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International Journal of Environmental Research and Public Health logoLink to International Journal of Environmental Research and Public Health
. 2022 Jan 31;19(3):1656. doi: 10.3390/ijerph19031656

Prevalence of Disability among the Chinese Older Population: A Systematic Review and Meta-Analysis

Pian-Pian Zheng 1, Zi-Le Guo 1, Xiao-Jing Du 2, Han-Mo Yang 3, Zhen-Jie Wang 1,*
Editor: Jean Woo
PMCID: PMC8835133  PMID: 35162679

Abstract

Background: Disability is an important problem in aging societies globally. However, the research findings of the prevalence of disability have been inconsistent. This study aims to estimate the prevalence of disability and its influencing factors among the Chinese older population from 1979 to 31 July 2021. Methods: A systematic review and meta-analysis were conducted using both international (PubMed, Web of Science, CBMdisc, PsycINFO, the Cochrane Library, and EMBASE) and Chinese (CNKI, CQVIP, and WanFang) databases. Meta-analysis was performed using a random-effects model to account for heterogeneity. Subgroup analyses were also done. Results: The pooled prevalence of disability across all 97 studies was 26.2% (95% CI: 23.7–28.6%). The estimates varied according to the types of activities of daily living (ADL), gender, age, and region. Studies based on the identification of cases by using the complete ADL scale showed a higher prevalence than those using the basic ADL scale. The prevalence was slightly higher among female older individuals than among male older individuals. The highest rates were seen in older individuals aged 80 years or older. Elders in central China, southwest China, and northwest China were more likely to be BADL-disabled. Conclusion: Prevalence of disability among the Chinese older population is high, around 26%. Using standardized diagnostic systems to correctly estimate the prevalence of disability would be helpful for public health professionals in China.

Keywords: prevalence, disability, activities of daily living, older population, Chinese, meta-analysis

1. Introduction

With the continuous extension of average life expectancy, the proportion of older individuals is increasing dramatically. In 2017, there were an estimated 962 million people aged 60 years or above, accounting for 13% of the global population [1]. The older population in China has reached a prevalence of 18.7%, according to China’s seventh census [2]. With the rapid aging that is occurring in all regions of the world, the prevalence of older individuals in the whole world, except Africa, will reach 25% by 2050. Therefore, the problems associated with an aging society are becoming more severe, and one of the associated problems is a high rate of disability. Figuring out the rate of disability and the number of disabled elders is vital for a country to make improvements and promotion strategies for the old, disabled population’s quality of life. Relevant agencies can also make better plans for financial support, nursing services, and medical services.

According to the World Health Organization (WHO) report on disability, the estimated prevalence of disability was 10.2% in people aged 60 years or above in 194 countries and regions around the world [3]. However, in China, the second national sample survey on disability pointed out that the disabled elders accounted for 24.43% of all older individuals [4]. This difference in the prevalence of disability was more apparent in individual studies. We found that the prevalence of disability among the Chinese older population varies greatly, ranging from 1.85% to 71.28% [5,6], after combining the results of different studies. The pooled prevalence rates given by three relevant meta-analyses were 20.1% (95% CI: 14.7–25.6%), 28.5% (95% CI: 25.9–31.2%), and 34% (95% CI: 14–53%), which also seem to be quite different [7,8,9].

One of the reasons for this discrepancy is the different understanding and measurements. Disability can be defined in various ways, including impairment, limitations in mobility, physical function decline, and activities of daily living (ADLs). World Health Organization (WHO)’s International Classification of Functioning, Disability, and Health (ICF) uses disability as an umbrella term for impairments, activity limitations, or participation restrictions [10]. Additionally, it points out that disability represents the negative aspects of the interaction between the health condition and life situations (personal factors and environmental factors). Thus, the ADL is considered a suitable measurement of disability and also has good robustness and comparability [11,12].

Although several studies calculated the prevalence of ADL disability among the older population, a synthesis of these studies to derive a general risk estimate has not been well conducted. Hence, we have carried out a systematic review and meta-analysis to comprehensively analyze the related studies and extract a more accurate and general prevalence of disability by avoiding differences in individual studies caused by biased samples and moderating factors.

2. Materials and Methods

2.1. Literature Search

This research protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO: CRD42021269367). The literature search was conducted using the Chinese National Knowledge Infrastructure (CNKI), VIP Database (CQVIP), China Biology Medicine disc (CBMdisc), Wanfang, PubMed, Web of Science, Embase, and the Cochrane Library. All databases were searched from 1979 (the earliest year available on the CNKI database) to 31 July 2021. The search terms were keywords related to the older population (elderly OR elder OR old population OR old adults), ADL (activities of daily living OR disable OR disabled OR ADL OR BADL OR IADL), and China (China OR Chinese).

The screening procedure is shown below: (a) the titles were reviewed to determine potential articles related to the topic, (b) the abstracts were reviewed to narrow down the list of articles, and (c) the full text of the articles was read to make a final decision.

2.2. Inclusion and Exclusion Criteria

The complete type of ADL includes basic activities of daily living (BADL) and instrumental activities of daily living (IADL). The most commonly used scales for assessing BADL are the Katz independence index and the Barthel independence index, which have 6 or 10 items, consisting of relatively simple self-care tasks such as dressing, eating, and bathing [13,14]. Moreover, the frequently used tool for assessing IADL was designed by Lawton and Brody and has 8 items, such as shopping, cooking, financial management, and other more complex activities [15]. Those standard scales provide good support for us to merge the rates and compare. Some researchers used a self-made scale or increased or decreased the ADL items, and we did not include these studies.

Studies were included only if they met the following criteria: (a) the study was published between 1979 and 31 July 2021; (b) the study was conducted using a questionnaire survey, and the measurement tool was the Barthel independence index, the Katz independence index or the scale designed by Lawton and Brody; (c) the study reported the prevalence of disability with accurate and clear data; and (d) all older respondents were aged 60 years or above and came from Mainland China.

Studies were excluded if they met the following criteria: (a) for literature published with the same data, only the latest data were included; (b) reviews, conferences, lectures, or unpublished essays; (b) an unscientific research design, such as convenience sampling, was used in the study; and (c) the study was based on a sample population involving patients, elders living in nursing homes, and other special groups with specific health-related characteristics.

2.3. Data Extraction

The data in the studies, including the authors, publication year, survey year, sampling locations, diagnostic tools, participants, and disability cases, were collected. Additionally, the prevalence of disability among older individuals of different diagnostic tools, genders, ages, and regions was collected.

All studies were reviewed and coded by two authors to determine the consistency of the inclusion and exclusion criteria. In addition, each study included in the meta-analysis was coded by two authors to extract major outcomes. The discrepancies were resolved through discussions.

2.4. Quality of Assessment

The quality of included studies was assessed by the 11-item checklist recommended by the Agency for Healthcare Research and Quality (AHRQ). The item would be scored 1 for the answer of “Yes” and would get a score of 0 if the answer was “No” or “Unclear” (opposite for the 5th item). A total score of 0–3 = low quality, 4–7 = moderate quality, and 8–11 = high quality [16].

2.5. Statistical Analysis

The meta-analysis was carried out by using STATA 16.0. Combined effect sizes with corresponding confidence intervals (95%) were calculated, and these indicated the magnitude of the effect across all studies. The Q test and I2 statistics were used to assess heterogeneity among the included studies. p > 0.05 and I2 < 50% indicated no statistical heterogeneity between the studies. If no heterogeneity was observed, the fixed-effects model was employed; otherwise, the random-effects model was used [17]. The homogeneity test showed that Q = 81,405.53 (p < 0.001) and I2 = 99.9%. Therefore, we adopted the random-effects model for all meta-analyses.

Subgroup analyses and meta-regression analyses were conducted to eliminate heterogeneity and identify potential influencing factors. Sensitivity analyses were conducted by removing one study at a time and then recalculating the prevalence of the remaining studies to test the robustness of the primary results. Publication bias was diagnosed through Begg’s test. The significance level was set at 0.05 (two-sided) in all analyses.

3. Results

3.1. Search Strategy and Selection Criteria

Figure 1 shows a flow diagram of the systematic search of the literature. A total of 6444 articles were identified in 8 electronic databases. Among them, 1666 duplicates were eliminated, the titles and abstracts studies were screened, and the full text of 484 studies was evaluated. In the end, 97 studies passed the evaluation and were included in the meta-analysis.

Figure 1.

Figure 1

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Flow chart of the study selection process.

3.2. Quality Assessment

The results of the quality assessment are shown in Table 1. Based on the AHRQ checklist, 97 studies reached moderate quality and above.

Table 1.

Risk of bias using quality assessment forms.

Item Yes No Unclear
(1) Define the source of information (survey, record review) 97 0 0
(2) List inclusion and exclusion criteria for exposed and unexposed subjects (cases and controls) or refer to previous publications 97 0 0
(3) Indicate time period used for identifying patients 78 19 0
(4) Indicate whether or not subjects were consecutive if not population-based 97 0 0
(5) Indicate if evaluators of subjective components of the study were masked to other aspects of the status of the participants 0 97 0
(6) Describe any assessments undertaken for quality assurance purposes (e.g., test/retest of primary outcome measurements) 60 36 1
(7) Explain any patient exclusions from the analysis 89 7 1
(8) Describe how confounding was assessed and/or controlled. 65 32 0
(9) If applicable, explain how missing data were handled in the analysis 13 82 2
(10) Summarize patient response rates and completeness of data collection 86 11 0
(11) Clarify what follow-up, if any, was expected and the percentage of patients for which incomplete data or follow-up was obtained 0 97 0
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3.3. Study Characteristics

Table A1 in Appendix A summarizes the characteristics and findings of the included studies. A total of 97 eligible studies reported the prevalence of disability in Chinese older individuals, with a total of 110 results. Eight studies reported multiple results because they used several cross-sectional data sets or used several types of ADL.

Most of the included studies were cross-sectional, and two were longitudinal. For the longitudinal study, we included only the results from the cross-sectional analysis of the baseline data. In addition, 18 studies used national data, while the remaining studies obtained samples from regions within China; 86 studies were conducted with the general older population (≥60 or ≥65 years), but 11 studies only included the oldest elders (≥80 years) or centenarians. The sample size ranged from 182 to 32,281. The time of data collection spanned nearly three decades.

3.4. Pooled Prevalence of Disability

In total, 97 studies met the inclusion criteria, with 110 results. The whole sample included 561,800 subjects, of whom 116,813 had disabilities. Table 2 shows that the pooled prevalence of disability among the Chinese older population was 26.2% (95% CI: 23.7–28.6%).

Table 2.

Pooled prevalence of disability and subgroup analyses.

Variables Classification Number of Studies Number of Results Event Rate (%) 95% CI (%) Heterogeneity I2 (%) Q-Value p-Value
Pooled prevalence 97 110 26.2 23.7–28.6 99.9 81,405.53
Type of ADL BADL 56 62 20.5 17.7–23.3 99.9 26.55 <0.001
IADL 7 7 31.8 21.2–42.4 99.9
BADL + IADL 41 41 33.8 29.4–38.3 99.6
Gender Male 53 60 22.7 20.0–25.5 99.7 5.35 0.021
Female 53 60 28.5 24.5–32.5 99.8
Age group 60–69 23 26 12.8 10.1–15.5 99.6 104.92 <0.001
70–79 23 26 22.4 16.5–28.3 99.7
≥80 36 44 36.8 33.1–40.5 99.6
Region Eastern China 32 33 27.0 22.3–31.7 99.8 2.44 0.786
Northern China 18 20 26.0 19.9–32.1 99.7
Southern China 6 6 24.2 8.0–40.3 99.7
Central China 7 7 26.9 17.9–35.8 99.4
Southwest China 10 13 30.9 22.3–39.4 99.7
Northwest China 4 4 21.3 12.3–30.3 97.8
Hukou Urban 17 22 22.4 16.9–27.9 99.9 2.13 0.143
Rural 26 31 28.0 22.9–33.0 99.9
Survey year 1999 and before 5 6 21.4 10.4–32.4 99.8 2.16 0.706
2000–2004 6 7 23.7 13.0–34.3 99.8
2005–2009 10 12 29.1 21.6–36.7 99.7
2010–2014 41 43 27.7 23.6–31.8 99.8
2015–2019 36 38 25.3 20.9–29.7 99.9
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3.5. Subgroup Analyses

The prevalence varied greatly according to the types of ADL. The prevalence of disability detected by BADL was 20.5% (95% CI: 17.7–23.3%), which was significantly lower than that detected by complete ADL (33.8%, 95% CI: 29.4–38.3%) (p < 0.001).

The prevalence in women (28.5%, 95% CI: 24.5–32.5%) was slightly higher than that in men (22.7%, 95% CI: 20.0–25.5%). A significant difference was found among different age groups (p < 0.001). The prevalence of disability in the oldest age group (≥80 years) was 36.8% (95% CI: 33.1–40.5%), which was higher than that in the 60–69 years age group (12.8%, 95% CI: 10.1–15.5%) and the 70–80 years age group (22.4%, 95% CI: 16.5–28.3%).

3.6. Assessment of Disability by Using a Specific Type of ADL

3.6.1. BADL

As Table 3 shown, 56 studies provided information about the BADL. The random-effects analysis showed that the pooled prevalence of BADL disability was 20.5% (95% CI: 17.7–23.3%). Furthermore, older individuals aged 80 years or over (30.0%, 95% CI: 26.2–33.9%, p < 0.001) had a significantly higher BADL disability rate than younger elders. To avoid the limitation of insufficient studies, we merged some regional subgroups and found that other parts of China had an obviously higher BADL prevalence (24.4%, 95% CI: 26.2–33.9%, p < 0.001) than northern China.

Table 3.

Pooled prevalence of BADL disabilities and subgroup analyses.

Variables Classification Number of Studies Number of Results Event Rate (%) 95% CI (%) Heterogeneity I2 (%) Q-Value p-Value
Pooled prevalence 56 62 20.5 17.7–23.3 99.9 45,852.90
Gender Male 37 41 19.4 16.4–22.4 99.7 3.95 0.047
Female 37 41 25.1 20.3–29.9 99.8
Age group 60–69 17 17 7.3 5.7–8.9 98.6 111.60 <0.001
70–79 17 17 13.1 10.4–15.9 98.4
≥80 29 33 30.0 26.2–33.9 99.6
Region Eastern China 15 15 16.8 13.5–20.1 99.3 10.45 0.005
Northern China 6 8 12.9 9.7–16.1 99.2
Other regions * 16 16 24.4 18.0–30.7 99.5
Hukou Urban 14 18 22.6 16.1–29.2 99.9 0.00 0.944
Rural 16 20 22.4 17.4–27.3 99.9
Survey year 2009 and before * 8 14 21.7 14.2–29.1 99.9 0.78 0.678
2010–2014 25 25 21.3 17.6–25.1 99.7
2015–2019 20 20 18.9 14.3–23.4 99.8
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* To avoid the limitation of insufficient studies, we merged Central China, Southwest China, and Northwest China into a group called “Other regions”. In addition, the studies published in 2009 and before were merged into one group.

3.6.2. Complete ADL

As Table 4 shown, 41 studies combined the basic and instrumental activities of daily living as a complete measurement tool, which consisted of 14 items. The pooled prevalence of disability according to complete ADL was 33.8% (95% CI: 29.4–38.3%). The oldest elders (≥80 years) also had an evidently higher prevalence (61.9%, 95% CI: 51.9–71.9%, p < 0.001).

Table 4.

Pooled prevalence of complete ADL and subgroup analyses.

Variables Classification Number of Studies Number of Results Event Rate (%) 95% CI (%) Heterogeneity I2 (%) Q-Value p-Value
Pooled prevalence 41 41 33.8 29.4–38.3 99.6 10,997.47
Gender Male 16 16 32.2 23.1–41.4 99.5 0.52 0.472
Female 16 16 36.7 28.7–44.7 99.4
Age group 60–69 6 6 25.5 14.0–36.9 99.7 22.45 <0.001
70–79 6 6 40.5 24.9–56.1 99.6
≥80 7 7 61.9 51.9–71.9 97.3
Region Eastern China 16 16 36.4 27.8–44.9 99.8 1.10 0.578
Northern China 12 12 34.7 26.9–42.4 99.6
Other regions * 12 12 31.2 25.1–37.2 99.6
Survey year 2009 and before * 10 10 32.9 24.4–41.5 99.5 0.38 0.827
2010–2014 15 15 35.9 27.4–44.3 99.7
2015–2019 16 16 32.5 24.9–40.1 99.7
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* To avoid the limitation of insufficient studies, we merged Central China, Southwest China, and Northwest China into a group called “Other regions”. In addition, the studies published in 2009 and prior to 2009 were merged into one group.

3.7. Meta-Regression

In this study, score, mean age, the proportion of females, the proportion of rural hukou, publication year, and survey year can be taken as a continuous variable. Additionally, meta-regression was performed to assess the relationship between those variables and the pooled prevalence. The results showed that only mean age had a significant linear relationship with the prevalence of disability (b = 0.0094, p < 0.001). Thus, the prevalence of ADL disability in Chinese older adults showed an ascending trend with age.

3.8. Publication Bias and Sensitivity Analyses

Begg’s test showed that there was no obvious publication bias (z = 1.65, p = 0.099). The results of sensitivity analysis were between 25.7% (95% CI: 23.3–28.2%) and 26.4% (95% CI: 23.9–28.9%), indicating that the primary result had good robustness.

4. Discussion

To the best of our knowledge, this is the first meta-analysis to examine the prevalence of disability among older adults in mainland China over such an extensive period based on both international and Chinese databases. The total number of older persons in this analysis was large enough to be conclusive on several issues. The meta-analysis of 97 studies revealed that the prevalence was 26.2% (95% CI: 23.7–28.6%), which means there are nearly 69 million older people suffering from disabilities in China. Additionally, the prevalence of disability presented differences in terms of types of ADL, gender, age, and region.

We divided ADL into three types when collating and analyzing the data and found that we obtained a higher pooled prevalence for complete ADL, especially compared with BADL. These findings might be related to the characteristics of BADL and IADL. BADL and IADL represent different positions along the spectrum of the disablement process. BADL reflects the elders’ basic self-care independence, whereas IADL reflects the ability of older people to live independently. The IADL disability is more likely to happen earlier with age. Hence, the more items that are included, the more sensitive the tool will be.

Several studies had shown that the ADL ability of older individuals was negatively correlated with age [18,19]. Therefore, it is not surprising that the pooled prevalence of disability among the oldest people (age 80 years or above) was significantly higher than that of younger individuals. Meanwhile, the regression results suggest that the increase in the prevalence of disability is about a 0.09 percent point for each 1-year increase in the mean age of the population. With increasing age, the physiological functions of older adults continue to decline, the risk of chronic disease and accidental injury increases, and the disability trend could further increase [20].

The prevalence of disability differs significantly by gender, and the prevalence in females was significantly higher than that in males. This finding is consistent with the results of numerous studies, showing that females are more likely to experience disabilities [21]. Especially in BADL disability, that females’ disability rate was 1.29 times that of males. This difference was mainly attributed to two aspects. Compared with females, males have usually had better social status, income, and degree of education since ancient times. They have a stronger awareness of health care and more social resources to obtain health care [22]. In addition, the average life expectancy of females is longer than that of males [23], leading to a higher risk of disability.

Compared with northern China, elders in other regions (including central China, southwest China, and northwest China) were more likely to be BADL-disabled. Although the economic conditions have been greatly improved recently in most areas of China, many older adults living in remote areas are still unable to obtain timely and high-quality medical services.

This analysis provides useful information for the public health professionals of China. Over a quarter of all Chinese older individuals may have different levels of disability. This result indicates that we should strengthen community-based intervention and provide more health services, such as disability assessments and functional exercises. Once an individual has a severe functional impairment, medical assistance and financial subsidies should be provided promptly. This is especially true for older individuals aged over 80 years and female older individuals.

5. Limitations

There are some limitations of our study. First, this study included only published studies, and there may have been publication bias even though no such bias was indicated by statistical tests. Second, the included studies suffered from high heterogeneity, although the measurement tools were controlled and subgroup analyses were performed to address this shortcoming. High heterogeneity may reflect differences in the design and conduct of the studies (methodological heterogeneity) or in the participants and outcomes measured (clinical heterogeneity) [24,25]. In this meta-analysis, we collected many studies published over nearly three decades. It was inevitable that we did not fully identify the studies with low-quality research design. Additionally, we may have ignored some important confounding factors, such as disease and social-economic status. Moreover, the large sample size included in the study may also make the I2 value increase [26]. In addition, when using dependency measures, much information about the severity of the disability was lost, which is worthy of further study.

6. Conclusions

The meta-analysis of 97 studies on the prevalence of disability among the Chinese elderly population from 1979 to 2021 found that (1) the pooled prevalence reached 26.2% (95% CI: 23.7–28.6%) and (2) differences in prevalence exist in terms of types of ADL, gender and age. Considering the negative impact of disability on personal well-being and financial expenditure, regular and appropriate interventions are needed for this vulnerable group.

Appendix A

Table A1.

Characteristics of the 97 studies included in the meta-analysis.

NO. Study Publication Year Language Survey Year Sampling Province Age
(Mean)		 Type of ADL Sample Size Female
(N, %)		 Rural
(N, %)		 Cases of Disability Rate Score of Quality
1. Huang et al. [27] 1993 CH 1991 * Sichuan ≥60 (68.8) PADL + IADL ** 1242 657 (52.9) NR 422 33.98 6
2. Meng et al. [28] 1996 CH 1992 Beijing ≥60 PADL + IADL 3257 1415 (43.44) NR 778 23.90 7
3. Tang et al. [29] 1999 EN 1990 Beijing ≥60 (71.0) PADL ** 3440 1733 (50.38) NR 629 18.28 9
4. Lv et al. [30] 2001 CH 2000 Anhui ≥60 PADL + IADL 1424 NR NR 274 19.24 5
5a. Meng et al. [31] 2002 CH 1992 Beijing ≥60 (72.3) PADL 2783 NR NR 262 9.41 8
5b. Meng et al. [31] 2002 CH 1997 Beijing ≥60 (72.0) PADL 2786 NR NR 171 6.14 8
5c. Meng et al. [31] 2002 CH 2000 Beijing ≥60 (72.9) PADL 2667 NR NR 214 8.02 8
6. Wang et al. [32] 2002 CH 2000 Guangzhou ≥60 PADL ** 1161 631 (54.35) NR 94 8.10 7
7. Lin et al. [33] 2002 CH 2000 Beijing ≥60 PADL 895 NR NR 174 19.44 7
8. Ji et al. [34] 2007 CH 2005 * Jiangsu ≥60 PADL + IADL 337 NR NR 103 30.56 6
9. Yin and Lu [35] 2007 EN 2002 National ≥80 PADL 8844 4938 (55.83) 4627 (52.3) 3153 35.65 10
10a. Huang et al. [36] 2008 CH 2006 * Guizhou ≥60 (70.2) PADL 3221 1995 (61.94) NR 171 5.31 7
10b. Huang et al. [36] 2008 CH 2006 * Guizhou ≥60 (70.2) IADL 3221 1995 (61.94) NR 382 11.86 7
11. Tang et al. [37] 2009 CH 2008 Hunan ≥60 PADL + IADL 203 124 (61.08) NR 102 50.25 7
12. Xu et al. [38] 2011 CH 2009 * Zhejiang ≥60 (70.0) PADL + IADL 753 404 (53.65) 753(100.00) 129 17.13 7
13. Chen et al. [39] 2011 CH 2010 Zhejiang ≥80 (84.8) PADL ** 454 268 (59.03) NR 138 30.40 9
14. Li et al. [40] 2011 EN 2006 Beijing ≥60 PADL + IADL 1882 990 (52.60) NR 817 43.41 5
15. Xue et al. [41] 2011 CH 2010 Shanghai ≥80 (83.1) PADL + IADL 1027 140 (13.63) NR 674 65.63 9
16. Li et al. [42] 2012 CH 2009 Shanghai ≥60 (73.3) PADL + IADL 11,338 6043 (53.30) NR 2013 17.75 8
17. Shi et al. [43] 2012 CH 2011 Shandong ≥65 PADL 504 234 (46.43) NR 96 19.05 8
18. Li et al. [44] 2012 CH 2010 * Ningxia ≥60 PADL + IADL ** 904 459 (50.77) NR 261 28.87 7
19. Zhang et al. [45] 2012 CH 2010 * Hebei ≥60 PADL + IADL ** 2161 NR NR 796 36.83 7
20. Yu et al. [46] 2012 CH 2011 Shanghai ≥60 PADL + IADL 1500 842 (56.13) NR 589 39.27 8
21. Huang et al. [47] 2012 CH 2008 Anhui ≥60 (70.2) PADL + IADL ** 1117 764 (68.40) 1117(100.00) 764 68.40 8
22. Yin et al. [48] 2012 CH 2009 Zhejiang ≥60 (71.2) PADL + IADL ** 2184 1218 (55.77) 2184(100.00) 566 25.92 8
23. Zhang and Wei [49] 2014 CH 2013 Beijing ≥60 PADL 2031 NR NR 200 9.85 9
24. Zhong et al. [50] 2014 CH 2008 Zhejiang, Gansu ≥60 PADL ** 1157 547 (47.28) 647 (55.92) 214 18.50 9
25. Yin et al. [51] 2014 EN 2011 National ≥80 (92.3) PADL 5495 3192 (58.09) NR 1856 33.78 9
26. Chen et al. [52] 2015 CH 2013* Fujian ≥60 (71.5) PADL 14,292 7404 (51.81) NR 610 4.27 8
27. Li et al. [53] 2015 CH 2013* Ningxia ≥60 (70.0) PADL + IADL ** 817 457 (55.94) NR 84 10.28 7
28. Li and Yuan [54] 2015 CH 2013 Shandong ≥60 PADL 416 276 (66.19) 172 (41.25) 67 16.11 7
29. Zhang et al. [55] 2015 CH 2011 Chongqing ≥80 PADL ** 227 131 (57.71) NR 84 37.00 9
30. Zhang et al. [56] 2016 EN 2013 Shanghai ≥60 (72.1) IADL 8237 4473 (53.26) NR 1360 16.51 7
31. Gong [57] 2016 CH 2014 Shanghai ≥60 PADL + IADL 1233 NR NR 226 18.33 6
32. Zhong [58] 2016 CH 2012–2014 Guangdong ≥60 PADL 1706 NR NR 331 19.40 7
33. Liu et al. [59] 2016 EN 2013 Beijing ≥60 (71.4) PADL 1036 522 (50.40) NR 219 21.10 7
34. Peng and Wu [60] 2016 CH 2011 National ≥65 PADL 9097 4918 (54.06) 4755 (52.27) 1948 21.41 10
35. Huang et al. [61] 2016 CH 2013–2015 Zhejiang ≥60 (73.8) PADL 883 490 (55.49) NR 191 21.63 8
36a Su et al. [62] 2016 EN 2013 Shanghai ≥80 PADL 2058 1191 (57.87) NR 478 23.23 7
36b. Su et al. [62] 2016 EN 2013 Shanghai ≥80 IADL 2058 1191 (57.87) NR 780 37.90 7
37. Yue and Liu [63] 2016 CH 2011 National ≥65 PADL 5118 2861 (55.90) NR 1214 23.72 10
38. Chen et al. [64] 2016 CH 2014 Shanghai ≥60 (74.2) PADL + IADL 3556 2114 (59.45) NR 879 24.72 8
39. Yi et al. [65] 2016 CH 2013 Hubei ≥65 (73.3) PADL + IADL ** 4002 2058 (51.42) 4002(100.00) 1375 34.36 8
40. Zhang et al. [66] 2016 CH 2014* Hebei ≥60 (68.7) PADL + IADL ** 2548 1322 (51.88) 1350 (52.98) 1076 42.23 7
41. Zhai et al. [67] 2016 CH 2011 Shandong ≥65 PADL + IADL 1355 706 (52.10) 729 (53.80) 921 67.97 10
42. Luo et al. [68] 2016 CH 2011 Shandong, Henan, Hebei, Hunan, Guangdong, Guangxi, Hainan, Jiangsu ≥65 PADL ** 2227 1227 (55.10) NR 553 24.83 10
43. Dong et al. [5] 2017 EN 2011 Shanghai ≥60 (71.6) PADL 1997 1153 (57.74) NR 37 1.85 6
44a. Zhang et al. [69] 2017 EN 2005–2014 National ≥65 (72.0) PADL 26,604 13,515 (50.80) 16,022 (60.22) 1862 7.00 9
44b. Zhang et al. [69] 2017 EN 2005–2014 National ≥65 (72.0) IADL 26,604 13,515 (50.80) 16,022 (60.22) 8513 32.00 9
45. Zhou and Ma [70] 2017 CH 2013 National ≥60 (68.9) PADL 7629 3988 (52.27) 7629 (100.00) 668 8.76 9
46. Ding and Wang [71] 2017 CH 2014 National 60–79 (67.7) PADL + IADL 6959 3549 (50.99) 3897 (56.00) 1038 14.92 9
47. Jin [72] 2017 CH 2011 National ≥60 PADL ** 9765 NR NR 2084 21.34 9
48. Li et al. [73] 2017 CH 2013 Anhui ≥60 (72.3) PADL + IADL ** 746 438 (58.71) NR 211 28.28 9
49. Hao et al. [74] 2017 EN 2016 Beijing ≥60 PADL + IADL 1083 543 (50.14) NR 347 32.04 8
50. Liu et al. [75] 2017 CH 2016 Shandong ≥65 PADL ** 1196 NR NR 404 33.78 8
51. Wang et al. [76] 2017 CH 2015* Hebei ≥60 (75.5) PADL + IADL ** 724 378 (52.20) NR 309 42.68 6
52a. Hu et al. [77] 2017 CH 2014 National ≥65 (66.4) PADL ** 6168 2813 (45.61) NR 1517 24.59 8
52b. Hu et al. [77] 2017 CH 2014 National ≥65 (66.4) IADL ** 6168 NR NR 3864 62.65 8
53. Wu et al. [78] 2017 EN 2010 Chongqing ≥100 PADL 564 471 (83.51) 564 (100.00) 370 65.60 9
54. Yang et al. [79] 2018 EN 2015–2016 Hubei ≥65 (72.6) PADL ** 2096 1065 (50.81) NR 149 7.11 8
55. Liu et al. [80] 2018 CH 2013 National ≥60 PADL ** 8751 NR NR 842 9.62 8
56. Ding and Yan [81] 2018 CH 2011 National ≥60 PADL 7626 3801 (49.84) 5765 (75.60) 845 11.08 8
57a. Chen et al. [82] 2018 EN 2016–2017 Guangxi ≥60 PADL 2300 1350 (58.70) NR 266 11.57 7
57b. Chen et al. [82] 2018 EN 2016–2017 Guangxi ≥60 IADL 2300 1350 (58.70) NR 976 42.43 7
57c. Chen et al. [82] 2018 EN 2016–2017 Guangxi ≥60 PADL + IADL 2300 1350 (58.70) NR 998 43.39 7
58. Zhai et al. [83] 2018 CH 2016* Shanghai ≥60 PADL + IADL 4003 2257 (56.38) NR 473 11.82 7
59. Liu et al. [84] 2018 CH 2010–2014 Beijing ≥60 (70.3) PADL 4499 2684 (59.66) 2397 (53.28) 544 12.10 8
60. Xu et al. [85] 2018 CH 2016 Sichuan ≥60 PADL 890 577 (64.83) NR 119 13.37 9
61. Wu et al. [86] 2018 CH 2016* Beijing ≥60 PADL + IADL 1158 713 (61.57) NR 220 19.00 8
62. Fu et al. [87] 2018 EN 2014 Hubei ≥65 (74.3) PADL + IADL 1210 672 (55.54) NR 249 20.58 7
63. Liu et al. [88] 2018 EN 2016* Hubei ≥65 PADL + IADL 622 358 (57.56) NR 179 28.78 6
64. Gu and Feng [89] 2018 EN 2000–2009 National ≥65 (88.1) PADL ** 32,281 18,914 (58.59) NR 9361 29.00 9
65a. Hou et al. [90] 2018 EN 1998 National ≥80 (92.0) PADL 8768 5240 (59.76) 5455 (62.21) 3236 36.91 10
65b. Hou et al. [90] 2018 EN 2000 National ≥80 (91.1) PADL 10,940 6356 (58.10) 4181 (38.22) 3805 34.78 10
65c. Hou et al. [90] 2018 EN 2002 National ≥80 (92.3) PADL 10,905 6579 (60.33) 5785 (53.05) 4414 40.48 10
65d. Hou et al. [90] 2018 EN 2005 National ≥80 (92.5) PADL 10,393 6260 (60.23) 5723 (55.07) 3516 33.83 10
65e. Hou et al. [90] 2018 EN 2008 National ≥80 (92.4) PADL 11,658 7074 (60.68) 7016 (60.18) 3318 28.46 10
66. Bai et al. [91] 2018 CH 2013 Hebei ≥60 PADL + IADL 1374 785 (57.13) NR 584 42.50 7
67. Gong et al. [92] 2018 EN 2016* Anhui ≥60 (70.7) PADL + IADL 3182 1862 (58.52) 3182 (100.00) 1942 61.03 6
68. Dong et al. [93] 2018 EN 2014 Anhui ≥60 PADL + IADL 945 580 (61.38) 945 (100.00) 599 63.39 9
69a. Zhang et al. [94] 2019 CH 2015 Beijing, Shanghai, Hebei, Sichuan, Yunnan, Guangxi ≥60 PADL 23,803 13,234 (55.60) 11,029 (46.33) 500 2.10 9
69b. Zhang et al. [94] 2019 CH 2015 Beijing, Shanghai, Hebei, Sichuan, Yunnan, Guangxi ≥60 IADL 23,803 13,234 (55.60) 11,029 (46.33) 4570 19.20 9
79. Li et al. [95] 2019 CH 2015 Fujian ≥60 PADL ** 5174 2716 (52.49) NR 280 5.41 9
71. Liu et al. [96] 2019 CH 2016–2017 Hebei ≥60 PADL + IADL 3125 1670 (53.44) NR 324 10.37 8
72. Fu et al. [97] 2019 CH 2017 * Sichuan ≥60 PADL ** 1000 562 (56.20) NR 158 15.80 7
73. Chen et al. [98] 2019 EN 2016 Jiangsu ≥60 PADL 2493 1314 (52.71) 1584 (63.54) 402 16.13 7
74. Chen et al. [99] 2019 EN 2014 National ≥80 PADL 4076 2308 (56.62) 2259 (55.42) 1083 26.57 9
75. Xu et al. [100] 2019 CH 2017 Hunan ≥60 PADL + IADL ** 194 NR 194 (100.00) 55 28.35 8
76. Bai et al. [101] 2019 CH 2016–2017 Hebei ≥60 PADL + IADL ** 6171 3024 (49.00) NR 2489 40.33 9
77. Ma et al. [102] 2019 CH 2016–2017 Hebei ≥60 PADL + IADL ** 6171 NR NR 2489 40.33 8
78. Zhao et al. [103] 2019 CH 2017 * Hebei ≥60 (75.5) PADL + IADL 724 NR NR 309 42.68 7
79. Yao et al. [6] 2019 CH 2014–2016 Hainan ≥100 (102.8) PADL 940 765 (81.38) NR 670 71.28 9
80. Chen et al. [104] 2020 CH 2015 National ≥60 PADL 4485 2422 (54.00) NR 297 6.62 9
81. Ning et al. [105] 2020 CH 2018 Shandong ≥60 (69.9) PADL 3349 1715 (51.21) NR 229 6.84 9
82. Xu et al. [106] 2020 CH 2018 * Hainan ≥60 PADL ** 365 213 (58.36) 221 (60.55) 29 7.95 8
83. Gu et al. [107] 2020 CH 2018 Jiangsu ≥60 (69.4) PADL 3259 1644 (50.44) 1544 (47.38) 344 10.56 9
84. Peng et al. [108] 2020 EN 2018 Guangdong ≥60 (71.6) PADL 1321 NR NR 160 12.11 8
85. Xu et al. [20] 2020 EN 2018 Ningxia ≥60 (70.5) PADL 1040 513 (49.33) NR 179 17.21 8
86. Zhang et al. [109] 2020 CH 2018* Henan ≥60 (70.9) PADL 5570 2825 (50.72) 4074 (73.14) 1139 20.45 6
87. Cai et al. [110] 2020 CH 2015 Yunnan ≥60 (70.9) PADL + IADL 3978 2213 (55.63) 2000 (50.28) 1017 25.57 9
88. Song et al. [111] 2020 CH 2014 Shandong ≥65 PADL ** 559 254 (45.44) 312 (55.81) 143 25.58 9
89. Liu et al. [112] 2020 CH 2015–2018 Guangdong ≥60 (74.3) PADL + IADL ** 221 104 (47.06) NR 58 26.24 9
90. Du et al. [113] 2020 CH 2016 Anhui ≥60 (71.7) PADL 983 527 (53.61) NR 312 31.74 10
91. Zhang et al. [114] 2020 CH 2016 Chongqing ≥65 PADL + IADL ** 1341 609 (45.41) NR 596 44.44 8
92. Lin et al. [115] 2020 CH 2018 * Yunnan ≥60 (76.7) PADL 182 118 (64.84) NR 96 52.75 8
93. Xiao et al. [116] 2021 EN 2018 Guizhou, Yunnan, Sichuan, Xinjiang ≥60 (69.4) PADL 3770 NR NR 488 12.94 8
94. Cheng and Yan [117] 2021 EN 1998–2014 National ≥80 PADL 30,317 17,663 (58.26) NR 4884 16.11 10
95. Gao et al. [118] 2021 CH 2017 Shandong ≥60 (69.8) PADL + IADL 7070 4224 (59.75) NR 1603 22.67 9
96. Chen et al. [119] 2021 CH 2014 National ≥60 (70.5) PADL 6182 3305 (53.46) 3337 (53.98) 1517 24.54 9
97. Yan et al. [120] 2021 CH 2018 National ≥65 (85.6) PADL 15,771 8902 (56.45) NR 4196 26.61 10
Open in a new tab

* Survey year is the year the data were collected; if the survey year was not reported, the data was computed by subtracting two from the year of publication; ** Some studies did not indicate the source of the scale. We determined by the items and calculation methods used; CH = Chinese; EN = English; NR = not reported.

Author Contributions

Conceptualization, Z.-J.W. and P.-P.Z.; Collection, curation, and interpretation of data: Z.-L.G. and X.-J.D.; writing—original draft preparation, P.-P.Z.; writing—review and editing, P.-P.Z., H.-M.Y. and Z.-J.W.; supervision, Z.-J.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by UKRI’s Global Challenge Research Fund (ES/P011055/1).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All relevant data are within the paper.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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