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      Implementing a sustainable health insurance system in Cambodia: a study protocol for developing and validating an efficient household income-level assessment model for equitable premium collection

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          Abstract

          Background

          As elsewhere in low- and middle-income countries, due to limited fiscal resources, universal health coverage (UHC) remains a challenge in Cambodia. Since 2016, the National Social Security Fund (NSSF) has implemented a social health insurance scheme with a contributory approach for formal sector workers. However, informal sector workers and dependents of formal sector workers are still not covered by this insurance because it is difficult to set an optimal amount of contribution for such individuals as their income levels are inestimable. The present study aims to develop and validate an efficient household income-level assessment model for Cambodia. We aim to help the country implement a financially sustainable social health insurance system in which the insured can pay contributions according to their ability.

          Methods

          This study will use nationally representative data collected by the Cambodia Socio-Economic Survey (CSES), covering the period from 2009 to 2019, and involving a total of 50,016 households. We will employ elastic net regression analysis, with per capita disposable income based on purchasing power parity as the dependent variable, and individual and community-level socioeconomic and demographic characteristics as independent variables. These analyses aim to create efficient income-level assessment models for health insurance contribution estimation. To fully capture socioeconomic heterogeneity, sub-group analyses will be conducted to develop separate income-level assessment models for urban and rural areas, as well as for each province.

          Discussion

          This research will help Cambodia implement a sustainable social health insurance system by collecting optimal amount of contributions from each socioeconomic group of the society. Incorporation of this approach into existing NSSF schemes will enhance the country’s current efforts to prevent impoverishing health expenditure and to achieve UHC.

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          Most cited references4

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          A variable selection method for genome-wide association studies.

          Genome-wide association studies (GWAS) involving half a million or more single nucleotide polymorphisms (SNPs) allow genetic dissection of complex diseases in a holistic manner. The common practice of analyzing one SNP at a time does not fully realize the potential of GWAS to identify multiple causal variants and to predict risk of disease. Existing methods for joint analysis of GWAS data tend to miss causal SNPs that are marginally uncorrelated with disease and have high false discovery rates (FDRs). We introduce GWASelect, a statistically powerful and computationally efficient variable selection method designed to tackle the unique challenges of GWAS data. This method searches iteratively over the potential SNPs conditional on previously selected SNPs and is thus capable of capturing causal SNPs that are marginally correlated with disease as well as those that are marginally uncorrelated with disease. A special resampling mechanism is built into the method to reduce false positive findings. Simulation studies demonstrate that the GWASelect performs well under a wide spectrum of linkage disequilibrium patterns and can be substantially more powerful than existing methods in capturing causal variants while having a lower FDR. In addition, the regression models based on the GWASelect tend to yield more accurate prediction of disease risk than existing methods. The advantages of the GWASelect are illustrated with the Wellcome Trust Case-Control Consortium (WTCCC) data. The software implementing GWASelect is available at http://www.bios.unc.edu/~lin. Access to WTCCC data: http://www.wtccc.org.uk/.
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            Updating and validation of the socioeconomic status scale for health research in Egypt.

            This study aimed to update and re-validate the scoring system of Fahmy and El-Sherbini for measurement of socioeconomic status in health research in Egypt. The new socioeconomic status scale has 7 domains with a total score of 84. Intra-and inter-observer variability and the internal consistency of the scale were assessed. A linear regression model was performed to determine the relative importance of each domain to the total score. Kappa coefficient was used to measure the agreement between the socioeconomic levels of the new and the old scales. There was a strong correlation between most of the 7 domains of the scale. Cronbach alpha for the scale was 0.66. The education domain contributed to 0.898 of variation in total score. There was a moderate agreement (kappa = 0.76) and strong positive significant correlation (r = 0.93) between the socioeconomic levels and scores of both scales. We conclude that the new socioeconomic status scale is valid and reliable.
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              Constructing Pragmatic Socioeconomic Status Assessment Tools to Address Health Equality Challenges

              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.
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                Author and article information

                Contributors
                hnakamura@m.u-tokyo.ac.jp
                Journal
                Int J Equity Health
                Int J Equity Health
                International Journal for Equity in Health
                BioMed Central (London )
                1475-9276
                31 January 2020
                31 January 2020
                2020
                : 19
                : 17
                Affiliations
                [1 ]ISNI 0000 0001 2151 536X, GRID grid.26999.3d, Department of Global Health Policy, Graduate School of Medicine, , The University of Tokyo, ; 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033 Japan
                [2 ]GRID grid.8295.6, Faculty of Medicine, , Eduardo Mondlane University, ; Maputo, Mozambique
                [3 ]KHANA Center for Population Health Research, Phnom Penh, Cambodia
                [4 ]ISNI 0000 0004 0623 6962, GRID grid.265117.6, Center for Global Health Research, , Touro University California, ; Vallejo, CA USA
                [5 ]ISNI 0000 0001 2180 6431, GRID grid.4280.e, Saw Swee Hock School of Public Health, , National University of Singapore and National University Health System, ; Singapore, Singapore
                [6 ]ISNI 0000 0001 2178 130X, GRID grid.454175.6, Japan International Cooperation Agency, ; Tokyo, Japan
                [7 ]ISNI 0000 0001 2347 9884, GRID grid.412160.0, Research Center for Health Policy and Economics, Hitotsubashi Institute for Advanced Study, , Hitotsubashi University, ; Tokyo, Japan
                [8 ]ISNI 0000 0004 1936 9959, GRID grid.26091.3c, Department of Health Policy and Management, School of Medicine, , Keio University, ; Tokyo, Japan
                Author information
                http://orcid.org/0000-0002-1557-0781
                Article
                1126
                10.1186/s12939-020-1126-8
                6995079
                32005237
                2318c22b-b297-4ee0-be86-09027c868a28
                © The Author(s). 2020

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 9 October 2019
                : 9 January 2020
                Categories
                Study Protocol
                Custom metadata
                © The Author(s) 2020

                Health & Social care
                health financing,financial sustainability,health insurance,contribution,income-level assessment,equity,cambodia

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