Introduction: Stroke is a devastating medical disorder associated with significant morbidity and mortality among adults and the elderly worldwide. Although numerous primary studies have been conducted to determine the pooled predictors of poststroke cognitive decline among stroke survivors in Sub-Saharan Africa, these studies presented inconsistent findings. Hence, the review aimed to determine the pooled predictors of poststroke cognitive decline among stroke survivors in Sub-Saharan Africa. Methods: The eligible studies were accessed through Google Scholar, Scopus, PubMed, and Web of Science databases. A manual search of the reference lists of included studies was performed. A weighted inverse-variance random-effects model was used to determine the pooled predictors of poststroke cognitive decline among stroke survivors in Sub-Saharan Africa. Results: A total of 1,710 stroke survivors from 10 primary studies were included in the final meta-analysis. Increased age (≥45 years) (adjusted odds ratio [AOR] = 1.32, 95% CI: 1.13, 1.54), lower educational level (AOR = 4.58, 95% CI: 2.98, 7.03), poor functional recovery (AOR = 1.75, 95% CI: 1.42, 2.15), and left hemisphere stroke (AOR = 4.88, 95% CI: 2.98, 7.99) were significantly associated with poststroke cognitive decline. Conclusions: Increased age, lower educational level, poor functional recovery, and left hemisphere stroke were the pooled independent predictors of poststroke cognitive decline in Sub-Saharan Africa Healthcare providers, and other concerned bodies should give attention to these risk factors as the early identification may help to improve the cognitive profile of stroke survivors.

Stroke is a devastating medical disorder associated with significant morbidity and mortality among adults and the elderly worldwide, particularly in Sub-Saharan Africa (SSA) [1‒4]. It affects over 67 million people globally and results in about 5,700,000 stroke-related deaths yearly [5, 6]. Globally, the prevalence of poststroke cognitive decline ranges from 32 to 92% [7‒9]. It involves multi-domain disruptions including attention, concentration, executive function, language, memory, and visuospatial function with executive dysfunction being the earliest and predominantly affected domain [10‒13]. Poststroke cognitive decline is associated with reduced quality of life, disability, high dependency, increased, likelihood of depressive symptoms, increased healthcare costs, lost wages, and social isolation [14‒19].

The possible risk factors of poststroke cognitive decline include the clinical and demographic characteristics of the patient, cardiovascular risk factors, and stroke-related characteristics such as the extent and site of brain injury [4, 10, 20‒22]. Identifying these risk factors early and managing them where possible can help to improve the cognitive profile of stroke survivors [21]. SSA is the area in the African continent, consisting of all African countries that are fully or partially located south of the Sahara desert.

The focus on SSA is required as there are substantial cultural variation and low socio-economic level in SSA compared to other regions of Africa, contributing a significant health impact in the region. Although numerous primary studies have been conducted to determine the pooled predictors of poststroke cognitive decline among stroke survivors in SSA, these studies presented inconsistent findings. Therefore, the review aimed to determine the pooled predictors of poststroke cognitive decline among stroke survivors in SSA.

Reporting and Registration Protocol

The PRISMA checklist [23] was used to report the results of this systematic review and meta-analysis (online suppl. file 1; for all online suppl. material, see https://doi.org/10.1159/000539449). The review protocol was registered with the PROSPERO database (PROSPERO, 2023: CRD42023494791).

Databases and Search Strategy

The primary studies were accessed through Google Scholar, Scopus, PubMed, and Web of Science databases using the following search terms and phrases: (“Post-stroke cognitive decline” [MeSH term] OR “Post-stroke cognitive impairment” [MeSH term]) OR (“Post-stroke dementia” [MeSH term]) AND (“Predictors” [MeSH term] OR “Associated factors” [MeSH term] OR “Risk factors” [MeSH term] OR “Determinants” [MeSH term]) AND (“Sub-Saharan Africa”). The search string was developed using “AND” and “OR” Boolean operators. Moreover, a manual search of the reference lists of included studies was performed. The searched studies were published between 2011 and 2023 in SSA and published in English.

Eligibility Criteria

All observational studies that were conducted among stroke survivors, reported the predictors of poststroke cognitive decline, and written in English were included in the review. However, citations without abstracts, and/or full texts, anonymous reports, editorials, systematic reviews, meta-analyses, and qualitative studies were excluded from the review.

Study Selection

All the retrieved studies were exported to the EndNote version 7 reference manager to remove duplicated studies. Initially, two independent reviewers (T.M.A. and S.D.K.) screened the titles and abstracts, followed by the full-text reviews to determine the eligibility of each study. The disagreement between the reviewers was solved through dialogue.

Data Extraction

Two independent reviewers (T.M.A. and W.N.A.) have extracted the data using a structured Microsoft Excel spreadsheet. Whenever discrepancies were observed in the extracted data, the phase was repeated. While the disagreements between the data extractors were continued, a third reviewer (S.A.) was involved. The name of the first authors, year of publication, study area, study design, sample size, and response rate of the included primary studies were extracted.

Primary Outcome Measure of Interest

The primary outcome measure was the pooled predictors of poststroke cognitive decline among stroke survivors in SSA.

Operational Definition of Variables

Poststroke cognitive decline is commonly assessed with the Montreal Cognitive Assessment (MoCA) tool. The total score of MoCA ranges from 0 to 30. A score <26/30 indicates cognitive decline, while a MoCA score of ≥26/30 indicates normal cognitive function [24].

The extracted data were exported to STATA version 17 for statistical analysis. A weighted inverse-variance random-effects model [25] was used to determine the pooled predictors of poststroke cognitive decline among stroke survivors. The presence of publication bias was checked by observing the symmetry of the funnel plot and Egger’s test with a p value of <0.05 that used to determine a significant publication bias [26]. A forest plot was used to estimate the effect of independent factors on the outcome variable and a measure of association at 95% CI was reported. The adjusted odds ratio (AOR) was the reported measure of association in the eligible primary studies.

Search Results

The search strategy retrieved a total of 978 studies from PubMed (n = 458), Google Scholar (n = 346), Scopus (n = 27), and Web of Science (n = 147) studies. Upon removing the irrelevant studies based on their titles and abstracts (n = 732) and duplicated studies (n = 56), a total of 190 studies were selected for full-text review. Then, full-text reviews were conducted, resulting in the removal of 151 studies due to a lack of complete texts. Then, 39 studies were assessed for full article’s review and 29 studies were excluded. Finally, 10 studies were found relevant to determine the pooled predictors of poststroke cognitive decline among stroke survivors. We traced the PRISMA flowchart [27] to show the selection process from initially identified records to finally included primary studies (Fig. 1).

Fig. 1.

PRISMA flowchart showing the study selection process, 2024.

Fig. 1.

PRISMA flowchart showing the study selection process, 2024.

Close modal

Characteristics of the Included Studies

Based on the methodology of the eligible primary studies, six studies [4, 28‒32], three studies [33‒35], and one study [10] were conducted using cross-sectional (CS), cohort, and case-control study designs, respectively. Concerning geographical regions, four studies [28, 31, 32, 34] were conducted in Ethiopia, two studies [29, 30] in Uganda, one study [4] in Ghana, one study [33] in Tanzania, one study [10] in Nigeria, and one study [35] was conducted in Democratic Republic of Congo. The total sample size of the included studies was 1,710, where the smallest and the largest sample size were 67 [31, 32] and 422 [28] among studies conducted in Ethiopia, respectively. The pooled predictors of poststroke cognitive decline were obtained from all the included primary studies [4, 10, 28‒35] with a response rate ranging from 61.96 to 100% (Table 1).

Table 1.

General characteristics of the included primary studies, 2024

IDAuthor, yearStudy areaStudy designSample sizeResponse rate, %Quality
Akinyemi et al. [10] (2014) Nigeria Case control 143 100 Low risk 
Alphonce et al. [33] (2023) Tanzania Cohort 158 61.96 Low risk 
Ayehu et al. [34] (2023) Ethiopia Cohort 403 100 Low risk 
Cherkos et al. [28] (2023) Ethiopia CS 422 100 Low risk 
Kaddumukasa et al. [29] (2023) Uganda CS 131 100 Low risk 
Mukisa et al. [30] (2011) Uganda CS 85 100 Low risk 
Mwamba et al. [35] (2023) DRC Cohort 87 100 Low risk 
Sarfo et al. [4] (2017) Ghana CS 147 73.5 Low risk 
Zewde et al. [31] (2023) Ethiopia CS 67 84.81 Moderate risk 
10 Zewde [32] (2022) Ethiopia CS 67 97.4 Moderate risk 
IDAuthor, yearStudy areaStudy designSample sizeResponse rate, %Quality
Akinyemi et al. [10] (2014) Nigeria Case control 143 100 Low risk 
Alphonce et al. [33] (2023) Tanzania Cohort 158 61.96 Low risk 
Ayehu et al. [34] (2023) Ethiopia Cohort 403 100 Low risk 
Cherkos et al. [28] (2023) Ethiopia CS 422 100 Low risk 
Kaddumukasa et al. [29] (2023) Uganda CS 131 100 Low risk 
Mukisa et al. [30] (2011) Uganda CS 85 100 Low risk 
Mwamba et al. [35] (2023) DRC Cohort 87 100 Low risk 
Sarfo et al. [4] (2017) Ghana CS 147 73.5 Low risk 
Zewde et al. [31] (2023) Ethiopia CS 67 84.81 Moderate risk 
10 Zewde [32] (2022) Ethiopia CS 67 97.4 Moderate risk 

CS, cross-sectional; DRC, Democratic Republic of Congo.

Quality Appraisal of the Included Studies

Two independent reviewers (T.M.A. and S.D.K.) appraised the quality of the included studies and scored for the validity of the results. The quality of each study was evaluated using the Joanna Briggs Institute (JBI) quality appraisal criteria [36]. Six studies [4, 28‒32], three studies [33‒35], and two studies [10, 37] were appraised using the JBI checklist for CS, cohort, and case-control studies, respectively. Thus, among the six CS studies, four studies scored seven of eight questions, 87.5% (low risk); one study scored six of eight questions, 75% (low risk); and the remaining one study scored five of eight questions, 62.5% (low risk). Similarly, among the three cohort studies, two studies scored eight of ten questions, 80% (low risk); and the third study scored seven of ten questions, 70% (low risk). Moreover, of the 2 case-control studies, one study scored eight of ten questions, 80% (low risk). However, the second study [37] scored four of ten questions, 40% (high risk). The CS studies scored between 5 and 7 out of a total of 8 points, whereas the cohort and case-control studies scored between 4 and 8 out of a total of 10 points (online suppl. file 2). Studies were considered to be of low risk when they scored 50% or higher on the quality assessment indicators. Therefore, the second case-control study [37] had lower quality, and it has been removed from the study.

Risk of Bias Assessment

The assessment tool [38] was adopted to assess the risk of bias. The tool consists of ten items that assess four areas of bias: internal validity and external validity. Items 1–4 evaluate selection bias, nonresponse bias, and external validity. Items 5–10 assess measure bias, analysis-related bias, and internal validity. Consequently, of the total of the ten included studies, eight studies scored eight of ten questions, and the two studies scored seven of ten questions. Studies were classified as “low risk” if eight and above of ten questions received a “Yes,” as “moderate risk” if six to seven of ten questions received a “Yes,” and as “high risk” if five or lower of ten questions received a “Yes.” Therefore, all the included studies [4, 10, 28‒35] had a low risk of bias (high quality) (online suppl. file 2).

The symmetry of the included primary studies on the funnel plot suggests the absence of publication bias (Fig. 2), and the p value of Egger’s test (p = 0.8936) also revealed the absence of publication bias.

Fig. 2.

Funnel plot showing the publication bias of poststroke cognitive decline among stroke survivors in SSA, 2024.

Fig. 2.

Funnel plot showing the publication bias of poststroke cognitive decline among stroke survivors in SSA, 2024.

Close modal

Pooled Predictors of Poststroke Cognitive Decline

Finally, the ten eligible primary studies [4, 10, 28‒35] were included in the final meta-analysis. In the study, seven studies [4, 10, 28‒31, 34] indicated that increased age (≥45 years) was significantly associated with poststroke cognitive decline. The pooled AOR of poststroke cognitive decline for stroke survivors with the age of ≥45 years was 1.32 (95% CI: 1.13, 1.54; I2 = 91.98%; p < 0.001) (Fig. 3).

Fig. 3.

Forest plot of the AORs with 95% CIs of studies on the association of increased age and poststroke cognitive decline among stroke survivors in SSA, 2024.

Fig. 3.

Forest plot of the AORs with 95% CIs of studies on the association of increased age and poststroke cognitive decline among stroke survivors in SSA, 2024.

Close modal

Five studies [4, 10, 28, 29, 31] reported a significant association between lower educational level and poststroke cognitive decline. The pooled AOR of poststroke cognitive decline for stroke survivors with a lower educational level was 4.58 (95% CI: 2.98, 7.03; I2 = 0.00%; p < 0.94) (Fig. 4).

Fig. 4.

Forest plot of the AORs with 95% CIs of studies on the association of lower educational level and poststroke cognitive decline among stroke survivors in SSA, 2024.

Fig. 4.

Forest plot of the AORs with 95% CIs of studies on the association of lower educational level and poststroke cognitive decline among stroke survivors in SSA, 2024.

Close modal

Five studies [4, 29, 31, 32, 34] showed that poor functional recovery was significantly associated with poststroke cognitive decline. The pooled AOR of poststroke cognitive decline for stroke survivors with poor functional recovery was 1.75 (95% CI: 1.42, 2.15; I2 = 45.33%; p < 0.12) (Fig. 5).

Fig. 5.

Forest plot of the AORs with 95% CIs of studies on the association of poor functional recovery and poststroke cognitive decline among stroke survivors in SSA, 2024.

Fig. 5.

Forest plot of the AORs with 95% CIs of studies on the association of poor functional recovery and poststroke cognitive decline among stroke survivors in SSA, 2024.

Close modal

Three studies [28, 32, 33] revealed a significant association between left hemisphere stroke and poststroke cognitive decline. The pooled AOR of poststroke cognitive decline for stroke survivors with left hemisphere stroke was 4.88 (95% CI: 2.98, 7.99; I2 = 0.00%; p < 0.99) (Fig. 6).

Fig. 6.

Forest plot of the AORs with 95% CIs of studies on the association of left hemisphere stroke and poststroke cognitive decline among stroke survivors in SSA, 2024.

Fig. 6.

Forest plot of the AORs with 95% CIs of studies on the association of left hemisphere stroke and poststroke cognitive decline among stroke survivors in SSA, 2024.

Close modal

The findings of the study indicated that stroke survivors with increased age (≥45 years) were 1.32 times more likely to develop poststroke cognitive decline compared to those with the age of <45 years old. The finding of this study was consistent with the finding of a study conducted in Egypt [39]. It could be explained due to the nature of stroke, which brings cognitive decline with age [40].

The findings of this study also revealed that stroke survivors with a lower educational level were 4.58 folds more likely to develop poststroke cognitive decline compared to stroke survivors with a higher educational level. The finding of this study was supported by the finding of a study conducted in the USA and China [22, 41, 42]. It might be because higher education provides good dynamic-based cognitive stimulation and is effective in improving cognitive performance [43], and a lower educational level has a negative impact on brain reserves and decreased synaptic connections during the schooling years [41].

Similarly, the findings of this study indicated that stroke survivors with poor functional recovery were 1.75 times more likely to face poststroke cognitive decline compared to their counterparts. The finding of this study was congruent with the finding of a study conducted in the UK, China, and Japan [44‒46]. Though studies used different cognitive assessment tools, the possible reason could be poor functional recovery, an indicator of stroke severity, clinical deficit, and vascular burden [47].

Moreover, the findings of this study reported that stroke survivors with left hemisphere stroke were 4.88 times more likely to encounter poststroke cognitive decline compared to stroke survivors with right hemisphere stroke. The finding of this study was similar with the study finding conducted among elderly stroke survivors in Egypt [48]. Language is a key domain in the MoCA assessment [49], and it is a left hemispheric cognitive domain for more than 90% of the population worldwide, potentially affected by left hemisphere stroke [48, 50, 51].

Strengths and Limitations of the Study

To the best of our knowledge, this was the first study to combine the results of multiple studies conducted in SSA, providing stronger evidence on poststroke cognitive decline. While all the included studies are of good quality, it should be noted that most of the studies were CS.

Increased age, lower educational level, poor functional recovery, and left hemisphere stroke were the pooled independent predictors of poststroke cognitive decline in SSA. Healthcare providers and other concerned bodies should give attention to these risk factors as the early identification may help to improve the cognitive profile of stroke survivors. Future researchers shall conduct further studies using triangulated study designs to identify additional predictors of poststroke cognitive decline among stroke survivors in SSA.

We would like to extend our deepest gratitude to Mr. Henok Andualem for his unreserved support throughout the study.

An ethics statement is not applicable because this study is based exclusively on the published literature.

All the authors have declared no conflicts of interest.

There was no any funding source for the study because the study was based exclusively on the published literature.

T.M.A. has generated the idea for this review and wrote the first draft of this manuscript. S.D.K. and W.N.A. contributed to data collection and statistical analysis. S.A. revised the manuscript. All the authors were responsible for the accuracy of the analysis. Finally, all the authors read and approved the final version of the manuscript for publication.

All the necessary data and supplementary materials were included in the manuscript. Further inquiries can be directed to the corresponding author.

1.
Adeloye
D
.
An estimate of the incidence and prevalence of stroke in Africa: a systematic review and meta-analysis
.
PloS one
.
2014
;
9
(
6
):
e100724
.
2.
Feigin
VL
,
Forouzanfar
MH
,
Krishnamurthi
R
,
Mensah
GA
,
Connor
M
,
Bennett
DA
, et al
.
Global and regional burden of stroke during 1990–2010: findings from the global burden of disease study 2010
.
The lancet
.
2014
;
383
(
9913
):
245
54
.
3.
Owolabi
MO
,
Akarolo-Anthony
S
,
Akinyemi
R
,
Arnett
D
,
Gebregziabher
M
,
Jenkins
C
, et al
.
The burden of stroke in Africa: a glance at the present and a glimpse into the future
.
Cardiovasc J Afr
.
2015
;
26
(
2 Suppl 1
):
S27
38
.
4.
Sarfo
FS
,
Akassi
J
,
Adamu
S
,
Obese
V
,
Ovbiagele
B
.
Burden and predictors of poststroke cognitive impairment in a sample of Ghanaian stroke survivors
.
J Stroke Cerebrovasc Dis
.
2017
;
26
(
11
):
2553
62
.
5.
Kulesh
A
,
Drobakha
V
,
Kuklina
E
,
Nekrasova
I
,
Shestakov
V
.
Cytokine response, tract-specific fractional anisotropy, and brain morphometry in post-stroke cognitive impairment
.
J Stroke Cerebrovasc Dis
.
2018
;
27
(
7
):
1752
9
.
6.
Zhu
Z
,
Chen
L
,
Guo
D
,
Zhong
C
,
Wang
A
,
Bu
X
, et al
.
Serum rheumatoid factor levels at acute phase of ischemic stroke are associated with poststroke cognitive impairment
.
J Stroke Cerebrovasc Dis
.
2019
;
28
(
4
):
1133
40
.
7.
Gong
L
,
Gu
Y
,
Yu
Q
,
Wang
H
,
Zhu
X
,
Dong
Q
, et al
.
Prognostic factors for cognitive recovery beyond early Poststroke Cognitive Impairment (PSCI): a prospective cohort study of spontaneous intracerebral hemorrhage
.
Front Neurol
.
2020
;
11
:
278
.
8.
Nys
GM
,
Van Zandvoort
MJ
,
De Kort
PL
,
Jansen
BP
,
De Haan
EH
,
Kappelle
LJ
.
Cognitive disorders in acute stroke: prevalence and clinical determinants
.
Cerebrovasc Dis
.
2007
;
23
(
5–6
):
408
16
.
9.
Sharma
R
,
Mallick
D
,
Llinas
RH
,
Marsh
EB
.
Early post-stroke cognition: in-hospital predictors and the association with functional outcome
.
Front Neurol
.
2020
;
11
:
613607
.
10.
Akinyemi
RO
,
Allan
L
,
Owolabi
MO
,
Akinyemi
JO
,
Ogbole
G
,
Ajani
A
, et al
.
Profile and determinants of vascular cognitive impairment in African stroke survivors: the CogFAST Nigeria Study
.
J Neurol Sci
.
2014
;
346
(
1–2
):
241
9
.
11.
Ballard
C
,
Rowan
E
,
Stephens
S
,
Kalaria
R
,
Kenny
RA
.
Prospective follow-up study between 3 and 15 months after stroke: improvements and decline in cognitive function among dementia-free stroke survivors> 75 years of age
.
Stroke
.
2003
;
34
(
10
):
2440
4
.
12.
Gorelick
PB
,
Scuteri
A
,
Black
SE
,
DeCarli
C
,
Greenberg
SM
,
Iadecola
C
, et al
.
Vascular contributions to cognitive impairment and dementia: a statement for healthcare professionals from the American Heart Association/American Stroke Association
.
Stroke
.
2011
;
42
(
9
):
2672
713
.
13.
Henon
H
,
Pasquier
F
,
Leys
D
.
Poststroke dementia
.
Cerebrovasc Dis
.
2006
;
22
(
1
):
61
70
.
14.
Fride
Y
,
Adamit
T
,
Maeir
A
,
Ben Assayag
E
,
Bornstein
NM
,
Korczyn
AD
, et al
.
What are the correlates of cognition and participation to return to work after first ever mild stroke
.
Top Stroke Rehabil
.
2015
;
22
(
5
):
317
25
.
15.
Jeffares
I
,
Rohde
D
,
Doyle
F
,
Horgan
F
,
Hickey
A
.
The impact of stroke, cognitive function and Post-Stroke Cognitive Impairment (PSCI) on healthcare utilisation in Ireland: a cross-sectional nationally representative study
.
BMC Health Serv Res
.
2022
;
22
(
1
):
414
3
.
16.
Lees
RA
,
Hendry Ba
K
,
Broomfield
N
,
Stott
D
,
Larner
AJ
,
Quinn
TJ
.
Cognitive assessment in stroke: feasibility and test properties using differing approaches to scoring of incomplete items
.
Int J Geriatr Psychiatry
.
2017
;
32
(
10
):
1072
8
.
17.
Levine
DA
,
Wadley
VG
,
Langa
KM
,
Unverzagt
FW
,
Kabeto
MU
,
Giordani
B
, et al
.
Risk factors for poststroke cognitive decline: the REGARDS study (reasons for geographic and racial differences in stroke)
.
Stroke
.
2018
;
49
(
4
):
987
94
.
18.
Pollock
A
,
St George
B
,
Fenton
M
,
Firkins
L
.
Top ten research priorities relating to life after stroke
.
Lancet Neurol
.
2012
;
11
(
3
):
209
.
19.
Mohd Zulkifly
MF
,
Ghazali
SE
,
Che Din
N
,
Subramaniam
P
.
The influence of demographic, clinical, psychological and functional determinants on post-stroke cognitive impairment at day care stroke center, Malaysia
.
Malays J Med Sci
.
2016
;
23
(
2
):
53
64
.
20.
Burton
EJ
,
Kenny
RA
,
O’Brien
J
,
Stephens
S
,
Bradbury
M
,
Rowan
E
, et al
.
White matter hyperintensities are associated with impairment of memory, attention, and global cognitive performance in older stroke patients
.
Stroke
.
2004
;
35
(
6
):
1270
5
.
21.
Danovska
M
,
Peychinska
D
.
Post-stroke cognitive impairment–phenomenology and prognostic factors
.
JofIMAB
.
2012
;
18, 3
(
2012
):
290
7
.
22.
Gottesman
RF
,
Hillis
AE
.
Predictors and assessment of cognitive dysfunction resulting from ischaemic stroke
.
Lancet Neurol
.
2010
;
9
(
9
):
895
905
.
23.
Page
MJ
,
McKenzie
JE
,
Bossuyt
PM
,
Boutron
I
,
Hoffmann
TC
,
Mulrow
CD
, et al
.
The PRISMA 2020 statement: an updated guideline for reporting systematic reviews
.
Int J Surg
.
2021
;
88
:
105906
.
24.
Pendlebury
ST
,
Mariz
J
,
Bull
L
,
Mehta
Z
,
Rothwell
PM
.
MoCA, ACE-R, and MMSE versus the National Institute of Neurological Disorders and Stroke–Canadian Stroke Network vascular cognitive impairment harmonization standards neuropsychological battery after tia and stroke
.
Stroke
.
2012
;
43
(
2
):
464
9
.
25.
DerSimonian
R
,
Kacker
R
.
Random-effects model for meta-analysis of clinical trials: an update
.
Contemp Clin Trials
.
2007
;
28
(
2
):
105
14
.
26.
Peters
JL
,
Sutton
AJ
,
Jones
DR
,
Abrams
KR
,
Rushton
L
.
Comparison of two methods to detect publication bias in meta-analysis
.
JAMA
.
2006
;
295
(
6
):
676
80
.
27.
Stovold
E
,
Beecher
D
,
Foxlee
R
,
Noel-Storr
A
.
Study flow diagrams in cochrane systematic review updates: an adapted PRISMA flow diagram
.
Syst Rev
.
2014
;
3
:
54
.
28.
Cherkos
K
,
Jember
G
,
Mihret
T
,
Fentanew
M
.
Prevalence and associated factors of cognitive impairment among stroke survivors at comprehensive specialized hospitals in northwest Ethiopia: multi-centered cross-sectional study
.
Vasc Health Risk Manag
.
2023
;
19
:
265
77
.
29.
Kaddumukasa
MN
,
Kaddumukasa
M
,
Katabira
E
,
Sewankambo
N
,
Namujju
LD
,
Goldstein
LB
.
Prevalence and predictors of post-stroke cognitive impairment among stroke survivors in Uganda
.
BMC Neurol
.
2023
;
23
(
1
):
166
8
.
30.
Mukisa
R
,
Ddumba
E
,
Musisi
S
,
Kiwuwa
SM
.
Prevalence and types of cognitive impairment among patients with stroke attending a referral hospital in Uganda
.
Afr J Neurol Sci
.
2011
;
30
(
2
).
31.
Zewde
Y
,
Alem
A
,
Seeger
SK
.
Magnitude and predictors of post-stroke cognitive impairment among Ethiopian stroke survivors: a facility-based cross-sectional study
.
32.
Zewde
YZ
.
Post-stroke dementia and its determinant factors among Ethiopian stroke survivors: a cross-sectional study
.
Alzheimer's Dementia
.
2022
;
18
(
S7
):
e060612
.
33.
Alphonce
B
,
Meda
J
,
Nyundo
A
.
Correlates of the Post-Stroke cognitive impairment among patients with first-ever stroke admitted at tertiary hospitals in Dodoma, Tanzania: A prospective cohort study
.
PLoS One
.
2024
;
19
(
4
):
e0287952
.
34.
Ayehu
GW
,
Admasu
FT
,
Yitbarek
GY
,
Agegnehu Teshome
A
,
Amare
AT
,
Atlaw
D
, et al
.
Early post-stroke cognitive impairment and in-hospital predicting factors among stroke survivors in Ethiopia
.
Front Neurol
.
2023
;
14
:
1163812
.
35.
Mwamba
K
,
Kazenza
B
,
Mbenza
BL
,
Kalula
TK
,
Ayanne
MT
,
Bumoko
G
.
Evolving profile and determinants of post-stroke cognitive impairment in the 3rd month among kinshasa’s survivors (democratic republic of the Congo)
.
World J Neurosci
.
2023
;
13
(
03
):
160
72
.
36.
Peters
MD
,
Godfrey
CM
,
McInerney
P
,
Soares
CB
,
Khalil
H
,
Parker
D
. The Joanna Briggs Institute reviewers' manual 2015: methodology for JBI scoping reviews.
37.
Fatoye
FO
,
Komolafe
MA
,
Eegunranti
BA
,
Adewuya
AO
,
Mosaku
SK
,
Fatoye
GK
.
Cognitive impairment and quality of life among stroke survivors in Nigeria
.
Psychol Rep
.
2007
;
100
(
3 Pt 1
):
876
82
.
38.
Hoy
D
,
Brooks
P
,
Woolf
A
,
Blyth
F
,
March
L
,
Bain
C
, et al
.
Assessing risk of bias in prevalence studies: modification of an existing tool and evidence of interrater agreement
.
J Clin Epidemiol
.
2012
;
65
(
9
):
934
9
.
39.
Esmael
A
,
Elsherief
M
,
Eltoukhy
K
.
Prevalence of cognitive impairment in acute ischaemic stroke and use of Alberta Stroke Programme Early CT Score (ASPECTS) for early prediction of post-stroke cognitive impairment
.
Neurol Neurochir Pol
.
2021
;
55
(
2
):
179
85
.
40.
De Ronchi
D
,
Palmer
K
,
Pioggiosi
P
,
Atti
AR
,
Berardi
D
,
Ferrari
B
, et al
.
The combined effect of age, education, and stroke on dementia and cognitive impairment no dementia in the elderly
.
Dement Geriatr Cogn Disord
.
2007
;
24
(
4
):
266
73
.
41.
He
A
,
Wang
Z
,
Wu
X
,
Sun
W
,
Yang
K
,
Feng
W
, et al
.
Incidence of post-stroke cognitive impairment in patients with first-ever ischemic stroke: a multicenter cross-sectional study in China
.
Lancet Reg Health West Pac
.
2023
;
33
:
100687
.
42.
Qu
Y
,
Zhuo
L
,
Li
N
,
Hu
Y
,
Chen
W
,
Zhou
Y
, et al
.
Prevalence of post-stroke cognitive impairment in China: a community-based, cross-sectional study
.
PLoS One
.
2015
;
10
(
4
):
e0122864
.
43.
Casemiro
FG
,
Quirino
DM
,
Diniz
MA
,
Rodrigues
RA
,
Pavarini
SC
,
Gratão
AC
.
Effects of health education in the elderly with mild cognitive impairment
.
Rev Bras Enferm
.
2018
;
71
(
Suppl 2
):
801
10
.
44.
Hartley
P
,
Gibbins
N
,
Saunders
A
,
Alexander
K
,
Conroy
E
,
Dixon
R
, et al
.
The association between cognitive impairment and functional outcome in hospitalised older patients: a systematic review and meta-analysis
.
Age Ageing
.
2017
;
46
(
4
):
559
67
.
45.
Kiyohara
T
,
Kumai
Y
,
Yubi
T
,
Ishikawa
E
,
Wakisaka
Y
,
Ago
T
, et al
.
Association between early cognitive impairment and short-term functional outcome in acute ischemic stroke
.
Cerebrovasc Dis
.
2023
;
52
(
1
):
61
7
.
46.
Li
J
,
Wang
J
,
Wu
B
,
Xu
H
,
Wu
X
,
Zhou
L
, et al
.
Association between early cognitive impairment and midterm functional outcomes among Chinese acute ischemic stroke patients: a longitudinal study
.
Front Neurol
.
2020
;
11
:
20
.
47.
Leys
D
,
Hénon
H
,
Mackowiak-Cordoliani
MA
,
Pasquier
F
.
Poststroke dementia
.
Lancet Neurol
.
2005
;
4
(
11
):
752
9
.
48.
Lo Coco
D
,
Lopez
G
,
Corrao
S
.
Cognitive impairment and stroke in elderly patients
.
Vasc Health Risk Manag
.
2016
;
12
:
105
16
.
49.
Hobson
J
.
The Montreal Cognitive Assessment (MoCA)
.
Occup Med
.
2015
;
65
(
9
):
764
5
.
50.
Fridriksson
J
,
den Ouden
DB
,
Hillis
AE
,
Hickok
G
,
Rorden
C
,
Basilakos
A
, et al
.
Anatomy of aphasia revisited
.
Brain
.
2018
;
141
(
3
):
848
62
.
51.
Packheiser
J
,
Schmitz
J
,
Arning
L
,
Beste
C
,
Güntürkün
O
,
Ocklenburg
S
.
A large-scale estimate on the relationship between language and motor lateralization
.
Sci Rep
.
2020
;
10
(
1
):
13027
.