Disability-Adjusted Life Expectancy and Cognitive Function among Community-Dwelling Adults

Abstract

We aimed to calculate disability-adjusted life expectancy (DALE) for Korean older adults based on their sex, educational attainment, and residential region across their cognitive status. We included 3,854 participants (aged 65–91 years) from the Korean Longitudinal Study of Aging’s seventh survey data. The participant’s cognitive function status (normal, moderately impaired, or severely impaired) was determined based on cognitive examination and physical function independence, which was used to calculate their DALE. Females with normal cognition had higher DALE (7.60 years, Standard Deviation (SD) = 3.88) than males (6.76, SD = 3.40); however, both sexes had comparable DALE for cognitive impairment. In contrast, the DALE values increased with higher educational achievements. Regarding residential areas, the DALE value for participants with normal cognition and moderate impairment was the highest among urban dwellers, while DALE for participants with severely impaired cognitive function was highest among rural dwellers; however, there were no statistically significant differences based on residential conditions. Our findings suggest that demographic characteristics should be considered when developing health policies and treatment strategies to meet the needs of the aging population in Korea.

Keywords

cognition instrumental activities of daily living aging older adult life expectancy

Introduction

As of 2019, the life expectancy in Korea is 80.3 years for males and 86.3 years for females, which is 2.2 years and 2.9 years higher, respectively, than the average reported by the Organization for Economic Co-operation and Development (OECD) (Statistics Korea, 2019). This increase in the life expectancy of the Korean population signifies the success of their national health policies. However, increasing life expectancy also indicates a surge in the proportion of the aging population. In 2020, adults aged ≥65 years comprised 15.7% of Korea’s domestic population; this share is expected to reach 20.3% by 2025, making Korea a super-aged society (Statistics Korea, 2020).

However, an increase in the older population can introduce multiple public health problems. Mild cognitive impairment is often observed with advancing age, which may progress to severe impairment, such as dementia (Liu et al., 2017), which can reduce the quality of life in the elderly (Stites et al., 2017). In addition, people with cognitive impairment require extensive support from family members or caregivers to carry out activities of daily living (Hawkley et al., 2020). Consequently, as the severity of cognitive impairment increases, medical expenses and the burden of support for caregivers also increase (Park et al., 2019).

In recent years, extensive research has been conducted on cognitive impairment in old age to generate primary health policy data. Particularly, studies have investigated cognitive function using disability-free life expectancy (DFLE). Garcia et al. (2019) compared life expectancy in older adults of different sexes from different races grouped as cognitively normal, cognitively impaired/no dementia, and dementia. They reported that the length of life in older adults with dementia was 1.6 years for Whites, 3.9 years for Blacks, 4.7 years for American-born Hispanics, and 6.0 years for foreign-born Hispanics. Furthermore, another study exploring life expectancy among Americans ≥65 years of age with or without dementia found African Americans had a significantly higher prevalence of dementia but tended to live longer than other Americans (Farina et al., 2020). Likewise, when comparing the life expectancy in Mexican older adults with cognitive impairment, it was found that foreign-born Mexican immigrants had a higher total life expectancy than US-born Mexican Americans regardless of sex (Garcia et al., 2018).

Despite listing numerous cognitive risk factors, only a few studies have delineated an association between these risk factors (such as sex, educational attainment, and residential area) and life expectancy across cognitive function (Lee et al., 2020; Saenz et al., 2018). Currently, the urban population in Korea is 47,279, which is about 10 times the rural population (4,550) (e-Country Indicators, 2020). A recent study pointed out that the average educational achievement was 7.12 years for all older adults in Korea, 7.40 years for urban-dwelling older adults, and 5.79 years for rural older adults (Choi et al., 2018). These differences highlight the role of cognitive abilities in affecting life expectancy.

In Korea, the Korean version of the Mini-Mental Status Examination (K-MMSE) is widely used for evaluating cognitive function (Park & Yoo, 2002). The Korean Longitudinal Study on Aging (KLoSA) also used this tool. However, the MMSE has been criticized for its inaccuracy in diagnosing mild cognitive impairment and early dementia (Körver et al., 2019). A potential solution for this is to combine the parameters of K-MMSE with the instrumental activities of daily living (IADL) (Luttenberger et al., 2012; Mejia-Arango & Gutierrez, 2011) since daily living activities are included in the clinical criteria for dementia diagnosis (McKhann et al., 2011). A previous study elucidating the 10 year longitudinal relationship between an individual’s cognitive change and independence in IADLs reported a 16% increase in the risk of IADL damage with every one-word decrease in the word list comprehension assessment (Passler et al., 2020).

Accordingly, this study was conducted to determine the effect of cognitive function on life expectancy and quality of life after developing cognitive disabilities in the Korean population. First, we combined K-MMSE and IADLs to classify subjects according to their cognition into normal, moderate cognitive impairment, and severe cognitive impairment. Next, we determined their disability-adjusted life expectancy (DALE) by sex, educational attainment, and region using the weights for their respective cognitive grades. We believe that the DALE values calculated in this study based on cognitive function can be used as evidence-based data by health policymakers in Korea.

Methods

Study Design and Data Source

This study is a secondary analysis of data from the KLoSA. The KLoSA is a panel survey first conducted in 2006; since then, the survey has been administered every 2 years (Boo & Chang, 2006). The index survey targeted participants aged above 45 years; the initial sample size was 10,254 adults (Boo & Chang, 2006). For the current study, we used the most recent data from the seventh wave survey collected in 2018, wherein 6,940 Korean adults responded; the age range for this data was above 57 years. The survey questions collected information about the health, income, and assets of all household members of the participant (Boo & Chang, 2006). More information about the KLoSA can be found at https://survey.keis.or.kr/.

For our study, we included data from participants aged 65–91 years; accordingly, 3,086 individuals (age <65 years) were excluded. A final 3,854 participants were included in the study and divided into three groups according to cognitive function status: normal (n = 2,392), moderate cognitive impairment (n = 1,113), and severe cognitive impairment (n = 349) (Downer et al., 2016; McKhann et al., 2011; Mejia-Arango & Gutierrez, 2011; Nam et al., 2021). The KLoSA data does not contain proxy responses to the K-MMSE and IADL items; hence, our analysis also included items directly reported by the study participants. The KLoSA databases are publicly available; however, participants’ confidential health information was disclosed by the Korea Employment Information Service (Boo & Chang, 2006).

The protocol for our study was reviewed by the local Institutional Review Board; since it is a secondary analysis, the board exempted us from the need for written informed consent.

Defining Cognitive Function Status

The cognitive function was investigated using the K-MMSE which assesses the subject’s (1) orientation to time, (2) orientation to place, (3) registration, (4) attention and calculation, (5) recall, (6) language, and (7) visual construction (Kang et al., 1997). The total score was 30 points; a score of ≥24 signified normal cognitive function (Folstein et al., 1975). The K-MMSE has been reported to have an inter-rater reliability of 0.96 and test-retest reliability of 0.86 (Na et al., 1999).

Next, the participant’s physical function was investigated by the number of IADLs requiring external assistance. The KLoSA data contained 10 IADL entries – (1) grooming (brushing teeth, combing hair, maintaining personal hygiene, etc.); (2) routine housework (such as cleaning); (3) preparing food; (4) washing laundry; (5) traveling to short distances; (6) using transportation; (7) managing assets; (8) buying things; (9) making and receiving phone calls; (10) taking medication.

Finally, both assessments were combined to determine cognitive functional status which was classified as normal cognitive status, moderate cognitive impairment, or severe cognitive impairment. A K-MMSE score of ≤24 was classified as cognitive dysfunction, and requiring help in >1 IADLs was classified as a physical dysfunction. Participants without any cognitive dysfunction (K-MMSE score ≥24) were classified as having normal cognitive status regardless of their IADL status. Those with cognitive dysfunction but without physical dysfunction were classified as having moderate cognitive impairment, and those with both cognitive and physical dysfunction were classified as having severe cognitive impairment. These classification criteria are consistent with other studies (Downer et al., 2016; Nam et al., 2021) and clinical diagnostic criteria (McKhann et al., 2011).

Definition of Disability-Adjusted Life Expectancy and Disability Life Expectancy

This study used Sullivan’s method (Sullivan, 1971) to analyze differences in DALE across different grades of cognitive function stratified according to sex (male/female), educational attainment (elementary school, middle school, high school, or college degree), and regional subgroups (urban, suburban, or rural). DALE was developed in the World Health Organization Burden of Disease Study in the late 1990s. DALE refers to the remaining life expectancy that a person lives in a healthy state after adjusting for years lost due to disability compared to the average life expectancy of a population group.

In this study, the 2018 life table from KLoSA provided by Statistics Korea was used to calculate DALE. The life table includes the number of survivors and the stationary population stratified by age, which is essential to calculate the DALE. In addition, to obtain the weight required for the DALE calculation, the disability rate ( $D_{x}$ ) for each cognitive impairment grade was calculated. The disability rate ( $D_{x}$ ) was obtained by dividing the number of people with each of the three grades of cognitive function status by the total number of people in the study. Using these values, DALE was calculated as: First, the number of survival years ( $L_{x}$ ) in the life table for each age field was multiplied by the weight for each cognitive impairment to obtain the number of years of survival for each disability ( $L_{x}^{*}$ ) (equation (1)). Second, the total number of years of survival ( $T_{x}^{*}$ ) was calculated using the $L_{x}^{*}$ value based on the life table calculation method (equation (2)). Finally, DALE for each cognitive status grade ( $e_{x}^{*}$ ) was obtained by dividing $T_{x}^{*}$ by the number of survivors ( $I_{x}$ ) in the corresponding age group (equation (3)). Expressing this as a formula

L_{x}^{*} = L_{x} * D_{x}

(1)

T_{x}^{*} = \sum L_{x}^{*}

(2)

e_{x}^{*} = T_{x}^{*} / I_{x}

(3)

We also aimed to calculate life expectancy (LE) by different demographic characteristics; however, the 2018 life table includes only the stationary population values according to sex, so we only calculated LE according to sex.

Statistical Analysis

The Sullivan method calculates LE for both normal cognitive status and moderate and severe cognitive impairment. Statistical analysis of descriptive statistics was performed using Student’s t-test or Analysis of Variance (ANOVA) for continuous variables and the Chi-square test for categorical variables. A t-test or ANOVA was used as applicable to determine the difference in DALE according to sex, educational attainment, and region using cognitive weighting. We also used the t-test and Chi-square test to determine the possible differences in the demographic characteristics of the participants, including age, sex, residential area, educational attainment, marital status, employment status, weight status, self-rated health, drinking, smoking, diabetes, hypertension, and stroke. All statistical analysis was done using a two-sided hypothesis, and a p-value <0.05 was considered statistically significant. SAS version 9.4 software was used to perform all statistical analyses.

Results

The average LE calculated using the 2018 life table for participants of the 65–91 years age group was 11.32 years (SD = 5.19). Table 1 presents the demographic characteristics of the final study sample stratified according to cognitive function status in 2018. A total of 2,392 participants (62.07%) were classified as having normal cognitive function, 1,113 (28.88%) had moderate cognitive impairment, and 349 (9.06%) had severe cognitive impairment. Individuals with severe cognitive impairment were mostly female (62.75%) and generally older (mean age = 80.82 years, standard deviation, SD = 6.72) than those with normal cognition (mean age = 73.24 years, SD = 6.06). Participants with severe cognitive impairment had lower levels of educational achievement and were less likely to be married (50.72%) than those with normal cognition. In terms of residential areas, the percentage of participants with severe cognitive impairment living in rural areas (36.39%) was greater than that for individuals with normal cognition (25.50%). Furthermore, individuals with severe cognitive impairment had a greater prevalence of diabetes (n = 101, 28.94%), hypertension (n = 235, 67.34%), and stroke (n = 81, 23.21%) as compared to those with normal cognition.

Table 1.

Demographics According to the Cognitive Status Classifications N (%)

Characteristics^a	Normal Cognition (n = 2,392, 62.07%)	Moderate Cognition (n = 1,113, 28.88%)	Severe Cognition (n = 349, 9.06%)	p
Age, mean (SD)	73.24 (6.06)	77.22 (6.26)	80.82 (6.72)	0.0052^*
Sex				<.0001^**
Male	1161 (48.54)	349 (31.36)	130 (37.25)
Female	1231 (51.46)	764 (68.64)	219 (62.75)
Residential area				<.0001^**
Urban	1042 (43.56)	386 (34.68)	131 (37.54)
Suburban	740 (30.94)	329 (29.56)	91 (26.07)
Rural	610 (25.50)	398 (35.76)	127 (36.39)
Educational attainment				<.0001^**
Elementary school	979 (40.93)	807 (72.51)	264 (75.64)
Middle school	505 (21.11)	143 (12.85)	36 (10.32)
High school	654 (27.34)	141 (12.67)	34 (9.74)
College degree	254 (10.62)	22 (1.98)	15 (4.30)
Marital status				<.0001^**
Single	557 (23.29)	436 (39.17)	172 (49.28)
Married	1835 (76.71)	677 (60.83)	177 (50.72)
Employment status				<.0001^**
Employed	706 (29.52)	203 (18.24)	24 (6.88)
Unemployed	1686 (70.48)	910 (81.76)	325 (93.12)
Weight status				<.0001^**
Obesity	648 (27.09)	253 (22.73)	62 (17.77)
Overweight	721 (30.14)	301 (27.04)	86 (24.64)
Normal	963 (40.26)	500 (44.92)	155 (44.41)
Underweight	60 (2.51)	59 (5.30)	46 (13.18)
Self-rated health, mean (SD)	3.01 (0.75)	3.36 (0.82)	4.06 (0.81)	0.0003^*
Drinking				<.0001^**
Current drinker	732 (30.60)	226 (20.31)	28 (8.02)
Past drinker	477 (19.94)	234 (21.02)	104 (29.80)
Non-drinker	1183 (49.46)	653 (58.67)	217 (62.18)
Smoking				<.0001^**
Current smoker	195 (8.15)	69 (6.20)	15 (4.30)
Past smokers	599 (25.04)	220 (19.77)	88 (25.21)
Non-smoker	1598 (66.81)	824 (74.03)	246 (70.49)
Chronic conditions
Diabetes	542 (22.66)	319 (28.66)	101 (28.94)	<.0001^**
Hypertension	1233 (51.55)	663 (59.57)	235 (67.34)	<.0001^**
Stroke	146 (6.10)	97 (8.72)	81 (23.21)	<.0001^**
Life expectancy of the 2018 life table (65–91)	11.32 (5.19)

Note. Life expectancy of the 2018 life table, age, and self-rated health are expressed as mean and standard deviation, and the remaining values are expressed as numbers (percentages).

*p < .05. **p < .0001.

LE for Different Cognitive Function Status Categories Stratified by Sex

Table 2 presents the sex-wise LE calculated for participants with normal cognition and moderate and severe cognitive impairment. The average LE for our study sample (65–91 years) as calculated from the 2018 life table was 9.96 years (SD = 4.69) for males and 12.42 years (SD = 5.76) for females. The DALE calculated using cognitive grade weighting for male participants was 6.76 years (SD = 3.40) for participants with normal cognition, 5.10 years (SD = 6.03) for those with moderate cognitive impairment, and 3.46 years (SD = 6.98) for those with severe cognitive impairment. Likewise, DALE calculated using weighted cognitive status values for females was 7.60 years (SD = 3.88) with normal cognition, 5.03 years (SD = 6.83) for participants with moderate cognitive impairment, and 3.41 years (SD = 8.25) for those with severe cognitive impairment. There was no statistically significant difference in the DALE values calculated using cognitive weighting across sexes (normal cognition: p = .4008, moderate cognitive impairment: p = .9727, severe cognitive impairment: p = .9873).

Table 2.

Normal Cognition Life Expectancy, and Moderate and Severe Cognitive Impairment Life Expectancy by Sex, KLoSA 2018

Sex	Male (n = 1,640)	Female (n = 2,214)	p-value
LE(x)^a (sex)
M (SD)^b	9.96 (4.69)	12.42 (5.76)	.0913
DALE (x)^c (normal)^d
M (SD)^b	6.76 (3.40)	7.60 (3.88)	.4008
DALE (x)^c (moderate)^e
M (SD)^b	5.10 (6.03)	5.03 (6.83)	.9727
DALE (x)^c (severe)^f
M (SD)^b	3.46 (6.98)	3.41 (8.25)	.9873

Note. Statistical analysis: t-test.

^aLife Expectancy.

^bMean and standard deviation.

^cDisability-Adjusted Life Expectancy.

^dNormal cognition.

^eModerate cognitive impairment.

^fSevere cognitive impairment.

*p < .05.**p < .0001.

LE for Different Cognitive Function Categories Stratified According to Educational Attainment

We observed that, in general, the higher the educational attainment, the higher the DALE value among the participants. Among elementary school graduates, the DALE calculated using cognitive status weighting was 7.45 years (SD = 3.81) for people with normal cognition, 5.26 years (SD = 6.22) with moderate cognitive impairment, and only 0.58 years (SD = 11.28) with severe cognitive impairment (Table 3). On the other hand, participants who received a college degree had a DALE of 9.61 years (SD = 5.19) with normal cognition, 8.00 years (SD = 5.24) with moderate cognitive impairment, and 9.37 years (SD = 6.65) despite severe cognitive impairment. There was a statistically significant difference in DALE values among participants with moderate (p = .0251) and severe cognitive impairments (p = .0004) across different educational attainment groups.

Table 3.

Normal Cognition Life Expectancy, Moderate and Severe Cognitive Impairment Life Expectancy by Educational Attainment, KLoSA 2018

Educational Attainment	Elementary School (n = 2,050)	Middle School (n = 684)	High School (n = 829)	College Degree (n = 291)	p-value
DALE (x)^a (normal)^b
M (SD)^c	7.45 (3.81)	9.68 (4.51)	8.63 (4.62)	9.61 (5.19)	.3907
DALE (x)^a (moderate)^d
M (SD)^c	5.26 (6.22)	10.52 (7.14)	6.29 (5.60)	8.00 (5.24)	.0251^*
DALE (x)^a (severe)^e
M (SD)^c	0.58 (11.28)	6.27 (4.94)	6.39 (4.55)	9.37 (6.65)	.0004^*

Note. Statistical analysis: Analysis of variance.

^aDisability-Adjusted Life Expectancy.

^bNormal cognition.

^cMean and standard deviation.

^dModerate cognitive impairment.

^eSevere cognitive impairment.

*p < .05.**p < .0001.

LE for Different Cognitive Function Categories Stratified According to Residential Area

Table 4 presents the LE calculated for different cognitive status groups categorized according to their area of residence. Using normal cognitive function weighting, people living in urban areas had a DALE of 8.11 years (SD = 3.99), those in suburban areas had 7.94 years (SD = 4.02), and the rural population had a mean of 7.24 years (SD = 3.48). Similarly, participants with moderate cognitive impairments had a DALE of 6.56 years (SD = 3.68) when living in urban areas, 5.20 years (SD = 4.53) when living in suburban areas, and only 3.82 years (SD = 4.86) when living in rural areas. Lastly, for participants with severe cognitive impairment, the DALE for urban dwellers was 4.22 years (SD = 6.78), 3.31 years (SD = 3.89) for suburban dwellers, and 6.50 years (SD = 11.10) for rural dwellers. In general, the DALE for participants with normal cognition or moderate cognitive impairment was higher among urban dwellers than the rural population; however, this difference was not statistically significant (p > 0.05 for each).

Table 4.

Normal Cognition Life Expectancy, Moderate and Severe Cognitive Impairment Life Expectancy by Region, KLoSA 2018

Region	Urban (n = 1,559)	Suburban (n = 1,160)	Rural (n = 1,135)	p-value
DALE (x)^a (normal)^b
M (SD)^c	8.11 (3.99)	7.94 (4.02)	7.24 (3.48)	.6922
DALE (x)^a (moderate)^d
M (SD)^c	6.56 (3.68)	5.20 (4.53)	3.82 (4.86)	.0943
DALE (x)^a (severe)^e
M (SD)^c	4.22 (6.78)	3.31 (3.89)	6.50 (11.10)	.3251

Note. Statistical analysis: Analysis of variance.

^aDisability-Adjusted Life Expectancy.

^bNormal cognition.

^cMean and standard deviation.

^dModerate cognitive impairment.

^eSevere cognitive impairment.

*p < .05.**p < .0001.

Discussion

Despite a rapid increase in the proportion of the aging population in Korea, there is a lack of substantial evidence that can be used to support policymaking directed toward addressing the health concerns of an aging population. In this study, we obtained DALE for the older Korean population (65–91 years) according to different sexes, levels of educational attainment, and residential conditions based on their K-MMSE scores and independence in IADLs. We observed females retaining normal cognition during this age have higher DALE while males with moderate or severe cognitive impairment have higher DALE values; however, there were no statistically significant differences among these groups. Second, when stratified based on educational attainments, all participants showed an increasing DALE with higher educational attainment, and the differences among participants with moderate and severe forms of cognitive impairment were statistically significant when compared based on educational attainment. Finally, DALE values for participants with normal and moderately impaired cognitive status were higher for urban dwellers. Interestingly, DALE values for participants with severe cognitive impairment were higher among those living in rural areas. However, none of the differences were statistically significant.

As of 2020, females residing in all OECD countries are reported to have a higher LE than males (Korean Statistical Information Service, 2020). In our study, although there were sex-wise differences in DALE values, they were not statistically significant. These results corroborate the aforementioned LE reported in OECD countries, as well as the results of previous studies reporting the relationship between cognitive function in males and females. Díaz-Venegas et al. (2019) investigated how changes in educational achievements have influenced cognitive function in older adults using data from the Mexican Health and Aging Study conducted in 2001 and 2012. They reported that the sex-related difference in the total cognitive score in 2012 was smaller compared to 2001. Miu et al. (2016) stated that age, rural life, low-income earners, and daily activities affect cognitive function in Mexican older adults; however, they too could not find any statistically significant difference between sexes affecting cognitive function. Therefore, our findings concur with the recent evidence that there are no sex-wise significant differences among older adults regarding cognitive function in today’s time.

Like sex-wise differences, several studies have evaluated the relationship between LE and education levels. As reported in previous studies, we found that LE was higher among participants with higher educational achievement. Lövdén et al. (2020) reported that education promotes cognitive function and lowers the risk of dementia in old age. Interestingly, Kim and Park (2017) attempted to identify factors that affect cognitive function in community-dwelling older adults and found a statistically significant difference in cognitive function based on the participant’s sex, even when age was controlled; however, this sex-based difference disappeared when education was controlled. They also reported females had a higher proportion of participants with an education period of <3 years than their contemporary males. These results support the notion that education can affect cognitive function irrespective of sex.

Regarding the influence of residential conditions on cognitive status, our results were in contrast with those reported by previous studies. According to Lee (2008), the cognitive function of older adults residing in urban areas of Korea was higher than that of the rural-dwelling subjects. In contrast, we observed that rural-dwelling older adults were healthier and independent in terms of physical and sensory functions and daily activities. Still, the cognitive and social-psychological functional status of the urban dwellers was better. Zhu et al. (2016) reported that urban older adults were less physically active than rural older adults. However, according to Bae and Kim (2022), there was no significant difference among the risk factors for cognitive function decline in retired elderly based on their residential conditions. Our findings concur with these results. Since there is no conclusive evidence delineating the role of one’s residential area on cognitive function, detailed association studies are warranted to explore this aspect.

Etgen et al. (2011) identified certain risk factors for mild cognitive impairment and stated that these factors must be addressed to ensure the optimal treatment for improving cognitive abilities in these patients and preventing the progression of pre-existing deficits. Based on our results, we suggest that treatment strategies and health policies must consider sex, educational attainment, and regional variables while dealing with the aging population of varying cognitive function status. There is a need to revise the direction of treatment and health policy to reduce the overall burden of an aging society.

Our study had several limitations. First, the KLoSA data are difficult to generalize to adult populations in other countries because they are solely based on Korean adults. Second, we only included adults aged 65–91 years; therefore, these results cannot apply to older adults from other age groups. Future research must compare statistics from different countries using panel survey data of various age groups to generate more homogenizing results.

Conclusion

In this study, we calculated LE in Korean older adults based on the individual’s cognitive function status to identify vulnerable subgroups. While sex-based and residential condition differences may not necessarily affect a person’s cognitive function, their educational attainment most certainly does. Such information can help identify cognitive risk factors and assist public health policymakers and practitioners in designing effective intervention strategies to reduce the risk of cognitive impairment in an aging population.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Yonsei University Future-leading Research Initiative of 2021 [RMS2 2021-62-002].

Ethics Approval Statement

This study was approved by the Yonsei institutional review board (#1041849-202108-SB-117-01) and met the research exemption criteria of the participating institutions.

Availability of Data and Material

The data used by this research team is from the Korean Longitudinal Study of Aging, and more information can be found on the Web site ().

ORCID iDs

Sanghun Nam

Ickpyo Hong

Appendix

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