Micro-level Estimation of Child Undernutrition Indicators in Cambodia

Tomoki FUJII (tfujii@smu.edu.sg), Singapore Management University

Presented at the First Asian ISI Satellite Meeting on Small Area Estimation (SAE) Bangkok, Thailand.

September 2, 2013

Child Undernutrition: Why matter?

3.7 million deaths of young children related to malnutrition worldwide (WHO, 2002).In Cambodia, almost half of the children are malnourished in Year 2000.Child malnutrition associated with higher mortality, morbidity and delayed physical and mental development.

Policy Issues

Limited resources to address child undernutrition.Targeting helpful for efficient use of resources.Necessary information often not available.In Cambodia, the DHS allows us to estimate prevalence of undernutrition only at the level of 17 strata.

Objective

Develop methodology to estimate commune-level prevalence of child undernutrition.– Useful for formulating targeting policies– Can be presented as a map.– Disaggregate estimates from 17 strata to about 1,600

communes.

OutlineMeasurement of undernutritionMethodology– Overview– Estimation– Simulation

DataResultsConclusion

Measurement of Undernutrition (1)

Individual nutrition status is measured by how many SDs from the median of reference healthy population. (Z-score).– Height-for-age: Z<-2 → stunted– Weight-for-age: Z<-2 → underweight

Standardize height-for-age and weight-for-age Z-scores to 24-month old girl. Call them standardized height and weight. – The choice of reference age and sex doesn’t affect the

results.

Measurement of Undernutrition (2)

Height-for-age and weight-for-age reflect different aspects of undernutrition.One can lose weight, but not height.Linear growth slower than growth in body mass.Height-for-age reflects status of nutrition in a longer-term than weight-for-age does.

Overview of estimation method

Built on the small area estimation by Elbers, Lanjouw & Lanjouw (2002,2003; ELL).– Combine census and survey.– Relate them via regression by using common variables and

tertiary data set that can be liked to both

Methodology works in two steps. – Estimation stage: Find the model parameters and

distribution of error terms.– Simulation stage: Randomly draw model parameters and

error terms to impute dependent variables using estimated distribution..

Methodology: Estimation (1)

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x RegressionHeight, Location Household Individual IndividualCoefficientWeight Effect Effect EffectVariables

Index for indicators (height or weight) : :h iLocation, Household, Individual

Standardized height and weight are related to a set of variables common between census and survey, and variables that can be linked to both census and surveyEstimate regression coefficients are by GLS

Methodology: Estimation (2)

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Location Household Individual Intrapersonal Effect Effect Effect Correlation

For GLS, first need to get above parameters.Note heteroskedasticity and intra-personal correlation.Get residuals from OLS (First-stage regression). Use them to calculate the estimates of above parameters.Use logistic regression for heteroskedastic model.Also obtain empirical distribution of each error component.

Methodology: Simulation (1)

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:Drawn standardized error component

Carry out Monte-Carlo simulation to explicitly evaluate standard errors of estimates.Impute standardized height and weight to each census record in each round of simulation.Draw parameters and error components.

Methodology: Simulation (2)

S t a n d a r d i z e d h e i g h t / w e i g h t a t z = - 2 . ( P o v . L i n e )( ) ( ) ( )

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S e t o f i n d i v i d u a l s ( e . g . c o m m u n e )

In each round of simulation, we calculate prevalence of undernutrition for each indicator.We can also calculate inequality.Take the mean and standard deviation of estimates over r to get the point estimates and their standard errors.

Poverty vs nutrition mapping

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ocation Household Individual

Effect Effect Effect

Index for indicators (height or weight) Location, Household, Individual

Poverty mapping

Nutrition mapping

Difference between poverty mapping (ELL) and nutrition mapping.• Type of data set (anthropometrics vs consumption)• Household effect• Explicit treatment of finite sample property • Individual effect correlated across (multiple) indicators.

Data

Cambodian Demographic and Health Survey (CDHS) 2000. Includes anthropometric indicators. About 3,600 children under five.Cambodian National Population Census 1998. Covers 1.4 million children under five in Cambodia. Does not have anthropometric indicators.Geographic data set (compilation of satellite data, census means and other geographic data).

– Both can be linked to both CDHS and Census

Results (1)

Split the data into five ecozones (Coastal, Plain, Plateau, Tonle Sap and Urban)In the benchmark result, village is taken as a unit of clustering– The results for commune-level and district-level clustering are

similar.

In the first stage regression, about 40-60% of the variation in anthropometric indicators were captured.

Results (2)

Location variables improved the explanatory power of the regression.Individual-specific random effects dominates the cluster-specific and household-specific effects.Correlations of unobserved individual effect was .42-.53.

Results (3)

The standard errors at the ecozone level are smaller for this study than DHS only (see next)Median SEs for commune- and district-level estimates are less than 4% and 3%, respectively, for both stunting and underweight. There are, however, communes with high SEs (Max around 20%).

Results (4)

Survey-only estimates and SAE are consistent.– They don’t differ significantly at aggregate levels (see

next)– SAE estimates are generally more accurate.– Correlation between stunting and underweight is

reproduced thanks to the intrapersonal correlation.• Survey only correlation at district-level: 62.6% (s.e., 6.9%)• SAE with the intrapersonal correlation: 53.7% (s.e.: 6.6%)• SAE without the intrapersonal correlation: 26.7% (s.e.: 6.2%)

Results (5)

Both ecozone-level and provincial level estimates are consistent with DHS only.

Mean (SE) Mean (SE)

Urban 37.9 (2.9) 37.0 (1.6)Plain 47.6 (2.8) 50.8 (1.8)

Tonle Sap 42.9 (2.4) 44.0 (2.1)Coastal 47.2 (5.1) 47.1 (3.4)Plateau 47.1 (3.1) 46.9 (1.4)Urban 39.6 (2.7) 38.7 (1.6)Plain 47.8 (2.7) 46.6 (1.7)

Tonle Sap 45.8 (2.5) 43.1 (1.9)Coastal 39.0 (5.3) 39.0 (3.3)Plateau 46.4 (3.2) 45.9 (1.7)

Stunting

Underweight

Indicator EcozoneDHS Only This Study

Prevalence of Stunting

The most intuitive representation.

Stunting vs Underweight

High and low are in comparison with the national average.May indicate the change in nutritional status in the commune.

In comparison with national average

Difference from the national average divided by the standard error.

Density

Density of undernutrition.

Summary

Developed a methodology to derive estimates of prevalence of undernutrition in small areasAllowed for a richer structure of error terms suitable for the estimation of multiple undernutrition indicatorsEstimates were consistent with the survey and SEs were at an acceptable levelThis methodology is applicable to other countries

Thank you!