Modelling Modifiable Risk Factors of Stroke in South Africa: Classical and Bayesian Logistic Regression Models

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Abstract Background: Stroke is the second leading cause of death and long-term disability in South Africa (SA). Yet little is known in SA about the modelling of modifiable risk factors of stroke. Information on the relative contribution of modifiable risk factors on stroke incidence is needed for early interventions in SA. Identification of risk factors remains the principal aspect of stroke prevention. This study aims to identify and quantify the risk of stroke associated with modifiable stroke predictors in SA. Methods: A cross-sectional hospital-based study was employed to identify and quantify the risk of stroke associated with modifiable stroke predictors using 35730 individual patient data retrieved from selected private and public hospitals between January 2014 and December 2018 in SA. Multivariate logistic regression analysis was employed to assess the effect of modifiable factors on stroke. Bayesian logistic regression analysis was employed to understand the uncertainty in the estimation of model parameters. Results: The dominant modifiable risk factors were: hypertension, cholesterol, heart problems and diabetes and all depend on the age of an individual and the interaction of these factors. The parameters, in the simpler model, of having hypertension, cholesterol, heart problems and diabetes were positive and significant confirming greater impact on the risk of stroke distribution. For instance, the odds ratio(OR) of patients with hypertension developing stroke when compared to those without hypertension is 80% higher. The OR of diabetic patients developing stroke when compared to non-diabetic was 194% higher.Conclusions: Most strokes are attributable to modifiable factors. Study findings will be used to raise awareness of modifiable risk factors to prevent strokes and recommend regular screening and treatment of identified risk factors. This will reduce the burden of stroke in SA.

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