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Article

Frailty and Its Contributory Factors in Older Adults: A Comparison of Two Asian Regions (Hong Kong and Taiwan)

1
Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China
2
The Chinese University of Hong Kong Jockey Club Institute of Ageing, Shatin, Hong Kong, China
3
Department of Public Health, College of Medicine, National Cheng Kung University, Tainan 701, Taiwan
4
The Chinese University of Hong Kong Jockey Club Centre for Osteoporosis Care and Control, Shatin, Hong Kong, China
*
Authors to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2017, 14(10), 1096; https://doi.org/10.3390/ijerph14101096
Submission received: 18 July 2017 / Revised: 12 September 2017 / Accepted: 15 September 2017 / Published: 21 September 2017
(This article belongs to the Special Issue Ageing Well: The Role of Age-Friendly Environments)

Abstract

:
This study aimed to compare the prevalence of frailty across three Chinese populations: Hong Kong, Taiwan-urban and Taiwan-rural. Contributing factors to disparities in frailty were also examined. Data were derived from the Osteoporotic Fractures in Men (MrOs) and Women (MsOs) (Hong Kong) Study (n = 4000) and the Taiwan Longitudinal Study on Aging (n = 2392). Frailty was defined as an index calculated from 30 multiple deficits. The ratio of the frailty index to life expectancy at birth (FI/LE) was used as an indicator of compression of morbidity. Frailty was more prevalent in Taiwan-urban (33.1%) and Taiwan-rural (38.1%) compared to Hong Kong (16.6%, p < 0.05) and was higher in women (22.6–49.7%) than in men (10.5–27.5%, p < 0.05). The ratios of FI/LE were higher in Taiwan-urban and Taiwan-rural (both 0.27) compared to Hong Kong (0.20, p < 0.05). Multivariate analyses revealed that older age, being a woman and low levels of physical activity were common risk factors for frailty across the three populations. Alcohol use was inversely associated with frailty in both Hong Kong and Taiwan-urban populations, but not in Taiwan-rural. Living alone was associated with frailty in Hong Kong men, but not in Hong Kong women or Taiwanese people. For all study populations, older age and being a woman constituted the highest attributable factor. This comparison provides useful data to inform government policies.

1. Introduction

With the increase in life expectancy, the prevalence of multi-morbidity is expected to increase [1], resulting in increased demand for healthcare services. Many studies have examined trends in disability among older adults aiming to identify patterns of ageing [2,3] and whether there has been a compression of morbidity [4] (i.e., the increased life expectancy accompanied by a shortening of the length of morbid life) as proposed by Fries [5,6,7]. However, disability is influenced not only by physiological functioning, but also by the environment in which it takes place. Frailty, the state representing a decline in functional reserves, has emerged as a key aspect in research on population ageing. It is used as a health indicator for how well populations are aging and is commonly used in the context of the elderly facing functional disabilities, as many studies have demonstrated that frail individuals are at a high risk of becoming disabled, independent of the presence of co-morbid diseases [8,9,10,11,12,13]; thus, frailty has been suggested as a better predictor of health and well-being than the presence or absence of disease and has been used as an indicator of the compression of morbidity to predict the demands on the healthcare system [14].
Currently, there are two frameworks (approaches) that are referred to for the assessment of frailty. A popular approach as proposed by Fried et al. [8] (i.e., the phenotype approach) encompasses the assessment of five criteria based primarily on physical attributes and capabilities including poor grip strength, slow walking speed, low levels of physical activity, exhaustion and unintentional weight loss, whereas an individual is considered to be frail if he or she meets three or more of the five criteria. Another notable approach is that of Rockwood and Mitnitski [15,16] (i.e., the multiple deficits approach) in which frailty is viewed in terms of the number of health deficits that manifest in the individual, leading to a continuous measure of frailty (Frailty Index (FI)).
The prevalence of frailty in different populations has been widely studied. Depending on how frailty is measured, prevalence can range from 4.0% in the United States to 59.1% in the Netherlands for those aged 65 and above [17,18,19,20,21] and 13.0% in the United States to 52.0% in Australia for those aged 85 and above [8,11,22,23]. Different rates of frailty across populations suggest that the contributory factors of frailty that are culturally unique may also be different across populations. However, comparative studies of the levels of frailty across populations are sparse; although in a previous study using the Survey of Health, Ageing and Retirement in Europe (SHARE) and the study on global ageing and adult health (SAGE), frailty was reported as a useful construct that was associated with social support and healthcare systems [24].
In-depth cross-cultural comparisons of frailty could provide a better understanding of levels in the health of older adults, providing useful data to examine the role of personal, social and environmental factors in contributing to disparities in frailty, as well as to inform government policies. In this study, we compared the prevalence of frailty and its contributory factors across three Chinese populations: Hong Kong, Taiwan-urban and Taiwan-rural. In addition, the ratio of FI to life expectancy at birth (FI/LE) was used as an indicator of compression of morbidity for comparison across the three populations.

2. Materials and Methods

2.1. Data Sources

This is a comparison of cohorts of Hong Kong and Taiwan using two sets of aggregated secondary data on frailty prevalence and contributory factors. Since data were aggregated at the group level, relationships at the individual level were not determined.
In Hong Kong, 4000 Chinese adults aged 65 years and above were recruited by placing advertisements in housing estates and community centers, as part of a bone health survey (Osteoporotic Fractures in Men (MrOs) and Women (MsOs) (Hong Kong) Study), which started in August 2001–December 2003, with follow-up studies administered in 2003–2005 and 2005–2007. The 14-year follow-up is ongoing. The sample was stratified to recruit approximately the same number of people in each of the three age strata: 65–74, 75–84, 85+ years. Those who were unable to walk independently, had bilateral hip replacement or were not competent to give informed consent were excluded. Eligible persons were invited to attend a health check at the School of Public Health, The Chinese University of Hong Kong. A team of trained research assistants administered the study questionnaire and took physical measurements for each participant on the same day. To be comparable to the Taiwan dataset, this analysis reported results based on data from the baseline (2001–2003) only. Details of the survey population have been reported elsewhere [25]. Ethical approval was obtained from the Clinical Research Ethics Committee of the Chinese University of Hong Kong and Hospital Authority New Territories East Cluster (CRE-2003.102).
In Taiwan, 4049 individuals aged 60 years and above from the household registration population were recruited for the Taiwan Longitudinal Study on Aging (TLSA), a nationally-representative survey of adults aged 60 and above, which started in 1989, with follow-up surveys administered in 1993, 1996, 1999, 2003, 2007 and 2011. A three-stage systematic random sampling method was used for the selection of an equal probability sample. First, 56 townships and districts were selected from 331 townships and districts in Taiwan. Second, villages and neighborhoods were selected. Third, two individuals aged 60 years and above were enrolled from each selected village and neighborhood. Data were collected with face-to-face interview questionnaires by trained interviewers. To be comparable to the Hong Kong dataset, this analysis reported results based on data from the 2003 follow-up study only. In this study, after excluding those who were living in long-term care institutions and having an unknown living area, a sample of 2392 older adults aged 65 years and above was used. Detailed information of the study methodology and TLSA data collection is provided by the Health Promotion Administration of Ministry of Health and Welfare in Taiwan [26]. Research ethics committee approval was obtained from the National Cheng Kung University (A-ER-105-149).

2.2. Frailty Index Construction

The multiple deficits approach was used to construct the FI, which was constructed from 30 items covering self-reported medical and drug histories (14 items), functional assessments and psychological well-being (5 items), as well as geriatric syndromes (11 items). There were some variations in the definitions of some items between the Hong Kong cohort and the Taiwanese cohorts. For example, in the Hong Kong cohort, cognitive function was assessed using Mini-Mental State Examination (MMSE) [27]; depression was assessed using the 15-item Geriatric Depression Scale (GDS) [28]. In the Taiwanese cohorts, these were assessed using the Short Portable Mental Status Questionnaire (SPMSQ) [29] and the Center for Epidemiologic Studies Depression Scale (CES-D) [30], respectively. Details of the measurement methods of assessing the items are described in Appendix A Table A1. All items for FI had less than 5% missing values and were dichotomized into the presence or absence of a frailty marker, and a score of 1 representing a deficit was given to each item. The FI was calculated as the proportion of the number of deficits for an individual to the maximum total number of deficits. A cut-off point of ≥0.25 was used to indicate frailty [11,16].

2.3. Potential Contributory Factors

To examine factors that may contribute to frailty that are common to both datasets, the following variables were included in the analysis: basic socio-demographic characteristics including age, sex, educational attainments and living arrangement (living alone vs. not living alone) as well as lifestyle factors including smoking (current and ex-smokers vs. never smokers), alcohol use (drinkers (consumed >12 alcoholic drinks in the past 12 months) vs. non-drinkers (consumed ≤12 alcoholic drinks in past 12 months)) and physical activity (low levels (exercise <5–7 days/week or exercise <6 times/week) vs. high levels (exercise ≥5–7 days/week or exercise ≥6 times/week)).

2.4. Statistical Analysis

Four thousand Hong Kong men and women aged 65 years and above and 2392 Taiwanese men and women aged 65 years and above were included in this analysis. To control for the differences in age distributions across the three study populations (Hong Kong, Taiwan-urban and Taiwan-rural), standardization was applied to each of the populations using the sex and age (5-year age groups) proportions according to the 2011 Hong Kong Census Population. All analyses were performed after standardization. After standardization, the age distribution of the three populations being compared followed the same distribution as the 2011 Hong Kong Census Population. Population characteristics of Hong Kong and Taiwan were compared using analysis of variance (ANOVA) for continuous variables or the chi-square test for categorical variables. Analyses were repeated by stratifying age groups of 65–74, 75–84 and 85+ years. Prevalence of frailty was compared in each cohort for men and women, controlling for age, by the chi-square test. In addition, a ratio of the frailty index (FI) to life expectancy at birth (FI/LE) was used as an indicator of the compression of morbidity. Lower values (i.e., low FI and high LE) indicate morbidity compression. The LE is cohort- and sex-specific. In 2003, the average LE was 78.5 years for Hong Kong men and 84.4 years for Hong Kong women [31]; and 74.8 years for Taiwanese men and 80.3 years for Taiwanese women [32]. FI/LE was calculated for each participant by dividing the FI of each participant by the cohort- and sex-specific LE. Differences of FI/LE between the three populations were examined by ANOVA. This indicator has been used in a previous study [14]. Risk factors were compared between the three cohorts using crude odds ratio (OR). Variables with p-value < 0.1 were included in multiple logistic regressions. The dependent variable was frailty. Independent variables were age group, education level, living arrangement, smoking, alcohol use and physical activity. Attributable fractions (AF) for risk factors contributing to frailty were then compared. The AF is the proportion of the incidence of a disease in the exposed that is due to the exposure. It is the proportion of the incidence of a disease in the exposed that would be eliminated if exposure were eliminated. The AF was calculated using the formula (AF = (OR − 1)/OR)) [33]. t-tests were used for comparing mean continuous variables, log OR of frailty and Area Under the Curve (AUC) in logistic regression. Chi-square tests were used for comparing categorical variables. Missing observations were excluded in the analysis. Statistical analyses were performed using the Window-based SPSS Statistical Package (Version 23.0; SPSS Inc., Chicago, IL, USA). All statistical tests were two-sided. A p-value of < 0.05 was considered statistically significant.

3. Results

The FI was constructed from 30 items, and the prevalence of each was sorted into categories (medical and drug history, functional assessment and psychological well-being and geriatric syndrome) (Table 1). With respect to medical and drug history, both Taiwan-urban and -rural populations had higher prevalence rates compared to the Hong Kong population, especially in arthritis, gastropathy/gastrectomy, heart diseases, Chronic Obstructive Pulmonary Disease (COPD) and kidney disease, with the prevalence in Taiwan approximately twice that of Hong Kong. Hypertension and cataract were the top two highest prevalences of chronic disease among the three populations. The Taiwan-urban and -rural populations consumed more drugs than the Hong Kong population.
With respect to functional assessment and psychological well-being, the Taiwan-urban and -rural populations had more functional limitations (low lower limb strength and poor walking performance), but lower prevalence of cognitive impairment. In addition, the prevalence of depression was three-times higher in the Taiwan-urban and -rural populations than the Hong Kong population. With respect to geriatric syndrome, the top three syndromes with the highest prevalence showed very different patterns between the two regions. The Taiwan-urban and -rural populations had poor health and difficulty in moderate activities and climbing several stairs; however, the Hong Kong population had back pain, higher risk of falls and less activity.
There were significant differences in socio-demographic characteristics and lifestyle between the three groups (Table 2). Overall, the population characteristics of Taiwan-rural significantly differed from those of Hong Kong. The highest percentage of low education occurred in the Taiwan-rural population, followed by Hong Kong and then Taiwan-urban. Living alone was the commonest among Hong Kong women, followed by Taiwanese rural men, with the lowest percentage among men in Hong Kong. Compared to Hong Kong men, Taiwanese men had a higher percentage of smoking. An opposite pattern was observed for women. The percentage of alcohol use was the highest among Taiwanese rural men, while the percentages for Taiwanese urban men and Hong Kong men were similar. Taiwanese urban men were more active compared with Hong Kong men. However, Taiwanese women were less active compared with Hong Kong women. These differences remained significance across different age groups (Appendix A Table A2, Table A3 and Table A4).
The prevalence of frailty increased with age in all three populations and approximately doubled for every 10 years until around age 85, and the Hong Kong cohort had the lowest prevalence rate (Table 3). Overall, the prevalence of frailty was higher in women (22.6–49.7%) than in men (10.5–27.5%), and the differences were found in all age groups (p < 0.05). Using the ratio of FI divided by LE (i.e., FI/LE) as an indicator of the compression of morbidity, the highest ratio was observed in the Taiwan populations, with the Hong Kong population having the lowest ratio (Table 3). Similarly, the ratio was higher in women than in men in all age groups.
Risk factors for frailty were similar in all three populations (Table 4a); those having the highest odds ratios (ORs) were older, women, had low levels of physical activity and had a low educational level. Smoking and alcohol use were significantly associated with a lower risk of frailty in all three populations. Living alone was only significant in the Hong Kong cohort. In multiple logistic models, variations in the ORs and their magnitude were noted between the three populations. The highest ORs occurred in the Hong Kong cohort, for the following risk factors: older age, being a woman, low levels of physical activity and smoking. For the Taiwanese cohorts, older age, being a woman, low levels of physical activity and a low educational level (Taiwan-urban only) were significant risk factors. The risk of frailty among the older age group (aged 85+) and those who had low levels of physical activity was significantly different between the Hong Kong cohort and the Taiwanese cohorts. However, living alone was not associated with frailty in any of the three populations after controlling for covariates.
Furthermore, sex-specific analyses were performed (Table 4b,c). Except for living alone, risk factors for frailty were the same between Hong Kong and Taiwanese rural male populations. Alcohol use was inversely associated with frailty. In multiple logistic models, among Hong Kong men, the highest ORs were observed in older people, followed by those living alone and with low levels of physical activity. In Taiwanese men, older age and low levels of physical activity (Taiwan-rural only) were significant risk factors. Alcohol use was associated with a lower risk of frailty in the Hong Kong and the Taiwan-urban populations only. Among women populations, while older age (75–84) and low levels of physical activity were significant risk factors, living alone was not significant at all. In multiple logistic models, a similar pattern was observed in Hong Kong women, except that living alone was associated with a lower risk of frailty. However, living alone was not significant in Taiwanese women.
AF for frailty in the three populations are shown in Table 5. For all three populations, older age and women constituted the highest AF, while alcohol was inversely associated with frailty. AF of older age (85+) between the Hong Kong cohort and the Taiwan-urban cohort and AF of alcohol use between the Taiwan-urban and the Taiwan-rural cohorts differed significantly.

4. Discussion

This study compared the prevalence of frailty and its contributory factors in samples of older Chinese adults in Hong Kong and Taiwan. With a harmonized assessment of frailty, the results showed significantly different patterns. While the prevalence of frailty for Hong Kong was much lower than that in the Taiwanese cohorts, the prevalence of frailty for the Taiwanese urban population was similar to that for the Taiwanese rural population. For all cohorts, FI increased with age and was higher amongst women. However, differences in risk factors in terms of socio-demographic characteristics and lifestyle and the AF to frailty were observed across the three populations. These differences may partly explain the variations in the prevalence of frailty between studies.
With respect to socio-demographic factors, the comparison shows that there was an association between low education and frailty for Taiwanese urban women. In addition, this study found that living alone was positively associated with frailty in men, but was inversely associated with frailty in women in the Hong Kong cohort. The differences between men and women could be due to differences between the two sexes in terms of social support networks. It has been suggested that women tend to have larger and more diverse social networks than men [34,35]; therefore, women may be more able to adapt to living alone compared to men. Some studies have suggested that upon losing a spouse, men had greater mortality risk in widowhood compared with women [34,36]. In Hong Kong, living alone is a common phenomenon among older populations (Year 1991, 11.6%; Year 2014, 14.8%) [37]. The increasing number of people who live alone will probably increase the number of people with frailty. Hence, these findings emphasize the importance of social support networks in the prevention of frailty. However, the association between living arrangement and frailty was less strong in the Taiwanese cohorts compared with the Hong Kong cohort. Further research is certainly needed to fully explore whether culture differences lead to disparities in frailty.
With respect to lifestyle factors, we found that alcohol consumption was associated with lower risk of frailty in all studied cohorts. Individuals who consumed >12 alcoholic drinks in the past 12 months were less frail than those who consumed ≤12 alcoholic drinks in past 12 months. The positive association observed could be due to the abstainer/quitter bias as individuals with poor health, particularly older people, drink less than those individuals with good health. Nevertheless, previous studies showed that moderate alcohol consumption was associated with less frailty [38,39,40], possibly through anti-inflammatory mechanisms. A number of studies have also demonstrated an association between inflammatory markers (e.g., Interleukin 6 (IL-6) and C-Reactive Protein (CRP)) and prevalent frailty [41,42,43]. Unfortunately, the present study did not collect information regarding inflammatory markers. This area needs further studies and has important public health implications. As expected, there was an association between low levels of physical activity and frailty in the Hong Kong and the Taiwanese cohorts. This finding reaffirms the importance of physical activity in healthy ageing and retarding the onset of frailty [44].
Our findings also showed that functional limitations (e.g., ‘low lower limb strength’, ‘poor walking performance’) and some geriatric syndromes (e.g., ‘difficulty in moderate activities’, ‘difficulty in climbing several stairs’) were more prevalent in the Taiwanese cohorts compared to the Hong Kong cohort. The differences could be due to the use of self-reported data in the Taiwanese cohorts versus the use of objective measures (e.g., chair stands, gait speed) in the Hong Kong cohort. The Taiwanese cohorts also had a higher prevalence of depressive symptoms compared with the Hong Kong cohort, although the difference in depressive symptoms may be related to differences in the measurement used (the GDS vs. the CES-D). The higher prevalence of depressive symptoms for the Taiwanese populations suggests that there is a need to focus on measures to prevent geriatric depression. For example, screening for depression as a routine psychiatric prevention strategy is recommended. The Hong Kong cohort, on the contrary, had higher prevalence of hypertension, osteoporosis, back pain, risk of falls and fractures compared with the Taiwanese cohorts. The higher prevalence of hypertension in the Hong Kong cohort might be due, in part, to the inclusion of objective blood pressure measurement data. The Hong Kong cohort also had a high prevalence of cognitive impairment, supporting the need for including cognitive assessments in primary care settings.
In this study, we used the FI/LE ratio as an indicator of the compression of morbidity. The higher ratio for the Taiwanese cohorts compared with the Hong Kong cohort suggests that population ageing in Taiwan is projected to be accompanied by increasing frailty. This finding allows a quick comparison of the compression of morbidity between populations and provides useful data to inform government policies for health and social care planning. Frailty interventions focusing on physical activity, nutrition and cognitive impairment [45,46,47], coupled with early detection, should be developed to combat the increasing rates of frailty.
There are limitations to this study. It is a secondary comparison of cohorts from Hong Kong and Taiwan using two different datasets with data collected at different time points. In the construction of the FI, the operative definitions of some FI components differ between Hong Kong and Taiwan. Nevertheless, it has been suggested that as long as the FIs cover a range of health deficits, they could yield comparable results even though their components are not the same [48,49,50,51]. The sampling strategies are different between cohorts. In Hong Kong, a non-probability sample was used, in which subjects were more educated, more physically active and may be healthier than the general elderly population of Hong Kong. In Taiwan, an equal probability sample was used in which subjects were recruited by a multi-stage stratified random sampling method; therefore, the different strategies might have recruited older adults with different characteristics, which may have biased the impact of socioeconomic status, living arrangement and lifestyle factors on frailty. In addition, questions and definitions used in both studies varied slightly, which might lead to discrepancies. For example, in the Hong Kong cohort, low levels of physical activity were defined as less than five days of exercise per week; in the Taiwanese cohorts, it was defined as less than six times of exercise per week. These differences need to be considered in interpreting the findings. Nevertheless, this comparative study was conducted within a framework of comparable measurements, which provides useful information for the analysis of policy impact compared to studies of single countries/regions. The inclusion of performance measures of functioning in the FI, in addition to the presence of specific diseases and a set of geriatric syndromes, would also provide a basis for descriptions of health states and for undertaking comparisons of health states across populations.

5. Conclusions

This cross-cultural study allows us to compare the prevalence of frailty and its contributory factors in two different settings, which will enhance our understanding of variations in the underlying dynamics of population ageing in Hong Kong and Taiwan. These findings will assist policymakers of the two regions in the planning of health and social care services. This study supports the need for prevention and early detection of frailty to enable older people to achieve healthy and active ageing. Further research is needed to explore cross-cultural differences in the trajectories of frailty and its application in predicting adverse health outcomes.

Acknowledgments

This research was funded by the Taiwan Collaboration Fund 2016–2017, The Chinese University of Hong Kong.

Author Contributions

R.Y., W.C.W., S.C.H. and J.W. conceived of and designed the experiments. R.Y., W.C.W. and J.L analyzed the data. R.Y., W.C.W., S.C.H. and J.W. wrote the paper.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Calculation of the frailty index.
Table A1. Calculation of the frailty index.
ComponentHong KongTaiwan
DescriptionValueDescriptionValue
Chronic disease history
HypertensionSelf-reported doctor-diagnosed hypertension or blood pressure ≥150/90 mmHg0 (No), 1 (Yes)Self-reported doctor-diagnosed high blood pressure0 (No), 1 (Yes)
CataractSelf-reported doctor-diagnosed cataracts0 (No), 1 (Yes)Self-reported doctor-diagnosed cataracts0 (No), 1 (Yes)
ArthritisSelf-reported doctor-diagnosed arthritis0 (No), 1 (Yes)Self-reported doctor-diagnosed arthritis or rheumatism0 (No), 1 (Yes)
Gastropathy/gastrectomySurgery to remove all or part of stomach or intestines0 (No), 1 (Yes)Self-reported doctor-diagnosed gastric ulcer or stomach ailment0 (No), 1 (Yes)
Heart diseasesSelf-reported doctor-diagnosed heart attack, coronary or myocardial infarction0 (No), 1 (Yes)Self-reported doctor-diagnosed heart diseases (palpitation does not count)0 (No), 1 (Yes)
OsteoporosisSelf-reported doctor-diagnosed osteoporosis0 (No), 1 (Yes)Self-reported doctor-diagnosed osteoporosis0 (No), 1 (Yes)
Diabetes type I or IISelf-reported doctor-diagnosed diabetes0 (No), 1 (Yes)Self-reported doctor-diagnosed diabetes0 (No), 1 (Yes)
COPDSelf-reported doctor-diagnosed chronic obstructive lung disease, chronic bronchitis, asthma, emphysema or COPD0 (No), 1 (Yes)Self-reported doctor-diagnosed bronchitis, pneumonia, asthma, pulmonary diseases or other respiratory ailment0 (No), 1 (Yes)
GoutSelf-reported doctor-diagnosed gout0 (No), 1 (Yes)Self-reported doctor-diagnosed gout0 (No), 1 (Yes)
Kidney diseaseSelf-reported doctor-diagnosed kidney stones0 (No), 1 (Yes)Self-reported doctor-diagnosed renal disease (including stone)0 (No), 1 (Yes)
StrokeSelf-reported doctor-diagnosed stroke0 (No), 1 (Yes)Self-reported doctor-diagnosed stroke0 (No), 1 (Yes)
GlaucomaSelf-reported doctor-diagnosed glaucoma0 (No), 1 (Yes)Self-reported glaucoma and other eye diseases (presbyopia and blindness do not count)0 (No), 1 (Yes)
Cancer/malignant tumorSelf-reported doctor-diagnosed cancer0 (No), 1 (Yes)Self-reported doctor-diagnosed cancer or malignant tumor0 (No), 1 (Yes)
Medication useNumber of medication (stimulants, sedatives, aspirin, painkillers for arthritis)0 (No), 1 (≥1)Number of medication (stimulants, sedatives, aspirin, painkillers for arthritis)0 (No), 1 (≥1)
Functional assessment and psychological wellbeing
Low lower limb strengthRepeated chair stand (seconds to complete 5 stands)0 (≤15), 1 (>15)Self-reported difficulty in squatting0 (No), 1 (Yes)
DepressionGDS0 (<8), 1 (≥8)CES-D0 (<8), 1 (≥8)
Poor walking performanceSix meter usual pace (minimum time to walk 6 m, seconds) Male: 0 (≤7.65), 1 (>7.65) Female: 0 (≤8.63), 1 (>8.63)Self-reported difficulty in walking for 200–300 m0 (No), 1 (Yes)
Cognitive impairmentMMSE0 (≥24), 1 (<24)SPMSQ0 (≥6), 1 (<5)
Low grip strength Grip strength (max of right/left grip strength, kg) Male: 0 (≥25), 1 (<25) Female: 0 (≥17), 1 (<17)Self-reported difficulty in grasping or turning objects with figures0 (No), 1 (Yes)
Geriatric syndrome
Difficulty in moderate activitiesSelf-reported difficulty in moderate activities such as moving a table, pushing a vacuum cleaner0 (No, not limited at all), 1 (Yes, limited a lot/a little)Self-reported difficulty in running a short distance0 (No), 1 (Yes)
Difficulty in climbing several stairsSelf-reported difficulty in climbing several flights of stairs0 (No, not limited at all), 1 (Yes, limited a lot/a little)Self-reported difficulty in climbing several flights0 (No), 1 (Yes)
Poor healthSelf-rated health (compared to other people of your own age)0 (Excellent/good/fair for my age), 1 (Very poor/poor for my age)Self-rated current health0 (Excellent /good/fair), 1 (Very poor/poor)
Often feel fatigue or tiredHave had much energy during the past 4 weeks0 (A good bit of/most of/all of the time), 1 (None of/a little of/some of the time)A selected item from the SWLS (feeling most of the things done are dull (not fun))0 (No), 1 (Yes)
Fall in the past 12 monthsHave fallen and landed on the floor or fallen and hit an object like a table or a chair in the past 12 months0 (<2 time), 1 (≥2 time)Have ever tumbled or fallen in the past year (including a tumble during walking, slip, failure to sit well or stand firmly, or fall because of dizziness, or fall off the bed, regardless of getting injured or not)0 (No), 1 (Yes)
Risk of fallHave trouble with dizziness0 (No), 1 (Yes)Have trouble with dizziness0 (No), 1 (Yes)
Difficulty in movementPhysical limitations in work or other regular daily activities during the past 4 weeks0 (No), 1 (Yes)ADLs0 (<1), 1 (≥1)
Less activityLess activity during the past 4 weeks0 (No), 1 (Yes)Less activity during the past month due to illness or injury0 (No), 1 (≥1 day)
UnderweightBMI, kg/m20 (≥18.5 kg/m2), 1 (<18.5 kg/m2)BMI, kg/m20 (≥18.5 kg/m2), 1 (<18.5 kg/m2)
Ever fractureSelf-reported doctor-diagnosed fracture (have broken or fractured a bone)0 (0 time), 1 (≥1 time)Self-reported doctor-diagnosed hipbone fracture0 (No), 1 (Yes)
Back painSelf-reported back pain during the past 12 months0 (No), 1 (Yes)Self-reported back pain0 (No), 1 (Yes)
ADL, Activities of Daily Living; BMI, Body Mass Index; CES-D, Center for Epidemiologic Studies Depression Scale; COPD, Chronic Obstructive Pulmonary Disease; GDS, Geriatric Depression Scale; MMSE, Mini-Mental State Examination; SPMSQ, Short Portable Mental Status Questionnaire; SWLS, Satisfaction with Life Scale.
Table A2. Population characteristics between Hong Kong, Taiwan-urban and Taiwan-rural: age 65–74.
Table A2. Population characteristics between Hong Kong, Taiwan-urban and Taiwan-rural: age 65–74.
CharacteristicMean (SD)/n (%)
Hong KongTaiwan-UrbanTaiwan-Rural
Menn = 1087n = 284n = 403
Age, mean (SD)69.50 (2.37)69.62 (3.24)69.53 (2.94)
Low education, %620 (57.07)139 (48.77) 3308 (76.43) 1,2
Living alone, %37 (3.41)16 (5.61)48 (11.91) 1,2
Smoking, %662 (60.87)183 (64.44)312 (77.23) 1,2
Alcohol use, %293 (27.00)80 (28.07)128 (31.68)
Low levels of physical activity, %420 (38.63)100 (35.21)185 (45.91) 1,2
Womenn = 900n = 198n = 309
Age, mean (SD)69.44 (2.17)69.47 (2.92)69.54 (3.02)
Low education, %724 (80.38)166 (83.84)290 (93.85) 1,2
Living alone, %107 (11.86)17 (8.59)32 (10.36)
Smoking, %62 (6.91)7 (3.54)10 (3.24) 1
Alcohol use, %29 (3.22)14 (7.07) 316 (5.18)
Low levels of physical activity, %298 (33.12)97 (48.99) 3160 (51.61) 1
1 p-value < 0.05, comparing Taiwan-rural with Hong Kong; 2 p-value < 0.05, comparing Taiwan-rural with Taiwan-urban; 3 p-value < 0.05, comparing Hong Kong with Taiwan-urban. SD: Standard Deviation.
Table A3. Population characteristics between Hong Kong, Taiwan-urban and Taiwan-rural: age 75–84.
Table A3. Population characteristics between Hong Kong, Taiwan-urban and Taiwan-rural: age 75–84.
CharacteristicMean (SD)/n (%)
Hong KongTaiwan-UrbanTaiwan-Rural
Menn = 727n = 190n = 270
Age, mean (SD)78.48 (2.86)78.45 (2.76)78.61 (2.70)
Low education, %486 (66.80)85 (44.74) 3208 (77.04) 1,2
Living alone, %51 (7.03)23 (12.11) 338 (14.07) 1
Smoking, %512 (70.40)139 (73.16)208 (77.04) 1
Alcohol use, %116 (15.97)36 (18.85)65 (24.07) 1
Low levels of physical activity, %324 (44.62)66 (34.55) 3113 (42.01)
Womenn = 763n = 168n = 262
Age, mean (SD)78.94 (3.04)78.87 (2.76)79.01 (2.85)
Low education, %675 (88.50)136 (80.95) 3252 (96.18) 1,2
Living alone, %219 (28.64)24 (14.29) 335 (13.36) 1
Smoking, %113 (14.82)12 (7.19) 317 (6.49) 1
Alcohol use, %9 (1.15)8 (4.76) 38 (3.04) 1
Low levels of physical activity, %236 (30.94)87 (51.79) 3146 (55.51) 1
1 p-value < 0.05, comparing Taiwan-rural with Hong Kong; 2 p-value < 0.05, comparing Taiwan-rural with Taiwan-urban; 3 p-value < 0.05, comparing Hong Kong with Taiwan-urban. SD: Standard deviation.
Table A4. Population characteristics between Hong Kong, Taiwan-urban and Taiwan-rural: age 85+.
Table A4. Population characteristics between Hong Kong, Taiwan-urban and Taiwan-rural: age 85+.
CharacteristicMean (SD)/n (%)
Hong KongTaiwan-UrbanTaiwan-Rural
Menn = 186n = 49n = 69
Age, mean (SD)87.09 (4.51)87.71 (2.18)87.97 (3.08)
Low education, %121 (65.12)35 (71.43)56 (81.16) 1
Living alone, %30 (16.28)2 (4.08) 311 (15.94) 2
Smoking, %130 (69.77)33 (67.35)48 (70.59)
Alcohol use, %22 (11.63)9 (18.37)13 (18.84)
Low levels of physical activity, %60 (32.56)21 (42.86)41 (59.42) 1
Womenn = 337n = 74n = 116
Age, mean (SD)87.00 (6.20)87.44 (2.33)87.90 (3.02)
Low education, %296 (87.93)65 (87.84)113 (97.41) 1,2
Living alone, %75 (22.41)6 (8.11) 318 (15.52)
Smoking, %70 (20.69)9 (12.16)3 (2.59) 1,2
Alcohol use, %0 (0.00)6 (8.11) 30 (0.00) 2
Low levels of physical activity, %110 (32.76)50 (67.57) 374 (64.35) 1
1 p-value < 0.05, comparing Taiwan-rural with Hong Kong; 2 p-value < 0.05, comparing Taiwan-rural with Taiwan-urban; 3 p-value < 0.05, comparing Hong Kong with Taiwan-urban. SD: Standard Deviation.

References

  1. Wang, H.H.; Wang, J.J.; Wong, S.Y.; Wong, M.C.; Li, F.J.; Wang, P.X.; Zhou, Z.H.; Zhu, C.Y.; Griffiths, S.M.; Mercer, S.W. Epidemiology of multimorbidity in China and implications for the healthcare system: Cross-sectional survey among 162,464 community household residents in southern China. BMC Med. 2014, 12, 188. [Google Scholar] [CrossRef] [PubMed]
  2. Fuller-Thomson, E.; Yu, B.; Nuru-Jeter, A.; Guralnik, J.M.; Minkler, M. Basic ADL disability and functional limitation rates among older Americans from 2000 to 2005: The end of the decline? J. Gerontol. A Biol. Sci. Med. Sci. 2009, 64, 1333–1336. [Google Scholar] [CrossRef] [PubMed]
  3. Feng, Q.; Zhen, Z.; Gu, D.; Wu, B.; Duncan, P.W.; Purser, J.L. Trends in ADL and IADL disability in community-dwelling older adults in Shanghai, China, 1998–2008. J. Gerontol. B Psychol. Sci. Soc. Sci. 2013, 68, 476–485. [Google Scholar] [CrossRef] [PubMed]
  4. Crimmins, E.M.; Beltran-Sanchez, H. Mortality and morbidity trends: Is there compression of morbidity? J. Gerontol. B Psychol. Sci. Soc. Sci. 2011, 66, 75–86. [Google Scholar] [CrossRef] [PubMed]
  5. Fries, J.F. Aging, natural death, and the compression of morbidity. N. Engl. J. Med. 1980, 303, 130–135. [Google Scholar] [CrossRef] [PubMed]
  6. Vita, A.J.; Terry, R.B.; Hubert, H.B.; Fries, J.F. Aging, health risks, and cumulative disability. N. Engl. J. Med. 1998, 338, 1035–1041. [Google Scholar] [CrossRef] [PubMed]
  7. Swartz, A. James Fries: Healthy aging pioneer. Am. J. Public Health 2008, 98, 1163–1166. [Google Scholar] [CrossRef] [PubMed]
  8. Fried, L.P.; Tangen, C.M.; Walston, J.; Newman, A.B.; Hirsch, C.; Gottdiener, J.; Seeman, T.; Tracy, R.; Kop, W.J.; Burke, G.; et al. Frailty in older adults: Evidence for a phenotype. J. Gerontol. A Biol. Sci. Med. Sci. 2001, 56, M146–M156. [Google Scholar] [CrossRef] [PubMed]
  9. Fried, L.P.; Ferrucci, L.; Darer, J.; Williamson, J.D.; Anderson, G. Untangling the concepts of disability, frailty, and comorbidity: Implications for improved targeting and care. J. Gerontol. A Biol. Sci. Med. Sci. 2004, 59, 255–263. [Google Scholar] [CrossRef] [PubMed]
  10. Ensrud, K.E.; Ewing, S.K.; Taylor, B.C.; Fink, H.A.; Cawthon, P.M.; Stone, K.L.; Hillier, T.A.; Cauley, J.A.; Hochberg, M.C.; Rodondi, N.; et al. Comparison of 2 frailty indexes for prediction of falls, disability, fractures, and death in older women. Arch. Intern. Med. 2008, 168, 382–389. [Google Scholar] [CrossRef] [PubMed]
  11. Song, X.; Mitnitski, A.; Rockwood, K. Prevalence and 10-year outcomes of frailty in older adults in relation to deficit accumulation. J. Am. Geriatr. Soc. 2010, 58, 681–687. [Google Scholar] [CrossRef] [PubMed]
  12. Romero-Ortuno, R.; Kenny, R.A. The frailty index in Europeans: Association with age and mortality. Age Ageing 2012, 41, 684–689. [Google Scholar] [CrossRef] [PubMed]
  13. Chen, K.W.; Chang, S.F.; Lin, P.L. Frailty as a predictor of future fracture in older adults: A systematic review and meta-analysis. Worldviews Evid. Based Nurs. 2017, 14, 282–293. [Google Scholar] [CrossRef] [PubMed]
  14. Woo, J.; Zheng, Z.; Leung, J.; Chan, P. Prevalence of frailty and contributory factors in three Chinese populations with different socioeconomic and healthcare characteristics. BMC Geriatr. 2015, 15, 163. [Google Scholar] [CrossRef] [PubMed]
  15. Rockwood, K.; Song, X.; MacKnight, C.; Bergman, H.; Hogan, D.B.; McDowell, I.; Mitnitski, A. A global clinical measure of fitness and frailty in elderly people. Can. Med. Assoc. J. 2005, 173, 489–495. [Google Scholar] [CrossRef] [PubMed]
  16. Rockwood, K.; Andrew, M.; Mitnitski, A. A comparison of two approaches to measuring frailty in elderly people. J. Gerontol. A Biol. Sci. Med. Sci. 2007, 62, 738–743. [Google Scholar] [CrossRef] [PubMed]
  17. Santos-Eggimann, B.; Cuenoud, P.; Spagnoli, J.; Junod, J. Prevalence of frailty in middle-aged and older community-dwelling Europeans living in 10 countries. J. Gerontol. A Biol. Sci. Med. Sci. 2009, 64, 675–681. [Google Scholar] [CrossRef] [PubMed]
  18. Collard, R.M.; Boter, H.; Schoevers, R.A.; Oude Voshaar, R.C. Prevalence of frailty in community-dwelling older persons: A systematic review. J. Am. Geriatr. Soc. 2012, 60, 1487–1492. [Google Scholar] [CrossRef] [PubMed]
  19. Saum, K.U.; Dieffenbach, A.K.; Muller, H.; Holleczek, B.; Hauer, K.; Brenner, H. Frailty prevalence and 10-year survival in community-dwelling older adults: Results from the ESTHER cohort study. Eur. J. Epidemiol. 2014, 29, 171–179. [Google Scholar] [CrossRef] [PubMed]
  20. Woo, J.; Yu, R.; Wong, M.; Yeung, F.; Wong, M.; Lum, C. Frailty screening in the community using the FRAIL scale. J. Am. Med. Dir. Assoc. 2015, 16, 412–419. [Google Scholar] [CrossRef] [PubMed]
  21. Kojima, G.; Iliffe, S.; Taniguchi, Y.; Shimada, H.; Rakugi, H.; Walters, K. Prevalence of frailty in Japan: A systematic review and meta-analysis. J. Epidemiol. 2017, 27, 347–353. [Google Scholar] [CrossRef] [PubMed]
  22. Clegg, A.; Young, J.; Iliffe, S.; Rikkert, M.O.; Rockwood, K. Frailty in elderly people. Lancet 2013, 381, 752–762. [Google Scholar] [CrossRef]
  23. Noguchi, N.; Blyth, F.M.; Waite, L.M.; Naganathan, V.; Cumming, R.G.; Handelsman, D.J.; Seibel, M.J.; Le Couteur, D.G. Prevalence of the geriatric syndromes and frailty in older men living in the community: The Concord Health and Ageing in Men Project. Australas. J. Ageing 2016, 35, 255–261. [Google Scholar] [CrossRef] [PubMed]
  24. Harttgen, K.; Kowal, P.; Strulik, H.; Chatterji, S.; Vollmer, S. Patterns of frailty in older adults: Comparing results from higher and lower income countries using the survey of health, ageing and retirement in Europe (SHARE) and the study on global ageing and adult health (SAGE). PLoS ONE 2013, 8, e75847. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Wong, S.Y.; Kwok, T.; Woo, J.; Lynn, H.; Griffith, J.F.; Leung, J.; Tang, Y.Y.; Leung, P.C. Bone mineral density and the risk of peripheral arterial disease in men and women: Results from Mr. and Ms. Os, Hong Kong. Osteoporos. Int. 2005, 16, 1933–1938. [Google Scholar] [CrossRef] [PubMed]
  26. Bureau of Health Promotion; Department of Health; the Executive Yuan, R.O.C. Taiwan Longitudinal Study on Aging (TLSA). Available online: https://mybox.ncku.edu.tw/navigate/s/423DD76530AE4EC1877B26351D4B55F2GSY (accessed on 18 July 2017).
  27. Folstein, M.F.; Folstein, S.E.; Mchugh, P.R. Mini-Mental State—Practical method for grading cognitive state of patients for clinician. J. Psychiatr. Res. 1975, 12, 189–198. [Google Scholar] [CrossRef]
  28. Yesavage, J.A.; Sheikh, J.I. Geriatric depression scale (GDS). Clin. Gerontol. 1986, 5, 165–173. [Google Scholar] [CrossRef]
  29. Pfeiffer, E. A short portable mental status questionnaire for the assessment of organic brain deficit in elderly patients. J. Am. Geriatr. Soc. 1975, 23, 433–441. [Google Scholar] [CrossRef] [PubMed]
  30. Hsu, H.C.; Chang, W.C. Reducing the risks of morbidity, disability, and mortality using successful aging strategies. J. Am. Geriatr. Soc. 2015, 63, 2426–2428. [Google Scholar] [CrossRef] [PubMed]
  31. Centre for Health Protection. Life Expectancy at Birth (Male and Female), 1971–2015. Available online: http://www.chp.gov.hk/en/data/4/10/27/111.html (accessed on 18 July 2017).
  32. Ministry of the Interior. Life Expectancy. Available online: http://www.moi.gov.tw/stat/chart.aspx?ChartID (accessed on 18 July 2017).
  33. Gefeller, O. The concept of attributable risk in epidemiology: Yesterday, today and tomorrow. Stat. Methods Med. Res. 2001, 10, 157–158. [Google Scholar] [CrossRef] [PubMed]
  34. Shye, D.; Mullooly, J.P.; Freeborn, D.K.; Pope, C.R. Gender differences in the relationship between social network support and mortality—A longitudinal-study of an elderly cohort. Soc. Sci. Med. 1995, 41, 935–947. [Google Scholar] [CrossRef]
  35. McLaughlin, D.; Vagenas, D.; Pachana, N.A.; Begum, N.; Dobson, A. Gender differences in social network size and satisfaction in adults in their 70s. J. Health Psychol. 2010, 15, 671–679. [Google Scholar] [CrossRef] [PubMed]
  36. Smith, K.R.; Zick, C.D. Risk of mortality following widowhood: Age and sex differences by mode of death. Soc. Biol. 1996, 43, 59–71. [Google Scholar] [CrossRef] [PubMed]
  37. The Hong Kong Council of Social Service. Percentage of Elderly Aged 65 and over Living Alone. Available online: http://socialindicators.org.hk/en/indicators/elderly/31.11/percentage_of_elderly_aged_65_and_over_living_alone.pdf (accessed on 18 July 2017).
  38. Woods, N.F.; LaCroix, A.Z.; Gray, S.L.; Aragaki, A.; Cochrane, B.B.; Brunner, R.L.; Masaki, K.; Murray, A.; Newman, A.B.; Women’s Health Initiative. Frailty: Emergence and consequences in women aged 65 and older in the Women’s Health Initiative Observational Study. J. Am. Geriatr. Soc. 2005, 53, 1321–1330. [Google Scholar] [CrossRef] [PubMed]
  39. Ortola, R.; Garcia-Esquinas, E.; Leon-Munoz, L.M.; Guallar-Castillon, P.; Valencia-Martin, J.L.; Galan, I.; Rodriguez-Artalejo, F. Patterns of alcohol consumption and risk of frailty in community-dwelling older adults. J. Gerontol. A Biol. Sci. Med. Sci. 2016, 71, 251–258. [Google Scholar] [CrossRef] [PubMed]
  40. Shah, M.; Paulson, D. C-reactive protein level partially mediates the relationship between moderate alcohol use and frailty: The health and retirement study. Age Ageing 2016, 45, 874–878. [Google Scholar] [CrossRef] [PubMed]
  41. Fried, L.P.; Xue, Q.L.; Cappola, A.R.; Ferrucci, L.; Chaves, P.; Varadhan, R.; Guralnik, J.M.; Leng, S.X.; Semba, R.D.; Walston, J.D.; et al. Nonlinear multisystem physiological dysregulation associated with frailty in older women: Implications for etiology and treatment. J. Gerontol. A Biol. Sci. Med. Sci. 2009, 64, 1049–1057. [Google Scholar] [CrossRef] [PubMed]
  42. Hubbard, R.E.; O’Mahony, M.S.; Savva, G.M.; Calver, B.L.; Woodhouse, K.W. Inflammation and frailty measures in older people. J. Cell Mol. Med. 2009, 13, 3103–3109. [Google Scholar] [CrossRef] [PubMed]
  43. Collerton, J.; Martin-Ruiz, C.; Davies, K.; Hilkens, C.M.; Isaacs, J.; Kolenda, C.; Parker, C.; Dunn, M.; Catt, M.; Jagger, C.; et al. Frailty and the role of inflammation, immunosenescence and cellular ageing in the very old: Cross-sectional findings from the Newcastle 85+ study. Mech. Ageing Dev. 2012, 133, 456–466. [Google Scholar] [CrossRef] [PubMed]
  44. Peterson, M.J.; Giuliani, C.; Morey, M.C.; Pieper, C.F.; Evenson, K.R.; Mercer, V.; Cohen, H.J.; Visser, M.; Brach, J.S.; Kritchevsky, S.B.; et al. Physical activity as a preventative factor for frailty: The health, aging, and body composition study. J. Gerontol. A Biol. Sci. Med. Sci. 2009, 64, 61–68. [Google Scholar] [CrossRef] [PubMed]
  45. Tarazona-Santabalbina, F.J.; Gomez-Cabrera, M.C.; Perez-Ros, P.; Martinez-Arnau, F.M.; Cabo, H.; Tsaparas, K.; Salvador-Pascual, A.; Rodriguez-Manas, L.; Vina, J. A multicomponent exercise intervention that reverses frailty and improves cognition, emotion, and social networking in the community-dwelling frail elderly: A randomized clinical trial. J. Am. Med. Dir. Assoc. 2016, 17, 426–433. [Google Scholar] [CrossRef] [PubMed]
  46. Puts, M.T.E.; Toubasi, S.; Andrew, M.K.; Ashe, M.C.; Ploeg, J.; Atkinson, E.; Ayala, A.P.; Roy, A.; Rodríguez Monforte, M.; Bergman, H.; et al. Interventions to prevent or reduce the level of frailty in community-dwelling older adults: A scoping review of the literature and international policies. Age Ageing 2017, 46, 383–392. [Google Scholar] [CrossRef] [PubMed]
  47. Dedeyne, L.; Deschodt, M.; Verschueren, S.; Tournoy, J.; Gielen, E. Effects of multi-domain interventions in (pre) frail elderly on frailty, functional, and cognitive status: A systematic review. Clin. Interv. Aging 2017, 12, 873–896. [Google Scholar] [CrossRef] [PubMed]
  48. Rockwood, K.; Mitnitski, A. Frailty in relation to the accumulation of deficits. J. Gerontol. A Biol. Sci. Med. Sci. 2007, 62, 722–727. [Google Scholar] [CrossRef] [PubMed]
  49. Searle, S.D.; Mitnitski, A.; Gahbauer, E.A.; Gill, T.M.; Rockwood, K. A standard procedure for creating a frailty index. BMC Geriatr. 2008, 8, 24. [Google Scholar] [CrossRef] [PubMed]
  50. Gu, D. Health cumulative deficit index and its validity among the Chinese elderly. Popul. Econ. 2009, 5, 52–57. [Google Scholar]
  51. Yang, F.; Gu, D.N. Predictability of frailty index and its components on mortality in older adults in China. BMC Geriatr. 2016, 16, 145. [Google Scholar] [CrossRef] [PubMed]
Table 1. Components of the frailty index.
Table 1. Components of the frailty index.
ComponentPrevalence, n (%)
Hong KongTaiwan-UrbanTaiwan-Rural
n%n%n%
Medical and drug history
Hypertension231657.9043845.4956039.18
Cataract178944.7346648.4253637.50
Arthritis54513.6329730.8936725.66
Gastropathy/gastrectomy3348.3520621.3831922.33
Heart diseases40110.0326727.6831822.24
Osteoporosis136134.0326727.6930021.01
Diabetes type I or II58314.5818218.9121715.21
COPD3548.8513514.0420814.58
Gout3809.5011411.8815110.54
Kidney disease1654.139910.271399.76
Stroke1714.28666.82936.52
Glaucoma2005.00434.47855.94
Cancer/malignant tumor1744.35394.02332.31
Medication use (use of stimulants, sedatives, aspirin and painkillers for arthritis)60315.0821322.1527519.26
Functional assessment and psychological well-being
Low lower limb strength99024.7540041.5164745.24
Depression42510.6337538.9855638.88
Poor walking performance57614.4023524.4043030.09
Cognitive impairment119329.83586.0716811.79
Low grip strength49812.45777.991419.89
Geriatric syndrome
Difficulty in moderate activities2085.2055457.5286460.48
Difficulty in climbing several stairs2756.8827828.8455338.71
Poor health2756.8830331.4255238.62
Often feel fatigue or tired1744.3524625.5138727.07
Fall in past 12 months2626.5518819.5328119.64
Risk of falls109227.3013313.8326018.19
Difficulty in movement63815.9510510.891419.90
Less activity72318.08788.061379.06
Underweight2867.15565.57815.64
Ever fracture71717.93383.97563.93
Back pain191347.83272.82483.35
Table 2. Population characteristics between Hong Kong, Taiwan-urban and Taiwan-rural by sex.
Table 2. Population characteristics between Hong Kong, Taiwan-urban and Taiwan-rural by sex.
CharacteristicMean (SD)/n (%)
Hong KongTaiwan-UrbanTaiwan-Rural
Menn = 2000n = 523n = 742
Age, mean (SD)74.40 (6.38)74.51 (6.62)74.55 (6.69)
Low education, %1227 (61.36)258 (49.33) 3572 (77.09) 1,2
Living alone, %118 (5.92)41 (7.82)97 (13.07) 1,2
Smoking, %1303 (65.16)355 (67.88)568 (76.55) 1,2
Alcohol use, %431 (21.56)124 (23.71)205 (27.63) 1
Low levels of physical activity, %805 (40.24)186 (35.56)340 (45.82) 1,2
Womenn = 2000n = 440n = 687
Age, mean (SD)76.02 (7.08)76.08 (7.22)76.27 (7.40)
Low education, %1695 (84.75)368 (83.64)655 (95.34) 1,2
Living alone, %401 (20.04)47 (10.68) 386 (12.50) 1
Smoking, %245 (12.24)29 (6.59) 330 (4.37) 1
Alcohol use, %38 (1.89)28 (6.36) 324 (3.49) 1,2
Low levels of physical activity, %645 (32.23)234 (53.18) 3379 (55.17) 1
1 p-value < 0.05, comparing Taiwan-rural with Hong Kong; 2 p-value < 0.05, comparing Taiwan-rural with Taiwan-urban; 3 p-value < 0.05, comparing Hong Kong with Taiwan-urban. SD: Standard Deviation.
Table 3. Prevalence of frailty and mean of frailty index (FI)/life expectancy at birth (LE) in different areas by age and sex.
Table 3. Prevalence of frailty and mean of frailty index (FI)/life expectancy at birth (LE) in different areas by age and sex.
CharacteristicPrevalence of Frailty (FI ≥ 0.25), n (%)Mean (SD) of FI/LE Ratio
Hong KongTaiwan-UrbanTaiwan-RuralHong KongTaiwan-UrbanTaiwan-Rural
Men
65–7470 (6.43)53 (18.66) 388 (21.89) 10.15 (0.09)0.19 (0.16) 30.20 (0.17) 1
75–84102 (14.05)48 (25.26) 383 (31.00) 10.20 (0.13)0.24 (0.17) 30.25 (0.17) 1
85+39 (20.93)17 (36.96) 332 (46.38) 10.24 (0.24)0.32 (0.17) 30.31 (0.20) 1
Total211 (10.54)118 (22.69) 3203 (27.51) 10.17 (0.11)0.23 (0.16)30.23 (0.17) 1
Women
65–74134 (14.86)69 (35.03) 3124 (40.52) 10.19 (0.09)0.27 (0.17) 30.26 (0.17) 1
75–84219 (28.75)97 (57.74) 3146 (55.73) 10.25 (0.13)0.35 (0.16) 30.34 (0.17) 1
85+99 (29.31)33 (44.59) 335 (61.32) 10.26 (0.29)0.35 (0.19) 30.36 (0.14) 1
Total452 (22.59)199 (45.33) 3335 (49.70) 10.23 (0.11)0.32 (0.17) 30.31 (0.17) 1
Both sexes
65–74204 (10.25)122 (25.36) 3212 (29.94) 10.17 (0.09)0.23 (0.17) 30.23 (0.18) 1
75–84322 (21.58)145 (40.50) 3229 (43.29) 10.22 (0.13)0.30 (0.18) 30.30 (0.18) 1
85+138 (26.33)50 (41.67) 397 (55.43) 1,20.25 (0.27)0.35 (0.18) 30.35 (0.17) 1
Total663 (16.57)317 (33.06) 3538 (38.10) 1,20.20 (0.12)0.27 (0.18) 30.27 (0.18) 1
1 p-value < 0.05, comparing Taiwan-rural with Hong Kong; 2 p-value < 0.05, comparing Taiwan-rural with Taiwan-urban; 3 p-value < 0.05, comparing Hong Kong with Taiwan-urban. SD: Standard Deviation; FI: Frailty Index; LE: Life Expectancy.
Table 4. (a) Multiple logistic regression of frailty in Hong Kong, Taiwan-urban and Taiwan-rural (both sexes); (b) Multiple logistic regression of frailty in Hong Kong, Taiwan-urban and Taiwan-rural (men); (c) Multiple logistic regression of frailty in Hong Kong, Taiwan-urban and Taiwan-rural (women).
(a)
(a)
CharacteristicCrude OR (95% CI)Adjusted OR (95% CI)
Hong KongTaiwan-UrbanTaiwan-RuralHong KongTaiwan-UrbanTaiwan-Rural
Women2.48 (2.07, 2.96)2.81 (2.13, 3.71)2.60 (2.09, 3.25)2.43 (1.92, 3.06)2.41 (1.61, 3.60)2.11 (1.49, 3.00)
Age
   65–74ReferenceReferenceReferenceReferenceReferenceReference
   75–842.41 (1.99, 2.92)1.99 (1.48, 2.67)1.79 (1.41, 2.26)2.22 (1.83, 2.71)1.92 (1.40, 2.63)1.75 (1.37, 2.24)
   85+3.13 (2.46, 3.99)2.06 (1.36, 3.13)2.88 (2.05, 4.04)2.60 (2.02, 3.34)1.47 (0.94, 2.28) 32.38 (1.67, 3.39)
Low education1.77 (1.43, 2.18)2.09 (1.55, 2.83)2.05 (1.46, 2.87)1.23 (0.98, 1.54)1.52 (1.09, 2.13)1.26 (0.87, 1.82)
Smoking0.77 (0.64, 0.91)0.60 (0.45, 0.80)0.48 (0.38, 0.60) 11.26 (1.01, 1.58)1.38 (0.92, 2.09)0.95 (0.67, 1.34)
Alcohol use0.30 (0.20, 0.44)0.32 (0.20, 0.51)0.48 (0.35, 0.67)0.50 (0.34, 0.76)0.44 (0.27, 0.72)0.82 (0.57, 1.16) 2
Low levels of physical activity1.37 (1.16, 1.62)2.38 (1.81, 3.13) 32.45 (1.96, 3.05) 11.51 (1.27, 1.81)2.03 (1.52, 2.71)2.29 (1.82, 2.88) 1
Living alone1.34 (1.06, 1.69)0.88 (0.55, 1.42)1.02 (0.74, 1.41)0.88 (0.69, 1.12)0.77 (0.46, 1.28)1.02 (0.72, 1.43)
AUC 0.6790.7030.700
(b)
(b)
CharacteristicCrude OR (95% CI)Adjusted OR (95% CI)
Hong KongTaiwan-UrbanTaiwan-RuralHong KongTaiwan-UrbanTaiwan-Rural
Age
   65–74ReferenceReferenceReferenceReferenceReferenceReference
   75–842.38 (1.73, 3.28)1.45 (0.93, 2.26)1.62 (1.14, 2.30)2.05 (1.48, 2.84)1.36 (0.86, 2.14)1.63 (1.14, 2.33)
   85+3.86 (2.51, 5.92)2.55 (1.31, 4.94)3.04 (1.79, 5.16)3.18 (2.04, 4.96)2.25 (1.14, 4.44)2.71 (1.58, 4.65)
Low education1.50 (1.10, 2.04)1.27 (0.84, 1.92)1.52 (1.01, 2.29)1.35 (0.98, 1.86)1.20 (0.78, 1.83)1.38 (0.91, 2.11)
Smoking1.31 (0.96, 1.79)1.19 (0.76, 1.86)0.96 (0.65, 1.40)1.15 (0.83, 1.59)1.27 (0.80, 2.02)0.96 (0.65, 1.43)
Alcohol use0.50 (0.33, 0.76)0.48 (0.27, 0.83)0.67 (0.46, 0.97)0.57 (0.37, 0.88)0.48 (0.27, 0.86)0.73 (0.49, 1.08)
Low levels of physical activity1.48 (1.11, 1.97)1.56 (1.03, 2.37)1.96 (1.41, 2.72)1.47 (1.10, 1.98)1.49 (0.97, 2.28)1.87 (1.33, 2.61)
Living alone2.85 (1.82, 4.48)1.00 (0.47, 2.15) 31.23 (0.77, 1.97) 12.32 (1.45, 3.71)0.96 (0.44, 2.12)1.21 (0.75, 1.96)
AUC 0.6470.6170.622
(c)
(c)
CharacteristicCrude OR (95% CI)Adjusted OR (95% CI)
Hong KongTaiwan-UrbanTaiwan-RuralHong KongTaiwan-UrbanTaiwan-Rural
Age
   65–74ReferenceReferenceReferenceReferenceReferenceReference
   75–842.31 (1.82, 2.94)2.54 (1.66, 3.88)1.84 (1.32, 2.58)2.38 (1.86, 3.06)2.66 (1.69, 4.18)1.88 (1.33, 2.66)
   85+2.38 (1.76, 3.20)1.46 (0.84, 2.51)2.31 (1.47, 3.62)2.30 (1.69, 3.12)1.14 (0.64, 2.02) 32.27 (1.42, 3.63)
Low education1.28 (0.94, 1.74)1.99 (1.16, 3.40)1.34 (0.65, 2.73)1.18 (0.86, 1.62)2.25 (1.26, 4.01)0.96 (0.45, 2.05)
Smoking1.58 (1.17, 2.12)1.91 (0.88, 4.15)0.89 (0.43, 1.85)1.35 (0.99, 1.83)2.35 (0.95, 5.85)0.87 (0.40, 1.90)
Alcohol use0.19 (0.05, 0.79)0.32 (0.13, 0.79)1.02 (0.45, 2.30) 10.22 (0.05, 0.92)0.31 (0.11, 0.89)1.43 (0.60, 3.39) 1,2
Low levels of physical activity1.51 (1.22, 1.88)2.69 (1.82, 3.97) 32.77 (2.03, 3.79) 11.55 (1.24, 1.94)2.72 (1.80, 4.12) 32.74 (1.99, 3.78) 1
Living alone0.79 (0.60, 1.04)0.70 (0.37, 1.30)0.88 (0.56, 1.39)0.66 (0.50, 0.87)0.72 (0.36, 1.42)0.87 (0.54, 1.41)
AUC 0.6290.695 30.663
1 p-value < 0.05, comparing Taiwan-rural with Hong Kong; 2 p-value < 0.05, comparing Taiwan-rural with Taiwan-urban; 3 p-value < 0.05, comparing Hong Kong with Taiwan-urban. OR: Odds Ratio; AUC: Area Under the Curve.
Table 5. Attributable fraction for frailty in Hong Kong, Taiwan-urban and Taiwan-rural (both sexes).
Table 5. Attributable fraction for frailty in Hong Kong, Taiwan-urban and Taiwan-rural (both sexes).
CharacteristicAttributable Fraction (%) *
Hong KongTaiwan-UrbanTaiwan-Rural
Women58.85%58.51%52.61%
Age
   65–74ReferenceReferenceReference
   75–8454.95%47.92%42.86%
   85+61.54%31.97% 357.98%
Low education18.70%34.21%20.63%
Smoking20.63%27.54%−5.26%
Alcohol use−100.00%−127.27%−21.95% 2
Low levels of physical activity33.77%50.74%56.33% 1
Living alone−13.64%−29.87%1.96%
* A positive value indicates that the exposure is a potential risk factor for frailty, while a negative value indicates that it is a potential protective factor. 1 p-value < 0.05, comparing Taiwan-rural with Hong Kong; 2 p-value < 0.05, comparing Taiwan-rural with Taiwan-urban; 3 p-value < 0.05, comparing Hong Kong with Taiwan-urban.

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MDPI and ACS Style

Yu, R.; Wu, W.-C.; Leung, J.; Hu, S.C.; Woo, J. Frailty and Its Contributory Factors in Older Adults: A Comparison of Two Asian Regions (Hong Kong and Taiwan). Int. J. Environ. Res. Public Health 2017, 14, 1096. https://doi.org/10.3390/ijerph14101096

AMA Style

Yu R, Wu W-C, Leung J, Hu SC, Woo J. Frailty and Its Contributory Factors in Older Adults: A Comparison of Two Asian Regions (Hong Kong and Taiwan). International Journal of Environmental Research and Public Health. 2017; 14(10):1096. https://doi.org/10.3390/ijerph14101096

Chicago/Turabian Style

Yu, Ruby, Wan-Chi Wu, Jason Leung, Susan C. Hu, and Jean Woo. 2017. "Frailty and Its Contributory Factors in Older Adults: A Comparison of Two Asian Regions (Hong Kong and Taiwan)" International Journal of Environmental Research and Public Health 14, no. 10: 1096. https://doi.org/10.3390/ijerph14101096

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