The Composite Index of Anthropometric Failure (CIAF): An Alternative Indicator for Malnutrition
6.9 Conclusions
This chapter has shown how and why the CIAF is an important new policy-relevant anthropometric indicator of malnutrition. It showed how conventional indicators “miss” large numbers of malnour- ished children, and that the CIAF is the only indicator to provide a single fi gure, aggregate estimate
Box 6.1 Household survey data available on the Internet
Demographic and Health Surveys:
• www.measuredhs.com
DHS STATcompiler:
• www.statcompiler.com UNICEF’s Multiple Indicator Cluster Surveys:
• www.childinfo.org/mics.html
World Bank Living Standard Measurement Study:
• http://iresearch.worldbank.org/lsms/
lsmssurveyFinder.htm
World Health Organization World Health Surveys:
• www.who.int/healthinfo/survey/en/
index.html
Pan Arab Project for Family Health:
China Health and Nutrition Survey:
• www.cpc.unc.edu/projects/china/data/data.html World Health Organization Database on Child Growth and Malnutrition:
• www.who.int/
nutgrowthdb/en/
136 S. Nandy and P. Svedberg
of the overall burden of malnutrition among children in a population. The disaggregated CIAF allows for more nuanced assessments of the relationship between malnutrition, morbidity and poverty, and is increasingly being used in the assessments of child poverty and malnutrition (Gordon et al. 2003 ; Baschieri and Falkingham 2007 ; Seetharaman et al. 2007 ) . Given the ready availability of anthropo- metric data from household surveys, it is likely to be used much more frequently in future research.
Efforts to reduce malnutrition will depend heavily on reducing poverty and raising people’s living standards. This will require improving the quality of their homes, and their access to basic services, such as clean water and effective sanitation, as well as food of suffi cient quality and quantity. Access to affordable qualifi ed health care to prevent the debilitating effects of diseases like dysentery and pneumonia are essential if children’s nutritional status is to improve. The CIAF provides researchers and planners with another tool to assess change and see if progress is being made.
Summary Points
The conventional indicators of malnutrition – stunting, wasting and underweight – each individually
•
“miss” large numbers of malnourished children.
The CIAF provides an aggregate measure to estimate the overall burden of malnutrition among
•
young children.
In its disaggregated form, the CIAF can be used to predict the varying risks of morbidity for
•
different types of anthropometric failure.
The CIAF can be used to show the relationship between poverty and multiple anthropometric
•
failures.
Anthropometric data from hundreds of household surveys are available on the Internet and can be
•
downloaded and used by independent researchers.
References
ACC/SCN. Nutrition and poverty: papers from the ACC/SCN 24th session symposium, Kathmandu. Geneva, United Nations Administrative Committee on Coordination (ACC), Sub-Committee on Nutrition (SCN), World Health Organization; 1997.
Baschieri A, Falkingham J. Child poverty in Tajikistan: report for the UNICEF Country Offi ce. Southampton, UK:
University of Southampton; 2007.
Caulfi eld LE, de Onis M, Blossner M, Black RE. Am J Clin Nutr. 2004;80:193–8.
Cunha A. Acta Paediatrica. 2000;89:608–9.
Eveleth P, Tanner J. Worldwide variations in human growth. Cambridge: Cambridge University Press; 1990.
Falkingham J, Namazie C. Measuring health and poverty: a review of approaches to identifying the poor. London:
DFID Health Systems Resource Centre; 2002.
Fernald LC, Neufeld LM. Eur J Clin Nutr. 2007;61:623–32.
Filmer D, Pritchett LH. Demography. 2001;38:115–32.
Key Features
The CIAF can provide policy-relevant information on the pattern of child malnutrition in any
•
country.
The CIAF can be used to illustrate the complex relationship between morbidity risk and
•
malnutrition.
The CIAF can be easily computed using freely available, individual-level household survey
•
data from over 70 countries.
137 6 The Composite Index of Anthropometric Failure (CIAF): An Alternative Indicator…
Gordon D, Nandy S, Pantazis C, Pemberton S, Townsend P. Child poverty in the developing world. Bristol, UK:
Policy; 2003.
Howe L, Hargreaves JR, Huttly SRA. Emerg Themes Epidemiol. 2008;5.
IIPS & Macro International. National Family Health Survey (NFHS-3) 2005–06: India: Volume 1. Mumbai, India:
International Institute for Population Sciences (IIPS); 2007.
Montgomery M, Gragnolati M, Burke K, Paredes E. Demography. 2000;37:155–74.
Nandy S, Miranda JJ. Soc Sci Med. 2008 66:1963–6.
Nandy S, Irving M, Gordon D, Subramanian SV, Davey-Smith G. Bull World Health Org. 2005;83:210–6.
Rice A, Sacco L, Hyder A, Black R. Bull WHO. 2000;78:1207–21.
Schroeder D, Brown K. Bull World Health Org. 72:569–79.
Seetharaman N, Chacko TV, Shankar SLR, Matthew AC. Indian J Comm Med. 2007;32.
Smith TA, Lehmann D, Coakley C, Spooner V, MP A. Am J Clin Nutr. 1991;53:963–70.
Svedberg P. Poverty and undernutrition: theory, measurement and policy. Oxford: Oxford University Press; 2000.
Svedberg P. Can indicators of child malnutrition be improved – and for what purposes? Taking action for the world’s poor and hungry people. Beijing, China; 2007.
Tomkins A, Watson F. Malnutrition and infection; a review. Geneva: World Health Organization/ACC/SCN; 1989.
UNPOP. World population prospects: the 2008 revision. Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat; 2008.
Vaessen M. In: Khlat M, editor. Demographic evaluation of health programmes – proceedings of a seminar in Paris.
Paris: CICRED, UNFPA; 1996.
Wagstaff A, Watanabe N. Socio-economic inequalities in child malnutrition in the developing world. World Bank Policy Research Working Papers. Washington DC; 2000.
WHO/CDR. Household survey manual: diarrhoea and acute respiratory infections. Geneva: WHO; 1994.
World Health Organization. Expert Committee on Nutrition and Physical Status: uses and interpretation of anthropom- etry. Geneva: World Health Organization; 1995.
World Health Organization Multicentre Growth Reference Study Group. WHO child growth standards: length/height- for-age, weight-for-age, weight-for-length, weight-for-height and body mass index-for-age: methods and develop- ment. Geneva: World Health Organization; 2006.
V.R. Preedy (ed.), Handbook of Anthropometry: Physical Measures 139 of Human Form in Health and Disease, DOI 10.1007/978-1-4419-1788-1_7,
© Springer Science+Business Media, LLC 2012
Abstract Body shape during age-related human growth is customarily described using either the body mass index (BMI) or the Ponderal Index (PI). These indices describe the human body shape (HBS) with different proportions between body mass ( M ) and body height ( H ). Both indices are affected by age during childhood. The establishment and maintenance of age- and sex-specific data- bases is time consuming and an expensive process. The lack of a general model for abnormal HBS makes the comparison of different populations (both in respect to time and location) very difficult.
Efforts to evaluate the epidemic status of childhood obesity would benefit from the development of new tools and the refining of existing simple, reliable tools to evaluate and monitor HBS. An age invariant, sex-specific human body shape index (HBSI) has been recently introduced as an alterna- tive to age-specific BMI and PI models. The HBSI was based on a mathematical model of human growth of Polish children (5–18 year old) between years 1985 and 1990. The sample was character- ized by ideal fat composition (judged by 95th percentile of BMI values). To model growth, the best fit between individual body height ( h ) and body mass ( m ) was calculated separately between sexes with the function M = c p H c . The models of growth were M = 13.11 H 2.84 ( R 2 = 0.9) and M = 13.64 H
2.68 ( R 2 = 0.91) in girls and boys respectively. The HBSIs were calculated as (HBSI p = M / H c ), where HBSI B = 13.74 ± 1.72 (in boys), HBSI G = 13.21 ± 1.73 (in girls) and HBSI = 13.47 ± 1.74 (for both sexes). While the sensitivity and specificity of HBSI in the classification of the pathological changes in human body shape has not been explored, HBSI may be beneficial over other age-specific indices in the comparative analysis of HBS in children originating from different populations (location, diversity, and time periods) over long periods of growth.
Abbreviations
HBS Human body shape
M G , M B , M Body mass in girls, boys and pooled together treated as a physical variable (respectively) H G , H B , H Body height in girls, boys and pooled together treated as a physical variable
(respectively)