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Micro-level Estimation of Child Undernutrition Indicators in Cambodia

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?.

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Micro-level Estimation of Child Undernutrition Indicators in Cambodia

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  1. 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

  2. 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.

  3. 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.

  4. 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.

  5. Outline • Measurement of undernutrition • Methodology • Overview • Estimation • Simulation • Data • Results • Conclusion

  6. 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.

  7. 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.

  8. 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..

  9. Methodology: Estimation (1) • 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 survey • Estimate regression coefficients are by GLS

  10. Methodology: Estimation (2) • 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.

  11. Methodology: Simulation (1) • 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.

  12. Methodology: Simulation (2) • 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.

  13. Poverty vs nutrition mapping 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.

  14. 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

  15. 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.

  16. 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.

  17. 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%).

  18. 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%)

  19. Results (5) • Both ecozone-level and provincial level estimates are consistent with DHS only.

  20. Prevalence of Stunting The most intuitive representation.

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

  22. In comparison with national average Difference from the national average divided by the standard error.

  23. Density Density of undernutrition.

  24. Summary • Developed a methodology to derive estimates of prevalence of undernutrition in small areas • Allowed for a richer structure of error terms suitable for the estimation of multiple undernutrition indicators • Estimates were consistent with the survey and SEs were at an acceptable level • This methodology is applicable to other countries

  25. Thank you!

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