Progress and Perspectives of Study onDETERMINANTS OF HEALTHY

LONGEVITY IN CHINA

A. Introduction of Chinese longitudinal survey; B. Correcting underestimation of disabled life expectancy in conventional MSLT method and application to healthy longevity study in China

Zeng YiProfessor, Peking University and Duke University

A. A BRIEF INTRODUCTION to Chinese Longitudinal Survey on Healthy Longevity

• General Goals of the project

•To better understand determinants of healthy longevity, such as social, economical, behavioral, environmental and biological factors.

•To provide data information for academic research, health and aging policy analysis.

PROGRESS REPORT

1. Progress of longitudinal surveys in 1998, 2000, and 2002 (1) Achieved sample size distributions.

-- Extensive questionnaire data were gathered from 8,959, 11,161, and 11,163 oldest-old aged 80+ in 1998, 2000, and 2002, respectively.

-- Among them, 8,170, 10,457, and 12,656 interviews were conducted with centenarians, nonagenarians, and octogenarians, respectively;

-- we interviewed 1,100 oldest-old siblings (117 centenarians, 308 nonagenarians, 675 octogenarians; some were interviewed more than once), constituting about 550 oldest-old sibling-pairs.

-- Data on date/cause of death, health status, socioeconomic status, and degree/length of disability and suffering before dying, etc., were collected from 2,783, 2,406 and 1,449 deceased interviewees aged 100+, 90-99, and 80-89, respectively.

(2) Significant Extension of CLHLS study since 2002

-- Thanks to UNFPA, NIA, China Social Science Foundation, Peking University, Taiwan Province Academia Sinica and China Academy of Social Science, we added 4,894 younger elderly aged 65-79 and 4,478 elderly interviewees’ adult children aged 35-65 into our sample in 2002 wave.

-- The total sample size is now over 20,000 interviewees including oldest-old (focus), younger elderly (as a comparison group) and elders’ adult children (intergenerational relations and healthy longevity).

Sample Distribution, 1998-2002

 

Age

Survival interviewees deceased (proxy interview)follow-up newly interview Total

M F T M F T M F T M F T

1998 baseline survey80-89 NA NA NA 1787 1741 3528 1787 1741 3528 NA NA NA90-99 NA NA NA 1299 1714 3013 1299 1714 3013 NA NA NA100+ NA NA NA 481 1937 2418 481 1937 2418 NA NA NATotal NA NA NA 3567 5392 8959 3567 5392 8959 NA NA NA2000 follow-up survey80-89 996 1048 2044 1471 1403 2874 2467 2451 4918 339 262 60190-99 720 907 1627 925 1260 2185 1645 2167 3812 574 612 1186100+ 262 891 1153 256 1022 1278 518 1913 2431 348 1213 1561Total 1978 2846 4824 2652 3685 6337 4630 6531 11161 1261 2087 33482002 follow-up survey35-65 NA NA NA 3132 1346 4478 3132 1346 4478 NA NA NA65-79 NA NA NA 2456 2438 4894 2456 2438 4894 NA NA NA80-89 1454 1411 2865 673 672 1345 2127 2083 4210 483 377 86090-99 948 1236 2184 590 858 1448 1538 2094 3632 547 679 1226100+ 277 917 1194 442 1685 2127 719 2602 3321 295 941 1236Total 2679 3564 6243 7293 6999 14292 9972 10563 20535 1325 1997 3322

NA -- Not applicable

(3) Sampling areas

-- The survey was conducted in the randomly selected half of the counties and cities of 22 provinces out of the total of 31 provinces where Han are majority.

 

-- There were 631, 777, and 866 counties and cities in our 1998, 2000, and 2002 surveys, respectively.

 

-- The population in the survey areas constitutes about 85 percent of the total population in China.

(4) Sample Design.

-- We tried to interview all centenarians who voluntarily agreed to participate in the study in the sampled counties and cities of the 22 provinces.

-- For each centenarian, one nearby octogenarian and one nearby nonagenarian of predefined age and sex were interviewed. “Nearby” – could be in the same village or on the same street, or in the same town or in the same sampled county or city. The predefined age and sex are randomly determined, based on the code numbers of the centenarians, to have more or less randomly selected comparable numbers of males and females at each age from 80 to 99.

-- Following the same sampling procedure as for those aged 80-99, we selected the sub-sample of the younger interviewees aged 65-79 who lived near the centenarian interviewees, based on the codes of the centenarians.

-- Follow-up interviewees who were still surviving and all centenarians in the sampled areas in the next wave.

-- Those deceased interviewees were replaced by new interviewees of the same sex and age (or within the same 5-year age group).

-- Sub-sample of 4,478 elderly interviewees’ adult children aged 35-65 in 2002 in Gaungdong, Jiangsu, Fujian, Zhejiang, Shandong, Shanghai, Beijing, and Guangxi.

-- If an elderly interviewee had only one eligible child (i.e., aged 35-65 and living in the sampling areas), that child was interviewed.

If an elderly interviewee had two eligible adult children, the elder or the younger child was interviewed if the elderly interviewee was born in the first 6-months or the second 6-months.

If an elderly interviewee had three eligible adult children, the eldest, the middle, or the youngest child was interviewed if the elderly interviewee was born in the first 4-months, second 4-months or the third 4-months, and so on.

(5) Age reporting.

-- Coale and Li (1991) concluded that the age reporting of Han Chinese elderly persons is reliable. As a pilot study of this research project, Wang, Zeng, Jeune, Vaupel (1998) reached similar conclusion.  

-- Coale and Li (1991) discovered that reported ages of the elders in Xinjiang, where minority ethnic groups are majority are seriously exaggerated. Thus, we did not include Xinjiang, Qinghai, Ningxia, Inner Mongolia, Tibet, Gansu, Yunnan, Guizhou, and Hainan, which have a high proportion of inhabitants belonging to ethnic minorities in our study.  

-- In the 22 selected Han-majority provinces, we CANNOT exclude minority groups, since excluding minorities in the same survey area would violate principles of equality among ethnic groups.  

-- Data analysis has shown that the overall age reporting in our CLHLS survey, which also included 7.3% minority interviewees, is acceptably good up to age 105. E.g., age distribution of centenarians interviewed in the 1998 baseline is similar to that of Swedish centenarians (see Figure 1 in Zeng and Vaupel et al., 2001).

Figure1. The age distribution of interviewed Chinese centenarians in 1998 and Swedish centenarians 1984-1993

percentage

100

101

102

103

104

105

106

107

108

109

110a

ge

100

101

102

103

104

105

106

107

108

109

110

ag

eMale Female

50 40 30 20 10 0 10 20 30 40 50

ChineseSwedish

-- However, we are very cautious about the small number of persons (about 150 in each wave) who reported an age of 106 or older. Using a numerical example, Wang, Zeng, Vaupel and Jeune (1998) illustrated why the suspicious age reporting at age 106 and above is not inconsistent with our conclusion that age reporting for Han Chinese oldest-old including centenarians aged 100-105 appears to be of acceptable quality. My co-authors and I excluded super-centenarians aged 106 and above in all of our publications.  

-- How about the quality of the age-reporting of the 7.3% minority sub-sample in CLHLS? Researchers may suspect their age-reporting quality.

-- If age exaggeration among the 7.3% minority sub-sample is more serious than Han, what would be the impact on the analysis using CLHLS?

The impact seems not significant, simply because the minority sub-sample size is 7.3% only.

Note: X-axis is the oldest old’s age in months. E.g., 960 months equal to 80 year-old (960=80*12), and so on.

0

.2

.4

.6

.8

1

pro

bability

1000 1100 1200 1300Age in months

Han &Minorities

HanMinorities

Figure 1. Kaplan-Meier survival estimate for the oldest old in CLHLS(weighted)

0

2

4

6

8

cum

.haz.

1000 1100 1200 1300Age in months

Han &Minorities

HanMinorities

Figure 2. Nelson-Aalen cumulative hazard estimate for the oldest old in CLHLS(weighted)

Note: X-axis is the oldest old’s age in months. E.g., 960 months equal to 80 year-old (960=80*12), and so on.

(6) Data quality.

-- Data quality evaluations, such as reliability coefficients and factor analysis, and the rates of logically inconsistent answers, have shown that the data quality of the 1998, 2000, and 2002 surveys is generally good, as compared to some U.S. and Canadian elderly surveys (e.g., see Table 3 and Appendix in Zeng and Vaupel et al., 2001).

-- However, we also realize that some problems exist in the data set. For example, Chronic disease information were seriously under-reported.

We asked six Western-standard disposition-related questions in the 1998 baseline survey such as: “Some people stated that ‘I often feel lonely and isolated’; How similar are you to these people?” But a high proportion of the Chinese oldest-old were unable to answer these questions. Thanks to great help from Prof. Jacqui Smith and our Chinese colleagues, we revised these questions in 2000 and 2002 surveys. The index of personality measures reliability was substantially improved in 2000 (0.72) and 2002 (0.71) as compared to 1998 (0.63).

Compared to the census data, the CHLS death rates at ages 90 and over are fairly accurate, but the death rates at ages 80-89 were somewhat underestimated (-9.4% and -8.7% at ages 80-84 & 85-89 for males; -13.1% and –6.1% at ages 80-84 & 85-89 for females) – we need to pay serious attention to improve this in the next waves.

2. Data analysis to enhance healthy longevity research.

Up to May 31, 2003,

63 published papers, 63 conference papers, and8 graduate students theses, all based on CLHLS data.-- Much more further research is needed.

3. Academic workshops/conferences on CLHLS

(1) “Workshop on Chinese oldest-old: dialogues with policy makers” Dec. 31, 1999, in Beijing; 20 participants.

(2) “First national conference on determinants of healthy longevity of the oldest-old in China”, March 2-3, 2001 in Beijing. 105 participants.

(3) A workshop presenting the major results of our 1998/2000 surveys and exchanges with leading researchers of HRS/AHEAD, Berlin Aging Study, NLTCS, LSADT, August 2001 at MPIDR in Germany. 30 participants.

(4) “International Symposium on Healthy Aging Studies in China”(co-sponsored with IUSSP), Oct. 25, 2001, Beijing. 150 participants from 11 countries.

(5) “Second national conference on determinants of healthy longevity”, May 12-13, 2004, Beijing. 150 Participants.

(6) “International workshop on data analysis of the Chinese Longitudinal Healthy Longevity Survey” at MPIDR, Rostock, August 2-4, 2004. 30 participants from 6 countries.

PERSPECTIVE

1. General plan of our 2005 and 2008 follow-up surveys

(1) Survey areas: same as in 1998, 2000, and 2002.

(2) Questionnaire and sample design: basically the same as those used in the 2002 survey, given the nature of the longitudinal survey; to improve whenever necessary.

Table 4. Expected sample distributions of the 2005 and 2008 follow-up surveys

Surviving interviewees

Age Follow-up Newly added Total

Deceased

Interviewees

M F T M F T M F T M F T

2005 follow-up survey

35-68 (child) a 2274 999 3273 1029 429 1458 3303 1428 4731 65 16 81

65-79 1392 1481 2873 1124 945 2069 2516 2426 4942 245 172 417

80-89 1112 1202 2314 776 636 1412 1888 1838 3726 639 486 1125

90-99 724 1001 1725 898 1123 2021 1622 2124 3746 917 1043 1960

100+ 247 765 1012 510 1936 2446 757 2701 3458 446 1640 2086

Total 5749 5448 11197 4337 5069 9406 10086 10517 20603 2312 3357 5669

2008 follow-up survey

35-71 (child)a 2528 1105 3633 2528 1105 3633 79 19 98

65-79 1402 1490 2892 1402 1490 2892 337 162 499

80-89 1125 1218 2343 1125 1218 2343 626 474 1100

90-99 742 1020 1762 742 1020 1762 889 1026 1915

100+ 278 784 1062 278 784 1062 492 1687 2179

Total 6075 5617 11692 6075 5617 11692 2423 3368 5791

Note: a, those interviewees’ children who were aged 65 at the 2002 survey will be 68 and 71 in 2005 and 2008, respectively.

(3) Survey fieldwork, national and provincial training workshops: basically the same as in 2002 wave; to improve whenever necessary. 

(4) Informed consent, confidentiality, and voluntary participation will be carefully implemented in the survey. 

(5) Procedures of age validation will continue to be used; to improve whenever necessary.  

(6) Economics of aging. We plan to add about five questions that seek to delineate more detailed information concerning payments for health care, decision power over financial issues in the household, spouse’s paid job at the present time, and health insurance. 

(7) Dissemination. We will make our 2005 and 2008 follow-up survey micro data files (without individual identifiers) widely available to scholars in the fall of 2007 and 2010, respectively.

2. Data analysis to better understand the determinants of healthy longevity

(1) Conventional demographic and statistical analysis.  

(2) Multi-wave longitudinal data analysis with greatly increased statistical power to evaluate trends of change in disability, healthy survival, mortality, and extent of suffering before dying.  

(3) International comparative analysis on trends in disability and healthy survival. 

(4) Analysis of the association of early life conditions with healthy longevity.  

(5) Explanatory factors of the extent of disability & suffering before dying.  

(6) Multi-level analysis.  

(7) Multivariate analysis on the impacts of cognitive functions and subjective well-being on healthy longevity. 

(8) Economic analysis of disability.  

(9) Impact of social activities (e.g. traveling, watching TV & public media, religious participation) on healthy longevity. 

(10) Multidimensional analysis on quality of life.

CORRECTING THE UNDERESTIMATION OF DISABLED LIFE EXPECTANCY:

A New Method & Application to the Oldest Old In China

Zeng Yi, Gu Danan, Kenneth C. Land

I. Introduction

Previous research has indicated that the underestimation of disability is one of the major problems in research on population aging (Gill et al. 2002; Guralnik and Ferrucci 2002)

We found that extant studies of active/disabled life expectancy based on multi-state life table implicitly assume that persons who die between ages x and x+1 (or ages x and x+n) do not change their functional status between age x and time of death.

Such unreasonable assumption has resulted in that the widely used disabled life expectancies based on conventional methods are significantly underestimated.

Based on the unique CLHLS data on ADL before dying, we propose a new method to correct the underestimation of disabled life expectancy inherent in the MSLT method.

II. Methodology 1. Following formulas show that classical Multi-state Life Table method underestimate the disabled life expectancy:

)]1()([2

1)( ++= xlxlxL jiyjiyjiy )]1()([

2

1)1( xlxlxl jiyjiyjiy=

= )()()()( xpxlxpxljk

kjkiyjjjiy

+ ])()()()()([2

1

jk

kjkiyjjjiyjiy xpxlxpxlxl

=

jk

Ck

jkiy

jjiy xSxS

,1

)()( )(2

1)( + )(

2

1 )(

,1

xS kj

jk

Ckiy

+ )(

2

1)(

2

1 )()( xdxdjk

kjiy

jjiy

Bias in )(ye jiy])()([

)(4

1 )()(

jk

jkiy

yt jk

kjiy

iiy

tdtdyl

(2)

（ 4 ）

2. New method

（ 1 ） Assuming a uniform distribution of both deaths and transitions between functional statuses between age x and x+1 or between age x and age at death, the new method which corrects the bias is:

])()()([])()()([,1

)(21

,1

)(21)(

21

,1

)(21

,1

)(21)(

kj

Ck

jkiy

jk

Ck

kjiy

jjiy

kj

Ck

jkiy

jk

Ck

kjiy

jjiyjiy xdxdxdxSxSxS(x)L (3)

（ 2 ） Proportion of deaths with morbidity status k before dying after age y among those with ADL status i at age y is:

(y)l

(t)d

yGiiy

ω

yt

kjiy

C

jkiiy

)(

1)( )( (6)

IV. Results and Discussion

1. Underestimation of disabled life expectancy by classic MSLT method

①    Proportion of underestimated disabled life expectancy by conventional MSLT method at age 80 are 10% and 6% for males and females respectively. With advancing of age, such underestimation declines ----the underestimation of disabled life expectancy is statistical significant.

②    The underestimation of disabled life expectancy is larger for males than for females.

③ The underestimation of disabled life expectancy is around 12% and 6% for males and females at age 65 respectively based on the extrapolation.

Fi g 1. % of underest i mated di sabl ed l i f e expectancy (DLE) by convent i onal MSLT approach

0

2

4

6

8

10

12

80 85 90 95 100

Age

%

Male

Female

Gill et al. (2002) demonstrated that prevalence rates of disability at different waves with longer intervals between waves (e.g. 1-5 years) are substantially underestimated.

Gill and colleagues proposed to substantially reduce the length of the observation intervals between interviews or using monthly telephone interviews---- but largely increase the costs.

2. ADL status-specific mortality and transition rate by age and gender

3. Age and gender differentials in status-based ALE

Figure 4. Percent of Remaining Life Spent in Active Status

among Chinese Oldest Old Males and Females

Who Are Active or Disable at Initail Age y

0

20

40

60

80

100

80 85 90 95 100

Initial Age y

% Male

Female

Male

Female

ADL Active at

Initial Age y

ADL Disabled

at Initial Age y

4. Life table proportions of the extent of morbidity before dying among the oldest old who are active or disabled at initial ages

The slight morbidity profile: refers to those who were ADL active & bedridden for <5 days (including not bedridden).

•The moderate morbidity profile: refers to those who were ADL active &

bedridden for ≥5 days or ADL disabled & bedridden for <5 days.

•Severe morbidity profile: refers to those who were ADL disabled &

bedridden for 5-59 days.

•The long-term severe morbidity profile: refers to those who were ADL

disabled & bedridden for ≥60 days.

（ 1 ） ADL disability at Survey is strongly associated with the extent of morbidity before dying.

（ 2 ） As compared to their male counterparts, Chinese oldest old women not only survive in a more likely disabled status, but also suffer more before dying

（ 3 ） Our empirical results show that the life table proportions of slight morbidity death over initial age y generally do not decline with the increase of age y

----This is generally consistent with previous similar studies and provides additional evidence concerning debates on the hypothesis about compression of morbidity (Fries 1980), or Morbidity dynamic equilibrium (Manton 1982).

---- reject the hypothesis of a pandemic of disability (Gruenberg 1977)

---- healthy longevity (i.e., achieving longevity while relatively compressing morbidity) is not impossible (Hubert et al. 2002)

V. Summary

1. This study demonstrates that estimates of disabled life expectancy based on conventional multi-state life table approach (which lack of ADL data before dying) are significantly underestimated.

2.This is the first time to provide age-sex-ADL status-specific mortality and transition rates and the estimates of ADL status-specific ALE for the oldest olds in a developing country.

3. This study has investigated the extent of morbidity before dying.

4. Further multivariate explanatory research are needed to overcome the limitations of this study.

Thank you !