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Global estimation of neonatal mortality using a Bayesian hierarchical splines regression model

Author

Listed:
  • Monica Alexander

    (University of Toronto)

  • Leontine Alkema

    (University of Massachusetts Amherst)

Abstract

Background: In recent years, much of the focus in monitoring child mortality has been on assessing changes in the under-5 mortality rate (U5MR). However, as the U5MR decreases, the share of neonatal deaths (within the first month) tends to increase, warranting increased efforts in monitoring the neonatal mortality rate (NMR) in addition to the U5MR. Objective: Data on neonatal deaths comes from a range of sources across different countries, with the amount of data available and the quality of data varying widely. Our objective in estimating the NMR globally is to combine all data sources available to obtain accurate estimates, be able to project mortality levels, and have some indication of the uncertainty in the estimates and projections. Methods: We present a new model for estimating the NMR for countries worldwide, using a Bayesian hierarchical model framework. Contribution: Our modeling approach offers an intuitive way to share information across different countries and time points, and incorporates different sources of error into the estimates. It also improves on previous modeling approaches by allowing for trends observed in NMR to be more driven by the data available, rather than trends in covariates.

Suggested Citation

  • 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.
    • Handle: RePEc:dem:demres:v:38:y:2018:i:15
    DOI: 10.4054/DemRes.2018.38.15
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    References listed on IDEAS

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    4. Mikkel Zahle Oestergaard & Mie Inoue & Sachiyo Yoshida & Wahyu Retno Mahanani & Fiona M Gore & Simon Cousens & Joy E Lawn & Colin Douglas Mathers & on behalf of the United Nations Inter-agency Group f, 2011. "Neonatal Mortality Levels for 193 Countries in 2009 with Trends since 1990: A Systematic Analysis of Progress, Projections, and Priorities," PLOS Medicine, Public Library of Science, vol. 8(8), pages 1-13, August.
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    Cited by:

    1. Maroof Ahmad Khan & Sumit Kumar Das, 2024. "Revisiting Factors Influencing Under-Five Mortality in India: The Application of a Generalised Additive Cox Proportional Hazards Model," IJERPH, MDPI, vol. 21(10), pages 1-13, September.
    2. Herbert Susmann & Monica Alexander & Leontine Alkema, 2022. "Temporal Models for Demographic and Global Health Outcomes in Multiple Populations: Introducing a New Framework to Review and Standardise Documentation of Model Assumptions and Facilitate Model Compar," International Statistical Review, International Statistical Institute, vol. 90(3), pages 437-467, December.
    3. Dharamshi, Ameer & Antoninis, Manos & Montoya, Silvia & Barakat, Bilal Fouad, 2023. "A Bayesian cohort model for estimating out-of-school rates and populations," SocArXiv sqwb2, Center for Open Science.
    4. Barakat, Bilal Fouad & Dharamshi, Ameer & Alkema, Leontine & Antoninis, Manos, 2021. "Adjusted Bayesian Completion Rates (ABC) Estimation," SocArXiv at368, Center for Open Science.
    5. 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.
    6. Carl Schmertmann, 2021. "D-splines: Estimating rate schedules using high-dimensional splines with empirical demographic penalties," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 44(45), pages 1085-1114.
    7. Laura Schmidt & Mahmoud Elkasabi, 2022. "Accumulating Birth Histories Across Surveys for Improved Estimates of Child Mortality," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 41(5), pages 2177-2209, October.
    8. Ameer Dharamshi & Bilal Barakat & Leontine Alkema & Manos Antoninis, 2022. "A Bayesian model for estimating Sustainable Development Goal indicator 4.1.2: School completion rates," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1822-1864, November.

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    More about this item

    Keywords

    neonatal mortality; Bayesian hierarchical model; millennium development goals;
    All these keywords.

    JEL classification:

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

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