With the increasing number of aging individuals in China, it is important to focus on age-related cognitive impairment and its impact on health-related quality of life (HRQoL) among the elderly population. This study aimed to assess the HRQoL of elderly patients with cognitive impairment in China and identify associated factors. Data from the China Health and Retirement Longitudinal Study (CHARLS) in 2018, including 684 patients with cognitive impairment, were analyzed. The diagnosis of cognitive impairment was based on the Minimum Mental State Examination (MMSE), and the EuroQoL Five Dimensions Questionnaire (EQ-5D-3L) was used to measure HRQoL. The results indicated that age, education level, sleep duration, and social activities significantly influenced the HRQoL of these patients. Specifically, patients aged 75 years or older, those with lower education levels, and those with inadequate sleep had lower health utility values, while patients engaged in social activities exhibited higher health utility values. Tobit regression analysis confirmed the substantial impact of age, education level, sleep duration, and activities on health utility values. Overall, the findings revealed a strong association between diminished health-related quality of life and elderly Chinese patients with cognitive impairment. It was concluded that advanced age, lower education level, insufficient sleep, and limited social engagement were influential factors affecting the HRQoL of these patients. Therefore, the study suggested that collaborative efforts among patients, families, hospitals, and social service organizations are essential to enhance the HRQoL and overall physical and mental well-being of elderly patients with cognitive impairment.
Research Article
Health-related Quality of Life and the Influencing Factors in Chinese Elderly Patients with Cognitive Impairment
https://doi.org/10.21203/rs.3.rs-3961970/v1
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Health utility value
Cognitive impairment
EQ-5D-3L
Elder people
Health utility value
As the world's population continues to age, the health of elderly individuals has become an increasingly important topic of study. China has one of the largest ageing populations in the world [1] and previous studies[2] have shown that the elderly population is expected to grow significantly by 2050, with the number of people aged 65 and over reaching 400 million. Aging is a natural and inevitable process that affects multiple aspects of an individual's life. As a social issue that cannot be ignored, many issues related to the Chinese elderly have yet to be researched, such as healthcare facilities, factors affecting the quality of life, and various related diseases. Physiological changes associated with aging can lead to cognitive impairment and some other related diseases.
Cognitive impairment [3]is a decline in cognitive function that beyond what is considered a normal age-related change. It is the most consistent neurological complication of acquired and degenerative brain disorders[4], and can manifest as difficulties with memory, attention, problem-solving, language, and other cognitive abilities. It is estimated that a great amount of elderly people in China experience some form of cognitive impairment, ranging from mild cognitive impairment (MCI) to severe cognitive impairment (dementia). [5] Epidemiological surveys have reported that the prevalence of mild cognitive dysfunction in people over 60 years of age in China is approximately 14.71%, and the overall prevalence of dementia in mainland China to be 5.14%.[5, 6] In addition to its high prevalence, cognitive impairment can lead to difficulties in performing daily activities, social interactions, and maintaining relationships, resulting in a reduced quality of life.
Considering the intricate nature of the impact of cognitive impairment, health-related quality of life (HRQoL) is a requirement for a thorough assessment of patients. HRQoL is a multi-dimensional construct that encompasses physical, mental, emotional, and social functioning [7] and provides a comprehensive assessment of one's general health condition by considering the factors that have proven to influence health. Health utility value is a comprehensive index that express people's preference for a certain health state and reflects individual health status. It is a quantitative indicator of HRQoL.[8] There are both non-preference and preference-based, self-reported health outcomes can be accurately assessed, facilitating meaningful comparisons between populations with different health statuses, including healthy individuals and those with medical conditions.[9] Several studies[10–12] demonstrated the association between cognitive impairment and lower HRQoL, especially in patients with severe dysfunction. Understanding the specific factors that influence HRQoL in this population becomes essential for developing effective interventions and improving overall outcomes. Rabbitt et al. have suggested that risk factors from both socio-demographic and lifestyle have impact on cognitive function.[13] Some previous studies have confirmed this idea. For example, factors such as female gender[14], senior age bracket[15], alcohol consumption[16], and social activities[17] may be risk factors for cognitive impairment. However, the factors influencing HRQoL in Chinese elderly patients with cognitive impairment are not yet fully understood and remain ambiguous.
Therefore, this study employed the data from the fourth phase of the China Health and Retirement Longitudinal Study (CHARLS, 2018), a survey-based research conducted on the population of mainland China. The purpose of this research was to investigate the HRQoL by measuring health utility values with the 3-level version of the EuroQoL Five Dimensions Questionnaire (EQ-5D-3L), and analyze its influencing factors among elderly patients with cognitive impairment in China.
1. Data sources
The data was obtained from the 2018 China Health and Retirement Longitudinal Study (CHARLS), a high-quality study aimed at analyzing the current situation of ageing in China and promoting the development of ageing issues, with questionnaires and face-to-face interviews with middle-aged and older people aged 45 and above and their families in a multi-stage sample of 150 counties and 450 communities or villages in 28 provinces across China. [18]The survey included a wide range of information on personal information, economy, health and so on, and the survey method was multi-stage sampling, with PPS sampling at both the county/district and village sampling stages. The data was released on 23 September 2020, which includes the latest nationally representative survey data on middle-aged and elderly people with cognitive impairment in China. Therefore, we chose the data from the CHARLS 2018.
2. Study population
This study was conducted on older adults aged 55 years and older from CHARLS 2018. Data from the modules " Demographic Backgrounds", "Health Status and Functioning" and "Cognition and Depression" were selected for this study.
In the part of “Demographic Backgrounds”, gender, age, address, the highest level of education and marital status were considered. It should be noted that age was classified as 55–64,65–74,75 and above. Address included village or city/town. The highest level of education included “No formal education (illiterate)”, “Elementary school or did not finish primary school”, “Middle school and above”. Meanwhile, marital status marital status was divided into “married” and “others”. Secondly, in the “Health Status and Functioning” part, smoking and drinking status, Sleep duration, nap, activities were selected. Sleep duration was classified as insufficient, normal, or excessive according to the criteria of < 7h, 7 ~ 9h, and > 9h.[19] In addition, We calculated and used the MMSE scores from the "Cognition and Depression" module. 921 patients were diagnosed with cognitive impairment, and 684 valid samples were finally obtained by deleting samples with missing information on important variables.
Participants were excluded if they met any of the following criteria:(1) aged below 55 years, (2) did not finish the Minimum Mental State Examination (MMSE), (3) did not meet a diagnosis of cognitive impairment (4) incomplete information on EQ-5D-3L. (Fig. 1)
3. MMSE
The Mini-Mental State Examination (MMSE) is a widely used cognitive function assessment tool, and is known for its high degree of reliability and validity when used by general population or Chinese population.[20, 21] In this study, the MMSE was used to assess the cognitive function of older adults, measuring orientation, memory, attention, computation, and language. Higher scores indicate better cognitive function. In addition, MMSE thresholds consisted of 17 (illiterate participants), 20 (participants with elementary school or did not finish it), and 24 (participants with Middle school and above) based on the previous studies.[22, 23] Meanwhile, cognitive impairment was defined as those whose MMSE score was below the set threshold according to the level of education.
4. EQ-5D-3L
Recently, the 3-level version of the EuroQoL Five Dimensions Questionnaire (EQ-5D-3L) has been widely used to measure HRQoL and showed good reliability and validity in Chinese population.[24, 25]
The EQ-5D-3L questionnaire consists of five dimensions, including “Mobility”, “Self-Care”, “Usual Activities”, “Pain or Discomfort”, and “Anxiety or Depression”. Each dimension consists of three levels: “have no problems/be not,” “have some/moderate problems,” “have extremely problems/unable to.”[24, 26] Based on the meanings of the five dimensions, the corresponding questions were selected in the two modules of the CHARLS questionnaire, namely "Health Status and Functioning" and "Cognition and Depression". The correspondence between the questions of the CHARLS questionnaire and the dimensions of the EQ-5D-3L selected for this study is shown in the Table 1.
Based on a value set, we can convert EQ-5D states to health utility value, which was calculated using the utility value integral system constructed according to the preferences of the Chinese population. The calculation formula is: Health utility value = 1 - the sum of the corresponding level coefficients of each dimension.[25] Table 2. shows the coefficients of different levels for each dimension. Notably, the answer options for the questions in the CHARLS questionnaire are based on four items: no difficulty, can still be completed autonomously if there is difficulty, difficulty requires help from others, and cannot be completed. We combined the second and third options corresponding to the second level in the EQ-5D-3L "have some/moderate problems ". The worst-case state of any problem in each dimension (“have extremely problems/unable to.”> “have some/moderate problems,” > “have no problems/be not,”) is the final state for this dimension.
|
Dimension |
Level |
Coefficient |
|---|---|---|
|
Mobility |
have no problems/be not |
0.0000 |
|
have some/moderate problems |
0.0766 |
|
|
have extremely problems/unable to |
0.2688 |
|
|
Self-Care |
have no problems/be not |
0.0000 |
|
have some/moderate problems |
0.0441 |
|
|
have extremely problems/unable to |
0.2912 |
|
|
Usual Activities |
have no problems/be not |
0.0000 |
|
have some/moderate problems |
0.0370 |
|
|
have extremely problems/unable to |
0.0538 |
|
|
Pain or Discomfort |
have no problems/be not |
0.0000 |
|
have some/moderate problems |
0.0274 |
|
|
have extremely problems/unable to |
0.0409 |
|
|
Anxiety or Depression |
have no problems/be not |
0.0000 |
|
have some/moderate problems |
0.0359 |
|
|
have extremely problems/unable to |
0.1771 |
5. Statistical analysis
We applied SPSS 26.0 (SPSS Inc., Chicago, IL, US) for statistical analysis. Firstly, we tested all the health utility values of elderly patients with cognitive impairment by Kolmogorov-Smirnov method. It indicated that the health utility values did not conform to normal distribution, thus Median (P25, P75) was used to statistically describe it. Univariate analyses were performed by Mann-Whitney test or Kruskal-Wallis test.
Additionally, Since the health utility value as a dependent variable has a limited range of values and has a strong ceiling effect[27], in other words, it’s a restricted dependent variable with the characteristic of being intercepted. In order to deal with this situation, we conducted regression analyses using the Tobit regression model [28] to analyze the influencing factors of health-related quality of life. To solve the error caused by the ceiling effect, we assumed that an elderly cognitively impaired patient with a health utility value of 1 has a true value greater than or equal to 1. We used \({Y}_{i}\) to denote the utility value of a particular individual \(i\) computed by the scale, and \({Y}_{i}^{*}\) to denote the actual utility value of the individual \(i\), thus the Tobit model can be expressed as:
We used the Tobit regression model with Stata MP 17.0 (StataCorp LLC, Texas, US), \(\alpha\)=0.05. In all the results, p < 0.05 was used as the criterion for statistically significant differences.
|
Serial number |
Dimension |
Code of questions |
The content of questions |
|---|---|---|---|
|
1 |
Mobility |
DB002 |
Do you have difficulty with walking 1 km? |
|
2 |
DB003 |
Do you have difficulty with walking 100 meters? |
|
|
3 |
DB004 |
Do you have difficulty with getting up from a chair after sitting for a long period? |
|
|
4 |
DB006 |
Do you have difficulty with stooping, kneeling, or crouching? |
|
|
5 |
Self-Care |
DB010 |
Because of health and memory problems, do you have any difficulty with dressing? |
|
6 |
DB011 |
Because of health and memory problems, do you have any difficulty with bathing or showering? |
|
|
7 |
DB012 |
Because of health and memory problems, do you have any difficulty with eating, such as cutting up your food? |
|
|
8 |
DB013 |
Do you have any difficulty with getting into or out of bed? |
|
|
9 |
DB014 |
Because of health and memory problems, do you have any difficulties with using the toilet, including getting up and down? |
|
|
10 |
Usual Activities |
DB016 |
Because of health and memory problems, do you have any difficulties with doing household chores? |
|
11 |
DB017 |
Because of health and memory problems, do you have any difficulties with preparing hot meals? |
|
|
12 |
DB018 |
Because of health and memory problems, do you have any difficulties with shopping for groceries |
|
|
13 |
DB019 |
Because of health and memory problems, do you have any difficulties with managing your money, such as paying your bills, keeping track of expenses, or managing assets? |
|
|
14 |
Pain or Discomfort |
DA041_W4 |
Are you often troubled with any body pains? |
|
15 |
Anxiety or Depression |
DC009 |
I was bothered by things that don’t usually bother me |
|
16 |
DC010 |
I had trouble keeping my mind on what I was doing |
|
|
17 |
DC011 |
I felt depressed |
|
|
18 |
DC014 |
I felt fearful |
|
Dimension |
Level |
Coefficient |
|---|---|---|
|
Mobility |
have no problems/be not |
0.0000 |
|
have some/moderate problems |
0.0766 |
|
|
have extremely problems/unable to |
0.2688 |
|
|
Self-Care |
have no problems/be not |
0.0000 |
|
have some/moderate problems |
0.0441 |
|
|
have extremely problems/unable to |
0.2912 |
|
|
Usual Activities |
have no problems/be not |
0.0000 |
|
have some/moderate problems |
0.0370 |
|
|
have extremely problems/unable to |
0.0538 |
|
|
Pain or Discomfort |
have no problems/be not |
0.0000 |
|
have some/moderate problems |
0.0274 |
|
|
have extremely problems/unable to |
0.0409 |
|
|
Anxiety or Depression |
have no problems/be not |
0.0000 |
|
have some/moderate problems |
0.0359 |
|
|
have extremely problems/unable to |
0.1771 |
1. Basic information
This study included 684 elderly cognitively impaired patients with a median health utility value of 0.815 and an interquartile range (IQR) of 0.234, with a range of (0.187-1.000). The patients were unevenly distributed geographically, with 74.3% of the patients residing in rural areas. The proportion of male patients was 56.4% and 43.6% of female patients. The average age was (69.24 ± 7.81) years, with 34.9% aged 55–64 years, 38.6% aged 65–74 years, and 26.5% aged over 75 years. Educational level was 15.2% for those who had no education, 37.6% for primary school and below, and 47.2% for secondary school and above.49.1% of the patients had insufficient hours of sleep, 49.56% had never smoked, 46.9% had never consumed alcohol, 35.8% had no siesta habits, and 51.4% had no social activities. More details are given in Table 3.
| Characteristic | Category | \(n\) | \(\%\) | Health utility value (IQR) | \(p\) value | |
|---|---|---|---|---|---|---|
| Gender | < 0.001 | |||||
| Male | 386 | 56.4 | 0.847(0.242) | |||
| Female | 298 | 43.6 | 0.782(0.250) | |||
| Address | 0.529 | |||||
| City/town | 176 | 25.7 | 0.816(0.253) | |||
| Village | 508 | 74.3 | 0.815(0.237) | |||
| Marital status | 0.036 | |||||
| Married | 524(1) | 76.6 | 0.823(0.228) | |||
| Others | 160(39) | 23.4 | 0.772(0.280) | |||
| Smoke | 0.019 | |||||
| Still smoking | 216(28) | 31.3 | 0.847(0.267) | |||
| Quit | 129 | 18.9 | 0.843(0.242) | |||
| Never | 339 | 49.6 | 0.796(0.231) | |||
| Drink | 0.145 | |||||
| Still drinking | 301 | 44.0 | 0.838(0.255) | |||
| Quit | 62 | 9.1 | 0.757(0.256) | |||
| Never | 321 | 46.9 | 0.806(0.229) | |||
| Age (y) | 0.002 | |||||
| 55–64 | 239 | 34.9 | 0.847(0.242) | |||
| 65–74 | 264 | 38.6 | 0.808(0.244) | |||
| ≥ 75 | 181 | 26.5 | 0.746(0.273) | |||
| Education | < 0.001 | |||||
| No formal education (illiterate) | 104 | 15.2 | 0.712(0.222) | |||
| Elementary school or did not finish it | 257 | 37.6 | 0.803(0.228) | |||
| Middle school and above | 323 | 47.2 | 0.847(0.239) | |||
| Sleep duration | < 0.001 | |||||
| Normal | 336 | 49.1 | 0.847(0.221) | |||
| Insufficient | 271 | 39.6 | 0.759(0.222) | |||
| Excessive | 77 | 11.3 | 0.847(0.294) | |||
| Nap | 0.834 | |||||
| Yes | 439 | 64.2 | 0.815(0.235) | |||
| No | 245 | 35.8 | 0.816(0.236) | |||
| Activity | 0.035 | |||||
| Yes | 315 | 48.6 | 0.843(0.214) | |||
| No | 369 | 51.4 | 0.796(0.285) | |||
2. Univariate analysis of health-related quality of life influencing factors
As shown in Table 3, the results of univariate analysis showed that there were statistically significant differences in EQ-5D-3L health utility values between groups in terms of gender, age, education, marital status, sleep duration, smoking status, and activities.
3 Tobit regression analysis of health-related quality of life and its associated factors.
We applied Stata MP 17.0 (StataCorp LLC, Texas, US) to conduct the Tobit regression analysis, \(\alpha\)=0.05. It was constructed with the health utility value as the dependent variable, and Gender, Marital status, Age, Smoking, Education, Sleep duration, and Activities as the independent variables. As shown in Table 4, according to the partial regression coefficients, the effects of age, education level, sleep duration and activities on the health utility value were statistically significant. With regard to age, patients aged ≥ 75 years had lower health utility values compared to patients aged 55–64 years (regression coefficient = -0.046, \(\varvec{p}\)<0.05). Regarding education, patients with “Middle school and above” had higher health utility values than patients who had “No formal education or illiterate” (regression coefficient = 0.052, \(\varvec{p}\)<0.05). Patients who did not sleep enough at night had lower health utility values than those who slept normally (regression coefficient = − 0.051, \(\varvec{p}\)<0.001). Finally, patients with social activities had higher health utility values (regression coefficient = 0.031, \(\varvec{p}\)<0.05).
| Characteristic | Category | Coefficient | [95% conf. interval] | \(\varvec{p}\) value |
|---|---|---|---|---|
| Age (Guideline = “55–64”) | ||||
| 65–74 | -0.019 | -0.007, 0.061 | 0.228 | |
| ≥ 75 | -0.046 | -0.082, -0.010 | 0.012 | |
| Education (Guideline = “No formal education or illiterate”) | ||||
| Elementary school or did not finish it | -0.017 | -0.046, 0.012 | 0.248 | |
| Middle school and above | 0.052 | 0.011, 0.093 | 0.013 | |
| Sleep duration (Guideline = “Normal”) | ||||
| Insufficient | -0.051 | -0.079, -0.023 | < 0.001 | |
| Excessive | 0.004 | -0.039, 0.047 | 0.859 | |
| Activities (Guideline = “No”) | ||||
| Yes | 0.031 | 0.004, 0.057 | 0.024 | |
| _cons | 0.744 | 0.686, 0.803 | < 0.001 |
In this study, we screened 684 subjects that met the inclusion criteria from the China Health and Retirement Longitudinal Study (CHARLS) database 2018. 6.3% of the patients with cognitive impairment had the theoretically largest health utility value (HRQoL = 1), suggesting that there was a ceiling effect of the EQ-5D-3L, and we therefore used a Tobit model for multifactorial regression. The median health utility value of the 684 patients was 0.815. A national cross-sectional study found that the overall prevalence of dementia was 6.0%, representing 15.07 million adults aged 60 years or older with dementia in China. [29] Previous research showed that in comparison to people who are cognitively intact, people with cognitive impairment have lower health utility values. From mild to moderate or severe stages of cognitive impairment, there is a decline in health utility. [30] Thus, in addition to the degree of cognitive impairment, it may be important to analyze the influencing factors of the health utility value of elderly people with cognitive impairment.
We found that higher age, lower education level, lack of sleep at night, and lack of social activities are all risk factors for health-related quality of life in patients with cognitive impairment. One in six individuals on the planet will be 60 years of age or older by 2030. At this point, there will be 1.4 billion people over the age of 60, up from 1 billion in 2020. The number of individuals in the world who are 60 years of age or older is expected to double (to 2.1 billion) by 2050. [31]Geriatric syndromes such as decline in organ function, co-morbidities, cognitive dysfunction, anxiety and depressive mood, and nutritional dysfunction all negatively affect the health-related quality of life of patients. [32] It is suggested that elderly patients with cognitive impairment should be assessed for timely nutritional interventions, psycho-social cognitive functioning, and anxiety and depression symptoms to detect health problems as early as possible and address them in a timely manner.
The present study demonstrated that patients with lower education levels usually have lower health utility values. Among older adults, lower education levels are linked to greater prevalence of chronic illnesses, disabilities, and poor general health. [33–35] A lower quality of life may result from this. In addition, our study also found that the absence of social activities was another factor affecting the quality of life of cognitively impaired patients. Education can influence social engagement, social networks, and social support systems. Older adults with low education levels may have fewer social connections and reduced participation in social activities, leading to feelings of isolation and lower subjective well-being.[36, 37] Research on older individuals' mental health has shown that social activities can help prevent mental health problems including anxiety and depression. [38] Frequent participation in social activities can lessen feelings of loneliness, elevate psychological well-being, and boost mood. [39] Also, participating in socially stimulating activities, such games, learning new skills, or group conversations, can improve cognitive functioning and reduce the chance of cognitive decline in later life. Consequently, this has a favorable effect on the general standard of living. It suggests that we should pay attention to the quality of life of elderly cognitively impaired patients with lower education levels and the absence of social activities as early as possible, and pay attention to their health status in time and follow up more frequently.
We found that among old people with cognitive impairment, sleep deprivation significantly affects health-related quality of life. Firstly, physical health is greatly impacted by either insufficient or poor-quality sleep. [40] Chronic sleep deprivation raises the risk of falls, impairs immunological function, increases frailty, and causes cardiovascular issues, all of which can have a detrimental effect on this population's health-related quality of life. [41, 42] Secondly, the effects of sleep deprivation can affect mental health. [42] Among those with cognitive impairments, sleep abnormalities are linked to increased agitation, disorientation, and behavioral problems. These disturbances may exacerbate cognitive aging and add to the stress of caring for others. Improvements in mood, cognition, and general mental health can be attained by addressing sleep-related problems, which will have a favorable effect on health-related quality of life. Healthcare providers can improve the health-related quality of life in this vulnerable population by implementing methods to improve sleep hygiene, creating a comfortable sleep environment, and offering appropriate therapies for sleep disorders.
However, this study has a few limitations. First, since the study is cross-sectional, it is challenging to determine a causal association between the factors and the health-related quality of life in older persons with cognitive impairment. Therefore, in order to verify the causality, prospective cohort studies had to be carried out. Second, doubts remained on whether any other factors had an impact on the MMSE scores altogether. We only rate based on their performance and answers, and exclude those who cannot answer. Therefore, we may have overlooked some individuals with cognitive impairments. Third, EQ-5D-5L, a single general instrument, was used to evaluate HRQoL. Additionally, individuals with severe dementia may not be able to reliably report their subjective HRQoL due to impairments in cognitive function, communication memory, perception of time, and insight.[30]
In conclusion, the majority of elderly patients with cognitive impairment experience a lower health-related quality of life. Factors such as advanced age, lower education level, inadequate nighttime sleep, and a lack of social activities significantly impact the health-related quality of life in this patient group. Therefore, it is imperative for patients, families, hospitals, and social service organizations to collaborate on implementing integrated strategies and initiatives aimed at enhancing patients' understanding of the disease and promoting healthier lifestyles to address controllable risk factors. These efforts are crucial for enhancing both the physical and mental well-being of elderly patients with cognitive impairment and improving their overall health-related quality of life.
Author Contribution
Wanting Ma: Conceptualization, Methodology, Software, Data processing, wrote the main manuscript text. Weijia Wang: Data processing, data collection, wrote part of the manuscript. Yuanwen Zou: Supervision. All authors reviewed the manuscript.
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No competing interests reported.