Yasuhiko Saito, Nihon University Gu Danan, Duke University Presented at the workshop on

1. Active life expectancy among Chinese oldest-old: Are there any differences by gender, place of residence, ethnicity, and SES Yasuhiko Saito, Nihon University Gu Danan, Duke University Presented at the workshop on "Determinants of Health Longevity in China" Max Planck Institute for Demographic Research Rostock, Germany, August 2-4, 2004

2. Purpose of the study • Try to understand the factors affecting healthy longevity by computing active life expectancy by gender, place of residence, ethnicity, and SES • Try to present methodological issues in computing active life expectancy based on multistate life table methods

3. We have seen many studies to evaluate effects of factors on mortality and health status of oldest-old Chinese. One way of making these transition rates/ probabilities more tangible or intuitive is to compute active life expectancy.

4. Data • Chinese Longitudinal Healthy Longevity Survey (CLHLS) • 1998, 2000, and 2002

5. Definition of Active/Disabled • Measures used: 6 ADL items • Eating, Bathing, Transferring, Toileting, Dressing, Continence • Response categories: • can do it • can do it but need assistance • con not do it • Disabled: At least one ADL limitation

6. Method--step 1 • Create new variable which indicates health status at each wave • active, disabled and dead • Create interval observation data from 3 waves of CLHLS and pooled • health status at wave 1 as initial health status and at wave 2 as end • health status at wave 2 as initial health status and at wave 3 as end

7. Pooled Data Original Data

8. Method--step 2 • Applied Discrete Time Hazard Model to estimate transition rates (not transition probability from multinomial logistic regression model) • SAS: PROC LIFEREG • weights are applied wherever applicable

9. Active Disabled Dead Estimating 4 transition schedules

10. Method--step 3 • Construct multistate life tables (transition rates as input) as age and gender covariates • Population-based • Status-based • Construct multistate life tables by introducing other factors which may associate with each transitions • Status-based

11. Distribution of Sample Persons by Health Status (pooled data)

12. Possible factors affecting healthy longevity analyzed in this study • age: computed based on dates(no 106+years) • gender: females (0) / males (1) • ethnicity: Han (0) / minority (1) • education: no education (0) / 1+ education (1) • place of residence: rural (0) / urban (1) • economic independence: no (0) / yes (1) • marital status: other (0) / currently married (1)

13. Distribution of Sample Persons by Health Status and Ethnicity

14. Distribution of Sample Persons by Health Status and Education

15. Model Specifications

16. Results: Transition rates estimation for Model 1 (Gender as a covariate) ***: significant at 0.01 level; **: 0.05 level; *: 0.10 level

17. data workshop (label='healthy longevity data: for males'); * data workshop (label='healthy longevity data: for females'); male=1; * male=0; do b_age=80 to 100; e_age=b_age; tr_1_1=0; tr_1_2=exp(-(5.3118 -0.0374*b_age +0.3129*male)); tr_1_3=exp(-(8.1075 -0.0691*b_age -0.2222*male)); tr_2_1=exp(-(-4.0470 +0.0685*b_age -0.0668*male)); tr_2_2=0; tr_2_3=exp(-(4.9473 -0.0424*b_age -0.2257*male)); output; end; run; proc print; run;

18. Population-basedActive Life Expectancy

19. Status-based Active Life ExpectancyInitial Health Status: Active

20. Status-based Active Life ExpectancyInitial Health Status: Disabled

21. Results: Transition rates estimation for Model 2 (Ethnicity is added)

26. Active/Disabled Life Expectancy by Gender and Ethnicity at Age 80

29. Results: Transition rates estimation for Model 3 (Education is added)

30. Active/Disabled Life Expectancy by Gender and Education at Age 80

31. Results: Transition rates estimation for Model 4 (urban is added)

32. Active/Disabled Life Expectancy by Gender and Residence at Age 80

33. Transition rates estimation for Model 5 (economically independent is added)

34. Results: Transition rates estimation for Model 6 (Married is added)

35. Results: Transition rates estimation for Model 7

36. Active/Disabled Life Expectancy by Gender and Residence at Age 80, ceteris paribus

37. Initial Health: Active Initial Health: Disabled

38. Initial Health: Active Initial Health: Disabled

39. Status-based ALE at Age 80Initial Health Status: Activew/ & w/o Controlled for other variables

40. Status-based ALE at Age 80Initial Health Status: Disablew/ & w/o Controlled for other variables

41. Conclusions • In general, those who are active at the beginning of the interval have a significantly longer life expectancy, longer active life expectancy, shorter disabled life expectancy and higher proportion of active life over the total life expectancy. • In contrast, those with disability have shorter life expectancy, shorter active life expectancy, longer disabled life expectancy.

42. Conclusions-con't • Females tend to have longer total life expectancy and active life expectancy but smaller proportion of active life expectancy to total life expectancy tend to smaller. • Minority seems to have shorter total life expectancy, active life expectancy and higher proportion of active life but we may need to hold this conclusion because of the smaller sample size of minorities.

43. Conclusions--con't • Education, place of residence, economic independence, marital status, all have statistically significant effect on, at least some of the transition schedules. However, the effects of these factors on active/disabled life expectancy are mainly evaluated independently.

44. Possible Improvements • Need to estimate up to 105 • Need to consider some more covariates available in the survey • Need to test statistical significance of differences in active life expectancies • Need to look at interactions among covariates more carefully

45. In Addition

46. Introduction to The Nihon University Longitudinal Study of Aging (NUJLSOA)

47. Purpose of the Study • Investigate levels of and changes in health status of Japanese elderly • Investigate factors affecting health status and changes in health status over time • Observe effect of long-term care insurance program on attitute toward long-term care • Collect comparable data to other longitudinal data for cross-national comparisons • Help advancing research on Japanese elderly by releasing the data

48. Overview of the Survey • Wave 1 – November 1999 • Follow-up March 2000 • Wave 2 – November 2001 • Follow-up December 2001 • Wave 3 – November 2003 • Follow-up December 2003

49. Survey Design • Nationally representative sample of 65 and over • Initial sample of 6,700 persons selected by Multi-stage stratified sampling • oversampled those aged 75 and over by factor of 2 • In-person interview survey using structured survey questionnaire (proxy allowed) • Sample refreshing - New sample persons for those age 65 and 66 were added at each wave. • Approximately 2 years interval

50. Sample Size