This article is cited by Guo Kailin, Wang Shiqiang, Li Dan, Wang Yijie, Wang Shaokun, Xu Zhihan. The development trajectory of debilitating elderly people in mainland China: An analysis based on the growth model of latent variables. Chinese Journal of General Practice[J], 2022, 25(06): 742-749,755 doi:10.12114/j.issn.1007-9572.2021.02.119
GUOKailin, WANGShiqiang, LIDan, WANGYijie, WANGShaokun, XUZhihan. Developmental Trajectory of Frailty in Chinese Elderly People: an Analysis Based on the Latent Growth Model. Chinese General Practice[J], 2022, 25(06): 742-749,755 doi:10.12114/j.issn.1007-9572.2021.02.119
According to the World Population Prospects 2019 released by the United Nations, the global aging trend will further increase by 1.5 billion people over the age of 65 by 2050, and 1 in 6 people will be elderly.[1] Weakness is raised in the context of aging and is an important challenge for healthy aging [2]. Adverse events caused by debilitating [3] place a huge burden on the patient, the family, and society [4]. Research on debilitation first arose in foreign countries, from the discussion of the concept of debilitation [5] to the development of debilitation assessment tools [6], at present, a number of foreign studies have used longitudinal tracking data to investigate the debilitation of the elderly, pointing out that debilitation has different development trajectories [7,8]. The mainland's research in the field of weakness started late, and at present, it is mainly based on the assessment of weakness, influencing factors and review studies. Studies have shown that the incidence of debilitation among the elderly in mainland China is increasing year by year [9], the incidence of debilitation among the elderly in rural areas is higher than that in cities [10,11], and the incidence of debilitation in specific institutions such as hospitals and nursing homes is higher [12]. Studies on the influencing factors of older age weakness are mainly carried out around demographic factors [13,14] and lifestyle [15,16], and are mostly cross-sectional studies. Through literature combing, it is found that there are still the following deficiencies: (1) Mainland research on debilitation mostly uses a small range of cross-sectional data, although such studies provide a lot of valuable information, but the conclusions obtained are not suitable for extrapolation and cannot reflect the development trajectory of debilitation and the differences between individuals. (2) Previous studies have mostly investigated the influence of demographic characteristics and lifestyle on debilitation based on cross-sectional data, ignoring the role of these factors in the process of debilitating development. (3) Physical activity (PA) as the most effective way to prevent and delay debilitation, there are few studies on the relationship between PA and debilitation in mainland China, especially the lack of research on the effect of PA on debilitation based on longitudinal tracking data.
With the further intensification of the aging situation, debilitation has become an important social problem affecting the health of the elderly. In recent years, the number of domestic research on weakness has gradually increased, but the discussion on this topic is not perfect enough, and the relevant research is still dominated by small-scale cross-sectional investigations, which provide a lot of valuable information, but ignore the development trajectory of debilitation and individual differences.
This study uses the data of the China Health and Pension Tracking Survey (CHARLS) to identify the development trajectory of debilitation and analyze how individual differences affect the changes of debilitation, so as to grasp the development law of debilitating elderly in mainland China, and provide a theoretical basis for mainland China to actively cope with aging and promote the formation of non-medical health intervention models for the elderly.
In view of the above findings, based on the national sampling data of the China Health and Retirement Longitudinal Study (CHARLS), the frailty index (FI) was used to assess the level of debilitation of the elderly in mainland China, and the development trajectory of the elderly was examined by constructing a latent growth model (LGM). On the basis of understanding the trajectory of debilitating development, gender and education level are further incorporated as time constant factors, and PA, smoking, drinking, and sleep are used as time change factors, and conditional LGM is constructed, so as to explain the individual differences shown in the process of debilitation. On the one hand, it enriches the research in the field of elderly debilitation in mainland China, and on the other hand, it also identifies the factors affecting debilitation through nationwide longitudinal tracking data, providing more convincing evidence for the debilitating intervention practice of the elderly in mainland China than cross-sectional studies.
1 Information and methods
1.1 Sources
The data for this study is derived from the data of CHARLES in 2011, 2013, 2015 and 2018, which was conducted in 2011 and followed every 2 to 3 years, and the data was made public one year after the end of the survey (http://charls.pku.edu.cn/). Hosted by the National Institute of Development studies of Peking University, CHARLS pioneered the electronic mapping software (CHARLS-GIS) technology, using the map method to produce village-level sampling frames, using multi-stage PPS sampling, and conducting household surveys of middle-aged and elderly people in 28 provincial-level administrative regions in mainland China, covering 150 county-level units and 450 village-level units. Among them, 2011 was a national baseline survey, and 2013, 2015 and 2018 were a national follow-up survey. The questionnaire was designed with reference to international standards, access response rates and data quality were among the best in the world, and the data was widely used and recognized in academia.[17]
1.2 Research Methodology
1.2.1 Variable construction
Gender (assignment: 0 = female, 1 = male), educational attainment (assignment: 1 = uneducated, 2 = primary school, 3 = lower secondary school and above) are included as time constant factors. PA, smoking (assignment: 0 = non-smoking, 1 = smoking), drinking alcohol (assignment: 0 = no drinking, 1 = drinking) and sleep (assignment: 0 = <6 h/d, 1 = ≥6 h /d) were included as time factors, of which PA assessment was based on the time spent per day in each level of PA, the number of days of 1 week, and the metabolic equivalent (MET) of each PA mode (low PA MET assignment was 3.3, medium PA MET assignment was 4.0, high PA MET assignment was 8.0)[18] The total energy consumption of 1 week is calculated as: 1 week PA energy consumption = MET× daily activity time × 1 week of active days (where MET = ∑METn×hn/∑h) [19], and according to the International Physical Activity Scale (IPAQ) evaluation criteria [20], the PA level of the elderly is divided into low (assignment: 0 = <600 METs / week), medium and high (assignment: 1 = ≥600 METs / week).
FI is used to assess the debilitating status of the elderly. FI has a high effectiveness and stability in assessing and predicting the health status of the elderly, and has been widely used in gerontology, demography and sociology in recent years, and is also often used in large-scale population surveys such as epidemiology, and has been confirmed by several studies with good validity and reliability [21,22]. According to the criteria for constructing FI [23], variables must be health-related; variables must not saturate population prematurely; and variables must cover a range of systems in the body. Among them, the FI indicators can be freely constructed according to the needs on the basis of following the principle of health defect selection, and the number can vary (generally 30 to 92), but it should contain at least 30 health defect entries [24]. Referring to previous studies [9,25,26], 39 variables were selected from the data to construct the FI evaluation scale for this study. Includes: (1) Illness: 13 chronic diseases, 2 disabilities, 2 types of audiovisual conditions, and health changes compared to the previous survey (assignment: 0 = healthy, 1 = deterioration). (2) Disability: Includes 6 somatic life self-care scale (BADL) items, 5 instrumental activity of daily living energy strength scale (IADL) items, 3 mobility indicators and 5 muscle ability indicators. (3) Depression: The flow call self-assessment depression scale (CESD-10) was used to measure, which was divided into positive and negative factor structures, and the correlation coefficient was 0.56, which could effectively measure the depression level of the elderly in the TRAFFIC data [27], and the scale score ranged from 0 to 30 points, with a score of > 10 points, health deficiencies are assigned a value of 1, otherwise 0. (4) Cognitive ability: Assessed by the Revised Edition of the Cognitive Function Telephone Assessment Questionnaire (TICS-m), the score range is 0 to 21 points, and the cognitive value is assigned to the actual score/21. In addition to cognitive ability, the evaluation of the above dimensions assigns a health variable value of 0, 1 (0 = no health defect, 1 = health defect), and so on, depending on the variable type. FI is calculated by dividing the number of health deficiencies by the total number of inclusions (39 in this study), which ranges from 0 to 1, and the larger the value, the weaker it is. Due to the small value of FI, with reference to STUDIES such as KULMINSKI [28], FI was converted to 1% unit. Fi and health deficiencies are distributed in Table 1.
Table 1 Frailty index and the distribution of health defects in participants by the wave of CHARLS
1.2.2 Missing value handling
There are many missing variables in the disease dimension and cognitive dimension in the CHARLS data. Since CHARLES no longer asked returning visitors about the disease when it followed up the survey, but only asked whether the disease answered in the previous survey was correct, the missing data in this part could be filled by the data from the previous period. The assessment variables of the cognitive dimension all have different degrees of deletion, so to choose the appropriate filling method, the multiple filling method is still accurate in the parameter estimation when dealing with the data with a deletion rate of up to 25%[29], which assumes that the data are randomly missing, and the missing data in this study have significant results after the little test (P <0.05), indicating that the data are not completely randomly missing, and may be randomly missing or non-randomly missing. Continue to generate an additional diffuseric variable for each variable to indicate whether it is missing or not, and as a grouping of χ2 tests, χ2 tests are performed with other target variables, the results show that there are differences with age, sex, education, etc., indicating that the missing data depend on other variables, belong to the random missing type, and are suitable for filling with multiple filling methods. Refer to previous studies [30], use gender, age, and education as explanatory variables for cognitive dimensions to predict cognitive ability, and go through the following three steps: first replace each missing value with a series of possible values, then analyze multiple datasets resulting from multiple substitutions with standard statistical analysis procedures, and finally combine the statistical results from each dataset. Through the treatment of the above missing values, and the exclusion of the data of dead and lost visitors, the data of the four phases were matched and combined through personal codes, and finally 267 elderly people aged 60 years and above who participated in the four surveys were formed as the study sample.
1.3 Statistical methods
LGM construction using Mplus 8.0 was used to observe the development trajectory of debilitation in the elderly. LGM is a variant of the structural equation model that allows simultaneous estimation of cohort and individual variation during development [31]. LGM first defines two latent variable structures, namely intercept and slope, and then estimates the structure of two latent variables in the model with the actual measurements of a variable at different time points, this model that briefly describes the trajectory of change is called unconditional LGM. After unconditional LGM, if the variation in intercept or slope is significant, you can continue to investigate the factors that affect the intercept or slope, that is, the construction of conditional LGM. If a variable has a significant covariant relationship with the intercept or slope, you can determine that the variable is a factor that affects the intercept or slope. Variable correlation analysis uses Spearman rank correlation analysis. The difference in P<0.05 is statistically significant.
2 Results
2.1 Correlation Analysis
The results of the relevant analysis show that FI is related to PA, smoking, alcohol consumption, sleep, gender, and education level (P<0.05), see Table 2.
Table 2 Correlation coefficient matrix of frailty index with PA，smoking，alcohol consumption，sleep，gender，and education level in Chinese older people
2.2 The development trajectory of debilitating elderly people in mainland China: unconditional LGM
Construct three types of unconditional LGMs: (1) linear unconditional LGM, (2) quadratic function unconditional LGM, and (3) undefined curve unconditional LGM, see Table 3. It can be seen from the fitting index that the undefined curve unconditional LGM fits the data better than the linear unconditional LGM and the quadratic function unconditional LGM, for this reason, the debilitating trajectory of the elderly in the continent is determined to meet the unconditional LGM without the defined curve, indicating that the development trajectory of the debilitation of the elderly in the continent shows a curved growth trend. From the results of unconditional LGM without defining the curve, the initial level of debilitation in the elderly was significantly >0 (intercept = 11.83, P<0.01), and the decline showed an upward trend during four surveys (slope = 0.92, P<0.01), in addition, the variation of the intercept (σ2 = 53.16, P<0.01), the slope variation (σ2 = 1.13, P<0.01) were significantly >0, indicating that there were significant individual differences in the starting level of elderly debilitation. And there are also significant individual differences in the subsequent rate of development. There was a significant correlation between intercept and slope (r=0.41, P<0.01), indicating a significant correlation between the development rate and onset level of debilitation in the elderly in mainland China, as shown in Figure 1.
Figure 1
Figure 1 Unconditional latent growth model with undefined curve for analyzing the developmental trajectory and associated factors of frailty in Chinese older people
Table 3 Fitting indices of the unconditional latent growth model for analyzing the developmental trajectory of frailty in Chinese older people
2.3 Trajectory of debilitating elderly people in mainland China: Conditional LGM
In order to investigate whether there are differences in gender and educational attainment in the development trajectory of debilitation in the elderly, as well as the influence of PA, smoking, alcohol consumption and sleep factors, the undefined curve condition LGM shown in Figure 2 is constructed. The conditional model fits the data well: χ2(19)=300.79, Comparative Fit Index (CFI) = 0.947, Non-Canonical Fit Index (TLI) = 0.925, Approximate Rms Error (RMSEA) = 0.044, Standardized Rms Residual (SRMR) = 0.058. There were significant individual differences in the weakening of the elderly (σ2 =0.942, P<0.01), and there were also significant individual differences in the rate of development (σ2=0.923, P<0.01). In addition, in the prediction of the model intercept, there was a significant difference in the initial level of debilitation between male and female older adults (β = -0.113, P<0.01), and the lower the level of debilitation in male older adults, and there was also a significant difference in the initial level of debilitation in the elderly (β = -0.173, P<0.01), and the higher the education level, the lower the initial level of debilitation. In the prediction of the slope of the model, both sex and education had a significant negative predictive effect (sex: β = -0.181, P<0.01; education level: β = -0.151, P<0.01), and the debilitation of women and low-educated elderly developed faster than that of men and the elderly with high education.
Figure 2
Figure 2 Conditional latent growth model with undefined curve for analyzing the developmental trajectory and associated factors of frailty in Chinese older people
PA, smoking, drinking, sleep as a time change factor into the inclusion, the results show that PA and sleep have a significant negative impact on the elderly debilitation, this effect is consistent in the phase 4 survey, that is, the higher the PA level of the elderly, the more adequate sleep, the lower the level of weakness, taking 2011 as an example, compared with the elderly with low PA, the level of weakness of the elderly engaged in high and medium PA decreased by 0.026 units (2011: β = -0.026, P<0.05); compared with the elderly with insufficient sleep, the level of debilitation in the elderly with adequate sleep decreased by 0.077 units (2011: β = -0.077, P <0.01), it is shown that moderate to high PA and guaranteed sleep time help reduce debilitating levels in the elderly. In addition, although smoking and drinking have a positive effect on the decline of the elderly, that is, long-term smoking and excessive alcohol consumption will increase the level of debilitation in the elderly, there is only a significant role in the 2011, 2015, 2018, 2013 and 2015 surveys, respectively.
3 Discussion
Demographic changes have led to a gradual increase in the proportion of the debilitated population, which is expected to increase exponentially over the next few decades.[32] Based on the data of CHARLES surveys in 2011, 2013, 2015 and 2018, this study established the trajectory and influencing factors of debilitation of the elderly in mainland China. The results showed that in the 4th phase of the follow-up survey, the debilitation of the elderly in mainland China showed a curved growth trajectory, and there were significant individual differences between the initial level of debilitation and the subsequent growth rate, suggesting that the debilitation level of the elderly in mainland China did not rise linearly with age, which provided the possibility of preventing and delaying debilitation [33,34]. The conclusions of this study are consistent with the study of STOLZ et al. [35] using data from the European Healthy Aging and Retirement Survey (SHARE), which showed that the level of debilitation among elderly individuals has a large heterogeneity and develops in a non-linear growth pattern, but also contradicts other research conclusions, STOW et al. [36], AGUAYO et al. [37] studies show that the debilitation trajectory of the elderly shows a linear growth trend, which may be related to the inconsistency of the study sample, and the longitudinal data contain more missing data Different ways of handling missing data may also be one of the reasons for the difference in conclusions.
Among the constant time factors, this study shows that whether it is the initial level of debilitation or the development rate of debilitation, women, the elderly with low education level are faster than the male and the elderly with high education level, suggesting that the clinical focus should be on women and the elderly with low education level. A study using a comprehensive assessment of the frailty index (FI-CGA) constructed for the elderly to assess the FI of urban and rural residents in Beijing showed that women had higher FI than men, and that previous foreign studies concluded that female debilitation scores were higher than men[38,39], which may be related to estrogen levels, which rapidly decline in women after menopause [40], causing vitamin D deficiency, resulting in rapid deterioration of neuro-muscle balance and muscle strength and other physiological functions [41], followed by debilitation. While previous studies have mostly used cross-sectional studies to confirm that female elderly people are more serious, this study not only shows that the initial level of female debilitation is higher than that of men from a longitudinal perspective, but also its subsequent development rate is faster than that of men, which further confirms the severity of female elderly debilitation. Studies by YANG et al. [42] also point to the increasing gender imbalance in terms of weakness, showing that women as vulnerable groups are also showing less optimistic situations in terms of weakness. In addition, education level can predict the level of debilitation and development of the elderly, the initial level of debilitation and development rate of the elderly with high education level is lower than that of the elderly with low education level, so high education level is a protective factor for debilitation [43], BAKKER et al. [44] believe that the health literacy of people with high education level is also higher, and they have the ability to use relevant health knowledge to maintain their own health if they are willing, thereby reducing the incidence of debilitation. On the other hand, education level will affect the cognitive function of the elderly [45], Xie Ruining et al. [46] believe that the central nervous system of the brain will not stagnate due to maturity, after the individual matures, its education and experience and other internal and external factors will lead to further development of brain structure and function, so the higher the level of education, the higher the cognitive ability of the brain. Wang Huihui et al. [47] have found that education can indirectly affect the occurrence of debilitation through cognitive level, and cognitive status not only affects the memory of the elderly, but also the basis for self-care ability and social activities. It can be seen that in addition to the short-term effects of cognition, class, income and so on brought about by education, from the perspective of a longer-term life course, it also has a significant impact on the health security of the elderly, and these advantages due to education affect the initial level of weakness, and also affect the development speed of weakness through cumulative advantages.
Among the time factors, PA and sleep had a significant negative effect on the debilitation of the elderly in the phase 4 survey data, indicating that the higher the PA level, the more adequate sleep, which contributed to the reduction of the debilitation level of the elderly. In the 2019 edition of the International Debilitating Clinical Practice Guidelines developed by the International Conference of Frailty and Sarcopenia Research (ICFSR), PA is considered to be the most effective and preferred way to prevent the onset of debilitating and delay debilitating conditions, and is at the level of "strong recommendation" [48]. PA promotes improvement in physical and mental health, reverses the harmful effects of chronic diseases, and maintains functional autonomy in older adults [49,50], delays the onset of weakness in older adults, and inhibits their progression [51]. PA better controls blood pressure, cholesterol, and waist circumference in a dose-dependent manner, reduces the risk of cardiovascular and metabolic diseases [52], helps maintain cognitive function[53], and helps maintain the number of peripheral motor neurons in the leg muscles [54] improving balance and coordination to reduce the risk of falls [55]. If a fall occurs, people who exercise regularly are less likely to fracture because their bones are stronger and have higher bone density [56]. Sleep deprivation is a risk factor for debilitation in the elderly, and studies have speculated that sleep deprivation will lead to an imbalance of catabolic hormones and anabolic hormones in the body, resulting in an increase in inflammatory factors, which to some extent explains the relationship between sleep and debilitation [57]. In addition, smoking and alcohol consumption had a significant positive effect on the decline of the elderly in the phases 1, 3, 4, and 2 and 3, respectively, and long-term smoking not only aggravated atherosclerosis, but also led to an increase in inflammatory factors in the body, which promoted the occurrence of weakness [41]. The effect of excessive alcohol consumption on weakness may be the effect of ethanol, resulting in bone mass loss and decreased bone production, which can cause osteoporosis and promote the occurrence of weakness [58].
Based on the above findings, the following suggestions are put forward: First, pay attention to the problem of the debilitation of the elderly on the mainland. As the mainland is about to enter the era of moderate aging, this demographic change has made the problem of the elderly debilitated more and more severe, and a series of negative events caused by the weakening will seriously affect the quality of life of the elderly. Therefore, it is necessary to learn from foreign interdisciplinary research models, through multidisciplinary collaboration in geriatrics, exercise science, demography, nursing and other multidisciplinary collaboration, jointly carry out debilitating intervention research, and form a debilitating intervention prescription in different scenarios such as communities, hospitals and nursing homes.
The second is to implement targeted interventions. There are significant gender inequalities and educational inequalities in the weakening of the elderly in the mainland, and the level of weakness and development of women and the elderly with low education levels is not optimistic. Therefore, in addition to focusing on debilitating screening for women and the elderly with low education level, it is also necessary to re-educate the elderly, and popularize health literacy for the elderly in communities, hospitals, nursing homes, and families to improve their cognitive ability, thereby preventing and delaying weakness.
The third is to guide the elderly to develop a healthy lifestyle, reduce smoking and alcohol consumption, and carry out medium and high PA to improve the quality of sleep. Therefore, it is necessary to widely carry out the knowledge of healthy lifestyle on the prevention and treatment of debilitation, and provide a suitable sports environment and fitness environment, and actively guide the majority of the elderly to increase PA.
Although this study explores the development trajectory of the elderly in the mainland and enriches the research in the field of continental weakness, there are still many areas for improvement. On the one hand, the factors affecting the debilitation of the elderly are not comprehensive, and only 4 time change factors (PA, smoking, alcohol consumption, sleep) and 2 time constant factors (education level, gender) are examined. On the other hand, the impact of the sample of dead older adults has not been more excluded. Therefore, future studies can further integrate other variables to comprehensively investigate the effects and mechanisms of these factors on the debilitation of the elderly, and use more comprehensive models and statistical methods to exclude the impact of the dead elderly sample and dig deeper into the meaning behind the data.
There is no conflict of interest in this article.
The table in this article is omitted.
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The development trajectory of debilitating elderly people in mainland China: an analysis based on the growth model of latent variables