Research techniques and use of data in research on ageing

1. Research techniques and use of data in research on ageing Yvonne Wells Lincoln Centre for Research on Ageing

2. Where this workshop is going • What do you need? • Focus on quantitative data • Don’t under-rate qualitative research • Cross-sectional vs. longitudinal research • Measures of change • ABS and administrative by-product data sets • Content and examples of use • Discussion

3. What goes without saying … • Never generalize from younger populations to older ones • Never assume that all older people are alike

4. “Old people are pretty much alike” False • On a task measuring speed and accuracy performance (Stroop Task), older adults displayed as much individual variation as other populations (Rush, Panek, & Russell, 1990). • Individual variation in mental abilities increases as participants age (Rabbitt et al., 2001) • Older adults show large individual differences in terms of subjective well-being and quality of social relationships(Smith & Baltes, 1993).

5. Research on ageing is increasing Number of international PsycInfo journal publications addressing ageing, childhood and adolescence 1993-2002. Wells, Y. (2005). Research and practice with older adults: The picture in Australia. Australian Psychologist, 40(1), 2-7.

6. … especially in Australia Growth curves comparing Australian with international research on ageing, 1993-2002

7. Time in ageing research • Age – years since birth • Wave – number of data collection points since T0 • Analyses can centre on an event of interest • e.g., time to death, time before/after a dementia diagnosis • This technique can help separate normative from non-normative changes with ageing

8. Hazards of research with older people • Participation rates • Survivor effects • Physical health and disability issues • vision, deafness, fatigue, mental status, balance • methods of gathering data do have consequences. • Response bias may increase with age • Issues of meaning and acceptability • Ethical issues and privacy • Cross-sectional vs. longitudinal studies • In longitudinal studies, selective attrition

9. Longitudinal vs. cross-sectional • Cross-sectional studies can be misleading because • Cohort effects • Period effects • Selective attrition • Longitudinal research permits estimates of individual change over time • Easier to retain individuals with failing health to an ongoing study than recruit them to a new one • Longitudinal research is expensive • Cross-sequential analysis is a good compromise

10. Response bias and ageing McAvay, G. J. et al. (2005). Symptoms of depression in older home-care patients: Patient and informant reports. Psychology and Aging, 20, 3, 507-518. • Older, medically ill population reports of depressive symptoms vary systematically between patients and family member informants. • Agreement on somatic symptoms was poor, even though reported most often by patients and informants • Patients were more likely to report sleep problems whereas informants were more likely to report fatigue • Informants were more likely to report indecisiveness and inability to concentrate • Patients reported more suicidal thoughts than informants • Younger informants reported more cognitive symptoms than patients, whereas older informants reported fewer cognitive symptoms that patients

11. Response rate and ageing Kaldenberg, D. O., Koenig, H. E., & Becker, B. W. (1994). Mail survey response rate patterns in a population of the elderly: Does response deteriorate with age? Public Opinion Quarterly, 58, 68-76. • Response rate 58% for 60-62 year-olds • Fell by half a percentage point for each age group • Influence of age on data quality varied between items types • No age difference in missing data on open-ended questions • See graph …

12. Response rates by age and type of question (from Kaldenberg et al., 1994)

13. Attrition is considerable • Figure is from the Health Status of Older People study

14. Attrition & data quality in HSOP 1994-2005

15. Loss rate in HSOP Loss average of 12% per year, accelerating.

16. Figure 2 Attrition is not random Sliwinski, M., & Buschke, H. (1999). Cross-sectional and longitudinal relationships among age, cognition, and processing speed. Psychology and Ageing, 14, 18-33.

17. Figure 1 Retest effects are considerable Salthouse, T., Schroeder, D. H., & Ferrer, E. (2004). Estimating retest effects in longitudinal assessments of cognitive functioning in adults between 18 and 60 years of age. Developmental Psychology, 40, 813-822. Is improvement with retest error?

18. How? • Prospective vs. retrospective • Ways of collecting data (interview vs. self-complete questionnaire)

19. Retest vs self-ratings of change • Data from Healthy Retirement Project • Prospective panel study • n > 400 • Next table provides data on change in first 12 months of retirement

20. Retest vs self-ratings of change

21. What predicts discrepancy? • Floor and ceiling effects • Self-image • Not • Recency • Social comparison

22. Self-rated health in HSOP

23. Use of existing datasets • Australian Bureau of Statistics • Census data • National Health Survey • Survey Disability, Ageing and Carers • Administrative by-product data sets for • Home and Community Care program • Aged Care Assessment Program • Death Registry • Hospital Data • ACCMIS

24. Examples • Focus on CALD in health data • Interesting analyses from ACAP and HACC data

25. Using the ACAP and HACC MDSs

26. ACAP MDS • 200,000 assessments per year • 1.2% involve a psychologist • 27.5% of clients have a diagnosis of dementia • Increases to 34.5% of assessments that involve a psychologist

27. ACAP MDS domains • Registration • Accommodation and living arrangements • Dates • Carer data • Activity Limitations • Current assistance and recommended assistance • Current government service use and recommended use • Health conditions • Recommended accommodation • Assessor profession • Approvals • Care coordination

28. Reporting • Numbers • Timeliness • Access to key groups • Recommendations for key groups • Ad hoc data analyses

29. Example 1: Assessment numbers Total equivalent assessment numbers by location of assessment, ACAP, Australia 1994-1995 to 2005-2006

30. Example 2: Change in client age Figure 16: Change in client age over time, 1995-1996 to 2005-2006 (% of client group)

31. Example 3: Dependency How do dependency and service use at assessment influence recommendations? Mean dependency score by location at assessment, service use, and outcome

32. Example 4: ACAT Variability 40-80% of variance in recommendations explained

33. HACC and ACAP MDSs • Advantages of using both datasets together • HACC has much better data on service use • ACAP has much better data quality and information on outcomes

34. Are small amounts of service protective? 1 % recommended to community by assessment setting, hours of service and dependency

35. Are small amounts of service protective? 2 % recommended to community by assessment setting, cost of service and dependency A. By costs of services used

36. Using the National Health Survey

37. CALD groups aged 60+ in NHS 2001

38. Mental health of older Italians • Comparison with • Australian-born • migrants from English-speaking countries • migrants from other CALD countries

39. Italian-born group much more likely to have high scores indicating low well-being Mental Health (K10)

40. % with High/Very high scores by Sex Italian-born women much more likely to have high scores indicating low well-being

41. Mean scores on feeling depressed Italian-born group much more likely to have high scores indicating low well-being

42. Used medication for mental wellbeing

43. Medication use and mental health

44. Risk factors for poor mental health • Physical health problems • Health behaviours (inactivity, alcohol, smoking) • Social isolation • Financial insecurity

45. Self-rated healthItalian-born group much more likely to rate health as fair or poor

46. Arthritis Italian-born group much more likely to currently have arthritis

47. DiabetesItalian-born group more likely to currently have diabetes than Australian-born or ESB groups

48. Sedentary Italian-born group was more likely to be sedentary

49. Obesity Italian-born group was more likely to be obese than other groups

50. Italian-born group (especially women) much more likely to be in the lowest income group Income quintiles