Socio-economic disparity in the occurrence of disability among older adults in six low and middle income countries

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Abstract

Purpose

Nearly 200m people in the world experience considerable functioning difficulties. Also, more than three-fourth of the population aged 50 years and over is suffering from some kind of disability in India, China, Ghana, Russia, Mexico and South Africa. Despite the compelling nature of this issue, evidence on socioeconomic disparity in the occurrence of disability is lacking throughout the world and particularly in the aforementioned countries. The purpose of this paper is twofold – first, to examine the socioeconomic inequalities in the prevalence of disability in the selected countries; and second, to investigate the cross-country differentials in the prevalence of disability by socioeconomic characteristics.

Design/methodology/approach

The authors use data from the Study on Global Ageing and Adult Health (SAGE) conducted in China, Ghana, India, Mexico, Russia and South Africa during 2007–2010. Disability scores have been constructed using Item Response Theory Partial Credit Model based on eight health and functioning domains. Bivariate analysis, concentration curves, concentration indices and multivariate regressions have been used in the analysis presented in this paper.

Findings

The authors find that the prevalence of disability varied considerably across sociodemographic groups. Moreover, this variation is not uniform across all countries. Also, age, Sex, work status, years of schooling and economic status emerged out as significant predictors of disability among the studied countries.

Originality/value

This is perhaps the first study which examines the socioeconomic inequality in disability conceptualized in a comprehensive manner among older adults spread across low to upper middle income countries. The alarming level of prevalence of disability among sociodemographic disadvantage groups calls for immediate attention in terms of detailed study of risk factors, effective policy and timely intervention.

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Most cited references 45

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Estimating wealth effects without expenditure data—or tears: An application to educational enrollments in states of India

Deon Filmer, Lant H. Pritchett (2001)

Using data from India, we estimate the relationship between household wealth and children’s school enrollment. We proxy wealth by constructing a linear index from asset ownership indicators, using principal-components analysis to derive weights. In Indian data this index is robust to the assets included, and produces internally coherent results. State-level results correspond well to independent data on per capita output and poverty. To validate the method and to show that the asset index predicts enrollments as accurately as expenditures, or more so, we use data sets from Indonesia, Pakistan, and Nepal that contain information on both expenditures and assets. The results show large, variable wealth gaps in children’s enrollment across Indian states. On average a “rich” child is 31 percentage points more likely to be enrolled than a “poor” child, but this gap varies from only 4.6 percentage points in Kerala to 38.2 in Uttar Pradesh and 42.6 in Bihar.

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Constructing socio-economic status indices: how to use principal components analysis.

S Vyas, L Kumaranayake (2006)

Theoretically, measures of household wealth can be reflected by income, consumption or expenditure information. However, the collection of accurate income and consumption data requires extensive resources for household surveys. Given the increasingly routine application of principal components analysis (PCA) using asset data in creating socio-economic status (SES) indices, we review how PCA-based indices are constructed, how they can be used, and their validity and limitations. Specifically, issues related to choice of variables, data preparation and problems such as data clustering are addressed. Interpretation of results and methods of classifying households into SES groups are also discussed. PCA has been validated as a method to describe SES differentiation within a population. Issues related to the underlying data will affect PCA and this should be considered when generating and interpreting results.