title: Measuring socioeconomic status in multicountry studies: results from the eight-country MAL-ED study.
abstract: 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.
authors.forename: Stephanie R
authors.initials: SR
authors.lastname: Psaki
authors.forename: Jessica C
authors.initials: JC
authors.lastname: Seidman
authors.forename: Mark
authors.initials: M
authors.lastname: Miller
authors.forename: Michael
authors.initials: M
authors.lastname: Gottlieb
authors.forename: Zulfiqar A
authors.initials: ZA
authors.lastname: Bhutta
authors.forename: Tahmeed
authors.initials: T
authors.lastname: Ahmed
authors.forename: Am Shamsir
authors.initials: AS
authors.lastname: Ahmed
authors.forename: Pascal
authors.initials: P
authors.lastname: Bessong
authors.forename: Sushil M
authors.initials: SM
authors.lastname: John
authors.forename: Gagandeep
authors.initials: G
authors.lastname: Kang
authors.forename: Margaret
authors.initials: M
authors.lastname: Kosek
authors.forename: Aldo
authors.initials: A
authors.lastname: Lima
authors.forename: Prakash
authors.initials: P
authors.lastname: Shrestha
authors.forename: Erling
authors.initials: E
authors.lastname: Svensen
authors.affiliation: Fogarty International Center, National Institutes of Health, Bethesda, USA. wcheckl1@jhmi.edu.
authors.forename: William
authors.initials: W
authors.lastname: Checkley