A Dynamic Regression Modeling of the Prevalence of Stroke in South-East of Nigeria

Abstract

Stroke is increasingly becoming one of the major public health issues in Nigeria. This study aimed to predicting future prevalence of stroke in south-east Nigeria in 2022 and 2023, using previous prevalence. The number of patients recorded with stroke (transient ischemic attack) and risk factors hypertension (HT), diabetes mellitus (DM), dyslipidemia (DY), and alcohol (AL) on monthly basis from January 2017 to December 2021 in Enugu State University Teaching Hospital were extracted. The dynamic regression model was applied to the data, best model is selected using Akaike information criterion corrected (AICC), and the model generated is used to predict the number of patients with stroke in 2022 and 2023.  A total of 1216 patient records were included in this study. The proportion of hypertension was 51.49%, diabetes mellitus was 7.65%, alcohol was 6.4%, and dyslipidemia was 24.81%. Regression with ARIMA(0,0,1) was the best model. The prediction showed that by December 2022 the number of patients will increase by 29.63% and by December 2023 it will rise to 36.67%. The findings of this study suggest the prevalence of stroke in south-east Nigeria is high and will still rise in the future. There is still need of further research on stroke and other risk factors towards establishing appropriate policy, preventive and management measures.

Keywords: Nigeria, Risk Factors, Stroke, dynamic regression model, transient ischemic attack

DOI: https://doi.org/10.37745/ijphpp.15/vol7n43140

Article Review Status: Published

Pages: 31-40 (Download PDF)

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