Introduction
With increasing age, the susceptibility to age-associated cognitive impairment increases [
1]. Concerningly, the demographic profile of the world population is shifting towards older age with improved life expectancy. As of 2020, older Australians (aged ≥ 65 years) comprised 16% of the total Australian population and is projected to increase to 20.7% by 2066 [
2]. This unprecedented growth of population ageing presents significant challenges relating to healthcare, independence, social, and community interaction among the elderly.
Ageing is associated with cognitive impairment ranging from mild impairment to dementia (severe impairment). Such impairment is a major cause of dependency and disability in the elderly. The age at which cognitive abilities decline is subject to debate [
3‐
6]. However, longitudinal data has shown cognitive decline is evident at all ages between 45 and 70 years, with an accelerated decline in the oldest age groups [
7]. Amongst older Australians (aged ≥ 65 years), the estimated prevalence of cognitive impairment ranges from 7.7 to 33.3% [
8,
9]. Globally, the prevalence of cognitive impairment in those aged ≥ 50 years, ranges from 5.1 to 41.0%, with a median prevalence of 19.0% [
10]. It is estimated that the prevalence of severe cognitive impairment will be 82 million in 2030, and will rise to 152 million by 2050 [
11,
12].
There is well-established literature on the vital role that cognitive ability occupies in an individual’s daily functioning. Cognitive ability is defined as “the skills involved in performing the tasks associated with perception, learning, memory, understanding, awareness, reasoning, judgment, intuition, and language” [
13]. The degree of cognitive impairment may range from mild deficits in memory, language, executive functioning, or visuospatial capabilities [
14] to clinically significant deficits associated with other pathologies, such as dementia and Alzheimer’s disease [
15,
16]. Given the complex multitude of influences on cognitive impairment, a multi-dimensional measure of health-related outcomes is needed to assess health and well-being.
HRQoL is a multi-dimensional construct that encompasses physical, mental, emotional, and social functioning [
17]. It provides a broad summary of overall health status by incorporating elements that have been shown to affect health. Self-reported health outcomes can be measured accurately using generic non-preference and preference-based quality of life instruments, allowing for meaningful comparisons between healthy and clinical populations [
18]. However, it is argued that condition-specific preference-based measurements may be more successful than a generic preference-based measure of HRQoL because they are more responsive to a particular health condition [
19].
The association between cognitive impairment and HRQoL has been previously investigated, yielding conflicting findings. Notably, a number of these studies were undertaken in specific age groups and settings. Cognitive impairment has been associated with lower HRQoL in the Chinese (utilised EQ-5D) [
20], Swedish (utilised EQ-5D) [
21], and Turkish (utilised CDC HRQOL-4) [
22] elderly. Other studies have shown similar findings in older adults with ailments, such as Alzheimer’s disease (utilised Quality of Life–Alzheimer’s Disease Scale) [
23], dementia (utilised Qualidem) [
24], and neurological disease [
25]. Conversely, other evidence suggests that HRQoL is not impacted by cognitive impairment in nursing home residents (utilised Nursing Home Vision-Targeted Health-Related Quality of Life Questionnaire, VF-14, and the SF-36) [
26], dementia patients [
27] or institutionalized older Canadians (utilised EQ-5D-3L) [
28]. One study of older Belgian adults found that lower HRQoL (using Alzheimer's Disease Related Quality of Life) was associated with dementia, rather than mild cognitive impairment (MCI) [
29]. These inconsistent findings may be attributed to the subjective nature of HRQoL, instruments used, statistical methods and settings. Thus, a reliable comparison of findings across studies is challenging. Further, research on the complex interaction between degrees of cognitive impairment and HRQoL and how it evolves over time is needed across a broader spectrum of older age. This calls for further longitudinal research to elucidate how cognitive impairment impacts HRQoL over time.
The unprecedented proportional population growth of the elderly, in conjunction with the negative association between cognitive impairment and HRQoL, will pose significant logistical and financial healthcare challenges in the future. It is thus paramount that further research be conducted into developing medical treatments and funding preventative public health measures to address the cognitive decline. The current study will examine the association between cognitive impairment and HRQoL among older Australians (≥ 50 years old) using longitudinal data from the Household, Income and Labour Dynamics (HILDA) survey. Understanding the association between cognitive impairment and HRQoL may assist in identifying effective measures to support healthy ageing. It may also serve to optimize how resources are distributed into conserving and/or relieving cognitive impairment symptoms and subsequently, improving and maintaining HRQoL amongst the elderly.
Discussion
This study examined the association between cognitive impairment with HRQoL in a sample of older Australians, using data from the HILDA survey. The findings indicated that majority of older Australians do not have cognitive impairment (89%). However, 10% and 1% had moderate and severe cognitive impairment, respectively. Similar levels of cognitive impairment amongst older Australians (aged ≥ 65 years) have been documented elsewhere [
8,
9].
We measured HRQoL by the generic non-preference based measure (SF-36) and generic preference based measure (SF-6D). Generic tools have been criticised for not being sensitive for cognition. However, our results indicate that possibly SF-36 is a suitable tool for measuring change in quality of life related to cognitive status. The study showed strong evidence of an association between moderate and severe cognitive impairment with lower HRQoL concerning physical function (PCS) and the SF-6D. With regards to mental function (MCS), there was a strong association between moderate cognitive impairment and lower HRQoL after adjusting for confounding factors. A similar association between cognitive impairment and low HRQoL has been observed in older community-based participants from Australia [
40], the United States [
40], Japan [
41], China [
20] and Sweden [
21]. Cognitive impairment has been associated with lower ratings of physical and mental components of HRQoL [
20,
21]. Amongst community-dwelling older people, a 10-unit higher PCS and MCS score was associated with a 12% and 6% decreased risk of cognitive impairment, respectively [
40]. Alternatively, no association was found between cognitive impairment and HRQoL in a community-dwelling sample of older adults [
28]. However, this was attributed to the use of EuroQOL (EQ-5D) to measure HRQoL which does not contain a domain related to cognition, and thus may not be well associated with the cognitive impairment screening measure (i.e., Montreal Cognitive Assessment) used in the study [
42,
43]. This issue has been previously debated [
44]. Furthermore, as demonstrated in this study, severe cognitive impairment was associated with lower HRQoL scores compared to mild cognitive impairment in other studies [
21,
29]. The concordance in community-based findings across various samples and this study’s findings add weight to the association between cognitive impairment and HRQoL.
Additionally, this study revealed differing strengths in associations between the mental and physical components of HRQoL with cognitive impairment. MCS and PCS scores assess unique aspects of self-reported HRQoL, which may account for differing associations with cognitive impairment. The current study illustrated that cognitive impairment was negatively associated with PCS and MCS, albeit the negative association between severe cognitive impairment and lower MCS scores was not statistically significant. In contrast, previous research on healthy older adults have shown differing results on the mental and physical components of HRQoL. A cross-sectional study on healthy older adults (aged 55 and older) showed a negative association between the physical component of HRQoL with age but a positive association between the mental component of HRQoL with age [
45]. This is in line with theoretical views that older adults may maintain mental well-being despite objective health losses given that they possess self-regulatory mechanisms [
46].
A plausible reason for the association between cognitive impairment and low HRQoL is the perception of poor health. The deterioration of HRQoL amongst the cognitively impaired elderly has been primarily attributed to loss of autonomy, which affects the ability to complete basic daily tasks independently [
47,
48]. Cognitively impaired individuals may experience frustrations from the inability to complete basic daily tasks independently [
48]. Therefore, individuals may experience heightened depression, anxiety, and dysfunctional social interactions [
49], which may further deteriorate HRQoL. Prior studies have highlighted that older adult with cognitive impairment has a higher likelihood of reporting problems in HRQoL dimensions such as pain/discomfort, and anxiety/depression [
20,
50]. Thus, it has been argued that an awareness of cognitive impairment may lead to distress as a reaction to declining cognition in elderly adults [
50]. Independent of the severity of cognitive impairment, older adults aware of a mild cognitive impairment diagnosis and its prognosis had lower HRQoL, and more difficulties with daily functioning than those who were unaware [
51]. Conversely, if individuals with cognitive impairment do not perceive their functioning as impaired, they may report a non-deteriorated HRQoL. Given that individuals assess their HRQoL by comparing their expectations and experience, perceived functional limitations may lower HRQoL [
52]. Therefore, promoting practical coping strategies could improve HRQoL among cognitively impaired older adults. However, as cognitive impairment is associated with functional decline and multiple morbidities [
53], it may curtail compensatory mechanisms amongst cognitively impaired elderly to maintain good mental well-being in contrast to their healthier, community-dwelling counterparts.
This study adds to the existing evidence that cognitive impairment is associated with low HRQoL in older adults. The findings highlight the importance of sustaining the cognitive ability to improve HRQoL in healthy ageing, particularly for the physical component of HRQoL. This presents a public health opportunity for policymakers to design and implement strategies that will maintain cognitive functioning or relieve symptoms associated with cognitive decline. It is hoped that such efforts may meaningfully improve HRQoL and potentially prevent individuals from declining further into severe cognitive impairment seen in dementia.
As cognitive decline is associated with functional impairments, this may pose difficulties in fulfilling basic needs, thus leading to poor physical and mental health. Specifically, assisting cognitively impaired older adults with feelings of loneliness, pain and improving the ability to undertake basic activities of daily living may have important implications for mental and physical HRQoL [
54‐
56]. Therefore, promoting purposeful care to address a broad range of modifiable risk factors and encouraging protective factors may be key. These include social engagement, cognitive and physical activity and may serve as an effective strategy to maintain HRQoL with advancing age [
57‐
59]. Besides, there is evidence that a patient empowerment model that considers the patient as the prime member of the health team and care managers who provide services to patients suggested by physicians in the primary health care system is beneficial for improving health outcomes for patients with heart failure and diabetes [
60]. Hence, introducing a patient empowerment model might be helpful to reduce cognitive impairment among older Australians. Moreover, the literature has highlighted the importance of preventative measures in sustaining cognitive function. Recent evidence illustrated that 21.7% of MCI cases that deteriorated in dementia may have been preventable by targeting diet (8.7%), diabetes (1.5%) and neuropsychiatric symptoms (11.5%) [
61]. Furthermore, specific lifestyle factors such as engaging in social and artistic activities during midlife and late were protective against cognitive impairment at ages 85–89 [
62]. This evidence reinforces the need for further research to broadly understand health status across multiple domains, and formulate preventative measures against cognitive deterioration. Thus, by understanding the overall impact of cognitive impairment on HRQoL, cognitive measures may be utilised as a pragmatic tool to identify those who may benefit from such preventative strategies.
A key contribution of this study is the suggested cut-off scores to define cognitive impairment. To the authors’ knowledge, there is limited literature on appropriate cut-off scores that define cognitive impairment using the BDS and SDMT in older adults. BDS cut-off scores ≤ 4 have been used as a threshold to define cognitive impairment in older adults [
36,
63,
64]. Previous research has suggested SDMT cut-off scores between 24 and 40 to define cognitive impairment. However, such research was based on highly specific groups, particularly, those with multiple sclerosis [
34,
65‐
67]. The generated cut-off scores used in the current study generally align with previous literature discussed previously. Nevertheless, the current study suggested a SDMT cut-off score of ≤ 30, which lies in the range suggested by previous studies as discussed. However, the BDS cut-off score of ≤ 3 was more conservative than previous literature, and thus may be less sensitive in capturing milder cognitive impairment. Nevertheless, it is hoped that the suggested cut-off scores for cognitive impairment in the current study may contribute to the limited body of literature on optimal cut-off scores for defining cognitive impairment in the elderly. Much of the previous literature has focused on highly specific samples, yielding inconsistent findings on the association between cognitive impairment and HRQoL. This may be attributed to the heterogeneity in study samples, settings, and methodology. Furthermore, to the authors’ knowledge, majority of studies assessing the association between cognitive impairment and HRQoL have been cross-sectional in nature. Thus, no causative association could be firmly established. By undertaking this longitudinal study in a nationally representative cohort of older Australians, findings on the impact of cognitive impairment on HRQoL may be more generalisable to the normal ageing process and may be applied to future research and policy endeavours.
Strength and weaknesses
A major strength of this study was its large, population-based longitudinal nature, covering a wide spectrum of older age (50 years and older). This study utilised 10,737 person-year observations from 6892 unique individuals, using two waves of the HILDA survey to examine the association between cognitive impairment and HRQoL amongst older Australians. Further, a variety of factors were controlled for, and findings remained robust with adjustment. However, given the design of the study, unmeasured and unknown confounders could exist and impact the results.
The cognitive impairment measures (SDMT and BDS) are also validated and have good utility in reflecting core constructs of cognitive ageing and impairment. The use of validated tools facilitates the comparison of this study’s findings with previous research. A notable limitation of this study was the data collection methods for HRQoL. As HRQoL was self-reported, there is a risk of social desirability bias, leading to an inflation of HRQoL scores. Secondly, there is no consensus on clear cut-off scores for the SDMT and BDS scales to define cognitive impairment. Therefore, these measures are not diagnostic of cognitive impairment and may not accurately capture a full spectrum of cognitive impairment that is clinically significant. Thirdly, chronic conditions and pharmacological treatment are two crucial confounding variables in the relationship between cognitive impairment and HRQoL. Due to the unavailability of chronic conditions and pharmacologic data, the authors can’t include these confounding factors in the multivariate regression. Therefore, the estimated co-efficient could be underestimated or overestimated.
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