Experimental Gerontology 66 (2015) 32–38

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Trajectories of multimorbidity and impacts on successful aging Hui-Chuan Hsu ⁎ Department of Health Care Administration, Asia University, Taiwan, ROC Department of Medical Research, China Medical University Hospital, China Medical University, Taiwan, ROC

a r t i c l e

i n f o

Article history: Received 1 December 2014 Received in revised form 4 April 2015 Accepted 9 April 2015 Available online 12 April 2015 Section Editor: Diana Van Heemst Keywords: Multiple group-based trajectories Multimorbidity Comorbidity Chronic disease Successful aging

a b s t r a c t Elderly people usually have multiple chronic diseases concurrently. However, studies of multimorbidity patterns over long time periods are scarce. The purpose of this study was to examine the joint trajectories of chronic multimorbidity among the Taiwanese elderly and to examine related factors and to predict later successful aging outcomes. The data used in this study were from a nation-representative panel survey conducted in Taiwan from 1993 to 2007. Those who participated in at least three waves of the survey were included in the analysis (in total 2584 persons and 57,012 observations). The chronic diseases included cardiovascular disease (CVD), chronic non-specific lung disease (CNSLD), arthritis, cancer, gastrointestinal disease (GI), and kidney disease. The multiple group-based trajectories analysis approach was applied to identify the trajectory groups. Four trajectory groups of multimorbidity were identified: low risk (55.51%), CVD risk only (15.55%), GI & CNSLD risk (20.20%), and multiple risks (8.74%). Related factors included age, level of education, physical functioning, depressive symptoms, and undergoing health examinations. The multimorbidity trajectories affected later physical functioning, depressive symptoms, cognitive function, and life satisfaction. Multiple trajectories of multimorbidity show the patterns of health burden and risks to successful aging among the elderly over time. © 2015 Elsevier Inc. All rights reserved.

1. Introduction Elderly people usually have multiple chronic diseases, which affect their health status, quality of life, the burden of health care and family caregiving, and mortality (Anderson and Horvath, 2004; Fortin et al., 2004; Gijsen et al., 2001; Laux et al., 2008). Reducing and preventing the risks of multimorbidity are essential components of successful aging (Rowe and Kahn, 1997). If multimorbidity patterns may be appropriately identified, the risk factors and probable challenges of the specific multimorbidity types will be better known. Past studies used either the total number of chronic diseases as a burden (Anderson and Horvath, 2004; Fortin et al., 2004) or focused on one particular disease and its comorbidities (Caughey et al., 2008; van Oostrom et al., 2012). The former represents a relatively easy measure for defining the health burden but does not consider the types of diseases present, whereas the latter is more useful for identifying a particular disease rather than the co-occurrence of multiple diseases. Some studies have used a comorbidity score (Charlson et al., 1987), which gives a total score that includes weights of the multiple diseases; however, the index does not provide the disease patterns for identifying patients' risks. A few studies have used cluster analysis or factor analysis to categorize multiple diseases or to define groups by prevalence (Formiga et al., 2013; Prados-Torres et al., 2012; Steinman ⁎ No. 500, Lioufeng Road, Wufeng, Taichung, 41354, Taiwan, ROC. E-mail address: [email protected].

http://dx.doi.org/10.1016/j.exger.2015.04.005 0531-5565/© 2015 Elsevier Inc. All rights reserved.

et al., 2012), by factor analysis or by cluster analysis (Marengoni et al., 2009; Prados-Torres et al., 2012). Another way to define co-occurring combinations of chronic diseases is to use the observed and expected prevalence of the chronic conditions to examine the risk ratios (van den Bussche et al., 2011; Kirchberger et al., 2012; Schäfer, 2012). All of these methods have aided in categorizing patterns at a specific point in time; however, the categorization may not follow the same pattern over time. Recently, the guiding principles for older adults with multimorbidity have been developed for clinicians (American Geriatrics Society Expert Panel on the Care of Older Adults with Multimorbidity, 2012). However, the prognosis regarding quality of life and the dynamics of multimorbidity remain challenges. In general, multiple chronic diseases may be categorized into two ways: the disease-centered approach or the person-centered approach (Muthén and Muthén, 2000). That is, we may cluster the diseases or cluster the people (person-centered) with different disease patterns (disease-centered). However, the most prevalent chronic diseases do not necessarily dominate the combinations of multimorbidity patterns (van den Bussche et al., 2011). The probabilities at which the multimorbidity patterns appear among the elderly are more meaningful. The person-centered approach provides information about the high-risk population and the high-risk disease patterns within a certain group of people. Thus, in this study, we used a person-centered approach and a multiple group-based trajectory analysis (Nagin, 2005) to identify multimorbidity patterns across time among the elderly in Taiwan. The risk factors for the different multimorbidity trajectories

H.-C. Hsu / Experimental Gerontology 66 (2015) 32–38

and the impact of these multimorbidity trajectories on successful aging indicators were also examined. 2. Methods 2.1. Data Data were obtained from the Taiwanese Longitudinal Survey on Aging, which is a longitudinal survey that was first conducted in 1989. Face-to-face interviews were conducted with a random sample of individuals (aged N 60 years) drawn from the entire elderly population of Taiwan. A few of the participants lived in institutions, although most (99.0%) lived in a community. A three-stage proportional-to-size probability sampling technique was used. In 1989, the initial sample included 4049 people. Because some variables were unavailable in 1989, the data used in the study reported herein were obtained from the interviews conducted in 1993, 1996, 1999, 2003, and 2007. The number of missing cases and people who died increased over time. Only those who participated since 1993, completing three or more waves of interviews, and who selfreported were included in the analysis. For participants who completed the study but for whom certain items were missing, the missing data were imputed. In total, 2584 respondents (57,012 observations) were included in the group-based trajectory analysis, and 1370 participants survived and completed the 2007 survey. The death rate during the follow-ups was 46.3%. This study obtained the approval of the Research Ethics Committee of the Central Regional Research Ethics Center, Taiwan. 2.2. Measures 2.2.1. Chronic diseases The chronic diseases assessed in the study included diabetes mellitus, heart disease, stroke, cancer, lung diseases, rheumatism/ arthritis, liver or gallbladder disease, gastrointestinal disorders, and renal disease. If the respondent self-reported and the disease had been diagnosed by a physician, who then had considered it as a morbidity, the respondents were defined as presence of the disease. Due to the limited number of variables (i.e., six) that can be used in the multiple group-based trajectories program at a time, the diseases were grouped on a physiology or pathology basis. The six disease types were CVD (which included diabetes mellitus, heart disease, and stroke), chronic non-specific lung disease (CNSLD, including lung disease, asthma, bronchitis, and emphysema), gastrointestinal disease (GI, including liver disease, gallbladder disease, and gastrointestinal disorders), arthritis (or rheumatism), cancer, and renal diseases. 2.2.2. Successful aging indicators Successful aging indicators used in this study referred to the Rowe and Kahn's framework (Rowe and Kahn, 1997) and a previous study of successful aging among the Taiwanese elderly (Hsu and Jones, 2012). The successful aging indicators were measured at baseline in 1993 as the controlling factors, and they were also measured in 2007 as the outcomes of the multiple chronic disease trajectories. The seven indicators were as follows: 1. Physical function difficulty measures included difficulties with activities of daily living (Katz et al., 1963), which include eating, dressing, transferring, bathing, walking indoors, and using the toilet, and instrumental activities of daily living (Lawton and Brody, 1969), which include shopping for groceries, managing money, traveling alone by car or train, performing heavy housework, performing light housework, and making phone calls. Each item was scored from 0 (no difficulty) to 3 (unable to do at all), with the possible total score ranging from 0 to 36. The higher score indicated more physical function difficulties.

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2. Depressive symptoms were measured using the Center for Epidemiologic Studies Depression Scale; the 10-item version was used (Kohout et al., 1993). Each item was scored from 0 to 3, with possible total scores ranging from 0 to 30. The higher score indicated more depressive symptoms. 3. Cognitive function was measured using the Short Portable Mental Status Questionnaire (Pfeiffer, 1975). The following nine items were measured in both 1993 and 2007: Where are you located now?; What are the day, month, and year?; What weekday is today?; How old are you?; What is your mother's maiden name?; Who is the current president?; Who was the last president?; When were you born?; and counting backwards from 20 by 3s. The higher score represented a better cognitive function. 4. Social support included receiving and providing support. Receiving support was measured as the degree to which family/relatives/ friends care about you, listen to you, can be counted on when you are ill, and your satisfaction with the care and support that you receive. Providing support included two variables: How often did family or friends come to consult with you for your opinions, and to what degree did you feel that you were being helpful. The total score for social support ranged from 1 to 18; the higher score indicated more social support. 5. Productive activity participation included paid or unpaid work or participation in social groups, such as volunteer groups, community groups, religious groups, occupation associations, political groups or parties, social service groups, clan associations, elderly groups, or elderly colleges. Social participation was coded as yes/no. 6. Economic satisfaction was coded as unsatisfied to satisfied (score ranged from 1 to 5). The higher score indicated higher satisfaction. 7. Life satisfaction was measured using the Life Satisfaction Rating (LSR) (Neugarten et al., 1961). Four variables were available across waves, so only these four variables were used in this study. Each item was rated as yes or no (scored 1 or 0). The total scores ranged from 0 to 4; the higher score represented a better life satisfaction. 2.2.3. Baseline covariates Time was calculated as years since baseline (year 1993) divided by 10. Baseline covariates were measured according to the status in 1993, and they included demographics, health behaviors, and the baseline successful aging status. Demographics included age, sex, years of education, and marital status (having a spouse or not). Each of the following four health variables was coded as a binary outcome (yes/no): smoking and drinking alcohol were defined as occurring if the respondent was engaging in the behavior; physical activity was defined as walking, doing outdoor physical activities, or gardening at least 1–2 times per week; the health examination variable referred to whether or not a participant underwent general health checkups between 1993 and 1996. 2.3. Analysis The multiple trajectory model was used to analyze the data (Nagin, 2005). This method is designed to cluster individuals who exhibit a similar progression in some outcome (in this study, morbidity) over time. The group-based trajectory model assumes that the population is composed of a mixture of underlying trajectory groups. It assumes that Yi = { yi1, yi2, yi3, …. yiT}, where the longitudinal measurement is of an individual i over T periods and that P(Yi) = Σ πjP j (Yi), where P(Yi) is the probability of Yi given membership in group j, and πj is the probability of group j. The form of P(Yi) is determined by the type of data used in the analysis. In this study, morbidity was set to follow a logistic model (morbidity was defined as yes or no). Thus, it was assumed that: jt

β j0β j1

Timeitβ j2

P ðMorbidityit Þ ¼ e β j0β j1 1e

Timeit:

Time2itβ j3 β j2

Time3it

Time2itβ j3

Time3it

:

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H.-C. Hsu / Experimental Gerontology 66 (2015) 32–38

Fig. 1. Trajectories of six chronic kinds of chronic diseases. Note: The names of the 4 groups are as following: Group 1: GI & CNSLD Risks; Group 2: Low Risks, Group 3: CVD Only Risk; and Group 4: Multiple Risks.

The Proc Traj procedure in the SAS program can model up to six outcome variables for multiple trajectories. The form of the likelihood of the multi-trajectory model (Jones and Nagin, 2007) is: P ðY 1 ; Y 2 ; …:Y k Þ ¼

X

j

π j Π f k ðY k Þ;

where k is the number of different outcome trajectories (which is six in our current study) in each trajectory group j. The optimal trajectory group number was determined by comparing the Bayesian information criteria (BIC), fitting the theoretical explanations, and using the parsimony principle. The six types of diseases each were defined as binary variables (yes/no). We used the multiple trajectory model generated by the SAS TRAJ procedure (Jones and Nagin, 2007) for analysis; a 6-indicator trajectory model had been developed and applied (Hsu and Jones, 2012). The optimal trajectory group number was determined by comparing the Bayesian information criteria (BIC) and the parsimony principle. After the multimorbidity trajectories were determined, the differences of the groups were examined by Chi-square test and one-way ANOVA test. Then, multinomial logistic regression analysis

was used to compare the differences in characteristics among the multimorbidity trajectories. Next, the multimorbidity trajectory groups and other baseline covariates were analyzed by multiple linear regression and logistic regression (for productive activity) to examine the effect of multimorbidity trajectories on later successful aging in the last wave (2007). The non-normal distributed dependent variables (such as physical function and cognitive function) were transformed before entering into the linear regression model. 3. Results The multiple trajectories of the six types of chronic disease were analyzed, and the group numbers of trajectories were determined based on the BIC scores and the reasonableness of the results. The BIC score leveled off at more than five groups; thus, four groups of chronic disease trajectories were determined to be the best group number. The results of the multimorbidity trajectories are shown in the figures. Fig. 1 shows each chronic disease by the four trajectory groups. Four trajectory groups of multimorbidity were identified: low risk

H.-C. Hsu / Experimental Gerontology 66 (2015) 32–38

(55.51%), CVD risk only (15.55%), GI & CNSLD risk (20.20%), and multiple risks (8.74%). Fig. 1 shows that groups 3 and 4 had higher probability trajectories for CVD, whereas groups 1 and 2 had a lower CVD probability. Groups 1 and 4 had a higher probability for CNSLD. Group 4 showed a particularly high probability for arthritis compared to the other three groups. The probability of cancer was much lower than the probability of other chronic diseases. However, group 4 showed a larger increase in cancer over time than the other groups, and group 2 had the lowest probability for cancer. Group 4 also had the highest probability for GI, followed by group 1, whereas groups 2 and 3 had lower GI probabilities. Additionally, group 4 showed the highest probability for renal disease, and this probability increased over time (from 0.17 to 0.28). The other three groups had lower and stable probabilities for renal disease. Fig. 2 shows the multimorbidity trajectory groups by the six types of chronic diseases. The “GI & CNSLD risk” group consisted of members of group 1, who had higher GI and CNSLD risks than the other groups. Members of group 2, the “low-risk” group, had lower probabilities of multimorbidity. Although the probability of arthritis in this group was high relative to the other diseases, the probability of arthritis for this group was still lower than that in the other groups. Group 3 was named the “CVD risk only” group because members of this group had a particularly high probability of CVD. Members of group 4, the “multiple-risk” group, had higher probabilities of almost all of the morbidities compared to the other groups. Table 1 shows the characteristics of the 4 multimorbidity trajectory groups, and the differences across the four groups were examined. The six chronic disease morbidities were significantly different across the 4 groups and in most of the waves, as the findings show in Fig. 1. The characteristics of the 4 groups were somewhat different at baseline. The members in Group 2 (Low Risk) were more likely to be male, to be better educated, to have fewer physical function difficulties, to have fewer depressive symptoms, to have higher cognitive function, to have better life satisfaction, to smoke and drink, and to undergo fewer health examinations. The members in Group 4 (Multiple Risks) were more likely to be female, to be less educated, to have more physical function difficulties, to have more depressives symptoms, to have lower life satisfaction, not smoke or drink, and to undergo health examinations. The differences in smoking and drinking were possibly related to gender because very few elderly Taiwanese women smoked or drank at that time. Table 2 shows the results of the multinomial logistic regression analysis to examine the effects of baseline characteristics on the multimorbidity trajectories. The low-risk group (group 2) was used as the reference, and the odds ratios of the other three groups are shown in Table 2. Compared to the low-risk group, members of group 1 (GI & CNSLD risk) were more likely to have less education and to be younger. Members of group 3 (CVD risk only) were more likely to be young, were less likely to smoke, and were more likely to undergo health examinations. Members of group 4 (multiple risks) were more likely to be younger, to have more physical function difficulties, to have more depressive symptoms, and to undergo health examinations than members of the low-risk group. The multimorbidity trajectories were then used to predict successful aging performance in 2007 (Table 3). First, only the multimorbidity trajectories were added in the model (model 1), and then, the baseline covariates were added in model 2 as controlling factors. The reference group was the low-risk group. Next, the seven successful aging indicators in 2007 were used as the dependent variables in the analyses, respectively. When only the multimorbidity trajectories were used (model 1), the members of group 1 (GI & CNSLD risk) were more likely to have higher physical function difficulties (β = 0.120); however, more participated in productive activities (OR = 3.358) than did the low-risk group. The members of group 3 (CVD risk only) had more physical function difficulties (β = 0.188), had worse cognitive function (β = − 0.171), and were more likely to participate in productive activities (OR = 2.500). The members of group 4 (multiple risks) had

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Fig. 2. Multimorbidity trajectory groups.

more physical function difficulties (β = 0.251), more depressive symptoms (β = 0.227), worse cognitive function (β = − 0.122), less social support (β = − 0.107), were more likely to participate in productive activities (OR = 1.899), were less satisfied with their economic status (β = − 0.170), and had lower life satisfaction (β = −0.234). The odds ratios of participating in productive activities was observed to be insignificant after controlling for other variables, possibly because the three multimorbidity groups were younger than the low-risk group, and the younger elderly had a greater chance of participating in productive activities. When the covariates at baseline were added to the model (model 2), the GI & CNSLD group showed only higher physical function disabilities than the low-risk group (β = −0.099). The members of the CVD risk only group still had more physical function difficulties (β = 0.150) and worse cognitive function (β = − 0.104) than members of the low-risk group at baseline. Members of the multiple-risk group had more physical function difficulties (β = 0.205), more depressive symptoms (β = 0.162), worse cognitive function (β = − 0.104), lower economic satisfaction (β = −0.124), and lower life satisfaction

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H.-C. Hsu / Experimental Gerontology 66 (2015) 32–38

Table 1 Characteristics of samples by the four multimorbidity trajectory groups (Mean & SD or %). Characteristics in baseline (1993)

Group 1 (GI &CNSLD risks)

Group 2 (low risks)

Group 3 (CVD risk only)

Group 4 (multiple risks)

Significance

CVD 1993 1996 1999 2003 2007

15.2% 11.4% 10.8% 20.1% 22.4%

0.9% 0.9% 1.4% 3.8% 3.0%

52.3% 62.6% 74.6% 85.4% 57.8%

52.1% 66.2% 67.6% 81.7% 54.9%

*** *** *** *** ***

CNLSD 1993 1996 1999 2003 2007

23.6% 23.6% 22.3% 25.8% 26.7%

4.6% 1.7% 3.1% 4.7% 2.0%

7.0% 4.1% 4.9% 8.1% 6.3%

21.8% 18.4% 19.3% 33.2% 21.7%

*** *** *** *** ***

Arthritis 1993 1996 1999 2003 2007

23.1% 26.1% 27.7% 23.1% 22.6%

17.4% 15.9% 16.7% 17.8% 14.3%

20.8% 17.3% 16.3% 21.8% 17.7%

67.5% 65.7% 55.0% 66.8% 61.8%

*** *** *** *** ***

0.9% 3.0% 0.9% 2.6% 2.9%

0.9% 0.3% 0.5% 1.5% 3.4%

2.1% 0.5% 0.5% 1.2% 0.7%

1.9% 2.9% 1.5% 5.7% 5.7%

GI 1993 1996 1999 2003 2007

28.8% 33.8% 34.7% 30.4% 26.2%

1.1% 1.6% 1.1% 1.2% 0.9%

10.6% 9.1% 10.1% 9.4% 5.0%

31.9% 47.4% 39.4% 49.8% 36.6%

*** *** *** *** ***

Renal disease 1993 1996 1999 2003 2007

10.3% 6.2% 5.4% 6.3% 6.5%

1.9% 1.2% 2.0% 1.8% 1.3%

4.7% 5.7% 2.5% 4.9% 6.9%

15.0% 25.6% 18.8% 31.8% 28.0%

*** *** *** *** ***

Baseline Sex (female) Age Education Physical function difficulties Depressive symptoms Cognitive function Social support Life satisfaction Economic satisfaction Smoking (yes) Drinking (yes) Physical activity (yes) Health exams 1993–1996 (yes) Marital status (having spouse) Productive activity (yes)

42.6% 70.09 (4.85) 4.79 (4.34) 0.97 (2.21) 6.29 (6.26) 1.19 (1.87) 13.41 (3.47) 2.17 (1.38) 3.67 (0.95) 33.7% 18.6% 58.7% 43.9% 67.4% 64.3%

60.7% 69.07 (3.93) 4.52 (4.60) 2.03 (3.67) 8.11 (6.65) 1.21 (1.86) 13.33 (3.45) 2.08 (1.45) 3.49 (1.02) 18.0% 8.6% 62.1% 50.4% 73.3% 63.9%

*** * * ** ** *

Cancer 1993 1996 1999 2003 2007

39.4% 70.62 (5.17) 5.54 (4.64) 1.65 (3.69) 5.97 (5.87) 1.29 (1.93) 13.21 (3.61) 2.64 (1.36) 3.67 (0.94) 33.9% 16.5% 57.8% 40.8% 72.7% 61.8%

52.0% 69.84 (4.05) 5.55 (4.82) 1.21 (2.58) 6.28 (5.81) 0.99 (1.55) 13.57 (3.31) 2.48 (1.45) 3.70 (0.90) 18.4% 10.4% 64.1% 51.4% 73.0% 58.6%

*** ** *

*** *** * *

N = 2584. The differences across 4 groups were examined by Chi-square test or one-way ANOVA. *p b 0.05, **p b 0.01, ***p b 0.001.

(β = −0.172) than members of the low-risk group at baseline. In other words, the multiple-risk group showed worst health compared with other groups; the GI & CNSLD groups also showed worse health in some indicators compared with the low-risk group. 4. Discussion This study examined the multimorbidity trajectories of chronic diseases among the Taiwanese elderly over a time span of 14 years. Six types of chronic diseases were considered simultaneously, and then, four multimorbidity trajectory groups were identified: low risk, GI & CNSLD risk, CVD risk only, and multiple risks. More than half of the study participants had low risk of chronic disease multimorbidity,

and they had better successful aging performance. Although past cross-sectional studies have attempted to describe multimorbidity patterns (Kirchberger et al., 2012; Marengoni et al., 2008; Prados-Torres et al., 2012; Schäfer, 2012; van den Bussche et al., 2011), disease selection differed across the studies, and therefore, the results cannot be compared directly. This study identified the group of participants with the highest risk and multiple risks of developing chronic diseases, and the probability of CVD, CNSLD, cancer, GI, and renal disease in this group increased over time. Additionally, the differences observed among the multimorbidity trajectory groups reflected their initial successful aging status at baseline. That is, when participants had worse physical function and depressive symptoms at baseline, they

H.-C. Hsu / Experimental Gerontology 66 (2015) 32–38 Table 2 Multinomial logistic regression of characteristics of baseline to predict multimorbidity trajectory groups (the low risk group as the reference). Characteristics in baseline (1993)

Group 1 (GI &CNSLD risk)

Group 3 (CVD risk only)

Group 4 (Multiple-risk)

Odds ratio

Odds ratio

Odds ratio

Education 0.942⁎ 0.996 0.961 Age 0.940⁎⁎ 0.938⁎⁎ 0.858⁎⁎⁎ Physical function difficulties 1.308 1.074 1.173⁎⁎ Depressive symptoms 1.036 1.043 1.066⁎⁎ Cognitive function 1.029 1.025 1.001 Social support 0.984 0.984 1.011 Life satisfaction 0.869 0.996 0.921 Economic satisfaction 1.021 0.936 0.766 Sex (female) 0.902 1.134 1.531 Smoking (yes) 1.051 0.472⁎⁎ 0.641 Drinking (yes) 1.168 0.671 0.699 Physical activity (yes) 1.448 1.191 1.607 Health exams 1993–1996 (yes) 1.373 1.933⁎⁎ 2.266⁎⁎ Marital status (having spouse) 0.817 1.059 1.067 Productive activity (yes) 1.203 0.877 1.192 −2 log likelihood (intercept only) = 2.096E3, −2 log likelihood (final) = 1.963E3 Chi-square = 132.456 (d.f. = 45), p b 0.001 Note: n = 798. Intercept is omitted. The low risk group was the reference group of the multimorbidity trajectories. ⁎ p b 0.05. ⁎⁎ p b 0.01. ⁎⁎⁎ p b 0.001.

were more likely to be part of a higher-risk disease trajectory group (particularly the multiple-risk group). The multiple-risk group also performed worse in terms of the later successful aging indicators. Among all of the previous multimorbidity studies, the only consistent conclusion was that cardiovascular and metabolic disorders usually co-occur. In the present study, two groups (CVD only and multiple risks) had higher risks of CVD across time. Members of these groups also had more physical function difficulties and more depressive symptoms,

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although these were not significant for the CVD risk only group. The elderly in these groups may have had chronic diseases before the follow-ups, and thus, the physical disabilities and depressive symptoms reflected their multimorbidity. Thus, these participants may have been more likely to undergo health examinations or to smoke less because they were aware of the health management needed to control their diseases. Members of these two groups also had less education and were more likely to be female, which is consistent with the results of previous studies (Nagel et al., 2008; Prados-Torres et al., 2012). It is noteworthy that the participants in the CVD risk only group had lower probabilities of other chronic diseases. This finding implies that these elderly may have had some health strengths that were absent in others; these strengths, which could be related to genes, diet, or personality, were not identified in this study and should be explored in the future. Additionally, members of the CVD risk only group had more physical function difficulties and worse cognitive function than the low-risk group at the end of follow-up after controlling for other covariates. This finding indicates that older people in this group may suffer from CVD-related impairment of physical function or cognitive function, but they may still be similar to the low-risk group in other dimensions of successful aging. Although the prevalence of CVD is high among the elderly, if those with CVD risk only manage their health well and prevent other comorbidity risks, they have a good chance of aging successfully. Factors related to multimorbidity include age, sex, level of education, and health behaviors, and these factors seem to be valid across nationalities (Fortin et al., 2005; Marengoni et al., 2009; Nagel et al., 2008; Prados-Torres et al., 2012). In this study, age was significantly related to the multimorbidity trajectories; however, members of the three higher-risk groups were younger than members of the low-risk group. It is possible that survivors are healthier, and thus, the low-risk group members are older on average. Females were more likely to be in the CVD risk only group or in the multiple-risk group; however, this tendency was not significant after controlling

Table 3 Multimorbidity trajectories (1993–2007) predict later successful aging indicators (2007). Multimorbidtiy trajectories and covariates in 1993

GI &CNSLD group CVD only group Multiple-risk group R2 or −2 LL GI &CNSLD group CVD only group Multiple risk group Age Sex (female) education Economic satisfaction Marital status (have spouse) Physical function difficulties Depressive symptoms Cognitive function Social support Productive activity (yes) Life satisfaction Smoking (yes) Alcohol drinking (yes) Physical activity (yes) Health exams (yes) R2/−2LL

Physical function difficulties (Beta)

Depressive symptoms (Beta)

Model 1: Multimorbidity trajectories 0.120⁎⁎⁎ 0.047 0.188⁎ 0.066 0.251⁎⁎⁎ 0.227⁎⁎⁎

Cognitive function (Beta)

Social support (Beta)

Productive activity (OR)

Economic satisfaction (Beta)

Life satisfaction (Beta)

−0.056 −0.171⁎⁎ −0.122⁎

−0.034 −0.038 −0.107⁎ R2 = 0.009

3.358⁎⁎⁎ 2.500⁎⁎⁎ 1.899⁎⁎ -2LL = 1346.474

−0.011 −0.025 −0.170⁎⁎ R2 = 0.027

0.024 0.007 −0.234⁎⁎⁎ R2 = 0.059

0.024 −0.018 −0.044 −0.040 0.035 0.101 0.012 0.060 −0.049 −0.106 −0.020 0.140⁎⁎ −0.040 0.079 −0.067 −0.025 0.003 −0.018 R2 = 0.110

1.207 0.903 0.739 0.894⁎⁎⁎ 1.248 1.057⁎ 0.905 1.095 0.886 0.957⁎

0.010 −0.018 −0.124⁎ −0.041 −0.078 0.055 0.135⁎⁎ −0.047 0.100 −0.088 0.058 0.030 0.091 0.040 −0.146⁎⁎

0.080 0.020 −0.172⁎⁎ −0.019 0.005 0.056 0.076 0.044 −0.057 −0.028 −0.064 0.098 0.004 0.124⁎ −0.064 −0.049 −0.045 0.064 R2 = 0.148

R2 = 0.058 R2 = 0.044 R2 = 0.027 Model 2: Multimorbidity trajectories and covariates 0.099⁎ 0.020 −0.033 0.150⁎⁎ 0.041 −0.156⁎⁎ 0.205⁎⁎⁎ 0.162⁎⁎ −0.104⁎ 0.283⁎⁎⁎ 0.050 −0.251⁎⁎⁎ −0.119⁎ 0.043 −0.034 −0.125⁎ −0.037 0.105⁎ 0.041 −0.007 0.157⁎⁎⁎ 0.105⁎

0.012 0.032 0.086 0.170⁎⁎

−0.057 0.026 −0.067 −0.135⁎⁎

−0.029 −0.025 −0.047 0.024 −0.052 0.003 −0.023 0.002 R2 = 0.274

0.047 −0.017 0.043 −0.010 0.049 0.035 −0.044 0.085 R2 = 0.111

−0.078 0.030 −0.003 −0.028 0.057 −0.046 −0.015 −0.100⁎ R2 = 0.197

0.999 0.957 1.752⁎⁎ 0.941 0.728 1.017 1.014 1.204 −2LL = 857.497

−0.018 0.027 −0.015 R2 = 0.111

Note: The low risk group was the reference group of the multimorbidity trajectories. LL stands for log likelihood. The analyses were done by linear regression or logistic regression models. The constants are omitted in the table. The beta coefficients of the linear regression are standardized; the odds ratios are reported in the model for productive activity. ⁎ p b 0.05. ⁎⁎ p b 0.01. ⁎⁎⁎ p b 0.001.

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for other covariates. Moreover, members of the CVD risk only group and the multiple-risk group were more likely to undergo health examinations, which may reflect their awareness of symptoms or discomfort and their willingness to investigate these issues. This study had several limitations. First, attrition and deaths occurred between the follow-up surveys. Only those who participated in at least three waves were included in the group-based trajectory analysis, and only those who survived and completed the last wave were analyzed for the performance of successful aging. Second, timevarying covariates were not included in the analysis to examine the factors related to trajectory groups, such as the physical function, depressive symptoms, and cognitive function. Only the baseline covariates were added in the model. Third, data about certain related factors, such as dietary patterns or adaptation to chronic diseases, were unavailable at baseline. Fourth, some people may be aware of their health risks and may change their lifestyle accordingly. However, lifestyle changes in the early stages of life were not considered. Fifth, the data were obtained from older Taiwanese, and the results may not be generalizable to other populations. Generally, the older people with CVD, GI, CNSLD or multiple risks showed a worse performance in successful aging than the low-risk groups, indicating that these chronic diseases are more related to quality of life in older age. Although having one or more chronic diseases is common among older people, it does not signify that older people with chronic diseases have no chance to age successfully. In fact, most older people (except the multiple-risk trajectory group) exhibited fair or good performance in the successful aging indicators. Older people with chronic diseases should not be distressed or give up health management because they still have a good chance of living a successful aging life. The fewer health risks older people have, the greater is the probability of aging successfully. We encourage middleaged and older people to learn to manage their health despite the development of chronic diseases and to attempt to reduce the risks of developing any other chronic diseases. Primary care physicians and geriatricians should evaluate the multimorbidity risks of older patients in the clinical setting and should help older people to maintain their comprehensive health without solely focusing on a specific disease. Acknowledgments The research was supported by grants from the National Science Council, Taiwan, Republic of China (NSC 101-2410-H-468-008-MY2). The data were provided by the Health Promotion Administration, Ministry of Health and Welfare, Taiwan, Republic of China. The interpretation and conclusions contained herein do not represent those of the Health Promotion Administration. This study received the approval of the Research Ethics Committee of the Central Regional Research Ethics Center, Taiwan, R. O. C. (No. CRREC-101-062). The author appreciates the assistance of Dr. Bobby L. Jones in developing the SAS program for the multiple group-based trajectory analysis used in this study. References American Geriatrics Society Expert Panel on the Care of Older Adults with Multimorbidity, 2012. Guiding principles for the care of older adults with multimorbidity: an approach for clinicians. J. Am. Geriatr. Soc. 60, E1–E25. Anderson, G., Horvath, J., 2004. The growing burden of chronic disease in America. Public Health Rep. 119, 263–270.

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