Details

Autor(en) / Beteiligte

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

Titel

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

Ist Teil von

• Population health metrics, 2014-03, Vol.12 (1), p.8-8, Article 8

Ort / Verlag

England: BioMed Central Ltd

Erscheinungsjahr

2014

Link zum Volltext

Quelle

SpringerLink (Online service)

Beschreibungen/Notizen

• 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. 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. 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). 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.