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Cohort profile: The Dynamic Analyses to Optimize Ageing (DYNOPTA) project

Cohort profile: The Dynamic Analyses to Optimize Ageing (DYNOPTA) project How did the study come about? Like other industrialized countries, Australia is facing major population ageing. From 2000 to 2025, the number of Australians aged 65 years and over will more than double, as a result of the ageing of the baby boom cohort and increasing life expectancy, while the number of people in working age groups will decline.1 To guide constructive responses to this unprecedented change, the Government's Minister on Ageing released the National Strategy for an Ageing Australia2 which set key issues for national policy development, to be underpinned by policy. In 2003, the Australian Prime Minister's Science, Engineering and Innovation Council (PMSEIC) brought together leading researchers and policymakers who prepared a report articulating an evidence-based vision for healthy ageing in Australia and an associated programme of longitudinal research to guide the achievement of ‘an additional 10 years of healthy life expectancy’ by 2050.2 That same year, the Australian government established ‘Ageing Well, Ageing Productively’ as a National Research Priority goal. In 2004 the Australian Research Council (ARC) and the National Health and Medical Research Council (NHMRC) funded national research networks including the Research Network in Ageing Well (RNAW) to lead and facilitate collaboration in multidisciplinary large scale research on ageing, build research capacities and international collaborations, and improve communication and translation with key constituencies (www.ageingwell.edu.au). In 2004, the NHMRC and ARC announced a strategic funding initiative to facilitate research into ageing that is multisectorial, multidisciplinary and cross-institutional. The goal of this was to develop an authoritative evidence base for policy and practice in the priority area of Ageing Well, Ageing Productively. Collaborators from nine Australian Longitudinal Ageing studies, together with demographers, statistical and modelling experts, subsequently proposed the Dynamic Analyses to Optimize Ageing (DYNOPTA) project which received funding for a 5-year programme grant that commenced in 2007. The broad aims of DYNOPTA reflect the vision of the PMSEIC report to identify effective pathways to compressing morbidity and optimizing ageing. DYNOPTA has constructed a pooled dataset comprising information from nine Australian Longitudinal Studies of Ageing (LSA).3–9 Data were harmonized from the contributing studies (described in Table 1) to create an entirely new and unique dataset. This dataset is not the summation of the individual datasets, but rather comprises new variables and constructs derived from complex harmonization procedures. Where possible, variables were harmonized to enable comparison with Australian benchmarks. For example, the physical activity data have been harmonized to derive measures reflecting the recommended level of physical activity per week,12 and the alcohol consumption data have been harmonized to provide classifications in accordance with those endorsed by the National Health and Medical Research Council.13 Table 1 Studies contributing to the DYNOPTA dataset Study . Location . Wave . Year . N . Age . Deceased at wave . Australian Longitudinal Study of Ageing (ALSA)3 Adelaide 1 1992–93 2087 65–103 0 2 1993–94 1779 65–104 131 3 1994–95 1679 66–105 250 4 1995–96 1504 68–106 630 5 1998 1171 70–100 723 6 2000–01 791 72–101 1248 7 2003–04 487 75–102 1264 Australian Longitudinal Study of Women's Health Mid Cohort (ALSWH-mid), Old Cohort (ALSWH-old)5 National (Mid) 1 1996 13 706 45–51 0 2 1998 12 329 46–53 50 3 2001 11 185 49–56 116 4 2004 10 897 52–59 204 National (Old) 1 1996 12 431 68–76 0 2 1999 10 434 71–79 529 3 2002 8629 74–82 1098 4 2005 7152 77–85 1867 Australian Diabetes and Obesity and Lifestyle Study (AUSDIAB)4 National 1 1999–2000 7296 45–95 0 2 2004–05 4380 49–93 a Blue Mountains Eye Study (BMES)6 Blue Mountains 1 1992–93 3654 45–100 0 2 1997–2000 2334 50–98 a 3 2001–04 1952 55–99 a Canberra Longitudinal Study (CLS)7 Canberra, Queanbeyan 1 1990–91 1134 70–103 0 2 1994–95 637 74–102 306 3 1998 380 78–101 552 4 2002 213 82–105 744 Household, Income and Labour Dynamics of Australia (HILDA)8 National 1 2001–02 6164 45–90+ 0 2 2002–03 5454 45–90+ a 3 2003–04 5089 46–90+ a 4 2004–05 4769 47–90+ a 5 2005–06 4658 48–90+ a Melbourne Longitudinal Study Healthy Ageing (MELSHA)9 Melbourne 1 1994 1000 65–94 0 2 1995 979 66–95 35 3 1996 796 67–96 74 4 1997 718 68–97 91 5 1998 649 69–98 113 6 1999 648 70–99 128 7 2000 542 71–96 143 8 2002–03 372 73–98 170 9 2003 347 74–99 193 10 2004–05 326 75–96 193 11 2005–06 242 76–97 415 Personality and Total Health through life (PATH)10 Canberra, Queanbeyan 1 2001–02 2550 60–66 0 2 2005–06 2222 64–70 70 Sydney Older Person's Study (SOPS)11 Sydney 1 1991–93 630 75–97 0 2 1994–96 449 78–99 123 3 1996–97 367 78–100 163 4 1997–99 299 80–101 226 5 2001–03 62 84–106 318 Study . Location . Wave . Year . N . Age . Deceased at wave . Australian Longitudinal Study of Ageing (ALSA)3 Adelaide 1 1992–93 2087 65–103 0 2 1993–94 1779 65–104 131 3 1994–95 1679 66–105 250 4 1995–96 1504 68–106 630 5 1998 1171 70–100 723 6 2000–01 791 72–101 1248 7 2003–04 487 75–102 1264 Australian Longitudinal Study of Women's Health Mid Cohort (ALSWH-mid), Old Cohort (ALSWH-old)5 National (Mid) 1 1996 13 706 45–51 0 2 1998 12 329 46–53 50 3 2001 11 185 49–56 116 4 2004 10 897 52–59 204 National (Old) 1 1996 12 431 68–76 0 2 1999 10 434 71–79 529 3 2002 8629 74–82 1098 4 2005 7152 77–85 1867 Australian Diabetes and Obesity and Lifestyle Study (AUSDIAB)4 National 1 1999–2000 7296 45–95 0 2 2004–05 4380 49–93 a Blue Mountains Eye Study (BMES)6 Blue Mountains 1 1992–93 3654 45–100 0 2 1997–2000 2334 50–98 a 3 2001–04 1952 55–99 a Canberra Longitudinal Study (CLS)7 Canberra, Queanbeyan 1 1990–91 1134 70–103 0 2 1994–95 637 74–102 306 3 1998 380 78–101 552 4 2002 213 82–105 744 Household, Income and Labour Dynamics of Australia (HILDA)8 National 1 2001–02 6164 45–90+ 0 2 2002–03 5454 45–90+ a 3 2003–04 5089 46–90+ a 4 2004–05 4769 47–90+ a 5 2005–06 4658 48–90+ a Melbourne Longitudinal Study Healthy Ageing (MELSHA)9 Melbourne 1 1994 1000 65–94 0 2 1995 979 66–95 35 3 1996 796 67–96 74 4 1997 718 68–97 91 5 1998 649 69–98 113 6 1999 648 70–99 128 7 2000 542 71–96 143 8 2002–03 372 73–98 170 9 2003 347 74–99 193 10 2004–05 326 75–96 193 11 2005–06 242 76–97 415 Personality and Total Health through life (PATH)10 Canberra, Queanbeyan 1 2001–02 2550 60–66 0 2 2005–06 2222 64–70 70 Sydney Older Person's Study (SOPS)11 Sydney 1 1991–93 630 75–97 0 2 1994–96 449 78–99 123 3 1996–97 367 78–100 163 4 1997–99 299 80–101 226 5 2001–03 62 84–106 318 aDeath data not available. Open in new tab Table 1 Studies contributing to the DYNOPTA dataset Study . Location . Wave . Year . N . Age . Deceased at wave . Australian Longitudinal Study of Ageing (ALSA)3 Adelaide 1 1992–93 2087 65–103 0 2 1993–94 1779 65–104 131 3 1994–95 1679 66–105 250 4 1995–96 1504 68–106 630 5 1998 1171 70–100 723 6 2000–01 791 72–101 1248 7 2003–04 487 75–102 1264 Australian Longitudinal Study of Women's Health Mid Cohort (ALSWH-mid), Old Cohort (ALSWH-old)5 National (Mid) 1 1996 13 706 45–51 0 2 1998 12 329 46–53 50 3 2001 11 185 49–56 116 4 2004 10 897 52–59 204 National (Old) 1 1996 12 431 68–76 0 2 1999 10 434 71–79 529 3 2002 8629 74–82 1098 4 2005 7152 77–85 1867 Australian Diabetes and Obesity and Lifestyle Study (AUSDIAB)4 National 1 1999–2000 7296 45–95 0 2 2004–05 4380 49–93 a Blue Mountains Eye Study (BMES)6 Blue Mountains 1 1992–93 3654 45–100 0 2 1997–2000 2334 50–98 a 3 2001–04 1952 55–99 a Canberra Longitudinal Study (CLS)7 Canberra, Queanbeyan 1 1990–91 1134 70–103 0 2 1994–95 637 74–102 306 3 1998 380 78–101 552 4 2002 213 82–105 744 Household, Income and Labour Dynamics of Australia (HILDA)8 National 1 2001–02 6164 45–90+ 0 2 2002–03 5454 45–90+ a 3 2003–04 5089 46–90+ a 4 2004–05 4769 47–90+ a 5 2005–06 4658 48–90+ a Melbourne Longitudinal Study Healthy Ageing (MELSHA)9 Melbourne 1 1994 1000 65–94 0 2 1995 979 66–95 35 3 1996 796 67–96 74 4 1997 718 68–97 91 5 1998 649 69–98 113 6 1999 648 70–99 128 7 2000 542 71–96 143 8 2002–03 372 73–98 170 9 2003 347 74–99 193 10 2004–05 326 75–96 193 11 2005–06 242 76–97 415 Personality and Total Health through life (PATH)10 Canberra, Queanbeyan 1 2001–02 2550 60–66 0 2 2005–06 2222 64–70 70 Sydney Older Person's Study (SOPS)11 Sydney 1 1991–93 630 75–97 0 2 1994–96 449 78–99 123 3 1996–97 367 78–100 163 4 1997–99 299 80–101 226 5 2001–03 62 84–106 318 Study . Location . Wave . Year . N . Age . Deceased at wave . Australian Longitudinal Study of Ageing (ALSA)3 Adelaide 1 1992–93 2087 65–103 0 2 1993–94 1779 65–104 131 3 1994–95 1679 66–105 250 4 1995–96 1504 68–106 630 5 1998 1171 70–100 723 6 2000–01 791 72–101 1248 7 2003–04 487 75–102 1264 Australian Longitudinal Study of Women's Health Mid Cohort (ALSWH-mid), Old Cohort (ALSWH-old)5 National (Mid) 1 1996 13 706 45–51 0 2 1998 12 329 46–53 50 3 2001 11 185 49–56 116 4 2004 10 897 52–59 204 National (Old) 1 1996 12 431 68–76 0 2 1999 10 434 71–79 529 3 2002 8629 74–82 1098 4 2005 7152 77–85 1867 Australian Diabetes and Obesity and Lifestyle Study (AUSDIAB)4 National 1 1999–2000 7296 45–95 0 2 2004–05 4380 49–93 a Blue Mountains Eye Study (BMES)6 Blue Mountains 1 1992–93 3654 45–100 0 2 1997–2000 2334 50–98 a 3 2001–04 1952 55–99 a Canberra Longitudinal Study (CLS)7 Canberra, Queanbeyan 1 1990–91 1134 70–103 0 2 1994–95 637 74–102 306 3 1998 380 78–101 552 4 2002 213 82–105 744 Household, Income and Labour Dynamics of Australia (HILDA)8 National 1 2001–02 6164 45–90+ 0 2 2002–03 5454 45–90+ a 3 2003–04 5089 46–90+ a 4 2004–05 4769 47–90+ a 5 2005–06 4658 48–90+ a Melbourne Longitudinal Study Healthy Ageing (MELSHA)9 Melbourne 1 1994 1000 65–94 0 2 1995 979 66–95 35 3 1996 796 67–96 74 4 1997 718 68–97 91 5 1998 649 69–98 113 6 1999 648 70–99 128 7 2000 542 71–96 143 8 2002–03 372 73–98 170 9 2003 347 74–99 193 10 2004–05 326 75–96 193 11 2005–06 242 76–97 415 Personality and Total Health through life (PATH)10 Canberra, Queanbeyan 1 2001–02 2550 60–66 0 2 2005–06 2222 64–70 70 Sydney Older Person's Study (SOPS)11 Sydney 1 1991–93 630 75–97 0 2 1994–96 449 78–99 123 3 1996–97 367 78–100 163 4 1997–99 299 80–101 226 5 2001–03 62 84–106 318 aDeath data not available. Open in new tab What does DYNOPTA cover? The research programme focuses on four outcomes that contribute greatly to the burden of disease and disability, namely dementia and cognition, mental health, sensory impairment and mobility/activity limitations. Mortality is also included as a key outcome. The four health domains were chosen from recent research into the principal factors contributing to disease burden, or studies of the impact of diseases or conditions leading to injury, disability/impairment or premature death. Lopez and colleagues14 found that Alzheimer disease and other dementias, depressive disorders, hearing loss and conditions such as osteoarthritis were among the top 10 leading causes of burden of disease with loss of healthy-life years in higher income countries worldwide. Similar findings were made in a recent report from the Australian Institute of Health and Welfare.15 The DYNOPTA dataset also includes key sociodemographic variables such as marital and partner status, labour force participation and date of death. In addition to being risk factors for the four outcomes, analyses of these will enable a more detailed understanding of demographic and social processes in later life. The research programme is underpinned by an interdisciplinary life course approach to human development and ageing that recognizes interdependencies among demographic, social, lifestyle, economic and health factors.16,17 Who is in the sample? The geographic scope of the sample is shown in Figure 1. Of the nine contributing studies, three are nationally representative, comprising 65% of participants at baseline. The remaining six studies are based in specific Australian cities or regions, including Adelaide, the Blue Mountains, the Australian Capital Territory (ACT) and Queanbeyan, Melbourne and Sydney. Most of the sample (90%) live in either highly accessible or accessible areas (i.e. access to metropolitan services) according to the Australian Standard Geographical Classification system.18 Figure 1 Open in new tabDownload slide Locations of contributing Australian Longitudinal Ageing Studies Figure 1 Open in new tabDownload slide Locations of contributing Australian Longitudinal Ageing Studies The sample comprises 50 652 baseline participants (wave 1 of each study between 1990 and 2001). Of these, 39 085 (77.2%) were female, reflecting inclusion of the all-female Australian Longitudinal Study of Women's Health (particularly for the 45–54 and 65–74 age groups), and women's greater longevity. Selected sample characteristics are shown in Table 2. This reports frequency of participants’ characteristics as a proportion of the baseline sample. In the baseline sample, a total of 69.5% participants were married or partnered, while 16.7% were widowed with increasing proportions of widowed participants at older ages. About half of those with available data at baseline were in the workforce. Only 0.3% had no formal education, and 10.3% reported having attained tertiary education. Table 2 Selected demographic characteristics of the DYNOPTA cohort (unweighted) . . Age group . . Total . 45–54 . 55–64 . 65–74 . 75–84 . 85–94 . 95+ . . N (%) . N (%) . N (%) . N (%) . N (%) . N (%) . N (%) . Sex     Male 11 567 (22.8) 2719 (13.9) 3459 (48.5) 2820 (15.8) 2062 (41.4) 492 (45.4) 15 (20.5)     Female 39 085 (77.2) 16 784 (86.1) 3674 (51.5) 15 059 (84.2) 2919 (58.6) 591 (54.6) 58 (79.5) Marital status     Married or de-facto 35 180 (69.5) 15 929 (81.7) 5492 (77.0) 10 867 (60.8) 2546 (51.1) 337 (31.1) 9 (12.3)     Divorced or separated 4628 (9.1) 2350 (12.0) 891 (12.5) 1132 (6.3) 236 (4.7) 19 (1.8) 0 (0.0)     Widowed 8468 (16.7) 364 (1.9) 456 (6.4) 5054 (28.3) 1920 (38.5) 635 (58.6) 39 (53.4)     Single/never married 1957 (3.9) 783 (4.0) 286 (4.0) 600 (3.4) 223 (4.5) 58 (5.4) 7 (9.6) Level of education     No formal education 130 (0.3) 16 (0.1) 14 (0.2) 82 (0.5) 14 (0.3) 4 (0.4) 0 (0.0)     Primary school or secondary 27 145 (53.6) 10 881 (55.8) 2155 (30.2) 11 538 (64.5) 2234 (44.9) 316 (29.2) 21 (28.8)     Non-tertiary study 11 934 (23.6) 4950 (25.4) 2569 (36.0) 3077 (17.2) 1094 (22.0) 235 (21.7) 9 (12.3)     Tertiary study 5196 (10.3) 2952 (15.1) 1151 (16.1) 830 (4.6) 210 (4.2) 50 (4.6) 3 (4.1) Employment status     Employed 17 213 (34.0) 13 416 (68.8) 3178 (44.6) 527 (2.9) 80 (1.6) 12 (1.1) 0 (0.0)     Unemployed 1229 (2.4) 455 (2.3) 144 (2.0) 16 (0.1) 509 (10.2) 100 (9.2) 5 (6.8)     Not in labour force 17 768 (35.1) 4133 (21.2) 3735 (52.4) 5314 (29.7) 3620 (72.7) 915 (84.5) 51 (69.9) . . Age group . . Total . 45–54 . 55–64 . 65–74 . 75–84 . 85–94 . 95+ . . N (%) . N (%) . N (%) . N (%) . N (%) . N (%) . N (%) . Sex     Male 11 567 (22.8) 2719 (13.9) 3459 (48.5) 2820 (15.8) 2062 (41.4) 492 (45.4) 15 (20.5)     Female 39 085 (77.2) 16 784 (86.1) 3674 (51.5) 15 059 (84.2) 2919 (58.6) 591 (54.6) 58 (79.5) Marital status     Married or de-facto 35 180 (69.5) 15 929 (81.7) 5492 (77.0) 10 867 (60.8) 2546 (51.1) 337 (31.1) 9 (12.3)     Divorced or separated 4628 (9.1) 2350 (12.0) 891 (12.5) 1132 (6.3) 236 (4.7) 19 (1.8) 0 (0.0)     Widowed 8468 (16.7) 364 (1.9) 456 (6.4) 5054 (28.3) 1920 (38.5) 635 (58.6) 39 (53.4)     Single/never married 1957 (3.9) 783 (4.0) 286 (4.0) 600 (3.4) 223 (4.5) 58 (5.4) 7 (9.6) Level of education     No formal education 130 (0.3) 16 (0.1) 14 (0.2) 82 (0.5) 14 (0.3) 4 (0.4) 0 (0.0)     Primary school or secondary 27 145 (53.6) 10 881 (55.8) 2155 (30.2) 11 538 (64.5) 2234 (44.9) 316 (29.2) 21 (28.8)     Non-tertiary study 11 934 (23.6) 4950 (25.4) 2569 (36.0) 3077 (17.2) 1094 (22.0) 235 (21.7) 9 (12.3)     Tertiary study 5196 (10.3) 2952 (15.1) 1151 (16.1) 830 (4.6) 210 (4.2) 50 (4.6) 3 (4.1) Employment status     Employed 17 213 (34.0) 13 416 (68.8) 3178 (44.6) 527 (2.9) 80 (1.6) 12 (1.1) 0 (0.0)     Unemployed 1229 (2.4) 455 (2.3) 144 (2.0) 16 (0.1) 509 (10.2) 100 (9.2) 5 (6.8)     Not in labour force 17 768 (35.1) 4133 (21.2) 3735 (52.4) 5314 (29.7) 3620 (72.7) 915 (84.5) 51 (69.9) Percentages reported for the full sample and do not add to 100% due to missing data. Open in new tab Table 2 Selected demographic characteristics of the DYNOPTA cohort (unweighted) . . Age group . . Total . 45–54 . 55–64 . 65–74 . 75–84 . 85–94 . 95+ . . N (%) . N (%) . N (%) . N (%) . N (%) . N (%) . N (%) . Sex     Male 11 567 (22.8) 2719 (13.9) 3459 (48.5) 2820 (15.8) 2062 (41.4) 492 (45.4) 15 (20.5)     Female 39 085 (77.2) 16 784 (86.1) 3674 (51.5) 15 059 (84.2) 2919 (58.6) 591 (54.6) 58 (79.5) Marital status     Married or de-facto 35 180 (69.5) 15 929 (81.7) 5492 (77.0) 10 867 (60.8) 2546 (51.1) 337 (31.1) 9 (12.3)     Divorced or separated 4628 (9.1) 2350 (12.0) 891 (12.5) 1132 (6.3) 236 (4.7) 19 (1.8) 0 (0.0)     Widowed 8468 (16.7) 364 (1.9) 456 (6.4) 5054 (28.3) 1920 (38.5) 635 (58.6) 39 (53.4)     Single/never married 1957 (3.9) 783 (4.0) 286 (4.0) 600 (3.4) 223 (4.5) 58 (5.4) 7 (9.6) Level of education     No formal education 130 (0.3) 16 (0.1) 14 (0.2) 82 (0.5) 14 (0.3) 4 (0.4) 0 (0.0)     Primary school or secondary 27 145 (53.6) 10 881 (55.8) 2155 (30.2) 11 538 (64.5) 2234 (44.9) 316 (29.2) 21 (28.8)     Non-tertiary study 11 934 (23.6) 4950 (25.4) 2569 (36.0) 3077 (17.2) 1094 (22.0) 235 (21.7) 9 (12.3)     Tertiary study 5196 (10.3) 2952 (15.1) 1151 (16.1) 830 (4.6) 210 (4.2) 50 (4.6) 3 (4.1) Employment status     Employed 17 213 (34.0) 13 416 (68.8) 3178 (44.6) 527 (2.9) 80 (1.6) 12 (1.1) 0 (0.0)     Unemployed 1229 (2.4) 455 (2.3) 144 (2.0) 16 (0.1) 509 (10.2) 100 (9.2) 5 (6.8)     Not in labour force 17 768 (35.1) 4133 (21.2) 3735 (52.4) 5314 (29.7) 3620 (72.7) 915 (84.5) 51 (69.9) . . Age group . . Total . 45–54 . 55–64 . 65–74 . 75–84 . 85–94 . 95+ . . N (%) . N (%) . N (%) . N (%) . N (%) . N (%) . N (%) . Sex     Male 11 567 (22.8) 2719 (13.9) 3459 (48.5) 2820 (15.8) 2062 (41.4) 492 (45.4) 15 (20.5)     Female 39 085 (77.2) 16 784 (86.1) 3674 (51.5) 15 059 (84.2) 2919 (58.6) 591 (54.6) 58 (79.5) Marital status     Married or de-facto 35 180 (69.5) 15 929 (81.7) 5492 (77.0) 10 867 (60.8) 2546 (51.1) 337 (31.1) 9 (12.3)     Divorced or separated 4628 (9.1) 2350 (12.0) 891 (12.5) 1132 (6.3) 236 (4.7) 19 (1.8) 0 (0.0)     Widowed 8468 (16.7) 364 (1.9) 456 (6.4) 5054 (28.3) 1920 (38.5) 635 (58.6) 39 (53.4)     Single/never married 1957 (3.9) 783 (4.0) 286 (4.0) 600 (3.4) 223 (4.5) 58 (5.4) 7 (9.6) Level of education     No formal education 130 (0.3) 16 (0.1) 14 (0.2) 82 (0.5) 14 (0.3) 4 (0.4) 0 (0.0)     Primary school or secondary 27 145 (53.6) 10 881 (55.8) 2155 (30.2) 11 538 (64.5) 2234 (44.9) 316 (29.2) 21 (28.8)     Non-tertiary study 11 934 (23.6) 4950 (25.4) 2569 (36.0) 3077 (17.2) 1094 (22.0) 235 (21.7) 9 (12.3)     Tertiary study 5196 (10.3) 2952 (15.1) 1151 (16.1) 830 (4.6) 210 (4.2) 50 (4.6) 3 (4.1) Employment status     Employed 17 213 (34.0) 13 416 (68.8) 3178 (44.6) 527 (2.9) 80 (1.6) 12 (1.1) 0 (0.0)     Unemployed 1229 (2.4) 455 (2.3) 144 (2.0) 16 (0.1) 509 (10.2) 100 (9.2) 5 (6.8)     Not in labour force 17 768 (35.1) 4133 (21.2) 3735 (52.4) 5314 (29.7) 3620 (72.7) 915 (84.5) 51 (69.9) Percentages reported for the full sample and do not add to 100% due to missing data. Open in new tab Selected health characteristics and social contact are reported in Table 3. On a self-reported health measure, 35.9% reported their health as excellent and 3.0% as poor. Of those with available data, about half (52%) reported having never smoked. Of those with visual acuity data, 3% were classified with a visual impairment. Of the baseline sample data reporting information on social contact, 68.6% reported face to face contact with friends or family at least once per week. Table 3 Selected health and social characteristics of the DYNOPTA cohort (unweighted) . . Age group . . Total . 45–54 . 55–64 . 65–74 . 75–84 . 85–94 . 95+ . . N (%) . N (%) . N (%) . N (%) . N (%) . N (%) . N (%) . SRH     Excellent 18 174 (35.9) 9078 (46.5) 2797 (39.2) 5168 (28.9) 917 (18.4) 200 (18.5) 14 (19.2)     Good 17 138 (33.8) 7304 (37.5) 2047 (28.7) 6205 (34.7) 1340 (26.9) 231 (21.3) 11 (15.1)     Fair 7396 (14.6) 1973 (10.1) 805 (11.3) 3554 (19.9) 845 (17.0) 212 (19.6) 7 (9.6)     Poor 1517 (3.0) 359 (1.8) 197 (2.8) 638 (3.6) 242 (4.9) 78 (7.2) 3 (4.1) Visual impairment     Impaired vision: <6/12 1501 (3.0) 12 (0.1) 132 (1.9) 405 (2.3) 684 (13.7) 259 (23.9) 9 (12.3) Smoking status     Never smoker 26 362 (52.0) 9965 (51.1) 3530 (49.5) 9765 (54.6) 2499 (50.2) 573 (52.9) 30 (41.1)     Former smoker 15 860 (31.3) 5438 (27.9) 2479 (34.8) 5705 (31.9) 1880 (37.7) 346 (31.9) 12 (16.4)     Current smoker 6285 (12.4) 3440 (17.6) 972 (13.6) 1466 (8.2) 355 (7.1) 51 (4.7) 1 (1.4) Face to face contact with friends and relatives     Once a week or more 6479 (12.8) 1184 (6.1) 936 (13.1) 1642 (9.2) 2114 (42.4) 579 (53.5) 24 (32.9)     Less than once a month or more 2941 (5.8) 1110 (5.7) 668 (9.4) 587 (3.3) 448 (9.0) 125 (11.5) 3 (4.1) . . Age group . . Total . 45–54 . 55–64 . 65–74 . 75–84 . 85–94 . 95+ . . N (%) . N (%) . N (%) . N (%) . N (%) . N (%) . N (%) . SRH     Excellent 18 174 (35.9) 9078 (46.5) 2797 (39.2) 5168 (28.9) 917 (18.4) 200 (18.5) 14 (19.2)     Good 17 138 (33.8) 7304 (37.5) 2047 (28.7) 6205 (34.7) 1340 (26.9) 231 (21.3) 11 (15.1)     Fair 7396 (14.6) 1973 (10.1) 805 (11.3) 3554 (19.9) 845 (17.0) 212 (19.6) 7 (9.6)     Poor 1517 (3.0) 359 (1.8) 197 (2.8) 638 (3.6) 242 (4.9) 78 (7.2) 3 (4.1) Visual impairment     Impaired vision: <6/12 1501 (3.0) 12 (0.1) 132 (1.9) 405 (2.3) 684 (13.7) 259 (23.9) 9 (12.3) Smoking status     Never smoker 26 362 (52.0) 9965 (51.1) 3530 (49.5) 9765 (54.6) 2499 (50.2) 573 (52.9) 30 (41.1)     Former smoker 15 860 (31.3) 5438 (27.9) 2479 (34.8) 5705 (31.9) 1880 (37.7) 346 (31.9) 12 (16.4)     Current smoker 6285 (12.4) 3440 (17.6) 972 (13.6) 1466 (8.2) 355 (7.1) 51 (4.7) 1 (1.4) Face to face contact with friends and relatives     Once a week or more 6479 (12.8) 1184 (6.1) 936 (13.1) 1642 (9.2) 2114 (42.4) 579 (53.5) 24 (32.9)     Less than once a month or more 2941 (5.8) 1110 (5.7) 668 (9.4) 587 (3.3) 448 (9.0) 125 (11.5) 3 (4.1) Open in new tab Table 3 Selected health and social characteristics of the DYNOPTA cohort (unweighted) . . Age group . . Total . 45–54 . 55–64 . 65–74 . 75–84 . 85–94 . 95+ . . N (%) . N (%) . N (%) . N (%) . N (%) . N (%) . N (%) . SRH     Excellent 18 174 (35.9) 9078 (46.5) 2797 (39.2) 5168 (28.9) 917 (18.4) 200 (18.5) 14 (19.2)     Good 17 138 (33.8) 7304 (37.5) 2047 (28.7) 6205 (34.7) 1340 (26.9) 231 (21.3) 11 (15.1)     Fair 7396 (14.6) 1973 (10.1) 805 (11.3) 3554 (19.9) 845 (17.0) 212 (19.6) 7 (9.6)     Poor 1517 (3.0) 359 (1.8) 197 (2.8) 638 (3.6) 242 (4.9) 78 (7.2) 3 (4.1) Visual impairment     Impaired vision: <6/12 1501 (3.0) 12 (0.1) 132 (1.9) 405 (2.3) 684 (13.7) 259 (23.9) 9 (12.3) Smoking status     Never smoker 26 362 (52.0) 9965 (51.1) 3530 (49.5) 9765 (54.6) 2499 (50.2) 573 (52.9) 30 (41.1)     Former smoker 15 860 (31.3) 5438 (27.9) 2479 (34.8) 5705 (31.9) 1880 (37.7) 346 (31.9) 12 (16.4)     Current smoker 6285 (12.4) 3440 (17.6) 972 (13.6) 1466 (8.2) 355 (7.1) 51 (4.7) 1 (1.4) Face to face contact with friends and relatives     Once a week or more 6479 (12.8) 1184 (6.1) 936 (13.1) 1642 (9.2) 2114 (42.4) 579 (53.5) 24 (32.9)     Less than once a month or more 2941 (5.8) 1110 (5.7) 668 (9.4) 587 (3.3) 448 (9.0) 125 (11.5) 3 (4.1) . . Age group . . Total . 45–54 . 55–64 . 65–74 . 75–84 . 85–94 . 95+ . . N (%) . N (%) . N (%) . N (%) . N (%) . N (%) . N (%) . SRH     Excellent 18 174 (35.9) 9078 (46.5) 2797 (39.2) 5168 (28.9) 917 (18.4) 200 (18.5) 14 (19.2)     Good 17 138 (33.8) 7304 (37.5) 2047 (28.7) 6205 (34.7) 1340 (26.9) 231 (21.3) 11 (15.1)     Fair 7396 (14.6) 1973 (10.1) 805 (11.3) 3554 (19.9) 845 (17.0) 212 (19.6) 7 (9.6)     Poor 1517 (3.0) 359 (1.8) 197 (2.8) 638 (3.6) 242 (4.9) 78 (7.2) 3 (4.1) Visual impairment     Impaired vision: <6/12 1501 (3.0) 12 (0.1) 132 (1.9) 405 (2.3) 684 (13.7) 259 (23.9) 9 (12.3) Smoking status     Never smoker 26 362 (52.0) 9965 (51.1) 3530 (49.5) 9765 (54.6) 2499 (50.2) 573 (52.9) 30 (41.1)     Former smoker 15 860 (31.3) 5438 (27.9) 2479 (34.8) 5705 (31.9) 1880 (37.7) 346 (31.9) 12 (16.4)     Current smoker 6285 (12.4) 3440 (17.6) 972 (13.6) 1466 (8.2) 355 (7.1) 51 (4.7) 1 (1.4) Face to face contact with friends and relatives     Once a week or more 6479 (12.8) 1184 (6.1) 936 (13.1) 1642 (9.2) 2114 (42.4) 579 (53.5) 24 (32.9)     Less than once a month or more 2941 (5.8) 1110 (5.7) 668 (9.4) 587 (3.3) 448 (9.0) 125 (11.5) 3 (4.1) Open in new tab What is included in the pooled dataset? The pooled dataset contains demographic information, outcome variables for the four key domains (cognition, mental health, physical disability and sensory function) and data on risk factors and health behaviours. The risk factors include other medical conditions (e.g. arthritis, hypertension) as well as psychosocial measures, such as social networks. Mortality data are also included, as well as self-reported use of services. Table 4 lists the domains included in the dataset. Table 4 Domains included in the DYNOPTA pooled dataset Domain . Example measures within domain . Health outcomes     Cognition/dementia Probable dementia, cognitive impairment, processing speed, memory, verbal ability     Mobility Activities of daily living, instrumental activities of daily living, driving     Mental health Depression, psychological distress, self-rated health, life satisfaction     Sensory functioning Visual acuity, self reported visual impairment, ability to read print, audiometry, self-reported hearing difficulties     Mortality Age at death Predictors and correlates     Sociodemographic Age, sex, partner status, education, occupation, socioeconomic status, retirement status, domicile     Medical conditions, health behaviours, social support Arthritis, cancer, stroke, high blood pressure, cholesterol, falls history, anthropometric data, smoking, alcohol use, physical activity, grip strength, social support and networks     Service use Hospital visits, medical practitioner visits, other health professional visits, community services use Domain . Example measures within domain . Health outcomes     Cognition/dementia Probable dementia, cognitive impairment, processing speed, memory, verbal ability     Mobility Activities of daily living, instrumental activities of daily living, driving     Mental health Depression, psychological distress, self-rated health, life satisfaction     Sensory functioning Visual acuity, self reported visual impairment, ability to read print, audiometry, self-reported hearing difficulties     Mortality Age at death Predictors and correlates     Sociodemographic Age, sex, partner status, education, occupation, socioeconomic status, retirement status, domicile     Medical conditions, health behaviours, social support Arthritis, cancer, stroke, high blood pressure, cholesterol, falls history, anthropometric data, smoking, alcohol use, physical activity, grip strength, social support and networks     Service use Hospital visits, medical practitioner visits, other health professional visits, community services use Open in new tab Table 4 Domains included in the DYNOPTA pooled dataset Domain . Example measures within domain . Health outcomes     Cognition/dementia Probable dementia, cognitive impairment, processing speed, memory, verbal ability     Mobility Activities of daily living, instrumental activities of daily living, driving     Mental health Depression, psychological distress, self-rated health, life satisfaction     Sensory functioning Visual acuity, self reported visual impairment, ability to read print, audiometry, self-reported hearing difficulties     Mortality Age at death Predictors and correlates     Sociodemographic Age, sex, partner status, education, occupation, socioeconomic status, retirement status, domicile     Medical conditions, health behaviours, social support Arthritis, cancer, stroke, high blood pressure, cholesterol, falls history, anthropometric data, smoking, alcohol use, physical activity, grip strength, social support and networks     Service use Hospital visits, medical practitioner visits, other health professional visits, community services use Domain . Example measures within domain . Health outcomes     Cognition/dementia Probable dementia, cognitive impairment, processing speed, memory, verbal ability     Mobility Activities of daily living, instrumental activities of daily living, driving     Mental health Depression, psychological distress, self-rated health, life satisfaction     Sensory functioning Visual acuity, self reported visual impairment, ability to read print, audiometry, self-reported hearing difficulties     Mortality Age at death Predictors and correlates     Sociodemographic Age, sex, partner status, education, occupation, socioeconomic status, retirement status, domicile     Medical conditions, health behaviours, social support Arthritis, cancer, stroke, high blood pressure, cholesterol, falls history, anthropometric data, smoking, alcohol use, physical activity, grip strength, social support and networks     Service use Hospital visits, medical practitioner visits, other health professional visits, community services use Open in new tab How often have they been followed? The number and dates of waves for each study are shown in Table 1. Studies have an average of 4.4 waves over an average period of 9.4 years (SD = 2.99), though this is inflated by MELSHA with 11 waves over 12 years, and by ALSA with seven waves over 11 years. The mean number of surveys completed by each participant was 3.1 (SD = 1.51), with a mode of 4 and median of 3 surveys per participant. Development of weights for the DYNOPTA dataset The contributing studies use a variety of survey designs and differ in their geographic and demographic coverage. Some surveys are results of simple random samples, for others stratified, or cluster designs were used. The demographic coverage also differs between studies. Estimation weights are being developed for DYNOPTA that will reflect the different sample sizes and selection probablities of the studies and ensure that the different studies are approriately combined when they cover the same geographical and demographic subpopulations. Other complexities of the design will also have to be accounted for in analyses and any study effects identified and adjustments made. How were data harmonized? Data were mostly harmonized using the ‘by fiat’ method.19–22 This method assumes a common scoring system across studies19–22 and is recommended when the possibility of dispute is small, the number of categories is relatively consistent across studies and a clear authority can endorse the system. In some instances, variables were harmonized at several levels. The most generic level is most inclusive of the largest number of participants, while a more detailed level would be that which is most inclusive of information, but which may result in the inclusion of fewer participants. A latent variable approach is being used to harmonize different depression measures. Due to the nature of the studies that contributed to the creation of the pooled dataset, coverage of information varies considerably across the total sample. For example, at the first measurement occasion (wave 1 for all studies), self-rated health is available for 44 224 participants, whilst information about visits to a general practitioner is available for only half that number (n = 28 639). What are the main strengths and weaknesses of DYNOPTA? A major strength of this innovative study is the large sample size and multiple occasions of measurement. The increase in statistical power enables more reliable analyses than has hitherto not been possible in the Australian setting. This allows for the study of conditions that are relatively rare such as Parkinson disease, or for those conditions that Australian policy makers had previously relied on European data (e.g. Dementia15). The study also provides data on under-represented groups, for whom comparisons are often not feasible due to small sample sizes (e.g. adults aged 85+). The inclusion of cohorts studied at varying points of time will allow for the evaluation of cohort effects. The national coverage of this large sample adds to the utility of the findings as does the depth and breadth of this dataset. Although some epidemiological studies with large datasets include information gained purely from mailed questionnaires, limiting the type of data collected, a significant proportion of the data included in DYNOPTA is based on personal interview and assessments. Another strength of the study is the development of the infrastructure, documentation of Australian longitudinal research and the development of methodologies for harmonization. This will facilitate future research involving the harmonization of the DYNOPTA dataset with other Australian sources, and international sources such as U.S. Health and Retirement Survey,23 the Surveys of Health and Retirement in Europe24 and the Comparison of Longitudinal European Studies on Aging (CLESA) which has undertaken a similar harmonization and pooling approach to analyse data from six longitudinal studies.25 The pooled dataset does however have limitations. As not all contributing datasets were nationally representative, the sample needs to be weighted to produce population estimates. Most of the data on medical conditions is self-report although clinical data are available for sensory function, blood pressure, cognition, grip strength and some other functional measures. There is variability in the sampling methods used by the contributing studies, the inclusion and exclusion criteria, and modes of survey administration and this potential for heterogeneity will need to be tested and adjusted for. Finally, as with all longitudinal studies of ageing, there is sample attrition and missing data due to withdrawal, mortality and other non-response that increases with each occasion of measurement. What are some of the anticipated research questions and implications for the DYNOPTA project? With the breadth of domains assessed and the extent of longitudinal data, DYNOPTA will provide a unique window through which to investigate ways to optimize ageing outcomes, using an interdisciplinary approach. Epidemiological analyses of the pooled dataset will identify key incidence rates and risk factors for the targetted health outcome areas, as well as for mortality. Longitudinal analyses will model the transition from states of health to states of disability. These transitions will be used in the construction of a dynamic microsimulation model that will also allow for the costs of disability to be estimated. Scenarios involving modification of risk factors and rates of disability will be evaluated to inform Australian policy makers. One goal is to estimate the expected years of disability-free life expectancy within each targetted health outcome area. The DYNOPTA dataset will also provide an excellent platform in cross-national analyses.20 Similar longitudinal datasets from Europe and the United States offer the opportunity to explore similarities and differences in ageing trajectories in countries with broadly similar populations, including the impact of particular policies, such as different retirement ages. Where can I find out more? Further information is available on the DYNOPTA website http://DYNOPTA.anu.edu.au. The pooled dataset is governed by a Collaborative Research Agreement among several institutions. The first version of the dataset was released in November 2008. Those interested in collaborating on the project can contact the Scientific Committee at DYNOPTA@anu.edu.au. Funding National Health and Medical Research Council (410215); NHMRC Fellowships (#366756 to K.J.A. and #316970 to P.B.) Conflict of interest: None declared. Acknowledgements The data on which this research is based were drawn from several Australian longitudinal studies including: the Australian Longitudinal Study of Ageing (ALSA), the Australian Longitudinal Study of Women's Health (ALSWH), the Australian Diabetes, Obesity and Lifestyle Study (AusDiab), the Blue Mountain Eye Study (BMES), the Canberra Longitudinal Study of Ageing (CLS), the Household, Income and Labour Dynamics in Australia study (HILDA), the Melbourne Longitudinal Studies on Healthy Ageing (MELSHA), the Personality And Total Health Through Life Study (PATH) and the Sydney Older Persons Study (SOPS). These studies were pooled and harmonized for the Dynamic Analyses to Optimize Ageing (DYNOPTA) project. All studies would like to thank the participants for volunteering their time to be involved in the respective studies. Details of all studies contributing data to DYNOPTA, including individual study leaders and funding sources, are available on the DYNOPTA website (http://DYNOPTA.anu.edu.au). The findings and views reported in this paper are those of the author(s) and not those of the original studies or their respective funding agencies. References 1 Booth H , Tickle L . The future aged: new projections of Australia's elderly population , Australas J Ageing , 2003 , vol. 22 (pg. 38 - 44 ) Google Scholar Crossref Search ADS WorldCat 2 Andrews K . , Department of Health and Ageing (DoHA) National Strategy for an Ageing Australia: An Older Australia, Challenges and Opportunities for All , 2001 Canberra, ACT DoHA Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 3 Luszcz MA , Giles L , Eckermann S , Edwards P , Browne-Yung K , Hayles C . , The Australian Longitudinal Study of Ageing: 15 Years of Ageing in South Australia , 2007 South Australian Department of Families and Communities Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 4 Dunstan DW , Zimmet PZ , Welborn TA , et al. The Australian Diabetes, Obesity and Lifestyle Study (AusDiab)—methods and response rates , Diab Res Clin Pract , 2002 , vol. 57 (pg. 119 - 29 ) Google Scholar Crossref Search ADS WorldCat 5 Lee C , Dobson AJ , Brown WJ , et al. Cohort profile: The Australian Longitudinal Study of Women's Health , Int J Epidemiol , 2005 , vol. 35 (pg. 987 - 91 ) Google Scholar Crossref Search ADS WorldCat 6 Blue Mountains Eye Study. (Online) , 2007 (Accessed November 12, 2008) Available at: URL: http://www.cvr.org.au/bmes.htm 7 Christensen H , Mackinnon A , Jorm AF , et al. The Canberra longitudinal study: design, aims, methodology, outcomes and recent empirical investigations , Aging Neuropsychol C , 2004 , vol. 11 (pg. 169 - 95 ) Google Scholar Crossref Search ADS WorldCat 8 The Household, Income and Labour Dynamics in Australia (HILDA) Survey. (Online) , 2008 (Accessed November 12, 2008) Available at: URL: http://www.melbourneinstitute.com/hilda/ 9 Unsworth CA , Wells Y , Browning C , Thomas SA , Kendig H . To continue, modify or relinquish driving. Findings from a longitudinal study of healthy ageing , Gerontology , 2007 , vol. 53 (pg. 423 - 31 ) Google Scholar Crossref Search ADS PubMed WorldCat 10 PATH Through Life Project. (Online) , 2008 (Accessed November 12, 2008) Available at: URL: http://cmhr.anu.edu.au/path/ 11 Project summary The Sydney Older Persons Study: 1992 to 2003. (Online) , 2003 (Accessed November 12, 2008) [3 screens]. Available at: URL: http://www.sesiahs.health.nsw.gov.au/powh/arc/dwnlds/SOPS%20Project%20Summary.pdf 12 Department of Health and Ageing (DOHA) , An Active Way to Better Health: National Physical Activity Guidelines for Adults , 2005 Canberra, ACT DoHA Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 13 National Health and Medical Research Council (NHMRC) , Australian Alcohol Guidelines: Health Risks and Benefits , 2001 Canberra, ACT NHMRC Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 14 Lopez AD , Mathers CD , Ezzati M , Jamison DT , Murray CJL . Global and regional burden of disease and risk factors, 2001: systematic analysis of population health data , Lancet , 2006 , vol. 376 (pg. 1747 - 57 ) Google Scholar Crossref Search ADS WorldCat 15 Australian Institute of Health and Welfare (AIHW) , Australia's Health 2008 , 2008 Canberra, ACT AIHW Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 16 Baltes PB , Staudinger UM , Lindenberger U . Lifespan psychology: Theory and application to intellectual functioning , Annu Rev Psychol , 1999 , vol. 50 (pg. 471 - 507 ) Google Scholar Crossref Search ADS PubMed WorldCat 17 Kuh D , Ben-Shlomo Y , Lynch J , Hallqvist J , Power C . Life course epidemiology , J Epidemiol Community Health , 2003 , vol. 57 (pg. 778 - 83 ) Google Scholar Crossref Search ADS PubMed WorldCat 18 Pink B . , Australian Standard Geographical Classification. ABS Catalogue No. 1216.0 , 2007 Canberra, ACT Australian Government Publishers Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 19 Lipnicki D . , DYNOPTA technical report #1: Data harmonisation (unpublished) , 2006 Canberra, ACT Australian National University Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 20 van Buuren S , Eyres S , Tennant A , Hopman-Rock M . , Response Conversion: A New Technology for Comparing Existing Health Information , 2001 Leiden, Holland TNO Prevention and Health Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 21 van Buuren S , Eyres S , Tennant A , Hopman-Rock M . Assessing comparability of dressing disability in different countries by response conversion , Eur J Public Health , 2003 , vol. 13 (pg. 15 - 9 ) Google Scholar Crossref Search ADS PubMed WorldCat 22 van Buuren S , Eyres S , Tennant A , Hopman-Rock M . Improving comparability of existing data by response conversion , J Off Stat , 2005 , vol. 21 (pg. 53 - 72 ) Google Scholar OpenURL Placeholder Text WorldCat 23 Hauser RM , Willis RJ . Waite LJ . Survey design and methodology in the health and retirement study and the Wisconsin Longitudinal Study , Aging, Health and Public Policy: Demographic and Economic Perspectives , 2005 New York Population Council (pg. 209 - 35 ) Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 24 Borsch-Supan A , Agar B , Hendrik J , Johan M , Johannes SWG . , Health, Ageing and Retirement in Europe: First Results from the Survey of Health, Ageing and Retirement in Europe , 2005 Mannheim, Germany Mannheim Research Institute for the Economics of Aging Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 25 Minicuci N , Noale M , Bardage C , et al. Cross-national determinants of quality of life from six longitudinal studies on aging: The CLESA project , Aging Clin Exp Res , 2003 , vol. 15 (pg. 187 - 202 ) Google Scholar Crossref Search ADS PubMed WorldCat Published by Oxford University Press on behalf of the International Epidemiological Association © The Author 2009; all rights reserved. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com Published by Oxford University Press on behalf of the International Epidemiological Association © The Author 2009; all rights reserved. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Epidemiology Oxford University Press

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Publisher
Oxford University Press
Copyright
Copyright © 2022 International Epidemiological Association
ISSN
0300-5771
eISSN
1464-3685
DOI
10.1093/ije/dyn276
pmid
19151373
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See Article on Publisher Site

Abstract

How did the study come about? Like other industrialized countries, Australia is facing major population ageing. From 2000 to 2025, the number of Australians aged 65 years and over will more than double, as a result of the ageing of the baby boom cohort and increasing life expectancy, while the number of people in working age groups will decline.1 To guide constructive responses to this unprecedented change, the Government's Minister on Ageing released the National Strategy for an Ageing Australia2 which set key issues for national policy development, to be underpinned by policy. In 2003, the Australian Prime Minister's Science, Engineering and Innovation Council (PMSEIC) brought together leading researchers and policymakers who prepared a report articulating an evidence-based vision for healthy ageing in Australia and an associated programme of longitudinal research to guide the achievement of ‘an additional 10 years of healthy life expectancy’ by 2050.2 That same year, the Australian government established ‘Ageing Well, Ageing Productively’ as a National Research Priority goal. In 2004 the Australian Research Council (ARC) and the National Health and Medical Research Council (NHMRC) funded national research networks including the Research Network in Ageing Well (RNAW) to lead and facilitate collaboration in multidisciplinary large scale research on ageing, build research capacities and international collaborations, and improve communication and translation with key constituencies (www.ageingwell.edu.au). In 2004, the NHMRC and ARC announced a strategic funding initiative to facilitate research into ageing that is multisectorial, multidisciplinary and cross-institutional. The goal of this was to develop an authoritative evidence base for policy and practice in the priority area of Ageing Well, Ageing Productively. Collaborators from nine Australian Longitudinal Ageing studies, together with demographers, statistical and modelling experts, subsequently proposed the Dynamic Analyses to Optimize Ageing (DYNOPTA) project which received funding for a 5-year programme grant that commenced in 2007. The broad aims of DYNOPTA reflect the vision of the PMSEIC report to identify effective pathways to compressing morbidity and optimizing ageing. DYNOPTA has constructed a pooled dataset comprising information from nine Australian Longitudinal Studies of Ageing (LSA).3–9 Data were harmonized from the contributing studies (described in Table 1) to create an entirely new and unique dataset. This dataset is not the summation of the individual datasets, but rather comprises new variables and constructs derived from complex harmonization procedures. Where possible, variables were harmonized to enable comparison with Australian benchmarks. For example, the physical activity data have been harmonized to derive measures reflecting the recommended level of physical activity per week,12 and the alcohol consumption data have been harmonized to provide classifications in accordance with those endorsed by the National Health and Medical Research Council.13 Table 1 Studies contributing to the DYNOPTA dataset Study . Location . Wave . Year . N . Age . Deceased at wave . Australian Longitudinal Study of Ageing (ALSA)3 Adelaide 1 1992–93 2087 65–103 0 2 1993–94 1779 65–104 131 3 1994–95 1679 66–105 250 4 1995–96 1504 68–106 630 5 1998 1171 70–100 723 6 2000–01 791 72–101 1248 7 2003–04 487 75–102 1264 Australian Longitudinal Study of Women's Health Mid Cohort (ALSWH-mid), Old Cohort (ALSWH-old)5 National (Mid) 1 1996 13 706 45–51 0 2 1998 12 329 46–53 50 3 2001 11 185 49–56 116 4 2004 10 897 52–59 204 National (Old) 1 1996 12 431 68–76 0 2 1999 10 434 71–79 529 3 2002 8629 74–82 1098 4 2005 7152 77–85 1867 Australian Diabetes and Obesity and Lifestyle Study (AUSDIAB)4 National 1 1999–2000 7296 45–95 0 2 2004–05 4380 49–93 a Blue Mountains Eye Study (BMES)6 Blue Mountains 1 1992–93 3654 45–100 0 2 1997–2000 2334 50–98 a 3 2001–04 1952 55–99 a Canberra Longitudinal Study (CLS)7 Canberra, Queanbeyan 1 1990–91 1134 70–103 0 2 1994–95 637 74–102 306 3 1998 380 78–101 552 4 2002 213 82–105 744 Household, Income and Labour Dynamics of Australia (HILDA)8 National 1 2001–02 6164 45–90+ 0 2 2002–03 5454 45–90+ a 3 2003–04 5089 46–90+ a 4 2004–05 4769 47–90+ a 5 2005–06 4658 48–90+ a Melbourne Longitudinal Study Healthy Ageing (MELSHA)9 Melbourne 1 1994 1000 65–94 0 2 1995 979 66–95 35 3 1996 796 67–96 74 4 1997 718 68–97 91 5 1998 649 69–98 113 6 1999 648 70–99 128 7 2000 542 71–96 143 8 2002–03 372 73–98 170 9 2003 347 74–99 193 10 2004–05 326 75–96 193 11 2005–06 242 76–97 415 Personality and Total Health through life (PATH)10 Canberra, Queanbeyan 1 2001–02 2550 60–66 0 2 2005–06 2222 64–70 70 Sydney Older Person's Study (SOPS)11 Sydney 1 1991–93 630 75–97 0 2 1994–96 449 78–99 123 3 1996–97 367 78–100 163 4 1997–99 299 80–101 226 5 2001–03 62 84–106 318 Study . Location . Wave . Year . N . Age . Deceased at wave . Australian Longitudinal Study of Ageing (ALSA)3 Adelaide 1 1992–93 2087 65–103 0 2 1993–94 1779 65–104 131 3 1994–95 1679 66–105 250 4 1995–96 1504 68–106 630 5 1998 1171 70–100 723 6 2000–01 791 72–101 1248 7 2003–04 487 75–102 1264 Australian Longitudinal Study of Women's Health Mid Cohort (ALSWH-mid), Old Cohort (ALSWH-old)5 National (Mid) 1 1996 13 706 45–51 0 2 1998 12 329 46–53 50 3 2001 11 185 49–56 116 4 2004 10 897 52–59 204 National (Old) 1 1996 12 431 68–76 0 2 1999 10 434 71–79 529 3 2002 8629 74–82 1098 4 2005 7152 77–85 1867 Australian Diabetes and Obesity and Lifestyle Study (AUSDIAB)4 National 1 1999–2000 7296 45–95 0 2 2004–05 4380 49–93 a Blue Mountains Eye Study (BMES)6 Blue Mountains 1 1992–93 3654 45–100 0 2 1997–2000 2334 50–98 a 3 2001–04 1952 55–99 a Canberra Longitudinal Study (CLS)7 Canberra, Queanbeyan 1 1990–91 1134 70–103 0 2 1994–95 637 74–102 306 3 1998 380 78–101 552 4 2002 213 82–105 744 Household, Income and Labour Dynamics of Australia (HILDA)8 National 1 2001–02 6164 45–90+ 0 2 2002–03 5454 45–90+ a 3 2003–04 5089 46–90+ a 4 2004–05 4769 47–90+ a 5 2005–06 4658 48–90+ a Melbourne Longitudinal Study Healthy Ageing (MELSHA)9 Melbourne 1 1994 1000 65–94 0 2 1995 979 66–95 35 3 1996 796 67–96 74 4 1997 718 68–97 91 5 1998 649 69–98 113 6 1999 648 70–99 128 7 2000 542 71–96 143 8 2002–03 372 73–98 170 9 2003 347 74–99 193 10 2004–05 326 75–96 193 11 2005–06 242 76–97 415 Personality and Total Health through life (PATH)10 Canberra, Queanbeyan 1 2001–02 2550 60–66 0 2 2005–06 2222 64–70 70 Sydney Older Person's Study (SOPS)11 Sydney 1 1991–93 630 75–97 0 2 1994–96 449 78–99 123 3 1996–97 367 78–100 163 4 1997–99 299 80–101 226 5 2001–03 62 84–106 318 aDeath data not available. Open in new tab Table 1 Studies contributing to the DYNOPTA dataset Study . Location . Wave . Year . N . Age . Deceased at wave . Australian Longitudinal Study of Ageing (ALSA)3 Adelaide 1 1992–93 2087 65–103 0 2 1993–94 1779 65–104 131 3 1994–95 1679 66–105 250 4 1995–96 1504 68–106 630 5 1998 1171 70–100 723 6 2000–01 791 72–101 1248 7 2003–04 487 75–102 1264 Australian Longitudinal Study of Women's Health Mid Cohort (ALSWH-mid), Old Cohort (ALSWH-old)5 National (Mid) 1 1996 13 706 45–51 0 2 1998 12 329 46–53 50 3 2001 11 185 49–56 116 4 2004 10 897 52–59 204 National (Old) 1 1996 12 431 68–76 0 2 1999 10 434 71–79 529 3 2002 8629 74–82 1098 4 2005 7152 77–85 1867 Australian Diabetes and Obesity and Lifestyle Study (AUSDIAB)4 National 1 1999–2000 7296 45–95 0 2 2004–05 4380 49–93 a Blue Mountains Eye Study (BMES)6 Blue Mountains 1 1992–93 3654 45–100 0 2 1997–2000 2334 50–98 a 3 2001–04 1952 55–99 a Canberra Longitudinal Study (CLS)7 Canberra, Queanbeyan 1 1990–91 1134 70–103 0 2 1994–95 637 74–102 306 3 1998 380 78–101 552 4 2002 213 82–105 744 Household, Income and Labour Dynamics of Australia (HILDA)8 National 1 2001–02 6164 45–90+ 0 2 2002–03 5454 45–90+ a 3 2003–04 5089 46–90+ a 4 2004–05 4769 47–90+ a 5 2005–06 4658 48–90+ a Melbourne Longitudinal Study Healthy Ageing (MELSHA)9 Melbourne 1 1994 1000 65–94 0 2 1995 979 66–95 35 3 1996 796 67–96 74 4 1997 718 68–97 91 5 1998 649 69–98 113 6 1999 648 70–99 128 7 2000 542 71–96 143 8 2002–03 372 73–98 170 9 2003 347 74–99 193 10 2004–05 326 75–96 193 11 2005–06 242 76–97 415 Personality and Total Health through life (PATH)10 Canberra, Queanbeyan 1 2001–02 2550 60–66 0 2 2005–06 2222 64–70 70 Sydney Older Person's Study (SOPS)11 Sydney 1 1991–93 630 75–97 0 2 1994–96 449 78–99 123 3 1996–97 367 78–100 163 4 1997–99 299 80–101 226 5 2001–03 62 84–106 318 Study . Location . Wave . Year . N . Age . Deceased at wave . Australian Longitudinal Study of Ageing (ALSA)3 Adelaide 1 1992–93 2087 65–103 0 2 1993–94 1779 65–104 131 3 1994–95 1679 66–105 250 4 1995–96 1504 68–106 630 5 1998 1171 70–100 723 6 2000–01 791 72–101 1248 7 2003–04 487 75–102 1264 Australian Longitudinal Study of Women's Health Mid Cohort (ALSWH-mid), Old Cohort (ALSWH-old)5 National (Mid) 1 1996 13 706 45–51 0 2 1998 12 329 46–53 50 3 2001 11 185 49–56 116 4 2004 10 897 52–59 204 National (Old) 1 1996 12 431 68–76 0 2 1999 10 434 71–79 529 3 2002 8629 74–82 1098 4 2005 7152 77–85 1867 Australian Diabetes and Obesity and Lifestyle Study (AUSDIAB)4 National 1 1999–2000 7296 45–95 0 2 2004–05 4380 49–93 a Blue Mountains Eye Study (BMES)6 Blue Mountains 1 1992–93 3654 45–100 0 2 1997–2000 2334 50–98 a 3 2001–04 1952 55–99 a Canberra Longitudinal Study (CLS)7 Canberra, Queanbeyan 1 1990–91 1134 70–103 0 2 1994–95 637 74–102 306 3 1998 380 78–101 552 4 2002 213 82–105 744 Household, Income and Labour Dynamics of Australia (HILDA)8 National 1 2001–02 6164 45–90+ 0 2 2002–03 5454 45–90+ a 3 2003–04 5089 46–90+ a 4 2004–05 4769 47–90+ a 5 2005–06 4658 48–90+ a Melbourne Longitudinal Study Healthy Ageing (MELSHA)9 Melbourne 1 1994 1000 65–94 0 2 1995 979 66–95 35 3 1996 796 67–96 74 4 1997 718 68–97 91 5 1998 649 69–98 113 6 1999 648 70–99 128 7 2000 542 71–96 143 8 2002–03 372 73–98 170 9 2003 347 74–99 193 10 2004–05 326 75–96 193 11 2005–06 242 76–97 415 Personality and Total Health through life (PATH)10 Canberra, Queanbeyan 1 2001–02 2550 60–66 0 2 2005–06 2222 64–70 70 Sydney Older Person's Study (SOPS)11 Sydney 1 1991–93 630 75–97 0 2 1994–96 449 78–99 123 3 1996–97 367 78–100 163 4 1997–99 299 80–101 226 5 2001–03 62 84–106 318 aDeath data not available. Open in new tab What does DYNOPTA cover? The research programme focuses on four outcomes that contribute greatly to the burden of disease and disability, namely dementia and cognition, mental health, sensory impairment and mobility/activity limitations. Mortality is also included as a key outcome. The four health domains were chosen from recent research into the principal factors contributing to disease burden, or studies of the impact of diseases or conditions leading to injury, disability/impairment or premature death. Lopez and colleagues14 found that Alzheimer disease and other dementias, depressive disorders, hearing loss and conditions such as osteoarthritis were among the top 10 leading causes of burden of disease with loss of healthy-life years in higher income countries worldwide. Similar findings were made in a recent report from the Australian Institute of Health and Welfare.15 The DYNOPTA dataset also includes key sociodemographic variables such as marital and partner status, labour force participation and date of death. In addition to being risk factors for the four outcomes, analyses of these will enable a more detailed understanding of demographic and social processes in later life. The research programme is underpinned by an interdisciplinary life course approach to human development and ageing that recognizes interdependencies among demographic, social, lifestyle, economic and health factors.16,17 Who is in the sample? The geographic scope of the sample is shown in Figure 1. Of the nine contributing studies, three are nationally representative, comprising 65% of participants at baseline. The remaining six studies are based in specific Australian cities or regions, including Adelaide, the Blue Mountains, the Australian Capital Territory (ACT) and Queanbeyan, Melbourne and Sydney. Most of the sample (90%) live in either highly accessible or accessible areas (i.e. access to metropolitan services) according to the Australian Standard Geographical Classification system.18 Figure 1 Open in new tabDownload slide Locations of contributing Australian Longitudinal Ageing Studies Figure 1 Open in new tabDownload slide Locations of contributing Australian Longitudinal Ageing Studies The sample comprises 50 652 baseline participants (wave 1 of each study between 1990 and 2001). Of these, 39 085 (77.2%) were female, reflecting inclusion of the all-female Australian Longitudinal Study of Women's Health (particularly for the 45–54 and 65–74 age groups), and women's greater longevity. Selected sample characteristics are shown in Table 2. This reports frequency of participants’ characteristics as a proportion of the baseline sample. In the baseline sample, a total of 69.5% participants were married or partnered, while 16.7% were widowed with increasing proportions of widowed participants at older ages. About half of those with available data at baseline were in the workforce. Only 0.3% had no formal education, and 10.3% reported having attained tertiary education. Table 2 Selected demographic characteristics of the DYNOPTA cohort (unweighted) . . Age group . . Total . 45–54 . 55–64 . 65–74 . 75–84 . 85–94 . 95+ . . N (%) . N (%) . N (%) . N (%) . N (%) . N (%) . N (%) . Sex     Male 11 567 (22.8) 2719 (13.9) 3459 (48.5) 2820 (15.8) 2062 (41.4) 492 (45.4) 15 (20.5)     Female 39 085 (77.2) 16 784 (86.1) 3674 (51.5) 15 059 (84.2) 2919 (58.6) 591 (54.6) 58 (79.5) Marital status     Married or de-facto 35 180 (69.5) 15 929 (81.7) 5492 (77.0) 10 867 (60.8) 2546 (51.1) 337 (31.1) 9 (12.3)     Divorced or separated 4628 (9.1) 2350 (12.0) 891 (12.5) 1132 (6.3) 236 (4.7) 19 (1.8) 0 (0.0)     Widowed 8468 (16.7) 364 (1.9) 456 (6.4) 5054 (28.3) 1920 (38.5) 635 (58.6) 39 (53.4)     Single/never married 1957 (3.9) 783 (4.0) 286 (4.0) 600 (3.4) 223 (4.5) 58 (5.4) 7 (9.6) Level of education     No formal education 130 (0.3) 16 (0.1) 14 (0.2) 82 (0.5) 14 (0.3) 4 (0.4) 0 (0.0)     Primary school or secondary 27 145 (53.6) 10 881 (55.8) 2155 (30.2) 11 538 (64.5) 2234 (44.9) 316 (29.2) 21 (28.8)     Non-tertiary study 11 934 (23.6) 4950 (25.4) 2569 (36.0) 3077 (17.2) 1094 (22.0) 235 (21.7) 9 (12.3)     Tertiary study 5196 (10.3) 2952 (15.1) 1151 (16.1) 830 (4.6) 210 (4.2) 50 (4.6) 3 (4.1) Employment status     Employed 17 213 (34.0) 13 416 (68.8) 3178 (44.6) 527 (2.9) 80 (1.6) 12 (1.1) 0 (0.0)     Unemployed 1229 (2.4) 455 (2.3) 144 (2.0) 16 (0.1) 509 (10.2) 100 (9.2) 5 (6.8)     Not in labour force 17 768 (35.1) 4133 (21.2) 3735 (52.4) 5314 (29.7) 3620 (72.7) 915 (84.5) 51 (69.9) . . Age group . . Total . 45–54 . 55–64 . 65–74 . 75–84 . 85–94 . 95+ . . N (%) . N (%) . N (%) . N (%) . N (%) . N (%) . N (%) . Sex     Male 11 567 (22.8) 2719 (13.9) 3459 (48.5) 2820 (15.8) 2062 (41.4) 492 (45.4) 15 (20.5)     Female 39 085 (77.2) 16 784 (86.1) 3674 (51.5) 15 059 (84.2) 2919 (58.6) 591 (54.6) 58 (79.5) Marital status     Married or de-facto 35 180 (69.5) 15 929 (81.7) 5492 (77.0) 10 867 (60.8) 2546 (51.1) 337 (31.1) 9 (12.3)     Divorced or separated 4628 (9.1) 2350 (12.0) 891 (12.5) 1132 (6.3) 236 (4.7) 19 (1.8) 0 (0.0)     Widowed 8468 (16.7) 364 (1.9) 456 (6.4) 5054 (28.3) 1920 (38.5) 635 (58.6) 39 (53.4)     Single/never married 1957 (3.9) 783 (4.0) 286 (4.0) 600 (3.4) 223 (4.5) 58 (5.4) 7 (9.6) Level of education     No formal education 130 (0.3) 16 (0.1) 14 (0.2) 82 (0.5) 14 (0.3) 4 (0.4) 0 (0.0)     Primary school or secondary 27 145 (53.6) 10 881 (55.8) 2155 (30.2) 11 538 (64.5) 2234 (44.9) 316 (29.2) 21 (28.8)     Non-tertiary study 11 934 (23.6) 4950 (25.4) 2569 (36.0) 3077 (17.2) 1094 (22.0) 235 (21.7) 9 (12.3)     Tertiary study 5196 (10.3) 2952 (15.1) 1151 (16.1) 830 (4.6) 210 (4.2) 50 (4.6) 3 (4.1) Employment status     Employed 17 213 (34.0) 13 416 (68.8) 3178 (44.6) 527 (2.9) 80 (1.6) 12 (1.1) 0 (0.0)     Unemployed 1229 (2.4) 455 (2.3) 144 (2.0) 16 (0.1) 509 (10.2) 100 (9.2) 5 (6.8)     Not in labour force 17 768 (35.1) 4133 (21.2) 3735 (52.4) 5314 (29.7) 3620 (72.7) 915 (84.5) 51 (69.9) Percentages reported for the full sample and do not add to 100% due to missing data. Open in new tab Table 2 Selected demographic characteristics of the DYNOPTA cohort (unweighted) . . Age group . . Total . 45–54 . 55–64 . 65–74 . 75–84 . 85–94 . 95+ . . N (%) . N (%) . N (%) . N (%) . N (%) . N (%) . N (%) . Sex     Male 11 567 (22.8) 2719 (13.9) 3459 (48.5) 2820 (15.8) 2062 (41.4) 492 (45.4) 15 (20.5)     Female 39 085 (77.2) 16 784 (86.1) 3674 (51.5) 15 059 (84.2) 2919 (58.6) 591 (54.6) 58 (79.5) Marital status     Married or de-facto 35 180 (69.5) 15 929 (81.7) 5492 (77.0) 10 867 (60.8) 2546 (51.1) 337 (31.1) 9 (12.3)     Divorced or separated 4628 (9.1) 2350 (12.0) 891 (12.5) 1132 (6.3) 236 (4.7) 19 (1.8) 0 (0.0)     Widowed 8468 (16.7) 364 (1.9) 456 (6.4) 5054 (28.3) 1920 (38.5) 635 (58.6) 39 (53.4)     Single/never married 1957 (3.9) 783 (4.0) 286 (4.0) 600 (3.4) 223 (4.5) 58 (5.4) 7 (9.6) Level of education     No formal education 130 (0.3) 16 (0.1) 14 (0.2) 82 (0.5) 14 (0.3) 4 (0.4) 0 (0.0)     Primary school or secondary 27 145 (53.6) 10 881 (55.8) 2155 (30.2) 11 538 (64.5) 2234 (44.9) 316 (29.2) 21 (28.8)     Non-tertiary study 11 934 (23.6) 4950 (25.4) 2569 (36.0) 3077 (17.2) 1094 (22.0) 235 (21.7) 9 (12.3)     Tertiary study 5196 (10.3) 2952 (15.1) 1151 (16.1) 830 (4.6) 210 (4.2) 50 (4.6) 3 (4.1) Employment status     Employed 17 213 (34.0) 13 416 (68.8) 3178 (44.6) 527 (2.9) 80 (1.6) 12 (1.1) 0 (0.0)     Unemployed 1229 (2.4) 455 (2.3) 144 (2.0) 16 (0.1) 509 (10.2) 100 (9.2) 5 (6.8)     Not in labour force 17 768 (35.1) 4133 (21.2) 3735 (52.4) 5314 (29.7) 3620 (72.7) 915 (84.5) 51 (69.9) . . Age group . . Total . 45–54 . 55–64 . 65–74 . 75–84 . 85–94 . 95+ . . N (%) . N (%) . N (%) . N (%) . N (%) . N (%) . N (%) . Sex     Male 11 567 (22.8) 2719 (13.9) 3459 (48.5) 2820 (15.8) 2062 (41.4) 492 (45.4) 15 (20.5)     Female 39 085 (77.2) 16 784 (86.1) 3674 (51.5) 15 059 (84.2) 2919 (58.6) 591 (54.6) 58 (79.5) Marital status     Married or de-facto 35 180 (69.5) 15 929 (81.7) 5492 (77.0) 10 867 (60.8) 2546 (51.1) 337 (31.1) 9 (12.3)     Divorced or separated 4628 (9.1) 2350 (12.0) 891 (12.5) 1132 (6.3) 236 (4.7) 19 (1.8) 0 (0.0)     Widowed 8468 (16.7) 364 (1.9) 456 (6.4) 5054 (28.3) 1920 (38.5) 635 (58.6) 39 (53.4)     Single/never married 1957 (3.9) 783 (4.0) 286 (4.0) 600 (3.4) 223 (4.5) 58 (5.4) 7 (9.6) Level of education     No formal education 130 (0.3) 16 (0.1) 14 (0.2) 82 (0.5) 14 (0.3) 4 (0.4) 0 (0.0)     Primary school or secondary 27 145 (53.6) 10 881 (55.8) 2155 (30.2) 11 538 (64.5) 2234 (44.9) 316 (29.2) 21 (28.8)     Non-tertiary study 11 934 (23.6) 4950 (25.4) 2569 (36.0) 3077 (17.2) 1094 (22.0) 235 (21.7) 9 (12.3)     Tertiary study 5196 (10.3) 2952 (15.1) 1151 (16.1) 830 (4.6) 210 (4.2) 50 (4.6) 3 (4.1) Employment status     Employed 17 213 (34.0) 13 416 (68.8) 3178 (44.6) 527 (2.9) 80 (1.6) 12 (1.1) 0 (0.0)     Unemployed 1229 (2.4) 455 (2.3) 144 (2.0) 16 (0.1) 509 (10.2) 100 (9.2) 5 (6.8)     Not in labour force 17 768 (35.1) 4133 (21.2) 3735 (52.4) 5314 (29.7) 3620 (72.7) 915 (84.5) 51 (69.9) Percentages reported for the full sample and do not add to 100% due to missing data. Open in new tab Selected health characteristics and social contact are reported in Table 3. On a self-reported health measure, 35.9% reported their health as excellent and 3.0% as poor. Of those with available data, about half (52%) reported having never smoked. Of those with visual acuity data, 3% were classified with a visual impairment. Of the baseline sample data reporting information on social contact, 68.6% reported face to face contact with friends or family at least once per week. Table 3 Selected health and social characteristics of the DYNOPTA cohort (unweighted) . . Age group . . Total . 45–54 . 55–64 . 65–74 . 75–84 . 85–94 . 95+ . . N (%) . N (%) . N (%) . N (%) . N (%) . N (%) . N (%) . SRH     Excellent 18 174 (35.9) 9078 (46.5) 2797 (39.2) 5168 (28.9) 917 (18.4) 200 (18.5) 14 (19.2)     Good 17 138 (33.8) 7304 (37.5) 2047 (28.7) 6205 (34.7) 1340 (26.9) 231 (21.3) 11 (15.1)     Fair 7396 (14.6) 1973 (10.1) 805 (11.3) 3554 (19.9) 845 (17.0) 212 (19.6) 7 (9.6)     Poor 1517 (3.0) 359 (1.8) 197 (2.8) 638 (3.6) 242 (4.9) 78 (7.2) 3 (4.1) Visual impairment     Impaired vision: <6/12 1501 (3.0) 12 (0.1) 132 (1.9) 405 (2.3) 684 (13.7) 259 (23.9) 9 (12.3) Smoking status     Never smoker 26 362 (52.0) 9965 (51.1) 3530 (49.5) 9765 (54.6) 2499 (50.2) 573 (52.9) 30 (41.1)     Former smoker 15 860 (31.3) 5438 (27.9) 2479 (34.8) 5705 (31.9) 1880 (37.7) 346 (31.9) 12 (16.4)     Current smoker 6285 (12.4) 3440 (17.6) 972 (13.6) 1466 (8.2) 355 (7.1) 51 (4.7) 1 (1.4) Face to face contact with friends and relatives     Once a week or more 6479 (12.8) 1184 (6.1) 936 (13.1) 1642 (9.2) 2114 (42.4) 579 (53.5) 24 (32.9)     Less than once a month or more 2941 (5.8) 1110 (5.7) 668 (9.4) 587 (3.3) 448 (9.0) 125 (11.5) 3 (4.1) . . Age group . . Total . 45–54 . 55–64 . 65–74 . 75–84 . 85–94 . 95+ . . N (%) . N (%) . N (%) . N (%) . N (%) . N (%) . N (%) . SRH     Excellent 18 174 (35.9) 9078 (46.5) 2797 (39.2) 5168 (28.9) 917 (18.4) 200 (18.5) 14 (19.2)     Good 17 138 (33.8) 7304 (37.5) 2047 (28.7) 6205 (34.7) 1340 (26.9) 231 (21.3) 11 (15.1)     Fair 7396 (14.6) 1973 (10.1) 805 (11.3) 3554 (19.9) 845 (17.0) 212 (19.6) 7 (9.6)     Poor 1517 (3.0) 359 (1.8) 197 (2.8) 638 (3.6) 242 (4.9) 78 (7.2) 3 (4.1) Visual impairment     Impaired vision: <6/12 1501 (3.0) 12 (0.1) 132 (1.9) 405 (2.3) 684 (13.7) 259 (23.9) 9 (12.3) Smoking status     Never smoker 26 362 (52.0) 9965 (51.1) 3530 (49.5) 9765 (54.6) 2499 (50.2) 573 (52.9) 30 (41.1)     Former smoker 15 860 (31.3) 5438 (27.9) 2479 (34.8) 5705 (31.9) 1880 (37.7) 346 (31.9) 12 (16.4)     Current smoker 6285 (12.4) 3440 (17.6) 972 (13.6) 1466 (8.2) 355 (7.1) 51 (4.7) 1 (1.4) Face to face contact with friends and relatives     Once a week or more 6479 (12.8) 1184 (6.1) 936 (13.1) 1642 (9.2) 2114 (42.4) 579 (53.5) 24 (32.9)     Less than once a month or more 2941 (5.8) 1110 (5.7) 668 (9.4) 587 (3.3) 448 (9.0) 125 (11.5) 3 (4.1) Open in new tab Table 3 Selected health and social characteristics of the DYNOPTA cohort (unweighted) . . Age group . . Total . 45–54 . 55–64 . 65–74 . 75–84 . 85–94 . 95+ . . N (%) . N (%) . N (%) . N (%) . N (%) . N (%) . N (%) . SRH     Excellent 18 174 (35.9) 9078 (46.5) 2797 (39.2) 5168 (28.9) 917 (18.4) 200 (18.5) 14 (19.2)     Good 17 138 (33.8) 7304 (37.5) 2047 (28.7) 6205 (34.7) 1340 (26.9) 231 (21.3) 11 (15.1)     Fair 7396 (14.6) 1973 (10.1) 805 (11.3) 3554 (19.9) 845 (17.0) 212 (19.6) 7 (9.6)     Poor 1517 (3.0) 359 (1.8) 197 (2.8) 638 (3.6) 242 (4.9) 78 (7.2) 3 (4.1) Visual impairment     Impaired vision: <6/12 1501 (3.0) 12 (0.1) 132 (1.9) 405 (2.3) 684 (13.7) 259 (23.9) 9 (12.3) Smoking status     Never smoker 26 362 (52.0) 9965 (51.1) 3530 (49.5) 9765 (54.6) 2499 (50.2) 573 (52.9) 30 (41.1)     Former smoker 15 860 (31.3) 5438 (27.9) 2479 (34.8) 5705 (31.9) 1880 (37.7) 346 (31.9) 12 (16.4)     Current smoker 6285 (12.4) 3440 (17.6) 972 (13.6) 1466 (8.2) 355 (7.1) 51 (4.7) 1 (1.4) Face to face contact with friends and relatives     Once a week or more 6479 (12.8) 1184 (6.1) 936 (13.1) 1642 (9.2) 2114 (42.4) 579 (53.5) 24 (32.9)     Less than once a month or more 2941 (5.8) 1110 (5.7) 668 (9.4) 587 (3.3) 448 (9.0) 125 (11.5) 3 (4.1) . . Age group . . Total . 45–54 . 55–64 . 65–74 . 75–84 . 85–94 . 95+ . . N (%) . N (%) . N (%) . N (%) . N (%) . N (%) . N (%) . SRH     Excellent 18 174 (35.9) 9078 (46.5) 2797 (39.2) 5168 (28.9) 917 (18.4) 200 (18.5) 14 (19.2)     Good 17 138 (33.8) 7304 (37.5) 2047 (28.7) 6205 (34.7) 1340 (26.9) 231 (21.3) 11 (15.1)     Fair 7396 (14.6) 1973 (10.1) 805 (11.3) 3554 (19.9) 845 (17.0) 212 (19.6) 7 (9.6)     Poor 1517 (3.0) 359 (1.8) 197 (2.8) 638 (3.6) 242 (4.9) 78 (7.2) 3 (4.1) Visual impairment     Impaired vision: <6/12 1501 (3.0) 12 (0.1) 132 (1.9) 405 (2.3) 684 (13.7) 259 (23.9) 9 (12.3) Smoking status     Never smoker 26 362 (52.0) 9965 (51.1) 3530 (49.5) 9765 (54.6) 2499 (50.2) 573 (52.9) 30 (41.1)     Former smoker 15 860 (31.3) 5438 (27.9) 2479 (34.8) 5705 (31.9) 1880 (37.7) 346 (31.9) 12 (16.4)     Current smoker 6285 (12.4) 3440 (17.6) 972 (13.6) 1466 (8.2) 355 (7.1) 51 (4.7) 1 (1.4) Face to face contact with friends and relatives     Once a week or more 6479 (12.8) 1184 (6.1) 936 (13.1) 1642 (9.2) 2114 (42.4) 579 (53.5) 24 (32.9)     Less than once a month or more 2941 (5.8) 1110 (5.7) 668 (9.4) 587 (3.3) 448 (9.0) 125 (11.5) 3 (4.1) Open in new tab What is included in the pooled dataset? The pooled dataset contains demographic information, outcome variables for the four key domains (cognition, mental health, physical disability and sensory function) and data on risk factors and health behaviours. The risk factors include other medical conditions (e.g. arthritis, hypertension) as well as psychosocial measures, such as social networks. Mortality data are also included, as well as self-reported use of services. Table 4 lists the domains included in the dataset. Table 4 Domains included in the DYNOPTA pooled dataset Domain . Example measures within domain . Health outcomes     Cognition/dementia Probable dementia, cognitive impairment, processing speed, memory, verbal ability     Mobility Activities of daily living, instrumental activities of daily living, driving     Mental health Depression, psychological distress, self-rated health, life satisfaction     Sensory functioning Visual acuity, self reported visual impairment, ability to read print, audiometry, self-reported hearing difficulties     Mortality Age at death Predictors and correlates     Sociodemographic Age, sex, partner status, education, occupation, socioeconomic status, retirement status, domicile     Medical conditions, health behaviours, social support Arthritis, cancer, stroke, high blood pressure, cholesterol, falls history, anthropometric data, smoking, alcohol use, physical activity, grip strength, social support and networks     Service use Hospital visits, medical practitioner visits, other health professional visits, community services use Domain . Example measures within domain . Health outcomes     Cognition/dementia Probable dementia, cognitive impairment, processing speed, memory, verbal ability     Mobility Activities of daily living, instrumental activities of daily living, driving     Mental health Depression, psychological distress, self-rated health, life satisfaction     Sensory functioning Visual acuity, self reported visual impairment, ability to read print, audiometry, self-reported hearing difficulties     Mortality Age at death Predictors and correlates     Sociodemographic Age, sex, partner status, education, occupation, socioeconomic status, retirement status, domicile     Medical conditions, health behaviours, social support Arthritis, cancer, stroke, high blood pressure, cholesterol, falls history, anthropometric data, smoking, alcohol use, physical activity, grip strength, social support and networks     Service use Hospital visits, medical practitioner visits, other health professional visits, community services use Open in new tab Table 4 Domains included in the DYNOPTA pooled dataset Domain . Example measures within domain . Health outcomes     Cognition/dementia Probable dementia, cognitive impairment, processing speed, memory, verbal ability     Mobility Activities of daily living, instrumental activities of daily living, driving     Mental health Depression, psychological distress, self-rated health, life satisfaction     Sensory functioning Visual acuity, self reported visual impairment, ability to read print, audiometry, self-reported hearing difficulties     Mortality Age at death Predictors and correlates     Sociodemographic Age, sex, partner status, education, occupation, socioeconomic status, retirement status, domicile     Medical conditions, health behaviours, social support Arthritis, cancer, stroke, high blood pressure, cholesterol, falls history, anthropometric data, smoking, alcohol use, physical activity, grip strength, social support and networks     Service use Hospital visits, medical practitioner visits, other health professional visits, community services use Domain . Example measures within domain . Health outcomes     Cognition/dementia Probable dementia, cognitive impairment, processing speed, memory, verbal ability     Mobility Activities of daily living, instrumental activities of daily living, driving     Mental health Depression, psychological distress, self-rated health, life satisfaction     Sensory functioning Visual acuity, self reported visual impairment, ability to read print, audiometry, self-reported hearing difficulties     Mortality Age at death Predictors and correlates     Sociodemographic Age, sex, partner status, education, occupation, socioeconomic status, retirement status, domicile     Medical conditions, health behaviours, social support Arthritis, cancer, stroke, high blood pressure, cholesterol, falls history, anthropometric data, smoking, alcohol use, physical activity, grip strength, social support and networks     Service use Hospital visits, medical practitioner visits, other health professional visits, community services use Open in new tab How often have they been followed? The number and dates of waves for each study are shown in Table 1. Studies have an average of 4.4 waves over an average period of 9.4 years (SD = 2.99), though this is inflated by MELSHA with 11 waves over 12 years, and by ALSA with seven waves over 11 years. The mean number of surveys completed by each participant was 3.1 (SD = 1.51), with a mode of 4 and median of 3 surveys per participant. Development of weights for the DYNOPTA dataset The contributing studies use a variety of survey designs and differ in their geographic and demographic coverage. Some surveys are results of simple random samples, for others stratified, or cluster designs were used. The demographic coverage also differs between studies. Estimation weights are being developed for DYNOPTA that will reflect the different sample sizes and selection probablities of the studies and ensure that the different studies are approriately combined when they cover the same geographical and demographic subpopulations. Other complexities of the design will also have to be accounted for in analyses and any study effects identified and adjustments made. How were data harmonized? Data were mostly harmonized using the ‘by fiat’ method.19–22 This method assumes a common scoring system across studies19–22 and is recommended when the possibility of dispute is small, the number of categories is relatively consistent across studies and a clear authority can endorse the system. In some instances, variables were harmonized at several levels. The most generic level is most inclusive of the largest number of participants, while a more detailed level would be that which is most inclusive of information, but which may result in the inclusion of fewer participants. A latent variable approach is being used to harmonize different depression measures. Due to the nature of the studies that contributed to the creation of the pooled dataset, coverage of information varies considerably across the total sample. For example, at the first measurement occasion (wave 1 for all studies), self-rated health is available for 44 224 participants, whilst information about visits to a general practitioner is available for only half that number (n = 28 639). What are the main strengths and weaknesses of DYNOPTA? A major strength of this innovative study is the large sample size and multiple occasions of measurement. The increase in statistical power enables more reliable analyses than has hitherto not been possible in the Australian setting. This allows for the study of conditions that are relatively rare such as Parkinson disease, or for those conditions that Australian policy makers had previously relied on European data (e.g. Dementia15). The study also provides data on under-represented groups, for whom comparisons are often not feasible due to small sample sizes (e.g. adults aged 85+). The inclusion of cohorts studied at varying points of time will allow for the evaluation of cohort effects. The national coverage of this large sample adds to the utility of the findings as does the depth and breadth of this dataset. Although some epidemiological studies with large datasets include information gained purely from mailed questionnaires, limiting the type of data collected, a significant proportion of the data included in DYNOPTA is based on personal interview and assessments. Another strength of the study is the development of the infrastructure, documentation of Australian longitudinal research and the development of methodologies for harmonization. This will facilitate future research involving the harmonization of the DYNOPTA dataset with other Australian sources, and international sources such as U.S. Health and Retirement Survey,23 the Surveys of Health and Retirement in Europe24 and the Comparison of Longitudinal European Studies on Aging (CLESA) which has undertaken a similar harmonization and pooling approach to analyse data from six longitudinal studies.25 The pooled dataset does however have limitations. As not all contributing datasets were nationally representative, the sample needs to be weighted to produce population estimates. Most of the data on medical conditions is self-report although clinical data are available for sensory function, blood pressure, cognition, grip strength and some other functional measures. There is variability in the sampling methods used by the contributing studies, the inclusion and exclusion criteria, and modes of survey administration and this potential for heterogeneity will need to be tested and adjusted for. Finally, as with all longitudinal studies of ageing, there is sample attrition and missing data due to withdrawal, mortality and other non-response that increases with each occasion of measurement. What are some of the anticipated research questions and implications for the DYNOPTA project? With the breadth of domains assessed and the extent of longitudinal data, DYNOPTA will provide a unique window through which to investigate ways to optimize ageing outcomes, using an interdisciplinary approach. Epidemiological analyses of the pooled dataset will identify key incidence rates and risk factors for the targetted health outcome areas, as well as for mortality. Longitudinal analyses will model the transition from states of health to states of disability. These transitions will be used in the construction of a dynamic microsimulation model that will also allow for the costs of disability to be estimated. Scenarios involving modification of risk factors and rates of disability will be evaluated to inform Australian policy makers. One goal is to estimate the expected years of disability-free life expectancy within each targetted health outcome area. The DYNOPTA dataset will also provide an excellent platform in cross-national analyses.20 Similar longitudinal datasets from Europe and the United States offer the opportunity to explore similarities and differences in ageing trajectories in countries with broadly similar populations, including the impact of particular policies, such as different retirement ages. Where can I find out more? Further information is available on the DYNOPTA website http://DYNOPTA.anu.edu.au. The pooled dataset is governed by a Collaborative Research Agreement among several institutions. The first version of the dataset was released in November 2008. Those interested in collaborating on the project can contact the Scientific Committee at DYNOPTA@anu.edu.au. Funding National Health and Medical Research Council (410215); NHMRC Fellowships (#366756 to K.J.A. and #316970 to P.B.) Conflict of interest: None declared. Acknowledgements The data on which this research is based were drawn from several Australian longitudinal studies including: the Australian Longitudinal Study of Ageing (ALSA), the Australian Longitudinal Study of Women's Health (ALSWH), the Australian Diabetes, Obesity and Lifestyle Study (AusDiab), the Blue Mountain Eye Study (BMES), the Canberra Longitudinal Study of Ageing (CLS), the Household, Income and Labour Dynamics in Australia study (HILDA), the Melbourne Longitudinal Studies on Healthy Ageing (MELSHA), the Personality And Total Health Through Life Study (PATH) and the Sydney Older Persons Study (SOPS). These studies were pooled and harmonized for the Dynamic Analyses to Optimize Ageing (DYNOPTA) project. All studies would like to thank the participants for volunteering their time to be involved in the respective studies. Details of all studies contributing data to DYNOPTA, including individual study leaders and funding sources, are available on the DYNOPTA website (http://DYNOPTA.anu.edu.au). The findings and views reported in this paper are those of the author(s) and not those of the original studies or their respective funding agencies. References 1 Booth H , Tickle L . 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Findings from a longitudinal study of healthy ageing , Gerontology , 2007 , vol. 53 (pg. 423 - 31 ) Google Scholar Crossref Search ADS PubMed WorldCat 10 PATH Through Life Project. (Online) , 2008 (Accessed November 12, 2008) Available at: URL: http://cmhr.anu.edu.au/path/ 11 Project summary The Sydney Older Persons Study: 1992 to 2003. (Online) , 2003 (Accessed November 12, 2008) [3 screens]. Available at: URL: http://www.sesiahs.health.nsw.gov.au/powh/arc/dwnlds/SOPS%20Project%20Summary.pdf 12 Department of Health and Ageing (DOHA) , An Active Way to Better Health: National Physical Activity Guidelines for Adults , 2005 Canberra, ACT DoHA Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 13 National Health and Medical Research Council (NHMRC) , Australian Alcohol Guidelines: Health Risks and Benefits , 2001 Canberra, ACT NHMRC Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 14 Lopez AD , Mathers CD , Ezzati M , Jamison DT , Murray CJL . 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Improving comparability of existing data by response conversion , J Off Stat , 2005 , vol. 21 (pg. 53 - 72 ) Google Scholar OpenURL Placeholder Text WorldCat 23 Hauser RM , Willis RJ . Waite LJ . Survey design and methodology in the health and retirement study and the Wisconsin Longitudinal Study , Aging, Health and Public Policy: Demographic and Economic Perspectives , 2005 New York Population Council (pg. 209 - 35 ) Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 24 Borsch-Supan A , Agar B , Hendrik J , Johan M , Johannes SWG . , Health, Ageing and Retirement in Europe: First Results from the Survey of Health, Ageing and Retirement in Europe , 2005 Mannheim, Germany Mannheim Research Institute for the Economics of Aging Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 25 Minicuci N , Noale M , Bardage C , et al. Cross-national determinants of quality of life from six longitudinal studies on aging: The CLESA project , Aging Clin Exp Res , 2003 , vol. 15 (pg. 187 - 202 ) Google Scholar Crossref Search ADS PubMed WorldCat Published by Oxford University Press on behalf of the International Epidemiological Association © The Author 2009; all rights reserved. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com Published by Oxford University Press on behalf of the International Epidemiological Association © The Author 2009; all rights reserved.

Journal

International Journal of EpidemiologyOxford University Press

Published: Feb 1, 2010

Keywords: datasets

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