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Do childhood growth indicators in developing countries cluster? Implications for intervention strategies

Published online by Cambridge University Press:  02 January 2007

Bridget Fenn ,
Saul S Morris  and
Chris Frost
Bridget Fenn*
Affiliation:
Department of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, UK
Saul S Morris
Affiliation:
Department of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, UK
Chris Frost
Affiliation:
Department of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, UK
*
*Corresponding author: Email bridget.fenn@lshtm.ac.uk
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Abstract

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Objective:

The effectiveness of geographic targeting in nutrition programmes depends largely on the degree to which malnutrition clusters within particular areas. This study investigates the extent to which the childhood nutrition indicators, stunting (height-for-age Z-score <−2) and wasting (weight-for-height Z-score <−2), are spatially clustered; this information is used to determine the implications of spatial clustering for the effectiveness of geographic targeting.

Design:

Analysis of data from Demographic and Health Survey (DHS) results. Clustering is assessed by calculating intra-cluster correlation coefficients (ICCs). Estimating the proportion of malnourished children covered by a programme successfully targeting 10% of clusters with the highest malnutrition prevalences allows an assessment of the effectiveness of geographic targeting.

Setting:

Fifty-eight DHS III (1992–1997) and DHS IV (1998–2001) reports from 46 developing countries.

Subjects:

Pre-school children of mothers interviewed by DHS.

Main results: The extent of clustering of nutritional status was surprisingly low (median ICC for national samples: stunting=0.054, wasting=0.032) and most countries were characterised by having an ICC <0.1 – i.e. low clustering – for childhood undernutrition (91% of countries for wasting and 78% for stunting). Our assessment of the effectiveness of geographic targeting showed that coverage was better for wasting than for stunting; for wasting, 23% of countries would achieve less than 20% coverage, compared with 76% of countries achieving less than 20% coverage for stunting. Coverage is dependent on the overall prevalence of malnutrition and the ICC.

Conclusions:

Childhood nutritional status is determined at the household, or even individual, level; nutrition programmes that are geographically targeted may result in high levels of under-coverage and leakage, thereby compromising their cost-effectiveness; the lack of clustering questions the appropriateness of current nutrition interventions.

Keywords

TargetingSpatial clusteringGrowthPre-school childrenDeveloping countries
Type
Research Article
Information
Public Health Nutrition , Volume 7 , Issue 7 , October 2004 , pp. 829 - 834
Copyright
Copyright © The Authors 2004

References

1 Millennium Development Goals. Millennium Declaration 2000 [online]. Available at www.developmentgoals.org. Accessed 1 July 2002.Google Scholar
2Baker, JL, Grosh, ME. Poverty reduction through geographic targeting: how well does it work? World Development 1994; 22: 983–95.CrossRefGoogle Scholar
3Hoddinott, J. Targeting: Principles and Practice. Technical Report No. 9. Washington, DC: International Food Policy Research Institute, 1999.Google Scholar
4Morris, SS. Targeting urban malnutrition: a multi-city analysis of the spatial distribution of childhood nutritional status. Food Policy 2001; 26: 49–64.CrossRefGoogle Scholar
5Morris, SS, Levin, C, Armar-Klemesu, M, Maxwell, D, Ruel, M. Does Geographic Targeting of Nutrition Interventions Make Sense in Cities? Evidence from Abidjan and Accra. FCND Discussion Paper No. 61 Washington, DC: International Food Policy Research Institute, Food Consumption and Nutrition Division, 1999.CrossRefGoogle Scholar
6Bennett, S. The EPI cluster sampling method. A critical appraisal. Bulletin of the International Statistical Institute 1993; 55(Book 2): 21–35.Google Scholar
7Hamil, PVV, Drizid, TA, Johnson, CL, Reed, RB, Roche, AF, Moore, WM. NCHS Growth Curves for Children Birth–18 years. Vital and Health Statistics Series 11, No. 165. DHEW Publication No. (PHS):78–1650. Hyattsville, MD: Department of Health, Education and Welfare, 1977.Google Scholar
8World Health Organization (WHO). Physical Status: The Use and Interpretation of Anthropometry. Geneva: WHO, 1995.Google Scholar
9Megill, DJ. Recommended Sample Design for 1999 Enquete Permanenet Aupres des Ménages. Washington, DC: US Bureau of the Census, 1999.Google Scholar
10Yansaneh, IS, Eltinge, JL, Empirical Studies of Design Effect Components in the Demographic and Health Surveys 2003 [online]. Available at http://unstats.un.org/unsd/hhsurveys/final.htm. Accessed April 2003.Google Scholar
11Commenges, D, Jacqmin, H. The intraclass correlation coefficient: distribution-free definition and test. Biometrics 1994; 50: 517–26.CrossRefGoogle ScholarPubMed
12Snedecor, GW, Cochran, WG. Statistical Methods, 8th ed. Ames, IA: Iowa State University Press, 1989;Google Scholar
13Donald, A, Donner, A. Adjustment to the Mantel–Haenszel chi-square statistic and odds ratio variance estimator when data are clustered. Statistics in Medicine 1987; 6: 491–9.CrossRefGoogle Scholar
14Edwards, TC, Moisen, GG, Cutler, DR. Assessing Map Accuracy in Remotely-Sensed, Ecoregion-Scale Cover Map [online], 1998. Available at http://ella.nr.usu.edu/~utcoop/tce/publications/rse98.pdf. Accessed 9 July 2002.Google Scholar
15Bennett, S, Woods, T, Liyanage, W, Smith, D. A simplified general method for cluster-sample surveys of health in developing countries. World Health Statistics Quarterly 1991; 44: 98–106.Google ScholarPubMed
16Shackman, G. Sample size and design effect. Presented at Albany Chapter of American Statistical AssociationMarch 2001 [online]. Available at http://faculty.smu.edu/slstokes/stat6380/deff%20doc.pdf. Accessed 8 July 2002.Google Scholar
17Kish, L. Survey Sampling. New York: John Wiley & Sons, 1965.Google Scholar
18Katz, J, Carey, VJ, Zeger, SL, Sommer, A. Estimation of design effects and diarrhea clustering within households and villages. American Journal of Epidemiology 1993; 138: 994–1006.CrossRefGoogle ScholarPubMed
19Katz, J. Sample-size implications for population-based cluster surveys of nutritional status. American Journal of Clinical Nutrition 1995; 61: 155–60.CrossRefGoogle ScholarPubMed
20Hollander, M, Wolfe, DA. Nonparametric Statistical Methods. New York: John Wiley & Sons, 1973.Google Scholar
21Donald, AW, Gardner, IA, Wiggins, AD. Cut-off points for aggregate herd testing in the presence of disease clustering and correlation of test errors. Preventive Veterinary Medicine 1994; 19: 167–87.CrossRefGoogle Scholar
22Bohning, D, Greiner, M. Prevalence estimation under heterogeneity in the example of bovine trypanosomosis in Uganda. Preventive Veterinary Medicine 1998; 36: 11–23.CrossRefGoogle ScholarPubMed
23Menon, P, Ruel, MT, Morris, SS. Socioeconomic Differentials in Child Stunting are Consistently Larger in Urban than in Rural Areas. FCND Discussion Paper No. 97 Washington, DC: International Food Policy Research Institute, Food Consumption and Nutrition Division, 2000.Google Scholar
24Lé, TN, Verma, VK. An Analysis of Sample Designs and Sampling Errors of the Demographic and Health Surveys. DHS Analytical Reports. Calverton, MD: ORC Macro, 1997.Google Scholar
25Gulliford, MC, Ukoumunne, CU, Chinn, S. Components of variance and intraclass correlations for the design of community-based surveys and intervention studies. American Journal of Epidemiology 1999; 149: 876–83.CrossRefGoogle ScholarPubMed
26Bogin, B. Patterns of Human Growth, 2nd ed. Cambridge: Cambridge University Press, 1999.Google ScholarPubMed