Is Middle-Upper Arm Circumference "normally" distributed? Secondary data analysis of 852 nutrition surveys

Emerging Themes in Epidemiology

Volumn 13, Issue 1, 2016, Pages

  Frison, Severine ^a   Checchi, Francesco ^a   Kerac, Marko ^a   Nicholas, Jennifer ^a  

^a LONDON SCHOOL OF HYGIENE AND TROPICAL MEDICINE   (United Kingdom)

Author keywords

Child malnutrition; Middle Upper Arm Circumference; Normal distribution; Probit; Wasting

Indexed keywords

ARM CIRCUMFERENCE; ARTICLE; CHILD; CHILD NUTRITION; CROSS-SECTIONAL STUDY; HEALTH SURVEY; HUMAN; INFANT; MIDDLE UPPER ARM CIRCUMFERENCE; NORMAL DISTRIBUTION; NUTRITIONAL ASSESSMENT; SAMPLE SIZE;

EID: 84969611130     PISSN: None     EISSN: 17427622     Source Type: Journal    
DOI: 10.1186/s12982-016-0048-9     Document Type: Article

References (26)

1. UNICEF. The State of the World's Children. New York: UNICEF; 2014.
2. Black R, Victora C, Walker S, Bhutta Z, Christian P, De Onis M, Ezzati M, Grantham-Mcgregor S, Katz J, Martorell R, Uauy R. Maternal and child undernutrition and overweight in low-income and middle-income countries. Lancet. 2013;382:427-51.
3. Cluster I-ASCN: Transitioning to the WHO growth standards: implications for emergency nutrition programmes. 2008.
4. Myatt M, Khara T, Collins S. A review of methods to detect cases of severely malnourished children in the community for their admission into community-based therapeutic care programs. Food Nutr Bull. 2006;27:S7-23.
5. Kirkwood BR, Sterne JAC: Essential medical statistics. Blackwell Publishing; 2003.
6. Finney DJ. Probit analysis. Cambridge: Cambridge University Press; 1971.
7. Bulmer MG. Principles of statistics. New York: Dover Publications Inc; 1979.
8. DeCarlo LT. On the meaning and use of kurtosis. Psychol Methods. 1997;2:292-307.
9. Thode HC. Testing for normality. New York: Marcel Dekker; 2002.
10. D'Agostino RB, Stephens MA. Goodness-of-fit techniques. New York: Marcel Dekker; 1986.
11. De Boor C. A practical guide to splines (revised edition); 2001.
12. Cleveland WS. Lowess - a program for smoothing scatterplots by robust locally weighted regression. Am Stat. 1981;35:54.
13. Cleveland WS. Robust locally weighted regression and smoothing scatterplots. J Am Stat Assoc. 1979;74:829-36.
14. Carroll RJ, Ruppert D. On prediction and the power transformation family. Biometrika. 1981;68:609-15.
15. Osborne JW. Improving your data transformations: Applying the Box-Cox transformation. In: Improving your data transformations: applying the Box-Cox transformation (editor), vol 15, City;2010.
16. Osborne JW. Notes on the use of data transformations. In: Notes on the use of data transformations. Practical assessment, research and evaluation (editor). City;2002.
17. Altman DG, Martin BJ. Statistics notes: the normal distribution. BMJ. 1995;310:298.
18. Mei Z, Grummer-Strawn LM. Standard deviation of anthropometric Z-scores as a data quality assessment tool using the 2006 WHO growth standards: a cross country analysis. Bull World Health Organ. 2007;85:441-8.
19. Waterlow JC, Buzina R, Keller W, Lane JM, Nichaman MZ, Tanner JM. The presentation and use of height and weight data for comparing the nutritional status of groups of children under the age of 10 years. Bull World Health Organ. 1977;55:489-98.
20. Blanton C, Bilukha O. The PROBIT approach in estimating the prevalence of wasting: revisiting bias and precision. Emerg Themes Epidemiol. 2013;10:8.
21. Binkin N, Sullivan K, Staehling N, Nieburg P. Rapid nutrition surveys: How many clusters are enough? Disasters. 1995;16:99-103.
22. Spiegel PB, Salama P, Maloney S, van der Veen A. Quality of malnutrition assessment surveys conducted during famine in Ethiopia. JAMA. 2004;292:613-8.
23. WHO. 2006. World Health Organization (WHO) Child Growth Standards. http://www.who.int/childgrowth/en/. Accessed 23 June 2015.
24. Software: STATA 13. In: STATA 13 (editor). pp. StataCorp. 2013. Stata Statistical Software: Release 2013. College Station, TX: StataCorp LP.
25. RStudio Team (2015). RStudio: integrated development for R. RStudio, Inc., Boston, MA. http://www.rstudio.com/.
26. Dale NM, Myatt M, Prudhon C, Briend A. Assessment of the PROBIT approach for estimating the prevalence of global, moderate and severe acute malnutrition from population surveys. Public Health Nutr. 2013;16:858-63.