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. 2024 Aug 7;24:2143. doi: 10.1186/s12889-024-19684-3

Poststroke cognitive impairment among stroke survivors in Sub-Saharan Africa: a systematic review and meta-analysis

Tigabu Munye Aytenew 1,✉, Demewoz Kefale 2, Binyam Minuye Birhane 3,4, Solomon Demis Kebede 4, Worku Necho Asferie 4, Amare Kassaw 2, Yenework Mulu Tiruneh 5, Getasew Legas 6, Addisu Getie 7, Berihun Bantie 1, Sintayehu Asnakew 6
PMCID: PMC11308218  PMID: 39112982

Abstract

Introduction

Stroke is the leading cause of death and disability among adults and elderly individuals worldwide. Although several primary studies have been conducted to determine the prevalence of poststroke cognitive impairment among stroke survivors in sub-Saharan Africa, these studies have presented inconsistent findings. Therefore, this study aimed to determine the pooled prevalence of poststroke cognitive impairment and identify its associated factors among stroke survivors in sub-Saharan Africa.

Methods

The studies were retrieved from the Google Scholar, Scopus, PubMed, and Web of Science databases. A manual search of the reference lists of the included studies was performed. A random-effects DerSimonian-Laird model was used to compute the pooled prevalence of poststroke cognitive impairment among stroke survivors in sub-Saharan Africa.

Results

A total of 10 primary studies with a sample size of 1,709 stroke survivors were included in the final meta-analysis. The pooled prevalence of PSCI was obtained from the 9 included studies with a sample size of 1,566. In contrast, the data regarding the associated factors were obtained from all the 10 included studies with a sample size of 1,709. The pooled prevalence of poststroke cognitive impairment among stroke survivors was 59.61% (95% CI: 46.87, 72.35); I2 = 96.47%; P < 0.001). Increased age (≥ 45 years) [AOR = 1.23, 95% CI: 1.09, 1.40], lower educational level [AOR = 4.35, 95% CI: 2.87, 6.61], 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 impairment.

Conclusions

The pooled prevalence of poststroke cognitive impairment was considerably high among stroke survivors in sub-Saharan Africa. Increased age, lower educational level, poor functional recovery, and left hemisphere stroke were the pooled independent predictors of poststroke cognitive impairment in sub-Saharan Africa. Stakeholders should focus on empowering education and lifestyle modifications, keeping their minds engaged, staying connected with social activities and introducing rehabilitative services for stroke survivors with these identified factors to reduce the risk of developing poststroke cognitive impairment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-024-19684-3.

Keywords: Poststroke cognitive impairment, Stroke survivors, Sub-Saharan Africa, Meta-analysis

Introduction

Stroke is a leading cause of death and disability among adults and elderly individuals worldwide, particularly in low- and middle-income countries (LMICs) in sub-Saharan Africa (SSA) [1–4]. It affects more than 80 million people globally [5], and results in approximately 6,500,000 stroke-related deaths annually [6]. Most of the stroke-related deaths occur in LMICs [7].

Stroke causes cell damage and cell death in the brain, which may result in acute neuronal loss, disruption of brain functional networks, inflammation, social isolation, and poststroke cognitive impairment (PSCI) [8]. PSCI is a cognitive decline usually evidenced by a Montreal Cognitive Assessment (MoCA) score < 26/30 [9, 10]. Globally, the prevalence of PSCI varies and has been reported to range between 32 to 92% [11–13] with an estimated 64% of stroke survivors developing PSCI [14, 15].

PSCI involves multidomain disruptions including disruptions in attention, concentration, executive function, language, memory, and visuospatial function, with executive dysfunction being the earliest and predominantly affected domain [14, 16–18]. The PSCI significantly affects the victim’s quality of life, family, and community at large [19]. It is associated with reduced quality of life, disability, high dependency, increased likelihood of depressive symptoms, increased healthcare costs, lost wages, and social isolation [19–24].

The cognitive profile of stroke survivors depends on the demographic characteristics of the patient, such as differences in age, pre-stroke cognitive status and comorbidities, and stroke-related clinical characteristics such as the extent and site of brain injury [14, 25–28]. Identifying these factors early and managing them where possible can help improve the cognitive profile of stroke survivors [26].

Although several primary studies have been conducted to determine the prevalence of PSCI among stroke survivors in SSA, these studies have presented inconsistent findings. Therefore, this study aimed to determine the pooled prevalence of PSCI and identify its associated factors among stroke survivors in SSA.

Methods

Reporting and registration protocol

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) format [29] was used to report the findings of the study (Supplemental Table 1). The review protocol was registered with the Prospero database: (PROSPERO, 2023: CRD42023494791).

Table 1.

Subgroup analyses of poststroke cognitive impairment among stroke survivors in SSA, 2023

Variables Outcome Subgroup No. of studies Model Prevalence
(95%CI)		 I2 P = value
Study design PSCI CS 6(n = 1,062) Random 62.27 (54.79, 69.75) 79.84%  < 0.001
Cohort 3(n = 647) Random 55.09 (20.60, 89.58) 96.39%  < 0.001
Sample size PSCI  < 171 8(n = 884) Random 64.57 (55.58, 73.57) 86.13%  < 0.001
 ≥ 171 2(n = 825) Random 44.29 (16.85, 71.73) 98.60%  < 0.001
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CS Cross-sectional, CI Confidence interval, n Sample size

Databases and search strategy

The studies were subsequently retrieved from the Google Scholar, Scopus, PubMed, and Web of Science databases using search terms and phrases. Moreover, a manual search of the reference lists of the included studies was performed (Supplemental file 1). All studies were conducted in SSA and published in English between 2011 and 2023.

Eligibility criteria

All observational (cross-sectional, cohort, and case–control) studies that were conducted among stroke survivors in SSA, reported the prevalence of PSCI and/or at least one associated factor of PSCI and were written in English included in the study. However, citations without abstracts, full texts, anonymous reports, editorials, systematic reviews, meta-analyses, or qualitative studies were excluded from the study.

Study selection

All the retrieved studies were exported to the EndNote version 7 reference manager to remove the duplicate studies. Primarily, two independent authors (TMA and SA) screened the titles and abstracts, followed by the full-text reviews to determine the eligibility of each study. Disagreements between the reviewers were resolved through discussion.

Quality appraisal of the included studies

Two independent authors (TMA and SA) appraised the quality of the included primary studies. The quality of each study was appraised using the Joanna Briggs Institute (JBI) quality appraisal criteria [30]. Six studies [28, 31–35], three [36–38], and two [14, 39] were appraised using the JBI checklist for cross-sectional, cohort, and case–control respectively. Studies were classified as “high quality” if 50% or higher on the quality assessment indicators scored “Yes” and as “low quality” if lower than 50% on the quality assessment indicators scored “Yes”. Thus, among the 6 cross-sectional studies, 4 studies scored “Yes” on 7 of the 8 questions (high quality), 1 study scored “Yes” on 6 of the 8 questions (high quality), and the remaining study scored “Yes” on 5 of the 8 questions (high quality). Similarly, among the 3 cohort studies, two studies scored “Yes” on 8 of the 10 questions (high quality), and the third study scored “Yes” on 7 of the 10 questions (high quality). Moreover, of the 2 case–control studies, one study scored “Yes” on 8 of the 10 questions (high quality). However, the second case–control study [39] scored “Yes” on 4 of the 10 questions (low quality). The cross-sectional studies scored “Yes” on 5 to 7 of the 8 points, whereas the cohort and case–control studies scored “Yes” on 4 to 8 of the 10 points (Supplemental Table 2). Therefore, the second case–control study [39] was of lower quality, and was excluded from the study.

Risk of bias assessment of the included studies

The risk of bias was assessed using the adopted standardized assessment tool [40]. The tool consists of ten items that assess four areas of bias. To assess the risk of bias in the included studies, studies were classified as “low risk of bias” if 8 and above of the 10 questions scored “Yes”, as “moderate risk of bias” if 6 to 7 of the 10 questions scored “Yes” and as “high risk of bias” if ≤ 5 of the 10 questions scored “Yes”. Consequently, of the total of the 10 included studies, 8 studies scored “Yes” on 8 of the 10 questions (low risk of bias), and the 2 studies scored “Yes” on 7 of the 10 questions (moderate risk of bias) (Supplemental Table 3).

Data extraction

Two independent authors (TMA and AK) extracted the data using a structured Microsoft Excel sheet. When differences were detected in the extracted data, the step was repeated. When discrepancies between the extracted data were continued, the third reviewer (SDK) was consulted. The name of the first authors, year of publication, study area, study design, sample size, response rate, and effect size of the included primary studies were extracted.

Outcome measures

The study focused on two outcome measures. The primary outcome measure of interest was the prevalence of PSCI among stroke survivors in SSA. Additionally, the second outcome measure was identifying factors associated with PSCI among stroke survivors.

Operational definition of variables

“The total score of the MoCA ranges from 0 to 30. A score < 26/30 indicates cognitive impairment, while a MoCA score ≥ 26/30 indicates normal cognitive function” [10]. Similarly, the total score of the Mini-Mental State Examination (MMSE) ranges from 0 to 30, and a score < 25/30 indicates cognitive impairment, whereas a MMSE score ≥ 25/30 indicates normal cognitive function” [33]. These tools were translated into local African languages in each included study so as not to favor study participants with higher educational levels.

Data analysis

The extracted data were exported to STATA version 17 for statistical analysis. The pooled prevalence of PSCI was computed using a random-effects DerSimonian-Laird model [41]. The pooled prevalence of PSCI was obtained from the 9 included primary studies with a sample size of 1,566 [28, 31–38]. In contrast, the data regarding the associated factors were obtained from all the 10 included primary studies [14, 28, 31–38] with a sample size of 1,709. A funnel plot was used to determine publication bias, and Egger’s test with a P = value of < 0.05 was used to determine significant publication bias [42]. The heterogeneity across the included primary studies was assessed using I2 statistics [43]. The values of I2 statistic range from 0 to 100%, and I2 statistic values of 0, 25, 50, and 75% denote no, low, moderate, and high heterogeneity respectively [43].

A P = value of the chi-square test on the Cochrane’s Q statistic < 0.05 was used to indicate a significant heterogeneity [44, 45]. Sensitivity analysis was employed to determine the effect of a single study on the overall estimate. A forest plot was used to display the effect of independent factors on the outcome and a measure of association with a 95% CI was generated. The adjusted odds ratio (AOR) was the reported measure of association in the included primary studies.

Results

Search results

The search strategy retrieved a total of 978 studies from PubMed (458), Google Scholar (346), Scopus (27), and Web of Science (147). After removing the irrelevant studies based on their titles and abstracts (732) and duplicated studies (56), a total of 190 studies were selected for full-text review.

Afterward, 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 review and 29 studies were excluded. Finally, 10 studies were found to be relevant for determining the pooled prevalence of PSCI and identifying its associated factors. We have constructed the PRISMA flow chart [46] to show the selection process from the initially identified records to the ultimately included studies (Fig. 1).

Fig. 1.

Fig. 1

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PRISMA flow chart showing the studies selection process, 2024

Characteristics of the included studies

Six studies [28, 31–35], three studies [36–38], and one study [14] were conducted using cross-sectional, cohort, and case–control study designs respectively. Concerning geographical regions, four studies [31, 34, 35, 37] were conducted in Ethiopia, two [32, 33] in Uganda, one [28] in Ghana, one [36] in Tanzania, one [14] in Nigeria, and one [38] was conducted in the Democratic Republic of the Congo (DRC). The total sample size of the included studies was 1,709, where the smallest and largest sample sizes were 67 [34, 35] and 422 [31] respectively, among studies conducted in Ethiopia. The response rate of the included studies ranged from 61.96 to 100% (Supplemental Table 4).

Meta-analysis

Pooled prevalence of poststroke cognitive impairment

Finally, the 10 eligible primary studies [14, 28, 31–38] were included in the final meta-analysis, and the pooled prevalence of PSCI was 59.61% (95% CI: 46.87, 72.35); I2 = 96.47%; P < 0.001) (Fig. 2).

Fig. 2.

Fig. 2

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Forest plot showing the pooled prevalence of PSCI with 95% CIs in SSA, 2024

Publication bias

The asymmetric distribution of the funnel plot indicated the presence of publication bias in the study (Fig. 3).

Fig. 3.

Fig. 3

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Funnel plot showing the publication bias of PSCI among stroke survivors in SSA, 2024

Investigation of heterogeneity

The percentage of I2 statistics in the forest plot indicated a considerable heterogeneity among the included primary studies (I2 = 96.47%; P < 0.001) (Fig. 2). Therefore, sensitivity and subgroup analyses were performed to investigate potential sources of heterogeneity.

Sensitivity analysis

A sensitivity analysis was also conducted to determine the influence of a single study on the overall meta-analysis. The forest plot showed that the estimate of a single study was closer to the combined estimate, indicating the absence of a single study effect on the overall pooled estimate (Fig. 4).

Fig. 4.

Fig. 4

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Sensitivity analysis of PSCI among stroke survivors in SSA, 2024

Subgroup analysis

Subgroup analysis was performed based on the study design and sample size. The subgroup analysis performed using the study design revealed that the higher pooled prevalence of PSCI was detected among studies conducted with a cross-sectional study design [62.27, 95% CI: 54.79, 69.75; I2 = 79.84%, P < 0.001], and the lower pooled prevalence was among studies conducted with a cohort study design [55.09, 95% CI: 20.60, 89.58; I2 = 96.39%; P < 0.001]. Similarly, the subgroup analysis performed using the sample size indicated that the higher pooled prevalence of PSCI was among studies with a sample size < 171 [64.57, 95% CI: 55.58, 73.57; I2 = 86.13%; P < 0.001] followed by studies with a sample size ≥ 171 [44.29, 95% CI: 16.85, 71.73; I2 = 98.60%; P < 0.001] (Table 1). Thus, the heterogeneity of the study could be attributed to variances in study design and sample size across the primary studies.

Factors associated with poststroke cognitive impairment

In the study, eight studies [14, 28, 31–34, 36, 37] indicated that increased age (≥ 45 years) was significantly associated with PSCI. The pooled AOR of PSCI for stroke survivors aged ≥ 45 years was 1.23 (95% CI: 1.09, 1.40; I2 = 90.70%; P < 0.001) (Fig. 5).

Fig. 5.

Fig. 5

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

Five studies [14, 28, 31, 32, 34] reported a significant association between lower educational levels and PSCI. The pooled AOR of PSCI for stroke survivors with a lower educational level was 4.35 (95% CI: 2.87, 6.61; I2 = 0.00%; P < 0.89) (Fig. 6).

Fig. 6.

Fig. 6

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

Five studies [28, 32, 34, 35, 37] showed that poor functional recovery was significantly associated with PSCI. The pooled AOR of PSCI for stroke survivors with poor functional recovery was 1.75 (95% CI: 1.42, 2.15; I2 = 45.33%; P < 0.12) (Fig. 7).

Fig. 7.

Fig. 7

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

Three studies [31, 35, 36] also revealed a significant association between left hemisphere stroke and PSCI. The pooled AOR of PSCI for stroke survivors with left hemisphere stroke was 4.88 (95% CI: 2.98, 7.99; I2 = 0.00%; P < 0.99) (Fig. 8).

Fig. 8.

Fig. 8

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

Discussion

The study findings indicated that the pooled prevalence of PSCI was 59.61% (95% CI: 46.87, 72.35); I2 = 96.47%; P < 0.001). This finding is consistent with/higher than that of studies conducted in Japan (48%) [47], Austria (56.3%) [48], Norway (57%) [49], Finland (61.7%) [50], Indonesia (68.2%) [51], Holland (70%) [52], France (16.3%) [53], USA (19.3%) [54], Benin (20%) [55], Egypt (25.3%) [56], Chile (39%) [57], and China (39.4%) [58]. However, this finding is lower than that of a study conducted in China (80%) [59]. These discrepancies could be due to variations in the cognitive assessment tool used, inconsistency of treatment approaches, the cutoff point for diagnosis, or sample sizes across the studies [37, 60].

In addition, the findings of the study indicated that stroke survivors aged ≥ 45 years were 1.23 fold more likely to develop PSCI than those aged < 45 years. The finding of this study was consistent with the finding of a study conducted in Egypt [56]. This could be due to the nature of stroke, which brings cognitive decline in line with increased age [61]. Hence, encouraging lifestyle modifications could be a way to reduce the occurrence of cognitive impairment with increased age after stroke.

The findings of this study also revealed that stroke survivors with lower educational levels were 4.35 times more likely to develop PSCI than stroke survivors with higher educational levels. This study finding was supported by a study conducted in China [59]. Education increases the cognitive reserve and protects against cognitive impairment [62]. This could be the reason why a lower educational level favors the onset of PSCI. Therefore, improving access to education could be a way of decreasing the occurrence of cognitive impairment and dementia after stroke.

Similarly, the findings of this study indicated that stroke survivors with poor functional recovery were 1.75 times more likely to experience PSCI than their counterparts. This study finding was congruent with the study conducted in China [63]. A possible reason could be poor functional recovery is an indicator of stroke severity, clinical deficit, and vascular burden [64]. Consequently, initiating rehabilitative services early could enhance functional recovery, which may be a mean to decrease the occurrence of cognitive impairment after a stroke.

Moreover, the findings of this study revealed that stroke survivors with left hemisphere stroke were 4.88 times more likely to experience PSCI than stroke survivors with right hemisphere stroke. This finding was similar to those of a study conducted in Egypt [65]. Language is a key domain in the MoCA assessment [66], and it is a left hemispheric cognitive domain for more than 90% of the population worldwide and is potentially affected by left hemisphere stroke [65, 67, 68]. Thus, delivering rehabilitative services early could maintain the cognitive domain of left hemisphere, which could be a way to decrease the occurrence of cognitive impairment after a stroke.

Strengths and limitations of the study

To the best of our knowledge, this was the first systematic review and meta-analysis conducted to pool the results of various primary studies that have been conducted in SSA, providing strong evidence of PSCI among stroke survivors. Although the included studies were of good quality, most of the included primary studies were cross-sectional. Similarly, there was heterogeneity in PCSI diagnostic methods, absence of data indicating stroke severity, and a high proportion of loss to follow-up across the included studies. Moreover, this study included only studies that have been conducted using the English language.

Conclusions

The pooled prevalence of PSCI was considerably high among stroke survivors in SSA. Increased age, lower educational level, poor functional recovery, and left hemisphere stroke were the pooled independent predictors of PSCI in SSA. Stakeholders should focus on empowering education and lifestyle modifications, keeping their minds engaged, staying connected with social activities and introducing rehabilitative services for stroke survivors with these identified factors to reduce the risk of developing poststroke cognitive impairment.

Supplementary Information

12889_2024_19684_MOESM1_ESM.docx (14.6KB, docx)

Additional file 1:  Supplemental file 1. Search strategy.

12889_2024_19684_MOESM2_ESM.docx (26KB, docx)

Additional file 2:  Supplemental Table 1. PRISMA checklist.

12889_2024_19684_MOESM3_ESM.docx (66.3KB, docx)

Additional file 3:  Supplemental Table 3. Quality assessment of the included studies..

12889_2024_19684_MOESM4_ESM.docx (47.6KB, docx)

Additional file 4:  Supplemental Table 4. Risk of bias assessment of the included studies.

12889_2024_19684_MOESM5_ESM.docx (47.2KB, docx)

Additional file 5:  Supplemental Table 4. General characteristics of the included studies.

Acknowledgements

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

Abbreviations

AOR

Adjusted odds ratio

CI

Confidence interval

CSID

Community Screening Instrument for Dementia

DRC

Democratic Republic of the Congo

GCS

Glasgow Coma Scale

JBI

Joanna Briggs Institute

MMSE

Mini-Mental State Examination

MoCA

Montreal Cognitive Assessment

PICO

Population, Intervention, Context and Outcome

PRISMA

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

PSCI

Poststroke Cognitive Impairment

USA

United States of America

Authors’ contributions

TMA has generated the idea for this review. DK, BMB, SDK, WNA, and AK were involved in the data collection and statistical analysis. TMA wrote the first draft of this manuscript. YMT, GL, AG, BB, and SA revised the manuscript. All the authors were responsible for the accuracy of the analysis, and the contents of the study. Finally, all the authors read and approved the final version of the manuscript for publication.

Funding

Not applicable.

Availability of data and materials

All the necessary data and materials were included in the manuscript.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

All the authors declare that they have no conflicts of interest.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

12889_2024_19684_MOESM1_ESM.docx (14.6KB, docx)

Additional file 1:  Supplemental file 1. Search strategy.

12889_2024_19684_MOESM2_ESM.docx (26KB, docx)

Additional file 2:  Supplemental Table 1. PRISMA checklist.

12889_2024_19684_MOESM3_ESM.docx (66.3KB, docx)

Additional file 3:  Supplemental Table 3. Quality assessment of the included studies..

12889_2024_19684_MOESM4_ESM.docx (47.6KB, docx)

Additional file 4:  Supplemental Table 4. Risk of bias assessment of the included studies.

12889_2024_19684_MOESM5_ESM.docx (47.2KB, docx)

Additional file 5:  Supplemental Table 4. General characteristics of the included studies.

Data Availability Statement

All the necessary data and materials were included in the manuscript.

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