Nguefack-Tsague, Georges and Tanya K. N., Agatha (2011): Using weight-for-age for predicting wasted children.
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Background: The equipments for taking body weights (scales) are more frequent in Cameroon health centres than measuring boards for heights. Even when the later exist there are some difficulties inherent in their qualities; thus the height measurement is not always available or accurate. Objective: To construct statistical models for predicting wasting from weight-for-age. Methods: 3742 children a ged 0 to 59 months were enrolled in a cross-sectional household survey (2004 Cameroon Demographic and Health Surveys (DHS)) covering the entire Cameroon national territory. Results: There were highly significant association between underweight and wasting. For all discriminant statistical methods used, the test error rates (using an independent testing sample) are less than 5%; the Area Under the Curve (AUC) using the Receiver Operating Characteristic (ROC) is 0.86. Conclusions: Weight-for-age can be used for accurately classifying a child whose wasting status is unknown. The result is useful in Cameroon as too often the height measurements may not be feasible, thus the need for estimating wasted children.
|Item Type:||MPRA Paper|
|Original Title:||Using weight-for-age for predicting wasted children|
|Keywords:||Anthropometric measures, nutritional status, discriminant analysis, underweight, wasting|
|Subjects:||I - Health, Education, and Welfare > I1 - Health > I12 - Health Production
C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models; Multiple Variables > C35 - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
|Depositing User:||Georges Nguefack-Tsague|
|Date Deposited:||21. Apr 2011 12:06|
|Last Modified:||15. Feb 2013 19:40|
1. Black RE, Allen LH, Bhutta ZA et al. Maternal and child undernutrition: global and regional exposures and health consequences. Lancet 2008; 371: 243-260.
2. Cole TJ. A method for assessing age-standardized weight-for-height in children seen cross-sectionally. Ann Hum Biol 1979; 6: 249-268.
3. Cole TJ. A critique of the NCHS weight for height standard. Hum Biol 1985; 57: 183-196.
4. Cole TJ. A new index of child weight-for-height based on weight and height Z scores. Ann Hum Biol 1994; 21: 96.
5. Gorstein J, Sullivan K, Yip R et al. Issues in the assessment of nutritional status using anthropometry. Bull World Health Organ 1994; 72: 273–283.
6. World Health Organization. Physical status: the use and interpretation of anthropometry. WHO Technical Report Series 1995; N0 854. Geneva:WHO.
7. de Onís M & Habicht JP. Anthropometric reference data for international use: recommendations from a World Health Organization Expert Committee. Am. J. Clin. Nutr 1996; 64: 650–658.
8. United Nations. A better world for all. United Nations Development Programme. http://www.paris21.org/betterworld/pdf/bwa_e.pdf (accessed 26.05.08) 2000.
9. Pelletier D . The relationship between child anthropometry and mortality in developing countries. J. Nutr. (Supplement) 1994;124: 2047–2081.
10. UNICEF. The State of World’s Children: Focus on Nutrition 1998; New York : UNICEF.
11. de Onís M, Frongillo EA & Blössner M. Is malnutrition declining? An analysis of changes in levels of child malnutrition since 1980. Bull World Health Org 2000; 78: 1222-1233.
12. Smith L and Haddad L. How potent is economic growth in reducing undernutrition? What are the pathways of influence? New cross-country evidence. Econ Dev Cult Change 2002; 51:55–76.
13. Caputo A, Foraita R, Klasen, S et al. Undernutrition in Benin—an analysis based on graphical models. Soc Sci Med 2003; 56: 1677–1697.
14. Behrman J & Skoufias E. Correlates and determinants of child anthropometrics in Latin America: background and overview of the symposium. Econ Hum Biol 200; 42: 335–352.
15. Klasen S. Poverty, undernutrition, and child mortality: some interregional puzzles and their implications for research and policy. Journal of Economic Inequality 2008; 6: 89–115.
16. Cogill B. Anthropometric Indicators Measurement Guide. Food and Nutrition Technical Assistance Project. Washington D.C. : Academy for Educational Development 2003.
17. Rutstein SO & Rojas G. Guide to DHS Statistics, Demographic and Health Survey. Maryland : ORC Macro Calverton 2006.
18. WHO Anthro for personal computers, version 2. Software for assessing growth and development of the world's children WHO . Geneva. http://www.who.int/childgrowth/software/en/. 2007
19. Khattree R & Naik DN. Multivariate Data Reduction and Discrimination with SAS Software. Cary NC: SAS Institute Inc 2000.
20. Hastie T, Tibshirani R & Friedman J. The elements of statistical learning: data mining, inference and prediction. New York: Springer 2001.
21. SAS Institute Inc. SAS/STAT Software: Changes and Enhancements, through Release 6.11 and 6.12. Cary NC: SAS Institute Inc 1996.
22. R Development Core Team. R: A language and environment for statistical computing. Vienna, Austria : R Foundation for Statistical Computing. http://www.R-project.org 2008.
23. Kelsey JL, Thompson WD & Evans AS. Methods in observational epidemiology. New York: Oxford University Press 1986.
24. de Onís, M, Monteiro C, Akré J et al. The worlwide magnitude of protein-energy malnutrition : an overview from the WHO Global Database on Child Nutrition. Bull World Health Org 1993;71: 703–712.
25. Victora CG. The association between wasting and stunting: an international perspective. J Nutr 1992; 122: 1105-1110.
26. Centers for Disease Control and Prevention. Nutritional assessment of children drought-affected area-Haiti, 1990. Morbidity and mortality weekly report 1991; 40:222-225.
27. Blössner M, de Onis M & Uauy R. Estimating stunting from underweight survey data. Journal of Human Ecology 2006; 14:145-152.
28. Victora CG, Gigante DP, Barros AJ et al. Estimating the prevalence of height for age deficits based on the prevalence of weight for age deficits among Brazilian children. Rev Saúde públ 1998; 32(4):321-327.