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Population Ageing and Healthy Life Expectancy in Thailand

Rukchanok Karcharnubarn and Philip Rees School of Geography, University of Leeds, Leeds, UK [email protected] August 2009

Abstract

During the past several decades, Thailand has experienced a significant demographic transition from high to low of fertility and mortality. The United Nations has reported that the total fertility rate in Thailand has declined from over 6 births per woman in the mid 1960s to 1.8 in the period 2005-2010 and during the same period, life expectancy at birth increased from 56 years to 66 years for men and 60 years to 72 years for women. A major consequence of this rapid demographic transition is population ageing. This process will accelerate in the next two decades. The National Economic and Social Development Board (NESDB) of Thailand projects that in 2005 the population of persons aged 65+ was 4.5 million and that by 2025 population aged 65+ will increase to 9.8 million. Furthermore, there will be ageing within the older population, because life expectancy after 65 is improving. While life expectancy represents average length of life, the continued rise in life expectancy does not quantify the health status. Increasing longevity can also result in rising demands for health services and health care costs. Healthy life expectancy has been used to present the average life time in different health states. The aim of this study is to investigate changes in health expectancy in Thailand. The data on health status are derived from Surveys of Elderly in Thailand 2002 and 2007. The life table data are obtained from Thailand vital registration. The life expectancy in self-rated good health and disability free life expectancy were calculated using Sullivan’s method. The results indicate that the life expectancy had increased both for men and women between the period 2002 and 2007. However, the self-rated good health was stagnating whereas the disability free life expectancy was increased and the increase in the disability free life expectancy was greater than the life expectancy gain. As a result, the increase in life expectancy among the elderly Thai appear to be accompanied by improved health status when a measure of disability based on activities of daily living is used.

1. Introduction

Populations throughout the world are growing older and the number of people aged 60 years

and over are expected to nearly triple by 2050 and Thailand is no exception (United Nations,

2001). The increase in the elderly population is the result of the demographic transition from

high to low levels of fertility and mortality (Atchley, 1994, UNFPA, 2006, Phillips, 1991).

Not only are more people surviving to reach old age, but those who attain old age are living

longer than ever before. The older population is itself ageing. In Thailand, the decline in

fertility and the improvements in mortality are the demographic forces driving population

ageing. As the pace of population ageing is much faster in developing countries such as

Thailand than in developed countries, Thailand is likely have less time to adjust to the

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consequences of the population ageing. Moreover, population ageing in the developing

countries is taking place in the context of much lower levels of socio-economic development

than was the case in the developed countries (Knodel et al., 1999).

Increasing longevity can also result in rising medical costs and increasing demands for health

services, since older people are typically more vulnerable to chronic diseases and disability

(World Health Organization, 1998, Breakwell and Bajekal, 2006). The continuing growth of

the elderly population has made it a matter of increasing urgency to look for ways to maintain

their health status and to help them cope independently and keep the quality of their life. It is

important to know whether increased survival chances and longer life are associated with

continuing good health and longer healthier life.

Health expectancy has become a key indicator in studying health status in older ages

(Breakwell and Bajekal, 2006, Crimmins et al., 1989). However, one should not lose sight

that good health implies not only survival, but also a certain quality of life. While life

expectancy quantifies average length of life in a stationary population, with the influence of

current age structure removed, healthy life expectancy represents the average lifetime in

different health states and offers the possibility to evaluate the quality of life with respect to

health (European Health Expectancy Monitoring Unit, 2006). Therefore, longer life in old

ages may involve more healthy years as well as more unhealthy years.

The research question we wish to pose is as follow: Has the increase in life expectancy in

Thailand been accompanied by an increase or decrease in health problems? The aim of this

paper is to investigate changes in healthy life expectancy in Thailand between 2002 and 2007

when we have available national surveys of the activities of daily living (ADLs) of the Thai

population. In order to achieve the aim, this paper is organized into five sections. Population

ageing and the demographic transition, health status and the methodology of healthy life

expectancy computation are reviewed in section 2, followed by the description in data and

methods in section 3. The results are presented in section 4 and section 5 discusses the

findings and reaches conclusions.

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2. Review

Population Ageing and the Demographic Transition

During the past several decades, Thailand has experienced significant fertility decline within

a short period of time. The total fertility rate has declined from over 6 births per woman in

the mid 1960s to 1.8 births per woman in the period 2005-2010 (United Nations, 2008). The

period 2005-10 is the first projection period in the 2005 based World Population Prospects

forecasts. The figures for 2005-10 are forecast assumptions but are likely to be close to

contemporary experience. During the same period, life expectancy at birth increased from 56

years to 66 years for men and 60 years to 72 years for women. In the coming decades, besides

the lowering of the growth rate, a major demographic consequence of this rapid fertility

reduction will be an inevitable ageing of the population. However, the UN estimates and

forecasts of life expectancy are too conservative, according to national analysis (NESDB,

2003; Karcharnubarn 2009). Even more dramatic will be the rapid increase in the size of the

elderly population, a result of past high fertility levels and substantial declines of mortality.

The proportion of the population in their elderly years is anticipated to increase from 8.7

percent in 2000 to 13.7 percent in the year 2015, 19.0 percent in the year 2025, and 26.4

percent in the year 2050. The number of elderly Thai will continue to rise, from

approximately 5.3 million at present to 7.6 million in 2020 and will reach 11 million by 2030

(United Nations, 2008). The difference of life expectancy at birth between male and female is

6.3 in period 2005-2010 which is lower than Japan (7.2) in the same period. However, when

compare the difference between sexes with Singapore, it shows that the difference is higher

than Singapore and also higher than Laos and Cambodia and many European countries.

In 2000-2005, the life expectancies of Thai male and female at age 60 were 16.9 and 19.5

years respectively; at the age 65 they were 13.5 and 15.6 years and at the age 80 they were

5.7 years for male and 6.4 years for female. This shows that women live longer than men in

old age and will share a higher proportion among the elderly. Another important feature of

the population ageing in Thailand is the increasing proportion of the oldest old which means

population aged 80 years and over (UNFPA, 2006). Increasing survival rate to age 80 years

means that more and more the elderly persons will live to and beyond 80 years. It will affect

the extended duration of social security and welfare payments and increasing need for care of

the elderly morbidity and disability.

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Healthy Life Expectancy

Healthy life expectancy is an index of a population status of health derived from estimates for

mortality and morbidity, essentially addressing the question of whether observed increases in

life expectancy are also accompanied by decreased in morbidity. The answer to this question

is important both for the understanding of changes in the health status and for the direction of

the government policies. Interest in the relation between mortality and morbidity grew out

with the decrease in mortality observed in elderly people (Robine et al., 1999, Robine and

Ritchie, 1991). Report on health trends in elderly people in developed countries show an

inconsistent pattern. For example the study of health trends among the elderly aged 65 and

over in United States showed a significant improvement in self-rated health between 1993

and 2001 (Zack et al., 2004), whereas the study in United Kingdom found worsening self-

rated health during the 1980s (Spiers et al., 1996). The Austrian study showed that between

1978 and 1998, improvements in self-reported health were reported for the population aged

60 to 84 but not for older groups (Doblhammer and Kytir, 2001).

The discussion on the consequences of both increasing life expectancy and the reduction in

mortality of older people is linked to uncertainty about the future burden of morbidity. There

are three different hypothesis in explain changes in health status. Fries (1980) has proposed

that if the onset of health problems, morbidity or disability is postponed and the

postponement is greater than the increase in life expectancy, then the cumulative life time

with morbidity will be low. This is known as the compression of morbidity hypothesis. While

Gruenberg (1977) suggested that the fall in mortality has not been accompanied by a decline

in morbidity but it is a result of the increase in the life expectancy of people with poor health.

This assumption is known as the expansion of morbidity hypothesis. A third hypothesis is

that there is the dynamic equilibrium, in which the increase in morbidity is counterbalanced

by a decrease in the severity as proposed by Manton (1982). These health hypotheses are

illustrated by the different relative shifts in the survival curve of the mortality-disability-

morbidity model (Figure 1). The areas under the curves represent the average number of

years to be lived (a) free from morbidity or diseases, (b) with diseases but free from disability

and (c) with diseases and disability. This is based on the assumption that the mean age at

onset of morbidity will be less than the mean age of onset of disability because diseases

generally cause disability and then death occur some year after the onset of diseases and

disability (Manton, 2002). The leftmost graph present the usual type of the relation of the

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age-specific relation of morbidity, disability and mortality in population surveys. The middle

graph shows that the life expectancy increased but the life expectancy without disability and

free from diseases not increased at the same time. This curve illustrated the expansion of

morbidity. The rightmost graph shows that the disability free life expectancy and diseases

free life expectancy increase with corresponding to the change in life expectancy as known as

the compression of morbidity. Note that the lines for disability lie to the right of those for

morbidity, indicating that there is a progression from illness to disability.

[Figure 1 about here]

Another way of representing the three morbidity-mortality hypotheses is shown in Figure 2.

The graphs plot survival probabilities, without illness and with illness, against age between

50 and 100 for two time points, t=1 and t=2. The lines on the graph labelled i1 (light blue) and

i2 (dark blue) are survival curves for people without illness (in good health, without

disability). The lines on the graph labelled s1 (red) and s2 (green) are total survival curves for

people. In the Figure, graph (a) shows what happens when a population experiences

expansion of morbidity. Area B on the graph represents people with illness at t=1. Area C

(which includes B) represents people with illness at t=2. Area C is much greater than area B.

This is expansion. In the Figure, graph (b) shows what happens when a population

experiences compression of morbidity. Area B on the graph represents people with illness at

t=1. Area C represents people with illness at t=2. Area C is much smaller than area B. This is

compression. In the Figure, graph (c) shows what happens when a population experiences

equilibrium of morbidity. Area B on the graph represents people with illness at t=1. Area C

represents people with illness at t=2. Area C is about the same size as area B. This is

equilibrium.


Life tables which incorporate health status provide significant indicators representing the

health status of elderly people because they provide a means of dividing life expectancy into

life spent in various states of good and poor health, thus extending the concept of life

expectancy to morbidity and disability (Robine et al., 2001). Healthy life expectancy is now

increasingly used in developed countries to assess the health status of the population

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especially, the elderly. It has become an important measure of population health at both

national and international level (Crimmins et al., 1989, Breakwell and Bajekal, 2006). The

interest in healthy life expectancy has grown as an increase in the proportion of elderly

population impacts the demand for health and social care in the future. Even in developing

countries, the study showed that people are living longer, resulting in an increase in the

proportion of the elderly in their population (Lloyd-Sherlock, 2000). However, There are a

number of ageing studies which reported that elderly people have achieved a longer life, but

in worse health (Crimmins et al., 1994, Rogers, 2007, Bebbington, 1988, Wilkins and Adams,

1983). As a result, healthy life expectancies were developed as population health indicators

that combined mortality and morbidity.

In England and Wales, Bebbington has found that from 1976 to 1985 disability free life

expectancy increased more slowly than life expectancy for men. The proportion of years

spent without disability within the total life expectancy fell from 83.1% in 1976 to 81.8% in

1985. Disability free life expectancy for women ceased to increase when life expectancy

increased so that the proportion of years spent without disability fell from 81% to 79%. Thus

the results from this study confirmed the expansion of morbidity hypothesis (Bebbington,

1988). Furthermore the study of healthy life expectancy using incidence based estimates for

the United Kingdom also showed that the healthy life expectancy have risen between 1992

and 2002 but it is smaller than the increase in total life expectancy, then confirming the

expansion hypothesis (Khoman, 2008).

A study of health expectancy in Denmark between 1987 and 2000 using Sullivan’s method

showed that the life expectancy of a 65 year old men and women had increased and the

expected lifetime in self-rated good health and disability free life expectancy had also

improved both for men and women. But the trend in life expectancy without longstanding

illness had decreased (Bronnum-Hansen, 2005). The rise in life expectancy in Denmark

appears to be accompanied by improved health status among the elderly. The results of the

studies in healthy life expectancy have also confirmed that men spend a smaller proportion in

their life in poor health than women do although healthy life expectancy remain higher in

women. Therefore, women may live longer, but greater proportion of their life in bad health

(Wilkins and Adams, 1983, Robine and Ritchie, 1991).

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The trend in health expectancy depends on the health indicator chosen. Most studies have

used health indicator based on disability. Most often researchers measure it with some form

of primary activities of daily living (ADLs: e.g. ability to use toilet, bath, dress and eat). This

instrument has been used to measure health in both clinical study and community based

surveys of elderly people. However, to compare the results using this indicator is often

difficult due to the difference in wording and activities included. For example, some studies

ask if the respondent experiences difficulty in performing the activities, whereas the others

ask if the respondent needs help or is dependent. Different wordings or scales lead to

differences in prevalence rates (Freedman et al., 2004). Most of American studies have used

ADL disability as a major outcome and the results indicated improvement or no change in

ADL limitation during the 1990s, although the trend was not consistent across studies

(Freedman et al., 2004, Freedman et al., 2002). The other health indicator is self-rated health

which asks the respondents to their general health on three or five point scale. It has become a

widely used health indicator due to its ease of administration and its strength as a predictor of

mortality (Idler et al., 1999). Most studies in United Kingdom and United States (Spiers et al.,

1996, Zack et al., 2004) found improvement in self-rated health among the elderly whereas,

the results from Sweden showed significant worsening of self–rated health in old age

population between 1992 and 2002 (Parker et al., 2005).

We have already pointed to the difficulty of comparing results between studies because of the

different ways that ill health and disability are measured. Van Oyen et al. (2008) report on a

study of health expectancy in the older population of Belgium using four different measures

for the same population. These measures covered the the health domains of self-reported

health, one chronic disease, two or more chronic diseases and disability. They compare the

distribution of life expectancy by different health states for persons at age 65 and at age 80.

Their results were as follows: for men aged 65 the compression hypothesis held for the two

illness and the disability measures but not for self-reported health where the equilibrium

hypothesis was a better description. For women at ages 65 and 80 and for men at age 80 none

of the changes in health expectancy were significant but the expansion hypothesis described

the changes best. So we should expect differences to manifest themselves between the

measures used, between the sexes and between younger and older ages in the old age range. It

is not surprising therefore that reviews of changes in health expectancy change across a

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number of developed countries present a varied picture by country and time period of study

(Robine et al. 1999, Mathers and Robine 1997/2008 and Howse and Harper 2008).

Calculation of Healthy Life Expectancy

Healthy life expectancy is potentially useful as a basis for international comparisons of health

state and for allocating health resources. The concept of healthy life expectancy was first

established by Sanders in 1964 (Sanders, 1964) and then in the early 1970s, Sullivan

(Sullivan, 1971) proposed a simple method for estimating life expectancy as a function of

disability or health states (Robine and Ritchie, 1991). However, there are two methods of

calculating healthy life expectancy. First the Sullivan’s method which is one of the particular

methods to calculate health expectancy (European Health Expectancy Monitoring Unit,

2006). To calculate healthy expectancy at a particular age and time, it is necessary to

calculate the number of person years lived in the health state from that age at the particular

time. This method calculates healthy expectancy as the number of remaining years, at a given

age, which an individual can expect to live in a healthy state. The data required are the age-

specific prevalence of the population in healthy and unhealthy states which measured from

cross sectional health data and the total person years lived at a particular age taken from a

period life table. Sullivan’s method provides a means of comparing the health states of an

entire population at two time points of two different populations at the same time point,

despite differences in age composition. However, the same definition of health states and age

intervals must be used for at time point being compared. The second one is the multi-state

method (Rogers et al., 1990) which uses incidence probability to calculate prevalence and

allow for one or more disabilities or diseases. This method is the most demanding in terms of

data requirements. Longitudinal studies can provide to necessary data for this method.

Though there has been considerable consistency between studies about the method used to

calculate healthy life expectancy, there remain large differences in defining and measuring

disability or ill-health. Current difficulties in calculating healthy life expectancy as a

population indicator relate primarily to a lack of consensus on definitions of health,

morbidity, and disability and the calculation procedures. The use of different methods makes

it difficult to compare studies across countries or even within country. For example, the use

of proxy interviews and how missing data is handled. Freedman found that some small

change in methodology can influence results(Freedman et al., 2004). Moreover, the present

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calculations are restricted on the healthy population. The inclusion of probability of recovery

in calculating of healthy life expectancy would extend to the unhealthy population.

3. Data and Methods

In order to investigate the degree to which the Thai elderly will be living healthy or unhealthy

lives, healthy life expectancy will be calculated. There are two different types of method have

been used for calculating healthy life expectancy: The Sullivan’s method and Multi-state life

table method. However, in this study, The Sullivan’s method will be applied because the

necessary data on transitions between health states and death were not available. This method

involves using the prevalence of disability at each age in the population at a given point of

time to divide the years of life lived by a period life table at different ages into years with or

without disability as showed in Table 1. Then the prevalence rate of disability in older age

which calculated from the Survey of Elderly in Thailand will be used and the period life table

for Thailand also required. The Survey of Elderly in Thailand 2002 and 2007 will be used in

this study. It is a nationally representative data base of demographic, socio-economic, health

characteristics and living arrangement. The survey included questions intended to reflect the

health status of the respondents; asking for general self assessment of respondent’s health

status and ability to undertake activities of daily living (ADL). Period life tables are built up

from set of age-specific death rates which can be defined as the number of deaths occurring

in a given period at age x divided by the size of mid-year population at age x. In Thailand the

number of deaths can be obtained from vital registration.

[Table 1 about here]

Health status was measured with two outcomes: disability and self-rated health. Disability

was defined as the inability to perform at least one activity in daily life (ADL). Persons with

no ADL limitations were classified as ADL active and those with one or more limitations

were classified as “ADL disabled”. Self-rated health was defined by comparing those

indicating their health as very good and good (self-rated good health), to those who

experienced their health as very bad, bad and fair (self-rated bad health) when answering the

question “How is your physical health in the past 7days?”

4. Results

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Table 2 shows that between 2002 and 2007, the total life expectancy (LE) at age 65 increased

by 0.7 years for males and 1.0 year for females. For males, this increase is accompanied by

the increase in expected life time without disability, thus the life expectancy without

disability increased by 2.3 years and the life expectancy with disability for males at age 65

decreased by 1.6 years. Between 2002 and 2007, life expectancy increased from 15.6 to 16.3

years, whereas disability free life expectancy increased from 7.6 to 9.8 years (Figure 3). The

increase in the health expectancy was greater than the life expectancy gain, suggesting an

absolute compression. The proportion of expected years lived free from disability increased

from 48 percent to 60 percent, indicating a relative compression. The increase in disability

free life expectancy was also seen for females age 65. The life expectancy increased by 1

year from 16.7 years in 2002 to 17.7 years in 2007, whereas the expected lifetime without

disability increased by 2.6 years from 4.7 years in 2002 to 7.3 years in 2007. Thus the

proportion of expected years lived free from disability increased from 28 percent to 41

percent.



The life expectancy for males and females at age 80 increased by 0.7 years for both sexes.

For males, the increase in disability free life expectancy was greater than the life expectancy

gain. Life expectancy for males age 80 increased from 7.4 to 8.1 years, whereas disability

free life expectancy increased from 1.6 to 2.4 years (Figure 4). Thus the proportion of

expected years lived without disability increased from 21.6 percent to 29.6 percent. For

females age 80, the life expectancy also increased from 7.6 years to 8.3 years, whereas the

disability free life expectancy increased 0.6 year from 0.7 year to 1.3 years. Thus the increase

in health expectancy was less than the life expectancy gain. However, the proportion of

expected years lived free from disability increased from 9.2 percent to 15.7 percent.


Table 3 shows between 2002 and 2007, for males, the life expectancy in self-rated good

health is stable at 6.8 years and the life expectancy in self-rated bad health for males at age 65

increased by 0.7 year (Figure 5). The increase in the healthy life expectancy was less than the

life expectancy gain, suggesting an expansion hypothesis. The proportion of expected years

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lived in good health decreased from 43.6 percent to 41.7 percent, indicating a relative

expansion of morbidity. The increase in self-rated good health was seen for females age 65.

The life expectancy increased by 1 year from 16.7 years in 2002 to 17.7 years in 2007,

whereas the expected lifetime in good health increased by 0.4 years from 5.6 years in 2002 to

6.0 years in 2007. Thus the proportion of expected years lived in good health increased from

33.5 percent to 33.9 percent.



The life expectancy for males and females at age 80 increased by 0.7 years for both sexes.

For males, the increase in expected lifetime in self-rated good health was less than the life

expectancy gain. Life expectancy for males age 80 increased from 7.4 to 8.1 years, whereas

life expectancy in good health increased from 2.1 to 2.3 years (Figure 6). Thus the proportion

of expected years lived in good health did not increase. For females age 80, the life

expectancy also increased from 7.6 years to 8.3 years, whereas the expected lifetime in good

health increased 0.3 year from 1.8 year to 2.1 years. Thus the increase in health expectancy

was less than the life expectancy gain. However, the proportion of expected years lived in

good health increased from 23.7 percent to 25.3 percent.


5. Discussion and Conclusion

This study focused on the health status overtime and its implication on healthy life

expectancy. For expected lifetime without disability the results were clear and demonstrated

improved health among the elderly. The improvement in disability free life expectancy may

indicate compression of morbidity. While the expected lifetime with good health increased

between 2002 and 2007 for women but remained stable for men at the same time. However, a

similar phenomenon has been observed in other countries. In Denmark and Belgium,

disability free life expectancy and self-rated good health using Sullivan’s method increased

(Bronnum-Hansen, 2005, Van Oyen et al., 2008). Then the results indicate that the life

expectancy had increased both for men and women and the increase in life expectancy among

the elderly Thai appear to be accompanied by improved health status. However, the disability

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free life expectancy and expected lifetime in self-rated good health among men were higher

than among women.

As the health indicators are based on the same questions asked in both surveys, trends in

population health could be evaluated. However, some caution must be exercised when

interpreting trends in health expectancy estimated by Sullivan’s method, as this method is not

suitable for detecting sudden changes in population health (Mathers and Robine, 1997).

Health prevalence data derived from cross-sectional surveys only implicitly reflect past

transitions between state of health and changes in mortality rates. The period of observation

(2002-2007) remains rather short compare to time series available in developed countries.

This is only measures at two points in time and the time period only five years.

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Table1: Calculation of Healthy Life Expectancy (HLE) by Sullivan Method for Female 2002

Age nMx lx nLx ex Dx (1‐Dx)*nLx sum(1‐Dx)*nLX HLE

0 0.02241 100000 98023 72.7 0.1311 0.1311 4810740 48.11 0.00109 97803 390189 73.3 0.1381 0.1381 4725568 48.35 0.00060 97377 486152 69.6 0.1516 0.1516 4389264 45.1

10 0.00041 97084 484922 64.8 0.1682 0.1682 3976813 41.015 0.00074 96885 483523 60.0 0.1866 0.1866 3573454 36.920 0.00157 96525 480741 55.2 0.2070 0.2070 3180157 32.925 0.00282 95772 475508 50.6 0.2297 0.2297 2798929 29.230 0.00265 94431 469044 46.3 0.2548 0.2548 2432645 25.835 0.00266 93186 462855 41.9 0.2827 0.2827 2083114 22.440 0.00309 91956 456256 37.4 0.3136 0.3136 1751108 19.0

45 0.00440 90547 447810 32.9 0.3480 0.3480 1437934 15.950 0.00628 88578 436039 28.6 0.3860 0.3860 1145962 12.955 0.00890 85838 419847 24.4 0.4030 0.4030 878234 10.260 0.01356 82101 397048 20.4 0.5050 0.5050 627585 7.665 0.02097 76719 364483 16.7 0.5750 0.5750 431046 5.670 0.03252 69075 319404 13.3 0.6330 0.6330 276141 4.075 0.05362 58687 258747 10.2 0.6930 0.6930 158920 2.780 0.08956 44812 183068 7.6 0.7550 0.7550 79485 1.885 0.14480 28415 104316 5.5 0.7660 0.7660 34633 1.290 0.22654 13311 39625 3.9 0.7850 0.7850 10223 0.895 0.34309 4334 9847 2.7 0.8270 0.8270 1704 0.4

100+ 0.50295 956 1901 2.0 1.0000 1.0000 0 0.0

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Table2: Life expectancy, expected lifetime with and without disability and proportion of expected lifetime without disability at age 65 and 80 in Thailand in 2002 and 2007

Calendar year Life Expectanc

y

(years)

Expected Lifetime without

Disability and 95% Confidence

Interval

(years)

Expected Lifetime with

Disability (years)

Proportion of Expected

Lifetime without Disability (percent )

(LE) (DFLE) (DLE)

Males age 65 2002 15.6 7.5 (7.36-7.72) 8.1 48.08

2007 16.3 9.8 (9.60-9.91) 6.5 60.12

Females age 65 2002 16.7 4.7 (4.52-4.80) 12.0 28.14

2007 17.7 7.3 (7.13-7.41) 10.4 41.24

Males age 80 2002 7.4 1.6 (1.37-1.73) 5.8 21.62

2007 8.1 2.4 (2.22-2.59) 5.7 29.63

Females age 80 2002 7.6 0.7 (0.56-0.77) 6.9 9.21

2007 8.3 1.3 (1.14-1.38) 7.0 15.66

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Table3: Life expectancy, expected lifetime in self-rated good health and bad health and proportion of expected lifetime in self-rated good health at age 65 and 80 in Thailand in 2002 and 2007

Calendar year Life Expectancy

(years)

Expected Lifetime in Self-rated Good Health and 95%

Confident interval

(years)

Expected Lifetime IN Self-

rated Bad Health (years)

Proportion of Expected

Lifetime in Good Health

(percent)

(LE) (HLE) (UHLE)

Males age 65 2002 15.6 6.8 (6.57-6.94) 8.8 43.59

2007 16.3 6.8 (6.63-6.96) 9.5 41.72

Females age 65 2002 16.7 5.6 (5.46-5.78) 11.1 33.53

2007 17.7 6.0 (5.88-6.17) 11.7 33.90

Males age 80 2002 7.4 2.1(1.86-2.26) 5.3 28.38

2007 8.1 2.3 (2.06-2.44) 5.8 28.40

Females age 80 2002 7.6 1.8 (1.62-1.92) 5.8 23.68

2007 8.3 2.1 (1.98-2.27) 6.2 25.30

16

0

10

20

30

40

50

60

70

80

90

100

Age

% Surviving

Mortality Disability Morbidity

(a)

(b)(c)

0

10

20

30

40

50

60

70

80

90

100

Age

% Surviving

Mortality Disability Morbidity

(a)

(b)

(c)

Figure 1: Relationships among age-specific morbidity, disability and mortality

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i1

i2

s2

s1

B C

10050

100

0age

% surviving

KEYA = not ill and surviving at t=1 B = ill and surviving at t=1C = surviving and i ll at t=2D = not surviving at t=2Expansion of morbidity: C > BCompression of morbidity: C < BEquilibrium of morbidity: C = B

A

D

(a) Expansion of morbidity

A

B

D

C

C

(b) Compressionof morbidity

D

A

B

C(c)Equilibrium of morbidity

Figure 2: The three hypotheses for morbidity-mortality change

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Figure 3: Life Expectancy and Healthy Life without Disability at Age 65 by Gender, 2002 and 2007

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Figure 4: Life Expectancy and Healthy Life without Disability at Age 80 by Gender, 2002 and 2007

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Figure 5: Life Expectancy and Expected Lifetime in Good Health at Age 65 by Gender,

2002 and 2007

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Figure 6: Life Expectancy and Expected Lifetime in Good Health at Age 80 by Gender,

2002 and 2007

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