Health Care Use by Older Australian Women with Asthma

HEALTH CARE USE BY OLDER AUSTRALIAN WOMEN WITH ASTHMA

Parivash Eftekhari (PharmD, PhD)

Thesis submitted for the degree of Doctor of Philosophy (Gender and Health)

School of Medicine and Public Health Faculty of Health

University of Newcastle March 2018

ii Statement of Originality

“I, solemnly and sincerely declare that thesis entitled Quality of care in older women with asthma is my own research work and to the best of my knowledge contains no material that has been published previously or accepted for the award of any other degree in any university or other tertiary institution by me or any other person except where due references and acknowledgements are made. I give consent to the final version of my thesis being made available worldwide when deposited in the University’s Digital Repository**, subject to the provisions of the Copyright Act 1968.”

**Unless an Embargo has been approved for a determined period.

Parivash Eftekhari Date: 29/03/2018

iii Statement of Collaboration

“I hereby certify that the work embodied in this thesis has been done in collaboration with other researchers. I have included as part of the thesis a statement clearly outlining the extent of collaboration, with whom and under what auspices.”

Parivash Eftekhari Date: 29/03/2018

iv Copyright Permission

“I warrant that I have obtained, where necessary, permission from the copyright owners to use any third party copyright material reproduced in the thesis (e.g. questionnaires, artwork, unpublished letters), or to use any of my own published work (e.g. journal articles) in which the copyright is held by another party (e.g. publisher, co-author).”

Parivash Eftekhari Date: 29/03/2018

v Acknowledgements of authorship

“I hereby certify that the work embodied in this thesis contains a published paper/scholarly work of which I am joint author. I have included as part of the thesis a written statement, endorsed by my supervisor, attesting to my contribution to the joint publication/scholarly work.”

Parivash Eftekhari Date: 29/03/2018

vi Thesis publications and presentations

Published manuscripts

1. Eftekhari P, Forder PM, Majeed T, Byles JE. Impact of asthma on mortality in older women: An Australian cohort study of 10,413 women. Resp Med. 2016; 119: 102-108.

2. Eftekhari P, Forder PM, Byles JE. Asthma Cycle of Care uptake among Australian older women with asthma. Intern Med J. 2016; 46(8):990-1.

Conference presentations

1. Eftekhari P, Forder PM, Byles JE. Survival and Comorbidity Association in Older Women with Asthma: Australian Longitudinal Study on Women's Health (ALSWH). American Thoracic Society (ATS) International Conference; 2014 May 16-21 San Diego, California.

2. Eftekhari P, Forder PM, Byles JE. Medical Service Utilization among Australian Older Women with Asthma. American Thoracic Society (ATS) International Conference; 2016 May 13-18 San Francisco, California.

vii Statement of contribution for Chapter 4

viii Conflict of interest

“I hereby declare that there are no conflicts of interests.”

Parivash Eftekhari Date: 29/03/2018

ix Acknowledgements of data use

The research on which this thesis is based was conducted as part of the Australian Longitudinal Study on Women's Health by the University of Queensland and the University of Newcastle. We are grateful to the Australian Government Department of Health for funding and to the women who provided the survey data.

We acknowledge the Department of Health and Medicare Australia for providing the PBS and MBS data. We also acknowledge the Australian Institute of Health and Welfare (AIHW) as the integrating authority for these data.

We acknowledge the assistance of the Data Linkage Unit at the Australian Institute of Health and Welfare (AIHW) for undertaking the data linkage to the National Death Index (NDI).

x Acknowledgement

I am very fortunate to have undertaken this PhD at the University of Newcastle. Although these four and a half years haven’t always been the easiest, they have been the best years of my life. I would like to take this opportunity to thank everyone who played a role in completing this piece of work which is an important step in reaching my goals in life.

Foremost, I would like to thank my supervisors Professor Julie Byles and Ms. Peta Forder for your support, both professionally and emotionally. I am very grateful to you both for your guidance and encouragement over the past four and a half years, you always had time for me in your busy schedules. Without you, this thesis would not have been possible and I thank you for believing in me and giving me this opportunity.

Melissa, although I wasn’t fortunate enough to have you as a co-supervisor from the beginning, I have learned a lot from you. Thank you for your encouragement, and your advice on writing was very helpful.

I would also like to thank Associate Professor Erica James and Dr. Natalie Johnson for their support as Research Higher Degree Coordinators.

Acknowledgements to my friends at the Research Centre for Generational Health and Ageing. I have been very lucky to have all your support over the period of my candidature.

My friends and fellow students at HMRI, Tanmay, Shahinoor, Tazeen, Befikadu, Bernadette, Adam,

Iqbal, Candice, Maha, Shazia, Mijan, Melissa, Smriti and Nanda, I am grateful to you all and I feel privileged to have had you by my side during this journey. We have so many memories together which I will cherish for the rest of my life.

Dr. Robyn Kenough and Dr. Xenia Dolja-Gore, I consider you both friends and mentors, thank you for all your support on this piece of work. I really appreciate the emotional support you two

xi provided for me during my candidature. Robyn, I appreciate your kind offer to proof read my thesis, and Xenia, thank you for all the analysis tips and SAS codes.

Thank you to all the participants and staff of the ‘Australian Longitudinal Study on Women’s

Health’ for your time and effort during all these years, the data you have provided is invaluable.

Mum and Dad, you have always encouraged me to follow my dreams and become a better person.

Now here I am, submitting my thesis for a second PhD. Dad, I know it wasn’t easy to pay for my education and I thank you for the sacrifices you made. Mum, thank you for the countless nights that you stayed up and helped me with my assignments. You both always believed in me and I can assure you that your hard work has paid off.

My appreciation goes to my fiancé’s patents, Martin and Janet Ryan. You have always supported me emotionally and treated me like your daughter.

Jewels of my life, Suzie, Charlie, Dr. Mikie and Cinnamon. Although you are only birds, with your cuteness you give me the energy to go on. Thank you for chirping all the time and giving me company.

Lastly, I want to thank my love, my joy and my happiness, my fiancé, Hayden. Thank you for always being there for me and listening to my ideas, questions, complaints and everything else. You have supported me like no one else, and have been there no matter what. You held my hand this whole time and walked with me step by step through all the joy and pain. You have instilled in me confidence to aim high and never stopped believing in me. Thank you for being self-less and compromising. You are the most amazing person I have ever met and I am the luckiest women in the world to be your fiancé. I love you with all my heart and I would like to dedicate all the hard work that has gone into writing this thesis to you.

xii Contents Chapter 1 Background: Asthma in context ...... 17 Clinical definition of asthma ...... 17 Why is asthma important? ...... 18 1.2.1 Burden of asthma ...... 18 Costs of asthma ...... 19 Asthma mortality ...... 19 Asthma in older age ...... 20 Definition of older age ...... 20 Population Ageing ...... 20 Why is asthma important in older people? ...... 21 Significance of thesis investigations ...... 24 Thesis framework ...... 25 Aims and objectives ...... 25 Structure of this thesis ...... 26 Section 1. Background, study aims and literature review (Chapter 1 and 2) 26 Data sources, methods and analyses ...... 26 Section 2. Asthma and mortality (Chapter 4) ...... 26 Section 3. Asthma groups and self-reported health service use (Chapter 5 and 6) 26 Section 4. Asthma groups and Medicare health service use (Chapter 7 and 8) 26 Chapter 2 Literature review ...... 27 Introduction ...... 27 Asthma Incidence and prevalence ...... 27 Asthma incidence and prevalence among older people ...... 27 Asthma incidence and prevalence according to sex ...... 27 Definition and assessment of asthma in epidemiological studies ...... 28 Under-diagnosis of asthma in older ages ...... 31 Burden of asthma in older people...... 32

1 Asthma mortality among older adults ...... 33 Comorbidities and competing risks ...... 34 Quality of life for older people with asthma ...... 36 Health Related Quality of Life in older women with asthma ...... 37 Health behaviours and other risk factors affecting asthma in older ages ...... 38 Social isolation among older people with asthma ...... 39 Management of asthma in older adults ...... 40 Barriers to asthma therapy in older adults ...... 41 Global guidelines for best practice in asthma management ...... 42 Guidelines for managing asthma in older population ...... 43 Quality health care for people with asthma ...... 43 Optimum asthma treatment ...... 43 Primary health care ...... 44 Primary health services for asthma in Australia ...... 44 Specialist services ...... 46 Asthma Management Plan in Australia ...... 48 Asthma Cycle of Care (3+ Visits) ...... 48 Asthma Action Plans ...... 49 Assessing asthma management and outcomes ...... 50 Health service use by people with asthma ...... 51 Framework to evaluate health service utilisation ...... 52 The Andersen-Newman Behavioural Model of Health Service Utilisation 52 Employment of theoretical framework for drivers of health service use in other studies ...... 56 Use of Andersen-Newman framework in this thesis ...... 57 Drivers of health care use in older people (predisposing, enabling and needs factors) ...... 57 Gaps in literature ...... 59 Chapter 3 Data sources and methods ...... 61 Introduction ...... 61 The Australian Longitudinal Study on Women’s Health (ALSWH) ...... 61

2 Participation and attrition ...... 64 Representativeness of Australian population ...... 65 ALSWH survey variables ...... 65 Asthma, other respiratory diseases and symptoms ...... 65 Self-reported health service use variables ...... 70 Health Related Quality of Life (SF-36) ...... 72 Predisposing, enabling and needs factors affecting health care use by women with asthma ...... 73 Predisposing factors ...... 73 Enabling factors ...... 73 Needs factors ...... 77 Medicare Benefits Schedule (MBS) ...... 80 Linkage of MBS data with ALSWH participants ...... 81 MBS variables for health service use ...... 81 National Death Index (NDI) ...... 95 Statistical analyses ...... 96 Overview of analyses, according to aims and chapters ...... 96 Survival analysis ...... 98 Logistic regression ...... 100 Multinomial regression models ...... 101 Longitudinal analyses...... 102 Ethics approval and data access approval ...... 103 Collection of ALSWH Survey Data ...... 103 Data linkage ...... 103 Approval for projects in this thesis ...... 103 Summary ...... 104 Chapter 4 Impact of asthma on mortality ...... 105 Introduction ...... 105 Asthma case definition ...... 106 Impact of asthma on mortality among women from the 1921-26 cohort .... 107 Impact of asthma on mortality among women from the 1946-51 cohort .... 115

3 Prevalence of asthma and other respiratory conditions among women from the 1946-51 cohort ...... 115 Mortality for women from the 1946-51 cohort between 2001 and 2014 116 Conclusion ...... 119 Chapter 5 Asthma and other conditions and characteristics among women from the 1921-26 and 1946-51 ALSWH cohorts ...... 121 Introduction ...... 121 Identifying women with asthma and other respiratory conditions/symptoms 122 Classification of women according to self-reported asthma and bronchitis/emphysema ...... 126 Breathing difficulties among women, according to asthma groups ...... 127 Predisposing factors, according to asthma group ...... 137 Enabling factors according to asthma group ...... 140 Need factors according to asthma groups ...... 142 Health-related quality of life, according to asthma groups ...... 145 Physical health ...... 145 147 Mental and emotional health ...... 148 Conclusion ...... 151 Chapter 6 Asthma status and self-reported health care utilisation ...... 153 Asthma status and self-reported health service use at survey 1 ...... 154 Asthma groups and GP service use: changes over time ...... 157 Satisfaction with access to GP services ...... 160 Predisposing, enabling and need factors that may affect frequency of GP visits 161 Predisposing factors and frequency of GP visits according to asthma groups 161 Enabling factors and frequency of GP visits according to asthma groups 165 Needs and frequency of GP visits according to asthma groups ...... 168

4 Associations between asthma group, predisposing, enabling and need factors and GP visits ...... 171 Predictors of the frequency of GP visits among women from the 1921-26 cohort 171 Predictors of the frequency of GP visits among women from the 1946-51 cohort 183 Chapter 7 MBS records of health care utilisation according to asthma status..... 197 Introduction ...... 197 Health services relevant to asthma in older people ...... 198 Health services used for ALSWH women according to asthma group ...... 201 GP service use and asthma groups ...... 202 After-hours GP service use ...... 203 Length of GP visits ...... 204 Specialist visits ...... 210 Health assessment claims ...... 211 Chronic Disease Management claims ...... 212 Asthma Cycle of Care (ACC) and asthma groups ...... 213 ACC/CDM items ...... 214 Spirometry tests ...... 217 Conclusion ...... 218 Chapter 8 Determinants of health service use by women with asthma ...... 219 Introduction ...... 219 Associations between asthma groups and total annual GP visit time at survey 3 220 Univariate associations ...... 221 Multivariable and Longitudinal models ...... 225 1921-26 cohort ...... 225 1946-51 cohort ...... 233 Longitudinal analysis for total GP visit time ...... 238 Association between asthma groups and after-hours GP visits ...... 240 Cross-sectional associations (Survey 3) ...... 240 Longitudinal analysis for after-hours GP visits ...... 243

5 Association between asthma groups and specialist visits ...... 246 Cross-sectional nested models at survey 3 ...... 246 Longitudinal analysis for specialist visits ...... 249 Association between asthma groups and ACC/CDM service use ...... 253 Cross-sectional nested models at survey 3 ...... 253 Longitudinal analysis for ACC/CDM ...... 256 Conclusion ...... 260 Chapter 9 Thesis discussion and conclusion ...... 263 Discussion ...... 263 Impact of asthma on mortality in later life ...... 264 Prevalence and incidence of asthma and breathing difficulty and establishing asthma case definition for this thesis ...... 265 Predictors of health service use among older women ...... 266 Pattern of health service use by older women with asthma ...... 267 Association of asthma and health service use among older women ... 269 Policy suggestions ...... 270 Strengths and limitations ...... 271 Suggestions for further research ...... 273 Conclusion ...... 273

6 Table of Tables

Table 3-1. ALSWH survey follow-up for four cohorts, including year of survey, age at survey and number of respondents ...... 63 Table 2. Number of participants, respondents and deaths across surveys for the 1921-26 ALSWH cohort of women (N=12,432) ...... 64 Table 3. Number of participants, respondents and deaths across surveys for the 1946-51 ALSWH cohort of women (N=13,715) ...... 64 Table 3-2. Respiratory related questions for women from the 1921-26 and 1946-51 cohorts from Survey 1-6 ...... 66 Table 3-3. Definition of asthma group for women in 1921-26 cohort ...... 68 Table 3-4. Definition of asthma groups for 1946-51 cohort ...... 69 Table 3-5. Self-reported family doctor/GP visits in a year by women from the 1921-26 (surveys 1-6) and women from the 1946-51 (surveys 1-7) cohorts ...... 70 Table 3-6. Self-reported health service use (other than a GP) by women from the 1921-26 and 1946-51 cohorts at Survey 1 ...... 71 Table 3-7. SF-36 subscales and items in each subscale ...... 72 Table 3-8. Predisposing variables from the ALSWH surveys, 1921-26 cohort (survey 1-6) and 1946-51 cohort (survey 1-7) ...... 74 Table 3-9. Enabling variables from the ALSWH surveys, 1921-26 cohort (survey 1-6) and 1946- 51 cohort (survey 1-7) ...... 75 Table 3-10. Needs variables from the ALSWH surveys, 1921-26 cohort (survey 1-6) and 1946-51 cohort (survey 1-7) ...... 79 Table 3-11. Specification of item numbers for Asthma 3+ visits and Asthma Cycle of Care between 2001 and 2014 ...... 85 Table 3-12. Specification of item numbers for GP visits 1997-2010 ...... 87 Table 3-13. Specification of item numbers for GP visits 2010 onwards ...... 88 Table 3-14. Specification of health assessment items from Nov 1999 to November 2010 ...... 91 Table 3-15. Chronic disease management items before 30th November 2005 ...... 94 Table 3-16. Chronic disease management items after 30th November 2005 ...... 94 Table 4-1. Prevalence of respiratory case definitions at survey3 (2001) for women from the 1946-51 cohort (aged 51-56 years) ...... 115 Table 4-2. Asthma case difinitions and mortality from survey 3 (2001) to survey 7 (2013) for women from the 1946-51 cohort ...... 117

7 Table 4-3. Effect of asthma only, bronchitis/emphysema only and their combination on mortality from survey 3 (2001) to survey 7 (2013) for the 1946-51 cohort ...... 118 Table 5-1. Prevalence of reported respiratory conditions for women in the 1921-26 ALSWH cohort from 1996 to 2011 ...... 124 Table 5-2. Prevalence of reported respiratory conditions for women in the 1946-51 ALSWH cohort from 1996 to 2013 ...... 125 Table 5-3. Frequency of asthma groups for women in 1921-26 and 1946-51 cohort ...... 127 Table 5-4. Prevalence of breathing difficulty for women from the 1921-26 cohort, according to asthma groups ...... 128 Table 5-5. Prevalence of breathing difficulty for women from the 1946-51 cohort, according to asthma groups ...... 133 Table 5-6. Predisposing factors at Survey 1 for the women in the 1921-26 cohort according to asthma group (N=10761) ...... 138 Table 5-7. Predisposing factors at Survey 1 for the women in the 1946-51 cohort according to asthma group (N=11425) ...... 139 Table 5-8. Enabling factors at Survey 1 for the women in the 1921-26 cohort according to asthma group (N=10761) ...... 140 Table 5-9. Baseline enabling factors at Survey 1 for the women in the 1946-51 cohort according to asthma group (N=11425) ...... 141 Table 5-10. Comorbidities reported at Survey 2 by women in the 1921-26 cohort according to asthma group ...... 143 Table 5-11. Comorbidities reported at Survey 2 for women in the 1946-51 cohort according to asthma group ...... 144 Table 6-1. GP satisfaction at Survey 1among women from the 1921-26 and 1946-51 cohorts, according to asthma group ...... 160 Table 6-2. Number of self-reported GP visits at Survey 2 and Survey 6 for women from the 1921-26 cohort according to area of residence and asthma groups ...... 162 Table 6-3. Number of self-reported GP visits at Survey 1 and Survey 7 for women from the 1946-51 cohort according to area of residence and asthma groups ...... 164 Table 6-4. Number of self-reported GP visits at Survey 2 and Survey 6 for women from the 1921-26 cohort according to income management status and asthma groups ...... 166 Table 6-5. Number of self-reported GP visits at Survey 1 and Survey 7 for women from the 1946-51 cohort according to income management status and asthma groups ...... 167 Table 6-6. Number of self-reported GP visits at Survey 2 and Survey 6 for women from the 1921-26 cohort according to diabetes and asthma groups ...... 169

8 Table 6-7. Number of self-reported GP visits at Survey 1 and Survey 7 for women from the 1946-51 cohort according to diabetes and asthma groups ...... 170 Table 6-8. Univariate association between number of GP visits /year and predisposing, enabling and needs factors for women from the 1921-26 cohort at Survey 3 ...... 172 Table 6-9. Multivariate associations between number of GP visits /year and predisposing factors for women from the 1921-26 cohort at Survey 3 ...... 176 Table 6-10. Multivariate associations between number of GP visits /year and enabling factors for women from the 1921-26 cohort at Survey 3 ...... 177 Table 6-11. Multivariate associations between number of GP visits /year and need factors for women from the 1946-51 cohort at Survey 3 ...... 178 Table 6-12. Association between asthma groups and number of GP visits within 12 months prior to Survey 3 for women from the 1921-26 cohort using multivariate multinomial logistic regressions ...... 180 Table 6-13. Association between asthma or bronchitis/emphysema and number of GP visits for women from the 1921-26 cohort using a longitudinal analysis approach, from Survey 2 to Survey 6 ...... 182 Table 6-14. Univariate association between number of GP visits /year and predisposing, enabling and needs factors for women from the 1946-51 cohort at Survey 3 ...... 184 Table 6-15. Multivariate association between number of GP visits /year and predisposing factors for women from the 1946-51 cohort at Survey 3 ...... 188 Table 6-16. Multivariate association between number of GP visits /year and enabling factors for women from the 1946-51 cohort at Survey 3 ...... 189 Table 6-17. Multivariate association between number of GP visits /year and need factors for women from the 1946-51 cohort at Survey 3 ...... 190 Table 6-18. Association between asthma groups and number of GP visits within 12 months prior to Survey 3 for women from the 1946-51 cohort using multivariate multinomial logistic regressions ...... 192 Table 6-19. Association between asthma or bronchitis/emphysema and number of GP visits for women from the 1946-51 cohort using a longitudinal analysis approach, from survey 2 to survey 7 ...... 194 Table 7-1. Broad Type of Service (BTOS) list used in this thesis ...... 198 Table 7-2. Total minutes of GP visits in regular hours in 2000 by women from the 1921-26 and 1946-51 cohorts ...... 204 Table 7-3. Proportion of women with longer total time of GP visits in a year* according to asthma groups ...... 207

9 Table 8-1. Univariate associations between total GP visit time and asthma groups, predisposing, enabling and needs factors for women from the 1921-26 cohort at Survey 3 .. 221 Table 8-2. Univariate associations between total GP visit time and asthma groups, predisposing, enabling and needs factors for women from the 1946-51 cohort at Survey 3 .. 223 Table 8-3. Multivariate associations between total GP visit time and predisposing factors for women from the 1921-26 cohort at Survey 3 ...... 226 Table 8-4. Multivariate associations between total GP visit time and enabling factors for women from the 1921-26 cohort at Survey 3 ...... 227 Table 8-5. Multivariate associations between total GP visit time and need factors for women from the 1921-26 cohort at Survey 3 ...... 228 Table 8-6. Adjusted odds ratios (and 95% CI) for the effect of asthma group on total GP visit time among women from the 1921-26 cohort at Survey 3 (2002), while adjusting for predisposing, enabling and needs factors ...... 230 Table 8-7. Association between total GP visit time and asthma groups among women from the 1921-26 cohort, after adjusting for time (Survey 2 to 6), and predisposing, enabling and needs factors ...... 232 Table 8-8. Multivariate associations between total GP visit time and predisposing factors for women from the 1946-51 cohort at Survey 3 ...... 233 Table 8-9. Multivariate associations between total GP visit time and enabling factors for women from the 1946-51 cohort at Survey 3 ...... 234 Table 8-10. Multivariate associations between total GP visit time and need factors for women from the 1946-51 cohort at Survey 3 ...... 235 Table 8-11. Adjusted odds ratios (and 95% CI) for the effect of asthma group on total GP visit time among women from the 1946-51 cohort at Survey 3 (2001), while adjusting for predisposing, enabling and needs factors ...... 237 Table 8-12. Association between total GP visit time and asthma groups among women from the 1946-51 cohort, after adjusting for time (Survey 2 to 6), and predisposing, enabling and needs factors ...... 239 Table 8-13. Adjusted odds ratios (and 95% CI) for the effect of asthma group on after-hours GP visit time among women from the 1921-26 cohort at Survey 3 (2002), while adjusting for predisposing, enabling and needs factors ...... 241 Table 8-14. Adjusted odds ratios (and 95% CI) for the effect of asthma group on after-hours GP visit time among women from the 1946-51 cohort at Survey 3 (2001), while adjusting for predisposing, enabling and needs factors ...... 242

10 Table 8-15. Association between after-hours GP visit time and asthma groups among women from the 1921-26 cohort, after adjusting for time (Survey 2 to 6), and predisposing, enabling and needs factors ...... 244 Table 8-16. Association between after-hours GP visit time and asthma groups among women from the 1946-51 cohort, after adjusting for time (Survey 2 to 6), and predisposing, enabling and needs factors ...... 245 Table 8-17. Adjusted odds ratios (and 95% CI) for the effect of asthma group on specialist visits among women from the 1921-26 cohort at Survey 3 (2002), while adjusting for predisposing, enabling and needs factors ...... 247 Table 8-18. Adjusted odds ratios (and 95% CI) for the effect of asthma group on specialist visits among women from the 1946-51 cohort at Survey 3 (2001), while adjusting for predisposing, enabling and needs factors ...... 248 Table 8-19. Association between specialist visit time and asthma groups among women from the 1921-26 cohort, after adjusting for time (Survey 2 to 6), and predisposing, enabling and needs factors ...... 250 Table 8-20. Association between specialist visit time and asthma groups among women from the 1946-51 cohort, after adjusting for time (Survey 2 to 6), and predisposing, enabling and needs factors ...... 252 Table 8-21. Adjusted odds ratios (and 95% CI) for the effect of asthma group on ACC/CDM service use among women from the 1921-26 cohort at Survey 3 (2002), while adjusting for predisposing, enabling and needs factors ...... 254 Table 8-22. Adjusted odds ratios (and 95% CI) for the effect of asthma group on ACC/CDM service use among women from the 1946-51 cohort at Survey 3 (2001), while adjusting for predisposing, enabling and needs factors ...... 255 Table 8-23. Longitudinal association between ACC/CDM service use and asthma groups adjusting for predisposing, enabling and needs factors among women from the 1921-26 cohort from survey 2 to 6 ...... 257 Table 8-24. Association between ACC/CDM and asthma groups among women from the 1946- 51 cohort, after adjusting for time (Survey 2 to 6), and predisposing, enabling and needs factors ...... 259

11 Table of Figures

Figure 2-1.Prevalence of asthma in Australia by age and sex, 2014-15 (Source: AIHW analysis of ABS Microdata, National Health Survey (NHS) 2016, available from http://www.aihw.gov.au/asthma/prevalence/) ...... 28 Figure 2-2. Application of Andersen-Newman’s Behavioural model in health service use in this thesis to identifying determinants of health service use in older women with asthma ...... 55 Figure 4-1. Cohort profile for the 1946-51 cohort between 1996 (survey 1) and 2014 (survey 7) ...... 116 Figure 5-1. Breathing difficulty for women from the 1921-26 cohort for Surveys 1-4, according to asthma groups ...... 130 Figure 5-2. Breathing difficulty for surviving women from the 1921-26 cohort for Surveys 1-6, according to asthma group ...... 131 Figure 5-3. Breathing difficulty for women in the 1946-51 cohort for Surveys 1-4, according to asthma group ...... 135 Figure 5-4. Breathing difficulty for surviving women from the 1946-51 cohort for Surveys 1-7, according to asthma group...... 136 Figure 5-5. SF-36 Physical health quality of life items for women in the 1921-26 cohort from Survey1 (1996) to Survey 6 (2011), according to asthma group ...... 146 Figure 5-6. SF-36 Physical health quality of life items for women in the 1946-51 cohort from Survey1 (1996) to Survey 6 (2010), according to asthma group ...... 147 Figure 5-7. SF-36 Mental Health quality of life items for women from the 1921-26 cohort from Survey1 (1996) to Survey 7 (2011) according to asthma groups ...... 149 Figure 5-8. SF-36 Mental Health quality of life items for women from the 1946-51 cohort from Survey1 (1996) to Survey 7 (2013) according to asthma groups ...... 150 Figure 6-1. Self-reported health service use at Survey 1 by women from the 1921-26 cohort, according to asthma group ...... 155 Figure 6-2. Self-reported health service use at Survey 1 by women from the 1946-51 cohort, according to asthma group ...... 156 Figure 6-3. Frequency of GP visits in the last 12 months for women from the 1921-26 cohort across five surveys, according to asthma group ...... 158 Figure 6-4. Frequency of GP visits in the last 12 months for women from the 1946-51 cohort across seven surveys, according to asthma group...... 159

12 Figure 7-1. Use of Medicare BTOS A, B and M items in 2013 by women from the 1921-26 cohort (Age: 87-92, N=3433) ...... 200 Figure 7-2.Use of Medicare BTOS A, B and M items in 2013 by women from the 1946-51 cohort (age: 62-67, N=7940) ...... 201 Figure 7-3. Proportion of women with a regular hours GP service claim between 1997 and 2013 for women in the 1921-26 and 1946-51 cohorts, according to asthma group ...... 202 Figure 7-4. Proportion of women with an after-hours GP service claim between 1997 and 2013 for women in the 1921-26 and 1946-51 cohorts, according to asthma group ...... 203 Figure 7-5. Distribution of total minutes of GP visits in 2000 for women from both the 1921-26 and 1946-51 cohorts ...... 205 Figure 7-6. Average total time spent with within hours GP visits in each survey period for the 1921-26 cohort, according to asthma group ...... 208 Figure 7-7. Average total time spent with within hours GP visits in each survey period for the 1946-51 cohort, according to asthma group ...... 209 Figure 7-8. Proportion of women with a specialist visit claim between 1997 and 2013 for women in the 1921-26 and 1946-51 cohorts, according to asthma group ...... 210 Figure 7-9. Proportion of women with a health assessment claim between 1997 and 2013 for women in the 1921-26 and 1946-51 cohorts, according to asthma group ...... 211 Figure 7-10. Proportion of women with a CDM claim between 1997 and 2013 among women in the 1921-26 and 1946-51 cohorts, according to asthma group ...... 212 Figure 7-11. Proportion of women with an ACC claim between 1997 and 2013 for women in the 1921-26 and 1946-51 cohorts, according to asthma group ...... 213 Figure 7-12. Proportion of women with an ACC only, CDM only or no ACC/CDM claim between 1997 and 2013 for women in the 1921-26 cohorts, according to asthma group ...... 215 Figure 7-13. Proportion of women with an ACC only, CDM only or no ACC/CDM claim between 1997 and 2013 for women in the 1946-51 cohorts, according to asthma group ...... 216 Figure 7-14. Proportion of women with a Spirometry claim between 1997 and 2013 for women in the 1921-26 and 1946-51 cohorts, according to asthma group ...... 217

13 Abbreviations

ABS Australian Bureau of Statistics ACC Asthma Cycle of Care AHR Airway Hyper-responsiveness ALSWH Australian Longitudinal Study on Women’s Health BMI Body Mass Index CDM Chronic Disease Management COPD Chronic Obstructive Pulmonary Disease DALY Disability Adjusted Lost Years DVA Department of Veteran's Affair ECRHS European Community Respiratory Health Survey FEV1 Forced expiratory volume during the first second FVC Forced Vital Capacity GINA Global Initiative for Asthma GP General Practitioner HNHANES National Health and Nutrition Examination Survey HR Hazard Ratio HRQoL Health Related Quality of Life ISAAC The International Study of Asthma and Allergies in Childhood MBS Medicare Benefits Schedule NDI National Death Index NHMRC National Health and Medical Research Council NHS National Health Survey OR Odds Ratio SF-36 Sort Form – 36 Survey WHO World Health Organization YLD years lost due to disability YLL Years of Life Lost

14 Abstract

Abstract

Asthma Prevalence is higher in Australia Compared with global rates with older women having the highest frequency of the disease. In older people asthma is found to be a different phenotype with more severe symptoms resulting in worse outcomes and higher mortality rates. Given that there is an increasing trend in global ageing which is associated with elevated prevalence of chronic diseases including asthma and ageing of baby boomers in Australia, there is need for research on asthma in older population especially older women. This thesis aimed to I) investigate the impact of asthma on mortality for older women while considering confounding factors; II) examine self- reported health service use for older women according to asthma status; III) investigate cross sectional and longitudinal associations between asthma groups and self-reported health service use, adjusting for predisposing, enabling and needs factors; IV) examine Medicare records for health service use by older women according to asthma status; and V) investigate cross sectional and longitudinal associations between asthma groups and Medicare for health service use while also considering predisposing factors, enabling factors and needs. Data from 1921-26 and 1946-51 cohorts of the Australian Longitudinal study on women's health linked with Medicare records were used in analyses of this thesis. Women were categorised into five mutually exclusive groups according to their asthma status (percentages shown for 1921-26 and 1946-51 cohorts respectively): 1) past asthma (4.2% and 6.4%); 2) prevalent asthma (8.5% and 10.2%); 3) incident asthma (5.3% and 8.9%); 4) bronchitis/emphysema (17.6% and 15.2%); and 5) never asthma (64.4% and 59.3%). Logistic regression and multinomial regressions were used to investigate the cross sectional associations between asthma groups and both Self-reported and administrative health service use taking into account the effect of predisposing, enabling and needs factors. Longitudinal analyses were conducted to investigate the association of asthma groups with health service use by older women over time adjusting for repeated measures of predisposing, enabling and needs factors. Findings from the studies showed that asthma was associated with higher mortality

15 rates in older women from the 1921-26 cohort even after taking into account the effect of confounding factors. Larger proportions of women with asthma in both cohorts had comorbidities including heart diseases, diabetes, anxiety and depression. Women with asthma were more likely to have reported visits to their GPs/family doctors in a year compared with women without asthma even after adjusting for predisposing, enabling and needs factors. This finding was corroborated by results from the Medicare records, showing that asthma was associated with more frequent and longer visits even after taking into account the effect of predisposing, enabling and needs factors. Asthma was also associated with higher number of claims for specialist visits, after-hours GP visits, Chronic Disease Management (CDM) and Asthma Cycle of Care (ACC) items. After adjusting for asthma group, the use of these services, were mostly driven by possessing private health insurance and comorbidities. Although women with asthma had higher levels of health service use, the uptake of enhanced primary care items including assessments, CDM and ACC were low. Potentially, the better uptake and application of services subsidised by these items could improve the impact of asthma on older women’s quality of life and reduce asthma mortality rates in older women.

16 Chapter 1 Background: Asthma in context

The objective of this chapter is to provide contextual information about asthma and the importance of asthma research for older people, particularly women.

Clinical definition of asthma

Asthma is a syndrome described by episodic obstruction in airflow (1). The word ‘asthma’ originates from the Greek term ααζειν (aazein), which was used to describe breathing with open mouth or panting (2). Narrowing of the airways is caused by a specific type of inflammation in the airways and is usually reversible (spontaneously or by treatment). The inflammation makes airways more responsive to environmental and endogenous triggers compared with non-sensitized airways (3, 4). This hypersensitivity response includes bronchoconstriction and excess mucous production with excessive narrowing in the airways and reduced airflow. Clinical manifestations of asthma include tightness in the chest, cough, wheezing and dyspnoea which range in severity from mild and intermittent to severe and persistent, which can be life threatening for the patient (3). The clinical exhibitions of asthma are due to structural alterations in the airways including angiogenesis, fragility of epithelium, collagen deposition in the airways, goblet cell hyperplasia, increased airway smooth muscle mass and abnormalities in elastin (5). Although there is no one clear definition for asthma, the Global Initiative for Asthma (GINA) in 2016 defined asthma as a:

“…. heterogeneous disease, usually characterized by chronic airway inflammation. It is defined by the history of respiratory symptoms such as wheeze, shortness of breath, chest tightness and cough that vary over time and in intensity, together with variable expiratory airflow limitation” (3).

17 Why is asthma important?

1.2.1 Burden of asthma Asthma is a common chronic disease which affects approximately 300 million people in the world (1). Overall worldwide asthma incidence is 2.6-4/1000 per year, and the incidence varies according to some factors including age, area, gender and other sociodemographic factors. Since asthma is a chronic condition, the prevalence is greater than the incidence. Findings from the World Health Survey in 2002-2003 show that worldwide asthma prevalence is about 4.3% across all ages (6). However, asthma prevalence varies from country to country. Industrialised countries tend to have a higher prevalence, and urbanisation in developing countries also increases the prevalence of asthma in these countries (4). Worldwide asthma prevalence is rising and estimated to increase to 400 million people by 2025 regardless of age and gender (7). International population-based studies using data from 2002 to 2003 suggest that Australia with an overall prevalence rate of 21% has one of the highest prevalence rates of asthma in the world. In this report, asthma prevalence was calculated considering people who were ever diagnosed with asthma in their lifetime (6, 8). According to the Australian Institute of Health and Welfare (AIHW), in Australia over 2.5 million (11%) people were affected by asthma in 2014-2015 which explains why it is distinguished as a national health priority (7-11). It has been estimated that people with asthma spend 11% of their total life with disability caused by asthma (12). Disability-adjusted life years (DALYs) is the number of years lost due to disability, ill health or early death which was introduced in the 1990s as a uniform measure of comparison of the overall health between different countries (13). In 2014 in Australia, asthma accounted for 2.3% of the total number of DALYs (14). Worldwide, it is estimated that the number of DALYs lost is 15 million per year for asthma, similar to diabetes (15).

18 Costs of asthma

In addition to its high frequency, asthma is a significant burden on health care system worldwide (16). In Australia, Asthma exacerbations are the cause for 40,000 hospitalisations and 105,000 emergency visits to hospitals every year (17). Health expenditure due to asthma in the financial year of 2000-2001 was $693 million which was equal to 1.4% of the total health expenditure for Australia for that year(18) which by 2015 increased to $1.2 billion and total burden of disease costs of $23.7 (19). Asthma burden in female Australians older than 45 years is two-thirds of the overall asthma burden (20). Health care expenditure in 2015 was higher for females compared with males ($738.8 vs. $506.7 million) (19). Asthma is also associated with economic costs for the individual and their caregivers, and for the community. Costs are associated with medication, doctor visits and hospitalisation (8). Expenditure on prescription medication and hospital admissions in 2015 were respectively estimated $263 and $102 per person for the general population (19). People with asthma use over the counter mediations related to their disease as well as subsidised prescription medications. For example, costs of pharmaceuticals for asthma constitute over 50% of the treatment costs in older people (18). Tests and investigations may not be covered by their health insurance additional to transportation costs for medical visits or home care (19). Bahadori et al in 2009 in a systematic review showed that treatment cost for older people is twice as much as younger adults with asthma. Cost will increase with addition of comorbidities such as psychiatric diseases and cardiac diseases which are common in older people (21). The average cost of asthma per person in 2015 was considered to be as high as $11,500 per year, estimated to be much higher for people with severe asthma with medication and hospitalisation being the most important drivers of the cost (19, 22).

Asthma mortality

Despite all the methods of treatment for asthma which are reasonably effective in most people and even in developed countries where treatment is more easily accessible, asthma mortality and morbidities are still high (15, 23). Global asthma mortality accounted for 0.44 deaths per 100,000 people in 1993 which was declined to 0.19

19 deaths per 100,000 people in 2012 (24). In Australia, in 2012, asthma was the underlying cause of for 394 deaths which increased to 421 deaths in 2015 corresponding to 1.5 per 100,000 people (11). Mortality rate due to asthma is twice as much for Australian females compared to males (0.36% vs. 0.18% of all deaths in 2012) (14). Asthma mortality is higher in older people than in younger people (25). Asthma mortality will be discussed in more detail in Chapter 2.

Asthma in older age

Definition of older age

There are different definitions for older age according to country or region but, there is no set number at which people are called ‘old’. Biological age may be different to calendar age but calendar age is used to determine older age. The United Nations definition of older age is 60+ years although there is no standard numerical criterion (26). Australian Institute Of Health and Welfare (AIHW) defines older people in relation to asthma as 45 years and over (9). In this thesis, unless stated, older people are considered using age 60 years as the cut point.

Population Ageing

Underscoring the importance of research focused on older people is that the world’s population is ageing due to lower birth rates and increased life expectancy. WHO reports in 2015 show an increase in the proportion of people aged 60 years and over while the proportion of younger adults has dropped (27). In 2015, 900 million (11%) of the world’s population were aged 60 years and over. The proportion of older people is estimated to reach two billion (22%) in 2050 (27). From the mid to late 20th century, there has been an addition of approximately 30 years to the life expectancy of people in Australia, New Zealand, USA, Canada, Japan, Spain and Italy (28).

20 Longer life gives older people opportunities to contribute to society and be helpful to their families and communities. However, health is a determinant in level of activities by older people which has not improved in line with longevity (27). Although, there has been a decrease in mortality rates due to ageing, the incidence of diseases has not changed which results in more years lived with morbidities and disabilities (28, 29). In Australia, ‘baby boomers’ (born in 1946-65) with 5.5 million people as the largest cohort, have impacted the structure of the population. In 2050 a quarter of Australian population will be over 65 years old. The Australian healthcare system will face issues related to ageing especially, chronic conditions (30, 31). AIHW Reports in 2010 showed that the prevalence of asthma among older people (over 70 years old) from 2001 to 2004 increased from 8.1% to 9.4% (20). Also, Woolcock institute in Australa in 2011- 2012 reports asthma prevalence of 12.7% among women compared with 8% among men(14). Although mortality rate had dropped in younger people with asthma, the reduction did not have the same downwards trend in people aged 70 years and older (20).

Why is asthma important in older people?

Asthma management has improved in younger adults and mortality due to asthma has decreased (32). However, even though the management of the disease in older people is similar to younger adults, and despite the efforts in improving asthma by governmental and non-governmental initiatives, the outcome of the disease in older people is far from satisfactory (20). The reason behind poorer disease outcomes in older people is not well understood, but factors including under-diagnosis of asthma, comorbidities, health behaviours and other risk factors, social circumstances, and health service use have been implicated (4, 33). These issues will be discussed in more detail in Chapter 2.Clinical studies have also shown that older people have a different phenotype of asthma compared with younger people. Hannania and colleagues suggested that this may be the result of complex interactions between altered immune system, epigenetics, environment, viral bacterial triggers and comorbidities in older population (34). This potentially impacts the diagnosis of asthma in this population (35-38).

21 Asthma and older women

Ageing affects sexes differently. The ratio of males to females in the Australian population at birth is 1.06: 1 while above age 70, the sex ratio reduces to 0.9:1 due to higher mortality among males in this group of age (13). Apart from higher ratio of females in older age compared to males globally asthma is more prevalent among females (9). In Australia, statistics from 2004–05 show higher overall prevalence of asthma among female Australians (10.8%) compared to males (7.4%) (20). In 2011, the prevalence of asthma among females and males aged over 75 years was 13.4% and 7% respectively (9). Older women also have more severe asthma compared to older men with higher rates of exacerbations (39-41). Moreover, among people 70 years old and over mortality due to asthma is markedly higher in females (12.7%) compared to males (5.7%) (20). Women aged from 40 to 60 are more likely to have hospitalizations for their asthma (42). From 1999 to 2006, in Australia, hospitalisation rates due to asthma had decreased for people older than 45 regardless of gender. However, hospitalisation rates were still higher for older women compared to older men and the reduction in hospitalisation during this period was the least among women aged 70 years and older (20). In 2011- 2012 in Australia, there were 49,411 hospital patient-days for women attributed to asthma compared to 32,212 hospital patient-days for men (14). Severe asthma is characterised as having continuous symptoms (cough, wheezing, shortness of breath and chest tightness) including frequent night time symptoms. Severe asthma requires high doses of preventer and bronchodilator medications to prevent or treat the symptoms (3). Severe asthma is distinguished from poorly controlled asthma by the frequency and dose of the medication in controlling the symptoms (3). Hospitalisation is correlated with severity of asthma and it is known that women have a different phenotype of asthma which is more severe and hard to treat. This gender difference in asthma severity is partially attributed to ovarian hormones and hormonal changes during menopause and related risk factors such as obesity (41-43). The hormonal differences between men and women drives different types of inflammation in airways engaging different types of immune cells and different cytokines (43, 44).

22 Moreover, older women have more chronic diseases and comorbidities compared to older men which may impinge on their asthma management (45). The reason why women have more morbidities and mortality rates than older men is not well known. The gender difference in mortality is attributable to biological differences, risks and behaviour developed through social roles, illness perception, illness reporting, health care access, health care use and adherence to treatment (46). Women tend to have a better and more exaggerated perception of their illnesses and report them better than men especially for minor health issues (47). On the other hand, there is an association between comorbidities and mortality in older women (46). Older women also tend to have higher rates of disability (48). Greater disability may impact on women’s asthma management and access to health services, and could result in higher mortality rates (49). Women are more likely to be single and single older women are less likely to live in residential care and married women are less likely to live in the community and more likely to care for their partner. This results in more isolation of older women, poorer management of their asthma, poorer access to health services and lower level of care compared to men (48). In general, women report less access to health care and tend to defer their visit to a GP due to cost barriers (50). However, older women with asthma visit GPs more frequently than older men which could be due to worse severity of asthma among older women and consequently more frequent exacerbations (51).

23 Significance of thesis investigations

Asthma in older people remains an important and under investigated problem. The prevalence and mortality of asthma among older people is high and asthma care is complex in this age group. Asthma also, affects quality of life and health care use. Thus, effective methods to improve asthma care and outcomes need to be developed. There is currently a lack of comprehensive studies on all aspects of asthma in older people in Australia and elsewhere particularly their health service use, taking into account intrinsic and extrinsic contributing factors. To my knowledge, there are no studies that refer to the type of care received by older people or the effect of asthma care on health outcomes. Findings of this thesis provide knowledge on the types of health service use by Australian older women with asthma and contributes strong evidence for improving health outcomes in these women, considering possible contributing factors in health care use.

24 Thesis framework

Aims and objectives

The aim of this thesis is to investigate the level of health service use by Australian women with asthma and factors associated with health service use. The framework to achieve the aim of this thesis has three major dimensions:  Survival of women according to asthma status  Self-reported health service use and asthma using cross sectional and longitudinal perspectives  Medicare registered health service using cross sectional and longitudinal perspectives Specific research objectives of this thesis include:  To investigate the impact of asthma on mortality for older while considering confounding factors (Chapter 4)  To examine self-reported health service use for older women according to asthma status (Chapter 5)  To investigate cross sectional and longitudinal associations between asthma groups and self-reported health service use, adjusting for predisposing, enabling and need factors1 (Chapter 6)  To examine Medicare records for health service use by older women according to asthma status (Chapter 7)  To investigate cross sectional and longitudinal associations between asthma groups and Medicare for health service use while also considering predisposing factors, enabling factors and needs (Chapter 8)

1 Andersen-Newman’s behavioural model in health service use and the definition of predisposing, enabling and need factors is described in Chapter 2.

25 Structure of this thesis

Section 1. Background, study aims and literature review (Chapter 1 and 2)  Background and aims of this thesis and structure of the thesis (Chapter 1)  Review of the current literature on asthma in older people from an epidemiological point of view considering mis/under-diagnosis of asthma in older population, best practice in management of asthma for this age group, the availability of health services, and the use of health services according to the Andersen-Newman behavioural model. (Chapter 2) Data sources, methods and analyses  Description of the data sources including the Australian Longitudinal Study on Women’s Health and the Medicare Benefits Schedule (MBS). This chapter will cover, predisposing, enabling and needs variables from each data source, as well as the statistical analyses employed in this thesis (Chapter 3) Section 2. Asthma and mortality (Chapter 4)  Impact of asthma on mortality in older women Section 3. Asthma groups and self-reported health service use (Chapter 5 and 6)  Defining asthma groups (past asthma, prevalent asthma, incident asthma, bronchitis/emphysema and never asthma) based on women’s self-reported doctor diagnosed asthma using data from Australian Longitudinal Study on Women’s Health (ALSWH) surveys  Exploration of self-reported health service use (GP, Specialist, allied health and hospital doctor) for older women, according to asthma group (Chapter 5)  Investigation of the association between asthma groups and self-reported health service use after considering predisposing, enabling and need factors (Chapter 6) Section 4. Asthma groups and Medicare health service use (Chapter 7 and 8)  Exploration of Medicare health service use (GP and specialist visits, Health assessment, chronic disease management and asthma cycle of care) for older women, according to asthma group (Chapter 7)  Investigation of association between asthma groups and self-reported health service use after considering predisposing, enabling and need factors (Chapter 8)

26 Chapter 2 Literature review

Introduction

This chapter highlights recent literature on asthma in older people from an epidemiological point of view considering mis/under-diagnosis of asthma in older population, best practice in management of asthma for this age group, the availability of health services, and the use of health services according to the Andersen-Newman behavioural model. Databases searched to access the most recent literature including Cochrane, Medline, scienceDirect, EMBase, web of Science and AMED from 2000 to 2017.

Asthma Incidence and prevalence

Asthma incidence and prevalence among older people

Asthma was considered to be a childhood disease, but, nowadays it is also known as an important cause of death in older adults. Globally, asthma is highly frequent among older people. Global epidemiological studies show that incidence of asthma in adults older than 65, irrespective of sex, is about 1.03 per 1,000 population per year (52), compared to the overall incidence after the age of 25 of 2.1/1000 per year (12, 53). However, this incidence may be underestimated due to under/misdiagnosis of asthma (3). Asthma prevalence is also increasing in the Australian population, for those aged 75 years and older, the overall prevalence of asthma was 9.9% in 2015 compared to 7% in 2001 (11).

Asthma incidence and prevalence according to sex

Among children less than five years of age asthma incidence rate is 8.1-14/1000 and 4.3- 9/1000 per year for boys and girls respectively, (53). In Australia, in 2015, the prevalence of asthma was higher for boys under 14 compared with girls (12.4% vs. 9.6%) (11) while

27 the overall prevalence of asthma was higher among females compared with males (respectively 11.8% compared with 9.8%). The Australian Institute of Health and Welfare reported that between ages 64 to 75 years, asthma is more prevalent in women (10.9%) compared to men (8.9%) (Figure 2-1). However, this gender difference diminishes around age 75 years with similar prevalence for men and women (11, 54). Among female adults though, the prevalence is higher for women aged 75 years and over compared to women aged younger than 75 years old (13.4% vs. 7.0%) (9).

18.0

16.0

14.0

12.0

10.0

Percent 8.0

6.0

4.0

2.0

0.0 0–14 15–29 30–44 45–59 60–74 75+ All ages(a) Age group

Male Female

Definition and assessment of asthma in epidemiological studies

Estimates of asthma incidence and prevalence are highly variable according to the different approaches and methods employed to collect and report data (55). Currently there is no standardised operational definition for asthma and the existing definition

28 does not seem to be appropriate for older people (56). It is ideal to define asthma by clinical diagnosis using pulmonary function test or bronchial hyper-responsiveness and symptoms (57, 58). In population studies, performing physical examinations to ascertain asthma is not feasible due to large sample sizes. Moreover, conducting spirometry in older people is not easy and the results require specific interpretation according to physiological changes in older age (59). To date, the use of questionnaires in epidemiological studies on asthma has been the best method in defining asthma (57). Most surveys of asthma prevalence rely on self-reported doctor diagnosis of asthma, with a combination of questions to help improve the accuracy of reporting (6, 55). For example, in an analysis of the data on asthma from the National Health and Nutrition Examination Survey (NHANES) in the United States, 9.1% of the participants aged 65-74 responded ‘yes’ to the question ‘During the past 12 months, not counting colds or the flu, have you frequently had trouble with wheezing?’. Of the same age group, 5.9% answered ‘Yes’ to ‘Did a doctor ever tell you that you had asthma?’, Interestingly when the question changed to ‘Has a doctor ever told you that you had asthma and/or wheezing?’, 12.4% said “Yes” (60). Sa et al. in 2014 performed a systematic review to examine the definition of asthma used in epidemiological studies. The authors investigated studies which had used data from international Study of Asthma and Allergies in Childhood (ISAAC) and European Community Respiratory Health Survey (ECRHS) (55). In these studies prevalent asthma was defined by using a combination of, ‘ever diagnosed with asthma’ and ‘still having asthma’. Of the 117 studies that were included in the analysis, prevalent asthma was defined in 29 different ways and diagnosed asthma and lifetime asthma were defined in 12 and 8 different ways respectively (55). Applying the combination of definitions to data from the 2010 Portuguese National Asthma Survey (INAsma) the prevalence of different definitions of asthma ranged from 5.3-24.4% (61). Using the same case definitions on data from 2005-2006 National Health and Nutrition Examination Survey (NHANES) resulted in asthma prevalence of 1.1 to 17.2% (55). It was suggested that the addition of a symptoms such as wheezing or shortness of breath increased the specificity for asthma and reduce the risk of misclassification (55). Accordingly, Pekkanen and colleagues in 2005 suggested that using a combination of symptoms in self-reported asthma increased specificity of the definition against

29 bronchial hyper-responsiveness (62). In this study of 21,924 people (aged 25-44 years) from 18 countries, using different combinations of the symptoms reported in the survey, resulted in different estimates of asthma prevalence (62). In another study, researchers from the University of Sao Paulo, used a scoring system applied to the asthma module of the ISAAC questionnaire (Annex 1). To ascertain asthma, twenty specialists experienced in treating asthma, individually assigned scores to each of the questions (0- 2) on the ISAAC questionnaire on asthma symptoms including wheezing, cough and exacerbations. Scoring 5 out of 14 allowed people with asthma to be discriminated against controls (sensitivity: 93%. and specificity: 100%.) (63). Considering the range of methods and responses in these epidemiological studies suggest that variable prevalence estimates of the disease may be due to different questions and study methods. This implicates the need for having a standardised operational definition of asthma for survey research. Another reason that epidemiological studies have not been able to provide accurate estimates of the prevalence or incidence of asthma in older people is due to difficulties in ascertainment of asthma diagnosis, and the variability of the signs and symptoms in older adults (64). Asthma is a heterogeneous disease with symptoms which could be common with other respiratory diseases (33). In older people, there are other respiratory diseases namely, COPD which are usually misdiagnosed as asthma or vice versa due to lack of a gold standard in defining asthma (59). Thus, diagnosis varies from doctor to doctor and place to place, depending on case definitions, investigations, and clinical practices (65, 66). Accounting for misdiagnoses of asthma as other respiratory conditions, would result in higher prevalence and incidence of the disease (6). A further problem in asthma diagnosis is that the reduction in lung function may be the result of elasticity or respiratory muscle function decline due to ageing (24, 25). All cells age over time including lung cells and the ageing process results in changes in lung function. (23, 24). Conversely, older people with asthma may attribute their shortness of breath or asthma exacerbation as part of the natural ageing experience, hence less likely to seek treatment for asthma.

30 Under-diagnosis of asthma in older ages

In the past, the fact that asthma could affect older people was denied and asthma was considered a childhood disease which resulted in ignorance of the diagnoses and treatment plan specific to older age (4, 34, 59, 64, 67) . In older ages, respiratory and immune system’s function change. Also, older people may not have the ‘typical’ asthma symptoms or ignore their symptoms as part of natural ageing process. Currently, the diagnosis of asthma in older people is the same as the diagnosis for younger people using the same diagnostic and examination methods and tests. However, the interpretation of the results is more difficult in older people (54, 68, 69). In general, clinicians are not aware of the symptoms of asthma in older people or do not acknowledge the different nature of the disease in this age group (70, 71). Moreover, due to comorbid conditions and changes in airway physiology, the diagnosis of asthma is even more difficult in older people, and under-diagnosis and misdiagnosis of asthma is greater in older patients (64, 72). Older people have comorbid conditions which can share the same symptoms with asthma. Congestive heart disease, emphysema, gastroesophageal reflux and tumours in the respiratory system can cause similar symptoms as asthma. In some patients, asthma is not distinguishable from COPD even with available tests. Pressure on the respiratory tract (caused by trauma or an enlarged thyroid gland) can cause asthma symptoms as well (67, 73). Comorbidities and their effect on asthma diagnosis and treatment will be discussed in detail later in this chapter. Spirometry is usually a good way to diagnose asthma (74) however, in older patients spirometry is not employed well (75), partly because performing the test is difficult due to disabilities and partly due to loss of elastic recoil, chest wall stiffness and loss of respiratory muscle function and partial irreversibility of airway obstruction (75). In clinical practice, asthma symptoms and spirometric features at older age are similar and attributed to Chronic Obstructive Pulmonary Disease (COPD), leading to under-diagnosis and inadequate treatment (76, 77). Some older people may think respiratory symptoms are a normal part of ageing and therefore not seek medical attention for this problem (36, 78). Wheezing, cough and dyspnoea could be signs of asthma in older adults and since if asthma remains untreated it may result in death, it is important to pay attention to patients’ symptoms carefully

31 (79). It is stated that if shortness of breath exists at rest, it is more likely to be asthma rather than other similar conditions (4, 34). Due to changes in the skin at older age, the skin test method for assessing atopy, does not show reliable results in older adults. Also, specific IgE test results (a marker in diagnosing allergic asthma) are not specific in older people and hence they are not widely used for this group of age (80, 81). It has been shown that there is a Positive correlation between age and airway hyper-responsiveness in over 65 years old adults (82, 83). Lack of age specific diagnostic methods and an evidence based gold standard in asthma definition especially, in older population leads in under/misdiagnosis, under-treatment and increased burden of the disease.

Burden of asthma in older people

In Australia, burden of asthma in women aged 70 years and over is higher than men in the same group of age (31.0% vs 25.0%). Asthma burden in Australian women aged 45 and older is two-thirds of the overall asthma burden (20). An AIHW report in 2007-2008 showed hospitalisation rate due to asthma increased by age for those aged 45 years and older with people older than 70 years old having the highest rate. Overall length of stay in hospital on average was 2.2 days while for older people (45+) it was as long as 4.3 days. The increased length of hospital stay by age is attributed to various factors including higher number of comorbidities and physiological changes which retard the pace of recovery in older age (20). Asthma is found to have a different phenotype in older people which is more severe especially, in women (84). In a study conducted by Zein et al., 454 people with severe asthma were followed from 2002 to 2011. The researchers aimed to investigate the association between length of asthma and ageing on severe asthma. In this study, people older than 45 years with asthma had 2.7 times higher likelihood of having severe asthma, adjusted for comorbidities (OR: 2.73, 95 CI: 1.96, 3.81) (85). Also, they concluded that age has a greater effect on severity of asthma than the length of the disease (85).

32 Asthma is known as a disability that affects people’s lives (86). To measure burden of living with a disease or disability WHO defined measures of health in 1999 including ‘years of life lost (YLL)’ due to dying early and ‘years lost due to disability (YLD)’ (87). Among Australians over 45 years of age, the majority (71%) of the asthma burden is due to years lost on account of disability (YLD) while years of life lost due to mortality (YLL) is accountable for 29% of DALYs. However, as patients age, the impact of mortality on the burden of asthma increases (20).

Asthma mortality among older adults

There is a great variation in statistics on asthma deaths between countries. Various factors including age, area of residence and socio-economic status may affect deaths of patients with asthma. The overall global prevalence of asthma deaths, estimated by WHO, is about 250,000 each year which is about 1 in every 250 deaths (6, 88, 89). The American Lung association reported that asthma deaths have declined in the United States over a 10 year period leading up to 2012. The report showed the asthma mortality rate fell from 2.7 to 1.7 per 100,000 people which was believed to be driven by reduction in asthma attributed deaths in older people (90).However, more recent studies suggest that although asthma deaths have decreased in all age groups they have increased in older adults aged 70 years and older (91). Tsai et al. in 2012 showed that the mortality people aged 55 and over who visited emergency department, was four times higher than the overall mortality for younger adults even after adjusting for covariates including comorbidities (92, 93). To and colleagues (2014) in a study using canadian health administrative data on people aged 0-99 years old also showed that asthma attributed deaths were higher among older people which unlike other age groups, did not decline over time (1999-2008) (94). Rebuck suggested that the reduced mortality rate for asthma was due to the improvements in asthma management strategies and use of asthma preventers which reduced the asthma exacerbations (90). People with asthma have higher morbidity rates compared with general population. The likelihood of all-cause mortality in people with asthma is four times higher than asthma attributed mortality (90, 94).

33 In Australia, the rate of mortality attributed to asthma is 1.6 per 100,000 people which is high compared to international rates (91) and around 67% of asthma attributed deaths happen in older adults (17). Among Australians over 45 years of age, the majority (71%) of the asthma burden is due to years lost on account of disability (YLD) while years of life lost due to mortality (YLL) is only responsible for 29% of DALYs. However, as patients age, the impact of mortality on the burden of asthma increases (20). Deaths are also higher among females than males. According to the Australian Centre for Asthma Monitoring, in 2015, for all ages, asthma attributed deaths in Australian were 1.7 per 100,000 for women compared with 1.2 per 100,000 for men (95). Between 2002 and 2006, the mortality rate was around 7 deaths per 100,000 population for men aged 65 years and over with asthma, and 10 per 100,000 population for women with asthma (12, 21, 34, 54, 79). The results of these researches suggest a decline in asthma mortality due to better management of the disease. However, mortality rates for older people with asthma has not changed over time which may be suggestive of no efficient management of the disease in this age group.

Comorbidities and competing risks

Treating asthma in older people is more complicated than younger adults because of other existing chronic conditions in this age group which may be directly associated with asthma or occur concurrently with asthma. When treating asthma, comorbidities should be taken into account to achieve a better patient outcome (3). Atopy or rhinitis/rhinosinusitis, sleep apnoea, heart burn, cardiac diseases (atrial fibrillation and congestive heart diseases), COPD and psychological disorders are considered to be the most common comorbidities associated with asthma (22, 36, 96-98). The estimates for comorbidities are as low as 20-30% for younger adults and children while it exceeds 50% in older adults aged 65 and over. There is no doubt that treating asthma and comorbidities caused by asthma increases the costs of treatment, however, lack of simultaneous treatment results in higher frequency of visits to emergency departments

34 and hospitalisations and even higher costs of treatment in asthma patients which is more significant in older people (3, 7, 89). Also, as discussed before, asthma severity increases by age so does with comorbidities (85, 99, 100). Obesity is a contributing factor in severity of asthma and is associated with exacerbations (101). Diette and colleagues in 2002, investigated the impact of comorbidities on hospitalisation of people with asthma prospectively in 15 managed care organizations in the United States. Of 6590 people, 554 subjects were aged 65 years and older. Older people were more likely to have asthma symptoms including cough and wheezing as well as comorbidities. Comorbidities reported by the authors were sinusitis, heart diseases, chronic bronchitis/emphysema and heartburn at a greater frequency compared to people aged 18-34 (p<0. 001). During one year of follow up, older people were twice as likely to had been hospitalised (14%) than younger adults. Although age per se was not associated with hospitalisation, having more symptoms and comorbidities were associated with hospitalisation in older people (97). In another study Zhang et al. analysed data from the 2005 Canadian Community Health Survey (CCHS) with 132,221 participants of which 10,089 adults reported having asthma. People with asthma had a higher likelihood of reporting comorbidities and more likely to have rated their health to be fair/poor. Allergy, chronic respiratory conditions and psychological illnesses were associated with asthma (102). Mental health may impact patient’s cognitive abilities and consequently reduce the adherence to the treatment (103, 104). A cohort study by Krauskopf and colleagues on 317 people aged 60 years and older in the Unites States from 2010 to 2012 investigated the association between depressive symptoms and asthma treatment outcomes. In this study, older people with depression were more likely to have reported lower quality of life scores, poor asthma control, higher rates of hospitalisation due to asthma and lower treatment adherence even after adjusting for factors including age, sex, income, comorbidities, and length of asthma(104). Mental illnesses are independently correlated with mortality in older people (65 years and over) and are competing risks for mortality in people with asthma (105). Adding to comorbidities, associated polypharmacy can interfere with asthma treatments, modifying the absorption, distribution, metabolism, and excretion of drugs in body by affecting pharmacodynamics and pharmacokinetics of asthma medications.

35 This may cause drug-drug interaction and increased side effects and consequently lower adherence to asthma treatment (106, 107). The presence of comorbidities and polypharmacy emphasises the need for higher and more complex health care and treatment strategies for older people. Comorbidities should be considered in treatment plan and addressed at the same time with asthma treatment to improve the outcome of the disease as well as quality of life of the older people.

Quality of life for older people with asthma

Quality of life is the person’s perception of their overall status which includes health, symptoms of diseases, physical and social functioning (108). This comprehensive concept is impacted by various factors including socio-economic status and housing. In terms of health services for illnesses, health related quality of life (HRQoL) is used to provide information on health services evaluation. It is recommended to assess self- reported HRQoL as an outcome in measuring treatment efficacy by administering a questionnaire (109). The Medical Outcomes SF-36 questionnaire is commonly used in studies on quality of life of patients with asthma. This questionnaire includes subscales that measure different domains of health-related quality of life (and will be described more in chapter 3). Using this measure, people with asthma rate their physical and mental health lower than people without asthma (110-112). Among older people, those with asthma report their health status poor or fair compared with those without asthma (35% vs.17%) which shows a significant effect on quality of life (34). People with asthma have higher prevalence of depression especially if they have severe symptoms (113).

In Australia, in the National Health Survey (NHS) 2004-2005, fewer people with asthma rated their health as ‘excellent’ or ‘very good,’ compared to people without asthma (42% Vs. 58%) (18). In 2013 a cross-sectional study by Kia et al. in Iran evaluated quality of life using the SF-36 questionnaire, in 385 adults with asthma aged over 40 years old. Asthma was ascertained by physicians in an outpatient pulmonary clinic. This study reported

36 people with asthma had both lower physical and mental component scores (58.8 and 57.3 respectively) compared with people without asthma (114). In contrast, a cross-sectional study in Sweden in 2016 with 737 participants showed that people with doctor diagnosed asthma had lower physical score compared to people without asthma (44. Vs 49.4) but there was no significant difference in mental health score. Interestingly, participants with well controlled asthma had higher physical score than subjects with poorly controlled asthma (115). The NHS study was performed on a large sample size while both studies in Iran and Sweden were done on a limited sample size which caused larger variability in results and could limit the generalisability of the results. However, both the studies had significant differences in results after adjusting for confounding factors such as age, sex, smoking, marital status, etc.

Health Related Quality of Life in older women with asthma

Poor management of asthma, which is common in older patients, has a direct impact on quality of life in this age group which may be employed as a tool in evaluation of the treatment in these patients (116). Equally, poor quality of life can have an impact on health care use and asthma management. For example, Redondo-Sendino and colleagues in 2006 investigated the level of health service use and its predictors among Spanish population older than 60. Using the SF-36 questionnaire, the authors reported that compared with men, women had lower scores in both physical (42.66 vs. 46.19) and mental (47.43 vs. 52.40) components. Higher number of comorbidities, greater degree of disability and worse perception of health were attributed to lower scores of HRQoL by women. Also, HRQoL had a great effect on health service use by older people in this study (45).

37 Health behaviours and other risk factors affecting asthma in older ages

Although health behaviour affects asthma at all ages, in older age, they are of more importance due to their effect on organs and systems over long term and alongside age- related physiological changes. Adult onset asthma is associated with factors such as obesity and smoking (84, 117). Smoking impairs lung function and increases mortality in patients with asthma (36). More than 50% of older adults with asthma have been found to be smokers or ex-smokers (37). A study in the United States showed that 11% of older cigarette smokers have asthma which is significantly higher compared with 4% among non-smokers (34). In epidemiological studies, although older people with asthma do not smoke as much as younger adults, a higher percentage of them are ex-smokers (118). Smoking alters the phenotype of asthma by activating neutrophils in airways and creating neutrophilic inflammation which results in hard-to-control asthma which is resistant to steroids. Smoking affects the elastic recoil of the lungs and causes rapid decline in pulmonary function (119). On the other hand exercise helps improve chronic diseases and asthma management. Longitudinal multi-country studies have shown that mild physical activity reduces the number of asthma exacerbations (120, 121). The beneficial effect of exercise is due to a reduction in inflammation in airways and reduction in Body Mass Index (BMI) (120, 122). However, due to their asthma symptoms, people with asthma are less likely to engage in physical activities compared to people without asthma. In older people who have musculoskeletal problems, exercise is more challenging (123). Nutrition also has a direct impact on health and the body’s ability in overcoming diseases. This effect is two fold: I) obesity II) malnutrition. Among people with asthma, 20% are obese (BMI≥ 30) which is more prevalent in women (124). Expansion of adipose tissue in obese people causes inflammation and mechanical pressure on the respiratory system and may result in asthma and shortness of breath. Changes in mechanics of the chest reduces asthma control and increases the burden of asthma in patients (124). Insulin resistance which is more common in overweight and obese people is known to be associated with asthma. The intestinal microbiota which forms during the first 1000 days of life is a significant element in the development of the human immune system (125). Changes to the gut microbiota is considered as a connection between asthma and

38 obesity (126). Dietary fibre changes the gut microbiota and consequently reduces inflammation through nutrition-gut microbiome-physiology axis (127). Apart from the microbiota role in development of immune system, micronutrients play a pivotal role in controlling inflammation. Vitamin E, vitamin C, vitamin D, Zinc, Selenium, calcium and magnesium regulate inflammatory processes in airways and may impact asthma (128- 133). Macronutrients play role in immune system regulation too. Lack of protein may affect respiratory muscle strength or production of elastin and restrict chest movement (134). Despite the importance of nutrition in asthma management, malnutrition is more common among older people due to physiological changes and socioeconomic problems. Changes in taste or smell and lack of exercise may cause malnutrition in older people. Also, chronic conditions such as dementia, anxiety and depression alongside with social isolation contribute to malnutrition in older people (135, 136).

Social isolation among older people with asthma

Remaining in the community and social interaction improves older people’s quality of life and is protective against poor health. But ageing may result in social relationship changes, financial hardship, isolation, loneliness as well as physical issues which may complicate older people’s access to health services (137). Older people with asthma are more likely to be socially isolated which results in poor adherence to treatment and worse health outcomes. At older age many people live alone with limited support from their family or community (138, 139). They have to monitor symptoms, seek help and manage their own treatment. Managing on income is more difficult for older people who live alone compared with couples which could affect the affordability of asthma treatment (48, 140). Older people are more affected by the restrictions that asthma imposes on their lives compared to younger people. Socialising with family and friends and doing outdoor activities are often limited for older people with asthma (137).

39 Management of asthma in older adults

Management of asthma in older people requires specific considerations that account for the different perception of breathlessness and other symptoms, physiological changes of ageing, social circumstances, and the impact of comorbid conditions and other therapies. At older ages, perception of asthma symptoms can be confounded as part of other age associated changes in function. The goals of treatment may also be different in older people. While achievement of improvement in lung function may be an immediate goal in younger populations, this goal may be less appropriate for older people who have difficulty with spirometry. In this age group asthma outcome may be more appropriately measured against satisfaction with care and quality of life. Other tools like patient’s personal diary and history forms can be helpful to measure the treatment outcome. It is suggested that assessing asthma impairment should be included in asthma control guidelines (141-144). Measuring peak expiratory flow (PEF) is more tolerable for the elderly but, it is not a good measure for airway obstruction (145). The major target for treatment is to prevent disability by retaining mental and physical function, and improve quality of life (146). In general asthma treatment targets symptom relief, attack prevention, restoration of pulmonary function, reduced emergency visits or hospitalization, reduced medication adverse effect and attain normal activity (147, 148). In older ages, asthma management should include controlling comorbid conditions (eg. hypertension, diabetes, coronary artery diseases and cognitive disorders) and environmental factors as well as pharmacotherapy. Although pharmacotherapy is similar in all ages, in older ages the efficient inhaling of steroids or adhering to the medication is a problem due to side effects such as osteoporosis, rise in blood sugar, or drug-drug interaction due to polypharmacy (54). Thus, administration of alternative therapies with less side effects is recommended (149, 150). Self-management plays an important role in asthma therapy and is an important mechanism for more effective therapy and better quality of life(151, 152). Accordingly, patient education plays an effective role in the management of asthma in this group of age and needs to be part of the treatment process (153).

40 Barriers to asthma therapy in older adults

Many conditions and circumstances can complicate or impede the management of asthma in older ages. Older people have multiple comorbid conditions which may impact diagnosis, symptoms, treatment and the outcome of asthma (48). Misdiagnosis of asthma with COPD at this age is common so is coexistence of the two conditions (15- 20%) (4). Of older people (65 years and older) with asthma, 90% have at least one other chronic condition (154). Comorbidities like arthritis (over 60%), hypertension, heart disease (20%), diabetes (16%) anxiety (51%) and depression (57%) are common in older people with asthma (48, 155, 156). Some of the comorbidities may affect the older person’s ability in managing their asthma. Older people with arthritis or cognitive disorders are more likely to have difficulty controlling their asthma (48). Lack of coordination, joint problems and mental status will affect the inhaler use and non- adherence to the treatment (103, 104, 157). Also, social isolation, limited income and limited health care cover may make asthma management difficult in older people (139). Access to medical services may be limited by frailty and cognitive problems and by lack of social support or financial means (158). Having several chronic diseases may cause drug interactions and impact older people’s compliance in taking asthma medications (48). Complicated medication schedules and drug-drug interaction are other potential problems among older people (138, 139). Additionally, some older patients with asthma are not able to take an active role in their treatment procedure or adhere to their medication and treatment schedule (140). Factors affecting older peoples’ adherence to asthma therapy include adverse effects of asthma medications, number of medications used to treat other comorbid conditions (poly-pharmacy) and lack of knowledge about how to use their inhaler effectively (159, 160) (48, 161). Comorbidities complicate asthma management in older people and have negative effect on asthma outcomes such asunder-diagnosis, under-treatment, hospitalisation, quality of life and mortality (162, 163). Also, treatment of comorbidities may cause or aggravate asthma symptoms. For instance, use of aspirin or beta-blockers is very common in older people however, these medications may trigger asthma exacerbations (162).

41 In summary, asthma therapy is very challenging among older people requiring specific considerations to be adequately managed (138, 139).

Global guidelines for best practice in asthma management

Evidence-based and consensus guidelines have been established to enhance asthma treatment. The Global Strategy for Asthma Management and Prevention produced by the Global Initiative for Asthma (GINA) provide evidence based guidelines to help asthma care. The guidelines apply evidence from randomised controlled trials, meta- analysis, non-randomised trials, observational studies, and panel consensus judgement, GINA guidelines have been revised regularly over the last 20 years and most recently in 2017 to provide accessible guidelines for all patients around the world. These guidelines are designed separately for adults aged 12 to 39, adults aged 40 years and over, children aged 6 to 11 and children under 5 years old which include step by step approaches to asthma diagnosis, stepwise asthma treatment and assessment guides for healthcare practitioners (67). The guidelines aim to control asthma symptoms to enable patients have normal activity levels and to minimise future exacerbations and medication side effects (67). GINA recommends primary health care as the first line in asthma management and suggests specialist’s help for special cases such as difficulty diagnosing asthma, hard to treat asthma and risk of mortality (67). Guidelines do not however are designed rigidly for global application and not adapted locally in each country which results in inadequate benefiting in many patients (67). GINA plans to work with primary health service and public health sectors as well as patient support organisations to improve asthma care guidelines to meet local people’s needs in various countries (67). Australian guidelines are similar to international guidelines and are evidence based. Australian asthma handbook categorises patients to children and adults categories in which ages 14-16 may be classed as adults which is different to GINA’s categories (164). The diagnosis and treatment process and goals in managing asthma are in line with international guidelines.

42 Guidelines for managing asthma in older population

Although both GINA and Australian guidelines acknowledge the difficulty in diagnosis of asthma in older people, it does not provide specific information or evidence on the management of asthma in older people, particularly those with multiple comorbidities (67, 162, 164, 165). It has been mentioned in management of asthma in older people that self-management knowledge, medication, comorbidities and inhaler technique should be considered in treatment plans but there are no specific guidelines regarding this (67, 164).

Quality health care for people with asthma

Quality health care for older people with asthma plays a crucial role in asthma management and improved survival and quality of life in this group of age. When health services are not adequate to meet older people’s needs, or they are not used appropriately and accordingly, this equates to failure in treatment. The demand for health care in ageing population is rising which makes it a high priority for governments (166). Provision of health services for older people requires knowledge on their needs and characteristics. Health system in Australia aims on equity in access and availability of the services to all the citizens including the choice of being treated in public or private system. The system is effective in both acute and chronic respiratory conditions (167). General practitioners are required to have timely access and knowledge on performing and interpreting the results of spirometry to be able to provide high quality care for asthma, up to Australian standards (167).

Optimum asthma treatment

There is no gold standard in who should treat asthma. However, in health care models the optimum and quality care for asthma is a mix of primary care and specialist services. Often, the primary care health care General Practitioner (GP) or a family doctor is the

43 first care provider (168). If the treatment process requires specialist services, the GP refers the patient to specialist who has expertise in treatment of asthma. effective communication between the health care professionals is important in quality care, cost effectiveness of the treatment and patient outcomes (169). Evidently, it is not feasible that all the people with asthma get specialist services all the time. It is best to treat mind or moderate asthma in primary care with occasional specialist consultations to reduce the costs for patients while the outcome is satisfactory. However, people with severe or hard to treat asthma should have more specialist visits to have better treatment outcomes (169). Below, the literature investigating the different levels of care for people with asthma is discussed.

Primary health care

Primary health care physicians are the first contact point for people with asthma (169). About two-thirds of people with asthma utilise primary care and one-third receive care from specialists. Primary care health care providers are mainly general practitioners who may be family doctors and specialists are allergists or pulmonologists (168). Patient assessment, diagnosis and treatment including patient education and providing an individualised asthma action plan is the GPs responsibility. Results from the Bettering the Evaluation and Care of Health (BEACH) survey which collects information from GPs in Australia suggested that from 2005-2006 to 2015-2016, the frequency of visits due to asthma were higher than other chronic diseases (170). High frequency of the disease and higher level of the need for health service use by people with asthma makes health professionals’ role critical in treating the disease (168).

Primary health services for asthma in Australia

In Australia, the health system is a combination of public and private sectors which is publically funded and available to all the citizens. Primary health services are mainly provided by General Practitioners (GPs). In 2007, about 85% of the Australians visited a GP at least once a year and 11% of consultations resulting in referrals to specialists (167,

44 171). In 2011 of 2,686 adult Australians (16+ years old) who had prevalent asthma, 29% required emergency health care for asthma of which 23% had emergency visit to their GP (172). However, long term studies indicate that asthma management encounters in Australian general practice shows 0.3% drop within a decade leading to 2015-16 (172). Australian health care system provides enhanced primary care items which are called ‘Chronic Disease Management (CDM)’ items now and delivered by general practitioners. These items are for patients with chronic or terminal conditions and may be conducted in multidisciplinary settings engaging a team of health care providers (173). Chronic conditions include asthma, diabetes, heart disease, hypertension, cancer or any other condition which has been present for six months or more (173). This type of services were introduced in November 1999, by the Australian federal government to reduce hospitalisation rates and burden of chronic diseases. CDM pays a financial incentive in addition to the GP visit fees to encourage the use of the service by GPs (174). CDM items are subsidised under Medicare Benefits Schedule and are generic items to be used in management of any chronic disease, including asthma. However, the uptake of these items has been slow and limited (167, 175). Martin and Rohan (2002) in a qualitative study on GP’s perception of chronic disease care suggested that CDM items were not of GP’s primary concern in everyday practice due to limitations in time, funding and balance of responsibilities (176). In 2011, Douglas and colleagues conducted a cross- sectional study on 102,934 people who participated in 45 and Up study in New South Wales. People with diabetes had the highest proportion of claims for health assessment items (44.9%) followed by people with heart disease (22.3%). In this study, 18.5% of people with asthma had claims for health assessment items over 18 months (January 2006 and July 2008). People were more likely to have CDM claims if they were older, female and obese and were from lower socio-economic background (177). Comprehensive and thorough assessment by a health professional in primary care is another strategy developed by the Australian government to screen health problems including chronic conditions in Australian population, especially in older generation (178, 179). In 1999, Medicare ‘75+ health assessment’ items were introduced by the Australian Federal Government in order for comprehensive health assessment of people aged 75 years and older (179). In 2010, these items changed to ‘health assessment’ items and included broader population (167). Byles et al. (2007) measured utilisation of

45 75+ health assessment items among 4,646 Australian older women (75-78 years old) from 1999-2003. The uptake of the items increased from 12% in 1999 to 49% in 2003. Repeated visits to GP, satisfaction with the availability of GPs, being a carer for another person and polypharmacy were predictors of having claims for health assessment items (180). Dolja-Gore and colleagues (2017) investigated the uptake of these items using the Australian Longitudinal Study on Women's Health (ALSWH) data linked with Medicare records. The outcome of this study suggested that many (38.2%) of the older women never claimed health assessment items and many had health assessment only once. The authors found living arrangement, private health insurance, marital status, ability to manage on available income and education, associated with health assessment in older people (179). Further investigation on the uptake of these items in regional areas of New South Wales showed that over 11 years from 1999 to 2010, there was an increase in the number of health assessment items. However, the overall uptake was low (20% of eligible people). The investigators suggested modifications in the items in order for early identification of diseases and bettering quality of health for older people in Australia (181). Studies on CDM and health assessment items in Australia suggest poor uptake of these items and the need for promotion of these items, especially for older people who have or are at risk of chronic diseases and comorbidities. There are also specific MBS items for asthma management called Asthma Cycle of Care which were originally established in 1999 previously known as asthma 3+ Visits. This will be discussed in detail in section 2.17.

Specialist services

Although most people with asthma are treated by GPs, evidence suggests that specialists are more aware of the most recent guidelines in diagnosis and treatment of asthma. Patients treated by specialists on average have better outcomes than patients treated by GPs (169, 182). Legorreta et al. (1998) studied asthma management in 5580 patients and reported that GPs within large health care organisations have poorer adherence to national guidelines compared with asthma specialists who provide a more

46 thorough care for their patients (183). More recently, Tavakoli and colleagues (2018) used British Columbian‘s administrative health data from 1997 to 2014 (n= 343,520) to investigate the predictors of high asthma reliever use. Interestingly, people with asthma who visited a GP during a year before the study, were more likely to have used their reliever inappropriately. The association became stronger with higher number of visits in a year (OR: 1.73 for two visits). On the other hand, patients who had specialist visits had lower risk of inappropriate use of asthma relievers. Among specialists, visits to an allergist reduced the likelihood of utilising asthma relievers more than visits to a respirologist (OR: 0.48 vs. 0.70, P < .0001). authors attributed the difference between having GP visits and specialist visits to specialists’ knowledge of and adherence to guidelines which resulted to appropriate prevention and lower use of relievers (182). Gibeon and colleagues (2015) investigated emergency department visits and quality of life of 346 British patients with severe asthma who were referred to specialists. The results suggested a reduction in health service use and emergency visits after 286 days. Also, significant improvement was seen in quality of life of the patients before and after seeing specialists (184). Similarly, Wu et al. (2016) surveyed 1954 adults with at least two emergency visits due to asthma within two years leading to the study and concluded that pulmonologists or allergists had better adherence to national guidelines compared with general practitioners. Also, patients treated by specialists had more knowledge on managing their exacerbations and were more likely to have discussed their triggers with their doctor (185). This finding was followed by a study in Italy on 210 people with asthma or COPD which investigated the effect of collaboration between GPs and specialists. The outcome of this study showed that specialists are more reliable in differential diagnosis between asthma and similar respiratory conditions such as COPD. However, when there is a reasonable communication and collaboration between GPs and pulmonologists/respiralogists, diagnosis and management of respiratory diseases is more efficient and successful (186).

47 Asthma Management Plan in Australia

In 1989, the asthma management plan was published in Australia by the National Asthma Council (187). This document targeted health care professionals and provided a six-step plan based on guidelines and recommendations to provide better asthma management. The plan has since been modified in accordance with emerging evidence and changes to accepted best practice. In 1999, Couglan et al reviewed the evidence for the asthma management plan in a randomized controlled trial and demonstrated that particular aspects of the asthma management plan were effective in asthma management such as, written asthma action plans, controlling asthma based on symptoms, and regular management review (187). Preventive medication, controllers and reliever medications have also been found to be of benefit for effective asthma management. According to these studies, patient education has a strong effect in self- management and monitoring of the symptoms in patients (4, 140, 188, 189). Since asthma management is crucial in asthma treatment, an effective asthma management plan is the mainstay in asthma care clinical practice in Australia.

Asthma Cycle of Care (3+ Visits)

In 1999, the federal government of Australia recognised asthma as a national health priority (8). Consequently, in 2001, the commonwealth government allocated $84 million for a GP asthma program for specific asthma care management for people with moderate to severe asthma (8). This program was introduced as the Asthma 3+ visits and meant to be the most efficient program in managing asthma, identifying long term care and regular treatment review as essential ingredients for effective management (190). The Asthma 3+ visit plan was the first program that was introduced in order to help with asthma management. In this program, patients were encouraged to have at least three general practitioner attendances over a period of four months (191). A study by Zwar and colleagues (2005) on 315 GPs in Sydney showed that the uptake of the Asthma 3+ visits item was poor and only 44.9% of the GP had used the items. Factors associated with poor use were inconvenience of the multiple visits in such a short time frame and complexities of the paperwork or administrative process (192). Later in 2006,

48 this program was revised to the Asthma Cycle of Care (ACC) which involves an active relationship between the patient and their health professionals requiring at least two asthma management consultations a year. Asthma control, severity, medication review and correct use of medication and devices by patient, patient education and written individualised asthma action plan should be assessed during these visits (193). It has been shown that having enough knowledge on asthma and how medications work, regular visits by a doctor and being given a written asthma action plan to get back to when needed, improves asthma symptoms and morbidity, higher quality of life, hospitalization, unscheduled doctor visits and days off work (190, 191). Eftekhari et al. (2016) investigated the uptake of ACC among Australian older women with asthma (75 years and older) from 2001 to 2013 using the Australian Longitudinal Study on Women’s Health linked with Medicare records. Of 776 women with prevalent asthma, only 67 had claims for ACC. Women who had ACC claims had higher number of claims for health assessment and GP visits (194).

Asthma Action Plans

The foundation of an effective Asthma Cycle of Care is a written individualized action plan for each patient irrespective of disease severity (195). The asthma action plan is necessary for both patient and healthcare professional in following the pattern of the disease. In Australia, the asthma action plan is the principal element in asthma management, but is under- employed (196). The main idea of an asthma action plan application is self-monitoring, self-acting and learning how to control the disease as part of routine life. The asthma action plan is composed of different components which are necessary for asthma control. An asthma action plan should include:

 Patient’s name  Date of issue or review  Doctors name and contact information  Current medicines, dosage and frequencies  Symptom levels and instruction for each level

49  Therapy instruction for each level  Emergencies (67)

The asthma action plan should be reviewed at each doctor visit to assess the effectiveness and update it regarding patient’s current needs (195). Studies have shown that utilizing asthma action plan helps in patient adherence to treatment, prevents hospitalization and reduces asthma burden of disease. More importantly, it is estimated that written asthma action plan may reduce asthma attributed mortality by 70% (8).

Assessing asthma management and outcomes

In 2005, the Australian National Health Survey results published by the AIHW, showed only 22.5% of people with asthma had a written action plan (32, 197). A larger proportion of people with asthma possessed action plans in 2011-12 compared with 2001 (24.0% vs. 17.0%). In 2014-15, of the 2.5 million Australians with asthma, 28.1% had a written asthma action plan (32). Rudolphy (2008) reported that enhanced primary care items and GP management plans including Asthma Cycle of Care were being used increasingly in asthma treatment (197). However, Zwar and colleagues (2005) in a cross sectional study on GP’s opinions on Asthma 3+ visit plan, found that only 14% of the GP’s had used this item due to difficulty in completing three visits in a year by patients (192). After the item changed to Asthma Cycle of Care in 2006 to accommodate only two visits in one year, MBS reports showed that from 2007 to 2009 there had been no change in item claims. In 2010, Byrnes et al. designed call recruitment for people with asthma for Asthma Cycle of Care (n=2941). In this study patients were recruited via phone to complete their Asthma Cycle of Care. The results of this study suggested that call recruitment increased the use of the item by 8% at first year, 64% in second year and 45% in third year and 59% in fourth year of the recruitment (198).

50 Health service use by people with asthma

Asthma management and exacerbation control, requires access to health service and health care professionals (199, 200). Uncontrolled or poorly controlled asthma leads in more emergency department visits especially among people with severe asthma (201). Behr and coleagues (2016) using samples from Hampton Roads (n=1678), found that people with poorly managed asthma were 1.9 times more likely to have used emergency department services and 2.2 times more likely to have been hospitalised (201). As discussed before, asthma is known to be more severe among older people thus, requires a better management in this group of age (85). In Australia, older people aged 65 years and over are more likely to use GP services compared to younger age (average number of visits 8.6 vs. 4.0 in 2005-6) with females having higher number of visits compared to males at all ages. The gender difference in number of GP visits is more significant in people aged 65 and older (1.4 times more) (171). Redondo-Sendino and colleagues (2006) conducted a cross-sectional study on 3030 people aged 60 and older. The result of their investigation showed that women use GP services 1.24 times more than men and 14% more likely to be using three or more medications (45). This result is in line with higher number of morbidities in women (45). According to Australian Institute of Health and Welfare, asthma management by GPs encountered for 2.2% of GP visits in 2007-08. There had been a decline in asthma management by GPs, yet asthma was 4.1% of the chronic diseases managed by GPs. GP encounters were higher for Australian younger people compared with adults older than 70 (20). Older Australians (45+) with asthma were more treated by the GP and there was a downwards trend from 2001 to 2008 in GP encounters for asthma for this age group. Moreover, hospital stays were more frequent (78%) among people aged 45 years and older which had an upwards trend by age with 70+ age group having the highest hospitalisation rate. Although hospitalisation had decreased during this period, the smallest change was observed among 70+ years old females (20). Higher frequencies of health care use is associated with near-fatal or fatal asthma. To and colleagues (2016) investigated health service use by 1503 individuals who died from asthma, during the year before their death. From 1996 to 2011, people with asthma were 8 times more

51 likely to have had hospitalisation(s) and 13 times more likely to have visited emergency departments compared to live people with asthma (202). Results of an investigation in North-East of England from 2013-2015 suggested that the use of hospital services for COPD increased in older people. However, people with asthma had used less hospital services at younger age but, women aged 90 years and older had increased hospital service use (203).

Framework to evaluate health service utilisation

Health service utilisation is driven by patient’s needs for the service and is dependent upon supply. When needs and supply meet, health service gets utilised by people (204). Social characteristics of patients impact health service use. Several studies have tried to develop frameworks to explain and identify factors predicting health care utilisation which are called behavioural models of health service use (205). Examples of these models are Andersen-Newman model and Penchansky’s theory of access. The Andersen-Newman model is based on population characteristics and health service delivery and has five components: I) characteristics of the population; II) characteristics of the health delivery system; II) health service use; IV) health policy; and V) consumer satisfaction (206). Penchansky’s model is based on: I) availability of health services; II) accessibility; III) accommodation of the services; IV) affordability; and V) acceptability by the users (207). In this thesis in order to provide a consistent and reliable model which is well-tested and proven as a successful model in investigating health service utilisation, Andersen- Newman’s behavioural model of health service use was employed as it provides a comprehensive framework that explains health service use drivers. This model will be described in the next section.

The Andersen-Newman Behavioural Model of Health Service Utilisation

In 1968, Ronald M. Andersen proposed a model for use of service by families. Subsequently in 1973, this model was modified by Andersen in collaboration with

52 Newman and Aday to explain the health service use by individuals. The model was initially intended for use with national survey data to measure access to health services in order to develop policies to promote equitable access to services, but it has now become known as the Behavioural Model of Health Service Utilisation (208-210). “All people have a right to good medical care whether they can pay for it or not” (211). Ronald M. Andersen

Anderson believed that changes that give individuals the entitlement to health services with the same quality for every individual must take place at the individual level (212). In fact, the model was designed to promote health policy and changes to health services and equity in access by describing health service use according to social and behavioural determinants and the effect of inequitable access on communities at risk (212). The behavioural model is a multi-level model that integrates individual determinants with contextual determinants of health services use. This model “… divides the major components of contextual characteristics in the same way as individual characteristics have traditionally been divided—those that predispose …, enable …, or suggest need for individual use of health services” (209). In 2001, Andersen & Davidson explicated the model further and described the three key components of the model which are described as follow (204). Predisposing factors: These factors predispose the individuals to utilise health services. Demographic factors such as age and sex (biological imperatives), social factors including education and social relationships (e.g., marital status) are examples of predisposing factors which are modelled in this thesis. Enabling factors: These factors enable individuals to use health services. Income and wealth, health insurance type and satisfaction with access to health services are enabling factors that are used in this thesis. Need factors: These are the factors that determine needs of individuals to use health services. Comorbidities, smoking, drinking alcohol and BMI are the needs that are considered in modelling health care use in this thesis (Figure 2-2).

53 The Andersen-Newman Behavioural Model of health service use has been employed commonly in studies (including systematic reviews) that investigate different aspects of health care (204, 213) The three key components influence the patient’s health care utilisation and they should be addressed at the initial consultation and barriers to access to health services should be assessed to insure that the patient will attend follow-ups (209). To provide practical and efficient health services, the resources should be distributed equitably and accessible to all the individuals, in particular to the patients that need it more and vulnerable population (214). As mentioned above the model is based upon three key components that will be used in this thesis in predicting self-reported GP service use (chapter 6) and MBS records of GP service use (chapter 8) in different asthma groups.

54 Determinants of Healthcare Use (e.g. GP visits) Predisposing factors

 Age, highest qualification, marital status, area of residence, country of birth

Enabling factors

 Ability to manage on available Income, private health insurance, satisfaction with access to a specialist, Hospital doctor, healthcare in emergencies, after hour doctor, bulk billing doctor and female doctor

Needs

 BMI, alcohol consumption, smoking and presence of other chronic diseases (comorbidities)

Health service utilisation Number of claims for: GP visits (regular or after-hours), ACC, CDM and Health Assessment

Figure 2-2. Application of Andersen-Newman’s Behavioural model in health service use in this thesis to identifying determinants of health service use in older women with asthma

55 Employment of theoretical framework for drivers of health service use in other studies

Although there are various models developed to identify drivers of health service use, Andersen-Newman’s behavioural model is by far the most widely used model. This model has been used since 1968. Parslow & Jorm in 2004, used the model to study the predictors of health care use among adults aged 40-45 and 60-65 years in Australia (215). In this cross-sectional study, the researchers found that older age, lower physical or mental score, chronic diseases and smoking were predictors of health service use (215). Two years later, Xu, et al. used data from the Health and Retirement Study in the United States to investigate health service use over 2 years among 12,600 women aged 55-64 years old (216). In this study, women without health insurance were less likely to have used health services. Better coverage by health insurance increased the likelihood of private health service use. In addition, not having private health insurance was associated with having one or more chronic diseases, and less health service use. In 2006, Redondo-Sendino et al. studied health service use by 3,030 people aged 60 years and over in Spain. In this cross-sectional study, chronic conditions and lower quality of life increased health service use by women more men (45). Piper et al. in 2010 studied the predictors of having an asthma action plan, using National Health Interview Survey data on 13,000 children aged 5-17 (217). The study showed that having an asthma action plan was associated with less health service utilization which was determined by having health insurance (217). Jandasek et al. in 2011 assessed asthma service use among 804 Latino children (5-17) using primary cross-sectional data. Findings of this study suggested that distribution of health services affects the type of service accessed by the participants (218). Newer versions of the model includes an extensive and more complex description of the core predisposing, enabling and need factors. However, in the studies which have used Andersen-Newman behavioural model, older versions of the model which are simpler are used. The model has been used successfully for cross sectional, longitudinal, and clinical trial study designs. The model explains disparities in health service use by people

56 with asthma consistently regarding predisposing, enabling and need factors individually or grouped although, variables in each category varies from study to study.

Use of Andersen-Newman framework in this thesis

The large data from the ALSWH allows inclusion of measures consistent with the Andersen-Newman framework which affects the outcome measure. In this thesis, predisposing, enabling and need factors from the ALSWH study were used to evaluate the association between self-reported doctor diagnosed asthma and health service use. The influence of the independent variables were studied using both self- reported health service use as well as administrative data (i.e. Medicare Benefits Schedule service) use. Factors with a potential influence were included in cross sectional or longitudinal nested models.

Drivers of health care use in older people (predisposing, enabling and needs factors)

Girma and colleagues in their investigation for factors affecting health service use by Ethiopian population found that a disabling disease or recurring episodes of a disease increased health service use by 3.8 and 10.5 times respectively (219). In this study which was conducted in Ethiopia people younger than 65 years old were shown to use health services more than people over 65 years old (219). Also, the same study suggested that women use health services twice as much as men and higher education and being unmarried decreases the level of health service use (219). Analyses of data from China Health and Retirement Longitudinal Study in 2013 showed that older age, lower qualification and non-urban residence had more GP visits (220). In this study, Gong et al. in 2013, showed that better financial situation and having private health insurance increased the number of GP visits by older people. Also, of chronic diseases, depression and poor memory are shown to be associated with lower number of GP visits (220). In a study in Ghana (2017), 4,724 people aged 50 and older from the World Health Organization Study on global AGEing and adult health (SAGE) Wave 1 were investigated for health service use and its predictors. Age, higher education, private health insurance

57 and higher socio-economic status was associated with private health service use. Having two or more chronic conditions was associated with higher frequencies of health service use (221). Similarly, Al Snih and colleagues (2006) showed among 1987 Mexican- Americans aged 65 and older, age, being female, private health insurance, comorbidities and polypharmacy were predictors of higher GP service utilisation (222). On the other hand, in another Mexican cross sectional study on 2030 Mexican adults over 60 years old, being unable to afford food (lower socio-economic status), not having private health insurance and longer time to get to health service site reduced the level of health service use, while ability to afford the fees for health service use and having a caregiver or children increased the level of health service use (223). The severity of illness and number of comorbidities affect health service use. Alkhawaldeh et al. in a cross sectional study on Healthcare use by older adults in Jordan found that chronic diseases are the strongest drivers of health care use (224). A cross-sectional study on Australian middle- aged women (50-55) in 2008 showed an association between higher BMI and health care use. High BMI and low physical activity increase the cost of care for these women as well (225). Reports from ALSWH shows older women living in remote areas have lower number of claims for GP visits (226). Predictors of health service use by people with asthma was investigated by Wisnivesky and colleagues (2005) on 198 adults. Advanced age, being a female and having cockroach allergy were considered predictors of hospitalisation or emergency department visits. While, having an established asthma care provider was a predictor of lower health care resource utilisation (227). Self-reported health service use by older people with asthma is associated with comorbidities, self-reported quality of life and previous health service utilisations for asthma. Having other comorbidities increases health service use by older people. Also, people who report their quality of life lower, has higher health service use (228). Also, improved medication adherence by people with asthma reduces hospitalisation and visits to the emergency department (229), as Balkrishnan and colleagues (2002) reported, inappropriate use of asthma preventers and depression is associate with increased health service use among older adults (230).

58 Gaps in literature

Despite the investigations on asthma in older people, the disease remains an important and under-investigated problem. Given the high prevalence of asthma among older people, the high mortality rate, complexities of asthma care in older people, and the effects of asthma on quality of life and health care costs, method to improve asthma care and outcomes are imperative. There is currently a lack of studies on the level and type of health services used by older people with asthma in Australia, and elsewhere, specifically, men and women have not been studied separately. To our knowledge, the impact of predisposing, enabling and need factors on health service use by older women with asthma is poorly studied. Moreover, association between asthma, in particular, different definitions of asthma with health service use and predictors of health service use has not been investigated. Research is needed to understand the needs of older people with asthma, their access to health care, and the potential for health services to result in improvements in quality of life and survival. This thesis therefore, aims to investigate the level of health service use by Australian women with asthma and factors associated with health service use.

60 Chapter 3 Data sources and methods

Introduction

This chapter describes the data sources and statistical methods that have been used in this thesis. There are three data sources that are used for analysis in this thesis. 1) Australian Longitudinal Study on Women’s Health (ALSWH) data; 2) Medicare Benefits Schedule (MBS) data; 3) National Death Index (NDI). Each of these data sources and their use in this thesis are described below. In this thesis, variables were categorised to predisposing, enabling and needs factors according to Anderson-Newman Behavioural Model of Health Service Use (Chapter 2) and will be described in the same categories in this chapter. The final section of this chapter describes the statistical methods used to predict the health service use of older Australian women. Ethics approvals for access to the data sources are provided at the end of this chapter.

The Australian Longitudinal Study on Women’s Health (ALSWH)

ALSWH is an ongoing population-based longitudinal cohort study designed to track the lifetime health of Australian women and to identify factors that influence their health and health care use. The study includes over 58,000 women who are broadly representative of the population of women in Australia across four different age cohorts. Many aspects of health during the women’s life course have been studied in ALSWH, and it has an international reputation for its multidisciplinary methodology. ALSWH has been funded by the Department of Health (previously Department of Health and Ageing) since its inception (231). ALSWH began in 1996 with the recruitment of women in three different birth groups. The oldest cohort at that time included women born between 1921 and 1926 who were aged 70-75 years at the time of first survey. The other two cohorts were born in 1946- 51 (aged 45-50 years at baseline) and 1973-78 (aged 18-23 years at baseline) (231). These three age groups were selected to follow women through critical life stages (such

61 as first pregnancy, retirement, menopause, onset of chronic diseases). Samples of women in each age range were randomly selected from the Medicare database, based on their date of birth and area of residence. Medicare, which will be described in detail later in this chapter, is the publicly funded national health insurer for primary health care in Australia, and the Medicare database contains the names, addresses and other details for all Australian citizens, permanent and temporary residents, and refugees who utilise the Australian health system. Women from rural and remote areas were sampled twice as much as urban areas to allow statistical comparisons for their quite different contexts and life experience, and differences in health and health service use (231, 232). Of the 106,000 women who were randomly selected and sent invitations, more than 40,000 agreed to participate in the study and subsequently returned the baseline survey (232). Response rate could not be calculated exactly due to uncertainty about the accuracy of Medicare data. The first survey was completed by: 12,432 women from the 1921 – 1926 cohort (response rate: 37-40%), 13,715 women from the 1946 – 1951 cohort (response rate: 53-56%) and 14,247 women from the 1973 – 1978 cohort (response rate: 41-42%) (233). After the baseline survey in 1996, the second survey was staggered such that each cohort was surveyed in a different year (1998 for the 1946-51 cohort, 1999 for the 1921-26 cohort and 2000 for the 1973-78 cohort). Thereafter, the women were surveyed every three years (see Table 3-1). From November 2011, the women from the 1921-26 cohort were surveyed every six months with much shorter surveys (234). More recently, in 2013, a fourth group of women born in 1989-95 (aged 18 – 23 years) were recruited.

62 Table 3-1. ALSWH survey follow-up for four cohorts, including year of survey, age at survey and number of respondents COHORT

1921-26 1946-51 1973-78 1989-95

Survey 1 1996 1996 1996 2013 Age 70-75 Age 45-50 Age 18-23 Age 18-23 N=12 432 N=13 715 N=14 247 N=17 012 Survey 2 1999 1998 2000 2014 Age 73-78 Age 47-52 Age 22-27 Age 19-24 N=10 434 N=12 338 N=9688 N=11 345 Survey 3 2002 2001 2003 2015 Age 76-81 Age 50-55 Age 25-30 Age 20-25 N=8646 N=11 226 N=9081 N=8961 Survey 4 2005 2004 2006 2016 Age 79-84 Age 53-58 Age 28-33 Age 21-26 N=7158 N=10 905 N=9145 N=9007

Survey 5 2008 2007 2009 Age 82-87 Age 56-61 Age 31-36 N=5560 N=10 638 N=8200

Survey 6 2011 2010 2012 Age 85-90 Age 59-64 Age 34-39 N=4055 N=10 011 N=8009

Survey 7 Surveyed at 2013 2015 six monthly Age 62-67 Age 37-42 intervals N=9151 N = 6051 from November 2011 2016 Survey 8 Age 65-70 N=8622

The anonymised survey data are made available to researchers, and contains repeated self-reported measures of health and lifestyle for each participant such as demographic characteristics, health behaviours, health related quality of life, health service use and satisfaction with access to medical care. Qualitative data are also available from free- text comments sought at the end of each survey (234).

63 Measures that are repeatedly included in ALSWH surveys allow assessment of longitudinal associations within a generation as well as across generations. Data from ALSWH allows investigation of the relationship between health outcomes and predictors of health as well as assessing the impact of changes in health policy on health outcomes (234). In this thesis, ALSWH data are used both cross-sectionally and longitudinally to evaluating the effect of asthma on health service use, while accounting for other predictors. This thesis will use data for the 1921-26 and 1946-51 cohorts.

Participation and attrition

The participation of women in 1921-26 and 1946-51 cohort in baseline and subsequent surveys is shown in Table 2 and Table 3.

Table 2. Number of participants, respondents and deaths across surveys for the 1921- 26 ALSWH cohort of women (N=12,432) Surveys Year Women’s Age Non- Deceased Respondent respondent Survey 1 1996 70-75 0 0 12432 Survey 2 1999 73-78 1447 551 10434 Survey 3 2002 76-81 2547 1238 8647 Survey 4 2005 79-84 2988 2286 7158 Survey 5 2008 82-87 3246 3626 5560 Survey 6 2011 85-90 3098 5279 4055

Table 3. Number of participants, respondents and deaths across surveys for the 1946- 51 ALSWH cohort of women (N=13,715)

Surveys Year Women’s Non-respondent Deceased Respondent

Age Survey 1 1996 45-50 0 0 13715

Survey 2 1998 47-52 1327 50 12338

Survey 3 2001 50-55 2370 119 11226 Survey 4 2004 53-58 2594 216 10905

Survey 5 2007 56-61 2749 328 10638 Survey 6 2010 59-64 3230 474 10011

Survey 7 2013 62-67 3893 671 9151

64 Representativeness of Australian population

Using the 1996 census data, it was confirmed that ALSWH participants were representative of the Australian general population in each of the cohorts. Also, the high response rates to the surveys makes ALSWH data generalizable.

ALSWH survey variables

Asthma, other respiratory diseases and symptoms

At each survey, women in the 1921-26 and 1946-51 cohorts were asked whether a doctor had diagnosed them with asthma, bronchitis/emphysema and whether they had experienced breathing difficulties as a respiratory symptom. Questions related to these respiratory conditions and response alternatives for each question in each survey for the 1921-26 cohort and 1946-51 cohort are shown respectively in Table 3-4.

65 Table 3-4. Respiratory related questions for women from the 1921-26 and 1946-51 cohorts from Survey 1-6

Cohort (Survey) Question Original Response options Re-coded values No 0=No Have you ever been told by a doctor that you have: Asthma? Yes 1=Yes No 0=No Have you ever been told by a doctor that you have : Bronchitis/emphysema both cohorts: Yes 1=Yes Survey 1 Never 0=No (never and rarely) Rarely In the last 12 months, have you had any of the following: Breathing difficulty 1=Yes (sometimes and Sometimes often) Often No 0=No In the last 3 years have you been told by a doctor that you have: asthma? Yes 1=Yes In the last 3 years have you been told by a doctor that you have: No 0=No 1921-26 cohort: Bronchitis/emphysema? Yes 1=Yes survey 2 No 0=No (no and rarely) Rarely Have you had any of the following problems in the last 12 months? Breathing difficulty 1=Yes (sometimes and Sometimes often) Often 0=No (never) Never 1=Yes (yes in the last 2 Have you ever been told by a doctor that you have: Asthma Yes, in the last 2 years years, Yes, more than 2 Yes, more than 2 years ago years ago) 0=No Never (never) 1946-51 cohort: Have you ever been told by a doctor that you have: Bronchitis/emphysema Yes, in the last 2 years 1=Yes survey 2 Yes, more than 2 years ago (yes in the last 2 years, Yes, more than 2 years ago) Never Rarely 0=No In the last 12 months, have you had any of the following: Breathing difficulty Sometimes 1=Yes Often

66 Cohort (Survey) Question Original Response options Re-coded values No 0=No In the past three years, have you been diagnosed with or treated for: Asthma? Yes 1=Yes 1921-26 cohort: In the past three years, have you been diagnosed with or treated for: No 0=No survey 3-6 bronchitis/emphysema? Yes 1=Yes 1946-51 cohort: No 0=No survey 3-7 In the last 12 months, have you had any of the following: Rarely (no and rarely) Breathing difficulty Sometimes 1=Yes Often (sometimes and often)

67 According to their asthma or bronchitis/emphysema status across surveys, women were classified into the following mutually exclusive groups: i) Past asthma; ii) Prevalent asthma; iii) Incident asthma; iv) Bronchitis/emphysema; v) Never asthma.

These groups will be used to examine the association between asthma and health service use in Chapters 6-9.

1921-26 cohort When defining asthma groups, women in the 1921-26 cohort had to have answered respiratory questions at Survey 1 as well as at least one other survey. Out of 12432 women in this cohort who returned Survey 1, 10761 women were eligible for analysis. Women in the 1921-26 cohort who answered ‘yes’ to the asthma question “ever” at Survey 1 but not any other survey were considered to have ‘Past asthma’. ‘Prevalent asthma’ was defined as answering ‘yes’ to the asthma question in Survey 1 and at least one other survey. Women who answered ‘Yes’ to the asthma question “in the last three years” at Survey 2, 3, 4, 5 or 6 but NOT Survey 1 were considered to have ‘Incident asthma’. The ‘Bronchitis/emphysema’ group was defined as women who did not report asthma in any survey but reported bronchitis/emphysema on at least one survey. The ‘Never asthma’ group included women who never answered ‘yes’ to either the asthma or the bronchitis/emphysema questions (see Table 3-5).

Table 3-5. Definition of asthma group for women in 1921-26 cohort Asthma status Answer to asthma questions S1 S2 S3 S4 S5 S6 Past asthma Yes No/. No/. No/. No/. No/. Prevalent asthma Yes Yes at any follow-up survey Incident asthma No Yes at any follow-up survey Bronchitis/emphysema No/. No/. No/. No/. No/. No/. Yes to bronchitis/emphysema at any survey Never asthma No asthma or bronchitis/emphysema reported at any survey

68 1946-51 cohort For 1946-51 cohort, the categorisation of the groups were slightly different because of the question asked in Survey 2 (Table 3-4), necessitating a slightly different algorithm for categorisation. Among this cohort, women had to have answered Survey 1 and at least two more following Surveys to be included in the analysis. Of the 13715 women who completed Survey 1, 11425 women had completed at least two more subsequent surveys. Women who answered ‘yes’ to the asthma question at Survey 1 and had indicated that they had been diagnosed with asthma more than 2 years ago at Survey 2 but not any other Survey were considered to have ‘Past asthma’. ‘Prevalent asthma’ was defined as answering ‘yes’ to the asthma question in Survey 1 or Survey 2 (more than 2 years) and at least one other follow-up Survey. Women who answered ‘yes’ to being diagnosed with asthma less than 2 years ago at Survey 2 or at subsequent Surveys (i.e. Survey 3,4, 5 or 6) but NOT Survey 1 or Survey 2 (if they had been diagnosed ‘more than 2 years’ prior) were considered to have ‘Incident asthma’. The ‘Bronchitis/emphysema’ group and ‘Never asthma’ group were defined the same as 1921-26 cohort (see Table 3-6). Table 3-6. Definition of asthma groups for 1946-51 cohort Asthma status Answer to asthma questions S1 S2 S3 S4 S5 S6 S7 Past asthma Yes (>2 yrs) Yes No No No No No No (<2 yrs) Prevalent asthma Yes (>2 yrs) Yes Yes at any follow-up survey Yes (<2 yrs) Incident asthma No (>2 yrs) No Yes at any follow-up survey Yes (<2 yrs) Bronchitis/emphyse No No No No No No ma Yes to bronchitis/emphysema at any survey Never asthma No asthma or bronchitis/emphysema at any survey

69 Self-reported health service use variables

Women in both cohorts were asked about their visits to a family doctor/general practitioner (GP). This question is used as the outcome variable in modelling self- reported health service use (Chapter 5 and 6). Questions and answer options on this variable are shown in Table 3-7 for both cohorts. Table 3-7. Self-reported family doctor/GP visits in a year by women from the 1921-26 (surveys 1-6) and women from the 1946-51 (surveys 1-7) cohorts

Cohort/ variable Original Response Coding for analysis Survey options 1921-26 How many times have you . None 0=None cohort: consulted the following people . Once or twice 1=1-2 visits survey 1 for your own health in the last . Three or four times 2=3-4 visits 1946-51 12 months? . Five or six times 3=5-6 visits cohort: Family doctor or another . Seven or more times 4=7 or more visits Survey 1 and general practitioner 2 1921-26 How many times have you . None 0=2 visits or less cohort: consulted a family doctor or . 1 or 2 times 1=3 or 4 visits survey 2-6 another general practitioner in . 3 or 4 times 2=5-8 visits the last 12 months? . 5-8 times 3=9 or more visits . 9-12 times . 13-15 times . 16-19 times . 20 or more times 1946-51 How many times have you . None 0=None cohort: consulted the following people . Once or twice 1=1 or 2 visits survey 3-7 for your own health in the last . 3 or 4 times 2=3 or 4 visits twelve months? . 5 or 6 times 3=5 or 6 visits A family doctor or another . 7-12 times 4=7 or more visits general practitioner (GP) . 13-24 times . 25 or more times

70 Categorisation of the GP visits for both cohorts is described in chapter 5. Other health service use questions include allied health professional, hospital doctor, alternative health practitioner and specialist. These variables were investigated at Survey 1. The survey questions for these variables and response options are shown in Table 3-8.

Table 3-8. Self-reported health service use (other than a GP) by women from the 1921-26 and 1946-51 cohorts at Survey 1

Cohort/ variable Original Response options Collapsed values Survey 1921-26 How many times have . None 0=None cohort: you consulted the . Once or twice 1=1-2 visits Survey 1 following people for . Three or four times 2=3-4 visits 1946-51 your own health in the . Five or six times 3=5-6 visits cohort: last 12 months? . Seven or more times 4=7 or more visits Survey 1  A specialist doctor  An allied health professional  An "alternative" health practitioner  A hospital doctor

71 Health Related Quality of Life (SF-36)

Health related quality of life measures can be used to provide an indication of the impact of asthma on wellbeing and daily functioning. The SF-36 is a widely used, well-validated and short form measure of self-reported health-related quality of life (116) This questionnaire has been extensively reviewed for use with older populations (34, 110, 111). Thirty-five items are used to construct eight subscales, each with a standardised score range of 0 to 100 (116) (see Table 3-9).

Table 3-9. SF-36 subscales and items in each subscale Subscale Abbreviation Number of items in subscale Physical Health Physical Functioning PF 10 Role Physical RP 4 Bodily Pain BP 2 General Health GH 5 Mental Health Vitality VT 4 Social Functioning SF 2 Role Emotional RE 3 Mental Health MH 5

72 Predisposing, enabling and needs factors affecting health care use by women with asthma These factors were described in Chapter 2 in relation to the Andersen-Newman behavioural model in health service use. This section identifies those variables available from ALSWH surveys.

Predisposing factors The questions and response values used to collect information on ALWH women’s predisposing factors used in this thesis are shown in Table 3-10 for both cohorts, along with how the variables have been re-classified for analysis purposes. Note: While Indigenous ethnicity is a predisposing factor and ALSWH includes Aboriginal and Torres Strait Islander women’s data, it could not be used as a source of data about Indigenous health. Australian research community believes that research involving Indigenous population should be designed, developed, conducted, and analysed consulting key stakeholders which was not the case with ALSWH. Also, due to the nature of ALSWH data collection, Indigenous women are likely to not be representative of the Indigenous population.

Enabling factors The questions and response values used to collect information on ALSWH women’s enabling factors which are used in this thesis are shown in Table 3-11 for both cohorts.

73 Table 3-10. Predisposing variables from the ALSWH surveys, 1921-26 cohort (survey 1-6) and 1946-51 cohort (survey 1-7) Cohort/ Variable Original Response options New categories Survey Survey question Both cohorts Marital status . Married 0=Never married/single All surveys What is your present marital . Defacto (in a relationship)* 1=Partnered (Married or defacto) status? . Separated 2=Separated/divorced/widowed . Divorced . Widowed . Never Married/ single Both cohorts Area of residence . Major cities of Australia 0=Outer regional and remote areas All surveys Accessibility/remoteness Index of . Inner regional Australia (Outer regional Australia, Remote Australia, Very remote Australia, ARIA+ . Outer regional Australia Australia) . Remote Australia 1=Metropolitan areas . Very remote Australia (Major cities of Australia) 2=Inner regional areas (Inner regional Australia) Both cohorts Country of birth . Australian born 0=Other non-English speaking countries Survey 1 only In which country were you born? . Other English Speaking 1=Australian born . Other non-English speaking 2=Other English speaking countries Both cohorts Highest qualification . No formal qualifications 0= No formal qualifications Survey 1 only What is the highest qualification . School or Intermediate Certificate (or equivalent) 1= School certificate or trade certificate you have completed? . Higher School or Leaving Certificate (or equivalent) (School or Intermediate Certificate (or equivalent), Higher . Trade/apprenticeship (eg Hairdresser, Chef) School or Leaving Certificate (or equivalent), . Certificate/diploma (eg Child Care, Technician) Trade/apprenticeship (eg Hairdresser, Chef), . University degree Certificate/diploma (eg Child Care, Technician)) . High University degree (eg Grad Dip, Masters, PhD) 2= University qualification (University degree, Higher University degree (eg Grad Dip, Masters, PhD)) *for the 1946-51 cohort this option includes de facto (opposite sex) and de facto (same sex)

74 Table 3-11. Enabling variables from the ALSWH surveys, 1921-26 cohort (survey 1-6) and 1946-51 cohort (survey 1-7)

Cohort/ Variable Original Response options New categories Survey Survey question 1921-26 cohort: Private health insurance for hospital cover or . Yes 0=No Survey 1, 2 and 3 ancillary services . I am covered by Veterans' Affairs 1=Yes ●This question is about health care (applicable for 1921-26 cohort only) 2= I am covered by Veterans' Affairs

Do you have private hospital insurance? . No 1946-51 cohort: ●This question is about health care Survey 1 and 3 Do you have private health insurance for ancillary services (eg. dental, physiotherapy, etc)?

1946-51 cohort: ●Do you have private health insurance for hospital . Yes 0=No Survey 2, 4, 5, 6 cover? . No - I am covered by Veterans' (No - because I can't afford the cost, No and 7 ●Do you have private health insurance for ancillary Affairs - because I don’t think you get value for services? (eg. dental, physiotherapy) . No - because I can't afford the cost money, No - because I don't think I need . No - because I don’t think you get it, No - because the services are not value for money available where I live, No - other reason) . No - because I don't think I need it 1=Yes . No - because the services are not 2= No - I am covered by Veterans' available where I live Affairs . No - other reason 1921-26 cohort: ●Which of the following types of cover do you have . Yes 0=No Survey 4, 5 and 6 for health services (excluding your Medicare card): . No 1=Yes Private health insurance for hospital cover ●Which of the following types of cover do you have for health services (excluding your Medicare card): Private health insurance for ancillary services/ extras cover (eg dental, physiotherapy)

75 Cohort/ Variable Original Response options New categories Survey Survey question Both cohorts: How do you manage on the income you have . It is impossible 0=easy All surveys available? . It is difficult all the time 1=difficult some of the time/not too . It is difficult some of the time bad . It is not too bad 2=Impossible/difficult all the time . It is easy 1946-51 cohort: Thinking about your own health care, how would you . Excellent 0=Excellent All surveys rate the following: . Very good 1=Good/very good 1921-26 cohort:  Access to medical specialists if you need them . Good 2=Poor/Fair Survey 2 and 3  Access to a hospital if you need it . Fair *Don’t know was classed as missing  Access to medical care in an emergency . Poor  Access to after-hours medical care . Don’t Know  Access to a GP who bulk bills  Access to a female GP

76 Needs factors

In relation to the Andersen-Newman framework, need factors include chronic conditions, risk factors and other health related needs. With respect to this body of work, the relevant variables are described below.

Chronic conditions

In all surveys there were questions relating to doctor diagnosed health conditions. Women in survey 1 in both cohorts were asked: “Has a doctor ever told you that you have…?” followed by a list of various chronic conditions, namely: diabetes, arthritis, heart disease, hypertension, stroke, thrombosis, osteoporosis, depression, anxiety, breast cancer and low iron level.

From survey 2 for the 1921-26 cohort and from survey 3 for the 1946-51 cohort, the question was changed to “In the past three years, have you been diagnosed or treated for:...?”. The response options to these questions were “Yes” or “No”. For women in 1946-51 cohort the question at survey 2 was “Have you ever been told by a doctor that you have:...?” with the response alternatives of ‘Never’, ‘Yes, in the last 2 years’ ‘Yes, more than 2 years ago’ and ‘Both’ which were collapsed to ‘No (Never)’ and ‘Yes (Yes- in the last 2 years, Yes- more than 2 years ago, and both)’.

Body Mass Index (BMI)

In ALSWH study, BMI was calculated using the height and weight of the participants and was provided for each survey for all cohorts. BMI was categorised into four groups according to the WHO criteria (Table 3-12) (235)

Smoking status

There are a number of questions about smoking in ALSWH surveys including “Have you ever been a regular smoker?”, “How old were you when you started smoking?”, “Are you a regular smoker now?”, “How many cigarettes or pipes and cigars do you smoke

77 on average each day?”. For women from the 1946-51 cohort the questions were asked in all surveys while for women from the 1921-26 cohort the questions on smoking were asked only in Survey 1 and Survey 2 with an abbreviated question asked at Survey 6. Using the survey responses, a new variable ‘smoking status’ was determined, based on guidelines from National Heart Foundation of Australia (1990), risk factor prevalence study survey no. 3 1989’ and National Heart Foundation of Australia and Australian Institute of Health. The smoking categories that were used in analyses in this thesis are presented in Table 3-12.

Alcohol consumption

Participants were asked about their alcohol drinking habits in ALSWH surveys. One drink was defined as a glass of wine, a middy of beer or a nip of spirits. Participants were asked “How often do you usually drink alcohol?”, “On a day when you drink alcohol, how many standard drinks do you usually have? “ and “How often do you have five or more standard drinks of alcohol on one occasion? “. The ALSWH originally based its definition of health risk associated with alcohol intake on the National Heart Foundation criteria 1. Subsequently, the definition has been revised in light of the National Health and Medical Research Council (NHMRC) guidelines 2. The three NHMRC categories of health risk associated with alcohol consumption currently are: ‘Low risk’ (Up to 14 drinks per week), ‘Risky’ (15 to 28 drinks per week) and ‘High risk’ (More than 28 drinks per week). Based on NHMRC guidelines, a variable for alcohol consumption status (AlcNHMRC) was provided, derived from the frequency and quantity items. The ALSWH variable has defined five categories of risk. The NHMRC definitions for ‘Risky’ and ‘High risk’ drinking are adopted in full. For women defined as ‘Low risk’ by NHRMC, the ALSWH have separately defined as ‘Rarely drinks’ those who drink only rarely, leaving the remainder of this group to be classified as ‘Low risk’. A fifth category for ‘non-drinkers’ was added (see Table 3-12) (234). For this thesis, the ALSWH categories were collapsed to provide a three-level categorical variable to use in analyses due to low frequencies of some of the extreme categories.

Table 3-12. Needs variables from the ALSWH surveys, 1921-26 cohort (survey 1-6) and 1946- 51 cohort (survey 1-7)

Cohort/ Variable Original Response options Collapsed values Survey Both BMI . Underweight: BMI ≤ 18.5 0=Underweight (BMI ≤ 18.5) cohorts . Healthy weight: 18.5 < BMI ≤ 1=Healthy weight (18.5 < All surveys 25.0 BMI ≤ 25.0) . Overweight: 25.0 < BMI ≤ 30.0 2=Overweight (25.0 < BMI ≤ . Obese: BMI > 30 30.0) 3=Obese (BMI > 30)

1921-26 Smoking . Never-smoker 0=Non-smoker cohort: status . Ex-smoker (Never smoker) Survey1 and . Smoker, less than 10 per day 1=Smoker Survey 2 . Smoker, 10-19 per day (Smoker, less than 10 per 1946-51 . Smoker, 20 or more per day day, Smoker, 10-19 per day, cohort: . Smoker, unknown cigarettes Smoker, 20 or more per day, Survey 1-7 per day Smoker, unknown cigarettes per day) 2=Ex-smoker (Ex-smoker) 1921-26 Smoking . I have never smoked 0=Non-smoker cohort: Status Short . I used to smoke (I have never smoked Survey 6 . I now smoke occasionally ) . I now smoke regularly 1=Smoker (I now smoke occasionally, I now smoke regularly) 2=Ex-smoker (I used to smoke) 1921-26 Alcohol . 'Low risk drinker' (Up to14 0=non-drinker cohort: status- drinks per week (Up to 2 drinks (Non-drinker) Survey1, 2, NHMRC per day)) '1= low risk drinker 3 and 6 (AlcNHMRC) . 'Non-drinker' (Low risk drinker and 1946-51 . 'Rarely drinks' Rarely drinker) cohort: . 'Risky drinker' (15 to 28 drinks 2= high risk drinker Survey 1, 2, per week (3 to 4 drinks per (Risky drinker and High 4, 5, 6 and7 day)) risk drinker) . 'High risk drinker' (More than 28 drinks per week (5 or more drinks per day))

79 Medicare Benefits Schedule (MBS)

In Australia, costs of many health services are subsidised through the Medicare Benefits Schedule which is the publicly funded national health insurance scheme, financed through taxation with the current Medicare Levy set at 2% of personal taxable income (236, 237). Universal health insurance was originally introduced in Australia in 1974, under the name Medibank. On 1st February 1984, after the passage of Health Legislation Amendment Act of 1983 (in September 1983), the Medicare Benefits Schedule (MBS) was introduced as a fee listing of all medical services and procedures subsidised by the Australian Government, managed by the Department of Health and Ageing (DoHA) and administered as Medicare by the Health Insurance Commission (237). Medicare was introduced as ‘a major social reform’ that was intended to provide an affordable, fair and basic health insurance system to all Australians. Since 1984, Medicare has had some major modifications to provide better services including subsidising new technologies and medication, funding new ways of delivering health care and including preventive health checks and care plans (237). The MBS is currently administered by Medicare Australia. The following people are eligible for Medicare benefits:  Australian citizens  New Zealand citizens who are lawfully living in Australia  Australian permanent visa holders or have applied for it  New Zealand citizens who have permission to work in Australia  New Zealand citizens who can prove a relationship to an Australian citizen or permanent resident Medicare benefits are not payable for prisoners or for services covered by third party insurance arrangements (such as life insurance) (237). When a health service use claim is submitted, a specific benefit is paid by Medicare in accordance with the scheduled fee for that service. The MBS includes a wide range of consultations, procedures and tests, defined items cover the clinical relevance of the service provided (necessary and appropriate according to treatment for the patient) and the designation of the provider. Services covered by Medicare are identified in the MBS with an item number which indicates the type and scope of the service, fees, Medicare

80 payable benefits, clinical requirements of the service and safety net benefits. In addition to service based fees, the MBS provides incentive payments for chronic disease management as well as bulk billing services for certain categories of patients. The fee that the patient pays is determined by the provider who may charge more than the scheduled fee. If the service is bulk billed, the health practitioner receives the Medicare benefit as full payment and the patient does not pay any extra (237). If the service is charged in excess of the scheduled fee, the patient incurs an “out-of-pocket” cost. Health consumers can claim 100% of the scheduled service fee for non-referred GP services, 85% of non-GP services out of hospital and 75% of the services provided in hospital from Medicare.

Linkage of MBS data with ALSWH participants

ALSWH participants have been linked with unit records from the Medicare Benefits Schedule (MBS) data from 1997 onwards, using the personal identifier numbers held by Medicare (i.e. participant’s Medicare number). These unit records data provide information on service item numbers and date of service. Consent for data linkage is on opt-out basis with all women who do not deny consent being eligible for linkage. Of women in the 1921-26 cohort, 6% and of women in the 1946-51 cohort, 7% opted out from data linkage. For this thesis, the linkage between ALSWH participants and MBS data was done in August 2014. ALSWH is also linked with NDI which allows ascertainment of Death. NDI provides information on women’s death from 1996 onwards.

MBS variables for health service use

Individual MBS items are listed under broad categories known as Broad Type of Services (BTOS). Categories of BTOS are indicated by letters from A to R. In this thesis, relevant items from BTOS A, B and M have been included in the analyses (Chapter 7 and 8). BTOS A includes item numbers for un-referred services that are provided by a GP, BTOS B are the un-referred services that are provided by a non-GP medical practitioner and BTOS

81 M are un-referred enhanced primary care services. These categories will be discussed in Chapter 7 in detail.

Asthma specific Medicare items

Asthma specific MBS items are nominated health services items for people with moderate to severe asthma. Severity of asthma is assessed by a GP and the patient is considered to have moderate to severe asthma if they have one of the conditions below:  Asthma symptoms on most days  Bronchodilator use at least 3 times per week  Use of preventer medication  Hospital attendance or admission following an acute exacerbation of asthma (173) These conditions were introduced and explained in detail in Chapter 1. Asthma care items are BTOS A or B items and are described below.

3.5.2.1.1 Asthma 3+ Visit Plan

Asthma-specific items were first introduced into the MBS in November 2001 via the ‘Asthma 3+’ visits, requiring GP to have three specific consultation for asthma with their patient over a maximum four month period (173) MBS Item Numbers: 2546, 2547, 2552, 2553, 2558, 2559, 2664, 2666, 2668, 2673, 2675 and 2677 Requirements for these items were:  At least 3 asthma related consultations in the previous 4 weeks (minimum) to 4 months (maximum) for a patient with moderate to severe asthma,  At least two of these consultations to have been planned recalls,  Diagnosis and assessment of severity,  Review of asthma related medication, and  Provision of written asthma action plan and education of the patient. The patient’s medical record should include documentation of each of these requirements and the clinical content of the patient held written action plan.

82 Medical actions to be taken during each visit which are mentioned below: Visit 1  Manage and discuss the issue that caused asthma or worsens the symptoms  Explain the ‘3+ Visit Plan’ to the patient and the need for review  Give handout for 3+ Visit Plan to the patient Visit 2  Assess patient’s present situation (review of medical records and history, medication and management)  Assess patient’s knowledge and perception on their condition and their expectation from their GP  Review medication and the technique to use devices to control or measure their asthma  Perform physical examination and spirometry  Measure asthma severity  Measure asthma control considering 2 weeks of peak expiratory flow rate (PEFR) records Visit 3 (approximately 2 weeks later)  Review patient’s PEFR records  Perform spirometry  Complete a written Asthma Action Plan for the patient  Identify triggers of asthma including allergy tests  Review medication  Reinforce education After visit 3, the GP initiated the payment of an incentive through the Practice Incentive Program (PIP) in addition to attracting a Medicare rebate. Visit 4 (approximately 4 weeks later)  Assess treatment progress  Review Asthma Action Plan  Discuss results of tests  Reinforce education (173)

83 3.5.2.1.2 Asthma Cycle of Care

The asthma 3+ visits was replaced with the Asthma Cycle of Care in 2006. Item numbers and treatment principals for the Asthma Cycle of Care are similar to the Asthma 3+ visits. Specifications for Asthma Cycle of Care are explained below. Item numbers: 2546, 2547, 2552, 2553, 2558, 2559 and 2664, 2666, 2668, 2673, 2675 and 2677 Requirements for Asthma Cycle of Care include:  At least 2 asthma related consultations within 12 months  One of the consultations has to be planned at a previous consultation  diagnosis and assessment of severity of asthma  Review of the patient's use of and access to asthma-related medication and devices  Provision to the patient of a written asthma action plan  Provision of asthma self-management education  Review of the written or documented asthma action plan The major difference between the two asthma care services is in the number of visits and the time period for claims. Each item number is described in Table 3-13.

84 Table 3-13. Specification of item numbers for Asthma 3+ visits and Asthma Cycle of Care between 2001 and 2014

Item Level/ length Specification number 2546 B (Less than 20 minutes) Surgery consultation 2547 B (Less than 20 minutes) Out-of-surgery consultation 2552 C (At least 20 minutes) Surgery consultation 2553 C (At least 20 minutes) Out-of-surgery consultation 2558 D (At least 40 minutes) Surgery consultation 2559 D (At least 40 minutes) Out-of-surgery consultation 2664 Standard consultations (5< <25) Surgery consultations 2666 Long consultation (25< <45) Surgery consultations 2668 Prolonged consultation (>45) Surgery consultations 2673 Standard consultations (5< <25) Out-of-surgery consultations 2675 Long consultation (25< <45) Out-of-surgery consultations 2677 Prolonged consultation (>45) Out-of-surgery consultations

To use these item numbers in the analyses, a new variable was created with two categories (yes/no). If a participant had claims for any of the asthma item numbers in a calendar year, the variable was given the value of ‘yes’ otherwise it was ‘no’.

General Practitioner (GP) visits (Items 1-51, 601 and 602)

Some item numbers under BTOS A and B changed in 2010. Item numbers are given for 1997-2010 and 2010 onwards are described below.

(1997-2010) GP attendances based on the level of complexity are divided into four categories

LEVEL A (Items 3, 4, 13, 19, 20) . Obvious and straightforward medical issues

85 . Limited examination of the affected part if required . Management . A brief attendance of at least 5 minutes

LEVEL B (Items 23, 24, 25, 33, 35) . Selective history . Implementation of a management plan such as advising or counselling the patient, ordering tests, or referring the patient to a specialist medical practitioner or other allied health professional. . Formulation of the decision . A physical attendance of less than 20 minutes The essential difference between Levels A and B relate not to time but to complexity.

LEVEL C (Items 36, 37, 38, 40, 43) . Taking a detailed history . Examination of multiple systems . A physical attendance of at least 20 minutes . Arranging investigations and implementing a management plan

LEVEL D (Items 44, 47, 48, 50, 51) . Difficult problems where the diagnosis is elusive and highly complex . Requiring consideration of several possible differential diagnoses . Making a decision about the most appropriate investigations and the order in which they should be performed . Physical attendance of at least 40 minutes

After Hours Services (Items 1, 2, 601, 602, 97, 98) After hours GP services are defined as services which are delivered: . Sunday, before 8 a.m. . After 1 p.m. On a Saturday . Public holiday . Week days between 8 p.m. And 8 a.m. (Table 3-14)

86 Table 3-14. Specification of item numbers for GP visits 1997-2010 Item Level/ length Specification number 3 A (at least 5 minutes) Surgery consultation 4 A (at least 5 minutes) Home visit 13 A (at least 5 minutes) Consultation at an institution other than a hospital or nursing home 19 A (at least 5 minutes) Consultation at a hospital 20 A (at least 5 minutes) Consultation at a nursing home 23 B (Less than 20 minutes) Surgery consultation 24 B (Less than 20 minutes) Home visit 25 B (Less than 20 minutes) Consultation at an institution other than a hospital or nursing home 33 B (Less than 20 minutes) Consultation at a hospital 35 B (Less than 20 minutes) Consultation at a nursing home 36 C (at least 20 minutes) Surgery consultation 37 C (at least 20 minutes) Home visit 38 C (at least 20 minutes) Consultation at an institution other than a hospital or nursing home 40 C (at least 20 minutes) Consultation at a hospital 43 C (at least 20 minutes) Consultation at a nursing home 44 D (at least 40 minutes) Surgery consultation 47 D (at least 40 minutes) Home visit 48 D (at least 40 minutes) Consultation at an institution other than a hospital or nursing home 50 D (at least 40 minutes) Consultation at a hospital 51 D (at least 40 minutes) Consultation at a nursing home

Emergency attendance-after hours 1 Other than 11pm-7am At a place other than consulting rooms 2 Other than 11pm-7am At consulting room 601 11pm-7am At a place other than consulting rooms 602 11pm-7am At consulting room 97 Other than 11pm-7am At a place other than consulting rooms 98 Other than 11pm-7am At consulting room

GP visit items after 2010 were modified to Items 3 to 51 and 5000-5067 which are described in Table 3-15.

87 Table 3-15. Specification of item numbers for GP visits 2010 onwards Item Level Specification number 3 A (at least 5 minutes) Consultation at consulting room 4 A (at least 5 minutes) Home visit or consultation at an institution other than a residential aged care 20 A (at least 5 minutes) Consultation at a residential aged care facility 23 B (Less than 20 minutes) Consultation at consulting room 24 B (Less than 20 minutes) Home visit or consultation at an institution other than a residential aged care 35 B (Less than 20 minutes) Consultation at a residential aged care facility 36 C (at least 20 minutes) Consultation at consulting room 37 C (at least 20 minutes) Home visit or consultation at an institution other than a residential aged care 43 C (at least 20 minutes) Consultation at a residential aged care facility 44 D (at least 40 minutes) Consultation at consulting room 47 D (at least 40 minutes) Home visit or consultation at an institution other than a residential aged care 51 D (at least 40 minutes) Consultation at a residential aged care facility

General practitioner-after hours

5000 A (at least 5 minutes) Consultation at consulting room 5003 A (at least 5 minutes) Home visit or consultation at an institution other than a residential aged care 5010 A (at least 5 minutes) Consultation at a residential aged care facility 5020 B (Less than 20 minutes) Consultation at consulting room 5023 B (Less than 20 minutes) Home visit or consultation at an institution other than a residential aged care 5028 B (Less than 20 minutes) Consultation at a residential aged care facility 5040 C (at least 20 minutes) Consultation at consulting room 5043 C (at least 20 minutes) Home visit or consultation at an institution other than a residential aged care 5049 C (at least 20 minutes) Consultation at a residential aged care facility 5060 D (at least 40 minutes) Consultation at consulting room 5063 D (at least 40 minutes) Home visit or consultation at an institution other than a residential aged care 5067 D (at least 40 minutes) Consultation at a residential aged care facility

To use GP visit item numbers in the analyses, a new variable was created with four categories (A, B, C and D). If a participant had claims for any of the item numbers in level A, the variable was given the value of ‘A’ and so on. Then, the total time of the GP visits

88 for each participant was calculated for each year assuming visit durations of 5, 15, 30 and 50 minutes for the A, B, C and D visits respectively. This procedure is described in detail in Chapter 7. For after-hours services, a new variable was created with two categories (yes/no). If a participant had claims for any of the after-hours visits, the variable was given the value of ‘yes’ otherwise it was ‘no’.

Respiratory tests

There are some Medicare items for assessing respiratory function which may be used in diagnosing and monitoring people with asthma. These items are as below

Item 11503 This item is for measurement of the following medical procedures: . Mechanical or gas exchange function of the respiratory system . Respiratory muscle function . Ventilatory control mechanisms Various respiratory tests such as volumes, flow, pressure, inspiratory and expiratory gas concentration, etc. may be claimed under this item number.

Item 11506 Under this item number respiratory function is measured and tracing is recorded before and after inhalation of bronchodilator.

Item 11509 The procedures performed under this item number is very similar to item 11509 with the addition of complex respiratory function tests in continuous presence of a technician.

89 Item 11512 Under this Medicare item the relationship between flow and volume during expiration or inspiration is continuously measured before and after inhalation of bronchodilator.

To use these item numbers in the analyses, a new variable (test) was created with two categories (yes/no). If a participant had claims for any of the item numbers for spirometry, the variable was given the value of ‘yes’ otherwise it was ‘no’ for each year of observation.

Health Assessments (Items 701 to 707)

Health assessment means the assessment of a patient’s health in all physical, psychological and social function aspects and the assessment of whether the patient requires preventative health care and/or education to improve their health. The Medicare health assessment items cover:  Blood pressure, heart rate and rhythm measurement  Medication assessment  Continence assessment  Immunisation status assessment for influenza, tetanus and pneumococcus  Physical functioning assessment (activities of daily living and fall in the last 3 months)  Psychological functioning assessment (cognition and mood)  Social functioning assessment (availability and adequacy of paid and unpaid help and caring for another person) A health assessment item is claimed for a service provided by a medical practitioner who has provided medical services to the patient in the last 12 months and/or will be providing services to the patient in the next 12 months. Health assessment items are BTOS M items, and from their introduction in 1999 till November 2010 were item numbers 700-706 which were claimable for people aged 75 years and over or 55 and over for Aboriginals or Torres Strait Islanders (Table 3-16).

90 Table 3-16. Specification of health assessment items from Nov 1999 to November 2010

Item Patient Specification number 700 75+ years old Consultation at consulting room 702 75+ years old Not an attendance at consulting rooms, a hospital or a residential aged care facility

704 55+ years old and of Consultation at consulting room Aboriginal or Torres Strait Islander descent 706 55+ years old and of Not an attendance at consulting rooms, a Aboriginal or hospital or a residential aged care facility Torres Strait Islander descent

However, from November 2010 the items were modified and item numbers changed to 701-707. There are four time-based health assessment items which are described below.

Brief Health Assessment (MBS Item 701) A brief health assessment is used to undertake simple health assessments. The health assessment should take no more than 30 minutes to complete.

Standard Health Assessment (MBS Item 703) A standard health assessment is used for straightforward assessments where the patient does not present with complex health issues but may require more attention than can be provided in a brief assessment. The assessment lasts more than 30 minutes but takes less than 45 minutes.

Long Health Assessment (MBS Item 705) A long health assessment is used for an extensive assessment, where the patient has a range of health issues that require more in-depth consideration, and longer-term

91 strategies for managing the patient’s health may be necessary. The assessment lasts at least 45 minutes but less than 60 minutes.

Prolonged Health Assessment (MBS Item 707) A prolonged health assessment is used for a complex assessment of a patient with significant, long-term health needs that need to be managed through a comprehensive preventive health care plan. The assessment takes 60 minutes or more to complete.

Eligibility for the modified health assessment items include:  Age 40 to 49 years (inclusive) and being at a high risk of developing type 2 diabetes  Age 45 and 49 (inclusive) and being at risk of developing a chronic disease  Age 75 years and older  Being a permanent residents of a Residential Aged Care Facility  Having an intellectual disability  Being a humanitarian entrant with access to Medicare services (eg. Refugees)  Being a former serving members of the Australian Defence Force

Health assessment for Aboriginal and Torres Strait Islander people (item 715)  This health assessment is used for the following age categories:  An Aboriginal or Torres Strait Islander child who is less than 15 years.  An Aboriginal or Torres Strait Islander person who is aged between 15 years and 54 years.  An Aboriginal or Torres Strait Islander older person who is aged 55 years and over. To use these item numbers in analyses, a new variable was created with two categories (yes/no). If a participant had claims for any of the item numbers for health assessment in a calendar year, the variable was given the value of ‘yes’ otherwise it was ‘no’.

92 Care planning and chronic disease management (Items 721 to 731)

The Chronic Disease Management plan is a GP service which enables GPs to plan and coordinate the services required to receive multidisciplinary, team-based care for patients with chronic or terminal medical conditions. These items can be claimed for a patient who suffers from at least one medical condition that has been (or is likely to be) present for at least 6 months, or that is terminal. A medical practitioner and at least two other health professionals who are involved in writing the plan deliver a multidisciplinary care plan which describes the following matters:

 Patient and their health care needs assessment  Management goals (patient agreement required)  An assessment of the patient’s needs for treatment types, required health services and desired health care or any other kind of service (eg. Home care)  Provisions for delivering the treatment, services and care  Review the plan There are two types of plans for Chronic Disease Management (CDM) including GP Management Plan (GPMP); and Team Care Arrangements (TCAs). If the patient has a chronic (or terminal) medical condition, the GP may suggest a GPMP. However, if the patient also has complex care needs which requires treatment from two or more other health care providers, TCAs may be suggested as well.

These BTOS M items identified as Enhanced Primary Care included items 720, 722, 724, 726, 728 and 730 up to 30th November 2005 (Table 3-17) Chronic Disease Management items after that date (Table 3-18).

93 Table 3-17. Chronic disease management items before 30th November 2005 Item Specification number 720 in consultation with a multidisciplinary care plan team 722 in consultation with a multidisciplinary discharge care plan team 724 to review a multidisciplinary community care plan or a discharge care plan prepared by that medical practitioner for a patient and claimed for under item 720 or 722 726 as a member of a multidisciplinary care plan team, to contribute to a multidisciplinary community care plan or to a review of a multidisciplinary community care plan prepared by another provider 728 as a member of a multidisciplinary care plan team, to contribute to a multidisciplinary discharge care plan or to a review of a multidisciplinary discharge care plan prepared by another provider 730 as a member of a multidisciplinary care plan team, to make a contribution to a multidisciplinary care plan in a residential aged care facility or to a review of a multidisciplinary care plan in a residential aged care facility prepared by the residential aged care facility

Table 3-18. Chronic disease management items after 30th November 2005 Item Specification number 721 Preparing a GP management plan (GPMP) 723 Coordinating the development of team care arrangements (TCA) 725 Reviewing a GP management plan 727 Coordinating a review of team care arrangements 729 Contributing to a multidisciplinary care plan or contributing to a review of a Multidisciplinary care plan for a patient who is not a resident of an aged care Facility 731 Contributing to another provider’s multidisciplinary care plan or contributing to A review of a multidisciplinary care plan for a patient who is a resident of an aged Care facility

94 National Death Index (NDI)

The National Death Index (NDI) provides death records including time of death and cause of death for each person in Australia since 1980. This data which can be linked with other epidemiological datasets is obtained from the Registrars of Births, Deaths and Marriage in each jurisdiction, the National Coronial Information System and the Australian Bureau of Statistics. The use of NDI data are restricted and secured by AIHW Ethics Committee for research purposes. Data linkage with NDI is provided by AIHW to maintain the security of data.

Although reports of deaths are reported by ALSWH participants’ families, the NDI is used on a yearly or bi-yearly basis to ascertain the death of the participants. This allows information on cause of death for the participants as well. The linkage process is conducted by sending a detailed list of participants (50,351) to Australian Institute of Health and Welfare (AIHW). Then, the information on death from NDI is matched with ALSWH data to prove the participants’ status (238).

To ascertain deaths in this thesis, NDI data were linked with ALSWH participants’ data in December 2016.

95 Statistical analyses

In this section, the statistical methods applied to the data throughout the thesis will be described including survival analysis and multinomial logistic regressions.

Overview of analyses, according to aims and chapters

Chapter 4: Association between asthma and mortality To study the associated between asthma and mortality while considering confounding factors, Cox proportional hazard analysis and survival analyses were conducted. Survival of the women from the 1921-26 cohort and women from the 1946-51 cohort were studied according to expanding asthma case definition over 12 and 13 years respectively. This analysis provided cumulative probabilities of mortality during the given time for women who had asthma at survey 2 (1921-26 cohort) or survey 3 (1946- 51).

Chapter 5: ALSWH survey data descriptive, according to asthma groups

Self-reported health service use and other factors (predisposing, enabling and needs) were described according to asthma group.

Chapter 6: Association between self-reported health service use and asthma group

There are two types of association studies in this thesis, cross sectional and longitudinal. Logistic regression (for dichotomous outcomes) and multinomial regression (for categorical outcomes with more than two categories) models were conducted to examine predictors of health service use among the 1921-26 and 1946-51 cohorts at Survey 3 using ALSWH self-reported survey data. Multivariate logistic regressions and multinomial multivariate regressions were performed on cross sectional data (Survey 3) to model the association between asthma groups and health service use in both cohorts.

96 Longitudinal analyses using robust variance estimators were conducted to investigate the association between asthma groups and health service use using survey self- reported data while taking time into account.

Both the cross sectional multinomial regression and longitudinal analyses incorporated the Andersen-Newman behavioural model in predictor variable selection and inclusion in the nested models.

Chapter 7: MBS data descriptive, according to asthma groups

In this chapter the level of health service use according to MBS claims is described according to asthma groups.

Chapter 8: Association between administrative MBS health survey use data and asthma group

This chapter is similar to Chapter 6 where logistic regressions models were conducted for dichotomous outcomes followed by multinomial regressions (for outcomes with more than two categories) models to examine individual predictors of health service use among the 1921-26 and 1946-51 cohorts at Survey 3 on MBS linked data. Multivariate multinomial logistic regressions were subsequently performed on the cross sectional data (Survey 3) followed by nested multinomial regressions to model the association between asthma groups and health service use in both cohorts adjusting for covariates according to the Andersen-Newman model.

Longitudinal analyses with robust variance estimators were conducted to investigate the association between asthma groups and health service use using MBS linked data over time.

97 Survival analysis

Survival analysis involves the modelling of time-to-event data. To study the impact of asthma on survival of older women over 12 years (chapter 4), survival analyses were performed. The survival function, S (t), gives the probability that a subject will survive past time t:

Where t is time of the tthevent, F(t) is the cumulative distribution function of T with corresponding probability density function. Probability density function is:

The hazard function which is also referred to as mortality rate, h(t), is the instantaneous rate at which events occur, given no previous events (239).

The cumulative hazard function, H(t), can be obtained by integrating h(u) over (0,t):

Using nonparametric methods like the Kaplan-Meier estimator (240) we can estimate H(t) and S(t).

Kaplan-Meier curves

The survival function can be estimated using the product-limit estimator which calculates the survival probability at each event time using life-table data and plotted as a Kaplan-Meier curve.

98 Kaplan-Meier curves are presented in Chapter 4 illustrating the probability of survival for women with and without asthma, using the LIFETEST procedure in SAS. If there is only one explanatory variable, a non-parametric method using Kaplan-Meier survival probabilities and comparing reasonably similar groups using the Log-rank test is sufficient however, often the groups being compared are different in many respects and continuous covariates cannot be evaluated using Kaplan-Meier plots and log-rank tests.

Cox Proportional Hazard Regression Model

The Cox proportional Hazards model (241) is the most widely used semi-parametric procedure for modelling survival data. In Chapter 4, the likelihood of mortality was calculated according to asthma status the measure of the association between asthma status and mortality rate was presented as a hazard ratio. Four nested models were performed to adjust for the covariates. The basic Cox model is:

Where h0 (t) is the baseline hazard which may vary arbitrarily over time, and Z is the covariate vector with β=(β1, β2,...,βp) being a vector of covariate coefficient. The hazard ratio for two individuals, Z and Z*, at any time point is:

Using asthma status as the predictor, a hazard ratio of 1 means the ratio of hazard rates for having asthma and not having asthma is the same and there is no difference between the groups in survival. If HR>1, the hazard rate of survival for asthma group is higher than the reference which means the asthma group has lower likelihood of survival. If the HR<1, the hazard rate of survival for asthma group is lower than the reference which means the likelihood of survival is higher in asthma group. Cox proportional hazard regressions were performed in SAS® using the PHREG procedure.

99 Logistic regression

Logistic regression is a method of analysis that is used to investigate the association between a binary, dependent outcome variable with an independent, explanatory variable(s), X, which may be categorical or continuous (242). To investigate the association between asthma or bronchitis/emphysema and self-reported or MBS records of health service use at Survey 3, initially univariate and then multivariate logistic regressions were performed. In this analysis the association between the predisposing, enabling and need factors and the outcome variable which was health service use (e.g. GP visits ACC, CDM and Health assessment) was investigated. GP visit categories are described in each of the chapters.

The probability (p) of the outcome (GP service use) is modelled by using a logistic link 푝 function, with the assumption that the relationship between ln⁡[ ]and X is linear in the 1−푝 following logistic regression equation: (242)

푝 In the above equation⁡ln⁡[ ] denotes the log of the odds for the outcome of interest, 1−푝

and are the estimates of the coefficients (242).

The Odds Ratio (OR) is a relative measure of effect provided through the logistic regressions. If the outcome is the same between the groups, the OR will be 1 indicating no difference between the groups. If the Odds Ratio>1, the predictor increases the odds of the outcome and if Odds Ratio<1, the predictor decreases the odds of outcome. Odds Ratio=1 indicates no difference between the groups in outcome.

For both Chapter 6 and Chapter 8, logistic regressions were performed using the LOGISTIC procedure in SAS®.

100 Multinomial regression models

Multinomial regression is a generalized form of logistic regression which is applied when the outcome variable is nominal with more than two categories (243). The Multinomial regression model predicts the probabilities of different categories (category membership) of the outcomes compared to the referent outcome category, given a set of independent variables using maximum likelihood estimation methods (243) To study the association between asthma and self-reported or MBS records of health service use (e.g. number of GP visits), univariate and multivariate multinomial logistic regressions were used. Univariate multinomial analyses were initially performed in order to individually identify predisposing, enabling and needs variables that were associated with health service use. Multivariate multinomial analyses were subsequently performed separately for each of the predisposing, enabling and needs variable categories for variables that were suggestive of an association in the univariate analyses using the decision criteria of p<0.2 for further investigation. Variables which had p>0.2 in multinomial association models were used in nested multivariate models to investigate association of asthma with health service use while subsequently controlling for important predisposing, enabling and needs factors. Sequentially nested models included: . Model 1: Asthma group . Model 2: Asthma group + predisposing factors . Model 3: Asthma group + predisposing factors + enabling factors . Model 4: Asthma group + predisposing factors + enabling factors + needs

In Chapter 6, the outcome variable was self-reported GP visits. Chapter 8 covered a number of Medicare claimed health services including GP visits (categorised in four categories according to total visit times), after hours GP visits, specialist visits, chronic disease management and asthma cycle of care which were (categorised to Yes or No). Models were built the same for all the outcome variables using variables which had significant associations in the cross sectional models.

101 Longitudinal analyses

In Chapter 6 and Chapter 8, after studying the association between asthma groups and health service use, adjusting for predisposing, enabling and needs factors at Survey 3 the association between asthma and HSU (specifically GP service use) was investigated longitudinally over five surveys using multinomial logistic regressions, which fits logistic regression models for discrete responses from survey data by method of maximum likelihood. The analysis was performed using Proc SURVEY LOGISTIC in SAS. To use the procedure, repeated measures for predisposing, enabling and needs factors that were retained for multinomial regression nested models were used. These measures were collected for each participant at each survey. Any observations with missing values for a particular time point were removed from the analysis. For this reason missing values were imputed where appropriate as detailed in Chapter 6 and Chapter 8. Sequentially nested models included: . Model 1: Asthma group + time . Model 2: Asthma group + time + predisposing factors . Model 3: Asthma group + time + predisposing factors + enabling factors . Model 4: Asthma group + time + predisposing factors + enabling factors + needs

102 Ethics approval and data access approval

Collection of ALSWH Survey Data

The ALSWH study has been approved by the ethics committees of the University of Newcastle and University of Queensland Human Research Ethics (H076‐0795 + UQ 2004 000 224). the participants have given written consents for their participation in the study, longitudinal linkage of their survey data to administrative data set (including but not limited to MBS, PBS and NDI), and use of their data by approved researchers. Consent for linkage is provided on an opt out basis.

Data linkage

The MBS data have been linked to ALSWH survey data for ALSWH for all women except for those who have opted out or were not able to provide consent before the opt out linkage was approved in 2012. The linkage of the survey and MBS data are approved by the Department of Health Ethics Committee, and acknowledged by the University of Newcastle Ethics Committee (H – 2011-0371) with the Australian Institute of Health and Welfare (AIHW) as the integrating authority for the creation of the statistical linkage key that allows linkage of de-identified survey and PBS data. Linked datasets are prepared at the University of Queensland, and released to approved researchers with a specific project identifier. Access to the external linked data requires that all analyses are conducted at ALSWH offices (either in Newcastle or Brisbane) using a secure network and environment.

Approval for projects in this thesis

Approval for the study presented in Chapter 4 was granted by the ALSWH Publications, Sub-study and Analysis committee on 12th August 2013, with a variation to the approval number A346 (EoI title: Factors affecting survival among older women with asthma). Permission to analyse ALSWH data for Chapter 5 and Chapter 6 and ALSWH-MBS linked data for Chapter 7 and Chapter 8 was granted on 20th February 2014 for the 1921-26

103 cohort with the approval number A507 (EoI title: Quality health care in older women with asthma). In 2015, a variation to EoI A507 was submitted to access the data for the 1946-51 cohort and the permission for analysis was granted on 20th April 2015 (the approval number changed to A507A and A507 became inactive). To access the ALSWH data, researchers submit an Expression of Interest (EoI) form to the Publications, Analyses and Sub‐studies (PSA) Committee of the data custodians (244). This EoI contains information about the applicant and researchers who have requested for data access, datasets, variables of interest and justification for each variable requested, an abstract of the project and aims of the project. Analyses of the data can only be carried out according to the EoI and any variation to the analyses or aims of the project should be submitted to the PSA committee for approval. The policies and procedures for data access, analysis and publications can be found on ALSWH website (244). Before accessing data and conducting any analysis, all researchers are required to sign the Confidentiality Statement with a clear understanding of the ALSWH Privacy Protocol. All research must comply with the Information Privacy Principles Section 14 of the Privacy Act 1988 and the National Privacy principles contained in the 2000 Amendment to the Act (http://www.privacy.gov.au/). Researchers must also provide an annual update on study progress for subsequent annual reports to the Australian Department of Health.

Summary This chapter included detailed description of the participants, data sources, variables and analysis methods that are used in this thesis with respect to the thesis aims and corresponding chapters. The next five chapters are result chapters starting with the impact of asthma on mortality in older women with asthma, including a peer –reviewed published paper for the results from the women in the 1921-26 cohort as well as unpublished results from the women in the 1946-51 cohort.

104 Chapter 4 Impact of asthma on mortality

Introduction

There is conflicting evidence of the effect of asthma on mortality in older age, especially in women (34, 54, 79). Adding to this conflicting information, asthma can be difficult to distinguish from other respiratory conditions in older age since there is no gold standard for defining asthma. Different asthma phenotype, physiological changes due to ageing and older people’s perception of asthma symptoms play important roles in underdiagnoses of asthma on older adults. This conflicting information on the impact of asthma on mortality resulted in two research questions for this thesis, that is: 1) What is the impact of asthma on mortality in older women? And 2) given the uncertainty in asthma definition/diagnosis, how does this influence association with mortality? In this chapter, the impact of asthma on mortality was investigated for both the 1921- 26 and 1946-51 cohorts, using different case definitions that account for some of the difficulty in defining asthma in older age. The ALSWH database was linked with the NDI to provide death data on participants. The findings of this investigation for women from the 1921-26 cohort were published in a peer-reviewed journal article, which is presented in section 4.3. The findings for women from the 1946-51 cohort are discussed following this article.

105 Asthma case definition

For this chapter, asthma status at survey 2 for the 1921-26 cohort and survey 3 for the 1946-51 cohort was identified to allow mortality rates to be comparable for women with and without asthma at this time point. Given the uncertainty of asthma diagnosis, a combination of self-reported respiratory diagnoses and symptoms were used to identify asthma cases among older women. These various definitions allow for possible under-diagnosis of asthma at older ages, including the differential diagnosis of bronchitis/emphysema and symptoms of breathing difficulties. These case definitions for asthma include: (i) Asthma only; (ii) Asthma or bronchitis/emphysema; (iii) Asthma or breathing difficulties; (iv) Asthma or bronchitis/emphysema or breathing difficulties;

106 Impact of asthma on mortality among women from the 1921-26 cohort

Results of this analysis for women from the 1921-26 cohort are presented in a published paper, which follows in this section. Title: Impact of asthma on mortality in older women: An Australian cohort study of 10,413 women Authors: Parivash Eftekhari, Peta M. Forder, Tazeen Majeed, Julie E. Byles Journal: Respiratory Medicine Year: (2016) Vol: 119 Pages: 102-108 DOI: 10.1016/j.rmed.2016.08.026

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114 Impact of asthma on mortality among women from the 1946-51 cohort

As was done for women from the 1921-26 cohort, four different case definitions for asthma were examined for women from the 1946-51 cohort, using Survey 3 as baseline. Women at Survey 1 were asked if they had ever been diagnosed with asthma. At Survey 2, they were asked if they had been diagnosed with asthma within the previous two years or more than two years ago. From Survey 3 onwards, they were asked whether they had been diagnosed or treated for asthma in the past three years. In this chapter, to be consistent with the criteria that were used for the analysis of the 1921-26 cohort’s data, Survey 3 was used as the baseline survey for the analysis of the 1946-51 cohort’s data. The association between asthma and mortality over 13 years was subsequently examined among these women.

Prevalence of asthma and other respiratory conditions among women from the 1946-51 cohort

The case definition for asthma using self-reported asthma by itself had a prevalence of 9.9%, while the prevalence of asthma nearly doubled when self-reported bronchitis/emphysema and breathing difficulties were included in the case definition (18.6%) (See Table 4-1).

Table 4-1. Prevalence of respiratory case definitions at survey3 (2001) for women from the 1946-51 cohort (aged 51-56 years) Asthma-related case definitions N=11, 226 N (%) (i) Asthma 1113 (9.9) (ii) Asthma OR Bronchitis/emphysema 1478 (13.2) (iii) Asthma OR Breathing difficulty 1829 (16.3) (iv) Asthma OR Breathing difficulty OR Bronchitis/emphysema 2089 (18.6)

115 Mortality for women from the 1946-51 cohort between 2001 and 2014

Mortality over a 13 year period (2001 to 2014) for women who participated at Survey 3 was investigated according to asthma status. By the end of follow-up in 2014, 41 women who had reported asthma (3.7%) (Self-reported, asthma only) had died compared with 379 of women who did not report asthma (3.7%), whether they reported other respiratory conditions or not (see Figure 4-1).

Figure 4-1. Cohort profile for the 1946-51 cohort between 1996 (survey 1) and 2014 (survey 7)

116 Using Cox proportional hazard regression (a crude unadjusted model), there was no evidence of a difference in mortality due to asthma status using the strictest definition (asthma only) (HR: 0.98, CI: 0.71-1.36, p=0.92) (see Table 4-2). Women included in the asthma or bronchitis/emphysema case definition (ii) had a similar mortality hazard as women without asthma or bronchitis/emphysema (CI:1.10 (0.84-1.45), p=0.47) while women in the asthma or breathing difficulty case definition (iii) were 1.4 times more likely to have died over 13 years (CI:1.11-1.77, p=0.005) Women included in the broadest case definition for asthma (iv) were 34% more likely to have died over 13 years compared with women without the condition (HR:1.34, CI:1.10- 1.69, p=0.0102).

Table 4-2. Asthma case difinitions and mortality from survey 3 (2001) to survey 7 (2013) for women from the 1946-51 cohort Case definition Univariate Death (n=11220) association Non- Case HR (C.I.) P case (i) Asthma only 379 (3.7) 41 (3.7) 0.98 (0.71-1.36) 0.92 (ii) Asthma or 360 (3.7) 60 (4.1) 1.10 (0.84-1.45) 0.47 bronchitis/emphysema (iii) Asthma or breathing 331 (3.5) 89 (4.8) 1.40 (1.11-1.77) 0.005 difficulty (iv) Asthma or breathing difficulty or 322 (3.5) 98 (4.7) 1.34 (1.10-1.69) 0.0102 bronchitis/emphysema

117 Further analyses investigated the mortality hazard for ‘asthma only’, ‘bronchitis/emphysema only’ and ‘asthma and bronchitis/emphysema’ groups. The hazard ratios were not significant for any of these asthma case definitions for the 1946- 51 cohort (see Table 4-3). However, it should be noted that there are few events and the confidence intervals around the Hazard ratio estimates are wide.

Table 4-3. Effect of asthma only, bronchitis/emphysema only and their combination on mortality from survey 3 (2001) to survey 7 (2013) for the 1946-51 cohort Groups Death Model1 (n=11220) (%) HR (C.I.) P Asthma only (n=943) 31 (3.3) 0.87 ( 0.60-1.26) 0.4649 Bronchitis/emphysema only (n=362) 19 (5.3) 1.43 ( 0.90-2.27) 0.1274 Asthma and bronchitis/emphysema (n=173) 10 (5.8) 1.57 (0.84-2.93) 0.1604 *Reference: no asthma or bronchitis/emphysema

Since the hazard ratio for the case definitions were not statistically significant when modelled univariately, there was no need to conduct multivariate models.

118 Conclusion

Women from the 1946-51 cohort had a higher prevalence of asthma compared with women from the 1921-26 cohort. However, prevalence of the broader case definition (asthma or bronchitis/emphysema or breathing difficulty) was similar for the two cohorts. Among women from the 1921-26 cohort with a 17% relative increased risk of death over 12 years even after controlling for other risk factors, asthma was associated with a higher mortality rate. However, among women from the 1946-51 cohort, there was no evidence of an association between asthma and mortality over 13 years of follow-up. However, it should be noted that there are few events in this cohort and the power may not be enough, and the confidence intervals around the Hazard Ratio estimates are wide. The significant increase in association between asthma and mortality in older women may reflect the nature of the disease or mis/underdiagnoses among these people but may also indicate potential to improve asthma care at older ages. Asthma in older people can have a different nature than asthma in younger people, and may be a different phenotype which is more severe and requires a more strict control on symptoms (84). Treatment seems to be harder in older adults due to various reasons including resistance to preventive medication and lack of adherence to treatment (4). The broader definitions may include women who have conditions other than asthma which share similar symptoms or may be misdiagnosed with asthma. Breathing difficulty is a common symptom of various health conditions including heart diseases. This may be the reason for higher prevalence rates in both cohorts and higher mortality rates in broader definitions in the 1921-26 cohort and may not be attributable to asthma. A high proportion of deaths in the asthma group were due to respiratory conditions. Also, mortality rate in this study is all-cause mortality rather than deaths attributed to asthma. Although adjusting for comorbidities in the analysis counteracts for impact of comorbidities on mortality in these women, only comorbidities which were in ALSWH were adjusted for. In this study, there is no information on asthma severity, however we do know that older women with asthma have significantly lower health-related quality of life than women without (see Chapter 5 to follow). This study has also not investigated the treatments

119 for these older women with asthma, and further research is warranted to determine whether they are receiving pharmacological and non-pharmacological treatments in association with asthma management guidelines. Given that the world’s population is ageing, there is a clear need for more specific diagnosis methods for asthma among older people to reduce the chance of mis/under- diagnosis. Given the nature of the disease seems to be different in older people and more severe, better management of the disease noting comorbidities and more specialised, accessible health care for asthma especially for older people may improve treatment outcomes and reduce mortality rates.

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Chapter 5 Asthma and other conditions and characteristics among women from the 1921-26 and 1946-51 ALSWH cohorts

Introduction

This chapter provides detailed information on the characteristics of the women from the 1921-26 and 1946-51 cohorts of the ALSWH, employing Andersen-Newman behavioural model in health service use. The ALSWH provides an opportunity to identify women with asthma, and to follow their health care use and health outcomes over time. As described in Chapter 3, for this analysis, the diagnosis of asthma was ascertained by asking women if a doctor had ever told them they had asthma and if they had other respiratory conditions and related symptoms. The women have been surveyed at baseline in 1996, again in 1998 (1946-51cohort)/1999 (1921-26 cohort), and then each three years on a rolling basis (see Chapter 3). This chapter describes the groups of women classified according to their self-report of doctor diagnosed asthma status and compares each group in terms of breathing difficulty, predisposing, enabling and needs factors which may drive health care use according to the behavioural model. These groups are the basis for all other comparisons in this thesis, with the exception of Chapter 4. The impact of asthma or bronchitis/emphysema on health related quality of life will also be described in this chapter.

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Identifying women with asthma and other respiratory conditions/symptoms

Self-reported information is commonly used in epidemiological studies. For studies of asthma, self-reported symptoms, diagnoses, and medication use are often assessed through questionnaire or interview. It has been shown that there is a close relationship between self-reported doctor diagnosed asthma and asthma medication used by patients, suggesting that self-reported diagnosis is a reliable measure of the disease (245). As described in Chapter 3 (see Table 3-4), information on self-reported asthma, bronchitis/emphysema and breathing difficulty was collected at each survey for the 1921-26 and 1946-51 cohorts. The responses for asthma and bronchitis/emphysema in all surveys for both cohorts were “Yes” or “No” except for survey 2 for 1946-51 cohort. In this survey, “Never” was considered “No” and “yes in the last 2 years”, “Yes, more than 2 years ago”, “both” were considered “Yes” (Table x, y, chapter 4). Table 5-1 and Table 5-2 summarise the prevalence of self-reported doctor diagnosed respiratory conditions provided by women from the ALSWH 1921-26 and 1946-51 cohorts respectively. In these tables, “missing” refers to women who did not return a survey at that time point or did not provide an answer to the questions used for case definitions. For women from the 1921-26 cohort, a drop in the prevalence of asthma and bronchitis/emphysema was observed between Surveys 1 to 2. This was attributed to the change in the way the questions were asked (i.e. from “ever” to “in last three years”). For women from the 1946-51 cohort, the decline in prevalence occurred at survey 3 where the question was modified to focus on diagnoses and treatment “in the last three years”. The prevalence of both asthma and bronchitis/emphysema was higher in the 1946-51 cohort, but the prevalence of breathing difficulty was higher in the 1921-26 cohort. Asthma prevalence increased from Survey 2 to Survey 4 in the 1921-26 cohort and then decreased slightly over Surveys 5 and 6, while in the 1946-51 cohort there was an upward trend in reported asthma from Survey 3 to Survey 7. In all surveys, the prevalence of breathing difficulty (“often” and “sometimes”) among women in the 1921-26 cohort was almost equal to the sum of prevalence of both asthma and 122

bronchitis/emphysema. However, for women from the 1946-51 cohort the prevalence of breathing difficulty was less than the prevalence of asthma plus the prevalence of bronchitis/emphysema.

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Table 5-1. Prevalence of reported respiratory conditions for women in the 1921-26 ALSWH cohort from 1996 to 2011

Survey Respiratory condition 1 (1996) 2 (1999) 3 (2002) 4 (2005) 5 (2008) 6 (2011) N= 12432 N=10434 N= 8646 N= 7158 N= 5560 N= 4055 n (%) n (%) n (%) n (%) n (%) n (%) Asthma Yes 1607 (12.9) 829 (7.9) 794 (9.2) 665 (9.3) 494 (8.9) 341 (8.4)

No 10624 (85.5) 9379 (89.9) 7673 (88.7) 6407 (89.5) 5007 (90) 3670 (90.5) Missing 201 (1.6) 226 (2.2) 179 (2.1) 86 (1.2) 59 (1.1) 44 (1.1) Bronchitis/emphysema Yes 21.1 (17.0) 643 (6.2) 567 (6.56) 787 (11.0) 377 (6.78) 240 (5.9) No 10099 (81.2) 9565 (91.7) 7900 (91.4) 6285 (87.8) 5124 (92.2) 3771 (93.0) Missing 224 (1.8) 226 (2.2) 179 (2.1) 86 (1.2) 59 (1.1) 44 (1.1) Breathing difficulty Often 778 (6.3) 471 (4.5) 534 (6.2) 544 (7.6) 454 (8.2) 335 (8.3)

Sometimes 1697 (13.6) 960 (9.2) 956 (11.1) 1345 (18.8) 962 (17.3) 489 (12.1) Rarely 1521 (12.2) 307 (2.9) 460 (5.3) 1141 (15.9) 807 (14.5) 268 (6.6) Never 7671 (61.7) 7751 (74.3) 6232 (72.1) 3819 (53.3) 3073 (55.3) 2744 (67.7) Missing 765 (6.2) 945 (9.1) 464 (5.4) 309 (4.3) 264 (4.7) 219 (5.4)

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Table 5-2. Prevalence of reported respiratory conditions for women in the 1946-51 ALSWH cohort from 1996 to 2013

Survey Respiratory condition 1 (1996) 2 (1998) 3 (2001) 4 (2004) 5 (2007) 6 (2010) 7 (2013) N= 13 715 N=12 338 N=11 226 N=10 905 N=10 638 N=10 011 N=9151 n (%) n (%) n (%) n (%) n (%) n (%) n (%) Asthma Yes 2143 (15.6) 1722 (14.0) 1116 (9.9) 1104 (10.1) 1086 (10.2) 1105 (11.0) 1121 (12.2) No 11488 (83.8) 10616 (86.0) 9987 (89.0) 9514 (87.2) 9426 (88.6) 8797 (87.9) 7986 (87.3) Missing 84 (0.6) 0 (0.0) 123 (1.1) 287 (2.6) 129 (1.2) 109 (1.1) 44 (0.5)

Bronchitis/emphysema Yes 2317 (16.9) 1722 (14.0) 1335 (11.9) 1478 (13.5) 1562 (14.7) 1541 (15.4) 1735 (19.0) No 11238 (81.9) 9566 (77.5) 6102 (54.4) 8836 (81.0) 8774 (82.5) 8209 (82.0) 7097 (77.5) Missing 160 (1.2) 1050 (8.5) 3789 (33.7) 591 (5.4) 302 (2.8) 261 (2.6) 319 (3.5) Breathing difficulty Often 493 (3.6) 339 (2.7) 273 (2.4) 298 (2.7) 300 (2.8) 294 (2.9) 361 (3.9) Sometimes 1824 (13.3) 1383 (11.2) 1062 (9.5) 1180 (10.8) 1262 (11.9) 1247 (12.5) 1374 (15.0) Rarely 2020 (14.7) 1486 (12.0) 883 (7.9) 1058 (9.7) 1433 (13.5) 1444 (14.4) 1580 (17.3) Never 9218 (67.2) 8080 (65.5) 5219 (46.5) 7778 (71.3) 7341 (69.0) 6765 (67.6) 5517 (60.3) Missing 160 (1.2) 1050 (8.5) 3789 (33.7) 591 (5.2) 302 (2.8) 261 (2.6) 319 (3.5)

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Classification of women according to self-reported asthma and bronchitis/emphysema

In the ALSWH, questions about diagnosed respiratory conditions (asthma or bronchitis/emphysema) were asked in different ways to capture point prevalence as well as incidence. (See Chapter 3, Table 3-5 and Table 3-6). Asthma may affect people across the life course. It is possible that a person may not have active asthma at older age but have been affected by it in the past, and this past exposure may still affect the person’s current health. In addition, new onset of a chronic disease later in life could have a significant impact on people’s health and quality of life.

Therefore, for the purposes of this thesis, women were classified not only according to the existence of asthma as a current chronic condition (prevalent asthma group), but also according to whether they reported having been affected by the disease in the past (past asthma group), and whether they newly reported a diagnosis of asthma during the observation period (incident asthma group). Due to a common misdiagnosis of asthma when it would be more correct to diagnose bronchitis/emphysema (COPD), women who had never reported asthma, but had reported bronchitis/emphysema were also considered (bronchitis/emphysema group). Women who never reported asthma or bronchitis/emphysema were in never asthma group.

With the exception of Chapter 4, which investigated asthma mortality in older women using inclusive definitions of asthma, analyses for all other studies in this thesis categorised women into mutually exclusive groups according to their asthma status. The process of identifying women and categorising them into five groups is described in full in Chapter 3 (Section 3.3.1).

Of the women from the 1921-26 cohort, 8.5%, 5.3% and 4.2% were classified as having prevalent asthma, incident asthma and past asthma respectively. Women with bronchitis/emphysema constituted 17.6% of the women in 1921-26 cohort. Of the women from the 1946-51 cohort, 10.2%, 8.9% and 6.4% were classified as having prevalent asthma, incident asthma and past asthma respectively. In this cohort, 15.2% of women had bronchitis/emphysema (see Table 5-3).

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Table 5-3. Frequency of asthma groups for women in 1921-26 and 1946-51 cohort

Asthma status Cohort 1921-26 1946-51 N=10761 N= 11425 N (%) N (%) Past asthma 452 (4.2) 728 (6.4) Prevalent asthma 912 (8.5) 1170 (10.2) Incident asthma 571 (5.3) 1014 (8.9) Bronchitis/emphysema 1893 (17.6) 1737 (15.2)

Never asthma 6933 (64.4) 6776 (59.3)

Breathing difficulties among women, according to asthma groups

Breathing difficulty is a key symptom of asthma which is used as a diagnostic tool in clinical practice, and commonly used in epidemiological studies to determine asthma status. In this section, the prevalence of breathing difficulty in both the 1921-26 and 1946-51 cohorts will be explored, especially with respect to the changes in symptom reporting over time according to the asthma groups. Among women from the 1921-26 cohort, women who had prevalent asthma had the highest frequency of breathing difficulty at Survey 1 (68.9%), followed by women who had past asthma (48.6%) (Table 5-4). From Survey 2 onwards, women who had prevalent asthma still had the highest frequency of breathing difficulty (45.3-69.1%).This was followed by women with incident asthma (32.3-61.0%). Of the women who had never had asthma, less than 5.0% reported breathing difficulty ‘often’ at any survey.

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Table 5-4. Prevalence of breathing difficulty for women from the 1921-26 cohort, according to asthma groups Breathing Surveys Asthma groups Difficulty Bronchitis/ Past asthma Prevalent asthma Incident asthma Never asthma emphysema

Never 121 (28.7) 118 (13.5) 263 (49.2) 1033 (58.0) 5261 (80.8) Rarely 96 (22.7) 149 (17.1) 112 (21.0) 307 (17.2) 671 (10.3) 1 Sometimes 133 (31.5) 352 (40.0) 121 (22.7) 323 (18.1) 488 (7.5) Often 72 (17.1) 252 (28.9) 38 (7.1) 118 (6.6) 91 (1.4) Never 257 (69.3) 331 (39.5) 321 (62.6) 1284 (76.7) 5558 (91.2) Rarely 20 (5.4) 56 (6.7) 26 (5.1) 67 (4.0) 138 (2.3) 2 Sometimes 58 (15.6) 261 (31.1) 112 (21.8) 224 (13.4) 305 (5.0) Often 36 (9.7) 190 (22.7) 54 (10.5) 98 (5.9) 93 (1.5) Never 188 (65.5) 238 (33.6) 228 (47.1) 1029 (68.6) 4549 (87.4) Rarely 24 (8.4) 55 (7.8) 34 (7.0) 108 (7.2) 239 (4.6) 3 Sometimes 51 (17.8) 232 (32.8) 138 (28.5) 234 (15.6) 301 (5.8) Often 24 (8.4) 182 (25.7) 84 (17.4) 128 (8.5) 116 (2.2) Never 97 (44.5) 98 (16.5) 97 (21.1) 594 (44.2) 2933 (68.9) Rarely 39 (17.9) 85 (14.3) 74 (16.9) 245 (18.2) 698 (16.4) 4 Sometimes 58 (26.6) 245 (41.2) 172 (39.3) 360 (26.8) 510 (12.0) Often 24 (11.0) 166 (27.9) 95 (21.7) 144 (10.7) 115 (2.7) Never 77 (45.6) 113 (24.4) 90 (25.3) 466 (45.7) 2327 (70.7) Rarely 38 (22.5) 72 (15.5) 52 (14.6) 193 (18.9) 452 (13.7) 5 Sometimes 34 (20.1) 175 (37.8) 129 (36.2) 244 (23.9) 380 (11.5) Often 20 (11.8) 103 (22.2) 85 (23.9) 116 (11.4) 130 (3.9) Never 84 (68.8) 136 (44.3) 101 (39.3) 449 (61.1) 1974 (81.7) Rarely 12 (9.8) 32 (10.4) 20 (7.8) 65 (8.8) 139 (5.8) 6 Sometimes 13 (10.7) 76 (24.8) 73 (28.4) 129 (17.5) 198 (8.2) Often 13 (10.7) 63 (20.5) 63 (24.5) 92 (12.5) 104 (4.3)

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Figure 5-1 shows progressive stacked bar graphs of changes in the proportions of women in the 1921-26 cohort reporting existing and new breathing difficulty symptoms from Survey 1 to Survey 4, for each asthma group. In these graphs, women who did not answer the question on breathing difficulty, or who were deceased by that survey, were combined into a single category. While these are very different categories, the disaggregation of these states within the graph would make the graph difficult to interpret. They are therefore combined here for simplification. A large proportion of women with prevalent asthma reported experiencing breathing difficulty at Survey 1, with most of these women still reporting breathing difficulty or were deceased/had missing data by Survey 4. Some of the women who did not report breathing difficulty at Survey 1 went on to develop breathing difficulty by Survey 4. The complexity of the stacked bar graphs at Survey 4 shows the variability in symptoms reported by women in the 1921-26 cohort. The incident asthma group also showed a pattern of complexity by Survey 4, with a lower prevalence of breathing difficulty at Survey 1, and increasing prevalence of breathing difficulty across time, with most women reporting breathing difficulty or being missing/deceased by Survey 4. The bronchitis group had an increasing proportion of women with breathing difficulty from Survey 1 to Survey 4. Women who had never reported asthma showed a very stable pattern of low prevalence of breathing difficulties across all surveys. Due to the complexity of breathing difficulty reporting over time for each of the groups, the inclusion of Surveys 5 and Survey 6 in Figure 5-1 was not feasible. Figure 5-2 shows a more simplified version of Figure 5-1, which includes all six surveys for surviving women from the 1921-26 cohort who provided complete responses. In this figure, women in all the groups showed a reduction in experiencing breathing difficulty at Survey 5 and Survey 6 compared to Survey 4 (this is likely to be due to survivor bias or attrition).

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No Breathing difficulty Breathing difficulty Missing/dead

Figure 5-1. Breathing difficulty for women from the 1921-26 cohort for Surveys 1-4, according to asthma groups

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No Breathing difficulty Breathing difficulty

Figure 5-2. Breathing difficulty for surviving women from the 1921-26 cohort for Surveys 1-6, according to asthma group

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Although the prevalence of breathing difficulty among women from the 1946-51 cohort was lower than the 1921-26 cohort (See Table 5-5). Women who had prevalent asthma had experienced more breathing difficulty in all the surveys (49.6-65.6%), followed by women who had past asthma (34.0%), and women with incident asthma from Survey 3 onwards (20.5- 43.3%). Among women who had never had asthma or bronchitis/emphysema, 5.7-10.0% reported breathing difficulty from Survey 1 to Survey 7.

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Table 5-5. Prevalence of breathing difficulty for women from the 1946-51 cohort, according to asthma groups Breathing Surveys Asthma groups Difficulty Bronchitis/ Past asthma Prevalent asthma Incident asthma Never asthma emphysema Never 333 (39.1) 146 (12.6) 520 (57.6) 1094 (65.6) 6149 (82.9) Rarely 229 (26.9) 252 (21.8) 197 (21.8) 324 (19.4) 779 (10.5) 1 Sometimes 256 (30.1) 526 (45.5) 153 (17.0) 215 (12.9) 428 (5.8) Often 33 (3.9) 232 (20.1) 32 (3.5) 35 (2.1) 60 (0.8) No 399 (53.5) 201 (19.5) 463 (57.9) 1033 (69.5) 5634 (84.0) Rarely 170 (22.8) 230 (22.4) 139 (17.4) 248 (16.7) 645 (9.6) 2 Sometimes 161 (21.6) 418 (40.7) 160 (20.0) 172 (11.6) 388 (5.8) Often 16 (2.1) 179 (17.4) 38 (4.7) 34 (2.3) 39 (0.6) No 271 (56.2) 141 (16.9) 256 (43.5) 722 (69.7) 3803 (85.2) Rarely 104 (21.6) 159 (19.1) 96 (16.3) 155 (15.0) 368 (8.2) 3 Sometimes 94 (19.5) 393 (47.1) 186 (31.6) 127 (12.3) 257 (5.8) Often 13 (2.7) 141 (16.9) 50 (8.5) 32 (3.1) 35 (0.8) No 490 (70.4) 329 (31.9) 382 (48.3) 1028 (73.2) 5528 (87.0) Rarely 105 (15.1) 178 (17.3) 115 (14.6) 191 (13.6) 464 (7.3) 4 Sometimes 84 (12.1) 394 (38.2) 229 (29.0) 156 (11.1) 312 (4.9) Often 17 (2.4) 130 (12.6) 64 (8.1) 30 (2.1) 50 (0.8) No 463 (66.3) 255 (25.3) 344 (42.5) 951 (67.0) 5300 (83.3) Rarely 134 (19.2) 253 (25.1) 148 (18.3) 245 (17.2) 651 (10.2) 5 Sometimes 85 (12.2) 377 (37.4) 255 (31.5) 178 (12.5) 362 (5.7) Often 16 (2.3) 123 (12.2) 62 (7.7) 46 (3.2) 51 (0.8) No 437 (66.2) 246 (25.5) 287 (38.0) 874 (67.0) 4898 (81.2) Rarely 128 (19.4) 218 (22.6) 155 (20.5) 221 (16.9) 718 (11.9) 6 Sometimes 82 (12.4) 374 (38.8) 241 (31.9) 168 (12.9) 374 (6.2) Often 13 (2.0) 125 (13.0) 72 (9.5) 41 (3.1) 40 (0.7) No 314 (52.9) 145 (17.0) 222 (33.0) 721 (59.7) 4081 (74.9) Rarely 159 (26.8) 196 (22.9) 159 (23.7) 238 (19.7) 821 (15.1) 7 Sometimes 98 (16.5) 366 (42.9) 215 (32.0) 198 (16.4) 486 (8.9) Often 23 (3.9) 147 (17.2) 76 (11.3) 50 (4.1) 62 (1.1)

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The graphical presentation of breathing difficulty according to the asthma groups in the 1946- 51 cohort is shown in Figure 5-3. A large proportion of women with prevalent asthma reported breathing difficulty at Survey 1 which stayed high until Survey 4. In general, women who had prevalent asthma had a higher prevalence of breathing difficulty across Survey 1 to Survey 4 compared with all other groups, and in addition, they also had a higher degree of complexity. In the incident asthma group, the prevalence of breathing difficulty increased from Survey 1 to Survey 4, but with a high number missing at Survey 3. Overall, there were progressively more new cases of breathing difficulty at each survey. The bronchitis/emphysema group showed a similar pattern of complexity, whereas the past asthma group did not show progression of symptoms. Women who had never reported asthma showed a stable pattern of low prevalence of breathing difficulty across time. As the women from the 1946-51 cohort also demonstrates complexity in breathing difficulty over time, Surveys 5-7 were not included in Figure 5-3 due to feasibility. Figure 5-4 shows a more simplified version of Figure 5-3 which includes all surveys for surviving women from the 1946-51 cohort who provided complete responses. The prevalent asthma group showed a pattern of intermittent symptoms, with a high degree of complexity by Survey 6. The incident asthma group showed less complexity, with increasing numbers of women reporting breathing difficulty from Survey 1-6 and then dropping slightly at Survey 7. Women with bronchitis/emphysema and never asthma very rarely reported breathing difficulty in all the surveys. This is lower in prevalence compared to women from the same category in the 1921- 26 cohort. One-third of the women who had past asthma, reported breathing difficulty at Survey 1, declining to 14% by Survey 7. This pattern was also similar to the 1921-26 cohort.

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Missing/dead No Breathing difficulty Breathing difficulty

Figure 5-3. Breathing difficulty for women in the 1946-51 cohort for Surveys 1-4, according to asthma group

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No Breathing difficulty Breathing difficulty

Figure 5-4. Breathing difficulty for surviving women from the 1946-51 cohort for Surveys 1-7, according to asthma group.

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Predisposing factors, according to asthma group

As mentioned in Chapter 3, the factors that impact women’s health service use in this thesis are categorised as predisposing, enabling and needs using the Andersen-Newman framework. This section presents the distribution of predisposing factors including: area of residence; marital status; highest qualification; and country of birth, according to asthma group for both cohorts (see Table 5-6 and Table 5-7. These factors may potentially influence health service utilisation and level of asthma management in patients with asthma (9). Details on collecting information regarding these variables in the ALSWH surveys were previously described in Chapter 3 (Table 3-10 to Table 3-12).

Observation of Table 5-6 and Table 5-7 suggest that there are not large differences between asthma groups in predisposing factors between asthma groups.

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Table 5-6. Predisposing factors at Survey 1 for the women in the 1921-26 cohort according to asthma group (N=10761)

Demographic factors Past Prevalent Incident Bronchitis/ Never asthma asthma asthma emphysema asthma N (%) N (%) N (%) N (%) N (%) Marital status Partnered 224 (50.9) 443 (50.0) 263 (47.6) 882 (48.4) 3502 (52.3) Not partnered 216 (49.1) 443 (50.0) 290 (52.4) 940 (51.6) 3190 (47.7) Missing 1 4 3 6 27 Area Major cities 173 (39.3) 317 (35.7) 230 (41.4) 724 (39.7) 2590 (38.6) Inner Regional 179 (40.7) 390 (43.9) 220 (39.6) 771 (42.3) 2725 (40.6) Outer regional or remote 88 (20.0) 182 (20.5) 106 (19.1) 329 (18.0) 1390 (20.7) Missing 1 1 0 4 14 Education No formal qualification 142 (34.1) 284 (33.8) 170 (32.4) 576 (33.2) 2031 (31.8) School certificate only 256 (61.5) 520 (61.9) 333 (63.5) 1092 (63.0) 4119 (64.4) University degree 18 (4.4) 36 (4.3) 21 (4.0) 66 (3.8) 241 (3.8) Missing 25 50 32 94 328 Country of Birth Australia 339 (81.1) 682 (80.9) 388 (74.8) 1337 (77.8) 4936 (78.5) Other English speaking 48 (11.5) 102 (12.1) 80 (15.4) 240 (14.0) 776 (12.34) countries Other non-English 31 (7.4) 59 (7.0) 51 (9.8) 141 (8.2) 573 (9.1) speaking countries Missing 23 47 37 110 434

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Table 5-7. Predisposing factors at Survey 1 for the women in the 1946-51 cohort according to asthma group (N=11425)

Demographic factors Past Prevalent Incident Bronchitis/ Never asthma asthma asthma Emphysema asthma N (%) N (%) N (%) N (%) N (%) Marital status Partnered 561 (80.6) 888 (79.7) 776 (81.8) 1365 (81.8) 5510 (84.8) Not Partnered 135 (19.4) 226 (20.3) 176 (18.5) 303 (18.2) 986 (15.2) Missing 32 56 62 69 280 Area Major cities 289 (35.2) 371 (35.5) 244 (28.4) 558 (34.7) 2339 (32.6) Inner regional 315 (38.4) 449 (40.6) 386 (44.9) 667 (41.5) 2893 (40.4) Outer regional or remote 216 (26.3) 286 (25.9) 229 (26.7) 383 (23.8) 1932 (27.0) area Missing 3 7 4 9 34 Education No formal qualification 112 (15.5) 212 (18.3) 208 (20.7) 281 (16.3) 1052 (15.7) School certificate only 486 (67.2) 760 (65.6) 679 (67.5) 1196 (69.4) 4652 (69.2) University degree 125 (17.3) 186 (16.1) 119 (11.8) 246 (14.3) 1016 (15.1) Missing 5 12 8 14 56 Country of Birth Australia 576 (79.9) 953 (82.2) 786 (78.4) 1346 (78.5) 5099 (76.1) Other English speaking 95 (13.2) 144 (12.4) 129 (12.9) 216 (12.6) 943 (14.1) countries Other non-English 50 (6.9) 63 (5.4) 87 (8.7) 153 (8.9) 657 (9.8) speaking countries Missing 7 10 12 22 77

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Enabling factors according to asthma group

Enabling factors that impact women’s health service use that are considered in this thesis include private health insurance and the ability to manage on available income, as these factors may facilitate women’s use of health services. There were no large differences between the asthma groups with regards to private health insurance coverage or ability to manage on available income in the 1921-26 or 1946-51 cohorts.

Table 5-8. Enabling factors at Survey 1 for the women in the 1921-26 cohort according to asthma group (N=10761) Demographic factors Past Prevalent Incident Bronchitis/ Never asthma asthma asthma emphysema asthma N (%) N (%) N (%) N (%) N (%) Health insurance coverage DVA card 35 (10.0) 87 (11.1) 72 (15.2) 195 (12.4) 599 (10.7) Covered by private health 155 (44.4) 347 (44.3) 197 (41.6) 658 (41.9) 2568 (46.1) insurance No private health insurance 159 (45.6) 350 (44.6) 204 (43.1) 719 (45.7) 2408 (43.2) Missing 83 84 65 225 886 ability to manage on available income Easy 86 (19.7) 171 (19.2) 108 (19.3) 387 (20.7) 1666 (24.5) Not too bad/sometimes difficult 322 (73.7) 652 (73.2) 395 (70.7) 1321 (70.7) 4765 (70.2) Difficult/impossible 29 (6.6) 68 (7.6) 56 (10.0) 161 (8.6) 356 (5.2) Missing 15 21 12 24 146

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Table 5-9. Baseline enabling factors at Survey 1 for the women in the 1946-51 cohort according to asthma group (N=11425) Demographic factors Past Prevalent Incident Bronchitis/ Never asthma asthma asthma Emphysema asthma N (%) N (%) N (%) N (%) N (%) Private health insurance DVA card 0 (0.0) 2 (0.2) 1 (0.1) 7 (0.5) 18 (0.3) Yes 278 (38.9) 375 (38.1) 283 (37.0) 536 (37.0) 2491 (38.7) No 436 (61.1) 606 (61.6) 480 (62.8) 905 (62.5) 3923 (70.0) Missing 92 113 85 135 568 Income management Easy 112 (14.7) 137 (13.1) 90 (11.1) 218 (14.3) 1139 (16.7) Not too bad/sometimes 512 (67.2) 696 (66.3) 579 (71.2) 1045 (68.7) 4842 (70.9) difficult Difficult/impossible 138 (18.1) 216 (20.6) 144 (17.7) 258 (17.0) 846 (12.4) Missing 61 64 50 96 371

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Need factors according to asthma groups

According to the Andersen-Newman’s behavioural model, needs that impact women’s health service use which are considered in this thesis include: comorbidities, smoking, BMI and alcohol consumption. In this thesis, for both cohorts, comorbidities (as need factors) include: heart disease, thrombosis, osteoporosis, low iron levels, diabetes, breast cancer, stroke and hypertension, which have been asked across surveys. Also, women from the 1946-51 cohort were asked if they had any other ‘major illness’ which was included in this cohort’s analysis as a separate comorbidity variable. Survey 2 is used as the baseline survey for both cohorts since Survey 1 asked about “ever” reporting of conditions and did not include all relevant comorbidities.

1921-26 cohort Women with asthma or bronchitis/emphysema had higher prevalence of reported heart diseases, osteoporosis, hypertension and obesity. Higher proportion of women with asthma or bronchitis/emphysema were ex-smokers and diabetes was more prevalent among women past asthma.

1946-51 cohort A higher proportion of women with asthma or bronchitis/emphysema had thrombosis, low iron level, hypertension, diabetes and other major diseases and were obese or a smoker. In this cohort women with prevalent asthma had a higher prevalence of osteoporosis.

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Table 5-10. Comorbidities reported at Survey 2 by women in the 1921-26 cohort according to asthma group Past Prevalent Incident Bronchitis/ Never asthma Comorbidities asthma asthma asthma emphysema N (%) N (%) N (%) N (%) N (%) Heart Yes 78 (18.1) 171 (19.4) 99 (18.2) 400 (22.2) 620 (9.5) disease No 353 (81.9) 710 (80.6) 446 (81.8) 1399 (77.8) 5932 (90.5) Missing 10 9 11 29 167 Yes 3 (0.7) 19 (2.2) 15 (2.7) 43 (2.4) 81 (1.2) Thrombosis No 428 (99.3) 862 (97.8) 530 (97.2) 1756 (97.6) 6471 (98.8) Missing 10 9 11 29 167 Yes 63 (14.6) 150 (17.0) 86 (15.8) 264 (14.7) 720 (11.0) Osteoporosis No 368 (85.4) 731 (83.0) 459 (84.2) 1535 (85.3) 5832 (89.0) Missing 10 9 11 29 167 Low iron Yes 26 (6.0) 41 (4.5) 32 (5.9) 107 (5.9) 268 (4.1) level No 405 (94.0) 804 (95.3) 513 (94.1) 1692 (94.0) 628 (95.9) Missing 10 9 11 29 167 Yes 132 (30.6) 350 (39.7) 199 (36.5) 632 (35.1) 2130 (32.5) Hypertension No 299 (69.4) 531 (60.3) 346 (63.5) 1167 (64.9) 4422 (67.5) Missing 10 9 11 29 167 Yes 47 (10.9) 73 (8.3) 42 (7.7) 143 (7.9) 454 (6.9) Diabetes No 384 (89.1) 808 (91.7) 503 (92.3) 1656 (92.0) 6098 (93.1) Missing 10 9 11 29 167 Breast Yes 7 (1.6) 14 (1.6) 15 (2.7) 31 (1.7) 103 (1.6) cancer No 424 (98.4) 867 (98.4) 530 (97.2) 1768 (98.3) 6449 (98.4) Missing 10 9 11 29 167 Yes 18 (4.2) 30 (3.4) 18 (3.3) 68 (3.8) 155 (2.4) Stroke No 413 (95.8) 851 (96.6) 527 (96.7) 1731 (96.2) 6397 (97.6) Missing 10 9 11 29 167 BMI Underweight 18 (4.5) 18 (2.1) 10 (1.8) 54 (3.1) 178 (2.8) Acceptable weight 183 (45.4) 341 (40.4) 225 (41.6) 870 (49.8) 3337 (52.7) Overweight 134 (33.2) 323 (38.3) 202 (37.3) 558 (32.0) 2049 (32.4) Obese 68 (16.9) 161 (19.1) 104 (19.2) 264 (15.1) 766 (12.1) Missing 49 69 30 147 603 Alcohol Low risk drinker 147 (34.1) 285 (32.8) 181 (33.0) 600 (32.8) 2277 (34.0) consumption Non-drinker 265 (61.5) 560 (64.4) 341 (62.2) 1157 (63.2) 4203 (62.8) Risky drinker 19 (4.4) 25 (2.9) 26 (4.7) 73 (4.0) 213 (3.2) Missing 21 42 23 63 240 Smoking Non-smoker 228 (56.2) 495 (59.6) 301 (58.0) 973 (57.2) 4213 (68.0) status Ex-smoker 159 (39.2) 302 (36.3) 187 (36.0) 601 (35.3) 1722 (27.8) Smoker 19 (4.7) 34 (4.1) 31 (6.0) 127 (7.5) 261 (4.2) Missing 35 59 37 127 523

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Table 5-11. Comorbidities reported at Survey 2 for women in the 1946-51 cohort according to asthma group Bronchitis/ Past Prevalent Incident emphysem Never asthma Comorbidities asthma asthma asthma a N (%) N (%) N (%) N (%) N (%) Yes 20 (2.8) 39 (3.5) 25 (2.6) 41 (2.4) 102 (1.6) Heart disease No 682 (97.1) 1083 (96.5) 934 (97.4) 1636 (97.6) 6432 (98.4) Missing 26 48 55 60 242 Yes 46 (6.5) (66 (5.8) 38 (4.0) 84 (5.0) 178 (2.7) Thrombosis No 656 (93.4) 1056 (94.1) 921 (96.0) 1593 (95.0) 6356 (97.3) Missing 26 48 55 60 242 Yes 29 (4.1) 69 (6.1) 42 (4.4) 66 (3.9) 158 (2.4) Osteoporosis No 673 (95.9) 1053 (90.5) 917 (95.6) 1611 (96.1) 6376 (97.6) Missing 26 48 55 60 242 Low iron Yes 195 (27.8) 290 (25.8) 227 (23.7) 474 (28.3) 1374 (21.0) level No 507 (72.2) 832 (74.1) 732 (76.3) 1203 (71.4) 5160 (79.0) Missing 26 48 55 60 242 Yes 150 (21.4) 283 (25.2) 219 (22.8) 358 (21.3) 1071 (16.4) Hypertension No 552 (78.6) 839 (74.8) 740 (77.2) 1319 (78.6) 5463 (83.6) Missing 26 48 55 60 242 Yes 21 (3.0) 35 (3.1) 31 (3.2) 37 (2.2) 144 (2.2) Diabetes No 681 (97.0) 1087 (96.9) 928 (96.8) 1640 (97.8) 6390 (97.8) Missing 26 48 55 60 242 Yes 19 (2.7) 24 (2.1) 24 (2.5) 35 (2.1) 129 (2.0) Breast cancer No 683 (97.3) 1098 (97.9) 935 (97.5) 1642 (97.9) 6405 (98.0) Missing 26 48 55 60 242 Yes 11 (1.6) 21 (1.9) 12 (1.2) 18 (1.1) 33 (0.5) Stroke No 691 (98.4) 1101 (98.1) 947 (98.7) 1659 (98.9) 6501 (99.5) Missing 26 48 55 60 242 Other major Yes 72 (10.3) 149 (13.3) 100 (10.4) 174 (10.4) 410 (6.3) illness No 630 (89.7) 973 (86.7) 859 (89.6) 1503 (89.6) 6124 (93.7) Missing 26 48 55 60 242 BMI Underweight 14 (1.7) 23 (2.0) 13 (1.5) 31 (1.9) 112 (1.5) Acceptable 403 (48.5) 457 (40.4) 348 (39.2) 869 (53.5) 3898 (53.8) weight Overweight 233 (28.1) 351 (31.1) 278 (31.3) 435 (26.8) 2097 (29.0) Obese 180 (21.7) 299 (26.5) 249 (28.0) 289 (17.8) 1134 (15.7) Missing 27 42 25 61 250 Alcohol Low risk drinker 121 (14.2) 179 (15.4) 140 (15.4) 211 (12.6 ) 1066 (14.3) consumption Non-drinker 678 (79.7) 923 (79.3) 714 (78.7) 1350 (80.7) 6010 (80.9) Risky drinker 52 (6.1) 62 (5.3) 53 (5.8) 112 (6.7) 351 (4.7) Missing 6 8 6 12 64 Smoking Non-smoker 391 (59.5) 593 (55.3) 460 (50.2) 750 (47.0) 3772 (60.2) status Ex-smoker 162 (22.2) 297 (25.4) 279 (27.5) 394 (24.7) 1653 (24.4) Smoker 104 (14.3) 182 (15.6) 177 (17.5) 452 (28.3) 837 (12.3) Missing 71 98 98 121 514

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Health-related quality of life, according to asthma groups

The SF-36 scale is a widely used and well-validated measure of self-reported health-related quality of life. This measure is described in detail in Chapter 3 (Table 3-9). In all surveys, both cohorts were asked about their health related quality of life using the SF-36 instrument. Briefly, the SF-36 questionnaire contains eight subscales including: physical functioning; bodily pain; role physical; general health; mental health; role emotional; social functioning; and vitality. The subscale scores are all standardized to range from 0 to 100, with lower scores indicating worse status in the particular domain. In this section, changes in all subclasses of SF-36 health- related quality of life for all asthma groups from both 1921-26 and 1946-51 cohort across surveys will be presented.

Physical health

The physical functioning, bodily pain, role physical, and general health SF-36 subclasses are the physical aspects of health related quality of life. These Sub-scale scores of the SF-36 quality of life profile are shown in Figure 5-5 for the women from the 1921-26 cohort, and Figure 5-6 for the women from the 1946-51 cohort. Scores across all physical health domain were higher in the 1946-51 cohort than in the 1921- 26 cohort and decreased over time for both cohorts as the women aged. Compared to women without asthma, in the asthma and bronchitis/emphysema groups, women from both cohorts reported lower scores on all for subscales. Within the 1946-51 cohort, women with prevalent and incident asthma had the lowest scores for these subscales in all surveys. A relative decline in subscale scores can also be seen for women with incident asthma for women in both cohorts.

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Physical functioning Bodily Pain

1996 1999 2002 2005 2008 2011 1996 1999 2002 2005 2008 2011 (70-75) (73-78) (76-81) (79-84) (82-87) (85-89) (70-75) (73-78) (76-81) (79-84) (82-87) (85-89) Year (age) Year (age)

Role Physical General Health

1996 1999 2002 2005 2008 2011 1996 1999 2002 2005 2008 2011 (70-75) (73-78) (76-81) (79-84) (82-87) (85-89) (70-75) (73-78) (76-81) (79-84) (82-87) (85-89) Year (age) Year (age)

Figure 5-5. SF-36 Physical health quality of life items for women in the 1921-26 cohort from Survey1 (1996) to Survey 6 (2011), according to asthma group

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Physical Functioning Bodily Pain

1996 1998 2001 2004 2007 2010 2013 1996 1998 2001 2004 2007 2010 2013 (45-50) (47-52) (50-55) (53-58) (56-61) (59-64) (62-67) (45-50) (47-52) (50-55) (53-58) (56-61) (59-64) (62-67) Year (age) Year (age)

Role Physical General Health

Figure 5-6. SF-36 Physical health quality of life items for women in the 1946-51 cohort from Survey1 (1996) to Survey 6 (2010), according to asthma group

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Mental and emotional health

SF-36 mental and emotional health subscales include mental health, role emotional, social functioning and vitality. In both cohorts, women in the asthma or bronchitis/emphysema groups scored lower in all the subscales of mental and emotional health compared with women from the “never asthma” group, almost at all the surveys (Figure 5-7and Figure 5-8). The overall trend in subscales scores for the 1921-26 cohort from Survey 1 to Survey 6 (except for mental health) was downwards, while for the 1946-51 cohort the trend was upwards for all the subscales except social functioning. In the 1946-51 cohort, there was a tendency for the women in the “past asthma” and “bronchitis/emphysema” groups to have better scores than women in the “prevalent” and “incident” asthma groups.

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Mental Health Role Emotional

1996 1999 2002 2005 2008 2011 1996 1999 2002 2005 2008 2011 (70-75) (73-78) (76-81) (79-84) (82-87) (85-89) (70-75) (73-78) (76-81) (79-84) (82-87) (85-89) Year (age) Year (age)

Social Functioning Vitality

1996 1999 2002 2005 2008 2011 1996 1999 2002 2005 2008 2011 (70-75) (73-78) (76-81) (79-84) (82-87) (85-89) (70-75) (73-78) (76-81) (79-84) (82-87) (85-89) Year (age) Year (age)

Figure 5-7. SF-36 Mental Health quality of life items for women from the 1921-26 cohort from Survey1 (1996) to Survey 7 (2011) according to asthma groups

149

Mental Health Role Emotional

Social Functioning Vitality

Figure 5-8. SF-36 Mental Health quality of life items for women from the 1946-51 cohort from Survey1 (1996) to Survey 7 (2013) according to asthma groups

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Conclusion

This chapter provided information about the women in terms of reported diagnoses of asthma, bronchitis/emphysema, pattern of reports and breathing difficulty over time. Women in the 1946-51 cohort showed higher prevalence of asthma and bronchitis/emphysema over surveys whereas the prevalence breathing difficulty was more frequent for the women from the 1921-26 cohort. For both cohorts, breathing difficulty was reported more by women with prevalent asthma and the prevalence of this symptom increased for women with incident asthma. This chapter has also presented the distribution of factors that may affect health care use by women with asthma, grouped according to categories of predisposing, enabling and needs from Andersen-Newman behavioural model in health service use. In addition the quality of life of women was assessed for each group within each cohort which showed a similar downwards pattern over time in physical health subclasses of SF-36 for all the groups in both cohorts. Women with prevalent and incident asthma had distinctively lower score in all the subclasses of physical health. For the SF-36 subclasses in mental health, women from the 1921-26 cohort had a downward pattern over time compared with an upward pattern for the 1946-51 cohort. The scores were distinctly lower for women with prevalent and incident asthma. Understanding the factors which impact asthma and asthma management through health service availability and utilisation may enable more efficient asthma management in later life with potential benefits for women’s health-related quality of life.

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152

Chapter 6 Asthma status and self-reported health care utilisation

Chapter 5 was an overview of the asthma groups, and the characteristics which may impact on health service use. These characteristics were presented with regards to the Andersen- Newman’s behavioural model of predisposing, enabling and needs factors for health service utilisation. In this chapter the self-reported type of care and level of health care, used by women from both the 1921-26 and the 1946-51 cohorts will be described according to asthma groups and in relation to predisposing, enabling and need variables. The ALSWH survey data includes self-reported responses on women’s perception of their health, health service use, medications, access to health care and satisfaction regarding health service for both the 1921- 26 and 1946-51 cohorts. ALSWH health service use information include number of visits to a family doctor/GP, specialist doctor or other allied health care provider, and admission to hospital in the previous 12 months and satisfaction with their visits to these services. These items have been previously described in Chapter 3.

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Asthma status and self-reported health service use at survey 1

Health services that are explored in this section include number of visits to a family doctor/GP, hospital doctor, specialist, allied health professional and alternative medicine practitioner. For the 1921-26 cohort at Survey 1 (Figure 6-1), women with asthma or bronchitis/emphysema had more family doctor/ GP visits compared with women in the never asthma group. Women with prevalent asthma had the highest number of GP visits with 70.5% of the women having 7 or more visits in the last 12 months. Among women with past asthma, 65.9% reported 7 or more visits in the last 12 months, while only 46.0% of the women without asthma or bronchitis/emphysema had 7 or more visits to their family doctor/GP. Women with asthma or bronchitis/emphysema also had more visits to hospital doctors and specialists, compared to the never asthma group. For specialist visits, the differences were greatest for women with past asthma, prevalent asthma and bronchitis/emphysema, and not as marked for the incident asthma group. Few women used alternative services, but women with prevalent and past asthma appeared to use these more than old women in other groups. Self-reported health care use at Survey 1 for the 1946-51 cohort is shown in Figure 6-2. Note that response categories for these women were different to those for the 1921-26 cohort, in line with their lower level of health care use. Women in asthma or bronchitis/emphysema groups had higher frequencies of self-reported health service use compared women with no asthma. Women with prevalent or incident asthma had higher frequencies of family doctor/GP visits, with 25.3% and 20.8% respectively reporting 7 or more visits in a year compared with 8.7% of women in never asthma group. Women with prevalent asthma had higher prevalence of specialist visits (6.0%) and allied health practitioner (10.3%) compared with women with no asthma (2.1% and 5.3% respectively). Women with past asthma had higher number of visits to alternative health practitioner (e.g. chiropractor, naturopath, acupuncturist, herbalist etc.) (9.7%) compared with women in never asthma group (6.5%). For this cohort, pattern of service use was not substantially different from Survey 1 to Survey 2 (Survey 2 data shown in Appendix Figure A1).

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Figure 6-1. Self-reported health service use at Survey 1 by women from the 1921-26 cohort, according to asthma group

155

Figure 6-2. Self-reported health service use at Survey 1 by women from the 1946-51 cohort, according to asthma group

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Asthma groups and GP service use: changes over time

The number of visits to a General practitioner (GP) at each survey, according to asthma group is shown in Figure 6-3 and Figure 6-4 for 1921-26 and 1946-51 cohorts respectively. For the 1921-26 cohort, Survey 1 is not included in the graph due to different categorisation of GP visits at that survey. For the 1921-26 cohort, the proportion of women with very high visits (9 or more visits to a GP) was greater for women with asthma or bronchitis/emphysema (57.7-78.4%) than for women with no asthma (46.0-60.5%), consistent across all surveys (Figure 6-3). While women with prevalent asthma tended to have the most visits at the earlier surveys, women with incident asthma tended to have more visits at later surveys. For women from the 1946-51 cohort, women with asthma or bronchitis/emphysema reported a higher number of visits (7 or more) to their family doctor/GP in the last 12 months across all surveys (14.0%-28.4%) compared with women in the never asthma group (8.3%- 11.5%). Women with prevalent asthma had reported the highest frequency of family doctor/GP visits at all the surveys, however the frequency of GP visits increased for women with incident asthma on later surveys.

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Figure 6-3. Frequency of GP visits in the last 12 months for women from the 1921-26 cohort across five surveys, according to asthma group

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Figure 6-4. Frequency of GP visits in the last 12 months for women from the 1946-51 cohort across seven surveys, according to asthma group

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Satisfaction with access to GP services

Satisfaction with health service provision plays a significant role in the utilisation of healthcare services; women from both cohorts were asked to score their satisfaction with their GP with scores ranging from 1 to 5 where a higher score indicates greater satisfaction (Table 3-11). Table 6-1. GP satisfaction at Survey 1among women from the 1921-26 and 1946-51 cohorts, according to asthma group

GP satisfaction Past Prevalent Incident Bronchitis/ Never asthma asthma asthma asthma emphysema

1921-26 cohort

N 450 907 565 1874 6812

Mean±SD 4.3 ± 0.8 4.2 ± 0.8 4.2 ± 0.8 4.2 ± 0.8 4.2 ± 0.8

Median (Q1,Q3) 4.4 (4 , 5) 4.4 (3.8 , 5) 4.3 (3.8 , 5) 4.4 (3.8 , 5) 4.4 (3.8 , 5)

1946-51 cohort

N 854 1163 906 1677 7419

Mean±SD 4.0 ± 1.0 4.1 ± 0.9 3.9 ± 0.9 4.0 ± 0.9 4.0 ± 0.8

Median (Q1,Q3) 4.2 (3.2 , 5) 4.2 (3.4 , 4.8) 4.0 (3.2 , 4.8) 4.2 (3.4 , 5) 4.0 (3.4 , 4.8)

There was no apparent difference between the groups within both cohorts regarding GP satisfaction (Table 6-1). However, when comparing the two cohorts, the results show that women in all the groups from the 1946-51 cohort had lower GP satisfaction scores.

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Predisposing, enabling and need factors that may affect frequency of GP visits

As discussed in Chapter 2, health service use may be affected by predisposing, enabling and need factors. In this section, the number of visits to a GP by women in both 1921-26 and 1946-51 cohorts is explored according to asthma group and according to other predisposing, enabling and need factors. While all measured predisposing, enabling and need factors with potential to affect health service use were included in cross-sectional analyses (See Section 6.3) only example results are tabulated here. For the purpose of this chapter the results are shown for Survey 2 and 6 for the 1921-26 cohort and for Survey 1 and 7 for the 1946-51 cohort. Complete results of these cross-sectional analyses are provided in Appendices Table A-1 and Table A-2.

Predisposing factors and frequency of GP visits according to asthma groups

Predisposing factors available in ALSWH surveys include baseline age, area of residence, marital status, highest qualification and country of birth. Information on area of residence for both cohorts is presented here as an example of the cross-sectional association between predisposing factors and self-reported frequency of GP visits by women in different asthma groups.

For the 1921-26 cohort at both surveys, women who lived in major cities were more likely to have more GP visits regardless of asthma group. At both Survey 2 and Survey 6, women with asthma or bronchitis/emphysema had a higher proportion of ‘9 or more’ GP visits in a year regardless of their area of residence (27.8-54.3%) compared with women without bronchitis/emphysema (21.1-30.3%). At Survey 2, women with past asthma and those with prevalent asthma who lived in major cities had the highest proportion of ‘9 or more’ GP visits in a year (54.3%) (See Table 6-2). However, at Survey 6, women with incident asthma had the highest proportion of ‘9 or more’ GP visits.

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Table 6-2. Number of self-reported GP visits at Survey 2 and Survey 6 for women from the 1921-26 cohort according to area of residence and asthma groups

Survey Area of residence Number of GP visits Less than 2 3 or 4 5 to 8 9 or more n % n % n % n % Past asthma Survey 2 Major cities 11 6.4 34 19.7 39 22.5 89 51.4 Inner regional 13 7.3 43 24.0 53 29.6 70 39.1 Remote, very remote and outer regional 6 6.8 28 31.8 19 21.6 35 39.8 Survey 6 Major cities 8 10.8 16 21.6 19 25.7 31 41.9 Inner regional 8 20.0 7 17.5 11 27.5 14 35.0 Remote, very remote and outer regional 2 11.1 4 22.2 7 38.9 5 27.8 Prevalent asthma Survey 2 Major cities 16 5.0 47 14.8 82 25.9 172 54.3 Inner regional 30 7.7 84 21.5 123 31.5 153 39.2 Remote, very remote and outer regional 21 11.5 36 19.8 48 26.4 77 42.3 Survey 6 Major cities 15 10.3 23 15.9 49 33.8 58 40.0 Inner regional 12 9.4 37 29.1 30 23.6 48 37.8 Remote, very remote and outer regional 3 5.8 11 21.2 18 34.6 20 38.5 Incident asthma Survey 2 Major cities 15 6.5 52 22.6 74 32.2 89 38.7 Inner regional 21 9.5 55 25.0 60 27.3 84 38.2 Remote, very remote and outer regional 13 12.3 22 20.8 34 32.1 37 34.9 Survey 6 Major cities 5 3.9 20 15.7 39 30.7 63 49.6 Inner regional 9 10.6 18 21.2 25 29.4 33 38.8 Remote, very remote and outer regional 6 12.2 10 20.4 13 26.5 20 40.8 Bronchitis/emphysema Survey 2 Major cities 83 11.5 141 19.5 227 31.4 273 37.7 Inner regional 84 10.9 205 26.6 245 31.8 237 30.7 Remote, very remote and outer regional 43 13.1 74 22.5 109 33.1 103 31.3 Survey 6 Major cities 32 8.9 79 21.9 106 29.4 144 39.9 Inner regional 27 10.2 80 30.2 79 29.8 79 29.8 Remote, very remote and outer regional 19 13.8 30 21.7 37 26.8 52 37.7 Never asthma Survey 2 Major cities 404 15.6 722 27.9 679 26.2 785 30.3 Inner regional 594 21.8 843 30.9 714 26.2 574 21.1 Remote, very remote and outer regional 314 22.6 419 30.1 363 26.1 294 21.2 Survey 6 Major cities 152 13.1 330 28.5 356 30.8 319 27.6 Inner regional 135 14.4 306 32.7 262 28.0 233 24.9 Remote, very remote and outer regional 85 17.8 151 31.7 134 28.1 107 22.4

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For women from the 1946-51 cohort, the trends by area of residence were less clear. However the prevalence of 7 or more visits tended to be higher for women with asthma or bronchitis/emphysema compared with never asthma group irrespective of area of residence.

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Table 6-3. Number of self-reported GP visits at Survey 1 and Survey 7 for women from the 1946-51 cohort according to area of residence and asthma groups

Survey Area of residence Number of GP visits 0 1 or 2 3 or 4 5 or 6 7 or more

n % n % n % n % n % Past asthma Survey 1 Major cities 20 6.2 100 31.0 88 27.2 62 19.2 53 16.4 Inner regional 21 6.6 116 36.7 80 25.3 46 14.6 53 16.8 Remote, very remote and outer 14 6.4 79 36.2 55 25.2 29 13.3 41 18.8 regional Survey 7 Major cities 4 1.6 67 27.1 74 30.0 52 21.1 50 20.2 Inner regional 8 3.3 62 25.9 91 38.1 40 16.7 38 15.9 Remote, very remote and outer 3 2.4 28 22.6 36 29.0 25 20.2 32 25.8 regional Prevalent asthma Survey 1 Major cities 18 4.2 92 21.5 111 25.9 93 21.7 114 26.6 Inner regional 11 2.4 107 23.8 127 28.3 85 18.9 119 26.5 Remote, very remote and outer 19 6.4 76 25.8 71 24.1 67 22.7 62 21.0 regional Survey 7 Major cities 4 1.2 68 19.6 94 27.1 85 24.5 96 27.7 Inner regional 11 3.2 64 18.6 101 29.4 71 20.6 97 28.2 Remote, very remote and outer 10 6.0 32 19.2 60 35.9 30 18.0 35 21.0 regional Incident asthma Survey 1 Major cities 15 5.2 82 28.4 72 24.9 49 17.0 71 24.6 Inner regional 20 5.3 118 31.3 85 22.5 71 18.8 83 22.0 Remote, very remote and outer 22 8.9 84 34.0 71 28.7 35 14.2 35 14.2 regional Survey 7 Major cities 1 0.4 44 19.1 77 33.5 54 23.5 54 23.5 Inner regional 6 1.9 73 23.2 84 26.8 65 20.7 86 27.4 Remote, very remote and outer 3 2.2 32 23.5 33 24.3 40 29.4 28 20.6 regional Bronchitis/emphysema Survey 1 Major cities 35 5.5 197 31.0 196 30.9 101 15.9 106 16.7 Inner regional 49 7.3 227 33.9 185 27.6 113 16.9 96 14.3 Remote, very remote and outer 37 9.7 126 33.2 112 29.5 48 12.6 57 15.0 regional Survey 7 Major cities 8 1.6 131 26.8 158 32.3 99 20.2 93 19.0 Inner regional 22 4.4 137 27.2 148 29.4 113 22.5 83 16.5 Remote, very remote and outer 12 5.1 65 27.8 74 31.6 42 17.9 41 17.5 regional Never asthma Survey 1 Major cities 264 10 1052 39.8 708 26.8 354 13.4 267 10.1 Inner regional 321 11.2 1291 45.0 696 24.3 340 11.8 222 7.7 Remote, very remote and outer 244 12.4 873 44.2 492 24.9 209 10.6 157 7.9 regional Survey 7 Major cities 84 4.1 690 33.6 672 32.7 375 18.3 233 11.3 Inner regional 108 4.6 797 34.3 747 32.1 404 17.4 268 11.5 Remote, very remote and outer 63 5.4 398 34.1 393 33.6 176 15.1 138 11.8 regional

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Enabling factors and frequency of GP visits according to asthma groups

Enabling factors included in these analyses were ability to manage on available income, private health insurance and satisfaction with access to health services. Results for managing on available income are presented in Table 6-4 and Table 6-5 but complete results for private health insurance and satisfaction with access to health services are provided in Appendices Table A-3 and Table A-4.

In the 1921-26 cohort, at Survey 2, 63% of women with incident asthma who had difficulty managing on their income had ‘9 or more’ visits compared with 32.7% of those with never asthma (see Table 6-4). At survey 6, for this cohort, the greatest proportion with ‘9 or more’ visits was for women with incident asthma who had difficulty managing on their income. A similar effect with visits increasing according to asthma group and ability to manage on available income was also seen for the 1946-51 cohort (Table 6-5).

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Table 6-4. Number of self-reported GP visits at Survey 2 and Survey 6 for women from the 1921-26 cohort according to income management status and asthma groups Survey Income management Number of GP visits Less than 2 3 or 4 5 to 8 9 or more n % n % n % n % Past asthma Survey 2 Easy to manage 9 11.0 22 26.8 26 31.7 25 30.5 Not too bad to manage 17 6.6 59 23.0 68 26.5 113 44.0 Difficult to manage 0 0.0 5 17.9 6 21.4 17 60.7 Survey 6 Easy to manage 7 13.5 10 19.2 16 30.8 19 36.5 Not too bad to manage 8 11.3 17 23.9 19 26.8 27 38.0 Difficult to manage 2 25.0 0 0.0 2 25.0 4 50.0 Prevalent asthma Survey 2 Easy to manage 16 9.9 36 22.4 46 28.6 63 39.1 Not too bad to manage 44 7.0 115 18.3 179 28.5 289 46.1 Difficult to manage 3 7.0 2 4.7 14 32.6 24 55.8 Survey 6 Easy to manage 11 9.7 34 30.1 30 26.5 38 33.6 Not too bad to manage 16 8.3 34 17.6 64 33.2 79 40.9 Difficult to manage 2 12.5 2 12.5 3 18.8 9 56.3 Incident asthma Survey 2 Easy to manage 10 11.2 27 30.3 23 25.8 29 32.6 Not too bad to manage 35 9.4 85 22.7 126 33.7 128 34.2 Difficult to manage 0 0.0 7 15.2 10 21.7 29 63.0 Survey 6 Easy to manage 9 12.3 14 19.2 22 30.1 28 38.4 Not too bad to manage 10 5.9 29 17.2 52 30.8 78 46.2 Difficult to manage 1 6.7 3 20.0 2 13.3 9 60.0 Bronchitis/emphysema Survey 2 Easy to manage 38 11.7 89 27.4 110 33.8 88 27.1 Not too bad to manage 140 11.5 272 22.3 409 33.5 400 32.8 Difficult to manage 14 12.6 19 17.1 25 22.5 53 47.7 Survey 6 Easy to manage 30 11.6 66 25.5 77 29.7 86 33.2 Not too bad to manage 48 10.4 113 24.5 128 27.8 172 37.3 Difficult to manage 0 0.0 7 18.9 16 43.2 14 37.8 Never asthma Survey 2 Easy to manage 365 24.3 459 30.6 399 26.6 277 18.5 Not too bad to manage 764 18.0 1286 30.3 1145 27.0 104 24.6 3 Difficult to manage 59 19.9 72 24.2 69 23.2 97 32.7 Survey 6 Easy to manage 168 16.5 341 33.5 298 29.3 210 20.6 Not too bad to manage 188 12.9 424 29.0 430 29.4 421 28.8 Difficult to manage 8 12.7 13 20.6 16 25.4 26 41.3

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Table 6-5. Number of self-reported GP visits at Survey 1 and Survey 7 for women from the 1946-51 cohort according to income management status and asthma groups Income Survey management Number of GP visits 0 1 or 2 3 or 4 5 or 6 7 or more n % n % n % n % n % Past asthma Survey 1 Easy to manage 235 11.5 864 42.5 569 28.0 212 10.4 155 7.6 Not too bad to 890 9.3 3622 37.9 2501 26.2 1377 14.4 1174 12.3 manage Difficult to manage 153 7.5 580 28.6 446 22.0 315 15.5 536 26.4 Survey 7 Easy to manage 104 5.8 634 35.2 614 34.1 288 16.0 161 8.9 Not too bad to 222 3.5 1895 30.0 2065 32.7 1190 18.9 935 14.8 manage Difficult to manage 25 2.7 192 20.9 208 22.7 196 21.4 297 32.4 Prevalent asthma Survey 1 Easy to manage 9 6.4 41 29.1 47 33.3 24 17.0 20 14.2 Not too bad to 37 4.7 188 23.7 217 27.4 175 22.1 175 22.1 manage Difficult to manage 2 0.9 45 19.2 44 18.8 46 19.7 97 41.5 Survey 7 Easy to manage 7 4.3 51 31.5 44 27.2 37 22.8 23 14.2 Not too bad to 15 2.7 102 18.3 189 33.9 125 22.4 127 22.8 manage Difficult to manage 2 1.4 12 8.3 28 19.4 23 16.0 79 54.9 Incident asthma Survey 1 Easy to manage 11 8.7 46 36.5 32 25.4 17 13.5 20 15.9 Not too bad to 43 6.8 204 32.5 160 25.5 107 17.0 114 18.2 manage Difficult to manage 3 1.9 34 22.1 35 22.7 28 18.2 54 35.1 Survey 7 Easy to manage 2 2.0 33 33.7 37 37.8 14 14.3 12 12.2 Not too bad to 6 1.2 108 21.9 143 28.9 117 23.7 120 24.3 manage Difficult to manage 2 2.2 9 9.8 16 17.4 28 30.4 37 40.2 Bronchitis/emphysema Survey 1 Easy to manage 15 6.1 89 36.2 81 32.9 37 15.0 24 9.8 Not too bad to 87 7.5 388 33.5 346 29.9 184 15.9 152 13.1 manage Difficult to manage 18 6.5 72 26.1 63 22.8 40 14.5 83 30.1 Survey 7 Easy to manage 17 7.8 63 28.9 75 34.4 35 16.1 28 12.8 Not too bad to 21 2.4 244 28.3 262 30.4 192 22.3 142 16.5 manage Difficult to manage 5 3.5 22 15.4 40 28.0 27 18.9 49 34.3 Never asthma Survey 1 Easy to manage 174 13.9 572 45.7 332 26.5 108 8.6 67 5.3 Not too bad to 563 10.6 2318 43.5 1343 25.2 665 12.5 435 8.2 manage Difficult to manage 89 10.3 306 35.3 211 24.3 124 14.3 138 15.9 Survey 7 Easy to manage 75 6.2 450 37.0 418 34.4 184 15.1 89 7.3 Not too bad to 168 4.3 1317 33.6 1314 33.6 670 17.1 447 11.4 manage Difficult to manage 15 3.3 132 29.0 105 23.1 97 21.3 106 23.3

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Needs and frequency of GP visits according to asthma groups

Needs that were included in the analysis of this chapter include BMI, smoking status, alcohol consumption and comorbidities (including diabetes, hypertension, heart disease, osteoporosis, stroke, low iron level, depression, anxiety and other major illnesses). Here, the prevalence of diabetes in both cohorts according to asthma status is presented as an example (See Table 6-6 and Table 6-7). The results for other need factors are presented in Appendices Table A-5 to Table A-15.

The proportion with ‘9 or more’ visits is highest among women with both diabetes and asthma. For instance, in the 1921-26 cohort, women with prevalent asthma and diabetes had the highest prevalence of ‘9 or more’ visits at Survey 2, 61.6% compared with 43.8% for women with prevalent asthma and no diabetes, 45.2% for women in the never asthma group with diabetes, and 23.2% for women in the never asthma group with no diabetes. At Survey 6, for this cohort, the highest prevalence of ‘9 or more’ visits was among women with incident asthma and diabetes (59.5%) compared with 24.4% of women with neither conditions. A similar effect with visits increasing according to asthma group and diabetes was also seen for the 1946-51 cohort (See Table 6-7).

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Table 6-6. Number of self-reported GP visits at Survey 2 and Survey 6 for women from the 1921-26 cohort according to diabetes and asthma groups Survey Diabetes Number of GP visits Less than 2 3 or 4 5 to 8 9 or more n % n % n % n % Past asthma Survey 2 Yes 1 2.1 10 21.3 12 25.5 24 51.1 No 29 7.4 95 24.1 100 25.4 170 43.1 Survey 6 Yes 5 23.8 3 14.3 4 19.0 9 42.9 No 13 11.7 24 21.6 33 29.7 41 36.9 Prevalent asthma Survey 2 Yes 1 1.4 9 12.3 18 24.7 45 61.6 No 66 8.1 158 19.3 235 28.8 358 43.8 Survey 6 Yes 3 7.9 7 18.4 8 21.1 20 52.6 No 27 9.4 64 22.4 89 31.1 106 37.1 Incident asthma Survey 2 Yes 7 16.7 12 28.6 23 54.8 No 49 9.5 122 23.7 156 30.4 187 36.4 Survey 6 Yes 3 7.1 4 9.5 10 23.8 25 59.5 No 17 7.8 44 20.1 67 30.6 91 41.6 Bronchitis/emphysema Survey 2 Yes 7 4.9 27 18.9 35 24.5 74 51.7 No 203 12.0 394 23.4 547 32.5 541 32.1 Survey 6 Yes 10 8.4 28 23.5 33 27.7 48 40.3 No 69 10.7 161 24.9 189 29.3 227 35.1 Never asthma Survey 2 Yes 33 7.3 88 19.4 128 28.2 205 45.2 No 1283 20.5 1901 30.3 1630 26.0 1451 23.2 Survey 6 Yes 23 8.5 69 25.6 80 29.6 98 36.3 No 349 15.2 719 31.2 672 29.2 562 24.4

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Table 6-7. Number of self-reported GP visits at Survey 1 and Survey 7 for women from the 1946-51 cohort according to diabetes and asthma groups Diabet Survey es Number of GP visits 0 1 or 2 3 or 4 5 or 6 7 or more n % n % n % n % n % Past asthma Survey 1 Yes 0 0.0 6 20.7 4 13.8 7 24.1 12 41.4 No 55 6.6 289 34.9 219 26.4 130 15.7 135 16.3 Survey 7 Yes 1 1.9 4 7.5 11 20.8 13 24.5 24 45.3 No 14 2.5 156 27.7 192 34.0 106 18.8 96 17.0 Prevalent asthma Survey 1 Yes 0 0.0 7 16.3 8 18.6 10 23.3 18 41.9 No 48 4.3 268 23.7 301 26.7 235 20.8 277 24.5 Survey 7 Yes 4 3.5 7 6.1 27 23.7 34 29.8 42 36.8 No 22 2.9 158 20.8 235 31.0 152 20.1 191 25.2 Incident asthma Survey 1 Yes 3 7.3 8 19.5 5 12.2 9 22.0 16 39.0 No 54 6.2 276 31.7 223 25.6 146 16.7 173 19.8 Survey 7 Yes 1 1.0 9 9.1 21 21.2 27 27.3 41 41.4 No 11 1.9 143 24.1 177 29.8 134 22.6 129 21.7 Bronchitis/emphysema Survey 1 Yes 2 5.6 5 13.9 4 11.1 9 25.0 16 44.4 No 119 7.2 545 33.1 489 29.7 253 15.3 243 14.7 Survey 7 Yes 2 1.7 8 7.0 32 27.8 28 24.3 45 39.1 No 42 3.7 330 29.2 353 31.2 230 20.3 176 15.6 Never asthma Survey 1 Yes 8 4.5 39 21.8 45 25.1 39 21.8 48 26.8 No 821 11.2 3177 43.4 1852 25.3 864 11.8 598 8.2 Survey 7 Yes 3 0.7 68 16.0 152 35.8 109 25.7 92 21.7 No 265 5.1 1851 35.3 1708 32.6 856 16.3 559 10.7

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Associations between asthma group, predisposing, enabling and need factors and GP visits

To investigate the association between asthma status, predisposing, enabling and need factors and GP visits over time, regression analyses were conducted. Cross sectional univariate and multivariate multinomial logistic regressions at survey 3 were conducted for both cohorts in order to investigate the association between asthma or bronchitis/emphysema and frequency of GP visits. Nested models were then built with the sequential inclusion of predisposing, enabling and needs factors, which were significantly associated with GP visits in the univariate and multivariate analyses. A P-value < 0.2 was the cut-off for significantly association in these models to include any variables that showed some evidence of an association with the outcomes. After this, the association between asthma or bronchitis/emphysema and the frequency of GP visits were modelled longitudinally for both cohorts, using the same approach for model building and same covariates as the cross- sectional nested models. For the 1921-26 cohort the longitudinal analysis was done from Survey 2 to Survey 6, and for 1946-51 cohort data from Survey 1 to Survey 7 was modelled.

Predictors of the frequency of GP visits among women from the 1921-26 cohort

Univariate associations

Univariate multinomial logistic regressions were conducted for predisposing, enabling and need factors for the 1921-26 cohort with the outcome being GP visits. GP visits were categorised to 2 or less (reference category), 3-4, 5-8 and 9 or more visits. Table 6-8 shows results of univariate analysis for women from the 1921-26 cohort, for all of the predisposing, enabling and need factors. All factors were retained for the multivariate models (p<0.2).

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Table 6-8. Univariate association between number of GP visits /year and predisposing, enabling and needs factors for women from the 1921-26 cohort at Survey 3

Average GP visit time/year Predictor Categories (Ref: 0-75 minutes) 3-4 5-8 9 or more Odds Ratio (95% CI) Odds Ratio (95% CI) Odds Ratio (95% CI) Overall P Never asthma 1 1 1 <.0001 Past asthma 1.43 (0.82, 2.47) 3.02 (1.82, 5.02) 3.47 (2.09, 5.75) Asthma groups Prevalent asthma 1.90 (1.28, 2.82) 3.98 (2.74, 5.78) 6.08 (4.21, 8.78) Incident asthma 2.34 (1.49, 3.68) 3.40 (2.20, 5.27) 5.27 (3.43, 8.10) Bronchitis/emphysema 1.42 (1.13, 1.80) 2.28 (1.83, 2.85) 2.59 (2.08, 3.23) Predisposing factors Age 0.92 (0.87, 0.97) 0.90 (0.85, 0.95) 0.96 (0.91, 1.01 <.0001 Major cities 1 1 1 <.0001 Inner regional 0.76 (0.62, 0.94) 0.48 (0.40, 0.59) 0.37 (0.31, 0.46) Area Remote, very remote and outer 0.91 (0.76, 1.09) 0.73 (0.61, 0.86) 0.56 (0.47, 0.67) regional Partnered 1 1 1 <.0001 Marital status Never married 0.53 (0.36, 0.78) 0.52 (0.36, 0.75) 0.34 (0.23, 0.50) Seperated/Divorced/Widowed 0.86 (0.73, 1.01) 0.89 (0.76, 1.04) 0.90 (0.78, 1.05) University qualification 1 1 1 <.0001 Highest High school qualification 1.55 (1.08, 2.21) 2.06 (1.45, 2.92) 3.72 (2.56, 5.40) qualification No formal education 0.83 (0.61, 1.14) 0.92 (0.67, 1.26) 1.58 (1.12, 2.22) Australia 1 1 1 <.0001 Country of English speaking countries 1.62 (1.31, 2.00) 1.71 (1.39, 2.09) 1.25 (1.02, 1.52) birth Non-English speaking countries 1.65 (1.12, 2.44) 2.10 (1.45, 3.03) 2.02 (1.41, 2.89) Enabling factors No 1 1 1 0.0802 Private health Yes 1.69 (0.71, 4.03) 0.80 (0.38, 1.69) 1.79 (0.75, 4.28) insurance Yes, Veterans affair gold card 2.31 (0.91, 5.89) 0.99 (0.43, 2.27) 2.08 (0.81, 5.32) Easy to manage 1 1 1 <.0001

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Average GP visit time/year Predictor Categories (Ref: 0-75 minutes) 3-4 5-8 9 or more Odds Ratio (95% CI) Odds Ratio (95% CI) Odds Ratio (95% CI) Overall P Income Not too bad to manage 0.80 (0.53, 1.19) 1.93 (1.35, 2.76) 2.48 (1.75, 3.52) management Difficult to manage 1.08 (0.91, 1.29) 1.24 (1.04, 1.47) 1.29 (1.09, 1.53) Excellent/very good 1 1 1 <.0001 Access to a Good 0.90 (0.71, 1.14) 0.71 (0.56, 0.90) 0.66 (0.52, 0.84) specialist Fair/poor 0.85 (0.71, 1.01) 0.72 (0.61, 0.86) 0.56 (0.47, 0.66) Access to a Excellent/very good <.0001 bulk billing Good 0.89 (0.73, 1.08) 0.66 (0.55, 0.80) 0.54 (0.45, 0.66) doctor Fair/poor 1.15 (0.93, 1.43) 1.00 (0.81, 1.23) 0.76 (0.61, 0.94) Access to an Excellent/very good 1 1 1 <.0001 after-hours Good 1.31 (1.08, 1.58) 1.17 (0.98, 1.41) 1.14 (0.95, 1.37) doctor Fair/poor 1.29 (1.07, 1.56) 1.26 (1.05, 1.51) 0.96 (0.80, 1.15) Excellent/very good 1 1 1 <.0001 Access to a Good 0.78 (0.64, 0.95) 0.74 (0.61, 0.90) 0.76 (0.63, 0.92) female doctor Fair/poor 0.77 (0.64, 0.92) 0.69 (0.58, 0.83) 0.62 (0.52, 0.74) 0.0028 Excellent/very good 1 1 1 Access to a hospital doctor Good 1.34 (1.00, 1.81) 1.18 (0.88, 1.58) 1.12 (0.84, 1.50) Fair/poor 1.10 (0.93, 1.31) 1.02 (0.86, 1.20) 0.84 (0.71, 1.00) Needs Optimal weight 1 1 1 <.0001 Underweight 2.11 (1.01, 4.41) 1.43 (0.67, 3.05) 1.72 (0.81, 3.69) BMI Overweight 1.11 (0.88, 1.41) 1.17 (0.93, 1.47) 1.16 (0.92, 1.48) Obese 1.90 (1.24, 2.91) 2.52 (1.67, 3.81) 3.62 (2.40, 5.46) 0.1191 No drinking 1 1 1 Drinking alcohol Low risk drinking 0.96 (0.75, 1.21) 0.92 (0.73, 1.16) 0.80 (0.64, 1.02) High risk drinking 1.70 (0.68, 4.26) 1.22 (0.48, 3.09) 0.72 (0.26, 1.95) Non-smoker 1 1 1 0.0187 Smoking Smoker 0.55 (0.28, 1.08) 0.92 (0.50, 1.69) 0.57 (0.29, 1.12) status Ex-smoker 0.74 (0.58, 0.94) 0.88 (0.69, 1.11) 0.96 (0.76, 1.22)

173

Average GP visit time/year Predictor Categories (Ref: 0-75 minutes) 3-4 5-8 9 or more Odds Ratio (95% CI) Odds Ratio (95% CI) Odds Ratio (95% CI) Overall P No 1 1 1 <.0001 Diabetes Yes 1.69 (0.95, 3.02) 3.32 (1.92, 5.73) 4.45 (2.59, 7.67) No 1 1 1 <.0001 Heart diseases Yes 1.44 (0.92, 2.25) 3.07 (2.02, 4.65) 6.32 (4.19, 9.53) No 1 1 1 <.0001 Hypertension Yes 3.24 (2.53, 4.15) 3.99 (3.13, 5.08) 5.61 (4.38, 7.19) No 1 1 1 <.0001 Osteoporosis Yes 2.87 (1.95, 4.22) 3.31 (2.26, 4.84) 4.90 (3.36, 7.15) No 1 1 1 <.0001 Depression Yes 1.92 (0.92, 3.99) 2.39 (1.17, 4.87) 4.29 (2.14, 8.60) No 1 1 1 <.0001 Anxiety Yes 1.98 (0.81, 4.83) 4.21 (1.81, 9.78) 6.24 (2.71, 14.39)

174

Multivariate associations

The results of the univariate models for the 1921-26 cohort in the previous section suggested retaining all the investigated variables for multivariate models. These factors together could influence the strength of association of each other. In this section, the association between predisposing, enabling and need factors and number of GP visits will be examined. These analyses were performed in three separate multivariate multinomial analyses for the three categories of Andersen-Newman behavioural model in health service use. All the variables retained from the univariate models (p<0.2) were included in these analyses. The results of multinomial multivariate analyses for the 1921-26 cohort are shown in Table 6-9 to Table 6-11. For predisposing factors, all associations had p < 0.2 and were retained for inclusion in nested models. Likewise, for the model of enabling factors all associations had p < 0.2 and were retained for inclusion in the nested models. For this cohort, multivariate analysis of needs showed that the association between drinking alcohol and higher number of GP visits was > 0.2, alcohol consumption was subsequently dropped from further models.

175

Table 6-9. Multivariate associations between number of GP visits /year and predisposing factors for women from the 1921-26 cohort at Survey 3

Number of GP visits /year Predictor Categories (Ref: 2 or less) 3-4 5-8 9 or more Odds Ratio (95% Odds Ratio (95% CI) Overall P CI) Age 0.86 (0.80, 0.93) 0.85 (0.79, 0.92) 0.90 (0.83, 0.97) 0.0002 Major cities 1 1 1 <.0001 Inner regional 0.89 (0.66, 1.19) 0.42 (0.31, 0.57) 0.41 (0.30, 0.56) Area Remote, very remote and outer 0.93 (0.72, 1.19) 0.65 (0.51, 0.83) 0.63 (0.49, 0.81) regional Partnered 1 1 1 Marital status Never married 0.46 (0.29, 0.75) 0.48 (0.30, 0.77) 0.49 (0.30, 0.80) 0.0228 Separated/Divorced/Widowed 0.93 (0.74, 1.18) 0.92 (0.73, 1.16) 1.03 (0.81, 1.30) University qualification Highest High school qualification 1.61 (0.99, 2.62) 1.54 (0.96, 2.47) 3.22 (1.92, 5.40) <.0001 qualification No formal education 0.80 (0.53, 1.21) 0.66 (0.44, 0.97) 1.54 (0.98, 2.40) Australia 1 1 1 Country of English speaking countries 1.04 (0.56, 1.90) 1.89 (1.07, 3.33) 1.88 (1.06, 3.33) <.0001 birth Non-English speaking countries 0.45 (0.33, 0.62) 0.49 (0.36, 0.66) 0.64 (0.48, 0.87)

176

Table 6-10. Multivariate associations between number of GP visits /year and enabling factors for women from the 1921-26 cohort at Survey 3

Number of GP visits /year Predictor Categories (Ref: 2 or less) 3-4 5-8 9 or more Odds Ratio (95% Odds Ratio (95% CI) Overall P CI) No 1 1 1 0.0438 Private health Yes 1.89 (0.78, 4.55) 0.98 (0.46, 2.13) 2.51 (1.03, 6.13) insurance Yes, veteran’s affair gold card 2.60 (1.01, 6.71) 1.24 (0.53, 2.87) 2.86 (1.10, 7.48) Easy to manage 1 1 1 <.0001 Income Not too bad to manage 0.97 (0.76, 1.23) 1.28 (1.00, 1.62) 1.24 (0.97, 1.59) management Difficult to manage 0.92 (0.45, 1.89) 3.08 (1.61, 5.90) 4.89 (2.57, 9.30) Excellent/very good 1 1 1 <.0001 Access to a Good 0.73 (0.52, 1.01) 0.42 (0.30, 0.59) 0.40 (0.29, 0.57) specialist Fair/poor 0.70 (0.44, 1.12) 0.36 (0.22, 0.57) 0.35 (0.21, 0.57) Access to a Excellent/very good 1 1 1 0.0002 bulk billing Good 1.25 (0.87, 1.79) 1.00 (0.70, 1.44) 0.91 (0.62, 1.32) doctor Fair/poor 0.83 (0.62, 1.11) 0.73 (0.55, 0.98) 0.52 (0.38, 0.70) Access to Excellent/very good 1 1 1 <.0001 after-hours Good 1.93 (1.43, 2.60) 2.13 (1.59, 2.85) 1.82 (1.36, 2.45) doctors Fair/poor 2.25 (1.62, 3.13) 2.08 (1.50, 2.89) 2.13 (1.53, 2.97) Excellent/very good 1 1 1 <.0001 Access to a Good 0.49 (0.36, 0.66) 0.57 (0.43, 0.76) 0.50 (0.37, 0.67) female doctor Fair/poor 0.58 (0.42, 0.81) 0.72 (0.52, 1.00) 0.78 (0.56, 1.08) Excellent/very good 1 1 1 0.0559 Access to a Good 1.20 (0.84, 1.71) 1.62 (1.14, 2.30) 1.25 (0.87, 1.80) hospital doctor Fair/poor 1.30 (0.75, 2.24) 1.43 (0.82, 2.50) 1.66 (0.94, 2.92)

177

Table 6-11. Multivariate associations between number of GP visits /year and need factors for women from the 1946-51 cohort at Survey 3

Number of GP visits /year Predictor Categories (Ref: 2 or less) 3-4 5-8 9 or more Odds Ratio Odds Ratio (95% CI) Overall P (95% CI) Optimal weight 1 1 1 <.0001 Underweight 1.86 (0.87, 3.98) 1.25 (0.57, 2.74) 1.41 (0.63, 3.18) BMI Overweight 0.95 (0.74, 1.21) 0.95 (0.74, 1.21) 0.89 (0.69, 1.15) Obese 1.48 (0.96, 2.29) 1.84 (1.20, 2.81) 2.46 (1.60, 3.79) No drinking 1 1 1 0.2761 Drinking Low risk drinking 1.04 (0.81, 1.34) 1.00 (0.78, 1.28) 0.88 (0.68, 1.14) alcohol High risk drinking 1.90 (0.73, 4.91) 1.32 (0.50, 3.48) 0.84 (0.29, 2.41) Non-smoker 1 1 1 0.0178 Smoking Smoker 0.64 (0.32, 1.27) 1.18 (0.62, 2.23) 0.78 (0.37, 1.62) status Ex-smoker 0.76 (0.59, 0.98) 0.93 (0.72, 1.19) 1.04 (0.80, 1.36) No 1 1 1 <.0001 Diabetes Yes 1.46 (0.81, 2.64) 2.70 (1.54, 4.73) 3.15 (1.78, 5.57) No 1 1 1 <.0001 Heart diseases Yes 1.33 (0.85, 2.10) 2.87 (1.88, 4.39) 5.79 (3.80, 8.84) No 1 1 1 <.0001 Hypertension Yes 3.20 (2.49, 4.12) 3.78 (2.95, 4.85) 5.03 (3.88, 6.51) No 1 1 1 <.0001 Osteoporosis Yes 2.90 (1.96, 4.29) 3.43 (2.33, 5.05) 5.08 (3.44, 7.51) No 1 1 1 0.0093 Depression Yes 1.79 (0.84, 3.78) 1.74 (0.83, 3.64) 2.76 (1.32, 5.74) No 1 1 1 <.0001 Anxiety Yes 1.82 (0.73, 4.54) 3.76 (1.58, 8.94) 4.99 (2.09, 11.95)

178

Multivariate nested models

The aim of this study was to investigate the association between asthma groups and number of GP visits in a year at survey 3 adjusting for predisposing, enabling and need factors. Four nested models of multivariate multinomial logistic regression were constructed based on the separate multivariate analyses. Factors from multivariate multinomial analyses at Survey 3 that were associated with categories of higher average GP visit time (p<0.2), were included in the nested models as described in table footnotes.

For the 1921-26 cohort, Model 1 shows the association between asthma groups and frequency of GP visits without adjustment for any other factors. Women who had asthma or bronchitis/emphysema were more likely to have had 3-4, 5-8 and 9 or more GP visits a year compared with women without asthma or bronchitis/emphysema (never asthma group) except women with past asthma for whom the association with 3-4 GP visits was not significant (p=0.2) (see Table 6-12 ).

Once predisposing factors included in the model (Model 2), the association between asthma or bronchitis/emphysema and 3-4, 5-8 and 9 or more GP visits a year was diminished for incident asthma and bronchitis/emphysema groups and strengthened for past and prevalent asthma groups.

In Model 3, enabling factors were added to Model 2 and the association between asthma or bronchitis/emphysema groups with high frequencies of GP visits diminished compared to women in never asthma group.

Model 4 was adjusted for all predisposing, enabling and needs factors. For women with asthma or bronchitis/emphysema the association with all the GP visit categories was diminished again but still remained statistically significant.

The strongest association with GP visit was observed for women with past or prevalent asthma. Full model output is provided in Appendices Table A-16 to Table A-18.

179

Table 6-12. Association between asthma groups and number of GP visits within 12 months prior to Survey 3 for women from the 1921-26 cohort using multivariate multinomial logistic regressions

Effect of Asthma Groups* on GP visits

OUTCOME Past asthma Prevalent asthma Incident asthma Bronchitis/emphysema Number of GP Models visits /year OR P OR P OR P OR P Less than 2 1 1 1 1 Model 1 3-4 3.06 (1.28, 7.29) 0.0117 1.33 (0.77, 2.31) 0.31 1.96 (1.15, 3.33) 0.0128 1.03 (0.75, 1.42) 0.86 5-8 4.65 (1.99, 10.89) 0.0004 3.92 (2.36, 6.50) <.0001 2.45 (1.45, 4.13) 0.0008 1.84 (1.36, 2.50) <.0001 9 or more 6.30 (2.69, 14.73) <.0001 5.46 (3.29, 9.05) <.0001 2.94 (1.74, 4.97) <.0001 2.40 (1.76, 3.26) <.0001 Less than 2 1 1 1 1 Model 2 3-4 3.48 (1.45, 8.35) 0.0053 1.30 (0.75, 2.27) 0.35 1.95 (1.14, 3.34) 0.0145 1.02 (0.73, 1.41) 0.91 5-8 5.15 (2.18, 12.17) 0.0002 3.86 (2.31, 6.44) <.0001 2.46 (1.45, 4.18) 0.0009 1.84 (1.35, 2.51) 0.0001 9 or more 7.06 (2.99, 16.62) <.0001 5.57 (3.34, 9.28) <.0001 2.91 (1.71, 4.96) <.0001 2.44 (1.78, 3.33) <.0001 Less than 2 1 1 1 1 Model 3 3-4 3.57 (1.48, 8.66) 0.0048 1.24 (0.71, 2.18) 0.45 1.98 (1.15, 3.42) 0.0137 1.03 (0.74, 1.44) 0.86 5-8 4.88 (2.04, 11.66) 0.0004 3.57 (2.12, 6.02) <.0001 2.40 (1.40, 4.13) 0.0015 1.81 (1.32, 2.49) 0.0002 9 or more 6.52 (2.74, 15.55) <.0001 5.35 (3.18, 9.01) <.0001 2.60 (1.51, 4.49) 0.0006 2.35 (1.71, 3.23) <.0001 Less than 2 1 1 1 1 Model 4 3-4 3.50 (1.42, 8.64) 0.0066 1.00 (0.56, 1.78) 0.99 2.25 (1.28, 3.96) 0.005 0.99 (0.70, 1.40) 0.97 5-8 3.97 (1.62, 9.69) 0.0025 2.71 (1.58, 4.63) 0.0003 2.61 (1.48, 4.60) 0.0009 1.68 (1.21, 2.35) 0.0022 9 or more 4.34 (1.76, 10.73) 0.0015 3.86 (2.24, 6.65) <.0001 2.77 (1.55, 4.96) 0.0006 2.05 (1.45, 2.90) <.0001 *Reference: ‘2 or less’ GP visits for the outcome; ‘Never asthma’ is the reference category for asthma group Model 1: Asthma group Model 2: Asthma group + Predisposing factors (age, area of residence, marital status, highest qualification, country of birth) Model 3: Asthma group + Predisposing factors + Enabling factors (income management, private insurance and satisfaction with access to a specialist, Hospital doctor, healthcare in emergencies, after hour doctor, bulk billing doctor and female doctor) Model 4: Asthma group + Predisposing factors + Enabling factors + Needs (depression, anxiety, diabetes, heart disease, hypertension, osteoporosis, smoking and BMI)

180

Association between asthma group and GP visits over time

The association between asthma or bronchitis/emphysema and frequency of GP visits was examined longitudinally. Variables that were used in cross sectional nested models and had been measured in all surveys or could be assumed to remain fixed/constant across time (eg. country of birth) were used in the analysis. Table 6-13 shows the nested longitudinal models for the 1921-26 cohort. Model 1 shows the univariate association between asthma and frequency of GP visits. In this cohort, over time, women with asthma or bronchitis/emphysema had higher odds of having longer total GP visit time compared to women without the condition. Among women with asthma or bronchitis/emphysema, women who had prevalent asthma had the highest odds of having more GP visits. Model 2 showed a decrease in odds ratios as predisposing factors were added into model 1. Model 3 however, showed an increase in association between past and incident asthma which was attenuated in Model 4, once all predisposing, enabling and need factors were added to the previous models. Over time, there was still a statistically significant association between asthma group and higher number of GP visits. Full model output is presented in Appendices Table A-19 to Table A-21.

181

Table 6-13. Association between asthma or bronchitis/emphysema and number of GP visits for women from the 1921-26 cohort using a longitudinal analysis approach, from Survey 2 to Survey 6 Asthma Groups* Goodness of fit Outcome: Past asthma Prevalent asthma Incident asthma Bronchitis/emphysema Number of GP visits AIC -2 Log L Models per year OR (95% C.I.) P OR (95% C.I.) P OR (95% C.I.) P OR (95% C.I.) P Model 1 2 or less (ref) 1 1 1 1 94941.034 94887.034 3-4 1.53 (1.16, 2.00) 0.0027 1.56 (1.30, 1.88) <.0001 1.40 (1.11, 1.76) 0.0046 1.26 (1.11, 1.42) 0.0003 5-8 2.07 (1.54, 2.80) <.0001 2.64 (2.18, 3.20) <.0001 2.29 (1.81, 2.89) <.0001 1.87 (1.64, 2.13) <.0001 9 or more 3.15 (2.33, 4.25) <.0001 4.20 (3.45, 5.12) <.0001 3.39 (2.68, 4.30) <.0001 2.40 (2.10, 2.75) <.0001 Model 2 2 or less (ref) 1 1 1 1 71989.003 71881.003 3-4 1.45 (1.06, 1.97) 0.0191 1.48 (1.20, 1.82) 0.0002 1.44 (1.09, 1.89) 0.0096 1.23 (1.07, 1.42) 0.0039 5-8 1.93 (1.40, 2.66) <.0001 2.56 (2.06, 3.19) <.0001 2.47 (1.87, 3.26) <.0001 1.85 (1.60, 2.15) <.0001 9 or more 2.89 (2.06, 4.04) <.0001 4.08 (3.27, 5.11) <.0001 3.45 (2.61, 4.56) <.0001 2.39 (2.05, 2.78) <.0001 Model 3 2 or less (ref) 1 1 1 1 46041.896 45915.896 3-4 1.54 (1.09, 2.16) 0.0138 1.49 (1.18, 1.89) 0.0010 1.66 (1.20, 2.31) 0.0024 1.23 (1.04, 1.44) 0.0139 5-8 1.99 (1.40, 2.82) 0.0001 2.59 (2.02, 3.33) <.0001 3.00 (2.15, 4.17) <.0001 1.90 (1.61, 2.25) <.0001 9 or more 3.26 (2.28, 4.66) <.0001 4.09 (3.15, 5.30) <.0001 3.99 (2.87, 5.55) <.0001 2.35 (1.97, 2.80) <.0001 Model 4 2 or less (ref) 1 1 1 1 43384.906 43268.906 3-4 1.45 (1.03, 2.04) 0.033 1.41 (1.11, 1.79) 0.0055 1.60 (1.14, 2.23) 0.006 1.22 (1.04, 1.44) 0.0158 5-8 1.76 (1.23, 2.52) 0.002 2.29 (1.78, 2.96) <.0001 2.73 (1.95, 3.83) <.0001 1.80 (1.52, 2.13) <.0001 9 or more 2.55 (1.77, 3.67) <.0001 3.39 (2.6?, 4.43) <.0001 3.33 (2.38, 4.68) <.0001 2.06 (1.73, 2.46) <.0001 *outcome reference group was ‘2 or less’ visits a year for GP visits, the reference group used for the focus explanatory variable (asthma group) was ‘Never asthma’ Model 1: Time + Asthma group Model 2: Time + Asthma group + predisposing factors (age, area of residence, marital status, highest qualification, country of birth) Model 3: Time + Asthma group + Predisposing factors + Enabling factors (income management and private health insurance) Model 4: Time + Asthma group + Predisposing factors + Enabling factors + Needs (depression, anxiety, diabetes, heart disease, hypertension, osteoporosis, smoking and BMI)

182

Predictors of the frequency of GP visits among women from the 1946-51 cohort

Univariate associations

Univariate multinomial logistic regressions were conducted for predisposing, enabling and need factors for the 1946-51 cohort, with the number of GP visits categorised to 0 (reference category), 1 or 2, 3 or 4, 5 or 6 and 7 or more. For women from the 1946-51 cohort, of the predisposing, enabling and need factors that were studied in univariate models, satisfaction with access to a specialist and country of birth were not significantly associated with higher GP visit time/year compared with 0 visits category (p > 0.2) (see Table 6-14).

183

Table 6-14. Univariate association between number of GP visits /year and predisposing, enabling and needs factors for women from the 1946-51 cohort at Survey 3

Predictor Predictor category Number of GP visit /year (Ref: 0 visits) 1 or 2 3 or 4 5 or 6 7 or more Odds Ratio (95% Overall

CI) Odds Ratio (95% CI) Odds Ratio (95% CI) Odds Ratio (95% CI) P Never asthma 1 1 1 1 <.0001 Past asthma 0.85 (0.68, 1.06) 1.02 (0.81, 1.28) 1.32 (1.02, 1.71) 1.69 (1.31, 2.20) Asthma groups Prevalent asthma 0.78 (0.62, 0.97) 1.51 (1.22, 1.88) 2.23 (1.77, 2.82) 3.59 (2.86, 4.51) Incident asthma 0.77 (0.61, 0.97) 1.20 (0.95, 1.51) 1.65 (1.28, 2.13) 2.92 (2.29, 3.73) Bronchitis/emphysema 0.90 (0.76, 1.07) 1.34 (1.12, 1.59) 1.52 (1.25, 1.86) 1.83 (1.50, 2.25) Predisposing factors Age 1.01 (0.96, 1.06 1.05 (1.00, 1.11) 1.07 (1.01, 1.13) 1.11 (1.05, 1.18) <.0001 Major cities 1 1 1 1 <.0001 Inner regional 0.76 (0.62, 0.92) 0.68 (0.56, 0.83) 0.62 (0.50, 0.77) 0.55 (0.44, 0.69) Area Remote, very remote and outer 0.82 (0.69, 0.98) 0.68 (0.56, 0.81) 0.65 (0.54, 0.79) 0.69 (0.57, 0.84) regional Partnered 1 1 1 1 <.0001 Marital status Never married 0.86 (0.56, 1.31) 0.95 (0.62, 1.46) 0.85 (0.52, 1.37) 1.23 (0.77, 1.95) Separated/Divorced/Widowed 0.92 (0.75, 1.14) 0.94 (0.76, 1.16) 1.03 (0.82, 1.30) 1.73 (1.39, 2.17) University qualification 1 1 1 1 <.0001 Qualification High school qualification 0.78 (0.57, 1.07) 1.11 (0.80, 1.53) 1.38 (0.97, 1.96) 1.84 (1.29, 2.63) No formal education 1.22 (0.98, 1.52) 1.33 (1.05, 1.67) 1.45 (1.12, 1.88) 1.38 (1.06, 1.80) Australia 1 1 1 1 0.3056 Country of birth English speaking countries 0.87 (0.68, 1.12) 0.83 (0.64, 1.08) 0.79 (0.59, 1.05) 0.86 (0.64, 1.14) Non-English speaking countries 0.97 (0.78, 1.20) 0.92 (0.74, 1.15) 0.91 (0.72, 1.16) 0.77 (0.60, 0.99) Enabling factors No 1 1 1 1 <.0001 Private health 1.39 (1.20, 1.62) 1.50 (1.29, 1.75) 1.40 (1.19, 1.66) 1.06 (0.90, 1.26) insurance Yes Yes, veteran’s affair gold card 1.27 (0.37, 4.40) 1.79 (0.52, 6.18) 3.53 (1.03, 12.10) 4.17 (1.24, 13.99) Income management Easy to manage 1 1 1 1 <.0001

184

Predictor Predictor category Number of GP visit /year (Ref: 0 visits) 1 or 2 3 or 4 5 or 6 7 or more Odds Ratio (95% Overall

CI) Odds Ratio (95% CI) Odds Ratio (95% CI) Odds Ratio (95% CI) P Not too bad to manage 0.80 (0.59, 1.08) 1.27 (0.94, 1.72) 1.99 (1.43, 2.75) 4.53 (3.27, 6.27) Difficult to manage 0.98 (0.82, 1.18) 1.15 (0.95, 1.39) 1.35 (1.09, 1.68) 1.73 (1.37, 2.18) Excellent/very good 1 1 1 1 0.2334 Access to a specialist Good 0.89 (0.72, 1.10) 0.88 (0.71, 1.10) 0.93 (0.74, 1.18) 1.09 (0.87, 1.38) Fair/poor 0.86 (0.71, 1.04) 0.88 (0.73, 1.07) 0.91 (0.74, 1.12) 0.97 (0.79, 1.19) Excellent/very good 1 1 1 1 <.0001 Access to a bulk billing 1.12 (0.92, 1.35) 1.10 (0.91, 1.34) 0.97 (0.79, 1.20) 0.73 (0.59, 0.90) Good doctor Fair/poor 0.86 (0.68, 1.09) 0.70 (0.55, 0.90) 0.77 (0.59, 1.01) 0.64 (0.49, 0.83) Excellent/very good 1 1 1 1 0.0025 Access to an after- Good 0.99 (0.81, 1.20) 1.13 (0.93, 1.38) 1.20 (0.97, 1.49) 1.35 (1.09, 1.67) hours doctor Fair/poor 1.01 (0.82, 1.24) 1.10 (0.89, 1.35) 1.04 (0.83, 1.31) 1.08 (0.86, 1.36) Excellent/very good 0.0204 Access to a female Good 0.80 (0.66, 0.97) 0.80 (0.66, 0.98) 0.81 (0.65, 1.00) 0.84 (0.68, 1.04) doctor Fair/poor 0.72 (0.59, 0.88) 0.73 (0.59, 0.89) 0.85 (0.68, 1.06) 0.71 (0.56, 0.88) 1 1 1 1 <.0001 Excellent/very good Access to a hospital doctor Good 1.08 (0.82, 1.44) 1.08 (0.81, 1.44) 1.24 (0.91, 1.69) 1.73 (1.28, 2.33) Fair/poor 0.87 (0.72, 1.04) 0.91 (0.76, 1.09) 0.98 (0.80, 1.20) 1.06 (0.86, 1.29) 1 1 1 1 0.0335 Excellent/very good Access to an emergency doctor Good 0.95 (0.76, 1.20) 0.96 (0.76, 1.21) 1.14 (0.88, 1.46) 1.26 (0.98, 1.61) Fair/poor 0.91 (0.76, 1.10) 0.89 (0.74, 1.08) 1.01 (0.82, 1.24) 1.03 (0.83, 1.26) Needs Optimal weight 1 1 1 1 <.0001 BMI Underweight 1.23 (0.60, 2.52) 1.33 (0.64, 2.78) 2.17 (1.01, 4.68) 2.82 (1.32, 6.04)

185

Predictor Predictor category Number of GP visit /year (Ref: 0 visits) 1 or 2 3 or 4 5 or 6 7 or more Odds Ratio (95% Overall

CI) Odds Ratio (95% CI) Odds Ratio (95% CI) Odds Ratio (95% CI) P Overweight 1.06 (0.89, 1.26) 1.21 (1.01, 1.45) 1.45 (1.19, 1.77) 1.58 (1.29, 1.93) Obese 1.13 (0.91, 1.40) 1.73 (1.39, 2.15) 2.35 (1.86, 2.97) 3.53 (2.80, 4.46) No drinking 1 1 1 1 <.0001 Drinking alcohol Low risk drinking 1.36 (1.06, 1.74) 1.35 (1.05, 1.75) 1.23 (0.93, 1.62) 0.81 (0.62, 1.07) High risk drinking 1.27 (0.80, 2.03) 1.25 (0.77, 2.03) 1.19 (0.70, 2.01) 0.85 (0.50, 1.45) Non-smoker 1 1 1 1 <.0001 Smoking Smoker 0.81 (0.66, 1.00) 0.73 (0.59, 0.90) 0.76 (0.61, 0.97) 1.07 (0.86, 1.35) Ex-smoker 1.05 (0.87, 1.26) 1.15 (0.95, 1.38) 1.12 (0.92, 1.38) 1.26 (1.03, 1.55) No 1 1 1 1 <.0001 Diabetes Yes 1.43 (0.64, 3.18) 4.34 (2.01, 9.36) 6.69 (3.08, 14.53) 10.52 (4.89, 22.64) No 1 1 1 1 <.0001 Heart disease Yes 1.96 (0.59, 6.47) 4.57 (1.42, 14.71) 7.73 (2.39, 24.99) 18.59 (5.87, 58.83) No 1 1 1 1 <.0001 Hypertension Yes 2.59 (1.81, 3.71) 5.96 (4.18, 8.50) 9.01 (6.29, 12.92) 12.67 (8.85, 18.13) No 1 1 1 1 <.0001 Osteoporosis Yes 1.55 (0.77, 3.13) 2.95 (1.48, 5.86) 3.98 (1.98, 8.03) 8.45 (4.28, 16.70) No 1 1 1 1 <.0001 Low iron Yes 1.29 (0.93, 1.78) 1.65 (1.19, 2.28) 2.18 (1.56, 3.05) 2.65 (1.91, 3.70) No 1 1 1 1 <.0001 Depression Yes 3.06 (1.77, 5.29) 6.69 (3.89, 11.49) 11.92 (6.92, 20.54) 25.61 (14.94, 43.90) No 1 1 1 1 <.0001 Anxiety Yes 1.27 (0.77, 2.11) 2.69 (1.64, 4.39) 5.15 (3.14, 8.44) 10.39 (6.40, 16.86) Any other major No 1 1 1 1 <.0001 disease Yes 1.68 (0.76, 3.71) 3.34 (1.54, 7.25) 7.11 (3.28, 15.41) 15.46 (7.23, 33.07)

186

Multivariate association

In this section, the association between predisposing, enabling and needs factors and number of GP visits for women from the 1946-51 cohort will be examined. These analyses were performed in three separate multivariate multinomial analyses for the three categories of Andersen-Newman behavioural model in health service use. All the variables retained from the univariate models (p<0.2) were included in these analyses. However, in the multivariable model of need factors, alcohol consumption and smoking had a p-value >0.2, and these variables were not included in further analyses (Table 6-15 to Table 6-17).

187

Table 6-15. Multivariate association between number of GP visits /year and predisposing factors for women from the 1946-51 cohort at Survey 3

Predictor Predictor category Number of GP visits/year (Ref: 0 visits) 1-2 3-4 5-6 7 or more Odds Ratio (95% CI) Odds Ratio (95% CI) Odds Ratio (95% CI) Odds Ratio (95% CI) Overall P Age 1.00 (0.94, 1.07) 1.04 (0.98, 1.11) 1.07 (1.00, 1.15) 1.10 (1.03, 1.19) 0.0009 Major cities 1 1 1 1 <.0001 Inner regional 0.89 (0.69, 1.15) 0.85 (0.66, 1.11) 0.96 (0.72, 1.28) 1.60 (1.21, 2.12) Area Remote, very remote and outer 0.85 (0.51, 1.43) 0.93 (0.55, 1.58) 0.88 (0.49, 1.59) 1.23 (0.69, 2.20) regional Partnered 1 1 1 1 <.0001 Marital status Never married 0.81 (0.66, 1.00) 0.69 (0.55, 0.85) 0.62 (0.49, 0.79) 0.69 (0.54, 0.88) Separated/Divorced/Widowed 0.78 (0.61, 0.99) 0.74 (0.57, 0.94) 0.65 (0.50, 0.85) 0.54 (0.40, 0.71) University qualification 1 1 1 1 <.0001 Highest qualification High school qualification 1.22 (0.98, 1.54) 1.33 (1.05, 1.69) 1.47 (1.13, 1.91) 1.44 (1.10, 1.90) No formal education 0.79 (0.57, 1.08) 1.11 (0.80, 1.55) 1.41 (0.99, 2.03) 1.97 (1.37, 2.83)

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Table 6-16. Multivariate association between number of GP visits /year and enabling factors for women from the 1946-51 cohort at Survey 3

Predictor Predictor category Number of GP visits/year (Ref: 0 visits) 1-2 3-4 5-6 7 or more Odds Ratio (95% CI) Odds Ratio (95% CI) Odds Ratio (95% CI) Odds Ratio (95% CI) Overall P No 1 1 1 1 0.0077 Private health insurance Yes 1.20 (0.93, 1.53) 1.30 (1.01, 1.68) 1.31 (1.00, 1.72) 1.01 (0.77, 1.34) Yes, veteran’s affair gold card 1.10 (0.13, 9.49) 2.03 (0.25, 16.69) 3.28 (0.40, 27.07) 5.14 (0.65, 40.60) Easy to manage 1 1 1 1 <.0001 Income management Not too bad to manage 1.04 (0.77, 1.41) 1.20 (0.88, 1.64) 1.60 (1.13, 2.26) 1.70 (1.17, 2.45) Difficult to manage 0.68 (0.42, 1.09) 1.01 (0.63, 1.62) 1.94 (1.17, 3.21) 2.99 (1.79, 4.98) Excellent/very good 1 1 1 1 0.0002 Access to a bulk billing Good 1.00 (0.68, 1.45) 0.79 (0.54, 1.17) 0.67 (0.44, 1.02) 0.51 (0.33, 0.79) doctor Fair/poor 1.16 (0.86, 1.55) 1.07 (0.79, 1.45) 0.90 (0.65, 1.24) 0.73 (0.52, 1.01) Excellent/very good 1 1 1 1 0.0180 Access to an after-hours Good 1.07 (0.75, 1.53) 1.22 (0.85, 1.76) 1.11 (0.75, 1.65) 1.22 (0.81, 1.82) doctor Fair/poor 0.76 (0.52, 1.13) 1.17 (0.79, 1.74) 1.07 (0.69, 1.64) 1.23 (0.79, 1.91) Excellent/very good 1 1 1 1 0.1245 Access to a female Good 0.72 (0.52, 1.01) 0.68 (0.48, 0.95) 0.95 (0.66, 1.37) 0.72 (0.49, 1.05) doctor Fair/poor 0.73 (0.53, 1.00) 0.74 (0.54, 1.02) 0.82 (0.58, 1.16) 0.77 (0.54, 1.10) Excellent/very good 1 1 1 1 0.0484 Access to a hospital Good 1.25 (0.84, 1.86) 1.48 (0.99, 2.21) 1.56 (1.01, 2.41) 1.94 (1.25, 3.04) doctor Fair/poor 1.37 (0.79, 2.39) 1.50 (0.85, 2.64) 1.49 (0.82, 2.73) 2.35 (1.28, 4.31) Excellent/very good 1 1 1 1 0.1866 Access to an emergency Good 0.92 (0.61, 1.38) 0.75 (0.49, 1.13) 0.80 (0.51, 1.25) 0.78 (0.49, 1.23) doctor Fair/poor 1.02 (0.60, 1.73) 0.66 (0.39, 1.13) 0.91 (0.51, 1.61) 0.63 (0.35, 1.14)

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Table 6-17. Multivariate association between number of GP visits /year and need factors for women from the 1946-51 cohort at Survey 3

Predictor Predictor category Number of GP visits/year (Ref: 0 visits) 1-2 3-4 5-6 7 or more Odds Ratio (95% CI) Odds Ratio (95% CI) Odds Ratio (95% CI) Odds Ratio (95% CI) Overall P Optimal weight 1 1 1 1 <.0001 Underweight 0.99 (0.28, 3.50) 1.14 (0.31, 4.14) 1.80 (0.47, 6.90) 2.56 (0.65, 10.08) BMI Overweight 1.04 (0.76, 1.42) 1.11 (0.81, 1.53) 1.20 (0.84, 1.71) 1.55 (1.06, 2.29) Obese 0.96 (0.65, 1.40) 1.24 (0.84, 1.84) 1.44 (0.94, 2.19) 2.53 (1.62, 3.93) No drinking 1 1 1 1 0.4042 Drinking alcohol Low risk drinking 1.24 (0.80, 1.91) 1.18 (0.76, 1.84) 1.17 (0.72, 1.90) 0.86 (0.52, 1.42) High risk drinking 1.01 (0.48, 2.12) 0.70 (0.32, 1.53) 0.90 (0.39, 2.10) 0.72 (0.30, 1.74) Non-smoker 1 1 1 1 0.7602 Smoking Smoker 0.85 (0.55, 1.29) 0.86 (0.56, 1.33) 0.77 (0.47, 1.25) 1.14 (0.69, 1.87) Ex-smoker 0.96 (0.69, 1.34) 0.93 (0.66, 1.31) 0.97 (0.67, 1.40) 0.95 (0.64, 1.41) No 1 1 1 1 0.0013 Diabetes Yes 0.87 (0.25, 3.06) 1.61 (0.48, 5.44) 2.97 (0.87, 10.12) 2.70 (0.78, 9.37) No 1 1 1 1 0.0181 Heart disease Yes 0.70 (0.15, 3.37) 1.59 (0.36, 7.06) 1.83 (0.39, 8.52) 3.20 (0.70, 14.61) No 1 1 1 1 <.0001 Hypertension Yes 3.93 (1.80, 8.56) 8.04 (3.71, 17.43) 11.34 (5.18, 24.86) 13.87 (6.29, 30.59) No 1 1 1 1 <.0001 Osteoporosis Yes 1.65 (0.38, 7.23) 3.48 (0.82, 14.81) 4.78 (1.09, 20.89) 10.17 (2.36, 43.90) No 1 1 1 1 <.0001 Low iron Yes 2.15 (1.07, 4.32) 2.85 (1.41, 5.75) 3.00 (1.45, 6.24) 4.61 (2.21, 9.64) No 1 1 1 1 <.0001 Depression Yes 3.89 (1.20, 12.58) 7.37 (2.30, 23.62) 13.57 (4.21, 43.73) 23.29 (7.22, 75.13) No 1 1 1 1 <.0001 Anxiety Yes 1.02 (0.35, 2.98) 2.64 (0.93, 7.45) 4.06 (1.42, 11.62) 5.09 (1.77, 14.65) No 1 1 1 1 <.0001 Any other major disease Yes 0.73 (0.24, 2.21) 1.59 (0.55, 4.58) 3.44 (1.19, 9.96) 5.22 (1.80, 15.15)

190

Multivariate nested models

Four nested models of multivariate multinomial logistic regressions were constructed based on the separate multivariate analyses. Factors from multivariate multinomial analyses (previous section) that were associated with categories of higher average GP visit time (p < 0.2) were included in the nested models as described in table footnotes. For women in 1946-51 cohort (Table 6-18), women with asthma or bronchitis/emphysema had larger odds for higher number of GP visits compared with women without these conditions. Women with prevalent asthma had the highest odds of having more GP visits in a year (Model 1). Once predisposing (Model 2) and enabling factors (Model 3) were entered in the model, the association between having asthma or bronchitis/emphysema and higher frequency of GP visits diminished. In Model 4, controlling for predisposing, enabling and needs factors, the association between past asthma, incident asthma and bronchitis/emphysema was diminished even further. However, the association for having 7 or more visits a year remained statistically significant. Full models are presented in Appendices Table A-22 to Table A-24.

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Table 6-18. Association between asthma groups and number of GP visits within 12 months prior to Survey 3 for women from the 1946-51 cohort using multivariate multinomial logistic regressions Number of GP Models Groups* visits /year Past asthma Prevalent asthma Incident asthma Bronchitis/emphysema

OR P OR P OR P OR P

1-2 0.86 (0.49, 1.49) 0.59 2.79 (1.20, 6.49) 0.0171 1.10 (0.58, 2.08) 0.77 1.45 (0.88, 2.38) 0.14 Model 1 3-4 1.26 (0.72, 2.19) 0.42 5.23 (2.26, 12.10) 0.0001 1.93 (1.03, 3.62) 0.0411 2.33 (1.42, 3.82) 0.0008 5-6 1.56 (0.86, 2.81) 0.14 8.02 (3.42, 18.78) <.0001 2.36 (1.22, 4.56) 0.0111 2.04 (1.20, 3.49) 0.0086 7 or more 2.23 (1.21, 4.09) 0.0096 15.06 (6.43, 35.30) <.0001 3.97 (2.04, 7.72) <.0001 3.64 (2.13, 6.23) <.0001 1-2 0.86 (0.49, 1.50) 0.59 2.80 (1.20, 6.51) 0.0169 1.09 (0.58, 2.07) 0.79 1.44 (0.88, 2.36) 0.15 Model 2 3-4 1.28 (0.73, 2.23) 0.39 5.25 (2.27, 12.17) 0.0001 1.91 (1.02, 3.60) 0.0442 2.32 (1.41, 3.81) 0.0009 5-6 1.58 (0.87, 2.85) 0.13 8.02 (3.42, 18.80) <.0001 2.31 (1.19, 4.49) 0.0132 2.03 (1.19, 3.46) 0.0095 7 or more 2.26 (1.23, 4.17) 0.0089 14.86 (6.32, 34.90) <.0001 3.75 (1.92, 7.32) <.0001 3.54 (2.07, 6.08) <.0001 1-2 0.84 (0.48, 1.47) 0.55 2.84 (1.22, 6.62) 0.0155 1.09 (0.57, 2.06) 0.8 1.45 (0.88, 2.38) 0.14 Model 3 3-4 1.21 (0.69, 2.13) 0.5 5.19 (2.24, 12.04) 0.0001 1.89 (1.00, 3.56) 0.05 2.23 (1.35, 3.67) 0.0016 5-6 1.49 (0.82, 2.70) 0.19 7.72 (3.29, 18.13) <.0001 2.26 (1.16, 4.41) 0.0165 1.92 (1.12, 3.29) 0.0169 7 or more 2.08 (1.12, 3.86) 0.0201 14.06 (5.97, 33.14) <.0001 3.64 (1.85, 7.15) 0.0002 3.32 (1.93, 5.71) <.0001 1-2 0.86 (0.49, 1.50) 0.59 2.78 (1.19, 6.50) 0.0184 1.03 (0.54, 1.97) 0.92 1.44 (0.87, 2.37) 0.15 Model 4 3-4 1.20 (0.68, 2.12) 0.53 4.70 (2.01, 10.97) 0.0004 1.73 (0.91, 3.29) 0.1 2.14 (1.29, 3.54) 0.0031 5-6 1.41 (0.76, 2.61) 0.27 6.53 (2.75, 15.52) <.0001 2.00 (1.01, 3.97) 0.0464 1.81 (1.05, 3.13) 0.0336 7 or more 1.83 (0.95, 3.52) 0.07 10.63 (4.43, 25.51) <.0001 3.14 (1.56, 6.35) 0.0014 3.00 (1.71, 5.28) 0.0001 *Reference: ‘0’ GP visits and ‘Never asthma’ Model 1: Asthma Model 2: Asthma + Predisposing factors (age, area of residence, marital status and highest qualification) Model 3: Asthma + Predisposing factors + Enabling factors (income management and private insurance and satisfaction with access to a hospital doctor, healthcare in emergencies, after hour doctor, bulk billing doctor and female doctor) Model 4: Asthma + Predisposing factors + Enabling factors + Needs (depression, anxiety, diabetes, heart disease, hypertension, osteoporosis, low iron level and BMI)

192

Association between asthma group and GP visits over time

The association between asthma or bronchitis/emphysema and frequency of GP visits was examined longitudinally. Variables that were used in cross sectional nested models and had been measured in all surveys or could be assumed to remain fixed/constant across time (eg. Country of birth) were used in the analysis. Model 1 in Table 6-19 shows the univariate association between asthma or bronchitis/emphysema and frequency of GP time for women from the 1946-51 cohort. In this cohort, women with asthma or bronchitis/emphysema were more likely to have had higher frequency of GP visits compared to women without the condition. Among women with asthma or bronchitis/emphysema, women who had prevalent asthma had the highest odds of more GP visits. Model 2 showed a slight increase in odds of having higher number of GP visits for past and prevalent asthma and bronchitis/emphysema groups which was attenuated once enabling and need factors were adjusted for in Models 3 and 4. Over time, there was still a statistically significant association between asthma group and more frequent GP visits. Full models are presented in Appendices Table A-25 to Table A-27.

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Table 6-19. Association between asthma or bronchitis/emphysema and number of GP visits for women from the 1946-51 cohort using a longitudinal analysis approach, from survey 2 to survey 7 Number of Models GP visits Groups* /year Goodness of fit Past asthma Prevalent asthma Incident asthma Bronchitis/emphysema OR P OR P OR P OR P AIC -2 Log L 0 (ref) 1 1 1 1 72102.575 72062.575 1-2 1.00 (0.77, 1.30) 0.99 1.66 (1.25, 2.20) 0.0005 1.55 (1.13, 2.12) 0.0063 1.24 (0.99, 1.55) 0.06 Model 1 3-4 1.30 (0.98, 1.73) 0.07 3.12 (2.30, 4.23) <.0001 2.56 (1.82, 3.58) <.0001 1.67 (1.33, 2.10) <.0001 5-6 1.64 (1.21, 2.22) 0.0014 4.65 (3.40, 6.37) <.0001 3.23 (2.28, 4.57) <.0001 2.13 (1.67, 2.72) <.0001 7 or more 2.23 (1.62, 3.08) <.0001 8.50 (6.15, 11.77) <.0001 5.82 (4.08, 8.30) <.0001 3.05 (2.36, 3.96) <.0001 0 (ref) 1 1 1 1 71478.479 71382.479 1-2 1.01 (0.78, 1.32) 0.91 1.70 (1.27, 2.26) 0.0003 1.56 (1.14, 2.13) 0.0055 1.26 (1.01, 1.57) 0.0438 Model 2 3-4 1.34 (1.00, 1.78) 0.0466 3.23 (2.38, 4.39) <.0001 2.58 (1.84, 3.62) <.0001 1.70 (1.35, 2.15) <.0001 5-6 1.69 (1.25, 2.31) 0.0008 4.82 (3.51, 6.63) <.0001 3.24 (2.28, 4.59) <.0001 2.17 (1.70, 2.78) <.0001 7 or more 2.34 (1.68, 3.25) <.0001 8.61 (6.20, 11.96) <.0001 5.67 (3.96, 8.13) <.0001 3.09 (2.38, 4.02) <.0001 0 (ref) 1 1 1 1 70229.524 70013.524 1-2 1.01 (0.77, 1.32) 0.95 1.71 (1.28, 2.28) 0.0003 1.55 (1.13, 2.13) 0.0061 1.26 (1.00, 1.57) 0.0452 Model 3 3-4 1.32 (0.99, 1.76) 0.06 3.22 (2.37, 4.38) <.0001 2.55 (1.82, 3.59) <.0001 1.68 (1.33, 2.12) <.0001 5-6 1.64 (1.21, 2.23) 0.0015 4.62 (3.36, 6.36) <.0001 3.16 (2.22, 4.50) <.0001 2.09 (1.64, 2.67) <.0001 7 or more 2.20 (1.58, 3.05) <.0001 7.78 (5.60, 10.82) <.0001 5.43 (3.79, 7.79) <.0001 2.88 (2.21, 3.74) <.0001 0 (ref) 1 1 1 1 67122.113 66826.113 1-2 0.99 (0.76, 1.30) 0.95 1.68 (1.26, 2.24) 0.0004 1.54 (1.13, 2.11) 0.0068 1.24 (0.99, 1.55) 0.06 Model 4 3-4 1.24 (0.93, 1.65) 0.15 2.90 (2.12, 3.95) <.0001 2.39 (1.70, 3.37) <.0001 1.61 (1.27, 2.03) <.0001 5-6 1.47 (1.08, 2.00) 0.0152 3.88 (2.80, 5.36) <.0001 2.79 (1.96, 3.99) <.0001 1.93 (1.50, 2.47) <.0001 7 or more 1.82 (1.30, 2.54) 0.0004 5.95 (4.24, 8.34) <.0001 4.37 (3.02, 6.31) <.0001 2.50 (1.92, 3.26) <.0001 *outcome reference group was ‘0’ visits a year and ‘Never asthma’ Model 1: Time + Asthma group Model 2: Time + Asthma group + predisposing factors (age, area of residence, marital status and highest qualification) Model 3: Time + Asthma group + Predisposing factors + Enabling factors (income management and private insurance and satisfaction with access to a hospital doctor, healthcare in emergencies, after hour doctor, bulk billing doctor and female doctor) Model 4: Time + Asthma group + Predisposing factors + Enabling factors + Needs (depression, anxiety, diabetes, heart disease, hypertension, osteoporosis, low iron level and BMI)

194

Conclusion This chapter has provided information on self-reported health service use by women in both 1921-26 and 1946-51 cohort. Family doctor/GP visits were the most frequent self-reported health service use for the both cohorts. Women from the 1921-26 cohort had higher frequency of GP visits in a year and higher number of prescription medications than women from the 1946-51 cohort. In both cohorts women with asthma or bronchitis/emphysema had higher number of GP visits compared with women without these conditions. Women with prevalent asthma had the highest number of GP visits and prescription medications in a year as well as medical attention sought due to falls in both cohorts. Also the association between asthma or bronchitis/emphysema and the frequency of GP visits were investigated at survey 3 and longitudinally from survey 2 to survey 6/7 in this chapter. Based on the Anderson‐Newman behavioural model of health service use, after accounting for all possible known measured covariates, asthma or bronchitis emphysema was associated with higher number of GP visits in both cohorts especially for women with prevalent asthma.

195

196

Chapter 7 MBS records of health care utilisation according to asthma status

Introduction

This thesis aims to investigate health service use in older women evaluating both self- reported and Medicare data. Although epidemiological studies routinely use self-reported health related information and it has been shown to be robust (55, 62, 75), there may be some discrepancies between self- reported health service use and administrative population health registry data (e.g. Medicare). In addition, when administrative health service use data are collected routinely, there may be more detailed specific information available including date of service, length of visit, place of visit, type of service and cost of service. The data linkage between the ALSWH survey data and Medicare Benefits Schedule (MBS) database was described in Chapter 3. This allows exploration of health service use comparing cohorts especially over time and examining subgroups within each cohort. Chapter 5 presented self-reported health service use according to asthma groups. In this chapter, use of health services collected through Medicare database is described for both cohorts with respect to asthma groups. Women who are covered by the Department of Veterans’ Affairs (DVA) did not have complete records in the MBS database. Their health service use records is provided through the DVA if they use DVA facilities as subsidised by DVA not Medicare. There are two types of DVA health cover, gold card and white card. DVA Gold card cover is broader and more similar to having a private health insurance. A White Card entitles the holder to care and treatment for:  Accepted injuries or conditions that are war caused or service related  Malignant cancer, pulmonary tuberculosis, any mental health condition whether war caused or not  The symptoms of unidentifiable conditions that arise within 15 years of service (other than peacetime service).

Services covered by a DVA White Card are the same as those for a Gold Card but must be for one of the above conditions.

197

In ALSWH dataset, there was a minimal number of health services used by gold card holders, thus, women who had DVA gold card were excluded from the analyses in this chapter and Chapter 8. As a result, a total of 1327 women from the 1921-26 cohort and 74 women from the 1946-51 cohort were excluded from the analyses. However, there are another 199 white card holders who might have some services covered by DVA.

Health services relevant to asthma in older people

Health services that were relevant to asthma in older people were described in detail in Chapter 3 (namely GP services, Chronic Disease Management and Asthma Cycle of Care). Similar service items are collected through the MBS system and are categorised under Broad Type of Services (BTOS) in MBS. Services under BTOS A, B and M that have been used in this thesis are shown in Table 7-1. Full description of the item numbers for each service is presented in Chapter 3 (Table 3-13 to Table 3-18). For the purpose of the analyses, claims under BTOS A have been combined with corresponding claims under BTOS B to be able to categorise the services as discussed below.

Table 7-1. Broad Type of Service (BTOS) list used in this thesis

BTOS BTOS Name Delivered by Code Levels A-D, professional attendances Non-referred attendances vocationally (varying lengths), urgent attendance A regular hours and after- registered GP (unsociable after hours), GP after hours hours and Asthma Cycle of Care. Levels A-D, After-Hours Attendances, Non-referred attendances Telehealth Patient-end Support Services B (non-GP) regular hours and Other than a GP by Health Professionals, after hours and after-hours Asthma Annual Cycle of Care Non-referred attendances - GP or other than a chronic disease management and health M Enhanced Primary Care - GP assessment Other

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In this chapter the following health services will be explored for both cohorts: . GP visits o Day visits o After-hours visits o Asthma Cycle of Care (ACC) o Chronic Disease Management o Health assessment . Specialist visits . Spirometry test Figure 7-1 and Figure 7-2 show the types of services provided in 2013 under BTOS A, B and M for both the 1921-26 and 1946-51 cohorts respectively. In 2013 most BTOS claims were for GP attendances of 5-20 minutes duration, either by a GP or another doctor working in general practice (67.8%). The other major category was for GP visits level C for attendance 20-40 minutes duration (11.5%) in 2013, for women from the 1921-26 cohort. Only 6.1% of the women had after hours GP claims and there were only 5 claims for ACC (see Figure 7-1).

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GP visits

Figure 7-1. Use of Medicare BTOS A, B and M items in 2013 by women from the 1921-26 cohort (Age: 87-92, N=3433)

*There was only 5 claims for ACC which makes it difficult to spot it in the graph.

The 1946-51 cohort had similar pattern of claims, but with a larger proportion of GP visit level B claims (72.8%) and smaller proportion of claims being for after-hours visits (2.2%). There were 41 claims for Asthma Cycle of Care (see Figure 7-2).

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GP visits

Figure 7-2.Use of Medicare BTOS A, B and M items in 2013 by women from the 1946-51 cohort (age: 62-67, N=7940)

*There are very few claims for ACC and health assessment items The next section presents a description of the use of these items for women from both the 1921-26 and 1946-51 cohorts according to asthma groups.

Health services used for ALSWH women according to asthma group

For both cohorts, the proportion of women who had used health services under BTOS A, B and M, was graphed over time according to asthma group. These graphs are based on women who were alive at the end of each year from 1997 to 2013.

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GP service use and asthma groups

Regular hours GP service use

Figure 7-3 and Figure 7-4 show the proportion of women who have had at least one GP visit in regular hours (regardless of duration) in each year, according to asthma group. For the 1921-26 cohort, a larger proportion of women with past asthma had visited their GP each year from 1997-2013, followed by women with prevalent asthma while women with incident asthma had the lowest proportion of GP visits across time. From 1997 to 2013, there was a decline in proportion of women who visited their GP for all the groups except past asthma group which declined from 1997 to 2010 and subsequently increased between 2010 and 2013 (see Figure 7-3). For the 1946-51 cohort, a higher proportion of women with asthma or bronchitis/emphysema had visited a GP compared to women with no asthma. Women with prevalent asthma had the highest number of GP visits among all the groups across most years from 1997 to 2013. From 1997 to 2013, there was an increase in the proportion of women who visited their GP.

Figure 7-3. Proportion of women with a regular hours GP service claim between 1997 and 2013 for women in the 1921-26 and 1946-51 cohorts, according to asthma group

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After-hours GP service use

In both cohorts, a larger proportion of women with asthma or bronchitis/emphysema had used GP services after-hours. The proportion of women who visited an after-hours GP had increased for both cohorts from 1997 to 2013 although the rise was steeper for the 1921-26 cohort (Figure 7-4). Comparing the groups in each cohort from 1997 to 2013, a higher percentage of women with prevalent asthma from the 1946-51 cohort had used after-hours GP services while in the 1921-26 cohort a higher percentage of women with past asthma saw an after-hours GP. The dramatic rise in after-hours GP visits in 2005 was due to introduction of new MBS item numbers (the 5000 series for non-urgent after-hours visits, see Chapter 3).

Figure 7-4. Proportion of women with an after-hours GP service claim between 1997 and 2013 for women in the 1921-26 and 1946-51 cohorts, according to asthma group

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Length of GP visits

The previous section provided an exploration on the number of women with at least one GP visit claim in a year. However, the number of GP visits by an individual and the length of each visit may reflect different levels of need for medical care. GP visits (excluding after-hours) have four levels (A-D) depending on the length of the visits. In this section, the average minutes for regular hours GP visit claims by each woman in both cohorts for each year were calculated based on the service level (assumes Level A: 5 min, Level B: 15 min, Level C: 30 min and Level D: 50 min). for women in the 1921-26 cohort the total accumulated minutes for each year were categorised to a) 0≤ min ≤75, b) 75< min ≤225 and c) >225 min, according to the 25th and 75th percentiles for total GP visit minutes claimed. For women from the 1946-51 cohort, the total minutes for each year were categorised into a) 0≤ min ≤30, b) 30< min ≤120 and c) > 120 min according to 25th and 75th percentiles for total GP visit minutes claimed for these women. The percentile distribution for GP visit time in 2000 is presented in Table 7-2 while the histograms for the same data are presented in Figure 7-5.

Table 7-2. Total minutes of GP visits in regular hours in 2000 by women from the 1921-26 and 1946-51 cohorts

Percentile Minutes of GP visits 1946-51 1921-26 cohort cohort 1 0 0 5 15 15 10 30 15 25 75 30 50 135 75 75 225 120 90 345 200 95 440 270 99 725 495

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1921-26 cohort

Total minutes

1946-51 cohort

Total minutes

Figure 7-5. Distribution of total minutes of GP visits in 2000 for women from both the 1921-26 and 1946-51 cohorts

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For both cohorts six time periods were defined which are described below for each cohort. These periods were from the time surveys were returned until the time the following survey responses were received. 1921-26 cohort Period 1: 01 APR 1996 to 01 APR 1999; Period 2: 01 APR 1999 to 01 APR 2002; Period 3: 01 APR 2002 to 01 APR 2005; Period 4: 01 APR 2005 to 01 APR 2008; Period 5: 01 APR 2008 to 01 APR 2011; Period 6: 01 APR 2011 to 01 APR 2014.

1946-51 cohort Period 1: 01 APR 1996 to 01 APR 1998; Period 2: 01 APR 1998 to 01 APR 2001; Period 3: 01 APR 2001 to 01 APR 2004; Period 4: 01 APR 2004 to 01 APR 2007; Period 5: 01 APR 2007 to 01 APR 2010; Period 6: 01 APR 2010 to 01 APR 2013.

In both the 1921-26 and 1946-51 cohorts, a greater proportion of women with asthma or bronchitis/emphysema had longer GP visits (defined as: ≥225 and ≥120 minutes respectively) during all the periods compared to women who did not have asthma (1921-26 cohort: 9.4%- 33.1% vs. 6.5%-18.3% and 1946-51 cohort: 12.1%-44.8% vs. 7.2%-25.5%). Women with prevalent asthma from the 1921-26 and 1946-51 cohort had the highest proportion of longer GP visits (see Table 7-3). The average time spent with a GP within regular practice hours as well as attrition due to death prior to each survey is shown in Figure 7-6 and Figure 7-7 for each cohort according to asthma group. For the 1921-26 cohort there was a rise in the proportion of women having 225 minute or more in GP visit time from period 1 to period 3 when considering the deceased, the proportion of women who had 225 minutes or more of GP visit time was reduced from period 3 to period 6 for all groups. However, in all the groups in 1946-51 cohort, the proportion of

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women who had 120 minutes or more in GP visit time increased from period 1 to period 6 (see Figure 7-6 and Figure 7-7). Table 7-3. Proportion of women with longer total time of GP visits in a year* according to asthma groups

Asthma group Period 1 2 3 4 5 6 1921-26 cohort Past asthma 18.6 28.4 27.4 21.9 19.1 14.2 Prevalent asthma 17.0 33.1 29.8 26.3 20.3 15.1 Incident asthma 9.4 29.1 30.1 33.1 24.0 19.2 Bronchitis/emphysema 9.8 23.2 23.2 23.4 17.9 13.5 Never asthma 6.5 16.2 18.3 17.1 15.5 12.2 1946-51 cohort Past asthma 13.6 29.6 30.1 26.6 30.6 34.5 Prevalent asthma 23.7 39.7 41.2 40.3 41.9 44.8 Incident asthma 16.4 33.4 35.0 38.2 42.2 43.9 Bronchitis/emphysema 12.1 26.5 29.1 27.4 31.4 34.1 Never asthma 7.2 17.3 19.0 20.0 21.7 25.5 *longer average time of GP visits in a year: 1921-26 cohort: ≥225 minute 1946-51 cohort: ≥120 minutes

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Figure 7-6. Average total time spent with within hours GP visits in each survey period for the 1921- 26 cohort, according to asthma group

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Figure 7-7. Average total time spent with within hours GP visits in each survey period for the 1946- 51 cohort, according to asthma group

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Specialist visits

Women with asthma or bronchitis/emphysema in both cohorts had used specialist services more than women in the never asthma group. There was a rise in the proportion of women seeing a specialist across all groups from the 1946-51 cohort while for the 1921-26 cohort there was a general steady decline for all groups from 1997 to 2005 followed by a decline in specialist visits to 2013. For women from the 1921-26 cohort, a larger proportion of women with past asthma had visited a specialist in almost all the years compared to other groups. However, for women from the 1946-51 cohort, a higher percentage of women with prevalent asthma had seen a specialist in all years (Figure 7-8).

Figure 7-8. Proportion of women with a specialist visit claim between 1997 and 2013 for women in the 1921-26 and 1946-51 cohorts, according to asthma group

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Health assessment claims

Health assessment items are part of BTOS M which are described earlier in this chapter and previously in Chapter 3. These items were introduced in 1999 as the 75+ health assessment claims and were later modified in 2010 to include people aged 55 and over. In both cohorts, a higher percentage of women with asthma or bronchitis/emphysema had claims for health assessment items compared with never asthma group (see Figure 7-9). For the 1946-51 cohort, after the modification of the items in 2010, there was a modest uptake of health assessment items with around 5.0% of women having health assessment claims in 2013.

Figure 7-9. Proportion of women with a health assessment claim between 1997 and 2013 for women in the 1921-26 and 1946-51 cohorts, according to asthma group

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Chronic Disease Management claims

Chronic disease management (CDM) items were introduced in 1999 and are under BTOS M (described in Chapter 3). CDM items were used more by women from the 1921-26 cohort compared with women from the 1946-51 cohort. In both cohorts, a rise in CDM item use from their introduction in late 1999 was seen in a greater proportion of women with asthma or bronchitis/emphysema compared with women without asthma. Women with prevalent or incident asthma had higher percentage of claims for CDM among women with asthma. Difference according to asthma groups looks to be more obvious in the 1946-51 cohort than the 1921-26 cohort (Figure 7-10).

Figure 7-10. Proportion of women with a CDM claim between 1997 and 2013 among women in the 1921-26 and 1946-51 cohorts, according to asthma group

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Asthma Cycle of Care (ACC) and asthma groups

As described in Chapter 3, the ACC is an asthma specific service under BTOS A and B. The pattern of Asthma Cycle of Care (ACC) use over time was similar in both cohorts although there were a small number of ACC claims overall. A larger proportion of women from both cohorts with prevalent asthma or incident asthma had used ACC compared with women from the never asthma, past asthma or bronchitis/emphysema groups. However, total claims for ACC were low and the service was underused. For the 1946-51 cohort there seems to be an increase in ACC item use for women with prevalent and women with incident asthma in recent years while for women from the 1921- 26 cohort there is a decline in item use in recent years for these women. ACC was introduced in 2001 as asthma3+ visits and reformed in 2006 to ACC (See Chapter 3). As seen in Figure 7-11, immediately after these two time points, the use of this service increased in both cohorts.

Figure 7-11. Proportion of women with an ACC claim between 1997 and 2013 for women in the 1921-26 and 1946-51 cohorts, according to asthma group

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ACC/CDM items

ACC items are often used for patients with moderate to severe asthma. However, CDM items may be used by GPs for the management of chronic diseases including asthma in place of a singular ACC claim. If a patient receives one of these services (either ACC or CDM) it may improve asthma management and outcome of the disease. As such the use of CDM or ACC items was explored in both cohorts according to asthma groups. Figure 7-12 and Figure 7-13 show the proportion of women having ACC or CDM while also observing deaths over time. No woman had claims for both ACC and CDM in any one year. Although, the proportion of women who had claims for ACC or CDM items increased between 1997 and 2013. In both cohorts, the ACC and CDM items were used by a greater proportion of women with asthma or bronchitis/emphysema compared with women without asthma. Women with prevalent asthma or incident asthma had the highest number of ACC or CDM claims compared to all other groups (see Figure 7-12 and Figure 7-13).

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100 100 90 90 80 80 70 70 60 60 50 50 40 40 30 30 20 20

100 100 90 90 80 80 70 70 60 60 50 50 40 40 30 30 20 20 10 10 0 0

100 90 80 70 60 50 40 30 20 10 0

Figure 7-12. Proportion of women with an ACC only, CDM only or no ACC/CDM claim between 1997 and 2013 for women in the 1921-26 cohorts, according to asthma group

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100 100 90 90 80 80 70 70 60 60 50 50 40 40 30 30 20 20 10 10 0 0

100 90 80 70 60 50 40 30 20 10 0

Figure 7-13. Proportion of women with an ACC only, CDM only or no ACC/CDM claim between 1997 and 2013 for women in the 1946-51 cohorts, according to asthma group

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Spirometry tests

In both cohorts, only a small percentage of women had claims for spirometry in any year between 1997 and 2013. Women with prevalent asthma or incident asthma had higher claims for spirometry. Spirometry claims by women from the 1946-51 cohort showed an increase from 1997 to 2013, whereas it declined slightly for women from the 1921-26 cohort. A higher percentage of women in the past asthma group from the 1921-26 cohort had a spirometry claim compared with the corresponding group from the 1946-51 cohort (see Figure 7-14).

Figure 7-14. Proportion of women with a Spirometry claim between 1997 and 2013 for women in the 1921-26 and 1946-51 cohorts, according to asthma group

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Conclusion

In order to treat a chronic condition like asthma effectively, long term management plans are required. Results of this chapter showed that ACC and Spirometry which are MBS specific items in asthma treatment and diagnosis (respectively) had very low uptake and were used only by a small proportion of women with asthma even though the items have been available since 2001. This may influence effective asthma monitoring as well as offered treatments. The decline of spirometry items at older ages may also contribute in mis/under-diagnoses of asthma. Other chronic disease or older age specific items were also investigated followed by general health services including GP and specialist visits. CDM is a Medicare item which may be used for these women to manage their asthma, although it was used by a small proportion of women in both cohorts. Health assessment items had been used by women in 1921-26 cohort more than women in the 1946-51 cohort, although it was only for one third of the women in this cohort. In contrast, most women in both cohorts had visits to their GP. Interestingly, older women with past asthma saw their GP more frequently as they aged. Moreover, specialist visits were well utilised by women from the 1921-26 cohort over time. The results of the studies in this chapter showed that health service use by women changes over time which suggests longitudinal studies on service use especially its association with asthma or bronchitis/emphysema. The following chapter focuses on health care use among women according to asthma groups and determinants of the service use.

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Chapter 8 Determinants of health service use by women with asthma

Introduction

In Chapter 7, health service use (using Medicare data) was described according to asthma groups. Women with asthma or bronchitis/emphysema had used GP services more frequently than women without these conditions, in both the 1921-26 and 1946-51 cohorts. Health service use by these women will be influenced by various factors which are investigated in this chapter.

The associations between health service use (as captured by administrative health data) and asthma groups are examined using the Anderson-Newman framework for health service utilisation (i.e. taking into account the effects of predisposing, enabling and needs factors). Initially, factors associated with health service use by women in both the 1921-26 and 1946- 51 cohorts were explored through univariate and multivariate models at one time point. Final cross-sectional model examining total GP visit time and asthma group was built while accounting for predisposing, enabling and need variables. Factors included in the final cross- sectional models were subsequently entered into longitudinal models to account for time as well as other factors affecting health service use. The Andersen-Newman behavioural model was used to guide the analyses, identifying predisposing, enabling and needs factors that may be associated with higher health service use according to asthma group.

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Associations between asthma groups and total annual GP visit time at survey 3

Using the Andersen-Newman framework, associations between predisposing, enabling and needs factors and the total GP visit time for 12 months prior to survey 3 for women from the 1921-26 and 1946-51 cohorts were explored using univariate multinomial logistic regression analysis. In Chapter 7, calculation and categorisation of total time spent for GP visits in a year was described (Section 7.3.3). For these initial cross-sectional analyses, the chosen index year for the 1921-26 cohort and the 1946-51 cohorts was 2002 and 2001 respectively, which corresponds to Survey 3 for each cohort. For women from the 1921-26 cohort, total GP visit time was grouped into: 0- ≤75 minutes (reference category); 75-≤225 minutes; and >225 min, based on the distribution of total time for that year. For women from the 1946-51 cohort, total GP visit time for a year were categorised as: 0-≤30 minutes (reference category), 30- ≤120 minutes; and > 120 minutes.

Asthma and bronchitis/emphysema groups were all more likely to have had longer total GP visit time compared to the no asthma group. Other factors that were studied for association with total GP visit time, in accordance with aspects of the Andersen-Newman model, were as below;

Predisposing factors: age, area of residence, marital status, highest qualification, country of birth

Enabling factors: income management, private health insurance and satisfaction with access to specialist, hospital doctor, emergency doctor, after-hours doctor, bulk billing doctor and female doctor

Needs: Depression, anxiety, diabetes, heart disease, hypertension, osteoporosis, low iron level, stroke, any other major diseases (in 1946-51 cohort), alcohol consumption, smoking and BMI.

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Univariate associations

1921-26 cohort

Univariate associations between Predisposing, enabling and needs factors and GP visit time for the 1921-26 cohort is shown in Table 8-1. Factors were considered for inclusion into the multivariate analyses when there was evidence of association with total GP visit time in the univariate model (p-value<0.2). For these women, all factors except satisfaction with access to after-hours doctors met this criterion (see Table 8-1).

Table 8-1. Univariate associations between total GP visit time and asthma groups, predisposing, enabling and needs factors for women from the 1921-26 cohort at Survey 3

Total GP visit time/year Predictor Categories (Ref: 0-75 minutes) 76-225 min more than 225 min Odds Ratio (95% CI) Odds Ratio (95% CI) P* Never asthma 1 1 <.0001 Past asthma 1.12 (0.83, 1.52) 2.07 (1.51, 2.84) Asthma groups Prevalent asthma 1.74 (1.35, 2.24) 3.17 (2.44, 4.11) Incident asthma 2.28 (1.61, 3.22) 3.98 (2.78, 5.69) Bronchitis/emphysema 1.72 (1.44, 2.04) 2.19 (1.81, 2.64) Predisposing factors Age 1.05 (0.99, 1.10) 1.06 (1.00, 1.13) 0.1501 Major cities 1 1 <.0001 Inner regional 0.65 (0.54, 0.78) 0.47 (0.38, 0.57) Area Outer regional, remote and very 0.51 (0.41, 0.63) 0.45 (0.36, 0.58) remote Partnered 1 1 Marital status Never married 0.62 (0.41, 0.93) 0.57 (0.35, 0.91) 0.0216 Separated/Divorced/Widowed 0.83 (0.71, 0.98) 0.79 (0.66, 0.95) University qualification 1 1 0.0075 Highest High school qualification 1.14 (0.81, 1.60) 1.25 (0.84, 1.87) qualification No formal education 1.32 (0.91, 1.90) 1.75 (1.14, 2.67) Australia 1 1 0.1817 Country of English speaking countries 1.03 (0.83, 1.29) 1.17 (0.92, 1.51) birth Non-English speaking countries 1.14 (0.85, 1.52) 1.38 (1.01, 1.89) Enabling factors Private health No 1 1 0.0046 insurance Yes 1.23 (1.07, 1.41) 1.25 (1.07, 1.45) Concession/ No 1 1 <.0001 Seniors health Yes 1.26 (1.08, 1.46) 2.77 (2.36, 3.26) card Easy to manage 1 1 <.0001 Income Not too bad to manage 1.12 (0.94, 1.34) 1.42 (1.15, 1.74) management Difficult/impossible to manage 1.52 (1.03, 2.25) 3.00 (1.99, 4.53) Excellent/very good 1 1 0.0012

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Total GP visit time/year Predictor Categories (Ref: 0-75 minutes) 76-225 min more than 225 min Odds Ratio (95% CI) Odds Ratio (95% CI) P* Access to a Good 1.02 (0.81, 1.29) 0.73 (0.56, 0.95) specialist 0.72 (0.53, 0.98) 0.62 (0.44, 0.87) Fair/poor

Access to a Excellent/very good 1 1 <.0001 bulk billing Good 0.90 (0.68, 1.19) 0.68 (0.50, 0.93) doctor Fair/poor 0.66 (0.51, 0.85) 0.45 (0.34, 0.61) Access to an Excellent/very good 1 1 0.79 after-hours Good 1.14 (0.89, 1.46) 1.06 (0.80, 1.40) doctor Fair/poor 1.06 (0.82, 1.36) 1.09 (0.83, 1.44) Excellent/very good 1 1 0.0393 Access to a Good 0.91 (0.71, 1.15) 0.70 (0.54, 0.91) female doctor Fair/poor 0.95 (0.72, 1.24) 0.97 (0.73, 1.30) Excellent/very good 1 1 0.0874 Access to a Good 1.30 (1.02, 1.64) 1.04 (0.81, 1.35) hospital doctor Fair/poor 1.09 (0.75, 1.57) 1.11 (0.75, 1.65) Needs Optimal weight (20-25) 1 1 0.0155 Underweight (<20) 0.82 (0.56, 1.21) 0.82 (0.52, 1.28) BMI Overweight (>25 to 30) 1.19 (1.00, 1.43) 1.22 (1.00, 1.49) Obese (>30) 1.31 (1.02, 1.69) 1.58 (1.20, 2.08) No drinking 1 1 0.0478 Low risk drinking (up to 2 0.87 (0.74, 1.03) 0.77 (0.64, 0.93) Alcohol drinks/day) consumption High risk drinking (3 drinks or 1.35 (0.76, 2.39) 1.33 (0.71, 2.47) more/day) Non-smoker 1 1 Smoking Smoker 0.71 (0.48, 1.06) 1.19 (0.79, 1.81) 0.0001 status Ex-smoker 1.12 (0.94, 1.34) 1.43 (1.17, 1.74) No 1 1 <.0001 Diabetes Yes 1.03 (0.82, 1.30) 1.76 (1.38, 2.23) No 1 1 <.0001 Heart diseases Yes 1.41 (1.13, 1.75) 2.65 (2.12, 3.32) No 1 1 <.0001 Hypertension Yes 1.59 (1.39, 1.81) 1.95 (1.69, 2.27) No 1 1 <.0001 Osteoporosis Yes 1.46 (1.20, 1.76) 2.40 (1.96, 2.93) No 1 1 0.0020 Stroke Yes 1.00 (0.68, 1.49) 1.65 (1.10, 2.48) No 1 1 <.0001 Depression Yes 1.55 (1.03, 2.34) 3.28 (2.17, 4.95) No 1 1 <.0001 Anxiety Yes 1.40 (0.90, 2.16) 2.62 (1.68, 4.07) * overall Type 3 p-value

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1946-51 cohort

For women from the 1946-51 cohort, there was sufficient evidence of association for all predisposing, enabling and needs factors and they were retained for inclusion in multivariate analyses with the exception of satisfaction with access to a specialist (See Table 8-2).

Table 8-2. Univariate associations between total GP visit time and asthma groups, predisposing, enabling and needs factors for women from the 1946-51 cohort at Survey 3

Predictor Predictor category Total GP visit time/year (Ref: 0-30 minutes) 31-120 min more than 120 min Odds Ratio Odds Ratio

(95% CI) (95% CI) P* Never asthma 1 1 <.0001 Past asthma 1.37 (1.10, 1.70) 2.32 (1.82, 2.95) Asthma groups Prevalent asthma 1.95 (1.56, 2.44) 4.89 (3.86, 6.18) Incident asthma 1.92 (1.50, 2.45) 4.00 (3.08, 5.19) Bronchitis/emphysema 1.42 (1.21, 1.67) 2.29 (1.91, 2.75) Predisposing factors Age 1.04 (1.00-1.08) 1.09 (1.05-1.14) 0.0003 Major cities 1 1 <.0001 Inner regional 0.84 (0.74, 0.96) 0.61 (0.53, 0.70) Area Outer regional, Remote and 0.78 (0.67, 0.90) 0.58 (0.49, 0.68) very remote Partnered 1 1 <.0001 Marital status Never married 0.85 (0.62, 1.17) 1.36 (0.96, 1.94) Separated/Divorced/Widowed 1.02 (0.88, 1.19) 1.60 (1.34, 1.89) University qualification 1 1 <.0001 Highest qualification High school qualification 1.28 (1.09, 1.50) 1.52 (1.24, 1.87) No formal education 1.40 (1.10, 1.79) 2.34 (1.76, 3.12) Australia 1 1 0.1795 Country of birth English speaking countries 0.87 (0.75, 1.02) 0.87 (0.72, 1.04) Non-English speaking countries 1.01 (0.83, 1.23) 1.16 (0.92, 1.45) Enabling factors No 1 1 0.0008 Private health insurance Yes 1.21 (1.09, 1.35) 1.08 (0.95, 1.23) Discounted health care No 1 1 card Yes 1.26 (1.08, 1.46) 2.77 (2.36, 3.26) Easy to manage 1 1 <.0001 Income management Not too bad to manage 1.02 (0.89, 1.17) 1.50 (1.26, 1.79) Difficult to manage 1.39 (1.11, 1.75) 4.16 (3.23, 5.37) Excellent/very good 1 1 0.87 Access to a specialist Good 0.97 (0.85, 1.11) 1.01 (0.87, 1.19) Fair/poor 0.94 (0.81, 1.09) 0.97 (0.82, 1.16) Excellent/very good 1 1 <.0001 Access to a bulk billing Good 0.88 (0.73, 1.05) 0.60 (0.49, 0.73) doctor Fair/poor 0.89 (0.78, 1.01) 0.47 (0.40, 0.55) Access to an after- Excellent/very good 1 1 0.0948 hours doctor Good 1.09 (0.95, 1.26) 0.99 (0.83, 1.17)

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Predictor Predictor category Total GP visit time/year (Ref: 0-30 minutes) 31-120 min more than 120 min Odds Ratio Odds Ratio

(95% CI) (95% CI) P* 1.17 (1.02, 1.34) 1.16 (0.99, 1.36) Fair/poor

Excellent/very good 1 1 0.0125 Access to a female Good 0.82 (0.71, 0.95) 0.77 (0.65, 0.91) doctor Fair/poor 0.85 (0.75, 0.98) 0.81 (0.69, 0.96) Excellent/very good 1 1 0.0058 Access to a hospital Good 1.00 (0.88, 1.15) 1.06 (0.91, 1.23) doctor Fair/poor 1.08 (0.89, 1.31) 1.43 (1.15, 1.79) Excellent/very good 1 1 0.0315 Access to an Good 1.07 (0.94, 1.22) 0.99 (0.84, 1.15) emergency doctor Fair/poor 1.03 (0.87, 1.21) 1.22 (1.01, 1.47) Heading? Optimal weight (20-25) 1 1 <.0001 Underweight (<20) 1.15 (0.71, 1.88) 1.86 (1.07, 3.21) BMI Overweight (> 25 to 30) 1.07 (0.95, 1.22) 1.31 (1.13, 1.53) Obese (>30) 1.35 (1.16, 1.57) 2.49 (2.10, 2.96) No drinking 1 1 0.0059 Low risk drinking (up to 2 0.98 (0.80, 1.18) 0.72 (0.58, 0.90) Alcohol consumption drinks/day) High risk drinking (3 drinks or 0.95 (0.68, 1.34) 0.65 (0.44, 0.98) more/day) Non-smoker 1 1 <.0001 Smoking Smoker 0.90 (0.78, 1.05) 1.13 (0.94, 1.35) Ex-smoker 1.07 (0.94, 1.22) 1.46 (1.26, 1.70) No 1 1 <.0001 Diabetes Yes 2.27 (1.43, 3.59) 6.19 (3.91, 9.81) No 1 1 <.0001 Heart disease Yes 4.53 (1.98, 10.36) 13.59 (5.95, 31.05) No 1 1 <.0001 Hypertension Yes 3.23 (2.65, 3.95) 5.49 (4.44, 6.78) No 1 1 <.0001 Osteoporosis Yes 1.76 (1.15, 2.67) 4.44 (2.90, 6.77) No 1 1 0.0039 Stroke Yes 3.83 (0.50, 29.18) 11.08 (1.46, 84.04) No 1 1 <.0001 Low iron Yes 1.32 (1.07, 1.63) 1.87 (1.49, 2.36) No 1 1 <.0001 Depression Yes 2.50 (1.95, 3.21) 6.64 (5.15, 8.57) No 1 1 <.0001 Anxiety Yes 2.69 (1.91, 3.80) 7.72 (5.46, 10.92) No 1 1 <.0001 Any other major disease Yes 1.57 (1.08, 2.28) 4.24 (2.92, 6.17) * Overall Type 3 p-value

224

Multivariable and Longitudinal models

Three multivariate multinomial analyses for the three components of the Andersen-Newman behavioural model for health service use were evaluated separately. Variables with sufficient evidence of association with total GP visit time in these models were then used in a model to examine the association between asthma groups and total GP visit time simultaneously. Cross sectional regression analysis gives valuable information on association between the drivers and health service use at one time point. However, to investigate the impact of various factors on health service use in long term considering the changes in population and factors, longitudinal analyses are required. Cross-sectional analysis was followed by longitudinal analysis which also considered the influence of time (or ageing) on these effects. The following section first presents results for the full model building process for the 1921-26 cohort, followed by a similar process for the 1946-51 cohort.

1921-26 cohort

Multivariable associations with total GP visit time at Survey 3

8.2.3.1.1 Predisposing factors When all predisposing factors with sufficient evidence of association, were included in one model, there was insufficient evidence to support an association between GP visit time and country of birth (p=0.88) (Table 8-3). These two variables were not retained in further models.

8.2.3.1.2 Enabling factors There was enough supporting evidence of association for all the enabling factors in multivariate models with total GP visit time (p<0.2) and thus, all the factors were retained for further analysis (Table 8-4).

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8.2.3.1.3 Needs There was insufficient evidence supporting the association between BMI and GP visit time once other need factors were accounted for in the multivariate model (p=0.66). This variable was not retained in further models. All other variables were retained (p <0.2) (See Table 8-5).

Table 8-3. Multivariate associations between total GP visit time and predisposing factors for women from the 1921-26 cohort at Survey 3

Total GP visit time/year Predictor Categories (Ref: 0-75 minutes) 76-225 min more than 225 min Odds Ratio (95% CI) Odds Ratio (95% CI) P* Age 1.04 (0.99-1.09) 1.05 (1.00-1.11) 0.1554 Major cities 1 1 <.0001 Inner regional 0.64 (0.54-0.74) 0.48 (0.40-0.57) Area Outer regional, remote and very 0.56 (0.46-0.68) 0.48 (0.39-0.59) remote Partnered 1 1 Marital status Never married 0.61 (0.42-0.87) 0.57 (0.37-0.87) 0.0001 Separated/Divorced/Widowed 0.77 (0.67-0.89) 0.72 (0.61-0.84) University qualification 1 1 0.0002 Highest High school qualification 1.04 (0.75-1.43) 1.16 (0.80-1.68) qualification No formal education 1.31 (0.93-1.83) 1.72 (1.16-2.53) Australia 1 1 0.88 Country of English speaking countries 0.98 (0.80-1.19) 1.01 (0.81-1.26) birth Non-English speaking countries 1.08 (0.85-1.39) 1.15 (0.88-1.52) * Overall Type 3 p-value

226

Table 8-4. Multivariate associations between total GP visit time and enabling factors for women from the 1921-26 cohort at Survey 3

Total GP visit time/year Predictor Categories (Ref: 0-75 minutes) 76-225 min more than 225 min Odds Ratio (95% CI) Odds Ratio (95% CI) P* Private health No 1 1 0.0093 insurance Yes 1.34 (1.07-1.68) 1.47 (1.14-1.88) Easy to manage 1 1 0.0004 Income Not too bad to manage 1.24(0.96-1.60) 1.45 (1.08-1.93) management Difficult/impossible to manage 1.69 (1.00-2.85) 3.05 (1.76-5.29) Excellent/very good 1 1 0.0024 Access to a Good 0.84 (0.61-1.15) 0.62 (0.43-0.89) specialist Fair/poor 0.60 (0.40-0.89) 0.41 (0.26-0.65) Access to a Excellent/very good 1 1 0.0002 bulk billing Good 0.74 (0.52-1.04) 0.69 (0.47-1.02) doctor Fair/poor 0.65 (0.49-0.87) 0.45 (0.32-0.63) Excellent/very good 1 1 0.0348 Access to a Good 0.89 (0.67-1.18) 0.74 (0.54-1.01 female doctor Fair/poor 1.11 (0.81-1.52) 1.27 (0.90-1.78) Excellent/very good 1 1 0.0012 Access to a Good 1.71 (1.23-2.37) 1.81 (1.25-2.60) hospital doctor Fair/poor 1.79 (1.12-2.86) 2.45 (1.47-4.10) * Overall Type 3 p-value

227

Table 8-5. Multivariate associations between total GP visit time and need factors for women from the 1921-26 cohort at Survey 3

Total GP visit time/year Predictor Categories (Ref: 0-75 minutes) 76-225 min more than 225 min Odds Ratio (95% CI) Odds Ratio (95% CI) P* Optimal weight (20-25) 1 1 0.66 Underweight (<20) 0.90 (0.62-1.31) 0.94 (0.61-1.43) BMI Overweight (> 25 to 30) 1.12 (0.94-1.32) 1.10 (0.91-1.33) Obese (>30) 1.14 (0.90-1.44) 1.23 (0.95-1.59) No drinking 1 1 0.0877 Alcohol Low risk drinking (up to 2 drinks/day) 0.90 (0.76-1.05) 0.83 (0.69-0.99) consumption High risk drinking (3 or more drinks/day) 1.32 (0.78-2.23) 1.51 (0.86-2.65) Non-smoker 1 1 0.0001 Smoking Smoker 0.68 (0.48-0.95) 0.93 (0.64-1.37) status Ex-smoker 1.08 (0.91-1.29) 1.41 (1.16 -1.71) No 1 1 <.0001 Diabetes Yes 0.85 (0.66-1.11) 1.34 (1.02-1.77 No 1 1 <.0001 Heart diseases Yes 1.39 (1.09-1.78) 2.37 (1.83-3.06) No 1 1 <.0001 Hypertension Yes 1.55 (1.33-1.80) 1.79 (1.51-2.12) No 1 1 <.0001 Osteoporosis Yes 1.46 (1.18-1.81) 2.32 (1.85-2.91) No 1 1 0.0634 Stroke Yes 0.75 (0.49-1.16) 1.12 (0.71-1.76) No 1 1 <.0001 Depression Yes 1.48 (1.01-2.16) 2.43 (1.65-3.60) No 1 1 0.0014 Anxiety Yes 1.26 (0.84-1.89) 1.90 (1.25-2.89) * Overall Type 3 p-value

228

Multivariate cross-sectional models examining total GP visit time and asthma group, while accounting for predisposing, enabling and need variables

Four nested models were constructed to examine the association between asthma groups and total GP visit time, sequentially accounting for predisposing, enabling and need factors that had sufficient supporting their association. Table 8-6 presents the adjusted odds ratios between total GP visit time and asthma groups for the four models. Model 1 shows the known univariate association between total GP time and asthma groups. Models 2-4 show the change in odds ratios for asthma groups on total GP time as predisposing, enabling and need factors are sequentially added into each successive model. In Model 4 when all known factors have been included, there remained an association between asthma group and the longest category of GP visit time compared to 0- 75 minutes, but not for the 76-225 minute category. The association between total GP visit time and asthma group was not significant for 76-225 minutes category in any of the Models except for Model 1 for prevalent asthma and bronchitis/emphysema group. For the 225 minutes or more category, there was a significant association for all groups but the association diminished from Model 1 to Model 4.

229

Table 8-6. Adjusted odds ratios (and 95% CI) for the effect of asthma group on total GP visit time among women from the 1921-26 cohort at Survey 3 (2002), while adjusting for predisposing, enabling and needs factors

OUTCOME * Asthma groups Total GP visit Past asthma Prevalent asthma Incident asthma Bronchitis/emphysema Model time /year OR (95% CI) P OR (95% CI) P OR (95% CI) P OR (95% CI) P Model 76-225 min 1.12 (0.83, 1.52) 0.32 1.74 (1.35, 2.24) 0.0004 2.28 (1.61, 3.22) 0.13 1.72 (1.44, 2.04) 0.0008 1 >225 min 2.07 (1.51, 2.84) <.0001 3.17 (2.44, 4.11) <.0001 3.98 (2.78, 5.69) <.0001 2.19 (1.81, 2.64) <.0001 Model 76-225 min 1.04 (0.77-1.41) 0.7911 1.14 (0.92-1.40) 0.2310 1.04 (0.81-1.34) 0.7386 1.03 (0.90-1.19) 0.6418 2 >225 min 2.06 (1.46-2.92) <.0001 2.49 (1.98-3.14) <.0001 2.29 (1.74-3.00) <.0001 1.44 (1.22-1.71) <.0001 Model 76-225 min 1.07 (0.80-1.43) 0.6451 1.20 (0.97-1.48) 0.0883 1.06 (0.83-1.36) 0.64 1.09 (0.95-1.26) 0.2205 3 >225 min 2.09 (1.49-2.93) <.0001 2.60 (2.06-3.28) <.0001 2.26 (1.72-2.95) <.0001 1.51 (1.28-1.79) <.0001 Model 76-225 min 1.08 (0.80-1.45) 0.6074 1.18 (0.96-1.47) 0.1205 1.06 (0.82-1.36) 0.6733 1.10 (0.95-1.26) 0.2063 4 >225 min 1.82 (1.29-2.57) 0.0006 2.34 (1.85-2.96) <.0001 2.03 (1.54-2.66) <.0001 1.38 (1.16-1.63) 0.0003 *Outcome Reference: ’75 minutes or less’ for total GP visit time; asthma group reference: ‘Never asthma’ Model 1: Asthma group Model 2: Asthma group + Predisposing factors (age, area of residence, marital status and highest qualification) Model 3: Asthma group + Predisposing factors + Enabling factors (income management and private insurance) Model 4: Asthma group + Predisposing factors + Enabling factors + Needs (Depression, anxiety, diabetes, heart disease, stroke, hypertension, osteoporosis, low iron level, alcohol consumption and smoking)

230

Longitudinal analysis for total GP visit time

The preceding cross-sectional models provided initial evidence that having asthma, either incident, prevalent or past asthma, is associated with longer GP visit times. The ALSWH data allows for further examination of this association over a 17-year period, utilising the MBS data between 1996 and 2014. The variables that were included in the final cross sectional models were included in the longitudinal models, taking account of changes over time as well as the effect of predisposing, enabling and need variables.

Preparation of the longitudinal data Variables that were used in cross sectional nested models and had been measured in all surveys or could be assumed to remain fixed/constant across time (e.g. Country of birth) were used in the analysis. For every survey, MBS service use within the following three years was matched with survey variables.

8.3.1.1.1 Results of the longitudinal analyses on total GP visit time

Table 8-7 shows the nested longitudinal models for the 1921-26 cohort. In this cohort women with asthma or bronchitis/emphysema had higher odds of having 75-225 minutes or >225 minutes of total GP visit time with each successive time point. Model 1 shows the univariate association between asthma and total GP visit, after adjusting for survey time point. Women with asthma or bronchitis/emphysema had higher odds of having longer total GP visit time compared to women who never had asthma. Model 2 shows the change in odds ratios as predisposing factors were added into Model 1. Model 3 and 4, showed attenuation of association between asthma groups and total GP visit time when all predisposing, enabling and need factors were added to the previous models however, the association was still strong. In all the models, there was a statistically significant association between asthma group and longer GP visit time.

231

Table 8-7. Association between total GP visit time and asthma groups among women from the 1921-26 cohort, after adjusting for time (Survey 2 to 6), and predisposing, enabling and needs factors

Models Average GP Asthma groups visit time /year Past asthma Prevalent asthma Incident asthma Bronchitis/emphysema Goodness of fit OR (CI) P OR (CI) P OR (CI) P OR (CI) P AIC -2Log L Model 1 75-225 min 1.30 (0.96, 1.75) 0.0902 1.72 (1.37, 2.16) <.0001 2.03 (1.50, 2.76) <.0001 1.43 (1.23, 1.65) <.0001 35053.139 35017.139 >225 min 2.43 (1.72, 3.45) <.0001 3.59 (2.77, 4.66) <.0001 4.27 (3.08, 5.93) <.0001 1.97 (1.65, 2.35) <.0001 Model 2 75-225 min 1.30 (0.96, 1.77) 0.0915 1.75 (1.39, 2.20) <.0001 2.07 (1.52, 2.81) <.0001 1.42 (1.22, 1.64) <.0001 34713.712 34641.712 >225 min 2.43 (1.70, 3.48) <.0001 3.68 (2.84, 4.77) <.0001 4.35 (3.12, 6.06) <.0001 1.93 (1.62, 2.31) <.0001 Model 3 75-225 min 1.32 (0.94, 1.86) 0.1049 1.83 (1.43, 2.34) <.0001 2.26 (1.64, 3.11) <.0001 1.48 (1.27, 1.73) <.0001 29502.838 29414.838 >225 min 2.54 (1.69, 3.80) <.0001 3.77 (2.84, 5.01) <.0001 4.66 (3.27, 6.64) <.0001 2.09 (1.73, 2.53) <.0001 Model 4 75-225 min 1.26 (0.90, 1.78) 0.1774 1.72 (1.34, 2.21) <.0001 2.08 (1.50, 2.87) <.0001 1.42 (1.22, 1.67) <.0001 28968.231 28824.231 >225 min 2.12 (1.41, 3.18) 0.0003 3.27 (2.46, 4.35) <.0001 3.87 (2.71, 5.53) <.0001 1.83 (1.51, 2.22) <.0001 *Outcome Reference: ’75 minutes or less’ for total GP visit time; asthma group reference: ‘Never asthma’ Model 1: Asthma group Model 2: Asthma group + Predisposing factors (age, area of residence, marital status and highest qualification) Model 3: Asthma group + Predisposing factors + Enabling factors (income management, concession/seniors health card and private insurance) Model 4: Asthma group + Predisposing factors + Enabling factors + Needs (Depression, anxiety, diabetes, heart disease, stroke, hypertension, osteoporosis, low iron level, alcohol consumption and smoking)

232

1946-51 cohort Multivariable associations with total GP visit time at Survey 3

8.3.2.1.1 Predisposing factors There was sufficient evidence for all the predisposing factors in multivariate model of the association between predisposing factors and GP visit time (p<0.2) and all the factors were retained for further analysis (Table 8-8).

8.3.2.1.2 Enabling factors There was insufficient evidence for association between satisfaction with access to a specialist and GP visit time (p=0.58) once other enabling factors were accounted for in the multivariate model. This variable was not retained in further models (Table 8-9).

8.3.2.1.3 Needs Of all the need factors there was insufficient evidence for association between stroke and GP visit time (p=0.82) once other need factors were accounted for in the multivariate model. This variable was not retained in further models. All other variables were retained (p <0.2) (Table 8-10).

Table 8-8. Multivariate associations between total GP visit time and predisposing factors for women from the 1946-51 cohort at Survey 3

Predictor Predictor category Total GP visit time/year (Ref: 0-30 minutes) 31-120 min more than 120 min Odds Ratio (95% CI) Odds Ratio (95% CI) P* Age 1.06 (1.01-1.11) 1.10 (1.04-1.16) 0.0031 Major cities 1 1 <.0001 Inner regional 0.77 (0.65-0.90) 0.52 (0.43-0.63) Area Outer regional, remote and very 0.71 (0.59-0.86) 0.48 (0.39-0.60) remote Partnered 1 1 <.0001 Marital status Never married 0.82 (0.55-1.21) 1.12 (0.72-1.75) Separated/Divorced/Widowed 1.00 (0.81-1.22) 1.47 (1.17-1.84) University qualification 1 1 <.0001 Highest High school qualification 1.27 (1.07-1.50) 1.47 (1.19-1.80) qualification No formal education 1.45 (1.12-1.89) 2.43 (1.80-3.28) Australia 1 1 0.1163 Country of birth English speaking countries 0.81 (0.66-0.98) 0.74 (0.59-0.94) Non-English speaking countries 0.92 (0.71-1.21) 1.00 (0.74-1.36) *Overall Type 3 p-value

233

Table 8-9. Multivariate associations between total GP visit time and enabling factors for women from the 1946-51 cohort at Survey 3

Predictor Predictor category Total GP visit time/year (Ref: 0-30 minutes) more than 120

31-120 min min Odds Ratio

Odds Ratio (95% CI) (95% CI) P* No 1 1 0.0014 Private health insurance Yes 1.27 (1.09-1.48) 1.37 (1.15-1.62) Easy to manage 1 1 <.0001 Not too bad to 0.86 (0.71-1.05) 1.36 (1.08-1.73) Income management manage Difficult/impossible 1.27 (0.92-1.76) 4.11 (2.90-5.84) to manage Excellent/very good 1 1 0.58 Access to a specialist Good 0.92 (0.74-1.15) 0.95 (0.74-1.21) Fair/poor 0.84 (0.65-1.08) 0.80 (0.60-1.07) Excellent/very good 1 1 <.0001 Access to a bulk billing doctor Good 0.88 (0.73, 1.05) 0.60 (0.49, 0.73) Fair/poor 0.89 (0.78, 1.01) 0.47 (0.40, 0.55) Excellent/very good 1 1 0.0948 Access to an after-hours doctor Good 1.09 (0.95, 1.26) 0.99 (0.83, 1.17) Fair/poor 1.17 (1.02, 1.34) 1.16 (0.99, 1.36) Excellent/very good 1 1 0.0125 Access to a female doctor Good 0.82 (0.71, 0.95) 0.77 (0.65, 0.91) Fair/poor 0.85 (0.75, 0.98) 0.81 (0.69, 0.96) Excellent/very good 1 1 0.0014 Access to a hospital doctor Good 1.31 (1.01-1.70) 1.66 (1.23-2.23) Fair/poor 1.19 (0.83-1.70) 1.91 (1.28-2.84) Excellent/very good 1 1 0.1487 Access to an emergency doctor Good 1.11 (0.86-1.44) 0.87 (0.64-1.16) Fair/poor 0.92 (0.66-1.28) 0.81 (0.56-1.18) *Overall Type 3 p-value

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Table 8-10. Multivariate associations between total GP visit time and need factors for women from the 1946-51 cohort at Survey 3

Predictor Predictor category Total GP visit time/year (Ref: 0-75 minutes) more than 120

31-120 min min Odds Ratio (95% Odds Ratio

CI) (95% CI) P* Optimal weight (20-25) 1 1 <.0001 Underweight (<20) 1.47 (0.76-2.84) 2.16 (1.04-4.48) BMI Overweight (> 25 to 30) 1.03 (0.89-1.19) 1.14 (0.95-1.36) Obese (>30) 1.10 (0.91-1.31) 1.61 (1.30-1.99) No drinking 1 1 0.1496 Alcohol consumption Low risk drinking (up to 2 drinks/day) 0.99 (0.79-1.22) 0.82 (0.64-1.06) High risk drinking (3 or more drinks/day) 0.90 (0.62-1.31) 0.62 (0.40-0.98) Non-smoker 1 1 <.0001 Smoking Smoker 0.90 (0.74-1.10) 1.04 (0.81-1.32) Ex-smoker 1.06 (0.90-1.24) 1.49 (1.24-1.80) No 1 1 <.0001 Diabetes Yes 2.38 (1.19-4.76) 4.47 (2.20-9.06) No 1 1 <.0001 Heart disease Yes 2.33 (0.92-5.87) 5.12 (2.01-13.06) No 1 1 <.0001 Hypertension Yes 3.63 (2.76-4.76) 5.12 (3.83-6.83) No 1 1 <.0001 Osteoporosis Yes 1.63 (0.96-2.78) 3.52 (2.01-6.14) No 1 1 0.82 Stroke Yes 1.93 (0.24-15.41) 1.93 (0.22-16.94) No 1 1 <.0001 Low iron Yes 1.25 (0.96 -1.62) 1.63 (1.21-2.18) No 1 1 <.0001 Depression Yes 2.63 (1.85-3.75) 5.29 (3.67-7.63) No 1 1 <.0001 Anxiety Yes 2.45 (1.49-4.01) 4.51 (2.71-7.49) No 1 1 <.0001 Any other major disease Yes 1.37 (0.84 -2.22) 2.95 (1.79 -4.86) *Overall Type 3 p-value

235

Multivariate cross-sectional model examining total GP visit time and asthma group, while accounting for predisposing, enabling and need variables

Table 8-11 shows the association between GP visit time at Survey 3 and asthma groups, after sequential adjustment for predisposing, enabling and needs factors. The association between asthma or bronchitis/emphysema and GP time attenuated when other factors were added, but remained statistically significant (<0.05). In all models, women with prevalent or incident asthma had the strongest level of association with both GP time categories. At survey 3, these women were around twice as likely to have had a total of 31-120 minutes in GP consultation and had 4-5 times the odds of having more than 120 minutes total GP time. Although including all predisposing, enabling and needs factors in Model 4 diminished the association between asthma groups and total GP visit time, it still remained statistically significant.

236

Table 8-11. Adjusted odds ratios (and 95% CI) for the effect of asthma group on total GP visit time among women from the 1946-51 cohort at Survey 3 (2001), while adjusting for predisposing, enabling and needs factors

Models OUTCOM* Asthma groups Average GP visit time /year Past asthma Prevalent asthma Incident asthma Bronchitis/emphysema OR (95% CI) P OR (95% CI) P OR (95% CI) P OR (95% CI) P Model 31-120 min 1.37 (1.10, 1.70) 0.0042 1.95 (1.56, 2.44) <.0001 1.92 (1.50, 2.45) <.0001 1.42 (1.21, 1.67) <.0001 1 >120 min 2.32 (1.82, 2.95) <.0001 4.89 (3.86, 6.18) <.0001 4.00 (3.08, 5.19) <.0001 2.29 (1.91, 2.75) <.0001 Model 31-120 min 1.33 (1.00-1.78) 0.0490 2.25 (1.66-3.07) <.0001 1.79 (1.30-2.45) 0.0003 1.42 (1.15-1.77) 0.0013 2 >120 min 2.17 (1.57-3.00) <.0001 5.56 (4.02-7.68) <.0001 3.47 (2.48-4.87) <.0001 2.23 (1.75-2.84) <.0001 Model 31-120 min 1.34 (0.93-1.95) 0.1155 2.31 (1.55-3.43) <.0001 1.91 (1.24-2.93) 0.0031 1.62 (1.21-2.18) 0.0012 3 >120 min 2.06 (1.36-3.10) 0.0006 5.50 (3.63-8.33) <.0001 3.78 (2.39-5.99) <.0001 2.51 (1.81-3.49) <.0001 Model 31-120 min 1.34 (0.88-2.03) 0.1692 2.11 (1.36-3.28) 0.0009 1.63 (1.03-2.57) 0.0369 1.56 (1.12-2.17) 0.0083

4 >120 min 1.80 (1.10-2.93) 0.0189 4.20 (2.61-6.76) <.0001 3.05 (1.83-5.09) <.0001 2.13 (1.45-3.13) 0.0001 * Outcome Reference: ’30 minutes or less’ total GP visit time and ‘Never asthma’ Model 1: Asthma group Model 2: Asthma group + Predisposing factors (age, area of residence, marital status, highest qualification, country of birth) Model 3: Asthma group + Predisposing factors + Enabling factors (income management, private health insurance and access to hospital doctor, emergency doctor, after-hours doctor, bulk billing doctor and female doctor) Model 4: Asthma group + Predisposing factors + Enabling factors + Needs (Depression, anxiety, diabetes, heart disease, hypertension, osteoporosis, low iron level, alcohol consumption, smoking and BMI)

237

Longitudinal analysis for total GP visit time

Table 8-12 shows the longitudinal association between asthma and total GP visit time for women from the 1946-51 cohort. Results were similar to the cross-sectional models, with time having little effect on the overall estimates of the association between asthma group and GP visits.

238

Table 8-12. Association between total GP visit time and asthma groups among women from the 1946-51 cohort, after adjusting for time (Survey 2 to 6), and predisposing, enabling and needs factors

Models Average GP visit Asthma groups time /year Past asthma Prevalent asthma Incident asthma Bronchitis/emphysema Goodness of fit OR (95% CI) P OR (95% CI) P OR (95% CI) P OR (95% CI) P AIC -2 Log L Model 1 75-225 min 1.34 (1.07, 1.67) 0.0101 1.98 (1.57, 2.51) <.0001 1.51 (1.18, 1.93) 0.0009 1.47 (1.24, 1.74) <.0001 35407.032 35371.032 >225 min 2.13 (1.61, 2.82) <.0001 4.92 (3.78, 6.41) <.0001 3.24 (2.45, 4.28) <.0001 2.33 (1.89, 2.87) <.0001 Model 2 75-225 min 1.35 (1.08, 1.68) 0.0076 1.99 (1.57, 2.52) <.0001 1.51 (1.18, 1.92) 0.0011 1.47 (1.24, 1.74) <.0001 35106.664 35034.664 >225 min 2.15 (1.63, 2.85) <.0001 4.83 (3.70, 6.31) <.0001 3.17 (2.40, 4.19) <.0001 2.30 (1.87, 2.84) <.0001 Model 3 75-225 min 1.32 (1.06, 1.64) 0.0135 1.95 (1.54, 2.47) <.0001 1.49 (1.16, 1.91) 0.0016 1.43 (1.21, 1.69) <.0001 34385.933 34257.933 >225 min 2.03 (1.54, 2.67) <.0001 4.36 (3.34, 5.70) <.0001 3.02 (2.27, 4.01) <.0001 2.09 (1.69, 2.58) <.0001 Model 4 75-225 min 1.25 (1.00, 1.56) 0.0501 1.77 (1.39, 2.25) <.0001 1.39 (1.09, 1.78) 0.0087 1.37 (1.16, 1.63) 0.0003 33153.194 32965.194 >225 min 1.74 (1.31, 2.29) 0.0001 3.47 (2.65, 4.54) <.0001 2.52 (1.89, 3.35) <.0001 1.87 (1.52, 2.32) <.0001 *Outcome Reference: ’30 minutes or less’ total GP visit time and ‘Never asthma’ Model 1: Time+ Asthma group Model 2: Time+ Asthma group + Predisposing factors (age, area of residence, marital status, highest qualification, country of birth) Model 3: Time+ Asthma group + Predisposing factors + Enabling factors (income management, discounted health care card, private health insurance and access to Hospital doctor, emergency doctor, after-hours doctor, bulk billing doctor and female doctor) Model 4: Time+ Asthma group + Predisposing factors + Enabling factors + Needs (Depression, anxiety, diabetes, heart disease, hypertension, osteoporosis, low iron level, alcohol consumption, smoking and BMI)

239

Association between asthma groups and after-hours GP visits

After-hour GP visits are described in Chapter 3 (3.5.2.2). Briefly, this service is conducted by GPs outside regular hours, weekends and public holidays. Unlike regular hour GP visits, these items are not defined by length of service. In this section the association between predisposing, enabling and need factors with after-hours GP visits as a binomial variable (yes/no) is presented.

Cross-sectional associations (Survey 3)

To identify the predictors of after-hours GP service use, univariate and multivariate analyses were performed, similar to the analyses that were done in the previous section for total GP visit time.

1921-26 cohort

From the cross-sectional multivariable models, none of the predisposing factors demonstrated sufficient evidence of an association with after-hours GP visits. In view of this, only the relevant enabling and needs factors were included in the nested multivariable models when examining the association between asthma group and after hours GP visits (see Table 8-13). Without controlling for other factors (Model 1), women with asthma had higher odds of seeing an after-hours GP compared to women who never had asthma. In Models 2 and 3, there was an attenuation of the association between asthma groups and after-hours GP visits. The association remained significant for only women with prevalent asthma.

1946-51 cohort At Survey 3, in model 1, the univariate association between asthma and after-hours GP visits was significantly higher compared to women without asthma. However, after considering predisposing, enabling and needs factors in models 2-4, the association was attenuated and was only significant for women with prevalent and incident asthma (Table 8-14).

240

Table 8-13. Adjusted odds ratios (and 95% CI) for the effect of asthma group on after-hours GP visit time among women from the 1921-26 cohort at Survey 3 (2002), while adjusting for predisposing, enabling and needs factors

Models OUTCOME * Asthma groups after-hours GP visit Past asthma Prevalent asthma Incident asthma Bronchitis/emphysema OR (CI) P OR (CI) P OR (CI) P OR (CI) P Model 1 Yes 2.07 (1.57, 2.73) <.0001 1.41 (1.11, 1.78) 0.0042 1.45 (1.08, 1.93) 0.0121 1.19 (0.99, 1.44) 0.06 Model 2 Yes 1.56 (0.78, 3.12) 0.21 1.91 (1.25, 2.93) 0.0027 1.34 (0.78, 2.30) 0.29 1.39 (0.96, 2.01) 0.08 Model 3 Yes 1.39 (0.69, 2.81) 0.36 1.79 (1.17, 2.75) 0.0078 1.21 (0.70, 2.10) 0.49 1.29 (0.89, 1.87) 0.18 *Outcome Reference: No after-hours GP visits and ‘Never asthma’ Model 1: Asthma group Model 2: Asthma group + Enabling factors (access to an after-hours GP) Model 3: Asthma group + Enabling factors + Need factors (depression, heart disease and osteoporosis)

241

Table 8-14. Adjusted odds ratios (and 95% CI) for the effect of asthma group on after-hours GP visit time among women from the 1946-51 cohort at Survey 3 (2001), while adjusting for predisposing, enabling and needs factors

Models OUTCOME* Asthma groups after-hours GP visit Past asthma Prevalent asthma Incident asthma Bronchitis/emphysema OR (CI) P OR (CI) P OR (CI) P OR (CI) P Model 1 Yes 1.59 (1.13, 2.23) 0.0072 2.17 (1.66, 2.84) <.0001 1.60 (1.15, 2.22) 0.0055 1.26 (0.95, 1.66) 0.11 Model 2 Yes 1.30 (0.69, 2.42) 0.41 1.90 (1.19, 3.03) 0.0070 1.88 (1.10, 3.20) 0.0205 1.01 (0.60, 1.70) 0.97 Model 3 Yes 1.39 (0.74, 2.61) 0.31 2.00 (1.24, 3.21) 0.0043 2.00 (1.17, 3.42) 0.0117 1.08 (0.63, 1.82) 0.79 Model 4 Yes 1.30 (0.69, 2.45) 0.42 1.88 (1.17, 3.04) 0.0093 1.97 (1.15, 3.38) 0.0142 1.01 (0.59, 1.72) 0.97 * Outcome Reference: No after-hours GP visits and ‘Never asthma’ Model 1: Asthma group Model 2: Asthma group + Predisposing factors (area of residence) Model 3: Asthma group + Predisposing factors + Enabling factors (income management, access to an after-hours GP) Model 4: Asthma group + Predisposing factors + Enabling factors + Need factors (Depression, osteoporosis, any other major disease)

242

Longitudinal analysis for after-hours GP visits

For the final set of models of the association between asthma groups and after-hours GP visits, all variables that were included in the previous cross sectional models were included in longitudinal models, taking account of changes over time as well as the effect of need variables. For this cohort there was not enough supporting evidence for association between any of the predisposing and enabling factors with after-hours GP visits.

1921-26 cohort Table 8-7 shows the sequential nested longitudinal models for the 1921-26 cohort. Model 1 shows the univariate association between asthma and after-hours GP visits. Over time women in this cohort had increased association with after-hours GP visits. In this cohort, over time, women with asthma or bronchitis emphysema had higher odds ratios for visiting after- hours GPs. In Model 2 after inclusion of needs factors in the model, the association between asthma groups and GP visits declined but remained statistically significant.

1946-51 cohort In model 1,Table 8-16, women with asthma or bronchitis/emphysema were more likely to have had after-hours GP visits compared to women without the condition. Model 2-4 showed minor attenuation of association between asthma and after-hours GP visits when all predisposing, enabling and need factors were added to the previous models. Over time, there was still a statistically significant association between asthma group and after-hours GP visits.

243

Table 8-15. Association between after-hours GP visit time and asthma groups among women from the 1921-26 cohort, after adjusting for time (Survey 2 to 6), and predisposing, enabling and needs factors

Models OUTCOME* Asthma groups After-hours GP visit Past asthma Prevalent asthma Incident asthma Bronchitis/emphysema OR (CI) P OR (CI) P OR (CI) P OR (CI) P Model 1 Yes 1.48 (1.15, 1.92) 0.0025 1.46 (1.23, 1.74) <.0001 1.46 (1.18, 1.79) 0.0005 1.32 (1.16, 1.50) <.0001 Model 2 Yes 1.40 (1.09, 1.80) 0.0092 1.39 (1.17, 1.66) 0.0002 1.37 (1.10, 1.70) 0.0042 1.24 (1.09, 1.42) 0.0001 *Reference: No after-hours GP visits and ‘Never asthma’ Model 1: Time + Asthma group Model 2: Time + Asthma group+ Enabling factors + Needs (depression, heart disease and osteoporosis)

244

Table 8-16. Association between after-hours GP visit time and asthma groups among women from the 1946-51 cohort, after adjusting for time (Survey 2 to 6), and predisposing, enabling and needs factors

Models OUTCOME* Asthma groups After-hours GP visit Past asthma Prevalent asthma Incident asthma Bronchitis/emphysema Goodness of fit OR (CI) P OR (CI) P OR (CI) P OR (CI) P AIC -2 Log L Model 1 Yes 1.35 (1.12, 1.63) 0.0020 1.71 (1.48, 1.99) <.0001 1.44 (1.21, 1.72) <.0001 1.29 (1.12, 1.47) 0.0003 15192.008 15174.008 Model 2 Yes 1.33 (1.10, 1.61 0.0033 1.69 (1.46, 1.96) <.0001 1.47 (1.23, 1.75) <.0001 1.28 (1.12, 1.46) 0.0004 14983.069 14961.069 Model 3 Yes 1.33 (1.10, 1.61) 0.003 1.66 (1.43, 1.93) <.0001 1.46 (1.22, 1.74) <.0001 1.27 (1.11, 1.45) 0.0006 14904.161 14872.161 Model 4 Yes 1.30 (1.07, 1.57) 0.0071 1.59 (1.37, 1.85) <.0001 1.42 (1.19, 1.69) 0.0001 1.24 (1.08, 1.42) 0.0020 14867.595 14829.595 *Outcome Reference: No after-hours GP visits and ‘Never asthma’ Model 1: Time + Asthma group Model 2: Time + Asthma group + Predisposing factors (area of residence) Model 3: Time + Asthma group + Predisposing factors + Enabling factors (income management, access to an after-hours GP and discounted health care card) Model 4: Time + Asthma group + Predisposing factors + Enabling factors + Needs (Depression, osteoporosis, any other major disease)

245

Association between asthma groups and specialist visits

Specialist visits are provided by health professionals who had special training in treating certain diseases. To identify the predictors of specialist service use, univariate and multivariate analyses were performed, similar to the analyses that were done in the previous section for total GP visit time.

Cross-sectional nested models at survey 3

Four nested models were constructed to examine the association between asthma groups and specialist visits sequentially accounting for predisposing, enabling and needs factors. Variables that had sufficient evidence of association with specialist visits in univariate and multivariate analyses were retained in nested models.

1921-26 cohort

In Model 1, at survey 3, women with past, incident asthma and bronchitis/emphysema had higher odds of seeing a specialist in a year compared to women who never had asthma. From Model 2-4 there is an attenuation in association between asthma or bronchitis emphysema and specialist visits in all the groups. The association remained significant for women with incident asthma and bronchitis/emphysema, but not for women with past asthma. For women with prevalent asthma there was no association with specialist visits in any of the models (p>0.24) (See Table 8-17).

1946-51 cohort At survey 3, univariate association between asthma groups and specialist visits are shown in Model 1, Table 8-18. among asthma or bronchitis groups there was only association between incident asthma and specialist visits. This association did not change substantially from Model 1 to Model 4.

246

Table 8-17. Adjusted odds ratios (and 95% CI) for the effect of asthma group on specialist visits among women from the 1921-26 cohort at Survey 3 (2002), while adjusting for predisposing, enabling and needs factors

Models OUTCOME* Asthma groups specialist visit Past asthma Prevalent asthma Incident asthma Bronchitis/emphysema OR (CI) P OR (CI) P OR (CI) P OR (CI) P Model 1 Yes 2.57 (1.10, 5.98) 0.0291 1.28 (0.85, 1.93) 0.24 2.72 (1.45, 5.11) 0.0019 1.68 (1.18, 2.37) 0.0037 Model 2 Yes 2.41 (1.03, 5.63) 0.0424 1.29 (0.85, 1.95) 0.23 2.72 (1.44, 5.13) 0.0020 1.63 (1.15, 2.32) 0.0061 Model 3 Yes 2.32 (0.99, 5.44) 0.05 1.32 (0.87, 2.01) 0.19 2.83 (1.50, 5.36) 0.0013 1.72 (1.20, 2.44) 0.0028 Model 4 Yes 1.99 (0.84, 4.73) 0.12 1.10 (0.72, 1.69) 0.66 2.47 (1.29, 4.71) 0.0061 1.54 (1.07, 2.21) 0.0187 *Outcome Reference: No specialist visits and ‘Never asthma’ Model 1: Asthma group Model 2: Asthma group + Predisposing factors (age and area of residence) Model 3: Asthma group + Predisposing factors + Enabling factors (private health insurance and access to a specialist) Model 4: Asthma group + Predisposing factors + Enabling factors + Need factors (anxiety, diabetes, heart disease, osteoporosis, alcohol consumption and smoking)

247

Table 8-18. Adjusted odds ratios (and 95% CI) for the effect of asthma group on specialist visits among women from the 1946-51 cohort at Survey 3 (2001), while adjusting for predisposing, enabling and needs factors

Models OUTCOME* Asthma groups specialist visit Past asthma Prevalent asthma Incident asthma Bronchitis/emphysema OR (CI) P OR (CI) P OR (CI) P OR (CI) P Model 1 Yes 1.31 (0.99, 1.75) 0.06 1.18 (0.93, 1.49) 0.18 1.35 (1.02, 1.80) 0.0382 1.23 (0.99, 1.53) 0.06 Model 2 Yes 1.30 (0.98, 1.74) 0.07 1.17 (0.92, 1.49) 0.19 1.38 (1.04, 1.84) 0.0266 1.24 (1.00, 1.53) 0.06 Model 3 Yes 1.29 (0.97, 1.73) 0.08 1.19 (0.93, 1.51) 0.16 1.42 (1.06, 1.90) 0.0182 1.25 (1.00, 1.55) 0.0463 Model 4 Yes 1.22 (0.91, 1.64) 0.18 1.11 (0.87, 1.42) 0.40 1.38 (1.03, 1.85) 0.0312 1.21 (0.97, 1.51) 0.09 *Outcome Reference: No specialist visits and ‘Never asthma’ Model 1: Asthma group Model 2: Asthma group + Predisposing factors (area of residence) Model 3: Asthma group + Predisposing factors + Enabling factors (private health insurance, access to a specialist and a bulk billing doctor) Model 4: Asthma group + Predisposing factors + Enabling factors + Need factors (Depression, diabetes and heart disease)

248

Longitudinal analysis for specialist visits

For the final set of models of the association between asthma groups and specialist visits, all variables that were included in the previous cross sectional models were included in longitudinal models, taking account of changes over time as well as the effect of predisposing, enabling and need variables.

1921-26 cohort

Table 8-7 shows the nested longitudinal models for the 1921-26 cohort. Once time was accounted for, women with asthma or bronchitis/emphysema had higher odds (1.36-1.45) of having specialist visits compared to women without the condition (Model 1). Once predisposing, enabling and need factors and time were controlled for (Models 2-4), the association between asthma or bronchitis/emphysema and specialist visits diminished but remained significant except for past asthma group. These results are in contrast to those from the cross-sectional models described in section 8.5.1. Where differences were mostly not significant, however these point estimates are similar but Confidence Intervals have better precision. This is an advantage of longitudinal data with repeated measures. The major difference is in the point estimate for the incident asthma group, reflecting the effect of time and changes in health and health care use. Again, this shows the advantage of longitudinal data which can account for time in the analyses.

249

Table 8-19. Association between specialist visit time and asthma groups among women from the 1921-26 cohort, after adjusting for time (Survey 2 to 6), and predisposing, enabling and needs factors

Models OUTCOME* Asthma groups specialist visit Past asthma Prevalent asthma Incident asthma Bronchitis/emphysema OR (CI) P OR (CI) P OR (CI) P OR (CI) P Model 1 Yes 1.44 (1.08, 1.91) 0.0129 1.37 (1.13, 1.67) 0.0014 1.45 (1.15, 1.82) 0.0017 1.36 (1.18, 1.56) <.0001 Model 2 Yes 1.41 (1.06, 1.87) 0.0193 1.38 (1.13, 1.67) 0.0012 1.44 (1.15, 1.81) 0.0017 1.34 (1.16, 1.54) <.0001 Model 3 Yes 1.44 (1.09, 1.91) 0.0102 1.45 (1.19, 1.75) 0.0002 1.48 (1.17, 1.88) 0.0009 1.42 (1.24, 1.64) <.0001 Model 4 Yes 1.29 (0.97, 1.72) 0.0775 1.34 (1.10, 1.62) 0.0031 1.34 (1.06, 1.70) 0.0144 1.33 (1.16, 1.53) <.0001 *Reference: No specialist visits and ‘Never asthma’ Model 1: Time + Asthma group Model 2: Time + Asthma group + Predisposing factors (age and area of residence) Model 3: Time + Asthma group + Predisposing factors + Enabling factors (private health insurance and access to a specialist) Model 4: Time + Asthma group + Predisposing factors + Enabling factors + Need factors (anxiety, diabetes, heart disease, osteoporosis, alcohol consumption and smoking)

250

1946-51 cohort

Model 1 in Table 8-20 shows that for women from the 1946-51 cohort, after accounting for time, women with asthma or bronchitis/emphysema were more likely to have had specialist visits compared to women without the condition. Addition of predisposing factors to model 1 (Model 2) attenuated the association between asthma or bronchitis/emphysema and specialist visits. However, when enabling factors were accounted for in Model 3, the association strengthened but in Model 4, adjusting for needs factors as well, weakened the association over time although, there was still a statistically significant association between asthma or bronchitis/emphysema and specialist visits.

251

Table 8-20. Association between specialist visit time and asthma groups among women from the 1946-51 cohort, after adjusting for time (Survey 2 to 6), and predisposing, enabling and needs factors

Models OUTCOME* Asthma groups specialist visit Past asthma Prevalent asthma Incident asthma Bronchitis/emphysema OR (CI) P OR (CI) P OR (CI) P OR (CI) P Model 1 Yes 1.23 (1.06, 1.44) 0.0079 1.35 (1.18, 1.55) <.0001 1.29 (1.11, 1.51) 0.0011 1.18 (1.05, 1.32) 0.0047 Model 2 Yes 1.23 (1.05, 1.43) 0.0095 1.34 (1.17, 1.54) <.0001 1.30 (1.12, 1.52) 0.0007 1.17 (1.05, 1.32) 0.0057 Model 3 Yes 1.23 (1.06, 1.44) 0.0073 1.38 (1.20, 1.58) <.0001 1.33 (1.14, 1.55) 0.0003 1.20 (1.07, 1.34) 0.0020 Model 4 Yes 1.19 (1.03, 1.38) 0.0227 1.28 (1.12, 1.47) 0.0004 1.26 (1.08, 1.47) 0.0032 1.15 (1.03, 1.29) 0.0156 *Outcome Reference: No specialist visits and ‘Never asthma’ Model 1: Time + Asthma group Model 2: Time + Asthma group + Predisposing factors (area of residence) Model 3: Time + Asthma group + Predisposing factors + Enabling factors (private health insurance, access to a specialist and a bulk billing doctor) Model 4: Time + Asthma group + Predisposing factors + Enabling factors + Need factors (Depression, diabetes and heart disease,)

252

Association between asthma groups and ACC/CDM service use

These services are specific in treatment of chronic diseases and described in Chapter 3 (Section 3.5.2.1.2 and Section 3.5.2.5) and Chapter 7 (Section 7.3.7). Predictors of ACC/CDM service use, were identified conducting univariate and multivariate analyses and the results of these analyses are presented in appendices Table B-1 and Table B- 2 for the 1921-26 cohort and Table B-4 and B-5 for the 1946-51 cohort. Of all predisposing, enabling and needs factors there was not supporting evidence for association between enabling factors and ACC/CDM service use, so, none of these variables were retained for nested models.

Cross-sectional nested models at survey 3

Three nested models were performed to sequentially investigate the association between asthma groups and ACC/CDM service use at survey 3 for both the 1921-26 and 1946-51 cohort. Variables that had a significant association with ACC/CDM service use in univariate and multivariate analyses were retained in nested models.

1921-26 cohort At Survey 3, women with prevalent asthma had a significantly higher ACC/CDM service use (OR: 2.79-3.02). Although the association between prevalent asthma and ACC/CDM service use declined after adjusting for predisposing and need factors, it still remained significant (Table 8-21). Full models can be found in appendix Table B-3.

1946-51 cohort At survey 3, univariate association between asthma groups and ACC/CDM service use are shown in Model 1, Table 8-22. There was an association between prevalent and incident asthma and ACC/CDM service use. Adjusting for predisposing, enabling and need factors in Model 2-4 diminished these associations but, they remained significant. Full models can be found in appendix Table B-6.

253

Table 8-21. Adjusted odds ratios (and 95% CI) for the effect of asthma group on ACC/CDM service use among women from the 1921-26 cohort at Survey 3 (2002), while adjusting for predisposing, enabling and needs factors

Models OUTCOME* Asthma groups ACC/CDM service use Past asthma Prevalent asthma Incident asthma Bronchitis/emphysema OR (CI) P OR (CI) P OR (CI) P OR (CI) P Model 1 Yes 1.49 (0.75, 2.98) 0.26 3.02 (2.08, 4.39) <.0001 1.64 (1.00, 2.70) 0.05 1.06 (0.71, 1.56) 0.78 Model 2 Yes 1.45 (0.73, 2.91) 0.29 2.99 (2.05, 4.35) <.0001 1.61 (0.98, 2.67) 0.06 1.07 (0.72, 1.59) 0.73 Model 3 Yes 1.15 (0.56, 2.35) 0.70 2.79 (1.90, 4.10) <.0001 1.49 (0.89, 2.50) 0.13 1.02 (0.68, 1.51) 0.94 *Outcome Reference: No ACC/CDM service use and ‘Never asthma’ Model 1: Asthma group Model 2: Asthma group + predisposing factors (country of birth) Model 3: Asthma group + predisposing factors + needs factors (depression, diabetes and osteoporosis)

254

Table 8-22. Adjusted odds ratios (and 95% CI) for the effect of asthma group on ACC/CDM service use among women from the 1946-51 cohort at Survey 3 (2001), while adjusting for predisposing, enabling and needs factors

Models OUTCOME* Asthma groups ACC/CDM service use Past asthma Prevalent asthma Incident asthma Bronchitis/emphysema OR (CI) P OR (CI) P OR (CI) P OR (CI) P Model 1 Yes 1.66 (0.73, 3.77) 0.23 3.34 (1.91, 5.83) <.0001 2.88 (1.48, 5.61) 0.0019 1.51 (0.78, 2.92) 0.23 Model 2 Yes 1.70 (0.75, 3.86) 0.21 3.33 (1.90, 5.83) <.0001 2.77 (1.42, 5.41) 0.0029 1.48 (0.76, 2.87) 0.25 Model 3 Yes 1.65 (0.72, 3.78) 0.23 3.13 (1.77, 5.51) <.0001 2.76 (1.38, 5.53) 0.0053 1.40 (0.72, 2.73) 0.33 Model 4 Yes 1.21 (0.51, 2.90) 0.66 2.58 (1.43, 4.67) 0.0017 2.61 (1.33, 5.13) 0.0043 1.42 (0.72, 2.82) 0.32 *Reference: No ACC/CDM service use and ‘Never asthma’ Model 1: Asthma group Model 2: Asthma group + Predisposing factors (age, highest qualification) Model 3: Asthma group + Predisposing factors + Enabling factors (management on income, private health insurance, access to a specialist) Model 4: Asthma group + Predisposing factors + Enabling factors + Need factors (Depression, diabetes, hypertension, alcohol consumption and BMI)

255

Longitudinal analysis for ACC/CDM

For the final set of models of the association between asthma groups and ACC/CDM service use, all variables that were included in the previous cross sectional models were included in longitudinal models, taking account of changes over time as well as the effect of relevant predisposing, enabling and need variables.

1921-26 cohort

In 1921-26 cohort, women with asthma had higher odds of having ACC/CDM service use compared to women without the condition (Table 8-23). For women with bronchitis/emphysema, the association over time was not significant (Model 1). Over time, the association between asthma and ACC/CDM service use attenuated once predisposing and need factors were adjusted for in Model 2 and 3, but, remained statistically significant except for past asthma which was not significant in model 3. Full model available in Appendices Table B-7 and Table B-8.

256

Table 8-23. Longitudinal association between ACC/CDM service use and asthma groups adjusting for predisposing, enabling and needs factors among women from the 1921-26 cohort from survey 2 to 6

Models OUTCOME* Asthma groups ACC/CDM service use Past asthma Prevalent asthma Incident asthma Bronchitis/emphysema OR (CI) P OR (CI) P OR (CI) P OR (CI) P Model 1 Yes 1.34 (1.02, 1.77) 0.0355 1.79 (1.48, 2.17) <.0001 1.66 (1.33, 2.07) <.0001 1.10 (0.96, 1.26) 0.1906 Model 2 Yes 1.36 (1.03, 1.79) 0.0286 1.81 (1.49, 2.19) <.0001 1.65 (1.32, 2.06) <.0001 1.09 (0.95, 1.26) 0.2033 Model 3 Yes 1.23 (0.93, 1.62) 0.1400 1.71 (1.41, 2.07) <.0001 1.55 (1.23, 1.94) 0.0002 1.04 (0.90, 1.19) 0.6014 *Outcome Reference: No ACC/CDM service use and ‘Never asthma’ Model 1: Time + Asthma group Model 2: Time + Asthma group + predisposing factors (country of birth) Model 3: Time + Asthma group + predisposing factors + Needs (depression, diabetes and osteoporosis)

257

1946-51 cohort

Model 1 in Table 8-24 shows that over time, women with prevalent or incident asthma or bronchitis/emphysema were more likely to have used ACC/CDM services compared to women without the condition. The association between past asthma and ACC/CDM service use was not significant. Among women with asthma or bronchitis/emphysema, women who had prevalent asthma had the highest odds of ACC/CDM service use. Model 2-4 showed attenuation of association between prevalent or incident asthma or bronchitis/emphysema and ACC/CDM service use when all predisposing, enabling and need factors were added to the previous models. Over time, there was still a statistically significant association between asthma or bronchitis/emphysema and longer GP visit time. For women with past asthma, the association strengthened once predisposing factors were adjusted for, in Model 2 and became statistically significant, however, in Model 3 and 4, the association was not statistically significant (See full models in Appendices Table B-9 to Table B-11). Compared with women from the 1921-26 cohort, women from the 1946-51 cohort showed stronger association between asthma groups and ACC/CDM service use.

258

Table 8-24. Association between ACC/CDM and asthma groups among women from the 1946-51 cohort, after adjusting for time (Survey 2 to 6), and predisposing, enabling and needs factors

Models OUTCOME* Asthma groups ACC/CDM service use Past asthma Prevalent asthma Incident asthma Bronchitis/emphysema Goodness of fit OR (CI) P OR (CI) P OR (CI) P OR (CI) P AIC -2Log L Model 1 Yes 1.22 (0.99, 1.50) 0.0625 2.50 (2.13, 2.93) <.0001 2.11 (1.76, 2.54) <.0001 1.41 (1.20, 1.65) <.0001 13731.508 13713.508 Model 2 Yes 1.25 (1.01, 1.54) 0.0402 2.49 (2.12, 2.92) <.0001 2.07 (1.73, 2.49) <.0001 1.40 (1.20, 1.64) <.0001 13639.897 13615.897 Model 3 Yes 1.20 (0.98, 1.48) 0.083 2.24 (1.90, 2.64) <.0001 1.92 (1.59, 2.31) <.0001 1.26 (1.08, 1.48) 0.0038 13270.665 13238.665 Model 4 Yes 1.11 (0.90, 1.36) 0.3187 1.99 (1.69, 2.35) <.0001 1.74 (1.42, 2.09) <.0001 1.22 (1.04, 1.43) 0.0158 12462.684 12414.684 *Outcome Reference: No ACC/CDM service use and ‘Never asthma’ Model 1: Time + Asthma group Model 2: Time + Asthma group + Predisposing factors (age, highest qualification) Model 3: Time + Asthma group + Predisposing factors + Enabling factors (management on income, discounted health care card, private health insurance, access to a specialist) Model 4: Time + Asthma group + Predisposing factors + Enabling factors + Need factors (Depression, diabetes, hypertension, alcohol consumption and BMI)

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Conclusion

There are different levels of health services which may be used for asthma care. These services include broad and specific care. The specific MBS health service to manage asthma in Australia is ACC and then CDM. The results of this chapter suggest that total GP visit time, after-hours visit and specialist visits in both cohorts were higher for women with asthma. Moreover, women with asthma were more likely than other women to have ACC/CDM service use. However, overall use of ACC/CDM was low, even among women with asthma indicating potential to increase the use of these services for women with chronic and complex conditions in later life. While these analyses cannot determine whether the increased health service use by women with asthma was for their asthma, it does suggest an additional need for health care for these women over and above other factors that have been included in these models. It also suggests that these are multiple opportunities to attend to the health care needs of women with asthma. The analyses also cannot determine what happened during these health service encounters, nor whether they were initiated proactively by the health care provider as part of ongoing care (other than ACC or CDM) or weather they were initiated by the women in response to an intercurrent event or change in their condition. The analyses have also not taken account of other health services such as hospital outpatient (not charged to Medicare) or admissions. These data are available to ALSWH and further analyses of the integration between GP, specialist and hospital care is suggested. ALSWH also has access to Pharmaceutical Benefits Scheme (PBS) data and analysis of these could also provide insight into the nature of care provided to these women, specially prescribed medications for asthma as funded through PBS. In terms of the analyses which have been presented given the effects were similar for both cohorts, these could also be extended to combine the two cohorts and to assess whether cohort is a statistically significant effect modifier. These analyses can be undertaken as an extension of this thesis and for publications.

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Both GP and specialist service use may be affected by area of residence. The results of this thesis confirms higher GP visits for women in urban areas compared with women in regional or rural areas. It should be considered that this thesis does not include hospital data. As number of hospital visits increase in later life, use of health services outside hospital may decline. Two further considerations include the effect of dementia and proximity to death. Dementia is likely to be a major comorbidity for women in the 1921-26 cohort. A recent assessment of dementia in this cohort, using multiple sources of data to identify women with dementia, showed that around 29 % of women in the 1921-26 cohort developed dementia. In other preliminary analyses by the ALSWH team (unpublished), a significant increase in GP and specialist use in the last 2 years of life was suggested, particularly in the last 6 months for women with lung disease. Comorbid dementia does not appear to make a difference to GP service use in these later months of life, but is associated with a lower use of specialist services. Taking these effects into consideration, it should be considered that increase in service use by women with asthma may be partially explained by these higher mortality rate (as seen in Chapter 4). However, the longitudinal models which adjust for time should account for these potentially to some extent. A reverse model with these …death included in the analysis could also be considered as another way to investigate these end of life effects. Ideally, the higher use of health services by women with asthma represents a significant opportunity to provide a preventive approach to their asthma management, to improve their quality of life, and potentially prevent untimely deaths.

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Chapter 9 Thesis discussion and conclusion

Discussion, strength and limitations of the studies and final conclusion of the findings of the five preceding result chapters will be discussed in this chapter.

Discussion

Older people may experience asthma differently to younger adults and may have different needs and outcome of the disease. There is no gold standard in defining asthma which makes it difficult to diagnose the condition. In addition, there are no age adjusted diagnoses methods for the disease, especially in older people with age related decreased lung function or frailty which affects the diagnosis. Hence, the prevalence, incidence and mortality rates for asthma may not be accurate. Considering that the condition often remains underdiagnosed and undertreated, mortality rates are expected to be higher than the reported statistics. Moreover, men and women are affected differently by asthma in later life, with women having higher frequencies of asthma. Health service use has a great impact on outcome of the disease in people with asthma, particularly in older people. However, there are factors which play a major role in utilising health service use by older people in either the short term or long term. It was highlighted in the literature review that despite the impact of asthma on mortality in older women, and the importance of health service use in management of the disease, little attention has been given to the significance of the association between asthma and health service utilisation in this age group. This thesis endeavoured to address the gaps in knowledge on health service use by older women with asthma. The principal aim of this thesis was to investigate the level of health service use by Australian women with asthma and factors associated with health service use.. The first two chapters of the thesis presented the background of asthma, particularly as it relates to older women and a review of the literature. The following five chapters incorporated the results of both cross- sectional and longitudinal studies.

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The findings of this thesis add significant knowledge to the understanding of the patterns of health service use by older women with asthma, highlighting the specific services available for the disease. This chapter is a discussion of the study findings as well as suggestions for policy reform. The five studies presented in this thesis include: 1) Impact of asthma on mortality for older while considering confounding factors (Chapter 4) 2) Self-reported health service use for older women according to asthma status (Chapter 5) 3) Cross-sectional and longitudinal associations between asthma groups and self- reported health service use, adjusting for predisposing, enabling and need factors (Chapter 6) 4) Medicare records for health service use by older women according to asthma status (Chapter 7) 5) Cross-sectional and longitudinal associations between asthma groups and Medicare for health service use while also considering predisposing factors, enabling factors and needs (Chapter 8) The findings from these studies will be discussed below, in context and in accordance with the research aims presented in Chapter 1.

Impact of asthma on mortality in later life

The initial study presented survival analysis on study population of ALSWH 1921-26 and the 1946-51 cohort (Aim 1). The findings of this study for the 1921-26 cohort have been published in a peer reviewed Journal(194). Findings from this study indicated that women from the 1946-51 cohort had a higher prevalence of asthma at the age of 50-55 compared with women from the 1921-26 cohort at the age of 73-78. This finding is in accordance with Australian Bureau of Statistics (ABS) report in 2014-15 (154). However, prevalence of the broader case definitions (asthma or bronchitis/emphysema or breathing difficulty) was similar for the two cohorts. Asthma was associated with a higher mortality rate among women from the 1921-26 cohort with a 17% relative increased risk of death over 12 years even after controlling for other risk

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factors. However, among women from the 1946-51 cohort, there was no evidence of an association between asthma and mortality over 13 years of follow-up. The significant increase in association between asthma and mortality in older women may reflect the different phenotype of the disease among these people (84, 246). Reports from ABS suggest significantly higher mortality rates for women with asthma compared to men. Women aged 75 and older had significantly higher mortality rates each year from 2014 to 2016 compared to women aged 45-54 (247). Also, the fact that women with ‘asthma or breathing difficulty’ and women with ‘bronchitis/emphysema only’ had higher likelihoods of mortality, re- enforces the argument regarding the lack of a gold standard in defining asthma and mis/under-diagnosis of asthma in older population due to the gap in guidelines (248). These results are in accordance with existing literature which suggests a higher prevalence and mortality among older adults with COPD (10, 249, 250). However, it has been proven that asthma can be misdiagnosed as COPD and vice versa (59, 251). Moreover, breathing difficulty is one of the symptoms for ageing lungs which could be ignored as an asthma symptom at this age (248). Consequently, misdiagnoses of asthma with breathing difficulty or COPD May result in under-treatment of asthma and increased mortality (4).

Prevalence and incidence of asthma and breathing difficulty and establishing asthma case definition for this thesis

The study of self-reported health service use for older women according to asthma status in Chapter 5 (aim 2), provided information about women in terms of reported diagnoses of asthma, bronchitis/emphysema, the pattern of reports and breathing difficulty over time. Women in the 1946-51 cohort showed higher prevalence of asthma and bronchitis/emphysema over surveys whereas the prevalence of breathing difficulty was higher for women from the 1921-26 cohort. For both cohorts, breathing difficulty was reported more by women with prevalent asthma over time. Prevalence of this symptom increased for women with incident asthma from survey 1-6 (7 for the 1946-51 cohort). In this thesis, according to suggestions of some epidemiological studies, women were categorised to ‘past asthma’, ‘prevalent asthma’, ‘incident asthma’, ‘bronchitis/emphysema’ and ‘never asthma’ groups which were mutually exclusive (55).

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Predictors of health service use among older women

Following categorisation of women into five groups, distribution of factors that may impact health care use by these women, were investigated according to the Andersen-Newman’s behavioural model of health service use. With respect to the predisposing factors, living outside urban areas, not being partnered and having higher qualifications were associated with less health service use. Having private health insurance, as well as having difficulty managing on available income, were enabling factors which increased health service use. Other studies suggest that having private health insurance increases the likelihood of GP visits (221, 222). Having private health insurance provides power for health care purchase. Research has shown association between having private health insurance and GP visits especially, in older people. Using ALSWH data, Dolja-Gore and colleagues in 2017 suggested that private health insurance was a predictor of fitst-time use for health assessment items in older women (75+ years) (179). Possession of pensioner’s concession card or Commonwealth seniors health card reduces inequality in primary health care use by making the service use affordable (252). However, unlike the findings of this thesis, studies in lower socio-economic countries show that lower income is associated with less GP visits (220). This difference may be due to the differences in health care systems in Australia and other countries. In Australia, all the citizens and permanent residents are covered by a universal health service, Medicare, which allows equitable access to health services (173). Need factors including high BMI (>25), being an ex-smoker and comorbid conditions were associated with higher health service use. In a number of investigations, non-communicable (chronic) diseases have been shown to be strong predictors of health service use (220, 222, 253).

Health related quality of life

Asthma as a chronic disease impacts people’s quality of life (254). Health related quality of life is measured in ALSWH surveys using SF-36 questionnaire and it has been validated for use in asthma patients. The questionnaire measures physical and mental health (255). In Chapter

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5, using SF-36, the quality of life of women was assessed for each group within each cohort. As reported in the earlier work, (add the ref 18 again maybe) a downward pattern in physical health subclasses of SF-36 for all the groups in both cohorts was seen over time. Women with prevalent and incident asthma had distinctly lower score in all subclasses of physical health. For the SF-36 subclasses in mental health, women from the 1921-26 cohort had a downward pattern over time compared with an upward pattern for the 1946-51 cohort. The scores were distinctly lower for women with prevalent and incident asthma. Although, SF-36 questionnaire has not been used before in measuring quality of life in older people with asthma, other measures for quality of life, such as mini Asthma Quality of Life Questionnaire have shown that asthma is associated with poor quality of life (256). Use of SF-36 questionnaire in this thesis allowed the distinctive difference between the groups.

Pattern of health service use by older women with asthma

In Chapter 6, Self-reported health service use by women from both cohorts was investigated. Family doctor/GP visits were the most frequent self-reported health service use for the both cohorts. Women from the 1921-26 cohort had higher frequency of GP visits in a year and higher number of prescription medications than women from the 1946-51 cohort. It is apparent that as people get older, number of chronic conditions increase and primary health care use or hospitalisation increases accordingly (257, 258). In both cohorts, women with asthma or bronchitis/emphysema had higher number of GP visits compared with women without these conditions. Women in both cohorts with prevalent asthma had the highest number of GP visits and prescription medications in a year, as well as medical attention sought due to falls. The role of GP in managing asthma is well understood (259). Use of primary care in asthma reduces the rate of hospitalisation in these people (259). The results of Chapter 8 suggested that, most women in both cohorts had visits to their GPs each year which corroborates with the outcome of self-reported investigations. Interestingly, older women with past asthma as they aged saw their GP more frequently while in self- reported analyses, women with prevalent asthma had the highest number of GP visits in a year. Studying MBS service use provided an opportunity to investigate health services beyond GP visits. As women in the 1921-26 cohort get older number of regular hours GP visits in a year drops while number of after-hours GP visits rise (Chapter 7). This shift may be the due to

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increased frailty and decreased physical functioning (Chapter 6) which results in being reliant on family members and carers who work regular hours. Specialist visits were well utilised by women from the 1921-26 cohort over time. Although it is believed that specialists can deliver a better patient-centred and focused care than GPs, there is still need for further investigations in larger populations to prove the difference in outcome of the disease (168, 169).

Asthma Cycle of Care (ACC) use

In order to treat a chronic condition like asthma effectively, long term management plans are required. ACC is a specific item in asthma treatment in Australia. Asthma action plan which is part of best practice guidelines in Australia, is an integral part of ACC. Having ACC improves patient’s quality of life and increases their knowledge on asthma management. Better management of asthma results in lower emergency visits to GPs or hospitals (191). ACC has been suggested as a discharge strategy for people with asthma who have emergency departments or hospitals (191). Unfortunately, this thesis found that ACC items had suboptimal uptake and were used only by a small proportion of women with asthma even though the items have been available since 2001. Previous studies showed similar results on the uptake of the items which was attributed to the time consuming process and the paperwork required for documentation and claims (192, 198). Although in Australia resources are provided and an incentive of $100 is foreseen for health care providers who deliver ACC, the item is not taken up satisfactorily (260). Also, a decline in the use of asthma action plan has been found along with a reduction in using asthma preventers. This suggests, asthma management is suboptimal in Australia. Not only are designated items for asthma underused, the disease is not managed well in general practice either (260). It is important that the uptake of ACC, possession of asthma action plan and patient’s knowledge about their asthma, and its management, is studied nationally to find out if patients are receiving the care they require.

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Enhanced primary care and spirometry use

Enhanced Primary Care items, known as Chronic Disease Management (CDM) items are Medicare items which could be used for these women to help manage their asthma, although they were used by a small proportion of women in both cohorts. Moreover, health assessment items had been used by women in the 1921-26 cohort more than women in the 1946-51 cohort, although, it was only for one third of the women in this cohort. In the 1921- 26 cohort, as women get older CDM item claims increase which may be due to increasing prevalence of chronic diseases by age. These two MBS services are delivered by GPs under different item numbers than regular GP visits. Although CDM could be a substitute for ACC in treating asthma, the uptake of the item has not been satisfactory despite an increase in item utilisation (261, 262). Also, according to the findings in Chapter 7, the use of spirometry tests declined at older ages. This may result in mis/under-diagnoses of asthma. It was mentioned in the literature review that older people may have difficulty performing spirometry tests and the findings of the thesis supports this finding (58, 75).

Association of asthma and health service use among older women

Chapter 6 provided information on cross-sectional and longitudinal association of asthma and self-reported health service use (Aim 3) and Chapter 8 presented cross-sectional and longitudinal association between asthma and MBS service use (Aim 5). Frequency of self-reported GP visits and its association with asthma groups was investigated at survey 3 and longitudinally from survey 2 to survey 6/7. Findings of Chapter 6 suggested that asthma or bronchitis emphysema was associated with higher number of GP visits in both cohorts especially for women with prevalent asthma. The association remained significant after accounting for predisposing, enabling and need factors. The results of Chapter 8 corroborated self-report results in Chapter 6 suggesting that women with prevalent or incident asthma, considering other covariates, over one year, were more likely to have had longer GP visit time during both regular hours but only women with prevalent and incident asthma had higher odds of after-hours GP visits compared with women without the condition. However, in long term, women with asthma or

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bronchitis/emphysema were more likely to have longer GP visit time during regular hours and higher odds of seeing an after-hours GP. Also, women with prevalent or incident asthma were more likely to have had ACC or CDM service claims in short and long visit times compared with women without asthma. In cross sectional studies, women with incident asthma were more likely to have seen a specialist in a year but, longitudinal studies showed that over time, all women with asthma or bronchitis/emphysema were more likely to have seen a specialist compared with women without the condition. Although in short term (cross sectional studies), only women with prevalent or incident asthma had higher odds of using most of the health services studied in this chapter, in long term (longitudinal studies), all women with asthma or bronchitis/emphysema were more likely to have used health services in higher levels compared to women without asthma or bronchitis/emphysema even after adjusting for other drivers of health service use. Using specific health services for asthma may prevent after-hours or specialist visits, and may therefore reduce the burden of unnecessary claims on MBS, and improve patient outcomes.

Policy suggestions

The pattern of health service use by older women shows an increased need for after-hours GP visits which suggests the need for changes in health care system for this group of age to facilitate health service use. Also, appropriate care for asthma requires an individualised action plan for each patient which is part of ACC service. Asthma action plan has been found to help better management of the disease and reduce mortality (263). It has been 16 years since ACC became available on MBS to suit needs of people with asthma. Since the implementation of the ACC there has been research on uptake of the item and the findings are in accordance with this thesis, that “ACC is under-used” despite a significant amount of incentive payable. Although findings of this thesis showed that women with asthma have more frequent and longer GP visit time, the outcome of the treatment (eg. Mortality rate) is not satisfactory. There needs to be further investment in promoting ACC service and more trained nurses to take on the assessments and patient education.

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Strengths and limitations

The foremost strength of this thesis is the use of data from three large sources in the design and linkage between the datasets. Data from two large cohorts of the ALSWH was used to examine self-reported health service use by about 26,000 women in cross-sectional studies (Chapter 5). Longitudinal studies in Chapter 6 used these women’s data from three yearly surveys at 6/7 time points. ALSWH currently is the largest Australian study on women’s health and life events. Using data of this magnitude in these studies allowed for comparisons between the cohorts and groups in each cohort. Data collection at different time points over 17 years allowed the examination of various factors longitudinally. Chapter 7 and Chapter 8, used data from the Medicare linked with ALSWH data which In Chapter 4 ALSWH data was linked with NDI data which allowed survival studies. A further strength of this thesis was the use of cross-sectional studies followed by longitudinal studies. This allowed comparisons to be made about the short term and long term associations between asthma and health service use. Despite the strengths, this thesis had some limitations. ALSWH data which was the basis of this thesis is self-reported information on participants and recall bias could not be excluded. Doctor diagnosed health conditions (chronic conditions) and health service use that were formed the basis for defining the groups and other analyses, were self-reported. However, many studies have compared self-reported data with registry data (for chronic conditions) and proved it as a valid measure in epidemiological studies in large scale. Although self- reported chronic conditions were identified through self-reported survey data, the severity of the disease and exact duration of the disease was impossible to measure. It is possible that these components of the disease may impact asthma management and health service use. ALSWH sampling included twice as many women from rural and remote areas compared to urban areas to allow statistical comparisons to address the differences in health between the two areas. This may cause sampling bias in analysis. However, sampling from both areas were random and such a large sample size reduces the chance of bias.

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Moreover, the linkage between ALSWH and MBS data was done for women from ALSWH cohorts who did not opt-out from the linkage. This may bias the results due to the possibility of having different pattern of health service use or different socio-economic status. In longitudinal analyses, if variables were not measured in all surveys and they could not be considered constant, they were not retained for analyses which could cause bias in the results. Further research is required to investigate hospital service use by women according to asthma status. Also, comparison between late onset asthma (older age) and early onset asthma (childhood), association between severity of asthma in older age and health service use as well as gendered evaluations in late onset asthma is required which were beyond the scope of this thesis.

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Suggestions for further research

While this thesis showed remarkable results in health service use by older women with asthma, it is not a perfect indicator of the quality care in these women. The enormous quantity of information that was provided by the two linked datasets allowed for successful achievement of the aims of this thesis. One of the limitations of this thesis was access to only MBS records while some of these women may have been hospitalised for a period of time, especially due to asthma which may affect the magnitude or direction of the findings of this thesis. Therefore, in order to decide on quality care received by older women with asthma, in the future, hospital data may be linked with ALSWH data for the same women and, as a result, overall health service use (inpatient and outpatient) may be assessed. Moreover, gender comparison is suggested to be conducted using national databases for Australian men. Investigations on PBS data is also suggested since it may confirm self-reported asthma and provide valuable information on asthma medications used by women.

Conclusion

Findings of this thesis showed higher likelihood of mortality for women with asthma, regardless of the case definition. Health service use is a crucial element in asthma management and older women with asthma have different level, type and length of health service use according to asthma status, with women with prevalent asthma or incident asthma having higher health service use compared to women without the condition. However, asthma specific health service (ACC) use was under-used for older women with asthma. Attempts to improve the uptake of ACC since the conception of the service in 2001 have not been successful. Programs to increase older women’s awareness about or promote the use of ACC are suggested in order to increase the uptake of this item. The success of governments’ health policies depend on defining the right services for the right needs and for the right population. The results of this thesis provides knowledge on health service use by older women with asthma over 17 years which needs to be translated to policies after further research.

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Appendices

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Appendix A. Chapter 6 appendices

Figure A-1. Self-reported health service use at Survey 2 by women from the 1946-51 cohort, according to asthma group

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Table A-1. Number of self-reported GP visits at survey 2 and survey 6 for women from the 1921-26 cohort according to marital status and asthma groups Survey Marital status Number of GP visits Less than 2 3 or 4 5 to 8 9 or more n % n % n % n % Past asthma Survey 2 Never married 1 6.3 5 31.3 5 31.3 5 31.3 Partnered 12 5.4 58 25.9 56 25.0 98 43.8 Separated/Divorced/Widowed 17 8.5 41 20.5 51 25.5 91 45.5 Survey 6 Never married 0 0.0 1 14.3 2 28.6 4 57.1 Partnered 3 8.3 8 22.2 11 30.6 14 38.9 Separated/Divorced/Widowed 15 16.9 18 20.2 24 27.0 32 36.0 Prevalent asthma Survey 2 Never married 3 12.5 1 4.2 7 29.2 13 54.2 Partnered 40 9.0 80 18.1 131 29.6 192 43.3 Separated/Divorced/Widowed 24 5.7 86 20.5 112 26.7 197 47.0 Survey 6 Never married 1 11.1 3 33.3 3 33.3 2 22.2 Partnered 7 10.4 14 20.9 23 34.3 23 34.3 Separated/Divorced/Widowed 22 8.9 54 21.8 71 28.6 101 40.7 Incident asthma Survey 2 Never married 0 0.0 2 25.0 4 50.0 2 25.0 Partnered 23 8.7 70 26.6 79 30.0 91 34.6 Separated/Divorced/Widowed 26 9.2 57 20.2 84 29.8 115 40.8 Survey 6 Never married 2 50.0 0 0.0 1 25.0 1 25.0 Partnered 4 7.5 10 18.9 16 30.2 23 43.4 Separated/Divorced/Widowed 13 6.4 37 18.3 60 29.7 92 45.5 Bronchitis/emphysema Survey 2 Never married 9 16.4 17 30.9 15 27.3 14 25.5 Partnered 107 12.1 220 24.9 269 30.5 286 32.4 Separated/Divorced/Widowed 93 10.5 182 20.6 297 33.6 313 35.4 Survey 6 Never married 2 12.5 8 50.0 3 18.8 3 18.8 Partnered 21 14.3 35 23.8 40 27.2 51 34.7 Separated/Divorced/Widowed 56 9.3 145 24.2 179 29.9 219 36.6 Never asthma Survey 2 Never married 41 21.9 66 35.3 41 21.9 39 20.9 Partnered 660 18.8 104 30.0 932 26.6 861 24.6 9 Separated/Divorced/Widowed 610 20.3 867 28.9 778 25.9 748 24.9 Survey 6 Never married 10 15.2 26 39.4 21 31.8 9 13.6 Partnered 89 15.4 175 30.3 178 30.8 136 23.5 Separated/Divorced/Widowed 269 14.0 584 30.5 551 28.7 513 26.8

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Table A-2. Number of self-reported GP visits at survey 1 and survey 7 for women from the 1946-51 cohort according to marital status and asthma groups Survey Marital status Number of GP visits 0 1 or 2 3 or 4 5 or 6 7 or more n % n % n % n % n % Past Asthma Survey 1 Never married 0 0.0 11 45.8 7 29.2 3 12.5 3 12.5 Partnered 48 6.8 242 34.4 184 26.2 113 16.1 116 16.5 Separated/Divorced/Widowed 7 5.5 41 32.0 32 25.0 21 16.4 27 21.1 Survey 7 Never married 0 0.0 5 38.5 2 15.4 3 23.1 3 23.1 Partnered 13 2.8 110 24.0 160 34.9 88 19.2 88 19.2 Separated/Divorced/Widowed 2 1.4 44 31.7 40 28.8 27 19.4 26 18.7 Prevalent asthma Survey 1 Never married 3 7.1 4 9.5 13 31.0 13 31.0 9 21.4 Partnered 36 3.9 235 25.2 252 27.1 207 22.2 201 21.6 Separated/Divorced/Widowed 9 4.6 35 17.9 44 22.4 25 12.8 83 42.3 Survey 7 Never married 1 3.0 6 18.2 6 18.2 8 24.2 12 36.4 Partnered 17 2.8 116 19.0 195 31.9 137 22.4 147 24.0 Separated/Divorced/Widowed 7 3.2 43 19.5 60 27.1 41 18.6 70 31.7 Incident asthma Survey 1 Never married 0 0.0 9 42.9 3 14.3 3 14.3 6 28.6 Partnered 50 6.7 238 31.7 194 25.9 121 16.1 147 19.6 Separated/Divorced/Widowed 7 5.2 37 27.4 29 21.5 28 20.7 34 25.2 Survey 7 Never married 0 0.0 4 36.4 0 0.0 4 36.4 3 27.3 Partnered 8 1.6 117 23.5 152 30.6 109 21.9 111 22.3 Separated/Divorced/Widowed 2 1.1 31 17.2 44 24.4 48 26.7 55 30.6 Bronchitis/emphysema Survey 1 Never married 11 17.7 20 32.3 13 21.0 8 12.9 10 16.1 Partnered 98 7.1 459 33.2 420 30.4 215 15.5 191 13.8 Separated/Divorced/Widowed 11 4.7 70 29.9 57 24.4 38 16.2 58 24.8 Survey 7 Never married 2 5.6 14 38.9 7 19.4 6 16.7 7 19.4 Partnered 31 3.4 256 28.1 285 31.3 196 21.5 144 15.8 Separated/Divorced/Widowed 10 3.5 63 22.0 89 31.0 55 19.2 70 24.4 Never asthma Survey 1 Never married 33 14.7 73 32.6 55 24.6 32 14.3 31 13.8 Partnered 708 11.2 2769 43.6 160 25.3 754 11.9 511 8.1 5 Separated/Divorced/Widowed 83 9.4 358 40.6 225 25.5 114 12.9 101 11.5 Survey 7 Never married 7 4.8 54 36.7 45 30.6 21 14.3 20 13.6 Partnered 195 4.6 1464 34.3 142 33.3 720 16.9 465 10.9 1 Separated/Divorced/Widowed 57 4.8 390 32.7 373 31.3 215 18.0 158 13.2

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Table A-3. Number of self-reported GP visits at survey 2 and survey 6 for women from the 1921-26 cohort according to private health insurance status and asthma groups Private Survey insurance Number of GP visits Less than 2 3 or 4 5 to 8 9 or more n % n % n % n % Past asthma Survey 2 Yes 14 8.5 33 20.1 49 29.9 68 41.5 No 14 5.5 67 26.3 57 22.4 117 45.9 Survey 6 Yes 5 7.6 15 22.7 20 30.3 26 39.4 No 13 19.7 12 18.2 17 25.8 24 36.4 Prevalent asthma Survey 2 Yes 29 7.9 70 19.0 110 29.9 159 43.2 No 33 6.8 92 19.0 135 28.0 223 46.2 Survey 6 Yes 17 12.3 24 17.4 46 33.3 51 37.0 No 13 7.0 47 25.3 51 27.4 75 40.3 Incident asthma Survey 2 Yes 23 10.8 54 25.4 64 30.0 72 33.8 No 24 7.5 70 22.0 95 29.9 129 40.6 Survey 6 Yes 11 10.4 17 16.0 37 34.9 41 38.7 No 9 5.8 31 20.0 40 25.8 75 48.4 Bronchitis/emphysema Survey 2 Yes 77 10.9 179 25.3 242 34.2 209 29.6 No 127 12.1 230 22.0 319 30.5 371 35.4 Survey 6 Yes 32 10.1 74 23.3 107 33.6 105 33.0 No 47 10.5 115 25.7 115 25.7 170 38.0 Never asthma Survey 2 Yes 531 19.0 884 31.7 753 27.0 620 22.2 No 724 20.2 991 27.6 926 25.8 951 26.5 Survey 6 Yes 169 14.3 385 32.5 338 28.5 292 24.7 No 203 14.6 403 29.0 414 29.8 368 26.5

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Table A-4. Number of self-reported GP visits at survey 1 and survey 7 for women from the 1946-51 cohort according to private health insurance status and asthma groups Private Survey insurance Number of GP visits 0 1 or 2 3 or 4 5 or 6 7 or more n % n % n % n % n % Past asthma Survey 1 Yes 19 5.2 129 35.3 103 28.2 57 15.6 57 15.6 No 36 7.3 165 33.7 120 24.5 79 16.1 90 18.4 Survey 7 Yes 6 1.6 102 26.6 136 35.5 72 18.8 67 17.5 No 9 3.8 58 24.8 67 28.6 47 20.1 53 22.6 Prevalent asthma Survey 1 Yes 15 3.3 117 25.7 127 27.9 103 22.6 94 20.6 No 33 4.6 158 22.2 181 25.4 141 19.8 199 27.9 Survey 7 Yes 13 2.7 92 19.2 151 31.5 112 23.3 112 23.3 No 12 3.1 72 18.6 111 28.7 74 19.1 118 30.5 Incident asthma Survey 1 Yes 25 7.2 103 29.8 101 29.2 48 13.9 69 19.9 No 29 5.2 181 32.3 125 22.3 106 18.9 119 21.3 Survey 7 Yes 4 1.0 94 24.5 124 32.3 91 23.7 71 18.5 No 6 2.0 57 18.9 73 24.2 68 22.5 98 32.5 Bronchitis/emphysema Survey 1 Yes 39 6.0 212 32.8 210 32.5 96 14.8 90 13.9 No 81 7.9 332 32.3 281 27.4 166 16.2 167 16.3 Survey 7 Yes 15 2.2 198 28.6 238 34.3 139 20.1 103 14.9 No 26 4.8 138 25.4 146 26.8 117 21.5 117 21.5 Never asthma Survey 1 Yes 309 10.2 1297 42.9 827 27.3 362 12.0 230 7.6 No 512 11.6 1903 43 1061 24 539 12.2 409 9.2 Survey 7 Yes 134 4.0 1158 35.0 1154 34.9 534 16.1 331 10.0 No 130 5.6 753 32.4 696 29.9 428 18.4 317 13.6

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Table A-5. Number of self-reported GP visits at survey 2 and survey 6 for women from the 1921-26 cohort according to hypertension and asthma groups Hypertensio Survey n Number of GP visits Less than 2 3 or 4 5 to 8 9 or more n % n % n % n % Past asthma Survey 2 Yes 5 3.8 25 18.9 34 25.8 68 51.5 No 25 8.1 80 25.9 78 25.2 126 40.8 Survey 6 Yes 4 5.3 17 22.4 20 26.3 35 46.1 No 14 25.0 10 17.9 17 30.4 15 26.8 Prevalent asthma Survey 2 Yes 11 3.1 55 15.7 103 29.4 181 51.7 No 56 10.4 112 20.7 150 27.8 222 41.1 Survey 6 Yes 13 7.0 42 22.5 53 28.3 79 42.2 No 17 12.4 29 21.2 44 32.1 47 34.3 Incident asthma Survey 2 Yes 9 4.5 40 20.1 62 31.2 88 44.2 No 40 11.2 89 24.9 106 29.7 122 34.2 Survey 6 Yes 10 6.6 28 18.4 43 28.3 71 46.7 No 10 9.2 20 18.3 34 31.2 45 41.3 Bronchitis/emphysema Survey 2 Yes 31 4.9 112 17.7 229 36.2 260 41.1 No 179 15.0 309 25.8 353 29.5 355 29.7 Survey 6 Yes 30 6.7 100 22.2 152 33.7 169 37.5 No 49 15.6 89 28.3 70 22.3 106 33.8 Never asthma Survey 2 Yes 203 9.5 606 28.5 644 30.2 677 31.8 No 1113 24.3 1383 30.1 1114 24.3 979 21.3 Survey 6 Yes 140 9.2 464 30.6 496 32.7 417 27.5 No 232 22.0 324 30.7 256 24.3 243 23.0

297

Table A-6. Number of self-reported GP visits at survey 1 and survey 7 for women from the 1946-51 cohort according to hypertension and asthma groups Survey Hypertension Number of GP visits 0 1 or 2 3 or 4 5 or 6 7 or more n % n % n % n % n % Past asthma Survey 1 Yes 6 2.9 58 28.3 44 21.5 43 21.0 54 26.3 No 49 7.5 237 36.3 179 27.5 94 14.4 93 14.3 Survey 7 Yes 0 0.0 36 16.4 62 28.2 55 25.0 67 30.5 No 15 3.8 124 31.2 141 35.5 64 16.1 53 13.4 Prevalent asthma Survey 1 Yes 7 2.2 53 16.8 68 21.5 62 19.6 126 39.9 No 41 4.8 222 25.9 241 28.2 183 21.4 169 19.7 Survey 7 Yes 7 2.0 48 13.9 98 28.4 79 22.9 113 32.8 No 19 3.6 117 22.2 164 31.1 107 20.3 120 22.8 Incident asthma Survey 1 Yes 8 3.7 45 21.0 63 29.4 46 21.5 52 24.3 No 49 7.0 239 34.2 165 23.6 109 15.6 137 19.6 Survey 7 Yes 3 1.1 46 16.1 75 26.3 74 26.0 87 30.5 No 9 2.2 106 26.0 123 30.1 87 21.3 83 20.3 Bronchitis/emphysema Survey 1 Yes 23 5.7 88 21.6 116 28.5 81 19.9 99 24.3 No 98 7.7 462 36.2 377 29.5 181 14.2 160 12.5 Survey 7 Yes 2 0.5 70 15.9 154 34.9 112 25.4 103 23.4 No 42 5.2 268 33.3 231 28.7 146 18.1 118 14.7 Never asthma Survey 1 Yes 86 6.3 419 30.8 416 30.6 235 17.3 204 15.0 No 743 12.1 2797 45.6 1481 24.2 668 10.9 442 7.2 Survey 7 Yes 18 1.0 411 22.2 685 37.0 429 23.2 307 16.6 No 250 6.6 1508 39.5 1175 30.8 536 14.1 344 9.0

298

Table A-7. Number of self-reported GP visits at survey 2 and survey 6 for women from the 1921-26 cohort according to heart disease and asthma groups Heart Survey disease Number of GP visits Less than 2 3 or 4 5 to 8 9 or more n % n % n % n % Past asthma Survey 2 Yes 1 1.3 14 17.9 8 10.3 55 70.5 No 29 8.0 91 25.1 104 28.7 139 38.3 Survey 6 Yes 4 10.8 4 10.8 10 27.0 19 51.4 No 14 14.7 23 24.2 27 28.4 31 32.6 Prevalent asthma Survey 2 Yes 3 1.8 16 9.4 37 21.6 115 67.3 No 64 8.9 151 21.0 216 30.0 288 40.1 Survey 6 Yes 2 2.2 22 23.9 27 29.3 41 44.6 No 28 12.1 49 21.1 70 30.2 85 36.6 Incident asthma Survey 2 Yes 1 1.0 16 16.2 21 21.2 61 61.6 No 48 10.5 113 24.7 147 32.2 149 32.6 Survey 6 Yes 5 6.4 8 10.3 20 25.6 45 57.7 No 15 8.2 40 21.9 57 31.1 71 38.8 Bronchitis/emphysema Survey 2 Yes 18 4.5 58 14.5 125 31.3 199 49.8 No 192 13.4 363 25.4 457 32.0 416 29.1 Survey 6 Yes 12 5.7 41 19.4 56 26.5 102 48.3 No 67 12.1 148 26.7 166 30.0 173 31.2 Never asthma Survey 2 Yes 34 5.5 123 19.8 163 26.3 300 48.4 No 1282 21.0 1866 30.6 1595 26.2 1356 22.2 Survey 6 Yes 23 4.8 106 22.1 150 31.3 200 41.8 No 349 16.7 682 32.6 602 28.8 460 22.0

299

Table A-8. Number of self-reported GP visits at survey 1 and survey 7 for women from the 1946-51 cohort according to heart disease and asthma groups Heart diseas Survey e Number of GP visits 0 1 or 2 3 or 4 5 or 6 7 or more n % n % n % n % n % Past asthma Survey 1 Yes 2 6.7 6 20.0 5 16.7 4 13.3 13 43.3 No 53 6.4 289 34.9 218 26.4 133 16.1 134 16.2 Survey 7 Yes 0 0.0 5 11.4 7 15.9 10 22.7 22 50.0 No 15 2.6 155 27.1 196 34.2 109 19.0 98 17.1 Prevalent asthma Survey 1 Yes 2 4.8 4 9.5 8 19.0 9 21.4 19 45.2 No 46 4.1 271 24.0 301 26.6 236 20.9 276 24.4 Survey 7 Yes 1 1.4 2 2.7 10 13.5 20 27.0 41 55.4 No 25 3.1 163 20.4 252 31.6 166 20.8 192 24.1 Incident asthma Survey 1 Yes 0 0.0 5 27.8 0 0.0 3 16.7 10 55.6 No 57 6.4 279 31.2 228 25.5 152 17.0 179 20.0 Survey 7 Yes 1 1.5 8 12.3 12 18.5 14 21.5 30 46.2 No 11 1.8 144 22.9 186 29.6 147 23.4 140 22.3 Bronchitis/emphysema Survey 1 Yes 1 2.6 5 12.8 12 30.8 6 15.4 15 38.5 No 120 7.3 545 33.1 481 29.2 256 15.6 244 14.8 Survey 7 Yes 0 0.0 7 8.3 21 25.0 18 21.4 38 45.2 No 44 3.8 331 28.5 364 31.3 240 20.7 183 15.7 Never asthma Survey 1 Yes 6 5.0 35 28.9 33 27.3 16 13.2 31 25.6 No 823 11.2 3181 43.2 1864 25.3 887 12.0 615 8.3 Survey 7 Yes 0 0.0 34 13.3 84 32.9 70 27.5 67 26.3 No 268 5.0 1885 34.9 1776 32.8 895 16.5 584 10.8

300

Table A-9. Number of self-reported GP visits at survey 2 and survey 6 for women from the 1921-26 cohort according to osteoporosis and asthma groups Survey Osteoporosis Number of GP visits Less than 2 3 or 4 5 to 8 9 or more n % n % n % n % Past asthma Survey 2 Yes 4 6.3 7 11.1 13 20.6 39 61.9 No 26 6.9 98 25.9 99 26.2 155 41.0 Survey 6 Yes 3 10.7 6 21.4 9 32.1 10 35.7 No 15 14.4 21 20.2 28 26.9 40 38.5 Prevalent asthma Survey 2 Yes 9 6.0 25 16.7 29 19.3 87 58.0 No 58 7.8 142 19.2 224 30.3 316 42.7 Survey 6 Yes 5 5.1 20 20.2 29 29.3 45 45.5 No 25 11.1 51 22.7 68 30.2 81 36.0 Incident asthma Survey 2 Yes 4 4.7 16 18.6 28 32.6 38 44.2 No 45 9.6 113 24.0 140 29.8 172 36.6 Survey 6 Yes 5 6.6 13 17.1 20 26.3 38 50.0 No 15 8.1 35 18.9 57 30.8 78 42.2 Bronchitis/emphysema Survey 2 Yes 14 5.3 41 15.5 92 34.8 117 44.3 No 196 12.5 380 24.3 490 31.3 498 31.8 Survey 6 Yes 15 6.9 46 21.2 62 28.6 94 43.3 No 64 11.7 143 26.1 160 29.2 181 33.0 Never asthma Survey 2 Yes 69 9.6 156 21.7 224 31.1 271 37.6 No 1247 20.8 1833 30.6 1534 25.6 1385 23.1 Survey 6 Yes 46 8.6 145 27.3 171 32.1 170 32.0 No 326 16.0 643 31.5 581 28.5 490 24.0

301

Table A-10. Number of self-reported GP visits at survey 1 and survey 7 for women from the 1946-51 cohort according to osteoporosis and asthma groups Survey Osteoporosis Number of GP visits 0 1 or 2 3 or 4 5 or 6 7 or more n % n % n % n % n % Past asthma Survey 1 Yes 1 2.6 7 18.4 8 21.1 8 21.1 14 36.8 No 54 6.6 288 35.2 215 26.3 129 15.8 133 16.2 Survey 7 Yes 10 18.2 18 32.7 13 23.6 14 25.5 No 15 2.7 150 26.7 185 32.9 106 18.9 106 18.9 Prevalent asthma Survey 1 Yes 1 1.3 5 6.7 11 14.7 16 21.3 42 56.0 No 47 4.3 270 24.6 298 27.2 229 20.9 253 23.1 Survey 7 Yes 2 1.9 15 14.3 32 30.5 18 17.1 38 36.2 No 24 3.1 150 19.6 230 30.0 168 21.9 195 25.4 Incident asthma Survey 1 Yes 11 22.0 10 20.0 14 28.0 15 30.0 No 57 6.6 273 31.6 218 25.3 141 16.3 174 20.2 Survey 7 Yes 10 13.5 19 25.7 20 27.0 25 33.8 No 12 1.9 142 22.9 179 28.9 141 22.8 145 23.4 Bronchitis/emphysema Survey 1 Yes 3 3.7 14 17.1 18 22.0 19 23.2 28 34.1 No 118 7.4 536 33.4 475 29.6 243 15.2 231 14.4 Survey 7 Yes 19 15.8 33 27.5 30 25.0 38 31.7 No 44 3.9 319 28.3 352 31.3 228 20.2 183 16.3 Never asthma Survey 1 Yes 4 2.0 56 28.6 54 27.6 36 18.4 46 23.5 No 825 11.3 3160 43.3 1843 25.3 867 11.9 600 8.2 Survey 7 Yes 13 2.7 128 26.7 153 31.9 99 20.6 87 18.1 No 255 4.9 1791 34.6 1707 32.9 866 16.7 564 10.9

302

Table A-11. Number of self-reported GP visits at survey 2 and survey 6 for women from the 1921-26 cohort according to depression and asthma groups Survey Depression Number of GP visits Less than 2 3 or 4 5 to 8 9 or more n % n % n % n % Past asthma Survey 2 Yes 0 0.0 4 10.3 6 15.4 29 74.4 No 28 7.1 98 25.0 104 26.5 162 41.3 Survey 6 Yes 2 14.3 1 7.1 4 28.6 7 50.0 No 16 13.6 26 22.0 33 28.0 43 36.4 Prevalent asthma Survey 2 Yes 3 4.0 10 13.3 16 21.3 46 61.3 No 63 7.8 156 19.4 234 29.0 353 43.8 Survey 6 Yes 3 8.1 3 8.1 11 29.7 20 54.1 No 26 9.1 68 23.8 86 30.1 106 37.1 Incident asthma Survey 2 Yes 2 4.4 6 13.3 13 28.9 24 53.3 No 45 9.0 120 24.0 152 30.4 183 36.6 Survey 6 Yes 1 4.0 3 12.0 7 28.0 14 56.0 No 19 8.2 45 19.3 69 29.6 100 42.9 Bronchitis/emphysema Survey 2 Yes 8 5.8 19 13.7 39 28.1 73 52.5 No 196 11.8 392 23.6 533 32.1 539 32.5 Survey 6 Yes 3 4.6 8 12.3 15 23.1 39 60.0 No 71 10.2 181 26.1 206 29.7 235 33.9 Never asthma Survey 2 Yes 30 7.9 59 15.4 98 25.7 195 51.0 No 1244 20.2 1876 30.4 1625 26.3 1425 23.1 Survey 6 Yes 16 9.7 23 13.9 47 28.5 79 47.9 No 345 14.5 755 31.8 696 29.3 578 24.3

303

Table A-12. Number of self-reported GP visits at survey 1 and survey 7 for women from the 1946-51 cohort according to depression and asthma groups Depressio Survey n Number of GP visits 0 1 or 2 3 or 4 5 or 6 7 or more n % n % n % n % n % Past asthma Survey 2 Yes 25 12.0 40 19.1 53 25.4 33 15.8 58 27.8 No 94 15.3 236 38.4 148 24.1 82 13.4 54 8.8 Survey 7 Yes 2 2.4 9 10.8 22 26.5 20 24.1 30 36.1 No 13 2.4 151 28.3 181 33.9 99 18.5 90 16.9 Prevalent asthma Survey 2 Yes 30 11.2 30 11.2 67 25.1 49 18.4 91 34.1 No 72 8.5 227 26.8 233 27.5 145 17.1 169 20.0 Survey 7 Yes 3 1.9 16 10.1 35 22.2 24 15.2 80 50.6 No 23 3.2 149 20.9 227 31.8 162 22.7 153 21.4 Incident asthma Survey 2 Yes 14 7.1 37 18.7 44 22.2 33 16.7 70 35.4 No 80 12.0 200 30.1 188 28.3 92 13.8 105 15.8 Survey 7 Yes 0 0.0 13 9.8 27 20.5 34 25.8 58 43.9 No 12 2.1 139 24.8 171 30.5 127 22.6 112 20.0 Bronchitis/emphysema Survey 2 Yes 29 8.2 78 22.0 88 24.8 65 18.3 95 26.8 No 167 13.2 454 36.0 347 27.5 175 13.9 119 9.4 Survey 7 Yes 2 1.2 12 7.1 46 27.1 46 27.1 64 37.6 No 42 3.9 326 30.3 339 31.5 212 19.7 157 14.6 Never asthma Survey 2 Yes 107 10.8 272 27.4 247 24.9 190 19.2 175 17.7 No 994 16.0 2758 44.4 1489 24.0 572 9.2 394 6.3 Survey 7 Yes 5 0.9 89 16.0 166 29.8 145 26.0 152 27.3 No 263 5.2 1830 35.8 1694 33.2 820 16.1 499 9.8

304

Table A-13. Number of self-reported GP visits at survey 2 and survey 6 for women from the 1921-26 cohort according to anxiety and asthma groups Survey Anxiety Number of GP visits Less than 2 3 or 4 5 to 8 9 or more n % n % n % n % Past asthma Survey 2 Yes 1 3.0 4 12.1 4 12.1 24 72.7 No 27 6.8 98 24.6 106 26.6 167 42.0 Survey 6 Yes 1 6.7 2 13.3 5 33.3 7 46.7 No 17 14.5 25 21.4 32 27.4 43 36.8 Prevalent asthma Survey 2 Yes 4 5.8 7 10.1 16 23.2 42 60.9 No 62 7.6 159 19.6 234 28.8 357 44.0 Survey 6 Yes 2 6.7 3 10.0 12 40.0 13 43.3 No 27 9.2 68 23.2 85 29.0 113 38.6 Incident asthma Survey 2 Yes 3 6.1 9 18.4 12 24.5 25 51.0 No 44 8.9 117 23.6 153 30.8 182 36.7 Survey 6 Yes 1 4.0 2 8.0 7 28.0 15 60.0 No 19 8.2 46 19.7 69 29.6 99 42.5 Bronchitis/emphysema Survey 2 Yes 10 8.1 17 13.7 30 24.2 67 54.0 No 194 11.6 394 23.5 542 32.4 545 32.5 Survey 6 Yes 1 2.3 7 16.3 13 30.2 22 51.2 No 73 10.2 182 25.5 208 29.1 252 35.2 Never asthma Survey 2 Yes 20 6.7 56 18.7 75 25.1 148 49.5 No 1254 20.1 1879 30.0 1648 26.4 1472 23.5 Survey 6 Yes 12 8.8 27 19.7 43 31.4 55 40.1 No 349 14.5 751 31.3 700 29.1 602 25.1

305

Table A-14. Number of self-reported GP visits at survey 1 and survey 7 for women from the 1946-51 cohort according to anxiety and asthma groups Anxiet Survey y Number of GP visits 0 1 or 2 3 or 4 5 or 6 7 or more n % n % n % n % n % Past asthma Survey 2 Yes 22 13.2 33 19.8 44 26.3 26 15.6 42 25.1 No 97 14.8 243 37.0 157 23.9 89 13.6 70 10.7 Survey 7 Yes 2 3.0 8 12.1 25 37.9 11 16.7 20 30.3 No 13 2.4 152 27.6 178 32.3 108 19.6 100 18.1 Prevalent asthma Survey 2 Yes 24 11.5 25 12.0 48 23.0 37 17.7 75 35.9 No 78 8.6 232 25.7 252 27.9 157 17.4 185 20.5 Survey 7 Yes 1 0.9 12 10.3 26 22.4 18 15.5 59 50.9 No 25 3.3 153 20.2 236 31.2 168 22.2 174 23.0 Incident asthma Survey 2 Yes 16 9.6 28 16.9 37 22.3 30 18.1 55 33.1 No 78 11.2 209 30.0 195 28.0 95 13.6 120 17.2 Survey 7 Yes 12 12.2 21 21.4 26 26.5 39 39.8 No 12 2.0 140 23.5 177 29.7 135 22.7 131 22.0 Bronchitis/emphysema Survey 2 Yes 44 14.9 62 20.9 75 25.3 53 17.9 62 20.9 No 152 11.5 470 35.6 360 27.3 187 14.2 152 11.5 Survey 7 Yes 1 0.7 12 8.4 35 24.5 37 25.9 58 40.6 No 43 3.9 326 29.6 350 31.7 221 20.0 163 14.8 Never asthma Survey 2 Yes 95 12.6 182 24.2 207 27.6 132 17.6 135 18.0 No 1006 15.6 2848 44.2 1529 23.7 630 9.8 434 6.7 Survey 7 Yes 9 1.8 88 17.6 154 30.7 120 24.0 130 25.9 No 259 5.0 1831 35.5 1706 33.0 845 16.4 521 10.1

306

Table A-15. Number of self-reported GP visits at survey 1 and survey 7 for women from the 1946- 51 cohort according to major diseases and asthma groups

Major Survey disease Number of GP visits 0 1 or 2 3 or 4 5 or 6 7 or more n % n % n % n % n % Past asthma Survey 1 Yes 6 3.0 47 23.4 55 27.4 41 20.4 52 25.9 No 49 7.5 248 37.8 168 25.6 96 14.6 95 14.5 Survey 7 Yes 1 1.8 5 9.1 11 20.0 16 29.1 22 40.0 No 14 2.5 155 27.6 192 34.2 103 18.3 98 17.4 Prevalent asthma Survey 1 Yes 8 2.8 38 13.5 65 23.1 62 22.1 108 38.4 No 40 4.5 237 26.6 244 27.4 183 20.5 187 21.0 Survey 7 Yes 2 2.2 1 1.1 18 19.8 26 28.6 44 48.4 No 24 3.1 164 21.0 244 31.2 160 20.5 189 24.2 Incident asthma Survey 1 Yes 2 1.0 41 21.4 44 22.9 36 18.8 69 35.9 No 55 7.6 243 33.7 184 25.5 119 16.5 120 16.6 Survey 7 Yes 1 1.9 4 7.5 11 20.8 9 17.0 28 52.8 No 11 1.7 148 23.1 187 29.2 152 23.8 142 22.2 Bronchitis/emphysema Survey 1 Yes 15 4.1 87 23.7 107 29.2 60 16.3 98 26.7 No 106 8.0 463 35.1 386 29.3 202 15.3 161 12.2 Survey 7 Yes 4 5.0 7 8.8 18 22.5 22 27.5 29 36.3 No 40 3.4 331 28.4 367 31.5 236 20.2 192 16.5 Never asthma Survey 1 Yes 63 5.8 320 29.5 301 27.8 186 17.2 213 19.7 No 766 12.0 2896 45.2 1596 24.9 717 11.2 433 6.8 Survey 7 Yes 3 1.1 41 14.9 82 29.8 66 24.0 83 30.2 No 265 4.9 1878 34.9 1778 33.0 899 16.7 568 10.5

307

Table A-16. Association between asthma groups and number of GP visits within 12 months prior to Survey 3 for women from the 1921-26 cohort using multivariate multinomial logistic regressions (Model 1)

Number of GP visits /year Predictor Categories (Ref: 2 or less) 3-4 5-8 9 or more Odds Ratio Odds Ratio Odds Ratio

(95% CI) p-value (95% CI) p-value (95% CI) p-value Past asthma 3.48 (1.45, 8.35) 0.0053 5.15 (2.18, 12.17) 0.0002 7.06 (2.99, 16.62) <.0001 Prevalent asthma 1.30 (0.75, 2.27) 0.35 3.86 (2.31, 6.44) <.0001 5.57 (3.34, 9.28) <.0001 Asthma group Incident asthma 1.95 (1.14, 3.34) 0.0145 2.46 (1.45, 4.18) 0.0009 2.91 (1.71, 4.96) <.0001 Bronchitis/emphysema 1.02 (0.73, 1.41) 0.91 1.84 (1.35, 2.51) 0.0001 2.44 (1.78, 3.33) <.0001 Age 0.86 (0.80, 0.93) 0.0002 0.85 (0.79, 0.92) <.0001 0.90 (0.83, 0.97) 0.0065 Inner regional 0.92 (0.71, 1.18) 0.51 0.63 (0.49, 0.80) 0.0002 0.60 (0.46, 0.77) <.0001 Area of Remote, very remote and outer 0.89 (0.66, 1.19) 0.43 0.43 (0.32, 0.58) <.0001 0.42 (0.31, 0.57) <.0001 residence regional Separated/Divorced/Widowed 0.94 (0.74, 1.19) 0.61 0.91 (0.72, 1.15) 0.44 1.02 (0.80, 1.29) 0.89 Marital status Never married 0.46 (0.28, 0.75) 0.0017 0.47 (0.30, 0.76) 0.0018 0.48 (0.29, 0.79) 0.0039 Highest No formal education 1.61 (0.99, 2.62) 0.06 1.58 (0.98, 2.54) 0.06 3.34 (1.98, 5.64) <.0001 qualification High school qualification 0.81 (0.54, 1.22) 0.31 0.70 (0.47, 1.04) 0.08 1.68 (1.07, 2.65) 0.0246 English speaking countries 0.43 (0.31, 0.59) <.0001 0.47 (0.35, 0.64) <.0001 0.62 (0.45, 0.84) 0.0019 Country of birth Non-English speaking countries 1.05 (0.57, 1.93) 0.87 2.05 (1.16, 3.62) 0.0134 2.10 (1.18, 3.74) 0.0115

308

Table A-17. Association between asthma groups and number of GP visits within 12 months prior to Survey 3 for women from the 1921-26 cohort using multivariate multinomial logistic regressions (Model 2)

Number of GP visits /year Predictor Categories (Ref: 2 or less) 3-4 5-8 9 or more Odds Ratio Odds Ratio Odds Ratio (95% CI) p-value (95% CI) p-value (95% CI) p-value Asthma group Past asthma 3.57 (1.48, 8.66) 0.0048 4.88 (2.04, 11.66) 0.0004 6.52 (2.74, 15.55) <.0001 Prevalent asthma 1.24 (0.71, 2.18) 0.45 3.57 (2.12, 6.02) <.0001 5.35 (3.18, 9.01) <.0001 Incident asthma 1.98 (1.15, 3.42) 0.0137 2.40 (1.40, 4.13) 0.0015 2.60 (1.51, 4.49) 0.0006 Bronchitis/emphysema 1.03 (0.74, 1.44) 0.86 1.81 (1.32, 2.49) 0.0002 2.35 (1.71, 3.23) <.0001 Age 0.85 (0.79, 0.92) <.0001 0.84 (0.77, 0.91) <.0001 0.89 (0.82, 0.96) 0.0035 Area Inner regional 1.07 (0.82, 1.40) 0.62 0.75 (0.57, 0.97) 0.0288 0.75 (0.57, 0.98) 0.0341 Remote, very remote and outer 1.07 (0.78, 1.46) 0.68 0.53 (0.38, 0.73) 0.0001 0.53 (0.38, 0.74) 0.0002 regional Marital status Separated/Divorced/Widowed 0.89 (0.70, 1.14) 0.37 0.86 (0.67, 1.09) 0.22 0.95 (0.74, 1.21) 0.68 Never married 0.45 (0.27, 0.74) 0.0016 0.46 (0.28, 0.74) 0.0017 0.46 (0.27, 0.77) 0.0031 Highest No formal education 1.57 (0.95, 2.59) 0.08 1.48 (0.91, 2.42) 0.11 3.32 (1.94, 5.68) <.0001 qualification High school qualification 0.74 (0.49, 1.13) 0.16 0.60 (0.40, 0.91) 0.0152 1.54 (0.97, 2.45) 0.07 Country of birth English speaking countries 0.41 (0.29, 0.57) <.0001 0.42 (0.31, 0.58) <.0001 0.57 (0.42, 0.79) 0.0005 Non-English speaking countries 1.06 (0.57, 1.97) 0.85 2.05 (1.15, 3.66) 0.0148 2.14 (1.19, 3.86) 0.0111 Private health Yes 1.34 (0.54, 3.33) 0.52 0.67 (0.30, 1.50) 0.33 2.00 (0.79, 5.05) 0.14 insurance Yes, veteran’s affair gold card 1.65 (0.62, 4.39) 0.32 0.74 (0.31, 1.80) 0.51 1.88 (0.69, 5.13) 0.22

Not too bad to manage 0.97 (0.76, 1.25) 0.82 1.23 (0.96, 1.58) 0.11 1.13 (0.88, 1.47) 0.33

309

Number of GP visits /year Predictor Categories (Ref: 2 or less) 3-4 5-8 9 or more Odds Ratio Odds Ratio Odds Ratio (95% CI) p-value (95% CI) p-value (95% CI) p-value Income 0.79 (0.38, 1.64) 0.53 2.50 (1.27, 4.90) 0.0077 3.61 (1.85, 7.03) 0.0002 Difficult to manage management Access to a Good 0.64 (0.45, 0.89) 0.0092 0.43 (0.30, 0.60) <.0001 0.43 (0.30, 0.61) <.0001 specialist Fair/poor 0.56 (0.34, 0.93) 0.0241 0.36 (0.21, 0.59) <.0001 0.36 (0.21, 0.61) 0.0001 Access to a bulk Good 1.13 (0.78, 1.63) 0.51 0.96 (0.66, 1.38) 0.81 0.86 (0.59, 1.27) 0.46 billing doctor Fair/poor 0.71 (0.53, 0.96) 0.0277 0.64 (0.47, 0.86) 0.0035 0.47 (0.35, 0.65) <.0001 Access to after- Good 2.00 (1.47, 2.71) <.0001 2.13 (1.58, 2.87) <.0001 1.73 (1.28, 2.35) 0.0004 hours doctors Fair/poor 2.57 (1.83, 3.61) <.0001 2.40 (1.71, 3.36) <.0001 2.36 (1.67, 3.32) <.0001 Access to a female Good 0.47 (0.35, 0.64) <.0001 0.57 (0.43, 0.77) 0.0003 0.50 (0.37, 0.68) <.0001 doctor Fair/poor 0.51 (0.36, 0.72) 0.0001 0.75 (0.54, 1.06) 0.11 0.80 (0.56, 1.13) 0.20 Access to a Good 1.33 (0.92, 1.91) 0.13 1.61 (1.12, 2.32) 0.0094 1.18 (0.81, 1.72) 0.38 hospital doctor Fair/poor 1.36 (0.78, 2.39) 0.28 1.22 (0.68, 2.17) 0.51 1.42 (0.79, 2.55) 0.24

310

Table A-18. Association between asthma groups and number of GP visits within 12 months prior to Survey 3 for women from the 1921-26 cohort using multivariate multinomial logistic regressions (Model 4)

Number of GP visits /year Predictor Categories (Ref: 2 or less) 3-4 5-8 9 or more Odds Ratio Odds Ratio Odds Ratio p-value p-value p-value (95% CI) (95% CI) (95% CI) Past asthma 3.50 (1.42, 8.64) 0.0066 3.97 (1.62, 9.69) 0.0025 4.34 (1.76, 10.73) 0.0015 Prevalent asthma 1.00 (0.56, 1.78) 1.00 2.71 (1.58, 4.63) 0.0003 3.86 (2.24, 6.65) <.0001 Asthma group Incident asthma 2.25 (1.28, 3.96) 0.0050 2.61 (1.48, 4.60) 0.0009 2.77 (1.55, 4.96) 0.0006 Bronchitis/emphysema 0.99 (0.70, 1.40) 0.97 1.68 (1.21, 2.35) 0.0022 2.05 (1.45, 2.90) <.0001 Age 0.84 (0.78, 0.92) <.0001 0.84 (0.77, 0.91) <.0001 0.89 (0.82, 0.97) 0.0080 Inner regional 1.06 (0.80, 1.40) 0.68 0.70 (0.53, 0.92) 0.0114 0.66 (0.50, 0.89) 0.0061 Area Remote, very remote and outer 1.02 (0.73, 1.42) 0.91 0.46 (0.33, 0.65) <.0001 0.47 (0.33, 0.67) <.0001 regional Separated/Divorced/Widowed 0.86 (0.67, 1.11) 0.25 0.84 (0.65, 1.08) 0.17 0.89 (0.69, 1.17) 0.41 Marital status Never married 0.38 (0.22, 0.64) 0.0003 0.41 (0.24, 0.68) 0.0007 0.37 (0.21, 0.67) 0.0009 Highest No formal education 1.87 (1.11, 3.15) 0.0196 1.78 (1.06, 2.98) 0.0291 4.12 (2.31, 7.36) <.0001 qualification High school qualification 0.84 (0.54, 1.31) 0.44 0.70 (0.45, 1.09) 0.12 1.90 (1.15, 3.15) 0.0129 English speaking countries 0.40 (0.28, 0.56) <.0001 0.41 (0.29, 0.58) <.0001 0.56 (0.40, 0.80) 0.0015 Country of birth Non-English speaking countries 1.04 (0.55, 1.96) 0.90 1.84 (1.01, 3.37) 0.0476 1.80 (0.96, 3.36) 0.07 Private health Yes 1.20 (0.45, 3.19) 0.71 0.67 (0.27, 1.65) 0.39 2.21 (0.79, 6.20) 0.13 insurance Yes, veteran’s affair gold card 1.52 (0.54, 4.34) 0.43 0.73 (0.27, 1.93) 0.52 1.69 (0.56, 5.12) 0.35

Not too bad to manage 1.00 (0.78, 1.30) 0.97 1.23 (0.95, 1.61) 0.12 1.10 (0.83, 1.45) 0.51

311

Income Difficult to manage 0.86 (0.41, 1.83) 0.70 2.67 (1.32, 5.39) 0.0062 3.64 (1.79, 7.41) 0.0004 management Access to a Good 0.64 (0.45, 0.92) 0.0165 0.45 (0.31, 0.65) <.0001 0.44 (0.30, 0.65) <.0001 specialist Fair/poor 0.64 (0.38, 1.06) 0.08 0.42 (0.25, 0.72) 0.0013 0.45 (0.26, 0.78) 0.0048 Good 1.17 (0.79, 1.73) 0.43 0.99 (0.67, 1.47) 0.96 0.93 (0.62, 1.42) 0.75 Access to a bulk billing Fair/poor 0.62 (0.45, 0.85) 0.0029 0.56 (0.40, 0.77) 0.0003 0.39 (0.28, 0.56) <.0001 doctor Access to after- Good 2.20 (1.59, 3.04) <.0001 2.41 (1.75, 3.33) <.0001 1.96 (1.40, 2.75) <.0001 hours doctors Fair/poor 2.85 (1.99, 4.10) <.0001 2.66 (1.85, 3.84) <.0001 2.73 (1.87, 3.99) <.0001 Access to a Good 0.52 (0.38, 0.72) <.0001 0.64 (0.47, 0.89) 0.0068 0.58 (0.41, 0.81) 0.0013 female doctor Fair/poor 0.51 (0.36, 0.73) 0.0003 0.78 (0.55, 1.12) 0.18 0.85 (0.58, 1.23) 0.38 Access to a Good 1.21 (0.83, 1.76) 0.33 1.44 (0.99, 2.11) 0.06 1.06 (0.71, 1.59) 0.77 hospital doctor Fair/poor 1.46 (0.81, 2.63) 0.21 1.22 (0.66, 2.23) 0.52 1.30 (0.69, 2.46) 0.41 Underweight 1.36 (0.63, 2.98) 0.43 1.09 (0.48, 2.44) 0.84 1.16 (0.51, 2.67) 0.72 BMI Overweight 0.82 (0.63, 1.06) 0.13 0.82 (0.63, 1.07) 0.15 0.76 (0.58, 1.01) 0.06 Obese 1.35 (0.85, 2.14) 0.20 1.49 (0.95, 2.33) 0.08 1.91 (1.21, 3.02) 0.0054 High risk drinking 1.19 (0.91, 1.55) 0.20 1.11 (0.85, 1.45) 0.46 1.03 (0.78, 1.36) 0.85 Drinking alcohol Low risk drinking 2.62 (0.98, 7.03) 0.06 1.58 (0.57, 4.35) 0.38 1.03 (0.34, 3.15) 0.95 Smoker 0.54 (0.26, 1.10) 0.09 0.96 (0.49, 1.89) 0.90 0.64 (0.30, 1.39) 0.26 Smoking status Ex-smoker 0.81 (0.62, 1.07) 0.14 0.89 (0.67, 1.16) 0.38 0.96 (0.73, 1.28) 0.79 Diabetes Yes 1.50 (0.80, 2.80) 0.21 2.86 (1.57, 5.21) 0.0006 3.32 (1.81, 6.10) 0.0001 Heart diseases Yes 1.12 (0.70, 1.79) 0.63 2.34 (1.50, 3.65) 0.0002 4.86 (3.12, 7.57) <.0001 Hypertension Yes 3.44 (2.65, 4.48) <.0001 4.30 (3.30, 5.60) <.0001 5.88 (4.47, 7.74) <.0001 Osteoporosis Yes 3.16 (2.10, 4.75) <.0001 3.44 (2.29, 5.16) <.0001 5.18 (3.43, 7.81) <.0001

312

Depression Yes 1.76 (0.80, 3.86) 0.16 1.47 (0.67, 3.19) 0.34 2.02 (0.93, 4.41) 0.08 Anxiety Yes 1.39 (0.54, 3.57) 0.49 2.98 (1.21, 7.33) 0.0171 4.82 (1.94, 11.97) 0.0007

313

Table A-19. Association between asthma or bronchitis/emphysema and number of GP visits for women from the 1921-26 cohort using a longitudinal analysis approach, from survey 2 to survey 6 (Model 2)

Number of GP visits /year Predictor Categories (Ref: 2 or less) 3-4 5-8 9 or more Odds Ratio Odds Ratio Odds Ratio p-value p-value p-value (95% CI) (95% CI) (95% CI) Past asthma 1.448 (1.062,1.975) 0.0191 1.934 (1.405, 2.662) <.0001 2.888 (2.065, 4.038) <.0001 Prevalent asthma 1.482 (1.205, 1.824) 0.0002 2.564 (2.062, 3.189) <.0001 4.084 (3.266, 5.109) <.0001 Asthma group Incident asthma 1.439 (1.093, 1.894) 0.0096 2.469 (1.869, 3.263) <.0001 3.450 (2.611, 4.558) <.0001 Bronchitis/emphysema 1.234 (1.070, 1.423) 0.0039 1.852 (1.597, 2.148) <.0001 2.386 (2.047, 2.782) <.0001 Inner regional 0.908 (0.814, 1.012) 0.0816 0.733 (0.653, 0.824) <.0001 0.584 (0.517, 0.660) <.0001 Area of residence Remote, very remote and outer 0.775 (0.677, 0.888) 0.0002 0.666 (0.577, 0.769) <.0001 0.501 (0.431, 0.584) <.0001 regional Separated/Divorced/Widowed 0.932 (0.842, 1.032) 0.1758 0.970 (0.872, 1.079) 0.5716 1.003 (0.896, 1.122) 0.9613 Marital status Never married 0.852 (0.639, 1.136) 0.2737 0.728 (0.531, 0.998) 0.0487 0.646 (0.463, 0.901) 0.0101 No formal education 1.329 (1.061, 1.664) 0.0133 1.336 (1.051, 1.697) 0.0177 1.876 (1.441, 2.443) <.0001 Highest qualification High school qualification 1.346 (1.063, 1.703) 0.0136 1.493 (1.162, 1.919) 0.0017 2.594 (1.970, 3.415) <.0001 English speaking countries 0.43 (0.31, 0.59) <.0001 0.47 (0.35, 0.64) <.0001 0.62 (0.45, 0.84) 0.0019 Country of birth Non-English speaking countries 1.05 (0.57, 1.93) 0.87 2.05 (1.16, 3.62) 0.0134 2.10 (1.18, 3.74) 0.0115

314

Table A-20. Association between asthma or bronchitis/emphysema and number of GP visits for women from the 1921-26 cohort using a longitudinal analysis approach, from survey 2 to survey 6 (Model 3)

Number of GP visits /year Predictor Categories (Ref: 2 or less) 3-4 5-8 9 or more Odds Ratio Odds Ratio Odds Ratio

(95% CI) p-value (95% CI) p-value (95% CI) p-value Past asthma 1.53 (1.09, 2.16) 0.0138 2.00 (1.40, 2.82) 0.0001 3.26 (2.28, 4.66) <.0001 Prevalent asthma 1.49 (1.18, 1.89) 0.0001 2.59 (2.02,3.33) <.0001 4.09 (3.15, 5.30) <.0001 Asthma group Incident asthma 1.66 (1.20, 2.14) 0.0024 3.00 (2.15, 4.17) <.0001 4.00 (2.87, 5.55) <.0001 Bronchitis/emphysema 1.23 (1.04, 1.44) 0.0139 1.90 (1.61, 2.25) <.0001 2.35 (1.97, 2.80) <.0001 Inner regional 0.91 (0.81, 1.03) 0.1478 0.73 (0.66, 0.83) <.0001 0.56 (0.48, 0.64) <.0001 Area Remote, very remote and outer 0.80 (0.70,0.94) 0.0058 0.65 (0.55, 0.77) <.0001 0.48 (0.41, 0.57) <.0001 regional Separated/Divorced/Widowed 0.96 (0.85, 1.08) 0.4519 0.99 (0.88, 1.13) 0.9267 0.97 (0.85, 1.10) 0.5883 Marital status Never married 0.84 (0.60, 1.17) 0.2889 0.73 (0.51, 1.04) 0.0793 0.73 (0.50, 1.05) 0.0887 Highest No formal education 1.36 (1.05, 1.74) 0.0182 1.42 (1.09, 1.85) 0.0084 1.96 (1.46, 2.62) <.0001 qualification High school qualification 1.43 (1.09, 1.86) 0.0087 1.66 (1.26, 2.19) 0.0004 2.73 (2.01, 3.70) <.0001 English speaking countries 0.41 (0.29, 0.57) <.0001 0.42 (0.31, 0.58) <.0001 0.57 (0.42, 0.79) 0.0005 Country of birth Non-English speaking countries 1.06 (0.57, 1.97) 0.85 2.05 (1.15, 3.66) 0.0148 2.14 (1.19, 3.86) 0.0111 Private health Yes 1.34 (0.54, 3.33) 0.52 0.67 (0.30, 1.50) 0.33 2.00 (0.79, 5.05) 0.14 insurance Yes, veteran’s affair gold card 1.65 (0.62, 4.39) 0.32 0.74 (0.31, 1.80) 0.51 1.88 (0.69, 5.13) 0.22 Income Not too bad to manage 1.16 (1.04, 1.30) 0.0102 1.26 (1.12, 1.42) 0.0001 1.55 (1.37, 1.75) <.0001 management Difficult to manage 1.13 (0.84, 1.50) 0.4238 1.35 (1.01, 1.81) 0.0412 2.83 (2.13, 3.76) <.0001

315

Table A-21. Association between asthma or bronchitis/emphysema and number of GP visits for women from the 1921-26 cohort using a longitudinal analysis approach, from survey 2 to survey 6 (Model 4)

Number of GP visits /year (Ref: 2 or less) Predictor Categories 3-4 5-8 9 or more Odds Ratio Odds Ratio Odds Ratio p-value p-value p-value (95% CI) (95% CI) (95% CI) Past asthma 1.45(1.03 , 2.04) 0.0323 1.76 (1.23 , 2.52) 0.0021 2.54 (1.76 , 3.66) <.0001 Prevalent asthma 1.41 (1.11 , 1.79) 0.0055 2.29 (1.78 , 2.96) <.0001 3.39 (2.6 , 4.42) <.0001 Asthma group Incident asthma 1.60 (1.15 , 2.24) 0.0058 2.73 (1.95 , 3.82) <.0001 3.34 (2.38 , 4.68) <.0001 Bronchitis/emphysema 1.22(1.04 , 1.44) 0.0156 1.80 (1.52 , 2.13) <.0001 2.06 (1.73 , 2.46) <.0001 Inner regional 0.91 (.8 , 1.03) 0.1365 0.72 (.63 , .82) <.0001 0.55 (.47 , .63) <.0001 Area Remote, very remote and outer 0.80 (.69 , .94) 0.0048 0.65 (.55 , .77) <.0001 0.48 (.4 , .57) <.0001 regional Separated/Divorced/Widowed 0.97 (.86 , 1.09) 0.5624 0.99 (.87 , 1.12) 0.8136 0.94 (.82 , 1.08) 0.3871 Marital status Never married 0.84 (.6 , 1.18) 0.3126 0.72 (.51 , 1.02) 0.0619 0.72 (.5 , 1.05) 0.0855 Highest No formal education 1.37 (1.07 , 1.77) 0.0138 1.49 (1.14 , 1.95) 0.0032 2.07 (1.55 , 2.78) <.0001 qualification High school qualification 1.42 (1.09 ,1.86) 0.0093 1.68 (1.27 , 2.23) 0.0003 2.76 (2.02 , 3.76) <.0001 English speaking countries 0.40 (0.28, 0.56) <.0001 0.41 (0.29, 0.58) <.0001 0.56 (0.40, 0.80) 0.0015 Country of birth Non-English speaking countries 1.04 (0.55, 1.96) 0.90 1.84 (1.01, 3.37) 0.0476 1.80 (0.96, 3.36) 0.07 Private health Yes 1.20 (0.45, 3.19) 0.71 0.67 (0.27, 1.65) 0.39 2.21 (0.79, 6.20) 0.13 insurance Yes, veteran’s affair gold card 1.52 (0.54, 4.34) 0.43 0.73 (0.27, 1.93) 0.52 1.69 (0.56, 5.12) 0.35 Income Not too bad to manage 1.11 (0.99 , 1.25) 0.0752 1.17 (1.03 , 1.32) 0.0137 1.40 (1.23 , 1.59) <.0001 management Difficult to manage 1.07 (0.80 , 1.43) 0.6652 1.22 (0.90 , 1.65) 0.1940 2.34 (1.74 , 3.15) <.0001 Underweight 0.86 (0.66 , 1.13) 0.2762 0.92 (0.71 , 1.2) 0.5588 1.16 (0.88 , 1.53) 0.2928 BMI Overweight 1.07 (0.95 , 1.21) 0.2480 1.26 (1.11 , 1.43) 0.0003 1.27 (1.11 , 1.45) 0.0004 Obese 1.06 (0.89 , 1.28) 0.5089 1.33 (1.1 , 1.6) 0.0035 1.55 (1.28 , 1.89) <.0001 High risk drinking 1.19 (0.91, 1.55) 0.20 1.11 (0.85, 1.45) 0.46 1.03 (0.78, 1.36) 0.85 Drinking alcohol Low risk drinking 2.62 (0.98, 7.03) 0.06 1.58 (0.57, 4.35) 0.38 1.03 (0.34, 3.15) 0.95 Smoking status Smoker 0.68 (0.53 , .88) 0.0033 0.76 (0.57 , 1.02) 0.0647 0.73 (0.55 , 0.97) 0.0317

316

Number of GP visits /year (Ref: 2 or less) Predictor Categories 3-4 5-8 9 or more Odds Ratio Odds Ratio Odds Ratio p-value p-value p-value (95% CI) (95% CI) (95% CI) Ex-smoker 1.13 (.99 , 1.29) 0.0661 1.17 (1.02 , 1.34) 0.0293 1.27 (1.09 , 1.47) 0.0016 Diabetes Yes 1.29 (1.03 , 1.62) 0.0284 1.73 (1.38 , 2.19) <.0001 2.30 (1.81 , 2.92) <.0001 Heart diseases Yes 1.77 (1.45 , 2.15) <.0001 2.48 (2.03 , 3.02) <.0001 4.76 (3.91 , 5.8) <.0001 Hypertension Yes 2.41 (2.14 , 2.7) <.0001 3.15 (2.78 , 3.55) <.0001 3.36 (2.96 , 3.82) <.0001 Osteoporosis Yes 1.65 (1.4 , 1.94) <.0001 2.41 (2.04 , 2.85) <.0001 3.10 (2.61 , 3.67) <.0001 Depression Yes 1.22 (.91 , 1.64) 0.1934 2.17 (1.63 , 2.89) <.0001 3.30 (2.48 , 4.4) <.0001 Anxiety Yes 1.33 (.97 , 1.81) 0.0728 1.84 (1.36 , 2.48) <.0001 2.60 (1.93 , 3.52) <.0001

317

Table A-22. Association between asthma groups and number of GP visits within 12 months prior to Survey 3 for women from the 1946-51 cohort using multivariate multinomial logistic regressions (Model 2)

Number of GP visits /year Predictor Categories (Ref: 0 visits) 1-2 3-4 5-6 7 or more Odds Ratio p- Odds Ratio p- Odds Ratio p- Odds Ratio (95% CI) value (95% CI) value (95% CI) value (95% CI) p-value Past asthma 0.86 (0.49, 1.50) 0.59 1.28 (0.73, 2.23) 0.39 1.58 (0.87, 2.85) 0.13 2.26 (1.23, 4.17) 0.0089 Asthma Prevalent asthma 2.80 (1.20, 6.51) 0.0169 5.25 (2.27, 12.17) 0.0001 8.02 (3.42, 18.80) <.0001 14.86 (6.32, 34.90) <.0001 group Incident asthma 1.09 (0.58, 2.07) 0.79 1.91 (1.02, 3.60) 0.0442 2.31 (1.19, 4.49) 0.0132 3.75 (1.92, 7.32) 0.0001 Bronchitis/emphysema 1.44 (0.88, 2.36) 0.15 2.32 (1.41, 3.81) 0.0009 2.03 (1.19, 3.46) 0.0095 3.54 (2.07, 6.08) <.0001 Age 1.07 (0.97, 1.18) 0.18 1.09 (0.98, 1.20) 0.11 1.14 (1.02, 1.27) 0.0202 1.21 (1.08, 1.35) 0.0010 Area of Inner regional 1.14 (0.73, 1.78) 0.57 0.98 (0.62, 1.56) 0.95 1.17 (0.72, 1.90) 0.52 1.78 (1.10, 2.88) 0.0197 residence Remote, very remote and 1.00 (0.44, 2.28) 0.99 1.17 (0.51, 2.69) 0.72 1.06 (0.43, 2.62) 0.91 1.51 (0.60, 3.75) 0.38 outer regional Marital Separated/Divorced/Widowed 0.88 (0.63, 1.24) 0.48 0.80 (0.56, 1.13) 0.21 0.77 (0.53, 1.12) 0.17 0.96 (0.65, 1.42) 0.84 status Never married 0.72 (0.50, 1.05) 0.09 0.76 (0.52, 1.12) 0.17 0.65 (0.43, 0.98) 0.0406 0.59 (0.38, 0.92) 0.0208 Highest No formal education 1.21 (0.86, 1.70) 0.28 1.35 (0.95, 1.92) 0.10 1.35 (0.91, 1.98) 0.13 1.52 (1.01, 2.30) 0.0462 qualification High school qualification 1.34 (0.73, 2.46) 0.35 1.71 (0.92, 3.17) 0.09 2.10 (1.09, 4.04) 0.0258 3.15 (1.62, 6.14) 0.0007

318

Table A-23. Association between asthma groups and number of GP visits within 12 months prior to Survey 3 for women from the 1946-51 cohort using multivariate multinomial logistic regressions (Model 3)

Number of GP visits /year Predictor Categories (Ref: 2 or less) 1-2 3-4 5-6 7 or more Odds Ratio Odds Ratio Odds Ratio Odds Ratio (95% CI) p-value (95% CI) p-value (95% CI) p-value (95% CI) p-value Past asthma 0.84 (0.48, 1.47) 0.55 1.21 (0.69, 2.13) 0.50 1.49 (0.82, 2.70) 0.19 2.08 (1.12, 3.86) 0.0201 Prevalent asthma 2.84 (1.22, 6.62) 0.0155 5.19 (2.24, 12.04) 0.0001 7.72 (3.29, 18.13) <.0001 14.06 (5.97, 33.14) <.0001 Asthma group Incident asthma 1.09 (0.57, 2.06) 0.80 1.89 (1.00, 3.56) 0.05 2.26 (1.16, 4.41) 0.0165 3.64 (1.85, 7.15) 0.0002 Bronchitis/emphysema 1.45 (0.88, 2.38) 0.14 2.23 (1.35, 3.67) 0.0016 1.92 (1.12, 3.29) 0.0169 3.32 (1.93, 5.71) <.0001 Age 1.08 (0.98, 1.19) 0.13 1.09 (0.99, 1.21) 0.08 1.15 (1.03, 1.28) 0.0131 1.22 (1.09, 1.37) 0.0006 Area of Inner regional 1.22 (0.78, 1.91) 0.39 1.02 (0.64, 1.63) 0.92 1.12 (0.68, 1.83) 0.65 1.60 (0.98, 2.62) 0.06 residence Remote, very remote and outer regional 1.01 (0.44, 2.32) 0.98 1.19 (0.52, 2.76) 0.68 1.06 (0.43, 2.65) 0.90 1.49 (0.59, 3.75) 0.39 Marital status Separated/Divorced/Widowed 0.89 (0.62, 1.27) 0.52 0.80 (0.56, 1.15) 0.23 0.76 (0.51, 1.12) 0.17 0.94 (0.63, 1.41) 0.77 Never married 0.73 (0.49, 1.08) 0.11 0.76 (0.51, 1.13) 0.18 0.63 (0.41, 0.97) 0.0363 0.58 (0.37, 0.93) 0.0230 Highest No formal education 1.25 (0.88, 1.77) 0.21 1.39 (0.97, 2.00) 0.07 1.31 (0.88, 1.94) 0.18 1.37 (0.90, 2.09) 0.14 qualification High school qualification 1.46 (0.78, 2.71) 0.23 1.86 (0.99, 3.50) 0.05 2.06 (1.05, 4.01) 0.0343 2.74 (1.38, 5.42) 0.0039 Income Not too bad to manage 1.01 (0.71, 1.43) 0.97 1.10 (0.77, 1.58) 0.60 1.40 (0.94, 2.10) 0.10 1.44 (0.93, 2.22) 0.10 management Difficult to manage 0.79 (0.42, 1.47) 0.46 1.24 (0.66, 2.31) 0.50 2.38 (1.24, 4.59) 0.0096 2.78 (1.41, 5.48) 0.0031 Private health Yes 1.25 (0.93, 1.68) 0.14 1.40 (1.03, 1.89) 0.0305 1.37 (0.99, 1.90) 0.06 1.14 (0.80, 1.60) 0.47 insurance Yes, veteran’s affair gold card 0.54 (0.06, 5.30) 0.60 1.16 (0.13, 10.19) 0.90 1.14 (0.11, 11.36) 0.91 3.20 (0.37, 27.51) 0.29 Access to a Good 1.02 (0.66, 1.58) 0.93 0.83 (0.53, 1.31) 0.42 0.64 (0.39, 1.06) 0.09 0.52 (0.30, 0.89) 0.0165 bulk billing 1.20 (0.84, 1.72) 0.32 1.14 (0.79, 1.64) 0.49 1.00 (0.67, 1.49) 0.99 0.95 (0.63, 1.43) 0.80 Fair/poor doctor Access to Good 0.93 (0.62, 1.40) 0.74 1.13 (0.75, 1.71) 0.57 1.01 (0.65, 1.59) 0.96 1.05 (0.65, 1.69) 0.85 after-hours 0.76 (0.48, 1.22) 0.26 1.22 (0.76, 1.96) 0.42 1.05 (0.62, 1.76) 0.86 1.21 (0.71, 2.08) 0.48 Fair/poor doctors Access to a Good 0.62 (0.42, 0.90) 0.0127 0.62 (0.42, 0.92) 0.0167 0.89 (0.58, 1.35) 0.58 0.57 (0.36, 0.90) 0.0149 female doctor Fair/poor 0.82 (0.56, 1.21) 0.32 0.81 (0.55, 1.20) 0.30 0.87 (0.57, 1.34) 0.53 0.74 (0.47, 1.15) 0.18 Access to a Good 1.13 (0.70, 1.82) 0.62 1.42 (0.87, 2.31) 0.16 1.53 (0.90, 2.61) 0.11 2.37 (1.36, 4.11) 0.0022 hospital 1.34 (0.68, 2.65) 0.40 1.44 (0.72, 2.89) 0.30 1.60 (0.76, 3.37) 0.22 2.51 (1.16, 5.43) 0.0193 Fair/poor doctor Good 1.09 (0.67, 1.76) 0.73 0.87 (0.53, 1.43) 0.59 0.79 (0.46, 1.36) 0.40 0.76 (0.43, 1.33) 0.33

319

Number of GP visits /year Predictor Categories (Ref: 2 or less) 1-2 3-4 5-6 7 or more Odds Ratio Odds Ratio Odds Ratio Odds Ratio (95% CI) p-value (95% CI) p-value (95% CI) p-value (95% CI) p-value Access to 1.10 (0.58, 2.08) 0.78 0.70 (0.36, 1.33) 0.27 0.81 (0.40, 1.63) 0.55 0.54 (0.26, 1.13) 0.10 emergency Fair/poor health care Table A-24. Association between asthma groups and number of GP visits within 12 months prior to Survey 3 for women from the 1946-51 cohort using multivariate multinomial logistic regressions (Model 4)

Number of GP visits /year Predictor Categories (Ref: 0 visits) 1-2 3-4 5-6 7 or more Odds Ratio Odds Ratio Odds Ratio Odds Ratio (95% CI) p-value (95% CI) p-value (95% CI) p-value (95% CI) p-value Past asthma 0.86 (0.49, 1.50) 0.59 1.20 (0.68, 2.12) 0.53 1.41 (0.76, 2.61) 0.27 1.83 (0.95, 3.52) 0.07 Prevalent asthma 2.78 (1.19, 6.50) 0.0184 4.70 (2.01, 10.97) 0.0004 6.53 (2.75, 15.52) <.0001 10.63 (4.43, 25.51) <.0001 Asthma group Incident asthma 1.03 (0.54, 1.97) 0.92 1.73 (0.91, 3.29) 0.10 2.00 (1.01, 3.97) 0.0464 3.14 (1.56, 6.35) 0.0014 Bronchitis/emphysema 1.44 (0.87, 2.37) 0.15 2.14 (1.29, 3.54) 0.0031 1.81 (1.05, 3.13) 0.0336 3.00 (1.71, 5.28) 0.0001 Age 1.08 (0.98, 1.19) 0.13 1.09 (0.99, 1.21) 0.09 1.14 (1.02, 1.28) 0.0185 1.22 (1.08, 1.38) 0.0011 Area Inner regional 1.18 (0.75, 1.85) 0.48 0.95 (0.59, 1.53) 0.84 0.97 (0.58, 1.61) 0.91 1.21 (0.71, 2.04) 0.48 Remote, very remote and outer regional 0.99 (0.43, 2.30) 0.99 1.17 (0.50, 2.75) 0.72 1.01 (0.40, 2.60) 0.98 1.33 (0.50, 3.53) 0.56 Marital status Separated/Divorced/Widowed 0.91 (0.63, 1.30) 0.59 0.81 (0.56, 1.17) 0.26 0.76 (0.51, 1.14) 0.19 0.99 (0.64, 1.51) 0.95 Never married 0.73 (0.49, 1.09) 0.12 0.74 (0.49, 1.12) 0.15 0.60 (0.38, 0.93) 0.0236 0.53 (0.33, 0.87) 0.0115 Highest No formal education 1.23 (0.87, 1.76) 0.24 1.37 (0.95, 1.98) 0.09 1.30 (0.87, 1.96) 0.20 1.38 (0.88, 2.17) 0.16 qualification High school qualification 1.45 (0.78, 2.72) 0.24 1.75 (0.92, 3.32) 0.09 1.82 (0.91, 3.62) 0.09 2.36 (1.15, 4.85) 0.0193 Income Not too bad to manage 1.00 (0.70, 1.42) 0.98 1.10 (0.77, 1.59) 0.60 1.44 (0.95, 2.18) 0.09 1.45 (0.91, 2.29) 0.11 management Difficult to manage 0.77 (0.41, 1.44) 0.41 1.12 (0.59, 2.12) 0.73 1.96 (0.99, 3.88) 0.05 1.96 (0.96, 4.02) 0.07 Private health Yes 1.25 (0.93, 1.69) 0.13 1.41 (1.04, 1.92) 0.0284 1.38 (0.99, 1.93) 0.06 1.15 (0.80, 1.66) 0.44 insurance Yes, veteran’s affair gold card 0.43 (0.04, 4.32) 0.48 0.81 (0.09, 7.43) 0.85 0.71 (0.07, 7.61) 0.78 2.20 (0.24, 20.28) 0.48 Access to a Good 1.00 (0.64, 1.56) 0.99 0.79 (0.50, 1.26) 0.33 0.61 (0.36, 1.01) 0.06 0.49 (0.28, 0.87) 0.0145 bulk billing 1.17 (0.82, 1.68) 0.38 1.09 (0.75, 1.58) 0.66 0.95 (0.63, 1.43) 0.81 0.91 (0.59, 1.40) 0.67 Fair/poor doctor Good 0.93 (0.62, 1.40) 0.73 1.12 (0.74, 1.71) 0.58 0.99 (0.62, 1.57) 0.97 0.99 (0.60, 1.63) 0.96

320

Number of GP visits /year Predictor Categories (Ref: 0 visits) 1-2 3-4 5-6 7 or more Odds Ratio Odds Ratio Odds Ratio Odds Ratio (95% CI) p-value (95% CI) p-value (95% CI) p-value (95% CI) p-value Access to 0.75 (0.47, 1.21) 0.24 1.21 (0.75, 1.95) 0.45 1.00 (0.59, 1.71) 0.99 1.08 (0.61, 1.90) 0.79 after-hours Fair/poor doctors Access to a Good 0.61 (0.42, 0.90) 0.0118 0.62 (0.42, 0.92) 0.0184 0.90 (0.59, 1.39) 0.64 0.58 (0.36, 0.93) 0.0252 female doctor Fair/poor 0.84 (0.57, 1.23) 0.37 0.83 (0.56, 1.23) 0.35 0.90 (0.58, 1.40) 0.64 0.76 (0.48, 1.21) 0.25 Access to a Good 1.14 (0.70, 1.85) 0.59 1.42 (0.86, 2.34) 0.17 1.52 (0.88, 2.63) 0.13 2.38 (1.34, 4.25) 0.0032 hospital 1.31 (0.66, 2.60) 0.44 1.40 (0.69, 2.83) 0.35 1.58 (0.73, 3.39) 0.25 2.66 (1.18, 5.96) 0.0179 Fair/poor doctor Access to Good 1.09 (0.67, 1.78) 0.72 0.88 (0.53, 1.45) 0.62 0.80 (0.46, 1.40) 0.44 0.81 (0.45, 1.47) 0.50 emergency 1.16 (0.61, 2.20) 0.66 0.75 (0.39, 1.45) 0.39 0.87 (0.42, 1.80) 0.71 0.57 (0.26, 1.23) 0.15 Fair/poor health care BMI Underweight 0.85 (0.24, 3.08) 0.81 0.90 (0.24, 3.38) 0.88 1.09 (0.26, 4.56) 0.90 2.03 (0.48, 8.48) 0.33 Overweight 1.00 (0.72, 1.38) 1.00 1.05 (0.75, 1.47) 0.78 1.14 (0.79, 1.66) 0.49 1.39 (0.92, 2.10) 0.11 Obese 1.04 (0.68, 1.58) 0.86 1.22 (0.80, 1.87) 0.35 1.37 (0.87, 2.18) 0.18 2.24 (1.38, 3.63) 0.0011 Diabetes Yes 0.73 (0.20, 2.58) 0.62 1.31 (0.38, 4.50) 0.67 2.72 (0.79, 9.42) 0.11 2.19 (0.61, 7.83) 0.23 Heart disease Yes 0.67 (0.14, 3.22) 0.61 1.21 (0.26, 5.54) 0.81 1.38 (0.29, 6.64) 0.69 2.36 (0.50, 11.28) 0.28 hypertension Yes 4.18 (1.80, 9.66) 0.0008 8.49 (3.68, 19.57) <.0001 12.12 (5.20, 28.25) <.0001 14.96 (6.36, 35.17) <.0001 osteoporosis Yes 1.48 (0.34, 6.52) 0.60 3.11 (0.72, 13.46) 0.13 3.50 (0.77, 15.85) 0.10 9.18 (2.08, 40.59) 0.0035 Depression Yes 3.52 (1.08, 0.0363 6.58 (2.04, 21.24) 0.0016 12.41 (3.82, 40.31) <.0001 21.61 (6.63, 70.46) <.0001 11.45) Anxiety Yes 0.88 (0.30, 2.63) 0.82 2.09 (0.73, 6.01) 0.17 3.34 (1.15, 9.72) 0.0266 3.81 (1.30, 11.19) 0.0151 Low iron Yes 2.38 (1.13, 5.00) 0.0220 3.00 (1.42, 6.34) 0.0039 3.05 (1.40, 6.65) 0.0051 4.72 (2.14, 10.41) 0.0001 Any other Yes 0.97 (0.28, 3.40) 0.96 1.87 (0.55, 6.33) 0.32 4.07 (1.19, 13.88) 0.0248 5.81 (1.69, 20.00) 0.0053 major disease

321

Table A-25. Association between asthma or bronchitis/emphysema and number of GP visits for women from the 1946-51 cohort using a longitudinal analysis approach, from survey 2 to survey 7 (Model 2)

Number of GP visits /year Predictor categories (Ref: 0 visits) 1-2 3-4 5-6 7 or more Odds Ratio Odds Ratio Odds Ratio Odds Ratio (95% CI) p-value (95% CI) p-value (95% CI) p-value (95% CI) p-value Past asthma 1.01 (0.78, 1.32) 0.91 1.34 (1.00, 1.78) 0.0466 1.69 (1.25, 2.31) 0.0008 2.34 (1.68, 3.25) <.0001 Asthma Prevalent asthma 1.70 (1.27, 2.26) 0.0003 3.23 (2.38, 4.39) <.0001 4.82 (3.51, 6.63) <.0001 8.61 (6.20, 11.96) <.0001 group Incident asthma 1.56 (1.14, 2.13) 0.0055 2.58 (1.84, 3.62) <.0001 3.24 (2.28, 4.59) <.0001 5.67 (3.96, 8.13) <.0001 Bronchitis/emphysema 1.26 (1.01, 1.57) 0.0438 1.70 (1.35, 2.15) <.0001 2.17 (1.70, 2.78) <.0001 3.09 (2.38, 4.02) <.0001 Age 1.04 (1.03, 1.06) <.0001 1.08 (1.07, 1.09) <.0001 1.09 (1.08, 1.11) <.0001 1.09 (1.08, 1.11) <.0001 Area Inner regional 0.94 (0.79, 1.12) 0.50 0.94 (0.78, 1.13) 0.49 1.03 (0.85, 1.26) 0.75 1.46 (1.18, 1.79) 0.0004 Remote, very remote and outer regional 0.71 (0.52, 0.99) 0.0407 0.67 (0.46, 0.98) 0.0392 0.69 (0.46, 1.05) 0.08 1.00 (0.64, 1.57) 0.99 Marital Separated/Divorced/Widowed 0.97 (0.83, 1.14) 0.71 0.88 (0.74, 1.04) 0.14 0.86 (0.72, 1.03) 0.11 0.86 (0.71, 1.04) 0.11 status Never married 0.87 (0.73, 1.04) 0.13 0.79 (0.66, 0.96) 0.0168 0.78 (0.63, 0.95) 0.0144 0.74 (0.60, 0.92) 0.0074 Highest No formal education 1.08 (0.91, 1.28) 0.39 1.16 (0.97, 1.40) 0.11 1.22 (1.00, 1.49) 0.05 1.49 (1.19, 1.87) 0.0005 qualification High school qualification 0.92 (0.71, 1.20) 0.54 1.15 (0.86, 1.52) 0.34 1.41 (1.05, 1.91) 0.0235 2.42 (1.76, 3.32) <.0001

322

Table A-26. Association between asthma or bronchitis/emphysema and number of GP visits for women from the 1946-51 cohort using a longitudinal analysis approach, from survey 2 to survey 7 (Model 3)

Number of GP visits /year Predictor categories (Ref: 0 visits) 1-2 3-4 5-6 7 or more Odds Ratio Odds Ratio Odds Ratio Odds Ratio p-value p-value p-value p-value (95% CI) (95% CI) (95% CI) (95% CI) Past asthma 1.01 (0.77, 1.32) 0.95 1.32 (0.99, 1.76) 0.06 1.64 (1.21, 2.23) 0.0015 2.20 (1.58, 3.05) <.0001 Asthma Prevalent asthma 1.71 (1.28, 2.28) 0.0003 3.22 (2.37, 4.38) <.0001 4.62 (3.36, 6.36) <.0001 7.78 (5.60, 10.82) <.0001 group Incident asthma 1.55 (1.13, 2.13) 0.0061 2.55 (1.82, 3.59) <.0001 3.16 (2.22, 4.50) <.0001 5.43 (3.79, 7.79) <.0001 Bronchitis/emphysema 1.26 (1.00, 1.57) 0.0452 1.68 (1.33, 2.12) <.0001 2.09 (1.64, 2.67) <.0001 2.88 (2.21, 3.74) <.0001 Age 1.04 (1.03, 1.05) <.0001 1.08 (1.06, 1.09) <.0001 1.09 (1.08, 1.10) <.0001 1.09 (1.07, 1.10) <.0001 Inner regional 0.98 (0.82, 1.18) 0.86 0.95 (0.79, 1.15) 0.63 0.97 (0.80, 1.19) 0.80 1.24 (1.01, 1.53) 0.0431 Area Remote, very remote and outer regional 0.73 (0.53, 1.01) 0.05 0.69 (0.47, 1.00) 0.0499 0.68 (0.45, 1.02) 0.06 0.94 (0.60, 1.47) 0.78 Separated/Divorced/Widowed 1.01 (0.86, 1.19) 0.89 0.91 (0.77, 1.08) 0.30 0.89 (0.74, 1.07) 0.22 0.86 (0.71, 1.05) 0.14 Marital status Never married 0.94 (0.78, 1.13) 0.52 0.86 (0.70, 1.05) 0.13 0.82 (0.66, 1.01) 0.06 0.76 (0.60, 0.95) 0.0150 Highest No formal education 1.12 (0.94, 1.33) 0.22 1.19 (0.98, 1.43) 0.07 1.16 (0.95, 1.42) 0.14 1.30 (1.04, 1.62) 0.0226 qualification High school qualification 1.02 (0.78, 1.34) 0.87 1.26 (0.94, 1.68) 0.12 1.36 (1.00, 1.85) 0.0470 1.97 (1.43, 2.72) <.0001 Income Not too bad to manage 1.13 (0.97, 1.33) 0.12 1.35 (1.14, 1.59) 0.0005 1.59 (1.32, 1.91) <.0001 1.94 (1.59, 2.38) <.0001 management Difficult to manage 1.09 (0.85, 1.40) 0.48 1.44 (1.12, 1.86) 0.0047 2.56 (1.95, 3.35) <.0001 4.61 (3.47, 6.11) <.0001 Private health Yes 1.32 (1.15, 1.51) <.0001 1.38 (1.19, 1.59) <.0001 1.32 (1.13, 1.54) 0.0005 1.18 (1.00, 1.40) 0.0468 insurance Yes, veteran’s affair gold card 0.83 (0.34, 2.02) 0.68 1.56 (0.60, 4.09) 0.36 1.77 (0.65, 4.79) 0.26 2.13 (0.77, 5.91) 0.14 Access to a Good 0.92 (0.77, 1.11) 0.39 0.87 (0.72, 1.05) 0.14 0.61 (0.50, 0.75) <.0001 0.59 (0.48, 0.73) <.0001 bulk billing Fair/poor 1.16 (1.00, 1.35) 0.0462 1.16 (1.00, 1.36) 0.05 0.86 (0.73, 1.01) 0.07 0.71 (0.60, 0.85) 0.0002 doctor Access to Good 1.01 (0.85, 1.19) 0.92 1.16 (0.97, 1.37) 0.10 1.20 (1.00, 1.45) 0.0494 1.11 (0.92, 1.35) 0.28 after-hours Fair/poor 1.00 (0.82, 1.21) 0.97 1.16 (0.95, 1.42) 0.15 1.26 (1.02, 1.57) 0.0358 1.31 (1.04, 1.65) 0.0201 doctors

323

Number of GP visits /year Predictor categories (Ref: 0 visits) 1-2 3-4 5-6 7 or more Odds Ratio Odds Ratio Odds Ratio Odds Ratio p-value p-value p-value p-value (95% CI) (95% CI) (95% CI) (95% CI) Access to a Good 0.79 (0.68, 0.93) 0.0034 0.77 (0.66, 0.91) 0.0016 0.86 (0.72, 1.02) 0.07 0.78 (0.65, 0.93) 0.0060 female doctor Fair/poor 0.76 (0.65, 0.89) 0.0007 0.70 (0.59, 0.82) <.0001 0.77 (0.64, 0.92) 0.0046 0.75 (0.62, 0.90) 0.0026 Access to a Good 0.96 (0.79, 1.17) 0.70 0.97 (0.79, 1.18) 0.74 0.98 (0.80, 1.22) 0.89 1.08 (0.87, 1.35) 0.48 hospital Fair/poor 0.80 (0.62, 1.04) 0.09 0.81 (0.62, 1.06) 0.12 0.87 (0.65, 1.16) 0.33 1.01 (0.75, 1.36) 0.94 doctor Access to Good 1.09 (0.88, 1.34) 0.43 1.00 (0.81, 1.23) 1.00 1.01 (0.81, 1.26) 0.95 1.06 (0.84, 1.33) 0.65 emergency Fair/poor 1.06 (0.82, 1.38) 0.64 0.98 (0.75, 1.29) 0.90 0.97 (0.73, 1.30) 0.86 1.00 (0.74, 1.36) 0.98 health care

324

Table A-27. Association between asthma or bronchitis/emphysema and number of GP visits for women from the 1946-51 cohort using a longitudinal analysis approach, from survey 2 to survey 7 (Model 4)

Number of GP visits /year Predictor categories (Ref: 0 visits) 1-2 3-4 5-6 7 or more Odds Ratio Odds Ratio Odds Ratio Odds Ratio p-value p-value p-value p-value (95% CI) (95% CI) (95% CI) (95% CI) Past asthma 0.99 (0.76, 1.30) 0.95 1.24 (0.93, 1.65) 0.15 1.47 (1.08, 2.00) 0.0152 1.82 (1.30, 2.54) 0.0004 Prevalent asthma 1.68 (1.26, 2.24) 0.0004 2.90 (2.12, 3.95) <.0001 3.88 (2.80, 5.36) <.0001 5.95 (4.24, 8.34) <.0001 Asthma group Incident asthma 1.54 (1.13, 2.11) 0.0068 2.39 (1.70, 3.37) <.0001 2.79 (1.96, 3.99) <.0001 4.37 (3.02, 6.31) <.0001 Bronchitis/emphysema 1.24 (0.99, 1.55) 0.06 1.61 (1.27, 2.03) <.0001 1.93 (1.50, 2.47) <.0001 2.50 (1.92, 3.26) <.0001 Age 1.04 (1.03, 1.05) <.0001 1.06 (1.05, 1.08) <.0001 1.07 (1.06, 1.09) <.0001 1.07 (1.05, 1.08) <.0001 Inner regional 0.97 (0.81, 1.17) 0.77 0.91 (0.75, 1.10) 0.31 0.87 (0.71, 1.07) 0.18 1.01 (0.82, 1.26) 0.89 Area Remote, very remote and outer regional 0.73 (0.53, 1.01) 0.06 0.64 (0.44, 0.93) 0.0204 0.57 (0.38, 0.86) 0.0073 0.66 (0.42, 1.06) 0.09 Separated/Divorced/Widowed 1.00 (0.85, 1.17) 1.00 0.87 (0.73, 1.03) 0.10 0.83 (0.69, 1.00) 0.0447 0.79 (0.65, 0.97) 0.0220 Marital status Never married 0.94 (0.78, 1.13) 0.48 0.82 (0.67, 1.00) 0.05 0.77 (0.62, 0.95) 0.0149 0.69 (0.55, 0.87) 0.0018 Highest No formal education 1.11 (0.93, 1.33) 0.23 1.17 (0.97, 1.42) 0.10 1.15 (0.94, 1.41) 0.18 1.29 (1.03, 1.62) 0.0293 qualification High school qualification 1.03 (0.78, 1.35) 0.84 1.20 (0.90, 1.61) 0.21 1.28 (0.94, 1.75) 0.12 1.88 (1.35, 2.62) 0.0002 Income Not too bad to manage 1.13 (0.97, 1.33) 0.12 1.29 (1.09, 1.53) 0.0028 1.45 (1.21, 1.74) <.0001 1.66 (1.35, 2.04) <.0001 management Difficult to manage 1.07 (0.84, 1.37) 0.58 1.23 (0.95, 1.58) 0.12 1.89 (1.44, 2.49) <.0001 2.89 (2.16, 3.86) <.0001 Private health Yes 1.32 (1.15, 1.51) <.0001 1.39 (1.20, 1.61) <.0001 1.36 (1.16, 1.59) 0.0001 1.26 (1.06, 1.49) 0.0079 insurance Yes, veteran’s affair gold card 0.75 (0.31, 1.83) 0.53 1.32 (0.51, 3.39) 0.56 1.50 (0.53, 4.21) 0.44 1.93 (0.69, 5.40) 0.21 Access to a Good 0.92 (0.77, 1.11) 0.39 0.87 (0.72, 1.05) 0.16 0.63 (0.51, 0.77) <.0001 0.63 (0.51, 0.78) <.0001 bulk billing Fair/poor 1.16 (1.00, 1.34) 0.05 1.15 (0.99, 1.35) 0.08 0.86 (0.72, 1.01) 0.07 0.73 (0.61, 0.88) 0.0009 doctor Access to Good 1.01 (0.86, 1.20) 0.87 1.17 (0.98, 1.39) 0.08 1.21 (1.00, 1.46) 0.0449 1.10 (0.90, 1.35) 0.33 after-hours Fair/poor 0.99 (0.81, 1.21) 0.94 1.14 (0.93, 1.40) 0.21 1.22 (0.97, 1.52) 0.08 1.22 (0.97, 1.55) 0.09 doctors

325

Number of GP visits /year Predictor categories (Ref: 0 visits) 1-2 3-4 5-6 7 or more Odds Ratio Odds Ratio Odds Ratio Odds Ratio p-value p-value p-value p-value (95% CI) (95% CI) (95% CI) (95% CI) Access to a Good 0.79 (0.68, 0.93) 0.0037 0.78 (0.66, 0.91) 0.0025 0.86 (0.73, 1.03) 0.10 0.79 (0.65, 0.95) 0.0113 female doctor Fair/poor 0.77 (0.65, 0.90) 0.0009 0.70 (0.60, 0.83) <.0001 0.78 (0.65, 0.94) 0.0087 0.76 (0.63, 0.93) 0.0062 Access to a Good 0.97 (0.79, 1.18) 0.75 0.98 (0.80, 1.20) 0.85 1.00 (0.81, 1.25) 0.99 1.09 (0.87, 1.38) 0.45 hospital Fair/poor 0.80 (0.61, 1.04) 0.09 0.81 (0.62, 1.07) 0.13 0.86 (0.64, 1.16) 0.33 0.99 (0.72, 1.35) 0.94 doctor Access to Good 1.08 (0.87, 1.33) 0.48 0.99 (0.80, 1.22) 0.93 1.00 (0.80, 1.26) 0.99 1.08 (0.85, 1.38) 0.52 emergency Fair/poor 1.06 (0.81, 1.38) 0.68 0.98 (0.74, 1.29) 0.87 0.97 (0.72, 1.31) 0.86 1.03 (0.76, 1.40) 0.85 health care Underweight 0.81 (0.52, 1.26) 0.35 0.65 (0.39, 1.10) 0.11 0.76 (0.42, 1.36) 0.35 0.91 (0.46, 1.81) 0.78 BMI Overweight 0.97 (0.83, 1.12) 0.67 1.10 (0.94, 1.29) 0.24 1.14 (0.96, 1.35) 0.15 1.27 (1.05, 1.53) 0.0120

Obese 0.91 (0.76, 1.09) 0.29 1.17 (0.97, 1.42) 0.10 1.40 (1.14, 1.72) 0.0011 1.73 (1.39, 2.15) <.0001 Diabetes Yes 1.14 (0.71, 1.82) 0.58 2.37 (1.49, 3.76) 0.0003 3.28 (2.03, 5.28) <.0001 4.15 (2.57, 6.71) <.0001 Heart disease Yes 1.13 (0.62, 2.03) 0.70 1.75 (0.96, 3.19) 0.07 2.85 (1.54, 5.25) 0.0008 4.76 (2.55, 8.87) <.0001 hypertension Yes 2.32 (1.85, 2.90) <.0001 4.67 (3.72, 5.85) <.0001 5.66 (4.47, 7.16) <.0001 6.31 (4.95, 8.04) <.0001 osteoporosis Yes 1.50 (0.99, 2.27) 0.05 2.36 (1.54, 3.60) <.0001 2.82 (1.83, 4.35) <.0001 4.19 (2.69, 6.52) <.0001 Depression Yes 1.34 (1.02, 1.78) 0.0367 2.44 (1.84, 3.22) <.0001 3.68 (2.77, 4.90) <.0001 5.02 (3.75, 6.74) <.0001 Anxiety Yes 1.36 (0.98, 1.90) 0.07 2.15 (1.54, 2.98) <.0001 3.16 (2.25, 4.43) <.0001 4.16 (2.94, 5.88) <.0001 Low iron Yes 1.23 (0.99, 1.52) 0.06 1.41 (1.13, 1.76) 0.0023 1.60 (1.27, 2.02) <.0001 2.04 (1.60, 2.62) <.0001 Any other Yes 1.24 (0.86, 1.80) 0.25 2.53 (1.76, 3.65) <.0001 3.82 (2.65, 5.51) <.0001 6.90 (4.75, 10.02) <.0001 major disease

326

Appendix B. Chapter 8 appendices

Table B-1. Association between predisposing, enabling and needs factors and after-hours visits at Survey 3 for women from the 1921-26 cohort using univariate multinomial logistic regressions

After-hours visits Predictor Categories Reference (No)

Odds Ratio Overall (95% CI) p-value

Past asthma 2.07 (1.57, 2.73) <.0001

Prevalent asthma 1.41 (1.11, 1.78)

Incident asthma 1.45 (1.08, 1.93)

Bronchitis/emphysema 1.19 (0.99, 1.44)

Predisposing factors

Age 1.05 (0.96, 1.16) 0.2924

Area of residence Remote, very remote and outer 0.91 (0.69, 1.21) 0.7412 regional Inner regional 0.93 (0.75, 1.16)

Marital status Separated/Divorced/Widowed 0.97 (0.55, 1.72) 0.9397

Never married 1.03 (0.85, 1.26)

Highest qualification No formal education 1.01 (0.64, 1.59) 0.2532

High school qualification 0.85 (0.54, 1.31)

Country of birth English speaking countries 1.09 (0.78, 1.53) 0.5120

Non-English speaking countries 1.16 (0.89, 1.52)

Enabling factors

Private health Yes 1.22 (0.99, 1.49) 0.06 insurance

Concession card Yes 0.90 (0.68, 1.19) 0.46

Income management Difficult to manage 1.77 (1.21, 2.59) 0.0105

Not too bad to manage 1.10 (0.87, 1.40)

Access to a hospital 0.72 (0.48, 1.07) 0.2692 Fair/poor doctor

Good 0.95 (0.73, 1.23)

After-hours visits Predictor Categories Reference (No)

Odds Ratio Overall (95% CI) p-value

Access to after-hours 0.71 (0.53, 0.94) 0.0476 Fair/poor doctors

Good 0.80 (0.61, 1.05)

Access to a bulk billing 0.76 (0.55, 1.06) 0.2666 Fair/poor doctor

Good 0.92 (0.68, 1.26)

Access to a hospital 0.91 (0.60, 1.38) 0.7470 Fair/poor doctor

Good 0.91 (0.70, 1.18)

Access to a female 0.82 (0.61, 1.12) 0.4529 Fair/poor doctor

Good 0.92 (0.70, 1.20)

Needs

BMI Underweight 1.12 (0.67, 1.89) 0.2365

Overweight 1.11 (0.88, 1.39)

Obese 1.34 (1.01, 1.78)

Alcohol consumption High risk drinking 0.83 (0.68, 1.02) 0.1260

Low risk drinking 1.17 (0.66, 2.10)

Smoking Smoker 1.00 (0.60, 1.66) 0.8139

Ex-smoker 1.07 (0.86, 1.33)

Diabetes Yes 1.31 (1.03, 1.67) 0.0297

Heart disease Yes 1.92 (1.58, 2.33) <.0001

Hypertension Yes 1.04 (0.89, 1.22) 0.63

Osteoporosis Yes 1.29 (1.06, 1.57) 0.0097

Stroke Yes 1.79 (1.24, 2.60) 0.0020

Anxiety Yes 1.40 (0.94, 2.08) 0.09

Depression Yes 1.98 (1.44, 2.73) <.0001

Table B-2. Association between predisposing, enabling and needs factors and after-hours visits at Survey 3 for women from the 1921-26 cohort using multivariate multinomial logistic regressions

After-hours visits Predictor Categories Reference (No)

Odds Ratio Overall (95% CI) p-value

Past asthma 2.07 (1.57, 2.73) <.0001

Prevalent asthma 1.41 (1.11, 1.78)

Incident asthma 1.45 (1.08, 1.93)

Bronchitis/emphysema 1.19 (0.99, 1.44)

Enabling factors

Private health insurance Yes 0.94 (0.70, 1.25) 0.6647

Income management Difficult to manage 1.34 (0.76, 2.36) 0.5770

Not too bad to manage 1.03 (0.73, 1.44)

Access to after-hours 0.68 (0.48, 0.96) 0.0602 Fair/poor doctors

Good 0.74 (0.53, 1.04)

Needs

Alcohol consumption High risk drinking 0.87 (0.65, 1.17) 0.5466

Low risk drinking 1.21 (0.46, 3.21)

Diabetes Yes 1.09 (0.70, 1.71) 0.7062

Heart disease Yes 1.57 (1.12, 2.22) 0.0097

Hypertension Yes 0.91 (0.69, 1.22) 0.5419

Osteoporosis Yes 1.41 (1.01, 1.95) 0.0425

Stroke Yes 1.09 (0.50, 2.37) 0.8227

Anxiety Yes 1.11 (0.62, 1.98) 0.7297

Depression Yes 1.57 (0.94, 2.60) 0.0826

Table B-3. Association between predisposing, enabling and needs factors and after-hours visits at Survey 3 for women from the 1921-26 cohort using nested multivariate multinomial logistic regressions (Model 1 to 4)

After-hours visits Predictor Categories Reference (No) Odds Ratio p-value Overall p (95% CI)

Model 1

Past asthma 2.07 (1.57, 2.73) <.0001 <.0001

Prevalent asthma 1.41 (1.11, 1.78) 0.0042

Incident asthma 1.45 (1.08, 1.93) 0.0121

Bronchitis/emphysema 1.19 (0.99, 1.44) 0.06

Model 2

Past asthma 1.56 (0.78, 3.12) 0.21 0.0285

Prevalent asthma 1.91 (1.25, 2.93) 0.0027

Incident asthma 1.34 (0.78, 2.30) 0.29

Bronchitis/emphysema 1.39 (0.96, 2.01) 0.08

Access to after-hours doctors Fair/poor 0.74 (0.53, 1.04) 0.08 0.0706

Good 0.69 (0.49, 0.98) 0.0372

Model 3

Past asthma 1.39 (0.69, 2.81) 0.36 0.0988

Prevalent asthma 1.79 (1.17, 2.75) 0.0078

Incident asthma 1.21 (0.70, 2.10) 0.49

Bronchitis/emphysema 1.29 (0.89, 1.87) 0.18

Access to after-hours doctors Fair/poor 0.76 (0.54, 1.06) 0.11 0.0865

Good 0.70 (0.49, 0.99) 0.0421

Alcohol consumption High risk drinking 0.87 (0.65, 1.17) 0.36 0.5606

Low risk drinking 1.21 (0.46, 3.21) 0.70

diabetes Yes 1.05 (0.67, 1.64) 0.84 0.8391

Heart disease Yes 1.51 (1.07, 2.14) 0.0193 0.0193

Hypertension Yes 0.91 (0.68, 1.22) 0.53 0.5273

Osteoporosis Yes 1.35 (0.97, 1.88) 0.08 0.0768

After-hours visits Predictor Categories Reference (No) Odds Ratio p-value Overall p (95% CI)

Stroke Yes 1.09 (0.50, 2.38) 0.83 0.8288

Depression Yes 1.53 (0.92, 2.55) 0.10 0.0989

Anxiety Yes 1.07 (0.60, 1.91) 0.83 0.8251

Table B-4. Longitudinal association between after-hours service use and asthma groups adjusting for predisposing, enabling and needs factors among women from the 1921-26 cohort from survey 2 to 6 (Model2)

After-hours visits Predictor Categories Reference (No visits) OR P-value (95% CI) Never asthma 1 Past asthma 1.40 (1.09, 1.80) 0.0092 Prevalent asthma 1.39 (1.17, 1.66) 0.0002 Incident asthma 1.37 (1.10, 1.69) 0.0042 Bronchitis/emphysema 1.24 (1.09, 1.42) 0.0010 Osteoporosis Yes 1.17 (1.05,1.31) 0.0046 Heart disease Yes 1.48 (1.3,1.66) <.0001 Depression Yes 1.52 (1.28,1.80) <.0001

Table B-5. Association between predisposing, enabling and needs factors and specialist visits at Survey 3 for women from the 1921-26 cohort using univariate multinomial logistic regressions

Specialist visits Predictor Categories Reference (No)

Odds Ratio Overall (95% CI) p-value

Past asthma 2.57 (1.10, 5.98) 0.0291

Prevalent asthma 1.28 (0.85, 1.93)

Incident asthma 2.72 (1.45, 5.11)

Bronchitis/emphysema 1.68 (1.18, 2.37)

Predisposing factors

Age 1.07 (0.99, 1.16) 0.0916

Area of residence Remote, very remote and outer 0.57 (0.46, 0.70) <.0001 regional Inner regional 0.70 (0.59, 0.83)

Marital status 1.05 (0.66, 1.65) 0.0466 Separated/Divorced/Widowed

Never married 0.82 (0.70, 0.97)

Highest qualification 0.65 (0.43, 0.99) 0.1306 No formal education

High school qualification 0.69 (0.46, 1.04)

Country of birth English speaking countries 1.06 (0.81, 1.40) 0.1944

Non-English speaking countries 0.84 (0.68, 1.03)

Enabling factors

Private health Yes 2.08 (1.78, 2.44) <.0001 insurance

Concession card Yes 1.08 (0.77, 1.52) 0.64

Income management Difficult to manage 0.88 (0.63, 1.21) 0.4776

Not too bad to manage 1.05 (0.87, 1.26)

Access to a hospital 0.56 (0.40, 0.80) 0.0003 Fair/poor doctor

Good 0.64 (0.49, 0.83)

Specialist visits Predictor Categories Reference (No)

Odds Ratio Overall (95% CI) p-value

Access to after-hours 0.91 (0.68, 1.22) 0.7825 Fair/poor doctors Good 0.92 (0.69, 1.22)

Access to a bulk billing 1.41 (0.87, 2.28) 0.2367 Fair/poor doctor

Good 0.91 (0.70, 1.19)

Access to a hospital 1.21 (0.87, 1.68) 0.4473 Fair/poor doctor

Good 0.94 (0.68, 1.30)

Access to a female 0.81 (0.60, 1.09) 0.2913 Fair/poor doctor

Good 0.84 (0.63, 1.11)

Needs

BMI Underweight 0.91 (0.62, 1.36) 0.3356

Overweight 1.16 (0.97, 1.39)

Obese 1.09 (0.87, 1.38)

Alcohol consumption 1.16 (0.99, 1.36) 0.0326 High risk drinking

Low risk drinking 1.95 (1.06, 3.58)

Smoking Smoker 0.65 (0.46, 0.92) 0.0117

Ex-smoker 1.13 (0.95, 1.35)

Diabetes Yes 2.89 (1.66, 5.04) 0.0002

Heart disease Yes 2.74 (1.79, 4.20) <.0001

Hypertension Yes 1.11 (0.88, 1.41) 0.37

Osteoporosis Yes 1.79 (1.27, 2.51) 0.0009

Stroke Yes 0.82 (0.42, 1.59) 0.56

Anxiety Yes 1.61 (1.07, 2.43) 0.0215

Depression Yes 1.66 (1.15, 2.39) 0.0066

Table B-6. Association between predisposing, enabling and needs factors and specialist visits at Survey 3 for women from the 1921-26 cohort using multivariate multinomial logistic regressions

Specialist visits Predictor Categories Reference (No)

Odds Ratio Overall (95% CI) p-value

Past asthma 2.57 (1.10, 5.98) 0.0291

Prevalent asthma 1.28 (0.85, 1.93)

Incident asthma 2.72 (1.45, 5.11)

Bronchitis/emphysema 1.68 (1.18, 2.37)

Predisposing factors

Age 1.09 (1.00, 1.18) 0.05

Area of residence Remote, very remote and 0.47 (0.34, 0.64) <.0001 outer regional Inner regional 0.65 (0.49, 0.86)

Marital status Separated/Divorced/Wido 0.92 (0.47, 1.81) 0.5888 wed Never married 0.88 (0.68, 1.13)

Highest qualification No formal education 0.62 (0.32, 1.22) 0.2667

High school qualification 0.59 (0.31, 1.12)

Country of birth English speaking countries 0.93 (0.58, 1.49) 0.3182

Non-English speaking 0.78 (0.56, 1.08) countries Enabling factors

Private health insurance Yes 1.77 (1.38, 2.25) <.0001

Access to a specialist Fair/poor 0.64 (0.45, 0.92) 0.0055

Good 0.69 (0.53, 0.90)

Needs

Alcohol consumption High risk drinking 1.30 (1.01, 1.68) 0.0524

Low risk drinking 2.43 (0.83, 7.08)

Smoking Smoker 0.59 (0.34, 1.04) 0.1822

Ex-smoker 1.01 (0.76, 1.34)

Diabetes Yes 2.80 (1.59, 4.91) 0.0003

Heart disease Yes 2.61 (1.70, 4.00) <.0001

Specialist visits Predictor Categories Reference (No)

Odds Ratio Overall (95% CI) p-value

Osteoporosis Yes 1.76 (1.25, 2.49) 0.0013

Depression Yes 1.35 (0.75, 2.43) 0.3141

Anxiety Yes 1.93 (0.95, 3.93) 0.0688

Table B-7. Association between predisposing, enabling and needs factors and specialist visits at Survey 3 for women from the 1921-26 cohort using nested multivariate multinomial logistic regressions (Model 1 to 4)

Specialist visits Predictor Categories Reference (No) Odds Ratio p-value Overall p (95% CI)

Model 1

Past asthma 2.57 (1.10, 5.98) 0.0291 <.0001

Prevalent asthma 1.28 (0.85, 1.93) 0.24

Incident asthma 2.72 (1.45, 5.11) 0.0019

Bronchitis/emphysema 1.68 (1.18, 2.37) 0.0037

Model 2

Past asthma 2.41 (1.03, 5.63) 0.0424 0.0285

Prevalent asthma 1.29 (0.85, 1.95) 0.23

Incident asthma 2.72 (1.44, 5.13) 0.0020

Bronchitis/emphysema 1.63 (1.15, 2.32) 0.0061

Age 1.08 (0.99, 1.17) 0.07

Area of residence Remote, very remote and 0.66 (0.50, 0.87) 0.0035 0.0706 outer regional Inner regional 0.49 (0.36, 0.67) <.0001

Model 3

Past asthma 2.32 (0.99, 5.44) 0.05 0.0004

Prevalent asthma 1.32 (0.87, 2.01) 0.19

Incident asthma 2.83 (1.50, 5.36) 0.0013

Bronchitis/emphysema 1.72 (1.20, 2.44) 0.0028

Age 1.07 (0.99, 1.17) 0.09 0.0943

Area of residence Remote, very remote and 0.72 (0.54, 0.95) 0.0210 0.0035 outer regional

Inner regional 0.58 (0.42, 0.81) 0.0011

Private health insurance Yes 1.76 (1.38, 2.26) <.0001 <.0001

Access to specialist Fair/poor 0.75 (0.57, 0.98) 0.0381 0.0706

Good 0.74 (0.51, 1.07) 0.11

Model 4

Specialist visits Predictor Categories Reference (No) Odds Ratio p-value Overall p (95% CI)

Past asthma 1.99 (0.84, 4.73) 0.12 0.0090

Prevalent asthma 1.10 (0.72, 1.69) 0.66

Incident asthma 2.47 (1.29, 4.71) 0.0061

Bronchitis/emphysema 1.54 (1.07, 2.21) 0.0187

Age 1.06 (0.98, 1.16) 0.16 0.1607

Area of residence Remote, very remote and 0.74 (0.55, 0.99) 0.0398 0.0077 outer regional Inner regional 0.60 (0.43, 0.83) 0.0023

Private health insurance Yes 1.76 (1.37, 2.26) <.0001 <.0001

Access to specialist Fair/poor 0.76 (0.58, 1.01) 0.05 0.1228

Good 0.78 (0.53, 1.14) 0.20

Alcohol consumption High risk drinking 1.25 (0.96, 1.62) 0.10 0.0792

Low risk drinking 2.65 (0.89, 7.86) 0.08

Smoking Smoker 0.61 (0.34, 1.08) 0.09 0.2422

Ex-smoker 0.97 (0.73, 1.30) 0.85 diabetes Yes 2.85 (1.62, 5.03) 0.0003 0.0003

Heart disease Yes 2.36 (1.53, 3.65) 0.0001 0.0001

Osteoporosis Yes 1.57 (1.10, 2.24) 0.0122 0.0122

Depression Yes 2.07 (1.02, 4.20) 0.0436 0.0436

Table B-8. Longitudinal association between specialist service use and asthma groups adjusting for predisposing, enabling and needs factors among women from the 1921-26 cohort from survey 2 to 6 (Model2)

Specialist visits Predictor Categories Reference (No visits) OR P-value (95% CI) Never asthma 1 Past asthma 1.41 (1.06, 1.87) 0.0193

Prevalent asthma 1.38 (1.13, 1.67) 0.0012

Incident asthma 1.44 (1.15, 1.81) 0.0017

Bronchitis/emphysema 1.34 (1.16, 1.54) <.0001

Age 0.90 (0.78, 1.06) 0.2087 Remote, very remote and Area of residence 0.56 (0.49, 0.64) <.0001 outer regional

Inner regional 0.73 (0.65, 0.82) <.0001

Table B-9. Longitudinal association between specialist service use and asthma groups adjusting for predisposing, enabling and needs factors among women from the 1921-26 cohort from survey 2 to 6 (Model 3)

Specialist visits Predictor Categories Reference (No visits) OR P-value (95% CI) Never asthma 1 Past asthma 1.44 (1.09, 1.91) 0.0102

Prevalent asthma 1.45 (1.19, 1.75) 0.0002

Incident asthma 1.48 (1.17, 1.88) 0.0009

Bronchitis/emphysema 1.42 (1.24, 1.64) <.0001

Age 0.92 (0.78, 1.07) 0.2711 Remote, very remote and Area of residence 0.62 (0.54, 0.71) <.0001 outer regional

Inner regional 0.78 (0.69, 0.87) <.0001

Private health insurance Yes 2.44 (2.20, 2.72) <.0001

Table B-10. Longitudinal association between specialist service use and asthma groups adjusting for predisposing, enabling and needs factors among women from the 1921-26 cohort from survey 2 to 6 (Model 4)

Specialist visits Predictor Categories Reference (No visits) OR P-value (95% CI) Never asthma 1 Past asthma 1.29 (0.97, 1.72) 0.0775

Prevalent asthma 1.34 (1.10, 1.62) 0.0031

Incident asthma 1.34 (1.06, 1.70) 0.0144 Bronchitis/emphyse 1.33 (1.16, 1.53) <.0001 ma Age 0.93 (0.79, 1.09) 0.3582

Remote, very 0.62 (0.54, 0.71) <.0001 Area of residence remote and outer regional Inner regional 0.78 (0.70, 0.88) <.0001

Private health Yes 2.45 (2.20, 2.72) <.0001 insurance Alcohol 1.14 (1.03, 1.27) 0.0148 High risk drinking consumption

Low risk drinking 1.36 (0.97, 1.91) 0.0759

Smoking Smoker 0.73 (0.58, 0.92) 0.0065

Ex-smoker 1.01 (0.90, 1.14) 0.8821

diabetes Yes 1.75 (1.45, 2.12) <.0001

Heart disease Yes 1.91 (1.66, 2.21) <.0001

Osteoporosis Yes 1.42 (1.25, 1.62) <.0001

Depression Yes 1.04 (0.84, 1.28) 0.7199

Table B-11. Association between predisposing, enabling and needs factors and after-hours visits at Survey 3 for women from the 1946-51 cohort using univariate multinomial logistic regressions

After-hours visits Predictor Categories Reference (No)

Odds Ratio Overall (95% CI) p-value

Past asthma 1.59 (1.13, 2.23) <.0001

Prevalent asthma 2.17 (1.66, 2.84)

Incident asthma 1.60 (1.15, 2.22)

Bronchitis/emphysema 1.26 (0.95, 1.66)

Predisposing factors

Age 1.046 (0.98, 1.12) 0.1838

Area of residence Remote, very remote and outer 1.89 (1.45, 2.47) <.0001 regional Inner regional 1.63 (1.28, 2.08)

Marital status 1.54 (0.95, 2.52) 0.0583 Separated/Divorced/Widowed

Never married 1.26 (0.98, 1.62)

Highest qualification No formal education 1.31 (0.85, 2.01) 0.4387

High school qualification 1.07 (0.77, 1.48)

Country of birth English speaking countries 0.97 (0.68, 1.38) 0.509

Non-English speaking countries 0.83 (0.61, 1.13)

Enabling factors

Private health insurance Yes 1.00 (0.82, 1.22) 0.9964

Concession card Yes 1.75 (1.41, 2.16) <.0001

Income management Difficult to manage 1.61 (1.16, 2.22) <.0001

Not too bad to manage 0.87 (0.67, 1.13)

Access to a hospital doctor Fair/poor 0.73 (0.50, 1.06) 0.0351

Good 0.75 (0.59, 0.97)

Access to after-hours 0.56 (0.44, 0.72) <.0001 Fair/poor doctors

Good 0.61 (0.47, 0.79)

After-hours visits Predictor Categories Reference (No)

Odds Ratio Overall (95% CI) p-value

Access to a bulk billing 0.93 (0.73, 1.17) 0.4247 Fair/poor doctor

Good 1.13 (0.84, 1.53)

Access to a specialist Fair/poor 0.91 (0.69, 1.20) 0.7921

Good 0.99 (0.78, 1.26) 0.3387 Access to a female doctor Fair/poor 1.13 (0.89, 1.43)

Good 0.90 (0.69, 1.19)

Access to an emergency 0.59 (0.42, 0.83) 0.0063 Fair/poor doctor

Good 0.83 (0.65, 1.05)

Needs 0.1699 BMI Underweight 0.95 (0.38, 2.37)

Overweight 1.13 (0.89, 1.43)

Obese 1.32 (1.03, 1.70) 0.8472 Alcohol consumption High risk drinking 0.94 (0.67, 1.33)

Low risk drinking 0.83 (0.43, 1.60)

Smoking Smoker 1.19 (0.91, 1.56) 0.4400

Ex-smoker 1.05 (0.84, 1.33)

Diabetes Yes 2.00 (1.34, 3.01) 0.0008

Heart disease Yes 2.73 (1.72, 4.34) <.0001

Hypertension Yes 1.31 (1.04, 1.66) 0.0220

Osteoporosis Yes 2.35 (1.59, 3.49) <.0001

Low iron Yes 1.14 (0.82, 1.58) 0.43

Any other major disease Yes 2.19 (1.51, 3.18) <.0001

Stroke Yes 1.70 (0.40, 7.19) 0.47

After-hours visits Predictor Categories Reference (No)

Odds Ratio Overall (95% CI) p-value

Anxiety Yes 1.52 (1.09, 2.11) 0.0125

Depression Yes 1.67 (1.29, 2.15) <.0001

Table B-12. Association between predisposing, enabling and needs factors and after-hours visits at Survey 3 for women from the 1946-51 cohort using multivariate multinomial logistic regressions

After-hours visits Predictor Categories Reference (No)

Odds Ratio Overall (95% CI) p-value

Predisposing factors

Age 0.97 (0.86, 1.08) 0.5460

Area of residence Remote, very remote and outer 3.16 (1.96, 5.09) <.0001 regional Inner regional 2.36 (1.49, 3.72)

Marital status Separated/Divorced/Widowed 1.37 (0.59, 3.19) 0.6692

Never married 1.15 (0.72, 1.84)

Enabling factors

Concession card Yes 1.40 (0.95, 2.05) 0.0089

Income management Difficult to manage 0.87 (0.66, 1.16) 0.0004

Not too bad to manage 1.59 (1.23, 2.06)

Access to an emergency 0.61 (0.42, 0.86) 0.0012 Fair/poor doctor

Good 0.58 (0.42, 0.79)

Access to a hospital doctor Fair/poor 1.00 (0.58, 1.73) 0.9779

Good 0.96 (0.67, 1.39)

Access to after-hours 0.69 (0.40, 1.19) 0.2324 Fair/poor doctors

Good 1.05 (0.72, 1.55)

Needs

BMI Underweight 0.50 (0.07, 3.69) 0.3537

Overweight 1.35 (0.93, 1.98)

Obese 1.09 (0.70, 1.70) 0.8472 Alcohol consumption High risk drinking 0.94 (0.67, 1.33)

Low risk drinking 0.83 (0.43, 1.60)

After-hours visits Predictor Categories Reference (No)

Odds Ratio Overall (95% CI) p-value

Smoking Smoker 1.19 (0.91, 1.56) 0.4400

Ex-smoker 1.05 (0.84, 1.33)

Diabetes Yes 0.77 (0.34, 1.73) 0.5299

Heart disease Yes 0.60 (0.23, 1.56) 0.2931

Hypertension Yes 0.72 (0.48, 1.07) 0.1011

Osteoporosis Yes 0.48 (0.23, 0.98) 0.0447

Any other major disease Yes 1.64 (0.83, 3.25) 0.1578

Anxiety Yes 1.17 (0.58, 2.38) 0.6621

Depression Yes 0.66 (0.41, 1.08) 0.0969

Table B-13.Association between predisposing, enabling and needs factors and after-hours visits at Survey 3 for women from the 1946-51 cohort using nested multivariate multinomial logistic regressions (Model 1 to 4)

After-hours visits Predictor Categories Reference (No) Odds Ratio p-value Overall p (95% CI) Model 1 Past asthma 1.59 (1.13, 2.23) 0.0072

Prevalent asthma 2.17 (1.66, 2.84) <.0001

Incident asthma 1.60 (1.15, 2.22) 0.0055

Bronchitis/emphysema 1.26 (0.95, 1.66) 0.11

Model 2 Past asthma 1.30 (0.69, 2.42) 0.41 0.0239

Prevalent asthma 1.90 (1.19, 3.03) 0.0070

Incident asthma 1.88 (1.10, 3.20) 0.0205

Bronchitis/emphysema 1.01 (0.60, 1.70) 0.97

Remote, very remote and outer Area of residence 2.33 (1.48, 3.68) 0.0003 <.0001 regional Inner regional 3.12 (1.94, 5.01) <.0001 Model 3 Past asthma 1.48 (0.99, 2.21) 0.05 <.0001 Prevalent asthma 2.11 (1.56, 2.84) <.0001 Incident asthma 1.61 (1.11, 2.32) 0.0114 Bronchitis/emphysema 1.16 (0.83, 1.60) 0.38 Area of residence Inner regional 1.81 (1.38, 2.39) <.0001 <.0001 Remote, very remote and outer 2.03 (1.50, 2.74) <.0001 regional Income management Not too bad to manage 0.81 (0.61, 1.08) 0.16 0.0166

Difficult to manage 1.24 (0.84, 1.82) 0.29 Concession card Yes 1.45 (1.12, 1.89) 0.0048 0.0048 Access to an after-hours <.0001 Good 0.57 (0.44, 0.75) <.0001 doctor Fair/poor 0.48 (0.37, 0.62) <.0001 Model 4 Past asthma 1.40 (0.94, 2.09) 0.10

After-hours visits Predictor Categories Reference (No) Odds Ratio p-value Overall p (95% CI) Prevalent asthma 1.95 (1.44, 2.64) <.0001 Incident asthma 1.54 (1.06, 2.22) 0.0230 0.0003 Bronchitis/emphysema 1.11 (0.80, 1.54) 0.52 Area of residence Inner regional 1.82 (1.38, 2.40) <.0001 <.0001 Remote, very remote and outer 2.07 (1.53, 2.80) <.0001 regional Income management Difficult to manage 0.81 (0.61, 1.08) 0.16 0.0690

Not too bad to manage 1.12 (0.76, 1.66) 0.57 Concession card Yes 1.37 (1.06, 1.79) 0.0183 0.0183 Access to an after-hours <.0001 Good 0.57 (0.44, 0.75) <.0001 doctor Fair/poor 0.47 (0.36, 0.61) <.0001 Osteoporosis Yes 1.83 (1.16, 2.89) 0.0096 0.0096 Depression Yes 1.43 (1.07, 1.91) 0.0169 0.0169 Yes Any other major disease 1.78 (1.19, 2.67) 0.0054 0.0054 Yes Hypertension 1.15 (0.89, 1.49) 0.27 0.2740

Table B-14. Association between after-hours visits and asthma groups among women from the 1946-51 cohort, after adjusting for time (Survey 2 to 6), and predisposing, enabling and needs factors (Model 2)

After-hours visits Predictor Categories Reference (No visits) OR P-value (95% CI) Never asthma 1

Past asthma 1.33 (1.10, 1.60) 0.0033

Prevalent asthma 1.69 (1.46, 1.96) <.0001

Incident asthma 1.47 (1.23, 1.75) <.0001

Bronchitis/emphysema 1.28 (1.12, 1.46) 0.0004 Remote, very remote Area of residence 0.51 (0.44, 0.58) <.0001 and outer regional Inner regional 0.56 (0.50, 0.62) <.0001

Table B-15. Association between after-hours visits and asthma groups among women from the 1946-51 cohort, after adjusting for time (Survey 2 to 6), and predisposing, enabling and needs factors (Model 3)

After-hours visits Predictor Categories Reference (No visits) OR (95% CI) P-value Never asthma 1

Past asthma 1.33 (1.10, 1.61) 0.0030

Prevalent asthma 1.66 (1.43, 1.93) <.0001

Incident asthma 1.46 (1.22, 1.74) <.0001 Bronchitis/emphyse 1.27 (1.11, 1.45) 0.0006 ma Remote, very remote Area of residence 0.51 (0.45, 0.59) <.0001 and outer regional Inner regional 0.56 (0.50, 0.62) <.0001

Concession card Yes 1.03 (0.93, 1.15) 0.5229

Income Difficult to manage 1.23 (1.04, 1.45) 0.0144 management Not too bad to manage 0.96 (0.85, 1.08) 0.4825 Access to an after- Fair/poor 0.83 (0.75, 0.93) 0.0011 hours doctor Good 0.86 (0.77, 0.95) 0.0050

Table B-16. Association between after-hours visits and asthma groups among women from the 1946-51 cohort, after adjusting for time (Survey 2 to 6), and predisposing, enabling and needs factors (Model 4)

After-hours visits Predictor Categories Reference (No visits) OR P-value (95% CI) Never asthma 1

Past asthma 1.23 (1.07, 1.57) 0.0071

Prevalent asthma 1.59 (1.37, 1.85) <.0001

Incident asthma 1.42 (1.19, 1.69) 0.0001 Bronchitis/emphysem 1.24 (1.08, 1.42) 0.0020 a Remote, very remote and Area of residence 0.52 (0.46, 0.60) <.0001 outer regional Inner regional 0.56 (0.51, 0.63) <.0001

Concession card Yes 1.00 (0.90, 1.12) 0.9320

Income management Fair/poor 1.17 (0.99, 1.39) 0.0590

Good 0.95 (0.84, 1.07) 0.3804 Access to an after- Fair/poor 0.83 (0.74, 0.92) 0.0006 hours doctor Good 0.86 (0.77, 0.95) 0.0050

osteoporosis Yes 1.31 (1.09, 1.56) 0.0035

Depression Yes 1.30 (1.14, 1.47) <.0001

Other major diseases Yes 1.28 (1.10, 1.50) 0.0016

Table B-17. Association between predisposing, enabling and needs factors and specialist visits at Survey 3 for women from the 1946-51 cohort using univariate multinomial logistic regressions

Specialist visits Predictor Categories Reference (No) Odds Ratio p-value (95% CI) Past asthma 1.31 (0.99, 1.75) 0.0446 Prevalent asthma 1.18 (0.93, 1.49) Incident asthma 1.35 (1.02, 1.80) Bronchitis/emphysema 1.23 (0.99, 1.53) Predisposing factors Age 1.02 (0.97, 1.07) 0.4354 Remote, very remote and outer 0.71 (0.59, 0.86) <.0001 Area of residence regional Inner regional 0.71 (0.60, 0.84) Marital status Separated/Divorced/Widowed 1.35 (0.89, 2.05) 0.3152 Never married 1.07 (0.87, 1.32) Highest qualification No formal education 0.87 (0.66, 1.15) 0.5842 High school qualification 0.93 (0.77, 1.11) Country of birth English speaking countries 0.89 (0.67, 1.19) 0.4751 Non-English speaking countries 0.90 (0.73, 1.11) Enabling factors Private health 1.45 (1.26, 1.68) <.0001 Yes insurance

Concession card Yes 0.94 (0.85, 1.04) 0.26

Income management Difficult to manage 1.01 (0.77, 1.33) 0.3920 Not too bad to manage 0.90 (0.75, 1.08) Access to a specialist Fair/poor 0.93 (0.49, 1.78) 0.0025 Good 2.13 (1.34, 3.39) Access to an after- 0.90 (0.76, 1.06) 0.3593 Fair/poor hours doctor Good 1.00 (0.84, 1.19)

Specialist visits Predictor Categories Reference (No) Odds Ratio p-value (95% CI) Access to a bulk billing 1.01 (0.86, 1.18) 0.0314 Fair/poor doctor Good 0.77 (0.63, 0.96) Access to a hospital 0.76 (0.60, 0.97) 0.0033 Fair/poor doctor Good 0.77 (0.65, 0.92) Access to a female 0.84 (0.71, 0.99) 0.0753 Fair/poor doctor Good 0.86 (0.72, 1.03) Access to an 0.76 (0.62, 0.93) 0.0003 Fair/poor emergency doctor Good 0.74 (0.62, 0.87) Needs BMI Underweight 1.52 (0.79, 2.93) 0.0965 Overweight 1.19 (1.01, 1.40) Obese 1.18 (0.98, 1.42) Alcohol consumption High risk drinking 1.03 (0.81, 1.29) 0.7008 Low risk drinking 1.19 (0.79, 1.79) Smoking Smoker 1.03 (0.82, 1.30) 0.2619 Ex-smoker 1.15 (0.97, 1.37) Diabetes Yes 2.92 (1.73, 4.95) <.0001 Heart disease Yes 2.69 (1.35, 5.34) 0.0049 Hypertension Yes 1.14 (0.95, 1.38) 0.16 Osteoporosis Yes 2.04 (1.25, 3.33) 0.0045 Low iron Yes 1.34 (1.03, 1.73) 0.0272 Stroke Yes 5.76 (0.74, 44.86) 0.09 Any other major 1.51 (1.01, 2.26) 0.0442 Yes disease anxiety Yes 1.23 (0.90, 1.68) 0.19 depression Yes 1.46 (1.15, 1.86) 0.0019

Table B-18. Association between predisposing, enabling and needs factors and specialist visits at Survey 3 for women from the 1946-51 cohort using multivariate multinomial logistic regressions

Specialist visits Predictor Categories Reference (No) Odds Ratio p-value (95% CI)

Predisposing factors

Area of residence Remote, very remote and outer regional 0.71 (0.59, 0.86) <.0001

Inner regional 0.71 (0.60, 0.84)

Enabling

Private health insurance Yes 1.39 (1.20, 1.61) <.0001

Access to a specialist Fair/poor 0.70 (0.55, 0.90) 0.0016

Good 0.71 (0.57, 0.87) Access to an emergency 0.2784 Fair/poor 0.86 (0.64, 1.15) doctor Good 0.83 (0.66, 1.05)

Access to a hospital doctor Fair/poor 1.15 (0.82, 1.62) 0.5715

Good 1.14 (0.88, 1.46) Access to a bulk billing 0.0434 Fair/poor 1.14 (0.96, 1.36) doctor Good 0.88 (0.70, 1.11)

Access to a female doctor Fair/poor 0.96 (0.79, 1.16) 0.9192

Good 0.99 (0.81, 1.20)

Needs

BMI Underweight 1.44 (0.74, 2.79) 0.1778

Overweight 1.19 (1.01, 1.41)

Obese 1.09 (0.90, 1.32)

Diabetes Yes 2.73 (1.60, 4.66) 0.0002

Heart disease Yes 2.36 (1.17, 4.73) 0.0160

Hypertension Yes 1.03 (0.84, 1.25) 0.7878

Osteoporosis 1.90 (1.15, 3.12) 0.0115

Low iron Yes 1.30 (1.00, 1.69) 0.0468

Stroke Yes 3.62 (0.45, 29.22) 0.2277

Any other major disease Yes 1.26 (0.83, 1.90) 0.2766

Specialist visits Predictor Categories Reference (No) Odds Ratio p-value (95% CI)

Depression Yes 1.40 (1.08, 1.81) 0.0107

Anxiety 1.01 (0.73, 1.41) 0.9451

Table B-19. Association between asthma groups and specialist visits at Survey 3 for women from the 1946-51 cohort using nested multivariate multinomial logistic regressions (Model 1 to 4)

Specialist visits Predictor Categories Reference (No) Odds Ratio p-value Overall p (95% CI) Model 1 Past asthma 1.31 (0.99, 1.75) 0.23 0.0446 Prevalent asthma 1.18 (0.93, 1.49) <.0001 Incident asthma 1.35 (1.02, 1.80) 0.0019 Bronchitis/emphysema 1.23 (0.99, 1.53) 0.23 Model 2 Past asthma 1.30 (0.98, 1.74) 0.07 0.0388 Prevalent asthma 1.17 (0.92, 1.49) 0.19 Incident asthma 1.38 (1.04, 1.84) 0.0266 Bronchitis/emphysema 1.24 (1.00, 1.53) 0.06 Remote, very remote and outer 0.71 (0.60, 0.84) <.0001 <.0001 Area of residence regional Inner regional 0.71 (0.58, 0.85) 0.0003 Model 3 Past asthma 1.29 (0.97, 1.73) 0.08 0.0291 Prevalent asthma 1.19 (0.93, 1.51) 0.16 Incident asthma 1.42 (1.06, 1.90) 0.0182 Bronchitis/emphysema 1.25 (1.00, 1.55) 0.0463 Remote, very remote and outer 0.74 (0.62, 0.88) 0.0007 0.0024 Area of residence regional Inner regional 0.78 (0.63, 0.95) 0.0148 Private health insurance Yes 1.35 (1.17, 1.57) <.0001 <.0001 Access to a specialist Fair/poor 0.70 (0.58, 0.84) 0.0001 0.0001 Good 0.72 (0.58, 0.89) 0.0019 Access to a bulk billing 0.87 (0.70, 1.08) 0.21 0.0115 Fair/poor doctor Good 1.19 (1.00, 1.41) 0.0496

Specialist visits Predictor Categories Reference (No) Odds Ratio p-value Overall p (95% CI) Model 4 Past asthma 1.22 (0.91, 1.64) 0.18 0.1039 Prevalent asthma 1.11 (0.87, 1.42) 0.40 Incident asthma 1.38 (1.03, 1.85) 0.0312 Bronchitis/emphysema 1.21 (0.97, 1.51) 0.09 Remote, very remote and outer 0.74 (0.62, 0.88) 0.0008 0.0027 Area of residence regional Inner regional 0.78 (0.63, 0.95) 0.0160 Private health insurance Yes 1.39 (1.20, 1.61) <.0001 <.0001 Access to a specialist Good 0.69 (0.57, 0.83) <.0001 <.0001 Fair/poor 0.70 (0.57, 0.86) 0.0009 Access to a bulk billing 0.89 (0.71, 1.11) 0.30 0.0095 Fair/poor doctor Good 1.21 (1.02, 1.44) 0.0287 BMI Underweight 1.56 (0.80, 3.04) 0.19 0.0941 Overweight 1.22 (1.03, 1.44) 0.0230 Obese 1.13 (0.93, 1.36) 0.22 osteoporosis Yes 1.93 (1.17, 3.19) 0.0100 0.0100 diabetes Yes 3.00 (1.75, 5.14) <.0001 <.0001 Heart disease Yes 2.56 (1.27, 5.16) 0.0088 0.0088 Depression Yes 1.40 (1.09, 1.79) 0.0075 0.0075 Low iron Yes 1.28 (0.99, 1.67) 0.06 0.0608

Association between specialist visits and asthma groups among women from the 1946-51 cohort, after adjusting for time (Survey 2 to 6), and predisposing, enabling and needs factors (Model 2)

Specialist visits Predictor Categories Reference (No visits) OR 95% CI P-value Never asthma 1 Past asthma 1.23 (1.05, 1.43) 0.0095 Prevalent asthma 1.34 (1.17, 1.54) <.0001 Incident asthma 1.30 (1.12, 1.52) 0.0007 Bronchitis/emphyse 1.17 (1.05, 1.32) 0.0057 ma Remote, very remote Area of residence 0.70 (0.64, 0.78) <.0001 and outer regional Inner regional 0.80 (0.74, 0.88) <.0001

Table B-20. Association between specialist visits and asthma groups among women from the 1946-51 cohort, after adjusting for time (Survey 2 to 6), and predisposing, enabling and needs factors (Model 3)

Specialist visits Predictor Categories Reference (No visits) OR 95% CI P-value Never asthma 1 Past asthma 1.23 (1.06, 1.44) 0.0073 Prevalent asthma 1.38 (1.20, 1.58) <.0001 Incident asthma 1.33 (1.14, 1.55) 0.0003 Bronchitis/emphyse 1.20 (1.07, 1.34) 0.0020 ma Remote, very remote Area of residence 0.78 (0.70, 0.86) <.0001 and outer regional Inner regional 0.86 (0.79, 0.94) 0.0010 Private health Yes 1.47 (1.36, 1.58) <.0001 insurance Access to a Fair/poor 0.80 (0.72, 0.88) <.0001 specialist Good 0.82 (0.75, 0.90) <.0001 Access to a bulk Fair/poor 1.13 (1.05, 1.22) 0.0020 billing doctor Good 0.94 (0.85, 1.03) 0.2072

Table B-21. Association between specialist visits and asthma groups among women from the 1946-51 cohort, after adjusting for time (Survey 2 to 6), and predisposing, enabling and needs factors (Model 4)

Specialist visits Predictor Categories Reference (No visits) OR P-value (95% CI) Never asthma 1 Past asthma 1.19 (1.03, 1.38) 0.0227 Prevalent asthma 1.28 (1.12, 1.47) 0.0004 Incident asthma 1.26 (1.08, 1.47) 0.0032 Bronchitis/emphyse 1.15 (1.03, 1.29) 0.0156 ma Remote, very remote Area of residence 0.80 (0.71, 0.87) <.0001 and outer regional Inner regional 0.86 (0.79, 0.94) 0.0013 Private health Yes 1.50 (1.39, 1.62) <.0001 insurance Access to a Fair/poor 0.77 (0.70, 0.85) <.0001 specialist Good 0.81 (0.74, 0.89) <.0001 Access to a bulk Fair/poor 1.16 (1.07, 1.25) 0.0002 billing doctor Good 0.96 (0.87, 1.05) 0.3912 osteoporosis Yes 1.64 (1.39, 1.94) <.0001 diabetes Yes 1.94 (1.61, 2.33) <.0001 Heart disease Yes 1.72 (1.37, 2.14) <.0001 Depression Yes 1.43 (1.29, 1.59) <.0001 Low iron Yes 1.36 (1.21, 1.53) <.0001

Table B-22. Association between predisposing, enabling and needs factors and ACC/CDM visits at Survey 3 for women from the 1921-26 cohort using univariate multinomial logistic regressions

ACC/CDM visits Predictor Categories Reference (No) Odds Ratio Overall (95% CI) p-value Past asthma 1.49 (0.75, 2.98) <.0001 Prevalent asthma 3.02 (2.08, 4.39) Incident asthma 1.64 (1.00, 2.70) Bronchitis/emphysema 1.06 (0.71, 1.56) Predisposing factors Age 0.98 (0.89, 1.07) 0.6195 Area of residence Remote, very remote and outer 1.15 (0.81, 1.66) 0.7371 regional Inner regional 1.05 (0.77, 1.43) Marital status Separated/Divorced/Widowed 0.97 (0.46, 2.07) 0.7544 Never married 1.11 (0.84, 1.46) Highest qualification No formal education 0.69 (0.38, 1.25) 0.4331 High school qualification 0.79 (0.46, 1.38) Country of birth English speaking countries 0.24 (0.10, 0.59) 0.0040 Non-English speaking countries 1.18 (0.83, 1.69) Enabling factors Private health insurance Yes 1.15 (0.87, 1.51) 0.34 Concession card Yes 1.30 (0.85, 2.00) 0.23 Income management Difficult to manage 1.60 (0.92, 2.78) 0.1843 Not too bad to manage 1.29 (0.92, 1.82) Access to a hospital doctor Fair/poor 0.78 (0.49, 1.24) 0.4801 Good 0.88 (0.64, 1.20) Access to after-hours doctors Fair/poor 1.12 (0.81, 1.54) 0.5326 Good 0.92 (0.66, 1.29) Access to a bulk billing doctor Fair/poor 0.98 (0.68, 1.42) 0.1742 Good 1.38 (0.97, 1.96) Access to a hospital doctor Fair/poor 1.39 (0.88, 2.18) 0.3494 Good 1.10 (0.81, 1.49) Access to a female doctor Fair/poor 0.75 (0.52, 1.07) 0.2639

ACC/CDM visits Predictor Categories Reference (No) Odds Ratio Overall (95% CI) p-value Good 0.86 (0.63, 1.19) Needs BMI Underweight 1.14 (0.55, 2.36) 0.1284 Overweight 1.10 (0.80, 1.51) Obese 1.56 (1.08, 2.26) Alcohol consumption High risk drinking 1.09 (0.82, 1.46) 0.8148 Low risk drinking 1.18 (0.45, 3.11) Smoking Smoker 1.19 (0.60, 2.36) 0.7459 Ex-smoker 1.10 (0.82, 1.49) Diabetes Yes 2.99 (2.11, 4.22) <.0001 Heart disease Yes 1.42 (1.02, 1.98) 0.0404 Hypertension Yes 1.27 (0.96, 1.67) 0.09 Osteoporosis Yes 1.73 (1.27, 2.34) 0.0004 Stroke Yes 0.86 (0.37, 2.03) 0.74 Anxiety Yes 0.63 (0.31, 1.26) 0.19 Depression Yes 1.70 (1.07, 2.70) 0.0245

Table B-23. Association between predisposing, enabling and needs factors and ACC/CDM visits at Survey 3 for women from the 1921-26 cohort using multivariate multinomial logistic regressions

ACC/CDM visits Predictor Categories Reference (No)

Odds Ratio Overall (95% CI) p-value

Predisposing

Country of birth English speaking countries 0.24 (0.10, 0.59) 0.0039

Non-English speaking countries 1.19 (0.83, 1.70)

Enabling

Income management Difficult to manage 1.58 (0.91, 2.74) 0.2157

Not too bad to manage 1.27 (0.90, 1.79)

Access to a bulk billing 1.00 (0.69, 1.45) 0.2092 Fair/poor doctor

Good 1.36 (0.96, 1.93)

Needs

BMI Underweight 1.10 (0.53, 2.30) 0.5093

Overweight 1.04 (0.75, 1.43)

Obese 1.34 (0.91, 1.96)

Diabetes Yes 2.80 (1.96, 4.01) <.0001

Heart disease Yes 1.25 (0.89, 1.76) 0.2025

Hypertension Yes 1.13 (0.85, 1.50) 0.4189

Osteoporosis Yes 1.82 (1.33, 2.48) 0.0002

Depression Yes 1.45 (0.90, 2.33) 0.1293

Table B-24. Association between predisposing, enabling and needs factors and ACC/CDM visits at Survey 3 for women from the 1921-26 cohort using nested multivariate multinomial logistic regressions (Model 1 to 4)

ACC/CDM visits Predictor Categories Reference (No) Odds Ratio Overall p (95% CI) p-value

Model 1

Past asthma 1.49 (0.75, 2.98) 0.26 <.0001

Prevalent asthma 3.02 (2.08, 4.39) <.0001

Incident asthma 1.64 (1.00, 2.70) 0.05

Bronchitis/emphysema 1.06 (0.71, 1.56) 0.78

Model 2

Past asthma 1.45 (0.73, 2.91) 0.29 <.0001

Prevalent asthma 2.99 (2.05, 4.35) <.0001

Incident asthma 1.61 (0.98, 2.67) 0.06

Bronchitis/emphysema 1.07 (0.72, 1.59) 0.73

Country of birth English speaking 1.17 (0.81, 1.67) 0.40 0.0054 countries Non-English 0.24 (0.10, 0.60) 0.0023 speaking countries Model 3

Past asthma 1.15 (0.56, 2.35) 0.70 <.0001

Prevalent asthma 2.79 (1.90, 4.10) <.0001

Incident asthma 1.49 (0.89, 2.50) 0.13

Bronchitis/emphysema 1.02 (0.68, 1.51) 0.94

Country of birth English speaking 1.17 (0.81, 1.69) 0.40 0.0033 countries Non-English 0.23 (0.09, 0.56) 0.0013 speaking countries diabetes Yes 3.05 (2.13, 4.36) <.0001 <.0001

Osteoporosis Yes 1.75 (1.27, 2.40) 0.0006 0.0006

Depression Yes 1.49 (0.92, 2.42) 0.11 0.1073

Table B-25. Association between predisposing, enabling and needs factors and ACC/CDM visits at Survey 3 for women from the 1946-51 cohort using univariate multinomial logistic regressions

ACC/CDM visits Predictor Categories Reference (No) Odds Ratio p-value (95% CI) Past asthma 1.66 (0.73, 3.77) 0.0002 Prevalent asthma 3.34 (1.91, 5.83) Incident asthma 2.88 (1.48, 5.61) Bronchitis/emphysema 1.51 (0.78, 2.92) Predisposing factors Remote, very remote and outer 0.8192 Area of residence 1.19 (0.68, 2.06) regional Inner regional 1.04 (0.63, 1.73) 0.4417 Marital status Separated/Divorced/Widowed 1.79 (0.71, 4.53)

Never married 0.93 (0.49, 1.77) 0.0392 Highest qualification No formal education 2.61 (1.21, 5.65)

High school qualification 1.43 (0.77, 2.68) 0.3076 Country of birth English speaking countries 1.75 (0.86, 3.55)

Non-English speaking countries 1.08 (0.58, 2.01) Enabling factors Private health insurance Yes 0.54 (0.35, 0.83) 0.0052

Concession card Yes 2.98 (1.89, 4.67) <.0001

Income management Difficult to manage 3.60 (1.63, 7.96) 0.0043 Not too bad to manage 1.79 (0.91, 3.51) Access to a specialist Fair/poor 0.93 (0.49, 1.78) 0.0025 Good 2.13 (1.34, 3.39) Access to an after-hours Fair/poor 1.08 (0.65, 1.81) 0.6741 doctor Good 1.26 (0.75, 2.11) Access to a hospital Fair/poor 0.93 (0.42, 2.06) 1676 doctor

ACC/CDM visits Predictor Categories Reference (No) Odds Ratio p-value (95% CI) Good 1.55 (0.96, 2.50) Access to a bulk billing Fair/poor 0.84 (0.53, 1.33) 0.7563 doctor Good 0.90 (0.48, 1.70) Access to an emergency Fair/poor 1.11 (0.61, 2.01) 0.9457 doctor Good 1.01 (0.60, 1.71) Access to a female 0 0.81 (0.48, 1.36) 0.7260 doctor 2 0.94 (0.55, 1.60) Needs BMI Underweight 3.28 (0.74, 14.51) <.0001 Overweight 1.51 (0.83, 2.77) Obese 4.00 (2.33, 6.86) Alcohol consumption High risk drinking 0.44 (0.26, 0.75) 0.0090 Low risk drinking 0.39 (0.11, 1.33) Smoking Smoker 1.05 (0.53, 2.09) 0.9355 Ex-smoker 1.10 (0.66, 1.81) Diabetes Yes 9.77 (5.57, 17.15) <.0001 Heart disease Yes 2.91 (1.03, 8.23) 0.0441 Hypertension Yes 3.59 (2.33, 5.55) <.0001 Osteoporosis Yes 1.22 (0.38, 3.92) 0.74 Low iron Yes 1.45 (0.76, 2.77) 0.25 Stroke Yes 3.70 (0.47, 29.23) 0.21 Any other major disease Yes 0.92 (0.29, 2.96) 0.89 anxiety Yes 1.89 (0.93, 3.82) 0.08 depression Yes 2.38 (1.41, 4.00) 0.0011

Table B-26. Association between predisposing, enabling and needs factors and ACC/CDM visits at Survey 3 for women from the 1946-51 cohort using multivariate multinomial logistic regressions

ACC/CDM visits Predictor Categories Reference (No) Odds Ratio p-value (95% CI)

Predisposing factors

age 1.14 (0.99, 1.32) 0.0758

Highest qualification No formal education 2.47 (1.14, 5.37) 0.0561

High school qualification 1.39 (0.74, 2.61)

Enabling

Private health insurance Yes 0.77 (0.48, 1.22) 0.2035 Concession card Yes 2.31 (1.39, 3.83) 0.2643 Income management Difficult to manage 2.13 (0.91, 4.98) 0.0012 Not too bad to manage 1.41 (0.71, 2.80) Access to a specialist Fair/poor 0.79 (0.41, 1.53) 0.0058 Good 1.94 (1.20, 3.11) Needs

BMI Underweight 3.15 (0.71, 14.01) 0.0211

Overweight 1.29 (0.70, 2.38)

Obese 2.28 (1.27, 4.10)

Alcohol consumption High risk drinking 0.56 (0.32, 0.97) 0.1069

Low risk drinking 0.45 (0.13, 1.62)

Diabetes Yes 5.60 (3.06, 10.25) <.0001

Heart disease Yes 1.80 (0.60, 5.42) 0.2950

Hypertension Yes 2.34 (1.45, 3.75) 0.0005

Anxiety Yes 1.19 (0.54, 2.63) 0.6610

Depression Yes 2.22 (1.24, 3.98) 0.0071

Table B-27. Association between asthma groups and ACC/CDM visits at Survey 3 for women from the 1946-51 cohort using nested multivariate multinomial logistic regressions (Model 1 to 4)

ACC/CDM visits Predictor Categories Reference (No) Odds Ratio p-value Overall p (95% CI) Model 1 Past asthma 1.66 (0.73, 3.77) 0.23 0.0002 Prevalent asthma 3.34 (1.91, 5.83) <.0001 Incident asthma 2.88 (1.48, 5.61) 0.0019 Bronchitis/emphysema 1.51 (0.78, 2.92) 0.23 Model 2 Past asthma 1.70 (0.75, 3.86) 0.21 0.0003 Prevalent asthma 3.33 (1.90, 5.83) <.0001 Incident asthma 2.77 (1.42, 5.41) 0.0029 Bronchitis/emphysema 1.48 (0.76, 2.87) 0.25 Age 1.14 (0.99, 1.33) 0.07 0.0726 Highest qualification No formal education 1.42 (0.76, 2.66) 0.28 0.0733 High school qualification 2.41 (1.11, 5.24) 0.0266 Model 3

Past asthma 1.43 (0.59, 3.47) 0.42 0.0041

Prevalent asthma 2.82 (1.58, 5.03) 0.0004

Incident asthma 2.45 (1.24, 4.84) 0.0100

Bronchitis/emphysema 1.32 (0.68, 2.59) 0.41

Age 1.15 (0.99, 1.34) 0.07 0.0726

Highest qualification High school qualification 1.19 (0.61, 2.33) 0.60 0.4262

No formal education 1.68 (0.73, 3.86) 0.22

Income management Not too bad to manage 1.36 (0.68, 2.72) 0.39 0.3646

Difficult to manage 1.86 (0.78, 4.43) 0.16

Private health insurance Yes 0.78 (0.48, 1.26) 0.31 0.3071

ACC/CDM visits Predictor Categories Reference (No) Odds Ratio p-value Overall p (95% CI)

Concession card Yes 1.99 (1.18, 3.37) 0.0098 0.0098

Access to a specialist Good 1.87 (1.16, 3.02) 0.0102 0.0082

Fair/poor 0.76 (0.39, 1.49) 0.43

Model 4

Past asthma 1.08 (0.43, 2.72) 0.87 0.0119

Prevalent asthma 2.41 (1.32, 4.42) 0.0043

Incident asthma 2.70 (1.34, 5.43) 0.0054

Bronchitis/emphysema 1.38 (0.69, 2.75) 0.36

Age 1.16 (0.99, 1.35) 0.07 0.0710

Highest qualification High school qualification 1.02 (0.51, 2.03) 0.95 0.8383

No formal education 1.23 (0.52, 2.90) 0.64

Income management Not too bad to manage 1.36 (0.67, 2.77) 0.39 0.6191

Difficult to manage 1.54 (0.63, 3.78) 0.35

Private health insurance Yes 0.74 (0.46, 1.20) 0.22 0.2243

Concession card Yes 1.54 (0.89, 2.67) 0.12 0.1196

Access to a specialist Good 1.83 (1.11, 3.00) 0.0169 0.0097

Fair/poor 0.71 (0.36, 1.40) 0.32

BMI Underweight 3.19 (0.70, 14.51) 0.13 0.1250

Overweight 1.22 (0.65, 2.28) 0.53

Obese 1.85 (1.01, 3.38) 0.0473

Alcohol consumption High risk drinking 0.60 (0.34, 1.07) 0.08 0.2073

Low risk drinking 0.53 (0.15, 1.93) 0.34 hypertension Yes 2.02 (1.23, 3.29) 0.0052 0.0052

ACC/CDM visits Predictor Categories Reference (No) Odds Ratio p-value Overall p (95% CI) diabetes Yes 6.50 (3.46, 12.21) <.0001 <.0001

Depression Yes 2.30 (1.31, 4.02) 0.0036 0.0036

Table B-28. Longitudinal association between ACC/CDM service use and asthma groups adjusting for predisposing, enabling and needs factors among women from the 1921-26 cohort from survey 2 to 6 (Model2)

ACC/CDM visits Predictor Categories Reference (No visits) OR P-value (95% CI) Never asthma 1 Past asthma 1.36 (1.03, 1.79) 0.0286 Prevalent asthma 1.81 (1.49, 2.19) <.0001 Incident asthma 1.65 (1.32, 2.06) <.0001 Bronchitis/emphysema 1.09 (0.95, 1.26) 0.2033 Australian 1 English speaking Country of birth 1.06 (0.89, 1.28) 0.4986 countries Non-English speaking 1.25 (1.07, 1.45) 0.0045 countries

Table B-29. Longitudinal association between ACC/CDM service use and asthma groups adjusting for predisposing, enabling and needs factors among women from the 1921-26 cohort from survey 2 to 6 (Model 3)

ACC/CDM visits Predictor Categories Reference (No visits) OR P-value (95% CI) Never asthma 1 Past asthma 1.23 (0.93 1.62) 0.1400 Prevalent asthma 1.70 (1.41, 2.06) <.0001 Incident asthma 1.55 (1.23, 1.94) 0.0002 Bronchitis/emphyse 1.04 (0.90, 1.19) 0.6014

ma Australian 1 English speaking 1.03 (0.86, 1.24) 0.7642 Country of birth countries Non-English 1.25 1.07, 1.45) 0.0041

speaking countries No 1 Diabetes Yes 2.62 (2.26, 3.05) <.0001 No 1 Osteoporosis Yes 1.39 (1.24, 1.56) <.0001 No 1 Depression Yes 1.26 (1.06, 1.51) 0.0105

Table B-30. Association between ACC/CDM and asthma groups among women from the 1946-51 cohort, after adjusting for time (Survey 2 to 6), and predisposing, enabling and needs factors (Model 2)

ACC/CDM visits Predictor Categories Reference (No visits) OR P-value (95% CI)

Never asthma 1

Past asthma 1.25 (1.01, 1.54) 0.0402

Prevalent asthma 2.49 (2.12, 2.92) <.0001

Incident asthma 2.07 (1.72, 2.49) <.0001

Bronchitis/emphysema 1.40 (1.20, 1.64) <.0001

Age 1.09 (1.05, 1.13) <.0001

Tertiary qualification 1

Highest qualification No formal education 1.83 (1.52, 2.21) <.0001

High school 1.37 (1.18, 1.58) <.0001 qualification

Table B-31. Association between ACC/CDM and asthma groups among women from the 1946-51 cohort, after adjusting for time (Survey 2 to 6), and predisposing, enabling and needs factors (Model 3)

ACC/CDM visits Predictor Categories Reference (No visits) OR P-value (95% CI) Never asthma 1

Past asthma 1.20 (0.98, 1.48) 0.0830

Prevalent asthma 2.24 (1.90, 2.64) <.0001

Incident asthma 1.92 (1.59, 2.31) <.0001

Bronchitis/emphysema 1.26 (1.08, 1.48) 0.0038

Age 1.06 (1.02, 1.10) 0.0019

Highest qualification No formal education 1.27 (1.05, 1.55) 0.0154

High school qualification 1.12 (0.97, 1.29) 0.1284

Private health insurance Yes 0.80 (0.72, 0.90) <.0001

Income management Difficult to manage 1.94 (1.61, 2.34) <.0001

Not too bad to manage 1.37 (1.17, 1.59) <.0001

Concession card Yes 1.83 (1.64, 2.04) <.0001

Table B-32. Association between ACC/CDM and asthma groups among women from the 1946-51 cohort, after adjusting for time (Survey 2 to 6), and predisposing, enabling and needs factors (Model 4)

ACC/CDM visits Predictor Categories Reference (No visits) OR 95% CI P-value Never asthma 1

Past asthma 1.11 (0.90, 1.36) 0.3187

Prevalent asthma 1.99 (1.69, 2.35) <.0001

Incident asthma 1.72 (1.42, 2.09) <.0001

Bronchitis/emphysema 1.22 (1.04, 1.43) 0.0158

Age 1.05 (1.01, 1.09) 0.0060

Highest qualification No formal education 1.06 (0.87, 1.29) 0.5865

High school qualification 1.02 (0.88, 1.18) 0.7533

Private health insurance Yes 0.82 (0.73, 0.91) 0.0004

Income management Difficult to manage 1.49 (1.23, 1.82) <.0001

Not too bad to manage 1.23 (1.05, 1.43) 0.0090

Concession card Yes 1.64 (1.46, 1.84) <.0001

BMI Underweight 1.08 (0.67, 1.72) 0.7579

Overweight 1.13 (0.99, 1.28) 0.0704

Obese 1.72 (1.50, 1.97) <.0001

Alcohol consumption High risk drinking 0.78 (0.68, 0.90) 0.0008

Low risk drinking 0.58 (0.45, 0.75) <.0001

Hypertension Yes 1.52 (1.36, 1.70) <.0001 Yes Diabetes 4.44 (3.74, 5.27) <.0001 Yes Depression 1.44 (1.25, 1.66) <.0001