RESEARCH ARTICLE


Quantile Regression Analysis of Modifiable and Non-Modifiable Predictors of Stroke among Adults in South Africa



Delson Chikobvu1, Lyness Matizirofa2, *
1 Department of Mathematical Statistics and Actuarial Science, Faculty of Natural and Agricultural Sciences, University of the Free State, P.O. Box 339, Bloemfontein, South Africa
2 Department of Statistics, Florida Campus, College of Science, Engineering and Technology, University of South Africa, 28 Pioneer Avenue, Roodeport, Johannesburg, 1709, South Africa


Article Metrics

CrossRef Citations:
0
Total Statistics:

Full-Text HTML Views: 152
Abstract HTML Views: 87
PDF Downloads: 99
Total Views/Downloads: 338
Unique Statistics:

Full-Text HTML Views: 124
Abstract HTML Views: 74
PDF Downloads: 89
Total Views/Downloads: 287



Creative Commons License
© 2021 Chikobvu and Matizirofa.

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.

* Address correspondence to this author at Department of Statistics, College of Science, Engineering and Technology, Florida Campus, University of South Africa, 28 Pioneer Avenue, Roodeport, Johannesburg, 1709, South Africa;
Tel: 012 521 4969; E-mail: lynessmatizirofa@gmail.com


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.

Keywords: Stroke, Modifiable, Non-modifiable, Predictors, Quantile regression analysis, Logistic regression, South Africa.