Predictors of High Blood Pressure in South African Children: Quantile Regression Approach
DOI:
https://doi.org/10.6000/1929-6029.2017.06.02.4Keywords:
Body Mass Index (BMI), Diastolic Blood Pressure (DBP), Systolic Blood Pressure (SBP), Ordinary Least Squares Regression (OLS), Quantile Regression (QR).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.
References
Christofaro DGD, Fernandes RA, Polito MD, Romanzini M, Ronque ERV, Gobbo LA, Oliveira AR de. A comparison between overweight cutoff points for detection of high blood pressure in adolescents. J Pediatria 2009; 85(4): 353-8. https://doi.org/10.2223/JPED.1911 DOI: https://doi.org/10.2223/JPED.1911
Aounallah-Skhiri H, El Ati J, Traissac P, Ben Romdhane H, Eymard-Duvernay S, Delpeuch F, Achour N, Maire B. Blood pressure and associated factors in a north African adolescent population. A national cross-sectional study in Tunisia. BMC Public Health 2012; 98: 1-10. https://doi.org/10.1186/1471-2458-12-98 DOI: https://doi.org/10.1186/1475-2891-10-38
Chiolero A, Madeleine G, Gabriel A, Burnier M, Paccaud F, Bovet P. Prevalence of elevated blood pressure and association with overweight in children of a rapidly developing country. Journal of Human Hypertension 2006; 21(2): 120-7. https://doi.org/10.1038/sj.jhh.1002125 DOI: https://doi.org/10.1038/sj.jhh.1002125
Feber J, Ahmed M. Hypertension in children: New trends and challenges. Clinical Science 2010; 119(4): 151-61. https://doi.org/10.1042/CS20090544 DOI: https://doi.org/10.1042/CS20090544
Ayatollahi S, Vakili M, Behboodian J, Zare N. Reference Values for Blood Pressure of healthy schoolchildren in Shiraz (Southern Iran) using Quantile Regression. Iranian Cardiovascular Research Journal 2010; 4(2): 55-65.
Falkner B. Hypertension in children and adolescents: Epidemiology and natural history. Pediatric Nephrology 2009; 25(7): 1219-24. https://doi.org/10.1007/s00467-009-1200-3 DOI: https://doi.org/10.1007/s00467-009-1200-3
Kim HS, Park YH, Park HB, Kim SH. Estimation of effects of factors related to preschooler body mass index using Quantile regression model. Asian Nursing Research 2014; 8(4): 293-9. https://doi.org/10.1016/j.anr.2014.07.005 DOI: https://doi.org/10.1016/j.anr.2014.07.005
Crispim PAA, Peixoto M do RG, Jardim PCBV. Risk factors associated with high blood pressure in Two- to Five-Year-Old children. Arquivos Brasileiros de Cardiologia 2014; 102(1): 39-46. DOI: https://doi.org/10.5935/abc.20130227
Peer N, Lombard C, Steyn K, Levitt N. High prevalence of metabolic syndrome in the black population of Cape Town: The cardiovascular risk in black South Africans (CRIBSA) study. European Journal of Preventive Cardiology 2014; 22(8): 1036-42. https://doi.org/10.1177/2047487314549744 DOI: https://doi.org/10.1177/2047487314549744
He X, Liang H. Quantile regression estimates for a class of linear and partially linear errors-in-variables models. Statistica Sinica 2000; 10: 129-40.
Beyerlein A, Toschke AM, von Kries R. Risk factors for childhood overweight: Shift of the mean body mass index and shift of the upper percentiles: Results from a cross-sectional study. International Journal of Obesity 2010; 34(4): 642-8. https://doi.org/10.1038/ijo.2009.301 DOI: https://doi.org/10.1038/ijo.2009.301
Leibbrandt M, Woolard I, de DeVilliers L. National Income Dynamic Study. Methodology: report on NIDS wave 1, technical paper no. 1. 2009. Available at: http://www.nids.uct. ac.za/publications/technical-papers/108-nids-technical-paper-no1/file (Accessed on 15 December 2016).
Koenker R, Hallock KF. Quantile regression: An introduction. 2000; 15(4). Available from: https://www.researchgate.net/ publication/247312065_Quantile_Regression_An_Introduction. (Accessed on 22 November 2016).
IBM SPSS Missing Values 22. 1989; IBM Corporation. Available from: http://www.sussex.ac.uk/its/pdfs/ SPSS_Missing_Values_22.pdf (Accessed on 23 November 2016).
Shen X, Li K, Chen P, et al. Associations of blood pressure with common factors among lety-behind farmers in rural China: A cross-sectional study using quantile regression analysis. Medicine 2015; 94(2): e142. https://doi.org/10.1097/MD.0000000000000142 DOI: https://doi.org/10.1097/MD.0000000000000142
Cappuccio FP, Micah FB, Emmett L, Kerry SM, Antwi S, Martin-Peprah R, Phillips RO, Plange-Rhule J, Eastwood JB. Prevalence, detection, management, and control of hypertension in Ashanti, West Africa. Hypertension 2004; 43(5): 1017-22. https://doi.org/10.1161/01.HYP.0000126176.03319.d8 DOI: https://doi.org/10.1161/01.HYP.0000126176.03319.d8
Soudarssanane MB, Karthigeyan M, Stephen S, et al. Key predictors of high blood pressure and hypertension among adolescents: A simple prescription for prevention. Indian J Community Med 2006; 31(3): 164-169.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2017 Lyness Matizirofa, Anesu Gelfand Kuhudzai
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Policy for Journals/Articles with Open Access
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are permitted and encouraged to post links to their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
Policy for Journals / Manuscript with Paid Access
Authors who publish with this journal agree to the following terms:
- Publisher retain copyright .
- Authors are permitted and encouraged to post links to their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work .
