How did the study come about?

In Australia over 25% of the population experience a mental disorder each year and ∼12% of the population use a health service for mental health problems each year.1 The most prevalent of these disorders are depression, anxiety disorders and substance use. Dementia is currently the fifth leading cause of disease burden and, with an ageing population, by 2016, the disability burden of dementia will be the highest of any disease in Australia.2 There is a paucity of longitudinal research on factors influencing the development of and recovery from mental disorders over the adult age span. In response to this lack of research, the personality and total health through life (PATH) project was established in 1999 by A.F.J., H.C. and B.R. It has been funded almost entirely through grants from the National Health and Medical Research Council.

What does PATH cover?

The original aims of the study were These broad aims continue to provide the core direction for the project and, as such, the project goals relate to specific clinical outcomes that constitute the major burden of disease within the Australian community. The study aims to follow participants for 20 years, thus spanning the ages from 20 to 84 years.

  • to delineate the course of depression, anxiety, substance use and cognitive ability with increasing age across the adult life span;

  • to identify environmental and genetic risk factors influencing individual differences in the courses of these characteristics; and

  • to investigate inter-relationships over time between the three domains of depression and anxiety, substance use, and cognitive ability and dementia.

Design

PATH is based on three cohorts (birth years 1975–79, 1956–60 and 1937–41). At the start of the study, these cohorts were aged 20–24 years (The ‘20+’ cohort.), 40–44 years (‘40+’) and 60–64 years (‘60+’). By the end of 2010, three waves of data were collected for all three cohorts with 4-year intervals between interviews. Each cohort was interviewed over 1 year starting with the 20+. A broad range of fixed and time-varying risk factors and moderators have been collected in the interviews. These include genetic risk factors, early life adversity, other personal history (including past mental health problems and substance use, adolescent transitions, marital history and family formation), personality measures, life stress and social support, diet, occupational stress, recent anxiety and depression, recent substance use and cognitive abilities. In successive waves of data collection, new questions have been added to assess lifestyle changes appropriate to each cohort. These include (in)fertility and pregnancy, changes in family structure, relationship formation and separation, menopause, changes in work environments, job characteristics, workforce status and retirement.

Substudies

In addition to the main study, subsamples were drawn to undertake three more detailed studies.

  • Magnetic Resonance Imaging (MRI) substudy: at Wave 1, a subsample of 478 participants from the 60+ cohort received a brain MRI and blood tests. At Wave 2, this subsample was rescanned, and an additional 431 participants from the 40+ group received a brain MRI and blood test. At Wave 3, both these subsamples were recontacted and invited to undertake another brain scan. The aim of this neuoroimaging substudy is to study brain ageing in a non-clinical sample; the factors that predict accelerated brain atrophy, white matter hyperintensities and other neuropathology; and neural correlates of mental disorders, personality and cognitive function.

  • Health and Memory substudy: at Waves 1–3, participants in the 60+ cohort who performed poorly on selected cognitive tests were asked to complete a detailed neurocognitive assessment and to undergo an MRI scan and blood test. This examination provided clinical diagnoses of dementia or pre-clinical dementia syndromes such as mild cognitive impairment (MCI).3

  • Cardiovascular substudy: at Wave 3, the subsamples of participants from the 40+ and 60+ cohorts who participated in the MRI substudy were invited to take part in a substudy which aimed to investigate associations between more sensitive markers of cardiovascular function, indexed by measures of arterial stiffness (pulse wave velocity), left ventricular function (cardiac ultrasound) and conventional blood pressure measures, and cognition. This study may also identify cardiac biomarkers particularly predictive of cognitive decline and dementia.

Who is in the sample?

The project is being conducted in the Australian Capital Territory (ACT) and the neighbouring town of Queanbeyan in the adjoining state of New South Wales. The ACT is predominantly urban, but includes a number of small communities and farmlands within 30 min drive from the centre of Canberra. Canberra is the national capital of Australia. Its population at the 1996 census was nearly 300 000, whereas Queanbeyan had a population of 27 500.

The sample was drawn from the electoral rolls of the three federal electorates that make up the ACT and the electorate containing Queanbeyan. From this latter electorate, we selected only those giving Queanbeyan as their residential address. All Australian citizens aged ≥18 years are required by law to be enrolled. However, ∼5.5% of those who are entitled to vote are not enrolled at any one time for a number of reasons. Three age groups were selected for interview: residents between the ages of 20–24, 40–44 and 60–64 years. Each age group is interviewed, in turn, over a 1-year period allowing 1 year with no interviewing between Waves. The use of self-completion questionnaires on computers in PATH provides anonymity for participants and, we believe, encourages honest disclosure of usage of illicit drugs and alcohol.

To assist with follow-up at Wave 1, participants were asked to provide the name, address and phone numbers of two contacts (friends or relatives). These contacts were used at Wave 2 to help trace participants if necessary. In an effort to maintain contact with the PATH participants, each December participants are sent a card, a PATH Newsletter and a ‘change of address’ card. Participants are asked to update the information on these cards and post them back or telephone or email us with changes to their contact details. Participation rates have been high across all waves and cohorts. Follow-up participation rates have ranged from 89% to 93% across cohorts. Participation and follow-up rates are shown in Figure 1. A comparison between the demographical characteristics of the PATH cohorts and the Australian census data is shown in Table 1.

Figure 1

Sample size and participation rate by cohort and wave. P rate = participation rate. At Wave 1, this is the participation rate from the random sampling and at Waves 2 and 3, this is the percentage of participants interviewed from the previous wave

Figure 1

Sample size and participation rate by cohort and wave. P rate = participation rate. At Wave 1, this is the participation rate from the random sampling and at Waves 2 and 3, this is the percentage of participants interviewed from the previous wave

Table 1

Comparison of PATH cohorts at Wave 1 with Australian census data from 2001

 Males
 
Females
 
20+ cohort (%)
 
40+ cohort (%)
 
60+ cohort (%)
 
20+ cohort (%)
 
40+ cohort (%)
 
60+ cohort (%)
 
PATH Census PATH Census PATH Census PATH Census PATH Census PATH Census 
Registered marital status             
    Married 6.1 4.5 74.2 67.5 82.8 79.8 11.5 9.2 68.8 65.4 66.7 66.6 
    De facto 12.4 10.5 7.5 8.4 3.9 2.5 16.4 14.8 8.8 7.9 2.0 1.4 
    Separated 0.2 0.2 3.5 4.1 2.7 3.3 1.4 0.5 5.8 5.3 2.7 3.3 
    Divorced 0.1 0.1 5.5 6.8 6.6 7.8 0.2 0.1 9.0 10.8 12.5 13.8 
    Widowed 0.1 0.0 0.5 0.3 1.8 2.7 0.1 0.1 1.0 1.0 12.9 11.6 
    Never married 81.1 84.7 8.9 13.0 2.2 3.9 70.3 75.4 6.7 9.7 3.2 3.3 
Employment status             
    Employed (full- or part-time) 85.8 78.7 94.8 90.5 49.2 49.4 84.3 79.0 85.7 80.8 31.9 31.1 
    Unemployed 6.7 8.8 2.0 3.1 1.3 2.3 4.8 4.9 2.6 2.4 0.6 0.6 
    Not in labour force 7.4 12.5 3.2 6.5 49.5 48.3 10.9 16.2 11.7 16.9 67.5 68.2 
Education completed             
    Post-school qualifications 51.6 37.5 78.7 70.0 76.3 64.2 59.3 44.2 73.0 60.6 63.0 45.4 
Undertaking current study             
    Full- or part-time 48.4 39.6 15.1 9.1 2.9 2.1 42.9 41.4 15.4 10.9 2.8 2.4 
    Full-time 57.1 67.0 10.6 15.9 13.5 19.1 58.0 69.0 13.6 18.9 15.2 11.6 
    Part-time 42.9 33.0 89.4 84.1 86.5 80.9 42.0 31.0 86.4 81.1 84.8 88.4 
 Males
 
Females
 
20+ cohort (%)
 
40+ cohort (%)
 
60+ cohort (%)
 
20+ cohort (%)
 
40+ cohort (%)
 
60+ cohort (%)
 
PATH Census PATH Census PATH Census PATH Census PATH Census PATH Census 
Registered marital status             
    Married 6.1 4.5 74.2 67.5 82.8 79.8 11.5 9.2 68.8 65.4 66.7 66.6 
    De facto 12.4 10.5 7.5 8.4 3.9 2.5 16.4 14.8 8.8 7.9 2.0 1.4 
    Separated 0.2 0.2 3.5 4.1 2.7 3.3 1.4 0.5 5.8 5.3 2.7 3.3 
    Divorced 0.1 0.1 5.5 6.8 6.6 7.8 0.2 0.1 9.0 10.8 12.5 13.8 
    Widowed 0.1 0.0 0.5 0.3 1.8 2.7 0.1 0.1 1.0 1.0 12.9 11.6 
    Never married 81.1 84.7 8.9 13.0 2.2 3.9 70.3 75.4 6.7 9.7 3.2 3.3 
Employment status             
    Employed (full- or part-time) 85.8 78.7 94.8 90.5 49.2 49.4 84.3 79.0 85.7 80.8 31.9 31.1 
    Unemployed 6.7 8.8 2.0 3.1 1.3 2.3 4.8 4.9 2.6 2.4 0.6 0.6 
    Not in labour force 7.4 12.5 3.2 6.5 49.5 48.3 10.9 16.2 11.7 16.9 67.5 68.2 
Education completed             
    Post-school qualifications 51.6 37.5 78.7 70.0 76.3 64.2 59.3 44.2 73.0 60.6 63.0 45.4 
Undertaking current study             
    Full- or part-time 48.4 39.6 15.1 9.1 2.9 2.1 42.9 41.4 15.4 10.9 2.8 2.4 
    Full-time 57.1 67.0 10.6 15.9 13.5 19.1 58.0 69.0 13.6 18.9 15.2 11.6 
    Part-time 42.9 33.0 89.4 84.1 86.5 80.9 42.0 31.0 86.4 81.1 84.8 88.4 

What has been measured?

A list of all scales and tests used in PATH interviews, as well as the source references, can be found on the study website http://cmhr.anu.edu.au/path/. Table 2 shows the measures included at each wave. Table 3 reports demographical characteristics of the cohorts at Wave 1.

Table 2

Selected demographical and health characteristics of the PATH cohorts at Wave 1

 20+ cohort (%)
 
40+ cohort (%)
 
60+ cohort (%)
 
Total Total Total 
Region of birth          
    Australia 89.5 89.7 89.3 79.0 78.9 79.2 67.4 64.4 70.6 
    New Zealand 0.6 0.5 0.7 2.0 1.5 2.4 1.4 0.9 1.9 
    Oceania/Pacific Island 0.9 0.8 1.0 0.3 0.3 0.2 0.3 0.4 0.2 
    Europe and UK 3.4 3.5 3.4 11.9 13.2 10.9 24.9 28.2 21.2 
    Asia 3.6 3.3 3.8 4.0 3.7 4.2 3.5 3.3 3.6 
    North America 0.6 0.6 0.5 0.9 0.8 1.0 1.1 1.3 0.9 
    South America 0.3 0.5 0.1 0.4 0.4 0.4 0.2 0.1 0.3 
    Africa 0.8 0.7 0.9 0.9 1.1 0.8 0.8 0.9 0.7 
    Other/refused 0.4 0.5 0.3 0.5 0.2 0.8 0.5 0.4 0.6 
Financial hardship          
    No 91.5 92.3 90.9 94.6 95.6 93.7 97.8 98.5 97.0 
    Yes 8.5 7.7 9.1 5.4 4.4 6.3 2.2 1.5 3.0 
Smoking status          
    Never smoked 56.7 59.0 54.3 51.1 50.0 52.2 52.2 41.5 62.9 
    Past smoker 11.4 8.7 14.0 29.8 29.6 30.0 37.1 46.7 27.4 
    Current smoker 31.4 31.7 31.1 19.1 20.4 17.8 10.8 11.9 9.7 
Alcohol use          
    Abstain 8.3 7.2 9.4 9.3 7.6 10.9 14.2 9.5 18.8 
    Occasional 23.9 20.3 27.4 18.1 12.0 24.2 16.6 11.4 21.8 
    Light 49.2 54.0 44.4 47.5 58.0 37.0 42.9 55.0 30.8 
    Medium 11.7 11.6 11.8 18.3 15.9 20.7 20.4 17.4 23.3 
    Hazardous/harmful 6.6 6.1 7.0 6.6 6.3 6.9 6.1 6.7 5.4 
BMI          
    Underweight 5.4 2.2 8.5 1.0 0.5 1.5 0.8 0.3 1.3 
    Healthy 64.4 60.5 68.2 44.8 36.6 52.9 38.9 32.8 44.9 
    Overweight 20.6 25.9 15.2 35.3 44.4 26.1 41.0 49.8 32.2 
    Obese 7.2 6.3 8.1 19.0 18.5 19.5 19.4 17.1 21.6 
 20+ cohort (%)
 
40+ cohort (%)
 
60+ cohort (%)
 
Total Total Total 
Region of birth          
    Australia 89.5 89.7 89.3 79.0 78.9 79.2 67.4 64.4 70.6 
    New Zealand 0.6 0.5 0.7 2.0 1.5 2.4 1.4 0.9 1.9 
    Oceania/Pacific Island 0.9 0.8 1.0 0.3 0.3 0.2 0.3 0.4 0.2 
    Europe and UK 3.4 3.5 3.4 11.9 13.2 10.9 24.9 28.2 21.2 
    Asia 3.6 3.3 3.8 4.0 3.7 4.2 3.5 3.3 3.6 
    North America 0.6 0.6 0.5 0.9 0.8 1.0 1.1 1.3 0.9 
    South America 0.3 0.5 0.1 0.4 0.4 0.4 0.2 0.1 0.3 
    Africa 0.8 0.7 0.9 0.9 1.1 0.8 0.8 0.9 0.7 
    Other/refused 0.4 0.5 0.3 0.5 0.2 0.8 0.5 0.4 0.6 
Financial hardship          
    No 91.5 92.3 90.9 94.6 95.6 93.7 97.8 98.5 97.0 
    Yes 8.5 7.7 9.1 5.4 4.4 6.3 2.2 1.5 3.0 
Smoking status          
    Never smoked 56.7 59.0 54.3 51.1 50.0 52.2 52.2 41.5 62.9 
    Past smoker 11.4 8.7 14.0 29.8 29.6 30.0 37.1 46.7 27.4 
    Current smoker 31.4 31.7 31.1 19.1 20.4 17.8 10.8 11.9 9.7 
Alcohol use          
    Abstain 8.3 7.2 9.4 9.3 7.6 10.9 14.2 9.5 18.8 
    Occasional 23.9 20.3 27.4 18.1 12.0 24.2 16.6 11.4 21.8 
    Light 49.2 54.0 44.4 47.5 58.0 37.0 42.9 55.0 30.8 
    Medium 11.7 11.6 11.8 18.3 15.9 20.7 20.4 17.4 23.3 
    Hazardous/harmful 6.6 6.1 7.0 6.6 6.3 6.9 6.1 6.7 5.4 
BMI          
    Underweight 5.4 2.2 8.5 1.0 0.5 1.5 0.8 0.3 1.3 
    Healthy 64.4 60.5 68.2 44.8 36.6 52.9 38.9 32.8 44.9 
    Overweight 20.6 25.9 15.2 35.3 44.4 26.1 41.0 49.8 32.2 
    Obese 7.2 6.3 8.1 19.0 18.5 19.5 19.4 17.1 21.6 
Table 3

Variables included in the PATH project by wave and cohort

Variables Wave 1
 
Wave 2
 
Wave 3
 
 20 40 60 20 40 60 20 40 60 
Demographics          
    Age ✓ ✓ ✓       
    Gender ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Marital status ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Education ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Employment ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Housing   ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Income   ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Country of birth    ✓ ✓ ✓    
    Language ✓ ✓ ✓       
    Race ✓ ✓ ✓       
Health          
    BMI ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Medical conditions ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Head injury ✓ ✓ ✓    ✓ ✓ ✓ 
    Medication ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Women’s health ✓ ✓ ✓ ✓ ✓  ✓ ✓  
    Menopause    ✓ ✓ ✓ ✓ ✓ ✓ 
    Pregnancy ✓   ✓      
    Cigarette smoking ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Alcohol consumption ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Illicit drug use ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓  
    Gambling    ✓ ✓ ✓ ✓ ✓  
    Physical activity ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Mental activity ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
Stressors          
    Lifetime trauma    ✓ ✓ ✓ ✓ ✓ ✓ 
    Life events ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Stress ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Financial stress ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Work stress  ✓  ✓ ✓  ✓ ✓  
    Life transition ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Role strain ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
Social          
    Social support ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Social network       ✓ ✓ ✓ 
    Dyadic adjustment       ✓ ✓ ✓ 
    Pet ownership  ✓ ✓ ✓ ✓ ✓    
    Child care ✓ ✓ ✓ ✓ ✓  ✓ ✓  
    Care giving      ✓   ✓ 
    Volunteering      ✓   ✓ 
    Work  ✓  ✓ ✓  ✓ ✓  
Physical measures          
    Blood pressure ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Eye chart ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Hand grip ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Lung function ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Handedness   ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Physical tests (walk, stand, balance)      ✓    
    Reaction time ✓ ✓ ✓       
Cognitive measures          
    CVLT immediate recall ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    CVLT delayed recall ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Symbol Digit Modalities Test ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Digit span backwards ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Purdue pegboard   ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Trails A and B    ✓ ✓ ✓ ✓ ✓ ✓ 
    Faces    ✓ ✓ ✓ ✓ ✓ ✓ 
    Spot the word task ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Mini Mental State Examination   ✓   ✓   ✓ 
    Boston Naming test         ✓ 
Mental health self-report          
    Patient health questionnaire   ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Positive affect/negative affect scale ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Goldberg Anxiety and Depression scale ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Suicide ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Self-harm       ✓ ✓ ✓ 
    Seasonal Pattern Assessment Questionnaire ✓ ✓ ✓ ✓ ✓ ✓    
    Composite International Diagnostic interview ✓ ✓        
    Past depression ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
Psychological scales          
    Mastery ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Ruminative style ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Religiosity    ✓ ✓ ✓    
    Eysenck Personality questionnaire ✓ ✓ ✓ ✓ ✓ ✓    
    Behavioural Inhibition/Behavioural Activation scale ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Life satisfaction    ✓ ✓ ✓ ✓ ✓ ✓ 
    Bushfire experience    ✓ ✓ ✓    
    Brief COPE inventory       ✓ ✓ ✓ 
    Connor–Davidson Resilience scale       ✓ ✓ ✓ 
    Reciprocity         ✓ 
    Future time perspective         ✓ 
    Dialectical thinking         ✓ 
    Brief big five personality measure         ✓ 
General health self-report          
    SF-12 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Memory items   ✓   ✓    
    Self-report IQCODE         ✓ 
    Instrumental activities of daily living         ✓ 
    Driving         ✓ 
Variables Wave 1
 
Wave 2
 
Wave 3
 
 20 40 60 20 40 60 20 40 60 
Demographics          
    Age ✓ ✓ ✓       
    Gender ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Marital status ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Education ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Employment ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Housing   ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Income   ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Country of birth    ✓ ✓ ✓    
    Language ✓ ✓ ✓       
    Race ✓ ✓ ✓       
Health          
    BMI ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Medical conditions ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Head injury ✓ ✓ ✓    ✓ ✓ ✓ 
    Medication ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Women’s health ✓ ✓ ✓ ✓ ✓  ✓ ✓  
    Menopause    ✓ ✓ ✓ ✓ ✓ ✓ 
    Pregnancy ✓   ✓      
    Cigarette smoking ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Alcohol consumption ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Illicit drug use ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓  
    Gambling    ✓ ✓ ✓ ✓ ✓  
    Physical activity ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Mental activity ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
Stressors          
    Lifetime trauma    ✓ ✓ ✓ ✓ ✓ ✓ 
    Life events ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Stress ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Financial stress ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Work stress  ✓  ✓ ✓  ✓ ✓  
    Life transition ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Role strain ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
Social          
    Social support ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Social network       ✓ ✓ ✓ 
    Dyadic adjustment       ✓ ✓ ✓ 
    Pet ownership  ✓ ✓ ✓ ✓ ✓    
    Child care ✓ ✓ ✓ ✓ ✓  ✓ ✓  
    Care giving      ✓   ✓ 
    Volunteering      ✓   ✓ 
    Work  ✓  ✓ ✓  ✓ ✓  
Physical measures          
    Blood pressure ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Eye chart ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Hand grip ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Lung function ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Handedness   ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Physical tests (walk, stand, balance)      ✓    
    Reaction time ✓ ✓ ✓       
Cognitive measures          
    CVLT immediate recall ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    CVLT delayed recall ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Symbol Digit Modalities Test ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Digit span backwards ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Purdue pegboard   ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Trails A and B    ✓ ✓ ✓ ✓ ✓ ✓ 
    Faces    ✓ ✓ ✓ ✓ ✓ ✓ 
    Spot the word task ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Mini Mental State Examination   ✓   ✓   ✓ 
    Boston Naming test         ✓ 
Mental health self-report          
    Patient health questionnaire   ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Positive affect/negative affect scale ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Goldberg Anxiety and Depression scale ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Suicide ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Self-harm       ✓ ✓ ✓ 
    Seasonal Pattern Assessment Questionnaire ✓ ✓ ✓ ✓ ✓ ✓    
    Composite International Diagnostic interview ✓ ✓        
    Past depression ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
Psychological scales          
    Mastery ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Ruminative style ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Religiosity    ✓ ✓ ✓    
    Eysenck Personality questionnaire ✓ ✓ ✓ ✓ ✓ ✓    
    Behavioural Inhibition/Behavioural Activation scale ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Life satisfaction    ✓ ✓ ✓ ✓ ✓ ✓ 
    Bushfire experience    ✓ ✓ ✓    
    Brief COPE inventory       ✓ ✓ ✓ 
    Connor–Davidson Resilience scale       ✓ ✓ ✓ 
    Reciprocity         ✓ 
    Future time perspective         ✓ 
    Dialectical thinking         ✓ 
    Brief big five personality measure         ✓ 
General health self-report          
    SF-12 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 
    Memory items   ✓   ✓    
    Self-report IQCODE         ✓ 
    Instrumental activities of daily living         ✓ 
    Driving         ✓ 

CVLT, California Verbal Learning Test; SF-12, medical outcomes study, short form 12; IQCODE, Informant Questionnaire on Cognitive Decline in the Elderly.

What has been found from the first three waves of PATH?

Anxiety, depression and suicidality

The diagnostic measures included in PATH Wave 2 showed prevalence rates for depression (major or minor) and anxiety disorders consistent with established national figures, and replicated established age and gender differences. With between 3% and 11% of each cohort identified with either disorder, there are sufficient cases to examine individual differences within and between diagnostic categories. Consistent with the literature, PATH subjects show high co-morbidity4 between anxiety and depression suggesting shared aetiologies of these disorders. PATH is one of the largest sources of data on suicidal ideation and self-reported suicide attempts in Australia. This includes information on rates of suicidality in mid-life. At Wave 1, 8.2% of the sample reported suicidal ideation and 0.9% reported attempting suicide in the past 12 months.5

PATH has identified risk factors for depression, anxiety and suicidality in each cohort and demonstrated the need to consider both gender and age in relation to risk profiles for these common mental disorders.6–9 Consistent with other epidemiological work, PATH showed higher levels of symptoms in younger compared with older adults. Whereas levels of symptoms of depression and anxiety appear to be declining over time, at this stage, PATH data suggest that there are genuine cohort effects rather than ageing effects driving the different rates of common mental disorders over the lifecourse.10 Other key findings relate to the impact of physical illness as a risk factor for depression. This was observed in all cohorts. Of particular note is the finding of higher rates of depression among those with diabetes, history of head injury,11 higher body mass index (BMI) and lower levels of physical activity,7 although the effect of BMI on depression and anxiety was mediated by physical illness.12 Although we found that depression was associated with suicidality, this did not distinguish suicide attempters from among those who had suicidal ideation.5 Rather, we found that chronic medical conditions, unemployment and low sense of mastery (males only) were associated with suicide attempts among those with pre-existing ideation. Financial hardship, life events and poor work conditions were all associated with poorer mental health outcomes.13,14

Our published results have examined the social and economic factors associated with poorer mental health. This has confirmed the presence of socio-economic inequalities in depression but has further shown that the experience of hardship/deprivation is a more powerful predictor of poor mental health than other markers of disadvantage such as low educational attainment, labour-force status and housing tenure.13 Importantly, a measure of food insecurity (missing meals because of a lack of money) was the strongest single correlate of depression. Several articles have documented how aspects of psychosocial job quality (e.g. job strain, job insecurity) are associated with poorer mental and physical health independent of job status and income.15 Furthermore, those respondents in or moving into the poorest quality jobs report similar or poorer health compared with those who are unemployed.14 Results published have demonstrated that the effect of work stressors on mental health is independent of job status and income.14,15 Interestingly, those in jobs with multiple work stressors report similar health to those who are unemployed.

Cognitive development and transition to cognitive impairment

Analyses of PATH data have already demonstrated that lifestyle factors and physical function measures such as physical activity, smoking, lung function, physical health, mental health and substance use are associated with cognitive performance in 20- to 24-year-olds as well as in the older ages.16–18 A stream of research on cognitive development and cognitive disorders has identified risk factors for transition from normal ageing to MCI in late life. These included hypertension, alcohol abstinence, anti-depressant use, past smoking, inactivity, a high-fat diet and high calorie intake19 but not Apolipoprotein E (APOE) genotype or high cholesterol. By Wave 4, the oldest cohort will be aged 72–76 and the expected prevalence rate of dementia will be about ∼5%. This will be sufficient for analysis of predictors over 12 years of follow-up. Analysis of subgroups has also identified those ‘at-risk’ of cognitive decline, including participants with history of stroke, cancer or diabetes. Another focus of the analyses of the cognitive data in PATH has been on intra-individual variability in reaction times (IVV), a possible marker of neurological damage. We examined IVV in relation to health and lifestyle variables and found it most strongly associated with biomarkers (grip, Forced Expiratory Volume, vision), and have also shown that it is greater in individuals with MCI.16,20

Gene–environment interactions in relation to PATH mental health outcomes

Recent work has found significant associations for anti-social traits with Androgen Receptor and Estrogen Receptor 1 polymorphisms in men, and with polymorphisms within NR4A2 and TFAP2B in women suggesting that genetic variation within transcription factors may in part explain the variation in anti-social behavioural phenotypes.21 APOE has been studied extensively in relation to cognitive decline in PATH, with few significant findings. This has been attributed to the relatively young age of the cohorts, and small numbers of dementia cases in the available data set.22,23

Brain imaging

Longitudinal, population-representative imaging is a key feature of PATH. We have shown in our 60+ cohort that increased white matter changes are associated with physical disability, poor motor function, slowed information processing speed and depression.24,25 The main identifiable causes for white matter changes were found to differ somewhat between sexes but were strongly associated with physical and cardiovascular health. Moreover, the association between white matter changes and depression appeared to be mediated by physical health. Subtle but significant white matter changes were also detected in the 40+ cohort.26 Frontal white matter lesions were significantly associated with greater intra-individual reaction time variability in women, whereas temporal lesions were associated with face recognition deficits in men.27 Surprisingly, APOE*E4 genotype, the main known risk factor for Alzheimer’s disease, was found not to be associated with smaller regional grey matter volumes at Wave 1. In contrast, type 2 diabetes was found to be associated with greater brain atrophy at Wave 128, and hippocampal, ventricular and cerebral asymmetries were found to be predictive of impairment at Wave 2.29 These effects are currently being further investigated in longitudinal analyses.

Personality and social relationships

PATH research has also focused on associations of psychosocial characteristics including personality and social support, with aspects of adult development and well-being. Research on data at entry revealed associations between self-reported childhood adversity and trait tendencies towards negative emotionality in adulthood.30 Non-linear associations between personality and religiosity have been identified, with higher psychoticism and lower extraversion scores evident among the least and most religious respondents.31 Studies concerned with dispositional approach and avoidance sensitivities have revealed a positive association between avoidance sensitivity and hippocampal volume,32 and positive associations of approach motivation with generalized perceptions of control over the environment.33 Additional research concerned with control beliefs showed that those with stronger perceptions of control tend to perform better on cognitive tests,34 and to report better mental health.

Research concerned with social relations and social support has revealed that having supportive social exchanges with friends and family is associated with lower levels of psychological distress and higher positive affect.34 In contrast, negative social exchanges with friends and family (i.e., relationships characterized by tension and arguments) are associated with higher distress and higher negative affect. Further research has shown that the quality of spousal relationships indicated that older adults are more likely to rate their spousal relationship as being characterized by high level of support and low levels of negative exchanges relative to mid-life adults.35

Substance use

Research from PATH has shown that alcohol consumption has an inverse U-shaped association with cognitive performance, with light to moderate drinkers having the best performance in each cohort.36 Other work has linked heavy alcohol use with brain atrophy in the 60+ cohort.37 PATH is a unique source of Australian data on the use of illicit substances and, in particular, Ecstasy.38 Ecstasy use per capita is greater in Australia than in any other country.39 To our knowledge, PATH is the only population-based study of illicit substance use that includes cognitive assessment.

Strengths and weaknesses

Unique strengths of the PATH project include the large sample size, random selection from the population, narrow age cohort and longitudinal design and the concurrent assessment of three age groups. Together these aspects provide high statistical power, limit sampling bias, allow discrimination between age and cohort effects, limit extraneous bias through prospective longitudinal analyses and ultimately will provide a lifespan snapshot between the ages of 20 and 84 years in a well-characterized population. The concurrent availability of cognitive, health, personality, employment, lifestyle, MRI, genetic and blood data in the same individuals is also of substantial importance. Potential limitations include the fact that participants had relatively high levels of education, general good health and higher socio-economic status than average. Moreover, whereas the attrition rate has been remarkably low, its progression introduces additional bias at each assessment. The development of longitudinal weights aids in addressing concerns about potential bias due to selection and attrition. Due to the breadth of the study, measures cannot always be as comprehensive as those of more specific and limited investigations. The study is limited to measures that can be lay-administered and must rely on self-report rather than clinical assessment of disorders, except for cognitive impairment and dementia where clinical assessments are undertaken.

Where can I find out more?

Further information including a list of publications is available at http://cmhr.anu.edu.au/path. There is no open access to the data set, but strategic collaborations are welcome and contact information is available on the website for interested parties to learn more about formal application procedures.

Funding

National Health and Medical Research Council (Grants 973302, 179805, 157125); NHMRC Fellowships (#366756 to K.J.A., #525411 to H.C., #525410 to P.B., #471501 to N.C., #471429 to B.R. and # 40001 to A.F.J.).

Acknowledgements

The authors thank the PATH participants, PATH interviewers, the Centre for Mental Health Research at the Australian National University and their collaborators.

Conflict of interest: None declared.

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