Measuring socioeconomic status in multicountry studies: results from the eight-country MAL-ED study
Psaki, Stephanie; Seidman, Jessica; Miller, Mark; Gottlieb, Michael; Bhutta, Zulfiqar A.; Ahmed, Tahmeed; Ahmed, A. M. Shamsir; Bessong, Pascal; John, Sushil M.; Kang, Gagandeep; Kosek, Margaret; Lima, Aldo; Shrestha, Prakash S.; Svensen, Erling; Checkley, William; MAL-ED Network Investigators
Peer reviewed, Journal article
Published version
Date
2014-03-21Metadata
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Original version
https://doi.org/10.1186/1478-7954-12-8Abstract
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.
Publisher
BioMed CentralJournal
Population Health MetricsCopyright
Copyright 2014 Psaki et al.; licensee BioMed Central Ltd.Stephanie R Psaki et al.; licensee BioMed Central Ltd.