Journal of Environment Protection and Sustainable Development
Articles Information
Journal of Environment Protection and Sustainable Development, Vol.5, No.2, Jun. 2019, Pub. Date: Jun. 24, 2019
The Effect of Water, Sanitation and Hygiene (WaSH) on Nutrition, for Sri Lankan Children Under Five Years of Age
Pages: 75-81 Views: 102 Downloads: 38
Authors
[01] Marina Roshini Sooriyarachchi, Department of Statistics, Faculty of Science, University of Colombo, Colombo, Sri Lanka.
Abstract
This study aims to determine whether drinking, cooking, handwashing water, Sanitation and Hygiene (WaSH) are associated with each of the three nutrition measures stunting, wasting and underweight jointly after adjusting for important covariates and taking in to consideration the correlation within clusters, for the districts of Sri Lanka for children under 5 years. The data from the Demographic and Health survey 2016 gives detailed information on WaSH variables, Nutrition variables and a number of other probable prognostic factors. This data has been collected by the Department of Census and Statistics. The design of the sample is a two stage cluster design with census blocks at the first stage and households at the second stage. Joint Generalized Estimation Equation (GEE) estimation has been used within Generalized Linear models (GLM) for modeling the data. Important conclusions are that when it comes to stunting and wasting tap water is better for cooking and handwashing. Good sanitation improves stunting. Urban sector has less stunting than rural sector and this has less stunting than the estate sector. Western province has lower odds of stunting. Wasting mainly depends on the proxy of wealth. Well water for drinking improves underweight. Simple methods of living improve underweight.
Keywords
Stunting, Wasting, Underweight, Water, Sanitation, Hygiene, Joint Generalized Estimating Equations
References
[01] Cumming, O and Cairncross, S (2016). Can water, sanitation and hygiene help eliminate stunting? Current evidence and policy implication. Maternal and Child Nutrition. Wiley. 12 (51): 91-105.
[02] Freeman, M. C. et al 2017. The impact of sanitation on infectious disease and nutritional status: A systematic review and meta-analysis. International Journal of Hygiene and Environmental Health. Volume 220, Issue. August 2017, Pages 928-949 6.
[03] Rah, J. H., et al. (2015). Household sanitation and personal hygiene practices are associated with child stunting in rural India: a cross-sectional analysis of surveys. BMJ Open 2015; 5: e005180. doi: 10.1136/ bmjopen-2014-005180.
[04] Raihan, M. J. et al. (2017). Examining the relationship between socio-economic status, WASH practices and wasting PLOS ONE. https://doi.org/10.1371/journal.pone.0172134.
[05] G. Halcrow, S. Lala, L. Sherburne, T. Tho & M. Griffiths (Australia). Integrating WASH and nutrition to reduce stunting in Cambodia: from discourse to practice. 40th WEDC International Conference, Loughborough, UK, 2017.
[06] Humphreys, L. (2013). Mobile social media: Future challenges and opportunities. Sage journals. https://doi.org/10.1177/2050157912459499.
[07] Kavosi, E, et al. (2014). Prevalence and determinants of under-nutrition among children under six: a cross- sectional survey in Fars province, Iran. Int J Health Policy MNanag. 2014 Jul; 3 (2): 71–76.
[08] Kimani, E. W. M. et al/ (2014), Vulnerability to Food Insecurity in Urban Slums: Experiences from Nairobi, Kenya. Journal of Urban Health Volume 91, Issue 6, pp 1098–1113.
[09] IMPROVING NUTRITION OUTCOMES WITH BETTER WATER, SANITATION AND HYGIENE. https://www.unicef.org/media.
[10] http://www.washnet.de/en/epaper/ Retrieved in April, 2018.
[11] Nelder, P. Wedderburn. J. A. Generalized Linear Models. Chapman and Hall. London.
[12] Dobson A. et al. An Introduction to Generalized Linear Models, 4th edition. CRC press.
[13] Kung-Yee Liang; Scott L. Zeger. Longitudinal Data Analysis Using Generalized Linear Models. Biometrika, Vol. 73, No. 1. (Apr., 1986), pp. 13-22.
[14] Sooriyarachchi, M. R. JOINT GENERALIZED ESTIMATING EQUATIONS (GEE) FOR MULTIVARIATE BINARY OUTCOMES: AN APPLICATION TO NUTRITIONAL DATA FOR SRI LANKAN CHILDREN UNDER FIVE YEARS OF AGE. Published in the proceedings of the International Conference in Computational Statistics and Mathematics (ICCSM), 2019, Singapore.
[15] Lipsitz, S. R., Fitzmaurice, G. M., et al. (2009) Joint generalized estimating equations for multivariate longitudinal binary outcomes with missing data: An application to AIDS data. January 2009. Journal of the Royal Statistical Society Series A (Statistics in Society) 172 (1): 3-20.
[16] Collett, D. (2003). Modeling Survival Data in Medical Research. New York: Chapman & Hall/CRC.
[17] Casella, G. Berger, R. (2002) Statistical Inference, 2nd Edition. Duxbury Press.
[18] Ballinger, G. A. (2004). Using Generalized Estimating Equations for Longitudinal Data Analysis. Sage Journals. https://doi.org/10.1177/1094428104263672.
[19] Agresti, A. (2012) Categorical Data Analysis 3rd Edition, Wiley, USA.
[20] Pan, W. (2001) Akaike's Information Criterion in Generalized Estimating Equations. Biometrics. Volume 57, Issue 1, Pages 120-125.
600 ATLANTIC AVE, BOSTON,
MA 02210, USA
+001-6179630233
AIS is an academia-oriented and non-commercial institute aiming at providing users with a way to quickly and easily get the academic and scientific information.
Copyright © 2014 - American Institute of Science except certain content provided by third parties.