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


METHODS
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.


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


METHODS
Pre-school children of mothers interviewed by DHS.


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.
Fenn
Bridget
B
Department of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK. bridget.fenn@lshtm.ac.uk
Morris
Saul S
SS
Frost
Chris
C
D001827:Body Height; D001835:Body Weight; D015362:Child Nutrition Disorders / Q000175:diagnosis* / Q000453:epidemiology*; D002675:Child, Preschool; D016000:Cluster Analysis; D003906:Developing Countries; D005260:Female; D005843:Geography; D006306:Health Surveys; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D009752:Nutritional Status; D011159:Population Surveillance; D015995:Prevalence; D012680:Sensitivity and Specificity