Int. J. Epidemiol. Advance Access published December 1, 2014 International Journal of Epidemiology, 2014, 1–10 doi: 10.1093/ije/dyu196 Cohort Profile
Cohort Profile
Cohort Profile: The Australian Longitudinal Study of Ageing (ALSA) Mary A Luszcz,1,2* Lynne C Giles,3 Kaarin J Anstey,4 Kathryn C Browne-Yung,1,5 Ruth A Walker1,6 and Tim D Windsor1,2 Downloaded from http://ije.oxfordjournals.org/ at University of Otago on November 14, 2015
1
Flinders Centre for Ageing Studies, and 2School of Psychology, Flinders University, Adelaide, SA, Australia, Discipline of Public Health, University of Adelaide, Adelaide, SA, Australia, 4Centre for Research on Ageing, Health and Wellbeing, Australian National University, Canberra, ACT, Australia, 5Southgate Institute, and 6Disability and Community Inclusion Unit, Flinders University, Adelaide, SA, Australia 3
*Corresponding author. School of Psychology, Flinders University, GPO Box 2100, Adelaide, SA 5001, Australia. E-mail: [email protected] Accepted 5 September 2014
Abstract In response to the expressed need for more sophisticated and multidisciplinary data concerning ageing of the Australian population, the Australian Longitudinal Study of Ageing (ALSA) was established some two decades ago in Adelaide, South Australia. At Baseline in 1992, 2087 participants living in the community or in residential care (ranging in age from 65 to 103 years) were interviewed in their place of residence (1031 or 49% women), including 565 couples. By 2013, 12 Waves had been completed; both face-to-face and telephone personal interviews were conducted. Data collected included self-reports of demographic details, health, depression, morbid conditions, hospitalization, gross mobility, physical performance, activities of daily living, lifestyle activities, social resources, exercise, education and income. Objective performance data for physical and cognitive function were also collected. The ALSA data are held at the Flinders Centre for Ageing Studies, Flinders University. Procedures for data access, information on collaborations, publications and other details can be found at [http://flinders.edu.au/sabs/fcas/].
Key Messages • The population-based ALSA is one of the longest-running cohort studies of older people in the world. The frequency
of data collection, spanning some 12 waves over 22 years, has allowed insights about ageing rarely possible from other longitudinal studies of ageing. • While the majority of participants at every wave appear to be experiencing ageing as a positive process, with preser-
vation of cognitive, affective and functional health, wide interindividual differences, including in intra-individual change, were also observed. • Many of the identified factors that promote longevity and quality of life are aspects of lifestyle that are amenable to
change. Early screening could identify risk factors. Intervention strategies that encourage regular exercise, support social networks and engender a positive state of mind may help promote survival and a good quality of life.
C The Author 2014; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association V
1
2
International Journal of Epidemiology, 2014, Vol. 0, No. 0
A loyal participant “Sheila” is shown on three occasions over her 21 years of continuous involvement in the ALSA. She was 80 years of age at Wave 3, shown doing the Clinical Assessment; 95 at Wave 11 doing her home interview and 99 years at Wave 12. In 2014 she celebrated her 100th birthday.
The proportion of Australians over 65 years old is projected to increase from 14% in 2012 to 27.1% by 2101, with South Australia having the second oldest population in Australia.1 In response to the need for more sophisticated data concerning ageing of the Australian population, the Australian Longitudinal Study of Ageing (ALSA) was established two decades ago in Adelaide, capital of South Australia. The ALSA is multidisciplinary and designed to improve understanding of how a broad range of individual and structural factors are associated with age-related changes in health and well-being. Following an extensive pilot study2 in 1988, ALSA Wave 1 (Baseline) commenced in 1992. Needs for comprehensive information on ageing still exist, and a strength of the study is that the surviving ALSA cohort are now aged 85 years or older: the most rapidly growing portion of the older population.
What does ALSA cover? The overarching aim was to investigate how social, biomedical, behavioural, economic, and environmental factors affect ageing. Specific objectives of ALSA were to: i. determine health and functional status and track changes in these characteristics over time; ii. identify risk factors for major chronic conditions and normative age-related changes; iii. assess effects of disease processes and lifestyle choices on functional status, and the demand for acute and longer-term aged care services; and iv. examine mortality outcomes.
Who is in the cohort? The ALSA is a population-based cohort of older men and women who resided in the Adelaide Statistical Division
and were aged 70 years or more on 31 December 1992. Both community-dwelling and people living in residential care were eligible and were randomly selected from the South Australian Electoral Roll. The primary sample was stratified by age groups (70-74, 75-79, 80-84 and 85 years or more), gender and local government area. Letters of introduction and invitation were sent to 3263 people; 560 were not eligible (210 were deceased, 88 had no translator available, 189 could not be contacted at the address, 37 were out of the geographical scope and 36 were ineligible for other reasons, e.g. for incorrect date of birth). Of the 2703 eligible persons, 1477 consented and completed an interview (response fraction: 54.6%). In addition to the primary sample, spouses and other household members of eligible persons were invited to take part. The age requirement for spouses was relaxed to age 65 years. An additional 597 spouses and 13 household members were recruited. In total, 2087 people took part in a Wave 1 interview, including 565 couples. Key Baseline characteristics are presented in Table 1. After consenting, arrangements were made for the structured personal interview to be conducted at the participant’s usual place of residence. Then participants were invited to take part in a detailed clinical assessment and complete self-administered questionnaires. To minimize participant attrition, at each wave members of the cohort were asked to provide contact details of three people who could provide information about their whereabouts should their residential location change. Birthday and Christmas cards, and periodic newsletters, were sent to participants between waves. To assess the representativeness of the Baseline sample, we compared them on a range of health-related markers to the overall over-70 population in South Australia.3 Weights were applied to the data to adjust for age and sex stratification, and the probability of selection of respondents within each local
Downloaded from http://ije.oxfordjournals.org/ at University of Otago on November 14, 2015
Why was the ALSA cohort set up?
International Journal of Epidemiology, 2014, Vol. 0, No. 0
3
Table 1. Key characteristics of the ALSA participants at
Table 2. Summary of target sample characteristics and differ-
Baseline
ences compared with full sample residing in community
Characteristics
Variable 1056 (51) 1031 (49) 78.3 (6.7) 140 (7) 562 (27) 524 (25) 429 (21) 432 (21) 1367 (65.0) 594 (28.6) 76 (3.6) 49 (2.4) 790 (38) 633 (30.4) 658 (31.6) 1155 (55.3) 906 (43.4) 686 (35.5) 1083 (56.1) 136 (7.1) 25 (1.3)
government area sampled. The percentages for the weighted and unweighted values showed high correspondence on activities of daily living, self-ratings of health, number of health professionals consulted and hospital stays in the previous year. At Baseline, 14% of participants used formal services, comparing favourably to 12% of the over-70 population. Attrition was examined at Wave 3 in relation to partaking in the clinical assessment.4 Those who did not complete the clinical assessment at Wave 3 had had poorer health and cognitive function at Wave 1, independently of age and gender. Rates of possible dementia were also higher in participants who did not undertake Wave 3 clinical assessment, compared with both those who did so and population data. Possible sample selectivity was also assessed by Luszcz et al.5 using Baseline data to explain agerelated differences in memory. We compared the target community-dwelling sample who met inclusion criteria with all community-dwellers who completed the structured interview. Table 2 shows a high degree of similarity between the groups; differences were in all cases a small fraction of the standard deviation. The target sample was slightly younger, healthier and more cognitively able.
Differencea
Sub-sample
Age Gender Male Female Married School (left 16 years) Illnesses Medications Self-rated health (good) Activities of daily living (1) Instrumental activities of daily living (1) Depressive symptoms Adelaide Activities Profileb Home maintenance Domestic Social Service to others NART errors IQ estimatec Processing accuracy Processing speed Picture naming Recall Address Symbol Picture
%
M
SD
77.6 54.2 515 45.8 436 70 29 5.5 2.6 74 11
%
5.5
M 0.65
þ0.1 0.1 þ3.5 þ4.2 2.8 1.9
0.18 þ0.02 þ5.0 5.0
32
3.0 7.4
6.7
0.53
52.5 52.2 51.3 52.3 22.2 102.0 0.97 29.9 13.7
18.7 18.5 19.8 20.3 8.4 9.5 0.07 10.9 1.6
þ2.5 þ2.2 þ1.3 þ2.3 0.27 þ0.32 0.00 þ0.68 þ0.18
1.6 2.0 2.3
þ0.20 þ0.06 þ0.17
8.60 6.28 5.62
NART: National Adult Reading Test. a Raw score difference relative to all community residents. b Clark and Bond (1995)51 standardized these scales to M ¼ 50 (SD ¼ 20). c IQ estimate is based on NART errors. Statistics extracted from Luszcz et al.5
More generally, missing data resulting from attrition have been analysed using maximum likelihood estimation, e.g. growth curve modelling,6 which produces estimates based on all available data under missing-at-random assumptions.7
How often have they been followed up? There have been 12 waves of data collection. Wave 1 took place from September 1992 to March 1993. The next three waves took place 1, 2 or 3 years thereafter. Subsequent waves were approximately 6, 8, 11, 13, 15, 16, 18 and 21 years after baseline, with funding secured for a 22-year follow-up. Unequal intervals reflect funding availability for follow-up. There were two key modalities of data collection: in person and by telephone. Waves 1, 3, 6, 7, 9, 11 and
Downloaded from http://ije.oxfordjournals.org/ at University of Otago on November 14, 2015
Gender, n ¼ 2087 (%) Male Female Age (mean years, SD) Age in 5-year bands, n ¼ 2087 (%) 65–69 70–74 75–79 80–84 85 Marital status, n ¼ 2086 (%) Married/de facto Widowed Never married Divorced/separated Self-rated health, n ¼ 2081 (%) Excellent / very good, Good Fair/poor Education: age left school, n ¼ 2061 (%) 14 years >15 years Annual income, n ¼ 1930 (%) $AUD
Cohort Profile
Cohort Profile: The Australian Longitudinal Study of Ageing (ALSA) Mary A Luszcz,1,2* Lynne C Giles,3 Kaarin J Anstey,4 Kathryn C Browne-Yung,1,5 Ruth A Walker1,6 and Tim D Windsor1,2 Downloaded from http://ije.oxfordjournals.org/ at University of Otago on November 14, 2015
1
Flinders Centre for Ageing Studies, and 2School of Psychology, Flinders University, Adelaide, SA, Australia, Discipline of Public Health, University of Adelaide, Adelaide, SA, Australia, 4Centre for Research on Ageing, Health and Wellbeing, Australian National University, Canberra, ACT, Australia, 5Southgate Institute, and 6Disability and Community Inclusion Unit, Flinders University, Adelaide, SA, Australia 3
*Corresponding author. School of Psychology, Flinders University, GPO Box 2100, Adelaide, SA 5001, Australia. E-mail: [email protected] Accepted 5 September 2014
Abstract In response to the expressed need for more sophisticated and multidisciplinary data concerning ageing of the Australian population, the Australian Longitudinal Study of Ageing (ALSA) was established some two decades ago in Adelaide, South Australia. At Baseline in 1992, 2087 participants living in the community or in residential care (ranging in age from 65 to 103 years) were interviewed in their place of residence (1031 or 49% women), including 565 couples. By 2013, 12 Waves had been completed; both face-to-face and telephone personal interviews were conducted. Data collected included self-reports of demographic details, health, depression, morbid conditions, hospitalization, gross mobility, physical performance, activities of daily living, lifestyle activities, social resources, exercise, education and income. Objective performance data for physical and cognitive function were also collected. The ALSA data are held at the Flinders Centre for Ageing Studies, Flinders University. Procedures for data access, information on collaborations, publications and other details can be found at [http://flinders.edu.au/sabs/fcas/].
Key Messages • The population-based ALSA is one of the longest-running cohort studies of older people in the world. The frequency
of data collection, spanning some 12 waves over 22 years, has allowed insights about ageing rarely possible from other longitudinal studies of ageing. • While the majority of participants at every wave appear to be experiencing ageing as a positive process, with preser-
vation of cognitive, affective and functional health, wide interindividual differences, including in intra-individual change, were also observed. • Many of the identified factors that promote longevity and quality of life are aspects of lifestyle that are amenable to
change. Early screening could identify risk factors. Intervention strategies that encourage regular exercise, support social networks and engender a positive state of mind may help promote survival and a good quality of life.
C The Author 2014; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association V
1
2
International Journal of Epidemiology, 2014, Vol. 0, No. 0
A loyal participant “Sheila” is shown on three occasions over her 21 years of continuous involvement in the ALSA. She was 80 years of age at Wave 3, shown doing the Clinical Assessment; 95 at Wave 11 doing her home interview and 99 years at Wave 12. In 2014 she celebrated her 100th birthday.
The proportion of Australians over 65 years old is projected to increase from 14% in 2012 to 27.1% by 2101, with South Australia having the second oldest population in Australia.1 In response to the need for more sophisticated data concerning ageing of the Australian population, the Australian Longitudinal Study of Ageing (ALSA) was established two decades ago in Adelaide, capital of South Australia. The ALSA is multidisciplinary and designed to improve understanding of how a broad range of individual and structural factors are associated with age-related changes in health and well-being. Following an extensive pilot study2 in 1988, ALSA Wave 1 (Baseline) commenced in 1992. Needs for comprehensive information on ageing still exist, and a strength of the study is that the surviving ALSA cohort are now aged 85 years or older: the most rapidly growing portion of the older population.
What does ALSA cover? The overarching aim was to investigate how social, biomedical, behavioural, economic, and environmental factors affect ageing. Specific objectives of ALSA were to: i. determine health and functional status and track changes in these characteristics over time; ii. identify risk factors for major chronic conditions and normative age-related changes; iii. assess effects of disease processes and lifestyle choices on functional status, and the demand for acute and longer-term aged care services; and iv. examine mortality outcomes.
Who is in the cohort? The ALSA is a population-based cohort of older men and women who resided in the Adelaide Statistical Division
and were aged 70 years or more on 31 December 1992. Both community-dwelling and people living in residential care were eligible and were randomly selected from the South Australian Electoral Roll. The primary sample was stratified by age groups (70-74, 75-79, 80-84 and 85 years or more), gender and local government area. Letters of introduction and invitation were sent to 3263 people; 560 were not eligible (210 were deceased, 88 had no translator available, 189 could not be contacted at the address, 37 were out of the geographical scope and 36 were ineligible for other reasons, e.g. for incorrect date of birth). Of the 2703 eligible persons, 1477 consented and completed an interview (response fraction: 54.6%). In addition to the primary sample, spouses and other household members of eligible persons were invited to take part. The age requirement for spouses was relaxed to age 65 years. An additional 597 spouses and 13 household members were recruited. In total, 2087 people took part in a Wave 1 interview, including 565 couples. Key Baseline characteristics are presented in Table 1. After consenting, arrangements were made for the structured personal interview to be conducted at the participant’s usual place of residence. Then participants were invited to take part in a detailed clinical assessment and complete self-administered questionnaires. To minimize participant attrition, at each wave members of the cohort were asked to provide contact details of three people who could provide information about their whereabouts should their residential location change. Birthday and Christmas cards, and periodic newsletters, were sent to participants between waves. To assess the representativeness of the Baseline sample, we compared them on a range of health-related markers to the overall over-70 population in South Australia.3 Weights were applied to the data to adjust for age and sex stratification, and the probability of selection of respondents within each local
Downloaded from http://ije.oxfordjournals.org/ at University of Otago on November 14, 2015
Why was the ALSA cohort set up?
International Journal of Epidemiology, 2014, Vol. 0, No. 0
3
Table 1. Key characteristics of the ALSA participants at
Table 2. Summary of target sample characteristics and differ-
Baseline
ences compared with full sample residing in community
Characteristics
Variable 1056 (51) 1031 (49) 78.3 (6.7) 140 (7) 562 (27) 524 (25) 429 (21) 432 (21) 1367 (65.0) 594 (28.6) 76 (3.6) 49 (2.4) 790 (38) 633 (30.4) 658 (31.6) 1155 (55.3) 906 (43.4) 686 (35.5) 1083 (56.1) 136 (7.1) 25 (1.3)
government area sampled. The percentages for the weighted and unweighted values showed high correspondence on activities of daily living, self-ratings of health, number of health professionals consulted and hospital stays in the previous year. At Baseline, 14% of participants used formal services, comparing favourably to 12% of the over-70 population. Attrition was examined at Wave 3 in relation to partaking in the clinical assessment.4 Those who did not complete the clinical assessment at Wave 3 had had poorer health and cognitive function at Wave 1, independently of age and gender. Rates of possible dementia were also higher in participants who did not undertake Wave 3 clinical assessment, compared with both those who did so and population data. Possible sample selectivity was also assessed by Luszcz et al.5 using Baseline data to explain agerelated differences in memory. We compared the target community-dwelling sample who met inclusion criteria with all community-dwellers who completed the structured interview. Table 2 shows a high degree of similarity between the groups; differences were in all cases a small fraction of the standard deviation. The target sample was slightly younger, healthier and more cognitively able.
Differencea
Sub-sample
Age Gender Male Female Married School (left 16 years) Illnesses Medications Self-rated health (good) Activities of daily living (1) Instrumental activities of daily living (1) Depressive symptoms Adelaide Activities Profileb Home maintenance Domestic Social Service to others NART errors IQ estimatec Processing accuracy Processing speed Picture naming Recall Address Symbol Picture
%
M
SD
77.6 54.2 515 45.8 436 70 29 5.5 2.6 74 11
%
5.5
M 0.65
þ0.1 0.1 þ3.5 þ4.2 2.8 1.9
0.18 þ0.02 þ5.0 5.0
32
3.0 7.4
6.7
0.53
52.5 52.2 51.3 52.3 22.2 102.0 0.97 29.9 13.7
18.7 18.5 19.8 20.3 8.4 9.5 0.07 10.9 1.6
þ2.5 þ2.2 þ1.3 þ2.3 0.27 þ0.32 0.00 þ0.68 þ0.18
1.6 2.0 2.3
þ0.20 þ0.06 þ0.17
8.60 6.28 5.62
NART: National Adult Reading Test. a Raw score difference relative to all community residents. b Clark and Bond (1995)51 standardized these scales to M ¼ 50 (SD ¼ 20). c IQ estimate is based on NART errors. Statistics extracted from Luszcz et al.5
More generally, missing data resulting from attrition have been analysed using maximum likelihood estimation, e.g. growth curve modelling,6 which produces estimates based on all available data under missing-at-random assumptions.7
How often have they been followed up? There have been 12 waves of data collection. Wave 1 took place from September 1992 to March 1993. The next three waves took place 1, 2 or 3 years thereafter. Subsequent waves were approximately 6, 8, 11, 13, 15, 16, 18 and 21 years after baseline, with funding secured for a 22-year follow-up. Unequal intervals reflect funding availability for follow-up. There were two key modalities of data collection: in person and by telephone. Waves 1, 3, 6, 7, 9, 11 and
Downloaded from http://ije.oxfordjournals.org/ at University of Otago on November 14, 2015
Gender, n ¼ 2087 (%) Male Female Age (mean years, SD) Age in 5-year bands, n ¼ 2087 (%) 65–69 70–74 75–79 80–84 85 Marital status, n ¼ 2086 (%) Married/de facto Widowed Never married Divorced/separated Self-rated health, n ¼ 2081 (%) Excellent / very good, Good Fair/poor Education: age left school, n ¼ 2061 (%) 14 years >15 years Annual income, n ¼ 1930 (%) $AUD
