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      Socioeconomic Inequalities in Non-Communicable Diseases Prevalence in India: Disparities between Self-Reported Diagnoses and Standardized Measures

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          Abstract

          Background

          Whether non-communicable diseases (NCDs) are diseases of poverty or affluence in low-and-middle income countries has been vigorously debated. Most analyses of NCDs have used self-reported data, which is biased by differential access to healthcare services between groups of different socioeconomic status (SES). We sought to compare self-reported diagnoses versus standardised measures of NCD prevalence across SES groups in India.

          Methods

          We calculated age-adjusted prevalence rates of common NCDs from the Study on Global Ageing and Adult Health, a nationally representative cross-sectional survey. We compared self-reported diagnoses to standardized measures of disease for five NCDs. We calculated wealth-related and education-related disparities in NCD prevalence by calculating concentration index (C), which ranges from −1 to +1 (concentration of disease among lower and higher SES groups, respectively).

          Findings

          NCD prevalence was higher (range 5.2 to 19.1%) for standardised measures than self-reported diagnoses (range 3.1 to 9.4%). Several NCDs were particularly concentrated among higher SES groups according to self-reported diagnoses (C srd) but were concentrated either among lower SES groups or showed no strong socioeconomic gradient using standardized measures (C sm): age-standardised wealth-related C: angina C srd 0.02 vs. C sm 0.17; asthma and lung diseases C srd 0.05 vs. C sm 0.04 (age-standardised education-related C srd 0.04 vs. C sm 0.05); vision problems C srd 0.07 vs. C sm 0.05; depression C srd 0.07 vs. C sm 0.13. Indicating similar trends of standardized measures detecting more cases among low SES, concentration of hypertension declined among higher SES (C srd 0.19 vs. C sm 0.03).

          Conclusions

          The socio-economic patterning of NCD prevalence differs markedly when assessed by standardized criteria versus self-reported diagnoses. NCDs in India are not necessarily diseases of affluence but also of poverty, indicating likely under-diagnosis and under-reporting of diseases among the poor. Standardized measures should be used, wherever feasible, to estimate the true prevalence of NCDs.

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          Most cited references35

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          A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010

          The Lancet, 380(9859), 2224-2260
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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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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2013
                15 July 2013
                : 8
                : 7
                : e68219
                Affiliations
                [1 ]South Asia Network for Chronic Diseases, Public Health Foundation of India, New Delhi, India
                [2 ]Department of Society, Human Development and Health, Harvard School of Public Health, Harvard University, Boston, Massachusetts, United States of America
                [3 ]Department of Primary Care and Public Health, Imperial College London, London, United Kingdom
                [4 ]Prevention Research Center, Stanford University, Stanford, California, United States of America
                [5 ]Department of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, United Kingdom
                [6 ]Department of Sociology, Oxford University, Oxford, United Kingdom
                [7 ]Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
                Indiana University, United States of America
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: SV SVS SE. Performed the experiments: SV SVS CM SB DS SE. Analyzed the data: SV. Contributed reagents/materials/analysis tools: SV SVS CM SB DS SE. Wrote the paper: SV SVS CM SB DS SE. Conceptualized the study: SV SE. Did the data analysis, interpretation of results and first draft of the paper: SV. Contributed during the conceptualization and interpretation of results and substantially contributed to the revision: CM SB DS SVS SE. Reviewed the final draft of the paper: SVS CM SB DS SE SV. Supervised the study: SE SVS.

                Article
                PONE-D-13-08387
                10.1371/journal.pone.0068219
                3712012
                23869213
                aff4e513-bc09-4120-8c34-3eed8b6da852
                Copyright @ 2013

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 24 February 2013
                : 28 May 2013
                Page count
                Pages: 12
                Funding
                No specific funding has been received for this study, however, SE and SV are supported by a Wellcome Trust strategic award 084674/Z/08/Z, and CM with fellowship award from Leverhulme Trust. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology
                Population Biology
                Epidemiology
                Economic Epidemiology
                Social Epidemiology
                Medicine
                Clinical Research Design
                Epidemiology
                Epidemiology
                Biomarker Epidemiology
                Economic Epidemiology
                Social Epidemiology
                Global Health
                Mental Health
                Non-Clinical Medicine
                Socioeconomic Aspects of Health
                Public Health
                Behavioral and Social Aspects of Health
                Socioeconomic Aspects of Health
                Social and Behavioral Sciences

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                Uncategorized

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