| Literature DB >> 34526674 |
Rajesh N Kalaria1,2, Mayowa O Owolabi1,3,4, Rufus O Akinyemi5,6,7,8, Bruce Ovbiagele4,9,10,11, Olaleye A Adeniji12, Fred S Sarfo10,11, Foad Abd-Allah13, Thierry Adoukonou14, Okechukwu S Ogah1,15, Pamela Naidoo16, Albertino Damasceno17, Richard W Walker1,18,19, Adesola Ogunniyi1,4.
Abstract
Stroke is a leading cause of disability, dementia and death worldwide. Approximately 70% of deaths from stroke and 87% of stroke-related disability occur in low-income and middle-income countries. At the turn of the century, the most common diseases in Africa were communicable diseases, whereas non-communicable diseases, including stroke, were considered rare, particularly in sub-Saharan Africa. However, evidence indicates that, today, Africa could have up to 2-3-fold greater rates of stroke incidence and higher stroke prevalence than western Europe and the USA. In Africa, data published within the past decade show that stroke has an annual incidence rate of up to 316 per 100,000, a prevalence of up to 1,460 per 100,000 and a 3-year fatality rate greater than 80%. Moreover, many Africans have a stroke within the fourth to sixth decades of life, with serious implications for the individual, their family and society. This age profile is particularly important as strokes in younger people tend to result in a greater loss of self-worth and socioeconomic productivity than in older individuals. Emerging insights from research into stroke epidemiology, genetics, prevention, care and outcomes offer great prospects for tackling the growing burden of stroke on the continent. In this article, we review the unique profile of stroke in Africa and summarize current knowledge on stroke epidemiology, genetics, prevention, acute care, rehabilitation, outcomes, cost of care and awareness. We also discuss knowledge gaps, emerging priorities and future directions of stroke medicine for the more than 1 billion people who live in Africa.Entities:
Mesh:
Year: 2021 PMID: 34526674 PMCID: PMC8441961 DOI: 10.1038/s41582-021-00542-4
Source DB: PubMed Journal: Nat Rev Neurol ISSN: 1759-4758 Impact factor: 42.937
Studies of stroke incidence and prevalence in Africa
| Country, region | Study period | Type of study | Case definition | Neuroimaging confirmationa | Stroke subtyping | Crude annual incidence or crude prevalence rateb | Age-adjusted incidence or prevalence rateb | Ref. |
|---|---|---|---|---|---|---|---|---|
| Nigeria, Ibadan | 1973–1975 | Community | Not stated | No | Yes | 26 | Not stated | [ |
| Libya, Benghazi | 1983–1984 | Hospital | US national survey of stroke | Yes | Yes | 63 | Not stated | [ |
| South Africa, Pretoria | 1984–1985 | Hospital | Harvard Cooperative Stroke Registry | Yes | Yes | 101 | Not stated | [ |
| Zimbabwe, Harare | 1991 | Hospital | WHO criteria | No | No | 31 | 68 | [ |
| Libya, Benghazi | 1991–1993 | Hospital | Not stated | Yes | Yes | 48 | Not stated | [ |
| Egypt, Sohag | 1992–1993 | Community | WHO criteria | Yes | Yes | 180 | Not stated | [ |
| Tanzania, Hai | 2003–2006 | Mixed | WHO criteria | Yes | Yes | 95 | 109 | [ |
| Tanzania, Dar es Salaam | 2003–2006 | Mixed | WHO criteria | Yes | Yes | 108 | 316 | [ |
| Mozambique, Maputo | 2005–2006 | Hospital | WHO criteria | Yes | Yes | 149 | 260 | [ |
| Egypt, Al-Kharga | 2005–2008 | Community | WHO criteria | Yes | No | 260 | 560 | [ |
| Nigeria, Lagos | 2007 | Community | WHO criteria | Yes | No | 25 | 54 | [ |
| Egypt, Al-Quseir | 2010–2011 | Community | WHO criteria | Yes | No | 181 | Not stated | [ |
| Nigeria, Akure | 2010– 2011 | Mixed | Not stated | Yes | Yes | 61 | 61 | [ |
| Nigeria, Igbo-Ora, rural | 1983–1984 | Community | WHO criteria | No | No | 56 | Not stated | [ |
| Tunisia, Kelibia | 1985 | Community | WHO criteria | Yes | Yes | 42 | Not stated | [ |
| Ethiopia, central and rural | 1988 | Community | WHO criteria | No | No | 15 | Not stated | [ |
| Egypt, Sohag | 1992–1993 | Community | WHO criteria | Yes | Yes | 508 | Not stated | [ |
| Tanzania, Hai | 1994–1995 | Community | WHO criteria | No | Yes | 127 | Not stated | [ |
| South Africac, Limpopo province, rural | 2001 | Community | WHO criteria | No | Yes | 243 | 300 | [ |
| Nigeria, Lagos, urban | 2007 | Community | WHO criteria | No | No | 114 | 204 | [ |
| Egypt, Al-Kharga | 2005–2009 | Community | WHO criteria | Yes | Yes | 580 | Not stated | [ |
| Nigeria, Niger Delta | 2008 | Community | WHO criteria | No | No | 851 | 1,230 | [ |
| Benin, Cotonou, urban | 2008–2009 | Community | WHO criteria | Yes | Yes | 460 | 771 | [ |
| Morocco, Rabat | 2008–2009 | Community | WHO criteria | Yes | Yes | 284 | 292 | [ |
| Nigeria, Kwara, semi-urban | 2009–2010 | Community | WHO criteria | No | No | 1,310 | Not stated | [ |
| Tanzania, Hai | 2009–2010 | Community | WHO criteria | No | No | 2,420 (>70 years of age) | 2,300 (>70 years) | [ |
| Egypt, Assiut | 2010 | Community | WHO criteria | Yes | Yes | 963 | 980 | [ |
| Egypt, Al Quseird | 2010–2011 | Community | WHO criteria | Yes | Yes | 655 | Not stated | [ |
| Nigeria, SE | 2011 | Community | WHO criteria | No | No | 163 | 163 | [ |
| Egypt, Qena | 2011–2013 | Community | WHO criteria | Yes | Yes | 922 | 567 | [ |
| Nigeria, Niger Delta | 2014 | Community | WHO criteria | No | No | 1,331 | 1,460 | [ |
| Nigeria, Odeda | 2015 | Community | WHO criteria | No | No | 711 | Not stated | [ |
| Benin, Parakou | 2016 | Community | WHO criteria | No | Yes | 1,156 | 3,223 | [ |
a‘Yes’ >80%. bPer 100,000 population. cSouth Africa, Agincourt Health and Population Unit. dThe prevalence study performed in urban Egypt (Al Quseir) by El-Tallawy et al. fulfilled the gold-standard criteria for a stroke epidemiological study.
Fig. 1Risk factors for stroke in Africa.
a | The population attributable risk associated with 10 potentially modifiable risk factors in the INTERSTROKE study (Africa sub-cohort). The study included 973 case–control pairs of Indigenous Africans from sites in Mozambique, Nigeria, Sudan, South Africa and Uganda. Data from ref.[103] and ref.[105]. b | The population attributable risk associated with 11 potentially modifiable risk factors in the Stroke Investigative Research and Educational Network (SIREN) study. The study included 2,118 case–control pairs of Indigenous Africans from multiple sites across Nigeria and Ghana. Data from ref.[104].
Fig. 2Effect of race and geography on risk factors for stroke.
Graph shows the frequency of eight risk factors in Indigenous Africans, African Americans and Americans of European descent. Study participants were >55 years of age. The data for this analysis came from 1,928 individuals with stroke who met the selection criteria and consisted of 811 Indigenous Africans recruited into the Stroke Investigative Research and Educational Network (SIREN) study, 452 African Americans and 665 Americans of European descent who were participants in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study. The group of participants of African ancestry had a significantly higher prevalence of hypertension and diabetes, similar frequency of dyslipidaemia and lower prevalence of cardiac disorders than Americans of European descent. However, obesity and lifestyle factors, including smoking, alcohol consumption and physical inactivity, were more prevalent among African Americans and Americans of European descent than Indigenous Africans. These data illustrate the complex interaction between racial (genetic) and geographical (environmental, for example, lifestyle) factors in the neurobiology of stroke. Data from ref.[70].
Fig. 3A life course approach to factors driving stroke burden in Africa.
The population-level risk factors for stroke change across the lifespan. Here, we summarize the risk factors at each stage of the life course that are driving the increasing burden of stroke in Africa. We also highlight the molecular mechanisms and processes involved in stroke risk at each stage of life. HIV, human immunodeficiency virus.
Stroke complications and sequelae in Africa
| Post-stroke complication | Features | Reported prevalence (%)a | Predictors | Countries | Refs |
|---|---|---|---|---|---|
| Delirium | Onset 1 week after stroke; confusional states | 32–33 | Age, NIHSS score | Nigeria | [ |
| Aspiration pneumonia | Cause of death after stroke | 64–79 | Age, stroke severity, consciousness | Benin, Burkina Faso, Ethiopia, Mozambique, Tanzania | [ |
| Bacteriuria and urinary tract infection | Defined as >105 CFU/ml | 9.3–12.3 | Infections | Ghana, Nigeria | [ |
| Aphasia and deglutition disorders | Aphasia can last for up to 60 months after stroke | ~50 | Age, left-hemispheric stroke, cognitive impairment | Tanzania | [ |
| Anxiety | Accumulative over 12 months | 10–34 | Female sex, low socioeconomic status, haemorrhagic lesions, depression | Nigeria, Tanzania | [ |
| Fatigue | Peak at 6 months after stroke | 60 | Poor quality of life | Ghana, Nigeria | [ |
| Sexual dysfunction | Most common: erectile dysfunction and low libido | >80 | Age, male sex | Nigeria | [ |
| Pain | Usually musculoskeletal pain or central post-stroke pain syndrome | 5–80 | Background history of pain, differential thresholds, age at stroke onset | Nigeria, Zimbabwe | [ |
| Depression | Most common: mood disorders, suicidality and tedium vitae | 16–80; median 30 | Age | Democratic Republic of Congo, Gabon, Ghana, Nigeria | [ |
| Cognitive impairment | Vascular cognitive impairment, dementia | 30–50 | Age, low literacy, vascular risk factors, Black race, recent infection, MTLA, WMH | Ghana, Nigeria, South Africa | [ |
On the basis of data from hospital-based studies. For details on post-stroke epilepsy and functional disability, see Tables 3 and 4. CFU, colony-forming unit; MTLA, medial temporal lobe atrophy; NIHSS, National Institutes of Health Stroke Scale; WMH, white matter hyperintensities. aPercentage of individuals with stroke who go on to develop these complications.
Estimated prevalence of post-stroke epilepsy in Africa
| Country | Year | Study type | Sample size | Diagnostic criteria | Prevalence (%)a | Ref. |
|---|---|---|---|---|---|---|
| Burkina Faso | 2006–2014 | Hospital based; retrospective | 1,616 | Clinical, EEG, brain CT | 1.98 | [ |
| Sudan | 2006–2008 | Hospital based | 165 | Clinical, eye-witness reports, EEG, brain CT | 16.90 | [ |
| Benin | 2015–2016 | Hospital based; retrospective | 1,703 | Clinical, EEG, brain CT | 2.00 | [ |
| Ghana | 2018–2020 | Hospital based; cross-sectional | 1,101 | Clinical, brain CT, EEG | 11.40 | [ |
aPercentage of individuals with stroke who go on to develop post-stroke epilepsy.
Estimated frequency of functional disability after stroke in Africa
| Country (City) | Year | Type of study | Study population | Sample size | Tools used | Percentage with disability | Ref. |
|---|---|---|---|---|---|---|---|
| Gambia (Banjul) | 1990–1991 | Hospital | Not stated | 106 | Barthel Index | 91.5 at 1 month after stroke | [ |
| Tanzania (Hai) | 1994 | Community and census | 85,152 (aged >15 years) | 108 | Barthel Index | 60 | [ |
| South Africa (Cape Town) | 2004–2006 | Hospital | 4,524,335 | 196 | MRS, Barthel Index | 48 (moderate to severe disability) | [ |
| Benin (Cotonou) | 2009 | Community | 69,869 | 14,969 | MRS, FIM, MADRS | 60 | [ |
| South Africa (Johannesburg) | Not known | Hospital and community | Not stated | 68 | Barthel Index | 47 at discharge; 93 at 6 weeks after discharge | [ |
| South Africa (Cape Town) | 2010 | Hospital | Not stated | 67 | Barthel Index | 81.82 at discharge | [ |
| Nigeria (Ibadan) | 2013 | Hospital | Not stated | 128 | MRS | 60.9 | [ |
| Benin (Parakou) | 2013 | Hospital | Not stated | 85 | MRS | 53 at 1 month after stroke | [ |
| Nigeria (Benin) | Not known | Hospital and outpatient | Not stated | 102 | MRS | 71.6 | [ |
| Uganda (Kampala) | 2014 | Hospital | Not stated | 127 | Barthel Index | 46.1 at 1 month after stroke | [ |
| Egypt (Cairo) | 2015–2016 | Hospital | Not stated | 397 | MRS | 18 | [ |
| Egypt (Cairo) | 2018–2019 | Hospital | Not stated | 61 (posterior circulation stroke) | MRS | 72.13 at 3 months after stroke | [ |
FIM, functional independence measure; MADRS, Montgomery–Asberg depression rating scale; MRS, modified Rankin score.
Stroke candidate gene studies in Africa
| Study | Gene name | Study population | Salient findings |
|---|---|---|---|
| Saidi et al. (2007)[ | Plasminogen activator inhibitor 1 ( | Tunisian | |
| Saidi et al. (2007)[ | Apolipoprotein E ( | Tunisian | |
| Saidi et al. (2008)[ | Human platelet alloantigen 1–5 ( | Tunisian | Lower |
| Saidi et al. (2008)[ | Tunisian | ||
| Saidi et al. (2009)[ | Tunisian | Higher | |
| Saidi et al. (2009)[ | Angiotensinogen ( | Tunisian | Multiple |
| Saidi et al. (2010)[ | Aldosterone synthase ( | Tunisian | |
| Chehaibi et al. (2013)[ | Peroxisome proliferator-activated receptor-δ ( | Tunisian | |
| Fekih-Mrissa et al. (2013)[ | Methylenetetrahydrofolate reductase ( | Tunisian | |
| Atadzhanov et al. (2013)[ | Zambian | ||
| Chehaibi et al. (2014)[ | Matrix metalloproteinase 1 ( | Tunisian | |
| Diakite et al. (2015)[ | C2491T | Moroccan | C2491T |
| Diakite et al. (2014)[ | Moroccan | ||
| Rezk et al. (2015)[ | IL-1 cluster genes: | Egyptian | |
| Diakite et al. (2016)[ | Moroccan | ||
| Akinyemi et al. (2017)[ | West African (Nigeria and Ghana) | ||
| Akinyemi et al. (2018)[ | Apolipoprotein 1 ( | West African (Nigeria and Ghana) |
Fig. 4Studies on stroke in Africa.
Here, we summarize the published literature on stroke in Africa, from 1999 to November 2020, and including 107 case reports or series, 29 epidemiological studies, 562 clinical studies, 5 clinical trials, 4 international studies, 30 genetic studies, 21 preclinical studies, 136 reviews, 52 letters or editorials, 6 clinical guidelines and 4 quality improvement-related publications. aIncludes community-based prevalence and incidence studies on stroke in Africa. bIncludes hospital-based studies, whether of observational or interventional, retrospective or prospective, longitudinal or case–control designs. cRefers to studies that report the testing of a drug, procedure or other medical treatment in animals, where the disease of interest in the study was a stroke. dIncludes narrative reviews, scoping reviews, systematic reviews and meta-analyses.
Fig. 5The stroke quadrangle.
The four pillars of the stroke quadrangle are surveillance, prevention, acute care and rehabilitation. Together, these pillars can lead to the reduction of stroke incidence, prevalence, disability and mortality. DALYs, disability-adjusted life years.
Stroke care in Africa: challenges and solutions
| Aspect of stroke care | Challenges | Pragmatic solutions |
|---|---|---|
| Surveillance | Valid, reliable data on stroke incidence, prevalence, mortality and disability in Africa are extremely limited; no surveillance system is in place to track trends in the burden of stroke at continental, regional and country levels | Establish stroke surveillance systems to measure and monitor the burden of stroke |
| Prevention | No robust systems for detection and control of major stroke risk factors such as hypertension, diabetes mellitus and dyslipidaemia; high rates (93%) of uncontrolled hypertension | Increase awareness, screening and control of hypertension, dyslipidaemia, diabetes mellitus and other major stroke risk factors at the primary health-care level in synergy with programmes for NCDs; implement sensitization programme to involve the entire population across the lifespan |
| Acute care | Scarcity of high-quality hyperacute and acute care services; very low rates of thrombolysis and thrombectomy; few multidisciplinary stroke units | Synergistic action by all stakeholders, including pharmaceutical companies and stroke experts, to improve the availability of services and increase the number of stroke units |
| Rehabilitation | Few multidisciplinary stroke rehabilitation centres | Increase the number of centres and settings that offer multidisciplinary care; promote recovery and re-integration |
NCDs, non-communicable diseases.
Fig. 6Conceptual framework of the African Stroke Organization.
The aim of the African Stroke Organization (ASO) is to reduce the burden of stroke in Africa. This figure illustrates the framework through which the ASO plans to meet this goal. The colourful network represents the rich genetic, cultural and geographical diversity in Africa, and the neuronal network of the brain. The interconnected individuals represent the African Ubuntu Philosophy of inclusiveness, cooperation and collaboration. The pale green layer depicts the four core pillars of ASO activities: research; capacity-building programmes; development of stroke services; promotion of stroke awareness, and advocacy and empowerment of survivors of stroke, their families and their caregivers. The dark green layer represents the broader core values of the ASO: working with partners to involve people and positively influence practice and policy. Adapted with permission from ref.[312], SAGE.