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      Improved Socio-Economic Status of a Community Population Following Schistosomiasis and Intestinal Worm Control Interventions on Kome Island, North-Western Tanzania

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Research on micro-level assessment of the changes of socio-economic status following health interventions is very scarce. The use of household asset data to determine wealth indices is a common procedure for estimating socio-economic position in resource poor settings. In such settings information about income is usually lacking, and the collection of individual consumption or expenditure data would require in-depth interviews, posing a considerable risk of bias. In this study, we determined the socio-economic status of 213 households in a community population in an island in the north-western Tanzania before and 3 year after implementation of a participatory hygiene and sanitation transformation (PHAST) intervention to control schistosomiasis and intestinal worm infections. We constructed a household 'wealth index' based housing construction features (e.g., type of roof, walls, and floor) and durable assets ownership (e.g., bicycle, radio, etc.). We employed principal components analysis and classified households into wealth quintiles. The study revealed that asset variables with positive factor scores were associated with higher socio-economic status, whereas asset variables with negative factor scores were associated with lower socio-economic status. Overall, households which were rated as the poorest and very poor were on the decrease, whereas those rated as poor, less poor, and the least poor were on the increase after PHAST intervention. This decrease/increase was significant. The median shifted from -0.4376677 to 0.5001073, and the mean from -0.2605787 (SD; 2.005688) to 0.2605787 (SD; 1.831199). The difference in socio-economic status of the people between the 2 phases was highly statistically significant ( P<0.001). We argue that finding of this study should be treated with caution as there were other interventions to control schistosomiasis and intestinal worm infections which were running concurrently on Kome Island apart from PHAST intervention.

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          Most cited references 23

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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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            Constructing socio-economic status indices: how to use principal components analysis.

            Theoretically, measures of household wealth can be reflected by income, consumption or expenditure information. However, the collection of accurate income and consumption data requires extensive resources for household surveys. Given the increasingly routine application of principal components analysis (PCA) using asset data in creating socio-economic status (SES) indices, we review how PCA-based indices are constructed, how they can be used, and their validity and limitations. Specifically, issues related to choice of variables, data preparation and problems such as data clustering are addressed. Interpretation of results and methods of classifying households into SES groups are also discussed. PCA has been validated as a method to describe SES differentiation within a population. Issues related to the underlying data will affect PCA and this should be considered when generating and interpreting results.
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              Inequalities in health care use and expenditures: empirical data from eight developing countries and countries in transition.

              This paper summarizes eight country studies of inequality in the health sector. The analyses use household data to examine the distribution of service use and health expenditures. Each study divides the population into "income" quintiles, estimated using consumption expenditures. The studies measure inequality in the use of and spending on health services. Richer groups are found to have a higher probability of obtaining care when sick, to be more likely to be seen by a doctor, and to have a higher probability of receiving medicines when they are ill, than the poorer groups. The richer also spend more in absolute terms on care. In several instances there are unexpected findings. There is no consistent pattern in the use of private providers. Richer households do not devote a consistently higher percentage of their consumption expenditures to health care. The analyses indicate that intuition concerning inequalities could result in misguided decisions. It would thus be worthwhile to measure inequality to inform policy-making. Additional research could be performed using a common methodology for the collection of data and applying more sophisticated analytical techniques. These analyses could be used to measure the impact of health policy changes on inequality.
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                Author and article information

                Affiliations
                [1 ]National Institute for Medical Research, P.O. Box 1462, Mwanza, Tanzania
                [2 ]Good Neighbors International, Tanzania Western Chapter, P.O. Box 367, Mwanza, Tanzania
                [3 ]Department of Parasitology, Medical Research Institute and Parasite Resource Bank, Chungbuk National University School of Medicine, Cheongju 28644, Korea
                [4 ]Department of Environmental Medical Biology, Institute of Tropical Medicine and Arthropods of Medical Importance Resource Bank, Yonsei University College of Medicine, Seoul 03722, Korea
                [5 ]Department of Parasitology and Tropical Medicine, Seoul National University College of Medicine, Seoul 03080, Korea
                [6 ]Department of Immunology and Microbiology, Eulji University School of Medicine, Daejeon 35233, Korea
                [7 ]Department of Parasitology, College of Medicine, Korea University, Seoul 02841, Korea
                Author notes
                [* ] Corresponding author ( keeseon.eom@ 123456gmail.com )
                Journal
                Korean J Parasitol
                Korean J. Parasitol
                KJP
                The Korean Journal of Parasitology
                The Korean Society for Parasitology and Tropical Medicine
                0023-4001
                1738-0006
                October 2015
                29 October 2015
                : 53
                : 5
                : 553-559
                26537034 4635828 10.3347/kjp.2015.53.5.553 kjp-53-5-553
                © 2015, Korean Society for Parasitology and Tropical Medicine

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Categories
                Articles from Symposium on Controls of NTDs around Lake Victoria, Tanzania

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