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      Socioeconomic differences in mortality in the antiretroviral therapy era in Agincourt, rural South Africa, 2001–13: a population surveillance analysis


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          Understanding the effects of socioeconomic disparities in health outcomes is important to implement specific preventive actions. We assessed socioeconomic disparities in mortality indicators in a rural South African population over the period 2001–13.


          We used data from 21 villages of the Agincourt Health and socio-Demographic Surveillance System (HDSS). We calculated the probabilities of death from birth to age 5 years and from age 15 to 60 years, life expectancy at birth, and cause-specific and age-specific mortality by sex (not in children <5 years), time period, and socioeconomic status (household wealth) quintile for HIV/AIDS and tuberculosis, other communicable diseases (excluding HIV/AIDS and tuberculosis) and maternal, perinatal, and nutritional causes, non-communicable diseases, and injury. We also quantified differences with relative risk ratios and relative and slope indices of inequality.


          Between 2001 and 2013, 10 414 deaths were registered over 1 058 538 person-years of follow-up, meaning the overall crude mortality was 9·8 deaths per 1000 person-years. We found significant socioecomonic status gradients for mortality and life expectancy at birth, with outcomes improving with increasing socioeconomic status. An inverse relation was seen for HIV/AIDS and tuberculosis mortality and socioeconomic status that persisted from 2001 to 2013. Deaths from non-communicable diseases increased over time in both sexes, and injury was an important cause of death in men and boys. Neither of these causes of death, however, showed consistent significant associations with household socioeconomic status.


          The poorest people in the population continue to bear a high burden of HIV/AIDS and tuberculosis mortality, despite free antiretroviral therapy being made available from public health facilities. Associations between socioeconomic status and increasing burden of mortality from non-communicable diseases is likely to become prominent. Integrated strategies are needed to improve access to and uptake of HIV testing, care, and treatment, and management of non-communicable diseases in the poorest populations.


          Wellcome Trust, South African Medical Research Council, and University of the Witwatersrand, South Africa.

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          Most cited references44

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          Discrete-Time Methods for the Analysis of Event Histories

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            On the measurement of inequalities in health.

            This paper offers a critical appraisal of the various methods employed to date to measure inequalities in health. It suggests that only two of these--the slope index of inequality and the concentration index--are likely to present an accurate picture of socioeconomic inequalities in health. The paper also presents several empirical examples to illustrate of the dangers of using other measures such as the range, the Lorenz curve and the index of dissimilarity.
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              Measuring the magnitude of socio-economic inequalities in health: an overview of available measures illustrated with two examples from Europe.

              In this paper we review the available summary measures for the magnitude of socio-economic inequalities in health. Measures which have been used differ in a number of important respects, including (1) the measurement of "relative" or "absolute" differences; (2) the measurement of an "effect" of lower socio-economic status, or of the "total impact" of socio-economic inequalities in health upon the health status of the population; (3) simple versus sophisticated measurement techniques. Based on this analysis of summary measures which have previously been applied, eight different classes of summary measures can be distinguished. Because measures of "total impact" can be further subdivided on the basis of their underlying assumptions, we finally arrive at 12 types of summary measure. Each of these has its merits, and choice of a particular type of summary measure will depend partly on technical considerations, partly on one's perspective on socio-economic inequalities in health. In practice, it will often be useful to compare the results of several summary measures. These principles are illustrated with two examples: one on trends in the magnitude of inequalities in mortality by occupational class in Finland, and one on trends in the magnitude of inequalities in self-reported morbidity by level of education in the Netherlands.

                Author and article information

                Lancet Glob Health
                Lancet Glob Health
                The Lancet. Global Health
                Elsevier Ltd
                11 August 2017
                September 2017
                11 August 2017
                : 5
                : 9
                : e924-e935
                [a ]MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
                [b ]School of Demography, Australian National University, Canberra, ACT, Australia
                [c ]CU Population Center, Institute of Behavioral Science, University of Colorado at Boulder, Boulder, CO, USA
                [d ]INDEPTH Network, Accra, Ghana
                [e ]Umeå Centre for Global Health Research, Division of Epidemiology and Global Health, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
                [f ]Department of Science and Technology/Medical Research Council, South African Population Research Infrastructure Network (SAPRIN), Acornhoek, South Africa
                [g ]Department of Sociology, The Ohio State University, Columbus, OH, USA
                Author notes
                [* ]Correspondence to: Mr Chodziwadziwa W Kabudula, MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), University of the Witwatersrand, PO Box 2, Acornhoek 1360, South AfricaCorrespondence to: Mr Chodziwadziwa W KabudulaMRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt)University of the WitwatersrandPO Box 2Acornhoek1360South Africa chodziwadziwa.kabudula@ 123456wits.ac.za
                © 2017 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).



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