Development of an international scale of socio-economic position based on household assets

Emerging Themes in Epidemiology

Peter Burney²  Jaymini Patel²  Karima El-Rhazi³  Daniel O. Obaseki¹  Imed Harrabi⁴  Cosetta Minelli²  John Townend² 

[1] Department of Medicine, Obafemi Awolowo University, Ile-Ife, Nigeria;National Heart and Lung Institute, Imperial College, Emmanuel Kaye Building, 1b Manresa Road, London SW3 6LR, UK;Laboratoire d’épidémiologie, Recherche Clinique et Santé Communautaire, Faculté de Médecine de Fès, Université SidiMohammed Ben Abdellah, Fez, Morocco;Faculté de Médecine, Sousse, Sousse, Tunisia

关键词: Respiratory diseases;    Developing countries;    Socio-economic position;    Measurement tool development;    Poverty;   

Others  :  1228182
DOI  :  10.1186/s12982-015-0035-6

 received in 2015-05-13, accepted in 2015-09-08,  发布年份 2015

【 摘 要 】

Background

The importance of studying associations between socio-economic position and health has often been highlighted. Previous studies have linked the prevalence and severity of lung disease with national wealth and with socio-economic position within some countries but there has been no systematic evaluation of the association between lung function and poverty at the individual level on a global scale. The BOLD study has collected data on lung function for individuals in a wide range of countries, however a barrier to relating this to personal socio-economic position is the need for a suitable measure to compare individuals within and between countries. In this paper we test a method for assessing socio-economic position based on the scalability of a set of durable assets (Mokken scaling), and compare its usefulness across countries of varying gross national income per capita.

Results

Ten out of 15 candidate asset questions included in the questionnaire were found to form a Mokken type scale closely associated with GNI per capita (Spearman’s rank r _s  = 0.91, p = 0.002). The same set of assets conformed to a scale in 7 out of the 8 countries, the remaining country being Saudi Arabia where most respondents owned most of the assets. There was good consistency in the rank ordering of ownership of the assets in the different countries (Cronbach’s alpha = 0.96). Scores on the Mokken scale were highly correlated with scores developed using principal component analysis (r _s  = 0.977).

Conclusions

Mokken scaling is a potentially valuable tool for uncovering links between disease and socio-economic position within and between countries. It provides an alternative to currently used methods such as principal component analysis for combining personal asset data to give an indication of individuals’ relative wealth. Relative strengths of the Mokken scale method were considered to be ease of interpretation, adaptability for comparison with other datasets, and reliability of imputation for even quite large proportions of missing values.

【 授权许可】

   
2015 Townend et al.

【 图 表 】

Fig.1.

Fig.2.

Fig.3.

Fig.4.

Fig.5.

Fig.6.

【 参考文献 】

• [1]Gwatkin DR, Rutstein S, Johnson K, Suliman E, Wagstaff A, Amouzou A. Socio-economic differences in health, nutrition and population within developing countries—an overview. The World Bank, Washington DC; 2007.
• [2]Galobardes B, Lynch J, Smith GD. Measuring socioeconomic position in health research. Br Med Bull. 2007; 81:21-37.
• [3]Buist AS, Vollmer WM, Sullivan SD, Weiss KB, Lee TA, Menezes A et al.. The burden of obstructive lung disease initiative (BOLD): rationale and design. COPD. 2005; 2:277-283.
• [4]Burney P, Jithoo A, Kato B, Janson C, Mannino D, Niżankowska-Mogilnicka E et al.. Chronic obstructive pulmonary disease mortality and prevalence: the associations with smoking and poverty—a BOLD analysis. Thorax. 2014; 69:465-473.
• [5]Registrar General. Anon. Occupational mortality: the registrar general’s decennial supplement for Great Britain, 1979–80, 1982–83. Office of Population Censuses and Surveys, London: H.M. Stationery Office; 1986.
• [6]Barker DJP, Osmond C. Childhood respiratory infection and adult chronic bronchitis in England and Wales. Br Med J. 1986; 293:1271-1275.
• [7]Hegewald M, Crapo R. Socioeconomic status and lung function. Chest. 2007; 132:1608-1614.
• [8]Howe LD, Galobardes B, Matijasevich A, Gordon D, Johnston D, Onwujekwe O et al.. Measuring socio-economic position for epidemiological studies in low- and middle-income countries: a methods of measurement in epidemiology paper. Int J Epidemiol. 2012; 41:871-886.
• [9]Reidpath DD, Ahmadi K. A novel nonparametric item response theory approach to measuring socioeconomic position: a comparison using household expenditure data from a Vietnam health survey, 2003. Emerg Themes Epidemiol. 2014; 11:9. BioMed Central Full Text
• [10]Gillespie M, Tenvergert EM, Kingma J. Using Mokken scale analysis to develop unidimensional scales: do the six abortion items in the NORC GSS form one or two scales? Qual Quant. 1987; 21:393-408.
• [11]Mokken RJ. A theory and procedure for scale analysis: with applications in political research. de Gruyter, Berlin; 1971.
• [12]Guttman L. A basis for scaling qualitative data. Am Social Rev. 1944; 9:139-150.
• [13]Wright BD, Masters GN. Rating scale analysis. Mesa Press, Chicago; 1982.
• [14]Hardouin J, Bonnaud-Antignac A, Bille V. Nonparametric item response theory using Stata. Stata J. 2011; 11:30-51.
• [15]Zinn F, Henderson D, Nystuen J, Drake WD. A stochastic cumulative scaling method applied to measuring wealth in Indonesian villages. Environ Plan A. 1992; 24:1155-1166.
• [16]Karami H. An introduction to differential item functioning. Int J Educ Psychol Assess. 2012; 11:59-76.
• [17]GNI per capita, PPP (current international $). The World Bank. 2014. http://data.worldbank.org/indicator/NY.GNP.PCAP.PP.CD. Accessed 13 July 2015.
• [18]Hardouin J. Imputation of missing item responses with the Mokken scaling (help file for Stata add-in command Imputemok). http://fmwww.bc.edu/repec/bocode/i/imputemok.html. Accessed 16 July 2015.
• [19]Filmer D, Pritchett L. The effect of household wealth on educational attainment: evidence from 35 countries. Popul Dev Rev. 1999; 25:85-120.
• [20]Filmer D, Pritchett L. Estimating wealth effects without expenditure data—or tears: an application to educational enrollments in states of India. Demography. 2001; 38:115-132.
• [21]Rutstein SO, Johnson K. The DHS wealth index. DHS comparative reports 6. Calverton: ORC Macro; 2004.
• [22]Vyas S, Kumaranayake L. Constructing socio-economic status indices: how to use principal components analysis. Health Policy Plan. 2006; 21:459-468.
• [23]Kolenikov S, Angeles G. Socioeconomic status measurement with discrete proxy variables: is principal component analysis a reliable answer? Rev Income Wealth. 2009; 55:128-165.
• [24]Traissac P, Martin-Prevel Y. Alternatives to principal components analysis to derive asset-based indices to measure socio-economic position in low- and middle-income countries: the case for multiple correspondence analysis. Int J Epidemiol. 2012; 41:1207-1208.
• [25]Howe LD, Hargreaves JR, Huttly SRA. Issues in the construction of wealth indices for the measurement of socio-economic position in low-income countries. Emerg Themes Epidemiol. 2008; 5:3. BioMed Central Full Text
• [26]Rutstein SO, Staveteig S. Making the Demographic and Health Surveys Wealth Index comparable. DHS methodological reports 9. Rockville: ICF International; 2014.
• [27]Smits J, Steendijk R. The International Wealth Index (IWI). NiCE working paper 12-107. Nijmegen: Nijmegen Centre for Economics; 2013.