Determinants of Under-Five Children Body Mass Index in Sudan; Application of Quantile Regression: A Systematic Review
Background: One of the health challenges in Sub-Saharan countries is child malnutrition. Body Mass Index (BMI) can be defined as a measure of nutritional status. Examining the determinants of under-five children’s BMI is a significant subject that needs to be studied. For this study, quantile regression was used to identify the determinants of under-five children's BMI in Sudan.
Methods: We used the 2014 Sudan Multiple Indicator Cluster Survey (MICS) conducted by the Central Bureau of Statistics. Quantile regression was used.
Results: Place of residence, state, mother’s educational level, gender, age of the child, and wealth index were an important effect significantly affecting under-five children’s BMI at different quantile levels.
Conclusion: Taking measures on the nutritional status of mothers will accordingly resolve the nutritional status of their children. Therefore, the focus of policymakers should be on the influential significant factors which were found across all quantile levels to plan and develop strategies to enhance the normal or healthy weight status of under-five children in Sudan.
2. Pietrobelli A, Faith MS, Allison DB, Gallagher D, Chiumello G, Heymsfield SB (1998). Body mass index as a measure of adiposity among children and adolescents: a validation study. J Pediatr, 132(2):204-210.
3. Barlow SE (2007). Expert committee recommendations regarding the prevention, assessment, and treatment of child and adolescent overweight and obesity: summary report. Pediatrics, 120 Suppl 4:S164-S192.
4. Centers for Disease Control and Prevention (2009). About Adult BMI. http://www.cdc.gov/healthyweight/assessing/bmi/adult_bmi/index.html
5. Centers for Disease Control and Prevention (2009) BMI for children and teens. http://www.cdc.gov/healthyweight/assessing/bmi/childrens_bmi/about_childrens_bmi.html
6. United Nations (2020) Millennium Development Goals: Sudan: https://www.indexmundi.com/sudan/millennium-development-goals.html. Accessed 09/15/2020.
7. Central Bureau of Statistics, UNICEF Sudan (2016) Multiple Indicator Cluster Survey 2014 of Sudan, Final Report. UNICEF and Central Bureau of Statistics (CBS), Khartoum, Sudan
8. De Onis M, Brown D, Blossner M, E. B (2012). Levels and trends in child malnutrition. UNICEF-WHO-The World Bank Joint Child Malnutrition Estimates.
9. Habyarimana F, Zewotir T, Ramroop S, Ayele D (2016). Spatial Distribution of Determinants of Malnutrition of Children under Five Years in Rwanda: SimultaneousMeasurement of Three Anthropometric Indices. Journal of Human Ecology, 54:138-149.
10. Himes JH, Dietz WH (1994). Guidelines for overweight in adolescent preventive services: recommendations from an expert committee. Am J Clin Nutr, 59(2):307-316.
11. de Moraes Ferrari GL, Matsudo V, Katzmarzyk PT, Fisberg M (2017). Prevalence and factors associated with body mass index in children aged 9–11 years. J Pediat (Rio J), 93(6):601-609.
12. Guiné RP, Fernandes SR, Abrantes JL, Cardoso AP, Ferreira M (2016). Factors affecting the body mass index in adolescents in Portuguese schools. Hrvatski časopis za prehrambenu tehnologiju, biotehnologiju i nutricionizam, 11:58-64.
13. Kumar R, Abbas F, Mahmood T, Somrongthong R (2019). Prevalence and factors associated with underweight children: a population-based subnational analysis from Pakistan. BMJ Open, 9:e028972.
14. Jabakhanji SB, Boland F, Ward M, Biesma R (2018). Body mass index changes in early childhood. J Pediatri, 202:106-114.
15. Taal HR, vd Heijden AJ, Steegers EA, Hofman A, Jaddoe VW (2013). Small and large size for gestational age at birth, infant growth, and childhood overweight. Obesity (Silver Spring), 21(6):1261-1268.
16. Yirga AA, Ayele DG, Melesse SF (2018). Application of Quantile Regression: Modeling Body Mass Index in Ethiopia. The Open Public Health Journal, 11.
17. Koenker R, Bassett Jr G (1978). Regression quantiles. Econometrica, 46(1): 33-50.
18. Koenker R, Hallock KF (2001). Quantile regression. J Econ Perspect, 15:143-156.
19. Bushinsky M (1998). Recent advances in quantile regression models. J Hum Resour, 33:88-126.
20. Davino C, Furno M, Vistocco D (2013). Quantile regression: theory and applications. 1st ed. John Wiley & Sons.
21. Koenker R (2005). Econometric Society Monographs: Quantile Regression. New York: Cambridge University.
22. Neter J, Kutner MH, Nachtsheim CJ, Wasserman W (1996). Applied linear statistical models.4th ed.
23. Sen A, Srivastava M (2012). Regression analysis: theory, methods, and applications. ed. Springer Science & Business Media.
24. Koenker R, Machado JA(1999). Goodness of fit and related inference processes for quantile regression. Journal of the American Statistical Association,94(448):1296-1310.
25. Nordin R, Said N, Nordin FF, Adnan NF (2018). Factors Influence on Body Mass Index (BMI) among Overweight and Obese School Children. Journal of ASIAN Behavioural Studies, 3:11-21.
|Issue||Vol 50 No 1 (2021)|
|Body mass index; Under-five; Multiple indicator cluster survey|
|Rights and permissions|
|This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.|