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Review

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

by
Pian-Pian Zheng
1,
Zi-Le Guo
1,
Xiao-Jing Du
2,
Han-Mo Yang
3 and
Zhen-Jie Wang
1,*
1
Institute of Population Research, Peking University, Beijing 100871, China
2
School of Humanities and Social Sciences, Xi′an Jiaotong University, Xi′an 710049, China
3
National School of Development, Peking University, Beijing 100871, China
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2022, 19(3), 1656; https://doi.org/10.3390/ijerph19031656
Submission received: 25 December 2021 / Revised: 24 January 2022 / Accepted: 28 January 2022 / Published: 31 January 2022
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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.

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.

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.

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%).

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.

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).

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.

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.

Appendix A

Table
Table A1. Characteristics of the 97 studies included in the meta-analysis.
Table A1. Characteristics of the 97 studies included in the meta-analysis.
NO.StudyPublication YearLanguageSurvey YearSampling ProvinceAge
(Mean)		Type of ADLSample SizeFemale
(N, %)		Rural
(N, %)		Cases of DisabilityRateScore of Quality
1.Huang et al. [27]1993CH1991 *Sichuan≥60 (68.8)PADL + IADL **1242657 (52.9)NR42233.986
2.Meng et al. [28]1996CH1992Beijing≥60PADL + IADL32571415 (43.44)NR77823.907
3.Tang et al. [29]1999EN1990Beijing≥60 (71.0)PADL **34401733 (50.38)NR62918.289
4.Lv et al. [30]2001CH2000Anhui≥60PADL + IADL1424NRNR27419.245
5a.Meng et al. [31]2002CH1992Beijing≥60 (72.3)PADL2783NRNR2629.418
5b.Meng et al. [31]2002CH1997Beijing≥60 (72.0)PADL2786NRNR1716.148
5c.Meng et al. [31]2002CH2000Beijing≥60 (72.9)PADL2667NRNR2148.028
6.Wang et al. [32]2002CH2000Guangzhou≥60PADL **1161631 (54.35)NR948.107
7.Lin et al. [33]2002CH2000Beijing≥60PADL895NRNR17419.447
8.Ji et al. [34]2007CH2005 *Jiangsu≥60PADL + IADL337NRNR10330.566
9.Yin and Lu [35]2007EN2002National≥80PADL88444938 (55.83)4627 (52.3)315335.6510
10a.Huang et al. [36]2008CH2006 *Guizhou≥60 (70.2)PADL32211995 (61.94)NR1715.317
10b.Huang et al. [36]2008CH2006 *Guizhou≥60 (70.2)IADL32211995 (61.94)NR38211.867
11.Tang et al. [37]2009CH2008Hunan≥60PADL + IADL203124 (61.08)NR10250.257
12.Xu et al. [38]2011CH2009 *Zhejiang≥60 (70.0)PADL + IADL753404 (53.65)753(100.00)12917.137
13.Chen et al. [39]2011CH2010Zhejiang≥80 (84.8)PADL **454268 (59.03)NR13830.409
14.Li et al. [40]2011EN2006Beijing≥60PADL + IADL1882990 (52.60)NR81743.415
15.Xue et al. [41]2011CH2010Shanghai≥80 (83.1)PADL + IADL1027140 (13.63)NR67465.639
16.Li et al. [42]2012CH2009Shanghai≥60 (73.3)PADL + IADL11,3386043 (53.30)NR201317.758
17.Shi et al. [43]2012CH2011Shandong≥65PADL504234 (46.43)NR9619.058
18.Li et al. [44]2012CH2010 *Ningxia≥60PADL + IADL **904459 (50.77)NR26128.877
19.Zhang et al. [45]2012CH2010 *Hebei≥60PADL + IADL **2161NRNR79636.837
20.Yu et al. [46]2012CH2011Shanghai≥60PADL + IADL1500842 (56.13)NR58939.278
21.Huang et al. [47]2012CH2008Anhui≥60 (70.2)PADL + IADL **1117764 (68.40)1117(100.00)76468.408
22.Yin et al. [48]2012CH2009Zhejiang≥60 (71.2)PADL + IADL **21841218 (55.77)2184(100.00)56625.928
23.Zhang and Wei [49]2014CH2013Beijing≥60PADL2031NRNR2009.859
24.Zhong et al. [50]2014CH2008Zhejiang, Gansu≥60PADL **1157547 (47.28)647 (55.92)21418.509
25.Yin et al. [51]2014EN2011National≥80 (92.3)PADL54953192 (58.09)NR185633.789
26.Chen et al. [52]2015CH2013*Fujian≥60 (71.5)PADL14,2927404 (51.81)NR6104.278
27.Li et al. [53]2015CH2013*Ningxia≥60 (70.0)PADL + IADL **817457 (55.94)NR8410.287
28.Li and Yuan [54]2015CH2013Shandong≥60PADL416276 (66.19)172 (41.25)6716.117
29.Zhang et al. [55]2015CH2011Chongqing≥80PADL **227131 (57.71)NR8437.009
30.Zhang et al. [56]2016EN2013Shanghai≥60 (72.1)IADL82374473 (53.26)NR136016.517
31.Gong [57]2016CH2014Shanghai≥60PADL + IADL1233NRNR22618.336
32.Zhong [58]2016CH2012–2014Guangdong≥60PADL1706NRNR33119.407
33.Liu et al. [59]2016EN2013Beijing≥60 (71.4)PADL1036522 (50.40)NR21921.107
34.Peng and Wu [60]2016CH2011National≥65PADL90974918 (54.06)4755 (52.27)194821.4110
35.Huang et al. [61]2016CH2013–2015Zhejiang≥60 (73.8)PADL883490 (55.49)NR19121.638
36aSu et al. [62]2016EN2013Shanghai≥80PADL20581191 (57.87)NR47823.237
36b.Su et al. [62]2016EN2013Shanghai≥80IADL20581191 (57.87)NR78037.907
37.Yue and Liu [63]2016CH2011National≥65PADL51182861 (55.90)NR121423.7210
38.Chen et al. [64]2016CH2014Shanghai≥60 (74.2)PADL + IADL35562114 (59.45)NR87924.728
39.Yi et al. [65]2016CH2013Hubei≥65 (73.3)PADL + IADL **40022058 (51.42)4002(100.00)137534.368
40.Zhang et al. [66]2016CH2014*Hebei≥60 (68.7)PADL + IADL **25481322 (51.88)1350 (52.98)107642.237
41.Zhai et al. [67]2016CH2011Shandong≥65PADL + IADL1355706 (52.10)729 (53.80)92167.9710
42.Luo et al. [68]2016CH2011Shandong, Henan, Hebei, Hunan, Guangdong, Guangxi, Hainan, Jiangsu≥65PADL **22271227 (55.10)NR55324.8310
43.Dong et al. [5]2017EN2011Shanghai≥60 (71.6)PADL19971153 (57.74)NR371.856
44a.Zhang et al. [69]2017EN2005–2014National≥65 (72.0)PADL26,60413,515 (50.80)16,022 (60.22)18627.009
44b.Zhang et al. [69]2017EN2005–2014National≥65 (72.0)IADL26,60413,515 (50.80)16,022 (60.22)851332.009
45.Zhou and Ma [70]2017CH2013National≥60 (68.9)PADL76293988 (52.27)7629 (100.00)6688.769
46.Ding and Wang [71]2017CH2014National60–79 (67.7)PADL + IADL69593549 (50.99)3897 (56.00)103814.929
47.Jin [72]2017CH2011National≥60PADL **9765NRNR208421.349
48.Li et al. [73]2017CH2013Anhui≥60 (72.3)PADL + IADL **746438 (58.71)NR21128.289
49.Hao et al. [74]2017EN2016Beijing≥60PADL + IADL1083543 (50.14)NR34732.048
50.Liu et al. [75]2017CH2016Shandong≥65PADL **1196NRNR40433.788
51.Wang et al. [76]2017CH2015*Hebei≥60 (75.5)PADL + IADL **724378 (52.20)NR30942.686
52a.Hu et al. [77]2017CH2014National≥65 (66.4)PADL **61682813 (45.61)NR151724.598
52b.Hu et al. [77]2017CH2014National≥65 (66.4)IADL **6168NRNR386462.658
53.Wu et al. [78]2017EN2010Chongqing≥100PADL564471 (83.51)564 (100.00)37065.609
54.Yang et al. [79]2018EN2015–2016Hubei≥65 (72.6)PADL **20961065 (50.81)NR1497.118
55.Liu et al. [80]2018CH2013National≥60PADL **8751NRNR8429.628
56.Ding and Yan [81]2018CH2011National≥60PADL76263801 (49.84)5765 (75.60)84511.088
57a.Chen et al. [82]2018EN2016–2017Guangxi≥60PADL23001350 (58.70)NR26611.577
57b.Chen et al. [82]2018EN2016–2017Guangxi≥60IADL23001350 (58.70)NR97642.437
57c.Chen et al. [82]2018EN2016–2017Guangxi≥60PADL + IADL23001350 (58.70)NR99843.397
58.Zhai et al. [83]2018CH2016*Shanghai≥60PADL + IADL40032257 (56.38)NR47311.827
59.Liu et al. [84]2018CH2010–2014Beijing≥60 (70.3)PADL44992684 (59.66)2397 (53.28)54412.108
60.Xu et al. [85]2018CH2016Sichuan≥60PADL890577 (64.83)NR11913.379
61.Wu et al. [86]2018CH2016*Beijing≥60PADL + IADL1158713 (61.57)NR22019.008
62.Fu et al. [87]2018EN2014Hubei≥65 (74.3)PADL + IADL1210672 (55.54)NR24920.587
63.Liu et al. [88]2018EN2016*Hubei≥65PADL + IADL622358 (57.56)NR17928.786
64.Gu and Feng [89]2018EN2000–2009National≥65 (88.1)PADL **32,28118,914 (58.59)NR936129.009
65a.Hou et al. [90]2018EN1998National≥80 (92.0)PADL87685240 (59.76)5455 (62.21)323636.9110
65b.Hou et al. [90]2018EN2000National≥80 (91.1)PADL10,9406356 (58.10)4181 (38.22)380534.7810
65c.Hou et al. [90]2018EN2002National≥80 (92.3)PADL10,9056579 (60.33)5785 (53.05)441440.4810
65d.Hou et al. [90]2018EN2005National≥80 (92.5)PADL10,3936260 (60.23)5723 (55.07)351633.8310
65e.Hou et al. [90]2018EN2008National≥80 (92.4)PADL11,6587074 (60.68)7016 (60.18)331828.4610
66.Bai et al. [91]2018CH2013Hebei≥60PADL + IADL1374785 (57.13)NR58442.507
67.Gong et al. [92]2018EN2016*Anhui≥60 (70.7)PADL + IADL31821862 (58.52)3182 (100.00)194261.036
68.Dong et al. [93]2018EN2014Anhui≥60PADL + IADL945580 (61.38)945 (100.00)59963.399
69a.Zhang et al. [94]2019CH2015Beijing, Shanghai, Hebei, Sichuan, Yunnan, Guangxi≥60PADL23,80313,234 (55.60)11,029 (46.33)5002.109
69b.Zhang et al. [94]2019CH2015Beijing, Shanghai, Hebei, Sichuan, Yunnan, Guangxi≥60IADL23,80313,234 (55.60)11,029 (46.33)457019.209
79.Li et al. [95]2019CH2015Fujian≥60PADL **51742716 (52.49)NR2805.419
71.Liu et al. [96]2019CH2016–2017Hebei≥60PADL + IADL31251670 (53.44)NR32410.378
72.Fu et al. [97]2019CH2017 *Sichuan≥60PADL **1000562 (56.20)NR15815.807
73.Chen et al. [98]2019EN2016Jiangsu≥60PADL24931314 (52.71)1584 (63.54)40216.137
74.Chen et al. [99]2019EN2014National≥80PADL40762308 (56.62)2259 (55.42)108326.579
75.Xu et al. [100]2019CH2017Hunan≥60PADL + IADL **194NR194 (100.00)5528.358
76.Bai et al. [101]2019CH2016–2017Hebei≥60PADL + IADL **61713024 (49.00)NR248940.339
77.Ma et al. [102]2019CH2016–2017Hebei≥60PADL + IADL **6171NRNR248940.338
78.Zhao et al. [103]2019CH2017 *Hebei≥60 (75.5)PADL + IADL724NRNR30942.687
79.Yao et al. [6]2019CH2014–2016Hainan≥100 (102.8)PADL940765 (81.38)NR67071.289
80.Chen et al. [104]2020CH2015National≥60PADL44852422 (54.00)NR2976.629
81.Ning et al. [105]2020CH2018Shandong≥60 (69.9)PADL33491715 (51.21)NR2296.849
82.Xu et al. [106]2020CH2018 *Hainan≥60PADL **365213 (58.36)221 (60.55)297.958
83.Gu et al. [107]2020CH2018Jiangsu≥60 (69.4)PADL32591644 (50.44)1544 (47.38)34410.569
84.Peng et al. [108]2020EN2018Guangdong≥60 (71.6)PADL1321NRNR16012.118
85.Xu et al. [20]2020EN2018Ningxia≥60 (70.5)PADL1040513 (49.33)NR17917.218
86.Zhang et al. [109]2020CH2018*Henan≥60 (70.9)PADL55702825 (50.72)4074 (73.14)113920.456
87.Cai et al. [110]2020CH2015Yunnan≥60 (70.9)PADL + IADL39782213 (55.63)2000 (50.28)101725.579
88.Song et al. [111]2020CH2014Shandong≥65PADL **559254 (45.44)312 (55.81)14325.589
89.Liu et al. [112]2020CH2015–2018Guangdong≥60 (74.3)PADL + IADL **221104 (47.06)NR5826.249
90.Du et al. [113]2020CH2016Anhui≥60 (71.7)PADL983527 (53.61)NR31231.7410
91.Zhang et al. [114]2020CH2016Chongqing≥65PADL + IADL **1341609 (45.41)NR59644.448
92.Lin et al. [115]2020CH2018 *Yunnan≥60 (76.7)PADL182118 (64.84)NR9652.758
93.Xiao et al. [116]2021EN2018Guizhou, Yunnan, Sichuan, Xinjiang≥60 (69.4)PADL3770NRNR48812.948
94.Cheng and Yan [117]2021EN1998–2014National≥80PADL30,31717,663 (58.26)NR488416.1110
95.Gao et al. [118]2021CH2017Shandong≥60 (69.8)PADL + IADL70704224 (59.75)NR160322.679
96.Chen et al. [119]2021CH2014National≥60 (70.5)PADL61823305 (53.46)3337 (53.98)151724.549
97.Yan et al. [120]2021CH2018National≥65 (85.6)PADL15,7718902 (56.45)NR419626.6110
* 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.

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Ijerph 19 01656 g001 550
Figure 1. Flow chart of the study selection process.
Figure 1. Flow chart of the study selection process.
Ijerph 19 01656 g001
Table
Table 1. Risk of bias using quality assessment forms.
Table 1. Risk of bias using quality assessment forms.
ItemYesNoUnclear
(1) Define the source of information (survey, record review)9700
(2) List inclusion and exclusion criteria for exposed and unexposed subjects (cases and controls) or refer to previous publications9700
(3) Indicate time period used for identifying patients78190
(4) Indicate whether or not subjects were consecutive if not population-based9700
(5) Indicate if evaluators of subjective components of the study were masked to other aspects of the status of the participants0970
(6) Describe any assessments undertaken for quality assurance purposes (e.g., test/retest of primary outcome measurements)60361
(7) Explain any patient exclusions from the analysis8971
(8) Describe how confounding was assessed and/or controlled.65320
(9) If applicable, explain how missing data were handled in the analysis13822
(10) Summarize patient response rates and completeness of data collection86110
(11) Clarify what follow-up, if any, was expected and the percentage of patients for which incomplete data or follow-up was obtained0970
Table
Table 2. Pooled prevalence of disability and subgroup analyses.
Table 2. Pooled prevalence of disability and subgroup analyses.
VariablesClassificationNumber of StudiesNumber of ResultsEvent Rate (%)95% CI (%)Heterogeneity I2 (%)Q-Valuep-Value
Pooled prevalence9711026.223.7–28.699.981,405.53
Type of ADLBADL566220.517.7–23.399.926.55<0.001
IADL7731.821.2–42.499.9
BADL + IADL414133.829.4–38.399.6
GenderMale536022.720.0–25.599.75.350.021
Female536028.524.5–32.599.8
Age group60–69232612.810.1–15.599.6104.92<0.001
70–79232622.416.5–28.399.7
≥80364436.833.1–40.599.6
RegionEastern China323327.022.3–31.799.82.440.786
Northern China182026.019.9–32.199.7
Southern China6624.28.0–40.399.7
Central China7726.917.9–35.899.4
Southwest China101330.922.3–39.499.7
Northwest China4421.312.3–30.397.8
HukouUrban172222.416.9–27.999.92.130.143
Rural263128.022.9–33.099.9
Survey year1999 and before5621.410.4–32.499.82.160.706
2000–20046723.713.0–34.399.8
2005–2009101229.121.6–36.799.7
2010–2014414327.723.6–31.899.8
2015–2019363825.320.9–29.799.9
Table
Table 3. Pooled prevalence of BADL disabilities and subgroup analyses.
Table 3. Pooled prevalence of BADL disabilities and subgroup analyses.
VariablesClassificationNumber of StudiesNumber of ResultsEvent Rate (%)95% CI (%)Heterogeneity I2 (%)Q-Valuep-Value
Pooled prevalence566220.517.7–23.399.945,852.90
GenderMale374119.416.4–22.499.73.950.047
Female374125.120.3–29.999.8
Age group60–6917177.35.7–8.998.6111.60<0.001
70–79171713.110.4–15.998.4
≥80293330.026.2–33.999.6
RegionEastern China151516.813.5–20.199.310.450.005
Northern China6812.99.7–16.199.2
Other regions *161624.418.0–30.799.5
HukouUrban141822.616.1–29.299.90.000.944
Rural162022.417.4–27.399.9
Survey year2009 and before *81421.714.2–29.199.90.780.678
2010–2014252521.317.6–25.199.7
2015–2019202018.914.3–23.499.8
* 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.
Table
Table 4. Pooled prevalence of complete ADL and subgroup analyses.
Table 4. Pooled prevalence of complete ADL and subgroup analyses.
VariablesClassificationNumber of StudiesNumber of ResultsEvent Rate (%)95% CI (%)Heterogeneity I2 (%)Q-Valuep-Value
Pooled prevalence414133.829.4–38.399.610,997.47
GenderMale161632.223.1–41.499.50.520.472
Female161636.728.7–44.799.4
Age group60–696625.514.0–36.999.722.45<0.001
70–796640.524.9–56.199.6
≥807761.951.9–71.997.3
RegionEastern China161636.427.8–44.999.81.100.578
Northern China121234.726.9–42.499.6
Other regions *121231.225.1–37.299.6
Survey year2009 and before *101032.924.4–41.599.50.380.827
2010–2014151535.927.4–44.399.7
2015–2019161632.524.9–40.199.7
* 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.
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MDPI and ACS Style

Zheng, P.-P.; Guo, Z.-L.; Du, X.-J.; Yang, H.-M.; Wang, Z.-J. Prevalence of Disability among the Chinese Older Population: A Systematic Review and Meta-Analysis. Int. J. Environ. Res. Public Health 2022, 19, 1656. https://doi.org/10.3390/ijerph19031656

AMA Style

Zheng P-P, Guo Z-L, Du X-J, Yang H-M, Wang Z-J. Prevalence of Disability among the Chinese Older Population: A Systematic Review and Meta-Analysis. International Journal of Environmental Research and Public Health. 2022; 19(3):1656. https://doi.org/10.3390/ijerph19031656

Chicago/Turabian Style

Zheng, Pian-Pian, Zi-Le Guo, Xiao-Jing Du, Han-Mo Yang, and Zhen-Jie Wang. 2022. "Prevalence of Disability among the Chinese Older Population: A Systematic Review and Meta-Analysis" International Journal of Environmental Research and Public Health 19, no. 3: 1656. https://doi.org/10.3390/ijerph19031656

APA Style

Zheng, P.-P., Guo, Z.-L., Du, X.-J., Yang, H.-M., & Wang, Z.-J. (2022). Prevalence of Disability among the Chinese Older Population: A Systematic Review and Meta-Analysis. International Journal of Environmental Research and Public Health, 19(3), 1656. https://doi.org/10.3390/ijerph19031656

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