RESEARCH ARTICLE
Knowledge and awareness of and perception
towards cardiovascular disease risk in sub-
Saharan Africa: A systematic review
Daniel Boateng1,2*, Frederick Wekesah1,3, Joyce L. Browne1, Charles Agyemang4,
Peter Agyei-Baffour2, Ama de-Graft Aikins5, Henriette A. Smit1, Diederick E. Grobbee1,
Kerstin Klipstein-Grobusch1,6
1 Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center
Utrecht, Utrecht University, Utrecht, The Netherlands, 2 School of Public Health, Kwame Nkrumah University
of Science and Technology, Kumasi, Ghana, 3 African Population and Health Research Center, Nairobi,
a1111111111 Kenya, 4 Department of Public Health, Academic Medical Center, University of Amsterdam, Amsterdam
a1111111111 Public Health Institute, Amsterdam, The Netherlands, 5 Regional Institute for Population Studies, University
of Ghana, Legon, Ghana, 6 Division of Epidemiology & Biostatistics, School of Public Health, Faculty of
a1111111111
Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
a1111111111
a1111111111 * d.boateng@umcutrecht.nl
Abstract
OPENACCESS
Citation: Boateng D, Wekesah F, Browne JL, Introduction
Agyemang C, Agyei-Baffour P, Aikins Ad-G, et al.
(2017) Knowledge and awareness of and Cardiovascular diseases (CVDs) are the most common cause of non-communicable dis-
perception towards cardiovascular disease risk in ease mortality in sub-Saharan African (SSA) countries. Gaps in knowledge of CVD condi-
sub-Saharan Africa: A systematic review. PLoS
tions and their risk factors are important barriers in effective prevention and treatment. Yet,
ONE 12(12): e0189264. https://doi.org/10.1371/
journal.pone.0189264 evidence on the awareness and knowledge level of CVD and associated risk factors among
populations of SSA is scarce. This review aimed to synthesize available evidence of the
Editor: Albert Lee, The Chinese University of Hong
Kong, HONG KONG level of knowledge of and perceptions towards CVDs and risk factors in the SSA region.
Received: July 11, 2017
Methods
Accepted: November 23, 2017
Five databases were searched for publications up to December 2016. Narrative synthesis
Published: December 12, 2017
was conducted for knowledge level of CVDs, knowledge of risk factors and clinical signs,
Copyright: © 2017 Boateng et al. This is an open factors influencing knowledge of CVDs and source of health information on CVDs. The
access article distributed under the terms of the
review was registered with Prospero (CRD42016049165).
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original Results
author and source are credited.
Of 2212 titles and abstracts screened, 45 full-text papers were retrieved and reviewed and
Data Availability Statement: All relevant data are
20 were included: eighteen quantitative and two qualitative studies. Levels of knowledge
within the paper and its Supporting Information
files. and awareness for CVD and risk factors were generally low, coupled with poor perception.
Most studies reported less than half of their study participants having good knowledge of
Funding: DB is supported by the Global Health
Scholarship Programme, University Medical Centre CVDs and/or risk factors. Proportion of participants who were unable to identify a single risk
Utrecht, The Netherlands. factor and clinical symptom for CVDs ranged from 1.8% in a study among hospital staff in
Competing interests: The authors have declared Nigeria to a high of 73% in a population-based survey in Uganda and 7% among University
that no competing interests exist. staff in Nigeria to 75.1% in a general population in Uganda respectively. High educational
PLOS ONE | https://doi.org/10.1371/journal.pone.0189264 December 12, 2017 1 / 21
Knowledge and awareness of and perception towards cardiovascular disease risk in sub-Saharan Africa
Abbreviations: NCD, Non-communicable diseases; attainment and place of residence had a significant influence on the levels of knowledge for
CVD, Cardiovascular disease; CHD, Coronary Heart CVDs among SSA populations.
Disease; MI, Myocardial infarction; SSA, Sub-
Saharan Africa.
Conclusion
Low knowledge of CVDs, risk factors and clinical symptoms is strongly associated with the
low levels of educational attainment and rural residency in the region. These findings pro-
vide useful information for implementers of interventions targeted at the prevention and con-
trol of CVDs, and encourages them to incorporate health promotion and awareness
campaigns in order to enhance knowledge and awareness of CVDs in the region.
Introduction
Non-communicable diseases (NCDs) pose a major health challenge globally, currently causing
more deaths than all other causes combined.[1] In 2012, about 38 million people died from
NCDs and this is expected to increase to 52 million by 2030.[1] About 80% of these deaths are
caused by four NCDs: cardiovascular diseases (CVDs), cancers, chronic respiratory diseases
and diabetes. CVDs account for almost half of NCDs deaths,[1,2] estimated at an annual 17.3
million deaths, and 10% of the global disease DALY burden.[2,3] It is expected that by the year
2030, more than 23 million deaths will be caused by CVDs,[3,4] with stroke and coronary
heart disease (CHD) being the leading contributors.[5,6]
Deaths from CVDs have declined progressively over the past three decades in high-income
countries because of implementation of population-wide preventive strategies, effective pri-
mary and secondary preventive healthcare, and availability of improved treatment for acute
events.[7] However, rates of CVD deaths have increased in LMICs over the same period.[8,9]
In addition to increased prevalence of risk factors of CVDs in these settings, this rise in CVD
deaths reflects lower availability of population strategies for prevention and health care.[1] The
rise in CVD risk factors in sub-Saharan Africa (SSA) is attributed to rapid urbanization, glob-
alization and urban poverty.[10] Both are associated with a change in diets and lifestyle, where
traditional diets are replaced with energy-dense and processed foods and increasing physical
inactivity.[10] As poverty and inequality trigger the upsurge of communicable diseases,[11] as
well as propagate risk factors for NCDs as smoking, drinking and poor diet,[11] the burden of
disease disproportionally affects the urban poor.
Gaps in knowledge of CVD conditions and their risk factors in the general population are
important barriers in the effective prevention and treatment of CVDs.[12] The role of knowl-
edge in health behaviours and sustained behavioural changes has been proposed by several
models including the health belief model.[13–15] This models posit that knowledge of a dis-
ease condition influences patient’s attitude and practice, improves compliance with treatment
and has been shown to lead to reduction in prevalence and aversion of complications.[16]
These models, although they may differ in content and viewpoint, emphasize the importance
of appraising the beliefs, views and attitudes of individuals to apprehend observed behaviours
and to guide behavioural change.
Success in the implementation of any health promotion program is dependent on context-
specific information on knowledge, awareness and perception of the targeted population.
There is however a regional level scarcity of evidence on the knowledge and awareness levels
of CVDs and risk factors among the populations of SSA.[17] This systematic review therefore
PLOS ONE | https://doi.org/10.1371/journal.pone.0189264 December 12, 2017 2 / 21
Knowledge and awareness of and perception towards cardiovascular disease risk in sub-Saharan Africa
aims at synthesizing existing evidence on knowledge, awareness and perception towards these
conditions.
Methods
This review was conducted according to the recommendations outlined in the PRISMA (Pre-
ferred Reporting Items for Systematic Reviews and Meta-Analyses) statement.[18] (S1 File). It
was registered with Prospero (CRD42016049165).
Search strategy
We searched PubMed, Medline, Science Direct, Google Scholar, Africa Index Medicus (AIM),
Africa Journals Online (AJOL) databases to retrieve relevant primary studies conducted in
SSA, using pre-defined search (Title/Abstract) and indexing terms (MeSH/Emtree). Keywords
and MeSH terms and their combinations used in the searches were “knowledge”, “stroke”,
“heart attack”, “coronary heart disease”, “myocardial infarction”, “congenital heart disease”,
“heart diseases”, “vascular diseases”. Reference lists of full-text papers were hand searched for
additional articles and reviewed for relevance in this review. The strategy is provided as a sup-
plementary file (S1 Text).
Inclusion criteria
We included studies that were published in SSA, in English, and in peer-reviewed journals
between 2007 and 2015. Papers were from primary research of any design and methodology:
quantitative and qualitative and exploring knowledge, awareness and perception of CVD and
the risk factors. Studies that were carried out among SSA populations living in Western coun-
tries or only described interventions leading to increased knowledge and awareness of CVDs
or risk factors and symptoms of CVDs were also excluded.
Definition of terms/concepts
CVDs include vascular diseases in general, CHD, cerebrovascular disease (e.g. stroke), myo-
cardial infarction (MI) and congenital heart diseases. Individuals were required to correctly
identify CVD conditions, risk factors and clinical symptoms from a list to gauge their knowl-
edge. Perception was based on individuals’ self-assessment of chances of developing CVDs, as
well as their understanding of who was at risk to develop the condition. Perception was mostly
explored in qualitative studies. The SSA region was classified based on the United Nations clas-
sification of countries.[19]
Data extraction
Two reviewers (DB, FW) conducted data extraction from the identified studies. Information
was extracted on: authors, year of publication, study design and population, research methods,
types of CVDs studied, findings on the knowledge, awareness of and perception towards
CVDs and the risk factors. We extracted additional data on the factors influencing knowledge
and perceptions of CVD and the reported sources of information on CVD and risk factors.
The exercise was reviewed by JB and KKG, who were also consulted on the extraction process.
Quality assessment
The quality of the quantitative studies, were assessed based on National Institute of Health
(NIH) Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies.[20]
This form appraised the reliability, validity and generalizability of the quantitative studies. The
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Knowledge and awareness of and perception towards cardiovascular disease risk in sub-Saharan Africa
NIH quality assessment tool uses 13 criteria to assess and rate the quality of studies. This
included the research question, study population, sample size estimation, exposure and out-
come assessment, loss to follow-up and statistical analysis. General guidance is provided for
determining the overall quality of the studies and to grade their level of quality as good, fair or
poor.
Qualitative studies were appraised using the Critical Appraisal Skill Programme (CASP)
tool.[21] The CASP tool has 10 items that look at the relevance and clarity of research goals,
appropriateness of the research design and methodology in addressing the research question,
recruitment strategies, data collection, data analysis, findings, ethical consideration and value
of the research. Questions attached to these items enable critical self-reflection about biases
and assess the extent to which findings from the study could be transferred to other settings or
groups. The quality assessment and criteria are available as a supplementary file (S2 File).
Synthesis of findings
Qualitative data synthesis of the findings on the knowledge, awareness of and perception
towards CVD risk and risk factors in SSA was conducted. Findings from the quantitative
papers were absorbed using the multi-source synthesis method, an analytical technique that
enhances transparency when synthesizing quantitative and/or contextual data, thus providing
a platform for comparison between studies.[22] Findings from qualitative articles were inte-
grated with those from the quantitative studies based on similar themes or topics. Due to the
heterogeneity in outcomes, data were not pooled to conduct a meta-analysis.
Results
Study characteristics
A total of 2212 titles were identified from electronic database searches. 2167 titles were
excluded for being irrelevant to the review question, and 45 full-text articles were assessed for
inclusion. Twenty-five articles were excluded based on reasons such as not reporting the link
between risk factors to general knowledge and awareness of CVDs or reporting results of an
impact of an intervention in the levels of knowledge and awareness of CVD and risk factors. In
the end, 20 articles were included in the review. The assessment and inclusion criteria are
reported in Fig 1. One of the 18 quantitative studies out of the final 20 studies was quasi experi-
mental, while the rest were cross-sectional. Respondents were recruited from varied settings,
including from general population samples living in urban and rural areas, and from specific
samples like academic staff, hospital staff and health professionals, patients, and employees in
banks and in the military. The age of the participants in the different studies ranged from 16 to
82 years. More information on characteristics of study participants is presented in Table 1.
Quality of included studies
The majority of the quantitative studies were rated to be of good or high quality (n = 10). They
described in detail the design and methodology used, the process of recruiting participants,
justification and methods of arriving at required sample size, study setting, clear and detailed
presentation of findings. Studies that were rated to be of fair or poor quality (n = 8) were
papers that failed to describe details of subject recruitment processes including inclusion crite-
ria and sampling strategies and lacked justification of sample size and other issues that could
lead to a high risk of bias and undermine generalizability of the study (S1 File).
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Knowledge and awareness of and perception towards cardiovascular disease risk in sub-Saharan Africa
Fig 1. Flow chart of inclusion and exclusion of relevant articles.
https://doi.org/10.1371/journal.pone.0189264.g001
Knowledge and awareness regarding cardiovascular diseases
Most studies in this review did not state a priori the criteria used in measuring and classifying
levels of knowledge and awareness. However, most of them classified knowledge and aware-
ness of CVD or the risk factors as poor, acceptable or good. In the study by Akintunde et al,
[23] among university staff, a knowledge score of<50% was classified as low; 50–69% moder-
ate and70% good. Nakibuuka et al[24], in a study in Uganda classified urban and rural resi-
dents who could identify 5–10, 2–4 and<2 CVD risk factors or warning signs as having good,
fair and poor knowledge respectively.
Awareness of CVDs was high among studies that reported on it; 76.2% among bankers and
teachers[25] and 75.6% among military personnel[26] in Nigeria. Most people in a low-income
peri-urban community in South Africa,[27] were familiar with the terminology used to
describe CVDs. However, the studies reported generally low knowledge levels of CVDs with
most studies reporting less than 50.0% of respondents having good knowledge. In studies con-
ducted among workers in a Nigerian University Hospital, one reported that 19.0% had good
knowledge of CVDs[23] while another showed that 53.5% knew the mechanism through
which stroke occurs.[28] Findings on the knowledge and awareness of CVDs in SSA is sum-
marized in Table 2 and Fig 2.
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Knowledge and awareness of and perception towards cardiovascular disease risk in sub-Saharan Africa
Table 1. Characteristics of included studies.
Study, year, country Design and methods Sample size Study population and setting Quality†
Akintunde et al (2015)[23]; Study design: Descriptive cross-sectional. / 206 (M 96, Adult university staff (academic and non- Fair
Nigeria Methods: Quantitative; random sampling W110) academic); Mean age 45.3years
Mohammed (2012)[26] Study design: Cross-sectional. / Methods: 82 (M 80; W Military personnel (Army, Navy, Air force) of the Poor
Nigeria Quantitative. / Sampling: Not stated 2) Nigerian Armed Forces; 30-60years (mean
49years)
Uchenna, Ambakederomo, Study design: Cross-sectional. / Methods: 236 (M 136, Outpatients of university teaching hospital; 16– Poor
Jesuorobo (2012)[42]; Quantitative; convenient sampling W 100) 82 years (mean 42.1years)
Nigeria
Awosan et al (2013)[25] Study design: Cross-sectional. / Methods: 210 (M 141; Bankers and secondary school teachers (>1yr Good
Nigeria Quantitative, multistage random sampling W 69) experience) in a metropolis; 25-56years
teachers; 20-49years bankers
Oladapo et al (2013)[33] Study design: Cross-sectional survey. / 2000 (M 873; Rural community members in Southwestern Good
Nigeria Methods: Quantitative. / Sampling: W 1127) Nigeria
Systematic random
Akinyemi et al (2009)[28], Study design: Cross-sectional survey. / 400 (M 137; Hospital staff of federal medical centre; 20- Fair
Nigeria Methods: Quantitative, systematic random W 233) 64years (mean age 34.4years)
Wahab, Kayode & Musa Study design: Cross-sectional survey. / 354 (M 148; Patients on follow-up for hypertension and/or Good
(2015)[37] Nigeria Methods: Quantitative W 166) diabetes at specialist medical outpatient clinics;
Mean age 56.4years
Obembe et al (2014)[29] Study design: Cross-sectional survey. / 494 (M 284; Staff of government-owned tertiary institution Good
Nigeria Methods: Quantitative, multistage stratified W 210)
sampling
Komolafe et al (2015)[31] Study design: Cross-sectional survey. / Size: 114 (M Secondary school teachers of 2 towns in Fair
Nigeria Methods: Quantitative 51; W 63) Nigeria; 20-50years
Ajayi and Ojo (2007)[40] Study design: Descriptive cross-sectional. / 155 (M 87; Patients attending a medical out-patient clinic; Poor
Methods: Quantitative W 68) Mean age 58.4
Akinyemi RO et al (2015) Study design: Quasi experimental. / Methods: 116 (M 50; Non-neurologist health workers; Mean age 46.1 Fair
[30]§ Nigeria Quantitative W 66)
Ansa, Oyo-Ita and Essien Study design: Cross-sectional. / Methods: 500 (M 302; Staff of university hospital; 41-50years Fair
(2007)[17] Nigeria Quantitative; systematic random sampling W 198)
Donkor et al (2014)[35] Study design: Cross-sectional survey. / 693 (M 374; Inhabitants of a metropolitan city, Mean age, Good
Ghana Methods: Quantitative, systematic random W 319) 36.8years
Cossi et al (2012)[36] Benin Study design: Cross-sectional survey. / 15155 (M Adults in an urban district; Mean age, 31years Good
Methods: Quantitative. / Sampling: All 6293; W
included 8862)
Kaddumukasa et al (2015) Study design: Cross-sectional survey. / 370 (M 117; Households in selected urban and rural areas; Good
[32] Methods: Quantitative multistage stratified W 253) 18-85years; Median age, 34years
Uganda random
Nakibuuka et al (2014)[24] Study design: Cross-sectional. / Methods: 1616 (M 510; Urban and rural residents; 1161 urban, 455 Good
Uganda Quantitative; multistage stratified sampling. / W 1,106) rural; Mean age 39.6
Analysis: Chi square, logistic regression
Temu et al (2015)[41] Kenya Study design: Cross-sectional. / Methods: 300 (M108; PLWH on or not yet on ART (outpatients) from Good
Quantitative; convenient sampling W 192) HIV clinic of Teaching and referral hospital; > =
18years
Yuqiu & Wright (2008)[34] Study design: Cross-sectional survey. / 551 (M 302; Adults of working age living in a community; 18- Fair
South Africa Methods: Quantitative, census sampling W 249) 40years
Qualitative study
Surka et al (2015)[27] South Study design: Cross-sectional. / Methods: 28 (M 4; W Male and female community members ( _25 Good
Africa Qualitative (FGDs of 8–10 participants); 24) years) with no previous experience in being
Purposive sampling assessed for CVD risk; Mean age 53years
Awah et al (2008)[39] Study design: Cross-sectional. / Methods: 82 (M 44; W Community members, health workers, policy Good
Cameroon Qualitative (FGDs and IDI); Purposive 38) makers
sampling;
* HDFQ = Heart Disease Fact Questionnaire; CVD = cardiovascular disease; IHD = ischemic heart disease; PLWH = People living with HIV/AIDS;
CHD = Coronary Heart Disease; BP = Blood pressure; PLWH = People living with HIV/AIDS;
†The quality assessment and criteria are available in the S2;
§Only those in the pre-intervention phase included in this review; FGD = Focus group discussions; IDI = In depth interviews
https://doi.org/10.1371/journal.pone.0189264.t001
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Knowledge and awareness of and perception towards cardiovascular disease risk in sub-Saharan Africa
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Table 2. Outcome assessment and findings of included studies.
Study CVDs Assessment of knowledge General knowledge/ awareness of CVDs Knowledge of risk factors Knowledge of warning signs/symptoms Factors related
studied
Akintunde et al [23] CVD HDFQ scores were used to 50% had low knowledge of CVDs; 31.1% Poor knowledge on cholesterol and heart disease. / Age, gender and education not associated
determine the level of moderate; 19.9% high. Moderate knowledge of smoking, diabetes, overweight with knowledge of CVDs.
knowledge and high BP.
Uchenna, CVD Structured questionnaire- 91.2% never been counselled on heart disease 51.7% had no knowledge of any cause of heart disease. Low knowledge of symptoms of heart disease; 24.6% Education; gender not associated with
Ambakederomo, researcher administered prevention. awareness of CVDs.
Jesuorobo[42]
Mohammed [26] CVD Self-designed knowledge and 75.6% enlightened on CVD. Low knowledge level of;
awareness Questionnaire ➢Primary risk factors; 31.7%
➢Secondary risk factors; 41.5%
Identified risk factors: Smoking, 70.6%; excessive
alcohol, 52.8%; stress, 87.5%; sedentary lifestyle,
16.6%; poor dietary intake, 6.4%.
Awosan et al[25] CHD Questionnaire adapted from High level of awareness of CHD, 76.2%. Up to 50% knew 4/7 risk factors among teachers and 1/7
the American Heart among bankers. / Identified risk factors; Hypertension,
Association’s questionnaire 50.5% teachers and 59% bankers; overweight/obesity,
47.6% teachers and 55.2% bankers; physical activity,
cigarette smoking and fatty foods; up to 50% among
teachers; less among bankers.
Oladapo et al [33] Stroke Structured questionnaire Low knowledge of clinical features of stroke 56% unable to identify a single risk factor Age, gender, family history, history of stroke
Heart 21.9% and heart attack or angina, 0.4%. Identified risk factors: Hypertension, 16.2%; diabetes, not related Tertiary education (OR, 95%
failure 5.4%; tobacco use, 36.2%; obesity, 1.6%; lack of CI = 3.11, 2.06–7.14).
exercise, 1.2%; stress (42.7%).
Akinyemi et al [28] Stroke Structured, semi-closed Knowledge of organ affected by stroke; 16.9% 4.3% could not identify a single risk factor; 27.6% 8.6% could not identify a single warning symptom. / Tertiary education demonstrated better
questionnaire among clinical workers; 35.0 among non- identified 1–3 risk factors; 68.1% identified > = 4 risk Identified symptoms: One sided body weakness (most knowledge of how stroke occurs (p<0.001). /
clinical workers. / Knowledge of mechanism factors identified), 61.9%; slurring of speech, 52.2%; dizziness, Tertiary education knew < = 4 warning
through which stroke occurs; 53.5%. / Identified risk factors: Hypertension, 88.6%; stress, altered consciousness, loss of vision, chest pain least symptoms (p<0.004).
Knowledge of occurrence of stroke through 70.8%; high cholesterol, 43.8%; alcohol consumption, identified.
rupture of vessels; 64.9%. 43.4%, Smoking, 35.9%, lack of exercise, 34.6%, ageing
30.8%; diabetes 29.5%; bad diet 22.4%. / Some
participants cited evil spirits, 13.8% and will of God,
1.1%.
Wahab, Kayode & Stroke Author designed Only 39.8% correctly mention 1 modifiable stroke risk Age < 55 years (OR, 1.832; 95% CI, 1.160–
Musa [37] questionnaires factor. / Identified risk factors: Hypertension, 34.7%; 2.893); >12 years of formal education (OR,
diabetes, 7.3%; smoking, 3.8%; alcohol, 4.5%; stress, 2.712; 95% CI, 1.678–4.382); family history
12.7%; overweight/obesity, 1.9%; Sedentary lifestyle, (OR, 2.112; 95% CI, 1.116–3.998); urban
0.6%. residence (OR, 2.726; 95% CI, 1.256–
5.919).
Obembe et al [29] Stroke Author designed 1.8%% knew no risk factor 7.7% identified no warning sign; only 15.2% identified all Age, education, family history significantly
questionnaires Identified risk factors: Hypertension (most identified), warning signs. / Identified symptoms: Slurred speech, influenced awareness of stroke.
91.7%; stress, 80.2%; ageing, 63.8%; cholesterol, 58.7%; dizziness, 52.8%; numbness, 69.4%; weakness,
51.4%; smoking, 46.2%; obesity, 56.1%; lack of 69.8%; headache, 39.9%; vision problem, 39.5%;
exercise, 50.8%; family history, 55.5%; diabetes 45.7%; difficulty in understanding, 34.4%.
alcohol, 40.3%; diet, 36.0%.
Komolafe et al [31] Stroke Previously validated Inadequate awareness of stroke. 13.2% identified no risk factor. 23.7% identified no warning sign; only 3.5% identified all
questionnaire to recognize and Identified risk factors: Hypertension, 79.8%; ge, warning signs. Identified warning signs: Slurred
identify risk factors and early 43.9%; Stress, 65.8%; cholesterol, 50.9%; obesity, speech, 50%; dizziness, 27.2%; numbness, 33.3%;
warning signs 49.1%; lack of exercise, 57%; family history, 52.6%; weakness, 42.1%; headache, 36.8%; vision problem,
diabetes, 47.4%; alcohol, 52.6%; diet, 99.1%; 20.2%; shortness of breath, 32.5%.
hyperlipidemia, 22.8%; smoking, 49.6%; ageing, 43.9%.
Donkor et al [35] Stroke Author designed, validated Inadequate awareness of stroke. 19% identified no risk factor. 22% identified no warning sign. Age, gender, education not related to stroke
questionnaire, based on Identified risk factors: Lack of exercise, 37%; Identified warning signs: Slurred speech, 37%; awareness.
previously used hypertension, 34%; alcohol 33%; high cholesterol, dizziness, 17%; numbness, 21%; weakness, 38%;
questionnaires 32.0%; family history 28%; smoking, 24%; stress 22%; severe headache, 25%; vision problem, 15%; shortness
(heart disease, obesity, diabetes) <15%. of breath 13%.
Ajayi and Ojo (2007) Stroke Structured questionnaire- Identified risk factors: Hypertension (most identified), Identified warning signs: Paralysis on one side of Higher education association with increase
[40] researcher administered 60.6%; previous history, 16.1%; cholesterol, 3.2%; body, 55.6%; weakness on one side, 27.1%; sudden awareness of stroke risk factors.
family history, 3.2%; smoking, 1.3%. / None identified difficult in speaking and understanding, 7.1%; tingling
drinking of alcohol as risk factor. sensation, 5.8%; blurred vision, vertigo, difficulty
swallowing <1%. / None identified chest pain.
Akinyemi RO et al Stroke Self-administered Knowledge of epidemiology of stoke, 81%. 90.5% identified > = 4 risk factors; 79.3% identified > = 4 risk symptoms; 19% identified
[30]§ questionnaire. 7.8% identified 1–3 risk factors; 1.7% identified no risk 1–3 symptoms. / Identified warning signs: Face drop,
factor. / 95.7% identified hypertension as major risk 11.2%; arm weakness, 12.1%; slurred speech 18.1%.
factor.
Ansa, Oyo-Ita and IHD Self-administered Identified risk factors: Smoking, 70.6%; excessive Higher education increased knowledge of
Essien[17] questionnaire alcohol, 52.8%; obesity, 41.6%; sedentary lifestyle, risk factors.
16.6%; oral contraceptives, 6.4%.
Cossi et al [36] Stroke Author designed semi- Majority were unable to name organ affected by 21.8%% knew no risk factor. 22.7% knew no warning sign of stroke; 33% knew > = 1 Education, age, occupation associated with
structured questionnaires stroke. Identified risk factors: Hypertension most identified, warning sign. knowledge of stroke risk factors.
adopted from previous studies 34.5%; Stress, 7.6%; diet, 4.7%; diabetes, 0.3%; cardiac Identified warning signs: Paralysis and hemiplegia,
problems, 0.3%; obesity, 1%. 34.4%, Weakness, walking in speaking and seeing
12.8%; Headache and dizziness, 11.8%.
(Continued)
Knowledge and awareness of and perception towards cardiovascular disease risk in sub-Saharan Africa
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Table 2. (Continued)
Study CVDs Assessment of knowledge General knowledge/ awareness of CVDs Knowledge of risk factors Knowledge of warning signs/symptoms Factors related
studied
Kaddumukasa et al Stroke Modified standardized 59.4% did not know brain as site affected by 42.4% knew no risk factor. 57% knew no warning sign. Residence associated with knowledge of
[32] questionnaire already used in stroke. Identified risk factors: Stress (most identified), 43%; Identified warning signs: Paralysis (most identified), stroke (p = 0.038)
SSA settings hypertension, 28.9%; (Age, diabetes, fats, diet, lack of 18%; Body weakness, 12%; numbness 10%.
exercise), <10%. / None identified smoking
Nakibuuka et al[24] Stroke Structured questionnaires, 76.2% urban, 78.9% rural did not know organ 73% knew no stroke risk factor. 75.1% knew no stroke symptom; 40.3% only 1 Tertiary education associated with good
modified from previous studies affected by stroke. / Some believe stroke affect Identified risk factors: hypertension, 56%; stress, symptom; Only 3% knew  5 symptoms. knowledge of; -risk factors, (OR 5.96; 95%
the heart, liver and kidneys. / 39.5% knew 51.4%; bad diet, 29.6%; lack of exercise 25.7%; Identified symptoms: paralysis on one side, 28.6%; CI 2.94–12.06). Warning symptoms (OR
stroke is preventable. diabetes, 14.9%; old age, 12.6%; High cholesterol, weakness on one side, 26.1%; dizziness, 23.6%; 4.29; 95% CI 2.13–8.62). Urban residence
12.2%; obesity, alcohol <10%; smoking, 0.7%. / Some paralysis on any part, 17.4%; tiredness, 16.4%; increased knowledge or CVD.
cited demons or witchcraft, 0.9%; God’s will, 6%. headache 16.2%; shortness of breath, fever/sweating,
9.7%; weakness on any part of body, 7.5%, blackout,
6.5%; blurred vision, 2.7%; speaking difficulty, 2.2%.
Temu et al [41] CVD; CHD Questionnaire constructed Mean knowledge score 1.3/10. Mean knowledge score 0.28/7. 77.3% didn’t know heart Education! (OR 5.21, 95% CI 0.99–27.37)
from multiple validated Identified risk factors: Stress, 74%; obesity, 9.3%; attack. / <3% could identify chest pain, excessive
surveys. / Knowledge raised BP, 9%; excessive alcohol, 7.6%; smoking, 4%; sweating, nausea vomiting, pain in teeth, jaw or arm as
measured on a continuous age, 2.3; family history 1.3%. symptoms.
scale and scored
Yuqiu & Wright [34] CVDs Author designed questionnaire Generally low knowledge of CVDs. Identified risk factors: Stress, 53.5%; physical
inactivity obesity, alcohol, <30% in males and females)
and diabetes (<10%).
Surka et al [27] CVD, heart Thematic discussions on Majority familiar with terminologies for CVDs; Cited risk factors for CVD: Tobacco smoking,
attack, Knowledge of CVD and its Limited insight into the conditions. excessive alcohol consumption, stress, unhealthy diets.
stroke, MI prevention, perception of risk
Awah et al[39] CVD FGD and IDI guides Perceived risk factors of CVDs: Diet, obesity,
smoking, alcohol, sedentary lifestyle.
BP = Blood pressure; MI = Myocardial infarction
https://doi.org/10.1371/journal.pone.0189264.t002
Knowledge and awareness of and perception towards cardiovascular disease risk in sub-Saharan Africa
Knowledge of risk factors for cardiovascular diseases
To gauge knowledge of risk factors for CVDs, individuals were required to correctly identify
them from a list. Just like it was the case with CVD risk, majority of the studies also reported
low levels of knowledge on risk factors for CVDs. Hypertension and stress were the most
known and cited risk factors in most of the studies. Participants who were unable to identify a
single risk factor for CVDs ranged from as low as 1.8% in a study among hospital staff in Nige-
ria[29] to a high of 73.0% in a population-based survey in Uganda.[24] Specifically, among
studies that looked at stroke, participants who could not identify a single risk factor was <20%
among hospital workers,[28,30]university staff[29] and secondary school teachers[31] in
Fig 2. Summary of results. RF, Risk factors; HT, Hypertension; DM, Diabetes Mellitus; PA, Physical activity; FH, Family History; CVD,
Cardiovascular disease; MI, Myocardial infarction.
https://doi.org/10.1371/journal.pone.0189264.g002
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Knowledge and awareness of and perception towards cardiovascular disease risk in sub-Saharan Africa
Nigeria and 40–80% among rural and urban Ugandans[24,32] and rural Nigerians.[33] A
study that looked into coronary heart disease among teachers and bankers in Nigeria also
described about 20% of the study population as having no knowledge of risk factors for the dis-
ease.[25] The studies also reported some misconceptions regarding the risk factors for CVDs
to include evil spirits, demons and will of God as causes of CVDs.[24,28]
Hypertension
Knowledge levels of hypertension as a risk factor for CVD ranged from as low as 16.2% in a
study among rural community members in Nigeria[33] to 95.7% in a study among health
workers in Nigeria.[28] In a low-income peri-urban community in South Africa, none of the
respondents cited hypertension as a risk factor of CVD.[27] Low knowledge levels of hyperten-
sion as risk factor for CVDs, ranging from 16.2% to 34.5% were reported among studies con-
ducted within urban and rural communities[32–36] whereas high percentages were reported
in studies conducted among health workers (95.7%);[30] (88.6%),[28] secondary school teach-
ers (79.8%)[31] and staff of tertiary institution (91.7%).[29]
Diabetes
The knowledge level of diabetes as a risk factor of CVD ranged from 0.3% in a study among
urban adult population in Benin[36] to 47.4% among secondary school teachers in Nigeria.
[31] Two community-based studies from Ghana[35] and Uganda[24] reported less than 15%
of study participants possessing any knowledge of diabetes as a risk factor for stroke. Knowl-
edge of diabetes as a CVD risk factor among hypertension and diabetes patients at a specialist
medical centre in Southern Nigeria was very low, at 7.3%.[37]
Smoking
Knowledge of smoking as a CVD risk factor was 70.6% among military personnel in Nigeria
[26] and less than one percent among the general populations in Central Uganda.[24] Less
than 50% of respondents across all studies could identify smoking as a risk factor for CVD,
with the exception of the study among Armed Forces personnel in Nigeria, 70.6%.[26] In a
study in rural Uganda, none of the respondents identified smoking as a risk factor for CVD.
[32] In all, 14 studies reported on knowledge of smoking as CVD risk factor, three of which
reported<5% with knowledge of smoking as a risk factor for stroke26,42 and for CHD.[38]
Physical inactivity
Knowledge of physical inactivity or sedentary lifestyle as risk factors for CVD ranged from
0.6%[37] to 57%,[31] in Nigeria. Two other studies reported knowledge level of less than 10%;
1.2% in a rural Nigerian community[33] and 3.8% among hospital outpatients.[37]
Heavy alcohol consumption
Heavy alcohol consumption as a risk factor for CVD was reported by 4.5% in a study among
patients with hypertension and/or diabetes at specialist medical outpatient clinics in Nigeria
[37] to as high as 52.8% among staff at a University Hospital and same proportion among mili-
tary personnel in Nigeria.[26] Another study among secondary school teachers[31] and Hospi-
tal staff in Nigeria[28] reported 52.6% and 43.4% of knowledge of alcohol as risk factor,
respectively. Participants enrolled in qualitative studies conducted in South Africa and Camer-
oon[27,39] also mentioned heavy alcohol consumption as risk factor for CVD. Respondents in
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Knowledge and awareness of and perception towards cardiovascular disease risk in sub-Saharan Africa
a study among outpatients in Nigeria were, however, not able to identify heavy alcohol use as a
risk factor for CVD.[40]
Stress
Stress was reported as a risk factor for CVD by 7.6% of adults in an urban district of Benin[36]
to 87.5% of members of Nigerian Armed forces.[26] Other studies conducted among formally
employed workers reported high knowledge level; health workers 70.8%,[28] among university
staff 80.2%[29] and secondary school teachers 65.8%.[31] Among community level studies,
knowledge level of 53.5% as among a South African community[34] whereas studies among
urban communities in Ghana[35] and Benin[36] reported low knowledge of stress (22% and
7.6%) respectively.
Other risk factors
Other risk factors for CVD were ageing, family history, obesity and unhealthy diet. Knowledge
of these risk factors was low across studies reviewed and was least cited or known among study
subjects. Ageing was identified as a risk factor for CVD by 63.8%, 43.9% and 38% among uni-
versity staff,[29] secondary school teachers[31] and hospital staff[28] respectively in Nigeria.
Among the studies that reported on family history, knowledge level was>50% in two of the
studies that were conducted among formal working populations in Nigeria,[29,31] 3.2%
among medical outpatients in Nigeria[40] and as low as 1.3% in the study conducted among
people living with HIV/AIDS in Cameroon.[38] Of nine studies, five that were conducted
among people living with HIV,[38] hypertension and/ or diabetes outpatients,[37] rural popu-
lation,[33] urban population[36] and the general population,[24] <10% identified obesity as a
CVD risk factor. The biggest proportion with knowledge of obesity as a CVD risk factor,
56.1% was reported among staff of a University in Nigeria.[29] Knowledge on diet as risk fac-
tor for CVD was 99.1% among secondary school teachers[31] and<10% among Armed Forces
personnel[26] in Nigeria and the general household population in Uganda.[32] Unhealthy diet
was also reported as a risk factor in two studies.[27,39]
Knowledge of symptoms/ clinical signs of cardiovascular disease
The proportion of respondents who could not identify a single symptom of any CVD condition
ranged from 7.0% among academic staff in a University in Nigeria[29] to 75.1% among the gen-
eral population in Uganda.[24] The proportion of respondents who could identify all symptoms
ranged from 3.5% among teachers[31] to 15.2% among health staff[29] in Nigeria. Knowledge of
chest pain, excessive sweating, nausea, vomiting, and pain, as symptoms of CVDs were also very
low (<3%).[38,40] Knowledge of symptoms of stroke was<50% in all the studies that reported
on stroke with the exception of three, which reported>50% knowledge level of one (paralysis,
55.6%) among medical outpatients,[40] two (weakness, 52.2%; slurring speech, 61.9%) among
hospital staff31 and four symptoms (slurring speech, 58.7%; dizziness, 52.8%; numbness, 69.4%
and weakness, 69.8%) among university teachers.32 The most reported symptoms of stroke were
weakness, 61.9%[28] and 69.8%,[29] slurring speech, 59%[29] and paralysis on one side, 55.6%.
[40] Dizziness, loss of vision, chest pain and altered consciousness, headache, vision problem,
shortness of breath and numbness were least reported across the studies.
Perception of cardiovascular disease risk
Four studies investigated the perception of CVDs.[24,27,40,41] Among people living with
HIV/AIDS, 31% believed they were at high risk of developing CVDs, while older women were
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Knowledge and awareness of and perception towards cardiovascular disease risk in sub-Saharan Africa
more likely to agree that they were at a higher risk for CVDs.[41] In a qualitative study from
South Africa,[27] participants were described as being generally unfamiliar with the concept of
risk, while the two respondents who were familiar with the concept of risk could also not
explain in detail what it actually meant. In a study of medical out-patients in a tertiary health
institution in Nigeria,[40] majority (65.8%) of the respondents were never concerned about
the possibility of developing stroke, 16.1% sometimes thought of it, 12.3% occasionally and
5.8% always had the concern. 34.1% of respondents in a population-based study from Uganda
[24] perceived no chance while 14.4% perceived high chance of possible stroke in lifetime.
Factors influencing knowledge of cardiovascular diseases and risk
factors among reported studies
Factors such as age and family history, type of residence and education were reported to be
associated with knowledge of CVDs. The significant influence of age on knowledge of CVD
was reported by three studies.[29,36,37] In two studies from Nigeria conducted among hospi-
tal outpatients[37] and university staff,[29] age<55 and<40 were a significant predictor of
knowledge of CVDs. There was a significant relationship between educational attainment and
knowledge of CVDs.[17,29,33,36,37,40] As reported in a study from rural South-Western
Nigeria,[33] people with tertiary education were three times more likely to be knowledgeable
of CVD risk factors and a study among hospital outpatients in Nigeria[37] showed that more
than 12 years of education increased the odds of being knowledgeable about CVD risk factors
by more than twice. A significant association between type of residence and knowledge of
CVD was also described: urban residents were more knowledgeable about CVDs compared to
their rural counterparts in a community study in Uganda[24] and a study among diabetic/
hypertensive outpatients.[37] No study reported a relationship between gender with knowl-
edge of CVDs.[33,42]
Sources of information on cardiovascular diseases
The sources of information for CVD and risk factors included electronic media like television,
[25,26,31,36,41] radio,[26,31,41] and print media in the form of magazines or newspapers,
[25,31,41] health care professionals[25,26,31,33,36,40,41] and family members or relatives.
[25,31,33,36] The internet was reported as a source of information among secondary school
teachers[31] and among people living with HIV/AIDS.[41] Television was the most cited
source of CVD information across the studies that reported on it, with a proportion of 31.7%
in a study of Nigerian Armed forces[26] to 75.5% in a study among University staff.[29]
Healthcare professionals as source of CVD information ranged from 4%[41] to 64.4%[17] in
people living with HIV and university staff respectively. In the study among hospital workers
in Nigeria,[28] 66.9% and 23.2% of clinical and non-clinical staff had read on CVDs from
other sources. Details of the sources of information reported across the studies are presented
in Table 3.
Discussion
This review identified low levels of knowledge and awareness of CVDs and associated risk fac-
tors and clinical signs or symptoms for CVDs among populations in SSA. The knowledge gap
is also apparent in the low perception regarding the risk of developing and dying from CVDs
in the region.[24,40] In population-based studies conducted in Uganda[24] and Benin,[36]
respondents were unable to identify the organ affected by stroke, despite it being a condition
with poor survival outcomes in this region.[43–45] Knowledge of clinical symptoms was as
low as 3.5% among teachers in Nigeria,[31] while as few as 16.2% in a rural Nigerian
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Knowledge and awareness of and perception towards cardiovascular disease risk in sub-Saharan Africa
Table 3. Sources of information about CVDs.
Source of Temu Mohammed Awosan et al[25] Oladapo Akinyemi et al[28] Komolafe Cossi Ansa, Oyo-Ita
information et al[41] [26] et al[33] et al[31] et al[36] and Essien[17]
Teachers Bankers Clinical Non-
clinical
Television 51 31.7 53.8 43.8 75.4 13.9§
Radio 44 12.2 56.1
Magazine/ 19 21.3 27.5 59.4
newspaper
Internet 4 40.4
Healthcare 4 22.9 7.5 13.8 9.1 45.0 11.8 64.4
professional
Media* 24.6 37.3 27.7 28.8
Family 59.9 30.3 20.5 27.3 25.1
Friend 44.4 33.3
School education 68.3 10.3 38.7 9.5
Seen someone with 81.0 82.0 16.6
the condition
Health campaigns 33.8
Read from other 66.9 23.2% 20.4 36.2
sources
*Radio, public enlightenment programmes, and newspapers;
§Include radio and Internet
https://doi.org/10.1371/journal.pone.0189264.t003
community[33] knew that of hypertension, 0.3% for diabetes and 1% for obesity and 7.6% for
stress in Urban Beninese population,[36] as risk factors or developing CVD.
A systematic review of awareness of hypertension in West Africa reported overall low
knowledge of hypertension.[46] Studies that explored knowledge and perceptions of obesity
and sedentary lifestyles showed poor perceptions and subjective norms such as overweight
being socially desirable, and a sign of beauty and riches thereby inducing unwillingness to lose
weight.[47,48] African belief systems are however not static–they are complex and dynamic,
tied as they are to shifting social identities. Other body of evidence suggests that contrary to
the often-cited fatness equals wealth, health and beauty theory, young African women view fat-
ness as a precursor for CVDs.[49] These women are interested in living a healthy life and are
willing to reduce their body size in order to reduce the risk of obesity-related diseases despite
the resistance to lose weight because of the cultural value on weight and the impact of the hus-
band’s preference.[50] These inherent perceptions and desire to lose weight should be impor-
tant considerations when designing educational interventions to improve knowledge of
CVDs.
Despite the rise in CVD risk factors in SSA populations, our findings indicate that the pop-
ulations generally did not recognize their potential relation to the development of CVDs. In
SSA, the incidence and prevalence of classical risk factors of CVDs such as smoking,[51]
hypertension,[52] obesity,[53–55] high cholesterol, fatty diets, alcohol consumption[56–58]
and lowered physical activity[59] are rising. This rise is linked to rapid urbanization, resulting
in an epidemiological and nutrition transition, where energy-dense diets replace traditional
diets and sedentary lifestyles prevail poverty.[10] As such, there is a shift in disease burden
from under-nutrition and highly active lifestyle to over-nutrition-related and sedentary life-
style related chronic diseases.
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Knowledge and awareness of and perception towards cardiovascular disease risk in sub-Saharan Africa
Knowledge of alcohol intake as a risk factor for CVD was low in the region. Four studies
[24,34,37,41] reported on this and found that <30% of study participants cited alcohol con-
sumption as a risk factor for CVDs; in a study among medical outpatients,[40] none identified
alcohol consumption as a risk factor for CVD. In most societies in SSA, use of alcohol has been
defined by cultural and religious parameters, with little acceptance of the potential health effect
of alcohol consumption on health.[60] This is of concern, considering the expansion of alco-
holic industries commercial activities in SSA to increase sales in this region.[61,62] Adequate
policies to address these challenges in SSA are however few whereas there are no developed
multi-sectorial approaches, that involves the private sector, civil society, informal sector, com-
munity leaders and traditional healers.[63] Further, in countries where there are preventive
interventions such as enactment of drinking and driving laws, taxation, restrictions on adver-
tising and community information, implementation is ad hoc, informal, fragmented and often
lacks adequate control and enforcement systems.[63]
The relationship between alcohol consumption and CVDs is nuanced. Light to moderate
drinking has been suggested to decrease the incidence of ischaemic stroke, whereas heavy
drinking has been implicated as an independent risk factor for ischaemic and haemorrhagic
stroke.[64–66] For hypertension, cardiac dysrhythmias and haemorrhagic stroke, alcohol is
considered to be an independent risk factor, regardless of the drinking pattern.[67] This
emphasizes the need for the development and enforcement of adequate and effective policy
measures, public awareness and surveillance mechanisms in the SSA region. Without aware-
ness of personal susceptibility and health consequences related to alcohol consumption, alco-
hol consumption behaviours are less likely to be modified to reduce risk of CVD.
Knowledge on stress as a risk factor of CVD was relatively high, especially among urban
populations, despite the complex relationship between stress and CVDs.[68] Susceptibility to
stress is influenced by type of personality, social support, coping strategies and genetic vulnera-
bility.[68] Stress could be positive, by forcing us to adopt and thus to increase the strength of
our adaptation mechanisms (eustress) or negative, when it exceeds our ability to cope, fatigues
body systems and causes behavioural or physical problems (stressors).[68,69] A strong associa-
tion has been observed between perceived stress and CHD[70–72] and current evidence shows
perceived stress to be an independent risk factor for stroke.[73] The belief and perception of
the influence of stress on CVDs in SSA populations could however be related to experiences of
psychosocial stressors arising out of urbanization and poverty.[74,75] Experiences of chronic
poverty-related stressors, such as inadequate housing, sanitation, water, overcrowding, envi-
ronmental conditions, low education and unemployment, are potent predictors of poor car-
diovascular health.[76–78] Strategies to deal with perceived psychosocial stress among these
populations, include smoking and alcohol consumption, which themselves are precursors of
poor cardiovascular health.[79,80]
This review shows knowledge of CVDs and their risk factors to be significantly related to
the type of population studied and place of residence, and the level of exposure to health infor-
mation about CVDs. Studies that formally tested the association between place of residence
and education on knowledge of CVDs, also reported a significant relationship.[24,32,37]
There is the possibility that the differences observed in the levels of knowledge among the
urban and the rural populations are driven by the fact that the urban, and mostly formally
employed/ working population is more likely to be educated and more exposed to the media
and other modern sources of health information, including the internet.[81,82] The rural pop-
ulation and uneducated on the other hand, are most likely to be poor, and less likely to be
exposed to print and electronic media which have been reported as major sources of informa-
tion on CVDs and risk factors. The rural populations in SSA have also been shown to utilize
health services less than their urban counterparts,[83,84] and rely on information from their
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Knowledge and awareness of and perception towards cardiovascular disease risk in sub-Saharan Africa
families.[33] Exploring the determinants of health in rural areas, such as the role of the family,
is therefore important if health promotion policies and strategies are to result in significant
improvements in health status.
Traditionally the major sources of information on CVD, respectively CVD risk factors have
been shown to include electronic and print media (television, radio, newspaper) and health
workers,[85,86]. Recent studies have quoted the internet as an important source of health
information, especially among urban populations, teachers and other formally employed indi-
viduals, clearly illustrating the influence of the internet in health care. This situation presents
an important consideration for public health policy and resource allocation for health promo-
tion strategies in these settings.
Strengths and limitations
This review presents evidence regarding the knowledge and awareness of CVDs in SSA. To the
best of our knowledge, this is the first systematic review of the knowledge and perceptions of
CVDs in SSA. Our results are based on a systematic search of five databases, integrating both
qualitative and quantitative evidence on the topic. The inclusion of qualitative studies in this
review meant that research findings on perceptions towards CVDs were incorporated and
contributed to our understanding of and explanation of the trends of knowledge of CVDs in
this study setting. As the criteria of measurement of knowledge of CVD (risk factors) was not
uniform across studies (different criteria were used for classifying knowledge into low,
medium or high resulting in heterogeneity across study findings), a meta- analysis could not
be conducted. As the study populations differ considerably within and between countries it is
difficult to disentangle to what extent educational level or cultural or country level determine
knowledge and awareness levels. Still, the qualitative synthesis of available evidence of knowl-
edge and perceptions of and perception towards CVD risk and risk factors presented in this
review should speak to the current situation as most studies were published.
Conclusions
Generally, inadequate knowledge of CVDs and the associated risk factors continues to be one
of the most important factors in determining health-seeking behaviours in SSA. Knowledge
levels of CVDs, risk factors and warning signs were mainly varied by type of populations and
influenced by the type of employment, education levels and place of residence. Formal workers
were more aware of and knowledgeable about CVD and the risk factors compared to studies
conducted within rural and urban households. What this means is that education must be tai-
lored for different groups. One-size fits all messaging is unlikely to work. Misconceptions
(damaging cultural beliefs such as witchcraft and spiritual causal theories) must be addressed
in ways that enhance biomedical understandings without stigmatizing cultural understand-
ings. Adequate attention and awareness creation on the adverse implications of CVD related
risk behaviours such as smoking, alcohol consumption and sedentary lifestyle on this popula-
tion cannot be overemphasized. Effective policy measures, public awareness and surveillance
mechanisms that takes into consideration the socio-cultural context of these behaviours need
to be developed and implemented in this region. Evidence provided in this study can guide
context specific interventions, aimed at mitigating CVDs by improving levels of knowledge
and awareness of the conditions and risk factors among SSA populations.
Supporting information
S1 Text. Search strategy for PubMed.
(DOCX)
PLOS ONE | https://doi.org/10.1371/journal.pone.0189264 December 12, 2017 15 / 21
Knowledge and awareness of and perception towards cardiovascular disease risk in sub-Saharan Africa
S1 File. PRISMA 2009 checklist.
(DOC)
S2 File. Results of quality assessment of the quantitative and qualitative studies.
(XLSX)
Acknowledgments
DB and FW are supported by the Global Health Support Programme, University Medical Cen-
tre Utrecht, Utrecht University, The Netherlands.
Author Contributions
Conceptualization: Daniel Boateng, Frederick Wekesah, Kerstin Klipstein-Grobusch.
Formal analysis: Daniel Boateng, Frederick Wekesah.
Investigation: Daniel Boateng, Frederick Wekesah, Joyce L. Browne, Kerstin Klipstein-
Grobusch.
Methodology: Daniel Boateng, Frederick Wekesah, Joyce L. Browne, Kerstin Klipstein-
Grobusch.
Supervision: Charles Agyemang, Kerstin Klipstein-Grobusch.
Writing – original draft: Daniel Boateng.
Writing – review & editing: Daniel Boateng, Frederick Wekesah, Joyce L. Browne, Charles
Agyemang, Peter Agyei-Baffour, Ama de-Graft Aikins, Henriette A. Smit, Diederick E.
Grobbee, Kerstin Klipstein-Grobusch.
References
1. WHO. Global status report on noncommunicable diseases 2014. World Health Organization. Geneva;
2014.
2. Mendis S, Puska P, Norrving B. Global atlas on cardiovascular disease prevention and control. World
Health Organization; 2011.
3. Mozaffarian D, Benjamin E, Go A, Arnet D, Blaha M, Cushman M, et al. Heart disease and stroke statis-
tics—2015 update: a report from the American Heart Association. Am Hear Assoc. 2015;(1):7–10.
4. Mathers CD, Loncar D. Projections of global mortality and burden of disease from 2002 to 2030. PLoS
Med. 2006; 3(11):e442. https://doi.org/10.1371/journal.pmed.0030442 PMID: 17132052
5. WHO. Cardiovascular diseases (CVDs): Fact sheet [Internet]. 2016 [cited 2016 Jul 27]. Available from:
http://www.who.int/mediacentre/factsheets/fs317/en/
6. World Health Statistics. Causes of death 2008: data sources and methods. World Health [Internet].
2011; 2010(September 2010):1–28. Available from: http://www.who.int/healthinfo/global_burden_
disease/cod_2008_sources_methods.pdf
7. O’Flaherty M, Buchan I, Capewell S. Contributions of treatment and lifestyle to declining CVD mortality:
Why have CVD mortality rates declined so much since the 1960s? Heart. 2012;159–62. https://doi.org/
10.1136/heartjnl-2012-302300 PMID: 22962283
8. Agyemang C. Cardiovascular diseases in poor resource settings A neglected disease. Glob Med [Inter-
net]. 2005;10. Available from: http://globalmedicine.nl/issues/gm10/article6.pdf
9. WHO. The world health report 2003: Shaping the future. Geneva Wold Heal Organ [Internet]. 2003;204.
Available from: http://www.who.int/whr/2000/en/whr00_en.pdf
10. Popkin BM, Adair LS, Ng SW. Global nutrition transition and the pandemic of obesity in developing
countries. Nutr Rev [Internet]. 2012 Jan [cited 2017 May 29]; 70(1):3–21. Available from: http://www.
ncbi.nlm.nih.gov/pubmed/22221213 https://doi.org/10.1111/j.1753-4887.2011.00456.x PMID:
22221213
PLOS ONE | https://doi.org/10.1371/journal.pone.0189264 December 12, 2017 16 / 21
Knowledge and awareness of and perception towards cardiovascular disease risk in sub-Saharan Africa
11. Naik R, Kaneda T. Non-communicable Diseases in Africa: Youth are key to curbing the epidemic and
achieving sustainable development. 2015; 2014(April).
12. Adkins H. Getting the message across. Cater Hotelk [Internet]. 2006; 196(4447):13. Available from:
http://search.ebscohost.com/login.aspx?direct=true&db=buh&AN=23331140&site=ehost-live&scope=
site
13. Prochaska JO, DiClemente CC, Norcross JC. In search of how people change: Applications to addictive
behaviors. Am Psychol [Internet]. 1992; 47(9):1102–14. Available from: http://informahealthcare.com/
doi/abs/10.3109/10884609309149692 PMID: 1329589
14. Stretcher V, Rosenstock IM. The Health Belief Model. Heal Behav Heal Educ Theory, Res Pract.
1997;31–6.
15. Rosenstock IM, Strecher VJ, Becker MH. Social learning theory and the health belief model. Health
Educ Q. 1988; 15(2):175–83. PMID: 3378902
16. Busari OA, Olusegun T, Olufemi O, Desalu O, Opadijo OG, Jimoh AK, et al. Impact of Patients ‘ Knowl-
edge, Attitude and Practices on Hypertension on Compliance with Antihypertensive Drugs in a
Resource-poor Setting. Prev Med (Baltim) [Internet]. 2010; 9(2):87–92. Available from: http://www.
scopemed.org/fulltextpdf.php?mno=836
17. Ansa VO, Oyo-Ita A, Essien OE. Perception of ischaemic heart disease, knowledge of and attitude to
reduction of its risk factors. East Afr Med J. 2007; 84(7):318–23. PMID: 17886425
18. Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gotzsche PC, Ioannidis JPA et al. The PRISMA statement
for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions:
explanation and elaboration. In: Journal of clinical epidemiology. 2009. p. e1–34. https://doi.org/10.
1016/j.jclinepi.2009.06.006 PMID: 19631507
19. UN. World Population Prospects—The 2012 Revision. ESA/P/WP.235. 2014;44. Available from: http://
esa.un.org/wpp/Documentation/pdf/WPP2012_Methodology.pdf
20. National Institute of Health. Quality Assessment Tool for Observational Cohort and Cross-Sectional
Studies [Internet]. 2014 [cited 2016 Jul 25]. Available from: http://www.nhlbi.nih.gov/health-pro/
guidelines/in-develop/cardiovascular-risk-reduction/tools/cohort
21. Critical Appraisal Skills Programme (CASP). Critical Appraisal Skills Programme (CASP) Qualitative
Research Checklist [Internet]. 2013 [cited 2016 Jul 25]. Available from: http://media.wix.com/ugd/
dded87_29c5b002d99342f788c6ac670e49f274.pdf
22. Pedersen VH, Dagenais P, Lehoux P. Multi-source synthesis of data to inform health policy. Int J Tech-
nol Assess Health Care [Internet]. 2011; 27(3):238–46. Available from: http://www.ncbi.nlm.nih.gov/
pubmed/21736858 https://doi.org/10.1017/S0266462311000213 PMID: 21736858
23. Akintunde A, Akintunde T, Opadijo O. Knowledge of heart disease risk factors among workers in a Nige-
rian University: A call for concern. Niger Med J [Internet]. 2015; 56(2):89. Available from: http://www.
nigeriamedj.com/text.asp?2015/56/2/89/150688
24. Nakibuuka J, Sajatovic M, Katabira E, Ddumba E, Byakika-Tusiime J, Furlan AJ. Knowledge and Per-
ception of Stroke: A Population-Based Survey in Uganda. ISRN stroke [Internet]. 2014; 2014:1–7. Avail-
able from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4156791&tool=
pmcentrez&rendertype=abstract
25. Awosan KJ, Ibrahim MTO, Sabir AA, Ejimodu P. Awareness and prevalence of risk factors of coronary
heart disease among teachers and bankers in Sokoto, Nigeria. J Med Med Sci. 2013; 4(Septem-
ber):335–42.
26. Mohammed J. Knowledge of, and Attitude to Cardiovascular Disease Risk Factors Among Members Of
The Nigerian Armed Forces. World J Public Heal Sci [Internet]. 2012; 11(2):23–7. Available from: http://
www.rrpjournals.com/
27. Surka S, Steyn K, Everett-Murphy K, Gaziano TA, Levitt N. Knowledge and perceptions of risk for car-
diovascular disease: Findings of a qualitative investigation from a low-income peri-urban community in
the Western Cape, South Africa. African J Prim Heal care Fam Med. 2015; 7(1):E1–8.
28. Akinyemi RO, Ogah OS, Ogundipe RF, Oyesola OA, Oyadoke AA, Ogunlana MO, et al. Knowledge
and perception of stroke amongst hospital workers in an African community. Eur J Neurol. 2009 Sep; 16
(9):998–1003. https://doi.org/10.1111/j.1468-1331.2009.02666.x PMID: 19486134
29. Obembe AO, Olaogun MO, Bamikole AA, Komolafe MA, Odetunde MO. Awareness of risk factors and
warning signs of stroke in a Nigeria University. J Stroke Cerebrovasc Dis. 2014; 23(4):749–58. https://
doi.org/10.1016/j.jstrokecerebrovasdis.2013.06.036 PMID: 23910515
30. Akinyemi RO, Owolabi MO, Adebayo PB, Akinyemi JO, Otubogun FM, Uvere E, et al. Task-shifting
training improves stroke knowledge among Nigerian non-neurologist health workers. J Neurol Sci. 2015
Dec; 359(1–2):112–6. https://doi.org/10.1016/j.jns.2015.10.019 PMID: 26671098
PLOS ONE | https://doi.org/10.1371/journal.pone.0189264 December 12, 2017 17 / 21
Knowledge and awareness of and perception towards cardiovascular disease risk in sub-Saharan Africa
31. Komolafe MA, Obembe AO, Olaogun MO, Adebiyi AM, Ugalahi T, Dada O, et al. Awareness of stroke
risk factors and warning signs in nigerian adolescents compared with adults. J Stroke Cerebrovasc Dis
[Internet]. 2015 Mar; 24(3):687–93. Available from: http://dx.doi.org/10.1016/j.jstrokecerebrovasdis.
2014.11.013 PMID: 25601175
32. Kaddumukasa M, Kayima J, Kaddumukasa MN, Ddumba E, Mugenyi L, Pundik S, et al. Knowledge,
attitudes and perceptions of stroke: a cross-sectional survey in rural and urban Uganda. BMC Res
Notes. 2015; 8:819. https://doi.org/10.1186/s13104-015-1820-6 PMID: 26708348
33. Oladapo O., Salako L, Sadiq L, Soyinka K, F A.O. Knowledge of Hypertension and other Risk Factors
for Heart Disease among Yoruba Rural Southwestern Nigerian Population. Br J Med Med Res [Inter-
net]. 2013; 3(4):983–1003. Available from: http://sciencedomain.org/abstract/1100
34. Yuqiu L, Wright SCD. Knowledge and awareness of risk factors for cardiovascular disease in the ga-
rankuwa community. Heal SA Gesondheid. 2008; 13(3):3–4.
35. Donkor ES, Owolabi MO, Bampoh P, Aspelund T, Gudnason V. Community Awareness of Stroke in
Accra, Ghana. BMC Public Health. 2014; 14:196. https://doi.org/10.1186/1471-2458-14-196 PMID:
24559414
36. Cossi M-JJ, Preux P-MM, Chabriat H, Gobron C, Houinato D. Knowledge of stroke among an urban
population in Cotonou (Benin). Neuroepidemiology. 2012; 38(3):172–8. https://doi.org/10.1159/
000336862 PMID: 22472485
37. Wahab KW, Kayode OO, Musa OI. Knowledge of stroke risk factors among Nigerians at high risk. J
Stroke Cerebrovasc Dis [Internet]. 2015; 24(1):125–9. Available from: http://dx.doi.org/10.1016/j.
jstrokecerebrovasdis.2014.07.053 PMID: 25440355
38. Temu TM, Kirui N, Wanjalla C, Ndungu AM, Kamano JH, Inui TS, Bloomfield GS. Cardiovascular health
knowledge and preventive practices in people living with HIV in Kenya. BMC Infect Dis [Internet]. 2015;
15(1). Available from: http://www.embase.com/search/results?subaction=viewrecord&from=
export&id=L606397595
39. Awah PK, Kengne AP, Fezeu LLK, Mbanya J-C. Perceived risk factors of cardiovascular diseases and
diabetes in Cameroon. Health Educ Res. 2008 Aug; 23(4):612–20. https://doi.org/10.1093/her/cym037
PMID: 17897932
40. Ajayi AO, Ojo OO. Knowledge and perception of stroke among at risk medical out-patients in a tertiary
health institution in Nigeria. Ann Afr Med. 2007 Jun; 6(2):51–3. PMID: 18240702
41. Temu TM, Kirui N, Wanjalla C, Ndungu AM, Kamano JH, Inui TS, et al. Cardiovascular health knowl-
edge and preventive practices in people living with HIV in Kenya. BMC Infect Dis [Internet]. 2015; 15
(1):421. Available from: http://www.scopus.com/inward/record.url?eid=2-s2.0-
84944383084&partnerID=tZOtx3y1
42. Ucheana D, Ambakederemo T, Jesuorobo D. Awareness of Heart Disease Prevention Among Patients
Attending a Specialist Clinic in Southern Nigeria. 2012; 1(3):40–3.
43. Kolapo KO, Vento S. Stroke: a realistic approach to a growing problem in sub-Saharan Africa is urgently
needed. Trop Med Int Heal [Internet]. 2011 Jun [cited 2017 Jun 24]; 16(6):707–10. Available from:
http://www.ncbi.nlm.nih.gov/pubmed/21557793
44. 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 [Internet].
2015 [cited 2017 Jun 24]; 26(2 Suppl 1):S27–38. Available from: http://www.ncbi.nlm.nih.gov/pubmed/
25962945 https://doi.org/10.5830/CVJA-2015-038 PMID: 25962945
45. Nakibuuka J, Sajatovic M, Nankabirwa J, Ssendikadiwa C, Furlan AJ, Katabira E, et al. Early mortality
and functional outcome after acute stroke in Uganda: prospective study with 30 day follow-up. Springer-
plus [Internet]. 2015 [cited 2017 Jun 24]; 4:450. Available from: http://www.ncbi.nlm.nih.gov/pubmed/
26322256 https://doi.org/10.1186/s40064-015-1252-8 PMID: 26322256
46. Bosu WK. The prevalence, awareness, and control of hypertension among workers in West Africa: A
systematic review. Glob Health Action. 2015; 8(1).
47. Appiah CA, Steiner-Asiedu M, Otoo GE. Predictors of Overweight/Obesity in Urban Ghanaian Women.
Int J Clin Nutr [Internet]. 2014; 2(3):60–8. Available from: http://pubs.sciepub.com/ijcn/2/3/3/index.html
48. Okop KJ, Mukumbang FC, Mathole T, Levitt N, Puoane T. Perceptions of body size, obesity threat and
the willingness to lose weight among black South African adults: a qualitative study. BMC Public Health
[Internet]. 2016; 16(1):365. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=
4850665&tool=pmcentrez&rendertype=abstract
49. Puoane T, Tsolekile L, Steyn N. Perceptions about body image and sizes among Black African girls liv-
ing in Cape Town. Ethn Dis [Internet]. 2010 [cited 2017 May 29]; 20(1):29–34. Available from: http://
www.ncbi.nlm.nih.gov/pubmed/20178179 PMID: 20178179
PLOS ONE | https://doi.org/10.1371/journal.pone.0189264 December 12, 2017 18 / 21
Knowledge and awareness of and perception towards cardiovascular disease risk in sub-Saharan Africa
50. Duda RB, Jumah NA, Hill AG, Seffah J, Biritwum R. Interest in healthy living outweighs presumed cul-
tural norms for obesity for Ghanaian women. Health Qual Life Outcomes [Internet]. 2006 Jul 20 [cited
2017 May 29]; 4:44. Available from: http://www.ncbi.nlm.nih.gov/pubmed/16857048 https://doi.org/10.
1186/1477-7525-4-44 PMID: 16857048
51. World Health Organization Regional office for Africa. Cardiovascular diseases in the African Region:
Current Situation and Perspectives. Rep Reg Dir Fifty-fifth Sess [Internet]. 2005; Available from: http://
scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:CARDIOVASCULAR+DISEASES+IN
+THE+AFRICAN+REGION:+CURRENT+SITUATION+AND+PERSPECTIVES#3
52. Addo J, Agyemang C, Smeeth L, de-Graft Aikins A, Edusei AK, Ogedegbe O. A review of population-
based studies on hypertension in Ghana. Ghana Med J [Internet]. 2012; 46(June):4–11. Available from:
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3645150/pdf/GMJ462S-0004.pdf
53. Campbell T, Campbell A. Emerging disease burdens and the poor in cities of the developing world. J
Urban Heal. 2007; 84(SUPPL. 1).
54. Agyemang C, Owusu-Dabo E, de Jonge A, Martins D, Ogedegbe G, Stronks K. Overweight and obesity
among Ghanaian residents in The Netherlands: how do they weigh against their urban and rural coun-
terparts in Ghana? Public Health Nutr. 2009; 12(7):909–16. https://doi.org/10.1017/
S1368980008003510 PMID: 18761759
55. Ofori-Asenso R, Agyeman AA, Laar A, Boateng D. Overweight and obesity epidemic in Ghana—a sys-
tematic review and meta-analysis. BMC Public Health [Internet]. 2016 Dec; 16(1):1239. Available from:
http://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-016-3901-4 https://doi.org/10.
1186/s12889-016-3901-4 PMID: 27938360
56. African Business. Alcohol consumption rising in Africa despite obstacles—African Business Magazine
[Internet]. 2017 [cited 2017 Oct 24]. Available from: http://africanbusinessmagazine.com/sectors/retail/
alcohol-consumption-rising-in-africa-despite-obstacles/
57. BeLue R, Okoror T, Iwelunmor J, Taylor K, Degboe A, Agyemang C, et al. An overview of cardiovascu-
lar risk factor burden in sub-Saharan African countries: a socio-cultural perspective. Global Health
[Internet]. 2009; 5(1):10. Available from: http://www.globalizationandhealth.com/content/5/1/10
58. World Health Organization. Global status report on alcohol and health [Internet]. World Health Organi-
zation. Geneva; 2011 [cited 2017 Oct 24]. Available from: http://www.who.int/substance_abuse/
publications/global_alcohol_report/msbgsruprofiles.pdf
59. Ofori-Asenso R, Garcia D. Cardiovascular diseases in Ghana within the context of globalization. Cardio-
vasc Diagn Ther [Internet]. 2016; 6(1):67–77. Available from: http://www.pubmedcentral.nih.gov/
articlerender.fcgi?artid=4731582&tool=pmcentrez&rendertype=abstract https://doi.org/10.3978/j.issn.
2223-3652.2015.09.02 PMID: 26885494
60. Akyeampong E. Alcoholism in Ghana?A socio-cultural exploration. Cult Med Psychiatry [Internet]. 1995
Jun [cited 2017 Nov 16]; 19(2):261–80. Available from: http://link.springer.com/10.1007/BF01379414
PMID: 7497735
61. Ferreira-Borges C, Rehm J, Dias S, Babor T, Parry CDH. The impact of alcohol consumption on African
people in 2012: an analysis of burden of disease. Trop Med Int Heal [Internet]. 2016 Jan [cited 2017 Oct
24]; 21(1):52–60. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26448195
62. Kickbusch I, Allen L, Franz C. The commercial determinants of health. Lancet Glob Heal [Internet]. 2016
Dec 1 [cited 2017 Nov 3]; 4(12):e895–6. Available from: http://www.ncbi.nlm.nih.gov/pubmed/27855860
63. Ferreira-borges C, Ketsela T, Munodawafa D, Alisalad A. Reduction of the harmful use of alcohol: a
strategy for the WHO African Region [Internet]. African Health Monitor. Geneva; 2013. Available from:
https://www.aho.afro.who.int/en/ahm/issue/16
64. World Health Organisation. Global status report on alcohol and health 2014. Glob status Rep alcohol
[Internet]. 2014;1–392. Available from: http://www.who.int/substance_abuse/publications/global_
alcohol_report/msbgsruprofiles.pdf
65. Kalla A, Figueredo VM. Alcohol and cardiovascular disease in the geriatric population. [cited 2017 Oct
24]; Available from: http://onlinelibrary.wiley.com/store/10.1002/clc.22681/asset/clc22681.pdf?v=1&t=
j95z12ss&s=458889761aebf023479f78f4ced887b19746288f
66. Palomäki H, Kaste M. Regular light-to-moderate intake of alcohol and the risk of ischemic stroke. Is
there a beneficial effect? Stroke [Internet]. 1993 Dec [cited 2017 Oct 24]; 24(12):1828–32. Available
from: http://www.ncbi.nlm.nih.gov/pubmed/8248963 PMID: 8248963
67. Rehm J, Baliunas D, Borges GLG, Graham K, Irving H, Kehoe T, et al. The relation between different
dimensions of alcohol consumption and burden of disease: an overview. Addiction [Internet]. 2010 May
[cited 2017 Oct 24]; 105(5):817–43. Available from: http://www.ncbi.nlm.nih.gov/pubmed/20331573
https://doi.org/10.1111/j.1360-0443.2010.02899.x PMID: 20331573
68. Salleh MR. Life event, stress and illness. Malays J Med Sci [Internet]. 2008 Oct [cited 2017 Oct 24]; 15
(4):9–18. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22589633 PMID: 22589633
PLOS ONE | https://doi.org/10.1371/journal.pone.0189264 December 12, 2017 19 / 21
Knowledge and awareness of and perception towards cardiovascular disease risk in sub-Saharan Africa
69. Selye H. The stress of life. McGraw-Hill; 1978. 515 p.
70. Rosengren A, Hawken S, Ôunpuu S, Sliwa K, Zubaid M, Almahmeed WA, et al. Association of psycho-
social risk factors with risk of acute myocardial infarction in 11 119 cases and 13 648 controls from 52
countries (the INTERHEART study): case-control study. Lancet [Internet]. 2004 Sep [cited 2017 Oct
25]; 364(9438):953–62. Available from: http://www.ncbi.nlm.nih.gov/pubmed/15364186 https://doi.org/
10.1016/S0140-6736(04)17019-0 PMID: 15364186
71. Stansfeld SA, Fuhrer R, Shipley MJ, Marmot MG. Psychological distress as a risk factor for coronary
heart disease in the Whitehall II Study. Int J Epidemiol [Internet]. 2002 Feb [cited 2017 Oct 25]; 31
(1):248–55. Available from: http://www.ncbi.nlm.nih.gov/pubmed/11914328 PMID: 11914328
72. Iso H, Date C, Yamamoto A, Toyoshima H, Tanabe N, Kikuchi S, et al. Perceived mental stress and
mortality from cardiovascular disease among Japanese men and women: the Japan Collaborative
Cohort Study for Evaluation of Cancer Risk Sponsored by Monbusho (JACC Study). Circulation [Inter-
net]. 2002 Sep 3 [cited 2017 Oct 25]; 106(10):1229–36. Available from: http://www.ncbi.nlm.nih.gov/
pubmed/12208798 PMID: 12208798
73. Booth J, Connelly L, Lawrence M, Chalmers C, Joice S, Becker C, et al. Evidence of perceived psycho-
social stress as a risk factor for stroke in adults: a meta-analysis. BMC Neurol [Internet]. 2015 Nov 12
[cited 2017 Oct 25]; 15:233. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26563170 https://doi.
org/10.1186/s12883-015-0456-4 PMID: 26563170
74. Olufemi O, Oluseyi M. The Urban Poor and Mobility Stress in Nigerian Cities. Environ Res J [Internet].
2007 [cited 2017 Oct 25]; 1(1):1–8. Available from: https://www.medwelljournals.com/abstract/?doi=erj.
2007.1.8
75. BeLue R, Schreiner AS, Taylor-Richardson K, Murray-Kolb LE, Beard JL. What Matters Most: An Investi-
gation of Predictors of Perceived Stress Among Young Mothers in Khayelitsha. Health Care Women Int
[Internet]. 2008 Jun 25 [cited 2017 Oct 25]; 29(6):638–48. Available from: http://www.tandfonline.com/
doi/abs/10.1080/07399330802089198 https://doi.org/10.1080/07399330802089198 PMID: 18569048
76. Baum A, Garofalo JP, Yali AM. Socioeconomic status and chronic stress. Does stress account for SES
effects on health? Ann N Y Acad Sci [Internet]. 1999 [cited 2017 Oct 24]; 896:131–44. Available from:
http://www.ncbi.nlm.nih.gov/pubmed/10681894 PMID: 10681894
77. Mfenyana K, Griffin M, Yogeswaran P, Modell B, Modell M, Chandia J, et al. Socio-economic inequali-
ties as a predictor of health in South Africa—the Yenza cross-sectional study. S Afr Med J [Internet].
2006 Apr [cited 2017 Oct 24]; 96(4):323–30. Available from: http://www.ncbi.nlm.nih.gov/pubmed/
16670806 PMID: 16670806
78. Gulis G, Mulumba JAA, Juma O, Kakosova B. Health status of people of slums in Nairobi, Kenya. Envi-
ron Res [Internet]. 2004 Oct [cited 2017 Oct 24]; 96(2):219–27. Available from: http://www.ncbi.nlm.nih.
gov/pubmed/15325882 https://doi.org/10.1016/j.envres.2004.01.016 PMID: 15325882
79. Stubbs B, Veronese N, Vancampfort D, Prina AM, Lin P-Y, Tseng P-T, et al. Perceived stress and
smoking across 41 countries: A global perspective across Europe, Africa, Asia and the Americas. Sci
Rep [Internet]. 2017 Dec 8 [cited 2017 Oct 25]; 7(1):7597. Available from: http://www.nature.com/
articles/s41598-017-07579-w https://doi.org/10.1038/s41598-017-07579-w PMID: 28790418
80. Watt MH, Eaton LA, Choi KW, Velloza J, Kalichman SC, Skinner D, et al. "It’s better for me to drink, at
least the stress is going away"; perspectives on alcohol use during pregnancy among South African
women attending drinking establishments. Soc Sci Med [Internet]. 2014 Sep [cited 2017 Oct 25];
116:119–25. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24997441 https://doi.org/10.1016/j.
socscimed.2014.06.048 PMID: 24997441
81. Altindag DT, Cannonier C, Mocan NH. The Impact of Education on Health Knowledge. NBER Work Pap
No 16422 [Internet]. 2010; Available from: http://www.nber.org/papers/w16422
82. Mehrotra R, Bajaj S, Kumar D, Singh KJ. Influence of education and occupation on knowledge about
diabetes control. Natl Med J India. 2000; 13(6):293–6. PMID: 11209483
83. Salinas JJ, Al Snih S, Markides K, Ray LA, Angel RJ. The rural-urban divide: health services utilization
among older Mexicans in Mexico. J Rural Health [Internet]. 2010 [cited 2017 Jun 23]; 26(4):333–41.
Available from: http://www.ncbi.nlm.nih.gov/pubmed/21029168 https://doi.org/10.1111/j.1748-0361.
2010.00297.x PMID: 21029168
84. Begashaw B, Tesfaye T. Healthcare Utilization among Urban and Rural Households in Esera District:
Comparative Cross-sectional Study. Am J Public Heal Res Vol 4, 2016, Pages 56–61 [Internet]. 2016
Feb 29 [cited 2017 Jun 23];4(2):56–61. Available from: http://pubs.sciepub.com/ajphr/4/2/3/
85. Finken LR, Coomes E, Bajaj RR, Sharieff W, Bagai A, Cheema AN. Has google replaced traditional
sources of cardiovascular disease and risk factor information? Can J Cardiol [Internet]. 2014 Oct
[cited 2017 Jun 2]; 30(10):S180. Available from: http://linkinghub.elsevier.com/retrieve/pii/
S0828282X14007600
PLOS ONE | https://doi.org/10.1371/journal.pone.0189264 December 12, 2017 20 / 21
Knowledge and awareness of and perception towards cardiovascular disease risk in sub-Saharan Africa
86. Redmond N, Baer HJ, Clark CR, Lipsitz S, Hicks LS. Sources of Health Information Related to Preven-
tive Health Behaviors in a National Study. Am J Prev Med [Internet]. 2010 Jun [cited 2017 Jun 2]; 38
(6):620–627.e2. Available from: http://www.ncbi.nlm.nih.gov/pubmed/20494238 https://doi.org/10.
1016/j.amepre.2010.03.001 PMID: 20494238
PLOS ONE | https://doi.org/10.1371/journal.pone.0189264 December 12, 2017 21 / 21