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

Population Health Metrics

William Checkley⁷  Erling Svensen²  Prakash Shrestha⁹  Aldo Lima⁵  Margaret Kosek⁷  Gagandeep Kang⁸  Sushil M John⁸  Pascal Bessong⁶  AM Shamsir Ahmed⁴  Tahmeed Ahmed⁴  Zulfiqar A Bhutta¹  Michael Gottlieb³  Mark Miller^1,10  Jessica C Seidman⁷  Stephanie R Psaki⁷ 

[1] Division of Women and Child Health, Aga Khan University, Karachi, Pakistan;Haydom Lutheran Hospital, Haydom, Tanzania;Science Division, Foundation for the National Institutes of Health, Bethesda, USA;Division of Nutrition and Food Security, International Centers for Diarrheal Disease Research, Matlab, Bangladesh;Clinical Research Unit and Institute of Biomedicine, Federal University of Ceara, Fortaleza, Brazil;HIV/AIDS and Global Health Research Programme, University of Venda, Thohoyandou, South Africa;Program in Global Disease Epidemiology and Control, Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, USA;Christian Medical College, Vellore, India;Institute of Medicine, Tribhuvan University, Kathmandu, Nepal;Fogarty International Center, National Institutes of Health, Bethesda, USA

关键词: Measurement;    Classification;    Child growth;    Socioeconomic status;   

Others  :  802650
DOI  :  10.1186/1478-7954-12-8

 received in 2013-07-02, accepted in 2014-02-14,  发布年份 2014

【 摘 要 】

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.

【 授权许可】

   
2014 Psaki et al.; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Adler NE, Boyce T, Chesney MA, Cohen S, Folkman S, Kahn RL, Syme SL: Socioeconomic status and health. The challenge of the gradient. Am Psychol 1994, 49:15-24.
• [2]Pollack CE, Chideya S, Cubbin C, Williams B, Dekker M, Braveman P: Should health studies measure wealth? A systematic review. Am J Prev Med 2007, 33:250-264.
• [3]Black RE, Allen LH, Bhutta ZA, Caulfield LE, de Onis M, Ezzati M, Mathers C, Rivera J, Maternal and Child Undernutrition Study Group: Maternal and child undernutrition: global and regional exposures and health consequences. Lancet 2008, 371:243-260.
• [4]Walker SP, Wachs TD, Gardner JM, Lozoff B, Wasserman GA, Pollitt E, Carter JA, International Child Development Steering Group: Child development: risk factors for adverse outcomes in developing countries. Lancet 2007, 369:145-157.
• [5]Galobardes B, Lynch JW, Davey Smith G: Childhood socioeconomic circumstances and cause-specific mortality in adulthood: systematic review and interpretation. Epidemiol Rev 2004, 26:7-21.
• [6]O’Donnell O, van Doorslaer E, Wagstaff A, Lindelow M: Analyzing Health Equity using Household Survey Data: a Guide to Techniques and their Implementation. Washington DC: World Development Institute, World Bank; 2007. http://www.worldbank.org/analyzinghealthequity webcite (ISBN: 0-8213-6933-4)
• [7]Filmer D, Pritchett LH: Estimating wealth effects without expenditure data-or tears: an application to educational enrollments in states of India. Demography 2001, 38:115-132.
• [8]Rutstein SO, Johnson K: The DHS Wealth Index: DHS comparative reports No. 6. Calverton, MD: ORC Macro; 2004.
• [9]Rutstein SO: The DHS Wealth Index: Approaches for rural and urban areas. DHS Working Papers No. 60. Calverton, MD: Macro International; 2008.
• [10]Pitchforth E, van Teijlingen E, Graham W, Fitzmaurice A: Development of a proxy wealth index for women utilizing emergency obstetric care in Bangladesh. Health Policy Plan 2007, 22:311-319.
• [11]van Bodegom D, May L, Kuningas M, Kaptijn R, Thomése F, Meij HJ, Amankwa J, Westendorp RG: Socio-economic status by rapid appraisal is highly correlated with mortality risks in rural Africa. Trans R Soc Trop Med Hyg 2009, 103:795-800.
• [12]Alkire S, Santos ME: Acute Multidimensional Poverty: A new index for developing countries. Oxford: Oxford Poverty & Human Development Initiative; 2010. OPHI Working Paper 38
• [13]USAID: Demographic and Health Surveys. Available at: http://www.measuredhs.com/ webcite. Accessed December 10, 2012
• [14]World Health Organization: Child Growth Standards: Anthropometry Macros. 2011. Available at: http://www.who.int/childgrowth/software/en/index.htm webcite
• [15]de Onis M, Garza C, Victora CG: The WHO Multicentre Growth Reference Study: strategy for developing a new international growth reference. Forum Nutr 2003, 56:238-240.
• [16]Cronbach LJ, Meehl PE: Construct Validity in Psychological Tests. Psychol Bull 1955, 52:281-302.
• [17]Hastie T, Tibshirani R, Friedman J: Elements of Statistical Learning: Data Mining, Inference, and Prediction. Second edition. New York, NY: Springer-Verlag; 2009.
• [18]Monteiro CA, Conde WL, Popkin BM: Obesity and inequities in health in the developing world. Int J Obes 2004, 28:1181-1186.
• [19]Desai S, Alva S: Maternal education and child health: Is there a strong causal relationship? Demography 1998, 35:71-81.
• [20]Vyas S, Kumaranayake L: Constructing socio-economic status indices: how to use principal components analysis. Health Policy Plan 2006, 21:459-468.
• [21]Pagano M, Gauvreau K: Principles of Biostatistics. 2nd edition. Pacific Grove, CA: Duxbury Thomson Learning; 2000.
• [22]DeVellis R: Scale Development: theory and applications. 2nd edition. Thousand Oaks, CA: SAGE Publications; 2003.
• [23]Braveman P, Cubbin C, Marchi K, Egerter S, Chavez G: Measuring socioeconomic status/position in studies of racial/ethnic disparities: maternal and infant health. Public Health Rep 2001, 116:449-463.
• [24]Hackman DA, Farah MJ: Socioeconomic status and the developing brain. Trends Cogn Sci 2009, 13:65-73.
• [25]Braveman PA, Cubbin C, Egerter S, Chideya S, Marchi KS, Metzler M, Posner S: Socioeconomic status in health research: one size does not fit all. JAMA 2005, 294:2879-2888.
• [26]Krieger N, Williams DR, Moss NE: Measuring social class in US public health research: concepts, methodologies, and guidelines. Annu Rev Public Health 1997, 18:341-378.
• [27]Daly MC, Duncan GJ, McDonough P, Williams DR: Optimal indicators of socioeconomic status for health research. Am J Public Health 2002, 92:1151-1157.
• [28]Fosto JC, Kuate-Defo B: Measuring Socioeconomic Status in Health Research in Developing Countries: Should we be focusing on households, communities or both? Soc Indicators Res 2005, 72:189-237.