Int. J. Epidemiol. Advance Access published July 6, 2015 International Journal of Epidemiology, 2015, 1–7 doi: 10.1093/ije/dyv110 Cohort Profile Update
Cohort Profile Update
Cohort Profile Update: Australian Longitudinal Study on Women’s Health Downloaded from http://ije.oxfordjournals.org/ at University of British Columbia Library on November 14, 2015
Annette J Dobson,1* Richard Hockey,1 Wendy J Brown,2 Julie E Byles,3 Deborah J Loxton,3 Deirdre McLaughlin,1 Leigh R Tooth1 and Gita D Mishra1 1
School of Public Health, 2School of Human Movement Studies, University of Queensland, Herston, QLD, Australia and 3University of Newcastle, Research Centre for Gender, Health and Ageing, Newcastle, NSW, Australia *Corresponding author. School of Public Health, University of Queensland, Herston, Queensland, 4006, Australia. E-mail: [email protected]
Abstract In 1996 the Australian Longitudinal Study on Women’s Health recruited a nationally representative sample of more than 40 000 women in three age cohorts, born in 1973–78, 1946– 51 and 1921–26. At least six waves of 3-yearly surveys have been completed. Although the focus remains on factors affecting the health and well-being of women and their access to and use of health services across urban, rural and remote areas of Australia, the study has now been considerably expanded by linkage to other health data sets. For most women who have ever participated in the study, linked records are now available for: governmentsubsidized non-hospital services (e.g. all general practitioner visits); pharmaceutical prescriptions filled; national death index, including codes for multiple causes of death; aged care assessments and services; cancer registries; and, for most states and territories, hospital admissions and perinatal data. Additionally, a large cohort of women born in 1989–95 have been recruited. The data are available to approved collaborators, with more than 780 researchers using the data so far. Full details of the study materials and data access procedures are available at [http://www.alswh.org.au/]. Key words: Australian women, cohort retention, linked records
Key Messages • Since 1996 at least six waves of data have been collected from a nationally representative random sample of
Australian women born in 1973–78, 1946–51 and 1921–26. • Regular comparisons with 5-yearly national census data are used to assess potential bias due to drop-out, withdrawal
and death.
C The Author 2015; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association V
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• Survey data can now be linked to administrative data on doctor visits, pharmaceutical prescriptions, hospital admis-
sions, aged care services, cancer registries and death records. Geocoding of participants’ residential addresses enables linkage to geographic information. • Record linkage has been used for research including: validation of self-reported diagnoses; polypharmacy; and costs
of medical conditions and risk factors.
What is the rationale for the new focus and data collection?
Who is in the cohort? The original ALSWH cohorts were randomly selected from all women in the Medicare database who were born in 1973–78, 1946–51 and 1921–26 (and aged 18–23, 45–50 and 70–75 when they were first surveyed in 1996). The initial response rates were estimated to be 41–42% for the 1973–78 cohort, 53–56% for the 1946–51 cohort and 37–40% for the 1921–26 cohort.4Participants in baseline surveys have been followed up in approximately 3-yearly waves, initially with mailed surveys and more recently with web-based surveys for women who prefer this format. Table 2 shows the numbers of participants in each cohort for each of the first six waves. Continuing participation is encouraged through regular newsletters, the website and social media, opportunities to participate in focused sub-studies and other activities. Non-respondents are followed up through secondary contacts, address changes identified through a range of sources
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The Australian Longitudinal Study on Women’s Health (ALSWH) was established in 1995.1 It is funded by the Australian Department of Health to examine the social, psychological, physical and environmental factors which determine good health, and those which cause ill health, in women across the life course, with particular emphasis on women’s access to and use of health services. An important feature of the study is that it was designed to enable record linkage of self-reported survey data to administrative datasets of health service use. The sampling frame was all women in selected age ranges in the database of the Health Insurance Commission (later called Medicare Australia), the universal health insurance scheme that includes all Australian residents and permanent citizens. Thus each participant has a unique Medicare personal identification number (held by Medicare and not the study team) that can be used for data linkage to Medicare data. Medicare databases comprise the Medical Benefits Scheme which subsidizes costs of visits to general practitioners and other medical specialists (but not hospital visits) and the Pharmaceutical Benefits Scheme, which subsidizes the costs of many medicines. Although the study was designed to facilitate record linkage, numerous ethical and administrative barriers have been encountered. After completing the first survey, women were asked to provide consent for linkage of survey data to Medicare data; approximately half the participants did so.2 Consequently, early analyses using linked Medicare data were potentially biased by differences between women who consented to linkage and those who did not. Only in 2012 was approval obtained for linkage, without explicit consent, of study data to Medicare and other national data sets, including retrospective data for all women who had ever participated in the study (except for those who had refused consent at any time). Annual linkage to these data sources is now anticipated into the future. These linkages are deterministic, based on the Medicare personal identification numbers. As the purpose of the Medicare data is costing and re-imbursement, they contain little diagnostic information. Linkage to the National Death Index has been available for all participants since 1999 and is now conducted every
6 months. This linkage is probabilistic, based on names, dates of birth and addresses.3 Dates of deaths are obtained with a lag of approximately 1 year, but delays in coding and release of multiple causes of death result in a current lag of about 2 years. In mid-2014, linkage for all participants born in 1921– 26 was obtained for aged care data. These data include assessments of eligibility for a range of services, from home and community support through to high-level nursing home care, with measures of functional dependency, cognitive impairment and use of residential aged care and other services. Other administrative data sets are held by state and territory health departments and agencies such as cancer registries. As each jurisdiction has different, and changing, legislation and policies on data release and linkage, data sets are inconsistent across jurisdictions (see Table 1). Geocoding is another aspect of record linkage. At each survey women provide their residential address. This information is used to obtain coordinates of latitude and longitude for each participant. Using these coordinates, ALSWH survey responses can be linked to geographical, climatic and land use data.
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Table 1. Linked data, showing jurisdictional level of data and first year that linked data are available for participants in the Australian Longitudinal Study on Women’s Health (born in 1921–26, 1946–51 and 1973–78); shaded boxes indicate that data are not yet available and blank boxes indicate where data are from a different jurisdictional level
For 1921–26 cohort only
Table 2. Numbers of participants in the first six waves of the
Table 3. Attrition across waves in the Australian Longitudinal
Australian Longitudinal Study on Women’s Health, together
Study on Women’s Health for women born in 1921–26
with the years when the surveys were conducted and the ages of the women
Wave
Cohort Wave
1 2 3 4 5 6
1973–78
1946–51
1921–26
Year
Age
N
Year
Age
N
Year
Age
N
1996 2000 2003 2006 2009 2012
18–23 22–27 25–30 28–33 31–36 34–39
14247 9688 9081 9145 8200 8126
1996 1998 2001 2004 2007 2010
45–50 47–52 50–55 53–58 56–61 59–64
13715 12338 11226 10905 10638 10011
1996 1999 2002 2005 2008 2011
70–75 73–78 76–81 79–84 82–87 85–90
12432 10434 8647 7158 5561 4055
(including social media and online directories), telephone, text messaging and e-mail contact. Whereas the response rate for the 1973–78 cohort dropped from wave 1 to wave 2, it has been more stable, exceeding 80% since then, with participants who miss one wave sometimes returning in subsequent waves.5,6 Retention has been best in the 1946–51 cohort. In contrast, the numbers of respondents in the 1921–26 cohort have dropped substantially. Based on linked data from the National Death Index, by wave 6 (in 2011), 40% of these women had died and increasing frailty was the reason for many withdrawals (see Table 3). The representativeness of continuing participants is assessed by comparisons with data from the 5-yearly national censuses. At wave 1, there was some overrepresentation of women with university education and under-representation of immigrants from non-English speaking countries.7 Comparisons between wave 6 data and the 2011 Australian census show that these biases
1 2 3 4 5 6
Respondent
Non-respondent
Withdrawn
N
%
N
%
N
%
12432 10434 8647 7158 5560 4055
83.9 69.6 57.6 44.7 32.6
790 1155 1104 1283 979
6.4 9.3 8.9 10.3 7.9
674 1446 1976 2109 2430
5.4 11.6 15.9 17.0 19.5
Deceased N
%
534 4.3 1184 9.5 2194 17.6 3480 28.0 4968 40.0
have increased, especially for the 1973–78 cohort. Far more ALSWH participants than women of the same age in the national population have completed university education since 1996 (Table 4). Immigration to Australia in recent years, especially by young people from non-English speaking countries, has further increased the differences in country of birth between the ALSWH participants and the general population (Table 5). In response to the rates of death and functional decline among women in the 1921–26 cohort, we now send these women a brief survey every 6 months. The other two cohorts continue to be surveyed at 3-yearly intervals. In 2012–13, a new cohort was recruited of women aged 18–23 (i.e. born in 1989–95) to fill gaps in knowledge about the current health experiences of young women and to provide comparisons with the 1973–78 cohort when they were the same age.8
What has been measured? For all cohorts the surveys cover demographic variables, health behaviours, diagnoses, symptoms, general measures
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Table 4. Highest educational qualification completed: comparisons of participants in the Australian Longitudinal Study on Women’s Health at waves 1 and 6 and women of the same ages in the national population at the 1996 and 2011 censuses (column percentages) 1973–78 cohort
No post-school qualifications Trade/certificate/diploma University Not adequately stated 1946–51 cohort
1921–26 cohort
No post-school qualifications Trade/certificate/diploma University Not adequately stated
Census 1996 age 18–23
Wave 6, 2012 age 34–39
Census 2011 age 33–38
69.8 17.5 12.1 0.6
69.3 13.9 7.7 9.1
15.0 24.9 58.3 1.8
33.5 27.6 33.7 5.2
Wave1, 1996 age 45–50
Census 1996 age 45–50
Wave 6, 2010 age 59–64
Census 2011 age 60–65
63.1 19.4 16.3 1.2
61.8 15.7 11.6 10.8
60.3 20.7 18.1 0.9
59.7 18.4 14.6 7.2
Wave1, 1996 age 70–75
Census 1996 age 70–75
Wave 6, 2011 age 85–90
Census 2011 age 85–90
79.8 11.0 4.0 5.2
70.4 6.0 2.4 21.2
76.4 13.6 5.8 4.0
66.5 6.2 3.2 24.1
Table 5. Country of birth: comparison of participants in the Australian Longitudinal Study on Women’s Health at waves 1 and 6 and women of the same ages in the national population at the 1996 and 2011 censuses (column percentages) 1973–78 cohort
Australia Mainly English speaking countries Non-English speaking countries Other/not adequately stated 1946–51 cohort
Australia Mainly English speaking countries Non-English speaking countries Other/not adequately stated 1921–26 cohort
Australia Mainly English speaking countries Non- English speaking countries Other/not adequately stated
Wave1, 1996 age 18–23
Census 1996 age 18–23
Wave 6, 2012 age 34–39
Census 2011 age 33–38
88.6 3.5 4.9 3.0
77.8 4.1 11.6 6.0
91.5 4.1 3.8 0.7
66.0 7.8 21.4 4.9
Wave1, 1996 age 45–50
Census 1996 age 45–50
Wave 6, 2010 age 59–64
Census 2011 age 60–65
69.0 13.9 13.0 4.2
62.6 11.6 19.2 6.5
72.0 15.7 11.3 1.0
61.3 12.5 20.9 5.2
Wave1, 1996 age 70–75
Census 1996 age 70–75
Wave 6, 2011 age 85–90
Census 2011 age 85–90
68.5 12.4 11.6 7.6
66.4 11.0 16.0 6.5
72.3 13.7 8.9 5.0
61.6 10.6 17.5 10.3
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No post-school qualifications Trade/certificate/diploma University Not adequately stated
Wave1, 1996 age 18–23
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of health such as the Health Survey 36 Short Form (SF-36)9 and access to and use of a range of health services. There are also cohort-specific questions. For example, women in the 1973–78 cohort are asked about contraception and reproduction, childbirth and parenting, experiences of intimate partner violence, and work-life balance. Women in the 1946–51 cohort have been asked about menopause transition and retirement plans. Women in the oldest cohort are asked about activities of daily living, housing and whether they provide or receive supportive care. Copies of all the surveys and details of the linked data are available at [http:// www.alswh.org.au/surveys].
To date, much of the research has only involved longitudinal analysis of the survey data. Examples include health patterns relating to: family formation, childbirth and children; menopause; factors predicting physical and mental health changes; behavioural risk factors such as tobacco and alcohol consumption, diet, weight gain and physical activity; multi-morbidity and frailty. However, record linkage has substantially increased the value of the study, especially for health service research, and the generalizability of the findings. Self-reported diagnoses and symptoms are often regarded as less valid than information obtained from clinical records. The validity of self-reported conditions can be assessed using record linkage. For example, for ALSWH participants there is substantial agreement between survey data and hospital records for diabetes, agreement is less good for heart disease and there is evidence that hypertension may be under-recorded in hospital data.10 Diagnoses of breast, lung and colorectal cancers are reported accurately11 and so is arthritisrelated surgery.12 Agreement between prescription data and self-reported osteoporosis is only moderate.13 In vitro fertilization is well reported but self-reported use of ovulation inductions is less likely to be valid.14 Record linkage also provides insights into women’s perceptions and understanding of clinical events; for example, stillbirths are over-reported15 and transient ischaemic attacks and other stroke-like conditions may be self-reported as strokes.16 Linkage of ALSWH survey data to prescription records has been used to examine the anticholinergic effects of polypharmacy in older women,17and external factors influencing changes in the use of osteoporosis medications18 and COX-2 inhibitors.19 It is also possible to examine potentially adverse events associated with various commonly used drugs such as statins.20
Analyses of health service use and costs, using ALSWH data linked to Medicare and hospital data, have produced estimates of geographical and socioeconomic differences in access to services,21,22 relative costs to governments and patients and the effects of policy changes.23,24 Costs have been compared for women with and without various medical conditions, such as diabetes,25 back pain26 or mental health problems,27 and behavioural risk factors such as physical inactivity.28,29 Using geocoded data to examine associations between climate-related factors (for example, increasing salinity in farmland, drought, fire, floods or air pollution) and women’s mental and physical health has produced consistent but unexpected results. No associations have been found— possibly due to different time frames for the exposures (long-term) and health outcomes (short-term) or to the multiple mitigating resources available in a high-income country like Australia.30–32 By mid-2014 there were almost 500 publications in peer-reviewed journals from the study, produced by more than 780 ALSWH investigators and collaborators. The study findings provided much of the evidence for the 2010 National Women’s Health Policy33 as well as for policies on other topics such physical activity,34 incontinence35 and respite care.36 ALSWH data have been pooled with other data for several national and international comparative studies37,38 including comparisons between men and women.39,40
What are the main strengths and weaknesses? The main strengths of the study are the initial large sample size and the good retention of participants for more than 15 years. Record linkage to administrative data sets adds breadth and depth to the study, through objective information on diagnoses and conditions (from hospitals, cancer registries and medications) and health service uses and costs (e.g. from Medicare data). For a study providing information to governments for policy, planning and evaluation, representativeness is important. These cohorts were initially fairly similar to the national population (with some excess of better-educated women).7 Through regular comparisons with census data and data from national health surveys, we are able to document the extent to which representativeness is maintained and to quantify biases that might affect the generalizability of findings. The main weakness of the study is that the survey data are self-reported, though record linkage to administrative data provides ways of checking the validity of this information. The other main limitation is that the study only includes women. This is especially important as ALSWH is
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What has it found? Key findings and publications
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also the largest study of health in rural areas of Australia, but findings about access to and use of health services may vary between women and men. In recognition of this limitation, the Australian Department of Health is now funding a men’s health study which will provide comparative data for younger and middle-aged men.
Can I get hold of the data? Where can I find out more?
Funding This work was supported by the Australian Government Department of Health. G.D.M. is funded by the Australian Research Council Future Fellowship [FT120100812]. Additional support is provided by the Australian National Health and Medical Research Council Centre of Research Excellence grant [APP1000986] and Program grant [grant 569940].
Acknowledgements The research on which this paper is based was conducted as part of the Australian Longitudinal Study on Women’s Health, the University of Newcastle, Australia, and the University of Queensland, Australia. We are grateful to the Australian Government Department of Health for funding and to the women who provided the survey data.
References 1. Lee C, Dobson AJ, Brown WJ et al. Cohort Profile: The Australian Longitudinal Study on Women’s Health. Int J Epidemiol 2005;34:987–91. 2. Young AF, Dobson AJ, Byles JE. Health services research using linked records: Who consents and what is the gain? Aust N Z J Public Health 2001;25:417–20. 3. Powers J, Ball J, Adamson L, Dobson A. Effectiveness of the National Death Index for establishing the vital statistics of older women in the Australian Longitudinal Study on Women’s Health. Aust N Z J Public Health 2000;24:526–28. 4. Brown WJ, Bryson L, Byles JE et al. Women’s Health Australia: recruitment for a national longitudinal cohort study. Women Health 1998;28:23–40. 5. Powers J, Loxton D. The impact of attrition in an 11-year prospective longitudinal study of younger women. Ann Epidemiol 2010;20:318–21.
6. Powers J, Tavener M, Graves A, Loxton D. Loss to follow-up was used to estimate bias in a longitudinal study: a new approach. J Clin Epidemiol 2015. doi: 10.1016/j.jcli nepi.2015.01.010. [Epub ahead of print.] 7. Brown WJ, Dobson AJ, Bryson L, Byles JE. Women’s Health Australia: on the progress of the main cohort studies. J Women’s Health Gender-based Med 1999;8:681–88. 8. Mishra GD, Hockey R, Powers J et al. Recruitment via the Internet and Social Networking Sites: The 1989–1995 Cohort of the Australian Longitudinal Study on Women’s Health. J Med Internet Res 2014;16:e279. 9. Snow K, Kosinski M, Gandek B. SF-36 Health Survey: Manual and Interpretation Guide. Boston, MA: Nimrod Press, 1993. 10. Navin TJ, Stewart-Williams J, Parkinson L, Sibbritt D, Byles JE. The identification of diabetes, heart disease, hypertension and stroke in mid- and older-aged women: comparing self-report and administrative hospital data records. Geriatr Gerontol Int 2015. doi: 10.1111/ggi.12442. [Epub ahead of print.] 11. Stavrou E, Vajdic C, Loxton D, Pearson S. The validity of selfreported cancer diagnoses and factors associated with accurate reporting in a cohort of older Australian women. Cancer Epidemiol 2011;35:e75–e80. 12. Parkinson L, Curryer C, Gibberd A, Cunich M, Byles J. Good agreement between self-report and centralised hospitalisations data for arthritis related surgeries. J Clin Epidemiol 2013;66:1128–34. 13. Peeters G, Tett S, Dobson A, Mishra G. Validity of self-reported osteoporosis in mid-age and older women. Osteoporos Int 2013; 24:91–927. 14. Herbert D, Lucke J, Dobson A. Agreement between self-reported use of in vitro fertilisation or ovulation induction, and medical insurance claims in Australian women aged 28–36 years. Hum Reprod 2012;279:2823–28. 15. Hure A, Chojenta C, Powers J, Byles J, Loxton D. Validity and reliability of stillbirth data using linked self-reported and administrative datasets. J Epidemiol 2015;25:30–37. 16. Jackson C, Mishra G, Tooth L, Byles J, Dobson A. Moderate agreement between self-reported stroke and hospital-recorded stroke in two cohorts of Australian women: a validation study. BMC Methodol 2015;15:7. 17. Parkinson L, Magin P Thomson A et al. Anticholinergic burden in older women: Not seeing the wood for the trees. Med J Aust 2015;202:91–94. 18. Peeters G, Tett S, Duncan E, Mishra G, Dobson A. Osteoporosis medication dispensing for older Australian women from 2002 to 2010: influences of publications, guidelines, marketing activities and policy. Pharmacoepidemiol Drug Saf 2014;23:1303–11. 19. Parkinson L, Dolja-Gore X, Gibson R et al. An observational study of the discrediting of COX-2 NSAIDs in Australia: Vioxx or class effect. BMC Public Health 2011;11:892. 20. Peeters G, Tett SE, Conaghan PG, Mishra GD, Dobson AJ. Is statin use associated with new joint-related symptoms, physical function and quality of life. The Australian Longitudinal Study on Women’s Health. Arthritis Care Res 2015;67:13–20. 21. Young AF, Dobson AJ. The decline in bulk billing and increase in out-of-pocket costs for general practice consultations in rural areas of Australia, 1995–2001. Med J Aust 2003;178:122–26.
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The data are available free of charge on request to bona fide researchers. The process is documented on the website [http://www.alswh.org.au/] which includes all the survey questionnaires, data books of frequency tables for all surveys, meta-data, conditions of data access and request forms. Restrictions are imposed by some of the human research ethics committees (both national and state-based) and some data custodians on where some of the linked data may be analysed.
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cohort of 45–61 year old rural Australian women. Aust N Z J Public Health 2015; doi:10.1111/1753-6405.12369. Fearnley EJ, Soares Magalha˜es RJ, Speldewinde P, Weinstein P, Dobson A. Environmental correlates of mental health measures for women in Western Australia. EcoHealth 2014. Doi: 10.1007/s10393-014-0966-3. Australian Government Department of Health and Ageing. National Women’s Health Policy 2010. Canberra: Commonwealth of Australia, 2010. Brown WJ, Bauman AE, Bull FC, Burton NW. Development of Evidence-based Physical Activity Recommendations for Adults (18–64 Years). Canberra: Australian Government Department of Health. 2012. Australian Government Department of Social Services. National Continence Management Strategy. http://bladderbowel.gov.au/ ncp/ncms/ (15 June 2015, date last accessed). Australian Government Department of Social Services. National Respite for Carers Program. http://www.myagedcare.gov.au/ aged-care-services/national-respite-carers-program (15 June 2015, date last accessed). Anstey K, Byles J, Luszcz M et al. Cohort Profile: The Dynamic Analyses to Optimize Ageing (DYNOPTA) project. Int J Epidemiol 2010;39:44–51. Mishra GD, Anderson D, Schoenaker DAJM et al. InterLACE: a New International Collaboration for a Life Course Approach to Women’s Reproductive Health and Chronic Disease Events. Maturitas 2013;74:235–40. Jamrozik K, McLaughlin D, McCaul K et al. Women who smoke like men die like men who smoke: Findings from two Australian cohort studies. Tob Control 2011;20:258–65. Dobson A, Almeida O, Brown W et al. Impact of behavioural risk factors on death within 10 years for women and men in their 70 s: absolute risk charts. BMC Public Health 2012;12:669.
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22. Korda R, Banks E, Clements M, Young A. Is inequity undermining Australia’s ‘universal’ health care system. Socio-economic inequalities in the use of specialist medical and nonmedical ambulatory health care. Aust N Z J Public Health 2009; 33:458–65. 23. Dolja-Gore X, Byles J, Loxton D, Hockey R, Dobson A. Increased bulk-billing for general practice consultations and reduced inequity in regional and remote areas, 2002–2008. Med J Aust 2011;195:203–04. 24. Byles J, Young A, Wheway V. Annual health assessments for older Australian women: Uptake and equity. Aust N Z J Public Health 2007;31:170–73. 25. Lowe J, Young A, Dolja-Gore X, Byles J. Costs of medications for older women. Aust N Z J Public Health 2008;32:89. 26. Kirby E, Broom A, Sibbritt D, Refshauge K, Adams J. Health care utilisation and out-of-pocket expenditure associated with back pain: A nationally representative survey of Australian women. PLoS One 2013;8:e83559. 27. Byles JE, Dolja-Gore X, Loxton D, Parkinson L, Stewart Williams J. Women’s uptake of Medicare Benefits Schedule mental health items for general practitioners, psychologists and other allied mental health professionals. Med J Aust 2011;194:175–79. 28. Brown WJ, Hockey R, Dobson AJ. Physical activity, body mass index and health care costs in mid-age Australian women. Aust N Z Public Health 2008;32:150–55. 29. Peeters GMEE, Mishra G, Dobson A, Brown WJ. Health care costs associated with prolonged sitting and inactivity. Am J Prev Med 2014;46:265–72. 30. Powers J, Loxton D, Baker J,Rich J, Dobson A. Empirical evidence suggests adverse climate events have not affected Australian women’s health and well-being. Aust N Z J Public Health 2012;36:452–57. 31. Powers J, Dobson A, Berry H, Graves A, Hanigan I, Loxton D. Lack of association between drought and mental health in a
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Cohort Profile Update
Cohort Profile Update: Australian Longitudinal Study on Women’s Health Downloaded from http://ije.oxfordjournals.org/ at University of British Columbia Library on November 14, 2015
Annette J Dobson,1* Richard Hockey,1 Wendy J Brown,2 Julie E Byles,3 Deborah J Loxton,3 Deirdre McLaughlin,1 Leigh R Tooth1 and Gita D Mishra1 1
School of Public Health, 2School of Human Movement Studies, University of Queensland, Herston, QLD, Australia and 3University of Newcastle, Research Centre for Gender, Health and Ageing, Newcastle, NSW, Australia *Corresponding author. School of Public Health, University of Queensland, Herston, Queensland, 4006, Australia. E-mail: [email protected]
Abstract In 1996 the Australian Longitudinal Study on Women’s Health recruited a nationally representative sample of more than 40 000 women in three age cohorts, born in 1973–78, 1946– 51 and 1921–26. At least six waves of 3-yearly surveys have been completed. Although the focus remains on factors affecting the health and well-being of women and their access to and use of health services across urban, rural and remote areas of Australia, the study has now been considerably expanded by linkage to other health data sets. For most women who have ever participated in the study, linked records are now available for: governmentsubsidized non-hospital services (e.g. all general practitioner visits); pharmaceutical prescriptions filled; national death index, including codes for multiple causes of death; aged care assessments and services; cancer registries; and, for most states and territories, hospital admissions and perinatal data. Additionally, a large cohort of women born in 1989–95 have been recruited. The data are available to approved collaborators, with more than 780 researchers using the data so far. Full details of the study materials and data access procedures are available at [http://www.alswh.org.au/]. Key words: Australian women, cohort retention, linked records
Key Messages • Since 1996 at least six waves of data have been collected from a nationally representative random sample of
Australian women born in 1973–78, 1946–51 and 1921–26. • Regular comparisons with 5-yearly national census data are used to assess potential bias due to drop-out, withdrawal
and death.
C The Author 2015; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association V
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• Survey data can now be linked to administrative data on doctor visits, pharmaceutical prescriptions, hospital admis-
sions, aged care services, cancer registries and death records. Geocoding of participants’ residential addresses enables linkage to geographic information. • Record linkage has been used for research including: validation of self-reported diagnoses; polypharmacy; and costs
of medical conditions and risk factors.
What is the rationale for the new focus and data collection?
Who is in the cohort? The original ALSWH cohorts were randomly selected from all women in the Medicare database who were born in 1973–78, 1946–51 and 1921–26 (and aged 18–23, 45–50 and 70–75 when they were first surveyed in 1996). The initial response rates were estimated to be 41–42% for the 1973–78 cohort, 53–56% for the 1946–51 cohort and 37–40% for the 1921–26 cohort.4Participants in baseline surveys have been followed up in approximately 3-yearly waves, initially with mailed surveys and more recently with web-based surveys for women who prefer this format. Table 2 shows the numbers of participants in each cohort for each of the first six waves. Continuing participation is encouraged through regular newsletters, the website and social media, opportunities to participate in focused sub-studies and other activities. Non-respondents are followed up through secondary contacts, address changes identified through a range of sources
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The Australian Longitudinal Study on Women’s Health (ALSWH) was established in 1995.1 It is funded by the Australian Department of Health to examine the social, psychological, physical and environmental factors which determine good health, and those which cause ill health, in women across the life course, with particular emphasis on women’s access to and use of health services. An important feature of the study is that it was designed to enable record linkage of self-reported survey data to administrative datasets of health service use. The sampling frame was all women in selected age ranges in the database of the Health Insurance Commission (later called Medicare Australia), the universal health insurance scheme that includes all Australian residents and permanent citizens. Thus each participant has a unique Medicare personal identification number (held by Medicare and not the study team) that can be used for data linkage to Medicare data. Medicare databases comprise the Medical Benefits Scheme which subsidizes costs of visits to general practitioners and other medical specialists (but not hospital visits) and the Pharmaceutical Benefits Scheme, which subsidizes the costs of many medicines. Although the study was designed to facilitate record linkage, numerous ethical and administrative barriers have been encountered. After completing the first survey, women were asked to provide consent for linkage of survey data to Medicare data; approximately half the participants did so.2 Consequently, early analyses using linked Medicare data were potentially biased by differences between women who consented to linkage and those who did not. Only in 2012 was approval obtained for linkage, without explicit consent, of study data to Medicare and other national data sets, including retrospective data for all women who had ever participated in the study (except for those who had refused consent at any time). Annual linkage to these data sources is now anticipated into the future. These linkages are deterministic, based on the Medicare personal identification numbers. As the purpose of the Medicare data is costing and re-imbursement, they contain little diagnostic information. Linkage to the National Death Index has been available for all participants since 1999 and is now conducted every
6 months. This linkage is probabilistic, based on names, dates of birth and addresses.3 Dates of deaths are obtained with a lag of approximately 1 year, but delays in coding and release of multiple causes of death result in a current lag of about 2 years. In mid-2014, linkage for all participants born in 1921– 26 was obtained for aged care data. These data include assessments of eligibility for a range of services, from home and community support through to high-level nursing home care, with measures of functional dependency, cognitive impairment and use of residential aged care and other services. Other administrative data sets are held by state and territory health departments and agencies such as cancer registries. As each jurisdiction has different, and changing, legislation and policies on data release and linkage, data sets are inconsistent across jurisdictions (see Table 1). Geocoding is another aspect of record linkage. At each survey women provide their residential address. This information is used to obtain coordinates of latitude and longitude for each participant. Using these coordinates, ALSWH survey responses can be linked to geographical, climatic and land use data.
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Table 1. Linked data, showing jurisdictional level of data and first year that linked data are available for participants in the Australian Longitudinal Study on Women’s Health (born in 1921–26, 1946–51 and 1973–78); shaded boxes indicate that data are not yet available and blank boxes indicate where data are from a different jurisdictional level
For 1921–26 cohort only
Table 2. Numbers of participants in the first six waves of the
Table 3. Attrition across waves in the Australian Longitudinal
Australian Longitudinal Study on Women’s Health, together
Study on Women’s Health for women born in 1921–26
with the years when the surveys were conducted and the ages of the women
Wave
Cohort Wave
1 2 3 4 5 6
1973–78
1946–51
1921–26
Year
Age
N
Year
Age
N
Year
Age
N
1996 2000 2003 2006 2009 2012
18–23 22–27 25–30 28–33 31–36 34–39
14247 9688 9081 9145 8200 8126
1996 1998 2001 2004 2007 2010
45–50 47–52 50–55 53–58 56–61 59–64
13715 12338 11226 10905 10638 10011
1996 1999 2002 2005 2008 2011
70–75 73–78 76–81 79–84 82–87 85–90
12432 10434 8647 7158 5561 4055
(including social media and online directories), telephone, text messaging and e-mail contact. Whereas the response rate for the 1973–78 cohort dropped from wave 1 to wave 2, it has been more stable, exceeding 80% since then, with participants who miss one wave sometimes returning in subsequent waves.5,6 Retention has been best in the 1946–51 cohort. In contrast, the numbers of respondents in the 1921–26 cohort have dropped substantially. Based on linked data from the National Death Index, by wave 6 (in 2011), 40% of these women had died and increasing frailty was the reason for many withdrawals (see Table 3). The representativeness of continuing participants is assessed by comparisons with data from the 5-yearly national censuses. At wave 1, there was some overrepresentation of women with university education and under-representation of immigrants from non-English speaking countries.7 Comparisons between wave 6 data and the 2011 Australian census show that these biases
1 2 3 4 5 6
Respondent
Non-respondent
Withdrawn
N
%
N
%
N
%
12432 10434 8647 7158 5560 4055
83.9 69.6 57.6 44.7 32.6
790 1155 1104 1283 979
6.4 9.3 8.9 10.3 7.9
674 1446 1976 2109 2430
5.4 11.6 15.9 17.0 19.5
Deceased N
%
534 4.3 1184 9.5 2194 17.6 3480 28.0 4968 40.0
have increased, especially for the 1973–78 cohort. Far more ALSWH participants than women of the same age in the national population have completed university education since 1996 (Table 4). Immigration to Australia in recent years, especially by young people from non-English speaking countries, has further increased the differences in country of birth between the ALSWH participants and the general population (Table 5). In response to the rates of death and functional decline among women in the 1921–26 cohort, we now send these women a brief survey every 6 months. The other two cohorts continue to be surveyed at 3-yearly intervals. In 2012–13, a new cohort was recruited of women aged 18–23 (i.e. born in 1989–95) to fill gaps in knowledge about the current health experiences of young women and to provide comparisons with the 1973–78 cohort when they were the same age.8
What has been measured? For all cohorts the surveys cover demographic variables, health behaviours, diagnoses, symptoms, general measures
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Table 4. Highest educational qualification completed: comparisons of participants in the Australian Longitudinal Study on Women’s Health at waves 1 and 6 and women of the same ages in the national population at the 1996 and 2011 censuses (column percentages) 1973–78 cohort
No post-school qualifications Trade/certificate/diploma University Not adequately stated 1946–51 cohort
1921–26 cohort
No post-school qualifications Trade/certificate/diploma University Not adequately stated
Census 1996 age 18–23
Wave 6, 2012 age 34–39
Census 2011 age 33–38
69.8 17.5 12.1 0.6
69.3 13.9 7.7 9.1
15.0 24.9 58.3 1.8
33.5 27.6 33.7 5.2
Wave1, 1996 age 45–50
Census 1996 age 45–50
Wave 6, 2010 age 59–64
Census 2011 age 60–65
63.1 19.4 16.3 1.2
61.8 15.7 11.6 10.8
60.3 20.7 18.1 0.9
59.7 18.4 14.6 7.2
Wave1, 1996 age 70–75
Census 1996 age 70–75
Wave 6, 2011 age 85–90
Census 2011 age 85–90
79.8 11.0 4.0 5.2
70.4 6.0 2.4 21.2
76.4 13.6 5.8 4.0
66.5 6.2 3.2 24.1
Table 5. Country of birth: comparison of participants in the Australian Longitudinal Study on Women’s Health at waves 1 and 6 and women of the same ages in the national population at the 1996 and 2011 censuses (column percentages) 1973–78 cohort
Australia Mainly English speaking countries Non-English speaking countries Other/not adequately stated 1946–51 cohort
Australia Mainly English speaking countries Non-English speaking countries Other/not adequately stated 1921–26 cohort
Australia Mainly English speaking countries Non- English speaking countries Other/not adequately stated
Wave1, 1996 age 18–23
Census 1996 age 18–23
Wave 6, 2012 age 34–39
Census 2011 age 33–38
88.6 3.5 4.9 3.0
77.8 4.1 11.6 6.0
91.5 4.1 3.8 0.7
66.0 7.8 21.4 4.9
Wave1, 1996 age 45–50
Census 1996 age 45–50
Wave 6, 2010 age 59–64
Census 2011 age 60–65
69.0 13.9 13.0 4.2
62.6 11.6 19.2 6.5
72.0 15.7 11.3 1.0
61.3 12.5 20.9 5.2
Wave1, 1996 age 70–75
Census 1996 age 70–75
Wave 6, 2011 age 85–90
Census 2011 age 85–90
68.5 12.4 11.6 7.6
66.4 11.0 16.0 6.5
72.3 13.7 8.9 5.0
61.6 10.6 17.5 10.3
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No post-school qualifications Trade/certificate/diploma University Not adequately stated
Wave1, 1996 age 18–23
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of health such as the Health Survey 36 Short Form (SF-36)9 and access to and use of a range of health services. There are also cohort-specific questions. For example, women in the 1973–78 cohort are asked about contraception and reproduction, childbirth and parenting, experiences of intimate partner violence, and work-life balance. Women in the 1946–51 cohort have been asked about menopause transition and retirement plans. Women in the oldest cohort are asked about activities of daily living, housing and whether they provide or receive supportive care. Copies of all the surveys and details of the linked data are available at [http:// www.alswh.org.au/surveys].
To date, much of the research has only involved longitudinal analysis of the survey data. Examples include health patterns relating to: family formation, childbirth and children; menopause; factors predicting physical and mental health changes; behavioural risk factors such as tobacco and alcohol consumption, diet, weight gain and physical activity; multi-morbidity and frailty. However, record linkage has substantially increased the value of the study, especially for health service research, and the generalizability of the findings. Self-reported diagnoses and symptoms are often regarded as less valid than information obtained from clinical records. The validity of self-reported conditions can be assessed using record linkage. For example, for ALSWH participants there is substantial agreement between survey data and hospital records for diabetes, agreement is less good for heart disease and there is evidence that hypertension may be under-recorded in hospital data.10 Diagnoses of breast, lung and colorectal cancers are reported accurately11 and so is arthritisrelated surgery.12 Agreement between prescription data and self-reported osteoporosis is only moderate.13 In vitro fertilization is well reported but self-reported use of ovulation inductions is less likely to be valid.14 Record linkage also provides insights into women’s perceptions and understanding of clinical events; for example, stillbirths are over-reported15 and transient ischaemic attacks and other stroke-like conditions may be self-reported as strokes.16 Linkage of ALSWH survey data to prescription records has been used to examine the anticholinergic effects of polypharmacy in older women,17and external factors influencing changes in the use of osteoporosis medications18 and COX-2 inhibitors.19 It is also possible to examine potentially adverse events associated with various commonly used drugs such as statins.20
Analyses of health service use and costs, using ALSWH data linked to Medicare and hospital data, have produced estimates of geographical and socioeconomic differences in access to services,21,22 relative costs to governments and patients and the effects of policy changes.23,24 Costs have been compared for women with and without various medical conditions, such as diabetes,25 back pain26 or mental health problems,27 and behavioural risk factors such as physical inactivity.28,29 Using geocoded data to examine associations between climate-related factors (for example, increasing salinity in farmland, drought, fire, floods or air pollution) and women’s mental and physical health has produced consistent but unexpected results. No associations have been found— possibly due to different time frames for the exposures (long-term) and health outcomes (short-term) or to the multiple mitigating resources available in a high-income country like Australia.30–32 By mid-2014 there were almost 500 publications in peer-reviewed journals from the study, produced by more than 780 ALSWH investigators and collaborators. The study findings provided much of the evidence for the 2010 National Women’s Health Policy33 as well as for policies on other topics such physical activity,34 incontinence35 and respite care.36 ALSWH data have been pooled with other data for several national and international comparative studies37,38 including comparisons between men and women.39,40
What are the main strengths and weaknesses? The main strengths of the study are the initial large sample size and the good retention of participants for more than 15 years. Record linkage to administrative data sets adds breadth and depth to the study, through objective information on diagnoses and conditions (from hospitals, cancer registries and medications) and health service uses and costs (e.g. from Medicare data). For a study providing information to governments for policy, planning and evaluation, representativeness is important. These cohorts were initially fairly similar to the national population (with some excess of better-educated women).7 Through regular comparisons with census data and data from national health surveys, we are able to document the extent to which representativeness is maintained and to quantify biases that might affect the generalizability of findings. The main weakness of the study is that the survey data are self-reported, though record linkage to administrative data provides ways of checking the validity of this information. The other main limitation is that the study only includes women. This is especially important as ALSWH is
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What has it found? Key findings and publications
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also the largest study of health in rural areas of Australia, but findings about access to and use of health services may vary between women and men. In recognition of this limitation, the Australian Department of Health is now funding a men’s health study which will provide comparative data for younger and middle-aged men.
Can I get hold of the data? Where can I find out more?
Funding This work was supported by the Australian Government Department of Health. G.D.M. is funded by the Australian Research Council Future Fellowship [FT120100812]. Additional support is provided by the Australian National Health and Medical Research Council Centre of Research Excellence grant [APP1000986] and Program grant [grant 569940].
Acknowledgements The research on which this paper is based was conducted as part of the Australian Longitudinal Study on Women’s Health, the University of Newcastle, Australia, and the University of Queensland, Australia. We are grateful to the Australian Government Department of Health for funding and to the women who provided the survey data.
References 1. Lee C, Dobson AJ, Brown WJ et al. Cohort Profile: The Australian Longitudinal Study on Women’s Health. Int J Epidemiol 2005;34:987–91. 2. Young AF, Dobson AJ, Byles JE. Health services research using linked records: Who consents and what is the gain? Aust N Z J Public Health 2001;25:417–20. 3. Powers J, Ball J, Adamson L, Dobson A. Effectiveness of the National Death Index for establishing the vital statistics of older women in the Australian Longitudinal Study on Women’s Health. Aust N Z J Public Health 2000;24:526–28. 4. Brown WJ, Bryson L, Byles JE et al. Women’s Health Australia: recruitment for a national longitudinal cohort study. Women Health 1998;28:23–40. 5. Powers J, Loxton D. The impact of attrition in an 11-year prospective longitudinal study of younger women. Ann Epidemiol 2010;20:318–21.
6. Powers J, Tavener M, Graves A, Loxton D. Loss to follow-up was used to estimate bias in a longitudinal study: a new approach. J Clin Epidemiol 2015. doi: 10.1016/j.jcli nepi.2015.01.010. [Epub ahead of print.] 7. Brown WJ, Dobson AJ, Bryson L, Byles JE. Women’s Health Australia: on the progress of the main cohort studies. J Women’s Health Gender-based Med 1999;8:681–88. 8. Mishra GD, Hockey R, Powers J et al. Recruitment via the Internet and Social Networking Sites: The 1989–1995 Cohort of the Australian Longitudinal Study on Women’s Health. J Med Internet Res 2014;16:e279. 9. Snow K, Kosinski M, Gandek B. SF-36 Health Survey: Manual and Interpretation Guide. Boston, MA: Nimrod Press, 1993. 10. Navin TJ, Stewart-Williams J, Parkinson L, Sibbritt D, Byles JE. The identification of diabetes, heart disease, hypertension and stroke in mid- and older-aged women: comparing self-report and administrative hospital data records. Geriatr Gerontol Int 2015. doi: 10.1111/ggi.12442. [Epub ahead of print.] 11. Stavrou E, Vajdic C, Loxton D, Pearson S. The validity of selfreported cancer diagnoses and factors associated with accurate reporting in a cohort of older Australian women. Cancer Epidemiol 2011;35:e75–e80. 12. Parkinson L, Curryer C, Gibberd A, Cunich M, Byles J. Good agreement between self-report and centralised hospitalisations data for arthritis related surgeries. J Clin Epidemiol 2013;66:1128–34. 13. Peeters G, Tett S, Dobson A, Mishra G. Validity of self-reported osteoporosis in mid-age and older women. Osteoporos Int 2013; 24:91–927. 14. Herbert D, Lucke J, Dobson A. Agreement between self-reported use of in vitro fertilisation or ovulation induction, and medical insurance claims in Australian women aged 28–36 years. Hum Reprod 2012;279:2823–28. 15. Hure A, Chojenta C, Powers J, Byles J, Loxton D. Validity and reliability of stillbirth data using linked self-reported and administrative datasets. J Epidemiol 2015;25:30–37. 16. Jackson C, Mishra G, Tooth L, Byles J, Dobson A. Moderate agreement between self-reported stroke and hospital-recorded stroke in two cohorts of Australian women: a validation study. BMC Methodol 2015;15:7. 17. Parkinson L, Magin P Thomson A et al. Anticholinergic burden in older women: Not seeing the wood for the trees. Med J Aust 2015;202:91–94. 18. Peeters G, Tett S, Duncan E, Mishra G, Dobson A. Osteoporosis medication dispensing for older Australian women from 2002 to 2010: influences of publications, guidelines, marketing activities and policy. Pharmacoepidemiol Drug Saf 2014;23:1303–11. 19. Parkinson L, Dolja-Gore X, Gibson R et al. An observational study of the discrediting of COX-2 NSAIDs in Australia: Vioxx or class effect. BMC Public Health 2011;11:892. 20. Peeters G, Tett SE, Conaghan PG, Mishra GD, Dobson AJ. Is statin use associated with new joint-related symptoms, physical function and quality of life. The Australian Longitudinal Study on Women’s Health. Arthritis Care Res 2015;67:13–20. 21. Young AF, Dobson AJ. The decline in bulk billing and increase in out-of-pocket costs for general practice consultations in rural areas of Australia, 1995–2001. Med J Aust 2003;178:122–26.
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The data are available free of charge on request to bona fide researchers. The process is documented on the website [http://www.alswh.org.au/] which includes all the survey questionnaires, data books of frequency tables for all surveys, meta-data, conditions of data access and request forms. Restrictions are imposed by some of the human research ethics committees (both national and state-based) and some data custodians on where some of the linked data may be analysed.
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cohort of 45–61 year old rural Australian women. Aust N Z J Public Health 2015; doi:10.1111/1753-6405.12369. Fearnley EJ, Soares Magalha˜es RJ, Speldewinde P, Weinstein P, Dobson A. Environmental correlates of mental health measures for women in Western Australia. EcoHealth 2014. Doi: 10.1007/s10393-014-0966-3. Australian Government Department of Health and Ageing. National Women’s Health Policy 2010. Canberra: Commonwealth of Australia, 2010. Brown WJ, Bauman AE, Bull FC, Burton NW. Development of Evidence-based Physical Activity Recommendations for Adults (18–64 Years). Canberra: Australian Government Department of Health. 2012. Australian Government Department of Social Services. National Continence Management Strategy. http://bladderbowel.gov.au/ ncp/ncms/ (15 June 2015, date last accessed). Australian Government Department of Social Services. National Respite for Carers Program. http://www.myagedcare.gov.au/ aged-care-services/national-respite-carers-program (15 June 2015, date last accessed). Anstey K, Byles J, Luszcz M et al. Cohort Profile: The Dynamic Analyses to Optimize Ageing (DYNOPTA) project. Int J Epidemiol 2010;39:44–51. Mishra GD, Anderson D, Schoenaker DAJM et al. InterLACE: a New International Collaboration for a Life Course Approach to Women’s Reproductive Health and Chronic Disease Events. Maturitas 2013;74:235–40. Jamrozik K, McLaughlin D, McCaul K et al. Women who smoke like men die like men who smoke: Findings from two Australian cohort studies. Tob Control 2011;20:258–65. Dobson A, Almeida O, Brown W et al. Impact of behavioural risk factors on death within 10 years for women and men in their 70 s: absolute risk charts. BMC Public Health 2012;12:669.
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22. Korda R, Banks E, Clements M, Young A. Is inequity undermining Australia’s ‘universal’ health care system. Socio-economic inequalities in the use of specialist medical and nonmedical ambulatory health care. Aust N Z J Public Health 2009; 33:458–65. 23. Dolja-Gore X, Byles J, Loxton D, Hockey R, Dobson A. Increased bulk-billing for general practice consultations and reduced inequity in regional and remote areas, 2002–2008. Med J Aust 2011;195:203–04. 24. Byles J, Young A, Wheway V. Annual health assessments for older Australian women: Uptake and equity. Aust N Z J Public Health 2007;31:170–73. 25. Lowe J, Young A, Dolja-Gore X, Byles J. Costs of medications for older women. Aust N Z J Public Health 2008;32:89. 26. Kirby E, Broom A, Sibbritt D, Refshauge K, Adams J. Health care utilisation and out-of-pocket expenditure associated with back pain: A nationally representative survey of Australian women. PLoS One 2013;8:e83559. 27. Byles JE, Dolja-Gore X, Loxton D, Parkinson L, Stewart Williams J. Women’s uptake of Medicare Benefits Schedule mental health items for general practitioners, psychologists and other allied mental health professionals. Med J Aust 2011;194:175–79. 28. Brown WJ, Hockey R, Dobson AJ. Physical activity, body mass index and health care costs in mid-age Australian women. Aust N Z Public Health 2008;32:150–55. 29. Peeters GMEE, Mishra G, Dobson A, Brown WJ. Health care costs associated with prolonged sitting and inactivity. Am J Prev Med 2014;46:265–72. 30. Powers J, Loxton D, Baker J,Rich J, Dobson A. Empirical evidence suggests adverse climate events have not affected Australian women’s health and well-being. Aust N Z J Public Health 2012;36:452–57. 31. Powers J, Dobson A, Berry H, Graves A, Hanigan I, Loxton D. Lack of association between drought and mental health in a
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