Influence of age on links between major modifiable risk factors and stroke occurrence in West Africa

Sarfo, FS, Akpa O, Ovbiagele B, Akpalu A, Wahab K, Komolafe M, Obiako R, Owolabi L, Osaigbovo GO, Jenkins C, Ogbole G, Fakunle A, Tiwari HK, Arulogun O, Arnett DK, Asowata O, Ogah O, Akinyemi RO, Owolabi MO.  2021.  Influence of age on links between major modifiable risk factors and stroke occurrence in West Africa, 2021. Journal of the Neurological Sciences. 428


Background The burden of stroke in Africa is high. Understanding how age associates with major modifiable stroke risk factors could inform tailored demographic stroke prevention strategies. Purpose To quantify the magnitude and direction of the effect sizes of key modifiable stroke risk factors according to three age groups: <50 years (young), 50–65 years (middle age) and > 65 years (elderly) in West Africa. Methods This was a case-control study involving 15 sites in Ghana and Nigeria. Cases included adults aged ≥18 years with CT/MRI scan-typed stroke. Controls were age-and gender-matched stroke-free adults. Detailed evaluations for vascular, lifestyle and psychosocial factors were performed. We estimated adjusted odds ratios (aOR) using conditional logistic regression and population attributable risk (PAR) with 95% Confidence Interval of vascular risk factors by age groups. Results Among 3553 stroke cases, 813 (22.9%) were young, 1441 (40.6%) were middle-aged and 1299 (36.6%) were elderly. Among the 5 co-shared risk factors, dyslipidemia with PAR and aOR (95%CI) of 62.20% (52.82–71.58) and 4.13 (2.64–6.46) was highest among the young age group; hypertension with PAR of 94.31% (91.82–96.80) and aOR of 28.93 (15.10–55.44) was highest among the middle-age group. Diabetes with PAR of 32.29%(27.52–37.05) and aOR of 3.49 (2.56–4.75); meat consumption with PAR of 42.34%(32.33–52.35) and aOR of 2.40 (1.76, 3.26); and non-consumption of green vegetables, PAR of 16.81%(12.02–21.60) and aOR of 2.23 (1.60–3.12) were highest among the elderly age group. However confidence intervals of risk estimates overlapped across age groups. Additionally, among the young age group cigarette smoking, psychosocial stress and cardiac disease were independently associated with stroke. Furthermore, education, stress, physical inactivity and salt intake were associated with stroke in the middle-age group while cardiac disease was associated with stroke in the elderly age group. Conclusion There is a differential influence of age on the associations of major risk factors with stroke in this West African cohort. Targeting modifiable factors predominant within an age group may be more effective as a stroke prevention strategy.