (95%CI) of time to non- dialysis readmissions by methods used to identify health risk factors

Full adjus tme nt mode l# Three-year lookback period Index admission Country of birth Australia-born‡ 11 Vietnam-born 0.94 (0.79-1.13) 0.93 (0.78-1.12) Sex Male‡ 11 Female 1.00 (0.96-1.04) 0.99 (0.96-1.03) Age (years) Under 50‡ 11 50-59 1.22 (1.14-1.32) 1.24 (1.16-1.34) 60-69 1.40 (1.31-1.50) 1.42 (1.33-1.52) 70-79 1.71 (1.60-1.83) 1.72 (1.61-1.84) 80+ 1.76 (1.64-1.89) 1.74 (1.62-1.87) Relationship No partner‡ 11 Partner 0.94 (0.90-0.98) 0.94 (0.90-0.98) Patient public/private Public patient‡ 11 Private patient/Other 1.01 (0.97-1.06) 1.01 (0.97-1.05) IRSD of residence Less disadvantaged‡ 11 More disadvantaged 1.07 (1.02-1.11) 1.07 (1.03-1.11) Hospital peer group Principal referral‡ 11 Major 0.95 (0.90-1.00) 0.94 (0.89-0.99) Other 0.93 (0.88-0.97) 0.90 (0.86-0.94) Charlson index Zero‡ 11 1 to 2 1.38 (1.32-1.44) 1.42 (1.36-1.48) ≥3 1.67 (1.58-1.77) 1.76 (1.66-1.86) High blood pressure No/Unknown‡ 11 Yes 1.04 (1.00-1.09) 0.96 (0.92-1.00) High cholesterol No/Unknown‡ 11 Yes 1.08 (1.03-1.13) 0.99 (0.94-1.05) Smoking No/Unknown‡ 11 Current/Ex-smoker 1.05 (0.99-1.11) 1.04 (1.00-1.09) Obese No/Unknown‡ 11 Yes 1.14 (1.09-1.19) 1.02 (0.95-1.09) Emergency status Emergency‡ 11 Planned/Other 1.07 (1.02-1.12) 1.06 (1.02-1.12)

0.25 1 2 4 0.25 1 2 4 #: Adjusted for predisposing, enabling and need factors ‡: Reference category Forest graphs: The dots represent hazard ratios, the horizontal bars represent the 95%CIs, the bigger dots represent reference categories.

225 Appendix 10 Comparison of adjusted hazard ratios (95%CI) of time to mortality for all causes of death by methods used to identify health risk factors

Full adjus tme nt mode l# Three-year lookback period Index admission Country of birth Australia-born‡ 11 Vietnam-born 1.42 (1.07-1.88) 1.40 (1.06-1.86) Sex Male‡ 11 Female 0.97 (0.91-1.03) 0.95 (0.90-1.01) Age (years) Under 50‡ 11 50-59 0.96 (0.80-1.16) 0.96 (0.80-1.16) 60-69 1.06 (0.90-1.26) 1.04 (0.88-1.23) 70-79 1.19 (1.01-1.41) 1.15 (0.98-1.36) 80+ 1.51 (1.27-1.78) 1.44 (1.22-1.70) Relationship No partner‡ 11 Partner 0.98 (0.92-1.04) 0.98 (0.93-1.04) Patient public/private Public patient‡ 11 Private patient/Other 1.01 (0.94-1.08) 1.01 (0.94-1.08) IRSD of residence Less disadvantaged‡ 11 More disadvantaged 1.00 (0.94-1.06) 0.99 (0.94-1.06) Hospital peer group Principal referral‡ 11 Major 0.99 (0.91-1.07) 0.99 (0.92-1.08) Other 0.97 (0.90-1.04) 0.95 (0.88-1.02) Charlson index Zero‡ 11 1 to 2 1.30 (1.21-1.38) 1.33 (1.25-1.42) ≥3 1.75 (1.62-1.89) 1.84 (1.71-1.98) High blood pressure No/Unknown‡ 11 Yes 1.08 (1.01-1.15) 1.03 (0.96-1.09) High cholesterol No/Unknown‡ 11 Yes 1.05 (0.97-1.14) 1.03 (0.93-1.14) Smoking No/Unknown‡ 11 Current/Ex-smoker 1.03 (0.94-1.14) 1.07 (0.99-1.15) Obese No/Unknown‡ 11 Yes 1.17 (1.10-1.25) 0.96 (0.84-1.09) Emergency status Emergency‡ 11 Planned/Other 0.89 (0.82-0.95) 0.89 (0.83-0.96)

0.25 1 2 4 0.25 1 2 4 #: Adjusted for predisposing, enabling and need factors ‡: Reference category Forest graphs: The dots represent hazard ratios, the horizontal bars represent the 95%CIs, the bigger dots represent reference categories.

226 Appendix 11 Comparison of adjusted hazard ratios (95%CI) of diabetes- specific mortality by methods used to identify health risk factors

Full adjus tme nt mode l# Three-year lookback period Index admission Country of birth Australia-born‡ 11 Vietnam-born 1.58 (1.05-2.38) 1.53 (1.02-2.3) Sex Male‡ 11 Female 0.93 (0.85-1.01) 0.92 (0.84-1.00) Age (years) Under 50‡ 11 50-59 0.99 (0.76-1.30) 0.99 (0.76-1.29) 60-69 1.15 (0.91-1.47) 1.13 (0.89-1.44) 70-79 1.44 (1.14-1.83) 1.39 (1.10-1.77) 80+ 1.77 (1.39-2.24) 1.67 (1.31-2.11) Relationship No partner‡ 11 Partner 0.96 (0.88-1.04) 0.96 (0.89-1.05) Patient public/private Public patient‡ 11 Private patient/Other 0.97 (0.89-1.06) 0.97 (0.89-1.06) IRSD of residence Less disadvantaged‡ 11 More disadvantaged 1.02 (0.93-1.10) 1.00 (0.92-1.09) Hospital peer group Principal referral‡ 11 Major 0.96 (0.86-1.08) 0.97 (0.87-1.09) Other 0.97 (0.88-1.08) 0.95 (0.86-1.05) Charlson index Zero‡ 11 1 to 2 1.27 (1.16-1.40) 1.31 (1.19-1.43) ≥3 1.76 (1.59-1.96) 1.86 (1.68-2.06) High blood pressure No/Unknown‡ 11 Yes 1.08 (0.99-1.17) 1.03 (0.94-1.12) High cholesterol No/Unknown‡ 11 Yes 1.05 (0.95-1.17) 0.94 (0.82-1.08) Smoking No/Unknown‡ 11 Current/Ex-smoker 1.13 (0.99-1.28) 1.06 (0.96-1.17) Obese No/Unknown‡ 11 Yes 1.17 (1.07-1.28) 1.00 (0.84-1.20) Emergency status Emergency‡ 11 Planned/Other 0.86 (0.77-0.95) 0.86 (0.77-0.95)

0.25 1 2 4 0.25 1 2 4 #: Adjusted for predisposing, enabling and need factors ‡: Reference category Forest graphs: The dots represent hazard ratios, the horizontal bars represent the 95%CIs, the bigger dots represent reference categories.

227