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      Poor and non-poor gap in under-five child nutrition: a case from Nepal using Blinder-Oaxaca decomposition approach

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          Abstract

          Introduction

          Many low-and middle-income countries (LMICs) have improved health indicators in the past decades, however, there is a differential in outcomes between socioeconomic groups. Systematic analysis of drivers of child nutrition gap between non-poor and poor groups has a policy relevance in Nepal and other countries to make progress towards universal health coverage (UHC). The objective of this paper was to estimate the mean height-for-age z scores (HAZ) gap between under-five children belonging to non-poor and poor groups, divide the gap into components (endowments, coefficients and interaction), and identify the factors that contributed most to each of the component.

          Methods

          Information about 6277 under-five children was extracted from the most recent nationally representative Nepal Multiple Indicator Cluster Survey (MICS) 2019. HAZ was used to assess nutritional status of children. Wealth index was used to categorize children into non-poor and poor. Mean HAZ gap between groups was decomposed using Blinder-Oaxaca technique into components: endowments (group difference in levels of predictors), coefficients (group difference in effects of predictors), and interaction (group difference due to interaction between levels and effects of predictors). Detailed decomposition was carried out to identify the factors that contributed most to each component.

          Results

          There was a significant non-poor and poor gap in nutrition outcome measured in HAZ (0.447; p < 0.001) among under-five children in Nepal. The between-group mean differences in the predictors of study participants (endowments) contributed 0.210 (47%) to the gap. Similarly, the between-group differences in effects of the predictors (coefficients) contributed 0.308 (68.8%) towards the gap. The interaction contributed -0.071 (15.8%) towards minimizing the gap. The predictors/variables that contributed most towards the gap due to (i) endowments were: maternal education, province (Karnali, Sudurpaschim, Madhesh), residence (rural/urban), type of toilet facility and ethnic group (Dalit and Muslim); (ii) coefficients were: number of under-five children in family, ethnic group (Dalit and Muslim), type of toilet facility, maternal age and education.

          Conclusion

          Decomposition of the child nutrition gap revealed that narrowing the inequality between wealth groups depends not only on improving the level of the predictors (endowments) in the poor group but also on reducing differential effects of the predictors (coefficients).

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12913-022-08643-6.

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          Most cited references49

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          Wage Discrimination: Reduced Form and Structural Estimates

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            Male-Female Wage Differentials in Urban Labor Markets

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              How can I deal with missing data in my study?

              Missing data in medical research is a common problem that has long been recognised by statisticians and medical researchers alike. In general, if the effect of missing data is not taken into account the results of the statistical analyses will be biased and the amount of variability in the data will not be correctly estimated. There are three main types of missing data pattern: Missing Completely At Random (MCAR), Missing At Random (MAR) and Not Missing At Random (NMAR). The type of missing data that a researcher has in their dataset determines the appropriate method to use in handling the missing data before a formal statistical analysis begins. The aim of this practice note is to describe these patterns of missing data and how they can occur, as well describing the methods of handling them. Simple and more complex methods are described, including the advantages and disadvantages of each method as well as their availability in routine software. It is good practice to perform a sensitivity analysis employing different missing data techniques in order to assess the robustness of the conclusions drawn from each approach.
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                Author and article information

                Contributors
                umbhusal2020@gmail.com
                Journal
                BMC Health Serv Res
                BMC Health Serv Res
                BMC Health Services Research
                BioMed Central (London )
                1472-6963
                12 October 2022
                12 October 2022
                2022
                : 22
                : 1245
                Affiliations
                [1 ]Public Health and Social Protection Professional, Kathmandu, Nepal
                [2 ]GRID grid.1008.9, ISNI 0000 0001 2179 088X, Melbourne School of Population and Global Health, , The University of Melbourne, ; Melbourne, VIC Australia
                Author information
                http://orcid.org/0000-0001-9331-6028
                Article
                8643
                10.1186/s12913-022-08643-6
                9559871
                36224578
                9ab27705-d9c2-4bb1-968c-c0a4162419fd
                © The Author(s) 2022

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.

                History
                : 29 March 2022
                : 7 October 2022
                Categories
                Research
                Custom metadata
                © The Author(s) 2022

                Health & Social care
                child nutrition,gap,inequality,blinder-oaxaca decomposition,height-for-age z scores (haz),mics,nepal,sdgs,uhc

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