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Child mortality levels and trends: A new compositional approach

Author

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  • Fatine Ezbakhe

    (Université de Genève)

  • Agustí Pérez Foguet

    (Universitat Politecnica de Catalunya)

Abstract

Background: Trend analysis of child mortality is vital to evaluate countries’ progress towards achieving the Sustainable Development Goal on health (SDG 3). However, strictly speaking, child mortality data are probabilities, and thus subject to non-negativity and constant-sum constraints. Objective: Our objective is to assess the application of compositional data analysis for estimating levels and trends in child mortality. Methods: We compare two data transformations: logit, which is widely used in child mortality estimation, and isometric log-ratio (ILR), which is specifically designed for compositional data. We use publicly available household survey data on neonatal (NMR) and under-five (U5MR) mortality ratios in sub-Saharan Africa. Results: Although both data transformations yield similar estimates, only the ILR transformation is consistent with the compositional properties of child mortality data. However, the ILR suffers from one key drawback: it requires complete data series, with pairs of observations for both NMR and U5MR. As a result, ILR entails excluding a large amount of available data from the regression analysis. Conclusions: Complete data is needed to be able to undertake a compositional trend analysis of child mortality. This gap in data can be closed by employing imputation strategies that replace missing values in the existing datasets, and by developing new methods for the indirect estimation of NMR from summary birth history data, as it is currently done for U5MR. Contribution: This paper extends the literature on child mortality estimation by examining the application of compositional data analysis to this field. It constitutes a first step towards building a Bayesian compositional regression approach for child mortality estimation.

Suggested Citation

  • Fatine Ezbakhe & Agustí Pérez Foguet, 2020. "Child mortality levels and trends: A new compositional approach," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 43(43), pages 1263-1296.
    • Handle: RePEc:dem:demres:v:43:y:2020:i:43
    DOI: 10.4054/DemRes.2020.43.43
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    References listed on IDEAS

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    1. Raoul Bermejo III & Sonja Firth & Andrew Hodge & Eliana Jimenez-Soto & Willibald Zeck, 2015. "Overcoming Stagnation in the Levels and Distribution of Child Mortality: The Case of the Philippines," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-13, October.
    2. Andrew Hodge & Sonja Firth & Tiara Marthias & Eliana Jimenez-Soto, 2014. "Location Matters: Trends in Inequalities in Child Mortality in Indonesia. Evidence from Repeated Cross-Sectional Surveys," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-10, July.
    3. Marcillo-Delgado, J.C. & Ortego, M.I. & Pérez-Foguet, A., 2019. "A compositional approach for modelling SDG7 indicators: Case study applied to electricity access," Renewable and Sustainable Energy Reviews, Elsevier, vol. 107(C), pages 388-398.
    4. Leontine Alkema & Jin Rou New & Jon Pedersen & Danzhen You & all members of the UN Inter-agency Group for Child Mortality Estimation and its Technical Advisory Group, 2014. "Child Mortality Estimation 2013: An Overview of Updates in Estimation Methods by the United Nations Inter-Agency Group for Child Mortality Estimation," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-13, July.
    5. Monica Alexander & Leontine Alkema, 2018. "Global estimation of neonatal mortality using a Bayesian hierarchical splines regression model," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 38(15), pages 335-372.
    6. Marie-Pier Bergeron-Boucher & Violetta Simonacci & Jim Oeppen & Michele Gallo, 2018. "Coherent Modeling and Forecasting of Mortality Patterns for Subpopulations Using Multiway Analysis of Compositions: An Application to Canadian Provinces and Territories," North American Actuarial Journal, Taylor & Francis Journals, vol. 22(1), pages 92-118, January.
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    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics
    • Z0 - Other Special Topics - - General

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