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      Optimization and validation of the EconomicClusters model for facilitating global health disparities research: Examples from Cameroon and Ghana

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          Abstract

          Health disparities research in low- and middle-income countries (LMICs) is hampered by the difficulty of measuring economic status in low-resource settings. We previously developed the EconomicClusters k-medoids clustering-based algorithm for defining population-specific economic models based on few Demographic and Health Surveys (DHS) assets. The algorithm previously defined a twenty-group economic model for Cameroon. The aims of this study are to optimize the functionality of our EconomicClusters algorithm and app based on collaborator feedback from early use of this twenty-group economic model, to test the validity of the model as a metric of economic status, and to assess the utility of the model in another LMIC context. We condense the twenty Cameroonian economic groups into fewer, ordinally-ranked, groups using agglomerative hierarchical clustering based on mean cluster child height-for-age Z-score (HAZ), women’s literacy score, and proportion of children who are deceased. We develop an EconomicClusters model for Ghana consisting of five economic groups and rank these groups based on the same three variables. The proportion of variance in women’s literacy score accounted for by the EconomicClusters model was 5–12% less than the proportion of variance accounted for by the DHS Wealth Index model. The proportion of the variance in child HAZ and proportion of children who are deceased accounted for by the EconomicClusters model was similar to (0.4–2.5% less than) the proportion of variance accounted for by the DHS Wealth Index model. The EconomicClusters model requires asking only five questions, as opposed to greater than twenty Wealth Index questions. The EconomicClusters algorithm and app could facilitate health disparities research in any country with DHS data by generating ordinally-ranked, population-specific economic models that perform nearly as well as the Wealth Index in evaluating variability in health and social outcomes based on wealth status but that are more feasible to assess in time-constrained settings.

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          Most cited references15

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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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            Issues in the construction of wealth indices for the measurement of socio-economic position in low-income countries

            Background Epidemiological studies often require measures of socio-economic position (SEP). The application of principal components analysis (PCA) to data on asset-ownership is one popular approach to household SEP measurement. Proponents suggest that the approach provides a rational method for weighting asset data in a single indicator, captures the most important aspect of SEP for health studies, and is based on data that are readily available and/or simple to collect. However, the use of PCA on asset data may not be the best approach to SEP measurement. There remains concern that this approach can obscure the meaning of the final index and is statistically inappropriate for use with discrete data. In addition, the choice of assets to include and the level of agreement between wealth indices and more conventional measures of SEP such as consumption expenditure remain unclear. We discuss these issues, illustrating our examples with data from the Malawi Integrated Household Survey 2004–5. Methods Wealth indices were constructed using the assets on which data are collected within Demographic and Health Surveys. Indices were constructed using five weighting methods: PCA, PCA using dichotomised versions of categorical variables, equal weights, weights equal to the inverse of the proportion of households owning the item, and Multiple Correspondence Analysis. Agreement between indices was assessed. Indices were compared with per capita consumption expenditure, and the difference in agreement assessed when different methods were used to adjust consumption expenditure for household size and composition. Results All indices demonstrated similarly modest agreement with consumption expenditure. The indices constructed using dichotomised data showed strong agreement with each other, as did the indices constructed using categorical data. Agreement was lower between indices using data coded in different ways. The level of agreement between wealth indices and consumption expenditure did not differ when different consumption equivalence scales were applied. Conclusion This study questions the appropriateness of wealth indices as proxies for consumption expenditure. The choice of data included had a greater influence on the wealth index than the method used to weight the data. Despite the limitations of PCA, alternative methods also all had disadvantages.
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              Socioeconomic inequalities in child mortality: comparisons across nine developing countries.

              This paper generates and analyses survey data on inequalities in mortality among infants and children aged under five years by consumption in Brazil, Côte d'Ivoire, Ghana, Nepal, Nicaragua, Pakistan, the Philippines, South Africa, and Viet Nam. The data were obtained from the Living Standards Measurement Study and the Cebu Longitudinal Health and Nutrition Survey. Mortality rates were estimated directly where complete fertility histories were available and indirectly otherwise. Mortality distributions were compared between countries by means of concentration curves and concentration indices: dominance checks were carried out for all pairwise intercountry comparisons; standard errors were calculated for the concentration indices; and tests of intercountry differences in inequality were performed.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                23 May 2019
                2019
                : 14
                : 5
                : e0217197
                Affiliations
                [1 ] Department of Surgery, Center for Global Surgical Studies, University of California San Francisco, San Francisco, California, United States of America
                [2 ] Division of Biostatistics, School of Public Health, University of California Berkeley, Berkeley, California, United States of America
                [3 ] Department of Surgery, University of California Los Angeles, Los Angeles, California, United States of America
                University of Sheffield, UNITED KINGDOM
                Author notes

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

                Author information
                http://orcid.org/0000-0002-2967-0855
                http://orcid.org/0000-0002-3769-0127
                http://orcid.org/0000-0003-0847-8933
                Article
                PONE-D-18-26360
                10.1371/journal.pone.0217197
                6532895
                31120921
                f0198be6-2021-4c76-90f7-c00877fb2d8b
                © 2019 Eyler et al

                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
                : 8 September 2018
                : 8 May 2019
                Page count
                Figures: 9, Tables: 2, Pages: 22
                Funding
                The authors received no specific funding for this work.
                Categories
                Research Article
                Social Sciences
                Economics
                Medicine and Health Sciences
                Health Care
                Socioeconomic Aspects of Health
                Medicine and Health Sciences
                Public and Occupational Health
                Socioeconomic Aspects of Health
                Biology and Life Sciences
                Neuroscience
                Cognitive Science
                Cognitive Psychology
                Academic Skills
                Literacy
                Biology and Life Sciences
                Psychology
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                Academic Skills
                Literacy
                Social Sciences
                Psychology
                Cognitive Psychology
                Academic Skills
                Literacy
                People and Places
                Geographical Locations
                Africa
                Ghana
                People and Places
                Population Groupings
                Age Groups
                Children
                People and Places
                Population Groupings
                Families
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                Social Sciences
                Economics
                Health Economics
                Medicine and Health Sciences
                Health Care
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                Ecology
                Urban Ecology
                Ecology and Environmental Sciences
                Ecology
                Urban Ecology
                Social Sciences
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                Microeconomics
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                Custom metadata
                The data underlying the results presented in the study are available from the Demographic and Health Surveys. Downloads of these datasets may be requested from the DHS at: https://www.dhsprogram.com/data/available-datasets.cfm. The point-and-click R shiny app for defining EconomicClusters models can be downloaded from the UCSF Center for Global Surgical Studies at: https://global.surgery.ucsf.edu/resources/economicclusters-app.aspx.

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