Dominant modifiable risk factors for stroke in Ghana and Nigeria (SIREN): a case-control study

Summary Background Sub-Saharan Africa has the highest incidence, prevalence, and fatality from stroke globally. Yet, only little information about context-specific risk factors for prioritising interventions to reduce the stroke burden in sub-Saharan Africa is available. We aimed to identify and characterise the effect of the top modifiable risk factors for stroke in sub-Saharan Africa. Methods The Stroke Investigative Research and Educational Network (SIREN) study is a multicentre, case-control study done at 15 sites in Nigeria and Ghana. Cases were adults (aged ≥18 years) with stroke confirmed by CT or MRI. Controls were age-matched and gender-matched stroke-free adults (aged ≥18 years) recruited from the communities in catchment areas of cases. Comprehensive assessment for vascular, lifestyle, and psychosocial factors was done using standard instruments. We used conditional logistic regression to estimate odds ratios (ORs) and population-attributable risks (PARs) with 95% CIs. Findings Between Aug 28, 2014, and June 15, 2017, we enrolled 2118 case-control pairs (1192 [56%] men) with mean ages of 59.0 years (SD 13.8) for cases and 57.8 years (13.7) for controls. 1430 (68%) had ischaemic stoke, 682 (32%) had haemorrhagic stroke, and six (<1%) had discrete ischaemic and haemorrhagic lesions. 98.2% (95% CI 97.2–99.0) of adjusted PAR of stroke was associated with 11 potentially modifiable risk factors with ORs and PARs in descending order of PAR of 19.36 (95% CI 12.11–30.93) and 90.8% (95% CI 87.9–93.7) for hypertension, 1.85 (1.44–2.38) and 35.8% (25.3–46.2) for dyslipidaemia, 1.59 (1.19–2.13) and 31.1% (13.3–48.9) for regular meat consumption, 1.48 (1.13–1.94) and 26.5% (12.9–40.2) for elevated waist-to-hip ratio, 2.58 (1.98–3.37) and 22.1% (17.8–26.4) for diabetes, 2.43 (1.81–3.26) and 18.2% (14.1–22.3) for low green leafy vegetable consumption, 1.89 (1.40–2.54) and 11.6% (6.6–16.7) for stress, 2.14 (1.34–3.43) and 5.3% (3.3–7.3) for added salt at the table, 1.65 (1.09–2.49) and 4.3% (0.6–7.9) for cardiac disease, 2.13 (1.12–4.05) and 2.4% (0.7–4.1) for physical inactivity, and 4.42 (1.75–11.16) and 2.3% (1.5–3.1) for current cigarette smoking. Ten of these factors were associated with ischaemic stroke and six with haemorrhagic stroke occurrence. Interpretation Implementation of interventions targeting these leading risk factors at the population level should substantially curtail the burden of stroke among Africans. Funding National Institutes of Health.

[1]  M. Owolabi Taming the burgeoning stroke epidemic in Africa: stroke quadrangle to the rescue. , 2011, The West Indian medical journal.

[2]  J. Mckenney,et al.  Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). , 2001, JAMA.

[3]  R. Walker,et al.  Stroke risk factors in an incident population in urban and rural Tanzania: a prospective, community-based, case-control study , 2013, The Lancet. Global health.

[4]  D. Marchioni,et al.  A Quantile Regression Approach Can Reveal the Effect of Fruit and Vegetable Consumption on Plasma Homocysteine Levels , 2014, PloS one.

[5]  C. Anderson,et al.  Acute post-stroke blood pressure relative to premorbid levels in intracerebral haemorrhage versus major ischaemic stroke: a population-based study , 2014, The Lancet Neurology.

[6]  P. Zimmet,et al.  Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus. Provisional report of a WHO Consultation , 1998, Diabetic medicine : a journal of the British Diabetic Association.

[7]  Mark D. Huffman,et al.  AHA Statistical Update Heart Disease and Stroke Statistics — 2012 Update A Report From the American Heart Association WRITING GROUP MEMBERS , 2010 .

[8]  Olaf Gefeller,et al.  Epidemiology of Ischemic Stroke Subtypes According to TOAST Criteria: Incidence, Recurrence, and Long-Term Survival in Ischemic Stroke Subtypes: A Population-Based Study , 2001, Stroke.

[9]  J. Bamford,et al.  Classification and natural history of clinically identifiable subtypes of cerebral infarction , 1991, The Lancet.

[10]  Lei Pan,et al.  Red Meat Consumption and the Risk of Stroke: A Dose-Response Meta-analysis of Prospective Cohort Studies. , 2016, Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association.

[11]  Gregory S Kabadi,et al.  Stroke incidence in rural and urban Tanzania: a prospective, community-based study , 2010, The Lancet Neurology.

[12]  M. Kaste,et al.  SMASH-U: A Proposal for Etiologic Classification of Intracerebral Hemorrhage , 2012, Stroke.

[13]  Gregory S Kabadi,et al.  Post-stroke case fatality within an incident population in rural Tanzania , 2011, Journal of Neurology, Neurosurgery & Psychiatry.

[14]  O. Uthman,et al.  Stroke survivors in Nigeria: A door-to-door prevalence survey from the Niger Delta region , 2017, Journal of the Neurological Sciences.

[15]  R. Akinyemi,et al.  Multilingual Validation of the Questionnaire for Verifying Stroke-Free Status in West Africa , 2016, Stroke.

[16]  P. Schnohr,et al.  Self-Reported Stress and Risk of Endometrial Cancer: A Prospective Cohort Study , 2007, Psychosomatic medicine.

[17]  Ivy Shiue,et al.  Global burden of stroke and risk factors in 188 countries, during 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013 , 2016, The Lancet Neurology.

[18]  Alicja Wolk,et al.  Total and specific fruit and vegetable consumption and risk of stroke: a prospective study. , 2013, Atherosclerosis.

[19]  T. Truelsen,et al.  Self-Reported Stress and Risk of Stroke: The Copenhagen City Heart Study , 2003, Stroke.

[20]  S. Jee,et al.  Comparison of blood pressure-associated risk of intracerebral hemorrhage and subarachnoid hemorrhage: Korea Medical Insurance Corporation study. , 2005, Hypertension.

[21]  Mulugeta Gebregziabher,et al.  Stroke in Indigenous Africans, African Americans, and European Americans: Interplay of Racial and Geographic Factors , 2017, Stroke.

[22]  B. Ovbiagele,et al.  Recent patterns and predictors of neurological mortality among hospitalized patients in Central Ghana , 2016, Journal of the Neurological Sciences.

[23]  Sudha Seshadri,et al.  Lifetime risk of stroke and dementia: current concepts, and estimates from the Framingham Study , 2007, The Lancet Neurology.

[24]  Sherri Rose,et al.  The International Journal of Biostatistics Why Match ? Investigating Matched Case-Control Study Designs with Causal Effect Estimation , 2011 .

[25]  J Llorca,et al.  A comparison of several procedures to estimate the confidence interval for attributable risk in case-control studies. , 2000, Statistics in medicine.

[26]  J. Mckenney,et al.  National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) , 2002 .

[27]  B. Nordestgaard,et al.  The Copenhagen City Heart Study , 2003 .

[28]  D. Xavier,et al.  Rationale and Design of INTERSTROKE: A Global Case-Control Study of Risk Factors for Stroke , 2010, Neuroepidemiology.

[29]  S. Yusuf,et al.  Global and regional effects of potentially modifiable risk factors associated with acute stroke in 32 countries (INTERSTROKE): a case-control study , 2016, The Lancet.

[30]  Hemant K. Tiwari,et al.  Phenotyping Stroke in Sub-Saharan Africa: Stroke Investigative Research and Education Network (SIREN) Phenomics Protocol , 2015, Neuroepidemiology.

[31]  Hollis G. Potter,et al.  Author Manuscript , 2013 .

[32]  Mulugeta Gebregziabher,et al.  The burden of stroke in Africa: a glance at the present and a glimpse into the future , 2015, Cardiovascular journal of Africa.

[33]  M. Hennerici,et al.  New Approach to Stroke Subtyping: The A-S-C-O (Phenotypic) Classification of Stroke , 2009, Cerebrovascular Diseases.