Front. Aging Neurosci. Frontiers in Aging Neuroscience Front. Aging Neurosci. 1663-4365 Frontiers Media S.A. 10.3389/fnagi.2023.1227112 Aging Neuroscience Systematic Review Evidence from a meta-analysis and systematic review reveals the global prevalence of mild cognitive impairment Song Wen-xin 1 Wu Wei-wei 1 Zhao Yuan-yuan 1 Xu Hai-lun 1 Chen Guan-cheng 1 Jin Shan-yu 2 Chen Jie 1 3 * Xian Shao-xiang 1 3 * Liang Jing-hong 4 * 1The First Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China 2Gannan Medical University, Ganzhou, China 3The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China 4Department of Maternal and Child Health, School of Public Health, Sun Yat-sen University, Guangzhou, China

Edited by: Fangyi Xu, University of Louisville, United States

Reviewed by: Maria Casagrande, Sapienza University of Rome, Italy; Masafumi Yoshimura, Kansai Medical University, Japan

 *Correspondence: Jing-hong Liang, liangjh78@mail2.sysu.edu.cn Jie Chen, 2287550@qq.com Shao-xiang Xian, zjxzms2014@163.com 27 10 2023 2023 15 1227112 22 05 2023 26 09 2023 Copyright © 2023 Song, Wu, Zhao, Xu, Chen, Jin, Chen, Xian and Liang. 2023 Song, Wu, Zhao, Xu, Chen, Jin, Chen, Xian and Liang

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 Objective

Mild cognitive impairment (MCI) is a preclinical and transitional stage between healthy ageing and dementia. The purpose of our study was to investigate the recent pooled global prevalence of MCI.

 Methods

This meta-analysis was in line with the recommendations of Cochrane’s Handbook and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020. We conducted a comprehensive search using the PubMed, Embase, Web of Science, CNKI, WFD, VIP, and CBM from their inception to March 1, 2023. Quality assessment was guided by the Agency for Healthcare Research and Quality (AHRQ) methodology checklist. The pooled global prevalence of MCI was synthesized using meta-analysis via random effect model. Subgroup analyses were performed to examine considered factors potentially associated with MCI prevalence.

 Results

We identified 233 studies involving 676,974 individuals aged above 50 years. All the studies rated as moderated-to-high quality. The overall prevalence of MCI was 19.7% [95% confidence interval (95% CI): 18.3–21.1%]. Subgroup analyses revealed that the global prevalence of MCI increased over time, with a significant rise [32.1% (95% CI: 22.6–41.6%)] after 2019. Additionally, MCI prevalence in hospitals [34.0% (95% CI: 22.2–45.7%)] was higher than in nursing homes [22.6% (95% CI: 15.5–29.8%)] and communities [17.9% (95% CI: 16.6–19.2%)], particularly after the epidemic of coronavirus disease 2019 (COVID-19).

 Conclusion

The global prevalence of MCI was 19.7% and mainly correlated with beginning year of survey and sample source. The MCI prevalence increased largely in hospitals after 2019 may be related to the outbreak of COVID-19. Further attention to MCI is necessary in the future to inform allocation of health resources for at-risk populations.

 mild cognitive impairment global prevalence COVID-19 meta-analysis systematic review section-at-acceptance Neurocognitive Aging and Behavior Introduction

Mild cognitive impairment (MCI) is a condition characterized by mild cognitive deficit, while still retains the ability to perform daily living activities (Petersen et al., 2014). A recent review reported that up to 15.56% of community dwellers aged over 50 years were affected by MCI worldwide (Bai et al., 2022). MCI is considered as a symptomatic precursor of dementia, serving as an intermediate stage between normal aging and dementia. Over 46% of individuals with MCI progressed to clinical dementia within 3 years, which is one of the major causes of disability and dependency among older people (Trambaiolli et al., 2021). Therefore, MCI as predementia imposes potential economic burden on individuals, families, and society (Wang et al., 2022).

MCI is currently viewed as an “intervention window” for delaying the onset of dementia (Anderson, 2019; Liang et al., 2019; Wang et al., 2020). Understanding the global prevalence of MCI is essential for developing relevant strategies to prevent dementia. In recent years, several epidemiological studies have been conducted on MCI prevalence at different levels. For instance, Bai et al. revealed that the prevalence of MCI among community dwellers worldwide was over 15% and influenced by factors such as age, sex, educational level, and sample source (Bai et al., 2022). Deng et al. reported a prevalence rate of MCI in China was 15.4%, which was associated with unhealthy lifestyles such as alcohol consumption and lack of exercise, as well as health conditions like diabetes, hypertension, coronary heart disease, and depression (Deng et al., 2021). This information is crucial for developing prevention strategies aimed at addressing these risk factors. However, there are significant heterogeneities among previous studies. First, some studies may reveal the partial results when investigating the prevalence of MCI among the global population. On the one hand, differences in population characteristics could lead to variation in prevalence. Specifically, populations with the high-risk diseases, such as diabetes and depression, have a higher MCI prevalence (Hasche et al., 2010; Bo et al., 2015), which could affect the accuracy of total prevalence in healthy individuals. On the other hand, differences in geographical distribution could also affect the precision of global MCI prevalence when investigators omitted evidence from other geographical areas and sample source (Bai et al., 2022; Chen et al., 2023). Second, during the same period and in the same region, different studies have reported significant disparities in results. For instance, two studies from China in 2019 produced significantly different prevalence: one reported 9.67% (Ruan et al., 2020), while the other reported 27.8% (Lu et al., 2019). Similarly, two studies conducted 1 year apart reported nearly a threefold difference in MCI prevalence results in China: one reported 33.3% in 2015, while the other reported 10.42% in 2016 (McGrattan et al., 2021). These discrepancies may be attributed to variations in study design, such as search sources, screening tools, and diagnostic criteria for MCI. Lastly, the outbreak of the coronavirus disease 2019 (COVID-19) has significantly impacted society, affecting the lifestyle and health of everyone. There is evidence suggesting that some patients who have recovered from COVID-19 exhibit cognitive deficits (Liu et al., 2021; Crivelli et al., 2022). Consequently, the prevalence of neurological diseases, including MCI, may be even more severe as a result of COVID-19. However, whether COVID-19 has increased MCI prevalence remains unknown, highlighting the need for more updated research into the prevalence of MCI. Therefore, a comprehensive and updated meta-analysis on the global prevalence of MCI is urgently needed to identify the risk factors and provide a reference for researchers and clinicians. The purpose of this study is to investigate the recent global prevalence of MCI among the widest possible population.

 Methods

This systematic review was conducted in accordance with the recommendations of Cochrane’s Handbook (Cumpston et al., 2019) and the Systematic Reviews and Meta-Analyses (PRISMA) 2020 (Page et al., 2021) (Supplementary File S2). These analyses relied solely on previously published studies, so ethical approval or patient consent was not required.

Search strategies

The eligible studies were identified through a comprehensive literature search in PubMed, Embase, Web of Science, CNKI, WFD, VIP, and CBM databases from their inception to March 1, 2023. A search strategy was employed using Medical Subject Headings (MeSH) terms associated with keywords and Boolean operators on “cognitive dysfunction,” “mild cognitive impairment,” “mild cognitive disorder,” “prevalence,” “epidemiology,” and “epidemiological study” et al. In addition, manual retrieval was performed on the reference lists of relevant reviews and meta-analysis to search for additional studies on MCI prevalence. All database specific search queries could be found in Supplementary File S1.

 Inclusion and exclusion criteria

Inclusion criteria were developed based on the PICOS principle, including participants (P), outcomes (O), and study design (S):

Participants: Studies were included when participants were diagnosed with MCI using recognized criteria, such as Petersen criteria (P-MCI) (Ronald, 2011), Diagnostic and Statistical Manual of Mental Disorders (DSM) (Sharp, 2022), etc.

Outcomes: Prevalence of MCI (or any of MCI subtypes) or data regarding the prevalence of MCI were provided in the report. If multiple articles were published based on the same dataset, only the most recent study was included.

Study design: Our study included all types of cohort and cross-sectional studies without any restriction in language, region, or publication date.

Studies were excluded if they met the following conditions:

Participants: Studies involving other types of cognitive dysfunction, such as dementia.

Outcomes: Studies involving the prevalence of comorbidity with MCI, such as hypertension, coronary heart disease, and depression.

Study design: Randomized controlled trials (RCT), systematic reviews, meta-analysis, case–control studies, bibliographic review articles, letters to the editor, and articles published only in abstract form.

Full texts or data could not be obtained for our analyses.

 Literature selection and data extraction

All citations were downloaded and managed using EndNote X9 software (Thompson ISI Research Soft, Clarivate Analytics, Philadelphia, United States). First, duplicate items were retrieved and removed. Then, based on inclusion and exclusion criteria, three investigators (WXS, YYZ, and HLX) independently reviewed the titles, abstracts, and full texts of publications to exclude irrelevant studies. All the eligible citations were cross-checked again to ensure accuracy. The relevant key data from the included studies were extracted into Microsoft Excel worksheets: (1) basic information: first author, publication year; (2) baseline characteristics: sample size, cases, age, proportion of males, beginning of survey, diagnostic criteria, region. The corresponding authors were consulted to obtain the essential information missing in the original studies.

 Quality assessment

Three researchers (WXS, YYZ, and HLX) independently assessed the methodological quality of the included studies using the Agency for Healthcare Research and Quality (AHRQ) methodology checklist (Rostom et al., 2004). The checklist included 11 items: (I) Define the source of information; (II) List inclusion and exclusion criteria for exposed and unexposed subjects or provide a reference to previous publications that describe these criteria; (III) Indicate time period used for identifying patients; (IV) Indicate whether or not subjects were consecutive if not population-based; (V) Indicate if evaluators of subjective components of were masked to other aspects of the status of the participants; (VI) Describe any assessments undertaken for quality control purposes; (VII) Explain any patient exclusions from analysis; (VIII) Describe how confounding was assessed and/or controlled; (IX) If applicable, explain how missing data were handled in the analysis; (X) Summarize patient response rates and completeness of data collection; (XI) Clarify what follow-up, if any, was expected and the percentage of patients for which incomplete data or follow-up was obtained. The quality score for individual study ranges from 0 to 11, with 1 point for each item, and the study quality is separated into three levels: low (0–3), moderate (4–7), and high (8–11) (Hu et al., 2015). Any disagreements and uncertainty were resolved by discussion.

 Statistical analyses

The overall prevalence and 95% confidence intervals (95% CI) was estimated using a random-effects model (Hedges, 1984). Heterogeneity was assessed by utilizing I2 statistics, with I2 > 50% or p < 0.1 indicating high heterogeneity (Higgins et al., 2003). A series of subgroup analyses were conducted to examine considered factors potentially associated with MCI prevalence. The subgroup variables included study type (cohort, cross-sectional), diagnostic method (P-MCI, DSM), male-to-female ratio (male/female ≥1, male/female <1), region1 (developing country, developed country), regions2 (Asia, Europe, North America, Africa, Oceania, South America), beginning year of survey (≤ 2009, 2010–2018, ≥ 2019), sample size (0–1,000, 1,001–5,000, 5,001–10,000, ≥10,001), sample source (community, nursing home, hospital), MCI subtype (aMCI/naMCI ≥1, aMCI/naMCI <1), basic diseases/non basic diseases (≥ 1, < 1) and the time trends in prevalence from different sample sources. Potential publication bias was assessed by using the funnel plot (Sedgwick, 2015) and Egger’s test (Egger et al., 2003). All the aforementioned sequences of analyses were performed in Stata version 15.0 (Nyaga et al., 2014) using “metan” and “metabias” packages.

 Results Literature selection

We initially obtained 143,006 studies, including 142,706 citations from databases and 300 additional studies from manual retrieval. Then, 33,931 studies were excluded for duplication, 108,457 articles were removed due to irrelevant titles and abstracts. Subsequently, 385 studies were excluded for various reasons: 66 were not available in full, 31 were non-observational studies (RCT, reviews, commentaries, systematic reviews, meta-analysis, conference abstracts, case reports), 159 had no available data, 83 had unclear diagnostic criteria, and 46 were reduplicated. Finally, 233 studies were included in this meta-analysis. The study selection process is shown in Figure 1. And all included studies in this systematic review and meta-analysis showed in Supplementary File S4.

The screening process of the literature.

 Characteristics and quality of included studies

The 233 included studies were conducted between 1981 and 2021, enrolling 676,974 individuals aged from 50 to 107 years old. Most studies were cross-sectional studies (N = 207, 88.8%) and conducted in Asia (N = 171, 75.0%). The common diagnostic criteria for MCI was P-MCI (N = 150, 77.7%). Other detailed information on study characteristics is presented in Table 1.

Characteristics of studies included in this meta-analysis.

ID Study Study design Cases Sample Age, mean ± sd (range) Proportion of males (%) Beginning of survey Diagnostic criteria Region Quality score
1 Björk et al. (2018) Cross-sectional 1,067 4,545 85.50 ± 7.80 36.41% 2013–2014 P-MCI Swedish 9
2 Tiwari et al. (2013) Cross-sectional 98 2,146 ≥60 47.44% 2008–2010 P-MCI India 8
3 Rao et al. (2018) Cross-sectional 299 2,111 ≥65 40.50% 2009 P-MCI China 8
4 VancamPfort et al. (2017) Cross-sectional 5,005 32,715 ≥50 48.30% 2007–2010 DSM-IV China, Ghana, India, Mexico, Russia, South africa 7
5 Lu et al. (2019) Cross-sectional 1,541 5,542 ≥60 46.26% 2010 and 2015 P-MCI China 7
6 Su et al. (2013) Cross-sectional 145 796 ≥60 32.79% 2012 P-MCI China 4
7 Zhang et al. (2013) Cross-sectional 450 2,460 60–89 45.98% NR P-MCI China 5
8 Zhang et al. (2015) Cross-sectional 651 1,971 ≥60 37.44% NR DSM-IV China 6
9 Li et al. (2013) Cross-sectional 332 3,484 ≥65 41.30% 2007–2009 P-MCI China 9
10 Guo et al. (2013) Cross-sectional 136 940 ≥60 43.19% NR P-MCI China 6
11 Yin et al. (2012) Cross-sectional 67 1,011 ≥65 40.55% 2007–2009 P-MCI China 7
12 Pan et al. (2012) Cross-sectional 154 897 69.68 ± 7.06 48.38% 2011–2011 P-MCI China 9
13 Xia et al. (2006) Cross-sectional 16 145 67.96 ± 6.49 39.31% 2000–2004 DSM-IV China 7
14 Yang et al. (2017) Cross-sectional 296 1,000 71.45 ± 5.86 48.60% NR P-MCI China 7
15 Zhang et al. (2018) Cohort study 430 1,033 ≥55 33.69% 2016–2017 P-MCI China 7
16 Jiang et al. (2019) Cross-sectional 833 2,886 69.98 ± 5.90 41.61% 2017 P-MCI China 7
17 Dai et al. (2019) Cross-sectional 201 1,184 67.96 ± 6.49 50.17% 2019 CDGM China 8
18 Liu et al. (2019) Cross-sectional 73 554 ≥60 64.80% 2018 CDGM China 9
19 Yuan et al. (2019) Cross-sectional 199 1,032 66 ± 7 38.19% 2015 P-MCI China 6
20 Yuan et al. (2021) Cross-sectional 613 3,043 ≥60 51.36% 2016 P-MCI China 8
21 Luo et al. (2015) Cross-sectional 554 3,063 70.00 ± 7.70 45.60% 2010 P-MCI China 6
22 Xu et al. (2014) Cross-sectional 526 2,426 69.10 ± 6.80 39.30% 2010–2011 P-MCI China 8
23 Tang et al. (2007) Cross-sectional 217 1,865 60–100 48.10% 2004 P-MCI China 6
24 Gang et al. (2008) Cross-sectional 203 1,750 60–100 48.51% 2004 P-MCI China 8
25 Huang et al. (2008) Cross-sectional 257 4,697 ≥60 41.15% 2001–2002 P-MCI China 8
26 Ren et al. (2013) Cross-sectional 99 946 ≥60 50.74% 2011 DSM-IV China 8
27 Zhou et al. (2011) Cross-sectional 107 1,227 ≥60 43.68% 2009–2010 DSM-IV China 8
28 Chen et al. (2015) Cross-sectional 352 1,695 ≥60 46.90% NR P-MCI China 4
29 Pan et al. (2012) Cross-sectional 67 287 ≥60 42.86% NR P-MCI China 7
30 Song et al. (2012) Cross-sectional 167 2,279 ≥60 48.79% 2010–2011 P-MCI China 8
31 Zhu et al. (2009) Cross-sectional 148 1,511 ≥60 45.40% 2008 DSM-IV China 8
32 Wu et al. (2012) Cross-sectional 396 1,583 ≥60 50.28% 2011–2012 CDGM China 7
33 Liao et al. (2012) Cross-sectional 41 399 60–92 46.37% NR P-MCI China 5
34 Zhang et al. (2014) Cross-sectional 287 1,764 ≥60 44.05% 2012 P-MCI China 7
35 Afgin et al. (2012) Cohort study 303 944 ≥65 49.30% NR DSM-IV Israel 10
36 Artero et al. (2008) Cohort study 2,882 6,892 ≥65 53.19% 1991–2001 DSM-IV French 9
37 Lee et al. (2009) Cohort study 188 927 ≥60 33.66% 2005–2007 P-MCI Korea 8
38 Ogunniyi et al. (2016) Cross-sectional 111 613 72.90 ± 8.50 68.35% 2013–2014 DSM-IV and P-MCI Nigerian 9
39 Petersen et al. (2010) Cross-sectional 329 1,969 70–89 50.89% 2004–2007 DSM-IV United States 11
40 Pilleron et al. (2015) Cohort study 133 2,002 ≥65 NR 2011–2012 P-MCI Central Africa 6
41 Richard et al. (2013) Cohort study 429 2,160 NR NR 1999–2001 P-MCI United States 8
42 Kumar et al. (2005) Cohort study 93 2,518 NR NR 2001–2002 P-MCI Australia 9
43 Lee et al. (2009) Cohort study 197 714 71.90 ± 5.70 42.16% 2005 P-MCI Korea 6
44 Lee et al. (2012) Cross-sectional 67 318 65.90 ± 5.30 40.88% 2008–2009 P-MCI Malaysian 8
45 Purser et al. (2005) Cohort study 810 3,673 74 38.69% 1981, 1984, 1987, and 1990 P-MCI United States 8
46 De Jager et al. (2005) Cohort study 40 157 NR NR NR P-MCI United Kingdom 7
47 Khedr et al. (2015) Cross-sectional 12 691 ≥60 NR 2011–2013 DSM-IV Egypt 7
48 Yu et al. (2016) Cohort study 66 376 68.60 ± 4.70 NR NR DSM-IV China 5
49 Ma et al. (2016) Cross-sectional 574 5,241 72.13 ± 4.22 43.90% 2012–2012 P-MCI China 9
50 Wang et al. (2015) Cross-sectional 625 3,136 69.30 ± 6.80 40.66% 2012–2012 P-MCI China 8
51 Jia et al. (2013) Cross-sectional 2,137 10,276 NR 42.41% 2008–2009 DSM-IV China 9
52 Hu et al. (2012) Cross-sectional 1,782 9,146 65.62 ± 7.52 43.83% 2008–2009 DSM-IV China 7
53 Qiu et al. (2003) Cross-sectional 92 3,910 66.97 ± 8.44 49.68% 2000–2001 P-MCI China 8
54 Lei et al. (2008) Cross-sectional 680 4,419 66.40 ± 5.60 41.68% 2005 The diagnostic criteria for MCI in Sweden, 2001 China 8
55 Lao et al. (2011) Cross-sectional 326 7,665 ≥55 45.78% 2010 P-MCI China 5
56 Yang et al. (2011) Cross-sectional 337 454 72.67 ± 6.34 69.16% 2009 Chinese guidelines and P-MCI China 4
57 Yin et al. (2011) Cross-sectional 310 2,164 ≥60 45.84% 2010 P-MCI China 6
58 Tong et al. (2013) Cross-sectional 200 1,575 ≥60 NR 2012 P-MCI China 6
59 Xiong et al. (2013) Cross-sectional 339 2,978 ≥65 44.12% 2011 DSM-IV China 7
60 Zhang et al. (2013) Cross-sectional 450 2,460 ≥60 45.98% NR P-MCI China 8
61 Gu et al. (2014) Cohort study 92 679 60–91 44.33% 2010–2013 IWG China 7
62 Qin et al. (2014) Cross-sectional 612 4,086 ≥55 35.00% 2011–2012 P-MCI China 8
63 Sun et al. (2016) Cross-sectional 40 384 ≥65 52.08% NR IWG and ADNI China 4
64 Zhou et al. (2016) Cross-sectional 221 804 60–88 46.52% 2014–2015 Chinese guidelines and P-MCI China 7
65 Guo et al. (2012) Cross-sectional 35 264 ≥65 50.76% 2008–2009 P-MCI China 8
66 Jia et al. (2014) Cross-sectional 2,137 10,276 ≥65 42.61% 2008–2009 DSM-IV China 8
67 Li et al. (2013) Cross-sectional 160 1,020 ≥55 36.67% NR P-MCI China 8
68 Ding et al. (2015) Cross-sectional 601 2,985 ≥60 NR 2010–2011 DSM-IV China 8
69 Xu et al. (2014) Cross-sectional 526 2,426 ≥60 39.32% 2010–2011 P-MCI China 8
70 Zanetti et al. (2006) Cohort study 65 400 ≥65 NR 2000 DSM-IV Italy 7
71 Pioggiosi et al. (2006) Cross-sectional 11 34 96.40 ± 3.90 20.59% 1994–1996 DSM-IV Italy 7
72 Manly et al. (2005) Cohort study 372 1,315 ≥65 31.18% NR P-MCI United States 6
73 Purser et al. (2005) Cohort study 810 3,673 ≥65 38.69% 1981–1991 P-MCI United States 6
74 Kim et al. (2007) Cohort study 388 1,215 ≥60 42.80% 2004–2006 P-MCI Korea 8
75 Jungwirth et al. (2005) Cross-sectional 41 592 75 NR 2002 P-MCI Australia 7
76 Das et al. (2007) Cross-sectional 111 745 ≥50 49.26% 2003–2004 DSM-IV India 8
77 Tognoni et al. (2005) Cross-sectional 79 1,600 ≥65 40.38% 2000–2001 P-MCI Italy 8
78 Boeve et al. (2003) Cross-sectional 13 111 90–99 20.72% 1997–1999 P-MCI United States 8
79 Ganguli et al. (2004) Cohort study 40 1,248 NR 39.26% 1987–2001 P-MCI United States 7
80 Ravaglia et al. (2008) Cohort study 72 865 ≥65 NR 1999–2004 IWG United States 8
81 Xie et al. (2003) Cross-sectional 54 311 ≥75 100% 1998 P-MCI NR 4
82 Yu et al. (2003) Cross-sectional 216 2,674 ≥60 60.96% 2001 DSM-IV China 6
83 Wu et al. (2005) Cross-sectional 45 267 ≥80 37.08% NR Chinese guidelines and P-MCI China 4
84 Yang et al. (2008) Cross-sectional 647 3,175 ≥60 38.33% NR Chinese guidelines and P-MCI China 4
85 Liu et al. (2007) Cross-sectional 838 2,944 ≥60 84.65% NR Chinese guidelines and P-MCI China 6
86 Wada-isoe et al. (2012) Cohort study 211 723 77.80 ± 6.79 NR 2010 IWG Japan 7
87 Vlachos et al. (2020) Cohort study 243 1,960 ≥65 40.61% NR P-MCI Greece 4
88 Bickel et al. (2006) Cross-sectional 287 794 65–85 40.68% NR DSM-IV German 8
89 Busse et al. (2003) Cohort study 116 1,045 NR NR 1997–1998 P-MCI German 6
90 Rahman et al. (2009) Cross-sectional 104 268 60–76 54.48% NR DSM-IV Egypt 5
91 Yu et al. (2003) Cross-sectional 216 2,674 ≥60 60.96% NR P-MCI China 7
92 Assaf et al. (2021) Cross-sectional 50 337 ≥60 54.70% NR IWG Lebanon 8
93 Eramudugolla et al. (2022) Cohort study 132 1,427 60–64 44.11% NR DSM-IV Australia 8
94 Hussenoeder et al. (2020) Cross-sectional 110 903 86.50 ± 3.10 33.22% 2003–2013 IWG Germany 8
95 Mooldijk et al. (2022) Cohort study 648 7,058 ≥60 42.87% 2002–2014 P-MCI Netherland 8
96 Nakahata et al. (2021) Cross-sectional 191 2,286 69 NR 2014–2017 NIA-AA Japan 7
97 Samson et al. (2022) Cross-sectional 255 506 55–93 47.23% NR P-MCI United States 8
98 Lee et al. (2022) Cross-sectional 2,520 13,623 ≥65 45.50% 2007–2010 DSM-IV China, Ghana, India, Mexico, Russia, South Africa 7
99 Smith et al. (2022) Cross-sectional 5,005 32,715 50–65 48.30% 2007–2010 DSM-IV China, Ghana, India, Mexico, Russia, South Africa 7
100 Xu et al. (2021) Cross-sectional 55 171 70.68 ± 7.92 49.12% 2010–2010 P-MCI China 7
101 Yamane et al. (2022) Cross-sectional 61 865 ≥65 38.96% 2014–2017 P-MCI Japan 4
102 Yang et al. (2021) Cross-sectional 276 925 71.16 ± 4.41 NR NR DSM-IV China 7
103 Yu et al. (2022) Cross-sectional 86 163 81.20 ± 4.70 28.83% 2018–2021 ADNI Spanish 8
104 Tang al. 2007 Cross-sectional 217 1,865 ≥60 48.10% 2004–2004 P-MCI China 7
105 Gjøra et al. (2021) Cross-sectional 3,382 9,663 ≥70 43.25% 2017–2019 DSM-V Swedish 9
106 Ramlall et al. (2013) Cross-sectional 38 140 75.20 ± 8.90 30.71% NR IWG South Africa 6
107 Yang et al. (2019) Cross-sectional 318 2,015 79.5 NR 2014 NIA-AA China 10
108 Amoo et al. (2020) Cross-sectional 397 532 71.40 ± 8.86 35.30% NR P-MCI Nigera 5
109 Bae et al. (2017) Cross-sectional 698 3,312 NR 44.17% NR IWG Japan 6
110 Fernández-Blázquez et al. (2021) Cross-sectional 83 1,180 74.90 ± 3.90 36.44% 2011 NIA-AA Spanish 8
111 Ganguli et al. (2010) Cross-sectional 697 1,982 77.60 ± 7.40 38.90% NR P-MCI United States 6
112 González et al. (2019) Cross-sectional 5,851 59,714 63.00 ± 6.80 45.00% NR NIA-AA Spanish 8
113 Guaita et al. (2015) Cross-sectional 65 1,321 71.68 ± 1.43 54.05% NR P-MCI Italy 8
114 Heywood et al. (2017) Cross-sectional 507 2,599 ≥55 36.24% 2006–2009 P-MCI Singapore 9
115 Kivipelto et al. (2001) Cross-sectional 82 1,352 65–79 37.87% NR P-MCI Finland 6
116 Lara et al. (2016) Cross-sectional 348 3,625 66.26 ± 0.18 45.32% NR NIA-AA Spanish 6
117 Chong et al. (2019) Cross-sectional 158 1,209 68.08 ± 5.63 49.96% NR P-MCI Malaysia 6
118 Das et al. (2007) Cross-sectional 111 745 66.75 ± 9.96 49.26% 2003–2004 P-MCI India 7
119 Juarez- Cedillo et al. (2012) Cross-sectional 190 2,944 71.00 ± 7.10 42.19% NR P-MCI Mexico 7
120 Ding et al. (2015) Cross-sectional 601 3,141 73.30 ± 8.60 45.78% NR P-MCI China 9
121 Jia et al. (2014) Cross-sectional 2,137 13,806 ≥65 31.72% NR P-MCI China 8
122 Jia et al. (2020) Cross-sectional 7,215 46,011 70.00 ± 7.51 49.70% 2015–2018 NIA-AA China 11
123 Anstey et al. (2013) Cross-sectional 141 2,551 68–72 39.98% 1999–2007 P-MCI Australia 8
124 Dimitrov et al. (2012) Cross-sectional 37 605 73.20 ± 5.70 42.98% NR P-MCI Bulgaria 6
125 Gavrila et al. (2009) Cross-sectional 88 1,074 74.30 ± 6.50 48.23% 2003–2005 P-MCI Spanish 6
126 Han et al. (2017) Cross-sectional 305 755 ≥65 NR 2012 P-MCI Korea 7
127 Hänninen et al. (2002) Cross-sectional 43 806 68.10 ± 4.50 39.83% NR P-MCI Finland 6
128 Juncos-Rabadán et al. (2012) Cross-sectional 169 580 ≥50 30.86% NR P-MCI Spanish 5
129 Kim et al. (2011) Cross-sectional 1,455 6,141 ≥65 39.81% 2008 P-MCI Korea 5
130 Limongi et al. (2017) Cross-sectional 505 2,337 74 41.68% 2002–2004 P-MCI Italy 9
131 Liu et al. (2022) Cross-sectional 122 1,010 ≥60 31.49% 2011–2016 P-MCI Singapore 8
132 Lopez-Anton et al. (2015) Cross-sectional 323 4,803 ≥65 NR NR DSM-IV Spanish 6
133 Luck et al. (2007) Cross-sectional 499 3,242 ≥75 34.42% 2003–2004 IWG Germany 9
134 Mohan et al. (2019) Cross-sectional 111 426 69.90 ± 7.90 38.03% 2012–2014 P-MCI India 8
135 Mooi et al. (2016) Cross-sectional 1,442 2,112 68.80 ± 6.10 48.58% 2013–2014 P-MCI Malaysia 8
136 Moretti et al. (2013) Cross-sectional 3,351 7,930 61–107 39.66% NR IWG and P-MCI Italy 9
137 Noguchi-Shinohara et al. (2013) Cross-sectional 107 650 76 40.46% NR IWG and P-MCI Japan 7
138 Peltz et al. (2012) Cross-sectional 70 420 ≥90 34.05% 2003 and 2008 DSM-IV USA 5
139 Robertson et al. (2019) Cross-sectional 964 1,721 ≥65 40.44% 2008–2011 DSM-IV Canada 6
140 Sasaki et al. (2009) Cross-sectional 557 1,433 ≥65 NR 2001–2002 DSM-IV Japan 5
141 Shahnawaz et al. (2013) Cross-sectional 299 767 70–90 43.55% NR IWG Australia 4
142 Teh et al. (2021) Cross-sectional 32 2,165 ≥60 45.87% 2012–2013 IWG and P-MCI Singapore 7
143 Tsoy et al. (2019) Cross-sectional 201 662 ≥60 24.32% NR IWG Kazakhstan 8
144 Vlachos et al. (2020) Cross-sectional 243 1,960 73.46 ± 5.47 40.61% NR IWG and P-MCI Greece 6
145 Liu et al. (2022) Cross-sectional 5,432 10,432 ≥65 47.68% 2011–2013 ADNI China 7
146 Su et al. (2014) Cross-sectional 145 796 ≥60 32.79% NR P-MCI China 6
147 Mías et al. (2007) Cross-sectional 102 418 ≥50 22.01% 2004–2005 P-MCI Argentina 8
148 Pedraza et al. (2017) Cross-sectional 421 1,235 ≥50 24.78% NR P-MCI Bogotá 8
149 Sánchez et al. (2019) Cross-sectional 63 352 ≥60 27.05% NR P-MCI Peru 7
150 Monteagudo Torres et al. (2009) Cross-sectional 19 201 ≥60 NR 2006–2007 P-MCI Cuba 6
151 Wesseling et al. (2013) Cross-sectional 35 401 ≥65 39.65% 2010–2011 P-MCI Costa Rica 7
152 Li et al. (2020) Cohort study 535 3,135 71.58 ± 8.06 NR 2011–2012 P-MCI China 9
153 Rao et al. (2018) Cross-sectional 299 2,111 ≥65 40.50% NR P-MCI China 7
154 Sun et al. (2014) Cross-sectional 1,957 10,432 ≥65 47.70% NR ADNI China 5
155 Xiao et al. (2016) Cohort study 267 1,068 72.80 ± 8.50 42.23% NR P-MCI China 9
156 Liu et al. (2018) Cross-sectional 317 1,796 ≥60 46.05% NR DSM-IV China 6
157 Wu et al. (2017) Cross-sectional 371 1,846 69.52 ± 6.86 46.64% 2013–2014 P-MCI China 8
158 Chuang et al. (2021) Cross-sectional 82 470 71.20 ± 5.40 38.72% 2017–2019 NIA-AA China 7
159 Janelidze et al. (2018) Cross-sectional 113 851 56.50 ± 11.80 37.02% NR DSM-IV Georgia 6
160 Pilleron et al. (2015) Cross-sectional 266 2,002 ≥65 NR 2011–2012 P-MCI and DSM-IV South Africa 8
161 Vancampfort et al. (2017) Cross-sectional 5,005 32,715 62.10 ± 15.60 48.30% NR P-MCI China, Ghana, India, Mexico, Russia, South africa 9
162 Koyanagi et al. (2019) Cross-sectional 312 3,672 ≥50 44.01% 2007–2008 P-MCI South Africa 7
163 Li et al. (2013) Cross-sectional 160 1,020 63.90 ± 6.60 36.67% NR P-MCI China 8
164 Kang et al. (2016) Cross-sectional 180 1,248 ≥60 51.68% 2015–2016 P-MCI China 6
165 Huang et al. (2021) Cross-sectional 1,830 5,103 ≥55 44.95% 2018–2019 P-MCI China 6
166 Bai et al. (2021) Cross-sectional 92 428 86.34 ± 3.57 28.97% 2018–2019 P-MCI China 6
167 Lu et al. (2022) Cross-sectional 47 260 ≥60 53.46% 2021 CGDM China 6
168 Shi et al. (2019) Cross-sectional 175 513 40–98 86.74% 2015–2019 P-MCI China 6
169 Liu et al. (2005) Cross-sectional 88 410 ≥60 35.12% 2004 P-MCI China 5
170 Sun et al. (2013) Cross-sectional 53 471 83.00 ± 3.50 97.45% 2009–2010 IWG and P-MCI China 7
171 Hai et al. (2010) Cross-sectional 61 202 82.51 ± 2.14 74.26% 2007 IWG and P-MCI China 6
172 Yuan et al. (2017) Cross-sectional 158 1,013 60–96 52.82% 2014–2016 P-MCI. China 8
173 Ji et al. (2017) Cross-sectional 318 3,200 ≥60 49.76% NR P-MCI China 4
174 Wang et al. (2013) Cross-sectional 199 1,033 ≥55 38.14% NR P-MCI China 6
175 Zhao et al. (2015) Cross-sectional 171 976 ≥60 46.82% 2013–2014 P-MCI China 5
176 Li et al. (2013) Cross-sectional 115 1,226 ≥60 46.74% NR P-MCI China 5
177 Pan et al. (2020) Cross-sectional 214 1,012 ≥60 47.23% 2015 P-MCI China 6
178 Yu et al. (2012) Cross-sectional 168 1,086 84.80 ± 4.40 100% 2010 IWG China 7
179 Yu et al. (2002) Cross-sectional 123 1,630 65–92 100% 2001 P-MCI China 6
180 Cai et al. (2010) Cross-sectional 105 1,498 ≥60 NR 2004–2005 P-MCI China 7
181 Chen et al. (2009) Cross-sectional 195 925 ≥60 40.65% NR P-MCI China 5
182 Zhang et al. (2013) Cross-sectional 86 321 81.55 ± 4.14 100% 2009 P-MCI China 6
183 Sun et al. (2008) Cross-sectional 45 536 72.60 ± 5.60 79.85% 2005 P-MCI and DSM-IV China 5
184 Yu et al. (2004) Cross-sectional 36 420 73.60 ± 5.60 74.29% NR P-MCI and DSM-IV China 4
185 Zhang et al. (2008) Cross-sectional 104 586 75.92 ± 4.35 70.48% 2005–2007 P-MCI and DSM-IV China 6
186 Jiang et al. (2019) Cross-sectional 833 2,886 ≥60 41.61% 2017–2017 P-MCI and DSM-IV China 8
187 Hu et al. (2012) Cross-sectional 1,782 9,146 ≥55 43.83% 2008–2009 DSM-IV China 6
188 Guo et al. (2013) Cross-sectional 178 1,367 ≥60 49.60% 2011 DSM-IV China 5
189 Li et al. (2015) Cross-sectional 260 1,971 ≥60 37.39% NR DSM-IV China 5
190 Fan et al. (2014) Cross-sectional 73 213 65.70 ± 6.08 36.15% 2012 P-MCI and DSM-IV China 5
191 Lv et al. (2016) Cross-sectional 95 820 60–85 47.68% NR P-MCI and DSM-IV China 6
192 Zhang et al. (2021) Cross-sectional 253 309 58.85 ± 0.58 53.40% 2019 P-MCI China 7
193 Yuan et al. (2013) Cross-sectional 631 3,311 ≥60 32.47% NR P-MCI China 6
194 Fang et al. (2015) Cross-sectional 137 1,059 ≥60 46.18% NR P-MCI China 5
195 Pan et al. (2021) Cross-sectional 326 734 ≥60 40.74% 2019 P-MCI China 5
196 Tao et al. (2016) Cross-sectional 1,546 9,121 70.50 ± 7.68 53.95% 2013–2014 P-MCI China 7
197 Li et al. (2021) Cross-sectional 177 413 ≥60 41.65% 2019 P-MCI China 5
198 Xu et al. (2001) Cross-sectional 417 1,516 ≥65 NR NR P-MCI China 5
199 Zhou et al. (2020) Cross-sectional 49 114 81.30 ± 7.87 55.26% 2018–2019 P-MCI China 4
200 Qiu et al. (2018) Cross-sectional 65 239 65.68 ± 6.16 49.79% NR P-MCI China 4
201 Xia et al. (2011) Cross-sectional 47 20,367 NR NR 2009–2019 DSM-IV China 4
202 Wang et al. (2015) Cross-sectional 236 718 NR 47.63% 2013–2014 ADNI China 4
203 Zhang et al. (2020) Cross-sectional 260 1,614 ≥60 60.22% 2019–2019 P-MCI China 4
204 Gao et al. (2011) Cross-sectional 243 1,773 ≥60 44.21% 2010–2011 P-MCI China 8
205 Xue et al. (2010) Cross-sectional 93 1,713 ≥60 NR 2006 P-MCI China 6
206 Zhou et al. (2010) Cross-sectional 136 1,065 ≥60 43.29% NR DSM-IV China 5
207 Liang et al. (2008) Cross-sectional 220 2,895 ≥60 50.09% NR P-MCI China 4
208 He et al. (2013) Cross-sectional 69 598 60–90 71.57% 2011–2012 P-MCI China 5
209 Zhang et al. (2014) Cross-sectional 152 826 67.50 ± 7.03 60.65% 2012 P-MCI China 5
210 Sun et al. (2012) Cross-sectional 131 505 75.91 ± 7.96 34.46% 2011–2012 P-MCI China 5
211 Sun et al. (2019) Cross-sectional 402 2,105 74.35 ± 6.92 67.70% 2018 P-MCI China 6
212 Xiong et al. (2013) Cross-sectional 339 2,978 ≥65 44.12% NR Chinese guidelines and P-MCI China 4
213 Zhao et al. (2015) Cross-sectional 174 1,598 ≥60 54.26% NR DSM-IV China 5
214 Sun et al. (2013) Cross-sectional 74 427 79.17 ± 7.22 38.64% 2011 P-MCI China 5
215 Song et al. (2019) Cross-sectional 85 106 64.99 ± 7.05 NR 1987–2017 Chinese guidelines and P-MCI China 6
216 Wu et al. (2017) Cross-sectional 371 1,996 69.50 ± 6.86 46.39% NR P-MCI China 7
217 Yang et al. (2016) Cross-sectional 340 1,218 ≥65 44.01% NR Chinese guidelines and P-MCI China 5
218 Su et al. (2016) Cross-sectional 145 796 ≥60 32.79% NR P-MCI China 5
219 Xiang et al. (2009) Cross-sectional 72 532 ≥60 47.37% NR Chinese guidelines and P-MCI China 5
220 Xu et al. (2010) Cross-sectional 571 2,161 ≥60 50.49% 2007–2009 Chinese guidelines and P-MCI China 6
221 Ma et al. (2019) Cross-sectional 224 1,005 ≥60 41.69% 2017–2018 P-MCI China 5
222 An et al. (2020) Cross-sectional 396 3,247 71.58 ± 5.41 45.64% 2019 Chinese guidelines and P-MCI China 6
223 Yang et al. (2019) Cross-sectional 319 2,015 ≥65 NR 2014 NIA-AA China 7
224 Liu et al. (2022) Cross-sectional 69 476 ≥60 45.38% 2018–2021 CDGM China 7
225 Wang et al. (2017) Cross-sectional 209 1,781 ≥60 39.53% 2015 P-MCI China 6
226 Wang et al. (2017) Cross-sectional 25 84 ≥60 60.71% 2015 P-MCI China 6
227 Liu et al. (2021) Cross-sectional 64 287 ≥65 50.17% 2019–2020 Chinese guidelines and P-MCI China 6
228 Zhou et al. (2013) Cross-sectional 59 218 ≥60 49.08% 2012 Chinese guidelines and P-MCI China 5
229 Jia et al. (2020) Cross-sectional 87 255 >80 100% NR P-MCI China 5
230 Song et al. (2011) Cross-sectional 11 88 74–89 44.32% NR COMD-3 China 4
231 Xu et al. (2016) Cross-sectional 24 206 ≥75 100% 2012 DSM-IV China 6
232 Ma et al. (2017) Cross-sectional 148 895 ≥60 48.94% NR ADNI China 5
233 Zhang et al. (2014) Cross-sectional 287 1,764 ≥60 44.05% 2012 Chinese guidelines and P-MCI China 6

The number of amnestic MCI (aMCI) and no amnestic MCI (naMCI) were reported in these studies.

①NR, not reported; ②P-MCI, classical Petersen’s criteria of MCI; ③DSM, diagnostic and statistical manual of mental disorders; ④ANDI, Alzheimer’s disease neruimaging initiative; ⑤NIA-AA, the National Institute on Aging-Alzheimer’s Association; ⑥CDGM, Chinese guidelines for diagnosis and management of cognitive impairment and dementia; ⑦IWG, international working group; ⑧COMD-3, the spirit of China disorders classification and diagnostic criteria, third edition.

Study quality assessment scores ranged from 4 to 11, with 76 studies (32.6%) rated as “high quality” and 157 studies (67.4%) rated as “moderate quality.” All the 233 studies scored no less than 3, so no study was excluded. Further details of the quality assessment are shown in Supplementary File S3.

 Prevalence of MCI

A total of 233 studies were included in the analysis of overall pooled prevalence of MCI via a random effect model. The total global prevalence of MCI was 19.7% [(95% CI: 18.3–21.1%), p-value1 < 0.001, I2 = 99.80%], showing significant heterogeneity among studies. The funnel plot and Egger’s test (P-Egger’s test < 0.001) both detected potential publication bias among the pooled results (Figure 2).

Funnel plot of pooled prevalence of MCI.

 Subgroup analyses

Subgroup analyses indicated that the possible sources of heterogeneity were the sample source and beginning year of survey. The total prevalence of MCI in hospitals [34.0% (95% CI: 22.2–45.7%)] was the highest compared to that in nursing homes [22.6% (95% CI: 15.5–29.8%)] and communities [17.9% (95% CI: 16.6–19.2%)]. Moreover, MCI prevalence increased significantly over time. In particular, the global prevalence rose sharply after 2019 [32.1% (95% CI: 22.6–41.6%)] compared to the rates between 2010 and 2018 [19.8% (95% CI: 17.1–22.5%)] and before 2009 [14.5% (95% CI: 12.1–16.9%)]. Subsequently, we conducted further subgroup analyses to explore the time trends in prevalence from different sample sources (Table 2). Surprisingly, there were no significant differences in MCI prevalence among hospitals, nursing homes and communities before 2019. However, the MCI prevalence in hospitals [61.7% (95% CI: 27.8–95.7%)] was significantly higher than in nursing homes [16.1% (95% CI: 14.3–17.9%)] and communities [25.3% (95% CI: 17.4–33.2%)] after 2019. Additionally details of the subgroup analyses can be found in Table 3.

The time trends in MCI prevalence from different sample sources.

Subgroup No. of cases No. of samples Prevalence, 95%CI (%) p-value1 p-value2
≤ 2009 0.228
Community 18,914 106,057 15.8 (13.0–18.6) <0.001
Nursing home 356 3,460 13.1 (9.4–16.8) <0.001
Hospital 1,513 23,330 35.7 (4.2–67.1) 0.026
2010–2018 0.565
Community 33,245 169,301 18.7 (15.7–21.6) <0.001
Nursing home 999 9,438 27.7 (11.4–44.0) 0.001
Hospital 1,163 6,087 18.8 (13.8–23.8) <0.001
≥ 2019 0.003
Community 934 5,505 25.3 (17.4–33.2) <0.001
Nursing home 260 1,614 16.1 (14.3–17.9) <0.001
Hospital 579 1,054 61.7 (27.8–95.7) <0.001

p-value1 is the p-value within subgroups; p-value2 is the p-value across subgroups; 95%CI, 95% confidence interval.

Subgroup analyses of MCI prevalence.

Subgroup No. study No. of cases No. of sample Prevalence, 95%CI (%) p-value1 p-value2
Overall 233 115,958 676,974 19.7 (18.3–21.1) <0.001
Study type 0.976
Cross-sectional 207 106,067 627,798 19.7 (18.2–21.2) <0.001
Cohort 26 9,891 49,176 19.6 (15.3–24.0) <0.001
Diagnostic method 0.786
P-MCI 150 54,227 309,548 20.1 (18.5–21.6) <0.001
DSM 43 34,003 196,537 19.5 (15.7–23.3) <0.001
Male to female Ratio 0.918
Male/female ≥1 43 11,351 57,164 20.1 (16.9–23.3) <0.001
Male/female <1 164 99,214 560,490 20.3 (18.9–21.7) <0.001
Region1 0.856
Developing country 168 66,411 382,725 19.7 (17.9–21.5) <0.001
Developed country 60 31,958 182,170 20.0 (17.3–22.7) <0.001
Region2 0.909
Asia 171 70,205 400,010 19.8 (18.0–21.5) <0.001
Europe 27 20,703 125,743 18.0 (14.0–22.1) <0.001
North America 14 4,599 18,936 21.6 (14.1–29.1) <0.001
Africa 8 1,251 9,192 23.1 (14.5–31.6) <0.001
Oceania 4 713 5,337 19.3 (8.5–30.0) <0.001
South America 4 898 5,677 21.2 (7.0–35.3) 0.003
Beginning year of Survey <0.001
≤ 2009 54 162,314 20,548 14.5 (12.1–16.9) <0.001
2010–2018 72 195,203 40,908 19.8 (17.1–22.5) <0.001
≥ 2019 9 2,025 10,024 32.1 (22.6–41.6) <0.001
Sample size <0.001
0–1,000 94 10,760 48,769 23.5 (20.9–26.2) <0.001
1,001–5,000 115 39,651 246,475 16.4 (14.9–18.0) <0.001
5,001–10,000 12 21,099 88,648 23.9 (16.5–31.3) <0.001
≥10,001 12 44,448 293,082 18.2 (12.0–24.3) <0.001
Sample source 0.014
Community 170 84,742 498,057 17.9 (16.6–19.2) <0.001
Nursing home 21 8,754 30,251 22.6 (15.5–29.8) <0.001
Hospital 16 3,541 31,239 34.0 (22.2–45.7) <0.001
MCI subtype 0.555
aMCI/naMCI ≥1 17 7,174 41,589 16.2 (11.4–21.0) <0.001
aMCI/naMCI <1 5 1,252 6,535 18.4 (13.3–23.4) <0.001
Basic diseases/Non basic diseases 0.349
≥ 1 7 2,026 10,049 27.0 (17.2–36.7) <0.001
< 1 6 3,211 15,800 19.9 (8.6–31.1) 0.001

p-value1 is the p-value within subgroups; p-value2 is the p-value across subgroups; Region1 is classified according to developed/developing countries; Region2 is based on the region of each country.

①95%CI, 95% confidence interval; ②P-MCI, classical Petersen’s criteria of MCI; ③DSM, diagnostic and statistical manual of mental disorders; ④MCI, mild cognitive impairment.

 Discussion

Previous studies revealed partial results when investigating the prevalence of MCI with different degrees of limitation. In our study, we conducted an extensive literature search based on seven electronic databases and manual retrieval, ultimately identifying 233 studies with a total of 115,958 participants. Furthermore, we included more variables of interest into subgroup analyses, such as sample source, basic diseases, the beginning year of survey, and others. Considering the COVID-19 pandemic period, we attached importance to the MCI prevalence before and after 2019. To our knowledge, this is the most recent meta-analysis to provide a comprehensive overview of MCI prevalence without any limitations in age or region.

We concluded that the global total prevalence of MCI is 19.7% (95% CI: 18.3–21.1%) among 233 included studies. In addition, Subgroup analyses revealed that the sample source and beginning year of survey were considered factors potentially associated with MCI prevalence (p-value2 < 0.05) (Table 3).

On the one hand, the prevalence of MCI patients in hospitals [34.0% (95% CI: 22.2–45.7%)] was higher than those in nursing homes [22.6% (95% CI: 15.5–29.8%)] and communities [17.9% (95% CI: 16.6–19.2%)]. Several previous studies also draw the consistent conclusions. For example, Xue et al. reported that clinical patients [16.72% (95% CI: 15.6–17.7%)] have a higher MCI prevalence than nonclinical patients [14.61% (95% CI: 14.4–14.8%)] (Xue et al., 2018). The higher MCI prevalence in hospitals may be attributed to professional diagnosis and treatment procedures. Meanwhile, patients in hospitals have more apparent clinical symptoms of MCI and receive more attention from clinicians, which greatly improves the detection rate of MCI. Similarly, the population in nursing homes [21.2% (95% CI: 18.7–23.6%)] have a higher MCI prevalence than community dwellers [5.56% (95% CI: 13.2–18.0%)] (Bai et al., 2022; Chen et al., 2023). Compared to those living in nursing homes, people living in the communities have better material and emotional support from their families, which might make a difference in reducing MCI prevalence.

On the other hand, we found that the total prevalence of MCI increased over time, especially after 2019. Notably, before 2019, there were no significant differences in MCI prevalence among three sample sources. However, the MCI prevalence after 2019 in hospitals [61.7% (95% CI: 27.8–95.7%)] was significantly higher than those in nursing homes [16.1% (95% CI: 14.3–17.9%)] and communities [25.3% (95% CI: 17.4–33.2%)] (Table 2). Since the COVID-19 outbreak globally in 2019, hospital with the support of limited health resources and medical personnel with professional clinical knowledge has become the main refuge for COVID-19 patients (Kadri et al., 2020; Wadhera et al., 2020). There is cumulative evidence suggesting that COVID-19 impacts brain function and is associated with an elevated risk of neurodegenerative conditions, including cognitive dysfunction (Miners et al., 2020; Nath, 2020; Alquisiras-Burgos et al., 2021). Various post-COVID-19 symptoms indicate that coronaviruses, including SARS-CoV-2, could infect the central nervous system (CNS) through hematogenous pathways or neuronal retrograde neuro-invasion. This infiltration leads to subsequent microglial activation and enduring neuroinflammation, with dysregulated neuro-immunity serving as a foundational cause of nerve cell damage (Ellul et al., 2020; Troyer et al., 2020). Supporting the theory that COVID-19 can influence and exacerbate cognitive dysfunction, our data reveals a notable spike in the prevalence of MCI in hospitals post-2019. However, this rate may be conservative. The causes for this speculation are likely multifactorial, such as patients avoidance of emergency care due to fear of COVID-19 or the increased threshold for hospitalization of non-COVID-19 patients by clinicians due to the severity and urgency of COVID-19 (Blecker et al., 2021), which could masks the true prevalence. Therefore, more studies are needed in the future to investigate the potential link between COVID-19 and MCI.

 Strengths and limitations

Based on previous research, this meta-analysis is the latest meta-analysis to provide a comprehensive overview of MCI prevalence without any age and regional limitations. This meta-analysis may aid policymakers, clinicians in making decisions and clinical directions, thus facilitating future studies and clinical applications. Our study, including the most extensive information currently available, is the first to analyze the association between COVID-19 and global MCI prevalence. However, there are also some limitations. First, the included data is unevenly distributed across regions. A large number of studies have been included from Asia, Europe, and North America, while relatively few have been included from Africa, Oceania, and South America. This unbalanced distribution of literature across regions may introduce bias in subgroups. Naturally, due to the vast amount of data included, our study unavoidably presents significant publication bias. Finally, the MCI prevalence in post-COVID-19 era still requires further investigation to provide more accurate evidence for the allocation of medical and health resources.

 Conclusion

Our systematic review indicates that the current pooled global prevalence of Mild Cognitive Impairment (MCI) stands at 19.7%. Notably, we found a significant correlation between beginning year of survey and the global prevalence of MCI, with prevalence rates rising significantly after 2019. Furthermore, it is noteworthy that the prevalence of MCI in hospital settings outstripped those in nursing homes and community settings, especially after 2019. This trend may be in part attributable to the outbreak of COVID-19. The potential connection between COVID-19 and MCI warrants further investigation in future studies. Lastly, we posit that our review holds substantial value for policymakers and clinicians. The insights gleaned can guide health-related decision-making processes and inform the strategic allocation of health resources to better serve patients with MCI.

 Data availability statement

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors.

 Author contributions

J-hL, JC, and S-xX conceived and designed the study. W-xS and W-wW performed the data analysis and wrote the manuscript. Y-yZ, H-lX, S-yJ, and G-cC assessed the literature and extracted the data. All authors contributed to the article and approved the submitted version.

 Funding

This study was supported by the National Natural Science Foundation of China (82174316), Shenzhen Medical and Health Three Projects (SZZYSM202106006), National TCM Clinical Research Base Construction Project [No. State TCM Science and Technology Letter (2018) No. 131], Shaoxiang Xian National Famous Elder TCM Experts Inheritance Studio [State TCM Human Education Letter (2022) No. 75], Basic and Applied Research of Guangzhou Municipal University Joint Funding Project (202201020342), Qihuang Scholar Training program and Guangzhou Science and Technology Bureau 2022 Key R&D Project (2060404).

We affirm that the work submitted for publication is original and has not been published other than as an abstract or preprint in any language or format and has not been submitted elsewhere for print or electronic publication consideration. We affirm that each person listed as the author participated in the work in a substantive manner, in accordance with ICMJE authorship guidelines, and is prepared to take public responsibility for it. All authors consent to the investigation of any improprieties that may be alleged regarding the work.

 Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

 Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

 Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnagi.2023.1227112/full#supplementary-material

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