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      Socioeconomic Inequalities of Undiagnosed Diabetes in a Resource-Poor Setting: Insights from the Cross-Sectional Bangladesh Demographic and Health Survey 2011

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          Abstract

          Diabetes mellitus is rising disproportionately but is not frequently diagnosed until complications appear, which results in adverse health consequences. We estimated the prevalence of undiagnosed diabetes among adult diabetic patients and associated socioeconomic inequalities in Bangladesh. We used nationally representative cross-sectional Bangladesh Demographic and Health Survey (BDHS) 2011 data. Among patients with diabetes, we identified undiagnosed cases as having fasting plasma glucose ≥ 7.0 mmol/L, never having taken prescribed medicine and being told by health professionals. Among 938 patients with diabetes, 53.4% remained undiagnosed. The poorest (75.9%) and rural (59.0%) patients had significantly higher undiagnosed cases than the richest (36.0%) and urban (42.5%), respectively. Multiple logistic regression analysis revealed that the likelihood of being undiagnosed was lower among patients with age ≥ 70 years vs. 35–39 years (adjusted odds ratio (AOR) = 0.35; 95% confidence interval (CI) 0.19, 0.64) and patients with higher education vs. no education (AOR = 0.36; 95% CI 0.21, 0.62). Conversely, a high level of physical activity and being in a poor socioeconomic quintile were associated with a higher risk of remaining undiagnosed for diabetes. The Concentration Index (C) also showed that undiagnosed diabetes was largely distributed among the socioeconomically worse-off group in Bangladesh (C = −0.35). Nationwide diabetes screening programs may reduce this problem in Bangladesh and other similar low-income settings.

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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 Environ Res Public Health
                Int J Environ Res Public Health
                ijerph
                International Journal of Environmental Research and Public Health
                MDPI
                1661-7827
                1660-4601
                03 January 2019
                January 2019
                : 16
                : 1
                : 115
                Affiliations
                [1 ]Institute for Social Science Research, The University of Queensland, Indooroopilly 4068, Queensland, Australia
                [2 ]Maternal and Child Health Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), 1212 Dhaka, Bangladesh; fariharuhi@ 123456gmail.com
                [3 ]Nutrition and Clinical Services Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), 1212 Dhaka, Bangladesh; tariq.sbidu@ 123456gmail.com
                [4 ]Health Systems and Population Studies Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), 1212 Dhaka, Bangladesh; sayemahmed@ 123456icddrb.org
                [5 ]Health Economics and Policy Research Group, Department of Learning, Informatics, Management and Ethics, Karolinska Institute, SE-171 77 Stockholm, Sweden
                Author notes
                Author information
                https://orcid.org/0000-0001-7801-0506
                https://orcid.org/0000-0002-0172-9501
                https://orcid.org/0000-0001-9499-1500
                Article
                ijerph-16-00115
                10.3390/ijerph16010115
                6338882
                30609855
                5b58fea8-b0b7-4c4e-b614-0ffa709b0a6f
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 29 September 2018
                : 27 December 2018
                Categories
                Article

                Public health
                undiagnosed diabetes,socioeconomic condition,inequality,odds ratio,concentration index,bangladesh

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