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      Inequalities in multimorbidity in South Africa

      research-article
      1 ,
      International Journal for Equity in Health
      BioMed Central
      Multimorbidity, Socioeconomic inequality, South Africa

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          Abstract

          Background

          Very little is known about socioeconomic related inequalities in multimorbidity, especially in developing countries. Traditionally, studies on health inequalities have mainly focused on a single disease condition or different conditions in isolation. This paper examines socioeconomic inequality in multimorbidity in illness and disability in South Africa between 2005 and 2008.

          Methods

          Data were drawn from the 2005, 2006, 2007, and 2008 rounds of the nationally representative annual South African General Household Surveys (GHS). Indirectly standardised concentration indices were used to assess socioeconomic inequality. A proxy index of socioeconomic status was constructed, for each year, using a selected set of variables that are available in all the GHS rounds. Multimorbidity in illness and disability were constructed using data on nine illnesses and six disabilities contained in the GHS.

          Results

          Multimorbidity affects a substantial number of South Africans. Most often, based on the nine illness conditions and six disability conditions considered, multimorbidity in illness and multimorbidity in disability are each found to involve only two conditions. In 2008 in South Africa, the multimorbidity that affected the greatest number of individuals (0.6% of the population) combined high blood pressure (BP) with at least one other illness. The combination of sexually transmitted diseases (STDs) and other condition or conditions is the least reported (i.e. 0.02% of the population). Between 2005 and 2008, multimorbidity in illness and disability is more prevalent among the poor; in disabilities this is yet more consistent. The concentration index of multiple illnesses in 2005 and 2008 are −0.0009 and −0.0006 respectively. The corresponding values for multiple disabilities are −0.0006 and −0.0006 respectively.

          Conclusion

          While there is a dearth of information on the socioeconomic distribution of multimorbidity in many developing countries, this paper has shown that its distribution in South Africa indicates that the poor bear a greater burden of multimorbidity. This is more so for disability than for illness. This paper argues that, given the high burden and skewed socioeconomic distribution of multimorbidity, there is a need to design policies to address this situation. Further, there is a need to design surveys that specifically assess multimorbidity.

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          Most cited references14

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          Estimating wealth effects without expenditure data--or tears: an application to educational enrollments in states of India.

          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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            The bounds of the concentration index when the variable of interest is binary, with an application to immunization inequality.

            When the health sector variable whose inequality is being investigated is binary, the minimum and maximum possible values of the concentration index are equal to micro-1 and 1-micro, respectively, where micro is the mean of the variable in question. Thus as the mean increases, the range of the possible values of the concentration index shrinks, tending to zero as the mean tends to one and the concentration index tends to zero. Examples are presented on levels of and inequalities in immunization across 41 developing countries, and on changes in coverage and inequalities in selected countries. Copyright (c) 2004 John Wiley & Sons, Ltd.
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              Correcting the concentration index.

              In recent years attention has been drawn to several shortcomings of the Concentration Index, a frequently used indicator of the socioeconomic inequality of health. Some modifications have been suggested, but these are only partial remedies. This paper proposes a corrected version of the Concentration Index which is superior to the original Concentration Index and its variants, in the sense that it is a rank-dependent indicator which satisfies four key requirements (transfer, level independence, cardinal invariance, and mirror). The paper also shows how the corrected Concentration Index can be decomposed and generalized.
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                Author and article information

                Journal
                Int J Equity Health
                Int J Equity Health
                International Journal for Equity in Health
                BioMed Central
                1475-9276
                2013
                20 August 2013
                : 12
                : 64
                Affiliations
                [1 ]Health Economics Unit, School of Public Health and Family Medicine, University of Cape Town, Observatory, Cape Town 925, South Africa
                Article
                1475-9276-12-64
                10.1186/1475-9276-12-64
                3765402
                23962076
                f39ed88d-542b-4774-8e01-34da51e47659
                Copyright ©2013 Ataguba; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 15 January 2013
                : 13 July 2013
                Categories
                Research

                Health & Social care
                multimorbidity,socioeconomic inequality,south africa
                Health & Social care
                multimorbidity, socioeconomic inequality, south africa

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