RESEARCH ARTICLE
Quantile Regression Analysis of Modifiable and Non-Modifiable Predictors of Stroke among Adults in South Africa
Delson Chikobvu1, Lyness Matizirofa2, *
Article Information
Identifiers and Pagination:
Year: 2021Volume: 14
First Page: 409
Last Page: 416
Publisher ID: TOPHJ-14-409
DOI: 10.2174/1874944502114010409
Article History:
Received Date: 21/1/2021Revision Received Date: 28/6/2021
Acceptance Date: 30/6/2021
Electronic publication date: 17/09/2021
Collection year: 2021
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Background:
Stroke is the second largest cause of mortality and long-term disability in South Africa (SA). Stroke is a multifactorial disease regulated by modifiable and non-modifiable predictors. Little is known about the stroke predictors in SA, particularly modifiable and non-modifiable. Identification of stroke predictors using appropriate statistical methods can help formulate appropriate health programs and policies aimed at reducing the stroke burden. This study aims to address important gaps in stroke literature i.e., identifying and quantifying stroke predictors through quantile regression analysis.
Methods:
A cross-sectional hospital-based study was used to identify and quantify stroke predictors in SA using 35730 individual patient data retrieved from selected private and public hospitals between January 2014 and December 2018. Ordinary logistic regression models often miss critical aspects of the relationship that may exist between stroke and its predictors. Quantile regression analysis was used to model the effects of each predictor on stroke distribution.
Results:
Of the 35730 cases of stroke, 22183 were diabetic. The dominant stroke predictors were diabetes, hypertension, heart problems, the female gender, higher age groups and black race. The age group 55-75 years, female gender and black race, had a bigger effect on stroke distribution at the lower upper quantiles. Diabetes, hypertension and cholesterol showed a significant impact on stroke distribution (p < 0.0001).
Conclusion:
Most strokes are attributable to modifiable factors. Study findings will be used to raise awareness of modifiable predictors to prevent strokes. Regular screening and treatment are recommended for high-risk individuals with identified predictors in SA.
