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Predictors of High Blood Pressure in South African Children: Quantile Regression Approach
Pages 84-91
Lyness Matizirofa and Anesu Gelfand Kuhudzai
Published: 11 April 2017

Abstract: Objective: To identify predictors of blood pressure (BP) in children and explore the predictors` effects on the conditional quantile functions of systolic blood pressure and diastolic blood pressure.

Methods: A secondary data analysis was performed using data from the South African National Income Dynamics Study (2014-2015). From this particular secondary data, data for children aged between 10-17 years were extracted for analysis. The variables used in the study were systolic blood pressure (SBP), diastolic blood pressure (DBP), body mass index (BMI), age, smoking, alcohol consumption, exercises, gender and race. Two parameter estimation methods were used, ordinary least squares (OLS) and quantile regression (QR).

Results: BMI had positive statistically significant estimated OLS and conditional quantile functions with both the BP measures except the 95th quantile for SBP. Age had also positive statistically significant estimated OLS and QR coefficients except for the 95th percentile, with both DBP and SBP respectively. Gender was found to be inversely related to both DBP and SBP except the 10th quantile for DBP. Race was partially significant to DBP. Smoking, alcohol consumption and exercises did not present any statistically significant relations with both DBP and SBP for all the estimated OLS and QR coefficients.

Conclusion: BMI, age, gender and partially race were found to be predictors of BP in South African children using both OLS and QR techniques. Exercises, smoking and alcohol consumption did not present any statistically significant relations with both DBP and SBP probably because few participants exercise regularly, smoke and drink alcohol to bring out a significant change in both BP measurements.

Keywords: Body Mass Index (BMI), Diastolic Blood Pressure (DBP), Systolic Blood Pressure (SBP), Ordinary Least Squares Regression (OLS), Quantile Regression (QR).

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