Modelling of South African Hypertension: Application of Classical Quantile Regression

Authors

  • Anesu Gelfand Kuhudzai Ph.D. Candidate. University of Antwerp, Department of Social Epidemiology and Health Policy, Belgium & Statistical and Data Science Consultant. University of Johannesburg, Statistical Consultation Services, South Africa
  • Prof Guido Van Hal University of Antwerp, Department of Social Epidemiology and Health Policy, Belgium
  • Prof Stefan Van Dongen University of Antwerp, Department of Evolutionary Ecology and Biology, Belgium
  • Muhammed Ehsanul Hoque Senior Research Associate Research Department Management College of South Africa, Durban, South Africa

DOI:

https://doi.org/10.3329/bjms.v21i4.60238

Keywords:

Hierarchical Interactions; Group-lasso approach;Classical Quantile Regression; Hypertension; South Africa

Abstract

Background: High blood pressure, medically known as hypertension is the major risk factor for cardiovascular diseases (CVDs) and premature death globally. The aim of the present study was to explore possible interactions amongst systolic blood pressure`s (SBP) and diastolic blood pressure`s (DBP) risk factors in South Africa.

Methods:A retrospective study was conducted using data acquired from the South African National Income Dynamics Study Wave 5, Household Survey which was carried out in 2017-2018.A final data set of 21 180 adults was utilized for data analysis. An application of the hierarchical group-lasso approach to detect interactions between SBP`s and DBP`s risk factors and classical quantile regression analysis were performed in this study.

Results: By using only upper quantilesbody mass index (BMI), age, race, never exercised, and the following nine interactions: BMI and age, BMI and gender male, age and never exercised, gender male and race African, race coloured and depression some or little of the time, BMI and cigarette consumption, age and race white, gender male and employment status, never exercised and cigarette consumptionwere found to be significantdeterminantsof hypertension in South Africa.

Conclusion: The evidence of this study suggests that it is ideal to consider interactions amongst risk factors when modelling hypertension.

Bangladesh Journal of Medical Science Vol. 21 No. 04 October’22 Page : 772-781

Downloads

Download data is not yet available.
Abstract
330
PDF
314

Downloads

Published

2022-09-11

How to Cite

Kuhudzai, A. G. ., Hal, P. G. V. ., Dongen, P. S. V. ., & Hoque, M. E. . (2022). Modelling of South African Hypertension: Application of Classical Quantile Regression. Bangladesh Journal of Medical Science, 21(4), 772–781. https://doi.org/10.3329/bjms.v21i4.60238

Issue

Section

Original Articles