Berkvens, D.

Geckova, A.

Harper, S.

Hosseinpoor, A.R.

Konings, P.

Lorant, V.

Lynch, J.

Speybroeck, N.

English

Although "average'' health conditions at national and global levels may have improved, inequalities in health conditions still exist among and within countries (Mackenbach et al. 2008; Smits and Monden 2009). Epidemiologists often focus on average health and try to understand its determinants. This is important, but measuring and understanding inequalities in health, i.e. the gap in health between disadvantaged and better-off groups, is an additional concern for health policy makers, health communicators and health scientist

Proceedings - University of Groningen

   University of GroningenAnalysis of socioeconomic health inequalities using the concentration indexKonings, P.; Harper, S.; Lynch, J.; Hosseinpoor, A.R.; Berkvens, D.; Lorant, V.; Geckova, A.;Speybroeck, N.Published in:International Journal of Public HealthIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2010Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Konings, P., Harper, S., Lynch, J., Hosseinpoor, A. R., Berkvens, D., Lorant, V., Geckova, A., &Speybroeck, N. (2010). Analysis of socioeconomic health inequalities using the concentration index.International Journal of Public Health, 55(1), 71-74.CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 28-10-2022HINTS & KINKSAnalysis of socioeconomic health inequalitiesusing the concentration indexPeter Konings • Sam Harper • John Lynch •Ahmad Reza Hosseinpoor • Dirk Berkvens •Vincent Lorant • Andrea Geckova • Niko SpeybroeckReceived: 28 May 2009 / Revised: 11 September 2009 / Accepted: 14 September 2009 / Published online: 2 October 2009 Birkhäuser Verlag, Basel/Switzerland 2009Although ‘‘average’’ health conditions at national andglobal levels may have improved, inequalities in healthconditions still exist among and within countries (Mac-kenbach et al. 2008; Smits and Monden 2009).Epidemiologists often focus on average health and try tounderstand its determinants. This is important, but mea-suring and understanding inequalities in health, i.e. the gapin health between disadvantaged and better-off groups, isan additional concern for health policy makers, healthcommunicators and health scientists.This Hints & Kinks paper describes the computation ofthe concentration index. This index not only provides anindicator of health inequality but can also be decomposedproportionally into contributions of different inequality ofhealth determinants. In a subsequent issue of this journal,the technique of decomposing inequalities of health will beclarified.The relative concentration index (RCI) summarizesrelative inequality across the entire socioeconomic distri-bution rather than simply comparing extremes [an absoluteconcentration index (ACI) may also be calculated bymultiplying RCI by the mean level of health (Wagstaffet al. 1991)]. The RCI belongs to a general class of indi-cators sometimes called measures of ‘disproportionality’(Harper and Lynch 2007). Such measures all expressinequality as a function of differences between shares ofsome health outcome compared with shares of the popu-lation. The RCI measures this disproportionality acrossordered social groups, such as income or social class, and,therefore, reflects the direction of the social gradient indisease (Wagstaff et al. 1991).Assume yi is a continuous health outcome. The RCI of yresults from a relative concentration curve, which graphson the x-axis the cumulative percentage of the sample,ranked by an indicator of socioeconomic position, such asliving standards, or income beginning with the poorest. They-axis then indicates the cumulative percentage of thehealth outcome corresponding to each cumulative per-centage of the distribution of the socioeconomic indicator.Figure 1 provides an example of a relative concentrationcurve, where the health variable is malnutrition in Ghana in2003. Figure 1 is constructed using data from the GhanaDemographic Health Survey in 2003 which was analysedin Van de Poel et al. (2007). It shows that the level ofmalnutrition accumulates faster amongst the poor thanP. KoningsDepartment of Electrical Engineering, SCD (SISTA),K.U. Leuven, Leuven, BelgiumS. HarperDepartment of Epidemiology, Biostatistics, and OccupationalHealth, McGill University, Montreal, CanadaJ. LynchSansom Institute, University of South Australia,Adelaide, AustraliaJ. LynchDepartment of Social Medicine,University of Bristol, Bristol, UKA. R. HosseinpoorWorld Health Organization, Geneva, SwitzerlandD. Berkvens  N. Speybroeck (&)Institute of Tropical Medicine, Antwerp, Belgiume-mail: nspeybroeck@itg.beV. Lorant  N. SpeybroeckInstitute of Health and Society, Université Catholique deLouvain, Brussels, BelgiumA. GeckovaKosice Institute for Society and Health, Kosice, Slovak RepublicInt J Public Health (2010) 55:71–74DOI 10.1007/s00038-009-0078-yamongst the better-off. For example, the poorest 40%(x-axis) of the population has nearly 60% of the malnu-trition share. The RCI is defined as twice the area betweenthe concentration curve, L(p) (the L stands for Lorenzcurve), and the line of equality (the 45 diagonal from thebottom-left corner to the top-right). So, in the case wherethere is no income-related inequality, the RCI is zero.The RCI takes a negative value when the curve liesabove the line of equality, indicating disproportionateconcentration of the health outcome among the poor, and apositive value when it lies below the line of equality(O’Donnell et al. 2008). If the health variable represents a‘bad’ health state (e.g. malnutrition) a negative value of theRCI means ill health is higher among the poor.Figure 2 shows a theoretical example of three differentcurves. The first one has an area A between the relativeconcentration curve and the diagonal (light grey), and thelevel of ill health (e.g. malnutrition) accumulates fasteramongst the poor than amongst the better-off. The dottedline shows that the poorest 40% (x-axis) of the populationhas nearly 60% of the malnutrition share. The second curvehas an area A ? A0 between the concentration curve andthe diagonal (and additional darker grey area of A0) and thelevel of ill health (e.g. malnutrition) accumulates evenfaster amongst the poor than compared to the previoussituation. The RCI will be higher as well (i.e. malnutritionis more disproportionately concentrated amongst the poorin this case). The dotted line shows that the poorest 40%(x-axis) of the population has now about 80% of the badhealth share. Finally, a third curve has a concentrationindex which is even higher, getting closer to one (theadditional dark grey area A00 results in an area which coversa good part of the whole upper triangle and twice this areais 1), and here 40% of the poorest have nearly 100% of theill health share.The value of the RCI normally varies between -1 and?1 but it is not bounded within the range of [-1, 1] if thehealth variable of interest takes negative, as well aspositive values. Therefore, the health variable should besuch that it is restricted to positive values. Further, thebounds of the RCI depend upon the mean (i.e. overallprevalence) of the indicator when applied to binaryindicators (Wagstaff 2005). To avoid this problem, analternative but related RCI that was recently introducedmay be useful (Erreygers 2009). More formally, a RCI ofy can be computed as twice the covariance of the healthvariable and a person’s relative rank in terms of socio-economic position, divided by the mean health accordingto Eq. 1:RCI ¼ 2lcovðyi;RiÞ ð1Þwith yi and Ri, respectively, the health status of the ithindividual and the fractional rank of the ith individual indistribution of socioeconomic position (i.e. Ri = 1/N forthe poorest individual and Ri = N/N for the richest); l isthe mean of the health of the sample and cov denotes thecovariance. Notice that this assumes that the welfare var-iable is continuous. Methods for extending the calculationof RCI in cases where the welfare measure is categorical0.0 0.2 0.4 0.6 0.8 1.00.00.20.40.60.81.0Health Concentration Curvefractional rankcumulative distributionABFig. 1 The relative concentration curve: an example with malnutri-tion (Ghana 2003, Source: Demographic and Health Survey). Therelative concentration index equals 2 9 the area between the 45 lineand the relative concentration curve = A/(A ? B)Fig. 2 Different relative concentration curves: an example with badhealth. Section A demonstrates the lowest level of social inequality72 P. Konings et al.are available as well (Chen and Roy 2009). Furthermore,methods are available for calculating the RCI usingweighted data (O’Donnell et al. 2008).At the moment most of the software for inequality inhealth that is used in practice is written in Stata code(O’Donnell et al. 2008), e.g. the ‘‘concindexi’’ commandhas the Kakwani and Wagstaff covariance formulae, asoutlined with standard errors. There is no implementationavailable at the moment that runs entirely on free software,allowing for the calculation of the RCI and associatedconfidence intervals. The methods described above are nowimplemented in an R package called decomp (availablefrom the authors upon request). The R program is free ofcharge (http://www.r-project.org). This makes it anattractive option, especially in lower income countries. Inan Appendix at the end of this paper, the use of the code isexemplified. After installing the package decomp into R,the code can be copied and pasted into R and immediatelyused after having adapted the variables to the users’ needs.Finally, it can be noted that the concentration indexprovides an indication of health inequalities but it isimportant that public health science moves from purelydescribing health inequalities towards quantifying theimportance of different determinants of health inequality.As mentioned earlier, such a ‘‘decomposition’’ of healthinequalities will be the topic of an upcoming paper in thisjournal.Acknowledgments This paper resulted from cooperation initiatedthrough meetings organized by the WHO European Office forInvestment for Health and Development, Venice, Italy.AppendixAnalysis of socioeconomic health inequalities 73ReferencesChen Z, Roy K (2009) Calculating concentration index with repetitivevalues of indicators of economic welfare. J Health Econ28:169–175Erreygers G (2009) Correcting the concentration index. J Health Econ28:504–515Harper S, Lynch J (2007) Commentary: using innovative inequalitymeasures in epidemiology. Int J Epidemiol 36:926–928Mackenbach JP, Stirbu I, Roskam AJR, Schaap MM, Menvielle G,Leinsalu M, Kunst AE (2008) Socioeconomic inequalities inhealth in 22 European countries. N Engl J Med 358:2468–2481O’Donnell O, van Doorslaer E, Lindelöw M, World B (2008)Analyzing health equity using household survey data: a guide totechniques and their implementation. World Bank, Washington,DCSmits J, Monden C (2009) Length of life inequality around the globe.Soc Sci Med 86:1114–1123Van de Poel E, Hosseinpoor AR, Jehu-Appiah C, Vega J, SpeybroeckN (2007) Malnutrition and the disproportional burden on thepoor: the case of Ghana. Int J Equity Health 6:21Wagstaff A (2005) The bounds of the concentration index when thevariable of interest is binary, with an application to immuniza-tion inequality. Health Econ 14:429–432Wagstaff A, Paci P, vanDoorslaer E (1991) On the measurement ofinequalities in health. Soc Sci Med 33:545–55774 P. Konings et al.

Analysis of socioeconomic health inequalities using the concentration index

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Analysis of socioeconomic health inequalities using the concentration index

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