Experimental Aging Research, 41: 104–114, 2015 Copyright © Taylor & Francis Group, LLC ISSN: 0361-073X print/1096-4657 online DOI: 10.1080/0361073X.2015.978219

TRAJECTORIES OF FRAILTY AND RELATED FACTORS OF THE OLDER PEOPLE IN TAIWAN Hui-Chuan Hsu Department of Health Care Administration, Research Center of Health Policy and Management, Asia University, Taichung, Taiwan, Republic of China; and Department of Medical Research, China Medical University Hospital, China Medical University, Taiwan, Republic of China

Wen-Chiung Chang Institute of Health Policy and Management, National Taiwan University, Taipei, Taiwan, Republic of China Background/Study Context: This study aimed to identify the different trajectories of frailty and factors related to frailty among older adults over time. Methods: Data were obtained from a five-wave panel composed of older Taiwanese adults from 1993 to 2007 (N = 2306). Frailty was defined as the presence of three or more of the following criteria: shrinking, weakness, exhaustion, slowness, and low physical activity. A group-based model of trajectory analysis was applied with time-dependent and time-independent variables. Results: Three trajectory groups were identified: maintaining nonfrailty, developing frailty, and high risk of frailty. Being female, older, and having a lower level of education were risk factors for being in the developing frailty group or high risk of frailty group. Physical risk factors and psychological factors were associated with frailty within each group. Higher financial satisfaction and social participation were protective factors from frailty for the developing frailty group and high risk of frailty group, respectively. Conclusion: Older adults should promote their health physically, psychologically, and socially. Received 10 July 2013; accepted 6 December 2013. Address correspondence to Hui-Chuan Hsu, PhD, Professor, Department of Health Care Administration, Asia University, No. 500, Lioufeng Road, Wufeng, Taichung, 41354, Taiwan, Republic of China. E-mail: [email protected]

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Recent gerontological research has begun to define and understand the development of frailty in older adults. The presence of frailty is not only symptomatic in older adults but also leads to downstream changes in long-term health outcomes, such as mortality, incidence of disability, or hospitalization (Avila-Funes et al., 2008; Buchman, Wilson, Bienias, & Bennett, 2009). Up to this point, most studies of frailty have been crosssectional (Ahmed, Mandel, & Fain, 2007; Alvarado, Zunzunegui, Béland, & Bamvita, 2008; Chen, Wu, Chen, & Lue, 2010; Santos-Eggimann, Cuénoud, Spagnoli, & Junod, 2009; Woo, Goggins, Sham, & Ho, 2005). There have been some longitudinal studies about frailty in recent years (Avila-Funes et al., 2008; Buchman et al., 2009; Strawbridge, Shema, Balfour, Higby, & Kaplan, 1998; Xue, Fried, Glass, Laffan, & Chaves, 2008), but the relationships between frailty and other factors or health outcomes were based on averaged results across large samples. The heterogeneity of the different trajectories of frailty development in older adults over time has been little explored. Factors related to frailty have been explored in recent research. Factors related to frailty include being female, having a lower level of education, being of lower socioeconomic status, having no spouse, having a disability or a higher number of comorbidities, having more depressive symptoms or less social support, showing poor nutrition, displaying low participation in productive activities, and having the presence of biomarkers such as C-reactive protein (CRP), fibrinogen, D-dimer factor VIII, interleukin6, glucose intolerance, etc. (Ahmed et al., 2007; Alvarado et al., 2008; Bischoff, Staehelin, & Willett, 2006; Chen et al., 2010; Jung, Gruenewald, Seeman, & Sarkisian, 2010; Santos-Eggimann et al., 2009; Woo et al., 2005). In this study, we would like to examine trajectories of frailty among older adults by utilizing a five-wave panel data of Taiwanese older adults and studying factors related to particular trajectories of frailty development. A person-centered approach with a group-based trajectory analysis (Nagin, 2005) was applied to identify different trajectories of frailty over time. METHODS Data were analyzed from the Taiwanese Longitudinal Survey on Aging (TLSA) (originally named the “Health and Living Status of the older people in Taiwan survey”), which was first launched in 1989 and was subsequently followed up in five waves in 1993, 1996, 1999, 2003, and 2007. We analyzed the five-wave data from 1993 to 2007, which encompassed data from a total of 3363 individuals who were 64 years or older in

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1993. To identify different trajectories of frailty, data from study subjects with sufficient numbers of observed time points was needed. Therefore, only those who had been successfully interviewed at three or more waves were included in the analysis for a total of 2306 persons. The definition of frailty used in the current study was that used by Fried et al. (Fried et al., 2001), which is frequently cited in frailty research. Symptoms of frailty included the presence of three or more of the following five criteria: shrinking, weakness, exhaustion, slowness, and low physical activity. Shrinking was defined as self-reported poor appetite often or most of the time in the last week. Weakness was defined as having difficulty or being unable to carry 12-kg worth of groceries. Exhaustion was defined as subjects stating “I could not get going” or “I felt everything I did was an effort” often or most of the time in the last week. Individuals who had difficulty or were unable to walk a distance of 200 to 300 m were defined as being slow. The participants were defined to have low activity levels if one did not garden, take a walk, jog, climb mountains, or other outdoor activities at least once or twice a week. The variables related to frailty in this study included initially assessed factors that were unchanging over time and multiple time-sensitive factors, which were assessed at each wave. The time-constant factors included sex, age in 1993, and years of education. The time-varying covariates, which were measured at each survey, included chronic diseases, difficulty in activities of daily living (ADLs), financial satisfaction, social participation, depressive mood, smoking, alcohol drinking, and cognitive function. The total number across 10 categories of disease—hypertension, diabetes, heart diseases, stroke, cancer, chronic respiratory diseases, gastrointestinal diseases, hepatobiliary disease, kidney diseases, and arthritis—were counted as the number of chronic diseases. The level of difficulty of bathing, dressing, eating, transferring, walking inside, and toileting was accumulated. Difficulty in performing each ADL item was scored from 0 to 3, with higher scores representing a higher level of difficulty. Therefore, the total score of the ADL disability ranged from 0 to 18. Financial satisfaction was subjectively measured as satisfaction with their economic status. The individuals who had work or participated in community activities or engaged in voluntary work were defined as having social participation. The score of depressive mood, ranging from 0 to 4, was the frequency that the participant reported feeling a depressive mood during the past week. A person was recorded as smoking or drinking alcohol if he/she smoked or drank at the time of investigation. Cognitive function was measured by the following five test items: “where is this,” “what is the date,” “what day is it,” “how old are you,” and counting down by 3s from 20. The total score ranged from 0 to 5.

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We applied a group-based, person-centered trajectory model to identify our trajectory groups (Nagin, 2005; Nagin & Odgers, 2010). This method clusters individuals and follows similar paths of outcomes or behaviors over time. This model assumes that the population is composed of a mixture of underlying trajectory groups. It is assumed that Yi = {yi1 , yi2 , yi3 , . . . . yiT }, where the longitudinal measurement of an individual i occurs over T periods, and P(Yi ) = πj Pj (Yi ), where P(Yi ) is the probability of Yi given membership in group j, and πj is the probability of group j. The distribution of P(Yi ) is determined by the type of data. In this study, frailty is defined as a dichotomous variable; therefore, a logistic model was applied. The optimal trajectory group number was determined by comparing the change in the Bayesian information criteria (BIC), posterior probabilities while taking the parsimony principle into consideration. A SAS (SAS Institute, Cary, NC, USA) procedure, PROC TRAJ, was used for the analysis (Jones, Nagin, & Roeder, 2001). Subsequently, the time-constant factors were included in the model to predict which trajectory group individual patients fell into. The time-varying covariates were then added to examine the factors associated with frailty within each group. RESULTS Descriptive data for the samples are shown in Table 1. The analyzed study samples had a mean age and standard deviation (SD) of 70.7 (± 5.1) years. More than half of the samples were male (55.1%). The majority of our samples had a low educational level with an average of 4.4 (± 4.4) years of education. The prevalence of frailty in 1993, 1996, 1999, 2003, and 2007 was 12.7%, 15.4%, 23.3%, 24.8%, and 27.9%, respectively. In the unconditional model, the best group number of the model was determined to be 3. A quadratic model was initially assumed, and after refining our model, each group was then defined as intercept-only or quadratic. We identified three types of frailty trajectories in the unconditional model (including no other covariates except time). In the first group, the risk of frailty across all the time points was stable and low; therefore, it was named the maintaining nonfrailty group. The estimated prevalence of this group was 43.5%. The risk of frailty increased significantly in the second group, and it was named the developing frailty group, with an estimated prevalence of 38.8%. Individuals in the third group had a high risk of frailty from baseline through to the end of study measurement. This trajectory was named the high risk of frailty group, with an estimated prevalence of 17.7%. The probability of frailty for each group

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Table 1. Characteristics of the analyzed samples (N = 2306) 1993 Characteristic Frailty No Yes Sex (n (%)) Female Male Age at 1993 Mean (SD) Education (years) Mean (SD) Number of diseases Mean (SD) ADL difficulty Mean (SD) Financial satisfaction Mean (SD) Social engagement No Yes Depressive mood Mean (SD) Cognitive function Mean (SD)

1996

1999

2003

2007

n

%

n

%

n

%

n

%

n

%

1875 272

87.3 12.7

1721 313

84.6 15.4

1521 463

76.7 23.3

1112 366

75.2 24.8

725 280

72.1 27.9

1035 (44.9) 1271 (55.1) 70.7 (5.1) 4.4 (4.4) 1.1 (1.2)

1.3 (1.3)

1.6 (1.4)

1.8 (1.5)

1.9 (1.6)

0.2 (1.3)

0.4 (2.1)

1.1 (3.6)

1.8 (4.6)

2.8 (5.6)

3.6 (0.9)

3.3 (0.9)

3.2 (0.9)

3.3 (0.9)

3.3 (0.9)

1018 1188

46.1 53.9

1028 1162

46.9 53.1

1100 1113

49.7 50.3

997 714

58.3 41.7

800 439

64.6 35.4

0.5 (0.9)

0.6 (1.0)

0.6 (1.0)

0.6 (1.0)

0.7 (1.0)

4.5 (0.9)

4.5 (0.9)

4.4 (0.9)

4.2 (1.0)

4.1 (1.1)

Note. The cases with missing data were listwise deleted for each variable.

and the 95% confidence interval are shown in Figure 1. The average posterior probabilities ranged from .75 to .89, indicating a good fit for this model (Table 2). Next, the time-constant and time-varying covariates were added to the group-based trajectory model. The results are shown in Table 3. Compared with the maintaining nonfrailty group, being female, older, and of lower educational level were predictors of being in the developing frailty group or the high risk of frailty group. Within the maintaining nonfrailty group, having a higher level of ADL difficulties (B = 0.71, p < .001) and depressive mood (B = 1.21, p < .001) was associated with a higher risk of frailty; better cognitive function decreased the risk (B = −0.63, p < .001). Among the individuals in the developing frailty group, more chronic diseases (B = 0.29, p < .001), more ADL difficulty (B = 3.49, p < .001), and depressive mood (B = 0.63, p < .001) were risk factors for frailty; a protective factor was higher financial satisfaction (B = −0.54, p < .001). Similarly, in the high risk of frailty group, having more chronic diseases (B = 0.36, p < .001), ADL disability (B = 0.15, p < .01), and depressive mood (B =

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Figure 1. Trajectories of frailty of the unconditional model. Solid line: point estimation; dashed line: 95% confidence interval.

0.73, p < .001) were all risk factors; factors associated with lower risk of frailty were higher social engagement (B = −0.80, p < .001) and cognitive function (B = −0.37, p < .001). DISCUSSION This study identified three frailty trajectory groups from the TLSA data. These trajectories were derived from a 14-year span of data. The identified trajectory groups from this sample of older Taiwanese adults were identified as maintaining nonfrailty (43.5%), developing frailty (38.8%), and high risk of frailty (17.7%) groups. Being female, older, and having a lower education level increased the likelihood of being the developing frailty group or high risk of frailty group. With regard to risk factors of Table 2. Diagnostic statistics for the unconditional model Group Group 1 (Maintaining nonfraility) Group 2 (Developing frailty) Group 3 (High risk of frailty)

Average posterior probabilities

Proportion assigned to each group

Estimation using model

.80

58.6

50.5

.75

28.8

34.2

.89

12.5

15.3

110 Table 3. frailty Variable

H.-C. Hsu and W.-C. Chang Covariates predicting frailty trajectory memberships and risk of Group 1 (Maintaining nonfraility) B (SE)

Time-constant covariates Sex (male) Age Education level Time-varying covariates Intercept Linear Quadratic Number of chronic diseases ADL difficulty Financial satisfaction Social participation Depressive mood Cognitive function

Group 2 (Developing frailty) B (SE)

Group 3 (High risk of frailty) B (SE)

−0.76 (0.31)∗ 0.07 (0.04)∗ −0.13 (0.04)∗∗∗

−1.58 (0.30)∗∗∗ 0.17 (0.03)∗∗∗ −0.15 (0.04)∗∗∗

−1.45 (1.02) — — 0.10 (0.12)

−1.00 (0.68) 1.42 (0.81) 0.00 (0.49) 0.29 (0.08)∗∗∗

0.78 (0.60) 2.71 (0.70)∗∗∗ −2.17 (0.57)∗∗∗ 0.36 (0.09)∗∗∗

0.71 (0.12)∗∗∗ −0.19 (0.19)

3.49 (0.73)∗∗∗ −0.54 (0.15)∗∗∗

0.15 (0.06)∗∗ −0.21 (0.12)

−0.48 (0.40)

−0.44 (0.23)

−0.80 (0.22)∗∗∗

1.21 (0.19)∗∗∗ −0.63 (0.16)∗∗∗

0.63 (0.13)∗∗∗ −0.19 (0.13)

0.73 (0.12)∗∗∗ −0.37 (0.10)∗∗∗

— — —

Note. BIC = −3445.04 (N = 8205 observations); BIC = −3438.74 (N = 2026 persons). ∗ p < .05; ∗∗ p < .01; ∗∗∗ p < .001.

frailty between members of the same group, physical risk factors (having more chronic diseases and increased physical disability) and psychological risk factors (having a depressive mood and lower cognitive function) were associated with a higher probability of frailty. Higher financial satisfaction was a protective factor against frailty for the developing frailty group, whereas social participation reduced the probability of frailty for the high risk of frailty group. Identification of different trajectories of frailty provides more information about not only the heterogeneity of frailty among the older adults but also the associated risk factors in each trajectory group that vary over time. Having more chronic diseases was associated with a higher probability of frailty, as has been shown in previous studies (Chen et al., 2010; Strawbridge et al., 1998). This factor was significant for the developing frailty and the high risk of frailty group. Comorbidity may be accompanied by symptoms of a chronic condition and thus may increase the possibility of frailty. Having more functional disabilities was associated with a higher possibility of frailty across three trajectory groups, with a particularly profound effect exhibited in the developing frailty group. It seems that the members of the developing frailty group were more likely to be affected

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by physical disability, which increased their possibility of frailty, than the maintaining nonfrailty group and the high risk of frailty group. It is also possible that the maintaining nonfrailty group and the high risk of frailty group had a more stable rate of physical disability, making the effect of disability in this group less discernible. We found that psychological factors, cognitive function, and depressive mood also had effects on frailty. Better cognitive function was associated with a lower possibility of frailty for the maintaining nonfrailty group and the high risk of frailty group but not the developing frailty group. It is possible that the effect of ADL difficulty noted in the developing frailty group affects physical frailty to a much greater extent than it affects cognitive status. Frailty in this study was defined physically; however, frailty can be a combined physical, psychological, and social state (Panza et al., 2011). Our findings were in parallel with previous research. Past studies have found an association of frailty with lower cognitive function (Jacobs, Cohen, Ein-Mor, Maaravi, & Stessman, 2011; Jürschik et al., 2012; Macuco et al., 2012; Yassuda et al., 2012) and with depressive symptoms (Chen et al., 2010; Strawbridge et al., 1998). Education, having higher financial satisfaction, and social participation are protective factors to frailty. Education and financial satisfaction often reflects one’s socioeconomic status, and thus one’s subjective level of financial satisfaction is likely to indirectly predict one’s subjective wellbeing. Lower socioeconomic status has been shown to be related to frailty in previous studies (Ahmed et al., 2007; Alvarado et al., 2008; SantosEggimann et al., 2009; Woo et al., 2005). Previous studies have also found that social support and social participation may be related to lower probabilities of frailty (Jung et al., 2010; Woo et al., 2005). In our study, social participation was protective against frailty and was significant for the high risk of frailty group, although the physical disabilities were statistically adjusted for. Physical disability would not and should not stop older adults from participating in many types of social participation. It is important to note that older adults may gain psychological or social rewards through social participation and feel valued as a result of being more social, which may then serve as a protective factor against frailty. This study has several strengths. First, the use of a person-centered approach and group-based trajectory analysis allowed for the identification of different trajectories of frailty across years and subsequently allowed for identification of heterogeneity of the elderly population and the different risk factors across these groups. Second, this study used both time-constant and the time-varying covariates in the model. The contribution of several time-dependent covariates to the development of frailty was identified by the trajectory approach.

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There are also limitations in this study. First, there was attrition due to deaths or loss in follow-up. The results were from individuals who were survivors and were possibly healthier individuals. However, this study used the criteria that the participants must attend three or more interviews across five waves to be included in the analyses, which allowed for multiple, repeated measures and calculation of individual trajectories for each participant. Second, some of the measures were unavailable in the data. For example, only five items from the Short Portable Mental Status Questionnaire (SPMSQ) were consistent across the five waves, and thus our measures of cognitive function may not be comprehensive. The measure of depressive mood was taken from only a single item of the Center for Epidemiologic Studies Depression (CES-D) scale because other items from the CES-D scale were used for the proxy measures for frailty. Thus, the variable of depressive mood may not be a very stable measure. The biomarkers related to frailty were also unavailable for analysis. CONCLUSIONS This study identified three trajectories of frailty among older adults from the TLSA among older Taiwanese adults across 14 years of data collection. Risk factors and protective factors related to different trajectories of frailty were found. Prevention of chronic diseases may postpone the speed of frailty development. Maintaining a good emotional state and higher cognitive function may help to remain physically active. Participation in social groups or social affairs and maintaining connections in social networks may decrease the incidence of frailty. We suggest that public health policy should cover strategies for chronic disease prevention, mental health maintenance, and encouraging social participation for older adults. ACKNOWLEDGMENTS The data used were provided by the Population and Health Research Center, Bureau of Health Promotion, Department of Health, Taiwan, Republic of China. The interpretation and conclusions contained herein do not represent those of Bureau of Health Promotion. This study had obtained approval of Research Ethics Committee of Central Regional Research Ethics Center, Taiwan, Republic of China (no. CRREC-101062). This study does not have any potential conflicts of interest.

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FUNDING This work was supported by grants from the National Science Council, Taiwan, Republic of China (NSC 101-2410-H-468-008-MY2).

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Trajectories of frailty and related factors of the older people in Taiwan.

BACKGROUND/STUDY CONTEXT: This study aimed to identify the different trajectories of frailty and factors related to frailty among older adults over ti...
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