This articles was published in Modern China Studies. Vol. 26, 2019
A Study on the Changing Trend of Health Indicators of the Elderly in
Mainland China: 1998-2014
LU Jiehua, GUO Ran
(Department of Sociology, Peking University, Beijing, 100871, China)
Abstract: Along with the extending average life expectancy and the declining fertility rate, mainland China has experienced the much rapider process of population aging for the last two decades. Therefore, this paper uses Chinese Longitudinal
Healthy Longevity Survey (CLHLS) data to measure and estimate the changing process of health indicators of the elderly in mainland China. By exploring seven wave data including some principal indicators about the elderly health, such as
Activities of Daily Living (ADL), Self-Rated-Health (SRH), Mental Health (Mental),
Cognitive Level (MMSE) and Frailty Index (FI), we can highlight the dynamic change of major health indicators of Chinese elderly between 1998-2014. Firstly, findings turn out that average health indicators in earlier years have no significant difference between the later ones. Further analysis about health indicators showed that age and cohort are two main interference factors to estimate the changing trend of health status of the elderly because of selectivity. After controlling these two factors, we can figure out a deterioration in health index when the elderly grow older. In addition, the health levels of people in same cohort decreased with age. Objective and comprehensive indicators in younger cohorts deteriorated slower than older elderly cohort. However, this trend turns to opposite when comes to subjective health
1 indicators.
Key Words: Aging, Health Indicators, Trend, CLHLS
About the authors: Jiehua Lu is a professor at sociology department, Peking
University. His major research field is geriatric demography and economic demography. In recent years, he has focused on the health and pension of the elderly in the aging process of the mainland China. Ran Guo is a PhD candidate in sociology department, Peking University. His major research interest is economics of population and sociology of education.
1. Backgrounds
Along with the extending average life expectancy and the declining fertility rate, 吗 mainland China has witnessed the much rapider process of population aging in 21st century. Nowadays, China is aging much faster than other low- and middle-income countries.(WHO, 2015) Both the quantity and proportion in total population are increasing rapidly. Data bulletin of the 1% national population sampling survey in
2015 illustrated that the number of people over the age of 60 reached 222 million, accounting for more than 16% of total population, while the number of people aged over 65 reached 144 million, accounting for more than 10%. Figure 1 shows vividly that the quantity and proportion in total population of people aged over 65 have been growing rapidly. At the same time, the elderly dependency ratio is also increasing at a high speed.(Lu and Guo, 2016)
Figure 1. The size and proportion of elderly people aged over 65 and the elderly
dependency ratio in China
2 The amount of elderly people in mainland China is not only increasing rapidly, but also getting older and older. In 2010, the average life expectancy in mainland
China reached 74.83. But the number substantially improved 1.5 and then became
76.34 in 2015. (The State Council Information Office of the People's Republic of
China, 2016)The fifth census data showed that in 2000 the total population of people aged 80 and above was only 7,745 thousand, accounting for 15.99% of the total elderly population (aged 65 and over). Ten years later, the total population of the 80 year old reached 20,990 thousand in national sixth census, accounting for 17.65% of the total elderly population. It is estimated that by the middle of twenty-first Century, the amount of the elderly population in China will reach 100 million, equivalent to the number of elderly people summarized in all developed countries.(The Drafting Group of General Report, 2015)
3 In order to cope with the aging of the population, WHO called for an active response to the aging population and put forward the goal of "healthy aging". There are three key components in WHO’s framework: intrinsic capacity, functional ability, and subjective well-being. (Beard et al, 2016; WHO, 2016) These three parts defined the analysis path of healthy aging for the health of the elderly from three levels: the internal, external and subjective dimension. As for the Chinese mainland which is rapidly moving towards an aging society, the health of the elderly has become a major issue that needs urgent attention. Ageing is not only multidimensional but also a cumulative effect of the life course. (Evenhuis et al, 2001) The body function of elderly people is gradually deteriorated and decreased, which not only greatly weakened their intrinsic capacity as long as brought pain and torment to the elderly, but also place a huge burden to the social welfare and public health system for whole country. Furthermore, it will have a negative effect on the implementation of national long-term economic and social development strategic objectives.
Therefore, the attention and discussion of healthy aging also need to go back to the situation of time and space. In order to get better understanding of this progress, we use longitudinal data to observe the long-term health change. From the perspective of “Age-Period-Cohort”, we analyze the changing trend of health indicators of
Chinese elderly in the past twenty years in accordance with different indicators. There are two advantages to doing so. Firstly, we can accurately grasp the dynamic health trends and differences of the Chinese elderly from different dimensions. Secondly, it is possible to find the time nodes of health changes of the Chinese elderly, which will
4 help to take targeted measures to cope with the aging process. In a word, attention to the dynamic changes of aged health in mainland Chinese is not only the need of scientific research, but also the significance of responding to the appeal of WHO for healthy aging. Also, it is an important premise to realize the goal of healthy China.
2. Literature review
According to the definition of World Health Organization, “Health is a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity”.(WHO, 1946) Therefore, health is a multidimensional concept of interaction between mind and body. In order to cope with the global trend of population aging and health issues of the elderly, the definition of "healthy aging" recently released by WHO includes three parts: intrinsic capacity, functional ability, and subjective well-being. WHO believes that healthy aging is the process by which older people develop and maintain functional abilities and enhance their subjective well-being. (Beard et al, 2016) Elderly health not only includes psychological and physiological indexes, but also includes social participation indicators. (Xie and Zhou,
2016) Therefore, the accurate estimation of the health level, the changing trend of health and health burden of elderly people depends on the accurate definitions, measurements and predictions of the major health indicators for the elderly.(Li and
Zhang, 2014) In the practical study, we usually choose one to several representative indicators, and combine them with relevant social and economic factors to study the elderly health.(Zeng, 2010; Zeng et al, 2014) The first step in this study is to define the dimensions and extensions of health indicators of the elderly. Secondly, we need
5 to identify the dimensions and types of indicators so as to make them much more clear and accurate.
Previous studies are usually based on two perspectives to investigate health indicators of the elderly: static and dynamic dimensions. From the static perspective, health indicators is usually divided into several categories, including objective indicators, subjective indicators, as well as comprehensive ones. For example, objective indicators usually include ADL, IDAL, mental health and cognitive ability, while self-rated health is usually included in the subjective indicators. What’s more, the integration of subjective and objective indicators turns out to be frailty index.
From the dynamic perspective, health indicators, selected as the same way above, focus mainly on the changing trend of these health indicators in a certain period of time by using the individual panel data.
Tracking studies are distinguished from cross-sectional studies. This method is usually based on long-term data and holds a dynamic perspective, through which the data used here are usually panel data. The type of data that can be tracked over a long period of time to a particular subject is explored to investigate the time-series changes in health indicators at different times and between cohorts. (Xue, 2015; Zeng et al,
2014) However, the distinction between the two perspectives is only from the aspect of tracking data, there is no significant difference in the selection of health indicators.
The advantage that uses the perspective of dynamic changes in health is not only can overcome the malpractice of cross-sectional data, such as containing censored data, sample coverage error and causal inference problems, but also can more fully explore
6 the causal mechanism in health studies. With the collection and disclosure of all kinds of tracking data, studies of long-term trends based on this kind of data are gradually increasing, among which CLHLS data are very good representative.(Du and Wang,
2013; Li and Zhang, 2014)
In CLHLS questionnaire, ADL generally consists of 6 indicators of eating, bathing, dressing, toileting, continence, indoor transfer. According to the previous literature, general active ability will be valued 0 and 1 respectively based on whether the completion of action depend on the others. (Katz et al, 1963; Kempen and
Suurmeijer, 1990; Millán-Calenti et al, 2010) In contrast, the behavioral indicators contained in IADL is relatively complex, including telephone, laundry, doing housework, shopping and so on. In fact, ADL/IADL has a direct impact on ability to act as well as on the mental health.(Ormel et al, 2002) The previous studies also have a lot of discussions on the psychological health, including behavioral characterization of mental health, mental disorders and its conditions (Kessler and Ustün, 2004), stigmatization of mental illness (Pinto-Foltz and Logsdon, 2009). The Mini-Mental
State Examination (MMSE) is commonly used to measure cognitive abilities in older adults, especially those suffering from certain diseases, such as stroke. The scales usually contain several parts such as general ability, reaction ability, memory, language ability, and so on. The scores are evaluated by some form of conversion and addition. (Dong et al, 2010; Escobar et al, 1986; Pendlebury et al, 2011)
Subjective indicators of measurement are mainly self-rated health. (Fayers and
Sprangers, 2002) As the name suggests, self-rated health means that respondents
7 measure and evaluate their scores based on their physical and health status. In general, the standard of self-rated health has five dimensions: "very bad", "bad", "so-so",
"good" and "very good", and are graded according to the actual research situation.
Hence, self-rated health studies are extended to broader discussion boundaries and develop more topics. These studies are usually based on different populations of the group. For example, immigration could cause a paradox to self-rated health.(Gubernskaya, 2015) Some studies also investigated the factors, including education level (Hu and Hibel, 2013), parents and childcare (Chen and Liu, 2012), which have effects on self-rated health. There are also trend studies carried out on self-rated health (Craigs et al, 2014), based on a dynamic research perspective.
Among the studies of comprehensive indicators of elderly health, the frailty index is usually granted as a multidimensional indicator, but lacks a clear definition.
(Brown et al, 1995; Levers et al, 2006) Gerontologists define it as a kind of a syndrome that is "multiple physiological system to reduce the cumulative decline in reserves and lead to stress resistance, and lead to vulnerability to adverse outcomes."
(Walston et al, 2006) In general, the frailty index includes multiple factors, such as the physiological, psychological, social, and mental ones. Physiological factors include weight, balance, gait speed and stride (Vermeulen et al, 2011), as well as malnutrition and its effects. (Walston and Fried, 1999) In addition, scholars also pay attention to cognitive and psychological factors (Morley et al, 2002; Rockwood et al,
1994) through which make up the frailty index. At the same time, socioeconomic factors (Morley et al, 2002), such as education and personal income (Rockwood et al,
8 1994), and mental factors (Brown et al, 1995) are also important components of the frailty index.
To sum up, the dynamic study of the changing trend of health indicators of the elderly is mainly based on the time dimension, and analyzes the changes and trends of indicators in the perspective of “Age-Period-Cohort”. In practice, we select ADL,
IADL, self-rated health, mental health and cognitive ability, and constructs a comprehensive index of weakness index. Through these six health indicators, we conduct a comprehensive analysis of the 1998-2014 health trends of the elderly in mainland china.
3. Research Design
3.1 Data sources
In this study, we use Chinese Longitudinal Healthy Longevity Survey (CLHLS) data, which have a total of seven-wave studies conducted so far, including 1998, 2000,
2002, 2005, 2008, 2011-12, and 2014. CLHLS uses complex sampling methods and has a general-good national representation. Figure 2 illustrate the structure of CLHLS.
Figure 2 The sample structure of CLHLS between 1998-2014
9 3.2 Health Indicators
3.2.1 ADL & IADL
ADL is the abbreviation of activities of daily living, while IADL means instrumental activities of daily living. Due to questionnaires in some waves the ADL and IADL are not exactly the same, so when measuring these indicators, we respectively use different strategies, and filter them by the common denominator.
ADL is based on a comprehensive evaluation of the six basic living abilities, including eating, dressing, bathing, going to the toilet, continence, and indoor transfer.
If an activity cannot be completed independently, then he or she will get 1 points.
ADL calculates the total score of the six items, which ranges from 0~6. The higher the score, the poorer the ability to perform daily activities.
IADL's calculations also refer to this method, scoring eight life skills. These eight capabilities are: visiting the neighbors by themselves, going shopping alone, cooking meal when necessary, washing clothes, can walk continuously for one
10 kilometer at a time by oneself, lift a weight of 5kg, continuously crouch and stand up three times, taking public transportation alone. If one item cannot be completed, he or she will score 1 points, and the other will be 0 points, so the total of these eight items will be
0~8 points.
3.2.2 Self-rated-health (SRH)
SRH is generally granted as a subjective indicator. In CLHLS, the range of self-rated health is [0-4], which corresponds to the following: very bad, bad, so-so, good, and very good.
3.2.3 Mental health
According to the questionnaire and the existing methods (Li and Zhang, 2014), mental health is a comprehensive variable based on seven indicators, including four positive indicators and three negative indicators. Positive indicators include: the degree of optimism measurement of the elderly " Do you always look on the bright side of things?"; " Do you like to keep your belongings neat and clean?"; self-control ability "
Can you make your own decisions concerning your personal affairs?"; positive views of getting old “Are you as happy as when you were younger?”. Negative items include two questions: loneliness “Do you often feel lonely and isolated?”; losing of ability “Do you feel the older you get, the more useless you are?”.
In order to maintain the same direction of measurement, the negative indexes are at first transformed into corresponding positive scores. Therefore, the respective ranges of these indexes are 0-4, and these indexes are added together to reach the range of 0-28.
11 3.2.4 Cognitive ability (MMSE)
Cognitive ability was analyzed by data from section C of the questionnaire, which was adapted from Mini-Mental State Examination (MMSE) scale. In general, the MMSE questionnaire contains the following several aspects and up to 30 points.
The first part is general ability, accounting for maximum 10 points. Secondly, reaction ability could add 5 points at most, as well as attention and computing power. In addition, questions about memory are up to 3 points. Lastly, abilities including language using, understanding and self-coordination, can plus 9 points at most.
3.2.5 Frailty Index
The frailty index, which has been deformed and weighted, is calculated by ADL,
IADL, SRH, MMSE, and mental health. The higher the score, the worse the health level.
3.3 Reliability and validity test
There is no doubt that survey research is complex and comprehensive. Due to some potential problems in the process of data collection, sampling, questionnaire design and many other aspects, the accuracy of the estimating results will be seriously questioned, if not going through the inspection and evaluation of data quality, which will violate the original intention of data analysis. Therefore, it’s necessary to evaluate data quality. After eliminating the corresponding problems, the next step of data inference and analysis can be carried out. There are two kinds of common analysis standards: reliability and validity, and the quality of these two indicators will directly determine the accuracy of data analysis.(Gu, 2001; Qi, 2014; Xie and Zhou, 2016)
12 Reliability analysis generally uses Cronbach alpha coefficients to estimate the consistency of the measurement methods or to exclude the extent of random errors.
(Gu, 2001) Validity analysis generally used criterion related validity (between 0.4-0.8).
Continuous variables are analyzed using Pearson or Fisher correlation coefficients, and categorical variables using Kendall or Spearman rank correlation coefficients.
Validity indicates the effectiveness of the measurement and the ability of the measurement to make inferences about a larger population.(Gu, 2001)
4. Main Findings
4.1 Reliability and validity
4.1.1 Reliability
Reliability is an important indicator to measure the consistency of the results of repeated measurements. The higher the reliability, the higher the credibility of the results. In view of these seven-wave data, this study first carried out the reliability analysis and measured the stability of each index in the data of seven waves. The results show that most of the indexes are about 0.65, while IADL and mental health are over 0.7, indicating that the stability is pretty good, and the reliability of the measurement is acceptable
Figure 3 The Cronbach’s alpha coefficient of each index across seven waves
13 4.1.2 Validity
Validity is also an important index to evaluate the accuracy of the measuring tools used. The higher the validity, the more accurate the measurement is. Validity analysis of multi period data usually uses criterion related validity, the range of which is 0.4-0.8. We analyze the validity of each health index from 1998-2014 based on
CLHLS data. The continuous variables are estimated by Pearson or Fisher correlation coefficients, while the categorical variables estimate by Kendall or Spearman rank correlation coefficients. Since all indicators are converted into continuous variables in the data set, the standard used here is the Person correlation coefficient.
Since each variable corresponds to a 7 * 7 correlation coefficient matrix, for the sake of brevity, this study takes "mental health" as an example. As shown in Table 1 below, most of the correlation coefficients are within the [0.4-0.8] range and belong to relatively high validity. Other correlation coefficients that are not included in the form are generally in this range except for individual waves. From this we can infer that the aggregated seven-wave CLHLS data set has good validity.
Table 1 Correlation coefficient matrix of mental health
14 1998 2000 2002 2005 2008 2011 2014
1998 1.000
2000 0.550 1.000
2002 0.393 0.870 1.000
2005 0.688 0.654 0.465 1.000
2008 0.501 0.512 0.400 0.525 1.000
2011 0.389 0.648 0.740 0.337 0.376 1.000
2014 0.395 0.524 0.493 0.423 0.351 0.525 1.000
4.2 Age period cohort analysis of each indicator
4.2.1 Main analysis dimensions
The trend of absolute changes in health related indicators and relative trends have long been the focus of attention among research communities. Age, period and cohort factors, in particular, play an important role in demographic studies. CLHLS's long time tracking panel study overcomes the disadvantages of cross sectional data and provides a wealth of material for trend research.
Therefore, the "age period cohort" method is used to describe the inter group distribution of various indicators at different waves. In the light of the data aggregated by seven studies, this study firstly describes the sample mean of ADL, IADL, self-rated health, cognitive ability, mental health and frailty index. Secondly, all the indicators above are described and analyzed at the cohort and age levels. Except for
ADL, all other indicators change slightly cross different investigation points.
According to the age distribution of different cohorts, the sample mean difference between cohorts is remarkable, and at the same time, present some certain regularities.
15 The prediction of sample and cohort also has a similar regularity.
4.2.2 The distribution of all the indicators’ mean at different investigation points
Figures 4 and 5 show the distribution of the means of the six health indicators at different points of investigation.
In Figure 4, the changing trend of ADL in the seven-wave data is remarkable, which drop from 1998's highest point of 1.057, down to 2008's minimum point of
0.655. IADL has only five value points, because the first measurements was carried out in 2002. As can be seen from the diagram, the volatility of IADL is within the
[2.3~2.5] range, declining slightly from 2.515 in 2008 to 2.242 in 2014. Similar to the first two indicators, the self-rated health also declined slightly with the period of investigation, from 2.604 in 1998 to 2.242 in 2014. By contrast, the frailty index showed a small increase.
Figure 4 Period distribution of all indicators (1)
In Figure 5, the mean value of cognitive and mental health did not change significantly, both of which are slightly fluctuating and rising with a range of less than
16 5%. Among them, the mean of cognitive ability fluctuated within the [23~25] range, while the mean of mental health fluctuated within the [18~19.4] range. The health levels indicated by these two indicators did have improved continuously over the years.
Figure 5 Period distribution of all indicators (2)
Overall, four objective indexes, including ADL, IADL, cognitive ability and mental health, indicate that the health level of Chinese elderly is on the rise along with the survey point in advance, while subjective indicator and comprehensive indicator, self-rated health and frailty index, have demonstrated a certain decline. We can infer that in the past twenty years, the objective health indicators of the Chinese elderly have increased steadily, but not meet the subjective expectations. In addition, the smaller range of changes also confirms WHO's assertion that "there is little evidence that older people today are much healthier than their parents".(WHO, 2017)
4.2.3 The distribution of all the indicators’ mean in different cohorts
Figure 6 depicts the distribution and linear prediction of each health indicator
17 from two dimensions of age and cohort. First, ADL values show a linear growth at different ages, and this trend also shows a sustained decline in daily mobility as age increases. After controlling influence of age, the sample in younger cohort turn to be stronger daily activity. While after controlling cohort factors, the younger sample in the same cohort would also turn to be a higher health level.
Figure 6 Distribution of ADL by age and birth cohort
The distribution and trend prediction of IADL and ADL are basically the same.
As a whole, the absolute value of IADL increases in accordance with age, which means decrease in health levels. So do the different cohorts. The health of the
1936-1949 cohort is better than that of the other cohort, followed by 1926-1935 cohort, 1916-1925 cohort, 1906-1915 cohort, and 1905 cohort. In each cohort, the level of health declines with age.
Figure 7 Distribution of IADL by age and birth cohort
18 Figure 8 shows that self-rated health is negatively related to age in general, but this negative correlation decreases as the turning point among those aged 90 year old and above. What’s more, the self-rated health of people aged 90 and above would rise slightly. It is reasonable to speculate that poor physical old cohorts have been dead in lower age, while the rest healthier elderly were “filtered” out because the overall estimate does not control the disturbance of cohort factors and the existence of selective effects.(Li and Zhang, 2014) Therefore, the older they get, the higher self-rated health level they reach.
After controlling the effects of cohort and age separately, the results are similar to those of the previous ones. Those Chinese elderly in recent cohort have higher self-rated health level. However, self-rated health level is negatively correlated with age for a particular cohort, while the absolute value of the slope of the different cohorts decreases with time. Generally, the older the cohort, the slower the rate of
19 self-rated health declines.
Figure 8 Distribution of SRH by age and birth cohort
The mental health of the elderly is negatively related to age. Mental health of the Chinese elderly is higher in recent cohorts, but the absolute value of the slope decline along with the cohorts getting older. It is worth mentioning that mental health of the elderly increases with age for cohort 1905. This aspect confirms the health selectivity that the older the healthier. Two levels of physical and mental health also confirms the "happy -go-lucky(乐天知命)" of the China old saying.
Figure 9 Distribution of Mental by age and birth cohort
20 Cognitive ability, including attention, comprehension, and responsiveness, is attenuated by age, and overall cognitive ability decreases with age either. Among the more recent cohorts, or the younger elderly, cognitive ability is much stronger. From the attenuation of different cohort slopes, the older the cohort, the faster the cognitive decline.
Figure 10 Distribution of MMSE by age and birth cohort
21 Figure 11 shows the change in the frailty index. Because the frailty index combines the psychological, physiological and other comprehensive health levels of the elderly, it usually has a more comprehensive explanatory power.
In general, the frailty index is increasing with age, and the trend is accelerated by a quadratic fit. After controlling effects of cohort and age factors, this can also be seen in more recent cohort, the frailty index is lower, while the degree of health is better. At the same time, the fitting function of tangent slope is smaller in younger cohort. This indicates that, as the degree of aging increases, the deterioration of the health level is a process that accelerates with age.
Figure 11 Distribution of FI by age and birth cohort
22 5. Conclusion and discussion.
Accompanied with the deepening process of aging in China, active aging and healthy aging have become an important target to pay attention to the health of the elderly and realize the well-being of the Chinese elderly. It is also a positive practice in response to the WHO's response to the aging process.
This study adopts multi period CLHLS tracking data, and uses six indicators including ADL, IADL, SRH, mental health, cognitive level and frailty index to measure the evolution of elderly health in mainland China from 1998 to 2014. The
"age-period-cohort" frame was used here to assess and predict the evolution of health indicators among the Chinese elderly in different periods and cohorts. After examination, the seven-wave data used in this study have good reliability and validity, and the consistency and robustness of the multi period data in different indicators are even much stronger.
23 The findings turn out that the major health indicators of Chinese elderly people in different measurement time points are basically the same, although there are some fluctuations but no significant difference. It also confirms WHO's assertion that "there is little evidence that older people today are in better health than their parents".(WHO,
2017) From a dynamic age-increasing point of view, health of the Chinese elderly is progressively worse in each indicator with age or cohort aging.
Given that age and cohort are major disturbances in health changes in older adults, there is a question of selectivity when estimating age and health levels. (Fogel,
2003) Elderly people with poor health and disease are more likely to die at a lower age, while healthier individuals remain in the sample. On the other hand, with improving of health care, more elderly people who are less healthy could live longer.
From a major trend, the health indicators of the Chinese elderly decreased significantly with age, and the health level of the elderly in the cohort decreased with age after controlling the selectiveness of age and cohort. However, differences between the different indicators of the cohort did emerge. The younger cohort of elderly health deteriorated slower than older cohort, which shows that the slope of the fitted line slopes of the younger cohort is smaller than that of the older cohort, or the absolute slope of the tangent slope of the quadratic fitted line is smaller. Subjective indicators self-rated health showed the opposite trend, a recent cohort of self-rated health decreased quickly. As the cohort ages, the rate of self-rated health declines.
What’s more, in the oldest cohort, self-rated health increases with age.
In the study of health problems of the elderly, the selective survival and the
24 expansion of morbidity are two controversial theoretical hypotheses.(Li and Zhang,
2014) A selective survival hypothesis that relatively poor health status of the elderly would increase with age and eliminated at early stage, thus, surviving individuals are good individual health. This selectivity that the healthier live longer and the weaker filtered out in the early years will be enhanced when individuals entering the sample frame. The expansion of morbidity hypothesis suggests that the survival time will be longer with the improvement of medical conditions and living standards, and that individuals who may have been eliminated in the past can achieve longer survival by this condition.(Crimmins and Beltrán-Sánchez, 2011; Cutler et al, 2013; Fries, 1980)
However, the results of this study show that the two states may not be diametrically opposed to selective survival and expansion of morbidity. In different cohorts, health indicators may reflect these two situations at the same time. Table 2 shows the estimated errors that may be encountered in the estimation of health indicators. IADL and MMSE, after controlling for cohort and age factors, do not have significant estimates of bias. Among other indicators, there is more or less an estimate bias. Both the two indicators of ADL and Mental showed an expansion of morbidity among younger cohorts, whereas selective survival occurred among older cohorts.
The subjective index SRH emerged from the morbid state to the selective survival state among the youngest two cohorts, while the comprehensive Frailty index showed selective survival among the oldest cohort. Overall, selective survival and expansion of morbidity may not be antagonistic, but occur in different cohorts. When estimating health indicators, the estimation bias of expansion of morbidity is more likely to occur
25 among younger cohorts, while the selective survival problem is more likely to occur among older cohorts.
Table 2 Transformation of interference factors in different cohorts
Reference Beard, John R.，Alana M. Officer and Andrew K. Cassels, 2016, "The World Report On Ageing and Health," The Gerontologist 387 (Suppl_2): pp.2145-2154. Brown, I.，R. Renwick and D. Raphael, 1995, "Frailty: Constructing a Common Meaning, Definition, and Conceptual Framework.," International Journal of Rehabilitation Research 18 (2): pp.93-102. Chen, Feinian and Guangya Liu, 2012, "The Health Implications of Grandparents Caring for Grandchildren in China," The Journals of Gerontology: Series B 67B (1): pp.99-112. Craigs, Cheryl L. ，Maureen Twiddy，Stuart G. Parker and Robert M. West, 2014, "Understanding Causal Associations Between Self-Rated Health and Personal Relationships in Older Adults: A Review of Evidence From Longitudinal Studies," Archives of Gerontology and Geriatrics 59 (2): pp.211-226. Crimmins, Eileen M. and Hiram Beltrán-Sánchez, 2011, "Mortality and Morbidity Trends: Is there Compression of Morbidity?" The Journals of Gerontology: Series B 66 (1): pp.75-86. Cutler, David M. ， Kaushik Ghosh and Mary Beth Landrum, 2013, "Evidence for Significant Compression of Morbidity in the Elderly Us Population", National Bureau of Economic Research. Dong, Yan Hong，Vijay Kumar Sharma，Poon Lap Chan，Narayanaswamy Venketasubramanian， Hock Luen Teoh ， Raymond Chee Seong Seet ， Sophia Tanicala ， Yiong Huak Chan and Christopher Chen, 2010, "The Montreal Cognitive Assessment (Moca) is Superior to the Mini-Mental State Examination (Mmse) for the Detection of Vascular Cognitive Impairment After Acute Stroke," Journal of the Neurological Sciences 299 (1): pp.15-18. Du, Benfeng and Xuan Wang, 2013, "Health Inequality Among the Chinese Elderly: Changes ， Regional Disparities and Determinants," Population Research 37 (5): pp.81-90. Escobar, J. I.，A. Burnam，M. Karno，A. Forsythe，J. Landsverk and J. M. Golding, 1986, "Use of the Mini-Mental State Examination (Mmse) in a Community Population of Mixed Ethnicity. Cultural
26 and Linguistic Artifacts," Journal of Nervous & Mental Disease 174 (10): pp.607.
Evenhuis, H.，C. M. Henderson，H. Beange，N. Lennox and B. Chicoine, 2001, "Healthy Ageing –
Adults with Intellectual Disabilities: Physical Health Issues," Journal of Applied Research in
Intellectual Disabilities 14 (3): pp.175–194.
Fayers, Peter M. and Mirjam AG Sprangers, 2002, "Understanding Self-Rated Health," The Lancet 359 (9302): pp.187-188. Fogel, Robert W., 2003, "Changes in the Process of Aging During the Twentieth Century: Findings and Procedures of the Early Indicators Project," Nber Working Papers 30 (9941): pp.44. Fries, J. F., 1980, "Aging, Natural Death, and the Compression of Morbidity. 1980.," New England Journal of Medicine 303 (23): pp.1369. Gu, Da-nan, 2001, "A Discussion About Assessment of the Quality of Elder' S Health Data," Population & Economics (2): pp.38-43. Gubernskaya, Zoya, 2015, "Age at Migration and Self-Rated Health Trajectories After Age 50: Understanding the Older Immigrant Health Paradox.," Journals of Gerontology 70 (2): pp. Hu, Anning and Jacob Hibel, 2013, "Educational Attainment and Self-Rated Health in Contemporary China: A Survey-Based Study in 2010," The Social Science Journal 50 (4): pp.674-680. Katz, S. C.，A. B. Ford，R. W. Moskowitz，B. A. Jackson and M. W. Jaffe, 1963, "The Index of Adl:a Standardized Measure of Biological and Psychological Function," 185 (12): pp.914-9. Kempen, G. and T. Suurmeijer, 1990, "The Development of a Hierarchical Polychotomous Adl-Iadl Scale for Noninstitutionalized Elders1," The Gerontologist 30 (4): pp.497-502. Kessler, R. C. and T. B. Ustün, 2004, "The World Mental Health (Wmh) Survey Initiative Version of the World Health Organization (Who) Composite International Diagnostic Interview (Cidi).," International Journal of Methods in Psychiatric Research 13 (2): pp.93-121. Levers, Merry-Jo，Carole A. Estabrooks and Janet C. Ross Kerr, 2006, "Factors Contributing to Frailty: Literature Review," Journal of Advanced Nursing 56 (3): pp.282-291. Li, Ting and Yanlong Zhang, 2014, "Growth Curve Trajectories of Elderly People's Health Indicators in China: Cohort Variations and Rural-Urban Disparities," Population Research 38 (2): pp.18-35. Lu, Jie-hua and Ran Guo, 2016, "From New Situation to New National Policy: Strategic Thinking of Actively Coping with the Aging Population," Journal of Chinese Academy of Governance 104 (5): pp.27-34. Millán-Calenti, José C.，Javier Tubío，Salvador Pita-Fernández，Isabel González-Abraldes，Trinidad Lorenzo，Teresa Fernández-Arruty and Ana Maseda, 2010, "Prevalence of Functional Disability in Activities of Daily Living (Adl), Instrumental Activities of Daily Living (Iadl) and Associated Factors, as Predictors of Morbidity and Mortality," Archives of Gerontology and Geriatrics 50 (3): pp.306-310. Morley, John E.，III H. Mitchell Perry and Douglas K. Miller, 2002, "Something About Frailty," The Journals of Gerontology: Series A 57 (11): pp.M698-M704. Ormel, Johan，Frühling V. Rijsdijk，Mark Sullivan，Eric van Sonderen and Gertrudis I. J. M. Kempen, 2002, "Temporal and Reciprocal Relationship Between Iadl/Adl Disability and Depressive Symptoms in Late Life," The Journals of Gerontology: Series B 57 (4): pp.P338-P347. Pendlebury, S. T.，J. Mariz，L. Bull，Z. Mehta and P. M. Rothwell, 2011, "Moca, Ace-R, and Mmse Versus the National Institute of Neurological Disorders and Stroke-Canadian Stroke Network
27 Vascular Cognitive Impairment Harmonization Standards Neuropsychological Battery After Tia and Stroke," Stroke 43 (2): pp.464-469. Pinto-Foltz, Melissa D. and M. Cynthia Logsdon, 2009, "Reducing Stigma Related to Mental Disorders: Initiatives, Interventions, and Recommendations for Nursing," Archives of Psychiatric Nursing 23 (1): pp.32-40. Qi, Yaqiang, 2014, "Reliability and Validity of Self-Rated General Health," Chinese Journal of Sociology 34 (6): pp. Rockwood, K.，R. A. Fox，P. Stolee，D. Robertson and B. L. Beattie, 1994, "Frailty in Elderly People: An Evolving Concept.," Canadian Medical Association Journal 150 (4): pp.489. The Drafting Group of General Report, 2015, "The General Research Report of Chinese Strategic for Dealing with Population Aging," Scientific Research on Aging 03 (03): pp.4-48. The State Council Information Office of the People's Republic of China, 2016, "Assessment Report On the Implementation of the National Human Rights Action Plan of China (2012-2015)." online at: http://www.china.org.cn/chinese/2016-06/14/content_38665886.htm (accessed 4-5-2017) Vermeulen, Joan，Jacques CL Neyens，Erik van Rossum，Marieke D. Spreeuwenberg and Luc P. de Witte, 2011, "Predicting Adl Disability in Community-Dwelling Elderly People Using Physical Frailty Indicators: A Systematic Review," BMC Geriatrics 11 (1): pp.33. Walston, Jeremy and Linda P. Fried, 1999, "Frailty and the Older Man," Medical Clinics of North America 83 (5): pp.1173-1194. Walston, Jeremy，Evan C. Hadley，Luigi Ferrucci，Jack M. Guralnik，Anne B. Newman，Stephanie A. Studenski，William B. Ershler，Tamara Harris and Linda P. Fried, 2006, "Research Agenda for Frailty in Older Adults: Toward a Better Understanding of Physiology and Etiology: Summary From the American Geriatrics Society/National Institute On Aging Research Conference On Frailty in Older Adults," Journal of the American Geriatrics Society 54 (6): pp.991-1001. WHO, 2015, "China Country Assessment Report On Ageing and Health." online at: http://www.who.int/ageing/publications/china-country-assessment/en/ (accessed 4-3-2017) WHO, 2017, "10 Facts On Ageing and Health." online at: http://www.who.int/features/factfiles/ageing/en/ (accessed 4-3-2017) WHO, 1946, "Who Definition of Health", Preamble to the Constitution of the World Health Organization as adopted by the International Health Conference. New York: 19 June - 22 July, 1946. WHO, 2016, "World Report On Ageing and Health 2015." online at: http://www.who.int/ageing/events/world-report-2015-launch/en/# (accessed 5-1-2017) Xie, Shiqi and Jianrong Zhou, 2016, "Development of a Comprehensive Geriatric Assessment," Journal of Nursing Science 31 (13): pp.28-31. Xue, Xindong, 2015, "Analysis On the Trend and Determining Factors of Health Inequality Among China' S Older Population," Population and Development 21 (2): pp.84-92. Zeng, Xianxin, 2010, "A Comprehensive Analysis of the Health of China's Elderly Population," Population & Economics (5): pp.80-85. Zeng, Yi，Da-nan Gu，Purser Jama，Hoenig Helen and Christakis Nicholas, 2014, "Associations of Environmental Factors with Health and Mortality Among Chinese Elderly: A Sample Survey in 22 Provinces in China," Chinese Journal of Health Policy 7 (6): pp.53-62.
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