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Constructing Pragmatic Socioeconomic Status Assessment Tools to Address Health Equality Challenges

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      Abstract

      Background:

      A key challenge for equality evaluation and monitoring, mainly in developing countries, is assessing socioeconomic status (SES) of individuals. This difficulty along with low technical competency, have resulted in many health information collected in these countries which are devoid of suitable SES indices. However, simplifying data collection requirements for estimating economic parameters seems to guarantee their wide adoption by survey and health information system (HIS) designers, resulting in immediate production of equity-oriented policy-relevant information. The goal of this study is obtaining adequate number of variables, which their combination can provide a valid assessment of SES in Iranian population.

      Methods:

      The data source was Living Standards Measurement Study of Iran (2006). Data of 27,000 households on the ownership of 33 household assets was used for this analysis. Households of this study were divided into 5 groups in terms of SES status using principle component analysis. Then selection was made among the 33 variables so that a combination with minimum necessary number for obtaining SES status is reached. Agreement of the new combination (including minimum number of variables) with full variable combination (including all 33 variables) was assessed using weighted kappa.

      Results:

      A minimum set of six variables including having kitchen, bathroom, vacuum cleaner, washing machine, freezer and personal computer could successfully discriminate SES of the population. Comparing this 6 item-index with the whole 33 item-index revealed that 65% of households were in the same quintiles, with a weighted kappa statistics of 0.76. For households in different quintiles, movement was generally limited to one quintile, with just 2% of households moving two or more quintiles.

      Conclusions:

      The proposed simple index is completely applicable in current Iran's society. It can be used in different survey and studies. The development is quite simple and can be done on a yearly basis using the updated National level data. Having such standardized simplified and up to date SES indices and incorporating them into all health data sources can potentially ease the measurement and monitoring of equity of health services and indices.

      Related collections

      Most cited references 28

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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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        Socioeconomic status in health research: one size does not fit all.

        Problems with measuring socioeconomic status (SES)-frequently included in clinical and public health studies as a control variable and less frequently as the variable(s) of main interest-could affect research findings and conclusions, with implications for practice and policy. We critically examine standard SES measurement approaches, illustrating problems with examples from new analyses and the literature. For example, marked racial/ethnic differences in income at a given educational level and in wealth at a given income level raise questions about the socioeconomic comparability of individuals who are similar on education or income alone. Evidence also shows that conclusions about nonsocioeconomic causes of racial/ethnic differences in health may depend on the measure-eg, income, wealth, education, occupation, neighborhood socioeconomic characteristics, or past socioeconomic experiences-used to "control for SES," suggesting that findings from studies that have measured limited aspects of SES should be reassessed. We recommend an outcome- and social group-specific approach to SES measurement that involves (1) considering plausible explanatory pathways and mechanisms, (2) measuring as much relevant socioeconomic information as possible, (3) specifying the particular socioeconomic factors measured (rather than SES overall), and (4) systematically considering how potentially important unmeasured socioeconomic factors may affect conclusions. Better SES measures are needed in data sources, but improvements could be made by using existing information more thoughtfully and acknowledging its limitations.
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          Indicators of socioeconomic position (part 1).

          This glossary presents a comprehensive list of indicators of socioeconomic position used in health research. A description of what they intend to measure is given together with how data are elicited and the advantages and limitation of the indicators. The glossary is divided into two parts for journal publication but the intention is that it should be used as one piece. The second part highlights a life course approach and will be published in the next issue of the journal.
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            Author and article information

            Affiliations
            [1]Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
            [2]Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
            [3]Knowledge Utilization Research Center, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
            Author notes
            Correspondence to: Dr. Parvin Tajik, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Centre- University of Amsterdam, Room J1b-210; PO Box 22700 1100 DE Amsterdam, The Netherlands. E-mail: p.tajik@123456amc.uva.nl
            Journal
            Int J Prev Med
            Int J Prev Med
            IJPVM
            International Journal of Preventive Medicine
            Medknow Publications & Media Pvt Ltd (India)
            2008-7802
            2008-8213
            January 2014
            : 5
            : 1
            : 46-51
            3915472
            IJPVM-5-46
            Copyright: © International Journal of Preventive Medicine

            This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

            Categories
            Original Article

            Health & Social care

            equality, socioeconomic status measurement, iran

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