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      Multistate Markov chain modeling for child undernutrition transitions in Ethiopia: a longitudinal data analysis, 2002–2016

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          Abstract

          Background

          The use of the multistate Markov chain model is a valuable tool for studying child undernutrition. This allows us to examine the trends of children's transitions from one state to multiple states of undernutrition.

          Objectives

          In this study, our objective was to estimate the median duration for a child to first transition from one state of undernutrition to another as well as their first recurrence of undernutrition and also to analyze the typical duration of undernourishment. This involves understanding the central tendency of these transitions and durations in the context of longitudinal data.

          Methods

          We used a longitudinal dataset from the Young Lives cohort study (YLCS), which included approximately 1997 Ethiopian children aged 1–15 years. These children were selected from five regions and followed through five survey rounds between 2002 and 2016. The surveys provide comprehensive health and nutrition data and are designed to assess childhood poverty. To analyze this dataset, we employed a Markov chain regression model. The dataset constitutes a cohort with repeated measurements, allowing us to track the transitions of individual children across different states of undernutrition over time.

          Results

          The findings of our study indicate that 46% of children experienced concurrent underweight, stunting, and wasting (referred to as USW). The prevalence of underweight and stunted concurrent condition (US) was 18.7% at baseline, higher among males. The incidence density of undernutrition was calculated at 22.5% per year. On average, it took 3.02 months for a child in a wasting state to transition back to a normal state for the first time, followed by approximately 3.05 months for stunting and 3.89 months for underweight. It is noteworthy that the median duration of undernourishment among children in the US (underweight and stunted concurrently) state was 48.8 months, whereas those concurrently underweight and wasting experienced a median of 45.4 months in this state. Additionally, rural children (HR = 1.75; 95% CI: 1.53–1.97), those with illiterate fathers (HR = 1.50; 95% CI: 1.38–1.62) and mothers (HR = 1.45; 95% CI: 1.02–3.29), and those in households lacking safe drinking water (HR = 1.70; 95% CI: 1.26–2.14) or access to cooking fuel (HR = 1.95; 95% CI: 1.75–2.17) exhibited a higher risk of undernutrition and a slower recovery rate.

          Conclusions

          This study revealed that rural children, especially those with illiterate parents and households lacking safe drinking water but cooking fuels, face an increased risk of undernutrition and slower recovery.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12874-024-02399-9.

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          Most cited references33

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          Maternal and child undernutrition and overweight in low-income and middle-income countries

          The Lancet, 382(9890), 427-451
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            Maternal and child undernutrition: consequences for adult health and human capital

            Summary In this paper we review the associations between maternal and child undernutrition with human capital and risk of adult diseases in low-income and middle-income countries. We analysed data from five long-standing prospective cohort studies from Brazil, Guatemala, India, the Philippines, and South Africa and noted that indices of maternal and child undernutrition (maternal height, birthweight, intrauterine growth restriction, and weight, height, and body-mass index at 2 years according to the new WHO growth standards) were related to adult outcomes (height, schooling, income or assets, offspring birthweight, body-mass index, glucose concentrations, blood pressure). We undertook systematic reviews of studies from low-income and middle-income countries for these outcomes and for indicators related to blood lipids, cardiovascular disease, lung and immune function, cancers, osteoporosis, and mental illness. Undernutrition was strongly associated, both in the review of published work and in new analyses, with shorter adult height, less schooling, reduced economic productivity, and—for women—lower offspring birthweight. Associations with adult disease indicators were not so clear-cut. Increased size at birth and in childhood were positively associated with adult body-mass index and to a lesser extent with blood pressure values, but not with blood glucose concentrations. In our new analyses and in published work, lower birthweight and undernutrition in childhood were risk factors for high glucose concentrations, blood pressure, and harmful lipid profiles once adult body-mass index and height were adjusted for, suggesting that rapid postnatal weight gain—especially after infancy—is linked to these conditions. The review of published works indicates that there is insufficient information about long-term changes in immune function, blood lipids, or osteoporosis indicators. Birthweight is positively associated with lung function and with the incidence of some cancers, and undernutrition could be associated with mental illness. We noted that height-for-age at 2 years was the best predictor of human capital and that undernutrition is associated with lower human capital. We conclude that damage suffered in early life leads to permanent impairment, and might also affect future generations. Its prevention will probably bring about important health, educational, and economic benefits. Chronic diseases are especially common in undernourished children who experience rapid weight gain after infancy.
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              Multi-State Models for Panel Data: ThemsmPackage forR

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                Author and article information

                Contributors
                Getnetbogale145@gmail.com , getnetbogale@dbu.edu.et
                Journal
                BMC Med Res Methodol
                BMC Med Res Methodol
                BMC Medical Research Methodology
                BioMed Central (London )
                1471-2288
                15 November 2024
                15 November 2024
                2024
                : 24
                : 283
                Affiliations
                [1 ]Department of Statistics, College of Science, Bahir Dar University, ( https://ror.org/01670bg46) Bahir Dar, Ethiopia
                [2 ]Department of Data Science, College of Natural and Computational Science, Debre Berhan University, ( https://ror.org/04e72vw61) Debre Berhan, Ethiopia
                [3 ]School of Mathematics, Statistics and Computer Science, College of Agriculture, Engineering and Science, University of KwaZulu-Natal, ( https://ror.org/04qzfn040) Durban, South Africa
                [4 ]Center for Environmental and Respiratory Health Research (CERH), Research Unit of Population Health, University of Oulu, ( https://ror.org/03yj89h83) Oulu, Finland
                [5 ]Biocenter Oulu, University of Oulu, ( https://ror.org/03yj89h83) Oulu, Finland
                Article
                2399
                10.1186/s12874-024-02399-9
                11566054
                39548366
                0b6b7cd1-b057-4967-9a79-c16437017ae6
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, 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 you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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-nc-nd/4.0/.

                History
                : 29 December 2023
                : 31 October 2024
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                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2024

                Medicine
                lifetime undernourished period,median first passage time,median recurrence time,transition intensities,transition probability,young lives data

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