Measuring socioeconomic status in multicountry studies: Results from the eight-country MAL-ED study

Stephanie R. Psaki, Jessica C. Seidman, Mark Miller, Michael Gottlieb, Zulfiqar A. Bhutta, Tahmeed Ahmed, AM M.S. Ahmed, Pascal Bessong, Sushil M. John, Gagandeep Kang, Margaret Kosek, Aldo Lima, Prakash Shrestha, Erling Svensen, William Checkley

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168 Citations (Scopus)


Background: There is no standardized approach to comparing socioeconomic status (SES) across multiple sites in epidemiological studies. This is particularly problematic when cross-country comparisons are of interest. We sought to develop a simple measure of SES that would perform well across diverse, resource-limited settings.Methods: A cross-sectional study was conducted with 800 children aged 24 to 60 months across eight resource-limited settings. Parents were asked to respond to a household SES questionnaire, and the height of each child was measured. A statistical analysis was done in two phases. First, the best approach for selecting and weighting household assets as a proxy for wealth was identified. We compared four approaches to measuring wealth: maternal education, principal components analysis, Multidimensional Poverty Index, and a novel variable selection approach based on the use of random forests. Second, the selected wealth measure was combined with other relevant variables to form a more complete measure of household SES. We used child height-for-age Z-score (HAZ) as the outcome of interest.Results: Mean age of study children was 41 months, 52% were boys, and 42% were stunted. Using cross-validation, we found that random forests yielded the lowest prediction error when selecting assets as a measure of household wealth. The final SES index included access to improved water and sanitation, eight selected assets, maternal education, and household income (the WAMI index). A 25% difference in the WAMI index was positively associated with a difference of 0.38 standard deviations in HAZ (95% CI 0.22 to 0.55).Conclusions: Statistical learning methods such as random forests provide an alternative to principal components analysis in the development of SES scores. Results from this multicountry study demonstrate the validity of a simplified SES index. With further validation, this simplified index may provide a standard approach for SES adjustment across resource-limited settings.

Original languageEnglish
Article number8
JournalPopulation Health Metrics
Issue number1
Publication statusPublished - 21 Mar 2014


  • Child growth
  • Classification
  • Measurement
  • Socioeconomic status


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